In this chapter, the authors reflect on a case of seasonal rainfall forecast dissemination to farmers in the Sahel-Sudan region. In the case of climate forecasts, farmers' interpretations are anchored in their remembrance of desirable or dreaded situations they lived through, their observations about the conditions that brought them about, and their assessments of how they managed through them. Seasonal rainfall forecasts are based on the principle that sea surface temperatures influence global atmospheric circulation. Some farmers, especially where it had already started raining in nearby areas but not locally, understood it as a prediction that rains would be spatially localized, another key aspect of climate variability often mentioned in farmers' recollections of the 2000 season. The case study presented suggests that farmers interpretation of scientific forecasts is influenced by how farmers think about rainfall and what they are most interested in knowing. .

A generic agricultural drought index, called Agricultural Reference Index for Drought (ARID), was designed recently to quantify water stress for use in predicting crop yield loss from drought. This study evaluated ARID in terms of its ability to predict crop yields. Daily historical weather data and yields of cotton, maize, peanut and soybean were obtained for several locations and years in the south-eastern USA. Daily values of ARID were computed for each location and converted to monthly average values. Using regression analyses of crop yields vs. monthly ARID values during the crop growing season, ARID-yield relationships were developed for each crop. The ability of ARID to predict yield loss from drought was evaluated using the root mean square error (RMSE), the Willmott index and the modelling efficiency (ME). The ARID-based yield models predicted relative yields with the RMSE values of 0.144, 0.087, 0.089 and 0.142 (kg ha 1 yield per kg ha 1 potential yield); the Willmott index values of 0.70, 0.92, 0.86 and 0.79; and the ME values of 0.33, 0.73, 0.60 and 0.49 for cotton, maize, peanut and soybean, respectively. These values indicated that the ARIDbased yield models can predict the yield loss from drought for these crops with reasonable accuracy.

Infection of peanut (Arachis hypogaea L.) by Aspergillus flavus and subsequent aflatoxin contamination is a serious health and economic concern worldwide. Considering the role of calcium (Ca) in cell wall development, it has been shown to be associated with reduced A. flavus infection. In order to examine the influence of Ca content in shell (also called pod wall or hull), seed coat, and seed in relation to resistance to A. flavus infection, a pot experiment was conducted in a greenhouse of the Faculty of Agriculture, Chiang Mai University, Thailand during May- September, 2006, using three peanut genotypes: 419CC, drought and aflatoxin susceptible; 511CC, drought and aflatoxin resistant; and Tainan 9, a commercial variety in Thailand. Results showed that an increase in the concentration of Ca in the nutrient solution increased in the amount of Ca in shell, seed coat, and seed, and that higher Ca content was reduced the incidence of shell and seed infection by A. flavus for all three peanut genotypes. Genotype 511CC had the highest Ca content in shell (0.35 g/100 g), seed coat (0.37 g/100 g) and seed (0.40 g/100 g) and had the lowest incidence of A. flavus infection in shell (8.1%) and seed (2.2%) as compared to genotype 419CC, which had the lowest Ca content in shell (0.21 g/100 g), seed coat (0.18 g/100 g), and seed (0.20 g/100 g) and had the highest infection of shell (36.3%) and seed (13.3%). No seed infection was found in genotype 511CC under a high concentration of Ca (2500 ppm). From these results, it could be suggested that Ca content in peanut shell, seed coat, and seed might be applied as a criterion in selection for resistance to A. flavus infection. It was also found that genotype 511CC supported the lowest levels of A. flaws infection which suggested that this genotype might be a potential source of resistance that could be used to incorporate aflatoxin resistance into commercial peanut hybrids.

