Agriculture

Ag GeoSpatial Data Explorer (AgGeo): A Web-Based Tool for exploring Agricultural, Geospatial, and Climate Data

Posted on December 2, 2025December 2, 2025

In 2023, approximately 282 million people across 59 countries faced severe food insecurity, an increase of 24 million from the previous year. Sub-Saharan Africa (SSA) bears a disproportionate burden of this crisis. Climate change is already reducing cereal production across the region, with projections showing a further 20% decline by 2030. Food import bills in Southern Africa surged from $35 billion to $43 billion between 2019 and 2022. Africa is off track to meet the Sustainable Development Goal of ending hunger by 2030 and the Malabo Commitment to reduce poverty by half by 2025. Climate variability, rising temperatures, shifting rainfall patterns, and increasing frequency of droughts and floods, is fundamentally reshaping agricultural systems. Responding to climate shocks requires evidence-based policy, yet a significant gap persists between the availability of climate data and its practical usability for agricultural policy analysis.

The Data Access Challenge

High-quality climate datasets are increasingly available. The Climate Hazards Center (CHC) provides CHIRPS precipitation and CHIRTS temperature data at high resolution. NASA and other agencies offer temperature and vegetation data. These datasets are often free and well-documented, but “available” does not mean “accessible.”

Desktop software like GeoCLIM requires users to install QGIS, download several gigabytes of raw data, and have GIS expertise. Web-based alternatives like the Early Warning Explorer eliminate software installation but typically limit users to rectangular map regions and single-year time periods. Tableau needs pre-constructed datasets requiring massive storage. Most web tools provide only basic statistics rather than derived indicators that help identify drought-prone areas or understand seasonal dry spells. When researchers cannot easily access climate data, policy-relevant analyses either may not happen or rely on national-level averages that obscure local variation critical for small-scale producer (SSP) agriculture. The Ag GeoSpatial Data Explorer (AgGeo) addresses these barriers through a web interface that processes data on-demand, follows actual administrative boundaries, and allows custom seasonal definitions matching agricultural calendars.

How the Climate Data Explorer Works

EPAR’s Center on Risk and Inclusion in Food Systems (CRIFS) developed AgGeo to address these challenges. Rather than requiring users to download and process data locally, the platform processes climate data on-demand and delivers results through a web interface. This directly tackles the software installation, bandwidth, and technical expertise barriers.

The platform covers countries across sub-Saharan Africa and South Asia. It is built on public datasets: The CHC’s CHIRPS provides precipitation data over four decades, while GAEZ provides spatial classifications of land use.

Users select their country, layer, and time period through a sidebar interface. The tool offers annual trends, seasonal patterns, monthly details, or quarterly summaries. Users choose between interactive maps (detailed grid values or regional averages with color-coded boundaries) or time series charts that are rendered within seconds. Users can hover over locations for exact values, zoom into regions, compare multiple territories, and download visualizations.

**Figure 1:** Average Annual Rainfall in India in 2020-2024

What the Tool Provides

AgGeo currently offers three layers that help researchers and policymakers understand climate variability and its agricultural implications.

Rainfall volumes track precipitation. Users can calculate total rainfall for any time period, understand typical rainfall intensity through daily averages, and compare across different regions. This helps identify wet and dry seasons, understand year-to-year variations, and spot long-term trends. Researchers can analyze data annually, seasonally, monthly, or quarterly, depending on their specific questions.

Dry days monitor drought patterns by counting days with minimal rainfall. This indicator helps identify drought-prone areas, understand when and where seasonal dry spells occur, and assess water availability during critical growing periods. Knowing how many consecutive dry days occurred during planting or flowering stages reveals much more about potential crop stress than total seasonal rainfall alone. This information helps target drought-resistant varieties or irrigation investments to the areas that need them most.

Agro-ecological zones visualize agricultural potential based on climate, soil, and terrain. The tool shows both current conditions and future projections, helping researchers and policymakers understand which areas are suitable for different crops today and how climate change might shift these zones over time. This supports long-term agricultural planning and adaptation strategies.

Users can explore climate patterns at multiple administrative levels, from entire countries down to individual states, districts, or smaller regions. The ability to compare multiple territories side-by-side helps understand regional differences and identify areas facing similar climate challenges.

**Figure 2**: Agro-ecological Zones of the Ethiopian Region of Oromia

Why This Matters for Policy

Accessible climate data enables critical policy-relevant analysis. National planning offices can map rainfall variability to identify regions requiring different crop strategies or irrigation investments. Understanding spatial patterns helps target interventions effectively.

Program evaluations benefit from climate control variables that distinguish program effects from environmental factors. Food security monitoring reliability increases when analysts can quickly compare current rainfall to historical patterns, identifying regions at risk of production shortfalls. Researchers can identify drought-prone areas and analyze long-term trends to support climate adaptation planning.

**Figure 3**: Number of days with rainfall <1 mm in the Nigerian states of Ebonyi, Kebbi, and Niger during the 2010-2024 growing seasons (March through August)

Upcoming Features

The platform currently provides comprehensive coverage across sub-Saharan Africa and South Asia. Users can generate interactive maps and time series charts, compare multiple territories, and download results in multiple formats.

In the near future, EPAR plans substantial expansion including standardized drought indices, temperature anomalies, growing degree days for crop development tracking, and vegetation health monitoring. Integration plans include adding AgGeo within AgQuery+ and overlaying LSMS-ISA enumeration area coordinates for direct linkage with household survey data.

Getting Started

The Ag GeoSpatial Data Explorer is available at https://agquery.org/aggeo. The interface is designed for users without technical training. Select your parameters, generate visualizations, and download data through your web browser. You can click on the ‘About’ tab in the top-right corner of the app to learn more.

To cite this tool: UW EPAR (2025). Ag GeoSpatial Data Explorer: A platform for constructing and visualizing geospatial indicators for sub-Saharan African and South Asian countries.

