water-or nutrient-limited production levels in a given rice environment. Yield-reducing factors induce yield losses by reducing or hampering growth, including abiotic and biotic factors. Biotic factors include weeds, pests and diseases; abiotic factors include salinity, alkalinity and iron toxicity. Attainable yield (Ya) refers to the yield that can be achieved with best management practices that control yield-limiting and yield-reducing factors in a economically optimal manner. Under irrigated conditions, this is typically about 80% of Yp. The yield gap is commonly defined as the difference between Yp or Ya and actual average farmers' yields. A range of socioeconomic reasons underpin these yield gaps at harvest and the substantial losses that often occur after harvest (see Rickman et al., Chapter 27, this volume), such as lack of availability of key inputs (labour, fertilizer, etc.) and sub-optimal knowledge of improved management practices. In this chapter, we quantify farmer perceptions of major biotic and abiotic constraints that limit and reduce rice yields in farmers' fields in SSA. We also estimate the potential impact of research addressing such constraints.

Studies of improved seed adoption in developing countries are almost always based on household surveys and are premised on the assumption that farmers can accurately self-report their use of improved seed varieties. However, recent studies suggest that farmers' reports of seed varieties planted, or even whether the seed is local or improved, are sometimes inconsistent with the DNA fingerprinting results of those crops. We use household survey data from Tanzania to test the alignment between farmer-reported and DNA-identified maize seed types planted. In the sample, 70% of maize seed observations are correctly reported as local or improved, while 16% are type I errors (falsely reported as improved) and 14% are type II errors (falsely reported as local). Type I errors are more likely to have been sourced from other farmers, rather than formal channels. An analysis of input use,

Purpose The purpose of this paper is to examine the determinants of poverty and the persistence of poverty in Benin using a longitudinal data for the years 2006-2011. The paper also seeks to understand the dynamic of poverty and what factors explain households’ mobility across poverty status over time. Design/methodology/approach To answer the research questions, the paper develops and estimates logit and probit models of poverty. In addition to households’ characteristics as explanatory variables, the models control for the previous years’ poverty status to test for the hypothesis of persistence in poverty. Next, the paper extends the analysis to compute poverty transition matrix and estimates a multinomial models of the determinants of these transitions. Findings The paper finds that households’ demographic and socio-economic characteristics are strongly correlated with their poverty status. It also finds a strong evidence of persistence of poverty: being poor in a year increases the likelihood of remaining poor in the following years. The analysis of the dynamic of poverty reveals that there is a large and rapid change in poverty with households moving in and out of poverty. Across all models, it appears that human capital accumulation through education and labor skills are the strongest forces lifting households out of poverty and persistence of poverty. Practical implications Taken together, the results suggest that public policies should not only seek to lift households out of poverty, but also seek to reduce re-entries into poverty by investing in education and improving employment skills. Originality/value A key contribution of the study is the characterization of the profile of poor and persistently poor households in Benin, and the analysis of the factors explaining the dynamic of poverty. It updates and complements previous studies on poverty in Benin using the most recent nationally representative longitudinal data. The findings stress the crucial importance of human capital as a key force to lift households out of poverty.

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Designing effective policies for economic development often entails categorizing populations by their rural or urban status. Yet there exists no universal definition of what constitutes an “urban” area, and countries alternately apply criteria related to settlement size, population density, or economic advancement. In this study, we explore the implications of applying different urban definitions, focusing on Tanzania for illustrative purposes. Toward this end, we refer to nationally representative household survey data from Tanzania, collected in 2008 and 2014, and categorize households as urban or rural using seven distinct definitions. These are based on official administrative categorizations, population densities, daytime and nighttime satellite imagery, local economic characteristics, and subjective assessments of Google Earth images. These definitions are then applied in some common analyses of demographic and economic change. We find that these urban definitions produce different levels of urbanization. Thus, Tanzania's urban population share based on administrative designations was 28% in 2014, though this varies from 12% to 39% with alternative urban definitions. Some indicators of economic development, such as the level of rural poverty or the rate of rural electrification, also shift markedly when measured with different urban definitions. The periodic (official) recategorization of places as rural or urban, as occurs with the decennial census, results in a slower rate of rural poverty decline than would be measured with time-constant boundaries delimiting rural Tanzania. Because the outcomes of analysis are sensitive to the urban definitions used, policy makers should give attention to the definitions that underpin any statistics used in their decision making.