El Curso Masivo en Línea (MOOC) gratis sobre la Gestión de Datos Abiertos en Agricultura y Nutrición fue creado originalmente en el año 2016. El curso fue dado 5 veces entre Noviembre del 2017 y noviembre del 2018, alcanzando a más de 5000 personas mundialmente, antes de ser hecho disponible para su uso sin restricciones.
Ce cours en ligne massif (MOOC) gratuit sur la gestion des données ouvertes en agriculture et nutrition a été créé en 2016. Déjà en 2017 et 2018, plus de 5000 participants de partout dans le monde ont déjà suivi cette formation, laquelle est maintenant disponible pour une utilisation gratuite et sans restriction.
This free Massive Open Online Course (MOOC) on Open Data Management in Agriculture and Nutrition was first created in 2016. The course was delivered 5 times between November 2017 and November 2018, reaching over 5000 people globally, before being made available for unrestricted use
CABI and the Cereal Growers Association (CGA) have been sharing information with farmers in Kenya on how to effectively and safely manage the continuing threat of the invasive fall armyworm (Spodoptera frugiperda). This was achieved thanks to a development communication campaign that combined video sharing through a network of lead farmers and social media.
This paper considers genetically modified (GM) seed adoption decisions by farmers in a developing country under two alternative information regimes (with and without perfect information regarding production conditions) that allows the monopolist producer of GM seeds to either practice perfect discrimination or uniform pricing. Under each regime we analyze two scenarios: when the government can and cannot credibly commit to the announced form of welfare enhancing intervention in the domestic seed market.
Micronutrient malnutrition is a public health problem in many regions of the developing world. Severe vitamin A and iron deficiencies are of particular concern due to their high prevalence and their serious, multiple health effects on humans. This paper examines dietary patterns and nutrient intakes, as well as their socioeconomic determinants among households in the Philippines.
While several studies have shown that genetically modified Bt cotton can benefit smallholder farmers economically, the sustainability of these effects is still unclear and debated controversially between biotechnology proponents and critics. We use unique panel data of 533 cotton farmers, collected in India between 2002 and 2008, to analyze Bt impacts on cotton yield, profit, and household living standards. Results from fixed effects models show that the adoption of Bt cotton is associated with a net yield gain of 24% and a profit increase of 50%.
Labor saving innovations are essential to increase agricultural productivity, but they might also increase inequality through displacing labor. Empirical evidence on such labor displacements is limited. This study uses representative data at local and national scales to analyze labor market effects of the expansion of oil palm among smallholder farmers in Indonesia. Oil palm is labor-saving in the sense that it requires much less labor per unit of land than alternative crops.
In Sub-Sahara Africa, adoption rates of improved crop varieties remain relatively low, which is partly due to farmers’ limited access to information. In smallholder settings, information often spreads through informal networks. Better understanding of such networks could potentially help to spur innovation and farmers’ exposure to new technologies. This study uses survey data from Tanzania to analyze social networks and their role for the spread of information about improved varieties of maize and sorghum.
Most micro-level studies on the impact of agricultural technologies build on cross-section data, which can lead to unreliable impact estimates. Here, we use panel data covering two time periods to estimate the impact of tissue culture (TC) banana technology in the Kenyan small farm sector. TC banana is an interesting case, because previous impact studies showed mixed results. We combine propensity score matching with a difference-in-difference estimator to control for selection bias and account for temporal impact variability.