EPAR welcomes feedback and suggestions for platform improvements. More information about EPAR’s work on climate and food systems is available at epar.evans.uw.edu.

Blog written by Apurwa Rahulkar and Joaquin Mayorga.

How Well Does Machine Learning Predict Food Insufficiency? A Case Study from Malawi

Posted on October 23, 2025

The World Food Programme estimates (pdf) that approximately 3.5 million Malawians are chronically food insecure. That number rises, and hunger becomes more acute, in the lean season between harvests. Seasonal hunger is further aggravated by limited storage options and extreme weather events, such as the recent weak rains that rendered 25% of the population acutely food insecure. The impacts of crop losses go beyond rural subsistence producers; they also affect urban consumers through the resulting food price shocks.

In a recent article, we asked whether data related to crop production or markets could allow for greater precision in identifying which communities would face food insufficiency, which we define as a substantial proportion of households reporting “running out” of food in each month. Our data come from four waves of the Malawi Integrated Household Survey (IHS). Our goals were to understand whether using publicly available data derived from satellites and geo-coded market food prices with machine learning models could produce more accurate predictions than simpler approaches like classical regression or non-modeled human predictions based on past occurrences. To test the models, we first fit them using information from one or more survey rounds, then tested whether they could classify each community in the next survey round as food sufficient or food insufficient using updated predictor variables.

Our key takeaways were:

Producing accurate forecasts can require multiple years of data and, even in their simplest form, machine learning models still require some expertise
Prices, which reflect multiple factors on the supply and demand side, work as well as weather data
When food insufficiency recurs with some spatial and temporal predictability, machine learning models may not add substantial improvements to overall accuracy, but the distribution of communities predicted as food sufficient or food insufficient varies substantially by model, even at similar accuracy rates.

More details, including considerations for constructing indicators, specifying models and determining relevant predictors, are available in our paper.

1. Evaluating the Comparative Accuracy of Machine Learning Models

Machine learning models can discern subtle patterns in large datasets, but the size requirement can restrict where the models can be effectively used. Using datasets that are too small can lead to low generalizability – fits are highly accurate on the sample used to develop the models, but extrapolations on new data may be weaker.

To evaluate whether machine-learning was performing well given the available public data, we trained ML models on one to three rounds of the survey, testing on the second through fourth survey rounds, and compared them to classical regression and a non-modeled measure of checking the food security situation of the nearest neighboring community in the previous year. In addition to accuracy, we compared rates of false positive predictions (classifying food-sufficient communities as food-insufficient) and false negative predictions (classifying food-insufficient communities as food-sufficient) using two additional metrics: recall and precision. Recall represents the ratio of true positive predictions out of all observations of food insufficiency, and precision represents the ratio of true positive predictions to false positive predictions. Low recall scores indicate a bias toward producing false negative predictions, and low precision scores indicate a bias toward false positive predictions. We found that machine learning models tended to outperform on recall but under-perform on precision compared to the classical approaches and that model accuracy did not approach the simple non-modeled approach until three waves of survey data were used for training, a finding that indicates that although food insufficiency has high recurrence rates, the underlying reasons in a given year may vary.

Figure 1: Comparison of modeling approaches as the amount of training data increases (top to bottom: model 1: built using survey wave 1 and tested on survey wave 2; model 2: built using data from waves 1 and 2 and tested on wave 3; model 3: built using data from waves 1-3 and tested on wave 4). Blue bars represent simpler approaches (logit and LASSO, a minimal machine-learning example), red bars represent two alternative machine learning approaches, and the green bar represents a simple algorithm that uses the value of the nearest community in the previous survey.

2. Comparing Prices to Weather as Leading Indicators

Although the demand for staple crops is relatively stable, prices are affected by international trade, policy, weather, input prices, and other factors that affect supply. Hence, price movements offer a convenient summary of current and forecast changes in the availability of food relative to demand.

Using a primary measure of food affordability (observed maize market price over the prior year), combined with indicators of direction (maize price inflation and overall CPI inflation), we found that the predictions had similar or better accuracy compared to models that used variables that might influence agricultural productivity over the previous growing season, such as precipitation and temperature.

In our datasets, food insufficiency was highest in December, January, and February, typically dropping in March and April. In Wave 4 (2020-2021), March was an unusually severe month due to shocks to production in the previous growing season, and price-based models were slightly better at reacting to that difference than weather-based models, which may have been more reliant on seasonal variations. The price-based models were also more sensitive to the transition back to widespread food insufficiency at the end of the dry season.

One disadvantage of using machine learning compared to classical regression is that the former lack easily interpretable coefficients to assess the relative contributions of each variable. The Shapley Additive Values Framework was developed to help demystify modeling results and shows the effect each variable has on the final predicted value for each data point. Applying the Shapley framework to our fitted models suggested that prices in the past month and inflation in the previous year both had substantial, if mixed influences on predicted values. The most influential variable is the maize price in the previous month, but there is a mixed impact: low maize prices could be either a strong or a weak signal of future food insufficiency depending on the month in which the observation took place, while high maize prices tended to have a generally positive influence on predictions of insufficiency on positive predictions.

3. Recurrence of Food Insufficiency

Despite the weather and price volatility during the observation period, temporal stability, captured in a dummy variable representing the quarter when the observation occurred, was a highly influential factor in the model predictions. A large portion of the observed variation comes from seasonal swings in food availability. Much of the remainder is associated with the drought in southern Malawi, although spikes in flood-associated shocks to production also occur in the third survey round (Figure 2). These features of the agricultural sector in Malawi create a predictable spatio-temporal path for food insufficiency, making non-modeling prediction approaches based on historical occurrences of food insufficiency effective, although differences exist in which communities are flagged as food insufficient depending on the modeling approach being used.

Figure 2: Top: Observed food insufficiency over four survey rounds, derived from recall over the previous twelve months prior to the survey date, and events impacting Malawian agricultural production or overall food sufficiency. Bottom: nominal market maize prices during the survey period.

