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Innovation Policy and Scaling (IPS), Quality upgrading may be lagging in value chains where the assessment and traceability of the quality of the underlying commodity is challenging. In Uganda's southwestern milk shed, a variety of initiatives are trying to increase the quality of raw milk in dairy value chains. These initiatives generally involve the introduction of technologies that enable measurement of key quality parameters at strategic nodes in the value chain, in conjunction with a system that allows for tracking of these parameters throughout the supply chain. In this paper, we use a combination of focus group discussions, key informant interviews, and quantitative data that is generated by these initiatives to document outputs, describe emerging outcomes, and reflect on the potential impact. We find clear evidence that milk quality improved, but the effects on milk prices are more subtle.

Development Strategies and Governance (DSG); Transformation Strategies; Markets, Trade, and Institutions (MTI), The emergence of rural land rental markets in Sub-Saharan Africa is recognized as a key component of the region’s ongoing economic transformation. However, the evidence base on land market participation relies on survey-derived measures, which do not always cohere when compared and triangulated, suggesting the possibility of non-trivial measurement error. We report the results of a priming and list experiments designed to shed light on a persistent mystery in rural household survey data from Africa: why there are so many fewer self-reported landlords (renters-out) than tenants (renters-in)? Our design addresses two hypotheses using experimental data from Ethiopia. First, rented-out and rented-in land may be systematically underreported because enumerators and respondents are typically primed to emphasize parcels that are actively managed/cultivated by the household. Second, rented or sharecropped-out land may be systematically underreported because of respondents’ reluctance to acknowledge an activity for which public disclosure may have negative repercussions. We address the first hypothesis with a priming experiment by exposing a random subset of respondents to a nudge that explicitly reminded them to fully account for all land, including rented/sharecropped-in and rented/sharecropped-out. We address the second hypothesis with a double-list experiment, designed to elicit true rates of land renting and sharecropping-out. We find that nudging induces about 4 percentage points increase (or 13% in relative terms) in the share of households participating in renting in or sharecropping-in practices but has negligible effects on reported rates of renting and sharecropping-out. Interestingly, our list experiment indicates much higher revealed rates of renting-out (14-15%) than is reflected in the nominal parcel-roster responses (3%). The magnitude of the latter finding fully explains the apparent difference in renting in versus renting-out rates derived from the regular p

While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where “scaling” is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5–10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools’ failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional ‘blanket’ fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled ‘ready’ from p

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

PR, IFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; IFPRIOA, Innovation Policy and Scaling (IPS); Transformation Strategies, Ethiopia recently experienced a large-scale war that lasted for more than two years. Using unique High-Frequency Phone Survey (HFPS) data, which span several months before and after the outbreak of the war, this paper provides evidence on the immediate impacts of the conflict on households’ food security. We also assess potential mechanisms and evaluate impacts on proximate outcomes, including on livelihood activities and access to food markets. We use difference-in-differences and two-way fixed effects estimation to compare trends across affected and unaffected regions (households) and before and after the outbreak of the war. Seven months into the conflict, we find that the war was associated with a 37 percentage points increase in the probability of moderate to severe food insecurity. Using the Armed Conflict Location and Event Data (ACLED), we show that exposure to an additional battle leads to a 1 percentage point increase in the probability of moderate or severe food insecurity. The conflict was associated with significant reduction in access to food through supply chain disruptions and by curtailing non-farm livelihood activities. Non-farm and wage related activities were affected the most, whereas farming activities were relatively more resilient. Our estimates, which likely underestimate the true average effects on the population, constitute novel evidence on the near-real-time impacts of large-scale conflict. Our work highlights the potential of HFPS to monitor active and large-scale conflicts, especially in contexts where conventional data sources are not immediately available.

PR, IFPRI3; IFPRIOA; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance; ISI; Evaluating Impact and Building Capacity (EIBC), Development Strategies and Governance (DSG); Transformation Strategies; Markets, Trade, and Institutions (MTI); Food and Nutrition Policy, Despite enthusiasm for the potential of digital innovations to transform agricultural markets in Africa, progress made thus far has been limited to small-scale experiments that often fail to scale up. Realizing the full potential of digital innovations – tools, technologies, applications, and services – in Africa requires not just further development of these solutions at meaningful scales, but also more nuanced evidence from both successful and unsuccessful scaling efforts. This paper reviews the conceptual and empirical evidence on the transformative potential of digital innovations for African agricultural markets with an in-depth examination of solutions that have been rolled out to date in the continent. Specifically, the review addresses the following questions: (i) how can digital innovations improve the functioning of agricultural markets in Africa? (ii) what explains the apparent disconnect of most pilots to scale-ups? (iii) what is required to realize their full potential? and (iv) what are the emerging risks and opportunities associated with digital innovations for agricultural marketing? Although our review of the landscape and literature on market-focused digital innovations in Africa identifies several reasons to remain optimistic, the prevailing disconnect between pilots and scale-ups merits further evaluation. In particular, there is a need for more systematic assessments of both successes and failures at both the piloting and scaling stages.

