The effects of erosional and management history on soil organic carbon stores in ephemeral wetlands of hummocky agricultural landscapes

Geoderma ◽  
2006 ◽  
Vol 135 ◽  
pp. 296-306 ◽  
Author(s):  
A. Bedard-Haughn ◽  
F. Jongbloed ◽  
J. Akkerman ◽  
A. Uijl ◽  
E. de Jong ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Landscapes ◽  
Ephemeral Wetlands ◽  
Management History ◽  
Carbon Stores

Soil Organic Carbon Prediction at National Scale (Germany)

2020 ◽  
Author(s):  
Ali Sakhaee ◽  
Anika Gebauer ◽  
Mareike Ließ ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Model Performance ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Agricultural Landscapes ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
National Scale

<p>Soil Organic Carbon (SOC) plays a crucial role in agricultural ecosystems. However, its abundance is spatially variable at different scales. In recent years, machine learning (ML) algorithms have become an important tool in the spatial prediction of SOC at regional to continental scales. Particularly in agricultural landscapes, the prediction of SOC is a challenging task.</p><p>In this study, our aim is to evaluate the capability of two ML algorithms (Random Forest and Boosted Regression Trees) for topsoil (0 to 30 cm) SOC prediction in soils under agricultural use at national scale for Germany. In order to build the models, 50 environmental covariates representing topography, climate factors, land use as well as soil properties were selected. The SOC data we used was from the German Agricultural Soil inventory (2947 sampling points). A nested 5-fold cross-validation was used for model tuning and evaluation. Hyperparameter tuning for both ML algorithms was done by differential evolution optimization. </p><p>This approach allows exploring an extensive set of field data in combination with state of the art pedometric tools. With a strict validation scheme, the geospatial-model performance was assessed. Current results indicate that the spatial SOC variation is to a minor extent predictable with the considered covariate data (<30% explained variance). This may partly be explained by a non-steady state of SOC content in agricultural soils with environmental drivers. We discuss the challenges of geo-spatial modelling and the value of ML algorithms in pedometrics.</p>

Modelling impacts of climate change and alternative management interventions on the multi-functionality of agricultural landscapes in southern Africa

2020 ◽  
Author(s):  
Reimund Roetter ◽  
Simon Scheiter ◽  
Munir Hoffmann ◽  
Kwabena Ayisi ◽  
Paolo Merante ◽  
...  
Keyword(s):  
Climate Change ◽  
Ecosystem Services ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Landscapes ◽  
Sub Saharan Africa ◽  
Ecosystem Functions ◽  
Drought Risk ◽  
Crop Models ◽  
Landscape Level

<p><span><span>On the background of increasing welfare and continued population growth, there is an ever-increasing pressure on land and other natural resources in many parts of the world. The situation is, however, particularly severe in the drylands of Sub-Saharan Africa. Southern African landscapes, composed of arable lands, tree orchards and rangelands, provide a range of important ecosystem functions. These functions are increasingly threatened by land use changes through competing claims on land by agriculture, tourism, mining and other sectors, and by environmental change, namely climate change and soil degradation. Among others, climate models project that drought risk in the region will increase considerably. Based on comprehensive data sets originating from previous groundwork by several collaborative projects on the functioning of these ecosystems, a number of biophysical and bio-economic models have been developed and evaluated. In the framework of the South African Limpopo Landscapes network (SALLnet) we have now refined and tailored these models for combined use for the assessment of changes in multiple functions of the prevailing agroecosystems when affected by alternative climate and land management scenarios - from field to regional scale. We apply vegetation models (such as aDGVM), crop models (such as APSIM) and integrative farm level models (e.g. agent-based) for different farming systems in conjunction with geo-referenced databases. Model outputs are combined to assess the impact of management x environment interactions on various ecosystem functions. Of special interest in our study are the ecosystem services related to the provision of food, feed and fuel, soil and water conservation, as well as recycling and restoring carbon and nutrients in soil. To illustrate how the combination of various modelling components can work in assessing management intervention effects under different environmental conditions on landscape level ecosystem services, a case study was defined in Limpopo province, South Africa. We investigated effects of current management practices and an intensification scenario over a longer period of years on soil organic carbon change under rangeland and arable land, potential erosion, productive water use, biomass production, monthly feed gaps, and rangeland habitat quality. Tentative results showed that sustainable intensification closed the livestock feed gap, but further reduced soil organic carbon. More generally, coupling the output of vegetation and crop models regionally calibrated with sound ground/ experimental data appears promising to provide meaningful insights into the highly complex interconnections of different ecosystem services at a landscape level.</span></span></p>

scholarly journals World's soils are under threat

SOIL ◽  
2016 ◽  
Vol 2 (1) ◽  
pp. 79-82 ◽  
Author(s):  
Luca Montanarella ◽  
Daniel Jon Pennock ◽  
Neil McKenzie ◽  
Mohamed Badraoui ◽  
Victor Chude ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Crop Yields ◽  
Tropical Soils ◽  
Policy Formulation ◽  
Nitrogen And Phosphorus ◽  
Developing Regions ◽  
Soil Resources ◽  
National Governments ◽  
Carbon Stores

