Journal Paper Digests

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Journal Paper Digests 2018 #10

  • Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation
  • A Primer for Model Selection: The Decisive Role of Model Complexity
  • Spatial Variability of Soil Moisture and the Scale Issue: A Geostatistical Approach
  • Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns
  • Deep learning in agriculture: A survey
  • Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management
  • Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images
  • How much can natural resource inventory benefit from finer resolution auxiliary data?
  • A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data
  • The functional characterization of grass- and shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables
  • Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP
  • Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy
  • Soil mineral assemblage and substrate quality effects on microbial priming
  • The singularity index for soil geochemical variables, and a mixture model for its interpretation
  • A pedometric technique to delimitate soil-specific zones at field scale
  • Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models
  • A preliminary spatial quantification of the soil security dimensions for Tasmania
  • Soil quality - A critical review
  • The root of the matter: Linking root traits and soil organic matter stabilization processes

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

Authors: Akbar, R; Gianotti, DS; McColl, KA; Haghighi, E; Salvucci, GD; Entekhabi, D

Source: WATER RESOURCES RESEARCH, 54 (3):1476-1492; MAR 2018

Abstract: The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Delta z, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Delta z is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Delta z exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Delta z depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Delta z across CONUS is 135 mm. The spatial variance of Delta z is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

A Primer for Model Selection: The Decisive Role of Model Complexity

Authors: Hoge, M; Wohling, T; Nowak, W

Source: WATER RESOURCES RESEARCH, 54 (3):1688-1715; MAR 2018

Abstract: Selecting a “best” model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the “best” trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

Spatial Variability of Soil Moisture and the Scale Issue: A Geostatistical Approach

Authors: Zarlenga, A; Fiori, A; Russo, D

Source: WATER RESOURCES RESEARCH, 54 (3):1765-1780; MAR 2018

Abstract: We study the standard deviation of water saturation SDS as function of the mean saturation < S > by a stochastic model of unsaturated flow, which is based on the first-order solution of the three-dimensional Richards equation. The model assumes spatially variable soil properties, following a given geostatistical description, and it explicitly accounts for the different scales involved in the determination of the spatial properties of saturation: the extent L, i.e., the domain size, the spacing Delta among measurements, and the dimension l associated to the sampling measurement. It is found that the interplay between those scales and the correlation scale I of the hydraulic properties rules the spatial variability of saturation. A “scale effect’’ manifests for small to intermediate L/I, for which SDS increase with the extent L. This nonergodic effect depends on the structural and hydraulic parameters as well as the scales of the problem, and it is consistent with a similar effect found in field experiments. In turn, the influence of the scale l is to decrease the saturation variability and increase its spatial correlation. Although the solution focuses on the medium heterogeneity as the main driver for the spatial variability of saturation, neglecting other important components, it explicitly links the spatial variation of saturation to the hydraulic properties of the soil, their spatial variability, and the sampling schemes; it can provide a useful tool to assess the impact of scales on the saturation variability, also in view of the several applications that involve the saturation variability.

Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns

Authors: Dong, JN; Ochsner, TE

Source: WATER RESOURCES RESEARCH, 54 (3):2199-2211; MAR 2018

Abstract: Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (approximate to 1-100 km) soil moisture patterns and land surface and atmospheric factors along a 150 km transect using 4 km multisensor precipitation data and a cosmic-ray neutron rover, with a 400 m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients (r) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12-32 km, 13-20 km, and 14-45 km, respectively. Soil moisture was more strongly correlated with sand content (r=-0.536 to -0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

Deep learning in agriculture: A survey

Authors: Kamilaris, A; Prenafeta-Boldu, FX


Abstract: Deep learning constitutes a recent, modem technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management