Non-PR, IFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 5 Strengthening Institutions and Governance, Development Strategies and Governance (DSG); Transformation Strategies

PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; IFPRI3; DCA; ISI, Markets, Trade, and Institutions (MTI); Food and Nutrition Policy, Acid soils are a major constraint to agricultural productivity in many parts of sub-Saharan Africa, including Ethiopia. Restoring soil pH to optimal ranges for agriculture can have a significant impact on yields, particularly for acid intolerant crops like wheat and barley. The application of agricultural lime is the standard corrective, although the large application requirements, lack of farmer awareness, and weak or non-existent lime supply chains make this a complex problem to address at scale. To date, no large-scale farmer trials of lime application have been undertaken in Ethiopia. This leaves open the question to local policy makers as to the economic benefits given the enormous capital and logistics investments required. To help address this we leverage existing spatial edaphic data and longitudinal crop surveys to simulate the productivity impact of varying lime and fertilizer applications. Our estimates find the impact of moving pH from 5.5 to 6.5, modeled as a lime soil remediation strategy, increases yields by 22% and 19% for wheat and barley, respectively. In addition, at lower pH levels our models indicate that commonly used nitrogen-based fertilizers are less cost-effective. For wheat in highly acidic soils, we find that fertilizers cost over two times as much as a single application of lime over a five-year period. The cost savings of the use of lime reaches as high as 121% of average one-year agricultural household income for wheat; with barley these savings are lower but still substantial at 24%. In general, we advocate for an integrated soil fertility management strategy that applies appropriate levels of fertilizer on pH balanced soil. If successful, Ethiopia’s acid soil reclamation could become a modest version of Brazil’s successful “cerrado miracle” and serve as an example for Africa.

Non-PR, IFPRI1; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry, DSGD, CGIAR Research Program on Policies, Institutions, and Markets (PIM), We combine nationally representative data from Nigeria with spatiotemporal data from remote sensing and other sources to study how young migrants respond to observable characteristics of potential destinations, both in absolute terms and relative to origin locations. Migrants prefer destinations with better welfare, land availability and intensity of economic activity. We also find that migrants prefer shorter distances and those destinations with better urban amenities and infrastructure. However, responses vary by type of migrant and migration. For example, rural-rural migrants are more responsive to land availability and agricultural potential, while rural-urban and urban-urban migrants are more responsive to welfare and economic vibrancy (measured by nightlight intensity) in destinations. Distance induces varying impact on migration choices of poor and non-poor migrants as well as across more educated and less educated migrants. Longer distances discourage migration for female migrants, poorer migrants and less educated migrant while the implication for the non-poor and more educated migrants appears to be negligible. This is intuitive because poorer and less educated migrants have liquidity constraints to finance high migration costs. Our results suggest potential scope for predicting how labor mobility responds to alternative regional development policies.

PR, 5 Strengthening Institutions and Governance; G Cross-cutting gender theme; ISI; CRP2; IFPRIOA; IFPRI3, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Improving household resilience is becoming one of the key focus and target of social protection programs in Africa. However, there is surprisingly little direct evidence of the impacts of social protection programs on household resilience measures. We use five rounds of panel data to examine rural households’ resilience outcomes associated with participation in Ethiopia’s Productive Safety Nets Program (PSNP). Following Cissé and Barrett (2018), we employ a probabilistic moment-based approach for measuring resilience and evaluate the role of PSNP transfers and duration of participation on households’ resilience. We document four important findings. First, although PSNP transfers are positively associated with resilience, PSNP transfers below the median are less likely to generate meaningful improvements in resilience. Second, continuous participation in the PSNP is associated with higher resilience. Third, combining safety nets with income generating or asset building initiatives may be particularly efficacious at building poor households’ resilience. Fourth, our evaluation of both short-term welfare (consumption) and longer-term outcomes (resilience) suggests that these outcomes are likely to be driven by different factors, suggesting that optimizing intervention designs for improving short term welfare impacts may not necessarily improve households’ resilience, and vice versa. Together, our findings imply that effectively boosting household resilience may require significant transfers over multiple years. National safety nets programs that transfer small amounts to beneficiaries over limited time horizons may not be very effective.

PR, IFPRI3; 5 Strengthening Institutions and Governance, DSGD, Civil conflict began in Ethiopia in November 2020 and has reportedly caused major disrup tions in access to health services, food, and related critical services, in addition to the direct impacts of the conflict on health and well-being. However, the population-level impacts of the conflict have not yet been systematically quantified. We analyze high frequency phone surveys conducted by the World Bank, which included measures of access to basic ser vices, to estimate the impact of the first phase of the war (November 2020 to May 2021) on households in Tigray. After controlling for sample selection, a difference-in-differences approach is used to estimate causal effects of the conflict on population access to health services, food, and water and sanitation. Inverse probability weighting is used to adjust for sample attrition. The conflict has increased the share of respondents who report that they were unable to access needed health services by 35 percentage points (95% CI: 14–55 pp) and medicine by 8 pp (95% CI:2–15 pp). It has also increased the share of households unable to purchase staple foods by 26 pp (95% CI:7–45 pp). The share of households unable to access water did not increase, although the percentage able to purchase soap declined by 17 pp (95% CI: 1–32 pp). We document significant heterogeneity across popula tion groups, with disproportionate effects on the poor, on rural populations, on households with undernourished children, and those living in communities without health facilities. These significant disruptions in access to basic services likely underestimate the true bur den of conflict in the affected population, given that the conflict has continued beyond the survey period, and that worse-affected households may have higher rates of non-response. Documented spatial and household-level heterogeneity in the impact of the conflict may help guide rapid post-conflict responses.

