Smart Digital AgricultureJekyll2024-03-08T12:07:32+11:00/Smart Digital Agriculture/malone.brendan1001@gmail.com/2024/03/journalDigest2024-03-07T00:00:00-00:002024-03-07T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2024-5">Journal Paper Digests 2024 #5</h2>
<ul>
<li>Global cropland nitrous oxide emissions in fallow period are comparable to growing-season emissions</li>
<li>Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils</li>
<li>Why make inverse modeling and which methods to use in agriculture? A review</li>
</ul>
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<h3 id="why-make-inverse-modeling-and-which-methods-to-use-in-agriculture-a-review">Why make inverse modeling and which methods to use in agriculture? A review</h3>
<p>Inverse modeling (IM) is a valuable tool in agriculture for estimating model parameters that aid in decision-making. It is particularly useful when parameters cannot be directly measured or easily estimated due to logistical constraints in agricultural settings. Unlike other estimation methods, IM combines a mechanistic model with observations of its outputs to derive the parameters of interest, allowing for the integration of various sources of knowledge. The availability of numerous data sources, such as remote sensing and crowdsourcing, with high spatial and temporal resolution, has expanded the potential of IM in agriculture. Practitioners can now incorporate the spatial and temporal footprint of observational data into parameter estimation. However, common IM techniques currently applied in agriculture often struggle to account for effectively spatial and temporal variability. Relevant IM methods that address these challenges are usually isolated within specific developer and user communities and are not well known within the agricultural community. There is a lack of comprehensive reviews focusing on IM methods suitable for handling spatial and temporal data in agriculture. In parallel, the process of conducting IM in agriculture remains under-formalized. Typically, specific IM methods are chosen for specific combinations of models and types of observational data, but the rationale behind their selection is rarely explained in publications. The relationship between IM methods, models, and observational data is unclear, making it overwhelming for new practitioners to choose an appropriate method. This complex problem, along with the diversity of IM methods, has yet to be adequately addressed while taking into account the specificities of agricultural applications. To address these challenges, this review aims to provide a structured classification of IM methods based on the practical needs of new practitioners in agriculture. It examines a wide range of inversion methods applied in agriculture-related domains and covers four key topics: i) the essential elements and general process of IM, ii) the main families of IM methods in agriculture and their characteristics, iii) the circumstances in which practitioners prefer using IM over other approaches, and their motivations, and iv) practical guidance on choosing a method family based on operational criteria. The review aims to help readers develop a clear understanding of the practice of inverse modeling, gain insights into the diversity of IM methods, and make informed choices when selecting a method family for their agricultural applications.</p>
<h3 id="soil-properties-shape-the-heterogeneity-of-denitrification-and-n2o-emissions-across-large-scale-flooded-paddy-soils">Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils</h3>
<p>With widespread nitrogen fertilizer use and complex N2O sources in flooded paddy soils, understanding N2O emission dynamics is crucial but largely understudied. We investigate fungal, bacterial, and chemical denitrification pathways in N2O production after rewetting and nitrogen fertilizer application across southeastern China. Findings suggest significant fungal dominance, challenging previous undervaluations, with bacterial and chemical processes as secondary contributors. The spatial heterogeneity is linked to soil properties, particularly organic carbon and total nitrogen, laying the foundation for predictive models of future global N2O emissions from paddy soils.</p>
<h3 id="global-cropland-nitrous-oxide-emissions-in-fallow-period-are-comparable-to-growing-season-emissions">Global cropland nitrous oxide emissions in fallow period are comparable to growing-season emissions</h3>
<p>Fallow-period N2O emissions have been neglected in the estimation of whole-year greenhouse gas inventories for decades. It is estimated that the mean contribution of fallow period to whole-year N2O emissions (Rfallow) was 44% globally, with hotspots mainly in the northern high latitudes. The dominant driver of global variation in Rfallow was soil pH. To accurately estimate N2O emissions for national greenhouse gas inventories, it is crucial to update current EFs with full consideration of the fallow-period N2O emissions in the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method.</p>
<p><a href="/2024/03/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on March 07, 2024.</p>
/2024/02/journalDigest2024-02-28T00:00:00-00:002024-02-28T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2024-4">Journal Paper Digests 2024 #4</h2>
<ul>
<li>Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing</li>
<li>Spatial evaluation of the soils capacity and condition to store carbon across Australia</li>
<li>Suitability of microbial and organic matter indicators for on-farm soil health monitoring</li>
<li>Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada</li>
<li>Flexible marked spatio-temporal point processes with applications to event sequences from association football</li>
<li>Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow</li>
</ul>
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<h3 id="modelling-calibration-uncertainty-in-networks-of-environmental-sensorsget-accessarrow">Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow</h3>
<p>Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.</p>
<h3 id="flexible-marked-spatio-temporal-point-processes-with-applications-to-event-sequences-from-association-football">Flexible marked spatio-temporal point processes with applications to event sequences from association football</h3>
<p>We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively on the space of marks, allowing a separate model specification for the occurrence times. We develop a Bayesian framework for their inference and prediction that can naturally accommodate covariate information to drive cross-excitations, offering broad flexibility for real-world applications. The framework is applied to in-game event sequences from association football, resulting in inferences about previously unquantified characteristics of game dynamics, extraction of event-specific team abilities and predictions for event occurrences, such as goals or fouls in a specified interval of time.</p>
<h3 id="developing-scoring-functions-based-on-soil-texture-to-assess-agricultural-soil-health-in-quebec-canada">Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada</h3>
<p>Adoption of soil health indicators to assess physical, biological, and chemical properties involves adapting their interpretation for a specific region using scoring functions. Accordingly, we used data provided from 1166 soil samples distributed between fine-, medium-, and coarse-textured soils, collected in agricultural areas across the province of Quebec, Canada, and analyzed for 15 soil health indicators. Scoring functions were calculated according to the means and standard deviations obtained for each soil health indicator by textural group. Three scoring types were used: “more-is-better”, “less-is-better”, and “optimum-is-best”. The results showed that 12 indicators were significantly influenced by soil texture and need separate scoring functions, except for wet aggregate stability, penetration resistance of the surface hardness (0–15 cm), and pH. This led to the development of one to three scoring functions for each soil health indicator. Correlation analysis between soil health indicators was also investigated to better understand relationships between soil physical, biological, and chemical properties. We observed that soil biological indicators were moderately to strongly correlated with each other (r = 0.59–0.74) and with soil physical indicators (r = 0.60–0.76). Overall, the results of this study led to the development of new scoring functions based on soil texture to interpret soil health indicators objectively and accurately for the benefit of Quebec farmers and agricultural stakeholders. The findings of this study demonstrated the need to adapt scoring functions to better account for the impact of regional factors on agricultural soils for the interpretation of soil health indicators.</p>
<h3 id="suitability-of-microbial-and-organic-matter-indicators-for-on-farm-soil-health-monitoring">Suitability of microbial and organic matter indicators for on-farm soil health monitoring</h3>
<p>In addition to standard laboratory testing of soil samples, on-farm soil health monitoring methods are needed to help farmers assess progress in adopting new management practices. However, there is currently a lack of studies evaluating the suitability of semi-quantitative on-farm indicators to accurately rank target soil properties according to laboratory results. Therefore, this study assessed methods with potential for field use compared to common laboratory approaches for the determination of (i) soil organic carbon (SOC), (ii) carbon (C) fractions and (iii) microbial activity. The comparison allowed the evaluation of the validity, practicality and cost-effectiveness of the approaches. For this purpose, three sites in north-eastern Austria with contrasting soil textures (light, medium, heavy) and two different management systems (namely ‘pioneer’ and ‘standard’) were selected. Pioneer soils are managed long-term according to principles of soil health using conservation agricultural practices while neighbouring fields under standard management represent conventional practices. Beyond texture and site differences, both laboratory and field-adapted approaches revealed differences between the pioneer and standard systems. Overall, management-specific differences were most pronounced in the light and heavy textured soil. Although the laboratory methods provided more accurate results with less variability, the field-based approaches still identified trends in soil health parameters in the pioneer system. Our study can thus serve as a guide for the selection of suitable parameters and methods for assessing soil health in different areas of research and practical application.</p>
<h3 id="spatial-evaluation-of-the-soils-capacity-and-condition-to-store-carbon-across-australia">Spatial evaluation of the soils capacity and condition to store carbon across Australia</h3>
<p>The soil security concept has been put forward to maintain and improve soil resources inter alia to provide food, clean water, climate change mitigation and adaptation, and to protect ecosystems. A provisional framework suggested indicators for the soil security dimensions, and a methodology to achieve a quantification. In this study, we illustrate the framework for the function soil carbon storage and the two dimensions of soil capacity and soil condition. The methodology consists of (i) the selection and quantification of a small set of soil indicators for capacity and condition, (ii) the transformation of indicator values to unitless utility values via expert-generated utility graphs, and (iii) a two-level aggregation of the utility values by soil profile and by dimension. For capacity, we used a set of three indicators: total organic and inorganic carbon content and mineral associated organic carbon in the fine fraction (MAOC) estimated via their reference value using existing maps of pedogenons and current landuse to identify areas of remnant genosoils (total organic and inorganic carbon) and the 90th percentile for MAOC. For condition we used the same set of indicators, but this time using the estimated current value and comparing with their reference-state values (calculated for capacity). The methodology was applied to the whole of Australia at a spatial resolution of 90 m
90 m. The results show that the unitless indicator values supporting the function varied greatly in Australia. Aggregation of the indicators into the two dimensions of capacity and condition revealed that most of Australia has a relatively low capacity to support the function, but that most soils are in a generally good condition relative to that capacity, with some exceptions in agricultural areas, although more sampling of the remnant genosoils is required for corroboration and improvement. The maps of capacity and condition may serve as a basis to estimate a spatially-explicit local index of Australia’s soil resilience to the threat of decarbonization.</p>
<h3 id="preserving-soil-data-privacy-with-soilprint-a-unique-soil-identification-system-for-soil-data-sharing">Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing</h3>
<p>Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.</p>
<p><a href="/2024/02/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on February 28, 2024.</p>
/2024/02/journalDigest2024-02-27T00:00:00-00:002024-02-27T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2024-2">Journal Paper Digests 2024 #2</h2>
<ul>
<li>Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing</li>
<li>Spatial evaluation of the soils capacity and condition to store carbon across Australia</li>
<li>Suitability of microbial and organic matter indicators for on-farm soil health monitoring</li>
<li>Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada</li>
<li>Flexible marked spatio-temporal point processes with applications to event sequences from association football</li>
<li>Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow</li>
</ul>
<!--more-->
<h3 id="modelling-calibration-uncertainty-in-networks-of-environmental-sensorsget-accessarrow">Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow</h3>
<p>Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.</p>
<h3 id="flexible-marked-spatio-temporal-point-processes-with-applications-to-event-sequences-from-association-football">Flexible marked spatio-temporal point processes with applications to event sequences from association football</h3>
<p>We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively on the space of marks, allowing a separate model specification for the occurrence times. We develop a Bayesian framework for their inference and prediction that can naturally accommodate covariate information to drive cross-excitations, offering broad flexibility for real-world applications. The framework is applied to in-game event sequences from association football, resulting in inferences about previously unquantified characteristics of game dynamics, extraction of event-specific team abilities and predictions for event occurrences, such as goals or fouls in a specified interval of time.</p>
<h3 id="developing-scoring-functions-based-on-soil-texture-to-assess-agricultural-soil-health-in-quebec-canada">Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada</h3>
<p>Adoption of soil health indicators to assess physical, biological, and chemical properties involves adapting their interpretation for a specific region using scoring functions. Accordingly, we used data provided from 1166 soil samples distributed between fine-, medium-, and coarse-textured soils, collected in agricultural areas across the province of Quebec, Canada, and analyzed for 15 soil health indicators. Scoring functions were calculated according to the means and standard deviations obtained for each soil health indicator by textural group. Three scoring types were used: “more-is-better”, “less-is-better”, and “optimum-is-best”. The results showed that 12 indicators were significantly influenced by soil texture and need separate scoring functions, except for wet aggregate stability, penetration resistance of the surface hardness (0–15 cm), and pH. This led to the development of one to three scoring functions for each soil health indicator. Correlation analysis between soil health indicators was also investigated to better understand relationships between soil physical, biological, and chemical properties. We observed that soil biological indicators were moderately to strongly correlated with each other (r = 0.59–0.74) and with soil physical indicators (r = 0.60–0.76). Overall, the results of this study led to the development of new scoring functions based on soil texture to interpret soil health indicators objectively and accurately for the benefit of Quebec farmers and agricultural stakeholders. The findings of this study demonstrated the need to adapt scoring functions to better account for the impact of regional factors on agricultural soils for the interpretation of soil health indicators.</p>
<h3 id="suitability-of-microbial-and-organic-matter-indicators-for-on-farm-soil-health-monitoring">Suitability of microbial and organic matter indicators for on-farm soil health monitoring</h3>
<p>In addition to standard laboratory testing of soil samples, on-farm soil health monitoring methods are needed to help farmers assess progress in adopting new management practices. However, there is currently a lack of studies evaluating the suitability of semi-quantitative on-farm indicators to accurately rank target soil properties according to laboratory results. Therefore, this study assessed methods with potential for field use compared to common laboratory approaches for the determination of (i) soil organic carbon (SOC), (ii) carbon (C) fractions and (iii) microbial activity. The comparison allowed the evaluation of the validity, practicality and cost-effectiveness of the approaches. For this purpose, three sites in north-eastern Austria with contrasting soil textures (light, medium, heavy) and two different management systems (namely ‘pioneer’ and ‘standard’) were selected. Pioneer soils are managed long-term according to principles of soil health using conservation agricultural practices while neighbouring fields under standard management represent conventional practices. Beyond texture and site differences, both laboratory and field-adapted approaches revealed differences between the pioneer and standard systems. Overall, management-specific differences were most pronounced in the light and heavy textured soil. Although the laboratory methods provided more accurate results with less variability, the field-based approaches still identified trends in soil health parameters in the pioneer system. Our study can thus serve as a guide for the selection of suitable parameters and methods for assessing soil health in different areas of research and practical application.</p>
<h3 id="spatial-evaluation-of-the-soils-capacity-and-condition-to-store-carbon-across-australia">Spatial evaluation of the soils capacity and condition to store carbon across Australia</h3>
<p>The soil security concept has been put forward to maintain and improve soil resources inter alia to provide food, clean water, climate change mitigation and adaptation, and to protect ecosystems. A provisional framework suggested indicators for the soil security dimensions, and a methodology to achieve a quantification. In this study, we illustrate the framework for the function soil carbon storage and the two dimensions of soil capacity and soil condition. The methodology consists of (i) the selection and quantification of a small set of soil indicators for capacity and condition, (ii) the transformation of indicator values to unitless utility values via expert-generated utility graphs, and (iii) a two-level aggregation of the utility values by soil profile and by dimension. For capacity, we used a set of three indicators: total organic and inorganic carbon content and mineral associated organic carbon in the fine fraction (MAOC) estimated via their reference value using existing maps of pedogenons and current landuse to identify areas of remnant genosoils (total organic and inorganic carbon) and the 90th percentile for MAOC. For condition we used the same set of indicators, but this time using the estimated current value and comparing with their reference-state values (calculated for capacity). The methodology was applied to the whole of Australia at a spatial resolution of 90 m
90 m. The results show that the unitless indicator values supporting the function varied greatly in Australia. Aggregation of the indicators into the two dimensions of capacity and condition revealed that most of Australia has a relatively low capacity to support the function, but that most soils are in a generally good condition relative to that capacity, with some exceptions in agricultural areas, although more sampling of the remnant genosoils is required for corroboration and improvement. The maps of capacity and condition may serve as a basis to estimate a spatially-explicit local index of Australia’s soil resilience to the threat of decarbonization.</p>
<h3 id="preserving-soil-data-privacy-with-soilprint-a-unique-soil-identification-system-for-soil-data-sharing">Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing</h3>
<p>Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.</p>
<p><a href="/2024/02/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on February 27, 2024.</p>
/2024/01/journalDigest2024-01-17T00:00:00-00:002024-01-17T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2024-2">Journal Paper Digests 2024 #2</h2>
<ul>
<li>Physics-Informed Neural Networks for solving transient unconfined groundwater flow</li>
<li>Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning</li>
<li>An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter</li>
<li>Estimating soil organic carbon content at variable moisture contents using a low-cost spectrometer</li>
</ul>
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<h3 id="estimating-soil-organic-carbon-content-at-variable-moisture-contents-using-a-low-cost-spectrometer">Estimating soil organic carbon content at variable moisture contents using a low-cost spectrometer</h3>
<p>Research-grade spectrometers such as ASD are widely used in the lab to estimate soil properties, but they are bulky, heavy, and not easily deployable to measure field soils. The newer FT-NIR spectrometers are compact, lightweight, and robust, suitable for developing portable sensors for emerging applications such as field-based soil carbon stock assessment. In this study, we investigated the usefulness of an FT-NIR spectrometer (NanoQuest) for estimating SOC content while correcting for the effect of soil moisture using External Parameter Orthogonalization (EPO), and its performance was compared to that of ASD. To develop EPO transformation, five levels of soil moisture were used at 0, 0.07, 0.13, 0.18, 0.24, and 0.30 g g−1. We tested two modeling approaches: Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR). The results showed that EPO was more effective in correcting for the moisture effect as samples became drier. ASD gave a better performance in estimating SOC with SVR (R2: 0.17 to 0.84, RMSE: 6.1 to 3.9 g C kg−1, bias: −0.3 to 0.1 g C kg−1) after EPO transformation. NanoQuest gave slightly lower, but still satisfactory performance in SOC estimation (R2: 0.17 to 0.70, RMSE: 9.2 to 5 g C kg−1, bias: −0.3 to 0.1 g C kg−1). EPO substantially reduced the bias of the SOC models for both ASD and NanoQuest. This study demonstrates the usefulness of low-cost FT-NIR spectrometers for SOC measurement at varying moisture contents and their great potential for field-deployable soil sensor development.</p>
<h3 id="an-interlaboratory-comparison-of-mid-infrared-spectra-acquisition-instruments-and-procedures-matter">An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter</h3>
<p>Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments’ dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments’ characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures.</p>
<h3 id="towards-a-cost-effective-framework-for-estimating-soil-nitrogen-pools-using-pedotransfer-functions-and-machine-learning">Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning</h3>
<p>Globally, the strategic use of nitrogen (N) is important in optimizing economic returns and reducing soil nitrogen losses to the environment. Incorporating reliable estimates of nitrogen (N) mineralized over a growing season (GSN) into N-fertilizer rate prescriptions is critical, but may often lack a direct measurement. For this purpose, Pedotransfer functions (PTFs) of total nitrogen (TN) – representing the stable pool from which N is mineralized and biological nitrogen availability (BNA) – representing the labile pool of N mineralization were used to estimate GSN. GSN was calculated based on TN and BNA results from a soil health database (SHD), which also includes a suite of related soil health parameters (n = 2222). Using a process of recursive feature elimination (RFE) and cost-benefit feature elimination (CBFE), the best predictors of TN, BNA, and GSN were identified using a suite of machine learners (MLs) and regression analysis. For TN, RFE revealed that BNA, active carbon (AC), sand (Sa), and soil organic matter (OM) were the best predictors yielding a Lin’s concordance correlation coefficient (CCC) of 0.80 and a reduction in theoretical cost of 41 % compared to the control. CBFE resulted in AC, soil respiration (SR), clay, Sa, and OM as the most cost-effective predictors of TN with a CCC of 0.79 and a theoretical cost savings 49 % below the cost of using all appropriate soil health parameters in the SHD. With respect to BNA, the best predictors from RFE were aggregate stability (AS), AC, SR, and TN with a CCC of 0.78 and a theoretical cost reduction of 23 %. CBFE retained AC, SR, S, TN, OM and pH as predictors of BNA with a CCC of 0.78 and reduction of 29 % in theoretical cost. Finally, GSN results from RFE identified AS, AC, SR, OM and pH as the best predictors with a 0.82 CCC and 17 % reduction in theoretical cost. CBFE, on the other hand, identified AC, SR, sand, OM, and pH as the most cost-efficient predictors while maintaining a CCC of 0.82 and theoretical cost reduction of 29 %. Of the MLs used for pattern recognition (i.e., cubist, random forest, support vector machine, and stochastic gradient boosting), cubist model outperformed the others for the majority of iterations of the RFE and CBFE processes. The cost-effective framework, and the N-related PTFs developed in this study will greatly enhance our ability to predict of soil N-pool dynamics and the ability to incorporate GSN estimates into N-fertilizer recommendations for producers worldwide. Improvements in predictive strength could be achieved by incorporating climate and soil management practices into PTF development. Another area for improvement and future study would include addition of spatial and landscape variability related to N-measures via digital soil mapping applications.</p>
<h3 id="physics-informed-neural-networks-for-solving-transient-unconfined-groundwater-flow">Physics-Informed Neural Networks for solving transient unconfined groundwater flow</h3>
<p>Neural networks excel in various machine learning applications; however, they lack the physical interpretability and constraints crucial for numerous scientific and engineering problems. This limitation hinders their ability to accurately capture and predict complex physical systems’ behavior, potentially yielding inaccurate or unreliable results. Physics-Informed Neural Networks (PINNs) are a class of machine learning models that integrate the power of neural networks with the physical laws governing natural phenomena. PINNs provide an effective tool for solving intricate physical problems, ranging from fluid dynamics to materials science, by incorporating physical constraints into the neural network architecture. PINNs can substantially enhance the accuracy and efficiency of model predictions, even in data-limited situations. This work offers insight into recent developments in the PINN field, including their mathematical formulation and training algorithms, and emphasizes their application in solving transient unconfined groundwater flow. In this context, the phreatic surface acts as a spatiotemporally varying boundary condition, and properly accounting for its position is vital for precise predictions of unconfined groundwater flow and related environmental and engineering applications. The study’s objective is to develop a reliable model for estimating the phreatic surface and the spatiotemporal distribution of piezometric heads in a vertical cross-section of an unconfined aquifer. Two cases are examined: the first involves a homogeneous and isotropic aquifer, while the second comprises a mildly heterogeneous and anisotropic one. The challenges and opportunities arising from this emerging research area are also explored, and essential directions for future research are underscored.</p>
<p><a href="/2024/01/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on January 17, 2024.</p>
/2024/01/journalDigest2024-01-12T00:00:00-00:002024-01-12T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2024-1">Journal Paper Digests 2024 #1</h2>
<ul>
<li>Temporal Gap-Filling of 12-Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting</li>
<li>Carbon sequestration in soils and climate change mitigation—Definitions and pitfalls</li>
<li>Pyrogeography in flux: Reorganization of Australian fire regimes in a hotter world</li>
<li>Distinct, direct and climate-mediated environmental controls on global particulate and mineral-associated organic carbon storage</li>
<li>Stabilisation of soil organic matter with rock dust partially counteracted by plants</li>
<li>A technical evaluation on the mathematical attitudes and fitting accuracy of soil moisture retention curve models</li>
<li>Glacial rock flour reduces the hydrophobicity of Greenlandic cultivated soils</li>
<li>Determination of soil water retention curves from thermal conductivity curves, texture, bulk density, and field capacity</li>
<li>Incorporating soil knowledge into machine-learning prediction of soil properties from soil spectra</li>
</ul>
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<h3 id="incorporating-soil-knowledge-into-machine-learning-prediction-of-soil-properties-from-soil-spectra">Incorporating soil knowledge into machine-learning prediction of soil properties from soil spectra</h3>
<p>Various machine-learning models have been extensively applied to predict soil properties using infrared spectroscopy. Beyond the interpretability and transparency of these models, there is an ongoing discussion on the reliability of the prediction of soil properties generated from soil spectra. In this review, we contribute to this discussion by advocating for the integration of soil knowledge into machine-learning models. By doing so, researchers can delve deeper into the underlying soil constituents, ultimately enhancing prediction accuracy. Our review explores the soil information present in spectral data, the fallacy of model interpretability, methods to incorporate soil knowledge into machine-learning techniques, and the ways in which machine learning and soil spectroscopy can assist soil science. The combination of machine learning and domain knowledge is recommended to develop more meaningful models for predicting soil properties within the field of soil science.</p>
<h3 id="determination-of-soil-water-retention-curves-from-thermal-conductivity-curves-texture-bulk-density-and-field-capacity">Determination of soil water retention curves from thermal conductivity curves, texture, bulk density, and field capacity</h3>
<p>The soil water retention curve (SWRC) is frequently expressed using the van Genuchten (VG) model, which has four parameters: saturated water content (θs), residual water content (θr), α, and m (1–1/n). Soil thermal conductivity (λ), which is linked to the hydraulic properties of unsaturated soil, has been a proxy variable used to estimate SWRC. In this study, we present a new approach to estimate the VG model parameters. Parameters θs, α and m are calculated from the information of soil texture, bulk density (ρb), and a measured water content at field capacity (θfc, at −33 kPa or −10 kPa), and θr is estimated from the thermal conductivity versus water content curve, λ(θ), based on similarities between SWRCs and λ(θ) curves. The new approach was evaluated with laboratory and field measurements on 23 soils of various textures, ρb values, and θ values. Results showed that for repacked core samples, intact core samples, and in situ field soils, the new approach estimated SWRCs with average root mean square errors (RMSEs) of 0.042, 0.030, and 0.049 m3 m‐3, respectively. The new approach offers a quick and effective way to estimate SWRCs accurately with measured λ(θ) curves, texture, bulk density, and θ at field capacity.</p>
<h3 id="glacial-rock-flour-reduces-the-hydrophobicity-of-greenlandic-cultivated-soils">Glacial rock flour reduces the hydrophobicity of Greenlandic cultivated soils</h3>
<p>Applying fine-grained glacial rock flour (GRF) may alleviate highly hydrophobic subarctic soils.
