Our program staff has extensive experience in irrigation agronomy, in the relationships between soil, plant and water, and in the temporal and seasonal sensitivity of crops to water deficit by using indicators of the level of water deficit and relating them to their physiological and productive effects. All aligned with the productive response of different irrigation strategies in different crops (almond trees, vineyards, olive trees, apple trees, peach trees, cherry trees, corn, etc.).
As a scientific basis for precision irrigation, special emphasis has been placed on the analysis of spatial and temporal variability of irrigation needs and crop sensitivity to water stress. We have improved the practical application of this knowledge by developing intelligent decision-making tools, both for precision irrigation and for predicting water demands of irrigator communities. These tools are based on the synergy between physiological and agronomic knowledge of plants, modeling, soil and plant sensors, and remote sensing and crop modeling.
Soil-plant-water interactions
We comprehensively evaluate soil-plant-water relationships in different cultivation systems, both woody and extensive, under current environmental conditions and also under more extreme simulated scenarios, such as heat waves or prolonged droughts. To do this, we measure physiological and structural parameters of crops, as well as gas exchange flows between plants and the atmosphere. This approach allows us to understand the mechanisms of adaptation and response of crops to environmental stress and to generate management strategies aimed at improving the resilience and sustainability of agricultural systems.
Agronomic strategies for adaptation to climate change
We analyse the impact of various agronomic practices for soil and crop management (e.g. training systems) on water requirements, productivity and water use efficiency, with the aim of also contributing to the mitigation of the effects of climate change, such as heat waves and droughts. To this end, we conduct integrated studies of soil-plant-water-atmosphere interactions, which allows us to better understand how the system works and propose more resilient and efficient management strategies.
Irrigation agronomy
Accurate determination of the water requirements of various crops using weighing lysimeters, flow towers and other advanced monitoring tools. We evaluate the impact of different controlled deficit irrigation strategies on production and quality parameters, as well as the seasonal sensitivity of crops to water deficit in order to identify critical periods. Recently, studies have also focused on analysing crop survival with supplementary irrigation, with the aim of optimising water use in scenarios of limited availability.
Fertigation, plant nutrition and circular economy
Optimisation of irrigation and nutrition through fertigation strategies in fruit and horticultural crops to improve water efficiency and reduce environmental impact. Study of water and nutrient dynamics in the soil, with technology and automation support for nutritional control. Analysis of water and fertiliser quality to adjust nutrient solutions and reduce leaching. The circular economy is also promoted through the evaluation of the liquid fraction of slurry in fertigation and its impact on food security.
Irrigation with non-conventional water sources
We analyse the opportunities and risks associated with the use of unconventional water sources, including saline and treated wastewater, for the irrigation of woody and horticultural crops, evaluating their effects on plant growth, productivity and soil. We develop management strategies and agronomic practices to reduce salt accumulation and the transfer of emerging contaminants, seeking safe, efficient and sustainable hybrid use.
Water digitisation and modelling
We work with different types of sensors to monitor soil water content and crop water status, and their integration into decision-making systems. We also model crop water requirements in different agronomic and climatic scenarios, and use digital twins and artificial intelligence to determine and predict irrigation needs, irrigator behaviour and water demands.
