Irrigated cotton (Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content (θ), soil temperature (${\mathrm{T}}_{\mathrm{soil}}^{°}$) and plant transpiration (T), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and ${\mathrm{T}}_{\mathrm{soil}}^{°}$, as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha⁻¹) and net income (US $3165 ha⁻¹), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha⁻¹ mm⁻¹) and net water production (0.94 $ m⁻³). These results provided farmers guidelines to adopt deficit irrigation strategies.

The drip fertigation technique is a modern, efficient irrigation method to alleviate water scarcity and fertilizer surpluses in crop production, while the precise quantification of water and fertilizer inputs is difficult for drip fertigation systems. A field experiment of maize (Zea mays L.) in a solar greenhouse was conducted to meet different combinations of four irrigation rates (I₁₂₅, I₁₀₀, I₇₅ and I₅₀) and three nitrogen (N) fertilizer rates (N₁₂₅, N₁₀₀ and N₇₅) under surface drip fertigation (SDF) systems. The Root Zone Water Quality Model (RZWQM2) was used to assess the response of soil volumetric water content (VWC), leaf area index (LAI), plant height and maize yield to different SDF managements. The model was calibrated by the I₁₀₀N₁₀₀ scenario and validated by the remaining five scenarios (i.e., I₁₂₅N₁₀₀, I₇₅N₁₀₀, I₅₀N₁₀₀, I₁₀₀N₁₂₅ and I₁₀₀N₇₅). The predictions of VWC, LAI and plant height were satisfactory, with relative root mean square errors (RRMSE) < 9.8%, the percent errors (PBIAS) within ±6%, indexes of agreement (IoA) > 0.85 and determination of coefficients (R²) > 0.71, and the relative errors (RE) of simulated yields were in the range of 1.5–7.2%. The simulation results showed that both irrigation and fertilization had multiple effects on water and N stresses. The calibrated model was subsequently used to explore the optimal SDF scenarios for maximizing yield, water use efficiency (WUE) or nitrogen use efficiency (NUE). Among the SDF managements of 21 irrigation rates × 31 N fertilizer rates, the optimal SDF scenarios were I₁₂₀N₁₃₀ for max yield (10516 kg/ha), I₅₀N₇₀ for max WUE (47.3 kg/(ha·mm)) and I₁₂₅N₇₅ for max NUE (30.2 kg/kg), respectively. The results demonstrated that the RZWQM2 was a promising tool for evaluating the effects of SDF management and achieving optimal water and N inputs.

Water scarcity is the major limiting factor for oasis-desert agricultural production of cotton. It is necessary to improve cotton for drought tolerance and minimize drought-related crop losses, and the transgenic approach is efficient for cotton improvement. In order to evaluate the value of ScALDH21 transgenic cotton (G. hirsutum L.), it was tested in the main cotton region of south Xinjiang, in an environment of extreme drought around the desert. Transgenic cotton, overexpressing aldehyde dehydrogenase gene (ScALDH21) from the desiccation-tolerant moss Syntrichia caninervis in cotton variety Xin Nong Mian 1, was field-tested under six treatments based on three irrigation schedules and two irrigation levels (full (FI) and deficit (DI) irrigation) as follows: root zone model-simulated forecast irrigation (F) (FFI and FDI), soil moisture sensor-based irrigation (S) (SFI and SDI), and flood irrigation based on experience estimates (E) (EFI and EDI) to evaluate growth and yield performances. The results revealed that plant height and leaf area increased significantly in ScALDH21-transgenic cotton genotypes under all treatments. Physiological parameters such as chlorophyll content, net photosynthesis rate, and instantaneous water use efficiency were not significantly highly in transgenic lines compared to non-transgenic plants (NT). However, transgenic lines showed significantly improved yield and superior fiber quality than NT plants regardless of irrigation. The results demonstrate that ScALDH21-transgenic lines were excellent compared to NT plants under different water deficiency conditions. The study also provides guidelines for optimal irrigation protocol and minimum water requirements for the use of the ScALDH21-transgenic cotton lines in arid zones.

