The frequent occurrence of drought–flood abrupt alternation (DFAA) seriously affects crop yield. It is particularly important to explore the dynamics of material accumulation and distribution under DFAA stress to analyze the mechanism of yield formation. In this study, a bucket experiment with DFAA stress groups, drought control (DC) groups, flood control (FC) groups, and normal irrigation (CK) groups was set up from the jointing to the heading stage of rice to analyze the interaction effects of DFAA stress on rice yield and dry matter partitioning. The results showed that compared with the CK group, the average yield reduction rate of rice in the DFAA groups was 23.03%, and the number of grains per panicle, total grain number, thousand-seed mass, and seed setting rate decreased. Compared with the DC groups, the DFAA groups had a significant reduction in yield and its components during the flooding period. Compared with the FC groups, the DFAA groups showed a compensation phenomenon in the yield and its components during the drought period. From the end of DFAA stress to the harvest period, the root partitioning index (PI) of the DFAA groups decreased, the stem PI increased first and then decreased, the leaf PI decreased, and the panicle PI increased. The results showed that the rice leaves increased and thickened, and the stems thickened under DFAA conditions to enhance the ability to resist drought and flooding stress, but the panicle rate was reduced, the growth period of rice was delayed, and the redundant growth of stems and leaves was increased. It is suggested that the depth and duration of stagnant water storage during the flood period of DFAA should be controlled, and the transfer and supply of photosynthetic products to grains should be increased to avoid serious yield reductions. The research results provide a theoretical basis for the rational development of farmland DFAA mitigation measures.

To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m³ in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m³ in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m³ in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin.

There are some problems in Northwest China, such as the fragile ecological environment, poor basic conditions of agricultural production, low efficiency of agricultural water use, and difficulty in clarifying the path of agricultural water use efficiency (AWUE) improvement. Based on the superefficiency data envelopment analysis (DEA) model, this study increases the amount of ‘green water’ resources in the agricultural water consumption index of the input index and increases the wastewater pollutants (total chemical oxygen demand emissions; total ammonia nitrogen emissions) in the undesired output index to measure the AWUE in the northwest region. Based on the calculation results of AWUE, combined with the modified gravity model, the connection strength of AWUE between any two provinces in Northwest China is calculated, and the spatial structure and network characteristics of AWUE in this area are analysed via the social network analysis (SNA) method. The results show that the average AWUE in 2020 is nearly two times higher than that in 2011. From the situation of the northwest provinces, the average AWUE of the five provinces is in the order of Qinghai > Shaanxi > Gansu > Ningxia > Xinjiang. The AWUE value, total population, real GDP and per capita GDP of the capital cities of the five provinces in Northwest China are the key factors for the improvement of the connection intensity of AWUE. From 2011 to 2020, the connection intensity and closeness of AWUE in Northwest China increased, and there was a clear network hierarchy. The improvement in overall AWUE in the region is mainly due to the radiation and driving effect of the central province on other provinces. Based on this, the study proposes policy recommendations for gradually realising the AWUE improvement path of the central province (Shaanxi; Gansu)—cooperation circle (Shaanxi–Ningxia; Gansu–Qinghai)—the whole region. The results provide theoretical support and a quantitative basis for optimising the spatial pattern of agricultural water resources and improving AWUE in Northwest China.