Non-PR, IFPRI1; EgyptSSP; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies, DSGD; MTID, This policy note synthesizes the key messages and lessons from existing evidence and trends in the development, deployment and scale up of ICT-enabled marketing tools. It is based on the recently published discussion paper titled “Digital tools and agricultural market transformation in Africa: Why are they not at scale yet, and what will it take to get there”. Key messages • Many digital innovations have been developed and deployed in recent years in Africa, many of which have only been implemented at pilot stages, with limited evidence of successful scaling. • There remains significant marketing and institutional constraints hindering the development of some of these digital innovations, which may further explain disparate progress in countries. • Differential access to digital innovations across genders and different typologies of households may trigger alternative variants of digital divide. • Although the landscape of digital innovations in Africa offers several reasons to remain optimistic, the prevailing disconnect between pilots and scale-ups merits further evaluation.

Non-PR, IFPRI1; 1 Fostering Climate-Resilient and Sustainable Food Supply; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 5 Strengthening Institutions and Governance; G Cross-cutting gender theme; CRP2, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Ethiopia is currently embroiled in a large-scale civil war that has continued for more than a year. Using unique High-Frequency Phone Survey (HFPS) data, which spans several months before and after the outbreak of the war, this paper provides fresh evidence on the ex durante impacts of the conflict on the food security and livelihood activities of affected households. We use difference-in-differences estimation to compare trends in the outcomes of interest across affected and unaffected regions (households) and before and after the outbreak of the civil war. Seven months into the conflict, we find that the outbreak of the civil war increased the probability of moderate to severe food insecurity by 38 percentage points. Using the Armed Conflict Location and Event Data (ACLED) on households’ exposure to violent conflict, we show that exposure to one additional battle leads to 1 percentage point increase in the probability of moderate to severe food insecurity. The conflict has reduced households’ access to food through supply chain disruptions while also curtailing non-farm livelihood activities. Non-farm and wage related activities were the most affected by the conflict while farming activities were relatively more resilient. Similarly, economic activities in urban areas were much more affected than those in rural areas. These substantial impact estimates, which are likely to be underestimates of the true average effects on the population, constitute novel evidence on the near-real-time impacts of an on-going civil conflict, providing direct evidence on how violent conflict disrupts the functioning of market supply chains and livelihoods activities. Our work highlights the potential of HFPS to monitor active and large-scale conflicts, especially in contexts where conventional data sources are not immediately available.

Non-PR, IFPRI1; 1 Fostering Climate-Resilient and Sustainable Food Supply; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 5 Strengthening Institutions and Governance, DSGD, Smallholder agriculture in sub-Saharan Africa is commonly characterized by high levels of technical inefficiency. However, much of this characterization relies on self-reported input and production data, which are prone to systematic measurement error. We theoretically show that non-classical measurement error introduces multiple identification challenges and sources of bias in estimating smallholders’ technical inefficiency. We then empirically examine the implications of measurement error for the estimation of technical inefficiency using smallholder farm survey data from Ethiopia, Malawi, Nigeria, and Tanzania. We find that measurement error in agricultural input and production data leads to a substantial upward bias in technical inefficiency estimates (by up to 85 percent for some farmers). Our results suggest that existing estimates of technical efficiency in sub-Saharan Africa may be severe underestimates of smallholders’ actual efficiency and what is commonly attributed to farmer inefficiency may be an artifact of mismeasurement in agricultural data. Our results raise questions about the received wisdom on African smallholders’ production efficiency and prior estimates of the productivity of agricultural inputs. Improving the measurement of agricultural data can improve our understanding of smallholders’ production efficiencies and improve the targeting of productivity-enhancing technologies.

PR, IFPRI3; ISI; ESSP; CRP2; 4 Transforming Agricultural and Rural Economies, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous ap

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous ap

PR, IFPRI3; ISI; CRP2; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; IFPRIOA, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Although still at incipient stages in most areas, agricultural land markets in sub-Saharan Africa (SSA) are growing rapidly. While the literature on the region’s land markets is expanding, there has been little attention thus far paid to the drivers of land rental prices. We know quite little about whether and how land markets and land contracts respond to meso-scale factors such as spatial variations in land abundance, or to micro-level factors, such as household land endowments. In this paper, we study the response of land markets, and hence whether land market participation trends and land market rental prices respond to land scarcity. Drawing on nationally representative household survey data from Ethiopia, Malawi and Tanzania, which represent a large range of relative land scarcity conditions, we examine how land markets are responding to cross-sectional variations in land scarcity in terms of rental market participation rates, prices, and contractual arrangements. In all three countries, we document that rental market participation rates and land rental prices increase with land scarcity. We also find that land rental prices per hectare decrease with plot size. These land market responses and their heterogeneities have important implications for land distribution and land market policies.

Maize yields can be improved through many individual technologies and management practices, but the full realization of potential benefits is generally understood to require integrated use of complementary practices. We employed two years of survey data and alternative econometric models to better understand the use of individual and bundled packages of technologies and management practices in Ethiopian maize production, i.e., fertilizers, improved varieties, herbicides, pesticides, manure, intercropping, erosion control and crop rotation (the last three labeled integrated management). Although fertilizer and improved varieties were used on 85% of maize fields, with average yields of 3.4 ton/ha, large yield gaps remain. Complementary management practices improved these yields by as much as 22%, although in variable ways. Integrated management contributed to maize yield only when combined with crop protection (herbicides and/or pesticides). Combining manure with fertilizer and improved variety decreased maize yields, possibly due to manure quality and less inorganic fertilizer used on fields that received manure. Packages including crop protection increased labor productivity by 16–70%, while using integrated management decreased labor productivity by almost half. In summary, the combination of management practices did not automatically lead to increased yields, partly related to the conditions under which practices were applied, indicating the need for site-specific research and recommendations for sustainable intensification.

Non-PR, IFPRI1; ESSP; CRP2; 2 Promoting Healthy Diets and Nutrition for all; DCA, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), We examine the implication of the Productive Safety Nets Program (PSNP) in Ethiopia on the economic resilience of rural households. Using five-rounds of household panel data covering nine years, we implement a recently developed probabilistic moment-based approach to measure resilience and evaluate the role of PSNP transfers and duration of participation in PSNP on household resilience. We document three important findings. First, although PSNP transfers are positively strongly associated with resilience, we find that transfers below the median are less likely to generate meaningful improvements in resilience. Second, continuous participation in PSNP is associated with higher resilience. Third, our evaluation of both short-term welfare outcomes and longer-term resilience suggests that these outcomes are likely to be driven by different factors. These findings suggest boosting household resilience will require significant investments in social protection programs and continuous participation in these programs. Our findings have important implications for the design and targeting of social protection programs in Africa, where safety nets programs generally operate at small scale with small transfers to beneficiaries over relatively short durations.

While adoption rates for inorganic fertiliser are relatively high in Ethiopia, application rates are generally considered agronomically suboptimal. Using recent data on Ethiopian smallholder maize producers, we showed that maize response to nitrogen, and the profitability of fertiliser use depended on maize agronomy. The agronomic optimum ranged from 0 to 344 kg/ha with a mean value of 209 kg/ha. The actual nitrogen application rates were only about half the agronomic optimum, on average, and were less than the farm-specific economic optimum on 80% of maize fields. The average economic optimum level was 145 kg N/ha, but when we account for risk aversion, the resulting average optimum level is very close to the average observed usage level of 88 kg N/ha. Addressing risk aversion may help to induce greater levels of fertiliser investments at current prices and yield response rates. Our analysis also suggests that key pathways for increasing the economic returns to smallholder fertiliser investments include: complementing nitrogen inputs with phosphorus inputs and improved varieties, using lower levels of nitrogen under intercropping and manure inputs, enabling farmers to delay output sales beyond the immediate post-harvest period, and lowering the costs of accessing input and output markets.

Non-PR, IFPRI1; CRP2; DCA; 4 Transforming Agricultural and Rural Economies; ESSP, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Improving household resilience is becoming one of the key focus and target of social protection programs in Africa. However, there is surprisingly little direct evidence of the impacts of social protection programs on household resilience measures. We use five rounds of panel data to examine rural households’ resilience outcomes associated with participation in Ethiopia’s Productive Safety Nets Program (PSNP). Following Cissé and Barrett (2018), we employ a probabilistic moment-based approach for measuring resilience and evaluate the role of PSNP transfers and duration of participation on households’ resilience. We document four important findings. First, although PSNP transfers are positively associated with resilience, PSNP transfers below the median are less likely to generate meaningful improvements in resilience. Second, continuous participation in the PSNP participation is associated with higher resilience. Third, combining safety nets with income generating or asset building initiatives may be particularly efficacious at building poor households’ resilience. Fourth, our evaluation of both short-term welfare outcomes and longer-term resilience suggests that these outcomes are likely to be driven by different factors, suggesting that optimizing intervention designs for improving short term welfare impacts may not necessarily improve households’ resilience, and vice versa. Together, our findings imply that effectively boosting household resilience may require significant transfers over multiple years. National safety nets programs that transfer small amounts to beneficiaries over limited time horizons may not be very effective.

