Hydro-Climatic Information Needs of Smallholder Rice Farmers in Savelugu Municipality in Northern Region of Ghana

Abstract

Farmers in northern Ghana are vulnerable to climate change, partly because the region is dry and experiences uni-modal rainfall. The livelihood of these farmers is directly affected by rainfall variability and its effects on crop production. Reliable, timely and area-specific hydro-climatic information services could increase food production and security and play a significant role in poverty reduction. To achieve these results, there is the need to understand the hydro-climatic information needs of smallholder farmers and the role of such information in farmers’ decision-making processes in rice production, which is the call for this paper. Savelugu Municipality in the Northern of Ghana is known for its intensive lowland rice production and vulnerability to climate change. Data were collected at three (3) independent stages namely; focus group discussions, individual farmer interviews and expert interviews with organizations offering hydro-climatic information services to smallholder farmers using separate questionnaires for understanding the farmers’ needs. The results indicated that farmers adopt both scientific and local forecast methods to support the agricultural decision-making processes. Moreover, agriculture experts provide technical services, capacity building, financial support, and information on seasonal weather forecasts to farmers, however, these information are too general and not location-specific and tailored to farmers’ needs. Though farmers’ access information on rainfall, temperature, relative humidity, and storm occurrences, their needs are timely and area/ location specific information for accurate predictions and not the general or regional-based weather forecast information currently sourced from Agricultural Extension Agents, radios, televisions, peers and Agricultural Organizations. The study recommends the integration of local and scientific forecast knowledge to reduce the current prediction failures.