Authors: Romero, M; Luo, YC; Su, BF; Fuentes, S


Abstract: Remote sensing can provide a fast and reliable alternative for traditional in situ water status measurement in vineyards. Several vegetation indices (Ws) derived from aerial multispectral imagery were tested to estimate midday stem water potential (Psi(stem)) of grapevines. The experimental trial was carried out in a vineyard in the Shangri-La region, located in Yunnan province in China. Statistical methods and machine learning algorithms were used to evaluate the correlations between Psi(stem) and VIs. Results by simple regression between VIs individually and Psi(stem) showed no significant relationships, with coefficient of determination (R-2) for linear fitting smaller than 0.3 for almost all the indices studied, except for the Optimal Soil Adjusted Vegetation Index (OSAVI); R-2 = 0.42 with statistical significance (p <= 0.001). However, results from a model obtained by fitting using Artificial Neural Network (ANN), using all VIs calculated as inputs and real Psi(stem) from plants within the study site (n = 90) as targets (Model 1), showed high correlation between the estimated water potential through ANN (Psi(stem ANN)) and the actual measured Psi(stem). Training, validation and testing data sets presented individual correlations of R = 0.8, 0.72 and 0.62 respectively. The models obtained from the study site were then applied to a wider area from the vineyard studied and compared to further Psi(stem) measured obtained from different sites (n = 23) showing high correlation values between Psi(stem) (ANN) and real Psi(stem) (R-2 = 0.83; slope = 1; p <= 0.001). Finally, a pattern recognition ANN model (Model 2) was developed for irrigation scheduling purposes using the same Psi(stem) measured in the study site as inputs and with the following thresholds as outputs: Psi(stem) below -1.2 MPa considered as severe water stress (SS), Psi(stem) between -0.8 to -1.2 MPa as moderate stress (MS) and Psi(stem) over -0.8 MPa with no water stress (NS). This model can be applied to analyze on a plant by plant basis to identify sectors of stress within the vineyard for optimal irrigation management and to identify spatial variability within the vineyards.

Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images

Authors: Jia, K; Jiang, WG; Li, J; Tang, ZH


Abstract: Terrestrial water, an important indicator of inland hydrological status, is sensitive to land use cover change, natural disaster and climate change. An accurate and robust water extraction method can determine the surface water distribution. In this paper, a new method, called the spectrum matching based on discrete particle swarm optimization (SMDPSO) is proposed to recognize water and nonwater in Landsat 8 Operational Land Imager (OLI) images. Only two parameters, the standard water spectrum and the tile size, are considered. These parameters are sufficiently stable so it is unnecessary to change their values for different conditions. By contrast, in supervised methods, samples are chosen based on conditions. Eight test sites covering various water types in different climate conditions are used to assess the performance relative to that of unsupervised and supervised methods in terms of overall accuracy (OA), kappa coefficients (KC), commission error (CE) and omission error (OE). The results show that: (1) SMDPSO achieves the highest accuracy and robustness; (2) SMDPSO has lower OE but higher CE than the supervised method, which means that SMDPSO is the least likely to misclassify water as nonwater, but is more likely to misclassify nonwater as water; (3) SMDPSO has advantages with respect to removing shallows and dark vegetation and preserving the real distribution of small ponds, but cannot recognize shadows, ice, or clouds without the help of other data such as DEM. In addition, a case of flooding in northeastern China is analyzed to demonstrate the applicability of SMDPSO in water inundation mapping. The findings of this study demonstrate a novel robust, low-cost water extraction method that satisfies the requirements of terrestrial water inundation mapping and management.

How much can natural resource inventory benefit from finer resolution auxiliary data?

Authors: Hou, ZY; McRoberts, RE; Stahl, G; Packalen, P; Greenberg, JA; Xu, Q


Abstract: For remote sensing-assisted natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the response variable of interest was firewood volume (m(3)/ha). A sample consisting of 160 field plots was selected from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, mu, and the variance of the estimator of the population mean, V ((mu) over cap), were estimated using two inferential frameworks, model-based and model-assisted, and compared. For each framework, V((mu) over cap) was estimated both analytically and empirically. Empirical variances were estimated using bootstrapping that accounted for the two-stage sampling. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributed to greater precision for estimators of population parameter, but despite the finer spatial resolution of RapidEye, the increase was only marginal, on the order of 10% for model-based variance estimators and 36% for model assisted variance estimators; (2) subpixel information on texture was marginally beneficial for inference of large area population parameters; (3) RapidEye did not offer enough of an improvement to justify its cost relative to the free Landsat 8 imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) sampling distribution for the model based V((mu) over cap) was more concentrated and smaller on the order of 42% to 59% than that for the model-assisted V((mu) over cap), suggesting superior consistency and efficiency of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.