Moisture-dependent hydrophobicity was assessed in two field trials in South Greenland.
Parameters evaluated included both total area under the hydrophobicity curve and single-point values.
Hydrophobicity was reduced at GRF applications ≥300 ton ha−1, particularly in the less clayey field.
Normalizing the level of hydrophobicity to the level of water retention enabled comparisons between soils.</p>
<h3 id="a-technical-evaluation-on-the-mathematical-attitudes-and-fitting-accuracy-of-soil-moisture-retention-curve-models">A technical evaluation on the mathematical attitudes and fitting accuracy of soil moisture retention curve models</h3>
<p>Numerous mathematical equations have been formulated in the literature of different researchers for describing soil moisture retention curve (SMRC), which can be applied to simulate and solve soil hydraulic modeling problems. The primary concern lies in selecting an efficient model to simulate accurately the S-shaped curve or sigmoid-type of the SMRC for soils with different textures. Therefore, the objective of this study was a comprehensive and technical evaluation of 50 developed models of the SMRC based on the influence of parameters on the behavior of the curve and the ability of their fitting accuracy on 728 soil samples of the UNSODA dataset, that has not been investigated so far. Statistical criteria including corrected Akaike’s information criterion (AICc), root mean square error (RMSE) and coefficient of determination (R2) together with Duncan’s multiple range test and cluster analysis were employed to assess the fitting accuracy of SMRC models to measured data. Results from fitting accuracy on the UNSODA dataset indicated that Brutsaert model provided the best fit to the measured data compared to other models in 14.6% of the soil samples with RMSE = 0.0125 and AICc = -94.18. This model was classified in the same cluster with Groenevelt and Grant (GG1, GG2 and GG3), Dexter, Mualem, and Fredlund and Xing models and did not have a significant difference in terms of RMSE. Also, Brutsaert model had the highest fitting accuracy in 67% of different soil textural classes compared to other models. Finally, the technical evaluation in terms of accuracy, flexibility and simplicity of the fitting process showed that the Brutsaert, Mualem, Dexter and GG3 models can be selected for better simulation of the SMRC in water and soil research.</p>
<h3 id="temporal-gap-filling-of-12-hourly-smap-soil-moisture-over-the-conus-using-water-balance-budgeting">Temporal Gap-Filling of 12-Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting</h3>
<p>Twelve-hourly satellite soil moisture (SM) data were gap-filled using a water balance based on SM and precipitation observations</p>
<p>Gap-filled data had good accuracy and temporal consistency with in situ data and captured SM peaks to heavy rainfall</p>
<p>Exclusive fill-in SM values exhibited comparable performance to the Soil Moisture Active Passive observations</p>
<h3 id="carbon-sequestration-in-soils-and-climate-change-mitigationdefinitions-and-pitfalls">Carbon sequestration in soils and climate change mitigation—Definitions and pitfalls</h3>
<p>The term carbon (C) sequestration has not just become a buzzword but is something of a siren’s call to scientific communicators and media outlets. Carbon sequestration is the removal of C from the atmosphere and the storage, for example, in soil. It has the potential to partially compensate for anthropogenic greenhouse gas emissions and is, therefore, an important piece in the global climate change mitigation puzzle. However, the term C sequestration is often used misleadingly and, while likely unintentional, can lead to the perpetuation of biased conclusions and exaggerated expectations about its contribution to climate change mitigation efforts. Soils have considerable potential to take up C but many are also in a state of continuous loss. In such soils, measures to build up soil C may only lead to a reduction in C losses (C loss mitigation) rather than result in real C sequestration and negative emissions. In an examination of 100 recent peer-reviewed papers on topics surrounding soil C, only 4% were found to have used the term C sequestration correctly. Furthermore, 13% of the papers equated C sequestration with C stocks. The review, further, revealed that measures leading to C sequestration will not always result in climate change mitigation when non-CO2 greenhouse gases and leakage are taken into consideration. This paper highlights potential pitfalls when using the term C sequestration incorrectly and calls for accurate usage of this term going forward. Revised and new terms are suggested to distinguish clearly between C sequestration in soils, SOC loss mitigation, negative emissions, climate change mitigation, SOC storage, and SOC accrual to avoid miscommunication among scientists and stakeholder groups in future.</p>
<h3 id="pyrogeography-in-flux-reorganization-of-australian-fire-regimes-in-a-hotter-world">Pyrogeography in flux: Reorganization of Australian fire regimes in a hotter world</h3>
<p>Changes to the spatiotemporal patterns of wildfire are having profound implications for ecosystems and society globally, but we have limited understanding of the extent to which fire regimes will reorganize in a warming world. While predicting regime shifts remains challenging because of complex climate–vegetation–fire feedbacks, understanding the climate niches of fire regimes provides a simple way to identify locations most at risk of regime change. Using globally available satellite datasets, we constructed 14 metrics describing the spatiotemporal dimensions of fire and then delineated Australia’s pyroregions—the geographic area encapsulating a broad fire regime. Cluster analysis revealed 18 pyroregions, notably including the (1) high-intensity, infrequent fires of the temperate forests, (2) high-frequency, smaller fires of the tropical savanna, and (3) low-intensity, diurnal, human-engineered fires of the agricultural zones. To inform the risk of regime shifts, we identified locations where the climate under three CMIP6 scenarios is projected to shift (i) beyond each pyroregion’s historical climate niche, and (ii) into climate space that is novel to the Australian continent. Under middle-of-the-road climate projections (SSP2-4.5), an average of 65% of the extent of the pyroregions occurred beyond their historical climate niches by 2081–2100. Further, 52% of pyroregion extents, on average, were projected to occur in climate space without present-day analogues on the Australian continent, implying high risk of shifting to states that also lack present-day counterparts. Pyroregions in tropical and hot-arid climates were most at risk of shifting into both locally and continentally novel climate space because (i) their niches are narrower than southern temperate pyroregions, and (ii) their already-hot climates lead to earlier departure from present-day climate space. Such a shift implies widespread risk of regime shifts and the emergence of no-analogue fire regimes. Our approach can be applied to other regions to assess vulnerability to rapid fire regime change.</p>
<h3 id="stabilisation-of-soil-organic-matter-with-rock-dust-partially-counteracted-by-plants">Stabilisation of soil organic matter with rock dust partially counteracted by plants</h3>
<p>Soil application of Ca- and Mg-rich silicates can capture and store atmospheric carbon dioxide as inorganic carbon but could also have the potential to stabilise soil organic matter (SOM). Synergies between these two processes have not been investigated. Here, we apply finely ground silicate rock mining residues (basalt and granite blend) to a loamy sand in a pot trial at a rate of 4% (equivalent to 50 t ha−1) and investigate the effects of a wheat plant and two watering regimes on soil carbon sequestration over the course of 6 months. Rock dust addition increased soil pH, electric conductivity, inorganic carbon content and soil-exchangeable Ca and Mg contents, as expected for weathering. However, it decreased exchangeable levels of micronutrients Mn and Zn, likely related to the elevated soil pH. Importantly, it increased mineral-associated organic matter by 22% due to the supply of secondary minerals and associated sites for SOM sorption. Additionally, in the nonplanted treatments, rock supply of Ca and Mg increased soil microaggregation that subsequently stabilised labile particulate organic matter as organic matter occluded in aggregates by 46%. Plants, however, reduced soil-exchangeable Mg and Ca contents and hence counteracted the silicate rock effect on microaggregates and carbon within. We suggest this cation loss might be attributed to plant exudates released to solubilise micronutrients and hence neutralise plant deficiencies. The effect of enhanced silicate rock weathering on SOM stabilisation could substantially boost its carbon sequestration potential.</p>
<h3 id="distinct-direct-and-climate-mediated-environmental-controls-on-global-particulate-and-mineral-associated-organic-carbon-storage">Distinct, direct and climate-mediated environmental controls on global particulate and mineral-associated organic carbon storage</h3>
<p>Identifying controls on soil organic carbon (SOC) storage, and where SOC is most vulnerable to loss, are essential to managing soils for both climate change mitigation and global food security. However, we currently lack a comprehensive understanding of the global drivers of SOC storage, especially with regards to particulate (POC) and mineral-associated organic carbon (MAOC). To better understand hierarchical controls on POC and MAOC, we applied path analyses to SOC fractions, climate (i.e., mean annual temperature [MAT] and mean annual precipitation minus potential evapotranspiration [MAP-PET]), carbon (C) input (i.e., net primary production [NPP]), and soil property data synthesized from 72 published studies, along with data we generated from the National Ecological Observatory Network soil pits (n = 901 total observations). To assess the utility of investigating POC and MAOC separately in understanding SOC storage controls, we then compared these results with another path analysis predicting bulk SOC storage. We found that POC storage is negatively related to MAT and soil pH, while MAOC storage is positively related to NPP and MAP-PET, but negatively related to soil % sand. Our path analysis predicting bulk SOC revealed similar trends but explained less variation in C storage than our POC and MAOC analyses. Given that temperature and pH impose constraints on microbial decomposition, this indicates that POC is primarily controlled by SOC loss processes. In contrast, strong relationships with variables related to plant productivity constraints, moisture, and mineral surface availability for sorption indicate that MAOC is primarily controlled by climate-driven variations in C inputs to the soil, as well as C stabilization mechanisms. Altogether, these results demonstrate that global POC and MAOC storage are controlled by separate environmental variables, further justifying the need to quantify and model these C fractions separately to assess and forecast the responses of SOC storage to global change.</p>
<p><a href="/2024/01/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on January 12, 2024.</p>
/2023/12/journalDigest2023-12-18T00:00:00-00:002023-12-18T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-24">Journal Paper Digests 2023 #24</h2>
<ul>
<li>Physics-Informed Neural Networks for solving transient unconfined groundwater flow</li>
<li>A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia</li>
<li>Visible and near infrared spectroscopy for predicting soil nitrogen mineralization rate: Effect of incubation period and ancillary soil properties</li>
<li>Real-Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human-Machine Interactions</li>
<li>A Complete Water Balance of a Rain Garden</li>
<li>Agricultural value chains and food security in the Pacific: Evidence from Papua New Guinea and Solomon Islands</li>
</ul>
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<h3 id="agricultural-value-chains-and-food-security-in-the-pacific-evidence-from-papua-new-guinea-and-solomon-islands">Agricultural value chains and food security in the Pacific: Evidence from Papua New Guinea and Solomon Islands</h3>
<p>Small island developing states in the Pacific face multiple development challenges driven by rapid population growth and high transportation costs due to remoteness and isolation. Combined with the adverse consequences of extreme weather events and climate change, these challenges exacerbate poverty and food insecurity. Agricultural value chain development presents a pathway to poverty reduction and food security. In this paper, we assess the impacts of two value chain development projects in Papua New Guinea and Solomon Islands on dietary diversity and food security of small-scale producers. Project impacts on dietary diversity are positive and significant in both countries, but improved food security is only observed in Solomon Islands. These impacts are mainly driven by crop yields, value of crop production and sales, crop diversification and share of crop sales. We find that treatment households are more likely to consume less nutritious foods such as sweets and oils. Our findings expand the literature in a data-scarce region and caution that value chain interventions without nutrition-focused components to induce behavioral change may have unintended impacts on healthy diets.</p>
<h3 id="a-complete-water-balance-of-a-rain-garden">A Complete Water Balance of a Rain Garden</h3>
<p>A bioinfiltration rain garden was retrofitted from an existing traffic island at Villanova University in 2001. It has been monitored continuously since 2003 at a 5-min timeseries resolution and with instrumentation that would enable a water balance calculation. This 20-year data set allows for an in-depth analysis of the hydrologic pathways and management in the rain garden. Using physical equations and modeled data (based on real-time measurements), a balance of all influent, stored, and effluent water within the rain garden was constructed. Analysis shows the rain garden captures 73.5% of runoff, resulting in a post-implementation management of 86.2% of all rainfall in its watershed. In comparison to the hydrology of other land covers, implementing the rain garden resulted in the management of 37.6% more rainfall than pre-implementation, producing a hydrological signature similar to that of cultivated land or low development levels (e.g., 30% impervious). Additionally, with the long data record, several statistical techniques were applied to determine the amount of monitoring needed for a certain level of precision in system performance assessment. For 5% uncertainty, approximately 3 years of continuous data is needed to assess performance. This analysis not only facilitates understanding the function of rain garden systems, but also provides conclusions and methodology for understanding the uncertainty associated with the extent of monitoring performed on these green stormwater infrastructure systems. These findings provide practical knowledge as monitoring of stormwater management infrastructures is becoming a more standard part of their operation.</p>
<h3 id="real-time-irrigation-scheduling-based-on-weather-forecasts-field-observations-and-human-machine-interactions">Real-Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human-Machine Interactions</h3>
<p>Real-time irrigation schedules have been shown to outperform predetermined irrigation schedules that do not consider the present state and requirements. However, implementing real-time irrigation scheduling requires reliable present soil-crop-atmosphere dynamics and weather predictions; moreover, enabling farmers to adopt recommended water applications remains challenging as they rely on personal experience and knowledge. Farmers and computer-based tools are rarely connected in a closed-loop and farmers’ feedback are usually not incorporated into a real-time modeling procedure. To resolve these critical issues, this paper addresses the feasibility of a real-time irrigation scheduling tool (RTIST) based on weather forecasts, field observations, and human-machine interactions. RTIST integrates a simulation & optimization model, a data assimilation (DA) technique, and a human-computer interaction method, and enables optimality, accuracy, and applicability of the tool. The principle of the RTIST is to engage farmers directly into computer modeling, and support irrigation scheduling decisions jointly based on model provided information and farmers’ own justification. The optimization and simulation are validated by running the tool on two crop fields, showing the accuracy of present estimation and future prediction of soil moisture and leaf area index, taking advantage of field observation and DA. The applicability of RTIST is tested via virtual irrigation exercises with a group of farmers for a corn field in Eastern Nebraska. RTIST with farmers’ direct engagement shows increased productivity in comparison to traditional practices. Especially, farmers’ feedbacks show interest in using the tool in real-world irrigation scheduling and providing meaningful suggestions to improve the tool for real-world application.</p>
<h3 id="visible-and-near-infrared-spectroscopy-for-predicting-soil-nitrogen-mineralization-rate-effect-of-incubation-period-and-ancillary-soil-properties">Visible and near infrared spectroscopy for predicting soil nitrogen mineralization rate: Effect of incubation period and ancillary soil properties</h3>
<p>Soil nitrogen mineralization rate (SNMR) influences crop N uptake and nitrate leaching leading to environmental pollution. This study aims at (i) examining whether visible and near-infrared reflectance spectroscopy (vis-NIRS) can predict SNMR and (ii) investigating if incubation periods and ancillary soil attributes can improve the prediction accuracy. Total 133 soil samples collected from seven fields were incubated under aerobic conditions for 60 days with seven batches of sub-samples. Mineral N was measured at regular time intervals and soil samples were scanned using a vis-NIRS sensor (Tec5 Technology, Germany) parallelly. SNMR was determined by fitting a zero-order kinetic to the net mineralized N as a function of the incubation time. Soil total nitrogen (TN), total carbon (TC) and electrical conductivity (EC) were determined once. Partial least squares regression (PLSR) models were calibrated individually for each field both for vis-NIR spectra and its combinations with TN, TC and EC. Six out of seven batches of sub-samples were used for calibrating PLSR when remaining one batch was used for model validation, and it rotated across all seven batches. Vis-NIRS alone predicted SNMR with moderate accuracy in five of seven fields (coefficient of determination, 0.53 ≤ R2 ≥ 0.66, ratio of prediction to deviation, 1.51 ≤ RPD ≥ 1.76), while models were poor in two fields (R2 = 0.23–0.26, RPD = 1.18 – 1.20). Inclusion of soil TC, TN and/or EC was expected to improve accuracy, but improvements varied across fields (R2 = 0.23–0.79, RPD = 1.18 – 2.26). Similarly, the incubation period increased vis-NIRS prediction accuracy, but frequently occurred among 2nd to 6th batches (R2 = 0.35–0.82, RPD = 1.28 – 2.44). Even incorporating secondary properties and increasing incubation duration hardly improved predictions, improvement can be compromised since it is not significant mostly and often underperformed or remained unchanged. Considering the time and effort required to incubate and analyze soil properties, this study suggests using a vis-NIRS sensor to estimate SNMR in fresh soil conditions i.e., without incubation and incorporation of secondary properties.</p>
<h3 id="a-copula-based-parametric-composite-drought-index-for-drought-monitoring-and-applicability-in-arid-central-asia">A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia</h3>
<p>Due to the complexity of meteorological and hydrological conditions in a changing environment, previous drought indices for monitoring a specific drought type do not reflect the overall regional situation of water scarcity. Therefore, in order to obtain accurate and reliable drought monitoring, a more integrated drought index should be developed to identify drought events comprehensively. In this paper, a non-linear trivariate drought index (NTDI) was constructed based on the joint probability distribution of parametric copulas, combining precipitation (P), potential evapotranspiration (PET), and root zone soil moisture (SM) variables. Subsequently, it was respectively compared with four drought indices, SPEI, SSMI, SC-PDSI and TVDI, and cross-validated with actual recorded drought events and annual crop yield to evaluate its applicability in arid Central Asia (ACA). The results indicated that: (1) Frank copula (1-,3-month scale) and Gumbel copula (6-,12-month scale) were considered to be the best-fitted copula functions for constructing joint probability distributions in the ACA. (2) The NTDI integrated the P-PET and SM drought signals to sensitively capture drought onset and duration, reflecting the combined characteristics of meteorological and agricultural drought. (3) The drought information expressed by NTDI was generally consistent with recorded drought events, and the monitoring results are accurate. (4)The NTDI performed better in agricultural drought monitoring than other drought indices. This study provides a reliable multivariate composite indicator which is significant for drought monitoring, prevention and risk assessment in ACA.</p>
<h3 id="physics-informed-neural-networks-for-solving-transient-unconfined-groundwater-flow">Physics-Informed Neural Networks for solving transient unconfined groundwater flow</h3>
<p>Neural networks excel in various machine learning applications; however, they lack the physical interpretability and constraints crucial for numerous scientific and engineering problems. This limitation hinders their ability to accurately capture and predict complex physical systems’ behavior, potentially yielding inaccurate or unreliable results. Physics-Informed Neural Networks (PINNs) are a class of machine learning models that integrate the power of neural networks with the physical laws governing natural phenomena. PINNs provide an effective tool for solving intricate physical problems, ranging from fluid dynamics to materials science, by incorporating physical constraints into the neural network architecture. PINNs can substantially enhance the accuracy and efficiency of model predictions, even in data-limited situations. This work offers insight into recent developments in the PINN field, including their mathematical formulation and training algorithms, and emphasizes their application in solving transient unconfined groundwater flow. In this context, the phreatic surface acts as a spatiotemporally varying boundary condition, and properly accounting for its position is vital for precise predictions of unconfined groundwater flow and related environmental and engineering applications. The study’s objective is to develop a reliable model for estimating the phreatic surface and the spatiotemporal distribution of piezometric heads in a vertical cross-section of an unconfined aquifer. Two cases are examined: the first involves a homogeneous and isotropic aquifer, while the second comprises a mildly heterogeneous and anisotropic one. The challenges and opportunities arising from this emerging research area are also explored, and essential directions for future research are underscored.</p>
<p><a href="/2023/12/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on December 18, 2023.</p>
/2023/12/journalDigest2023-12-08T00:00:00-00:002023-12-08T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-23">Journal Paper Digests 2023 #23</h2>
<ul>
<li>Equalizing urban agriculture access in Glasgow: A spatial optimization approach</li>
<li>Reproducing computational processes in service-based geo-simulation experiments</li>
<li>Prediction of soil organic matter by Kubelka-Munk based airborne hyperspectral moisture removal model</li>
<li>Cropping intensity map of China with 10 m spatial resolution from analyses of time-series Landsat-7/8 and Sentinel-2 images</li>
<li>Diffuse reflectance mid-infrared spectroscopy is viable without fine milling</li>
<li>Optimising POXC effective sensitivity as a soil indicator in Australian soils</li>
<li>A Blue Water Scarcity-Based Method for Hydrologically Sustainable Agricultural Expansion Design</li>
<li>A global indicator of soil macroinvertebrate community composition, abundance and diversity</li>
<li>Fifty years after deep-ploughing: Effects on yield, roots, nutrient stocks and soil structure</li>
</ul>
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<h3 id="fifty-years-after-deep-ploughing-effects-on-yield-roots-nutrient-stocks-and-soil-structure">Fifty years after deep-ploughing: Effects on yield, roots, nutrient stocks and soil structure</h3>
<p>Deep-ploughing far beyond the common depth of 30 cm was used more than 50 years ago in Northern Germany with the aim to break root-restricting layers and thereby improve access to subsoil water and nutrient resources. We hypothesized that effects of this earlier intervention on soil properties and yields prevailed after 50 years. Hence, we sampled two sandy soils and one silty soil (Cambisols and a Luvisol) of which half of the field had been deep-ploughed 50 years ago (soils then re-classified as Treposols). The adjacent other half was not deep-ploughed and thus served as the control. At all the three sites, both deep-ploughed and control parts were then conventionally managed over the last 50 years. We assessed yields during the dry year 2019 and additionally in 2020, and rooting intensity at the year of sampling (2019), as well as changes in soil structure, carbon and nutrient stocks in that year. We found that deep-ploughing improved yields in the dry spell of 2019 at the sandy sites, which was supported by a more general pattern of higher NDVI indices in deep-ploughed parts for the period from 2016 to 2021 across varying weather conditions. Subsoil stocks of soil organic carbon and total plant-available phosphorus were enhanced by 21%–199% in the different sites. Root biomass in the subsoil was reduced due to deep-ploughing at the silty site and was increased or unaffected at the sandy sites. Overall, the effects of deep-ploughing were site-specific, with reduced bulk density in the buried topsoil stripes in the subsoil of the sandy sites, but with elevated subsoil density in the silty site. Hence, even 50 years after deep-ploughing, changes in soil properties are still detectable, although effect size differed among sites.</p>
<h3 id="a-global-indicator-of-soil-macroinvertebrate-community-composition-abundance-and-diversity">A global indicator of soil macroinvertebrate community composition, abundance and diversity</h3>
<p>Macroinvertebrate communities are highly sensitive indicators of physical and chemical soil qualities. Their evaluation in field conditions is rather simple and could serve as proxy for soil-based ecosystem services that farmers and field technicians could use. We tested the hypothesis that an indicator of soil macroinvertebrate communities, with 14 taxonomic groups characterized at the order level, could be used in any region of the world to assess these communities. A synthetic indicator was calculated using data from 9 reference sites from tropical, subtropical and temperate regions, a set of 3694 data of the open access Macrofauna database and a new site for validation. Invertebrates were extracted with the standard ISO/TSBF methodology and characterized with a set of 14 large taxonomic units, plus an index of taxonomic richness and total density. At each of the 9 reference sites, 27 to 252 sample points, representing different types of plant covers and/or soil management options, were considered and compared with their respective local synthetic GISQ indicator values. These indicators, elaborated from Principal Component Analysis of the sample points data, are set to vary from 0.1 to 1.0 according to the composition, diversity and abundance of the community. Analyses showed great similarities among sites, with Factors 1 (from 21.9 to 36.9 % variance explained) expressing the overall abundance and diversity of the communities and Factors 2 (8.9 to 15.83 %) opposing sites with dominant soil ecosystem engineer populations (Earthworms, Ants, Termites and some Coleoptera) to sites dominated by litter transformer populations (Diplopoda, Isopoda and others). Indicator formulae were designed based on PCA analyses of each data set and a global formula was established with the Macrofauna database. Very high correlations (>0.95) were obtained among values calculated with local data set and the general formula calculated with data of the Macrofauna database, in 8 of 9 sites and in the validation site. We discuss the importance of having a single formula to transform data obtained with a simple standard field method in the building of public policies for soil-based ecosystem services payment.</p>
