Jawoo Koo’s research while affiliated with International Food Policy Research Institute and other places

Climate change poses a greater threat for more exposed and vulnerable countries, communities and social groups. People whose livelihood depends on the agriculture and food sector, especially in low- and middle-income countries (LMICs), face significant risk. In contexts with gendered roles in agri-food systems or where structural constraints to gender equality underlie unequal access to resources and services and constrain women’s agency, local climate hazards and stressors, such as droughts, floods, or shortened crop-growing seasons, tend to negatively affect women more than men and women’s adaptive capacities tend to be more restrained than men’s. Transformation toward just and sustainable agri-food systems in the face of climate change will not only depend on reducing but also on averting aggravated gender inequality in agri-food systems. In this paper, we developed and applied an accessible and versatile methodology to identify and map localities where climate change poses high risk especially for women in agri-food systems because of gendered exposure and vulnerability. We label these localities climate-agriculture-gender inequality hotspots. Applying our methodology to LMICs reveals that the countries at highest risk are majorly situated in Africa and Asia. Applying our methodology for agricultural activity-specific hotspot subnational areas to four focus countries, Mali, Zambia, Pakistan and Bangladesh, for instance, identifies a cluster of districts in Dhaka and Mymensingh divisions in Bangladesh as a hotspot for rice. The relevance and urgency of identifying localities where climate change hits agri-food systems hardest and is likely to negatively affect population groups or sectors that are particularly vulnerable is increasingly acknowledged in the literature and, in the spirit of leaving no one behind, in climate and development policy arenas. Hotspot maps can guide the allocation of scarce resources to most-at-risk populations. The climate-agriculture-gender inequality hotspot maps show where women involved in agri-food systems are at high climate risk while signaling that reducing this risk requires addressing the structural barriers to gender equality.

The number of people living in rural areas of low and middle-income countries is projected to increase in the coming decades. It is in the rural areas of these countries where a large majority of the world’s extreme poor reside. The livelihoods of two to three billion rural people depend on small farms. These small farms are responsible for the production and supply of a large portion of the calories feeding low- and middle-income countries. Small farms are also preservers of crops and associated biodiversity and with the right incentives can contribute to land stewardship. Small farms are diverse, and, hence, so are their associated challenges. We categorize small farms as commercial farms, small farms in transition and subsistence-oriented farms and highlight evidence-based innovations for the sustainable transformation of each type of small farm. Broadly, small farms face high transaction costs, lack collective action, and experience coordination failure in production and marketing. Lack of market access is also a major challenge. Investments in infrastructure, including those that support access to digital technologies, can improve farmers’ access to markets and incentives as well as foster growth in the midstream segments of the value chain that provide inputs, storage, processing, and logistics to small farms. Rural Non-Farm Employment (RNFE) is increasingly the main source of income for most small farmers and provides them with a risk diversification strategy and cash, both to purchase food and for farm investments to raise productivity, expand commercial activities, and produce higher-value products. Public investments and policies that facilitate growth of the agrifood system must pay more attention to creating enabling environments for the development of RNFE and strengthening the synergy between agriculture and RNFE in rural areas.

Women are at a particular disadvantage by the stress that climate change poses on food systems in low- and middle-income countries (LMICs) as their adaptive capacity is hampered by unequal access to resources and services and constraints to their agency. This paper proposes a methodology to identify climate–agriculture–gender inequality hotspot LMICs and subnational areas where climate hazards converge with large concentrations of women participating in food systems and social conditions that disadvantage women. The methodology applies data reduction techniques on publicly available data to compute a hotspot index that forms the basis for ranking and mapping hotspots. Applying the methodology illustrates the hottest of 87 LMICs are located in Africa and identifies crop-specific hotspot subnational areas in four focus countries. Identifying hotspots can enable targeting populations at highest risk and render future efforts to support women’s agency for climate resilience and avert increasing gender inequalities more effective.

The CGIAR Platform for Big Data in Agriculture was a cross-cutting program of the global CGIAR consortium. The Platform tended to data standards and data sharing, digital innovation strategy and technology transfer, and research into the intersection of digital technologies and agricultural development in emerging regions. https://bigdata.cgiar.org/

