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While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.

A high-resolution nutrient emission inventory can provide reliable and accurate identification of priority control areas, which is crucial for efficient decisions on water quality restoration. However, the inventories widely used in large-scale modeling are usually based on provincial inputs, which induce the challenges of lacking localized parameters and missing localized characteristic when provincial scale inputs are converted to finer scales with the down-scale methods. Based on elaborate investigations and statistical data at the county scale with multi-scale data conversion, the China Emission Inventory of Nutrients (CEIN) was developed with a spatial resolution of a 0.1° grid and sub-basin scales. The Yangtze River Basin was used as a case study to illustrate the potential applications of CEIN. The emissions of total nitrogen (TN) and total phosphorus (TP) of Yangtze River Basin is 0.43 Mt and 0.04 Mt for point sources, 11.09 Mt and 4.64 Mt for diffuse sources in 2017. The hotspot analysis for 2606 sub-basins indicated that cropland is the key source of nutrient emissions, accounting for 58.88% and 79.15% of TN and TP, respectively. Industrial sewage and freshwater aquaculture accounted for 27.39% (TN) and 21.98% (TP) of the point sources, which is substantial due to their direct discharge into surface waters. The current results also reveal that, in contrast to CEIN, the previously used common emission factors based on GDP per capita produced considerable overestimations of 2.37 and 2.65 times the actual TN and TP emissions, respectively. Additional advantages of the CEIN have been demonstrated in identifying priority control areas more accurately with reduced bias and quantifying the effects of policies at much smaller scales. For example, the CEIN helps to distinguish hotspots, which was neglected when identifying sources at the level-III sub-basin scale, and indicates that the management of fractional areas (TN: 16.97%; TP: 13.44%) provides the highest nutrient emissions control (TN: 44.34%; TP: 48.65%) for the entire basin. The evaluation of China's toilet revolution policy demonstrates that achieving equitable access to safe sanitation has resulted in a reduction of 7240 t of TN and 833 t of TP, which is extremely critical for rural water quality and health.

Global gridded crop models (GGCMs) are essential tools for estimating agricultural crop yields and externalities at large scales, typically at coarse spatial resolutions. Higher resolution estimates are required for robust agricultural assessments at regional and local scales, where the applicability of GGCMs is often limited by low data availability and high computational demand. An approach to bridge this gap is the application of meta-models trained on GGCM output data to covariates of high spatial resolution. In this study, we explore two machine learning approaches – extreme gradient boosting and random forests - to develop meta-models for the prediction of crop model outputs at fine spatial resolutions. Machine learning algorithms are trained on global scale maize simulations of a GGCM and exemplary applied to the extent of Mexico at a finer spatial resolution. Results show very high accuracy with R2>0.96 for predictions of maize yields as well as the hydrologic externalities evapotranspiration and crop available water with also low mean bias in all cases. While limited sets of covariates such as annual climate data alone provide satisfactory results already, a comprehensive set of predictors covering annual, growing season, and monthly climate data is required to obtain high performance in reproducing climate-driven inter-annual crop yield variability. The findings presented herein provide a first proof of concept that machine learning methods are highly suitable for building crop meta-models for spatio-temporal downscaling and indicate potential for further developments towards scalable crop model emulators.

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

Phosphorus (P) is a major agricultural nutrient and, in its mineable form, a potentially scarce resource. Countries with limited physical access to P should hence develop an effective national P governance. This requires analyses of trends and variations in P flows and stocks over time. Here, we present a long-term P flow analysis for the Indian agri-food sector from 1988 to 2011. Major P flows are imports of mineral P, fertilizer application, and uptake of animal fodder. The mineral P import dependency ratio is constant at around 93%. On average, 20% of P inputs to soils are lost through erosion. Key drivers of changes in P flows include population growth, dietary change, and agricultural intensification. To reduce its P fertilizer import dependence, India could, for example, substitute up to 19% of the presently applied mineral P if manure used as a household fuel were recycled, and up to 21% if P was fully recovered from wastewater and household waste. Comparing selected indicators for P use in agriculture with China and the European Union (EU) reveals that there are structural similarities, such as increasing fertilizer application rates and P accumulation in soils, with the first but large differences compared to the latter. The analyses highlight that in contrast to static indicators, the time-continuous tracking of P flows provides substantial advantages, such as the identification of long-term trends, drivers, and intervention options for sustainable P management, given that it allows for the interpretation of present indicators in the context of past trends and legacies.

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

In agriculture, balancing water use and retention is an issue dealt with in most regions and for many crops. In this study, we suggest agricultural water equilibrium (AWE) as a new concept that can facilitate a spatially explicit management of agricultural water. This concept is based on the principle of supply and demand of agricultural water, where the virtual water content of crops (VWC) can be defined as the demand, and cropland water budget (CWB) as the supply. For assessing the AWE of the Korean Peninsula, we quantified the CWB based on the hydrological cycle and the VWC of rice, a key crop in the Peninsula. Five factors, namely crop yield, growing season evapotranspiration, annual evapotranspiration, runoff, and annual precipitation, were used to assess the AWE, of which the first four were estimated using the spatially explicit large-scale crop model, Geographical Information System (GIS)-based Environmental Policy Integrated Climate (GEPIC). The CWB and VWC were calculated for a period of three decades, and the AWE was computed by deducting the VWC from the CWB. Our results show a latitudinal difference across the Korean Peninsula. On analyzing the AWE of the major river basins, we found most basins in North Korea showed very low values inferring unsustainable overconsumption of water. The latitudinal difference in AWE is a reflectance of the latitudinal changes in the VWC and CWB. This can be explained by decoupling the demand and supply of agricultural water. Although the AWE values presented in this study were not absolute, the values were sufficient to explain the latitudinal change, and the demand and supply of agricultural water, and establish the usefulness of the indicator.

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 103 Mt in the 2050s and 2090s, respectively. However, the exploitable and potential production gap will stay above 350 and 580 Mt, 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.

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

Maize yields in sub-Saharan Africa (SSA) are at the lower end of the global range of yields since decades. This study used the large-scale agricultural crop growth model GEPIC to simulate maize yield responses to different management scenarios concerning: (a) level of nutrient supply; (b) extent of irrigated areas; and (c) low- or high-yielding cultivars. The results show that extending irrigation or planting an improved cultivar produced little effect on maize yield at the current level of nitrogen (N) and phosphorus (P) application rates. Increasing nutrient supply to the level commonly applied in high-input regions would allow for a tripling of maize yields from the current 1.4–4.5 Mg ha−1 and could increase yields even to 7 Mg ha−1 in combination with an improved cultivar. Irrigation was found to be especially effective for lifting very low yields concomitant to improved nutrient supply and cultivar. The highest yields when combining all the three improved management practices were predicted for East and Southern Africa with mostly 8–10 Mg ha−1, and West Africa with 7–9 Mg ha−1. The lowest yield potentials were found for the Western parts of Central Africa where they often reached only about 4–6 Mg ha−1, due to low solar radiation and low nutrient availability on highly weathered soils. The inputs required for reaching these high yield levels would be very costly. Nevertheless, the simulation shows that with a supply of only 50 kg N ha−1 and 18 kg P ha−1, which is less than one third of the current level in high-input countries, the maize yield could be doubled for most areas in SSA. The resulting increase in maize production would be about 10 times of the amount currently imported to the sub-continent including food aid.

Spatially explicit large-scale crop growth models are often applied at the global scale with no or little adjustments to regional conditions, which may produce unreliable model results. To tackle this issue, we have regionalized a large-scale crop model for simulating maize cultivation in sub-Saharan Africa (SSA). Planting dates were estimated using reported planting seasons, plant growth parameters were adopted from literature to reflect a low-yielding cultivar, and agricultural practice was mimicked by simulating continuous cultivation of maize with removal of plant residues. The analysis of different estimates of planting date showed that a monthly time step was too coarse in (semi-)arid regions and a weekly step should be used. Limiting planting date estimates by reported seasons is especially important in regions with bimodal rain seasons. The parameterization of a low-yielding cultivar by decreasing the maximum and minimum harvest index (HI) in the model resulted in HI estimates within the range of values reported in the literature. The most important step in the model adjustment was found to be the removal of plant residue. This leads together with little fertilizer inputs to soil nutrient and organic carbon depletion, which has been taking place in most parts of SSA during the past decades. If residue removal is not taken into account, the simulation results in organic carbon sequestration and only minor nutrient depletion. With the adjustments of cultivar, planting dates, and agricultural practice in the model setup, crop growth is in most areas of SSA mainly constrained by nutrient stress as compared to water and temperature. The estimated national and regional average yields compared well with reported yields for the major maize producing countries, suggesting that the regionalized model is suitable for supporting policies on water and soil management in SSA.

Two approaches to determine pesticide (bio-)availability in soils (i) batch experiments with "extraction with an excess of water" (EEW) and (ii) the recently introduced "soil pore water (PW) extraction" of pesticide incubated soil samples have been compared with regard to the sorption behavior of the model compound isoproturon in soils. A significant correlation between TOC and adsorbed pesticide amount was found when using the EEW approach. In contrast, there was no correlation between TOC and adsorbed isoproturon when using the in situ PW extraction method. Furthermore, sorption was higher at all concentrations in the EEW method when comparing the distribution coefficients (K(d)) for both methods. Over all, sorption in incubated soil samples at an identical water tension (-15 kPa) and soil density (1.3 g cm(-3)) appears to be controlled by a complex combination of sorption driving soil parameters. Isoproturon bioavailability was found to be governed in different soils by binding strength and availability of sorption sites as well as water content, whereas the dominance of either one of these factors seems to depend on the individual composition and characteristics of the respective soil sample. Using multiple linear regression analysis we obtained furthermore indications that the soil pore structure is affected by the EEW method due to disaggregation, resulting in a higher availability of pesticide sorption sites than in undisturbed soil samples. Therefore, it can be concluded that isoproturon sorption is overestimated when using the EEW method, which should be taken into account when using data from this approach or similar batch techniques for risk assessment analysis.

Aim of this work was the development of a new non-biological factor to determine microbial in situ bioavailability of chemicals in soils. Pesticide residues were extracted from ten highly different agricultural soils that had been incubated with the (14)C-herbicide isoproturon (IPU) under comparable soil conditions (water tension - 15 kPa; soil density 1.3 g cm(-3)). Two different pesticide extraction approaches were compared: (i) (14)C-pesticide residues were measured in the pore water (PW) which was extracted from soil by centrifugation; (ii) (14)C-pesticide residues were extracted from soil samples with an excess of water (EEW). We introduce the pesticide's in situ mass distribution quotient (iMDQ) as a measure for pesticide bioavailability, which is calculated as a quotient of adsorbed and dissolved chemical amounts for both approaches (iMDQ(PW), iMDQ(EEW)). Pesticide mineralization in soils served as a reference for real microbial availability. A highly significant correlation between iMDQ(PW) and mineralization showed that PW extraction is adequate to assess IPU bioavailability. In contrast, no correlation exists between IPU mineralization and its extractability from soil with EEW. Therefore, it can be concluded that soil equilibration at comparable conditions and subsequent PW extraction is vital for a isoproturon bioavailability ranking of soils.