Los casos incluidos en el presente documento se construyeron a partir de información suministrada, mediante entrevistas, por personas vinculadas a las organizaciones socias de ACORDAR, que son las protagonistas de estos casos. La escritura de los 10 casos de éxito fue realizada por el Centro Internacional de Agricultura Tropical (CIAT) y retroalimentada por las organizaciones que suministraron la información y por Catholic Relief Services (CRS)
The 2016 Global Agricultural Productivity Report advocates policies and innovations in five key areas to help the agriculture and food sectors manage uncertain seasons of fluctuating business cycles and climate change, while fostering competitiveness today and sustainable growth tomorrow.
IFPRI’s flagship report reviews the major food policy issues, developments, and decisions of 2016, and highlights challenges and opportunities for 2017 at the global and regional levels. This year’s report looks at the impact of rapid urban growth on food security and nutrition, and considers how food systems can be reshaped to benefit both urban and rural populations. Drawing on recent research, IFPRI researchers and other distinguished food policy experts consider a range of timely questions:
■ What do we know about the impacts of urbanization on hunger and nutrition?
This report aims to o estimate the current use of machinery in rice and bananas value chains; To establish determinants of mechanization in rice and bananas along the entire value chains; and estimate the effects of the determinants on mechanization levels. This study therefore seeks to identify factors that influence
mechanization levels for rice and bananas value chains. The findings from this study will help provide technical and policy recommendations for the improvement of not only the rice and banana value chains but the entire agriculture sector
This report assesses trends in investments, human resource capacity, and outputs in agricultural research in SSA, excluding the private (for-profit) sector. The analysis uses information collected by Agricultural Science and Technology Indicators (ASTI)—led by the International Food Policy Research Institute (IFPRI) within the portfolio of the CGIAR Research Program on Policies, Institutions, and Markets (PIM).
This study introduces a framework for managing information flow in innovation systems. An organisation's capacity to receive information, to share it with others and to learn from it is assumed to be the key factor that shapes the flow patterns and, hence, the performance of the innovation system concerned. The framework is applied to characterise the information structure underlying the agricultural innovation system of Azerbaijan and to develop an information strategy for the system to accelerate the information flow.
Learn about the Women’s Empowerment Farmer Business Schools (WE-FBS) implemented in Kenya through FAO’s Flexible Multi-Partner Mechanism (FMM). The approach prompts men and women to reflect critically on their roles, resources, and activities in farming, and to develop strategies that are needed to maximize their commercial potential.
The presented model of operations capacity planning allows obtaining quantitative dimensions of the service system parameters for Administrative Services Centre. A methodology for the practical application of this model has also been presented here. The present paper aims at offering support for operations managers in the service sector for decision making regarding the operations capacity. The paper was presented at 7th annual International scientific conference "New dimensions in the development of society", held in Jelgava, Latvia, in 2011.
This guide is the second in a series of documents designed to support agencies implementing participatory agroenterprise development program operating within defined geographical areas.
The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers.