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Introduction: The COVID-19 pandemic lockdowns (people confined to home, with movement restrictions) presented an external shock to livelihoods and food systems worldwide, most severely affecting vulnerable households in low-income countries. While evidence is available regarding how COVID-19 generally affected low-income countries, the specific dynamics of local food-system responses and sustainably-certified coffee farm households has not been examined, despite them being usually deemed to be more resilient to shocks. This research examines how local food systems in Honduras changed during lockdowns, how certified coffee households coped with the shock, especially food insecurity, and the potential role of coffee cooperatives in increasing households’ resilience under future shocks and stressors. Methods: We applied a mixed-methods approach that combined a structured household survey with semi-structured qualitative interviews with 91 households, 6 cooperative representatives, and 18 food-system representatives. Results: We found that coffee-income-dependent households experienced greater food insecurity during lockdown than coffee households with diversified incomes. Before lockdown the local food system was highly dependent on external fresh food from outside the state. Food suppliers changed altered fresh-food procurement strategies, mostly to maintain fresh-food availability at the beginning of the pandemic. However, more than half the interviewed households lacked confidence regarding food security, amid rising food prices and local shortages. Certified coffee cooperatives supported their members by providing food assistance, cash transfers, and credit. Discussion: Some of these strategies are difficult to maintain where crises are recurrent and that may render households more vulnerable to future extreme events. Rather, coffee cooperatives could diversify and support their members in growing and marketing additional food crops. This could be a key approach fo

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies –WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995–2014), 2030 (2020–2039), and 2050 (2040–2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights a

Efforts toward the development of common bean varieties that can perform better under the drought conditions in Eastern Africa are constrained by significant genotype–environment–management (G × E × M) interactions. To address this, an attempt was made previously to assess drought stress for Eastern Africa using the Target Population of Environments (TPE) approach, albeit by using a single cultivar. Here, we extend earlier TPE work with a larger sample of representative genotypes from East Africa including commercial and breeding lines. By using a larger genotype pool, we are able to (1) assess the extent to which TPEs identified vary across genotypes (i.e., generality of the TPEs) and (2) identify best-performing genotypes for the various environments identified. To perform the analyses, we used the CROPGRO-DRYBEAN cropping system model available in the Decision Support System for Agrotechnology Transfer (DSSAT) v4.7 calibrated and evaluated from field trials in Africa and Colombia with 10 genotypes. Our findings indicate that despite some level of genotype-related variation, the dominant drought stress patterns, and their priorities—drought stress-free conditions remain the dominant TPE, followed by terminal drought stress—remain unaltered. Moreover, we find that it is possible to reduce the extent and intensity of drought stress in various TPEs by adequately targeting genotypes from the pool considered herein. Importantly, the genotype characteristics for stress-free, extreme terminal drought and moderate terminal drought stress are significantly different, confirming a differentiated breeding strategy is required between the TPEs. The findings from this study are useful for the modernization of the bean breeding programs in Africa.

Societal Impact Statement: As the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation. Summary: There is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments. Meeting this challenge will require a clear understanding of how existing germplasm behave across Genotype ∗ Environment (G ∗ E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accurately simulate genotypic variation in key traits across target population of environments. Accordingly, we develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learning models and find that the best performing models display high levels of accuracy across G ∗ E. We advance understanding of the environmental drivers of flowering time in equatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models.

The dataset described includes second bias corrected daily weather data using ground-based weather data and simulated crop growth data under potential and water-limited potential conditions for 109 sites across ten countries in SSA including Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia. Whereas the weather data covers all sites, the simulated growth data for the four crops (maize, millet, sorghum, wheat) covers subsets of the sites depending on the geographic distribution of cultivation areas for the given crop. The stations can represent 65% of the harvested area for maize, 90% for millet, 83% for sorghum, and 59% for wheat the four crops together can represent 72% of the total harvested area in those countries. The data covers three time periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059).

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamical models to predict droughts at 8-month lead-times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross-silo collaborations.

The growing complexity of the relationship between climate information and agricultural decision-making necessitates the development of relevant and timely climate services for farmers. These services can effectively support risk management strategies in agriculture by fostering a comprehensive understanding of the intricacies involved in farmer decision-making dynamics. This paper addresses this critical gap by analyzing the drivers influencing decision-making processes that shape adaptation strategies for staple grain and coffee farming systems in Central America. The study answers the following research questions: (i) Does the mind map tool effectively provide a holistic understanding of farmers' decision-making processes? (ii) How do Central American farmers make decisions within their farm systems at multiple timescales? (iii) Which climate factors trigger these decisions? Employing a combination of systematic literature review and a case study in Honduras, the study identifies 13 critical decisions farmers make throughout their crop cycle and their respective triggers. These decisions were grouped into three clusters (production, household, and environmental) and classified into lead-time categories (operational, tactical, and strategic). Findings reveal that farmers base their decisions regarding future climate expectations on their traditional knowledge, religious dates, and memories of recent past seasons' rainfall patterns, and that one of the most significant factors influencing farmers' decisions is food security shortages resulting from extreme events. For example, recent mid-summer droughts have led farmers to prioritize sowing beans over maize in the Primera season, while during the Postrera season, they face challenges due to excess rainfall and the hurricane season. We conclude that the mind map tool developed in this paper provides an effective and appropriate method and that the variation in farmers' decision-making complexity across systems and l

This perspective describes the opportunities and challenges of data-driven approaches for crop diversity management (genebanks and breeding) in the context of agricultural research for sustainable development in the Global South. Data-driven approaches build on larger volumes of data and flexible analyses that link different datasets across domains and disciplines. This can lead to more information-rich management of crop diversity, which can address the complex interactions between crop diversity, production environments, and socioeconomic heterogeneity and help to deliver more suitable portfolios of crop diversity to users with highly diverse demands. We describe recent efforts that illustrate the potential of data-driven approaches for crop diversity management. A continued investment in this area should fill remaining gaps and seize opportunities, including i) supporting genebanks to play a more active role in linking with farmers using data-driven approaches; ii) designing low-cost, appropriate technologies for phenotyping; iii) generating more and better gender and socioeconomic data; iv) designing information products to facilitate decision-making; and v) building more capacity in data science. Broad, well-coordinated policies and investments are needed to avoid fragmentation of such capacities and achieve coherence between domains and disciplines so that crop diversity management systems can become more effective in delivering benefits to farmers, consumers, and other users of crop diversity.