Soil texture characteristics play an important role in determining the amount of soil moisture and the availability of water for evapotranspiration. As a result, they might affect the components of the water and the surface energy balance. An anthropogenic practice that also influences these components is irrigation, enhancing soil moisture, which subsequently leads to an increase in latent heat flux. Moreover, in some irrigation parameterizations, soil texture characteristics such as field capacity and permanent wilting point determine the irrigation amount, potentially affecting the overall impact of irrigation on surface variables. Some studies evaluated the impact of different soil texture maps globally and regionally in climate models, while other studies investigated the impact of irrigation on surface and atmospheric variables. However, the combined effect of soil parameters and irrigation on Earth system modeling, particularly in the context of uniform soil texture maps, has not been investigated yet. In this study, we assess the impact of two uniform soil texture maps on the surface energy and water budget components over the EURO-CORDEX domain during the years 2017–2018, both in isolation and in combination with irrigation. We choose uniform loam and sand soil type maps to evaluate the potential extent of change in the interactions between soil, climate and irrigation. Moreover, these soil textures are more common in agricultural land. We conduct a set of five simulations with the ICON-nwp model at 3 km resolution, a control run and four experiments. The control run includes the default soil map and no irrigation. Then, we perform two sets of experiments utilizing uniform soil maps, one representing loam and the other sand, both with and without irrigation. The surface energy balance decomposition (SEB) method (Thiery et al., 2017) allows us to determine which components of the surface energy balance influence any temperature changes. Some preliminary results indicate that the uniform sandy soil experiment intensifies the heat wave of 2018. In the same year, experiments with irrigation show that the temperature cooling is stronger over the uniform sandy soil experiment in the Alps, East Europe and Mid-Europe. In contrast, the temperature cooling is stronger over the uniform loamy soil experiment in moisture-limited regions such as the Iberian Peninsula and the Mediterranean. The SEB identifies the variables that mostly influence the temperature changes across different prudence regions.

Robust mapping of large-scale irrigation area is critical for sustainable water management and climate-resilient agriculture. However, a comprehensive synthesis of the conceptual foundations, inherent limitations, comparative strengths, and developmental trends of existing methodologies and datasets remains lacking, hindering methodological advancement and context-appropriate dataset selection. To bridge this gap, we (i) take stock of global and regional irrigation area datasets, elucidating their methodological frameworks, characteristics, interrelations, and current spatial–temporal–thematic gaps; (ii) benchmark ten datasets across Europe against EUROSTAT 2020 gridded-type statistics, showing the importance of survey-based statistics for ensuring mapping accuracy, particularly in humid regions where remote sensing exhibits inherent limitations; (iii) review prevailing mapping frameworks, identifying scarce, inconsistent, and restricted-access ground-truth and statistics data as the primary bottleneck, while proposing response strategies for future research. By integrating these dimensions, our analysis delineates the state of global irrigation mapping and charts a pathway toward precision monitoring, ultimately empowering robust water resource modeling and safeguarding the resilience of agricultural ecosystems in an era of escalating climatic variability.

Reduced tillage (RT) is widely promoted as an agroecological practice to enhance soil organic carbon (SOC) sequestration and mitigate greenhouse gas (GHG) emissions. However, its long-term effects on SOC stocks, crop yields, and soil GHG fluxes remain debated, particularly under changing climatic conditions. We investigated the impacts of RT and conventional tillage (CT) in the Garte-Sued field trial established in 1970 in Goettingen, Germany on a silt loam Haplic Luvisol under a humid temperate climate. Over two years, we assessed crop yield, SOC stocks and fractions, soil mineral N, and soil CO2 and N2O fluxes under ambient rainfall and 50 % rainfall exclusion (via rainout shelters). After 53 years, RT resulted in a stratification of SOC within the plough layer, but SOC stocks at 0-90 cm did not differ between tillage practices. Crop yields were, in general, lower under RT compared to CT but the significance of the effect depended on crop species. Rainfall exclusion did not significantly affect yields or cumulative CO2 and N2O fluxes, likely due to high soil water retention and moderate water deficit. Soil CO2 and N2O fluxes were mainly driven by soil temperature, water-filled pore space, and mineral N, with emission peaks following management events. Reduced tillage did not affect soil CO2 efflux, while it tended to increase N2O fluxes, especially under reduced rainfall. Our findings suggest that, in humid temperate and medium-textured soils, RT does not enhance crop yield, SOC stocks, or GHG mitigation relative to CT under ambient and reduced rainfall.