<h3 id="a-blue-water-scarcity-based-method-for-hydrologically-sustainable-agricultural-expansion-design">A Blue Water Scarcity-Based Method for Hydrologically Sustainable Agricultural Expansion Design</h3>
<p>A new methodology for designing sustainable agricultural expansion while preventing water scarcity is developed</p>
<p>The methodology selects areas with high water availability while ensuring that neither local nor downstream water scarcity is triggered</p>
<p>An application on coffee expansion in Kenya finds more areas than foreseen by policy, leaving action space for further selection criteria</p>
<h3 id="optimising-poxc-effective-sensitivity-as-a-soil-indicator-in-australian-soils">Optimising POXC effective sensitivity as a soil indicator in Australian soils</h3>
<p>The continuum of soil organic carbon is currently not well represented by any single metric. Permanganate oxidizable carbon (POXC) has been widely utilized as a soil condition indicator due to its correlation with biological indicators and sensitivity to management effects over relatively short time periods. However, the ability of POXC to represent the continuum of soil organic carbon, and how this could improve the characterization of management effects, has not been sufficiently explored. This study investigated the relationship between permanganate concentration and POXC across nine permanganate concentrations ranging from 3 to 300 mM. An initial investigation was performed on ten cropped and uncropped topsoil pairs representing a diverse range of soil types across New South Wales, Australia, and an additional 52 sites were investigated in a farm-scale study. POXC was observed to increase monotonically and non-linearly with increasing permanganate concentration. POXC characteristics were developed by fitting a logistic function to the observed data, which facilitated calculation of the area under the curve (POXCAUC) and the theoretical maximum POXC (POXCmax). The utility of the POXC characteristic was demonstrated with highly significant differences (p ≤ 0.002) observed in POXCAUC between cropped and uncropped sites. POXCAUC also displayed larger probability test statistics compared to any single permanganate concentration. The investigation also revealed that if a single concentration were to be utilized for australian soils, the 50 mM concentration was more effective at discerning land use effects in clayey (p = 0.000) and sandy (p = 0.049) sites compared to the widely adopted 20 mM concentration (p = 0.001; p = 0.312).</p>
<h3 id="diffuse-reflectance-mid-infrared-spectroscopy-is-viable-without-fine-milling">Diffuse reflectance mid-infrared spectroscopy is viable without fine milling</h3>
<p>While diffuse reflectance Fourier transform mid-infrared spectroscopy (mid-DRIFTS) has been established as a viable low-cost surrogate for traditional soil analyses, the assumed need for fine milling of soil samples prior to analysis is constraining the commercial appeal of this technology. Here, we reevaluate this assumption using a set of 2380 soil samples collected across North American agricultural soils. Cross-validation indicated that the best preprocessing (standard normal variate) and model form (memory-based learning) resulted in very good and nearly identical predictions for the <2 mm preparation and fine-milled preparation of these soils for total organic carbon (TOC), clay, sand, pH and bulk density (BD). Application of larger models built from the USDA NRCS mid-DRIFTS library also resulted in minimal performance differences between the two sample preps. Lower predictive performance of the existing library was attributed to less-than-perfect spectral representativeness of the library. Regardless of model form, there was very little variability between replicates of the <2 mm prep, suggesting that the lack of fine milling did not lead to more heterogeneous subsamples. Additionally, there was no relationship between residual error and soil texture, implying these results should be robust across most soil types. Overall, in agreement with other recent findings, these results suggest that routine scanning of standard <2 mm preparation does not degrade predictive performance of mid-DRIFTS-based inference systems. With good standard operating procedures including quality control and traditional analysis on a small percent of samples, mid-DRIFTS can become a routine tool in commercial soil laboratories.</p>
<h3 id="cropping-intensity-map-of-china-with-10-m-spatial-resolution-from-analyses-of-time-series-landsat-78-and-sentinel-2-images">Cropping intensity map of China with 10 m spatial resolution from analyses of time-series Landsat-7/8 and Sentinel-2 images</h3>
<p>Cropping intensity maps at high spatial resolution play a crucial role in guiding agricultural policies and ensuring food security. So far, most of nationwide cropping intensity maps have been developed from satellite images at moderate or coarse resolutions. In this study, we first assembled and integrated time-series dataset with high spatial resolution, specifically Landsat-7, Landsat-8 and Sentinel-2 imagery in 2017. We then used an object- and phenology-based algorithm and integrated images to create a 10-m resolution cropping intensity map over China. The map evaluation results revealed an overall accuracy of 96.68 ± 0.01 % and a Kappa coefficient of 0.90. In 2017, single cropping dominated the agricultural practices in China, with an approximate area of 1.189 × 106 km2 ± 7.90 × 103 km2, constituted 79.26 % of the entire cropland area. Simultaneously, double and triple cropping covered approximately 0.306 × 106 km2 ± 8.03 × 103 km2 and 5.00 × 103 ± 1.75 × 103 km2, corresponding to 20.41 % and 0.33 % of the entire cropland area, respectively. On average, the national multiple cropping index (MCI) was 1.21. The results in the study prove the reliability of the generated mapping products and high potential of the developed mapping framework (the algorithm and integrated datasets), which can be readily applied to quantify the interannual changes of cropping pattern on a nationwide level with a high spatial resolution.</p>
<h3 id="prediction-of-soil-organic-matter-by-kubelka-munk-based-airborne-hyperspectral-moisture-removal-model">Prediction of soil organic matter by Kubelka-Munk based airborne hyperspectral moisture removal model</h3>
<p>Obtaining high-precision soil organic matter (SOM) spatial distribution information is of great significance for applications such as precision agriculture. But in the current hyperspectral SOM inversion work, soil moisture greatly influences the representation of the sensitive information of SOM on the spectrum. Therefore, a Kubelka-Munk theory based spectral correction model for soil moisture removal is proposed to improve the spectral sensitivity of SOM. Firstly, the soil moisture content was obtained by the use of a Kubelka-Munk based physical soil moisture model and an unmixing method. Then, the spectral correction model for soil moisture removal was implemented based on the quantitative description of the Beer-Lambert law. The results show that the proposed spectral correction model for soil moisture removal can significantly enhance the expression of the sensitive spectral features of SOM, especially for the short-wave infrared range. After moisture removal, the imaging spectral data were used for inversion, using the sensitive band at 0.69 μm and a support vector machine regression (SVR) modeling method. The Kubelka-Munk moisture removal model for soil moisture removal can improve the accuracy of SOM inversion by at least 22% comparing with the 0.69 μm original spectral inversion model, with R2 of 0.42. Moreover, the proposed model can also improve the accuracy of SOM inversion by 19% for the SVR statistical regression method, with R2 of 0.69. Finally, the SOM distribution maps based on sensitive band model and SVR regression method were analyzed. Findings show that the two methods have high consistency, but the statistical method obtains better details of the SOM spatial distribution, due to its higher accuracy.</p>
<h3 id="reproducing-computational-processes-in-service-based-geo-simulation-experiments">Reproducing computational processes in service-based geo-simulation experiments</h3>
<p>Geo-simulation experiments (GSEs) are experiments allowing the simulation and exploration of Earth’s surface (such as hydrological, geomorphological, atmospheric, biological, and social processes and their interactions) with the usage of geo-analysis models (hereafter called ‘models’). Computational processes represent the steps in GSEs where researchers employ these models to analyze data by computer, encompassing a suite of actions carried out by researchers. These processes form the crux of GSEs, as GSEs are ultimately implemented through the execution of computational processes. Recent advancements in computer technology have facilitated sharing models online to promote resource accessibility and environmental dependency rebuilding, the lack of which are two fundamental barriers to reproduction. In particular, the trend of encapsulating models as web services online is gaining traction. While such service-oriented strategies aid in the reproduction of computational processes, they often ignore the association and interaction among researchers’ actions regarding the usage of sequential resources (model-service resources and data resources); documenting these actions can help clarify the exact order and details of resource usage. Inspired by these strategies, this study explores the organization of computational processes, which can be extracted with a collection of action nodes and related logical links (node-link ensembles). The action nodes are the abstraction of the interactions between participant entities and resource elements (i.e., model-service resource elements and data resource elements), while logical links represent the logical relationships between action nodes. In addition, the representation of actions, the formation of documentation, and the reimplementation of documentation are interconnected stages in this approach. Specifically, the accurate representation of actions facilitates the correct performance of these actions; therefore, the operation of actions can be documented in a standard way, which is crucial for the successful reproduction of computational processes based on standardized documentation. A prototype system is designed to demonstrate the feasibility and practicality of the proposed approach. By employing this pragmatic approach, researchers can share their computational processes in a structured and open format, allowing peer scientists to re-execute operations with initial resources and reimplement the initial computational processes of GSEs via the open web.</p>
<h3 id="equalizing-urban-agriculture-access-in-glasgow-a-spatial-optimization-approach">Equalizing urban agriculture access in Glasgow: A spatial optimization approach</h3>
<p>Glasgow, Scotland, United Kingdom, has long-term issues with inequalities in health and food security, as well as large areas of vacant and derelict land. Urban agriculture projects can increase access to fresh food, improve mental health and nutrition, and empower and bring communities together. We investigated the distribution of urban agriculture in Glasgow and found that the current configuration of urban agriculture projects is mostly located centrally in the city, covering 36 % of the total population (approximately 635,000) within 10-minute walking distance. We also found a positive correlation (r = 0.13, p = 0.0003) between the walking travel time to the nearest urban agriculture project and the food desert status. To increase urban agriculture access across the city, we used the Maximal Covering Location Problem (MCLP) model to optimally situate new urban agriculture projects on vacant and derelict land to maximize the covered population. We identified that a minimum of 15 new urban agriculture projects could increase the population coverage to 49 % and equalize the access disparity to a statistically non-significant level. This research shows that converting vacant and derelict land in Glasgow into urban agriculture projects could both help with the city’s problem of vacant and derelict land and bring many potential benefits to local communities.</p>
<p><a href="/2023/12/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on December 08, 2023.</p>
/2023/11/journalDigest2023-11-27T00:00:00-00:002023-11-27T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-22">Journal Paper Digests 2023 #22</h2>
<ul>
<li>Long-term ecological research in freshwaters enabled by regional biodiversity collections, stable isotope analysis, and environmental informatics Get access Arrow</li>
<li>Do carbon farming practices build bioavailable nitrogen pools?</li>
<li>Combining ground penetrating radar methodologies enables large-scale mapping of soil horizon thickness and bulk density in boreal forests</li>
<li>The appraisal of pedotransfer functions with legacy data; an example from southern Africa</li>
<li>Interactions among soil texture, pore structure, and labile carbon influence soil carbon gains</li>
<li>A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression</li>
<li>Soil total suction sensing using fiber-optic technology</li>
<li>Can we use X-ray CT to generate 3D penetration resistance data?</li>
<li>Estimating plant-available nutrients with XRF sensors: Towards a versatile analysis tool for soil condition assessment</li>
<li>Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities</li>
</ul>
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<h3 id="beyond-plfa-concurrent-extraction-of-neutral-and-glycolipid-fatty-acids-provides-new-insights-into-soil-microbial-communities">Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities</h3>
<p>The analysis of phospholipid fatty acids (PLFAs) is one of the most common methods used to quantify the abundance, and analyse the community structure, of soil microbes. The PLFA extraction method can yield two additional lipid fractions—neutral lipids and glycolipids—which potentially hold additional, valuable information on soil microbial communities. Yet its quantitative sensitivity on complete neutral lipid (NLFA) and glycolipid fatty acid (GLFA) profiles has never been validated. In this study we tested (i) if the high-throughput PLFA method can be expanded to concurrently extract complete NLFA and GLFA profiles, as well as sterols, (ii) whether taxonomic specificities of signature fatty acids are retained across the three lipid fractions in pure culture strains, and (iii) whether NLFAs and GLFAs allow soil-specific fingerprinting to the same extent as PLFA analysis. By adjusting the polarity of chloroform with 2% ethanol for solid phase extraction, pure lipid standards were fully fractionated into neutral lipids, glycolipids, and phospholipids. Sterols eluted in the neutral lipid fraction, and a betaine lipid co-eluted with phospholipids. We found consistent taxonomic specificities of fatty acid markers across the three lipid fractions by analysing pure culture extracts representative of soil microbes. Fatty acid profiles from soil extracts, however, showed stronger differences between PLFAs, NLFAs, and GLFAs than between soil types. This indicates that PLFAs and NLFAs signify different community properties (biomass vs. carbon storage, putatively), and that GLFAs are sensitive markers for community traits which behave differently than PLFAs. Although we consistently found high abundances of characteristic sterols in fungal extracts, the PLFA extraction method only yielded miniscule amounts of ergosterol from soil extracts. We argue that concomitant measurement of fatty acid profiles from all three lipid fractions is a low-effort and potentially information-rich addition to the PLFA method, and discuss its applicability for soil microbial community analyses.</p>
<h3 id="estimating-plant-available-nutrients-with-xrf-sensors-towards-a-versatile-analysis-tool-for-soil-condition-assessment">Estimating plant-available nutrients with XRF sensors: Towards a versatile analysis tool for soil condition assessment</h3>
<p>The timely diagnosis of plant-available soil nutrient contents is crucial in enhancing agricultural intensification and bridging yield gaps. There is a global demand for a practical and easy-to-use analytical tool capable of predicting the nutrient status of agricultural soils to make the soil chemical diagnosis faster, cheaper, and environmentally friendly. A growing body of research has highlighted the potential of energy dispersive X-ray fluorescence (XRF) sensors for monitoring the condition of agricultural soils. This study critically reviews current knowledge on the feasibility of using XRF sensors and suggests ways forward to predict plant-available soil nutrients. The review finds that some challenges need to be addressed, including: (i) mitigating the matrix effect in XRF spectral libraries and (ii) calibrating models that can capture the local context of the ratio between total and available nutrient content (T/A ratio). This study further discusses knowledge gaps related to the abovementioned challenges and proposes the following future research areas: (i) understanding the impact of soil management on the temporal stability of T/A ratio and XRF model performance; (ii) assessing advanced predictive modelling strategies to address the challenges related to XRF spectral libraries, i.e., to deal with matrix effect and local context of the relationship between total and available content of nutrients, and (iii) evaluating data acquisition and modelling strategies that optimize the in situ application of portable XRF. Understanding these points is critical to advancing the technological maturity of predicting available nutrients in situ to fulfil plant nutrient requirements along with its development. Finally, portable, easy-to-use analytical tools are key to enhancing soil health/condition monitoring and proposing best management practices in agricultural areas worldwide, particularly in regions with limited infrastructure of soil laboratories. Soil monitoring is critical to preserve, sustain and recover soil condition/health, one of the main manageable drivers of soil and food security.</p>
<h3 id="can-we-use-x-ray-ct-to-generate-3d-penetration-resistance-data">Can we use X-ray CT to generate 3D penetration resistance data?</h3>
<p>Noninvasive imaging of soils with X-ray CT has proven to be a useful method to assess soil structure from a pore space perspective. In contrast, methods like cone penetration tests reflect soil structure from the perspective of the soil matrix as assessed by its mechanical strength. Because both the gray value (GV) obtained with X-ray CT and the penetration resistance (PR) obtained with a cone penetration test depend on soil density there should be a relationship between the two. To the best of our knowledge, no studies attempted so far to investigate the nature of the PR ∼ GV relationship and to understand how well PR and GV are correlated. We aimed at bridging that gap and carried out a combined analysis of local GV and PR with undisturbed soil cores sampled in two soil textures, i.e., loam and sand. To carry out the GV measurements, we developed a new approach which considers an adaptive volume of the zone of influence of the penetrometer tip as a function of soil density. For sand and when looking at samples individually, the correlation between PR and GV was best when the soil microscale heterogeneity was high, i.e., when dense and loose zones of soil were present on the course of the penetrometer tip. For loam, the correlation between PR and GV was not dependent on soil heterogeneity. When looking at the whole dataset, the agreement between PR and GV was better in loam than in sand, with a distance correlation metric of 0.66 for loam and 0.34 for sand, respectively. For loam, the relationship PR ∼ GV had a trend which was similar to that of a hyperbola, i.e., with escalating PR values in a narrow GV range. For sand, no particular model could be recognized. In order to provide a proof-of-concept on how to generate 3D PR maps, the co-located measurements of GV and PR were used to establish an empirical relationship and X-ray CT was used to extrapolate it in 3D. This was carried out with the loam dataset by fitting a hyperbolic function to the PR ∼ GV data pairs. This model was then used to convert GVs into PR values, at a spatial resolution equal to that of the shaft diameter of the penetrometer tip we have used. Notwithstanding the fact that the suggested approach is dependent on numerous experimental conditions and edaphic factors, we advocate for the use of 3D PR maps. These maps could be used in root-soil interactions research, for which the study and breeding of cultivars that could show plastic response in their root systems under mechanical stress is becoming more and more important. This is particularly relevant in the context of mechanized modern agriculture.</p>
<h3 id="soil-total-suction-sensing-using-fiber-optic-technology">Soil total suction sensing using fiber-optic technology</h3>
<p>Measurement of the soil water suction is important for investigating geotechnical problems and mitigating associated risks; however, conventional high-range suction measurement techniques have some limitations for accurate, long-term, and quasi-distributed suction measurement in the field. Fiber-optic humidity sensors provide a viable solution due to their unique properties. Here, we present a novel microfabricated fiber-optic suction sensor for measuring the suction of water in unsaturated soils. We evaluated the performance of the sensor on four bentonite–sand mixtures by comparing it with two laboratory suction measurement methods (chill-mirror hygrometer, salt solution vapor equilibrium) and a capacitive relative humidity sensor. We determined that this sensor has a measurement range of 5–300 MPa with a root mean square error of less than 3.5 MPa, and a response time of 2–9 min over the tested suction variation range (6.5–80.5 MPa). The results of the evaporation model test showed that three fiber-optic sensors installed at different depths of bentonite could effectively capture changes in temperature, relative humidity in soil pores, and total suction during evaporation. Together, these results demonstrate the great potential of the fiber-optic suction sensor for in-situ, long-term, and quasi-distributed suction monitoring.</p>
<h3 id="a-framework-for-recalibrating-pedotransfer-functions-using-nonlinear-least-squares-and-estimating-uncertainty-using-quantile-regression">A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression</h3>
<p>Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed PTFs often lack accompanying uncertainty estimations. To address these issues, a framework is proposed where existing equation-based PTFs are recalibrated using a nonlinear least squares (NLS) approach and validated on two regions of Canada; this process is coupled with the use of quantile regression (QR) to generate uncertainty estimates. Many PTFs have been developed to predict soil bulk density, so this variable is used as a case study to evaluate the outcome of recalibration. New coefficients are generated for existing soil bulk density PTFs, and the performance of these PTFs is validated using three case study datasets, one from the Ottawa region of Ontario and two from the province of British Columbia, Canada. The improvement of the performance of the recalibrated PTFs is evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Uncertainty estimates produced using QR are communicated through the mean prediction interval (MPI) and prediction interval coverage probability (PICP) graphs. This framework produces dataset-specific PTFs with improved accuracy and minimized uncertainty, and the method can be applied to other regional datasets to improve the estimations of existing PTF model forms. The methods are most successful with large datasets and PTFs with fewer variables and minimal transformations; further, PTFs with organic carbon (OC) as one of or the sole input variable resulted in the highest accuracy.</p>
<h3 id="interactions-among-soil-texture-pore-structure-and-labile-carbon-influence-soil-carbon-gains">Interactions among soil texture, pore structure, and labile carbon influence soil carbon gains</h3>
<p>Perennial vegetation with high plant diversity, e.g., restored prairie, is known for stimulation of soil carbon (C) gains, due in part to enhanced formation of pore structure beneficial for long-term C storage. However, the prevalence of this phenomenon across soils of different types remains poorly understood. The aim of the study was to assess the associations between pore structure, soil C, and their differences in monoculture switchgrass and polyculture restored prairie vegetation across a wide range of soils dominating the Upper Midwest of the USA. Six experimental sites were sampled, representing three soil types with texture ranging from sandy to silt loams. The two vegetation systems studied at each site were (i) monoculture switchgrass (Panicum virgatum L.), and (ii) polyculture restored prairie, also containing switchgrass as one of its species. X-ray computed micro-tomography (µCT) was employed to analyze soil pore structure. Structural equation modeling and multiple path analyses were used to assess direct and indirect effects of soil texture and pore characteristics on microbial biomass C (MBC), particulate organic matter (POM), dissolved organic C (DOC), short-term respiration (CO2), and, ultimately, soil organic C (SOC). Across studied sites, prairie increased fractions of medium (50–150 µm Ø) pores by 11–45 %, SOC by 3–69 %, and MBC by 18–59 % (except for one site). The greater were the prairie-induced increases in the medium pore volumes, the greater were the prairie-induced SOC gains. Greater C losses via CO2 and DOC contributed to slower C accumulation in the prairie soil. We surmise that the interactive feedback loop relating medium pores and soil C acts across a wide range of soil textures and is an important mechanism through which perennial vegetation with high plant diversity, such as restored prairie, promotes rapid SOC gains.</p>
<h3 id="the-appraisal-of-pedotransfer-functions-with-legacy-data-an-example-from-southern-africa">The appraisal of pedotransfer functions with legacy data; an example from southern Africa</h3>
