Enhancing smallholder sociohydrological predictions at plot scale by novel data assimilation of high-resolution soil moisture and biomass data

Increasing competition for water resources and rainfall variability driven by climate change have led to irrigation water scarcity, particularly in drought-prone regions such as Vidarbha, Maharashtra (India). Enhancing irrigation water efficiency is essential for sustainable agricultural intensification. However, adopting new technologies poses a risk for farmers, as it requires significant investment of time and financial resources to modify their practices. In this context, we have developed a mobile application that implements a hybrid model combining a sociohydrological approach with a KPCA-based structural error model, providing farmers with timely information to support decision-making in the adoption of new irrigation technologies and the implementation of Good Agricultural Practices (GAPs), such as irrigation and fertilization. Although the model explains 20% of the observed variance in yields at the plot scale, its main purpose is to provide farm-scale predictions to encourage the adoption of GAPs. In this work, we venture into providing more precise forecasting to the users for direct applicability for forward-looking field advisories. By integrating higher-resolution data (e.g., Sentinel-2A) and exploring Bayesian methods along with machine learning techniques, the accuracy of the state variables, such as biomass and water storage, was enhanced. This advancement is incorporated into the mobile application to provide opportunistic and precise forecasting to farmers in their decision-making process when implementing GAPs.