Purpose – The purpose of this paper is to explain the factors affecting crop insurance purchases by farmers in Inner Mongolia, China. Design/methodology/approach – A survey of farmers in Inner Mongolia, China, is undertaken. Selected variables are used to explain crop insurance purchases, and a probit regression model is used for the analysis.
The report was authored by Olivier Mahul (Insurance for the Poor Program, GCMNB, World Bank, co-task team leader), Henry Bagazonzya (SASFP, World Bank, co-task team leader), Charles Stutley (Agricultural Insurance Specialist, Consultant) and Kirti Devkota (Agricultural Credit Specialist, Consultant).
In India, agricultural risks are exacerbated by a variety of factors, ranging from climate variability and change, frequent natural disasters, uncertainties in yields and prices, weak rural infrastructure, imperfect markets and lack of financial services including limited span and design of risk mitigation instruments such as credit and insurance.
The distribution of commodity-related payments and Federal crop insurance indemnities to U.S. farmers has shifted to larger farms as more and more U.S. agricultural production is done on those farms. Since the operators of larger farms tend to have higher household incomes than other farm operators, commodity-related program payments and Federal crop insurance indemnities also have shifted to higher income households.
This paper presents the findings of almost eight years of research and development into the topic of weather index insurance at the World Bank. It is the culmination of the efforts of a relatively small and dedicated team of individuals (both staff and consultants). It has been a challenging “voyage of discovery,” addressing both technological and practical barriers to the development of weather index insurance products in a number of developing countries.
Agrometeorological systems for regional crop yield forecasting have traditionally relied on weather data derived from weather stations for crop simulation and yield prediction. In recent years, numerical weather prediction (NWP) models have become an interesting source of weather data with the potential to replace observed weather data.
Climate change inevitably leads to large regional variations in risks and opportunities and is likely to affect most farmers in the Mediterranean in the next decades. The interpretation of climate projections to determine appropriate policy responses is not without difficulties, such as understanding local uncertainty and responses of specific crops to sets of conditions.
The intensity and area of occurrence of pest species are strongly determined by the overall climate conditions of a locality and the weather pattern within a given season in combination with other factors (e.g. host plant abundance). While inter-seasonal weather variability and consequent fluctuations of individual pest species are well-known phenomena, changes in overall climate conditions and associated range shifts of particular species have likewise become important areas of research, especially during the last decade.
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