Irrigation scheduling of an almond orchard using the water balance and remote and proximal sensing

Abstract

Precise irrigation scheduling is critical to efficient water use, particularly in high-value tree crops with limited irrigation supply. This study evaluates an irrigation scheduling methodology for almond orchards, previously studied in experimental fields, applied to a commercial almond farm (cv. Lauranne) over a four-year period and incorporating new technologies for canopy characterization and water stress assessment. Four irrigation treatments were studied: a control (F) based on the crop evapotranspiration estimated using crop coefficient derived from LiDAR, two over-irrigation treatments (SR1, SR2) with 22 % and 44 % increment of irrigation, and a deficit (D) with 30 % reduction over F. Despite increasing the amount of water by 22 % and 44 % in the over-irrigated treatments, no significant increase in yield was observed compared to the control treatment (2800 kg/ha), suggesting that F represents an optimal irrigation level. By contrast, the deficit treatment showed a cumulative yield reduction of 20 % over the four years, highlighting the impact of the reduction in water supply below that of F on cumulative yield. Advanced technologies such as LiDAR to capture canopy information and remote thermography to monitor the crop’s water status were essential to finetuning and improving irrigation scheduling decisions. This study emphasizes the need for long-term studies in established commercial orchards to enhance the sustainability and efficiency of agricultural practices.