Non-PR, IFPRI1; CRP2; Evaluating Impact and Building Capacity (EIBC); 4 Transforming Agricultural and Rural Economies, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Despite enthusiasm on the potential of digital innovations to transform agricultural markets in Africa, progress made thus far has been limited to small-scale experiments that often fail to scale up. Realizing the full potential of digital innovations—tools, technologies, applications, and services—in Africa requires not just further development of these solutions at meaningful scales, but also more nuanced evidence from both successful and unsuccessful scaling efforts. This paper reviews the theoretical and empirical evidence on the transformative potential of digital innovations for African agricultural markets with an in-depth examination of solutions that have been rolled out to date in the continent. Specifically, the review addresses the following questions: (i) how can digital innovations improve the functioning of agricultural markets in Africa? (ii) what explains the apparent failure of most pilots to scale up? (iii) what is required to realize their full potential? and (iv) what are the emerging risks and opportunities associated with these digital innovations for agricultural marketing? Although our review of the landscape and literature on market-focused digital innovations in Africa identifies several reasons to remain optimistic, the prevailing disconnect between pilots and scale-ups merits further evaluation. In particular, there is a need for more systematic assessments of both successes and failures at different stages of piloting and scaling of digital solutions.

PR, IFPRI3; ISI; IFPRIOA; 2 Promoting Healthy Diets and Nutrition for all; 5 Strengthening Institutions and Governance; CRP2, DSGD; PIM, CGIAR Research Programs on Policies, Institutions, and Markets (PIM), This paper combines pre-pandemic face-to-face survey data with follow up phone surveys collected in April-May 2020 to examine the implication of the COVID-19 pandemic on household food security and labor market participation outcomes in Nigeria. To examine these relationships and implications, we exploit spatial variation in exposure to COVID-19 related infections and lockdown measures, along with temporal differences in our outcomes of interest, using a difference-in-difference approach. We find that households exposed to higher COVID-19 case rates or mobility lockdowns experience a significant increase in measures of food insecurity. Examining possible transmission channels for this effect, we find that the spread of the pandemic is associated with significant reductions in labor market participation. For instance, lockdown measures are associated with 6–15 percentage points increase in households' experience of food insecurity. Similarly, lockdown measures are associated with 12 percentage points reduction in the probability of participation in non-farm business activities. These lockdown measures have limited implications on wage-related activities and farming activities. In terms of food security, households relying on non-farm businesses, poorer households, and those living in remote and conflicted-affected zones have experienced relatively larger deteriorations in food security. These findings can help inform immediate and medium-term policy responses, including social protection policies aiming at ameliorating the impacts of the pandemic.

PR, IFPRI3; ISI; 4 Transforming Agricultural and Rural Economies; G Cross-cutting gender theme; CRP2; CRP3.2; DCA, DSGD; PIM, CGIAR Research Programs on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Maize (MAIZE)

PR, IFPRI3; CRP2; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance; G Cross-cutting gender theme; Feed the Future Innovation Laboratory for Food Security Policy (FSP); ISI, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Scholarship on African land markets has proliferated in recent years. We contend that this scholarship consists of distinct literatures that examine non-overlapping parts of the system and diverge in their conclusions. Some studies view rising land markets as providing greater options for smallholder farmers and positive impacts on agricultural productivity and equity, while others emphasize large-scale land acquisitions and disenfranchisement as the dominant narrative. This article reviews these disparate literatures, identifies points of convergence and disagreement, and outlines steps required to achieve greater consensus on the effects of rising commodification of land. We conclude that land prices are rapidly rising throughout the region, that smallholder participation in land rental and purchase markets are generally rising, and that increased land market activity is generating complex distributional effects that remain poorly understood.

Understanding yield responses to nutrient application is a key input for extension advice and strategic agricultural investments in developing countries. A commonly used model for yield responses to nutrient inputs in tropical smallholder farming systems is QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils). While QUEFTS has a strong conceptual foundation, a key assumption is that nutrients are the only limiting factors. One implication of this is the required assumption of ‘perfect management’. This may be problematic in the application of QUEFTS in smallholder farming systems with a wide variety of yield limiting factors.In a previous study, QUEFTS was calibrated using farm trials in two major maize production zones in Nigeria. To reduce observed variability in correlations between estimated soil nutrient (N, P, K) supply and soil parameters (e.g. soil organic carbon, soil pH; step 1 of QUEFTS) a Mahalanobis distance method was used to remove data points not adhering to expected correlations. In this study, we assessed an alternative approach: can the QUEFTS model be adapted to fit smallholder farming systems and associated variation in management? Using 676 observations from the same nutrient omission trials in two major maize production zones in Nigeria, we compare a standard linear regression approach with a quantile regression approach to calibrate QUEFTS.We find that under the standard linear regression approach, there is a poor relation between predicted and observed yields. Using quantile regression, however, QUEFTS performed better at predicting attainable yields – defined as the 90th percentile of observed yields – under a wide variety of production conditions. Our results indicate that using quantile regression as a way to predict attainable yields, is a useful alternative implementation of QUEFTS in smallholder farming systems with high variability in management and other characteristics.

