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Smallholder farmers are critical to global food production and natural resource management. Due to increased competition for water resources and variability in rainfall due to climate change, chronic irrigation water scarcity is rising particularly in drought-prone regions like Vidarbha, Maharashtra (India). Improving irrigation water efficiency is critical to sustainable agricultural intensification; however, adopting a new technology represents a certain level of risk for the farmers, who invest time and economic resources in changing their practices. Because of the uncertainties in the rainfall (monsoon onset and dry spells), in addition to the upcoming global change, the expected yield is unsure and variable; so, a paradigm comparison between different irrigation technologies is not clear. We have developed software that allows farmers to make decisions in real time based on the implemented practices (fertilisation and irrigation) in their crops, mainly cotton. By implementing a socio-hydrological dynamic model, the software provides a risk forecast of the yield and profit the user can expect at the end of the season under the current practices; in addition, the software computes the forecast of the production under a provided best practices scenario, so the users can compare and improve their practices. The model also considers state variables the water storage water and biomass production, providing an understanding of the impact of the executed practices in the natural resources. Finally, we implemented a kernel principal component analysis (KPCA) to consider the impact of socioeconomic factors on the yearly outcome, based on previous surveys performed in the area. Weve focused on object-oriented programming (OOP) approach in order to optimise the management of the information. The app not only processes social and agricultural information provided by the user but also retrieves and continually updates climate datasets from the web, as well as market prices. The farmers can request the execution of the social-hydrological model to our servers from their own mobile devices, helping to the adoption of technologies. By following an agile methodology, the mobile app has been tested with farmers in order to get feedback from real users; this brought the opportunity to redesign the functionality based on the correct understanding of information and, a fast and clear management of the tool. In addition, this software represents a useful tool to capture and follow information about the use of water by farmers.
Pacheco et al. (Mon,) studied this question.