Abstract. The Intergovernmental Technical Panel on Soils has completed the first State of the World's Soil Resources Report. Globally soil erosion was identified as the gravest threat, leading to deteriorating water quality in developed regions and to lowering of crop yields in many developing regions. We need to increase nitrogen and phosphorus fertilizer use in infertile tropical and semi-tropical soils – the regions where the most food insecurity among us are found – while reducing global use of these products overall. Stores of soil organic carbon are critical in the global carbon balance, and national governments must set specific targets to stabilize or ideally increase soil organic carbon stores. Finally the quality of soil information available for policy formulation must be improved – the regional assessments in the State of the World's Soil Resources Report frequently base their evaluations on studies from the 1990s based on observations made in the 1980s or earlier.

scholarly journals Monitoring soil organic carbon stock changes in agricultural landscapes: Issues and a proposed approach

2006 ◽  
Vol 86 (3) ◽  
pp. 451-463 ◽  
Author(s):  
A J VandenBygaart
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Landscape ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Agricultural Landscapes ◽  
Spatial And Temporal Scales ◽  
Landscape Processes ◽  
Daunting Task ◽  
Soc Stock

The distribution of soil organic carbon (SOC) in the landscape is governed by multiple factors and processes occurring at multiple scales. Thus, an understanding of landscape processes and pedology should aid in designing approaches to study SOC stock changes. Numerous factors affect distribution of SOC in the landscape at varying spatial and temporal scales. Each of these is summarized to set the stage for outlining a proposed approach to monitoring SOC in the agricultural landscape. Many tools are used to assess the variability of soil properties at varying spatial scales. Pedological knowledge and interpretation of landscape processes can be used to understand the spatial distribution of SOC in the landscape. I show that semi-variograms and the minimum detectable difference may be of limited value in deriving a universal approach to assess SOC change. Issues to be considered or resolved before initiating a monitoring system include depth of sampling and influence of management, compositing and sub-sampling, changes in bulk density, landscape effects and SOC dynamics. After considering these issues, I propose an approach to monitor SOC stock change in agroecosystems, acknowledging that any methodology likely cannot be strictly and universally applicable. The approach considers issues such as location, plot layout, and experimental and statistical design. Such an approach, derived from a landscape and pedology perspective, may make the measurement and verification of SOC at varying scales a less daunting task. Key words: Soil organic carbon change, landscape, pedology, experimental design

Soil Organic Carbon Distribution and Isotope Composition Response to Erosion in Cropland under Soybean/Maize Production

2020 ◽  
Author(s):  
Greg McCarty ◽  
Xia Li
Keyword(s):  
Soil Erosion ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Landscapes ◽  
Poor Correlation ◽  
Carbon Distribution ◽  
Deposition Patterns ◽  
Soil Redistribution ◽  
Soc Density ◽  
Topographic Constraints

<p>Soil erosion and deposition patterns can affect the fate of soil organic carbon (SOC) in agroecosystems. Topographic constraints affect soil redistribution processes and create spatial structure in SOC density. We combined isoscape (isotopic landscape) analyses for δ<sup>13</sup>C and cesium-137 (<sup>137</sup>Cs) inventory via digital terrain analysis quantifying SOC dynamics and soil redistribution patterns to gain insight on their responses to topographic constraints in an Iowa cropland field under soybean/maize (C3/C4) production. Additionally, historic bare soil orthophotos were used to determine soil carbon distribution before the 1960s (prior to global <sup>137</sup>CS fallout). Topography‐based models were developed to estimate <sup>137</sup>Cs inventory, SOC density, and δ<sup>13</sup>C distributions using stepwise principal component regression. Findings showed that spatial patterns of SOC were similar to soil erosion/deposition patterns with high SOC density in depositional areas and low SOC density in eroded areas. Soil redistribution, SOC density, and δ<sup>13</sup>C signature of SOC were all highly correlated with topographic metrics indicating that topographic constraints determined the spatial variability in erosion and SOC dynamics. The δ<sup>13</sup>C isoscape indicated that C3‐derived SOC density was strongly controlled by topographic metrics whereas C4‐derived SOC density showed much weaker expression of spatial pattern and poor correlation to topographic metrics. The resulting topography‐based models captured more than 60% of the variability in total SOC density and C3‐derived SOC density but could not reliably predict C4‐derived SOC density. This study demonstrated the utility of exploring relationships between δ<sup>13</sup>C and <sup>137</sup>Cs isoscapes to gain insight on fate of SOC within eroding agricultural landscapes.</p>

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