PR, IFPRI3; ISI; CRP3.2; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies, DSGD, CGIAR Research Program on Maize (MAIZE), Agricultural statistics and applied analyses have benefitted from moving from farmer estimates of yield to crop cut based estimates, now regarded as a gold standard. However, in practice, crop cuts and other sample-based protocols vary widely in the details of their implementations and little empirical work has documented how alternative yield estimation methods perform. Here, we undertake a well-measured experiment of multiple yield estimation methods on 237 smallholder maize plots in Amhara region, Ethiopia. We compare yield from a full plot harvest with farmer assessments and with estimates from a variety of field sampling protocols: W-walk, transect, random quadrant, random octant, center quadrant, and 3 diagonal quadrants. We find that protocol choices are important: alternative protocols vary considerably in their accuracy relative to the whole plot, with absolute mean errors ranging from 23 (farmer estimates) to 10.6 (random octant). Furthermore, while most methods approximate the sample mean reasonably well, the divergence of individual measures from true plot-level values can be considerable. We find that randomly positioned quadrants outperform systematic sampling schemes: the random octant had the best accuracy and was the most cost-effective. The nature of bias is non-classical: bias is correlated with plot size as well as with plot management characteristics. In summary, our results advocate that even “gold standard” crop cut measures should be interpreted cautiously, and more empirical work should be carried out to validate and extend our conclusions.

PR, IFPRI3; 4 Transforming Agricultural and Rural Economies; ISI; CRP3.2, DSGD, CGIAR Research Program on Maize (MAIZE), While much has been said in recent years about the importance of engaging rural youth in sub-Saharan Africa’s development, the factual data about how African youth currently engage in rural economies remain sparse. We use recent nationally representative household survey data from six countries to describe the patterns of such engagement. We find that young people participate in agriculture at similar rates to older people. However, participation in non-farm wage employment and business activity changes with age, peaking in the 30s. The likelihood of reporting no activity is greatest for people in their 20s. In more remote places, people leave school earlier and are less likely to engage in the non-farm sector, compared with people in more accessible places. We also find evidence that the non-farm economy is more diversified in relatively more accessible places, offering a larger set of options for economic engagement. We show that ‘landscapes of opportunity’ , defined by economic remoteness and agricultural potential, are an important way of assessing the choices available to young rural people. A key conclusion is that efforts to develop a ‘youth lens’ for rural development should not abandon the mainstays of rural investment strategies such as infrastructure, education, and agricultural R&D.

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.

The original version of this article unfortunately contained the following errors. (1) Title of co-author Pytrik Reidsma should be Associate Professor instead of Assistant Professor. (2) In Fig. 6, the explanations in the legend are switched. The corrected figure is shown here. (Figure presented.).

Ethiopia has achieved the second highest maize yield in sub-Saharan Africa. Yet, farmers’ maize yields are still much lower than on-farm and on-station trial yields, and only ca. 20% of the estimated water-limited potential yield. This article provides a comprehensive national level analysis of the drivers of maize yields in Ethiopia, by decomposing yield gaps into efficiency, resource and technology components, and accounting for a broad set of detailed input and crop management choices. Stochastic frontier analysis was combined with concepts of production ecology to estimate and explain technically efficient yields, the efficiency yield gap and the resource yield gap. The technology yield gap was estimated based on water-limited potential yields from the Global Yield Gap Atlas. The relative magnitudes of the efficiency, resource and technology yield gaps differed across farming systems; they ranged from 15% (1.6 t/ha) to 21% (1.9 t/ha), 12% (1.3 t/ha) to 25% (2.3 t/ha) and 54% (4.8 t/ha) to 73% (7.8 t/ha), respectively. Factors that reduce the efficiency yield gap include: income from non-farm sources, value of productive assets, education and plot distance from home. The resource yield gap can be explained by sub-optimal input use, from a yield perspective. The technology yield gap comprised the largest share of the total yield gap, partly due to limited use of fertilizer and improved seeds. We conclude that targeted but integrated policy design and implementation is required to narrow the overall maize yield gap and improve food security.

Non-PR, IFPRI1; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance; G Cross-cutting gender theme; UNFSS, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), This paper combines pre-pandemic face-to-face survey data with follow up phone surveys collected in April-May 2020 to quantify the overall and differential impacts of COVID-19 on household food security, labor market participation and local food prices in Nigeria. We exploit spatial variation in exposure to COVID-19 related infections and lockdown measures along with temporal differences in our outcomes of interest using a difference-in-difference approach. We find that those households exposed to higher COVID-19 cases or mobility lockdowns experience a significant increase in measures of food insecurity. Examining possible transmission channels for this effect, we find that COVID-19 significantly reduces labor market participation and increases food prices. We find that impacts differ by economic activities and households. For instance, lockdown measures increased households' experience of food insecurity by 12 percentage points and reduced the probability of participation in non-farm business activities by 13 percentage points. These lockdown measures have smaller impacts on wage-related activities and farming activities. In terms of food security, households relying on non-farm businesses, poorer households, those with school-aged children, and those living in remote and conflicted-affected zones have experienced relatively larger deteriorations in food insecurity. These findings can help inform immediate and medium-term policy responses, including social protection policies aiming at ameliorating the impacts of the pandemic, as well as guide targeting strategies of governments and international donor agencies by identifying the most impacted sub-populations.

PR, IFPRI3; ISI; CRP3.2, EPTD, CGIAR Research Program on Maize (MAIZE), Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010–2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77–1.12), Nigeria (0.83–1.34), Senegal (0.73–1.40), Tanzania (0.90–1.29) and Uganda (0.93–1.30), but much lower in Burkina Faso (0.96–1.04), Burundi (0.95–1.05), and Togo (0.94–1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.

