L’ouest du Rio Grande do Sul est dominé par la culture du soja, du riz et par l’élevage bovin. Dans la partie sableuse, le milieu est affecté par des phénomènes d’érosion produisant des modelés éoliens spectaculaires (arenização) rappelant dans l’imaginaire ceux des déserts. La production agricole est importante ce qui engendre des prélèvements d’eau pour l’irrigation du riz, mais aussi l’utilisation de pesticides pour l’ensemble des cultures. La gestion durable des ressources en eau et en sol de cette région nécessite la mise en place d’action de conservation.
Given the diversity and context-specificity of innovation systems approaches, in March 2007 the World Bank organized a workshop in which about 80 experts (representing donor agencies, development and related agencies, academia, and the World Bank) took stock of recent experiences with innovation systems in agriculture and reconsidered strategies for their future development. This paper summarizes the workshop findings and uses them to develop and discuss key issues in applying the innovation systems concept. The workshop’s recommendations, including next steps for the wider
This paper synthesizes Component 2 of the Regoverning Markets Programme. It is based on 38 empirical case studies where small-scale farmers and businesses connected successfully to dynamic markets, doing business with agri-processors and supermarkets. The studies aimed to derive models, strategies and policy principles to guide public and private sector actors in promoting greater participation of small-scale producers in dynamic markets. This publication forms part of the Regoverning Markets project.
This paper examines the role of postsecondary agricultural education and training (AET) in sub-Saharan Africa in the context of the region’s agricultural innovation systems. Specifically, the paper looks at how AET in sub-Saharan Africa can contribute to agricultural development by strengthening innovative capacity, or the ability of individuals and organisations to introduce new products and processes that are socially or economically relevant, particularly with respect to smallholder farmers who represent the largest group of agricultural producers in the region.
The universal application of the T&V model of agricultural extension in more than 50 countries is one of agricultural development’s best known failures. The approach worked well in places where it was originally developed, but proved inappropriate almost everywhere else. In this report Rasheed Sulaiman V. and Andy Hall worry that an apparently successful extension innovation piloted in India is set to suffer a similar fate.
Over the past few decades, some countries in Asia have been more successful than others in addressing poverty and malnutrition. The key question is what policies, strategies, legislation and institutional arrangements have led to a transformed agricultural sector, effectively contributing to poverty alleviation and addressing malnutrition. The great majority of national policymakers within and outside the Asia-Pacific region are keen to understand the causes of agricultural development and transformation in successful countries in Asia.
Due to the increasing gap between input costs and the final prices they receive for their produce, Indian farmers have been increasingly affected by the current agrarian crisis. It is within this context that Zero Budget Natural Farming (ZBNF) - a farming method promising low to zero input costs - has been gaining momentum.
This paper contends that the exclusion of millions of poor from agricultural development gains is inexorably linked to the innovation system features that have evolved over time. An oft repeated lament of the Government of India about the inadequacy of reforms in agricultural research and extension, is used to explore the structure and institutions of agricultural innovation. Three main components of the agricultural innovation system, are the agricultural research and extension actors, the farming communities, and policy making agencies.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.
Productivity growth in smallholder agriculture is an important driver of rural economic development and poverty reduction. However, smallholder farmers often have limited access to information, which can be a serious constraint for increasing productivity. One potential mechanism to reduce information constraints is the public agricultural extension service, but its effectiveness has often been low in the past.