Search

Monitoring smallholder agricultural productivity growth, one of the targets of the Sustainable Development Goals, rests on accurate measures of crop production and land area. Existing methods and protocols for measuring smallholder production and plot size are prone to various sources and forms of mismeasurement. Inaccuracies in production and land area measurement are likely to distort descriptive and predictive inferences. We examine the sensitivity of empirical assessments of the relationship between agricultural productivity and land area to alternative measurement protocols. We implement six production and six land area measurement protocols, and show that most of these protocols differ systematically in their accuracy. We find that an apparent inverse size–productivity relationship in our data is fully explained by measurement error in both production and plot size. Moreover, we show that some of the previously used "gold standard" measures are themselves prone to nonclassical measurement error, and hence can generate spurious inverse size–productivity findings. Our results also show that slight improvements in the precision of objective measures significantly reduce the inferential bias associated with the size–productivity relationship. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the "true yield" measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available. [ABSTRACT FROM AUTHOR]

Credit markets are key instruments by which liquidity-constrained smallholder farmers may finance productive investments. However, the documented low demand and uptake of agricultural credit by smallholder farmers in sub-Saharan Africa pose challenges for energizing rural transformation in the region. In this paper, we investigate the impact of rainfall uncertainty—a major source of production risk—on the uptake of credit by rural farm households in Ethiopia. We further examine whether rainfall uncertainty explains credit rationing among those households not participating in rural credit markets. We find that rainfall variability discourages the uptake of agricultural credit. We also find that rainfall variability is associated with credit risk rationing, expressed as low demand for agricultural credit. We show that our findings are robust to alternative ways of constructing rainfall variability (inter-annual or inter-seasonal) and a battery of robustness checks. For instance, we show that rainfall variability is a strong predictor of credit uptake in rural areas while less relevant in urban areas. We also document heterogeneous responses to rainfall variability; those households living in the arid and semi-arid lands of Ethiopia, which are believed to be more vulnerable to recurrent weather shocks, are more responsive to rainfall variability in terms of reduced uptake of agricultural credit. Our results highlight the impacts of uninsured production risk on the demand for agricultural credit and hence smallholder agricultural investments. Our findings suggest the importance of interventions aimed at relaxing smallholders' credit rationing while also reducing their production risk. [ABSTRACT FROM AUTHOR]

• This paper provides fresh evidence on the ex durante impacts of an armed conflict on households' food security in Ethiopia. • We also assess potential mechanisms and hence evaluate impacts on livelihood activities and access to food markets. • Seven months into the conflict, we find that the war increased the probability of moderate to severe food insecurity by 37 percentage points. • 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 significantly reduced 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. 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. [ABSTRACT FROM AUTHOR]

• We use five rounds of panel data to examine rural households' resilience outcomes associated with participation in Ethiopia's Productive Safety Nets Program (PSNP). • We employ a probabilistic moment-based approach for measuring resilience and evaluate the role of PSNP transfers and duration of participation on households' resilience. • PSNP transfers below the median are less likely to generate meaningful improvements in resilience. • Continuous participation in the PSNP is associated with higher resilience. • Combining safety nets with income generating or asset building initiatives may be particularly efficacious at building poor households' resilience. • Boosting household resilience may require significant transfers over multiple years. 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. [ABSTRACT FROM AUTHOR]