Conclusion

While model accuracy may be higher in the absence of shocks like flooding and cyclones, historical observations of food insufficiency suggest that insufficiency in one year can arise from the consequences of a poor harvest in the previous year. Therefore, where recurring spatial patterns exist, decision makers could already have the information needed to act. Instead, modeling may offer benefits like increasing the utility of ongoing data collection for extrapolating to the rest of the country or providing visualizations of spatial patterns. The decreasing cost and increasing resolution of spatial data products could allow for detailed analyses to help inform policy-making, but only with frequent collection of training data. In resource-constrained environments, timely interventions based on informed priors may be preferable to gathering more information.

The Data and Policy open-access manuscript is available here.

Blog written by C. Leigh Anderson, Didier Alia, and Andrew Tomes.

When Do Farmers Adopt and Respond to Early Warning Systems?

Posted on April 15, 2025April 16, 2025

At first glance, one might presume the answer to this question to be an emphatic “always”. After all, these systems are designed to shield communities from severe environmental hazards, thereby boosting agricultural productivity, so it would seem that farmers would jump at the chance to use them. In reality, however, even when access isn’t a barrier, the use of Early Warning Systems can vary (Sharafi et al., 2021; Andersson et al., 2020). This raises some interesting questions: What drives some farmers to embrace EWSs? What holds others back? And how can research on climate risk communication help improve the adoption of these life-saving technologies?

In exploring these questions with existing literature, EPAR PhD student Nnenna Ogbonnaya-Orji conducted a Web of Science (WOS) search using the criteria “early warning system” AND farmer AND climate on studies published between January 2015 and March 2025. The majority of studies retrieved were focused on early warning systems pertaining to specific hazards, most notably droughts. Papers were excluded for one or more of the following reasons: either they did not address factors relevant to the uptake of early warning systems or other weather/climate information service; focused on advanced economies, or did not pertain to the early warning of climate-related events or disasters.

Why EWSs Matter to Farmers in Low- and Middle-Income Countries

Climate adaptation is increasingly critical for agriculture, especially rainfed and small-scale producer systems in countries where farming is not just an economic activity but a way of life, woven into social and cultural fabrics. In these regions, farming is often tied to deeply rooted beliefs and traditions. This creates a complex decision-making environment that makes it difficult to promote new information-based technologies. The high uncertainty of climate impacts introduces cognitive biases, which further complicates decisions to adopt new practices and technologies.

Though definitions of EWSs may vary, they all revolve around two main goals: detecting risks early and advising specific actions to reduce impact. Risk in this context is defined as the probability of an adverse event occurring, with its impact contingent on the frequency and intensity of exposure to the event and the capacity to mitigate or adapt to it (IPCC, 2022). Exposure and vulnerability depend on the scope and scale of the particular event, and are shaped by socioeconomic and political factors such as literacy levels, access to infrastructure, government policies and priorities, and trust in institutions – which in turn, influence how warning messages are communicated and the extent to which they prompt action. Therefore, to be effective, EWSs must be regularly updated and customized to fit the changing dynamics of local contexts. A key component of many EWSs are Seasonal Climate Forecasts (SCFs), which estimate rainfall and other important meteorological variables for periods ranging from months to entire seasons Despite increased availability, and proven gains to agricultural yield (e.g. see Lupiya et al., 2024), adoption among farmers is, at times, considered suboptimal (Andersson et al., 2020).

What drives EWS Adoption by Farmers?

Though some progress has been made in recent times, not all countries have functional EWSs. As at 2023, 69 of 100 countries assessed under the Sendai Framework for Disaster Risk Reduction reported having “comprehensive coverage” of local or national dissemination mechanisms for early warning information (WMO, 2023). Notably, none of the 11 Least Developed Countries (LDCs) assessed were included in this number – highlighting wide disparities in EWS access and significant knowledge gaps on the nature of this problem, especially as it pertains to developing countries. In some contexts where this technology is available, some evidence suggests that uptake by farmers is low, due in part, to a range of socio-cultural, demographic, economic, and institutional factors. For example, wealth and socioeconomic status reportedly play a big role in determining which farmers have access to these technologies, meaning that uptake can be limited to those with more resources (Nyoni et al., 2024). Adoption by farmers has also been found to be more likely with higher literacy rates and less likely with increasing age (Kolawole et al., 2018). Relative to other climate smart agricultural practices, information-based interventions such as EWSs are distinct in their primary reliance on effective communication rather than on physical tools or techniques. In Sub Saharan Africa, for example, low uptake of weather and climate services has been linked to limited comprehensibility of forecast information, inappropriate use of language and perceived incompatibility with indigenous practices (Nkiaka et al., 2019). Thus, to dig deeper into what drives EWS adoption, it helps to break down the communication process. Berlo’s (1960) SMCR model (where SMCR denotes the Source, Message, Channel, and Receiver), offers a useful framework for exploring these factors.

The Source of the Message Matters

EWSs are often managed and delivered through a top-down structure, starting from national or regional levels and moving down to local authorities, who then communicate with end-users (Andersson et al., 2020). This structure means that how farmers view the credibility of the government or scientific institutions often influences their trust in these systems. In many low-income countries, farmers rely on traditional sources of weather information, embedded in their cultural frameworks, rather than on scientific sources. For example, a study in North Central Namibia found that while about 50% of households accessed climate information, many still found it insufficient and continued to trust traditional knowledge instead (Gitonga et al., 2020). Promising paths to circumventing this challenge emphasize scientific interdisciplinarity and participatory approaches to the design and delivery of EWSs (Van Ginkel & Biradar, 2021; Walker, 2021; Hermans et al., 2022).

