African indigenous vegetables (AIVs) have the potential to increase food and nutritional security and contribute to improved livelihoods, but farmers’ capacity to meet the growing demand for them has been constrained by a lack of good quality seed and technical knowhow. The Good Seed Initiative (GSI), funded by Irish Aid and active in Tanzania from 2013 to 2015, targeted both seed and vegetable growers, linking them to markets through an innovation platform (IP) approach.
El objetivo general de la guía es generar las competencias necesarias para que los técnicos del MIDA puedan formular proyectos con base en los principios de la planificación estratégica y de la gestión por resultados. De manera específica, se pretende que los lectores puedan:Aplicar correctamente la metodología de marco lógico; Establecer indicadores adecuados; Incorporar la gestión de riesgos; Implementar la gestión por resultados y mejorar la sistematización de información para el monitoreo y seguimiento.
This set of guidance notes is designed to support practitioners and evaluators in conducting retrospective evaluations of a capacity development intervention or portfolio to assess and document results. Users will enhance their understanding of the capacity development process, of what works and what does not work in promoting change and to inform future programs. The standard M&E approach for assessing capacity development results has not been sufficient.
Small millets, a group of highly nutritious food, have taken a back seat in the Indian agriculture landscape in recent years, due to government policies and failings in the value chain. In this commentary, the unusual decline of small millets in comparison to its substitutes, and the repercussions thereof, were first presented as context. Thereafter, based on analysis of data from literature, survey, and stakeholder contributions, a cluster map for the Indian small millets value chain was designed, and its competitive state presented.
In the existing literature, the effects of contract farming on household welfare were examined with mixed results. Most studies looked at single contract types. This paper contributes to the literature by comparing two types of contracts – simple marketing contracts and resource- providing contracts – in the Ghanaian oil palm sector. We investigate the effects of both contracts on farm income, as well as spillovers on other household income sources. We use survey data collected with an innovative sampling design and a control function approach to address possible issues of endogeneity.
This publication comprises 24 full papers/abstracts presented at the “High Level Policy Dialogue on Investment in Agricultural Research for Sustainable Development in Asia and the Pacific” (Bangkok, 8-9 December 2015).
We look at the trade-off between smallholder cocoa intensification and the ecosystem in Indonesia and investigate the determinants of environmental efficiency in cocoa production. In our analysis, we apply a distance output function that includes cocoa production and the abundance of native rainforest plants as outputs. Our data set, based on a household and environment survey conducted in 2015, allows us to analyze 208 cocoa producers with both measured and self-reported data. We find that the intensification of cocoa farms results in higher ecosystem degradation.
Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field.
Though extension services have long since proved their value to agricultural production and farmer prosperity, their record in sub-Saharan Africa has been mixed. To study the impact of such programs on farmers' learning about agricultural technologies, we implemented a quasi-randomized controlled trial and collected detailed panel data among Malawian farmers. Based on those findings, we develop a two-stage learning framework, in which farmers formulate yield expectations before deciding on how much effort to invest in learning about these processes.