Several drought indices are available to compute the degree of drought to which crops are exposed. Th ey vary in complexity, generality, and the adequacy with which they represent processes in the soil, plant, and atmosphere. Agricultural Reference Index for Drought (ARID) was developed as a reference index to approximate the water stress factor that is used to aff ect growth and other physiological processes in crop simulation models. Using RMSE, Willmott d index, and modeling effi ciency (ME) as performance measures, ARID was evaluated using soil water contents in the root zone measured daily in two grass fi elds in Florida. Th e ability of ARID was assessed through comparison with the water defi cit index (WSPD) of the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model. Seven other drought indices were compared with WSPD to identify the most appropriate agricultural drought index. Values of each index were computed for full canopy cover periods of maize (Zea mays L.) crops for 16 locations in the U.S. Southeast. Using periodic values, the performance of each index was assessed in terms of its correlation (r) with and departure from WSPD. Th e ARID reasonably predicted soil water contents (RMSE = 0.01-0.019, d index = 0.92-0.94, ME = 0.66-0.73) and adequately approximated WSPD (r = 0.90, RMSE = 0.15). Among the indices compared, ARID mimicked WSPD the most closely (RMSE smaller by 1-83%, r larger by 1-630%) and captured weather fl uctuation eff ects the most accurately. Results indicated that ARID may be used as a simple index for quantifying drought and its eff ects on crop yields.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

The Columbia Basin in the Pacific Northwest is a highly productive area for potatoes in the United States. Here, nitrate is the most frequently documented groundwater contaminant, and the challenge of maximizing crop productivity while minimizing the nitrate pollution still remains. This study assessed the responses of tuber yield, nitrate leaching, and profit margin to irrigation water amount, irrigation interval, nitrogen application rate, and soil type using 30 years of historical weather data and two representative soils in three locations of this region. A potato model was used to simulate the response variables for a total of 7500 scenarios (5 irrigation intervals x 5 irrigation amounts x 5 nitrogen rates x 2 soil types x 30 years) for each location. The results showed that nitrate leaching was greater with a larger irrigation-, a longer irrigation interval, a higher nitrogen rate, and a lighter soil. Tuber yield was larger with a smaller irrigation, a higher nitrogen rate, and a heavier soil. Profit margin was larger with a smaller irrigation and a heavier soil. The optimum amount of irrigation water for the study region was 400 mm, at which both tuber yields and profit margins were the largest with the nitrogen application rate of 336 kg ha-1. The increase in leaching with a larger irrigation was smaller for a longer irrigation interval and a lighter soil but larger for a higher nitrogen rate. These findings might be helpful to potato growers in this region in identifying irrigation and nitrogen application rates aimed toward maximizing yields and profits while minimizing the nitrate contamination of groundwater.

or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. bd a, m i i D = βζ Θ −θ [4]where β is the drainage coeffi cient, bd a, Θ i is the available soil water content on the ith day before drainage (mm mm -1 ), and θ m is the plant-available water capacity (mm mm -1 ). Using the ABSTRACT Th e Agricultural Reference Index for Drought (ARID) is a newly designed decision support tool to quantify plant water defi cit and predict the eff ect of defi cit on crop yields. Th is study explored uncertainties in ARID associated with its parameters and the sensitivity of ARID to its parameters. Daily values of ARID were computed for fi ve selected locations in the southeastern United States using historical weather data for a 30-yr period . Uncertainty and sensitivity analyses were performed using the Fourier amplitude sensitivity test. According to the results, available water capacity was the most infl uential parameter, explaining about 60% of the total variance in ARID, followed by root zone depth, which contributed about 30% to the total variance. Of the fi ve parameters, runoff curve number and drainage coeffi cient had insignifi cant infl uence. Th e eff ect of the water uptake coeffi cient was negligible in most cases, and this parameter never contributed more than 15% of the total variance. Except for soils with large moisture content, ARID had small uncertainties associated with its parameters. Even under wet conditions, ARID mostly concentrated around its default values, indicating small uncertainties, which were mainly due to available water capacity. Th e type of distribution selected for the parameters was found to have signifi cant infl uence on sensitivity and uncertainty. With a shift from uniform to normal distribution, uncertainty in ARID decreased by 15 to 50%. Results indicated that although ARID uses a fi xed set of parameter values, it is applicable to a wide range of crops, soils, topographies, and management and has fairly small uncertainties. P. Woli, Agricultural and Biological Engineering Dep., Mississippi State

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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.

The winter wheat (Triticum aestivum L.) growing season in the southeastern United States occurs during the period when the climate of this region is strongly influenced by El Niño–Southern Oscillation (ENSO). The ENSO‐based interannual climate variability might influence growth, maturity, and yield of winter wheat. Because different maturity groups of wheat cultivars head at different times of the year, the groups are expected to have different impacts of climate variability. This study examined whether the yield difference between early and late maturity groups of winter wheat cultivars grown in this region were associated with ENSO‐based climate. Data on yield, planting date, and heading date were obtained for a number of wheat cultivars grown at four locations in Georgia during the 1975 to 2012 period. Wheat cultivars were classified according to heading date as early or late maturity, and yield differences between maturity groups and among ENSO phases were examined using the Wilcoxon rank sum test. Results showed that the early maturity group could out‐yield the late maturity group in southern locations during La Niña, whereas the late group could out‐yield the early group in northern locations during El Niño. Of all ENSO phases, La Niña was associated with the largest yields. During El Niño, the yield difference between early and late groups increased with an increase in latitude, whereas during La Niña, the yield difference increased with a decrease in latitude. These findings might be helpful to wheat growers in this region in optimizing decisions regarding planting date and cultivar selection to reduce the risks related to climate variability.

Global solar radiation R g is an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records of R g is a serious limitation in many countries, R g is estimated using models. For crop-model application, empirical R g 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 R g 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-meansquare 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.