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Non-PR, IFPRI1; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance; G Cross-cutting gender theme; Capacity Strengthening, Foresight and Policy Modeling (FPM); Transformation Strategies, Smallholder farmers in low- and medium-income countries lack sufficient access to agricultural production credit that can help them adopt new technologies and improve their farm production. Compared to men, women smallholder farmers face additional social, and economic barriers that further limit their credit access. Bundling agricultural credit with insurance, or risk contingent credit (RCC), provides a mechanism for addressing some of the credit access constraints and reducing credit rationing among smallholder farmers. In this paper, we evaluate the gendered determinants of credit rationing and the gender differences of the effects of RCC innovation on credit uptake decisions. We use three-wave panel data from a randomized control trial (RCT) in Kenya. We find that female-headed households (FHH) are significantly more risk rationed (or demand-side credit constrained) compared to male-headed households (MHH), however, the gender of the household head does not significantly determine the household quantity rationing status (supply-side constrained). We also find that farmers randomly assigned to be offered the RCC are up to four percent more likely to take up credit. RCC’s impacts on credit uptake decisions do not vary with the gender of the household head, however, RCC has a differential positive and significant impact on the credit uptake decisions of farmers that were previously (at baseline) risk rationed. Based on these findings, we suggest that policies should focus on reducing gendered demand-side barriers to credit access, especially among poorer women households. Climate financing innovations such as RCC should also be designed and delivered in a gender-inclusive manner to accommodate women farmers who face time, liquidity, and financial literacy barriers.

Non-PR, IFPRI1; Capacity Strengthening; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance, Foresight and Policy Modeling (FPM); Transformation Strategies, Changes in frequency and intensity of climate and weather events are a key challenge to agricultural production among farmers in Zambia. Climate variability reduces farm productivity, which in turn contributes to household food insecurity, income variability, and reduced overall economic growth. Using improved technologies such as mechanization, improved seed varieties, irrigation, and fertilizer can improve climate resilience and farm production among smallholder farmers. However, in Zambia, as in many countries in sub-Saharan Africa, most famers lack sufficient access to credit to purchase these technologies. Limited access to credit is mainly attributed to lack of collateral, fear of losing collateral in case of a default, and low financial literacy among smallholder famers (Balana et al. 2022). Information asymmetry also makes it risky and expensive for lenders to serve smallholder farmers, thus they ration the quantity of credit offered and/or raise the interest rates making credit too expensive and inaccessible for millions of smallholder farmers. Bundling agricultural credit with insurance, commonly referred to as risk-contingent credit (RCC), provides a mechanism for addressing some of the credit access constraints faced by smallholder farmers in developing countries. RCC is a loan product that is bundled with an insurance component. RCC seeks to enhance long-term resilience to climate uncertainties by promoting optimal farm investment and productivity among smallholders through sustainable access to credit markets. Under RCC, qualifying smallholder farmers borrow funds for agricultural production from formal financial institutions such as banks and microfinance institutions with minimum collateral requirements. The borrower’s ability to repay the loan is linked to climate outcomes, which are highly correlated with farm productivity. An insurance company underwrites the climate risks (either in the form of drought or flood), such that if that underlying risk

Non-PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; IFPRI1; MaSSP, Development Strategies and Governance (DSG); Transformation Strategies; Foresight and Policy Modeling (FPM), El Niño is a phase in an irregular periodic variation in winds and sea surface temperatures over the Pacific Ocean. It occurs on average every 2 to 7 years and typically lasts between 9 months and 2 years. El Niño affects the global weather patterns, resulting in above-average precipitation in some places and droughts in others. Malawi and its neighbors typically experience drier than usual weather during El Niño, which often leads to poor growing conditions and below-average harvests.

