Abstract
Quantifying evapotranspiration and drainage losses is essential for improving irrigation efficiency. The FAO-56 is the most popular method for computing crop evapotranspiration. There is, however, a need for locally derived crop coefficients (Kc) with a high temporal resolution to reduce errors in the water balance. The aim of this paper is to introduce a dynamic Kc approach, based on Leaf Area Index (LAI) observations, for improving water balance computations. Soil moisture and meteorological data were collected in a terraced nectarine (Prunus persica var. nucipersica) orchard in Cyprus, from 22 March 2019 to 18 November 2021. The Kc was derived as a function of the canopy cover fraction (c), from biweekly in situ LAI measurements. The use of a dynamic Kc resulted in Kc estimates with a bias of 17 mm and a mean absolute error of 0.8 mm. Evapotranspiration (ET) ranged from 41% of the rainfall (P) and irrigation (I) in the wet year (2019) to 57% of P + I in the dry year (2021). Drainage losses from irrigation (DR_I) were 44% of the total irrigation. The irrigation efficiency in the nectarine field could be improved by reducing irrigation amounts and increasing the irrigation frequency. Future studies should focus on improving the dynamic Kc approach by linking LAI field observations with remote sensing observations and by adding ground cover observations.
Highlights
Water plays a key role in agricultural production and food security
Quantifying actual crop water consumption and drainage losses is essential in order to improve irrigation efficiency
The main objective of this paper is the introduction of a dynamic crop coefficient (Kc ) approach based on in situ Leaf Area Index (LAI) measurements, soil moisture and meteorological observations to provide robust computations of the water balance components of irrigated fields
Summary
Water plays a key role in agricultural production and food security. Quantifying actual crop water consumption and drainage losses is essential in order to improve irrigation efficiency. According to climate model projections, extreme climate phenomena (droughts, floods, dust events) are expected in this region, which has been identified as a climate change hot spot [2–4]. These phenomena will have a serious impact on various sectors, such agriculture and water management [5,6]. Monitoring water balance components in agricultural sites is essential for optimizing irrigation and minimizing drainage losses
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