The El Nino Southern Oscillation (ENSO) climate-variability phenomenon greatly affects water availability in the Southeast United States. For example, it is well known that La Nina conditions bring drought to this region. In the past decade, several severe droughts have adversely impacted the water resources of many communities in this region, especially those that rely on surface-water systems. Because small- to mid-size communities are most vulnerable to climate variability, this study was undertaken to develop a climate variability-based community water deficit index (CWDI) for use by water managers in these communities. Although currently available drought indices can be useful tools for monitoring and forecasting purposes, they are not suitable for use in water-supply systems for small- to mid-size communities. The CWDI was conceptualized keeping in mind that it should (1)forecast hydrologic drought, (2)operate at a high spatial resolution, and (3)address both water supply and demand during droughts. The system dynamics-modeling software Structured Thinking Experiential Learning Laboratory with Animation was used to develop the modeling framework to estimate CWDI by evaluating differences in a community water supply and demand, and thus help forecast the severity of an impending drought. Another important feature of the CWDI is its ability to evaluate how drought-management policies can affect the severity of drought. The CWDI was tested in two small- to mid-size communities of this region (Auburn, Alabama, and Griffin, Georgia). The results indicate that the index not only can monitor drought in the studied water-supply systems, but can also forecast ENSO-induced hydrologic droughts in the region and can be used in drought planning. (C) 2013 American Society of Civil Engineers.

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 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 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 Agricultural Reference Index for Drought (ARID) is a newly designed decision support tool to quantify plant water deficit and predict the effect of deficit on crop yields. This study explored uncertainties in ARID associated with its parameters and the sensitivity of ARID to its parameters. Daily values of ARID were computed for five selected locations in the southeastern United States using historical weather data for a 30‐yr period (1971–2000). Uncertainty and sensitivity analyses were performed using the Fourier amplitude sensitivity test. According to the results, available water capacity was the most influential 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 five parameters, runoff curve number and drainage coefficient had insignificant influence. The effect of the water uptake coefficient 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. The type of distribution selected for the parameters was found to have significant influence 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 fixed set of parameter values, it is applicable to a wide range of crops, soils, topographies, and management and has fairly small uncertainties.

This book is based on a technical report the National Climate Assessment (NCA) document that was prepared for submission to the President of the United States and the United States Congress. That document summarized the scientific literature with respect to climate impacts on the Southeast (SE) USA, in particular the literature that has been published since 2004. A national assessment was produced in 2009; however, no technical report was developed in support of that document. For the Third US National Climate Assessment, the Southeast region includes 11 southern states (Figure 1.1), Puerto Rico, and the US Virgin Islands. This region differs slightly from the previous National Climate Assessment in that it follows state borders and does not include the Gulf Coast of Texas.

There is increased pressure on the water resources of the southeastern United States due to the rapidly growing population of the region. This pressure is further exacerbated by the severe seasonal to interannual climate variability this region experiences, most of which has been attributed to the El Niño Southern Oscillation (ENSO). Understanding the regional impacts of ENSO on precipitation and streamflow is a valuable tool for water resource managers in the region. This study was undertaken to develop a clear picture of the effect of ENSO on observed precipitation and streamflow anomalies in Alabama to help managers in the state with decision making. The effect of ENSO on precipitation in eight climate divisions of Alabama was assessed using 59 years (1950 to 2008) of monthly historical data. In addition, eight unimpaired streams (one in each climate division) were selected to study the relationship between ENSO and streamflow. Results indicate a significant relationship between ENSO and precipitation as well as between ENSO and streamflow. However, different parts of the state respond differently to ENSO. For precipitation, it was found that the relationship is significant during winter months with dry conditions being associated with La Niña in the southern climate divisions. A fairly strong relationship was also found during other months. Streamflows show high variability and positive correlation during winter months in the southern climate divisions. The results obtained can provide a basis for water resource managers in Alabama to incorporate climate variability caused by ENSO in their decision making related to soil and water conservation.

Several drought indices are available to compute the degree of drought to which crops are exposed. They 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 affect growth and other physiological processes in crop simulation models. Using RMSE, Willmott d index, and modeling efficiency (ME) as performance measures, ARID was evaluated using soil water contents in the root zone measured daily in two grass fields in Florida. The ability of ARID was assessed through comparison with the water deficit 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. The 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 fluctuation effects the most accurately. Results indicated that ARID may be used as a simple index for quantifying drought and its effects on crop yields.

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.

Interest of farmers in climate change has recently increased in response to intense media coverage of climate change, recent weather extremes in Florida such as 2 years of intense hurricane activity and a drought in 2007, and additional revenue possibilities in the carbon market. This article discusses the challenges involved and potential opportunities for the development and implementation of a climate change extension program at the University of Florida, complementing a recently established climate Extension program aimed at helping farmers cope with seasonal climate variability.

