Water use efficiency and its drivers of two typical cash crops in an arid area of Northwest China

Driving Factors and Numerical Simulation of Evapotranspiration of a Typical Cabbage Agroecosystem in the Shiyang River Basin, Northwest China

“…Based on our research, the following conclusions can be drawn: (1) During the growth period of cabbages from 2020 to 2021, the ET of cabbages was 260.1 ± 24.2 mm, which was basically lower than that of other crops in SYRB. (2) Through partial correlation analysis and principal component analysis, it can be found that environmental and physiological factors jointly drive the changes in ET during the growth period of cabbages in SYRB.…”

“…Evapotranspiration (ET) is the main consumer of agricultural water, so it will be worthwhile to clarify its change pattern and driving factors during the growth period [1]. Previous studies have typically included environmental and physiological factors as variable indicators for studying the driving factors of ET [2][3][4]; however, for each agroecosystem, the main driving factors of ET are not the same.…”

“…In conclusion, the RZWQM2 model has been widely used to simulate the ET of crops with a clear mechanism and higher accuracy. As a cash crop widely planted in the arid region of Northwest China [1], cabbage has high yield and economic benefits. However, based on the literature research, there is little research on the driving factors of ET changes in cabbage agroecosystems in this area.…”

Amendments of Severe Saline-Sodic Paddy Land: Optimal Combination of Phosphogypsum, Farmyard Fertilizer, and Wood Peat

“…Since the mathematical model was obtained after non-dimensional treatment and linear substitution, the absolute value of partial regression coefficients of each primary term can directly reflect the degree of influence of P, F, and W on the yield, which is shown as P>F>W. Meanwhile, the partial regression coefficient was positive, indicating that P, F, and W have a positive effect on the yield and growth of rice. Based on published methods [36][37][38], seven coding levels between P, F, and W and 152 sets of combination schemes exceeding the mean yield of 4449.00 kg•ha −1 were obtained, accounting for 44.32% of the total schemes. Frequency analysis was then used to calculate the frequency of each level of P, F, and W, and a management scheme with a yield greater than the mean yield was obtained (Table 9).…”

“…The experimental design includes 3 factors (here: P, F, and W) and 4 levels of administration, resulting in 14 treatments. It follows a D-optimal design for quadratic regression and has the advantages of reduced required processing and high efficiency [36][37][38]. Thus, this method was used in this experiment to comprehensively analyze and evaluate the effects of different dosage ratios on rice yield and its constituent factors, soil pH, and electrical conductivity (EC).…”