A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data

Authors: Houborg, R; McCabe, MF

Source: REMOTE SENSING OF ENVIRONMENT, 209 211-226; MAY 2018

Abstract: Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3-4 m resolution has recently become available via a constellation of > 130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by > 32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale reflectances over given Landsat-CubeSat acquisition timespans. With the observing potential of Planet’s CubeSats approaching daily nadir-pointing land surface imaging of the entire Earth, CESTEM offers the capacity to produce daily Landsat 8 consistent VNIR imagery with a factor of 10 increase in spatial resolution and with the radiometric quality of actual Landsat 8 observations. Realization of this unprecedented Earth observing capacity has far reaching implications for the monitoring and characterization of terrestrial systems at the precision scale.

Authors: Van Cleemput, E; Vanierschot, L; Fernandez-Castilla, B; Honnay, O; Somers, B

Source: REMOTE SENSING OF ENVIRONMENT, 209 747-763; MAY 2018

Abstract: Hyperspectral remote sensing is increasingly being recognized as a powerful tool to map ecosystem properties and functions through time and space. However, general information on the accuracy of this technology to assess the vegetation’s biophysical and-chemical trait composition, and on the variables which are mediating this accuracy, is often lacking so far. Here, we addressed this knowledge gap for grass- and shrubland ecosystems and applied novel three-level meta-analytical regression equations to 77 studies that validated hyperspectral remote sensing data with field observations. Our results showed that the accuracy of hyperspectral sensors is generally high, but strongly depends on the trait being studied (leaf area index: R-2 = 0.79 and nRMSE = 0.19, chlorophyll: R-2 = 0.77 and nRMSE = 0.21, carotenoids: R-2 = 0.80 and nRMSE = 0.29, phosphorus: R-2 = 0.75 and nRMSE = 0.14, nitrogen: R-2 = 0.74 and nRMSE = 0.09, water: R-2 = 0.69 and nRMSE = 0.13, and lignin content: R-2 = 0.64 and nRMSE = 0.26). Moreover, they indicated that the use of multivariate signal processing techniques could improve these estimation accuracies (adjusted p < 0.06 for LAI, chlorophyll and nitrogen). Finally, estimations from air- and spaceborne imaging spectrometers, allowing for functional mapping at broader spatial scales, were found to be as accurate as estimations from ground-based spectral measurements. Despite these promising findings, we revealed that leaf morphological properties (e.g. specific leaf area and leaf dry matter content) and biochemical traits which are not growth-related (e.g. lignin and cellulose) remain under explored in grass- and shrublands. Moreover there was a strong publication bias towards R2 for assessing model performance. Our findings foster and direct further methodological and technological developments for a more accurate and complete functional characterization of these ecosystems worldwide.

Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP

Authors: Sun, Y; Frankenberg, C; Jung, M; Joiner, J; Guanter, L; Kohler, P; Magney, T

