The Scaling Agricultural Innovations Workshop gathered scaling experts from a range of organizations and agriculture sectors to share their experiences and ideas on the findings and lessons learned from five case studies (hybrid maize in Zambia, irrigated rice in Senegal, Purdue Improved Crop Storage bags in Kenya, agricultural machinery services in Bangladesh, and Kuroiler chickens in Uganda).
This brief outlines why it is needed an index to measure and monitor women’s access to the services, markets, policies and other aspects constraining their ability to contribute to and benefit from opportunities in agriculture and agribusiness, especially in the developing world. This would allow policy-makers, women’s development advocates and development partners to better focus their efforts so they make agriculture work for women
In this study the farmers were first asked to answer two sets of statements related to views on climate change and experiences on changes so far in their own farm or nearby locations.
In this chapter, it is applied the CGPE model to analyzing the performance of policy processes with respect to the production of efficient policy choices. Within the CGPE approach participation of stakeholder organizations is modeled in two ways. First, as classical lobbying influence and second as informational influence within a model of political belief formation.
Governments of low-income countries and international development donors are increasing their funding for research at least in part on the assumption that research has positive impacts on socioeconomic development. Four pathways are commonly cited to describe how research will contribute to development: 1. Investment in research will drive economic growth; 2. Investment in research will increase human capital; 3. Investment in research will lead to the development of pro-poor products and technologies; 4.
What are key characteristics of rural innovators? How are their experiences similar for women and men, and how are they different? To examine these questions, this study draw on individual interviews with 336 rural women and men known in their communities for trying out new things in agriculture. The data form part of 84 GENNOVATE community case studies from 19 countries. Building on study participants’ own reflections and experiences with innovation in their agricultural livelihoods, we combine variable-oriented analysis and analysis of specific individuals’ lived experience.
This report analyses the experiences and lessons from three World Bank-Supported watershed development projects in the Indian states of Karnataka, Himachal Pradesh, and Uttarakhand.5 The primary reason for the analysis was to guide the development and execution of new watershed programs in India, including new Bank-supported state-level operations in Uttarakhand and Karnataka, and a proposed national project now under preparation.
This thesis is situated in this field of inquiry and studies entrepreneurship in agriculture. It explores how we can further develop both agriculture and sustainable rural areas. Farmers have traditionally played a significant role in rural areas and rural development, and still do. However in pace with societal development and the reduced number of farms and farm production, their role has changed. Today, they are considered as raw material producers, being the first link in a food chain, and active in landscape conservation in the countryside.
The presentation was given in January 2009 and introduced why a new approach for livestock development for poverty alleviation was desirable, innovation, innovation systems and value chains, building of innovation platforms, learning-oriented monitoring and evaluation, and scaling up and out.
This paper examined cowpea value chain mapping and marketing efficiency among cowpea farmers in Ga-Molepo of Capricorn district and Bela-Bela of Waterberg district. Primary data was collected through face to face interviews from 80 smallholder cowpea farmers using structured questionnaire. Value chain map, descriptive statistics and binary logistic regression model were used to analyse the data