Gridded precipitation products (GPPs) are widely used in climate-informed agricultural analyses in data-scarce regions, yet their suitability is often assessed using meteorological performance alone. This study evaluates how differences among rainfall datasets propagate into simulated maize yield and associated economic indicators using the Agricultural Production Systems sIMulator (APSIM) in two semi-arid systems, Kaffrine (Senegal) and Kongwa (Tanzania). Six GPPs (CHIRPS, CPC, ERA5, MERRA2, MSWEP, and TAMSAT) were evaluated against station observations and used as alternative rainfall inputs in APSIM.,Rainfall performance varied across indicators and between sites, with higher agreement for seasonal totals than for onset timing, and higher detection skill for dry days than for moderate and heavy rainfall classes. Several GPPs over-represented light rainfall events and under-represented heavy rainfall extremes, consistent with spatial averaging within grid cells. When used in crop simulations, these differences translated into site-specific yield responses driven by early-season rainfall conditions. In Kaffrine, yield distributions and nitrogen responses remained similar across datasets (KGE = 0.24–0.35). In Kongwa, yield variability and estimated optimal input levels were more sensitive to dataset choice (KGE = -0.24 to 0.60), particularly under wetter conditions.,Across sites, the general shape of yield responses to nitrogen and planting density remained similar, but economically optimal input levels varied across datasets. Agreement in rainfall indicators alone was therefore insufficient to infer the stability of crop model outputs. Evaluating GPP suitability for agronomic applications requires crop model diagnostics alongside meteorological validation, with explicit consideration of environmental context.

Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here we integrate multi-source Earth observation and environmental datasets and use machine learning to develop a medium-resolution (30 m) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. We subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management.

Irrigation significantly contributes to total water withdrawal and exhibits considerable spatial and temporal variability, particularly in more humid regions. This variability is caused by climate, soil properties, and crop water requirements. However, time series of high-resolution, crop-specific irrigated area data remain scarce in Europe. We developed and applied a method to harmonize input data on crop types and irrigation to obtain the European Crop-specific IRrigated Area (ECIRA) dataset, providing annual 1-km gridded crop-specific irrigated area for 16 crop types across 28 European countries for 2010-2020. The ECIRA dataset effectively identifies crop-specific irrigated hotspots, aligns with subnational census data, and strongly agrees with LUCAS field observations and other survey-based crop-specific irrigation area datasets. However, caution is needed for region- and location-specific studies, as the Europe-wide scope of ECIRA entails a trade-off between local details and overall consistency. It can be used in assessments of crop productivity and crop water use, as input in land surface-, crop-, and hydrological modeling, in climate impact studies and to support improved water resources management.

The global agrifood system is central to many challenges humanity faces today. Despite significant growth in total production, it fails to ensure food security for all, drives biodiversity decline, and majorly contributes to climate change. Research on agrifood-system burdens often focusses on the national level and isolated burdens, ignoring their systemic complexity. We address this knowledge gap by combining global subnational datasets proxying four key dimensions of agrifood-system burdens: environmental footprint, climate change, income poverty, and malnutrition. We map global hotspots of co-occurring agrifood-system burdens for 2017. We overlay these with data on ambient population counts, agricultural areas, farm size distributions, and lands inhabited by Indigenous peoples to identify spatial correspondence between people in vulnerable contexts and food production regions facing these burdens. We further assess countries’ relative burden against their inequality and governance indicators. Burden hotspots occupy many regions worldwide, especially in low-income, (sub)tropical regions. Single burdens occupy regions harbouring about 5 billion people (∼66% of the global population) and 1.8 billion ha of agricultural lands (∼40%), while multiple burdens occupy regions with about 1.9 billion people and 470 million ha agricultural lands. Environmental footprint is the strongest contributor to these burden profiles. Regions with traditionally marginalised communities (i.e. small-scale farmers and Indigenous peoples) disproportionally face multiple burdens. Agrifood-system burdens are more prevalent in countries with higher economic inequality and poorer governance. Burden profiles vary substantially within and between countries, necessitating regionalised and context-specific policies for effective, bundled, and targeted solutions. Addressing agrifood-system burdens can also synergise with tackling other current global challenges, like biodiversity loss and environmental justice.

Many agricultural regions rely on snowmelt runoff as a source of water for irrigation, but climate change is altering runoff dynamics, making it difficult to meet increasing irrigation water demands. It remains unclear whether irrigation water shortages are systematically occurring in snow-dependent regions across the globe. Here, we study global trends in surface runoff used to meet irrigation demands by linking rainfall and snowmelt runoff data with irrigation consumption data (1985-2020). Focusing on the most snow-dependent agricultural basins, we find that surface water runoff volumes have slightly decreased and snowmelt runoff is occurring significantly earlier in time (advancing an average of 8 d). These changes, coupled with an almost universal trend of increasing irrigation water consumption, have made snowmelt runoff less able to meet irrigation needs during crop growing seasons and increased reliance on alternative water sources (interbasin transfers or reservoirs). These results highlight potential future challenges for irrigated agriculture in snow-dependent regions.

Understanding how to optimize water and nutrient management is crucial for improving crop productivity in organic farming systems. In this study, we examined the effects of irrigation and fertilization on yield and nitrogen fixation in organically managed faba bean crops in temperate climates in six field trials covering three sites and two years. Irrigated plots showed a 54 % yield increase and higher nitrogen fixation (up to 105 %), while fertilization with rock phosphate and potassium sulfate, with or without micronutrients, had no significant impact. Irrigation induced higher yields as well as a significant increase in pod number, grain count, and chlorophyll content of leaves, suggesting improved photosynthesis, flowering and pod filling. Despite low soil nutrient levels for P, Mo and B, mineral fertilization showed no effect on faba bean yields and nitrogen fixation, likely due to long-term organic fertilization with cattle manure. Our results also indicate that irrigation enhances farmgate nitrogen balance by increasing nitrogen fixation without depleting soil nitrogen reserves. These findings suggest that water, rather than nutrient supply, is essential for maintaining productivity and nitrogen fixation in organic faba bean cultivation also in temperate regions. Economically, however, irrigation proved costeffective in only one of six trials, suggesting that irrigation rates need to be optimized.

Droughts pose a substantial threat to various sectors, including agriculture, human water supply but also natural ecosystems. While various studies have been conducted for drought evaluation, the majority of them have focused on a particular drought type. This may lead to a lack of comprehensive understanding of the features and progression of droughts among different drought types through time. For example, for water resources management and planning purposes, it is critical to understand the changes and temporal development of drought signals from abnormal meteorological conditions to soil moisture, groundwater levels, and streamflow. Within the OUTLAST project, which aims at developing an operational, multi-sectoral global drought hazard forecasting system, we develop a near real-time drought hazard monitoring and forecasting system which, for the first time, includes tailored indicators for various sectors, including water supply, riverine and non-agricultural land ecosystems, as well as rainfed and irrigated agriculture. In this context, the primary objectives of this study are to 1) develop different drought hazard indicators (DHI) to monitor and forecast the drought across different sectors; and 2) assess the spread and propagation of droughts across different sectors and regions at a global scale. For this purpose, DHIs were computed for a 40-year reference period (1981 to 2020) using ERA5 as meteorological forcing data to drive the DHIs using the global hydrological model (WaterGAP) and the global crop water model (GCWM). These DHIs cover meteorological (SPEI and SPI), hydrological (empirical percentiles and relative deviations of soil moisture and streamflow), as well as agricultural droughts (crop-specific DHIs for rainfed and irrigated croplands). In this project, we focus on the period 2011 to 2015, with 2012 being a year in which droughts had major impacts on various regions and sectors. The study investigates drought propagation from meteorological drought, extending to rainfed agriculture due to soil moisture deficiency, over streamflow, and eventually reaching irrigated agriculture. In doing so, region-specific features and the dependency of drought propagation on the magnitude of the drought are highlighted. Finally, as monitoring and projecting drought characteristics are important for comprehending drought-related issues, our multi-sectoral drought hazard forecasting system enables us to evaluate the state of drought propagation at a global scale.