Under certain conditions Aspergillus plants and produce aflatoxin, one of the most highly carcinogenic natural substances known. An ongoing project seeks to reduce aflatoxin contamination of peanut (Arachis hypogae L.). This paper describes new research tools, which have been developed to reach this goal. A minirhizotron system was used to study root growth and drought resistance in relation to aflatoxin resistance. The minirhizotron was combined with a microvideo camera, which has an ultravioletlight source to observe A. flavus infection using a strain of A. flavus that produces a green fluorescent protein (GFP). Fluorescence observed on roots, pods, and pegs decreased with time. Less than 5% of roots and pods observed with a minirhizotron fluoresced and less than 1% of pods or seeds cultured after harvest showed colonization by GFP A. flavus. Still, this technology provides an excellent tool for the study of infection pathways and A. flavus population development. To observe fluorescence athigher resolution, peanut was grown in 20-L containers in a growth chamber to which clear acrylic cuvettes filled with soil and inoculated with GFP A. flavus was attached. Although mycelia fluoresced on some pegs, A. flavus populations appeared small and levels of fluorescence were low. In a subsequent experiment using the same pod cuvette culture system, flowering plants was sprayed with A. flavus spores suspended in water.About 14 days after spraying the spore suspension on the flowers, A. flavus mycelia fluoresced on the surface of peanut flowers, but any fluorescence was observed in either excised ovules or in pegs excised before entering soil. Many minirhizotron images taken under ultraviolet illumination have little visible fluorescence. In order to quantifyfluorescence and to detect fluorescence in images having weak fluorescence, software was developed, QuaCos, to analyze the red-green-blue color values of pixels in digital images. These tools, applied in combination, offer great potential to improve theunderstanding of how A. flavus attacks peanut, and how varieties and management methods might be developed to reduce risk of aflatoxin contamination. Aspergillus flavus peut infecter les plantes et produire, dans certaines conditions, une substance naturelle cancérigène, l’aflatoxine. Des techniques ont été développées en vue de réduire la contamination des graines d’arachide (Arachis hypogae L.) par cette toxine.L’étude de la croissance des racines et de la résistance à la sécheresse des plantes en relation avec la résistance à l’aflatoxine a été réalisée grâce à un minirhizotron couplé à une microcaméra utilisant une source lumineuse ultraviolette. Pour observer l’infection des plantes, une souche fluorescente d’A. flavus a été utilisée. La fluorescence produitepar les racines, les gousses et les gynophores ont diminué en fonction du temps. Moins de 5% des racines et des gousses observées à l’aide du minirhizotron ont été fluorescentes et moins de 1% des gousses ou des graines d’arachide mises en culture a été colonisée par lasouche fluorescente d’A. flavus. L’utilisation de ces outils a permis l’étude du processus d’infection des plantes et du développement des populations d’A. flavus. Pour observer la fluorescence à une forte résolution, des graines d’arachide ont été semées dans des containers auxquels ont été attachées des cuvettes acryliques transparentes contenantdu sol inoculé avec la souche fluorescente d’A. flavus. Du mycélium fluorescent a été observé chez quelques gynophores, mais les populations d’A. flavus sont demeurées faibles de même que les niveaux de fluorescence. Les mêmes cuvettes ont été utilisées dans une autre expérimentation au cours de laquelle les plantes d’arachide en floraisonont été pulvérisées avec des spores d’A. flavus en suspension dans l’eau. 14 jours après pulvérisation, du mycélium fluorescent a été observé à la surface des fleurs, par contre aucune fluorescence n’a été observée aussi bien dans les ovules que dans les gynophores excisés. Plusieurs images obtenues à l’aide du minirhizotron ont montré une faible fluorescence. Pour déterminer et quantifier cette fluorescence, un logiciel, QuaCos, a étédéveloppé pour analyser les valeurs des couleurs rouge-vert-bleu présentes dans ces images. L’utilisation de ces différentes techniques permet une meilleure compréhension du processus d’infection des gousses d’arachide par A. flavus, et permettra à terme lamise au point de variétés résistantes et le développement d’une stratégie pour réduire le risque de contamination des arachides par l’aflatoxine.