National governments and multilateral institutions face difficult challenges reconciling biodiversity, climate, and economic development goals. We integrated spatial biophysical and economic data with optimization methods to develop sustainable landscape efficiency frontiers that show maximally feasible combinations of biodiversity conservation, land-based climate mitigation, and net economic value from agricultural crops, livestock, and forestry production. We applied this approach in 146 countries and found large potential gains in biodiversity, climate, and economic development from improved land use and land management. Summing national-level results shows the potential to increase climate mitigation by more than 200 billion metric tons of CO2 equivalents (>20% increase) or net economic value by more than US$350 billion (>80% increase), without loss in other objectives.

Arable land has been expanding since the advent of agriculture, and now sustains over eight billion people by supplying food, fiber, and fuel. However, the rapid expansion of built-up areas is increasingly displacing arable land across diverse regions, raising concerns about the long-term sustainability of global food systems. Addressing these concerns requires a fundamental understanding of global arable-land dynamics, which remains largely unknown. Using our newly developed high-quality arable-land product—integrated with global settlement layers, Köppen–Geiger climate classifications, and historical land-use reconstructions—we show that competition between arable land and urban systems for favorable climate fundamentally shapes global arable-land dynamics. Globally, urbanization in the Northern Hemisphere is displacing arable land from climatically favorable regions toward more marginal tropical and arid environments. In North America, Europe, and Asia, early co-development of arable land and urbanization has transitioned into intensified urban encroachment, pushing cultivation into adjacent arid zones. By contrast, low-latitude tropical regions continue to expand both systems at the expense of natural ecosystems, driven in part by agricultural investment from Northern Hemisphere countries. This framework not only offers a new perspective for synthesizing existing regional arable-land dynamics but is also closely linked to ongoing dryland greening, tropical deforestation, and groundwater depletion in drylands. Reconciling agricultural production with rapid urban growth is therefore essential to safeguarding sustainable land resources, forest health, water security, and global food security under accelerating demographic pressures.

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.

Agricultural production statistics underpin diverse research efforts and development activities. Yet despite their critical importance, efforts to collate, update, and harmonize detailed sub-national agricultural production statistics are frequently redundant and incomplete due to the substantial time, effort, and resources required. The persisting lack of coordination and standards in the food systems data community wastes valuable resources and hinders advances in action-oriented food systems knowledge. Here we introduce the HarvestStat sub-national data consortium as an open-source, collaborative, and transparent model to overcome these challenges. HarvestStat is collaboratively producing publicly available databases and datasets for the food systems community and the broader environmental and sustainability sciences by moving beyond closed and disjointed data-gathering efforts. We are guided by core principles of complete data openness—prioritizing high standards of quality assurance; active inclusion—emphasizing involvement from local experts; and collaboration—fostering engagement across communities of data producers and users. We extend an open global call to action, inviting organizations and individuals to engage in advancing this critical agenda.

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture.

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.

Greenhouse gas (GHG) emissions from beef production in the United States are unevenly distributed across the supply chain and production regions, complicating where and how to reduce emissions most effectively. Using spatially explicit life cycle assessment methods, we quantify the baseline GHG emissions and mitigation opportunities of 42 practices spanning the supply chain from crop and livestock production to processing. We find that the potential to reduce GHGs across the beef sector ranges up to 30% (20 million tonnes CO2e reduced and 58 million tonnes CO2 sequestered each year relative to the baseline) under ubiquitous adoption assumptions, largely driven by opportunities in the grazing stage. Opportunities to reduce GHGs in the feed, grazing and feedlot stages vary across regions, yet large-scale adoption across the entire beef supply chain is important. These findings reveal promising locations and practices to invest in to advance mitigation goals and an upper-end theoretical potential for mitigation in the beef industry. The United States is the world's largest beef producer. Identifying strategies to mitigate its GHG emissions remains a challenge due to sector complexity and heterogeneity. This study takes an LCA approach to quantify potential mitigation opportunities available or soon to be available for the beef sector.