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development.

Forecasting systems focusing on upcoming flood and drought events are essential to support various aspects such as disaster risk reduction, climate change mitigation, or long-term policy and planning. In particular, multiple model-based early warning systems have been developed to allow the simulation of future floods and droughts at different temporal-spatial scales. However, despite the successful development of many innovative and state-of-the-art modeling systems in the academic fields, their transition into an operational system is challenging, and it may take several years to set up appropriate technical requirements, especially into a new IT infrastructure. In this talk, we hence outline these challenges for the example of the ongoing project OUTLAST (operational, multi-sectoral global drought hazard forecasting system), where the main goal is to develop a modeling system that is ready for operational use. OUTLAST will provide model-based near real-time monitoring using recent updated ERA5 climate data and seasonal forecasting of drought globally across different sectors (water supply, riverine and non-agricultural land ecosystems, rainfed and irrigated agriculture). The system consists of a model chain of three models: (1) bias correction of global seasonal forecasting products SEAS5, (2) the global hydrological model WaterGAP, and (3) the global crop water model GCWM. The drought status in both monitoring and forecasting phase from OUTLAST will be provided globally for the next six months and be freely accessible via the HydroSOS portal, a Hydrological Status and Outlook System hosted by the World Meteorological Organization (WMO). Highlights of OUTLAST are the ability to run the whole system within a cloud-ready automated workflow to ensure seamless integration into the HydroSOS framework. This includes the so-called “trigger” to automatically download the newly released climate data (ERA5 and SEAS5) from the source (ECWMF). To achieve this goal, each model and its dependencies in the model chain in OUTLAST are encapsulated in a "container" by the core developer in the research institution before being transferred to run in an IT infrastructure at an external government institution. The containers will then be orchestrated to enable the upscaling of the system based on computational requirements and the availability of hardware resources. This approach aims to (i) enable a seamless transition of OUTLAST into operation, (ii) avoid any conflict with the host operating system, and (iii) ensure a fast boot system in case one of the servers fails. We hope that the proposed infrastructure design can serve as a blueprint for other efforts to transfer scientific workflows into an operational environment.

With increasing frequency and severity of drought hazards worldwide, reliable monitoring and forecasting of drought conditions becomes more and more relevant for efficient drought management. In this context, the OUTLAST project provides global monitoring and seasonal forecasting of drought hazard indicators (DHIs) across three sectors, ranging from meteorological and agricultural to hydrological DHIs. In OUTLAST, a consistent framework is developed in which ERA5 (for monitoring) and bias-corrected SEAS5 data (for seasonal forecasts) are used to calculate meteorological DHIs. The same climate data forces the Global Crop Water Model1 and the global hydrological model WaterGAP2 in order to derive agricultural and hydrological DHIs respectively. The global OUTLAST DHIs will be freely available via the WMO’s HydroSOS web portal.To adequately support drought management and decision-making, it is essential to identify and evaluate the accuracy of OUTLAST DHIs. Therefore, we apply a twofold evaluation procedure: 1) a global evaluation against various observation-based datasets with (nearly) global coverage, and 2) a regional evaluation in collaboration with experts who will potentially use OUTLAST products in their daily work. While the first provides a general assessment of the overall performance, the latter allows evaluation whether actual drought conditions are sufficiently monitored by the global OUTLAST system.Here, we focus on the global evaluation of DHIs for the historical period 1981-2020 by comprehensively comparing the performance of model-based DHIs from multiple sectors, including (1) the standard precipitation index, (2) the rainfed crop drought hazard indicator, and (3) the empirical percentiles of streamflow, against observation-based data, such as (a) remote sensing-based precipitation, (b) global evapotranspiration data, and (c) observed streamflow of large river basins. By analyzing DHIs from multiple sectors simultaneously, we show the effect of drought - and error- propagation in the hydrological cycle on the ability to capture observed drought conditions by model-based DHIs. Besides, the capability to accurately reproduce historic drought conditions represents the accuracy that users can expect when employing the OUTLAST near-real time monitoring and seasonal forecasts for drought management decisions. ---------------------------------------------------1Siebert, S., & Döll, P. (2010). Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. Journal of Hydrology, 384(3-4), 198-217. https://doi.org/10.1016/j.jhydrol.2009.07.0312Müller Schmied, H., Trautmann, T., Ackermann, S., Cáceres, D., Flörke, M., Gerdener, H., Kynast, E., Peiris, T. A., Schiebener, L., Schumacher, M. & Döll, P. (2024). The global water resources and use model WaterGAP v2. 2e: description and evaluation of modifications and new features. Geoscientific Model Development, 17(23), 8817-8852. https://doi.org/10.5194/gmd-17-8817-2024

