As the requirements for environmental protection and high-efficiency economies increase, grain production (GP) across the globe faces more stringent ecological constraints and economic pressure. Understanding the relationships between natural resources and economic and agricultural factors in grain-producing regions is paramount for ensuring global food security. This paper proposes a methodological framework to explore the relationships between water and soil resources (WSRs), economic input factors (EIFs), and GP. We employed the northeast region of China as a case study to advance our understanding of the factors driving the development of grain-producing capacity. We first constructed and calculated the region's water and soil comprehensive index (WSCI) to describe water-soil properties. We then used hotspot analysis to explore the spatial agglomeration characteristics of WSRs, EIFs, and GP. Finally, we used threshold regression analysis to understand the effects of EIFs and GP with the WSCI as the threshold variable. With the improvement of the WSCI, the positive impact of fertilizer and irrigation on GP shows a U-shaped curve in elasticity coefficients. The positive effect of agricultural machinery on GP decreases significantly, and the impact of labor input on GP is insignificant. These results provide new insights into the relationship between WSRs, EIFs, and GP and a reference for improving GP efficiency globally. This work thus contributes to advancing our capabilities to enable food security while considering aspects of sustainable agriculture in important grain-producing regions across the globe.

Affected by global climate change and water shortages, food security continues to be challenged. Improving agricultural water use efficiency is essential to guarantee food security. China has been suffering from water scarcity for a long time, and insufficient water supply in the agricultural sector has seriously threatened regional food security and sustainable development. This study adopted the super-efficiency slack-based model (SBM) to measure the provincial agricultural water use efficiency (AWUE). Then, we applied the vector autoregression (VAR) Granger causality test and social network analysis (SNA) method to explore the spatial correlation of AWUE between different provinces and reveal the interprovincial transmission mechanism of spillover effects in AWUE. The results show the following: (1) In China, the provincial AWUE was significantly enhanced, and the gaps in provincial AWUE have widened in the past 20 years. (2) There were apparent spatial heterogeneity and correlations of provincial AWUE. The provinces with higher AWUE were mainly located in economically developed and coastal areas. (3) The correlation of AWUE between provinces showed significant network structure characteristics. Fujian, Hebei, Jiangsu, Shandong, and Hubei Qinghai were central to the network, with high centrality. (4) The AWUE spatial correlation network could be divided into four blocks. Each block played a different role in the cross-provincial transmission of spillover effects. Therefore, it is necessary to manage the agricultural water resources and improve water use efficiency from the perspective of the network.

A stochastic frontier approach (SFA) model of translog production function was constructed to analyze the growth effect of agricultural production factors on grain production in China. Under the condition of unchanged cultivated land, the agricultural labor, capital, and water were regarded as input elements of the agricultural production function. The maximum likelihood estimation (MLE) method was used to analyze the technical efficiency, output elasticity, substitution elasticity, and relative variability of grain production in China from 2004 to 2018. The results showed that: (1) For the technical efficiency and output elasticity of the input factors of grain production, there were significant differences in different provinces. For example, the water resource was insufficient in Beijing and Shanghai, but the output elasticity of water was high. Heilongjiang was rich in water and had high technical efficiency. For Xinjiang, water was sufficient, but its output elasticity was deficient and the technical efficiency didn’t increase. (2) The overall technical efficiency level was relatively low and was still declining year by year; the output elasticity of water was much greater than that of capital. There was still great potential for grain growth. (3) Optimizing resource allocation and controlling the appropriate ratio of input factors to develop grain production could achieve the maximum benefits. Finally, according to the empirical results, this paper put forward some practical policy suggestions for optimizing the allocation of input factors, especially water and capital, which can ultimately improve agricultural productivity by improving technical efficiency.