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.

As one of the most dynamic natural elements, water resources play a vital role in both ecological and economic activities. The rational allocation of water resources is essential for the sustainable development of human society. Using data from prefecture-level cities in the Yangtze River Economic Belt from 2013 to 2022, this study employs the Cobb–Douglas (C-D) production function to measure the degree of water resource allocation distortion across provinces and cities. Additionally, a panel data model is applied to analyze the influencing factors. The key findings are as follows: The issue of excessive water resource allocation is widespread in the Yangtze River Economic Belt, with the highest degree of distortion observed in the middle reaches, where the average distortion index reaches 1.43—significantly higher than that in the upstream (1.15) and downstream (1.20) regions. Water resource endowment, the South-to-North Water Diversion Project, and rising water prices contribute significantly to improving water resource allocation, whereas increased industrial water use efficiency and excessive government intervention exacerbate allocation distortions. Regionally, the influencing factors exhibit broadly similar effects across provinces. In sectoral analysis, only the estimated coefficients of water resource policies show directional differences, whereas other factors have no significant impact on allocation distortion. By identifying the extent and causes of water resource misallocation, this study provides empirical evidence to support region-specific water management strategies, aiming to enhance resource efficiency and promote sustainable development.