Food security is threatened by compound events (extreme events like heat and drought occurring together), intensifying with climate change. Crucial for studying their impact on crop yield variability is the setting of temperature and precipitation thresholds. While relative thresholds (e.g., the 95th percentile) can hardly be justified concerning plant physiology, absolute thresholds (e.g., 30 degrees C) are expected to differ substantially between plant-level and large-scale assessments. As this contradiction has not yet been addressed, suitable relative and related absolute thresholds for the prominent crops grain maize and winter wheat are examined in this study. With these, it is analyzed whether extreme or compound events explain yield variability better and which development phase is sensitive to them. Also novel in the approach is to compare defining heat with daily mean and maximum temperatures and drought over 10 and 30 days. The analysis covers the years 1983 to 2021 and the 96 administrative districts of Bavaria, Germany, which are located in central Europe and exhibit a considerable precipitation gradient. Relative thresholds vary over this gradient, yet lead to similar absolute thresholds. This indicates that absolute thresholds are more suitable to explain crop yield variability. The discovered thresholds for daily maximum temperatures are at least 28 degrees C for grain maize and 24 degrees C to 25 degrees C for winter wheat, being lower than in plant-level analyses. Compound events have more impact on grain maize compared to individual extreme events. Yet, this effect was not revealed for winter wheat yields, showing the greatest sensitivity to individual heat events. During the vegetative phase, grain maize was most sensitive to heat. During the reproductive phase, grain maize was most sensitive to drought and winter wheat to heat. These results can be used in the methodology of further studies and for developing measures that buffer the impact of compound events on crop yields.

Climate change alters the climatic suitability of croplands, likely shifting the spatial distribution and diversity of global food crop production. Analyses of future potential food crop diversity have been limited to a small number of crops. Here we project geographical shifts in the climatic niches of 30 major food crops under 1.5-4 °C global warming and assess their impact on current crop production and potential food crop diversity across global croplands. We found that in low-latitude regions, 10-31% of current production would shift outside the climatic niche even under 2 °C global warming, increasing to 20-48% under 3 °C warming. Concurrently, potential food crop diversity would decline on 52% (+2 °C) and 56% (+3 °C) of global cropland. However, potential diversity would increase in mid to high latitudes, offering opportunities for climate change adaptation. These results highlight substantial latitudinal differences in the adaptation potential and vulnerability of the global food system under global warming.