Drip irrigation with saline water is frequently adopted to realize the sustainable utilization of saline–sodic soil with high water tables, and soil enzyme activities can be used to indicate changes in soil quality. In the current study, spatiotemporal changes in soil urease enzyme (URE), alkaline phosphatase (ALP) and invertase (INV) activities were investigated during consecutive growing seasons. Soil in beds was sampled before planting (0 y) and one, two, three and four years after the growing season (1 y, 2 y, 3 y, 4 y), and these samples were distributed at four horizontal distances from the drip line (0, 10, 20 and 30 cm) and four vertical soil depths (0–10, 10–20, 20–30 and 30–40 cm). The results showed that a distribution pattern of URE and ALP activities formed during the first growing season, while the distribution of INV activity formed until the third growing season. All three soil enzyme activities in the upper soil layers and positions close to the drip line were more greatly affected by planting year. The average URE activity of the soil profile decreased slightly during the first year and increased by about 220% and decreased by 20% after reclamation for two and three years, and finally, it increased to 4.9 μg NH₄⁺·g⁻¹·h⁻¹ at the end of the fourth growing season. ALP activity remained stable during the first two years and rapidly increased in the following years; in particular, in the fourth year, it reached 32.7 μg ph(OH)·g⁻¹·h⁻¹. INV activity increased continually with the number of years after planting and reached 1009.0 μg glu·g⁻¹·h⁻¹ at the fourth season’s end. An analysis of variance indicated that URE, ALP and INV activities varied insignificantly among the time points of 0 y, 1 y, 2 y and 3 y (p < 0.05), while they were significantly higher for 4 y than for 0 y and 1 y. In addition, all three enzyme activities of the soil profile had an exponentially increasing trend with the number of years after planting. These results indicated the soil quality in saline–sodic soils could be improved with time under drip irrigation with local saline groundwater, especially around the drip line.

Improving cotton (Gossypium hirsutum L.) yield and water use efficiency (WUE) under future climate scenarios by optimizing irrigation regimes is crucial in hyper-arid areas. Assuming a current baseline atmospheric carbon dioxide concentration (${\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}$) of 380 ppm (baseline, BL_0/380), the Root Zone Water Quality Model (RZWQM2) was used to evaluate the effects of four climate change scenarios—S_1.5/380 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=1.5 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=0$), S_2.0/380 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=2.0 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=0$), S_1.5/490 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=1.5 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=+110 \mathrm{p}\mathrm{p}\mathrm{m}$) and S_2.0/650 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=2.0 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=+270 \mathrm{p}\mathrm{p}\mathrm{m}$) on soil water content (θ), soil temperature (${\mathrm{T}}_{\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}}^{°}$), aboveground biomass, cotton yield and WUE under full irrigation. Cotton yield and irrigation water use efficiency (IWUE) under 10 different irrigation management strategies were analysed for economic benefits. Under the S_1.5/380 and S_2.0/380 scenarios, the average simulated aboveground biomass of cotton (vs. BL_0/380) declined by 11% and 16%, whereas under S_1.5/490 and S_2.0/650 scenarios it increased by 12% and 30%, respectively. The simulated average seed cotton yield (vs. BL_0/380) increased by 9.0% and 20.3% under the S_1.5/490 and S_2.0/650 scenarios, but decreased by 10.5% and 15.3% under the S_1.5/380 and S_2.0/380 scenarios, respectively. Owing to greater cotton yield and lesser transpiration, a 9.0% and 24.2% increase (vs. BL_0/380) in cotton WUE occurred under the S_1.5/490 and S_2.0/650 scenarios, respectively. The highest net income ($3741 ha⁻¹) and net water yield ($1.14 m⁻³) of cotton under climate change occurred when irrigated at 650 mm and 500 mm per growing season, respectively. These results suggested that deficit irrigation can be adopted in irrigated cotton fields to address the agricultural water crisis expected under climate change.

Deficit irrigation (DI) is a widely recognized water-saving irrigation method, but it is difficult to precisely quantify optimum DI levels in tomato production. In this study, the Root Zone Water Quality-Simultaneous Heat and Water (RZ-SHAW) model was used to evaluate the potential effects of different DI levels on tomato growth in a drip-irrigated field. Combinations of five DI scenarios were tested in greenhouse field experiments under plastic film mulching according to the percentage of crop evapotranspiration (ET), i.e., ET50, ET75, ET100, ET125, and ET150. The model was calibrated by using the ET100 scenario, and validated with four other scenarios. The simulation results showed that the predictions of tomato growth parameters and soil water were in good agreement with the observed data. The relative root mean square error (RRMSE), the percent bias (PBIAS), index of agreement (IoA) and coefficient of determination (R²) for leaf area index (LAI), plant height and soil volumetric water content (VWC) along the soil layers were 0.72 and >0.56, respectively. The relative errors (REs) of simulated biomass and yield were 3.5–8.7% and 7.0–14.0%, respectively. There was a positive correlation between plant water stress factor (PWSF) and DI levels (p < 0.01). The calibrated model was subsequently run with 45 different DI scenarios from ET0 to ET225 to explore optimal DI management for maximizing water productivity (WP) and yield. It was found that the maximum WP and yield occurred in ET95 and ET200, with values of 28.3 kg/(ha·mm) and 7304 kg/ha, respectively. The RZ-SHAW demonstrated its capacity to evaluate the effects of DI management on tomato growth under plastic film mulching. The parameterized model can be used to optimize DI management for improving WP and yield based on the water stress-based method.