Trust in the Message Itself

It’s not just about who delivers the message, but also about the message’s content, quality, and clarity. Farmers need to feel confident that the information is reliable and relevant. For an EWS to build this confidence, it typically needs to deliver consistently accurate and precise forecasts—a tall order given the high uncertainties inherent in climate modeling. This is complicated as communicating these uncertainties without compromising message credibility is difficult. Furthermore, many low-income countries lack the resources needed for up-to-date methods, further limiting the system’s ability to generate reliable predictions.

Studies also show that how a message is framed can impact its reception in a given context. For instance, Calvel et al. (2020) found that small-scale farmers in the Mangochi and Salima Districts of Malawi adopted and responded to early warning messages when framed as advice pertaining to agricultural practices (what to do) as opposed to weather-related information (when to do it). In a study of coastal communities in Vietnam, Ngo et al. (2022) found that when messages were more concrete, and framed as gains, they led to a stronger risk awareness and intent to act on climate adaptation than abstract- or loss- framed messages. To be effective, they conclude, EWS messages should be clear, culturally relevant, and specific in the actions they ask farmers to take.

Reaching Farmers: The Importance of Communication Channels

Whether it’s traditional media, new digital platforms, or word-of-mouth, the medium of communication is critical to broadening EWS access and uptake. Thus, to be effective, EWS messages must be disseminated via communication channels that are tailored, contextually relevant and accessible. This can be challenging, as in many poor or remote regions, access to these channels remains limited and can vary along gender lines, making it difficult to reach all potential users.

An illustrative case is the flood devastation of rural communities in Delta state, Nigeria despite timely early warnings disseminated via mass media. In a qualitative study, Ebhuoma & Leonard (2021) found that fatigue after a long day of work prevented farmers who owned radios from tuning into local news, while poverty restricted access to television broadcasts, suggesting that more personalized communication e.g. via extension workers may have been more impactful. Using intrahousehold survey data, Ngigi & Muange (2022) report gender variations in access to climate information services and preferences for specific communication channels. Whereas husbands were found to have significantly more access to early warning systems and advisory services on adaptation than their wives, wives were shown to have greater access to weather forecasts than their husbands. Additionally, husbands indicated a greater preference for obtaining climate information services from extension workers, print media, television and local leaders, while wives preferred to obtain such information from their social networks and/or the radio. Other channel-relevant challenges cited in literature include delays in data exchange between agencies and the need for timely, multi-channel dissemination, all of which can affect the salience of EWSs in local communities.

Understanding the Receiver

For EWSs to be impactful, they need to engage with farmers’ perspectives on risk and adaptation. Some factors, like innate cognitive biases, may be harder –if not infeasible – to address. But others, such as perceptions of self-efficacy and adaptive capacity, can be influenced through well-crafted messages. Research shows that with a higher material cost of adaptation (e.g. buying drought-resistant seeds or hiring equipment), discrepancies between plans and actions tend to widen (Sutcliffe et al., 2024). This suggests that when economic constraints prevent adaptation, communication strategies can sometimes help bridge the gap by fostering a sense of control. Farmers may be more likely to act if they feel capable of managing risks despite limited resources.

Bringing It All Together

The SMCR model focusing on the Source, Message, Channel, and Receiver provides a useful framework for dissecting the complex and layered communication environment in which farmers operate. By addressing gaps in trust, message framing, communication channels, and farmers’ perceptions, researchers and policymakers can improve EWS adoption, ultimately helping farmers make more informed, adaptive decisions in a changing climate.

There is no simple answer to why some farmers use and respond to climate information while others do not. Risk perception, often influenced by cognitive biases, remains a central factor in these decisions, but Early Warning Systems (EWSs) can also be optimized to improve uptake. EWS deployment must balance environmental and economic benefits with social realities to be effective in different cultural and economic contexts (Sharafi et al., 2021). Practical barriers, such as limited forecast precision, comprehension challenges, weak infrastructure, and perceived self-efficacy, underscore the need for EWS designs that engage local communities in identifying social priorities (Andersson et al, 2020; Otieno et al., 2024).

Studies suggest the importance of integrating EWSs with indigenous knowledge systems to build trust and foster effective two-way engagement with end-users of warning messages (Fragaszy et al., 2020; Otieno et al.,2024; Funk et al., 2023). Although some studies have looked into ways that practitioners have tried to do this (e.g. see Walker, 2021 and Hermans et al,. 2022 for examples) some questions remain. For instance, how might warning messages be communicated in ways that do not undermine/patronize indigenous belief systems? How can discrepancies which sometimes arise between scientifically derived information and farmers perceptions of climate realities be reconciled? (Solano-Hernandez et al., 2020).

Risk communication models that account for the behavioral nuances of adoption, such as consumer choice models with relaxed rationality assumptions or mental models that align messaging with farmer perspectives, could provide a stronger theoretical basis for adaptive decision-making.

Continuous EWS interventions face sustainability challenges, especially in changing policy landscapes. Research could address the effects of evolving political environments on EWS credibility, community buy-in, and funding stability. Institutionalizing EWSs, with stable support and adaptability to shifting needs, could enhance their long-term effectiveness.

Ultimately, addressing informational, attitudinal, and behavioral barriers through well-designed risk communication will be essential for supporting climate change adaptation. Future research that hones methods for effective risk messaging and clarifies potential trade-offs in EWS updates will enable decision-makers to allocate resources effectively and enhance farmers’ resilience in the face of growing climate challenges.

Blog written by Nnenna Ogbonnaya-Orji.

Overview of Recent and Ongoing Projects by the Evans School Policy Analysis and Research Group on Climate Impact in Food Systems

Posted on February 28, 2025February 27, 2025

Researchers at the Evans School Policy Analysis and Research Group (EPAR) engage in a wide range of projects spanning agriculture, development policy, financial services, poverty reduction, gender, and measurement and evaluation. Much of EPAR’s climate-related work is part of the newly established Center for Risk and Inclusion in Food Systems (CRIFS). CRIFS focuses on generating actionable, policy-driven insights to enhance the resilience of small-scale agricultural producers (SSPs) in low-income countries. Its research agenda addresses critical topics like climate adaptation, risk management, gender inclusion, and food system sustainability. By combining interdisciplinary methods and locally generated data, CRIFS supports innovative and cost-effective strategies to manage risks, reduce vulnerabilities, and improve livelihoods for SSPs.