PR, IFPRI3; ISI; DCA, Foresight and Policy Modeling (FPM); Transformation Strategies

PR, IFPRI3; ISI, Foresight and Policy Modeling (FPM); Transformation Strategies, That climate variability and change can potentially force multiple simultaneous breadbasket crop yield shocks has been established. But research quantifying the mechanisms behind such simultaneous shocks has been constrained by short records of crop yields. Here we compile a dataset of subnational crop yields in 25 countries dating back to 1900 to study the frequency and trends in multiple breadbasket yield shocks and how large-scale climate anomalies on interannual timescales have affected multiple breadbasket yield shocks over the last century. We find that major simultaneous breadbasket yield shocks have occurred in at least three, four, or five of nine breadbaskets 10.3%, 2.3% and 1.1% of the time for maize and 18.4%, 4.6% and 2.3% of the time for wheat. Furthermore, we find that multiple breadbasket yield shocks decreased in frequency even as those breadbaskets experience increasingly frequent climate-related shocks. For both maize and wheat breadbaskets, there were fewer simultaneous yield shocks during the 1975–2017 time period as compared to 1931–1975. Finally, we find that interannual modes of climate variability - such as the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO) - have all affected the relative probability of simultaneous yield shocks in pairs of breadbaskets by up to 20–40% in both maize and wheat breadbaskets. While past literature has focused on the effects of ENSO, we find that at the global scale the NAO affects the overall number of wheat yield shocks most strongly despite only affecting northern hemisphere breadbaskets.

PR, IFPRI3; ISI, Foresight and Policy Modeling (FPM); Transformation Strategies

PR, IFPRI1; DCA; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; Capacity Strengthening; CRP2, Foresight and Policy Modeling (FPM); Transformation Strategies; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Agricultural credit is an important instrument for improving farm productivity, the welfare of farm households, and their resilience to weather-related shocks. However, small-scale farmers’ access to credit is limited by demand- and supply-side constraints. Risk-contingent credit, which bundles credit with insurance against climate risks, is one promising solution. This study, conducted in three woredas in Ethiopia, implemented a game designed to increased farmers’ understanding of the product and finds that farmers recognize the potential of RCC.

PR, IFPRI3; CRP3.2; 1 Fostering Climate-Resilient and Sustainable Food Supply, Foresight and Policy Modeling (FPM); Transformation Strategies, CGIAR Research Program on Maize (MAIZE), The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment and other objectives. While much research has suggested that remote sensing can potentially help to address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperforms other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year’s data can be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale, high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms and well-measured ground control data and currently existing time series satellite data.

PR, IFPRI4; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all, Development Strategies and Governance (DSG); Transformation Strategies; Foresight and Policy Modeling (FPM); Transformation Strategies, Throughout Africa, climate change is posing severe challenges to agricultural production and food security. Agricultural risks—particularly those associated with drought—are a major cause of low agricultural productivity in most African countries, including Kenya. According to the Government of Kenya, four consecutive years (2008–2011) of drought caused US$12.1 billion in losses, accounting for about 8 percent of GDP, including losses in assets and disruptions to the economy across sectors (Kenya, Ministry of Agriculture, Livestock and Fisheries 2014). Currently, Kenya is in the middle of an acute drought following three consecutive poor rainy seasons. This has led to a drop in crop production nationally of about 70 percent, which has disproportionately exposed the communities of arid and semi-arid lands to hunger and malnutrition.

PR, IFPRI3; ISI; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply, Foresight and Policy Modeling (FPM); Transformation Strategies, Nitrogen (N) is an important nutrient for crop growth. However, the overuse of N fertilizers has led to a series of devastating global environmental issues. Recent studies show that multiple datasets have been created for agricultural N fertilizer application with varied temporal or spatial resolutions, nevertheless, how to synchronize and use these datasets becomes problematic due to the inconsistent temporal coverages, spatial resolutions, and crop-specific allocations. Here we reconstructed a comprehensive dataset for crop-specific N fertilization at 5-arc-min resolution (~10 km by 10 km) during 1961–2020, including N application rate, types, and placements. The N fertilization data was segmented by 21 crop groups, 13 fertilizer types, and 2 fertilization placements. Comparison analysis showed that our dataset is aligned with previous estimates. Our spatiotemporal N fertilization dataset could be used for the land surface models to quantify the effects of agricultural N fertilization practices on food security, climate change, and environmental sustainability.

