Journal Paper Digests

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Journal Paper Digests 2019 #4

  • Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions
  • A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model
  • How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery
  • National soil organic carbon estimates can improve global estimates
  • Merging country, continental and global predictions of soil texture: Lessons from ensemble modelling in France
  • Use of portable XRF: Effect of thickness and antecedent moisture of soils on measured concentration of trace elements
  • Mapping future soil carbon change and its uncertainty in croplands using simple surrogates of a complex farming system model
  • Soil fertility assessment by Vis-NIR spectroscopy: Predicting soil functioning rather than availability indices
  • Pre-treatment of soil X-ray powder diffraction data for cluster analysis
  • The Role of Urban Agriculture in a Secure, Healthy, and Sustainable Food System
  • Poetry as a Creative Practice to Enhance Engagement and Learning in Conservation Science

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions

Authors: Sheffield, J; Wood, EF; Pan, M; Beck, H; Coccia, G; Serrat-Capdevila, A; Verbist, K

Source: WATER RESOURCES RESEARCH, 54 (12):9724-9758; DEC 2018

Abstract: Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to im proving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.

A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

Authors: Zhang, YG; Schaap, MG; Zha, YY

Source: WATER RESOURCES RESEARCH, 54 (12):9774-9790; DEC 2018

Abstract: A correct quantification of mass and energy exchange processes among Earth’s land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global-scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, , 1996, ), which explicitly assume a lognormal pore size distribution and apply the Young-Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combinatio n of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data-poor to data-rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi-based PTFs outperformed two van Genuchten-based PTFs calibrated on the same data. The new PTFs were applied to a 1x1km(2) global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes.

How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery

Authors: Lagacherie, P; Arrouays, D; Bourennane, H; Gomez, C; Martin, M; Saby, NPA

Source: GEODERMA, 337 1320-1328; MAR 1 2019

Abstract: Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations that are used to determine various uncertainty indicators. However, the number and locations of the sites that constitute these evaluations may impact the value of these indicators.In this paper, a numerical experiment on uncertainty indicators was performed using the pseudo values of topsoil clay content obtained from an airborne hyperspectral image in the Cap Bon region (Tunisia). These pseudo values form a soil pattern with a large extent (46% of 300 km(2)), high resolution (5 m) and good accuracy (R-val(2) = 0.75) while being free of visible artefacts and pedologically plausible. Therefore, the dataset was considered a fair representation of reality while providing a quasi-unlimited choice of sites.The numerical experiment considered three Quantile Regression Forests as examples of DSM models by using inputs from relief soil covariates and geographical locations that were calibra ted from 200, 2000 and 100,000 individuals respectively (low, medium and high quality models). Their uncertainty indicators were first evaluated by calculating four uncertainty indicators (ME, MSE, SSMSE and PICP) from a large independent validation set of 100,000 sites. These uncertainty indicators were then computed from independent evaluation sets of different sizes (from 50 to 500 sites) and from different locations (500 evaluation sets of each size). The independent evaluation sets were selected following a stratified random sampling using compact geographical strata.The numerical experiment showed that the values of the uncertainty indicators were highly variable across numbers and locations of sites. The largest variations were observed for evaluation sets with fewer than 100 sites, but non-negligible variations remained for larger evaluation datasets. This result suggested that evaluations from independent sets convey a non-negligible error on the uncertainty indicat ors, which increases as the number of sites decrease.Evaluat! ions of DSM models from independent evaluation sets should be interpreted with care and uncertainty on validation results should be systematically estimated. For that, numerical experiments for benchmarking DSM models on known soil patterns across the world would be a valuable complement to the analytical expressions for the uncertainty indicators and the many DSM applications for which these analytical expressions are not valid. This would serve also to improve the sampling techniques for the calibration and evaluation datasets to reduce the error when estimating the uncertainty of a DSM product.