Source: REMOTE SENSING OF ENVIRONMENT, 209 808-823; MAY 2018

Abstract: The Orbiting Carbon Observatory-2 (OCO-2), launched in July 2014, is capable of measuring Solar-Induced chlorophyll Fluorescence (SIF), a functional proxy for terrestrial gross primary productivity (GPP). Although its primary mission is to measure the column-averaged mixing ratio of CO2 (Xco(2)) to constrain global carbon source/sink distribution, one of the OCO-2 spectrometers allows for a robust SIF retrieval solely based on solar Fraunhofer lines. Here we present a technical overview of the OCO-2 SIF product, aiming to provide the scientific community guidance on best practices for data analysis, interpretation, and application. This overview consists of the retrieval algorithms, OCO-2 specific bias correction, retrieval uncertainty evaluation, cross-mission comparison with other existing SIF products, and a global-scale examination of the SIF-GPP relationship. With the initial three years of data (September 2014 onward), we compared OCO-2 SIF with retrievals from Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and examined its relationship with FLUXCOM and MODIS GPP datasets. Our results show that OCO-2 SIF, along with GOSAT products, closely resemble the mean spatial and temporal patterns of FLUXCOM GPP from regions to the globe. Compared with GOME-2, however, OCO-2 depicts a more realistic spatial contrast between the tropics and extra-tropics. The linear relationship between OCO-2 SIF and existing modeled GPP products diverges somewhat across biomes at the global scale, consistent with previous GOSAT or GOME-2 based findings when modeled GPP products were used, but in contrast to a consistent cross-biome SIF-GPP relationship obtained at flux tower sites with OCO-2 products. This contrast suggests a critical need to reconcile differences in diverse SIF and GPP products and the relationships among them. Overall, the OCO-2 SIF products are robust and valuable for monitoring the global terrestrial carbon cycle and for constraining the carbon source/sink strengths of the Earth system. Finally, insights are offered for future satellite missions optimized for SIF retrievals.

Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy

Authors: Meyer, N; Meyer, H; Welp, G; Amelung, W

Source: GEODERMA, 323 31-40; AUG 1 2018

Abstract: Spatial patterns of soil respiration (SR) and its sensitivity to temperature (Q10) are one of the key uncertainties in climate change research but since their assessment is very time-consuming, large data sets can still not be provided. Here, we investigated the potential of mid-infrared spectroscopy (MIRS) to predict SR and Q10 values for 124 soil samples of diverse land use types taken from a 2868 km(2) catchment (Rur catchment, Germany/Belgium/Netherlands). Soil respiration at standardized temperature (25 degrees C) and soil moisture (45% of maximum water holding capacity, WHC) was successfully predicted by MIRS coupled with partial least square regression (PLSR, R-2 = 0.83). Also the Q10 value was predictable by MIRS-PLSR for a grassland submodel (R-2 = 0.75) and a cropland submodel (R-2 = 0.72) but not for forested sites (R-2 = 0.03). In order to provide soil respiration estimates for arbitrary conditions of temperature and soil moisture, more flexible models are required that can handle nonlinear and interacting relations. Therefore, we applied a Random Forest model, which includes the MIRS spectra, temperature, soil moisture, and land use as predictor variables. We could show that SR can be simultaneously predicted for any temperature (5-25 degrees C) and soil moisture level (30-75% of WHC), indicated by a high R-2 of 0.73. We conclude that the combination of MIRS with sophisticated statistical prediction tools allows for a novel, rapid acquisition of SR and Q10 values across landscapes and thus to fill an important data gap in the validation of large scale carbon modeling.

Soil mineral assemblage and substrate quality effects on microbial priming

Authors: Finley, BK; Dijkstra, P; Rasmussen, C; Schwartz, E; Mau, RL; Liu, XJA; Van Gestel, N; Hungate, BA

Source: GEODERMA, 322 38-47; JUL 15 2018

The singularity index for soil geochemical variables, and a mixture model for its interpretation

Authors: Lark, RM; Patton, M; Ander, EL; Reay, DM

Source: GEODERMA, 323 83-106; AUG 1 2018

Abstract: A geochemical anomaly is a concentration of an element or other constituent in a medium (soil, sediment or surface water) which is unusual in its local setting. Geochemical anomalies may be interesting as indicators of processes such as point contamination or mineralizations. They may therefore be practically useful, indicating sources of pollution or mineral deposits which may be of economic value. As defined, a geochemical anomaly is not merely a large (or small) concentration of a constituent as compared to the marginal distribution. To detect anomalies we must therefore do more than simply map the spatial distribution of the constituent. One proposed approach makes use of a singularity index based on fractal representation of spatial variation. The singularity index can be computed from local concentration measures in nested windows. In this paper we propose an approach to compute threshold values for the index to identify enrichment and depletion anomalies, separate from background information. The approach is based on a mixture model for the singularity index, and it can be supported by computing a distribution for background values of the index by parametric bootstrapping from a robustly-estimated variogram model for the target constituent. This approach is illustrated here using data on elements in the soil in four settings in Great Britain and Ireland.

