Future Yield Research Articles

Climate and vegetation change impacts on future conterminous United States water yield

Future water availability is influenced by both climate and associated vegetation dynamics. This study coupled vegetation projections from a dynamic global vegetation model (MC2) with an ecohydrological model, Water Supply Stress Index (WaSSI), to predict water yield at the 8-digit Hydrologic Unit Code (HUC8) watershed level for the conterminous United States (CONUS) for the 21st century. We considered two contrasting warming scenarios (Representative Concentration Pathways 8.5 and 4.5) and accounted for simulation uncertainty by using a large ensemble of climate model outputs. The coupled model projects a decrease in water yield across much of CONUS, especially towards end-century (2080–2099) under RCP 8.5 (warmer scenario), reaching up to −30% at the regional level, relative to the 2008–2027 baseline period. Overall, the projected water yield reduction under RCP 8.5 is roughly twice as high as under RCP 4.5. Substantial changes in water yield for watersheds in the central and southeastern United States are expected by mid-century (2040–2059), reaching up to −40% (RCP 4.5) and −75% (RCP 8.5) at at the century’s end (2080–2099), relative to 2008–2027, respectively. Climate change, rather than vegetation change, strongly dominates the projected future changes in water yield, with contributions typically one order of magnitude higher. For a small number of watersheds, the effects of vegetation change can mitigate or exacerbate the effects of climate change on water yield. Our simulation results suggest a widespread increase in aridity and evaporative indices and a decrease in soil moisture, especially under RCP 8.5. Our integrated modeling results can inform policy makers and resource development planners quantitative information of future water availability.

Agro-technology for climate-smart agriculture and resilience to climate extremes in sub-Saharan Africa

Agro-technologies such as irrigation and new crop varieties can reduce climate risk for agricultural production in sub-Sahara Africa (SSA). SSA has the highest maize yield gaps globally, despite its importance as a staple crop in the region. Reducing maize yield gaps is key to tackling food insecurity; however, closing yield gaps might imply an increased greenhouse gas (GHG) emission cost. Climate smart agriculture (CSA) seeks to minimise this cost whilst maximising productivity and resilience. One key element of CSA is resilience to extreme events, although this is rarely examined. Accordingly, we assess the climate smartness of contrasting agro-technology and climate scenarios to assess both resilience to extremes and the overall climate smartness of the scenarios. We use simulations from an existing integrated modelling framework for Malawi, Tanzania, and Zambia, centred on 2050. Four scenarios were examined, defined by combinations of high vs. low agro-technology adoption and high vs. low climate risk (RCP2.6 and RCP8.5). We calculated a climate smartness index (CSI) to the model outputs that quantify the trade-offs between greenhouse gas emissions and agricultural productivity. CSI scores showed that the increase in GHG emissions from improved agro-technology is compensated for the yield benefits. Agro-technology in SSA can therefore benefit the pillars of climate-smart agriculture, namely increased mitigation, adaptation, and productivity. Further, we show that improved maize varieties and irrigation can substantially reduce future yield shocks and enhance resilience to climate change extremes in SSA, pointing to best-bets for agro-technology adoption. Irrigation reduces mid-century yield shocks by 64% (RCP2.6) or 42% (RCP8.5). When combined with improved maize varieties, irrigation removes the majority of yield shocks (90%) in RCP8.5. We therefore conclude that: (i) irrigation has significant potential to increase resilience in SSA; and (ii) investment in strategies to improve crop varieties is critical if the benefits or irrigation are to be fully realized under an RCP8.5 future.