Guided by CRIFS’s research agenda, EPAR researchers are leading several projects at various stages of development. These projects are consistent with the center’s commitment to interdisciplinary research and collaborative partnerships. Our focus is on male and female SSP adaptation to changes in risk; specifically, SSP uptake of technologies and practices intended to reduce vulnerability to climate change – within an agri-food system (AFS). Because our ultimate interest is impact at the farmer level, we must consider national policies and infrastructure that affect AFS, as well as more localized access, norms, agro-ecologies, and climate vulnerabilities.

This blog highlights a selection of ongoing work, including studies on the factors driving small-scale producers’ perceptions of climate risks, the role of behavioral and psychological factors in shaping production decisions, the ways climate change transforms consumer diets through its impacts on crops and prices, and the influence of climate and national policies on the resilience of agricultural value chains.

Farmer level decision-making

How risks shape and influence small-scale producer uptake of agricultural technologies and climate adaptation solutions: EPAR researchers, in collaboration with local partners, are developing risk profiles for small-scale producers (SSPs) in Nigeria and India to understand adaptation challenges to climate shocks, their costs, and how decisions vary by gender. Using a mixed-methods approach, including primary data and remote sensing data, they explore risk perceptions, access to climate information, and the impact of gender on technology adoption. Researchers expect to find a positive but imperfect correlation between self-reported and measured climate hazards and uncover how demographics and information influence adaptation decisions. These insights will inform targeted, context-specific interventions.
Farm-Level Agricultural Productivity and Adaptation to Extreme Heat: EPAR researcher Joaquín Mayorga and researchers from Arizona State University investigated the impact of extreme heat on farm-level agricultural productivity and adaptation strategies in Nigeria, using data from the Nigeria Living Standards Measurement Study (LSMS-ISA) from 2010, 2012, and 2015. While high temperatures reduce crop yields, findings show that farmers compensate by expanding cultivation areas and reallocating inputs, shifting from productivity boosting input such as fertilizers to protective measures like pesticides. High temperatures also increase reliance on hired labor and mixed-cropping practices. These results highlight the need for understanding farmer-level input substation and production choices especially for initiatives promoting specific inputs, as extreme heat may hinder their effectiveness.
Comparing Self-Reported and Measured Climate Shocks: EPAR researchers analyze discrepancies between farmer-reported droughts and measured rainfall data in Ethiopia and Malawi. Using high-resolution satellite rainfall data and farming surveys, initial findings suggest that farmers are more likely to report droughts during growing seasons with low rainfall or prolonged dry spells. However, discrepancies exist, with false positives (reported droughts not reflected in data) and false negatives (missed actual droughts). Female-headed households in Ethiopia are more prone to false positives. These findings highlight the need to better understand how and why risk perceptions differ from measured risk, as an insight into SSP adaptation behaviors.

Fig. 1: Comparison of surveyed community-level food insufficiency (top) and observed market maize prices (MWK/kg) in Malawi from 2009-2020.

Broader Agri-Food System Dynamics

Climate shocks and changing diets in Sub-Saharan Africa: As more work continues to be done on the impacts of climate change, fewer studies focus on the climate-nutrition nexus. Leveraging a unique and extensive dataset (GitHub) on the patterns of food consumption (Figure) across 16 African countries over the period 2008-2021, EPAR researchers are assessing the impacts of climate shocks on diets. This study examines how climate stressors, such as temperature and rainfall shocks, are associated with the types of food consumed by households and the shift in the source of foods – own production versus market purchase. Initial analyses indicate that climate shocks influence food consumption differently across crop groups and sources. In areas where drought has become more prevalent, households consumed less cereals and legumes, and shifted their diets toward root and starchy tubers whose cultivation required less water. The analysis also shows that, across all locations – especially in rural areas – food purchase is increasing the dominant source of food acquisition as climate shocks reduced rural farmers productivity. This research highlights the role of alternative foods and food sources in agri-food systems, consumer’s responses to climate shocks, and the implications for nutrition security and food self-sufficiency.

Fig 3: Average annual evapotranspiration index, 2001-2021)

Resiliency of the rice sector in Nigeria in the context of a changing climate: Rice production in Nigeria has grown rapidly over the past few decades, becoming a staple food, particularly in peri-urban and urban areas. As part of its Agricultural Transformation Agenda (ATA), the Nigerian government aims to achieve rice self-sufficiency. However, the sector remains low in productivity and highly vulnerable to weather variability. In collaboration with researchers at the University of Arkansas, EPAR researchers are utilizing a global rice trade model and the latest climate change projections to analyze the Nigerian rice sector. This analysis assesses the potential impacts of climate change on yields, prices, and consumption. The model also evaluates various policy options to enhance small-scale rice producers’ productivity, increase milled rice production, and reduce dependency on imports. Initial findings suggest that climate change could sharply reduce domestic rice production by 2030, making self-sufficiency goals more challenging and increasing reliance on imports.

AgQuery 50X2030 Cambodia Initiative

Posted on January 21, 2025February 6, 2025

In December 2024, members of the Evans School Policy and Analysis Research (EPAR) group visited Cambodia, for the launch of AgQuery, an open-source, publicly-available data-visualization tool that EPAR developed to guide policy analysis and host rich agricultural data like the Cambodian Agricultural Survey. University of Washington Professors Leigh Anderson and Stanley Wood, and EPAR Research Scientist Andrew Tomes and Post-Doctoral Scholar Dr. Vanisha Sharma delivered the training. EPAR Post-Doctoral Scholars Dr. Amaka Nnaji and Dr. Joaquin Mayorga, and RAs Sam Kenney and Micah McFeely, also provided data and other content for the 50×2030 platform. The event was supported by Michael Steiner and Professor Valerio Leone Sciabolazza representing the U.N. International Fund for Agricultural Development (IFAD), and hosted by Todd Hunkin at the Phnom Penh U.N. Food and Agriculture Organization (FAO) office. Action for Research and Development (ARD) organization contributed to arrangements for the event including translations, transportation and more.

