A global clustering of terrestrial food production systems

Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world's foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions.

Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models

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.

Rice availability and stability in Africa under future socio-economic development and climatic change

As Africa is facing multiple challenges related to food security, frameworks integrating production and availability are urgent for policymaking. Attention should be given not only to gradual socio-economic and climatic changes but also to their temporal variability. Here we present an integrated framework that allows one to assess the impacts of socio-economic development, gradual climate change and climate anomalies. We apply this framework to rice production and consumption in Africa whereby we explicitly account for the continent's dependency on imported rice. We show that socio-economic development dictates rice availability, whereas climate change has only minor effects in the long term and is predicted not to amplify supply shocks. Still, rainfed-dominated or self-producing regions are sensitive to local climatic anomalies, while trade dominates stability in import-dependent regions. Our study suggests that facilitating agricultural development and limiting trade barriers are key in relieving future challenges to rice availability and stability.

A high-resolution nutrient emission inventory for hotspot identification in the Yangtze River Basin

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.

Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project's Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)-explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO₂ concentrations and expanded high-latitude gains. The 'emergence' of climate impacts consistently occurs earlier in the new projections-before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Large potential for crop production adaptation depends on available future varieties

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

Global irrigation contribution to wheat and maize yield

Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.

There are big uncertainties in the contribution of irrigation to crop yields. Here, the authors use Bayesian model averaging to combine statistical and process-based models and quantify the contribution of irrigation for wheat and maize yields, finding that irrigation alone cannot close yield gaps for a large fraction of global rainfed agriculture.

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

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.

A regional nuclear conflict would compromise global food security

A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

State-of-the-art global models underestimate impacts from climate extremes

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

Achieving High Crop Yields with Low Nitrogen Emissions in Global Agricultural Input Intensification

Increasing demand for food is driving a worldwide trend of agricultural input intensification. However, there is no comprehensive knowledge about the interrelations between potential yield gains and environmental trade-offs that would enable the identification of regions where input-driven intensification could achieve higher yields, yet with minimal environmental impacts. We explore ways of enhancing global yields, while avoiding significant nitrogen (N) emissions (N_e) by exploring a range of N and irrigation management scenarios. The simulated responses of yields and N_e to increased N inputs (N_in) and irrigation show high spatial variations due to differences in current agricultural inputs and agro-climatic conditions. Nitrogen use efficiency (NUE) of yield gains is negatively correlated with incremental N_e due to N_in additions. Avoiding further intensification in regions where high fractions of climatic yield potentials, ≥ 80%, are already achieved is key to maintain good NUE. Depending on the intensification scenarios, relative increases in N_e could be reduced by 0.3-29.6% of the baseline N_e with this intensification strategy as compared to indiscriminate further intensification, at the cost of a loss of yield increases by 0.2-16.7% of the baseline yields. In addition, irrigation water requirements and N_in would dramatically decrease by considering this intensification strategy.

Global patterns of crop yield stability under additional nutrient and water inputs

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980-2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.

Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Impacts and Uncertainties of +2°C of Climate Change and Soil Degradation on European Crop Calorie Supply

Even if global warming is kept below +2°C, European agriculture will be significantly impacted. Soil degradation may amplify these impacts substantially and thus hamper crop production further. We quantify biophysical consequences and bracket uncertainty of +2°C warming on calories supply from 10 major crops and vulnerability to soil degradation in Europe using crop modeling. The Environmental Policy Integrated Climate (EPIC) model together with regional climate projections from the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX) was used for this purpose. A robustly positive calorie yield change was estimated for the EU Member States except for some regions in Southern and South-Eastern Europe. The mean impacts range from +30 Gcal ha-1 in the north, through +25 and +20 Gcal ha-1 in Western and Eastern Europe, respectively, to +10 Gcal ha-1 in the south if soil degradation and heat impacts are not accounted for. Elevated CO2 and increased temperature are the dominant drivers of the simulated yield changes in high-input agricultural systems. The growth stimulus due to elevated CO2 may offset potentially negative yield impacts of temperature increase by +2°C in most of Europe. Soil degradation causes a calorie vulnerability ranging from 0 to 50 Gcal ha-1 due to insufficient compensation for nutrient depletion and this might undermine climate benefits in many regions, if not prevented by adaptation measures, especially in Eastern and North-Eastern Europe. Uncertainties due to future potentials for crop intensification are about 2-50 times higher than climate change impacts.

Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments

This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5° and +2.0°C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize Zea mays experiencing the largest losses, while soy Glycine max mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat Triticum aestivum, while soy prices and area decline (results for rice Oryza sativa are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0°C scenario which has a larger climate signal and lower mitigation requirement than the +1.5°C scenario. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO₂ benefits for diverse agricultural systems in crop models, as simulations without CO₂ benefits show widespread production losses that raise prices and expand agricultural area.

Understanding the weather signal in national crop-yield variability

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 US. 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 represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

Consistent negative response of US crops to high temperatures in observations and crop models

High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO₂ can only weakly reduce these yield losses, in contrast to irrigation.

Future agricultural productivity is threatened by high temperatures. Here, using 9 crop models, Schauberger et al. find that yield losses due to temperatures >30 °C are captured by current models where yield losses by mild heat stress occur mainly due to water stress and can be buffered by irrigation.

Climate analogues suggest limited potential for intensification of production on current croplands under climate change

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand.

Simulations of the impact of future climate change on crop yield vary considerably. Here, the authors use a climate analogue approach to estimate the response of maximum attainable yield to climate change and predict that large shifts in land use and crop choice would be required to meet demand.

Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations

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.

Global gridded crop models are increasingly used to assess climate change impacts on food production. Here, the authors assess crop yield uncertainty associated with soil data input, reporting that soil type strongly influences yield estimates, and may either buffer or amplify climate-related impacts.

African crop yield reductions due to increasingly unbalanced Nitrogen and Phosphorus consumption

The impact of soil nutrient depletion on crop production has been known for decades, but robust assessments of the impact of increasingly unbalanced nitrogen (N) and phosphorus (P) application rates on crop production are lacking. Here, we use crop response functions based on 741 FAO maize crop trials and EPIC crop modeling across Africa to examine maize yield deficits resulting from unbalanced N : P applications under low, medium, and high input scenarios, for past (1975), current, and future N : P mass ratios of respectively, 1 : 0.29, 1 : 0.15, and 1 : 0.05. At low N inputs (10 kg ha(-1)), current yield deficits amount to 10% but will increase up to 27% under the assumed future N : P ratio, while at medium N inputs (50 kg N ha(-1)), future yield losses could amount to over 40%. The EPIC crop model was then used to simulate maize yields across Africa. The model results showed relative median future yield reductions at low N inputs of 40%, and 50% at medium and high inputs, albeit with large spatial variability. Dominant low-quality soils such as Ferralsols, which are strongly adsorbing P, and Arenosols with a low nutrient retention capacity, are associated with a strong yield decline, although Arenosols show very variable crop yield losses at low inputs. Optimal N : P ratios, i.e. those where the lowest amount of applied P produces the highest yield (given N input) where calculated with EPIC to be as low as 1 : 0.5. Finally, we estimated the additional P required given current N inputs, and given N inputs that would allow Africa to close yield gaps (ca. 70%). At current N inputs, P consumption would have to increase 2.3-fold to be optimal, and to increase 11.7-fold to close yield gaps. The P demand to overcome these yield deficits would provide a significant additional pressure on current global extraction of P resources.

Climate change would increase the water intensity of irrigated corn ethanol

Changes in atmospheric CO2 concentrations, temperature, and precipitation affect plant growth and evapotranspiration. However, the interactive effects of these factors are relatively unexplored, and it is important to consider their combined effects at geographic and temporal scales that are relevant to policymaking. Accordingly, we estimate how climate change would affect water requirements for irrigated corn ethanol production in key regions of the U.S. over a 40 year horizon. We used the geographic-information-system-based environmental policy integrated climate (GEPIC) model, coupled with temperature and precipitation predictions from five different general circulation models and atmospheric CO2 concentrations from the Special Report on Emissions Scenarios A2 emission scenario of the Intergovernmental Panel on Climate Change, to estimate changes in water requirements and yields for corn ethanol. Simulations infer that climate change would increase the evaporative water consumption of the 15 billion gallons per year of corn ethanol needed to comply with the Energy Independency and Security Act by 10%, from 94 to 102 trillion liters/year (tly), and the irrigation water consumption by 19%, from 10.22 to 12.18 tly. Furthermore, on average, irrigation rates would increase by 9%, while corn yields would decrease by 7%, even when the projected increased irrigation requirements were met. In the irrigation-intensive High Plains, this implies increased pressure for the stressed Ogallala Aquifer, which provides water to seven states and irrigates one-fourth of the grain produced in the U.S. In the Corn Belt and Great Lakes region, where more rainfall is projected, higher water requirements could be related to less frequent rainfall, suggesting a need for additional water catchment capacity. The projected increases in water intensity (i.e., the liters of water required during feedstock cultivation to produce 1 L of corn ethanol) because of climate change highlight the need to re-evaluate the corn ethanol elements of the Renewable Fuel Standard.