National soil organic carbon estimates can improve global estimates

Authors: Vitharana, UWA; Mishra, U; Mapa, RB

Source: GEODERMA, 337 55-64; MAR 1 2019

Abstract: Global inventories of spatial and vertical distribution of soil organic carbon (SOC) stocks are being used in national and global initiatives targeted to mitigate climate change and land degradation impacts. Yet, national level high-resolution estimates of SOC stocks can be useful for improving the accuracy of global SOC inventories. We estimated spatially resolved SOC stocks of surface 0-30 cm and subsurface 30-100 cm layers at a spatial resolution of 30 m in tropical Island, Sri Lanka using a legacy harmonized soil database of 122 soil profiles. The national estimates were compared with two global estimates derived from WISE30sec and SoilGrids250m. The tropical Island (land area = 64,610 km(2)) occupying 0.03% of global land area showed a considerable heterogeneity in SOC stocks ranging from 2.0-342.5 Mg ha(-1) and 2.7-391.7 Mg ha(-1) in the surface and subsurface soil layers, respectively. We found, elevation, precipitation and slope angle as main environmental controllers of the spatial distribution of SOC stocks under tropical climate. Incorporating the pedogenic information (derived from soil series level legacy map, soil orders and suborders) with environmental controllers resulted in better regression models of predicting surface (R-2 = 0.61) and subsurface (R-2 = 0.81) SOC stocks. Geographically weighted regression kriging derived maps of SOC stocks revealed that 0-100 cm soil layer of the tropical Island stored 500 Tg C contributing for 0.04% of the global SOC stocks. The validation results of our estimates showed low Mean Estimation Error (MEE: surface -1.6 and subsurface -1.6 Mg ha(-1)) and Root Mean Square Error (RMSE: surface 29.5 and subsurface 24.9 Mg ha(-1)) indicating a low bias and satisfactory predictions. The relative improvement of the prediction accuracy of the SOC stocks of our geospatial estimates in the 0-30 cm layer in comparison to SoilGrids250m and WISE30sec data derived SOC stocks were 51.7% and 35.2%, respectively. The SOC stocks predictions of the 30-100 cm soil layer show! ed even better relative improvement compared to SoilGrids250m (78.4%) and WISE30sec (57.4%) SOC estimates. Compared to estimates of total SOC stocks resulted in this study, WISE30sec data derived SOC stock maps showed 30% over estimation of the C stock in surface 0-30 cm (332 Tg C) and 41% overestimation in 30-100 cm layer (343 Tg C). The over estimation of total SOC stocks by the SoilGrids250 SOC stocks map for the surface 0-30 cm layer was 122% (567 Tg C) and for the 30-100 cm layer it was 209% (750 Tg C). We conclude that the fusion of legacy soil information of SOC stocks with appropriate environmental covariates and pedogenic information derived from legacy area-class soil maps at national level can produce more accurate inventories of spatial and vertical distribution of SOC stocks. These national inventories have a great potential of upgrading global inventories of SOC stocks.

Merging country, continental and global predictions of soil texture: Lessons from ensemble modelling in France

Authors: Caubet, M; Dobarco, MR; Arrouays, D; Minasny, B; Saby, NPA

Source: GEODERMA, 337 99-110; MAR 1 2019

Abstract: The increasing demand for soil information has led to the rapid development of Digital Soil Mapping (DSM) products. As a consequence, multiple soil maps are sometimes available for a particular area. Rather than selecting the best map, model ensemble offers a way to capitalize on existing soil information, and to improve the map accuracy. In this study we ensemble four topsoil texture maps of France with different resolution made by different organizations at the national, European, and global scale. We investigated two methods of model ensemble: the Granger-Ramanathan (GR) and Variance-Weighted (VW) methods. Ensemble methods based on area stratification were also tested to take into account local soil information. We also assessed the impact of the number of calibration points on the evaluation indicators. Both ensemble methods improved the accuracy of the map compared to the best of the primary maps, while the GR method outperformed the VW method. We found that the differen t stratification strategies did not improve the accuracy significantly when compared to the global methods. Finally, we showed that a relatively low number of calibration points is required in the merging process if the sampling is well designed. This study demonstrates that digital soil mapping products at various scales from various data sources can be combined with the ensemble method taking advantage of all existing efforts and taking care of harmonization issues.

Use of portable XRF: Effect of thickness and antecedent moisture of soils on measured concentration of trace elements