<p>Predictions of soil hydraulic properties by pedotransfer functions (PTFs) must be treated with caution when they are used in an application domain which differs from the domain of their original development and calibration. However, in some settings, scientists may have little alternative but to use PTFs calibrated elsewhere. In this paper we consider how legacy data can be used to evaluate PTFs in new regions, paying particular attention to the challenges that arise when, as is often the case, the legacy data are not obtained by independent random sampling, and may be clustered at multiple scales. We undertook this work in southern Africa (Zimbabwe, Zambia and Malawi) where PTFs have been little-used, despite the scarcity of direct measurements of the soil properties of interest. We evaluated the extent to which existing PTFs provide a useful tool for the prediction of soil moisture content at field-capacity (−33 kPa) and permanent wilting-point (−1500 kPa) at different spatial scales. Soil legacy data for Zambia, Zimbabwe and Malawi were collated from various sources and PTFs from temperate and tropical domains were evaluated. We examined error variance components of predictions at within-profile, within-site and between-site scales; and estimated their mean errors. In general the better-performing PTFs (with respect to bias and the size of the error variance components) were ones calibrated with data from a tropical domain. This was most apparent at −1500 kPa. However, not all PTFs calibrated with data on tropical soils performed well, and predictions from some PTFs calibrated over a temperate domain were better at −33 kPa. The observations were spatially clustered, with data from different depth intervals in the same profile, from profiles in the same experimental site or farm, and from clusters across the region. This enabled us to show, with an appropriate mixed model analysis, that PTFs which effectively capture regional-scale variation may be less useful for predicting variation within a profile. We propose that such studies, based on legacy data, and with a suitable linear mixed model, should be used to screen PTFs of any provenance before their wider application.</p>
<h3 id="combining-ground-penetrating-radar-methodologies-enables-large-scale-mapping-of-soil-horizon-thickness-and-bulk-density-in-boreal-forests">Combining ground penetrating radar methodologies enables large-scale mapping of soil horizon thickness and bulk density in boreal forests</h3>
<p>Forest soil properties must be observed with the appropriate resolution by depth and landscape area to understand biogeomorphological controls on soil carbon (C). These observations, particularly in boreal forests, have been limited because of the poor resolution and unavailability of physical soil sampling results, especially for soil bulk density measurements. Ground penetrating radar (GPR) has been demonstrated to non-destructively and continuously estimate forest soil properties required in Cstock estimates, such as soil horizon thickness and soil bulk density, across small spatial scales and shallow depths. Yet, successful small-scale forest GPR approaches represent a potential opportunity to obtain soil property estimates at relevant resolution and depth across forest landscapes, enabling improvement to much needed soil mapping and stock estimates. This review discusses the existing soil property studies that utilize ground penetrating radar (GPR) and explores how the adaptation of GPR methodology can contribute to investigating soils in forest landscapes. We have identified common GPR surveying practices, data processing steps and interpretation methods employed in multiple studies. These approaches have proven effective in obtaining higher-resolution estimates of important soil properties, such as bulk density and horizon thickness, within small-scale forest plots. By applying relevant findings in this review to our own boreal forest investigation across an 80 m hillslope transect, we provide recommendations on how to tailor GPR methodology for landscape-scale estimates of soil horizon thickness and bulk density to examine forest soil property distribution. These findings should enable the future collection of soil datasets informing the distribution of soil C stocks and their relationship to landscape features, and thus their controls and responses to climate and environmental change.</p>
<h3 id="do-carbon-farming-practices-build-bioavailable-nitrogen-pools">Do carbon farming practices build bioavailable nitrogen pools?</h3>
<p>Agricultural soils contain large amounts of nitrogen (N), but only a small fraction is readily available to plants. Despite several methods developed to estimate the bioavailability of N, there is no consensus on which extraction methods to use, and which N pools are critically important. In this study, we measured six soil N pools from 20 farms, which were part of a multi-year soil carbon sequestration on-farm experiment (Carbon action, 2019–2023). The aim was to quantify the N pools and to evaluate if farming practices that aim to build soil carbon pools, also build bioavailable N pools. We also aimed to test if the smaller and rapidly changing N pools could serve as an indicator for the slower change in soil organic matter. The measured N pools decreased in size, when moving from total N (7700 ± 1500 kg/ha) to slowly cycling (Illinois Soil Nitrogen Test ISNT-N: 1063 ± 220 kg/ha, autoclave citrate-extracted ACE protein N: 633 ± 440 kg/ha), water-soluble organic N (50 ± 17 kg/ha), potentially mineralizable N (33 ± 13 kg/ha) and finally readily plant available inorganic pools (nitrate and ammonium, total: 14 ± 8 kg/ha). In total, the measured pools covered only 18%–44% of total N, indicating a large unidentified N pool, which is either tightly bound to soil mineral fraction and not easily extractable or is bound to undecomposed plant residues and not hydrolysed by the methods. Of the large N pools (ISNT-N, ACE protein and unidentified residual N), clay, carbon (C) and C:Clay ratios explained most of the variability (R2 = .90–.93), leaving a minor part of the variation to the management effect. A pairwise comparison of carbon farming and control plots concluded that farming practices had a small (3%–5%) but statistically significant (p < .05) effect on soil total N and ISNT-N pools, and a moderate and significant effect (18%, p < .01) on potentially mineralizable N. The large variation in protein N, water-soluble organic N and inorganic N reduced statistical significance, although individual C sequestration practices had large effects (−30% to +50%). In conclusion, carbon sequestration practices can build both slowly cycling N pools (ISNT) and increase the mineralisation rate of these pools to release plant available forms, resulting in an additional benefit to agriculture through reduced fertilizer application needs.</p>
<h3 id="long-term-ecological-research-in-freshwaters-enabled-by-regional-biodiversity-collections-stable-isotope-analysis-and-environmental-informatics-get-access-arrow">Long-term ecological research in freshwaters enabled by regional biodiversity collections, stable isotope analysis, and environmental informatics Get access Arrow</h3>
<p>Biodiversity collections are experiencing a renaissance fueled by the intersection of informatics, emerging technologies, and the extended use and interpretation of specimens and archived databases. In this article, we explore the potential for transformative research in ecology integrating biodiversity collections, stable isotope analysis (SIA), and environmental informatics. Like genomic DNA, SIA provides a common currency interpreted in the context of biogeochemical principles. Integration of SIA data across collections allows for evaluation of long-term ecological change at local to continental scales. Challenges including the analysis of sparse samples, a lack of information about baseline isotopic composition, and the effects of preservation remain, but none of these challenges is insurmountable. The proposed research framework interfaces with existing databases and observatories to provide benchmarks for retrospective studies and ecological forecasting. Collections and SIA add historical context to fundamental questions in freshwater ecological research, reference points for ecosystem monitoring, and a means of quantitative assessment for ecosystem restoration.</p>
<p><a href="/2023/11/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on November 27, 2023.</p>
/2023/11/journalDigest2023-11-23T00:00:00-00:002023-11-23T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-21">Journal Paper Digests 2023 #21</h2>
<ul>
<li>Towards multi-model soil erosion modelling: An evaluation of the erosion potential method (EPM) for global soil erosion assessments</li>
<li>Rainfall erosivity index for monitoring global soil erosion</li>
<li>QuadMap: Variable resolution maps to better represent spatial uncertainty</li>
<li>A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones</li>
<li>The relationship between wheat yield and sun-induced chlorophyll fluorescence from continuous measurements over the growing season</li>
<li>WAFER: A new method to retrieve sun-induced fluorescence based on spectral wavelet decompositions</li>
<li>Color calibration of moist soil images captured under irregular lighting conditions</li>
<li>Design and experimentation of soil organic matter content detection system based on high-temperature excitation principle</li>
<li>Agent-based sensor location strategy for smart irrigation of large crop fields</li>
<li>Specific surface area of soils with different clay mineralogy can be estimated from a single hygroscopic water content</li>
<li>Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields</li>
<li>Soil dielectric permittivity modelling for 50 MHz instrumentation</li>
<li>Land management affects soil structural stability: Multi-index principal component analyses of treatment interactions</li>
<li>Ten deep learning techniques to address small data problems with remote sensing</li>
</ul>
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<h3 id="ten-deep-learning-techniques-to-address-small-data-problems-with-remote-sensing">Ten deep learning techniques to address small data problems with remote sensing</h3>
<p>Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability. We then introduce an overview of ten promising DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. Our particular contribution was to develop a flowchart that helps DL users select which technique to use given by answering a few questions. We hope that our review article facilitate DL applications to tackle societally important environmental problems with limited reference data.</p>
<h3 id="land-management-affects-soil-structural-stability-multi-index-principal-component-analyses-of-treatment-interactions">Land management affects soil structural stability: Multi-index principal component analyses of treatment interactions</h3>
<p>Soil structure mediates soil functioning and can be influenced by agricultural management practices (cropping intensity, tillage, and nitrogen source). This study evaluated the effectiveness of multiple soil aggregate indices to quantify soil structural development using long-term data from three locations within the central Great Plains: Alternative Crop Rotation (ACR) and Long-Term Tillage (LTT) near Akron, Colorado, and Knorr-Holden (KH) near Mitchell, Nebraska. Tillage treatments include no-tillage (NT), reduced tillage (RT), conventional tillage (CT), and moldboard plow (MP). Commercial mineral fertilizer (F) was used as a nitrogen source in ACR and LTT sites while manure (M) plus F treatments were used in KH. Soil aggregate size classes were measured in the laboratory, and aggregate stability index (ASI), mean weight diameter (MWD), geometric mean diameter (GMD), and fractal dimension (FD) were calculated to evaluate soil aggregation using linear regression and principal component analysis (PCA). At 0–15 cm, intensive tillage treatments (CT and MP) in ACR and LTT, reduced (P < 0.05) ASI by 46.7%, MWD by 21.0% and GMD by 8.4% and increased FD by 0.77% compared with NT and RT treatments. The addition of manure increased (P < 0.05) ASI by 72.2%, MWD by 65.6%, GMD by 32.8%, and reduced FD by 5.5% compared with tillage treatments in ACR and LTT. The PCA indicated clear effects of M on aggregate structure and stability, indicating that manure addition could compensate for the tillage operations in sustaining soil structural stability. Explanatory variables FD and ASI were orthogonal across the three sites, while FD was negatively correlated with MWD and GWD; thus, FD provides information not captured by ASI and complements MWD and GWD. The evaluated indices, including FD, are effective in measuring soil aggregation and structural stability and should be considered further in management decisions to sustain soil resources and enhance economic returns. The methods developed here can be applied to any site where soils are sampled and analyzed for aggregates.</p>
<h3 id="soil-dielectric-permittivity-modelling-for-50-mhz-instrumentation">Soil dielectric permittivity modelling for 50 MHz instrumentation</h3>
<p>Near surface electromagnetic geophysical techniques are proven tools to support soil ecosystem services and soil exploration. Such geophysical techniques provide electromagnetic properties that are useful to characterize the studied soil. The link between relevant soil characteristics and geophysical properties, such as dielectric permittivity (ε), is commonly expressed by pedophysical models. However, some weaknesses remain in their application, such as the requirement of parameters that are difficult to measure or calculate. Therefore, these parameters are frequently fixed, but this oversimplifies the complexity of the investigated soils. Moreover, the validity of ε pedophysical models in the frequency range of operating soil moisture sensors (normally < 100 MHz) remains poorly investigated.</p>
<p>In this study, the accuracy and adaptability of ε pedophysical models at different electromagnetic frequency ranges was tested and improved using newly collected laboratory and field data. Such data was collected on soils over a wide range of textures, physical and chemical properties.</p>
<p>To achieve this, we review the measurement methods and characteristics of ε pedophysical models, soil phases and geometric parameters. Subsequently, we show how geometric parameters can explain the dependance of soil texture on ε by implementing pedotransfer functions. Then, drawing on a broad experimental basis of common soil types in Europe, we develop novel ε pedophysical models at 50 MHz. These models are not only easy to evaluate but also capture most of the soil’s complexity. Additionally, these new ε pedophysical models eliminate the need for calibration data due to the introduction of novel pedotransfer functions based on soil cation exchange capacity. An extensive model test shows an unprecedented decrease in the RMSE of the newly proposed models of up to 412%.</p>
<p>In conclusion, despite it is unlikely to characterize soil structure, bulk density, or temperature at 50 MHz, these updated PPMs are useful for highly accurate water content and ε predictions, in both laboratory and field conditions, without the need for calibration data. As the developed modelling procedures are valid for a wide range of electromagnetic frequencies, these can be applied to soil exploration with TDR and GPR instrumentation.</p>
<h3 id="multi-site-evaluation-of-stratified-and-balanced-sampling-of-soil-organic-carbon-stocks-in-agricultural-fields">Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields</h3>
<p>Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.</p>
<h3 id="specific-surface-area-of-soils-with-different-clay-mineralogy-can-be-estimated-from-a-single-hygroscopic-water-content">Specific surface area of soils with different clay mineralogy can be estimated from a single hygroscopic water content</h3>
<p>The soil specific surface area (SSA) is an important variable for soil science and geoenvironmental engineering applications, but traditional measurement methods are difficult and time-consuming. Regression models or pedotransfer functions are often used to estimate SSA from other soil properties (e.g., clay content and cation exchange capacity), but these models do not consider the impact of clay mineralogy. Hygroscopic water content (wh) is intimately linked to these soil properties, which suggests that wh may be a better parameter for SSA estimation. This study (i) proposes regression models that estimate SSA from wh at different relative humidity values (5 to 90%) for kaolinite-rich samples (KA), illite-rich or mixed clay samples (IL/MC), montmorillonite-rich samples (ML), and a combination of all samples (ALL) and (ii) compares the performance of the wh models to other published models that comprise clay, silt and soil organic carbon contents and cation exchange capacity. We found that the sample-specific wh regression models accurately estimated SSA for KA, IL/MC and ML samples. For KA and IL/MC samples, the performance of the KA model (e.g., for adsorption, average RMSE = 10.5 m2/g) and IL/MC model (average RMSE = 21.3 m2/g) were better than the ALL-calibration model (KA: average RMSE = 18.7 m2/g; ML: average RMSE = 22.4 m2/g). For ML samples, similar model performance between the ML-calibration model (average RMSE = 41.4 m2/g) and the ALL-calibration model (average RMSE = 41.1 m2/g) was observed. In addition, the model performance of regression models based on wh was superior to models published in the literature that are based on clay, silt and soil organic carbon contents and cation exchange capacity. Overall, this study confirms that a single measure of wh can provide reliable estimates of the SSA while revealing a significant impact of clay mineralogy on model performance.</p>
<h3 id="agent-based-sensor-location-strategy-for-smart-irrigation-of-large-crop-fields">Agent-based sensor location strategy for smart irrigation of large crop fields</h3>
<p>Efficient monitoring of large crop fields is important to ensure the optimal use of resources such as water in irrigation policies, but at the same time represents a challenge to determine the structure of the sensor network. A balance must be accomplished between the acquisition, operation, and maintenance costs of this sensor network with the amount of information that can be collected in real-time to support the optimal use of the resource, e.g., an optimal irrigation policy. In this study, a sensor location strategy is proposed based on an agent-based model (ABM) of the crop–soil system, a state estimation algorithm reconstructing non-measured variables, and an objective function balancing the convergence of the estimation technique and the costs of the sensor network. The ABM model describes the crop–soil dynamics and allows conveniently representing uneven landscapes where water exchanges take place between different portions of the land. Various state estimation techniques can be considered and an extended Kalman filter is implemented in the present study, whose error covariance matrix can be exploited to assess practical observability and observer convergence. Finally, an economic cost function combines the observability measure with the sensor costs in order to select an optimal or suboptimal sensor array. For validation purposes, a numerical simulation case study, corresponding to a rugged land located in Colombia, is used to test various scenarios including the variability of climatic inputs.</p>
<h3 id="design-and-experimentation-of-soil-organic-matter-content-detection-system-based-on-high-temperature-excitation-principle">Design and experimentation of soil organic matter content detection system based on high-temperature excitation principle</h3>
<p>With the continuous development of precision agriculture, the integration of sensor technology, control technology, and agricultural production activities has become increasingly tight. Soil organic matter (SOM) content is an important parameter in precision agriculture, closely related to the growth status of crops. Therefore, in-situ detection of SOM is an important part of variable seeding. Currently, most researchers use spectroscopy to detect SOM. However, the accuracy of spectroscopy is affected by factors such as soil texture, soil moisture content, soil iron oxide content, and soil particle size. Additionally, spectroscopic instruments are expensive and complex. To address these issues, this study designed and developed a real-time detection system for soil organic matter content based on the high-temperature excitation principle. The SOM detection system includes the selection and application of carbon dioxide sensor, the design of detection devices, the design of control systems, and the design of human–machine interface. Benchtop tests showed that the system’s response time standard deviation was 0.252 s, indicating good stability. The filtering and cooling effects of the system met the requirements of the detection system. Five different natural soils were collected, and two of them, along with organic soil, were used to create artificially graded soil samples. Multiple linear regression was used for modeling, selecting six models with an R2 greater than 0.8 for accurate prediction of the remaining three different soil textures. The modeling results showed that a heating depth of 10 mm was susceptible to external interference, and the accuracy of the model with a heating depth of 15 mm was similar to that of the model with a heating depth of 20 mm. When the heating time exceeded 10 s, both models with different heating depths had an R2 greater than 0.8. The accuracy of the models increased with increasing heating time. Among them, the 15 mm-20 s model had the highest accuracy, with an average prediction accuracy of 91.4 %, a maximum accuracy of 94.1 %, and a minimum accuracy of 86.7 %. The experimental results showed that the SOM detection system designed in this study had good predictive capabilities for SOM in different soil textures and could achieve in-situ detection of SOM.</p>
<h3 id="color-calibration-of-moist-soil-images-captured-under-irregular-lighting-conditions">Color calibration of moist soil images captured under irregular lighting conditions</h3>
<p>Soil color is a valuable indicator that visually reflects soil properties. In the field of soil science, soil color has been widely used for soil identification and property assessment. Soil color is influenced not only by soil properties but also by lighting conditions; therefore, adequately considering the effect of lighting conditions for the color-based soil assessment is essential. This paper proposes a color calibration method for moist soil images captured under irregular lighting conditions. Four samples of weathered granite soil with varying water contents were captured under nine different lighting conditions. The effect of lighting conditions on the color of moist soil was analyzed using linear regression analysis in the CIELAB color space. The findings demonstrate a linear relationship between soil color and lighting conditions. Furthermore, the slopes of the linear regression equations remained consistent across all moist soil samples, irrespective of soil type and water content. Based on this, calibration equations were derived to adjust the color of moist soil images captured under arbitrary lighting conditions to those under the desired lighting conditions. The proposed method was validated using soil images captured under natural lighting conditions, which show high accuracy in color calibration except for cases with illuminance exceeding 70,000 lux. This color calibration method provides a reliable approach to evaluating soil properties through accurate color representation and presents new possibilities for developing digital image-based soil property prediction techniques that are less sensitive to lighting conditions.</p>
<h3 id="wafer-a-new-method-to-retrieve-sun-induced-fluorescence-based-on-spectral-wavelet-decompositions">WAFER: A new method to retrieve sun-induced fluorescence based on spectral wavelet decompositions</h3>
<p>Sun-induced fluorescence (SIF) as a close remote sensing based proxy for photosynthesis is accepted as a useful measure to remotely monitor vegetation health and gross primary productivity. It is therefore important to develop methods that allow for its precise and reliable retrieval from radiance measurements with spectral resolutions that have been increasing over the past few years. Retrieval methods are catching up to the increasing complexity of the available datasets making use of their whole information extent (spectral, spatial and temporal) but the comparability of different SIF retrievals and consistency across scales is still limited.</p>
<p>In this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence. By comparing absolute absorption line depths by means of the corresponding wavelet coefficients, a relative reflectance is retrieved independently of the fluorescence, i.e. without introducing a coupling between reflectance and fluorescence. The fluorescence can then be derived as the remaining offset. This method can be applied to arbitrary chosen wavelength windows in the whole spectral range, such that all the spectral data available is exploited, including the separation into several frequency (i.e. width of absorption lines) levels and without the need of extensive training datasets.</p>
<p>At the same time, the assumptions about the reflectance shape are minimal and no spectral shape assumptions are imposed on the fluorescence, which not only avoids biases arising from wrong or differing fluorescence models across different spatial scales and retrieval methods but also allows for the exploration of this spectral shape for different measurement setups.</p>
<p>WAFER is tested on a synthetic dataset as well as several diurnal datasets acquired with a field spectrometer (FloX) over an agricultural site. We compare the WAFER method to two established retrieval methods, namely the improved Fraunhofer line discrimination (iFLD) method and spectral fitting method (SFM) and find a good agreement with the added possibility of exploring the true spectral shape of the offset signal and free choice of the retrieval window. On our synthetic dataset, WAFER seems to outperform the SFM and works best in a spectral window only containing solar Fraunhofer lines where we achieve a relative retrieval error of 10% on average. Applied to the real dataset, the method returns reasonable diurnal cycles for SIF and can, due to the decoupling of reflectance and fluorescence retrieval, reveal interesting trends at times when vegetation canopies may experience a midday depression that remain largely unobserved with current methods.</p>
<h3 id="the-relationship-between-wheat-yield-and-sun-induced-chlorophyll-fluorescence-from-continuous-measurements-over-the-growing-season">The relationship between wheat yield and sun-induced chlorophyll fluorescence from continuous measurements over the growing season</h3>
<p>Rapid and accurate estimation of crop yield using remote sensing technology could be an important tool for improved global food security. As an effective probe measuring photosynthesis, sun-induced chlorophyll fluorescence (SIF) has potential for predicting crop yield, particularly when SIF measurements are integrated over an extended time period. However, few studies have investigated how temporal scale, vegetation structure, physiology and environmental factors affect crop yield prediction using SIF. Therefore, in this study we evaluate uncertainties in the relationship between SIF and wheat yield, associated with changes in leaf area index (LAI), chlorophyll a and b content (Cab), photosynthetic active radiation (PAR), and the timing of measurements over a range of temporal scales. Wheat field experiments were carried out over two years. LAI, Cab, PAR and canopy SIF were measured at several temporal scales. We systematically compared the performance of SIF parameters [near-infrared canopy SIF normalized by PAR (SIFyNIR), total near-infrared at photosystem level normalized by PAR (SIFyNIR_tot), and normalized difference fluorescence index (NDFI)] and vegetation indices (VIs) [normalized difference vegetation index (NDVI), and NIR reflectance of vegetation (NIRv)] as predictors of yield estimation. Among the SIF parameters, NDFI appeared to be the most sensitive to LAI and Cab. SIFyNIR_tot at the anthesis stage was the best predictor of wheat yield. SIF outperformed VIs for wheat yield estimation during the late growth period. Moreover, as the temporal scale increased (i.e., as the data values were accumulated over longer intervals of time), the relationship between SIFyNIR and wheat yield tended to be more linear. Overall, the uncertainty in the relationship between SIF and yield was affected more by LAI than Cab, and higher PAR produced a stronger and more stable relationship between SIF and wheat yield. Our findings provide empirical support and an example of an approach for using SIF to predict crop yield, as well as elucidation of the mechanisms underlying the relationship between SIF and production.</p>
