Solving the problem of agricultural water use efficiency is an effective means to understand the agricultural ecological civilization and food security. In order to balance the relationship between agricultural water efficiency and regional economic size, this study examines the agricultural water use efficiency in China based on the Super-SBM model of unexpected output. At the same time, the Spatial Durbin Model (SDM) was introduced to analyze the spatial spillover effect and try to find ways to improve agricultural water use efficiency from the perspective of influencing factors. The present study used the panel data of 30 provinces in China from 1998 to 2018 to obtain empirical results. The results show that (1) China's agricultural water resources’ utilization efficiency in 21 years has not remained high: the results showed that it was 0.496 in 1998, 0.572 in 2008, and 0.657 in 2018, but it is slowly rising which explains that the low efficiency is mainly caused by the low pure technical efficiency. (2) The overall agricultural water use efficiency in China is in a situation of spatial agglomeration but there has been a trend of shifting positive correlations to negative correlations among neighboring provinces. It found that in 1998, the sum of the number of `High-high agglomeration type' and `low-low agglomeration type' provinces was 20, and the sum of `low-high agglomeration type' and `high-low agglomeration type' provinces was 10. In 2018, the total number of `high-high agglomeration type' and `low-low agglomeration type' provinces decreased to 12, while the total number of `low-high agglomeration type' and `high-low agglomeration type' provinces increased to 18, which specifically manifested as a negative spillover effect. (3) During the research period, the province's agricultural fixed asset investment, fiscal expenditure on agriculture, forestry, and water affairs, the number of years of education of rural residents and the increase in crop sown area have positive impacts on the efficiency of agricultural water use in the province. Meanwhile the transfer of labor and increase in the disposable income of rural residents reduced the efficiency of agricultural water use in the province. Fixed agricultural investment, labor transfers, and financial expenditures for agriculture, forestry, and water affairs in neighboring provinces have negative impacts on the province's agricultural water use efficiency. The impact is specifically manifested as a negative spatial spillover effect. As a result, China's agriculture will move towards healthy and green development. Therefore, the efficiency of agricultural water use needs to be comprehensively improved.

  • Agricultural water use efficiency and the spatial spillover effect.

  • Taking agricultural carbon emissions as an unexpected output.

  • The study used unexpected output Super-SBM model and SDM.

  • Agricultural water use efficiency among provinces shows a negative spatial spillover effect.

Water is the most critical input for agricultural production and also an important input for food security. Around the globe, almost 70% of the freshwater is used for agricultural production. Moreover, it will require a 50% increase in agricultural production to feed 9 billion people by 2025, and that 50% increase in agricultural production will require a 15% increase in water withdrawal (Khokhar, 2017). Agricultural production is composed of three elements: labor, land, and water resources. Sustainable agriculture is responsible for ecological changes and must be economically viable. Rapid population growth, as a major underlying force for environmental degradation, is a potential threat to the sustainable use of water resources and other natural resources (Maja & Ayano, 2021). At the same time, water scarcity has challenged the sustainability of agriculture, especially in arid and semi-arid regions, due to climatic effects. (Masoumeh & Ezatollah, 2011). Many scholars think about how to alleviate water shortage from the perspective of water evaporation (Valipour, 2017; El Shinawi et al., 2022) or water pollution management (Halsema & Linden, 2012; Abd-Elaty et al., 2022). So growing water scarcity and increasing demands for agricultural products have created a debate on improving agricultural water use efficiency and productivity.

Water resources are an important input for social and economic development. Being a largely agricultural country, China consumes a large amount of water resources in agriculture. In this modern era of science and technology, innovation in agricultural technology is continuously increasing and is being applied in the agricultural production process. Efficient agricultural machinery, pesticides, and fertilizers have improved the output of agricultural products and economic output as well. The utilization efficiency of new technology affects the improvement of the agricultural economy and social development. However, as one of the sources of nonpoint source pollution, agricultural activities have a negative effect on agricultural water quality (Badrzadeh et al., 2022), and due to the insufficient management efficiency of water resources and the increase of undesired output, the situation of agricultural water utilization is becoming increasingly severe. The overall plan for ecological civilization requires that regional development should not affect water resources. Therefore, it is imperative to ensure the sustainable and efficient utilization of agricultural water.

Aijun & Xianming (2010) and Guiliang et al. (2020) used the three-stage data envelope method (DEA) model and the Malmquist index method for the evaluation of China's water resources’ utilization efficiency. Shixiang et al., (2008) found the impact of the operation of the water rights trading market on the water resources’ utilization efficiency. Rilong & Mengyuan (2021) applied the meta frontier data envelopment analysis to measure the agricultural water efficiency based on the sample of 30 provinces in China from 2000 to 2017. Yaqing & Shipeng (2015) used the ratio analysis method to discuss the water resources’ use efficiency in China. Xiaobo & Boqiang (2017) revealed that improvement in technical efficiency in the agricultural sector has a significant impact on water demand and the spread of water-saving irrigation technologies (Caizhi et al., 2014). Based on the interprovincial water footprint and gray water footprint used for the interprovincial water resources’ utilization efficiency that is based on expected output and undesired output, Yu et al. (2007a, 2007b) and Xuhui et al. (2018) found through empirical research that expanding the proportion of food crops, increasing the rural labor force, and increasing the income of rural residents can effectively restrain the increase in agricultural water consumption. There is a positive correlation between the irrigated area of arable land and agricultural water consumption. Caizhi et al. (2017) investigated the influence of factors such as irrigation water-saving technology, engineering water-saving technology, water rights system, water price system, and water resources management system effect on the utilization efficiency of agricultural water resources. The results show that ecological benefits have become important factors affecting the level of regional water use efficiency. In terms of spatial research, Guoji et al. (2020) used DEA to measure China's water resources’ utilization efficiency by sector and stage, and also used Moran's index and impulse response function to analyze the spatial interaction. Feng et al. (2018) used the global Moran's I for spatial spillover effect of industrial water resources’ utilization efficiency tested with the Durbin model. Caizhi et al. (2014) and Gang et al. (2018) used exploratory spatial data analysis based on the perspective of water footprint to conduct water resource utilization efficiency analysis in the Yangtze River Economic Belt.