A pedometric technique to delimitate soil-specific zones at field scale

Authors: Castro-Franco, M; Cordoba, MA; Balzarini, MG; Costa, JL

Source: GEODERMA, 322 101-111; JUL 15 2018

Abstract: Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements.

Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models

Authors: Filippi, P; Cattle, SR; Bishop, TFA; Odeh, IOA; Pringle, MJ

Source: GEODERMA, 322 149-162; JUL 15 2018

Abstract: Intensive agricultural management practices and fluctuating rainfall patterns have the potential to significantly impact the status and change of important soil properties. This study looks at the change in soil pH during a 13-year period in a semi-arid, irrigated cotton-growing region in the lower Lachlan River valley in southern NSW, Australia under various land uses. Two soil surveys - one from 2002 and the other from 2015 - that had many of the same sites revisited and resampled, were used in conjunction with bivariate linear mixed models (BLMMs) to map soil pH status and change, at five depth increments to 1.2 m depth. The BLMM approach resulted in models with high predictive power and low prediction variance, likely due to the utilisation of the correlation of pH values at co-located sites through time. Results revealed an overall acidification trend throughout the soil profile, particularly in the subsoil. Test statistics were performed on this predicted acidification, showing that not all of this was statistically significant. This trend of decreasing pH over time is presumed to be at least partly due to management practices associated with irrigated cotton production, but also appears to be affected by increased water flow through the profile due to periods of very high rainfall. While acidification in the highly alkaline soils that cover much of the study area is of no great concern at this time, further acidification should be monitored. Further investigation into utilising the correlation from other co-located soil information should also be considered, such as different soil depths and soil properties in combination with the different time points used in this study.

A preliminary spatial quantification of the soil security dimensions for Tasmania

Authors: Kidd, D; Field, D; McBratney, A; Webb, M

Source: GEODERMA, 322 184-200; JUL 15 2018

Abstract: Soil Security is a holistic soil assessment approach that cogitates soil as a multi-dimensional medium. Rather than traditional single dimensional assessment approaches such as land capability mapping that largely considers only soil and landscape biophysical attributes, the Soil Security concept considers social aspects, education, policy, legislation, current land use, condition and the soils natural and economic value to society. It can identify discrete soils that are currently being used within their capacity, and areas where a use might be unsustainable, i.e. not secure. It would therefore make sense to map this concept, which aligns well with the aspirational and marketing policies of the Tasmanian Government, where increased agricultural expansion through new irrigation schemes and multiple-use State managed production forests co-exists beside pristine World Heritage conservation land, a major drawcard of the economically important tourism industry. The spatial quantification of soil security is seen as an emerging tool to effectively protect the soil resource in terms of food and water security, biodiversity maintenance and safeguarding fragile ecosystems. The recent development and application of Digital Soil Mapping and Assessment capacities in Tasmania to stimulate agricultural production and better target appropriate soil resource use has formed the foundational system that can enable the first efforts in quantifying and mapping Soil Security, in particular the five Soil Security dimensions (Capability, Condition, Capital, Codification and Connectivity). This forms a preliminary mapping product that demonstrates the feasibility of mapping the Soil Security concept. To provide a measure of overall soil security, it was necessary to separately assess the State’s three major soil uses; Agriculture, Conservation and Forestry. These outputs provide an indication of where different activities are sustainable or at risk, where more soil data is needed, and develops a tool to better plan for a State requiring optimal food and fibre production, without depleting its natural soil resources and impacting the fragile ecosystems providing environmental benefits and supporting the tourism industry. The following paper demonstrates why and how we might map Soil Security, describing a preliminary approach to mapping the separate dimensions; this approach could be adapted and applied elsewhere as an evaluation tool to identify soil threats relevant to current land use, biophysical properties, policy and management, and stimulate further research and debate into developing a global Soil Security mapping methodology.

