Keith T. Ingram

Forecast Skill and Farmers' Skills: Seasonal Climate Forecasts and Agricultural Risk Management in the Southeastern United States

During the last 10 yr, research on seasonal climate forecasts as an agricultural risk management tool has pursued three directions: modeling potential impacts and responses, identifying opportunities and constraints, and analyzing risk communication aspects. Most of these approaches tend to frame seasonal climate forecasts as a discrete product with direct and linear effects. In contrast, the authors propose that agricultural management is a performative process, constituted by a combination of planning, experimentation, and improvisation and drawing on a mix of technical expertise, situated knowledge, cumulative experience, and intuitive skill as farmers navigate a myriad of risks in the pursuit of livelihood goals and economic opportunities. This study draws on ethnographic interviews conducted with 38 family farmers in southern Georgia, examining their livelihood goals and social values, strategies for managing risk, and interactions with weather and climate information, specifically their responses to seasonal climate forecasts. Findings highlight the social nature of information processing and risk management, indicating that both material conditions and valuebased attitudes bear upon the ways farmers may integrate climate predictions into their agricultural management practices. These insights translate into specific recommendations that will enhance the salience, credibility, and legitimacy of seasonal climate forecasts among farmers and will promote the incorporation of such information into a skillful performance in the face of climate uncertainty.

Forecasting Drought Using the Agricultural Reference Index for Drought (ARID): A Case Study

AbstractDrought forecasting can aid in developing mitigation strategies and minimizing economic losses. Drought may be forecast using a drought index, which is an indicator of drought. The agricultural reference index for drought (ARID) was used as a tool to investigate the possibility of using climate indices (CIs) as predictors to improve the current level of forecasting, which is El Nino–Southern Oscillation (ENSO) based. The performances of models that are based on linear regression (LR), artificial neural networks (ANN), adaptive neuron-fuzzy inference systems (ANFIS), and autoregressive moving averages (ARMA) models were compared with that of the ENSO approach. Monthly values of ARID spanning 56 yr were computed for five locations in the southeastern United States, and monthly values of the CIs having significant connections with weather in this region were obtained. For the ENSO approach, the ARID values were separated into three ENSO phases and averaged by phase. For the ARMA models, monthly time ...