Figure 1: EPAR Members at the AqQuery launch hosted by FAO, Phnom Penh, Cambodia

Cambodia is emerging as a leader in committing to “evidence-based agricultural development”. EPAR members spent several days in Phnom Penh training more than 30 individuals from seven organizations on AgQuery, including representatives from the Ministry of Agriculture, Forestry and Fisheries (MAFF), National Institute of Statistics (NIS), Royal University of Agriculture (RAU), the Cambodia Australia Partnership for Resilient Economic Development (CAPRED), and Harvest III (a USAID funded project, with a monitoring and evaluation team from Abt Associates), FAO and ARD. The training preceded the December 19th launch of the 2022 Cambodian Agricultural Survey data as part of the multi-agency 50X2030 initiative. IFAD and the FAO, together with the World Bank, are key organizations within this global data initiative supporting 50 countries to transform the collection of key agricultural data by 2030.

Figure 2: AgQuery presents dynamically-synthesized information by region and commodity

While rich agricultural data are now being generated in Cambodia through the efforts of government ministries, the FAO, and other partners, those data will only have impact when they are embedded in policy and investment decision-making. AgQuery is designed to improve and accelerate the link between the production of data and its policy application. Disaggregated, enterprise-specific information can, for instance, provide useful insights into regional variation in poultry-vaccination rates and the design of animal health policies, while changes over time in crop or animal output can help shape and prioritize localized policies targeting productivity-growth.

While other data visualization tools exist, AgQuery is unique in extending beyond its inherent capabilities to flexibly summarize and explore processed survey data, guiding analysts through a series of policy analytic steps: understanding the policy context, articulating policy pathways and instrument options to achieve prioritized goals, flagging stakeholder considerations, recognizing decision-criteria tradeoffs, reviewing outside evidence from the literature, and potentially applying prioritization tools such as benefit-cost analysis to narrow options.

Figure 3: AgQuery for 50X30 Cambodia supports policy exploration built on rich data capabilities

Cambodian analysts have political support, increasing access to relevant, high-quality data, and solid partnerships. As one of those partners, EPAR plans to offer some ongoing desk support as requested by individuals from the MAFF, NIS, and RUA. These engagements are mutually beneficial, enabling us to validate that our novel AgQuery data and policy exploration platform, formulated as a customizable, open-source solution, is indeed context relevant and viable for on-going, in-country maintenance and evolution.

Based on our interactions and the feedback received, AgQuery seems well placed to further support Cambodia in translating its increased investment in agricultural data into better policy choices and outcomes and, thereby, contribute to faster progress toward the nation’s development goals.

Patterns of household food consumption across food groups and sources in sub-Saharan African countries

Posted on November 25, 2024November 23, 2024

Background

In most low- and middle-income countries (LMICs), per capita food expenditure has been steadily rising over the past few decades despite challenges from climate change, conflict, and COVID-19. Trends in food consumption are driven by urbanization, higher incomes, globalization, increased economic integration, and consumer preferences. In sub-Saharan Africa (SSA) there has been a shift away from the consumption of staple foods toward an increasingly diversified diet. Understanding these trends, however, remains constrained by the lack of large-scale cross-national data on the pattern of consumption across a broad set of food items. In particular, there is little information on cross-country and within-country variations in food consumption patterns and how households acquire food. Agricultural livelihoods dominate most LMICs, with many households’ food consumption coming predominantly from their own production. As economies transform and agriculture transitions from subsistence to commercial farming, it is expected that households will increasingly source food from markets. Increasing consumption from locally sourced production can incentivize investment in productive farm technologies and reduce import dependency, thereby contributing to food security. In this blog, we discuss an effort led by the University of Washington Evans School of Policy Analysis and Research (EPAR) group to standardize data on the value of food consumption patterns for a large number of food items and countries in SSA. We then leverage the data to discuss some insights regarding patterns of value of food consumption by food categories, food sources, and socio-demographics.

Standardizing food consumption indicators in large-scale household survey datasets

We leverage large-scale household datasets collected by the World Bank and country National Statistical Offices to construct food consumption indicators for 16 SSA countries over the period 2008-2021. These surveys ask households to report the amount of consumption from own-production and gifts, and the amount and value of food purchased over the past 7 days prior to the interview. Consumption from purchases comprises food items that are accessed from markets. Consumption from own production refers to consumed food items that are produced by households. Consumption from gifts encompasses food items households received from other households, non-governmental organizations, and the government. The value of food consumption from own production and gift was constructed using unit values estimated in reported quantities and values of purchases. For household food item observations for which no market purchase was reported, unit prices are imputed using the median purchase price of the same food items at the lowest administrative level with at least 10 observations. Food items were aggregated into broad categories: cereals, roots and tubers, pulses, legumes and nuts, dairy, fish and seafood, fruits and vegetables, livestock products, non-dairy beverages, oils and fats, processed food, other food, meals away from home, and tobacco. For comparability across countries, the monetary value of consumption was annualized and converted to 2017 Purchasing Power Parity (PPP).

Current patterns in average total value of food consumption at the aggregate level and by food items

Figure 1 presents a graph of the average annual per capita consumption in 2017 PPP for the most recent wave of data available for included countries. Nigeria had the highest average per capita value of food consumption in 2018, while Ethiopia had the lowest average per capita value of food consumption in 2021.