Batch experiments versus soil pore water extraction--what makes the difference in isoproturon (bio-)availability?

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.

In situ mass distribution quotient (iMDQ) - a new factor to compare bioavailability of chemicals in soils?

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.

Role of the kidney in primary hypertension: a renal transplantation study in rats

We have previously shown that transplantation of kidneys from genetically hypertensive to normotensive rats result in hypertension in renal graft recipients. To investigate whether this posttransplantation hypertension may have been the result of damage to the renal graft by high perfusion pressure before transplantation, we normalized blood pressure throughout life in spontaneously hypertensive rat (SHR) kidney donors by continuous antihypertensive treatment with the angiotensin-converting enzyme inhibitor ramipril (1 mg.kg-1.day-1 in drinking fluid). When kidneys from these rats were transplanted at age 20 wk to age-matched bilaterally nephrectomized F1 hybrids bred from SHR and Wistar-Kyoto (WKY) parents, posttransplantation hypertension still developed. In contrast, blood pressure did not change significantly in recipients of kidneys from ramipril-treated WKY rats. In the initial phase, recipients of SHR kidneys had a lower body weight and higher plasma urea concentrations than recipients of WKY kidneys. However, in the chronic phase, there were no significant differences between the two groups with respect to daily water intake, plasma urea concentration, glomerular filtration rate, renal blood flow, and weight of transplanted kidneys; no histological differences were observed between renal grafts from WKY and SHR donors, except for structural vascular hypertrophy in the latter group. We conclude that posttransplantation hypertension in recipients of SHR kidney grafts also develops, when the grafts have not been subjected to high renal perfusion pressure before transplantation. Our data support the hypothesis that SHR kidneys carry a primary defect, which can induce hypertension in renal graft recipients.

Role of the kidney in the pathogenesis of primary hypertension

Primary hypertension in animals and humans probably represents several different pathophysiological states rather than being a uniform nosological entity. Among other factors, renal mechanisms may be primarily and secondarily involved. The availability of genetically homologous animal models for hypertension has greatly promoted studies on the etiology and pathogenesis of high blood pressure disease. In particular, renal transplantation studies between genetically hypertensive and normotensive rats from three different models have provided strong evidence for a primary role of the kidney in genetic hypertension. Other factors, such as vascular, neural, and humoral mechanisms have also been shown to be involved and may be particularly effective in increasing blood pressure, when they act through the kidney. Several functional and biochemical differences have been identified between kidneys from genetically hypertensive and normotensive animals. However, the relative contribution of each of these factors to the development of primary hypertension remains to be determined. Evidence from studies on human renal graft recipients also indicates that, among other factors, the kidney plays an important role in the development of primary hypertension in humans.

Hypertension transmitted by kidneys from stroke-prone spontaneously hypertensive rats

We determined whether transplantations of kidneys from stroke-prone spontaneously hypertensive rats (SPSHR) and from normotensive Wistar-Kyoto rats (WKY) alter blood pressure in renal graft recipients. Kidneys taken from seven male SPSHR and seven male WKY rats (blood pressure 186 +/- 4.8 and 111 +/- 3.7 mmHg, respectively) at the age of 20 wk were transplanted, using microsurgical techniques, to bilaterally nephrectomized age-matched male F1 hybrids (blood pressure 136 +/- 2.6 and 138 +/- 6.3 mmHg, respectively) bred from SPSHR and WKY parents. After renal transplantation, blood pressure in recipients of SPSHR kidneys rose to 146 +/- 11.8 (week 2), 163 +/- 16.4 (week 3), 192 +/- 17.1 (week 4), 222 +/- 17.7 (week 5), 221 +/- 12.6 (week 6), 218 +/- 20.3 (week 7), and 239 +/- 9.2 mmHg (week 8). There was no significant change in blood pressure in recipients of WKY kidneys. All rats recovered rapidly from surgery. After renal transplantation, there was a significant increase in daily water intake, a decrease in plasma renin activity, and a slight rise in plasma urea concentration. Our data show that transplantation of kidneys from adult SPSHR causes hypertension in normotensive recipients, indicating a major function for the kidney in SPSHR hypertension.