Soil quality - A critical review

Authors: Bunemann, EK; Bongiorno, G; Bai, ZG; Creamer, RE; De Deyn, G; de Goede, R; Fleskens, L; Geissen, V; Kuyper, TW; Mader, P; Pulleman, M; Sukkel, W; van Groenigen, JW; Brussaard, L

Source: SOIL BIOLOGY & BIOCHEMISTRY, 120 105-125; MAY 2018

Abstract: Sampling and analysis or visual examination of soil to assess its status and use potential is widely practiced from plot to national scales. However, the choice of relevant soil attributes and interpretation of measurements are not straightforward, because of the complexity and site-specificity of soils, legacy effects of previous land use, and trade-offs between ecosystem services. Here we review soil quality and related concepts, in terms of definition, assessment approaches, and indicator selection and interpretation. We identify the most frequently used soil quality indicators under agricultural land use. We find that explicit evaluation of soil quality with respect to specific soil threats, soil functions and ecosystem services has rarely been implemented, and few approaches provide clear interpretation schemes of measured indicator values. This limits their adoption by land managers as well as policy. We also consider novel indicators that address currently neglected though important soil properties and processes, and we list the crucial steps in the development of a soil quality assessment procedure that is scientifically sound and supports management and policy decisions that account for the multi-functionality of soil. This requires the involvement of the pertinent actors, stakeholders and end-users to a much larger degree than practiced to date.

The root of the matter: Linking root traits and soil organic matter stabilization processes

Authors: Poirier, V; Roumet, C; Munson, AD

Source: SOIL BIOLOGY & BIOCHEMISTRY, 120 246-259; MAY 2018

Abstract: Plant roots contribute substantially to the formation of stable soil organic matter (SOM), and there is evidence that species differ in their contribution to SOM stabilization. However, it remains unclear what specific root traits contribute to the three SOM stabilization mechanisms: recalcitrance against decomposition, occlusion in soil aggregates and interaction with soil minerals and metals. This is likely because research is highly fragmented and hampered by disciplinary barriers. By reviewing both plant functional ecology and soil science literature, we identified 18 different traits: architectural, morphological, physiological, symbiotic and chemical root characteristics, influencing the three SOM stabilization mechanisms. We found that traits increasing root recalcitrance promote short term stabilization by slowing decomposition, but that traits reducing recalcitrance contribute to long term stabilization by reaction of microbial products with mineral surfaces. Root length density, mycorrhizal association and rhizodeposition contribute to microaggregation. These and other traits, such as hemicellulose, soluble compounds, and high root branching index, favor macroaggregation. For stabilization by minerals and metals, those root traits promoting higher microbial activity: root nitrogen, hemicellulose and soluble compound concentrations are fundamental, while polyphenols, and litter Al and Mn also contribute to complexification and stabilization. Root depth distribution is the most important trait to control root C storage and stabilization in the subsoil; once roots have reached deeper soil layers, other traits, such as rhizodeposition and root chemistry, influence interaction with minerals and metals. Both mycorrhizal presence and root suberin promote SOC stabilization by means of all three mechanisms, indicating that these are important targets for continued work. Surprisingly, morphological traits commonly measured, namely specific root length and root diameter, poorly relate to stabilization mechanisms. Alternative traits such as chemical composition of the different root orders, root apex characteristics, quantity and quality of rhizodeposits as well as mycorrhizal fungal traits, should be further investigated. For future research, this review highlights the need to evaluate root decomposition and root-C stabilization concomitantly over the long-term, considering simultaneously root litter quality, estimated by root traits, the microbial products and properties of the soil matrix. The information accrued in this review can be used to evaluate the potential of plant species and cultivars to promote SOM stabilization, based on their root traits.

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