Traditional agricultural research and extension relies on replicated field experiments, on-farm trials, and demonstration plots to evaluate and adapt agronomic technologies that aim to increase productivity, reduce risk, and protect the environment for a given biophysical and socio-economic context. To date, these efforts lack a generic and robust spatial framework for ex-ante assessment that: (i) provides strategic insight to guide decisions about the number and location of testing sites, (ii) define the target domain for scaling-out a given technology or technology package, and (iii) estimate potential impact from widespread adoption of the technology(ies) being evaluated. In this study, we developed a data-rich spatial framework to guide agricultural research and development (AR&D) prioritization and to perform ex-ante impact assessment. The framework uses “technology extrapolation domains”, which delineate regions with similar climate and soil type combined with other biophysical and socio-economic factors that influence technology adoption. We provide proof of concept for the framework using a maize agronomy project in three sub-Saharan Africa countries (Ethiopia, Nigeria, and Tanzania) as a case study. We used maize area and rural population coverage as indicators to estimate potential project impact in each country. The project conducted 496 nutrient omission trials located at both on-farm and research station sites across these three countries. Reallocation of test sites towards domains with a larger proportion of national maize area could increase coverage of maize area by 79–134% and of rural population by 14–33% in Nigeria and Ethiopia. This study represents a first step in developing a generic, transparent, and scientifically robust framework to estimate ex-ante impact of AR&D programs that aim to increase food production and reduce poverty and hunger.

Traditional agricultural research and extension relies on replicated field experiments, on-farm trials, and demonstration plots to evaluate and adapt agronomic technologies that aim to increase productivity, reduce risk, and protect the environment for a given biophysical and socio-economic context. To date, these efforts lack a generic and robust spatial framework for ex-ante assessment that: (i) provides strategic insight to guide decisions about the number and location of testing sites, (ii) define the target domain for scaling-out a given technology or technology package, and (iii) estimate potential impact from widespread adoption of the technology(ies) being evaluated. In this study, we developed a data-rich spatial framework to guide agricultural research and development (AR&D) prioritization and to perform ex-ante impact assessment. The framework uses “technology extrapolation domains”, which delineate regions with similar climate and soil type combined with other biophysical and socio-economic factors that influence technology adoption. We provide proof of concept for the framework using a maize agronomy project in three sub-Saharan Africa countries (Ethiopia, Nigeria, and Tanzania) as a case study. We used maize area and rural population coverage as indicators to estimate potential project impact in each country. The project conducted 496 nutrient omission trials located at both on-farm and research station sites across these three countries. Reallocation of test sites towards domains with a larger proportion of national maize area could increase coverage of maize area by 79–134% and of rural population by 14–33% in Nigeria and Ethiopia. This study represents a first step in developing a generic, transparent, and scientifically robust framework to estimate ex-ante impact of AR&D programs that aim to increase food production and reduce poverty and hunger.

PR, IFPRI4; CRP2, PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Non-PR, IFPRI1; ESSP, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Credit markets are key instruments by which liquidity constrained smallholder farmers may finance productivity investments. However, the documented low demand and uptake of agricultural credit by smallholder farmers in sub-Saharan Africa poses challenges for energizing rural transformation in the region. In this paper we investigate the impact of rainfall uncertainty (and risk more generally) on the expressed demand for credit among rural households in Ethiopia. We explore potential mechanisms through which weather risk may explain the low demand for credit. We also examine the consequences of uninsured rainfall uncertainty on productivity-enhancing and loss-reducing agricultural investments. We provide evidence that rainfall uncertainty dampens households’ demand for agricultural credit. Rainfall uncertainty is associated with credit risk-rationing, which underlies the low demand for agricultural credit in Ethiopia. We also show that rainfall uncertainty helps to explain the low uptake of productivity-enhancing agricultural technologies, such as fertilizers. On the other hand, rainfall uncertainty encourages investments in defensive agricultural inputs, such as pesticides, herbicides, and fungicides. Our results highlight the impacts of uninsured production risk on agricultural investments made by African smallholders.

PR, IFPRI3; ISI; CRP2, PHND; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; ISI; CRP2, PHND; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; ISI; CRP2, PHND; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI4; IFPRI2; GRP27; Theme 3; Subtheme 3.2, DSGD; MTID, The opportunities and constraints facing Ethiopian agriculture are strongly influenced by geographical location. Ethiopia’s diverse landscape defines certain agricultural production potentials, access to input and output markets, and local population densities, which determine both labor availability and local demand for food. Understanding the geographical expression of Ethiopia’s agricultural and rural development options provides greater information for more locally targeted policy options.