PR, IFPRI3; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies, Foresight and Policy Modeling (FPM); Transformation Strategies

PR, IFPRI3; ISI; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all, Foresight and Policy Modeling (FPM); Transformation Strategies

Non-PR, IFPRI1; SSSP; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance, Development Strategies and Governance (DSG); Transformation Strategies; Foresight and Policy Modeling (FPM), The confrontation in Sudan between the Sudanese Armed Forces (SAF) and Rapid Support Forces (RSF) had persisted for months before escalating into an armed conflict on April 15th, 2023. Besides the severe humanitarian catastrophe, the war has disrupted access to power, water, health services, and banking services while also disrupting access to markets triggering considerable scarcity of goods and services. The conflict has destroyed key infrastructure, constraining domestic and international trade and disrupting production activities and supply chains. In this paper, we utilize satellite data to provide a swift analysis of how the conflict has changed patterns of economic activity, which are bound to have severe implications on food security in Sudan. We also describe how remote sensing and data collection methods can be deployed to monitor economic activities amidst armed conflicts.

Non-PR, IFPRI1; SSSP; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance; EgyptSSP, Development Strategies and Governance (DSG); Foresight and Policy Modeling (FPM); Transformation Strategies, The confrontation in Sudan between the Sudanese Armed Forces (SAF) and Rapid Support Forces (RSF) had persisted for months before escalating into an armed conflict on April 15th, 2023. Besides the severe humanitarian catastrophe, the war has disrupted access to power, water, health services, and banking services while also disrupting access to markets triggering considerable scarcity of goods and services. The conflict has destroyed key infrastructure, constraining domestic and international trade and disrupting production activities and supply chains. In this paper, we utilize satellite data to provide a swift analysis of how the conflict has changed patterns of economic activity, which are bound to have severe implications on food security in Sudan. We also describe how remote sensing and data collection methods can be deployed to monitor economic activities amidst armed conflicts.

Non-PR, IFPRI5; 5 Strengthening Institutions and Governance; 4 Transforming Agricultural and Rural Economies, Foresight and Policy Modeling (FPM); Transformation Strategies, A large proportion of farm households in developing countries face a host of market and production risks that undermine their food security, make their income volatile, and make them hesitant to adopt new technologies or undertake new investments that might increase their long-term productivity and household welfare. Climate-related risks such as floods and droughts remain some of the most pervasive forms of production challenges. Adapting to climate variability and change is essential in safeguarding food security, ensuring economic growth, and advancing climate resilience among smallholder farmers. Recent research has shown that transferring some of the climate-related risks to the insurance market in exchange for a payout can shield the welfare of smallholders from the adverse effects of extreme weather conditions, while agricultural financing can help farmers to acquire and adopt agricultural inputs such as improved seed varieties, fertilizer, pesticides, and herbicides. However, in many developing countries, formal financial markets remain inaccessible to smallholder farmers.

PR, IFPRI3; ISI; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply, Foresight and Policy Modeling (FPM); Transformation Strategies

PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; IFPRI3; ISI, Foresight and Policy Modeling (FPM); Transformation Strategies, Ethiopia’s agriculture-based economy generates highly seasonal outputs, with most production occurring during the long-rains Meher season and a lesser amount during the short-rains Belg season. Despite numerous studies detailing the economic impacts of Meher precipitation, there is relatively little consideration of other seasonal or subseasonal climate impacts in Ethiopia, with most economic models only considering aggregated annual-scale production to simulate the Ethiopian economy. This paper serves to address both seasonal and subseasonal effects of climate using a partial equilibrium agroeconomic model of Ethiopia. First, an annual-scale model is disaggregated into a seasonal time step, corresponding to the two major cropping seasons in Ethiopia. Second, the effect of yield gains from a subseasonal forecast–based planting strategy is considered in the updated model. We found that crop yields and corresponding economic indicators varied widely by season and location, and that subseasonal forecast–based management strategies can, on average, increase gross domestic product (GDP), per capita calorie consumption, and lead toward reduced poverty, warranting the inclusion of seasonal and subseasonal processes in economic models and agricultural planning.

