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Prem Woli
and
Joel O. Paz

Abstract

Global solar radiation Rg is an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records of Rg is a serious limitation in many countries, Rg is estimated using models. For crop-model application, empirical Rg models that use commonly measured meteorological variables, such as temperature and precipitation, are generally preferred. Although a large number of models of this kind exist, few have been evaluated for conditions in the United States. This study evaluated the performances of 16 empirical, temperature- and/or precipitation-based Rg models for the southeastern United States. By taking into account spatial distribution and data availability, 30 locations in the region were selected and their daily weather data spanning eight years obtained. One-half of the data was used for calibrating the models, and the other half was used for evaluation. For each model, location-specific parameter values were estimated through regressions. Models were evaluated for each location using the root-mean-square error and the modeling efficiency as goodness-of-fit measures. Among the models that use temperature or precipitation as the input variable, the Mavromatis model showed the best performance. The piecewise linear regression–based Wu et al. model (WP) performed best not only among the models that use both temperature and precipitation but also among the 16 models evaluated, mainly because it has separate relationships for low and high radiation levels. The modeling efficiency of WP was from ~5% to more than 100% greater than those of the other models, depending on models and locations.

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Prem Woli
,
James Jones
,
Keith Ingram
, and
Joel Paz

Abstract

Drought 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 Niño–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 series of ARID were used. For the ANFIS, ANN, and LR models, ARID was predicted 1, 2, and 3 months ahead using the past values of the first principal component of the CIs. Model performances were assessed with the Nash–Sutcliffe index. Results indicated that drought forecasting could be improved for the southern part of the region using ANN models and CIs. The ANN outperformed the other models for most locations in the region. The CI-based models and the ENSO approach performed better during the winter, whereas the efficiency of ARMA models depended on precipitation periodicities. All models performed better for southern locations. The CIs showed good potential for use in forecasting drought, especially for southern locations in the winter.

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Todd A. Crane
,
Carla Roncoli
,
Joel Paz
,
Norman Breuer
,
Kenneth Broad
,
Keith T. Ingram
, and
Gerrit Hoogenboom
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Todd A. Crane
,
Carla Roncoli
,
Joel Paz
,
Norman Breuer
,
Kenneth Broad
,
Keith T. Ingram
, and
Gerrit Hoogenboom

Abstract

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 value-based 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.

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