Aim Landscape heterogeneity is a key driver of biodiversity, ecosystem functioning, and resilience. However, the complex relationships among different components of heterogeneity—compositional, configurational, vertical, and temporal—remain underexplored for large areas such as at the national scale. This study examines the associations among multiple landscape heterogeneity components across land-cover types to refine their use in ecological research. Location Germany Time Period Mainly 2017-2020 Major Taxa Studied Not taxa-specific; focuses on landscape heterogeneity as an ecological driver. Methods We analysed nationwide spatial datasets at very high resolution (10–30 m resolution) of land-cover types, dominant tree species, canopy height, and time-series of crop types as well as grassland mowing frequency. We applied Structural Equation Modeling (SEM) to assess the statistical relationship between heterogeneity indices and their interactions. Specifically, we examined (i) compositional vs. configurational heterogeneity (i.e., Shannon diversity vs. edge density), (ii) configurational heterogeneity vs. connectivity, (iii) horizontal vs. vertical and temporal heterogeneities, and (iv) heterogeneities across multiple land-cover types based on grid cells of 3 x 3 km2. Results Our findings reveal that compositional and configurational heterogeneities exhibit positive correlations, but their relationships are moderated by the proportions of land-cover types. Configurational heterogeneity does not enhance connectivity; after controlling for land-cover proportions, its partial association with connectivity is negative. Vertical and temporal heterogeneities show limited associations with horizontal compositional and configurational heterogeneities, suggesting relative independence. Principal component analysis indicates that landscape heterogeneity is primarily driven by heterogeneities of forest and overall land-cover, e.g., edge densities of forest dominant tree species and overall land-cover types, whereas cropland heterogeneity, e.g., Shannon diversity of crop types, contributes negatively. Main Conclusions Our study underscores the importance of accounting for land-cover proportions when analysing landscape heterogeneity relationships. Failing to do so can distort the model due to potential hidden collinearity. Additionally, our findings highlight the need to capture the multi-dimensional nature of landscape heterogeneity in biodiversity and ecosystem studies. Landscape heterogeneity is shaped by the interdependencies between prevailing land-cover patterns, likely influenced by land-use decisions and history as well as social-ecological contexts, highlighting the need for cross-national or cross-administrative studies.

AbstractIrrigation profoundly impacts ecology and agricultural productivity, with irrigated areas varying across regions and years. Interannual dynamics of irrigation extent are lacking, particularly in humid regions of Europe. We analyzed the response of irrigated areas to drought conditions in areas equipped for irrigation and used the derived relationships to estimate annual irrigated areas for 32 European countries in the period 1990–2020. Interannual variability of irrigated areas varied notably, particularly in more humid Northern and Western Europe. In most humid regions, irrigated area is larger in dry years, whereas in more arid regions like Spain, it is larger in wet years. The largest irrigated area across Europe occurred in dry years 2003 and 2018 (11.93 and 11.77 million hectares), while the smallest is estimated for the wet years 2002 and 2014 (10.71 and 10.31 million hectares). The findings of this study help to improve scenario development and water resources management.

The expansion of irrigated cropland exacerbates water scarcity, while geopolitical environment further intensifies the spatial imbalance of water resources, particularly in transboundary rivers. However, little is known about the evolution of water stress in upstream and downstream regions within transboundary river basins and their potential interrelationships. Here, we find that 396 of 431 sub-basins (91.9%) experience increasing irrigation water stress (IWS) between 1901 and 2005, with the number of sub-basins facing irrigation water scarcity doubling from 51 to 118. Disparities in IWS between upstream and downstream regions widen in 92.4% of transboundary river basins, especially in South Asia, Central Asia, and Africa. The expansion of upstream irrigated areas (6 Mha·yr-1) and associated water withdrawals (20.4 km3·yr-1) exacerbate downstream IWS by 34.3 ± 3.5% from 1901 to 2005, with this spatial spillover effect projected to intensify through 2099. Our findings emphasize the urgent need for cooperative water management in transboundary basins.

Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of ‘steady growth’ in actual and attainable yields, ‘stalled floor’ where yield is stagnated and ‘ceiling pressure’ where yield gaps are closing. Over 60% of maize area is experiencing ‘steady growth’, in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing ‘ceiling pressure’. We show that ‘ceiling pressure’ correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Freshwater is closely interconnected with multiple sustainable development goals (SDGs). Virtual water transfer associated with agricultural trade may help to mitigate water scarcity (SDG6). However, the resulting impacts on water scarcity distribution among income groups (SDG1) and subsequent effects on water use inequality and inequity (SDG10) remain largely unclear. Here we develop an integrated framework to reveal the asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity between and within developing and developed countries. We find that although agricultural trade generally relieves water scarcity globally, it disproportionately benefits the rich and widens both the water scarcity and inequity gap between the poor and the rich. Notably, in developing countries, the population (35%) suffering from both increased water scarcity and inequity are the poorest group (per capita income is 16% lower than average), whereas the relatively poor (13% population) in developed countries often simultaneously benefit from decreased water scarcity and reduced inequity synergies. Our results thereby highlight striking asymmetric and generally more favourable trade-induced water impacts for developed countries, urging future water and trade policies striving for a better balance across multiple critical SDGs and achieving sustainable development for all.