Authors: Padilla, JT; Hormes, J; Selim, HM

Source: GEODERMA, 337 143-149; MAR 1 2019

Abstract: The use of portable X-ray fluorescence (PXRF) for rapid measurements of concentrations of trace elements in soils is increasing. The purpose of this study was to assess in a systematic way the influence of soil moisture and sample thickness on concentrations of Ni, Zn, Cd, and Pb in a mixture of glass beads as well as Windsor loamy sand and Webster loam soils. The Windsor soil was collected from near Lebanon, NH, and the Webster soil was sampled from Story County, IA. In this context, the efficacy of the Compton Normalization (CN) calibration method to correct for changes in soil moisture was determined. Despite CN calibration, an inverse correlation between PXRF measured concentrations and soil moisture was observed. The magnitude of this effect depends upon the energy of the characteristic X-ray fluorescence of each element, with those emitting lower energy X-ray fluorescence being more greatly influenced. Moisture contents ranging from 0.09 to 0.26 cm(3) cm(-3) were found to generate trace element concentrations with no statistical difference, with significantly lower concentrations reported at greater moisture contents. In addition, measured concentrations of each trace element increased with increasing sample thickness until a constant measured concentration was attained, which was also correlated with the energy of the characteristic X-ray fluorescence of the corresponding trace element. Ni, with the lowest energy fluorescence, obtained constant measured concentration at 3 mm for all matrices, whereas a sample thickness of up to 10 mm was needed for Cd. In order to account for energy differences across a range of trace elements and matrices, the use of PXRF should be limited to soil samples having a thickness of at least 10 mm.

Mapping future soil carbon change and its uncertainty in croplands using simple surrogates of a complex farming system model

Authors: Luo, ZK; Eady, S; Sharma, B; Grant, T; Liu, DL; Cowie, A; Farquharson, R; Simmons, A; Crawford, D; Searle, R; Moor, A

Source: GEODERMA, 337 311-321; MAR 1 2019

Abstract: Soil organic carbon (SOC) in agricultural soils is vital for soil fertility for sustainable agricultural production and climate change resilience. Process-based farming system models are widely used to predict SOC dynamics in agricultural soils, but their application at regional scales is largely limited by computational requirements, data availability, and uncertainties in model predictions. Here we present an approach of combining a farming system model and a simplified surrogate model that emulates and mimics the behaviour of complex process-based models to predict SOC change (Delta SOC) and its uncertainty in Australian dryland cropping regions under anticipated climate change. We first calibrated and validated the farming system model APSIM for simulating Delta SOC (0-30 cm soil) using data from 90 farming-system trials at 28 sites across the study regions. Next we conducted a comprehensive simulation across the region using the validated APSIM model to predict Delta SOC over the period 2009-2070. Then simple surrogate models were developed based on the APSIM outputs. The surrogate models were able to explain > 96% of the variation in APSIM-predicted Delta SOC. Last the surrogate models were applied across the regions at the resolution of 1 km. In our simulations, Australian dryland cropping soils under farmers’ common management practices and future climate conditions were a net carbon source (0.66 Mg C ha(-1) with the 95% confidence interval ranging from -5.79 to 8.38 Mg C ha(-1)) during the 62-year period. Across the regions, simulated Delta SOC exhibited great spatial variability ranging from -108.8 to 9.89 Mg C ha(-1) at the resolution of 1 km, showing significant (P < 0.05) negative correlation with baseline SOC level, temperature and rainfall, and positive correlation with pasture frequency (the duration of pasture in the rotation divided by the whole duration of the rotation) and nitrogen application rate. The uncertainty in Delta S OC and the underlying drivers were also assessed. This study! presented a novel approach to efficiently predict future SOC dynamics and their uncertainty at fine resolutions, facilitating the development of site-specific management strategies for soil carbon sequestration.

Soil fertility assessment by Vis-NIR spectroscopy: Predicting soil functioning rather than availability indices

Authors: Recena, R; Fernandez-Cabanas, VM; Delgado, A

Source: GEODERMA, 337 368-374; MAR 1 2019

Abstract: Soil fertility is typically assessed by chemical analysis, which is expensive and time-consuming, and hence impractical for site-specific fertilizer management. Visible and near infrared (Vis-NIR) spectroscopy has been used for determining soil properties and chemically extractable plant nutrients. However, the suitability of Vis-NIR for accurate assessment of nutrient availability to plants has not yet been fully explored. In this work, we examined the accuracy of this technique as a new nutrient availability index, and in the case of P as a proxy for plant-available P. To this end, total plant-available P in soil was quantified in a P-depletion experiment with crops, and the availability of Ca, Mg, K, and Fe was assessed by chemical extraction.Vis-NIR spectroscopy allowed us to accurately estimate plant-available P, which depends not only on soil factors but also on the crop performance to take up P. Vis-NIR spectroscopy proved effective in identifying P, Ca, Mg, K, and Fe responsive sites. Precise estimation of plant-available P was a result of accurately predicting soil properties governing P availability to plants by Vis-NIR spectroscopy. In addition, this technique provided accurate predictions of soil properties influencing the dynamics of applied P and K fertilizer, which can be useful to adapt fertilization practices to soil properties. Vis-NIR spectroscopy can therefore enable a qualitative leap to cost-effective integral assessment of soil fertility by providing accurate predictions of soil functioning rather than mere estimates of availability indices, thereby facilitating more sustainable use of resources in agriculture.

