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Knowledge regarding uptake of water and nutrients as a function of their status in the soil is critical for smart fertigation management. Of particular interest is the uptake of water and potassium (K), each as a function of root zone salinity. The objective of this study was to quantify the response of tomato water uptake (transpiration) and K uptake to varied levels of K availability combined with salinity. Two independent lysimetric experiments were conducted and used to calibrate and validate models for water and K uptake under varied soil salinity. Tomato water and K uptake were determined by water and nutrient balance using the measured soil water content and K concentration in soil and drainage solution. Tomato water uptake was affected by root zone soil K and salinity. Salinity was the dominant factor driving uptake when irrigation solution had NaCl concentration of over 3 g L–1. Potassium uptake of tomato decreased with decreasing soil K content and increasing soil salinity. The linear relationship between tomato water uptake and K uptake rate was not influenced by soil salinity, indicating that the inhibition of K uptake was probably due to passive uptake of K with the flux of water from soil to roots decreased due to salinity. Tomato water and K uptake were simulated considering the effect of soil solution K concentration under simultaneous K and salinity stresses. Simulated daily average water and K uptake rates agreed well with measured values, with root mean squared error, normalized root mean squared error, and index of agreement of 144 cm3 d–1, 20.13% and 0.99 for average daily water uptake; and 24.43 mg d–1, 29.78% and 0.98 for K average daily uptake rate, respectively. These findings can be used to predict crop water and K requirements under combined salinity and K status conditions, which should contribute to efficient and sustainable fertigation scheduling. [ABSTRACT FROM AUTHOR]

It is crucial to understand and forecast future climate change and its impact on regional crop yield. This study employed the optimized World Food Studies (WOFOST) model and Coupled Model Intercomparison Project Phase 6 (CMIP6) data to assess the effects of climate change in different emission scenarios [ e.g. , ssp126 [sustainable development with low greenhouse gas (GHG) emissions], ssp245 (balanced development with medium GHG emissions), and ssp585 (conventional development with high GHG emissions)] on the spring wheat yield and growth period in Northwest China. Lasso regression, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), Random Forests (RF), and RNN showed superior optimization performance for radiation amount surface downwelling shortwave radiation flux (RSDS), maximum temperature (Tmax), minimum temperature (Tmin), wind speed (sfcWind), and precipitation (PR). These optimizations decreased the root mean square error (RMSE) of RSDS, Tmax, Tmin, sfcWind, and PR by 1.0285, 2.6396, 2.7839, 2.3763, and 0.3020, respectively, compared to the original data. Long-term annual PR will increase by 415 mm in the ssp126 scenario, whereas the increases were estimated to be 257 mm and 249 mm under the ssp245 and ssp585 scenarios, respectively. Regarding temperature, Tmax and Tmin are expected to rise by 0.97 °C and 1.08 °C, respectively, under the ssp126 scenario. These values are also projected to be 2.29 °C/1.70 °C and 1.81 °C/3.36 °C for Tmax/Tmin under the ssp245 and ssp585 scenarios, respectively. The optimized crop parameters can effectively improve the yield simulation accuracy of the WOFOST model and reduce RMSE. Yinchuan and Huinong are projected to experience an increase of 19.8 % and 15.8 % in their average yield under the ssp585 scenario in the short-term future, compared to the average yield during the baseline period. Qingtongxia is expected to reach a 20.2 % increase under the ssp126 model, while Zhongning will undergo an increase of 12.3 % under the ssp585 model, and Litong will observe an 11.2 % increase under the ssp245 model. Qingtongxia showed the highest long-term production growth, with increases of 18.0 % and 15.3 %, respectively, under the ssp126 and ssp245 scenarios. Higher temperatures, radiation levels, and PR are typical features of the future climate in the study area. The CMIP6 data simulations and the optimized WOFOST model predict that spring wheat yield will increase soon but will not increase as much in the long run. [ABSTRACT FROM AUTHOR]