Genome resources for three modern cotton lines guide future breeding efforts

Cotton (Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (‘UGA230’, ‘UA48’ and ‘CSX8308’) and updating the ‘TM-1’ cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from ‘Pima’ cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial

Common wheat (Triticum aestivum L.) is a major staple food crop, providing a fifth of food calories and proteins to the world’s human population. Despite the impressive growth in global wheat production in recent decades, further increases in grain yield are required to meet future demands. Here we estimated genetic gain and genotype stability for grain yield (GY) and determined the trait associations that contributed uniquely or in combination to increased GY, through a retrospective analysis of top-performing genotypes selected from the elite spring wheat yield trial (ESWYT) evaluated internationally during a 14-year period (2003 to 2016). Fifty-six ESWYT genotypes and four checks were sown under optimally irrigated conditions in three phenotyping trials during three consecutive growing seasons (2018–2019 to 2020–2021) at Norman E. Borlaug Research Station, Ciudad Obregon, Mexico. The mean GY rose from 6.75 (24th ESWYT) to 7.87 t ha−1 (37th ESWYT), representing a cumulative increase of 1.12 t ha−1. The annual genetic gain for GY was estimated at 0.96% (65 kg ha−1 year−1) accompanied by a positive trend in genotype stability over time. The GY progress was mainly associated with increases in biomass (BM), grain filling rate (GFR), total radiation use efficiency (RUE_total), grain weight per spike (GWS), and reduction in days to heading (DTH), which together explained 95.5% of the GY variation. Regression lines over the years showed significant increases of 0.015 kg m−2 year−1 (p < 0.01), 0.074 g m−2 year−1 (p < 0.05), and 0.017 g MJ−1 year−1 (p < 0.001) for BM, GFR, and RUE_total, respectively. Grain weight per spike exhibited a positive but no significant trend (0.014 g year−1, p = 0.07), whereas a negative tendency for DTH was observed (− 0.43 days year−1, p < 0.001). Analysis of the top ten highest-yielding genotypes revealed differential GY-associated trait contributions, demonstrating that improved GY can be attained through different mechanisms and indicating that no single trait criterion is adopted by CIMMYT breeders for developing new superior lines. We conclude that CIMMYT’s Bread Wheat Breeding Program has continued to deliver adapted and more productive wheat genotypes to National partners worldwide, mainly driven by enhancing RUE_total and GFR and that future yield increases could be achieved by intercrossing genetically diverse top performer genotypes.

The economics of farming expansion in the Brazilian Cerrado under possible effects of climate change

Abstract This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to predict potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis.

Global and local information efficiency: An examination of samuelson's dictum

The global version of Samuelson's Dictum is the conjecture that there is more informational inefficiency for global information than for country-specific information. Consistent with this conjecture, we find that sovereign CDS spreads can predict future sovereign bond yields of their underlying countries and this predictive power arises almost entirely from CDS spread's global—rather than country-specific—component. Noting the striking similarities between the evidence in stock and sovereign bond markets, we examine the underlying mechanism for the results in both markets in parallel and find further similarities across these two markets. In both cases, information appears to flow in one direction: from the sovereign CDS market to stock and sovereign bond markets and not the other way around. Information transmission occurs mostly during the days surrounding announcements of credit-rating or outlook changes, especially downgrades. These results are broadly consistent with a setup in which information acquisition and processing is costly.

Preliminary Exploration of Physiology and Genetic Basis Underlying High Yield in Indica–Japonica Hybrid Rice

The utilization of heterosis is of great significance in improving rice yield. To explore the physiological and genetic basis for high yield in indica–japonica hybrid rice, Zheyou18 (z18) and Yongyou12 (y12) were used as materials and compared with indica hybrid rice, Zheyou12 (z12); japonica hybrid rice, Liangyoupeijiu (LYPJ); and the conventional lines zhe04B (04B) and zhehui818 (h818) under seedling growth vigor, functional leaf morphology, chlorophyll content, yield component, panicle trait, and InDel heterosis analysis. Z18 and y12 showed the largest increase in plant height 6 d and 9 d after germination; the root dry weight of z18 was 31.2% and 42.0% higher than its parents on the 12th d. The length of functional leaves ranked in the middle, while the width was the largest, resulting in z18 and y12 having the largest leaf area. Yield components showed that z18 and y12 had the highest number of primary branches, spikelets, and grains, and grain yield, which was 58.1 g in z18, increased by 29.8% and 8.7%, respectively, in comparison with h818 and LYPJ. The InDel genetic distance was significantly positively correlated with single spike weight, with r reaching 0.771, making it the only consistent and most correlated among the seven traits. Therefore, we speculated that as the InDel genetic distance expands, heterosis mainly manifests in the increase in single spike weight. This study comprehensively explored the physiological mechanism of yield improvement in indica–japonica-hybrid rice and used InDel genetic distances to study the genetic basis of heterosis, which will be helpful for future rice yield improvement.

A Study of Irrigation Management in Smart Farming and IoT for Greenhouse Tomato Production

In this study of the relevant literature, we look at the development of smart farming and precision agriculture in greenhouse tomato cultivation, paying particular attention to the role that the Internet of Things (IoT) and automated irrigation systems have played thus far. Five terms were used to compile this literature review: automated watering systems, greenhouses, the Internet of Things, tomatoes, and smart farming. This article summarizes recent advancements in smart farming tech, their impact on greenhouse tomato farming, and their potential to boost future crop yield and quality. The evaluation starts by introducing 'smart farming' and its significance in agriculture, followed by a review of the greenhouse sector and an overview of the Internet of Things (IoT) within it, with subsequent examination of the pros and cons of employing smart farming and IoT in greenhouse tomato cultivation through specific industry technology examples. The following overview section discusses the benefits of cultivating tomatoes in a smart greenhouse, techniques for achieving optimal results in greenhouse tomato farming, and includes case studies to demonstrate the advantages of automated irrigation for smart greenhouse tomato cultivation, while this literature review analyzes the current state of innovative farming technology and its effect on greenhouse tomato cultivation, covering smart farming, greenhouse cultivation, the Internet of Things, tomato cultivation, and automated irrigation, summarizing significant results and recommendations for future research, including promising increases in crop yield and quality.

Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures

In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model’s performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper.

Mathematical modelling of quantifying bioethanol fuel production from pineapple waste

Lack of confidence in potential bioenergy production and net benefit hinders rapid implementations of such sustainable energy productions from different waste sources. With the aim of accelerating real-life implementations of more bioenergy productions, this paper presents development of a simple mathematical model, which can be used to evaluate potential bioethanol production capacity from pineapple waste under different input conditions. Based on an earlier experimental study, the mathematical model was developed depending on three contributing factors; pH, temperature and substrate concentration as considered in the earlier experimental study. Results from the developed mathematical formulation were compared with the experimental data from the earlier original study. It is found that the developed model is quite capable to estimate potential bioethanol productions from pineapple waste. Model estimated results are having a coefficient of correlation of 0.84 with the measured data. Standard errors of the model’s estimations are also quite low; RMSE = 0.49, MAE = 0.39 and RAE = 0.06. To facilitate a wider industrial generation, a basic mathematical model framework for economic analysis is proposed involving evaluation of net present values of expected future yields, as well as costs (initial and maintenance). Such mathematical model of economic analysis will help stakeholders on selecting optimum input parameters in achieving targeted benefit through optimised energy consumption.

Modeling and Optimization of Groundnut Production in Vijayapura District of Karnataka, India

Groundnut is a significant oilseed crop in India, with Karnataka being one of the largest producers. The agricultural economy of Vijayapura district relies heavily on crop production, including groundnut. Understanding the production patterns and forecasting future yields is crucial for agricultural planning and economic sustainability. The study aimed to investigate the production patterns and forecast the groundnut yield in Vijayapura district, Karnataka. The research problem focused on understanding the trends in groundnut area, production, and productivity over time and developing a forecasting model for future yields. Secondary data from 1966-67 to 2020-21 was collected from various sources. Statistical models including linear, quadratic, cubic, exponential, log-logistic and GAM were used to analyze the trends. The ARIMA method was employed for forecasting. The models' adequacy was assessed using MAPE, R2, AIC, and BIC criteria. The log-logistic model was found to be the best fit for groundnut area trends, while the cubic model and GAM were best for productivity and production, respectively. Forecasting using ARIMA initially indicated a slight increase in groundnut yield, but the GAM model predicted a decrease in future production. The findings provide insights for policymakers, agricultural extension services and farmers to make informed decisions regarding crop planning, resource allocation and economic sustainability. Understanding the production patterns and forecasting future yields is crucial for agricultural planning and economic sustainability in Vijayapura district.

Response of climate change and land use land cover change on catchment-scale water balance components: a multi-site calibration approach

Abstract The present study focused on evaluating the separate and combined response of land use land cover and climate change (CC) on future water balance components of a Subarnarekha River basin, spanning between the latitudes 21°33′N–23°18′N and longitudes 85°11′E–87°23′E, situated in the eastern India. The Soil and Water Assessment Tool is used for single-site calibration and multi-site calibration (MSC) of the model to characterize the future water balance components of the basin using the Cellular Automata-Markov model and climate projections under two representative concentration pathway (RCP) scenarios (4.5 and 8.5). The findings indicate that the model parameters obtained through MSC better represent spatial heterogeneity, making it the preferred calibration approach for model simulations. In the middle region of the basin, future annual water yield, groundwater recharge (GWR), and streamflow showed a reduction, respectively, by 46–47%, 29–30%, and 13–15%, while evapotranspiration showed an increase by 5–7% following projected CC under both RCP scenarios. The findings are relevant for policy-makers to mitigate the adverse effects of reduced GWR for sustainable water resources management. Future research may integrate reservoir operation frameworks to effectively address the water management issues of the basin.

Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation

AbstractCrop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

Bio-upcycling of even and uneven medium-chain-length diols and dicarboxylates to polyhydroxyalkanoates using engineered Pseudomonas putida

Bio-upcycling of plastics is an emerging alternative process that focuses on extracting value from a wide range of plastic waste streams. Such streams are typically too contaminated to be effectively processed using traditional recycling technologies. Medium-chain-length (mcl) diols and dicarboxylates (DCA) are major products of chemically or enzymatically depolymerized plastics, such as polyesters or polyethers. In this study, we enabled the efficient metabolism of mcl-diols and -DCA in engineered Pseudomonas putida as a prerequisite for subsequent bio-upcycling. We identified the transcriptional regulator GcdR as target for enabling metabolism of uneven mcl-DCA such as pimelate, and uncovered amino acid substitutions that lead to an increased coupling between the heterologous β-oxidation of mcl-DCA and the native degradation of short-chain-length DCA. Adaptive laboratory evolution and subsequent reverse engineering unravelled two distinct pathways for mcl-diol metabolism in P. putida, namely via the hydroxy acid and subsequent native β-oxidation or via full oxidation to the dicarboxylic acid that is further metabolized by heterologous β-oxidation. Furthermore, we demonstrated the production of polyhydroxyalkanoates from mcl-diols and -DCA by a single strain combining all required metabolic features. Overall, this study provides a powerful platform strain for the bio-upcycling of complex plastic hydrolysates to polyhydroxyalkanoates and leads the path for future yield optimizations.Graphical

Predictive Assessment of Climate Change Impact on Water Yield in the Meta River Basin, Colombia: An InVEST Model Application

This paper presents a hydrological assessment of the 113,981 km2 Meta River basin in Colombia using 13 global climate models to predict water yield for 2050 under two CMIP6 scenarios, SSP 4.5 and SSP 8.5. Despite mixed performance across subbasins, the model was notably effective in the upper Meta River subbasin. This study predicts an overall increase in the basin’s annual water yield due to increased precipitation, especially in flatter regions. Under the SSP 4.5, the Meta River basin’s water flow is expected to rise from 5141.6 m3/s to 6397.5 m3/s, and to 6101.5 m3/s under the SSP 8.5 scenario, marking 24% and 19% increases in water yield, respectively. Conversely, the upper Meta River subbasin may experience a slight decrease in water yield, while the upper Casanare River subbasin is predicted to see significant increases. The South Cravo River subbasin, however, is expected to face a considerable decline in water yield, indicating potential water scarcity. This study represents a pioneering large-scale application of the InVEST–AWY model in Colombia using CMIP6 global climate models with an integrated approach to produce predictions of future water yields.

Sustainable sweetpotato production in the United States: Current status, challenges, and opportunities

AbstractSweetpotato (Ipomoea batatas L.) is an important staple crop cultivated in over 100 countries, and the storage roots and vines provide food for humans and livestock. Sweetpotato consumption and demand for its value‐added products have increased significantly in the last two decades and have led to new cultivar development, expansion in acreage, and increased demand in the United States and its export markets. Despite the known nutritional components and other health benefits, further research is needed to characterize the genetic diversity and chemical composition related to their storage root qualities, essential in developing consumer‐preferred cultivars that offer host plant resistance against pests and pathogens. There is a critical need for research on non‐pesticidal control approaches that can provide safe, effective, economical, sustainable, and environmentally sound pest and disease management techniques, especially for socially disadvantaged small farmers in the United States. Moreover, climate change can significantly impact future production practices and yield and may directly or indirectly affect crop pests, weeds, and diseases. In this review, we discuss the current status, challenges, and future approaches associated with sweetpotato production practices; health‐promoting properties of sweetpotato cultivars; value‐added products; genetic diversity and germplasm; pest and disease management; weed and water management; pollination ecology; and other agronomic and cultural practices that may impact sustainable sweetpotato production by small‐scale, organic, and large‐scale growers.

An Intelligent System for Real Time Crop Monitoring and Yield Estimation using Machine Learning

Abstract: This ongoing effort aims to revolutionize the agricultural domain through the utilization of a microservices framework that leverages Spring Boot, REST API, and Bootstrap technologies. Its main goal is to offer real-time monitoring solutions, which are facilitated by an Oracle database for storing data acquired from various sensors, including those for weather, soil, and crop production. The overarching objectives include equipping farmers with instant insights into crop health and soil conditions, with a specific focus on increasing crop yields, reducing farming costs, and minimizing the environmental impact. The core functions of the project encompass data analysis to generate tailored farming recommendations, tracking the movements of pests and diseases, and predicting future crop yields. Expected advantages for farmers involve heightened productivity, costeffectiveness, sustainability enhancements, well- informed decision-making, and improved operational efficiency. As development advances, this endeavor holds significant promise for effecting a transformative influence on the agricultural sector.

Discovery and Characterization of Fluopipamine, a Putative Cellulose Synthase 1 Antagonist within Arabidopsis.

Herbicide-resistant weeds are increasingly a problem in crop fields when exposed to similar chemistry over time. To avoid future yield losses, identifying herbicidal chemistry needs to be accelerated. We screened 50,000 small molecules using a liquid-handling robot and light microscopy focusing on pre-emergent herbicides in the family of cellulose biosynthesis inhibitors. Through phenotypic, chemical, genetic, and in silico methods we uncovered 6-{[4-(2-fluorophenyl)-1-piperazinyl]methyl}-N-(2-methoxy-5-methylphenyl)-1,3,5-triazine-2,4-diamine (fluopipamine). Symptomologies support fluopipamine as a putative antagonist of cellulose synthase enzyme 1 (CESA1) from Arabidopsis (Arabidopsis thaliana). Ectopic lignification, inhibition of etiolation, phenotypes including loss of anisotropic cellular expansion, swollen roots, and live cell imaging link fluopipamine to cellulose biosynthesis inhibition. Radiolabeled glucose incorporation of cellulose decreased in short-duration experiments when seedlings were incubated in fluopipamine. To elucidate the mechanism, ethylmethanesulfonate mutagenized M2 seedlings were screened for fluopipamine resistance. Two loci of genetic resistance were linked to CESA1. In silico docking of fluopipamine, quinoxyphen, and flupoxam against various CESA1 mutations suggests that an alternative binding site at the interface between CESA proteins is necessary to preserve cellulose polymerization in compound presence. These data uncovered potential fundamental mechanisms of cellulose biosynthesis in plants along with feasible leads for herbicidal uses.

Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation.

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.