CONTEXT The accessibility and availability of forages is a common concern in crop-livestock systems in West Africa; however, options to increase forage production may entail trade-offs within the farm system that can be challenging to quantify explicitly. OBJECTIVE This study examined how maize (Zea mays L.) leaf stripping affected maize and sheep productivity and associated labour requirements, and farm system trade-offs and synergies in four communities in the Northern Region of Ghana. METHODS Maize leaf stripping involved removing almost senesced leaves from maize plants below the cob level at silking. We combined data from three sources: on-farm maize trials with 28 farmers from two seasons (2017 and 2018), on-farm sheep feeding trials where the pasture-based diets of weaner sheep were supplemented with stripped maize leaves fed in pens (conducted in 2019), and farm survey data from 117 households (conducted in 2014), seven of which were in the on-farm maize trials and owned sheep. We examined the trial data using linear mixed-effects models. RESULTS AND CONCLUSIONS Maize leaf stripping had no significant effect on maize grain yield but had a significant positive effect on maize forage protein yield from leaf and stover. Offering maize leaves to weaner sheep had a significant positive effect on average daily liveweight gain, estimated marginal mean was 29.3 g with maize leaves and −10.9 g without maize leaves. For the maize-sheep systems of the seven households, non-inferential statistics suggested that on average maize leaf stripping reduced total maize grain production by 12% (range −46 to 38) and increased maize forage protein production from leaf and stover by 90% (range −16 to 298). Stripping the maize leaves from one hectare of land took an extra 34 h (range 27 to 42) of labour, which was counterbalanced by reduced labour time for grazing as sheep were fed the maize leaves in pens. For the 117 farmers, heterogeneity in maize areas planted and livestock numbers resulted in heterogeneous production and labour effects of maize leaf stripping. Farmers qualitatively described how maize leaf stripping released labour so children could spend more time at school rather than shepherding. SIGNIFICANCE We quantified in northern Ghana how maize leaf stripping altered crop and livestock productivity and associated trade-offs and synergies in the farm system, including labour. Changes in crop management often have implications beyond the crop's field and examining these implications can provide insights into the suitability of alternative farm management options.

Small-scale irrigation has been identified as a potential adaptation strategy for climate change and boosting food security and livelihoods in dry regions. This study presents the analysis of the potential adoption of small-scale irrigation in two West African countries (Mali and Niger) by using a spatially explicit analytical framework. It underscores the need for strategically investing in the management of ground and surface water resources for the development of small-scale irrigation systems in the two countries. The study implemented an agent-based modeling technique to simulate small-scale irrigation decisions at the district and national level. The results revealed that, while small-scale irrigation can increase crop productivity in both countries, its adoption may be constrained by water scarcity and tensions in water allocation. Strategic water resource development plans should be established to ensure efficient and sustainable irrigation schemes, especially for areas with high potential profitability.

Most business-as-usual scenarios for farming under changing climate regimes project that the agriculture sector will be significantly impacted from increased temperatures and shifting precipitation patterns. Perhaps ironically, agricultural production contributes substantially to the problem with yearly greenhouse gas (GHG) emissions of about 11% of total anthropogenic GHG emissions, not including land use change. It is partly because of this tension that Climate Smart Agriculture (CSA) has attracted interest given its promise to increase agricultural productivity under a changing climate while reducing emissions. Considerable resources have been mobilized to promote CSA globally even though the potential effects of its widespread adoption have not yet been studied. Here we show that a subset of agronomic practices that are often included under the rubric of CSA can contribute to increasing agricultural production under unfavorable climate regimes while contributing to the reduction of GHG. However, for CSA to make a significant impact important investments and coordination are required and its principles must be implemented widely across the entire sector.

Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains ‘to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?’. Here, we address this question by critically reviewing how model‐based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better‐targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts.

Food security has been and will continue to be a major challenge in Ethiopia. The country's smallholder, rainfed agriculture renders its food production system extremely vulnerable to climate variability and extremes. In this study, we investigate the impact of past climate variability and change on the yields of five major cereal crops in Ethiopia—barley, maize, millet, sorghum, and wheat—during the period 1979–2014 using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model is calibrated at both the site and agroecological-zone scales. At the sites studied, the model results suggest that climate in the past four decades may have contributed to an increasing trend in maize yield, a decreasing trend in wheat yield, and no clear trend in the yields of barley and millet; cereal crop yield is positively correlated with growing season solar radiation and temperature, but negatively correlated with growing season precipitation. For modeled cereal crops across the nation during the study period, yield in western Ethiopia is positively correlated with solar radiation and day time temperature; in the eastern and southeastern Ethiopia where water is a limiting factor for growth, yield is positively correlated with precipitation but negatively correlated with solar radiation and both day time and night time temperature. The national average of simulated yields of most crops (except maize) showed an overall decreasing (although not statistically significant) trend induced by past climate variability and changes. Over a large portion of the highly productive areas where there is a negative correlation between yield and temperature, yield is simulated to have significantly decreased over the past four decades, an indication of adverse climate impact in the past and potential food security concern in the future.