Pre-treatment of soil X-ray powder diffraction data for cluster analysis

Authors: Butler, BM; Sila, AM; Shepherd, KD; Nyambura, M; Gilmore, CJ; Kourkoumelis, N; Hillier, S

Source: GEODERMA, 337 413-424; MAR 1 2019

Abstract: X-ray powder diffraction (XRPD) is widely applied for the qualitative and quantitative analysis of soil mineralogy. In recent years, high-throughput XRPD has resulted in soil XRPD datasets containing thousands of samples. The efforts required for conventional approaches of soil XRPD data analysis are currently restrictive for such large data sets, resulting in a need for computational methods that can aid in defining soil property soil mineralogy relationships. Cluster analysis of soil XRPD data represents a rapid method for grouping data into discrete classes based on mineralogical similarities, and thus allows for sets of mineralogically distinct soils to be defined and investigated in greater detail. Effective cluster analysis requires minimisation of sample-independent variation and maximisation of sample-dependent variation, which entails pre-treatment of XRPD data in order to correct for common aberrations associated with data collection.A 2(4) factorial design was used to investigate the most effective data pre-treatment protocol for the cluster analysis of XRPD data from 12 African soils, each analysed once by five different personnel. Sample-independent effects of displacement error, noise and signal intensity variation were pre-treated using peak alignment, binning and scaling, respectively. The sample-dependent effect of strongly diffracting minerals overwhelming the signal of weakly diffracting minerals was pre-treated using a square-root transformation. Without pre-treatment, the 60 XRPD measurements failed to provide informative clusters. Pre-treatment via peak alignment, square-root transformation, and scaling each resulted in significantly improved partitioning of the groups (p < 0.05). Data pre-treatment via binning reduced the computational demands of cluster analysis, but did not significantly affect the partitioning (p > 0.1). Applying all four pre-treatments proved to be the most suitable protocol for both non-hierarchical a nd hierarchical cluster analysis. Deducing such a protocol i! s considered a prerequisite to the wider application of cluster analysis in exploring soil property-soil mineralogy relationships in larger datasets.

The Role of Urban Agriculture in a Secure, Healthy, and Sustainable Food System

Authors: Nogeire-McRae, T; Ryan, EP; Jablonski, BBR; Carolan, M; Arathi, HS; Brown, CS; Saki, HH; Mckeen, S; Lapansky, E; Schipanski, ME

Source: BIOSCIENCE, 68 (10):748-759; OCT 2018

Abstract: Investments in urban agriculture (UA) initiatives have been increasing in the United States, but the costs and benefits to society are poorly understood. Urban agriculture can link socioeconomic and health systems, support education and societal engagement, and contribute to a range of conservation goals, including nutrient recycling and biodiversity conservation. Urban agriculture is spatially dispersed and small scale, creating opportunities to redirect underutilized land, water, and nutrient resources. Urban agriculture reduces water and carbon footprints when it replaces lawns. Labor and time requirements, potential for environmental and nutrient pollution, and scarce water resources are challenges that UA must address. Based on our review of the literature, it is unclear whether UA provides economic or nutritional benefits to urbanites, but our case study shows that UA can provide some benefits when replacing other land uses.

Poetry as a Creative Practice to Enhance Engagement and Learning in Conservation Science

Authors: Januchowski-Hartley, SR; Sopinka, N; Merkle, BG; Lux, C; Zivian, A; Goff, P; Oester, S

Source: BIOSCIENCE, 68 (11):905-911; NOV 2018

Abstract: Creativity is crucial to the capacity to do science well, to communicate it in compelling ways, and to enhance learning. Creativity can be both practiced and enhanced to strengthen conservation science professionals’ efforts to address global environmental challenges. We explore how poetry is one creative approach that can further conservation scientists’ engagement and learning. We draw on evidence from peer-reviewed literature to illustrate benefits of integrating science and poetry, and to ground our argument for the growth of a science-poetry community to help conservation scientists develop skills in creative practices as a component of professional development. We present examples from literature as well as two short poetry exercises for scientists to draw on when considering writing poetry, or deciding on forms of poetry to include, in their practice. Opportunity exists to grow science-poetry projects to further our understanding of what such initiatives can offer.

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