PR, IFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; Foresight and Policy Modeling (FPM); Transformation Strategies, Economic statistics are frequently produced at an administrative level such as the sub-national division. However, these measures may lack sufficient local variation in the economic activities to analyze local economic development patterns and the exposure to natural hazards. Agriculture GDP is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend on their livelihoods that provide a key source of income for the entire household (FAO, 2021). Through a data fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global gridded dataset at approximately 10 x 10 kilometers using satellite-derived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP is an estimated US$432 billion of agricultural GDP circa 2010, where nearly 1.2 billion people live. The data are available on the World Bank Development Data Hub (DOI: http://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022).

PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; IFPRI3; ISI; DCA, Foresight and Policy Modeling (FPM); Transformation Strategies, Ratoon rice has emerged as a promising rice cropping system to improve grain production and reduce labor costs compared with traditional single/double rice in China. However, the potential planting areas of ratoon rice in China remain unclear. This research investigated the potential northern limits and promotion extent of ratoon rice in China by considering its climatic suitability based on the optimized maximum entropy (MaxEnt) model as well as terrain and land use conditions. The MaxEnt model derived by all environmental variables yielded a good performance, with average AUC (area under the curve) and TSS (true skill statistic) over the validation dataset of 0.940 and 0.825, respectively. The comparison with field samples and previous studies revealed the reliability of the derived potential promotion areas. Potential northern limits contained a closed curve surrounding the Sichuan Basin, and the other curve ran from Yunnan Province to Jiangsu Province. Safe promotion areas of ratoon rice in China were 472,003 km2, mainly located in Sichuan, Hubei, Guangxi and Hunan. Risky promotion areas were 74,150 km2, which were dominant in Henan, Anhui and Yunnan. Our study provides crucial information for rice planting pattern adjustment to alleviate national food insecurity caused by the loss of double rice.

PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; IFPRI3; ISI, CPA; EPTD, The severity of the climate challenge requires a change in the climate response, from an incremental to a more far-reaching and radical transformative one. There is also a need to avoid maladaptation whereby responses to climate risk inadvertently reinforce vulnerability, exposure and risk for some sections of society. Innovative technological interventions are critical but enabling social, institutional and governance factors are the actual drivers of the transformative process. Bringing about this transformation requires inter- and transdisciplinary approaches, and the embracing of social equity. In this Perspective, we unpack what this means for agricultural research and, based on our collective experience, we map out a research agenda that weaves different research components into a holistic and transformative one. We do not offer best practice, but rather reflections on how agricultural research can more readily contribute to transformative adaptation, along with the personal and practical challenges of designing and implementing such an agenda.

PR, IFPRI3; 5 Strengthening Institutions and Governance, EPTD, We are pleased to announce the inauguration of Crop and Environment, an international peer-reviewed, open-access journal published by Elsevier in partnership with Huazhong Agricultural University, China. Crop and Environment aims to provide a forum for information exchange on innovation, research, and extension related to crop production science, environmental effects on crop production, and the impact of crop production on the environment.

PR, IFPRI3; 1 Fostering Climate-Resilient and Sustainable Food Supply; DCA; ISI, EPTD, Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.

PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; IFPRI3; ISI; DCA, EPTD, Excessive anthropogenic nitrogen (N) inputs to the biosphere have disrupted the global nitrogen cycle. To better quantify the spatial and temporal patterns of anthropogenic N inputs, assess their impacts on the biogeochemical cycles of the planet and the living organisms, and improve nitrogen use efficiency (NUE) for sustainable development, we have developed a comprehensive and synthetic dataset for reconstructing the History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere. The HaNi dataset takes advantage of different data sources in a spatiotemporally consistent way to generate a set of high-resolution gridded N input products from the preindustrial period to the present (1860–2019). The HaNi dataset includes annual rates of synthetic N fertilizer, manure application/deposition, and atmospheric N deposition on cropland, pasture, and rangeland at a spatial resolution of 5 arcmin × 5 arcmin. Specifically, the N inputs are categorized, according to the N forms and land uses, into 10 types: (1) -N fertilizer applied to cropland, (2) NO-N fertilizer applied to cropland, (3) -N fertilizer applied to pasture, (4) NO-N fertilizer applied to pasture, (5) manure N application on cropland, (6) manure N application on pasture, (7) manure N deposition on pasture, (8) manure N deposition on rangeland, (9) NHx-N deposition, and (10) NOy-N deposition. The total anthropogenic N (TN) inputs to global terrestrial ecosystems increased from 29.05 Tg N yr−1 in the 1860s to 267.23 Tg N yr−1 in the 2010s, with the dominant N source changing from atmospheric N deposition (before the 1900s) to manure N (in the 1910s–2000s) and then to synthetic fertilizer in the 2010s. The proportion of synthetic -N in fertilizer input increased from 64 % in the 1960s to 90 % in the 2010s, while synthetic NO-N fertilizer decreased from 36 % in the 1960s to 10 % in the 2010s. Hotspots of TN inputs shifted from Europe and North America to East and South Asia during the 1960s–2010s. Such

Non-PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all; 5 Strengthening Institutions and Governance; 4 Transforming Agricultural and Rural Economies; IFPRI5, EPTD, Farming Systems Analysis (FSA) is commonly used in CGIAR to assess ‘what works where, and for who?’. The findings are used to prioritize tailored/context-specific interventions and target investments for greater impact. An assessment of seven new CGIAR initiatives showed us that FSA is employed in a similar way across these initiatives, open ing up avenues for strong collaboration and sharing of data, methods and results to achieve better synergies than we have done in the past.

PR, IFPRI3; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; Capacity Strengthening, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; Advancing Research on Nutrition and Agriculture (ARENA); ISI; CRP4; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all; G Cross-cutting gender theme, DSGD; EPTD; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), The growing threat that climate shocks pose to food, water and nutrition security makes understanding the linkages between climate and nutrition increasingly urgent. The article demonstrates an empirical connection between rising temperatures, cereal yields, and poorer growth outcomes for children—the first time such a link has been established between climate shocks, agricultural productivity, and health. The authors find that extreme temperature shocks can severely reduce maize yields, and that lower yields in the season prior to birth are a strong predictor of reduced height in later years, especially among boys. They also show that lower maize yields are predictive of lower body mass among women but not predictive of diarrhea or fever incidence in children. Taken together, these results suggest that maternal malnutrition during pregnancy is a key pathway linking heat shocks and agricultural production to subsequent child health. The findings have important implications for policymakers responding to climate impacts, who must weigh whether to focus more on agricultural interventions and social protection or on public health interventions. Our research suggests the first approach could be particularly important in rural Africa.

PR, IFPRI3; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Spatially explicit global cropping system data products, which provide critical information on harvested areas, crop yields, other management variables, are imperative to tackle current grand challenges such as global food security and climate change. These cropping system datasets are also very useful for researchers as they can support various scientific analyses in research projects. Yet, effectively searching, navigating, and fully understanding various global datasets can be a daunting task for researchers and policy analysts. In this review, we first compare a few selected global data products, which use crop census and statistical data as the main data source, and identify key problems and challenges of the global crop mapping such as data accuracy and consistency. We then pointed out the future perspectives and directions in further improving the global cropping data products. Collective mechanisms and efforts with the support of open-access data hosting platforms, standard protocols, and consistent financial support are necessary to produce high-quality datasets for researchers, practitioners, and policymakers. Moreover, machine learning and data fusion approaches can also be further explored in future mapping exercises.

