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The current trajectory for crop yields is insufficient to nourish the world’s population by 2050 1 . Greater and more consistent crop production must be achieved against a backdrop of climatic stress that limits yields, owing to shifts in pests and pathogens, precipitation, heat-waves and other weather extremes. Here we consider the potential of plant sciences to address post-Green Revolution challenges in agriculture and explore emerging strategies for enhancing sustainable crop production and resilience in a changing climate. Accelerated crop improvement must leverage naturally evolved traits and transformative engineering driven by mechanistic understanding, to yield the resilient production systems that are needed to ensure future harvests. Genetic strategies for improving the yield and sustainability of agricultural crops, and the resilience of crops in the face of biotic and abiotic stresses contingent on projected climate change, are evaluated.

Industrially produced N-fertilizer is essential to the production of cereals that supports current and projected human populations. We constructed a top-down global N budget for maize, rice and wheat for a 50-year period (1961 to 2010). Cereals harvested a total of 1551 Tg of N, of which 48% was supplied through fertilizer-N and 4% came from net soil depletion. An estimated 48% (737 Tg) of crop N, equal to 29, 38 and 25 kg ha −1  yr −1 for maize, rice and wheat, respectively, is contributed by sources other than fertilizer- or soil-N. Non-symbiotic N 2 fixation appears to be the major source of this N, which is 370 Tg or 24% of total N in the crop, corresponding to 13, 22 and 13 kg ha −1  yr −1 for maize, rice and wheat, respectively. Manure (217 Tg or 14%) and atmospheric deposition (96 Tg or 6%) are the other sources of N. Crop residues and seed contribute marginally. Our scaling-down approach to estimate the contribution of non-symbiotic N 2 fixation is robust because it focuses on global quantities of N in sources and sinks that are easier to estimate, in contrast to estimating N losses per se, because losses are highly soil-, climate- and crop-specific.

Cropland is a main source of global nitrogen pollution 1 , 2 . Mitigating nitrogen pollution from global croplands is a grand challenge because of the nature of non-point-source pollution from millions of farms and the constraints to implementing pollution-reduction measures, such as lack of financial resources and limited nitrogen-management knowledge of farmers 3 . Here we synthesize 1,521 field observations worldwide and identify 11 key measures that can reduce nitrogen losses from croplands to air and water by 30–70%, while increasing crop yield and nitrogen use efficiency (NUE) by 10–30% and 10–80%, respectively. Overall, adoption of this package of measures on global croplands would allow the production of 17 ± 3 Tg (10 12  g) more crop nitrogen (20% increase) with 22 ± 4 Tg less nitrogen fertilizer used (21% reduction) and 26 ± 5 Tg less nitrogen pollution (32% reduction) to the environment for the considered base year of 2015. These changes could gain a global societal benefit of 476 ± 123 billion US dollars (USD) for food supply, human health, ecosystems and climate, with net mitigation costs of only 19 ± 5 billion USD, of which 15 ± 4 billion USD fertilizer saving offsets 44% of the gross mitigation cost. To mitigate nitrogen pollution from croplands in the future, innovative policies such as a nitrogen credit system (NCS) could be implemented to select, incentivize and, where necessary, subsidize the adoption of these measures. A meta-analysis of 1,521 field observations from the past two decades led to the identification of 11 key measures to cost-effectively mitigate nitrogen pollution from global croplands.

Precision agriculture technologies (PATs) transform crop production by enabling more sustainable and efficient agricultural practices. These technologies utilize data‐driven approaches to optimize the management of crops, soil, and resources, thus enhancing both productivity and environmental sustainability. This article reviewed the application of PATs for sustainable crop production and environmental sustainability around the globe. Key components of PAT include remote sensing, GPS‐guided equipment, variable rate technology (VRT), and Internet of Things (IoT) devices. Remote sensing and drones deliver high‐resolution imagery and data, enabling precise monitoring of crop health, soil conditions, and pest activity. GPS‐guided machinery ensures accurate planting, fertilizing, and harvesting, which reduces waste and enhances efficiency. VRT optimizes resource use by allowing farmers to apply inputs such as water, fertilizers, and pesticides at varying rates across a field based on real‐time data and specific crop requirements. This reduces over‐application and minimizes environmental impact, such as nutrient runoff and greenhouse gas emissions. IoT devices and sensors provide continuous monitoring of environmental conditions and crop status, enabling timely and informed decision‐making. The application of PAT contributes significantly to environmental sustainability by promoting practices that conserve water, reduce chemical usage, and enhance soil health. By enhancing the precision of agricultural operations, these technologies reduce the environmental impact of farming, while simultaneously boosting crop yields and profitability. As the global demand for food increases, precision agriculture offers a promising pathway to achieving sustainable crop production and ensuring long‐term environmental health.