Figure 2 is an interactive graph allowing a user to select one or multiple countries or years and display the average per capita value of food consumption disaggregated by food items. One takeaway is that for most countries, cereals continue to be the main food item consumed, with the highest average per capita value of consumption in all countries except Benin, Cote d’Ivoire, and Uganda. The other top food item consumed in terms of monetary value is fruits and vegetables, except in Nigeria and Uganda. The least consumed food items are pulses and legumes and roots and tubers, with the exception of Uganda where oils/fats and non-dairy beverages are the least consumed food items. For countries with multiple years of data, we can examine trends in the per capita value of food consumption over time. We see, for example, a consistent increase in the value of consumption of cereals, pulses, legumes and nuts in Malawi and Mali.

Patterns in the value of food consumption by sources

Figure 3 presents the average annual share of the value of household food consumption from purchases, own production, and gifts. Across all countries and years, about 75% of household value of food consumption is from market purchases, while own production and gifts represent 20% and 5% respectively. There are substantial variations across countries and over time. Senegal had about 93% of its household value of food consumption acquired through purchases in 2018 while the lowest share was recorded in Uganda in 2011 (46%). For most countries, the relative importance of market-sourced food is growing. For example, in Uganda, the share of food from markets increased from, 46% in 2011 to 59% in 2019. Similar growth was observed in Ethiopia, Tanzania, and Niger.

Figure 4 presents an interactive graph showing the average share of the value of household food consumption from purchases, own production, and gifts, disaggregated by major food categories. It reveals that for most food categories, more than 60% of consumption comes from purchases. This is particularly true for high-value commodities such as fish and seafood, livestock products, fruits and vegetables, oils and fats, non-dairy beverages, and processed food. For staple crops such as cereals, pulses, and roots and tubers, the purchased share is lower. The consistency of shares by sources over time also varies; for example, Tanzania consistently had more than 50% of its dairy consumption from own production while in Malawi, less than 20% of dairy consumption come from own production. We see a gradual decrease in consumption of pulses, legumes, and nuts from own-production and a resulting increase in consumption from purchases and gifts. The share of roots and tubers increased for most of the waves in Tanzania while there was a consistent decrease in Nigeria.

Spatial and gender heterogeneity in the value of household food consumption

Figure 5 presents an interactive spatial distribution of the total value of food consumption from purchases, own production, and gifts at both country and administration one levels. The maps can be further disaggregated by year, location, and gender of household head. These maps show that countries in East Africa had a greater value of food consumption from their own production compared to other SSA countries. The estimates mapped can also be disaggregated by place of residence and the gender of the head of household to produce location- and gender-specific insights. This insight holds for both male and female-headed households as well as households located in both rural and urban areas. This distinction is further strengthened when consumption is disaggregated by food items (see Figure 7).

Figure 6 explores differences in the share of the value of food consumption from different sources disaggregated by location. On average across countries, about 65% of food consumption for rural households comes from purchase. This percentage rises to about 90% for urban households in most countries, except Kenya and Uganda.

Figure 7 presents the same interactive graph as Figure 6 but is further disaggregated by the gender of the household head. Here, we see that for most countries, female-headed households (FHHs) residing in urban areas have a higher share of food consumption value from purchases compared to their counterparts in rural areas. The same distinction is also applicable for male-headed households (MHHs). The reverse is the case in Malawi where FHHs in both urban and rural areas have a lower share of food consumption value from purchases compared to MHHs. More generally, MHHs in both urban and rural areas tend to have a higher share of food consumption value from own production compared to FHHs.

Concluding remarks

This blog provides insights into the sources and patterns of the value of food consumption in SSA. It leverages a new dataset put together by EPAR processing and merging food consumption indicators in nationally representative large-scale household surveys collected in 16 SSA countries over the period 2008 – 2021. The analysis reveals that of the different sources of food examined, market-purchased consumption accounts for the highest value, even in rural areas. It also shows a rapid shift towards increased value of food consumption from purchases, marking a departure from traditional practices of consuming own-produced food and gifts. The analysis indicates that this shift is not uniform across countries and socio-demographic characteristics of households within countries. These shifts, rooted in socio-economic changes, gender roles, and urbanization, underscore the complex challenges and dynamics facing global food security and nutrition strategies. The dataset can be used to help understand changes in food sourcing and what this might mean for nutrition, resilience, and market access. The Stata codes to generate the dataset is available for download at the EPAR GitHub Repository. The more complete visualization data is also accessible on tableau visualization platform.

Blog written by Amaka Nnaji, Ahana Raina, Didier Alia and C. Leigh Anderson.

Where do African farmers obtain their seed from? Insights from Ethiopia and Nigeria

Posted on October 18, 2024

Background and motivation

Timely access to quality seed, among other factors, determines household planting decisions and has implications for agricultural productivity. Hence, it is important to understand where and how small-scale producers (SSP) obtain their seed. To answer this question, we use nationally representative agricultural households survey data from the Living Standards and Measurement Study – Integrated Surveys on Agriculture (LSMS – ISA) data for the Ethiopia Socioeconomic Survey or ESS and the Nigeria General Household Survey or GHS.

What are the main household seed sources?

The World Bank LSMS – ISA project, in partnership with countries’ national statistical offices and funders such as the Bill & Melinda Gates Foundation (BMGF), has been collecting rich panel survey data that can be used to analyze[i] seed sources over five waves for Ethiopia and four for Nigeria.

The surveys ask farmers to report crops planted and for each crop the type of seed -traditional or local vs improved or hybrid – and where the seed was acquired – purchased, leftover or home saved seed, and seed received for free. We further disaggregate purchased seed by type of seller: relative or friend or neighbor, village head, market or traders, government, or others.

What are the main sources of seed for rural households in Ethiopia and Nigeria?