PR, IFPRI3; CRP5; 1 Fostering Climate-Resilient and Sustainable Food Supply; Feed the Future Innovation Laboratory for Small-Scale Irrigation; ISI; IFPRIOA, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE), Sub-Saharan Africa has long been beset with food insecurity. Investment in small-scale irrigation may provide a solution to address the challenge by extending crop production into the dry season. We present an agent-based modeling system to assess the potential of dry-season small-irrigation development in Sub-Saharan Africa with an application to Ethiopia. We identify significant potential for investing in dry-season small-scale irrigation in Ethiopia and map geographic domains with highest investment opportunities.

PR, IFPRI3; ISI; DCA; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Paddy rice agriculture plays an important role in food security and has a considerable influence on natural systems. In the context of climate change, understanding the nature and drivers of shifts in the northern limit of paddy rice (NLPR) is crucial for adaptation strategies and food security. However, quantitative studies on the effect of climate change on paddy rice distribution shifts have not been well performed. Here, we mapped the NLPR in China using Landsat imagery from 1984 to 2013, analyzed the latitudinal and elevational dynamics of the NLPR using Fishnet analysis, and explored the factors driving the changes in rice area across the NLPR regions using a linear regression model. Our results show that between 1984 and 2013, the NLPR shifted 24.93 km northward (the greatest movement was 88.01 km occurring at approximately 133° E) and elevational limits increased by 39.15 m (the greatest movement was 117.08 m occurring at approximately 129° E). While socioeconomic factors (e.g. benefits, policies, irrigation, and mulch) played significant roles in rice area changes, the changes in rice area across the NLPR regions had the strongest positive association with the increase in the previous temperature, indicating that rice cultivation in the NLPR regions has moved to higher latitudes over the 30 year study period to adapt to climate change. Our study highlighted that quantifying the interactions between climate change and crop production systems can facilitate a better understanding of the human responses to changes in the growing conditions in the face of climate change and ensuring regional and global food security.

PR, IFPRI3; ISI; DCA; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; DCA; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Non-PR, IFPRI1; CRP4; Advancing Research on Nutrition and Agriculture (ARENA); 2 Promoting Healthy Diets and Nutrition for all; 4 Transforming Agricultural and Rural Economies, DSGD; EPTD; A4NH, CGIAR Research Programs on Agriculture for Nutrition and Health (A4NH), Supply chains for nutritious (“protective”) foods in Africa south of the Sahara are often poorly developed, especially for perishable crops that are vulnerable to wastage. We used LSMS-ISA surveys and geographic information system (GIS) data to explore which factors predict production patterns of four protective food crops (pulses, nuts and seeds, vegetables, and fruits) relative to cereals and starchy roots and tubers (grouped under staples) in Ethiopia, Malawi, Tanzania, and Uganda, with a focus on potential inter-ventions to improve production, trade, and consumption of these foods. Plot-level irrigation adoption is the strongest predictor of fruit and vegetable production, along with precipitation, suggesting that water availability is a major precondition for pro-ducing these protective foods. In contrast, pulses and nuts and seeds can be grown in drier and warmer conditions. Better market access is also associated with higher production of fruits and vegetables, but the association is weaker than that of water access. Investing in and expanding irrigation-especially small-scale irrigation-has strong poten-tial to yield high returns in East Africa, especially for poor households that cannot afford to invest in capital-intensive irrigation technologies. Irrigation investments may need to be complemented by investments in roads, rural elec-trification, and cold storage chains to promote efficiency of postharvest supply chains and reduce marketing costs.

Non-PR, IFPRI5; CRP5, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE), Climate change will have an impact on natural resources, water being one of them, affecting the availability of water including increasing the intensity of floods and droughts. It is also a known fact that the impact of natural hazards is felt more in developing countries compared to developed countries and especially by vulnerable and marginalised groups within communities. Such groups are more at risk due to their greater exposure (e.g. cultivation on low-lying land) and poor capacity to adapt (e.g. less access to irrigation and financial assets- poor literacy and agency) and hence lower resilience. This is compounded by the fact that financial support from the government is not readily available in many developing countries for all losses.

PR, IFPRI3; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; Capacity Strengthening, MTID; EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, Forecasting crop yields is becoming increasingly important under the current context in which food security needs to be ensured despite the challenges brought by climate change, an expanding world population accompanied by rising incomes, increasing soil erosion, and decreasing water resources. Temperature, radiation, water availability and other environmental conditions influence crop growth, development, and final grain yield in a complex nonlinear manner. Machine learning (ML) techniques, and deep learning (DL) methods in particular, can account for such nonlinear relations between yield and its covariates. However, they typically lack transparency and interpretability, since the way the predictions are derived is not directly evident. Yet, in the context of yield forecasting, understanding which are the underlying factors behind both a predicted loss or gain is of great relevance. Here, we explore how to benefit from the increased predictive performance of DL methods while maintaining the ability to interpret how the models achieve their results. To do so, we applied a deep neural network to multivariate time series of vegetation and meteorological data to estimate the wheat yield in the Indian Wheat Belt. Then, we visualized and analyzed the features and yield drivers learned by the model with the use of regression activation maps. The DL model outperformed other tested models (ridge regression and random forest) and facilitated the interpretation of variables and processes that lead to yield variability. The learned features were mostly related to the length of the growing season, and temperature and light conditions during this time. For example, our results showed that high yields in 2012 were associated with low temperatures accompanied by sunny conditions during the growing period. The proposed methodology can be used for other crops and regions in order to facilitate application of DL models in agriculture.

PR, IFPRI3; ISI; DCA; CRP2; CRP5; 1 Fostering Climate-Resilient and Sustainable Food Supply; Capacity Strengthening, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Water, Land and Ecosystems (WLE), Agrochemicals are overused in China. One strategy to reduce agrochemical use is to increase farm size because of the potential effect of economy of scale. Existing studies at a micro scale present mixed and often conflicting results on the relationship between agrochemical use and farm size. This study aimed to assess that relationship from a macro perspective using an aggregated panel dataset in 30 provinces in China from 2009 to 2016. The empirical results confirm the existence of both economy and diseconomy of scale effects on agrochemical use in China. The agrochemical application rates decreased as the proportion of farms between 0.667 and 2 ha increased. The diseconomy of scale existed when significantly larger farms, such as the farms larger than 3.34 ha, continued to emerge. Given the fact that 78.6% of farms are under 0.667 ha in China, our results suggest that the reduction strategy based on only expanding farm size might achieve some initial success in reducing agrochemical use, but the effect would fade away and be reversed as significantly large farms continue to emerge. These results have significant policy implications as China is proactively developing and implementing various policies and strategies to modernize its agriculture toward achieving its sustainability goals.

PR, IFPRI3; ISI; DCA; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; Capacity Strengthening, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; DCA; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; PIM, CGIAR Platform for Big Data in Agriculture (Big Data); CGIAR Research Program on Policies, Institutions, and Markets (PIM), Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/, last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: https://doi.org/10.7910/DVN/PRFF8V; IFPRI, 2019).

PR, IFPRI3; CRP2; SATISFy; 1 Fostering Climate-Resilient and Sustainable Food Supply; Capacity Strengthening, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; ISI; DCA; CRP5; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE), Groundwater (GW) overexploitation is a critical issue in North China with large GW level declines resulting in urban water scarcity, unsustainable agricultural production, and adverse ecological impacts. One approach to addressing GW depletion was to transport water from the humid south. However, impacts of water diversion on GW remained largely unknown. Here, we show impacts of the central South-to-North Water Diversion on GW storage recovery in Beijing within the context of climate variability and other policies. Water diverted to Beijing reduces cumulative GW depletion by ~3.6 km3, accounting for 40% of total GW storage recovery during 2006–2018. Increased precipitation contributes similar volumes to GW storage recovery of ~2.7 km3 (30%) along with policies on reduced irrigation (~2.8 km3, 30%). This recovery is projected to continue in the coming decade. Engineering approaches, such as water diversions, will increasingly be required to move towards sustainable water management.