The practice of cover crops has gained popularity as a strategy to improve agricultural sustainability, but its full potential is often limited by environmental trade-offs. Using meta-analytic and data-driven quantifications of 2302 observations, we optimized cover crop practices and evaluated their benefits for global agroecosystems. Cover crops have historically boosted crop yields, soil carbon storage, and stability, but also stimulated greenhouse gas emissions. However, combining them with long-term implementation (five years or more) and climate-smart practices (such as no-tillage) can enhance these services synergistically. A biculture of legume and non-legume cover crops, terminated 25 days before planting the next crop and followed by residue mulching, is the optimal portfolio. Such optimized practices are projected to increase agroecosystem multiservices by 1.25%, equivalent to annual gains of 97.7 million metric tons in crop production, 21.7 billion metric tons in carbon dioxide sequestration, and 2.41 billion metric tons in soil erosion reduction. By 2100, the continued implementation of optimized practices could mitigate climate-related yield losses and contribute to climate neutrality and soil stabilization, especially in harsh and underdeveloped areas. These findings underscore the promising potential of optimized cover crop practices to achieve the synergy in food security and environmental protection. Cover crops can improve agricultural sustainability. In this meta-analysis, the authors find that a biculture of legume and non-legume cover crops is optimal and may promote multiple agroecosystem services while mitigating climate-related yield losses by 2100.

Achieving food-system sustainability is a multidimensional challenge. In China, a doubling of crop production since 1990 has compromised other dimensions of sustainability 1 , 2 . Although the country is promoting various interventions to enhance production efficiency and reduce environmental impacts 3 , there is little understanding of whether crop switching can achieve more sustainable cropping systems and whether coordinated action is needed to avoid tradeoffs. Here we combine high-resolution data on crop-specific yields, harvested areas, environmental footprints and farmer incomes to first quantify the current state of crop-production sustainability. Under varying levels of inter-ministerial and central coordination, we perform spatial optimizations that redistribute crops to meet a suite of agricultural sustainable development targets. With a siloed approach—in which each government ministry seeks to improve a single sustainability outcome in isolation—crop switching could realize large individual benefits but produce tradeoffs for other dimensions and between regions. In cases of central coordination—in which tradeoffs are prevented—we find marked co-benefits for environmental-impact reductions (blue water (−4.5% to −18.5%), green water (−4.4% to −9.5%), greenhouse gases (GHGs) (−1.7% to −7.7%), fertilizers (−5.2% to −10.9%), pesticides (−4.3% to −10.8%)) and increased farmer incomes (+2.9% to +7.5%). These outcomes of centrally coordinated crop switching can contribute substantially (23–40% across dimensions) towards China’s 2030 agricultural sustainable development targets and potentially produce global resource savings. This integrated approach can inform feasible targeted agricultural interventions that achieve sustainability co-benefits across several dimensions. Spatial optimizations of high-resolution data from China on crop-specific yields, harvested areas, environmental footprints and farmer incomes shows that crop switching can enhance environmental sustainability and farmer incomes, and contribute substantially towards China’s agricultural sustainable development targets.

Many studies have estimated the adverse effects of climate change on crop yields, however, this literature almost universally assumes a constant geographic distribution of crops in the future. Movement of growing areas to limit exposure to adverse climate conditions has been discussed as a theoretical adaptive response but has not previously been quantified or demonstrated at a global scale. Here, we assess how changes in rainfed crop area have already mediated growing season temperature trends for rainfed maize, wheat, rice, and soybean using spatially-explicit climate and crop area data from 1973 to 2012. Our results suggest that the most damaging impacts of warming on rainfed maize, wheat, and rice have been substantially moderated by the migration of these crops over time and the expansion of irrigation. However, continued migration may incur substantial environmental costs and will depend on socio-economic and political factors in addition to land suitability and climate. Extreme high temperature events are increasing in frequency and severity, threatening the capacity for crops and farmers alike to adapt. Here Sloat and colleagues track the movement of cereal crops over the past 40 years, finding a global migration away from warming climates.

Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO 2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments. 'Crop growing periods and cultivars are key to crop adaptation. Here, the authors use a modelling approach that integrates farmers decision and biophysical crop models, showing the importance of cultivar, sowing date and growing period adaptation.'

Crop yields are projected to decrease under future climate conditions, and recent research suggests that yields have already been impacted. However, current impacts on a diversity of crops subnationally and implications for food security remains unclear. Here, we constructed linear regression relationships using weather and reported crop data to assess the potential impact of observed climate change on the yields of the top ten global crops-barley, cassava, maize, oil palm, rapeseed, rice, sorghum, soybean, sugarcane and wheat at ~20,000 political units. We find that the impact of global climate change on yields of different crops from climate trends ranged from -13.4% (oil palm) to 3.5% (soybean). Our results show that impacts are mostly negative in Europe, Southern Africa and Australia but generally positive in Latin America. Impacts in Asia and Northern and Central America are mixed. This has likely led to ~1% average reduction (-3.5 X 1013 kcal/year) in consumable food calories in these ten crops. In nearly half of food insecure countries, estimated caloric availability decreased. Our results suggest that climate change has already affected global food production.

Background The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments.