This paper introduces Rapid Appraisal of Agricultural Innovation Systems (RAAIS). RAAIS is a diagnostic tool that can guide the analysis of complex agricultural problems and innovation capacity of the agricultural system in which the complex agricultural problem is embedded. RAAIS focuses on the integrated analysis of different dimensions of problems (e.g. biophysical, technological, socio-cultural, economic, institutional and political), interactions across different levels (e.g.
Parasitic weeds such as Striga spp and Rhamphicarpa fistulosa in smallholder rice production systems form an increasing problem for food and income security in sub-Saharan Africa. In this paper we implement the Rapid Appraisal of Agricultural Innovation Systems (RAAIS) as a diagnostic tool to identify specific and generic entry points for innovations to address parasitic weeds in rain-fed rice production in Tanzania. Data were gathered across three study sites in Tanzania where parasitic weeds are eminent (Kyela, Songea Rural and Morogoro Rural districts).
This study identifies entry points for innovation for sustainable intensification of agricultural systems. An agricultural innovation systems approach is used to provide a holistic image of (relations between) constraints faced by different stakeholder groups, the dimensions and causes of these constraints, and intervention levels, timeframes and types of innovations needed. The authors aim at showing that constraints for sustainable intensification of agricultural systems are mainly of economic and institutional nature.
In this paper, is first described the design and development process of a modular ICT application system called GeoFarmer. Geofarmer was designed to provide a means by which farmers can communicate their experiences, both positive and negative, with each other and with experts and consequently better manage their crops and farms. We designed GeoFarmer in a collaborative, incremental and iterative process in which user needs and preferences were paramount.
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.