Food production data — such as crop, livestock, aquaculture and fisheries statistics — are critical to achieving multiple sustainable development goals. However, the lack of reliable, regularly collected, accessible, usable and spatially disaggregated statistics limits an accurate picture of the state of food production in many countries and prevents the implementation of effective food system interventions. In this Review, we take stock of national and international food production data to understand its availability and limitations. Across databases, there is substantial global variation in data timeliness, granularity (both spatially and by food category) and transparency. Data scarcity challenges are most pronounced for livestock and aquatic food production. These challenges are largely concentrated in Central America, the Middle East and Africa owing to a combination of inconsistent census implementation and a global reliance on self-reporting. Because data scarcity is the result of technical, institutional and political obstacles, solutions must include technological and policy innovations. Fusing traditional and emerging data-gathering techniques with coordinated governance and dedicated long-term financing will be key to overcoming current obstacles to sustained, up-to-date and accurate food production data collection, foundational in promoting and monitoring progress towards healthier and more sustainable food systems worldwide.

As wind energy expands to achieve the United States' net zero emission goals, compensatory mitigation will be required to offset negative effects on birds and bats. The golden eagle ( Aquila chrysaetos ) is particularly susceptible to collision with wind turbines, but only 1 option for offsetting mortalities has been approved by the United States Fish and Wildlife Service despite many sources of anthropogenic‐caused mortality. We update a previously developed vehicle‐collision model with data collected during 3 winters from 2016 to 2019 and integrate a resource equivalency analysis so that relocation of road‐killed game animals can be used as mitigation to offset incidental mortality. We parameterized golden eagle behaviors using motion‐sensitive cameras placed at roadside carcasses. We quantified the effects of different carcass‐relocation schemes based on vehicle and carcass characteristics observed for Wyoming, USA. Our model results indicate that while eagles saved per relocated carcass depends on relocation interval and vehicle traffic volume, carcass relocation is a viable mitigation strategy; up to 7 eagles could be saved each year in some counties. While some uncertainty remains about the precise credit received from each carcass relocated, delaying the inclusion of additional mitigation methods prevents opportunities for conservation action. An adaptive management program could be a way forward where management and monitoring are combined to further improve estimates of mitigation credit.

Abstract High food insecurity in sub-Saharan Africa (SSA) and expected adverse effects of climate change have appropriately focused attention on climate adaptation, but there has been less focus on mitigating SSA’s agricultural emissions. Emissions from the region’s agricultural production processes and recent rates of agricultural expansion are 2–3 tCO2e per capita per year and regionally more than 2 GtCO2e per year. Crop yield growth in recent years has been limited with some exceptions. With projected population of 2.15 billion in 2050 and extrapolating these recent yield trends, we estimate using the GlobAgri framework that agricultural production emissions for SSA will rise from 0.7 GtCO2e in 2010 to 1.3 GtCO2e by 2050. Conversion of 310 million hectares (Mha) of forests and savannas to cropland and 158 Mha to pasture will generate average annual emissions of 3.8 GtCO2e for 2010–2050. The combined ~ 5 GtCO2e per year would seriously undermine global climate goals. But mitigation options valuable to improve food insecurity could also greatly reduce emissions, including partially closing yield gaps, broad use of urea treatment of crop residues, and increasing per capita consumption of animal products from dairy and poultry rather than beef. These findings suggest that agricultural improvements in SSA needed to address food insecurity should also be a priority for climate mitigation.

Although extreme weather events recur periodically everywhere, the impacts of their simultaneous occurrence on crop yields are globally unknown. In this study, we estimate the impacts of combined hot and dry extremes as well as cold and wet extremes on maize, rice, soybean, and wheat yields using gridded weather data and reported crop yield data at the global scale for 1980–2009. Our results show that co-occurring extremely hot and dry events have globally consistent negative effects on the yields of all inspected crop types. Extremely cold and wet conditions were observed to reduce crop yields globally too, although to a lesser extent and the impacts being more uncertain and inconsistent. Critically, we found that over the study period, the probability of co-occurring extreme hot and dry events during the growing season increased across all inspected crop types; wheat showing the largest, up to a six-fold, increase. Hence, our study highlights the potentially detrimental impacts that increasing climate variability can have on global food production.

Continuous-living-cover (CLC) agriculture integrates multiple crops to create diversified agroecosystems in which soils are covered by living plants across time and space continuously. CLC agriculture can greatly improve production of many different ecosystem services from agroecosystems, including climate adaptation and mitigation. To go to scale, CLC agriculture requires crops that not only provide continuous living cover but are viable in economic and social terms. At present, lack of such viable crops is strongly limiting the scaling of CLC agriculture. Gene editing (GE) might provide a powerful tool for developing the crops needed to expand CLC agriculture to scale. To assess this possibility, a broad multi-sector deliberative group considered the merits of GE-relative to alternative plant-breeding methods-as means for improving crops for CLC agriculture. The group included many of the sectors whose support is necessary to scaling agricultural innovations, including actors involved in markets, finance, policy, and R&D. In this article, we report findings from interviews and deliberative workshops. Many in the group were enthusiastic about prospects for applications of GE to develop crops for CLC agriculture, relative to alternative plant-breeding options. However, the group noted many issues, risks, and contingencies, all of which are likely to require responsive and adaptive management. Conversely, if these issues, risks, and contingencies cannot be managed, it appears unlikely that a strong multi-sector base of support can be sustained for such applications, limiting their scaling. Emerging methods for responsible innovation and scaling have potential to manage these issues, risks, and contingencies; we propose that outcomes from GE crops for CLC agriculture are likely to be much improved if these emerging methods are used to govern such projects. However, both GE of CLC crops and responsible innovation and scaling are unrefined innovations. Therefore, we suggest that the best pathway for exploring GE of CLC crops is to intentionally couple implementation and refinement of both kinds of innovations. More broadly, we argue that such pilot projects are urgently needed to navigate intensifying grand challenges around food and agriculture, which are likely to create intense pressures to develop genetically-engineered agricultural products and equally intense social conflict.

ABSTRACT As wind energy expands to achieve the United States’ net zero emission goals, compensatory mitigation will be required to offset negative impacts to birds and bats. The golden eagle ( Aquila chrysaetos ) is particularly susceptible to collision with wind turbines but only one option for offsetting mortalities has been approved by the U.S. Fish and Wildlife Service despite many sources of anthropogenic-caused mortality. Here, we update a previously developed vehicle-collision model with empirical data and integrate a resource equivalency analysis so that removal of road-killed game animals can be used as mitigation to offset incidental mortality. We parameterized the golden eagle behavior parameters using camera-traps placed at roadside carcasses. We quantified the effects of different carcass-removal schemes based on vehicle and carcass characteristics observed for the state of Wyoming. Our model results indicate that while eagles saved per carcass removed depends on removal interval and vehicle traffic volume, carcass removal is a viable mitigation strategy; up to seven eagles could be saved per year in some counties. While some uncertainty remains about the precise credit received from each carcass removed, delaying the inclusion of additional mitigation methods prevents opportunities for conservation action. An adaptive management program could be a way forward where management and monitoring are combined to further improve estimates of mitigation credit.

Rising competition for crop usage presents policy challenges exacerbated by poor understanding of where crops are harvested for various uses. Here we create high-resolution global maps showing where crops are harvested for seven broad use categories-food, feed, processing, export, industrial, seed and losses. Yields for food crops are low relative to other crop-use categories. It is unlikely, given current trends, that the minimum calorie requirement to eliminate projected food undernourishment by 2030 will be met through crops harvested for direct food consumption, although enough calories will be harvested across all usages. Sub-Saharan African nations will probably fall short of feeding their increased population and eliminating undernourishment in 2030, even if all harvested calories are used directly as food.

The world’s farmers grow crops for food as well as other uses. Those other uses threaten to crowd out our chance to feed the world’s hungry. It’s sometimes bandied about that enough food is grown globally to feed everyone now and into the future. Undernourishment is ‘just a distribution challenge’. And it’s mostly true: enough […]

Nuclear weapons obliterate targets. The soot ejected into the stratosphere spreads, changing global weather patterns. When weapons are especially high yielding, the resultant soot could trigger global famine.

