Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and ...weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.
Water erosion removes soil nutrients, soil carbon, and in extreme cases can remove topsoil altogether. Previous studies have quantified crop yield losses from water erosion using a range of methods, ...applied mostly to single plots or fields, and cannot be systematically compared. This study assesses the worldwide impact of water erosion on maize and wheat production using a global gridded modeling approach for the first time. The EPIC crop model is used to simulate the global impact of water erosion on maize and wheat yields, from 1980 to 2010, for a range of field management strategies. Maize and wheat yields were reduced by a median of 3% annually in grid cells affected by water erosion, which represent approximately half of global maize and wheat cultivation areas. Water erosion reduces the annual global production of maize and wheat by 8.9 million tonnes and 5.6 million tonnes, with a value of $3.3bn globally. Nitrogen fertilizer necessary to reduce losses is valued at $0.9bn. As cropland most affected by water erosion is outside major maize and wheat production regions, the production losses account for less than 1% of the annual global production by volume. Countries with heavy rainfall, hilly agricultural regions and low fertilizer use are most vulnerable to water erosion. These characteristics are most common in South and Southeast Asia, sub-Saharan Africa and South and Central America. Notable uncertainties remain around large-scale water erosion estimates that will need to be addressed by better integration of models and observations. Yet, an integrated bio-physical modeling framework – considering plant growth, soil processes and input requirements – as presented herein can provide a link between robust water erosion estimates, economics and policy-making so far lacking in global agricultural assessments.
•Water erosion reduces maize and wheat yields by 3% on half of the global cropland.•Simulated erosion impacts are mostly driven by slope, precipitation and fertilizer rates.•Highest water erosion impacts are in Asia, sub-Saharan Africa and Latin America.•Maize and wheat losses due to water erosion account for less than 1% of the global production.
The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to ...a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R2 = 0.97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.
•Meta-model approach used to simplify process based crop model.•Exploration of the determinants of soil organic carbon responses to management.•Meta-model predictions validated with regional and local data.•Meta-models largely capture broad dynamics, underestimate magnitude of responses.•More accurate input data, calibration, and validation needed to improve accuracy.
Wheat is the third largest crop globally and an essential source of calories in human diets. Maintaining and increasing global wheat production is therefore strongly linked to food security. A large ...geographic variation in wheat yields across similar climates points to sizeable yield gaps in many nations, and indicates a regionally variable flexibility to increase wheat production. Wheat is particularly sensitive to a changing climate thus limiting management opportunities to enable (sustainable) intensification with potentially significant implications for future wheat production. We present a comprehensive global evaluation of future wheat yields and production under distinct Representative Concentration Pathways (RCPs) using the Environmental Policy Integrated Climate (EPIC) agro-ecosystem model. We project, in a geographically explicit manner, future wheat production pathways for rainfed and irrigated wheat systems. We explore agricultural management flexibility by quantifying the development of wheat yield potentials under current, rainfed, exploitable (given current irrigation infrastructure), and irrigated intensification levels. Globally, because of climate change, wheat production under conventional management (around the year 2000) would decrease across all RCPs by 37 to 52 and 54 to 103Mt in the 2050s and 2090s, respectively. However, the exploitable and potential production gap will stay above 350 and 580Mt, respectively, for all RCPs and time horizons, indicating that negative impacts of climate change can globally be offset by adequate intensification using currently existing irrigation infrastructure and nutrient additions. Future world wheat production on cropland already under cultivation can be increased by ~35% through intensified fertilization and ~50% through increased fertilization and extended irrigation, if sufficient water would be available. Significant potential can still be exploited, especially in rainfed wheat systems in Russia, Eastern Europe and North America.
•Assessment of global wheat production under Representative Concentration Pathways•Region-specific agricultural management flexibility to counteract climate impacts•Production with management as in 2000 would decrease by 54 to 103Mt in the 2090s.•Yet, the exploitable and potential production gap will stay above 350 and 580 Mt.
Soils as key component of terrestrial ecosystems are under increasing pressures. As an advance to current static assessments, we present a dynamic soil functions assessment (SFA) to evaluate the ...current and future state of soils regarding their nutrient storage, water regulation, productivity, habitat and carbon sequestration functions for the case-study region in the Lower Austrian Mostviertel. Carbon response functions simulating the development of regional soil organic carbon (SOC) stocks until 2100 are used to couple established indicator-based SFA methodology with two climate and three land use scenarios, i.e. land sparing (LSP), land sharing (LSH), and balanced land use (LBA). Results reveal a dominant impact of land use scenarios on soil functions compared to the impact from climate scenarios and highlight the close link between SOC development and the quality of investigated soil functions, i.e. soil functionality. The soil habitat and soil carbon sequestration functions on investigated agricultural land are positively affected by maintenance of grassland under LSH (20% of the case-study region), where SOC stocks show a steady and continuous increase. By 2100 however, total regional SOC stocks are higher under LSP compared to LSH or LBA, due to extensive afforestation. The presented approach may improve integrative decision-making in land use planning processes. It bridges superordinate goals of sustainable development, such as climate change mitigation, with land use actions taken at local or regional scales. The dynamic SFA broadens the debate on LSH and LSP and can reduce trade-offs between soil functions through land use planning processes.
•Dynamic soil functions assessment through simulation of SOC changes until 2100.•Land use change has a stronger effect on soil functionality than climate change.•By 2100, SOC stocks are higher under a land sparing than under a land sharing scenario.•Soil functions are sensitive to changes in SOC.•The approach enables soil-sensitive ex-ante evaluation of land use scenarios.
Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global ...scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
•Quantification of water use for rainfed production of Brazilian crops, and simulation of management scenarios.•Green water is a primary resource for production of the crops, both in the present and ...in prospective scenarios.•We found possibilities of improvement of green water productivity, and limited options for supplemental blue water use.•Irrigation-based intensification results in lower water productivity, when compared to fertilizer-based intensification.
Green water is a central resource for global agricultural production. Understanding its role is fundamental to design strategies to increase global food and feed production while avoiding further land conversion, and obtaining more crop per drop. Brazil is a country with high water availability, and a major exporter of agricultural goods and virtual water. We assess here water use and water productivity in Brazil for four major rainfed crops: cotton, maize, soybeans, and wheat. For this, we use the EPIC crop model to perform a spatially explicit assessment of consumptive water use and water productivity under crop management scenarios in Brazil between 1990 and 2013. We investigate four different land-water interactions: (i) water use and productivity for different management scenarios, (ii) the potential of supplemental irrigation for productivity improvement, (iii) changes in green water use throughout the study period, and finally (iv) potential reduction of land and water demand related to agricultural intensification. The results show that, for the studied crops, green water is the main resource for biomass production, and intensification can lead to great improvements in green water productivity. The results also suggest that, despite achieving higher yields, irrigation-based intensification tends to lower overall water productivity, compared to fertilizer-based intensification strategies. This is, however, regionally and crop-specific. Furthermore, due to higher yields and water productivity, producing the same amount of crop output in irrigated or rainfed intensification scenarios would result in the reduction of resource demand, in the order of 34–58 % for cropland, and 29–52 % for water.
•The Zoige wetlands accumulate>300tCha−1 of SOC in the top 1m of the soil.•The wetland soils were a net carbon sink of 0.25tCha−1yr−1 from 1980 to 2010.•Drainage since 1980 decreased SOC by ...4tCha−1.•Intense grazing had minor yet positive impacts on the SOC stock.•The results imply a need to control drainage to maintain the wetland SOC stock.
China's Zoige Plateau, located in the northeastern part of the Qinghai-Tibet plateau, has the largest alpine peat wetland in the world. However, little is known about how the soil organic carbon (SOC) stock of these wetlands has been influenced by human activities. In this study, we quantified the changes in the SOC stock in two counties (i.e., Hongyuan and Ruoergai) in the Zoige Plateau wetlands between 1980 and 2010 in response to progressive drainage of the wetlands and increased grazing intensity using the Environmental Policy Integrated Climate (EPIC) model. The results indicate that wetlands accumulate large amounts of SOC (>300tCha−1) in the upper 1m of the soil in the study area. Wetland soils sequestered ∼0.25tCha−1yr−1 despite the degradation that has occurred due to drainage and grazing. Drainage was one of the main driving factors for SOC loss in the wetlands. Conversion of wetlands to grassland via drainage since 1980 led to a loss of approximately 4tCha−1 from the SOC stock. On the other hand, grazing might have positive impact on root biomass accumulation, thus enhancing the SOC stock. As estimated by EPIC, more intensive grazing slightly increased the SOC stock. However, grazing is also a reason why wetlands were drained with all the negative effects on the SOC pool. The potential SOC sequestration of intensive grazing was offseted by the negative effect of drainage. The outcomes suggest not only to limit drainage and restore wetland, but also to control grazing which will in turn decrease drainages to sustain the ecosystem service of carbon sequestration provided by the Zoige wetlands.
•Evaluation of a field-scale crop model for reliable application at large scales.•European crop model validation using reported regional yields of four major crops.•Methodological improvements for ...sowing density and fertilizer application.•Crop yield sensitivity analysis of nutrient and irrigation management strategies.
Justifiable usage of large-scale crop model simulations requires transparent, comprehensive and spatially extensive evaluations of their performance and associated accuracy. Simulated crop yields of a Pan-European implementation of the Environmental Policy Integrated Climate (EPIC) crop model were satisfactorily evaluated with reported regional yield data from EUROSTAT for four major crops, including winter wheat, rainfed and irrigated maize, spring barley and winter rye. European-wide land use, elevation, soil and daily meteorological gridded data were integrated in GIS and coupled with EPIC. Default EPIC crop and biophysical process parameter values were used with some minor adjustments according to suggestions from scientific literature. The model performance was improved by spatial calculations of crop sowing densities, potential heat units, operation schedules, and nutrient application rates. EPIC performed reasonable in the simulation of regional crop yields, with long-term averages predicted better than inter-annual variability: linear regression R2 ranged from 0.58 (maize) to 0.91 (spring barley) and relative estimation errors were between ±30% for most of the European regions. The modelled and reported crop yields demonstrated similar responses to driving meteorological variables. However, EPIC performed better in dry compared to wet years. A yield sensitivity analysis of crop nutrient and irrigation management factors and cultivar specific characteristics for contrasting regions in Europe revealed a range in model response and attainable yields. We also show that modelled crop yield is strongly dependent on the chosen PET method. The simulated crop yield variability was lower compared to reported crop yields. This assessment should contribute to the availability of harmonised and transparently evaluated agricultural modelling tools in the EU as well as the establishment of modelling benchmarks as a requirement for sound and ongoing policy evaluations in the agricultural and environmental domains.
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