Raised-Bed Farming System Technology (RFST) in Egypt: Qualities and Determinants of Adoption in Different Agricultural Livelihood Conditions.

Abstract

Mechanized raised-bed technology (MRBT) has been recognized as an important component of integrated water management to achieve higher productivity in intensive irrigated systems, such as those found in the Nile Delta. Effective management and policies for spreading the technology for improving food security and resource use efficiency require adequate understanding of the qualities and drivers/determinants of farmers’ adoption of MRBT. Related research efforts on these issues have been challenged the diversity of socio-ecological contexts that shape farmers’ adoption and related driving effects. This study empirically investigates the issues using a system-based option-by-context approach for guiding concrete analytical steps and statistical methods in coping with the challenges of system complexity and contextual diversity in two governorates (Sharkia and Assiut) of Egypt. The main finding of the study is that, classifying the considered agrarian population into a limited number of agricultural livelihood system (ALS) types and conducting multivariate inferential statistics for both (1) whole sample population and (2) each specific ALS types helped discover hidden causal relationships shaping MRBT adoption which would have been identified by considering the whole sample only. For instance, roles of effectiveness in agricultural institutions, such as water use association (WUA) and agricultural market association (AMA), in MRRT adoption are found in specific ALS types rather through looking at the merged population. Some causal relationships are found significant via inferential statistics for the whole sample, but actually taking effects in a specific ALS group (e.g. the case for the effect of farm size on MRBT adoption of poor and non-farm based income group). The consideration of merged sample population is also complementary to ALS type-specific treatments: some causal effects (e.g. the role of education) can be evidentially inferred through the use of a large sample that provides enough statistical power for inferential statistics. The used approach and empirical results shall be useful for betterment of targeting in agricultural development (extension services, MRBT-related project and program) in practice.