<h3 id="a-deep-learning-approach-based-on-physical-constraints-for-predicting-soil-moisture-in-unsaturated-zones">A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones</h3>
<p>Water transport in the unsaturated zone is an important part of the hydrological cycle and is the link between the atmosphere‒soil-groundwater for material and energy transport. The accurate prediction of soil moisture (SM) is essential for the rational exploitation of water resources. Data-driven deep learning methods are widely used in many fields; however, the lack of physical mechanisms limits their application in hydrological fields, especially for SM prediction in unsaturated zones. To solve this problem, this study proposes a new deep learning method that introduces the water balance principle, Richard’s equation, and SM boundary conditions as constraints to construct the new loss function that guides the training process of deep learning, called physics-informed deep learning (PIDL). In tests consisting of a large number of data sets acquired from in situ observation sites in the field, PIDL exhibits higher accuracy than ordinary deep learning (long short-term memory) and physical models, with 51.03% and 53.46% reduction in root mean square error of SM prediction, respectively. PIDL performance significantly improved in predicting scenarios that are difficult for ordinary deep learning to handle, such as sparse data sets, extreme values, and mutated values. In addition, PIDL maintains high accuracy over a longer prediction period. The addition of physical mechanisms allows deep learning to mine patterns not only from the data itself but also from a priori physical theoretical knowledge for guidance, and this hybrid modeling approach can also be generalized to prediction problems in other hydrological domains.</p>
<h3 id="quadmap-variable-resolution-maps-to-better-represent-spatial-uncertainty">QuadMap: Variable resolution maps to better represent spatial uncertainty</h3>
<p>Uncertainty assessment is an integral component of spatial modelling not only from a analytical point of view but also as a communication tool. However, end users find uncertainty maps difficult to perceive alongside the prediction map. A common misconception is that finer resolution maps necessarily have higher precision. Here, we present an approach to take advantage of users’ perceptions of the resolution-quality relationship by incorporating prediction and uncertainty into a single, variable resolution digital map where the uncertainty is encoded as the pixel size. We use the quadtree algorithm to recursively partition the original map, aggregating pixels with uncertainty greater than a target threshold. In the resulting maps, users can immediately see where the uncertainty is large since it corresponds to coarser “pixelated” areas. This approach is not only useful to visualise and communicate uncertainty but it could be extended to integrate the quadtree into analytical workflows.</p>
<h3 id="rainfall-erosivity-index-for-monitoring-global-soil-erosion">Rainfall erosivity index for monitoring global soil erosion</h3>
<p>Rainfall erosivity (R) is commonly used to measure water and soil loss by representing the degree of rainfall-induced soil erosion. However, methods for calculating rainfall erosivity vary significantly regarding regional climatic and precipitation characteristics. How to quantitatively illustrate rainfall erosivity remains a key issue for soil erosion monitoring. In this paper, we summarize the basic principles in calculating rainfall erosivity, as well as the relationships and differences among mainstream methods. By referring to experiences gained from previous studies, this paper aims to better summarize and analyze the current rainfall erosivity estimation models and space–time distribution, so as to avoid the confused use of each estimation model as well as to proposes future researches. Currently, there is a widespread utilization of simple algorithms for rainfall erosivity estimation, and statistical methods like machine learning are also seen in such applications. Besides, while many have proposed to quantify local-scale rainfall erosivity, significant limitations are recognized for large-scale estimations. Future researches that emerge recently developed technologies such as remote sensing are expected to further improve rainfall erosivity estimation.</p>
<h3 id="towards-multi-model-soil-erosion-modelling-an-evaluation-of-the-erosion-potential-method-epm-for-global-soil-erosion-assessments">Towards multi-model soil erosion modelling: An evaluation of the erosion potential method (EPM) for global soil erosion assessments</h3>
<p>Soil erosion is expected to increase in the future due to climate change. Soil erosion models are useful tools that can be used by decision makers and other stakeholders to deal with soil erosion problems or the implementation of soil protection measures. Most of the modelling applications are using Universal Soil Loss Equation (USLE)-type models. In this study, we evaluate the applicability of the Erosion Potential Model (EPM) and its modified version (mEPM) for the estimation of the gross and net erosion rates at a global scale. The sensitivity analysis shows that the model results have the highest variability due to the soil protection (land cover) coefficient followed by the soil erodibility parameter. The models’ evaluations indicate that that the EPM cannot be applied to cold regions while the mEPM overcomes this issue. The erosion rates based on the EPM were 1.5–2.5 times larger than the ones obtained from the mEPM. Increasing the number of catchment properties as inputs to the model may help in improving the performance of the tested EPM and mEPM. Moreover, a comparison of net soil losses by mEPM with long-term suspended sediment yield data for 116 catchments located around the globe indicates a median bias of less than 10%, although the bias for around 1/3 of catchments was above 100%. Furthermore, a direct comparison with other soil erosion models such as USLE-type models is not possible since the EPM and mEPM do take into consideration other processes such as soil slumps and gully erosion and not just sheet and rill erosion. Therefore, as expected, the gross erosion rates by the EPM and mEPM are higher compared to the USLE-type models. Hence, the mEPM, despite its limitations, could be regarded as an interesting approach for the describing erosion processes around the globe and should be further tested using small- and medium-sized catchments from various climate zones.</p>
<p><a href="/2023/11/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on November 23, 2023.</p>
/2023/10/journalDigest2023-10-02T00:00:00-00:002023-10-02T00:00:00+11:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-20">Journal Paper Digests 2023 #20</h2>
<ul>
<li>A new concept for modelling the moisture dependence of heterotrophic soil respiration</li>
<li>Depth-dependent driver of global soil carbon turnover times</li>
<li>Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions</li>
<li>Enhanced carbon storage in semi-arid soils through termite activity</li>
<li>Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed</li>
<li>Modelling opportunities of potential European abandoned farmland to contribute to environmental policy targets</li>
<li>Specific surface area of soils with different clay mineralogy can be estimated from a single hygroscopic water content</li>
<li>Soil dielectric permittivity modelling for 50 MHz instrumentation</li>
<li>Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries</li>
<li>A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation</li>
</ul>
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<h3 id="a-novel-method-incorporating-large-rock-fragments-for-improved-soil-bulk-density-and-carbon-stock-estimation">A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation</h3>
<p>Soil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core-scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core-scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.</p>
<h3 id="failure-to-scale-in-digital-agronomy-an-analysis-of-site-specific-nutrient-management-decision-support-tools-in-developing-countries">Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries</h3>
<p>While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where “scaling” is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5–10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools’ failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional ‘blanket’ fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled ‘ready’ from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent.</p>
<h3 id="soil-dielectric-permittivity-modelling-for-50-mhz-instrumentation">Soil dielectric permittivity modelling for 50 MHz instrumentation</h3>
<p>Near surface electromagnetic geophysical techniques are proven tools to support soil ecosystem services and soil exploration. Such geophysical techniques provide electromagnetic properties that are useful to characterize the studied soil. The link between relevant soil characteristics and geophysical properties, such as dielectric permittivity (ε), is commonly expressed by pedophysical models. However, some weaknesses remain in their application, such as the requirement of parameters that are difficult to measure or calculate. Therefore, these parameters are frequently fixed, but this oversimplifies the complexity of the investigated soils. Moreover, the validity of ε pedophysical models in the frequency range of operating soil moisture sensors (normally < 100 MHz) remains poorly investigated.</p>
<h3 id="specific-surface-area-of-soils-with-different-clay-mineralogy-can-be-estimated-from-a-single-hygroscopic-water-content">Specific surface area of soils with different clay mineralogy can be estimated from a single hygroscopic water content</h3>
<p>The soil specific surface area (SSA) is an important variable for soil science and geoenvironmental engineering applications, but traditional measurement methods are difficult and time-consuming. Regression models or pedotransfer functions are often used to estimate SSA from other soil properties (e.g., clay content and cation exchange capacity), but these models do not consider the impact of clay mineralogy. Hygroscopic water content (wh) is intimately linked to these soil properties, which suggests that wh may be a better parameter for SSA estimation. This study (i) proposes regression models that estimate SSA from wh at different relative humidity values (5 to 90%) for kaolinite-rich samples (KA), illite-rich or mixed clay samples (IL/MC), montmorillonite-rich samples (ML), and a combination of all samples (ALL) and (ii) compares the performance of the wh models to other published models that comprise clay, silt and soil organic carbon contents and cation exchange capacity. We found that the sample-specific wh regression models accurately estimated SSA for KA, IL/MC and ML samples. For KA and IL/MC samples, the performance of the KA model (e.g., for adsorption, average RMSE = 10.5 m2/g) and IL/MC model (average RMSE = 21.3 m2/g) were better than the ALL-calibration model (KA: average RMSE = 18.7 m2/g; ML: average RMSE = 22.4 m2/g). For ML samples, similar model performance between the ML-calibration model (average RMSE = 41.4 m2/g) and the ALL-calibration model (average RMSE = 41.1 m2/g) was observed. In addition, the model performance of regression models based on wh was superior to models published in the literature that are based on clay, silt and soil organic carbon contents and cation exchange capacity. Overall, this study confirms that a single measure of wh can provide reliable estimates of the SSA while revealing a significant impact of clay mineralogy on model performance.</p>
<h3 id="modelling-opportunities-of-potential-european-abandoned-farmland-to-contribute-to-environmental-policy-targets">Modelling opportunities of potential European abandoned farmland to contribute to environmental policy targets</h3>
<p>Farmland abandonment is a major proximate driver of landscape change in European rural areas and is often followed by natural revegetation. In certain conditions, it might be preferable to prevent or reverse farmland abandonment or manage these areas towards active restoration (i.e., guided rewilding with wild or domesticated animals). These alternative responses to farmland abandonment lead to context-dependent impacts, which can potentially contribute to European Green Deal objectives for environment and rural areas. While previous studies analysed direct impacts of abandonment, there is little insight into how alternative ways of managing abandoned farmland can best contribute to environmental policy goals, and what type of management is preferred where. To assess opportunities in these areas, we compared three abandonment trajectories: natural revegetation, active restoration with rewilding, and extensive re-farming. We analysed the potential positive and negative environmental and cultural impacts of developing these management strategies in all farmland locations that could potentially be abandoned across Europe. Mapping and quantification of the benefits and risks associated with different management responses to abandonment indicate a large spatial variation across regions. While natural revegetation can support high benefits for carbon sequestration and erosion reduction, it is also linked to more frequent trade-offs than re-farming and rewilding. However, there is a very strong spatial variation in these trade-offs. It is worthwhile to focus on areas with the largest gains and fewest trade-offs when targeting investments for prevention of abandonment or rewilding. Our maps can help inform interventions in abandoned farmland to maximise the potential contributions of these lands to the European Green Deal environmental and rural policy targets.</p>
<h3 id="can-catpca-be-utilized-for-spatial-modeling-a-case-of-the-generation-susceptibility-of-gully-head-in-a-watershed">Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed</h3>
<p>Many spatial modeling methods have emerged; however, they require dependent variables, cannot reflect the relationship between categorical variables and numerical variables, and are limited by the interference of data collinearity. Categorical principal components analysis (CATPCA) has the potential to overcome these issues. Therefore, in order to investigate the suitability of CATPCA for spatial modeling, we conducted a case study based on the generation susceptibility of gully heads was determined, including 2310 gully head and 23 variables. CATPCA was first used in the spatial modeling of gully head generation. The first six principal components retained 76.4% of data trends. The area of the training and validation sensitivity curves were 75.4% and 75.7%, respectively, which reflected good levels. CATPCA can simulate gully head spatial differences, and thus, has great potential for spatial modeling applications. Among the 23 factors, elevation, distance to residential areas, human footprint, lithology, and soil type were identified as the main controlling factors affecting the generation susceptibility of gully heads, with high correlations between them. Low altitudes, close proximity to residential areas, high human footprint, and poor vegetation were associated with high susceptibility. The findings of this study provide a better understanding of the applicability of CATPCA for spatial modeling. CATPCA is a novel solution for spatial modeling strategies that can improve understanding and has great potential for various spatial modeling applications in the future.</p>
<h3 id="enhanced-carbon-storage-in-semi-arid-soils-through-termite-activity">Enhanced carbon storage in semi-arid soils through termite activity</h3>
<p>Termites are keystone species in natural ecosystems and their role in the C cycle is potentially substantial but poorly understood. Large (20–40 m) mounds (heuweltjies) of the harvester termite Microhodotermes viator occupy up to a quarter of the semi-arid west coast region of South Africa but their C storage potential is unknown. This study determined the organic and inorganic C fractions, C stocks, and their correlation with each other, depth, and biogenic features in these mounds. Trenches (30–60 m) were excavated through 3 mounds: Buffels River (m.a.p < 100 mm), Klawer (m.a.p 100–200 mm) and Piketberg (m.a.p 300–400 mm) and grid sampled. Mound soils had significantly higher soil organic carbon (SOC) and inorganic carbon (SIC) than surrounding soils. Total C was strongly correlated (ρ > 0.9; p < 0.001) with SIC in the arid mounds and SOC (ρ > 0.75; p < 0.001) in the higher rainfall mound. There was no consistent relationship between SOC and SIC distributions throughout the mounds, which is likely related to solubility-linked translocations of carbonates. For all mounds, SOC was highest in topsoils with a second clear peak in subsoils (>1 m) that was associated with biogenic features, termite channels and burrows. Subsoils contributed substantially (36–41 %) to the total C stock. Total C stocks for the intermediate rainfall mound (Klawer) were estimated at 14.6 tons per mound, with 1.1 tons SOC. In this region, mounds occupy 27 % of the total area but contribute 44 % of the total SOC stock to a depth of 80 cm. This highlights the disproportionate contribution termite mounds make to carbon stocks of these semi-arid environments and demonstrates the importance of deep (<1 m) soil carbon for C modelling. Termite activity needs to be recognized as a major contributor to C stock variability both laterally and at depth and accounted for in land-use change (CO2-LULUCF) models.</p>
<h3 id="cause-effect-relationships-using-structural-equation-modeling-for-soil-properties-in-arid-and-semi-arid-regions">Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions</h3>
<p>Predicting soil properties and evaluating their functions along with their related driving factors is useful for providing useful geographical information for soil management, which is especially important in arid and semi-arid regions. This study investigates the use of a structural equation modeling (SEM) approach for assessing the effects of soil forming factors through environ-mental proxies on three key soil properties, namely soil organic carbon (SOC), calcium carbonate equivalent (CCE) and clay content (clay) in an arid and semi-arid region of Iran. Using a set of 259 soil profiles collected over years 2016–2020 in the Qazvin plain, the cause-effect relationships were estimated between these soil properties and nine environmental factors derived from a digital elevation model and from satellite images. Focusing on two main horizons A and B, it was shown that normalized difference vegetation index, midslope position, elevation, multi-resolution valley bottom flatness, and saga wetness index are impacting these soil properties. Inside each horizon, the effect of CCE and clay on SOC was also evidenced, but to an extent that depends on the horizon. For each soil property, we were able to clearly identify the relationships between the two horizons. Although our SEM approach proved to be useful for identifying and estimating the cause-effect relationships, it failed to provide a good predictive model as required for a relevant digital soil mapping of these soil properties. However, as the SEM approach allows combining soil science knowledge inside a model that accounts for soil forming factors, external factors, and soil system at the same time, it permits an investigation of their potential cause-effect relationships in a rich theoretical framework. The SEM methodology is thus potentially useful for soil scientists that are studying various soil properties in other parts of the world, even if it cannot be advocated as an efficient digital soil mapping method in general.</p>
<h3 id="a-new-concept-for-modelling-the-moisture-dependence-of-heterotrophic-soil-respiration">A new concept for modelling the moisture dependence of heterotrophic soil respiration</h3>
<p>The moisture dependence of heterotrophic soil respiration is a key factor affecting the uncertainty in predicting the response of soil organic carbon (SOC) to global warming. Considering that heterotrophic respiration from unsaturated soils is primarily driven by microbial reduction of oxygen (O2), we propose a new concept to model the respiration by tracking dissolution of gaseous O2 and its subsequent diffusion and microbial reduction at hydrated microsite in the pore space of soil. Total respiration from a soil sample is calculated by summing the O2 reduced by all microbes in the soil. This allows us to separate physical processes and microbial activity occurring at microsites and incorporate pore-scale substrate heterogeneity, macropores and other factors explicitly into the model. We show that scaling up these microscopic physical processes over a soil sample makes soil moisture, temperature, and other factors inherently integrated in their influence on microbial respiration, and that a change in one of them affects the response of the respiration to the change in others. Comparison with experimental data shows the model can reproduce the diverse moisture-respiration relationships observed from various experiments and predict the change in soil respiration with temperature. It is noteworthy to point out that previous studies had attributed the variations in the moisture and temperature sensitivity of heterotrophic soil respiration to microbial adaptation; herein we demonstrate that changes in soil structure and physical processes can also give rise to such variations. Distinguishing between physical and microbial effects in data analysis and modelling is therefore crucial, as mistaking physical effects for microbial adaptation would lead to errors in predicting the response of SOC to environmental changes.</p>
<h3 id="depth-dependent-driver-of-global-soil-carbon-turnover-times">Depth-dependent driver of global soil carbon turnover times</h3>
<p>Soil carbon fixation has the potential to offset anthropogenic carbon emissions and mitigate climate change. However, the carbon fixation capacity still remains uncertain at the global scale, and little is known about the patterns and controls of soil carbon turnover times. Here we synthesize 5188 radiocarbon measurements at the global scale, and random forest models are applied to assess the key drivers of soil carbon turnover times in different soil layers. We find that across the globe, the mean soil carbon turnover time (τ) is 4178 ± 106 years (mean ± standard error), but the turnover time varies significantly across different regions and land cover types, with the longest values of τ being observed in tundra and the shortest in temperate forests. Longer soil carbon turnover times are observed in the northern permafrost regions, where the mean τ value is nearly twice that of the non-permafrost regions. Furthermore, τ is generally longer in subsoil than that in topsoil across all ecosystems. Moreover, we find the key drivers of τ are depth-dependent. The most important factors affecting topsoil τ are microbes (bacteria, fungi), while soil mineral protection is the major contributor to subsoil τ. These results highlight the necessity to integrate depth-specific soil carbon turnover time and its associated drivers in carbon cycling models into future climate change scenarios.</p>
<p><a href="/2023/10/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on October 02, 2023.</p>
/2023/09/journalDigest2023-09-10T00:00:00-00:002023-09-10T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-19">Journal Paper Digests 2023 #19</h2>
<ul>
<li>Exploratory Analysis of Surrogate Metrics to Assess the Resilience of Water Distribution Networks</li>
<li>Prolonged Drought in a Northern California Coastal Region Suppresses Wildfire Impacts on Hydrology</li>
<li>Evidence and Controls of the Acceleration of the Hydrological Cycle Over Land</li>
<li>Cycles-L: A Coupled, 3-D, Land Surface, Hydrologic, and Agroecosystem Landscape Model</li>
<li>Early Warning Indicators of Groundwater Drought in Mountainous Regions</li>
</ul>
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<h3 id="early-warning-indicators-of-groundwater-drought-in-mountainous-regions">Early Warning Indicators of Groundwater Drought in Mountainous Regions</h3>
<p>Generalized additive models are used to identify predictor variables associated with summer groundwater levels</p>
<p>Summer groundwater levels are influenced uniquely by region-specific seasonal climate and hydrological variables</p>
<p>Combinations of predictor variables can be used as early warning indicators of groundwater drought</p>
<h3 id="cycles-l-a-coupled-3-d-land-surface-hydrologic-and-agroecosystem-landscape-model">Cycles-L: A Coupled, 3-D, Land Surface, Hydrologic, and Agroecosystem Landscape Model</h3>
<p>Cycles-L is a coupled agroecosystem hydrologic modeling system that couples an agroecosystem model with a 3-D land surface hydrologic model</p>
<p>Cycles-L simulated well stream discharge, grain crops yield, and nitrogen exports in the stream at a 730-ha agricultural experimental watershed</p>
<p>Cycles-L can simulate landscape level processes affected by climate, topography, soil heterogeneity, and management practices</p>
<h3 id="evidence-and-controls-of-the-acceleration-of-the-hydrological-cycle-over-land">Evidence and Controls of the Acceleration of the Hydrological Cycle Over Land</h3>
<p>Several methods to approximate soil water residence time from commonly available hydrological variables are introduced and compared</p>
<p>The global terrestrial water cycle is currently accelerating and projected to do so in future climate scenarios</p>
<p>Precipitation changes play a more dominant role in the acceleration of the terrestrial water cycle when compared to evapotranspiration</p>
<h3 id="prolonged-drought-in-a-northern-california-coastal-region-suppresses-wildfire-impacts-on-hydrology">Prolonged Drought in a Northern California Coastal Region Suppresses Wildfire Impacts on Hydrology</h3>
<p>Little evidence of wildfire-related shifts in hydrology in drought-prone Northern California coastal region having a Mediterranean climate</p>
<p>When the percent of burned area increased beyond 30% of the watershed, the magnitude of the runoff response asymptotes</p>
<p>Post-wildfire hydrological variability did not extend outside of pre-wildfire streamflow conditions</p>
<h3 id="exploratory-analysis-of-surrogate-metrics-to-assess-the-resilience-of-water-distribution-networks">Exploratory Analysis of Surrogate Metrics to Assess the Resilience of Water Distribution Networks</h3>
<p>Comparative study of surrogate resilience metrics based on surplus energy, entropy and graph-theory</p>
<p>Sensitivity analysis allowed the selection of adequate metrics to assess resilience to demand increase and pipe failure</p>
<p>The weighted resilience index is the most complete metric, capable of assessing hydraulic resilience and network redundancy</p>
<p><a href="/2023/09/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on September 10, 2023.</p>
/2023/08/journalDigest2023-08-15T00:00:00-00:002023-08-15T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-18">Journal Paper Digests 2023 #18</h2>
<ul>
<li>Molecular complexity and diversity of persistent soil organic matter</li>
<li>Ecosystem-scale modelling of soil carbon dynamics: Time for a radical shift of perspective?</li>
<li>A numerical approach for modeling crack closure and infiltrated flow in cracked soils</li>
<li>Do diversified crop rotations influence soil physical health? A meta-analysis</li>
<li>Life cycle assessment, life cycle cost, and exergoeconomic analysis of different tillage systems in safflower production by micronutrients</li>
<li>Response of soil organic carbon stock to land use is modulated by soil hydraulic properties</li>
<li>Definition of Spatial Copula Based Dependence Using a Family of Non-Gaussian Spatial Random Fields</li>