Improving the prediction of crop production is critical for strategy development associated with global food security, particularly as the climate continues to change. Process-based ecosystem models are increasingly used for simulating global agricultural production. However, such simulations often use a single crop variety in global assessments, implying that major crops are identical across all regions of the world. To address this limitation, we applied a Bayesian approach to calibrate regional types of maize ( Zea mays L), capturing the aggregated traits of local varieties, for DayCent ecosystem model simulations, using global crop production data from 2001 to 2013. We selected major cropping regions from the FAO Global Agro-Environmental Stratification as a basis for the regionalization and identified the most important model parameters through a global sensitivity analysis. We calibrated DayCent using the sampling importance resampling algorithm and found significant improvement in DayCent simulations of maize yields with the calibrated regional varieties. Compared to a single type of maize for the world, the regionalization of maize leads to reductions in root mean squared error of 11%, 31%, 27%, 30%, 19%, and 27% and reductions in bias of 59%, 59%, 50%, 81%, 32%, and 56% for Africa, East Asia, Europe, North America, South America, and South and Southeast Asia, respectively. We also found the optimum parameter values of radiation use efficiency are positively correlated with the income level of different regions, which indicates that breeding has enhanced the photosynthetic efficiency of maize in developed countries. There may also be opportunities for expanding crop breeding programs in developing countries to enhance photosynthesis efficiency and reduce the yield gap in these regions. This study highlights the importance of representing regional variation in crop types for achieving accurate predictions of crop yields.

High crop yield variation between years—caused by extreme shocks on the food production system such as extreme weather—can have substantial effects on food production. This in turn introduces vulnerabilities into the global food system. To mitigate the effects of these shocks, there is a clear need to understand how different adaptive capacity measures link to crop yield variability. While existing literature provides many local‐scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean, and rice for the time period 1981–2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during “dry” shock and “hot” shock years. We used the machine learning algorithm XGBoost to assess the explanatory power of selected gridded indicators of anthropogenic factors globally (i.e., adaptive capacity measures such as the human development index, irrigation infrastructure, and fertilizer use) on yield variation at a 0.5° resolution within climatically similar regions (to rule out the role of average climate conditions). We found that the anthropogenic factors explained 40%–60% of yield loss risk variation across the whole time period, whereas the factors provided noticeably lower (5%–20%) explanatory power during shock years. On a continental scale, especially in Europe and Africa, the factors explained a high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.

Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values globally for the allocation processes. Therefore, this paper aims to evaluate the sensitivity of yield and HI of wheat and maize simulated with LPJ-GUESS to eight production allocation-related parameters and identify the most suitable parameter values for global simulations. The nitrogen demand reduction after anthesis, the minimum leaf carbon to nitrogen ratio (C:N) and the range of leaf C:N strongly affected carbon assimilation and yield, while the retranslocation of labile stem carbon to grains and the retranslocation rate of nitrogen and carbon from vegetative organs to grains after anthesis mainly influenced HI. A global database of observed HI for both crops was compiled for reference to constrain simulations before calibrating parameters for yield against reference data. Two high- and low-yielding maize cultivars emerged from the calibration, whilst spring and winter cultivars were found appropriate for wheat. The calibrated version of LPJ-GUESS improved the simulation of yield and HI at the global scale for both crops, providing a basis for future studies exploring crop production under different climate and management scenarios.

Armed conflicts often hinder food security through cropland abandonment and restrict the collection of on-the-ground information required for targeted relief distribution. Satellite remote sensing provides a means for gathering information about disruptions during armed conflicts and assessing the food security status in conflict zones. Using ~7,500 multisource satellite images, we implemented a data-driven approach that showed a reduction in cultivated croplands in war-ravaged South Sudan by 16% from 2016 to 2018. Propensity score matching revealed a statistical relationship between cropland abandonment and armed conflicts that contributed to drastic decreases in food supply. Our analysis shows that the abandoned croplands could have supported at least a quarter of the population in the southern states of South Sudan and demonstrates that remote sensing can play a crucial role in the assessment of cropland abandonment in food-insecure regions, thereby improving the basis for timely aid provision.