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AgQuery 50X2030 Cambodia Initiative

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Earlier this month, 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

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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.

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

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African-farmers

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