PR, IFPRI1; GRP32, DSGD, The authors identify a set of development priorities for agriculture that cut across West Africa at both the country and regional levels to achieve economywide growth goals in the region. To do this we adopt a modeling and analytical framework that involves the integration of spatial analysis to identify yield gaps determining the growth potential of different agricultural activities for areas with similar conditions and an economywide multimarket model to simulate ex ante the economic effects of closing these yield gaps. Results indicate that the greatest agriculture-led growth opportunities in West Africa reside in staple crops (cereals and roots and tubers) and livestock production. Contributing the most to agricultural growth in the Sahel are livestock, rice, coarse grains, and oilseeds (groundnuts); in Coastal countries, staple crops such as cassava, yams, and cereal seems to be relatively more important than other subsectors; and in Central Africa livestock and root crops are the sources of growth with highest potential. Results also point toward an essential range of policies and investments that are needed to stimulate the productivity growth of prioritized activities. These include developing opportunities for regional cooperation on technology adaptation and diffusion, strengthening regional agricultural markets, exploiting opportunities for greater regional cooperation and harmonization, diversifying traditional markets, and enhancing linkages between agricultural and nonagricultural sectors.

Non-PR, IFPRI1; GRP32; Subtheme 6.2; ESSP II, DSGD, The opportunities and constraints facing Ethiopian agriculture are strongly influenced by conditions which vary across geographical space. These conditions include basic agricultural production potentials, access to input and output markets, and local population densities which represent both labor availability and local demand for food. Understanding the geographical expression of these factors is an important way of making sense of Ethiopia’s agricultural and rural development options and for guiding the definition of supporting policies.

Non-PR, IFPRI2; GRP32; GRP36; ESSP II, DSGD, Rural non-farm development plays a key role in generating employment in many developing countries. Clustering is an important industrial organization in the rural non-farm sector. Based on primary surveys of both urban and rural handloom weaver clusters in Ethiopia which took place in May/June 2008, one of the most important rural nonfarm sectors, this paper examines the mechanism and performance of clustering. The clustering way of handloom production is observed even in remote rural areas, illustrating its vitality and flexibility in adapting to restricted environments. Despite its resilience in surviving in harsh environments, improvements in infrastructure can significantly increase labor productivity in a cluster. In towns with electricity access, producers work longer hours than those in towns without electricity and more entrepreneurs with limited access to capital are able to participate in handloom production because of finer division of labor.

Non-PR, IFPRI1; GRP32, "We identify a set of development priorities for agriculture that cut across West Africa, at both the country and the regional level, to achieve economy-wide growth goals in the region. To do this, we adopt a modeling and analytical framework that involves the integration of spatial analysis to identify yield gaps determining growth potential of different agricultural activities for areas with similar conditions and an economy-wide multimarket model to simulate ex ante the economic effects of closing these yield gaps. Results indicate that the greatest agriculture-led growth opportunities in West Africa reside in staple crops (cereals as well as roots and tubers) and livestock production. Rice is the commodity with the highest potential for growth and the one that could generate the greatest benefits for many countries. Activities contributing the most to agricultural growth in the Sahel are livestock, rice, coarse grains, and groundnuts; in coastal countries, staple crops like cassava, yams, and cereals seem to be relatively more important than the contributions of other subsectors; and livestock and root crops are the sources of growth with highest potential in Central Africa. Our results also point toward an essential range of policies and investments that are needed to stimulate productivity growth of prioritized activities. These include the following: development of opportunities for regional cooperation on technology adaptation and diffusion, strengthening of regional agricultural markets exploiting opportunities for greater regional cooperation and harmonization, diversification of traditional markets, and enhancement of linkages between agricultural and nonagricultural sectors." --from authors' abstract

Non-PR, IFPRI2; GRP32; GRP36; ESSP II, DSGD, This paper evaluates the impacts of access to infrastructure on development of handloom weaving clusters in Ethiopia based on a survey collected in both urban and rural areas. Geographical clustering enables entrepreneurs with limited capital to enter the business through shared workspaces and greater specialization of labor. In towns with electricity access, producers work longer hours by sharing workspaces with electric lights at lower rental cost, resulting in higher labor productivity.

Non-PR, IFPRI1; Subtheme 6.2; GRP32; GSSP, DSGD, "Strategies for boosting the agricultural economies of developing countries usually focus on small farms, attempting, for example, to link smallholders with markets through production chain development. However, such strategies often fail to differentiate between different types of small farmers or to investigate the distribution of assets within the group—efforts that are important because unequal distributions of assets can restrict pro-poor growth. Further, strategies to develop production chains favor some small farmers over others (i.e., those already participating in targeted chains and those with relatively more productive assets). Using landholding size as an organizational filter, we performed a basic descriptive analysis of smallholder traits in Ghana, using data from the 2005–2006 Ghana Living Standards Survey (GLSS5). We found strong inequalities in landholding distributions within Ghana’s small-farm sector in all regions of the country. Using a classification of smallholders we derived based on landholding size, we examined a variety of small-farm traits and found that many of the broadly perceived defining characteristics of smallholder agriculture—such as low input use and low market engagement—are negatively correlated with landholding size. The crowding of farms at the smaller end of the small-farm spectrum in Ghana suggests that rural development strategies based on expanding existing market chains will face challenges in connecting with the bulk of small producers, who are less well endowed than average statistics indicate." --from authors' abstract

Non-PR, IFPRI2; ReSAKSS, DSGD