Figures 1 and 2 show the trend in the percentage of rural farm households reporting using seed from different sources for Ethiopia and Nigeria. The results show that in general, a higher percentage of rural households use home saved or leftover seed relative to purchased seed. However, over the past decade the use of purchased seed is rising and home saved, or leftover seed is declining. In Ethiopia, the percentage of rural households reporting purchasing seed rose from a low in 2015 (Figure 1), and in Nigeria, after 2012 (Figure 2). On average, a higher percentage of rural households in Ethiopia use purchased seed than those in Nigeria. This notable growth in the use of purchased seeds in Ethiopia has been partly attributed to seed producer cooperatives that have improved the supply of seeds in the country. The results also show that in both countries, between 5 and 20 percent use seed received for free from other farmers or NGO or the government.

Figure 1: Overall trends in percentage of farm households using seed from various sources in Ethiopia, 2011 – 2021. Source: Based on Ethiopia ESS data. Sample restricted to rural households.

Figure 2: Overall trends in percentage of farm households using seed from various sources in Nigeria, 2010 – 2018. Source: Based on Nigeria GHS data. Sample restricted to rural households.

What is the share of seed from the main sources for rural households in Ethiopia and Nigeria?

The share of seed used by a household from each source is calculated as the quantity of seed from that source (purchased, saved/leftover or free) divided by the household’s total quantity of seed from the three sources. In Ethiopia, the share of purchased seed rose rapidly from 2015 – 2021 to exceed 50 percent. In Nigeria, the share of purchased seed rose in the last two waves to 27 percent in 2018.

Figure 3: Share of farm households seed from various sources in Ethiopia, 2011 – 2021. Source: Based on Ethiopia ESS data. Sample restricted to rural households.

Figure 4: Share of farm households seed from various sources in Nigeria, 2010 – 2018. Source: Based on Nigeria GHS data. Sample restricted to rural households.

Are there any gender differences in the share of seed used from the different sources by farm households in Ethiopia and Nigeria?

The share of purchased seed used by male headed households in Ethiopia between 2011 – 2015 was marginally higher than that of their female counterparts, with the trend reversing between 2015 – 2021 (Figure 5). In Nigeria, the share of purchased seed used by male headed households is generally lower than that of their female counterparts (Figure 6) except for 2018. This result is in line with the findings of a 2015 study showing that male headed households use less purchased seed than their male counterparts.

Figure 5: Share of farm households seed from various sources in Ethiopia, 2011 – 2021 by gender. Source: Based on Ethiopia ESS data. Sample restricted to rural households.

Figure 6: Share of farm households seed from various sources in Nigeria, 2010 – 2018 by household head gender. Source: Based on Nigeria GHS data. Sample restricted to rural households.

Concluding remarks

The descriptive results presented above generally show that rural farm households in Ethiopia and Nigeria predominantly use home saved or leftover seed. However, over the past decade, the use of market purchased seed, especially of legumes and nuts and horticultural crops, has been growing. Given that improved varieties tend to be market-sourced, this trend may show up in productivity numbers, though climate change remains an unpredictable factor.

[i] The codes that generate the results presented in this blog are published in the EPAR GitHub repository. EPAR has also produced and made publicly available in its GitHub repository additional code to process LSMS-ISA data, generate tailored indicators and obtain summary statistics for Ethiopia, Nigeria, Tanzania, Malawi and Uganda.

Blog written by Peter Agamile, Didier Alia and C. Leigh Anderson.

Year Over Year Smallholder Threshold Variability (Sub-Saharan Africa)

Posted on October 14, 2024May 27, 2025

Defining Smallholder Farmers
Smallholder Farmers or Small-Scale Producers, are frequently mentioned as targets for development interventions, to relieve hunger, alleviate poverty, or catalyze agricultural transformation. However, an operationalizable definition of a smallholder farmer is difficult to come by, with few sources even defining the term. When sources do offer a definition, they rarely agree on the indicators and thresholds to use. This blog post (forthcoming) from EPAR documents a recent literature review highlighting the lack of a clear definition. Below the visualization is further information about the data used to construct the visualization, and a link to the underlying data files. These visualizations are tools developed by EPAR to attempt to provide a clear and consistent answer to the question: “Who is a smallholder farmer?”

The Data
The visualization above is created using nationally representative data from the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). This is a publicly available household panel survey dataset for seven countries in Sub-Saharan Africa. The survey includes linked agricultural, livestock, household, and community level modules that provide information on a variety of topics including crops, farming practices, livestock, income sources, and socio-demographics.

Specifically, it displays cleaned data from the Nigeria General Household Survey, the Ethiopian Rural Socioeconomic Survey, and the Tanzania National Panel Survey. Each of these surveys represent panel data gathered in waves from the same households. In Ethiopia, the first wave was gathered in 2011-2012, the second wave was gathered in 2013-2014, the third wave was gathered in 2015-2016. In Tanzania the first wave was gathered in 2008-2009, the second wave was gathered in 2010-2011, and the third wave was gathered in 2012-2013. Tanzania also has a fourth wave gathered in 2014-2015, but using a new set of households. In Nigeria, the first wave was gathered in 2010-2011, the second wave was gathered in 2012-2013, and the third wave was gathered in 2015-2016. When only one year is shown, it is the most recent wave.

The visualization above is created using nationally reprsentative data from the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). This is a publicly available household panel survey dataset for seven countries in Sub-Saharan Africa. The survey includes linked agricultural, livestock, household, and community level modules that provide information on a variety of topics including crops, farming practices, livestock, income sources, and socio-demographics. Specifically it displays cleaned data from the Nigeria General Household Survey, the Ethiopian Rural Socioeconomic Survey, and the Tanzania National Panel Survey . The code used to generate the variables and estimates is available in a public GitHub repository. The estimates as well as more information about the specific construction decisions for each indicator are available through EPAR’s agricultural database. This visualization allows users to look at one particular custom definition in greater depth. This visualization looks at the AGRA definitions in greater depth. To view the visualization in full screen click here.

By Terry Fletcher

Summarizing research by Didier Alia, Terry Fletcher, Pierre Biscaye, C. Leigh Anderson, and Travis Reynolds