Speaker
Description
The future of agriculture is linked to our capability to find a way to maintain profitable production systems with limited water quantity and quality. Despite a large volume of research on irrigation, there is still a gap between research advancement and its implementation, due to a tradeoff between simplicity of use and precision of the various irrigation methods. Plant-based monitoring has stand out in the last decades for its precision; however, its practical implementation is hindered by the limited number of monitored plants, the lack of quantitative information on water needs, and the complexity of data interpretation. Empirical estimations of evapotranspiration (ET) are widely used for irrigation management due to their simplicity, although their reliance on a few crop coefficients (Kc) fails to adequately account for the diverse orchard features, thus affecting their precision. Actual ET (ETa) measurements are increasingly common thanks to new measuring and estimation methods, but they also suffer from drawbacks, including low temporal scales. In this study, we explored the potential of combining approaches spanning spatial and temporal scales to quantify irrigation needs. Specifically, we compared continuous and discrete tree-level measurements of water status and orchard-level measurements of water use using the energy balance method for olive under water limited conditions and for pistachio grown on saline-sodic soils. Overall, this study highlights the importance of adopting integrated approaches to enhance water use prediction and optimize water application in agricultural settings.