<li>Subsoil carbon loss</li>
<li>Real-time social media sentiment analysis for rapid impact assessment of floods</li>
<li>Filling the maize yield gap based on precision agriculture – A MaxEnt approach</li>
<li>A multi-scale algorithm for the NISAR mission high-resolution soil moisture product</li>
<li>Quantifying uncertainty in land-use land-cover classification using conformal statistics</li>
<li>Prediction of Na- and Ca-montmorillonite contents and swelling properties of clay mixtures using Vis-NIR spectroscopy</li>
</ul>
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<h3 id="prediction-of-na--and-ca-montmorillonite-contents-and-swelling-properties-of-clay-mixtures-using-vis-nir-spectroscopy">Prediction of Na- and Ca-montmorillonite contents and swelling properties of clay mixtures using Vis-NIR spectroscopy</h3>
<p>The aim of this study was to evaluate whether Na- and Ca-montmorillonite and the swell-indicating properties (i.e., free swell index, water uptake capacity, and cation exchange capacity (CEC)) of clay mineral mixtures can be estimated using visible-near-infrared (Vis-NIR) spectral features. The data regarding four types of reference clay minerals (KGa-1b, kaolinite; IYd, illite; SWy-3, Na-montmorillonite; STx-1b, Ca-montmorillonite) and binary and ternary mixtures of the reference clay minerals (SWy-3/KGa-1b/IYd, and STx-1b/KGa-1b/IYd) with specific mass percentage ratios were used as a calibration dataset. The absorption spectral features could be correlated well with changes in the clay mineral content of the mixtures. The leave-one-out cross-validation results showed that the partial least square regression (PLSR) calibration models produced the best prediction in the following order: Na or Ca-montmorillonite, kaolinite, and illite amounts in the mixtures. The calibration models produced better predictions in the ascending order of CEC, free swell index, and water uptake capacity, regardless of the multivariate statistical methods and type of exchangeable cations in montmorillonite. Validation using an independent dataset indicated that the PLSR model predicted the Na- and Ca-montmorillonite content in clay mineral mixtures and bentonite samples. The results of this study provide the possibility of using Vis-NIR absorption spectral features as a screening tool for predicting the montmorillonite content with different interlayer cations in relatively pure clay soils (e.g., bentonite), if the calibration model is developed using the specific montmorillonite contained in the clay soil of interest.</p>
<h3 id="quantifying-uncertainty-in-land-use-land-cover-classification-using-conformal-statistics">Quantifying uncertainty in land-use land-cover classification using conformal statistics</h3>
<p>Land-use land-cover (LULC) change is one of the most important anthropogenic threats to biodiversity and ecosystems integrity. As a result, the systematic generation of annual regional, national, and global LULC map products derived from the classification of satellite imagery data have become critical inputs for multiple scientific disciplines. The importance of quantifying pixel-level uncertainty to improve the robustness of downstream analyses has long been acknowledged but this practice is still not widely adopted in the generation of these LULC products. The lack of uncertainty quantification is likely due to the fact that most approaches that have been put forward for this task are too computationally intensive for large-scale analysis (e.g., bootstrapping). In this article, we describe how conformal statistics can be used to quantify pixel-level uncertainty in a way that is not computationally intensive, is statistically rigorous despite relying on few assumptions, and can be used together with any classification algorithm that produces class probabilities. Our simulation results show how the size of the predictive sets created by conformal statistics can be used as an indicator of classification uncertainty at the pixel level. Our analysis based on data from the Brazilian Amazon reveals that both forest and water have high certainty whereas pasture and the “natural (other)” category have substantial uncertainty. This information can guide additional ground-truth data collection and the resulting raster combining the LULC classification with the uncertainty results can be used to communicate in a transparent way to downstream users which classified pixels have high or low uncertainty. Given the importance of systematic LULC maps and uncertainty quantification, we believe that this approach will find wide use in the remote sensing community.</p>
<h3 id="a-multi-scale-algorithm-for-the-nisar-mission-high-resolution-soil-moisture-product">A multi-scale algorithm for the NISAR mission high-resolution soil moisture product</h3>
<p>This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m3/m3] with a bias of −0.051 [m3/m3]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m3/m3] and a low bias of 0.016 [m3/m3]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m3/m3] with an average bias of <0.07 [m3/m3]. The result confirms that the proposed algorithm meets the NISAR mission’s accuracy goals, i.e., 0.06 [m3/m3] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m2]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.</p>
<h3 id="filling-the-maize-yield-gap-based-on-precision-agriculture--a-maxent-approach">Filling the maize yield gap based on precision agriculture – A MaxEnt approach</h3>
<p>Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species’ niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatio-temporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by “Quinta da Cholda”, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm – maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.</p>
<h3 id="real-time-social-media-sentiment-analysis-for-rapid-impact-assessment-of-floods">Real-time social media sentiment analysis for rapid impact assessment of floods</h3>
<p>Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions.</p>
<p>Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time.</p>
<p>In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time.</p>
<h3 id="subsoil-carbon-loss">Subsoil carbon loss</h3>
<p>A field-based study of 4.5 years of whole-soil warming reveals that warming stimulates loss of structurally complex organic carbon at the same rate as that for bulk organic carbon in subsoil.</p>
<h3 id="definition-of-spatial-copula-based-dependence-using-a-family-of-non-gaussian-spatial-random-fields">Definition of Spatial Copula Based Dependence Using a Family of Non-Gaussian Spatial Random Fields</h3>
<p>Spatial structures of natural variables are often very complex due to the different physical chemical or biological processes which contributed to the emergence of the fields. These structures often show non-Gaussian spatial dependence. Unfortunately, there are only a limited number of approaches that can explicitly consider non-Gaussian behavior. In this contribution, a very flexible way of defining non-Gaussian spatial dependence is presented. The approach is based on a kind of continuous deformation of fields with different Gaussian spatial dependence. Theoretical examples illustrate the methodology for a wide variety of non-Gaussian structures. A real-life example of groundwater quality parameters shows the practical applicability of the geostatistical model.</p>
<p>Key Points
A method to define a wide range of non-Gaussian spatial dependence is presented</p>
<p>A conditional simulation approach for these non-Gaussian structures via Monte Carlo optimization is presented</p>
<p>A groundwater quality parameter study demonstrates the benefits of the approach</p>
<h3 id="response-of-soil-organic-carbon-stock-to-land-use-is-modulated-by-soil-hydraulic-properties">Response of soil organic carbon stock to land use is modulated by soil hydraulic properties</h3>
<p>Understanding the effects of land use on soil organic carbon (SOC) stock is important to develop practices that promote agricultural sustainability and mitigate climate change. The objective of this study was to explore whether the effects of land use on SOC stock differed with soil type and, particularly, with soil hydraulic properties. For this purpose, 90 sites from the Canterbury Plains, New Zealand, were sampled to a depth of 30 cm. The sampled sites consisted of long-term (>20 years) dryland pasture [DP], irrigated pasture [IP], and irrigated cropping [IC] covering three contrasting soil types with different soil drainage levels, including well drained Lismore (LIS) soil, imperfectly drained Templeton (TEM) soil, and poorly drained Waterton/Temuka (WAT) soil. On average, compared with DP, IP increased SOC and hot water extractable carbon (HWEC) stock at all depths; IC decreased SOC and HWEC at 0–15 cm. The results highlighted that the effects of land use change from DP to IP and IC on total SOC and HWEC stock significantly differed between soil types. The greatest gains in SOC and HWEC stocks following change from DP to IP were found in LIS soil; the greatest losses in SOC and HWEC stocks following change from DP to IC were found in WAT soil; and no significant differences in SOC stocks with land use changes were found in TEM soil. The results implied that the interaction between land use and soil type on SOC and HWEC stocks was associated with soil hydraulic properties (e.g. available water capacity and field capacity) that regulate the response of C inputs (net primary productivity) and outputs (microbial decomposition) to land use and management practices (e.g. irrigation and cultivation). Therefore, soil hydraulic properties should be taken into account in designing land use for the purpose of SOC sequestration.</p>
<h3 id="life-cycle-assessment-life-cycle-cost-and-exergoeconomic-analysis-of-different-tillage-systems-in-safflower-production-by-micronutrients">Life cycle assessment, life cycle cost, and exergoeconomic analysis of different tillage systems in safflower production by micronutrients</h3>
<p>In recent decades, one of the biggest challenges facing agriculture has been sustainability. To achieve sustainability goals, it is necessary to consider the compatibility of environmental, economic, and energy aspects. The main aim of this study is to conduct an environmental-economic-exergy assessment of safflower production under different tillage systems with Zn and Fe micronutrients using life cycle cost (LCC), life cycle assessment (LCA), and exergoeconomic analysis. For this purpose, three scenarios are evaluated: conventional tillage (Sc-CT), reduced tillage (Sc-RT), and no-tillage setup (Sc-NT). The LCA method ReCiPe2016 is used to assess environmental impacts based on one ton of safflower seeds as a functional unit. In addition, the emissions social cost (ESC) is considered as a hidden part of LCC in safflower production. Finally, a combination of cumulative exergy demand and net profit is used to compute an exergoeconomic index in agriculture as a novelty. Results indicate that On-Farm emissions derived from nitrogen and diesel fuel are the most significant contributor, accounting for over 50% of the impact on human health and ecosystems. Sc-RT with 62.62 Pt and Sc-NT with 71.66 Pt are the best and worst LCA scenarios of this study. Sc-CT has the highest ESC, with 51.14 $, and Sc-NT has the lowest LCC, with 380.39 $. Exergy results show that nitrogen fertilizer has the largest share of cumulative exergy demand, and Sc-RT, with 0.073 $ MJ−1, is the best scenario from an exergoeconomic perspective. In summary, Sc-RT, Sc-CT, and Sc-NT are ranked first, second, and third, respectively, from an environmental-economic-exergy-friendly point of view.</p>
<h3 id="do-diversified-crop-rotations-influence-soil-physical-health-a-meta-analysis">Do diversified crop rotations influence soil physical health? A meta-analysis</h3>
<p>Crop management practices such as rotation, as well as climatic and edaphic factors, modulate soil physical health. However, the overall magnitude of crop rotation benefits on soil physical health properties across a broad range of different conditions remains uncertain. To address this, we conducted a meta-analysis on 865 paired comparisons from 148 rotation studies to examine i) how crop diversity affected soil physical health properties: bulk density, aggregate stability, porosity, infiltration rate, and saturated hydraulic conductivity, and ii) how management practices, climatic, and edaphic factors influenced crop diversity effects. Overall, increased crop diversity (i.e., number of crop species in the rotation) significantly reduced bulk density (−1.6 ± 1.3%), enhanced soil aggregation (15.9 ± 12.7%), improved porosity (3.1 ± 2.0%), and saturated hydraulic conductivity (112.8 ± 57.9%), but did not significantly change infiltration rate (92.2 ± 98.7%) compared to less diverse systems. Compared to using conventional tillage and cereals-only rotations, diverse rotations combined with conservation tillage or including grain legumes performed even better in enhancing both soil aggregation and porosity. Diverse crop rotations managed for 5–10 yr showed greater benefits in regions experiencing mean annual precipitation > 900 mm, and in medium- and fine-textured soils. Among soil physical health properties, saturated hydraulic conductivity was the most responsive to management practices. Based on this meta-analysis, we conclude that rotations including diverse crop species and grain legumes, managed under conservation tillage are best for improving soil physical health, and thus should be considered when designing and developing sustainable cropping systems that promote soil health, system resilience, and crop productivity.</p>
<h3 id="a-numerical-approach-for-modeling-crack-closure-and-infiltrated-flow-in-cracked-soils">A numerical approach for modeling crack closure and infiltrated flow in cracked soils</h3>
<p>Soil cracks generated in natural fields will change soil structure and provide pathways for preferential flow. Although most simulations have focused on the influences of surface cracks or subsurface cracks in the fields, few studies are conducted on the deformable cracks extending from soil surface to deep soil layer. The present study addresses a fundamental issue and investigates the differences in water flow during infiltration processes of deformable and non-deformable cracked soils. Based on the discrete crack network model, a two-dimensional numerical approach is proposed to investigate the preferential flow in deformable cracked soil. Numerical simulations on water flow are implemented for matrix domain and cracks with the finite element method, which are discretized into solid elements and zero thickness elements, respectively. The proposed model is validated through explicit modeling and experimental observation and then compared with deformable crack approach. It is further employed to simulate the infiltration process of deformable cracked soil revealing significant differences in infiltration characteristics between deformable and non-deformable cracked soils, even if the cracks all act as preferential flow pathways. Although the saturation in crack closure of numerical model is variable during infiltration process, the effect of saturation is still affected by the number of cracks. Furthermore, cracks do not entirely close over a long period of infiltration, and thus they can become preferential flow pathways in subsequent infiltration events. These findings highlight the gap in simulations of water infiltration in cracked soil and the potential for erroneous simulations of water condition when cracks cannot be deformable during infiltration process.</p>
<h3 id="ecosystem-scale-modelling-of-soil-carbon-dynamics-time-for-a-radical-shift-of-perspective">Ecosystem-scale modelling of soil carbon dynamics: Time for a radical shift of perspective?</h3>
<p>Over the last few years, several researchers working on the development of “biogeochemical” or “ecosystem-scale” models of soil carbon dynamics have reported struggling with a number of difficult challenges. At the same time, work in this area has focused exclusively on microbial activity described at a macro-ecological level, and has entirely bypassed the abundant literature produced in the last two decades on the study of soil processes at the microscale. Juxtaposition of these different observations suggests that a radical shift of perspective is in order. In this general context, the present article carries out an in-depth analysis of several of the key limitations of current ecosystem-scale models and recommends a number of steps to shift the perspective to one that is argued to have a better chance of success in the relatively short time we have to address several pressing soil-related environmental problems. These steps, in particular, require the development of large-spatial-scale models of soil carbon dynamics to be far more interdisciplinary than it has been till now, and to adopt a “bottom-up” approach, building on what the research at the microscale reveals about soil processes. Nevertheless, because it may assist in upscaling efforts, it is argued that some room should be preserved for work to continue on the search for empirical models applicable at large spatial scales.</p>
<h3 id="molecular-complexity-and-diversity-of-persistent-soil-organic-matter">Molecular complexity and diversity of persistent soil organic matter</h3>
<p>Managing and increasing organic matter in soil requires greater understanding of the mechanisms driving its persistence through resistance to microbial decomposition. Conflicting evidence exists for whether persistent soil organic matter (SOM) is molecularly complex and diverse. As such, this study used a novel application of graph networks with pyrolysis-gas chromatography-mass spectrometry to quantify the complexity and diversity of persistent SOM, defined as SOM that persists through time (soil radiocarbon age) and soil depth. We analyzed soils from the Cooloola giant podzol chronosequence across a large gradient of soil depths (0–15 m) and SOM radiocarbon ages (modern to 19,000 years BP). We found that the most persistent SOM on this gradient was highly aromatic and had the lowest molecular complexity and diversity. By contrast, fresh surface SOM had higher molecular complexity and diversity, with high contributions of plant-derived lignins and polysaccharides. These findings indicate that persisting SOM declines in molecular complexity and diversity over geological timescales and soil depths, with aromatic SOM compounds persisting longer with mineral association.</p>
<p><a href="/2023/08/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on August 15, 2023.</p>
/2023/07/journalDigest2023-07-30T00:00:00-00:002023-07-30T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-17">Journal Paper Digests 2023 #17</h2>
<ul>
<li>Multidimensional Scaling of Varietal Data in Sedimentary Provenance Analysis</li>
<li>The ECO framework: advancing evidence-based science engagement within environmental research programs and organizations</li>
<li>Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley</li>
<li>Mapping global soil acidification under N deposition</li>
<li>No detectable upper limit of mineral-associated organic carbon in temperate agricultural soils</li>
<li>Limitations of farm management data in analyses of decadal changes in SOC stocks in the Danish soil-monitoring network</li>
<li>Characterization by X-ray μCT of the air-filled porosity of an agricultural soil at different matric potentials</li>
<li>Surrogate-Model Assisted Plausibility-Check, Calibration, and Posterior-Distribution Evaluation of</li>
<li>Do cover crops impact labile C more than total C?</li>
<li>A simple soil organic carbon level metric beyond the organic carbon-to-clay ratio</li>
</ul>
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<h3 id="a-simple-soil-organic-carbon-level-metric-beyond-the-organic-carbon-to-clay-ratio">A simple soil organic carbon level metric beyond the organic carbon-to-clay ratio</h3>
<p>Soil is a precious and non-renewable resource that is under increasing pressure and the development of indicators to monitor its state is pivotal. Soil organic carbon (SOC) is important for key physical, chemical and biological soil properties and thus a central indicator of soil quality and soil health. The content of SOC is driven by many abiotic factors, such as texture and climate, and is therefore strongly site-specific, which complicates, for example, the search for appropriate threshold values to differentiate healthy from less healthy soils. The SOC:clay ratio has been introduced as a normalized SOC level metric to indicate soils’ structural condition, with classes ranging from degraded (<1:13) to very good (>1:8). This study applied the ratio to 2958 topsoils (0–30 cm) in the German Agricultural Soil Inventory and showed that it is not a suitable SOC level metric since strongly biased, misleading and partly insensitive to SOC changes. The proportion of soils with SOC levels classified as degraded increased exponentially with clay content, indicating the indicator’s overly strong clay dependence. Thus, 94% of all Chernozems, which are known to have elevated SOC contents and a favourable soil structure, were found to have either degraded (61%) or moderate (33%) normalized SOC levels. The ratio between actual and expected SOC (SOC:SOCexp) is proposed as an easy-to-use alternative where expected SOC is derived from a regression between SOC and clay content. This ratio allows a simple but unbiased estimate of the clay-normalized SOC level. The quartiles of this ratio were used to derive threshold values to divide the dataset into the classes degraded, moderate, good and very good. These classes were clearly linked to bulk volume (inverse of bulk density) as an important structural parameter, which was not the case for classes based on the SOC:clay ratio. Therefore, SOC:SOCexp and its temporal dynamic are proposed for limited areas such as regions, states or pedoclimatic zones, for example, in a soil health monitoring context; further testing is, however, recommended.</p>
<h3 id="do-cover-crops-impact-labile-c-more-than-total-c">Do cover crops impact labile C more than total C?</h3>
<p>The potential of cover crops (CC) to increase total soil organic C (SOC) concentration can be inconsistent, but labile SOC is considered to be more sensitive to management than total SOC. This leads to two questions: Do CCs impact labile SOC more than total SOC? Do CCs increase labile SOC more rapidly than total SOC? This review compares CC impacts on labile and total SOC based on CC studies reporting both parameters up to 31 Dec 2022. Labile and total SOC concentrations were measured in 31 CC study locations. Cover crops increased labile SOC concentration in 58% (18 of 31) and had no effect in 42% (13 of 31) of locations, suggesting CCs do not increase labile SOC in all cases. Within the 18 locations, CCs increased labile SOC without increasing total SOC in only 19% (6 of 31 locations), while in the rest (12 of 31) of locations, CCs increased both labile and total SOC. Thus, CCs increased labile SOC more rapidly than total SOC in only one-fifth of cases. Also, the few studies that monitored changes in labile SOC with time found CCs do not always increase labile more rapidly than total SOC. In the 12 locations where CCs increased both labile and total SOC, CCs increased labile SOC by 54 ± 30% and total SOC by 23 ± 10%, indicating CCs can increase labile SOC by about two times compared with total SOC in some locations. Increased CC biomass production and reduced residue decomposition can increase labile SOC. Overall, CCs increase labile SOC in most cases but may not always increase labile SOC more rapidly than total SOC although more CC studies monitoring changes in SOC pools with time are needed to better understand CC impacts on SOC fractions under different CC management scenarios and climatic conditions.</p>
<h3 id="surrogate-model-assisted-plausibility-check-calibration-and-posterior-distribution-evaluation-of">Surrogate-Model Assisted Plausibility-Check, Calibration, and Posterior-Distribution Evaluation of</h3>
<ul>
<li>
<p>We use Gaussian Process Regression as proxy models to expedite the calibration of an expensive process-based hydrogeological model</p>
</li>
<li>
<p>We estimate the full posterior parameter distribution by Markov-Chain Monte Carlo sampling using the proxy models</p>
</li>
<li>
<p>We compare this distribution to results obtained by Neural Posterior Estimation</p>
</li>
</ul>
<h3 id="characterization-by-x-ray-μct-of-the-air-filled-porosity-of-an-agricultural-soil-at-different-matric-potentials">Characterization by X-ray μCT of the air-filled porosity of an agricultural soil at different matric potentials</h3>
<p>To describe various important soil processes like the release of greenhouse gases or the proliferation of microorganisms, it is necessary to assess quantitatively how the geometry and in particular the connectivity of the air-filled pore space of a soil evolves as it is progressively dried. The availability of X-ray computed microtomography (μCT) images of soil samples now allows this information to be obtained directly, without having to rely on the interpretation of macroscopic measurements using capillary theory, as used to be the case. In this general context, we present different methods to describe quantitatively the configuration of the air-filled pore space in 3D μCT images of 20 separate samples of a loamy soil equilibrated at different matric potentials. Even though measures using μCT on such multi-scale materials strongly depend on image resolution, our results show that in general, soil samples most often behave as expected, for example, connectivity increases with higher negative matric potential, while tortuosity decreases. However, simple correlations could not be found between the evolution of quantitative descriptors of the pore space at the different matric potentials and routinely measured macroscopic soil parameters. A statistical analysis of all soil samples concurrently confirmed this lack of correspondence.</p>
<h3 id="limitations-of-farm-management-data-in-analyses-of-decadal-changes-in-soc-stocks-in-the-danish-soil-monitoring-network">Limitations of farm management data in analyses of decadal changes in SOC stocks in the Danish soil-monitoring network</h3>
<p>Changes in soil organic carbon (SOC) storage in agricultural land are an important part of the Land Use, Land-Use Change and Forestry component of national greenhouse gas emission inventories. Furthermore, as climate mitigation strategies and incentives for carbon farming are being developed, accurate estimates of SOC stocks are essential to verify any management-induced changes in SOC. Based on agricultural mineral soils in the Danish soil-monitoring network, we analysed management effects on SOC stocks using data from the two most recent surveys (2009 and 2019). Between 2009 and 2019, the average increase in SOC stock was 1.2 Mg C ha−1 for 0–50 cm despite a loss of 1.2 Mg C ha−1 from the topsoil (0–25 cm), stressing the importance of including deeper soil layers in soil-monitoring networks. Comparing all four national surveys (1986, 1997, 2009, 2019), the mean SOC stock of mineral soils in Denmark appears stable. The change in SOC stock between 2009 and 2019 was analysed in detail in relation to management practices as reported by farmers. We found that the effects of single management factors were difficult to isolate from co-varying factors including soil parameters and that the use of farm management data to explain changes in SOC stocks observed in soil-monitoring networks appears limited. Uncertainty in SOC stock estimates also arises from low sampling frequency and statistical challenges related to regression to the mean. However, repeated stock measurements at decadal intervals still represent a benchmark for the overall development in regional and national SOC storage, as affected by actual farm management.</p>
<h3 id="no-detectable-upper-limit-of-mineral-associated-organic-carbon-in-temperate-agricultural-soils">No detectable upper limit of mineral-associated organic carbon in temperate agricultural soils</h3>
