Ecosystem services in agricultural landscapes: a spatially explicit approach to support sustainable soil management

Soil degradation has been associated with a lack of adequate consideration of soil ecosystem services. We demonstrate a broadly applicable method for mapping changes in the supply of two priority soil ecosystem services to support decisions about sustainable land-use configurations. We used a landscape-scale study area of 302 km(2) in northern Victoria, south-eastern Australia, which has been cleared for intensive agriculture. Indicators representing priority soil services (soil carbon sequestration and soil water storage) were quantified and mapped under both a current and a future 25-year land-use scenario (the latter including a greater diversity of land uses and increased perennial crops and irrigation). We combined diverse methods, including soil analysis using mid-infrared spectroscopy, soil biophysical modelling, and geostatistical interpolation. Our analysis suggests that the future land-use scenario would increase the landscape-level supply of both services over 25 years. Soil organic carbon content and water storage to 30 cm depth were predicted to increase by about 11% and 22%, respectively. Our service maps revealed the locations of hotspots, as well as potential trade-offs in service supply under new land-use configurations. The study highlights the need to consider diverse land uses in sustainable management of soil services in changing agricultural landscapes.Mohsen Forouzangohar, Neville D. Crossman, Richard J. MacEwan, D. Dugal Wallace, and Lauren T. Bennet

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Prediction of atrazine sorption coefficients in soils using mid-infrared spectroscopy and partial least-squares analysis

Copyright © 2008 American Chemical SocietyThis study explored the potential of mid-infrared spectroscopy (MIR) with partial least-squares (PLS) analysis to predict sorption coefficients (Kd) of pesticides in soil. The MIR technique has the advantage of being sensitive to both the content and the chemistry of soil organic matter and mineralogy, the important factors in the sorption of nonionic pesticides. MIR spectra and batch Kd values of atrazine were determined on a set of 31 soil samples as reference data for PLS calibration. The samples, with high variability in soil organic carbon content (SOC), were chosen from 10 southern Australian soil profiles (A1, A2, B, and C in one case). PLS calibrations, developed for the prediction of Kd from the MIR spectra and reference Kd data, were compared with predictions from Koc-based indirect estimation using SOC content. The reference Kd data for the 31 samples ranged from 0.31 to 5.48 L/kg, whereas Koc ranged from 30 to 680 L/kg. Both coefficients generally increased with total SOC content but showed a relatively poor coefficient of determination (R2 = 0.53; P > 0.0001) and a high standard error of prediction (SEP =1.22) for the prediction of Kd from Koc. This poor prediction suggested that total SOC content alone could explain only half of the variation in Kd. In contrast, the regression plot of PLS predicted versus measured Kd resulted in an improved correlation, with R2 = 0.72 ( P > 0.0001) and standard error of cross-validation (SECV) = 0.63 for three PLS factors. With the advantages of MIR-PLS in mind, (i) more accurate prediction of Kd, (ii) an ability to reflect the nature and content of SOC as well as mineralogy, and (iii) high repeatability and throughput, it is proposed that MIR-PLS has the potential for an improved and rapid assessment of pesticide sorption in soils

Midinfrared spectroscopy and chemometrics to predict diuron sorption coefficients in soils

The potential of mid-infrared (MIR) spectroscopy in combination with partial least-squares (PLS) regression was investigated to predict the soil sorption (distribution) coefficient (K(d)) of a nonionic pesticide (diuron). A calibration set of 101 surface soils collected from South Australia was utilized for reference sorption data and MIR spectra. Principal component analysis (PCA) was performed on the spectra to detect spectral outliers. The MIR-PLS model was developed and validated by dividing the initial data set into four validation sets. The model resulted in a coefficient of determination (R2) of 0.69, a standard error (SE) of 5.57, and a residual predictive deviation (RPD) of 1.63. The normalized sorption coefficient for the organic compound (K(oc)) approach, on the other hand, resulted in R2, SE, and RPD values of 0.42, 7.26, and 1.25, respectively. However, the significant statistical difference between the two models was mainly due to two outliers detected via PCA. Apart from spectral outliers, the performance of the two models was essentially similar for the rest of the calibration set. Outlier detection by the MIR-PLS model may gainfully be employed as a tool for improving prediction of K(d). The MIR-based model can provide a direct estimation of K(d) values based on the integrated properties of organic and mineral matter reflected in the infrared spectra.Mohsen Forouzangohar, Rai S. Kookana, Sean T. Forrester, Ronald J. Smernik and David J. Chittleboroug

Predicted consequences of increased rainfall variability on soil carbon stocks in a semiarid environment

Research on the impacts of climate change on soil organic carbon (SOC) stocks has focused on the effects of changes in average climate, but the potential effects of increased climate variability, including more frequent extreme events, remain under-examined. In this study, set in a semiarid agricultural landscape in southeastern Australia, we used the Rothamsted carbon (RothC) model to isolate the effects of interannual rainfall variability on SOC stocks over a 50 yr period. We modelled SOC trends in response to 3 scenarios that had the same 50 yr average climate but different interannual rainfall distributions: non-changing average climate, historic variability (H), and increased variability due to more frequent extreme rainfall years (XH). Relative to the non-changing average climate, RothC simulations predicted net decreases in mean SOC stocks to 50 yr of 11% under the H scenario and 13% under the XH scenario. These decreases were the result of predicted SOC decreases (and increased CO2 emissions) in extreme wet years (ca. 0.26 Mg ha(-1) yr(-1)) that were not counterbalanced by SOC increases in extreme dry years (ca. 0.11 Mg ha(-1) yr(-1)). No significant difference in mean SOC stocks at 50 yr between the H and XH scenarios was likely due to an increase in both extreme wet and counterbalancing extreme dry years in the latter. Strong negative correlations were found between annual changes in SOC stocks and rainfall. Our modelled predictions indicate the potential for extreme rainfall years to influence SOC gains and losses in semiarid environments and highlight the importance of maintaining plant inputs in these environments, particularly during extreme wet years

