Journal Paper Digests 2023 #18
- Molecular complexity and diversity of persistent soil organic matter
- Ecosystem-scale modelling of soil carbon dynamics: Time for a radical shift of perspective?
- A numerical approach for modeling crack closure and infiltrated flow in cracked soils
- Do diversified crop rotations influence soil physical health? A meta-analysis
- Life cycle assessment, life cycle cost, and exergoeconomic analysis of different tillage systems in safflower production by micronutrients
- Response of soil organic carbon stock to land use is modulated by soil hydraulic properties
- Definition of Spatial Copula Based Dependence Using a Family of Non-Gaussian Spatial Random Fields
- Subsoil carbon loss
- Real-time social media sentiment analysis for rapid impact assessment of floods
- Filling the maize yield gap based on precision agriculture – A MaxEnt approach
- A multi-scale algorithm for the NISAR mission high-resolution soil moisture product
- Quantifying uncertainty in land-use land-cover classification using conformal statistics
- 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
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.
Quantifying uncertainty in land-use land-cover classification using conformal statistics
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.
A multi-scale algorithm for the NISAR mission high-resolution soil moisture product
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.
Filling the maize yield gap based on precision agriculture – A MaxEnt approach
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.
Real-time social media sentiment analysis for rapid impact assessment of floods
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.
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.
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.
Subsoil carbon loss
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.
Definition of Spatial Copula Based Dependence Using a Family of Non-Gaussian Spatial Random Fields
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.
Key Points A method to define a wide range of non-Gaussian spatial dependence is presented
A conditional simulation approach for these non-Gaussian structures via Monte Carlo optimization is presented
A groundwater quality parameter study demonstrates the benefits of the approach
Response of soil organic carbon stock to land use is modulated by soil hydraulic properties
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.
Life cycle assessment, life cycle cost, and exergoeconomic analysis of different tillage systems in safflower production by micronutrients
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.
Do diversified crop rotations influence soil physical health? A meta-analysis
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.
A numerical approach for modeling crack closure and infiltrated flow in cracked soils
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.
Ecosystem-scale modelling of soil carbon dynamics: Time for a radical shift of perspective?
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.
Molecular complexity and diversity of persistent soil organic matter
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.