The future of precision agriculture and the rational application of deficit irrigation

Abstract

We are witnessing to the implementation of sensor technology and ITCs in agriculture. This is producing a huge amount of information from the soil, plant and atmosphere which is difficult to interpret to take decisions in irrigation scheduling. A short-term solution to deal with this Big Data accumulation is coming from the application of Machine Learning approaches which in the long-term are expected to provide no novel insights into the mechanisms involved in the response observed and what are the key physiological traits explaining the differences among cultivars or species. On the other hand, during the last three decades we have assisted to intense research efforts in the study of physiological mechanisms involved in the response of fruit trees and crops to water stress. This accumulated knowledge should start to yield new approaches to overcome the difficulty in the interpretation of plant-based sensors for irrigation scheduling. The choice of the key target variable to maximise or optimise yield is crucial and up-to-date there is not consensus in the scientific community about it. Stomatal conductance emerges as the best candidate due to several reasons that will be dissected in the presentation. It has been demonstrated recently that we can monitor automatically and in-continuous stomatal conductance in commercial orchards and estimate photosynthesis with confidence. It will be explained how stomatal conductance can be used to decide, with a rational basis, the level of stress we are implementing in our olive orchard during the application of a regulated deficit irrigation strategy.