Introducing a Mechanistic Model in Digital Soil Mapping to Predict Soil Organic Matter Stocks in the Cantabrian Region (Spain)

ABSTRACT: Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of -2 t SOM ha−1 and a root mean square error (RMSE) of 66t SOM ha-1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes.This research was financially supported by the Environmental Hydraulics Institute ‘IH Cantabria of Universidad de Cantabria’ and the CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS). The CCAFS project is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. Besides the financial support, we would like to thank Sara Alcalde Aparicio for collaboration in the collection and analyses of soil samples

Beyond the plot: technology extrapolation domains for scaling out agronomic science

Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland.Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate ‘technology extrapolation domains’ based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment

Climate change and cereal production evolution trend in the Sahel: case study in Mali from 1951 to 2010

Open Access Journal; Published online: 9 April 2019Mali is a Sahelian country with a large climatic contrast from North to South. The current climatic and production evolutionary study is focused on the six major agro-climatic cereal production zones ranging from Kayes (400 mm) to Sikasso (>1000 mm) of rainfalls. Climatic data are rainfall records, daily maximum and minimum temperatures from 60 years of the six major synoptic weather observation stations. Data were analyzed on comparing average decades of the two normal periods of 30 years (1951-1980) and (1981-2010). Annual agronomic production data for millet, sorghum, maize and rice are derived from Mali's agricultural statistics base from 1984 to 2013. Main climatic results analyses indicate that climate change resulted in a decrease of 100 mm isohyets between the 2 periods of 30 years. The structure of the rainy season was little changed between these two periods since the average start of the season was delayed by 6 days and the average end date of the season became earlier by 4 days. Maximum temperatures increased significantly from + 0.44°C to + 1.53°C and minimum temperatures significantly increased from + 1.05°C to + 1.93°C in varying way depending on the sites. Statistics of major agronomic food crop production in Mali from 1984 to 2013 indicate an average increase of 985 to 4492 thousand tones, or 22% increase per year. There is a positive upward in saw tooth trend in Malian production from 1984 to 2013. This positive trend is the result of a combination of agricultural extension, agronomic research application and the management of small farmer holder in the Sahel. This evolution needs better study for drawing necessary right conclusions

Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa

In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled georeferenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km2. The mean RZ-PAWHC for SSA is 74mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km3) due to soil conditions restricting root zone depth. Of these, 4800 km3 are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km3 due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km3, aluminium toxicity by 600 km3, porosity by 120 km3 and alkalinity by 20 km3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available

Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa

In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled georeferenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km2. The mean RZ-PAWHC for SSA is 74mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km3) due to soil conditions restricting root zone depth. Of these, 4800 km3 are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km3 due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km3, aluminium toxicity by 600 km3, porosity by 120 km3 and alkalinity by 20 km3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available

Use of agro-climatic zones to upscale simulated crop yield potential

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are cropspecific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species

Beyond the plot: technology extrapolation domains for scaling out agronomic science

Open Access Journal; Published online: 14 May 2018Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate 'technology extrapolation domains' based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment

Long-term evidence for ecological intensification as a pathway to sustainable agriculture

Ecological intensification (EI) could help return agriculture into a 'safe operating space' for humanity. Using a novel application of meta-analysis to data from 30 long-term experiments from Europe and Africa (comprising 25,565 yield records), we investigated how field-scale EI practices interact with each other, and with N fertilizer and tillage, in their effects on long-term crop yields. Here we confirmed that EI practices (specifically, increasing crop diversity and adding fertility crops and organic matter) have generally positive effects on the yield of staple crops. However, we show that EI practices have a largely substitutive interaction with N fertilizer, so that EI practices substantially increase yield at low N fertilizer doses but have minimal or no effect on yield at high N fertilizer doses. EI practices had comparable effects across different tillage intensities, and reducing tillage did not strongly affect yields.Intensifying food production sustainably is critical given growing demand and agriculture's environmental footprint. This meta-analysis finds that practices such as adding organic matter and increasing crop diversity can partly substitute for nitrogen fertilizer to sustain or increase yields