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The efficient management of irrigation water can affect crop profitability quite significantly. The application of precision irrigation based on soil monitoring can help manage water resources. In viticulture, the irrigation technique is thought to strongly influence grape ripening and the final grape composition. In this study, an irrigation decision support system was compared to a surface drip irrigation system in a commercial vineyard located in Andrea (Southern Italy) planted with Vitis vinifera cv. Montepulciano. We aimed to investigate the ability of the DSS to save water while maintaining an acceptable yield and quality of the grapes. To allow for the comparison, eco-physiological as well as yield parameters were measured during the irrigation periods in both irrigation systems over two years (2019 and 2020). The results indicate that the vines grown using the DSS treatment were less stressed compared to the plants grown using farm irrigation in both years. The yield attributes showed slight or no significant differences between the treatments. The quality results showed no significant differences between the treatments in both years. Our results indicate that with savings of 10% and 17% of the irrigation water in the first and second year, respectively, the DSS was able to maintain good yield and quality levels as compared to the farm irrigation system. These two-year results provide a promising implementation of its use in precision irrigation. [ABSTRACT FROM AUTHOR]

Contamination of crops by aflatoxins (AFs) is a real risk in the South-Eastern USA. Contamination risk at the county level based on soil type and weather in different years has been investigated. However, defining AFs contamination risk zones within fields has not yet been attempted. Drought conditions, particularly within the month of June have been linked to high levels of AFs contamination at the county level. Soil characteristics and topography are the factors influencing drought status that vary most within fields. Here, soil, plant, topography and remotely sensed information are used to define AFs contamination risk zones within two fields using different approaches. Normalized difference vegetation index (NDVI) data were used to indicate potential droughty areas and thermal IR data from LandSat imagery were used to identify hot areas. Topographic variables were also computed. Comparison tests showed that a combination of regression analysis of soil, plant and imagery data and bi-variate local Moran's I analysis of NDVI and Thermal IR data from several years was the best way to define zones for mean and maximum AFs levels. An approach based on principal components analysis of soil, plant and imagery data from 2010, a high-risk year, was best for defining zones for minimum AFs levels. Analysis of imagery from several years suggested that the zones are likely to be relatively stable in time and could be defined using only freely available sensor, topographic and soil series data. Once defined, such zones can be managed to increase profitability and reduce waste. [ABSTRACT FROM AUTHOR]