Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate

Abstract

Optimizing irrigation scheduling through a Decision Support System has shown promise to improve crop yield and water productivity in irrigated agriculture in an arid climate. The effects of an irrigation scheduling method on cotton (Gossypium hirsutum L.) yield and water productivity were investigated in Qira Oasis, China from 2016–2018. The Decision Support System for Irrigation Scheduling (DSSIS) was based on forecasted rainfall and water stress index simulated by the Root Zone Water Quality Model (RZWQM2). A field experiment was conducted to test the viability of the DSSIS in 2016. The design of the experiment was a randomized complete that included two factors and two levels for each factor: (i) irrigation scheduling method—DSSIS–based (DSS) and soil moisture sensor–based (SMS), and (ii) irrigation level—full irrigation (FI) and deficit irrigation (DI, 75 % of FI). Implementation of the DSS led to significant increases in seed cotton yield [1.05 Mg ha–1 (32 %)] and water productivity [1.64 kg ha–1 mm–1 (20 %)] compared to the SMS. Compared to DI, FI significantly increased cotton yield [0.69 Mg ha–1 (20 %)] but had no significant effect on water productivity. In general, the higher water productivity under DSS (vs. SMS) was attributed to the reduced water stress and increased seed cotton yield. While the DSS–FI treatment provided the greatest seed cotton yield (4.55 Mg ha–1) and net income (US $3427 ha–1), the highest water productivity (10.09 kg ha–1 mm–1) was achieved under the DSS–DI treatment. Water use under DSS–DI treatment significantly decreased by 51 mm (10 %) and 23 mm (5 %), respectively, compared to DSS–FI and SMS–FI treatments. Therefore, our results demonstrated that the DSS with deficit irrigation could maintain cotton yield and improve water productivity under an arid desert climate.