<p>Mineral-associated organic carbon (MAOC) is the stabilised fraction of soil organic carbon (SOC). Its accrual in the soil can help mitigating climate change. So far, the capacity of soils to store MAOC was believed to be limited by the amount of mineral surfaces available. Here, we provide evidence that up to a total SOC content of about 12%, MAOC is a surprisingly constant fraction of total SOC. This questions the notion of a maximum capacity of soils to store MAOC, at least for temperate soil within the tested range of SOC contents.</p>
<h3 id="mapping-global-soil-acidification-under-n-deposition">Mapping global soil acidification under N deposition</h3>
<p>The N deposition effects on soil pH across global terrestrial ecosystems remain poorly understood. By conducting a global meta-analysis with paired observations of soil pH under N addition and control from 634 studies spanning major types of terrestrial ecosystems, we showed that soil acidification increased rapidly with N addition amount and was most severe in neutral-pH soils. Grassland soil pH decreased most strongly under high N addition while wetlands were the least acidified. We extrapolated these relationships to global mapping and highlighted the hotspots of soil acidification under current and future atmospheric N deposition.</p>
<h3 id="soil-moisture-retrieval-from-sentinel-1-using-a-first-order-radiative-transfer-modela-case-study-over-the-po-valley">Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley</h3>
<p>Soil moisture is an important variable controlling many land surface processes and is used to quantify precipitation, drought, flooding, irrigation and other factors that influence decision making and risk-assessment. This paper presents the retrieval of high resolution (
1 km) soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1) previously only tested for scatterometer data. The model is applied over a diverse set of landcover types across the entire Po-Valley in Italy over a 4-year time-period from 2016 to 2019. The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The model parameterisation and retrieval method are chosen such as to constitute a trade-off between a physically plausible and a computationally feasible modeling approach. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.</p>
<h3 id="the-eco-framework-advancing-evidence-based-science-engagement-within-environmental-research-programs-and-organizations">The ECO framework: advancing evidence-based science engagement within environmental research programs and organizations</h3>
<p>Despite widespread interest in science communication, public engagement with science, and engaged research, a large gap exists between the theories behind science engagement and how it is practiced within the scientific community. The scholarship of science engagement is also fractured, with knowledge and insights fragmented across discourses related to science communication, informal science learning, participatory research, and sustainability science. In the present article, we share a planning tool for integrating evidence and theory from these discourses into effective programs and projects. The ECO framework promotes three distinct and interacting modes of science engagement practice: formative engagement (listening and relationship building), codesign and coproduction (action-oriented partnerships), and broader outreach (expanding networks and dissemination). By planning engagement activities with attention to these three modes of engagement, scientists and scientific research organizations will be better poised to address urgent needs for stronger connections between science and society and increased use of scientific research in decision-making.</p>
<h3 id="multidimensional-scaling-of-varietal-data-in-sedimentary-provenance-analysis">Multidimensional Scaling of Varietal Data in Sedimentary Provenance Analysis</h3>
<p>Varietal data are defined as lists of compositional tables</p>
<p>Given an appropriate dissimilarity measure, varietal data can be subjected to multidimensional scaling</p>
<p>This paper introduces three ways to quantify the pairwise dissimilarity of varietal data</p>
<p><a href="/2023/07/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on July 30, 2023.</p>
/2023/07/journalDigest2023-07-24T00:00:00-00:002023-07-24T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-16">Journal Paper Digests 2023 #16</h2>
<ul>
<li>A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation</li>
<li>Into the Pedocene – Pedology of a changing world</li>
<li>Empirical equations for estimating field capacity in dryland cropping soils of southeastern Australia</li>
</ul>
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<h3 id="empirical-equations-for-estimating-field-capacity-in-dryland-cropping-soils-of-southeastern-australia">Empirical equations for estimating field capacity in dryland cropping soils of southeastern Australia</h3>
<p>The root zone soil water content at field capacity, θFC, is often crucial for irrigation scheduling and soil-plant-atmosphere modelling. Although θFC is traditionally determined using methodologies based on matric head or when water flux at the bottom of the root-zone equal to a prescribed negligible flux, these approaches can be problematic for some soils and applications. In this study, a novel water storage approach was used to estimate θFC for 11 soil texture classes in southeastern Australia (northwest Victoria) by employing the Soil-Water-Atmosphere-Plant (SWAP) model to simulate gravity drainage from saturation in the active root zone (top 60 cm). Field capacity was specified as the average root zone water content corresponding to a 1% relative change in daily soil water storage. We have also estimated flux-based θFC when prescribed flux at the bottom of the root-zone was 0.1 cm d-1. Three new empirical equations were developed to estimate storage-based θFC as a function of SWAP-simulated drainage flux out of the root zone, the n parameter of the van Genuchten function, and the saturated soil hydraulic conductivity, Ks, or the soil water content at − 100 cm matric head. We have evaluated these three new equations in addition to nine published equations for estimating θFC. The three new equations were found to be better predictors of θFC than most of the nine popular equations reported in the literature when compared to flux-based θFC values. Based on findings from this study the new equations are considered to be effective for irrigation scheduling and crop/climate modelling on the dryland soils of Victoria. Future studies will assess the applicability of the new equations to other parts of Australia.</p>
<h3 id="into-the-pedocene--pedology-of-a-changing-world">Into the Pedocene – Pedology of a changing world</h3>
<p>Pedology, or the study of soil, is often viewed focusing on soil formation, morphology, mapping and classification. But the study of soil has largely expanded beyond those four areas and now includes quantitative studies using soil legacy data combined with technological advances in data collection, soil sampling and computation. We now have global availability of soil information and can retrieve pedological information for any location including some indication of its accuracy. Scientific and technological developments in pedology have been led by the rise of several subdisciplines including pedometrics, digital soil mapping, spectral pedology, digital soil morphometrics, hydropedology, microbial pedology, astropedology, and the development of pedotransfer functions. With the expansion of pedology and its relevance for understanding the earth system and tackling global change, it is postulated that soil science has now entered the ‘Pedocene’ – a soil epoch equivalent to the Anthropocene. The Pedocene is characterized by the quantitative understanding and evaluation of the global soil system, and the effects of human-induced changes brought to soil.</p>
<h3 id="a-novel-method-incorporating-large-rock-fragments-for-improved-soil-bulk-density-and-carbon-stock-estimation">A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation</h3>
<p>Soil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core-scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core-scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.</p>
<p><a href="/2023/07/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on July 24, 2023.</p>
/2023/07/journalDigest2023-07-23T00:00:00-00:002023-07-23T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-15">Journal Paper Digests 2023 #15</h2>
<ul>
<li>Downscaling legacy soil information for hydrological soil mapping using multinomial logistic regression</li>
<li>Measurement of lime movement and dissolution in acidic soils using mid-infrared spectroscopy</li>
<li>The EnMAP imaging spectroscopy mission towards operations</li>
<li>Filling the maize yield gap based on precision agriculture – A MaxEnt approach</li>
<li>Environmental factors and spatial dependence explain half of the inherent variation in carbon pools of tropical paddy soils</li>
<li>Bridging structural and functional hydrological connectivity in dryland ecosystems</li>
</ul>
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<h3 id="bridging-structural-and-functional-hydrological-connectivity-in-dryland-ecosystems">Bridging structural and functional hydrological connectivity in dryland ecosystems</h3>
<p>On dryland hillslopes, vegetation water availability is often subsidized by the redistribution of rainfall runoff from bare soil (sources) to vegetation patches (sinks). In regions where rainfall volumes are too low to support spatially continuous plant growth, such functional connectivity between bare soil and vegetated areas enables the establishment and persistence of dryland ecosystems. Increasing the connectivity within bare soil areas can intensify runoff and increase water losses from hillslopes, disrupting this redistribution and reducing the water available to sustain ecosystem function. Inferring functional connectivity (from bare to vegetated, or within bare areas) from structural landscape features is an attractive approach to enable rapid, scalable characterization of dryland ecosystem function from remote observations. Such inference, however, would rely on metrics of structural connectivity, which describe the contiguity of bare soil areas. Several studies have observed non-stationarity in the relations between functional and structural connectivity metrics as rainfall conditions vary. Consequently, the suitability of using structural connectivity to provide a reliable proxy for functional connectivity remains uncertain and motivates the work here.</p>
<p>Relations between structural and functional connectivity metrics are established based on model simulations of rainfall-runoff on hillslopes with varying soil properties and vegetation patterns. These relations vary between two hydrologic limits – a ‘local’ (patch-scale) limit, in which functional connectivity is related to structural connectivity, and a ‘global’ (hillslope-scale) limit, in which functional connectivity is most related to the hillslope vegetation fraction regardless of the structural connectivity of bare soil areas. The transition between these limits within the simulations depends on rainfall intensity and duration, and soil permeability. While the local limit may strengthen positive feedbacks between vegetation and water availability, the implications of these limits for dryland functioning need further exploration, particularly considering the timescale separation between storm runoff production and vegetation growth.</p>
<h3 id="environmental-factors-and-spatial-dependence-explain-half-of-the-inherent-variation-in-carbon-pools-of-tropical-paddy-soils">Environmental factors and spatial dependence explain half of the inherent variation in carbon pools of tropical paddy soils</h3>
<p>The current study examined macro-environmental (climate and topography), micro-environmental (soil chemical properties) drivers, and spatially derived parameters that are significantly associated with soil carbon pools (0–15 cm) across tropical paddy-growing areas in Sri Lanka using the data from 987 sampling sites across the country. Redundancy analysis was performed to identify the relationships between the explanatory variables and the variation in different soil carbon pools i.e., total carbon (TC), Microbial Biomass Carbon (MBC), Permanganate Oxidizable Carbon (POXC), and Dissolved Organic Carbon (DOC). The spatial patterns in soil carbon pools were evaluated using Moran’s eigenvector maps. Results indicated that macro, micro-environmental drivers and spatial variables explained 47% of the inherent variation of the TC, MBC, POXC and DOC. Micro-environmental drivers had a larger unique fraction relative to macro-environmental drivers (4% and 1% of the total variation, respectively). Most of the variation explained by macro-environmental drivers was shared by micro-environmental drivers (11% out of 15%). Among macro-environmental drivers, rainfall and enhanced vegetation index were more strongly related to the soil carbon pools compared to the topography-related factors. In terms of micro-environmental drivers, total N, available K, Ca, and soil pH (H2O) were the best explanatory variables of soil carbon pools. Spatial patterns in soil carbon pools were largely induced by the environmental predictors that are spatially structured. Our findings provide insights into improving the reliability of spatial estimation of the soil carbon by incorporating important soil carbon preditors and quantifying the impacts of environmental changes on soil carbon pools.</p>
<h3 id="filling-the-maize-yield-gap-based-on-precision-agriculture--a-maxent-approach">Filling the maize yield gap based on precision agriculture – A MaxEnt approach</h3>
<p>Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species’ niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatio-temporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by “Quinta da Cholda”, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm – maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.</p>
<h3 id="the-enmap-imaging-spectroscopy-mission-towards-operations">The EnMAP imaging spectroscopy mission towards operations</h3>
<p>EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2
and 2445.5
with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of
at a swath width of 30
, allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users.</p>
<h3 id="measurement-of-lime-movement-and-dissolution-in-acidic-soils-using-mid-infrared-spectroscopy">Measurement of lime movement and dissolution in acidic soils using mid-infrared spectroscopy</h3>
<p>Acidification of surface and sub-surface soils limits agricultural production globally. Conventional surface application of lime is the most common amelioration approach for surface acidity. However, amelioration of sub-surface acidity is challenging to achieve using this approach. Testing the efficacy of liming to treat acidity through the soil profile via the measurement of lime movement requires high throughput information about soil properties at a high spatial resolution, which can be time consuming and expensive using traditional laboratory analysis. Here, we investigated the potential of Mid Infrared (MIR) spectroscopy as a tool to monitor lime dissolution and vertical movement through soils at high spatial resolution. Soil samples were collected at 2.5 cm intervals to 20 cm depth at three trial sites in South Australia, with various lime treatments applied either 6 years or 1 year prior to sampling. MIR Partial least squares regression (PLSR) predictions were undertaken to measure lime dissolution and alkalinity movement via soil pH, and undissolved lime presence via soil carbonate concentrations. Lime balance calculations were then performed to determine the fate of applied lime and assess efficacy of various rates of lime products applied at the surface only or via incorporation. MIR-PLSR prediction model performance was strong for both soil pH (R2 = 0.923 and RMSE = 0.202) and carbonate (R2 =0.829 and RMSE=0.042 CO3%). Results indicated the movement of alkalinity at all sites was limited, and revealed increased movement at the longer-term (∼6 years) vs shorter-term (∼1 year) sites where lime was surface applied. Lime balance calculations indicated that residual lime remained in the top 7.5 cm of the soil profile while soils remained acidic below this depth. Findings suggest that incorporation of residual lime and additional lime applications may be necessary to remediate sub-surface acidity. A decision tree was developed to inform management of surface and sub-surface soil acidity. The study validates the potential of MIR spectroscopy to measure and monitor the effectiveness and movement of lime with improved resolution in acidic soils.</p>
<h3 id="downscaling-legacy-soil-information-for-hydrological-soil-mapping-using-multinomial-logistic-regression">Downscaling legacy soil information for hydrological soil mapping using multinomial logistic regression</h3>
<p>In South Africa, there is a growing demand for large scale detailed hydrological soil maps for modelling and management purposes. However, imbalanced legacy soil information often impedes the accurate creation of such maps by not being representative of the environmental complexity of large-scale catchments and containing imbalanced soil class distributions, often resulting in the loss of minority soil classes, which are often of great hydrological importance (e.g., wetland and riparian soils). In this study, we proposed a new downscaling approach to handle spatially localised legacy soil data within a larger low resolution legacy soil dataset to create an accurate hydrological soil map of the macro-scale (5790 km2) Sabie-Sand catchment using multinomial logistic regression (MNLR). The spatially localised legacy data was downscaled using k-means clustering and added to the broader legacy dataset. Five levels of legacy soil data were analysed in their representation of environmental covariates using QQ-plots and a Welsh’s t-test and their mapping accuracy using confusion matrix’s and Kappa coefficient statistics. However, MNLR also requires balanced soil classes. The value of the best performing legacy soil dataset was also compared to using all available soil information after both had their soil class distributions fully balanced using Synthetic Minority Oversampling Technique (SMOTE). The 500 ha/observation-SMOTE dataset resulted in the most accurate hydrological soil map with a validation point accuracy of 73% and a Kappa coefficient of 0.60, substantially outperforming the other downscaled soil maps as well as the SMOTE balanced dataset using all available soil information. This was due to the decreased variation between observations and catchment means, where the 500 ha/observation dataset yielded the least variation between soil observation and catchment datasets and well as reducing the class imbalance within the legacy soil data. Downscaling spatially localised legacy soil data for environmental representation is an effective tool to improve digital soil mapping accuracy using MNLR.</p>
<p><a href="/2023/07/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on July 23, 2023.</p>
/2023/07/journalDigest2023-07-05T00:00:00-00:002023-07-05T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-12">Journal Paper Digests 2023 #12</h2>
<ul>
<li>An integrated approach for estimating soil health: Incorporating digital elevation models and remote sensing of vegetation</li>
<li>Mapping Landslide Susceptibility Over Large Regions With Limited Data</li>
<li>An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks-Corey Model and Waxman-Smits Model</li>
<li>Conceptualising the drivers of ultra-processed food production and consumption and their environmental impacts: A group model-building exercise</li>
</ul>
<!--more-->
<h3 id="conceptualising-the-drivers-of-ultra-processed-food-production-and-consumption-and-their-environmental-impacts-a-group-model-building-exercise">Conceptualising the drivers of ultra-processed food production and consumption and their environmental impacts: A group model-building exercise</h3>
<p>Using group model building we developed a series of causal loop diagrams identifying the environmental impacts of ultra-processed food (UPF) systems, and underlying system drivers, which was subsequently validated against the peer-reviewed literature. The final conceptual model displays the commercial, biological and social drivers of the UPF system, and the impacts on environmental sub-systems including climate, land, water and waste. It displays complex interactions between various environmental impacts, demonstrating how changes to one component of the system could have flow-on effects on other components. Trade-offs and uncertainties are discussed. The model has a wide range of applications including informing the design of quantitative analyses, identifying research gaps and potential policy trade-offs resulting from a reduction of ultra-processed food production and consumption.</p>
<h3 id="an-unsaturated-hydraulic-conductivity-model-based-on-the-capillary-bundle-model-the-brooks-corey-model-and-waxman-smits-model">An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks-Corey Model and Waxman-Smits Model</h3>
<p>Soil unsaturated hydraulic conductivity (K), which depends on water content (?) and matric potential (?), exhibits a high degree of variability at the field scale. Here we first develop a theoretical hydraulic-electrical conductivity (s) relationship under low and high salinity cases based on the capillary bundle model and Waxman and Smits model which can account for the non-linear behavior of s at low salinities. Then the K-s relationship is converted into a K(?, ?) model using the Brooks-Corey model. The model includes two parameters c and ?. Parameter c accounts for the variation of the term (? + 2)/(? + 4) where ? is the pore size distribution parameter in the Brooks-Corey model, and the term m-n where m and n are Archie’s saturation and cementation exponents, respectively. Parameter ? is the sum of the tortuosity factor accounting for the differences between hydraulic and electrical tortuosity and Archie’s saturation exponent. Based on a calibration data set of 150 soils selected from the UNSODA database, the best fitting log(c) and ? values were determined as -2.53 and 1.92, -4.39 and -0.14, -5.01 and -1.34, and -5.79 and -2.27 for four textural groups. The estimated log(10)(K) values with the new K(?, ?) model compared well to the measured values from an independent data set of 49 soils selected from the UNSODA database, with mean error (ME), relative error (RE), root mean square error (RMSE) and coefficient of determination (R-2) values of 0.02, 8.8%, 0.80 and 0.73, respectively. A second test of the new K(?, ?) model using a data set representing 23 soils reported in the literature also showed good agreement between estimated and measured log(10)(K) values with ME of -0.01, RE of 9.5%, RMSE of 0.77 and R-2 of 0.85. The new K(?, ?) model outperformed the Mualem-van Genuchten model and two recently published pedo-transfer functions. The new K(?, ?) model can be applied for estimating K under field conditions and for hydrologic modeling without need for soil water retention curve data fitting to derive a K function.</p>
<h3 id="mapping-landslide-susceptibility-over-large-regions-with-limited-data">Mapping Landslide Susceptibility Over Large Regions With Limited Data</h3>
<p>Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model’s predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.</p>
<h3 id="an-integrated-approach-for-estimating-soil-health-incorporating-digital-elevation-models-and-remote-sensing-of-vegetation">An integrated approach for estimating soil health: Incorporating digital elevation models and remote sensing of vegetation</h3>
<p>The deterioration of soil health (SH) in agricultural lands is a global challenge that poses a threat to food and resource security. We developed a practical framework to facilitate the large-scale SH assessment in agricultural fields of northwestern Iran. A total of 350 soil samples were collected and soil properties were determined. Eight linear and non-linear Soil Health Indexes (SHIs) were developed. Digital Elevation Model (DEM) and multiple remote sensing indexes were obtained from satellite images. SHI prediction models were developed using an integrated approach and through a model selection procedure, the most relevant indexes were identified. The results showed significant (P < 0.05) positive correlation between the IHI-LT and elevation (r = 0.56), Vegetation Health Index (VHI) (r = 0.69), and Surface Water Condition Index (SWCI) (r = 0.79). The multiple regression model including the above indexes strongly explained the spatial variability of the Integrated Soil Health Index (IHI) with both total (LT) and minimum (LM) dataset approaches (R2 = 0.72; AIC =-1607.27; RMSE = 0.03; rho c = 0.65). The developed models can be utilized for large-scale assessment of soil health conditions, reducing the cost and effort of conventional ground-truth soil sampling and analysis. Furthermore, this approach may aid in monitoring and mitigating the soil degradation in agricultural lands.</p>
<p><a href="/2023/07/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on July 05, 2023.</p>
/2023/06/journalDigest2023-06-20T00:00:00-00:002023-06-20T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-11">Journal Paper Digests 2023 #11</h2>
<ul>
<li>An integrated approach for estimating soil health: Incorporating digital elevation models and remote sensing of vegetation</li>
<li>Mapping Landslide Susceptibility Over Large Regions With Limited Data</li>
<li>An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks-Corey Model and Waxman-Smits Model</li>
<li>Conceptualising the drivers of ultra-processed food production and consumption and their environmental impacts: A group model-building exercise</li>
</ul>
<!--more-->
<h3 id="conceptualising-the-drivers-of-ultra-processed-food-production-and-consumption-and-their-environmental-impacts-a-group-model-building-exercise">Conceptualising the drivers of ultra-processed food production and consumption and their environmental impacts: A group model-building exercise</h3>
<p>Using group model building we developed a series of causal loop diagrams identifying the environmental impacts of ultra-processed food (UPF) systems, and underlying system drivers, which was subsequently validated against the peer-reviewed literature. The final conceptual model displays the commercial, biological and social drivers of the UPF system, and the impacts on environmental sub-systems including climate, land, water and waste. It displays complex interactions between various environmental impacts, demonstrating how changes to one component of the system could have flow-on effects on other components. Trade-offs and uncertainties are discussed. The model has a wide range of applications including informing the design of quantitative analyses, identifying research gaps and potential policy trade-offs resulting from a reduction of ultra-processed food production and consumption.</p>
<h3 id="an-unsaturated-hydraulic-conductivity-model-based-on-the-capillary-bundle-model-the-brooks-corey-model-and-waxman-smits-model">An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks-Corey Model and Waxman-Smits Model</h3>
<p>Soil unsaturated hydraulic conductivity (K), which depends on water content (?) and matric potential (?), exhibits a high degree of variability at the field scale. Here we first develop a theoretical hydraulic-electrical conductivity (s) relationship under low and high salinity cases based on the capillary bundle model and Waxman and Smits model which can account for the non-linear behavior of s at low salinities. Then the K-s relationship is converted into a K(?, ?) model using the Brooks-Corey model. The model includes two parameters c and ?. Parameter c accounts for the variation of the term (? + 2)/(? + 4) where ? is the pore size distribution parameter in the Brooks-Corey model, and the term m-n where m and n are Archie’s saturation and cementation exponents, respectively. Parameter ? is the sum of the tortuosity factor accounting for the differences between hydraulic and electrical tortuosity and Archie’s saturation exponent. Based on a calibration data set of 150 soils selected from the UNSODA database, the best fitting log(c) and ? values were determined as -2.53 and 1.92, -4.39 and -0.14, -5.01 and -1.34, and -5.79 and -2.27 for four textural groups. The estimated log(10)(K) values with the new K(?, ?) model compared well to the measured values from an independent data set of 49 soils selected from the UNSODA database, with mean error (ME), relative error (RE), root mean square error (RMSE) and coefficient of determination (R-2) values of 0.02, 8.8%, 0.80 and 0.73, respectively. A second test of the new K(?, ?) model using a data set representing 23 soils reported in the literature also showed good agreement between estimated and measured log(10)(K) values with ME of -0.01, RE of 9.5%, RMSE of 0.77 and R-2 of 0.85. The new K(?, ?) model outperformed the Mualem-van Genuchten model and two recently published pedo-transfer functions. The new K(?, ?) model can be applied for estimating K under field conditions and for hydrologic modeling without need for soil water retention curve data fitting to derive a K function.</p>
<h3 id="mapping-landslide-susceptibility-over-large-regions-with-limited-data">Mapping Landslide Susceptibility Over Large Regions With Limited Data</h3>
<p>Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model’s predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.</p>
<h3 id="an-integrated-approach-for-estimating-soil-health-incorporating-digital-elevation-models-and-remote-sensing-of-vegetation">An integrated approach for estimating soil health: Incorporating digital elevation models and remote sensing of vegetation</h3>
<p>The deterioration of soil health (SH) in agricultural lands is a global challenge that poses a threat to food and resource security. We developed a practical framework to facilitate the large-scale SH assessment in agricultural fields of northwestern Iran. A total of 350 soil samples were collected and soil properties were determined. Eight linear and non-linear Soil Health Indexes (SHIs) were developed. Digital Elevation Model (DEM) and multiple remote sensing indexes were obtained from satellite images. SHI prediction models were developed using an integrated approach and through a model selection procedure, the most relevant indexes were identified. The results showed significant (P < 0.05) positive correlation between the IHI-LT and elevation (r = 0.56), Vegetation Health Index (VHI) (r = 0.69), and Surface Water Condition Index (SWCI) (r = 0.79). The multiple regression model including the above indexes strongly explained the spatial variability of the Integrated Soil Health Index (IHI) with both total (LT) and minimum (LM) dataset approaches (R2 = 0.72; AIC =-1607.27; RMSE = 0.03; rho c = 0.65). The developed models can be utilized for large-scale assessment of soil health conditions, reducing the cost and effort of conventional ground-truth soil sampling and analysis. Furthermore, this approach may aid in monitoring and mitigating the soil degradation in agricultural lands.</p>
<p><a href="/2023/06/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on June 20, 2023.</p>
/2023/06/journalDigest2023-06-11T00:00:00-00:002023-06-11T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-10">Journal Paper Digests 2023 #10</h2>
<ul>
<li>Complex systems modelling of UK winter wheat yield</li>
<li>Twenty-four years of contrasting cropping systems on a brown chernozem in Southern Alberta: crop yields, soil carbon, and subsoil salinity</li>
</ul>
<!--more-->
<h3 id="twenty-four-years-of-contrasting-cropping-systems-on-a-brown-chernozem-in-southern-alberta-crop-yields-soil-carbon-and-subsoil-salinity">Twenty-four years of contrasting cropping systems on a brown chernozem in Southern Alberta: crop yields, soil carbon, and subsoil salinity</h3>
<p>Cropping systems with perennial forages and reduced fallow frequency generally increase soil organic carbon and thus sub-sequent soil health and crop yield. We evaluated the impact of prior cropping systems on subsequent yields and soil properties in a semiarid region by using crop yields as a bioassay of soil health following the termination of a 24-year crop rotation study in the Brown soil zone in Alberta. During 24 growing seasons from 1992 to 2015, the study included three fallow-containing rotations, two annual crop rotations that were cropped continuously, and perennial grass hay, each with two to six fertilizer treatments. During the bioassay period from 2016 through 2020, all plots in the study were uniformly cropped. Compared to unfertilized fallow wheat, soil organic C in the fall of 2015 was 54% higher after 24 years of fertilized grass and up to 14% higher following annual crops in rotations without fallow. The most notable impact of the previous cropping system on yield during the bioassay years was low yield following perennial grass in 2016 and 2018. Soil electrical conductivity measurements showed that subsoil salinity was elevated following perennial grass, demonstrating the importance of subsoil characteristics for healthy soils. Crop yields in the fifth year of the crop bioassay were 10%-20% greater due to reduced fallow frequency or increased crop diversity. The long-term impact of the cropping system on crop yield in this study depended on drought intensity due to counteracting changes in soil organic matter and subsoil salinity.</p>
<h3 id="complex-systems-modelling-of-uk-winter-wheat-yield">Complex systems modelling of UK winter wheat yield</h3>
<p>Wheat is one of the most important global crops, understanding the drivers of wheat yield has significant societal benefits. Climate variables are particularly important in determining interannual variations in wheat yield, either as primary factors which directly influence the stages of wheat growth, or as secondary factors through their influence on pests, diseases and soil conditions. Here we present a new approach to model wheat yield; an empirical method based on nonlinear complex systems identification, known as NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs model). We deploy the NARMAX analytical approach for a specific site, Rothamsted, UK, where detailed meteorological variables are available, together with specific information on site conditions and crop growth stages. NARMAX yield forecasts are compared with those from the WOFOST crop model and nine state-of-the-art machine learning (ML) models; experimental results show that NARMAX outperforms all the compared methods in both prediction accuracy and model interpretability. We also develop regional wheat yield forecasts derived from a new gridded meteorological data product. The NARMAX approach produces skillful forecasts (r = 0.78) of Rothamsted wheat yield for a validation period, with small errors. The NARMAX regional forecasts, based on less specific information than WOFOST, also show a high degree of skill (r = 0.73). In addition, the predictor terms chosen for the model are identifiable and can help to give insight into potential key processes involved in the determination of wheat yield at a specific location. This approach can be extended in principle to other crop types and locations. It is straightforward and inexpensive to implement, using a limited number of meteorological predictor variables, which can be taken from site-based observations, or from gridded meteorological datasets. The method is a new tool to understand the environmental drivers of wheat yields on an annual basis.</p>
<p><a href="/2023/06/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on June 11, 2023.</p>
/2023/06/journalDigest2023-06-10T00:00:00-00:002023-06-10T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-9">Journal Paper Digests 2023 #9</h2>
<ul>
<li>Dual nature of soil structure: The unity of aggregates and pores</li>
<li>Structure liming reduces draught requirement on clay soil</li>
<li>Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping</li>
<li>Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation</li>
<li>What is wrong with biofortification</li>
</ul>
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<h3 id="what-is-wrong-with-biofortification">What is wrong with biofortification</h3>
<p>Malnutrition is now the leading avoidable cause of death and disability in the world. One aspect of malnutrition is hidden hunger, a lack of micronutrients in the diet resulting in poor health that may not manifest for months or years. One form of so-called biofortification - breeding staple crops to contain higher levels of vitamins and minerals - has received much praise as a potential solution and more than $500 million in funding over the past two decades. We show that biofortification has not delivered on its promises and that an associated yield penalty means that it may never be able to. It is likely that investment in biofortification has starved more difficult, although ultimately more sustainable, efforts to improve the overall quality of diets. Meeting Sustainable Development Goal No. 2 will depend on a more holistic improvement of culturally acceptable diets.</p>
<h3 id="countrywide-mapping-and-assessment-of-organic-carbon-saturation-in-the-topsoil-using-machine-learning-based-pedotransfer-function-with-uncertainty-propagation">Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation</h3>
<p>Stakeholders and policymakers have been becoming more and more interested not just in the potential organic carbon (SOC) saturation level of soils but also in spatially explicit information on the degree of SOC deficit, which can support future policy and sustainable management strategies, and carbon sequestration-associated spatial planning. Thus the objective of our study was to develop a cubist-based pedotransfer function (PTF) for pre-dicting and mapping the saturated SOC content of the topsoils (0-30 cm) in Hungary and then compare the resulting map with the actual SOC map to determine and assess the degree of SOC deficit. It was assumed that topsoils covered by permanent forests can be practically considered as saturated in SOC. Using the monitoring points of the Hungarian Soil Information and Monitoring System located in forests as reference soil profiles, we developed a cubist-based PTF. The transparent model structure provided by cubist allowed to show that not just the physicochemical properties of soils (e.g., texture, and pH) but also environmental conditions, such as topography (e.g., slope, altitude, and topographical position) and climate (e.g., long-term mean annual tem-perature, and evaporation), characterizing landscape are important factors in predicting the level of SOC satu-ration. Our results also pointed out that there is SOC deficit on large part of the country (similar to 80%) showing high spatial variability. It was also revealed that the most considerable potential for additional SOC sequestration can be found related to soils with medium to high actual SOC content.</p>
<h3 id="integrating-additional-spectroscopically-inferred-soil-data-improves-the-accuracy-of-digital-soil-mapping">Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping</h3>
<p>Digital soil mapping has been increasingly advocated as an efficient approach to deliver fine-resolution and up-to-date soil information in evaluating soil ecosystem services. Considering the great spatial heterogeneity of soils, it is widely recognized that more representative soil observations are needed for better capturing the soil spatial variation and thus to increase the accuracy of digital soil maps. In reality, the budget for the field work and soil laboratory analysis is commonly limited due to its high cost and low efficiency. In the last two decades, being an alternative to wet chemistry, soil spectroscopy, such as visible-near infrared (Vis-NIR), mid-infrared (MIR) spectroscopy has been developed in measuring soil information in a rapid and cost-effective manner and thus enable to collect more soil information for digital soil mapping (DSM). However, spectroscopically inferred (SI) data are subject to higher uncertainties than reference laboratory analysis. Many DSM practices integrated SI data with soil observations into spatial modelling while few studies addressed the key question that whether these non-errorless soil data improve map accuracy in DSM. In this study, French Soil Monitoring Network (RMQS) and Land Use and Coverage Area frame Survey Soil (LUCAS Soil) datasets were used to evaluate the potential of SI data from Vis-NIR and MIR in digital mapping of soil properties (i.e. soil organic carbon, clay, and pH) at a national scale. Cubist and quantile regression forests were used for spectral predictive modelling and DSM modelling, respectively. For both RMQS and LUCAS Soil dataset, different scenarios regarding varying proportions of SI data and laboratory observations were tested for spectral predictive models and DSM models. Repeated (50 times) external validation suggested that adding additional SI data can improve the performance of DSM models regardless of soil properties (gain of R2 proportion at 3-19%) when the laboratory observations are limited (<= 50%). Lower proportion of SI data used in DSM model and higher accuracy of spectral predictive models led to greater improvement of DSM. Our results also showed that a greater proportion of SI data lowered the prediction intervals which may result in an underestimation of prediction uncertainty. The determination of accuracy threshold on SI data for the use in DSM needs to be explored in future studies.</p>
<h3 id="structure-liming-reduces-draught-requirement-on-clay-soil">Structure liming reduces draught requirement on clay soil</h3>
<p>Liming with ‘structure lime’, comprising approximately 80-85% ground limestone and 15-20% slaked lime, has been promoted in subsidised environmental schemes in Sweden since 2010 to increase clay aggregate stability and mitigate particulate phosphorus losses to surface waters. To date, approximately 65,000 ha have been structure-limed. Apart from stabilising aggregates, liming may also improve other physical properties, such as soil strength. This study examined the effect of increasing application rate (0-16 t ha-1) of structure lime on soil strength, approximated by horizontal (draught requirement) and vertical (penetrometer resistance) measure-ments, in eight field soils (clay content 26-38%) to which structure lime had been applied two, three, four or six years previously. Draught requirement when cultivating with a multipurpose cultivator significantly decreased (by 11%) with the highest application rate of structure lime (16 t ha-1) compared with an unlimed control. This reduced the wheel power requirement by 7.1 kW and diesel consumption by 1.2-1.4 L ha-1, and lowered CO2 emissions by 3-4 kg ha-1. To clarify the general effect of structure liming, the mean value of all limed treatments was compared with that of the unlimed control. This showed that structure liming in general significantly reduced the draught requirement (by 7%). However, penetrometer resistance measurements revealed no sig-nificant effects of structure liming and no relation between draught requirement and penetrometer resistance measurements. Overall, the results indicate that structure liming can reduce fuel consumption, due to easier soil tillage, and thus lower CO2 emissions.</p>
<h3 id="dual-nature-of-soil-structure-the-unity-of-aggregates-and-pores">Dual nature of soil structure: The unity of aggregates and pores</h3>
<p>Soil is a hierarchical, self-organizing, and emergent system that supports plant and microbial growth, enables carbon sequestration, facilitates water fluxes, and provide habitat for microorganisms, all of which depend on soil structure. Recent debates have generally reduced soil functioning to geometry and topology of soil solids and pores and denied the existence and role of soil aggregates and hierarchy of solids. Here we argue that soil structure has a dual nature that essentially boils down to the interlocking of pores and solids in groupings of specific complexity and dynamics called aggregates. By comparing their architectural, chemical, and energetic parameters, we conclude that aggregates have a much higher information density than pores. Therefore, aggregates (as unity of solids and pores) perform much broader range of functions compared to pores alone, especially in long-term. A set of soil functions corresponding to each level of the soil structure hierarchy depends on aggregate type (macroaggregates, water-stable aggregates, microaggregates, and elementary soil particles) determined by their specific binding energy, dynamics, and lifetime. The introduced here energy-based concept justifies the hierarchy of soil structure, and is the base for the soil structuring and carbon stabilization processes in their most general form. We understand the soil structure implying the energy-based approach: each hierarchy level corresponds to specific bonding strength of mineral and organic particles forming aggregates. Aggregate formation is a bottom-up process because the energy binding elementary soil particles and microaggregates is orders of magnitude higher than that gluing macroaggregates. The duality of soil structure is manifested not only in the relationship between pores and solids in aggregates, but also in the interactions and competition between the biological and non-biological processes that aggregate and disaggregate the structure. The view of the pore space as a transport pathway and habitat for soil living phase and plant roots, the solid-pore interface as a setting for physico-chemical and biological transformations, and aggregates as a result of these phenomena, provides a context for mechanistic understanding and process-based modeling of soil functions and health.</p>
<p><a href="/2023/06/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on June 10, 2023.</p>
/2023/05/journalDigest2023-03-29T00:00:00-00:002023-05-09T00:00:00+10:00Smart Digital Agriculturemalone.brendan1001@gmail.com
<h2 id="journal-paper-digests-2023-8">Journal Paper Digests 2023 #8</h2>
<ul>
<li>An identified agronomic interpretation for potassium permanganate oxidizable carbon</li>
<li>A simple method to determine the reactivity of calcium carbonate in soils</li>
<li>Revisiting laboratory methods for measuring soil water retention curves</li>
<li>Estimating lime requirements for tropical soils: Model comparison and development</li>
<li>Upscaling soil moisture from point scale to field scale: Toward a general model</li>
<li>A field evaluation of the SoilVUE10 soil moisture sensor</li>
</ul>
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<h3 id="a-field-evaluation-of-the-soilvue10-soil-moisture-sensor">A field evaluation of the SoilVUE10 soil moisture sensor</h3>
<p>The U.S. Climate Reference Network (USCRN) has been engaged in ground-based soil water and soil temperature observations since 2009. As a nationwide climate network, the network stations are distributed across vast complex terrains. Due to the expansive distribution of the network and the related variability in soil properties, obtaining site-specific calibrations for sensors is a significant and costly endeavor. Presented here are three commercial-grade electromagnetic sensors, with built-in thermistors to measure both soil water and soil temperature, including the SoilVUE10 Time Domain Reflectometry (TDR) probe (hereafter called SP) (Campbell Scientific, Inc.), 50 MHz coaxial impedance dielectric sensor (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP), and the TDR-315L Probe (model TDR-315L, Acclima, Inc.) (hereafter called AP), which were evaluated in a relatively nonconductive loam soil in Oak Ridge, TN, from 2021 to 2022. The HP manufacturer-supplied calibration equation for loam soils was used in this study. While volumetric water content data from HP and AP were 82-99% of respective gravimetric observations at 10 cm, data from SP were only 65-81% of respective gravimetric observations in the top 20-cm soil horizon, where soil water showed relatively large spatial variability. The poor performance of the SP is likely due to poor contact between SP sensor electrodes and soil and the presence of soil voids caused by the installation method used, which itself may have caused soil disturbance.</p>
<h3 id="upscaling-soil-moisture-from-point-scale-to-field-scale-toward-a-general-model">Upscaling soil moisture from point scale to field scale: Toward a general model</h3>
<p>Field-scale soil moisture measurements are valuable but rarely available because the resolution of most satellite soil moisture products is too coarse, while most in situ sensors provide only point-scale data. Previous upscaling approaches for such data are mostly site-specific, and none are suitable to upscale data from the thousands of stations in existing monitoring networks. To help fill this gap, this research aims to develop a more broadly applicable upscaling approach using data from the Marena, Oklahoma, In Situ Sensor Testbed and a cosmic-ray neutron rover. Rover survey data were used to measure average soil moisture for the similar to 64-ha field on 12 dates in 2019-2020. Statistical modeling was used to identify the soil, terrain, and vegetation properties influencing the relationships between the field-scale rover data and point-scale in situ data from six monitoring sites. Site-specific linear upscaling models estimated the field average soil moisture with root mean squared error (RMSE) values ranging from 0.007 to 0.017 cm(3) cm(-3), but such models are not transferrable between sites. To create a more general model, Least Absolute Shrinkage and Selection Operator regression was used with a leave-one-out cross-validation approach to identify the key predictors for upscaling. The resulting parsimonious model required only the point-scale observations and sand content data and achieved RMSE values ranging from 0.006 to 0.031 cm(3) cm(-3) for the six monitoring sites. The texture-based model demonstrated reasonable accuracy and is a promising step toward a general model that could be broadly applied for upscaling point-scale in situ monitoring stations.</p>
<h3 id="estimating-lime-requirements-for-tropical-soils-model-comparison-and-development">Estimating lime requirements for tropical soils: Model comparison and development</h3>
<p>Acid tropical soils may become more productive when treated with agricultural lime, but optimal lime rates have yet to be determined in many tropical regions. In these regions, lime rates can be estimated with lime requirement models based on widely available soil data. We reviewed seven of these models and introduced a new model (LiTAS). We evaluated the models’ ability to predict the amount of lime needed to reach a target change in soil chemical properties with data from four soil incubation studies covering 31 soil types. Two foundational models, one targeting acidity saturation and the other targeting base saturation, were more accurate than the five models that were derived from them, while the LiTAS model was the most accurate. The models were used to estimate lime requirements for 303 African soil samples. We found large differences in the estimated lime rates depending on the target soil chemical property of the model. Therefore, an important first step in formulating liming recommendations is to clearly identify the soil property of interest and the target value that needs to be reached. While the LiTAS model can be useful for strategic research, more information on acidity-related problems other than aluminum toxicity is needed to comprehensively assess the benefits of liming.</p>
<h3 id="revisiting-laboratory-methods-for-measuring-soil-water-retention-curves">Revisiting laboratory methods for measuring soil water retention curves</h3>
<p>Traditional laboratory methods for measuring soil water retention curves (SWRCs) typically consist of suction tables, pressure cells, and pressure plate apparatus (i.e., traditional methods). However, technological advancement has resulted in newer methods based on precision mini-tensiometers and dew point water potential meters (i.e., modern methods). This study investigated the discrepancy between SWRCs measured using traditional and modern methods in three soil textures. Our results showed that SWRCs from both traditional and modern methods were similar at the wet end (i.e., matric potentials 0 to -10 kPa) and at the dry end (-500 to -1,500 kPa) of the SWRC, with an average mean absolute difference (MAD) across all three soils of 0.033 and 0.017 cm(3) cm(-3), respectively. The largest discrepancy between methods was consistently observed at moderate tensions of -33 and -70 kPa for the three soils, with an average MAD of 0.059 cm(3) cm(-3) for -33 kPa and a MAD of 0.083 cm(3) cm(-3) for -70 kPa. Plant available water capacity differed by up to 20% between the traditional and modern methods in a clay loam soil. While previous studies have mostly focused on the dry end of the SWRC, our study suggests that additional research comparing traditional and modern methods is required at moderate (-70 and -500 kPa) tension levels.</p>
<h3 id="an-identified-agronomic-interpretation-for-potassium-permanganate-oxidizable-carbon">An identified agronomic interpretation for potassium permanganate oxidizable carbon</h3>
<p>The absence of clear empirical relationships between soil health and agronomic outcomes remains an obstacle to widespread adoption of soil health assessments in row crop systems. The objectives of this research were to (1) determine whether soil health indicators are connected to corn (Zea mays L.) productivity and (2) establish interpretive benchmarks for soil health indicators in Missouri. The objectives were accomplished by collecting corn grain yield at 446 monitoring sites (37 m(2)) in 84 commercial production fields in 2018-2020. Soil health and soil fertility samples were collected prior to planting at each site. These data, along with site-specific soil and weather data, were modeled using traditional stepwise regression and nonparametric random forest (RF) and conditional inference forest (CIF) approaches. Root-mean-square errors were similar (1.4-1.5 Mg ha(-1)) with distinct R-2 improvements over stepwise regression for both CIF (R-2 = 0.45) and RF (R-2 = 0.46) algorithms. Only seasonal rainfall and potassium permanganate oxidizable carbon (POXC) were included as top factors governing grain productivity in each model approach, thus demonstrating a regionally robust empirical relationship between POXC and grain productivity. Partial dependency analysis and two decision tree approaches identified 415 mg POXC kg(-1) as a threshold for maximum grain productivity, providing a framework for regional interpretation of on-farm soil health assessments. Little evidence was found connecting grain productivity with autoclaved citrate extractable protein and soil respiration. These findings underscore the power of POXC as an emerging soil health indicator to assess and quantify soil management effects on grain productivity.</p>
<h3 id="a-simple-method-to-determine-the-reactivity-of-calcium-carbonate-in-soils">A simple method to determine the reactivity of calcium carbonate in soils</h3>
<p>Determination of soil carbonate is important for numerous chemical and physical soil processes in arid and semi-arid zones. Here, we modify a conventional method to more easily determine active CaCO3 (ACC) fraction in soils. Unlike the conventional method where the oxalate used up after reaction with carbonate is determined by titration with KMnO4, the proposed method uses the difference between total carbonate analyzed before and after reaction with oxalate to determine ACC. The method compared well to the conventional approach; the procedure was faster, did not require KMnO4, and appeared to be unaffected by exchangeable Ca. The proposed method is well-suited for samples across a wide range of total carbonate.</p>
<p><a href="/2023/05/journalDigest">Journal Paper Digests</a> was originally published by Smart Digital Agriculture at <a href="">Smart Digital Agriculture</a> on May 09, 2023.</p>