The creative process that leads to farmers’ innovations is rarely studied or described precisely in agricultural sciences. For academic scientists, obvious limitations of farmers’ experiments are e.g. precision, reliability, robustness, accuracy, validity or the correct analysis of cause and effect. Nevertheless, we propose that ‘farmers’ experiments’ underpin innovations that keep organic farming locally tuned for sustainability and adaptable to changing economic, social and ecological conditions.
This is the proceedings of the international conference ‘Innovations in Organic Food System for Sustainable Production and Enhanced Ecosystem Services’. The proceedings are a compilation of peer-reviewed articles based on presentations of 18 speakers invited conference speakers and published as a Special Issue of the scientific journal ‘Sustainable Agriculture Research’ by the Canadian Centre of Science and Education.
Organic farming is recognized as one source for innovation helping agriculture to develop sustainably. However, the understanding of innovation in agriculture is characterized by technical optimism, relying mainly on new inputs and technologies originating from research. The paper uses the alternative framework of innovation systems describing innovation as the outcome of stakeholder interaction and examples from the SOLID (Sustainable Organic Low-Input Dairying) project to discuss the role of farmers, researchers and knowledge exchange for innovation.
Farmers’ experiments can be defined as the autonomous activities of farmers to try or introduce something new at the farm, and include evaluation of success or failure with farmers’ own methods. Experiments enable farmers to adapt their farms to changing circumstances, build up local knowledge, and have resulted in countless agricultural innovations. Most research on the topic has been conducted in countries of the south.
This Economic and Sector Work paper, “Enhancing Agricultural Innovation: How to Go Beyond the Strengthening of Research Systems,” was initiated as a result of the international workshop, “Development of Research Systems to Support the Changing Agricultural Sector,” organized by the Agriculture and Rural Development Department of the World Bank in June 2004 in Washington, DC.
The first phase in the development of the Common Framework on Capacity Development for Agricultural Innovation systems (CD for AIS) consisted of the review of the existing literature, building up a repository of relevant documentation on agricultural innovation in general and AIS and CD for AIS. This report summarizes this first phase. In particular, Section 1 covers this brief introduction. Sections two and three focus on the review of relevant literature, presenting the methodology used and the structure of the repository itself.
African agriculture is currently at a crossroads, at which persistent food shortages are compounded by threats from climate change. But, as this book argues, Africa can feed itself in a generation and help contribute to global food security. To achieve this Africa has to define agriculture as a force in economic growth by: advancing scientific and technological research; investing in infrastructure; fostering higher technical training; and creating regional markets.
In this paper the authors provide climate smart agriculture (CSA) planners and implementers at all levels with a generic framework for evaluating and prioritising potential interventions. This entails an iterative process of mapping out recommendation domains, assessing adoption potential and estimating impacts. Through examples, related to livestock production in sub-Saharan Africa, they demonstrate each of the steps and how they are interlinked. The framework is applicable in many different forms, scales and settings.
This article explored patterns of farming system diversity through the classification of 70 smallholder farm households in two districts (Savelugu-Nanton and Tolon-Kumbungu) of Ghana’s Northern Region. Based on 2013 survey data, the typology was constructed using the multivariate statistical techniques of principal component analysis and cluster analysis.
This paper investigates Innovation Systems Concepts and Principles starting with an historical perspective. Then it analyzes their application to Integrated Agricultural Research for Development (IAR4D) and makes a comparison between the traditional Research and Development Systems Approaches and the Innovation Systems Approach.