Mid-infrared spectra predict nuclear magnetic resonance spectra of soil carbon

Abstract not availableMohsen Forouzangohar, Jeffrey A. Baldock, Ronald J. Smernik, Bruce Hawke, Lauren T. Bennet

Predicted consequences of increased rainfall variability on soil carbon stocks in a semiarid environment

Research on the impacts of climate change on soil organic carbon (SOC) stocks has focused on the effects of changes in average climate, but the potential effects of increased climate variability, including more frequent extreme events, remain under-examined. In this study, set in a semiarid agricultural landscape in southeastern Australia, we used the Rothamsted carbon (RothC) model to isolate the effects of interannual rainfall variability on SOC stocks over a 50 yr period. We modelled SOC trends in response to 3 scenarios that had the same 50 yr average climate but different interannual rainfall distributions: non-changing average climate, historic variability (H), and increased variability due to more frequent extreme rainfall years (XH). Relative to the non-changing average climate, RothC simulations predicted net decreases in mean SOC stocks to 50 yr of 11% under the H scenario and 13% under the XH scenario. These decreases were the result of predicted SOC decreases (and increased CO₂ emissions) in extreme wet years (ca. 0.26 Mg ha⁻¹ yr⁻¹) that were not counterbalanced by SOC increases in extreme dry years (ca. 0.11 Mg ha⁻¹ yr⁻¹). No significant difference in mean SOC stocks at 50 yr between the H and XH scenarios was likely due to an increase in both extreme wet and counterbalancing extreme dry years in the latter. Strong negative correlations were found between annual changes in SOC stocks and rainfall. Our modelled predictions indicate the potential for extreme rainfall years to influence SOC gains and losses in semiarid environments and highlight the importance of maintaining plant inputs in these environments, particularly during extreme wet years

Using the power of C-13 NMR to interpret infrared spectra of soil organic matter: A two-dimensional correlation spectroscopy approach

Soil organic matter (SOM) is a complex mixture containing a variety of organic molecular structures. As a consequence, interpretation of the infrared (IR) spectra of SOM is difficult and ambiguous and there is a necessity to establish more reliable IR spectral band assignments. We investigated a novel approach to identify IR spectral bands based on correlation with more easily interpreted nuclear magnetic resonance (NMR) spectra. The IR spectra of HF-treated soils were obtained in both near- and mid-infrared (NIR and MIR) regions and generalized two-dimensional (2D) correlation spectroscopy was employed as the computational correlation tool. 2D NMR/NIR and 2D NMR/MIR heterospectral correlation analyses were performed, separately. We found that NIR spectroscopy could identify aliphatic carbon in SOM as a broad peak occupying the entire NIR region. On the other hand, the MIR spectra contained stronger and more distinct signals than NIR from most of the major carbon types. Bands due to aromatic carbon and carboxyl groups were identified in the regions 4000-3500 cm-1 and 850-500 cm-1, respectively, and bands due to aliphatic carbon appeared around 3500-2600 cm-1 and 2000-1000 cm-1. Most (but not all) of these assignments are consistent with assignments based on MIR spectra of model compounds. These findings will assist in developing new IR spectroscopy tools for characterizing the chemistry of SOM more accurately and, perhaps, for monitoring its changes more sensitively. © 2013 Elsevier B.V.Mohsen Forouzangohar, Daniel Cozzolino, Ronald J. Smernik, Jeffrey A. Baldock, Sean T. Forrester, David J. Chittleborough, Rai S. Kookan

Using the power of C-13 NMR to interpret infrared spectra of soil organic matter: A two-dimensional correlation spectroscopy approach

Soil organic matter (SOM) is a complex mixture containing a variety of organic molecular structures. As a consequence, interpretation of the infrared (IR) spectra of SOM is difficult and ambiguous and there is a necessity to establish more reliable IR spectral band assignments. We investigated a novel approach to identify IR spectral bands based on correlation with more easily interpreted nuclear magnetic resonance (NMR) spectra. The IR spectra of HF-treated soils were obtained in both near- and mid-infrared (NIR and MIR) regions and generalized two-dimensional (2D) correlation spectroscopy was employed as the computational correlation tool. 2D NMR/NIR and 2D NMR/MIR heterospectral correlation analyses were performed, separately. We found that NIR spectroscopy could identify aliphatic carbon in SOM as a broad peak occupying the entire NIR region. On the other hand, the MIR spectra contained stronger and more distinct signals than NIR from most of the major carbon types. Bands due to aromatic carbon and carboxyl groups were identified in the regions 4000-3500 cm-1 and 850-500 cm-1, respectively, and bands due to aliphatic carbon appeared around 3500-2600 cm-1 and 2000-1000 cm-1. Most (but not all) of these assignments are consistent with assignments based on MIR spectra of model compounds. These findings will assist in developing new IR spectroscopy tools for characterizing the chemistry of SOM more accurately and, perhaps, for monitoring its changes more sensitively. © 2013 Elsevier B.V