Water shortage and excessive chemical fertilizers application result in low soil water and nutrient availability and limit crop production in the Loess Plateau of Northwest China. Ammoniated straw incorporation with N fertilization may be an efficient strategy to maintain agricultural sustainability. However, the interactive effects of straw incorporation and N fertilizer on the biomass water use efficiency (WUE) in the winter wheat–summer maize rotation system remain unclear. A 3-year field experiment was conducted to evaluate the effects of combining ammoniated straw incorporation and N fertilizer on soil water, biomass yield and biomass water use efficiency (WUE) in an annual summer maize (Zea mays L.)—Winter wheat (Triticum aestivum L.) rotation system. There were three treatments: (i) long straw (5 cm) mulching with N fertilizer (CK), (ii) long straw with N fertilizer plowed into the soil (LP), and (iii) ammoniated long straw with N fertilizer plowed into the soil (ALP). Compared with the CK treatment, LP and ALP led to a similar soil water storage capacity. ALP improved summer maize biomass yield and winter wheat biomass yield at the jointing-maturity stage. ALP improved summer maize WUE at the ten-leaf collar-tasseling stage and winter wheat WUE from the tillering stage to the maturity stage. Also, the ALP treatment increased the total water use efficiency (TWUE) of winter wheat by 4.1–22.0%. Overall, ammoniated straw incorporation produced the most favorable biomass yield and WUE in the summer maize—Winter wheat rotation system in the Loess Plateau of China.

The interaction between surface and subsurface water flows plays an important role in surface irrigation systems. This interaction can effectively be simulated by the physical-based models, which have been developed on the basis of the numerical solutions to the Saint-Venant and Richards’ equations. Meanwhile, the spatial variability of field physical properties (such as soil properties, surface micro-topography, and unit discharge) affects the interaction between surface and subsurface water flows and decreases the accuracy of simulating surface irrigation events at large scales. In this study, a new numerical methodology is developed based on the physical-based model of surface irrigation and the Monte Carlo simulation method to improve the modeling accuracy of surface irrigation performance at a field scale. In the proposed numerical methodology, soil properties, unit discharge, surface micro-topography, roughness, border length, and the cutoff time for the unit discharge are used as the stochastic parameters of the physical-based model, while field slope is assumed as the constant value because of the same field tillage and management conditions at a field scale. Monte Carlo simulation is used to obtain the stochastic parameter sample combinations of the physical-based model to represent the spatial variability of field physical properties. The updated stochastic simulation model of surface micro-topography, which is developed to model the spatial distribution of surface elevation differences (SED), is used to obtain the surface micro-topography samples at a field scale. Compared with the distributed-parameter modelling methodology and the field experimental data, the proposed numerical methodology presents the better simulation performance.

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964–2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha⁻¹ yr⁻¹. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008–2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region