The Agriculture Technology Program for Turkmenistan (AgTech) takes a comprehensive approach to agricultural development in Turkmenistan by implementing high-impact activities in the livestock and greenhouse horticulture sectors to achieve the two objectives: improve genetics, education and organizations for private livestock producers; introduce successful agribusiness practices.
The Agriculture Technology Program for Turkmenistan (AgTech), funded by USAID and implemented by Weidemann Associates, Inc., aims to increase and develop private enterprises, and improve productivity of private, small and household farms. The project has two key components: the improvement of genetics, education and organization as a means of increasing the incomes of private agribusiness involved in livestock; skills building for private producers, processors and marketers of fruits and vegetables.
This review aims to identify key issues and opportunities needed to bring current Agricultural Education and Training (AET) systems up to the needed capacity. This paper first looks at the opportunities identified in the preliminary research. Next the paper looks at some of the many pitfalls learned from previous AET work that should be avoided moving forward. Lastly the paper gives a brief explanation for some of the key areas that the preliminary research identified as requiring further research and study in a modern day context.
The Agriculture Technology Program for Turkmenistan (AgTech) takes a comprehensive approach to agricultural development in Turkmenistan by implementing high-impact activities in the livestock and greenhouse horticulture sectors to achieve the two objectives: improve genetics, education and organizations for private livestock producers; introduce successful agribusiness practices.
The Agriculture Technology Program for Turkmenistan (AgTech) takes a comprehensive approach to agricultural development in Turkmenistan by implementing high-impact activities in the livestock and greenhouse horticulture sectors to achieve the two objectives: improve genetics, education and organizations for private livestock producers; introduce successful agribusiness practices.
The contributions and dynamic interaction of thousands of stakeholders from all sectors have created the GCARD (Global Conference on Agricultural Research for Development) Roadmap, providing a clear path forward for all involved. The Roadmap highlights the urgent changes required in Agricultural Research for Development (AR4D) systems globally, to address worldwide goals of reducing hunger and poverty, creating opportunity for income growth while ensuring environmental sustainability and particularly meeting the needs of resource-poor farmers and consumer.
This study presents a quasi-experimental analysis of the impact of FairTrade certification on the commercial performance of coffee farmers in Tanzania. In doing so the study emphasises the importance of a well-contextualised theory of change as a basis for evaluation design. It also stresses the value of qualitative methods to control for selection bias. Based on a longitudinal (pseudo-panel) dataset comprising both certified and conventional farmers, it shows that FairTrade certification introduced a disincentive to farmers’ commercialisation.
The aim of this report is to provide a detailed review of documented social learning processes for climate changeand natural resource managementas described in peer-reviewed literature. Particular focus is on identifying (1) lessons and principles, (2) tools and approaches, (3) evaluation of social learning, as well as (4) concrete examples of impacts that social learning has contributed to.
This paper discusses a range of approaches and benchmarks that can guide future design of value chain impact evaluations. Twenty studies were reviewed to understand the status and direction of value chain impact evaluations. A majority of the studies focus on evaluating the impact of only a few interventions, at several levels within the value chains. Few impact evaluations are based on well-constructed, well-conceived comparison groups. Most of them rely on use of propensity score matching to construct counterfactual groups and estimate treatment effects.
The UNDP Capacity Assessment Methodology User‘s Guide gives UNDP and other development practitioners a detailed step-by-step guide to conducting a capacity assessment using the UNDP Capacity Assessment Methodology, which consists of the UNDP Capacity Assessment Framework, a three-step process and supporting tools.