PR, IFPRI3; DCA; CRP2; CRP5; Capacity Strengthening; 1 Fostering Climate-Resilient and Sustainable Food Supply; ISI, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Water, Land and Ecosystems (WLE), Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of the cropland area, which is independent of training samples. First, cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets. Secondly, statistics are allocated adaptively to the pixels with higher agreement scores until the cumulative cropland area is close to the statistics. The multilevel allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a 500 m spatial resolution for circa 2010. The accuracy assessments show that the synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics. The synergy cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map has been used as an essential input to the Spatial Production Allocation Model (SPAM) for producing the global dataset of agricultural production for circa 2010, which is described in the second part of the two-paper series.

PR, IFPRI3; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply; Capacity Strengthening; ISI, EPTD, CGIAR Platform for Big Data in Agriculture (Big Data)

PR, IFPRI3; Feed the Future Innovation Laboratory for Small-Scale Irrigation; ISI; CRP5; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE), Water resource development opens up opportunities for improving smallholder farmer livelihoods in sub-Saharan Africa; however, implementation of water resource interventions to ensure sustainability hinges on the availability of sufficient quantity and quality data for monitoring, analysis and planning. Such data is often acquired through instrumentation of water resources (e.g. stream flow monitoring) or the use of hydrological models. In sub-Saharan Africa, data scarcity has limited the ability to monitor and make appropriate decisions for water resource allocation and use. Data derived from remote sensing has been considered a viable option to fill this gap; however, there is limited research in the region that evaluate the quality of the remotely sensed based datasets. This study evaluated actual evapotranspiration (AET) estimates derived from Advanced Very High Resolution Radiometer (AVHRR AET) images and Moderate Resolution Imaging Spectrometer (MOD16 AET) images using estimates from a grid-based Soil and Water Assessment Tool (SWAT). The SWAT model was set up for the entire country of Ethiopia, and calibrated and validated using observed streamflow at several meso-scale watersheds in which satisfactory model performance was obtained. AET estimates from the calibrated and validated SWAT model were then used to evaluate remotely sensed based AET for three landscapes. The AVHRR AET better agreed with the SWAT-simulated AET than the MOD16 AET, although the AVHRR AET overestimated the SWAT-simulated AET in all of the landscapes. Both remotely sensed AET products showed better agreement with the SWAT-simulated AET over agriculture dominated landscapes compared to grassland and forest dominated landscapes. The findings of the study suggest that remotely sensed based AET may help to fine-tune hydrological models in agricultural landscapes in data-scarce regions to improve studies on the impacts of water management interventions aiming to ensure environmental sustaina

PR, IFPRI3; ISI; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies, EPTD

PR, IFPRI3; DCA; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD

PR, IFPRI3; ISI; DCA; Capacity Strengthening; 4 Transforming Agricultural and Rural Economies, EPTD

PR, IFPRI3; ISI; CRP5; Capacity Strengthening; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE)

PR, IFPRI3; ISI; CRP5; Capacity Strengthening; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE), Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to differe

PR, IFPRI3; CRP5; Capacity Strengthening; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE)

Non-PR, IFPRI5; CRP5; Mapping Solar Irrigation Potential; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD, CGIAR Research Program on Water, Land and Ecosystems (WLE), Sub -Saharan Africa has long been beset with food insecurity. Agriculture in the region is predominantly rainfed, which makes the sector highly vulnerable to climate variability. Irrigation is considered as a promising option to boost agricultural production and enhance agricultural resilience. At the same time, Sub-Saharan African countries also suffer from energy poverty. Lack of access to energy services limits the expansion of irrigated agriculture. A joint irrigation-energy planning framework is developed to estimate how much of the economic potential irrigated area could be powered with standalone solar photovoltaic (PV) energy. The modeling framework first compares the costs of motorized irrigation technology under solar and alternative energy solutions (e.g. diesel). The information generated from the comparative analysis informs power source selection in a model that simulates future pathways of irrigation expansion subject to water availability and market opportunities for irrigated crops. We present the joint irrigation-energy planning framework and a first set of results focusing on groundwater-fed irrigation in Sub-Saharan Africa.