Balancing economic benefits and environmental repercussions based on smart irrigation by regulating root zone water and salinity dynamics

Abstract

As a critical index for irrigation scheduling, plant water deficit index (PWDI) is defined as the ratio of water deficit to water demand to reflect the extent of abiotic stresses such as water and salinity. Recently, smart irrigation scheduling, according to PWDI thresholds to maintain desirable or acceptable stress levels, has been suggested to maximize yields while minimizing negative environmental effects under non-saline conditions. To investigate and quantify the potential for PWDI-driven irrigation on agricultural production under conditions of salinity, a two-year experiment with six specific thresholds was conducted in Shawan of Xinjiang for drip-irrigated cotton under film mulch. Results indicated that, with increasing PWDI threshold, irrigation depth per event increased, while irrigation frequency and total volume decreased. Consequently, the soil water and salt environment deteriorated, resulting in less nutrient uptake, slower growth, and lower yield and net profit. With particularly high PWDI thresholds leading to serious stress conditions, fiber quality was also negatively affected. Within a designed range of PWDI thresholds between 0.39 and 0.62, an elliptic function characterized the processes of water application, yield and net profit (R2 ≥ 0.95), and water productivity could be described by a parabolic function (R2 = 0.77). These quantitative results were used to provide guidelines for smart irrigation scheduling under local conditions considering water management measures and market prices of cotton. For a reference market price of 7.5 CNY kg-1, a PWDI threshold of 0.49 was found to optimize economic benefits while maximizing water productivity. When prices of cotton are prohibitively low, a lower threshold should be considered to obtain an acceptable net profit. Otherwise, a higher threshold would be preferable to use water more efficiently. Further verification and improvement are necessary to deal with more complex scenarios, such as, considering crop sensitivity to water and salinity stresses at different growth stages and optimizing irrigation depth per event.