Significance of the study

From the above review, the current academic research on agricultural water use efficiency and its spatial spillover effect mainly have the following shortcomings: the research on water use efficiency mainly focuses on the overall use of water resources and industrial water use. There are few studies related to agricultural water use. In previous studies, most scholars ignored the impact of undesired output on water use efficiency, although a few scholars selected undesired output indicators but could not reveal the impact of agricultural water resources on use efficiency. The actual loss is caused by the resources and environment, and the calculation results are not representative, which is not conducive to the accuracy of the evaluation of water resources’ utilization efficiency. The existing literature used the traditional DEA model to measure water resources efficiency without considering the ‘slack variable’ in it, and the output changes caused by the changes in technical efficiency have not been taken into account, making the calculation results of efficiency values unrepresentative.

Objectives of the study

The objective of this study based on the above-mentioned reality is to achieve the sustainable development goals in agriculture and improve agricultural water efficiency. In this paper, first, we will incorporate the undesired output into the efficiency evaluation index system and use the super efficiency of the SBM model to evaluate agricultural water efficiency from 1998 to 2018 in China; this paper also discusses the status quo of China's agricultural water use efficiency, by using Moran's I on the spatial correlation analysis, and illustrates the spatial agglomeration situation between the provinces. Then, the spatial Durbin model was used to analyze the spatial spillover effect of agricultural water use efficiency, and the factors affecting agricultural water use efficiency were discussed from the aspects of economy, society, and environment. Compared with previous studies, the possible marginal contributions of this paper are as follows:

(1) Taking agricultural water use as the research objective, measuring the water resources’ use efficiency has enriched the research into agricultural water. (2) Incorporating carbon emissions as an undesired output into the calculation system of agricultural water resources’ utilization efficiency, and to more truly and effectively understand the agricultural water resources’ utilization efficiency of various provinces. (3) In the measurement of agricultural water use efficiency, a Super-SBM model of undesired output is constructed. This model considers the non-radial and non-angle problems, and can effectively solve the slack variable caused by too many input variables. In addition, it can effectively solve the efficiency sorting problem when the decision unit efficiency value is greater than 1. (4) To seek the factors that affect China's agricultural water use efficiency and its interprovincial spatial spillover effects, this paper constructs a spatial econometric model and discusses agricultural fixed asset investment, labor transfer, fiscal expenditure on agriculture, forestry and water affairs, rural residents’ education and the effect of age, disposable income of rural residents, and crop sown area on agricultural water use efficiency.

Unexpected output Super-SBM model

DEA is an efficiency analysis method proposed by Charnes and Cooper in 1978 (Charnes et al., 1978). This method is different from the previous research methods. It can measure the relative efficiency of the research object through input-oriented and output-oriented research directions. It does not need to preset the specific function form between input and output, and also avoids parameters and weights. The subjective judgment of aspects is widely used in the evaluation of multiple input and output decision-making units. The traditional DEA model belongs to the radial and angular measurement methods, which can only be used for the research into expected output indicators in the social economy that cannot measure the data with the unexpected output indicators, and then the measurement efficiency of the evaluated object may be affected. Tone proposed the SBM model (Tone, 2001) based on traditional DEA. The measurement method of this model is non-radial and non-angular, which effectively solves the above problems. This paper focuses on the impact of carbon emissions, an undesired output, on agricultural water use, so an undesired output SBM model from the perspective of CRS (fixed expected output) has been adopted.

Water resources’ utilization efficiency value ranges from . In the traditional SBM model, when , input factors and undesired output factors will produce slack, while the expected output factors will produce slack. Redundancy is when the efficiency value is not optimal; the slack and redundancy can be improved through the optimization of factor input, to achieve optimal efficiency. But when the P-value is greater than 1, both slack and redundancy values are 0. Considering the possibility of redundant water input, the Super-SBM model considering undesired output is selected for corresponding research. Based on the actual research subject and research object of this paper, it is assumed that each decision-making unit is composed of m input elements, n expected output elements, and i undesired output elements. Referring to the previous research, the Super-SBM model is constructed as follows:
formula
(1)
formula
formula
formula
formula

In the above formula, p is the value of China's agricultural water use efficiency, are the slack of input elements, expected output, and undesired output, respectively, are the vectors of input elements, expected output, and undesired output, respectively, and is the weight.

Spatial autocorrelation model

The utilization of agricultural water resources in China is not an independent problem; it is affected by many factors and the development of various provinces in China will also affect its efficiency. Therefore, the analysis of the utilization efficiency of agricultural water resources in China from a spatial perspective can be more precise. Before using the spatial econometric model regression analysis, it is necessary to check whether there is a spatial correlation in the utilization efficiency of agricultural water resources in China. First, Global Moran's I is introduced, and the calculation formula is as follows:
formula
(2)
At the same time, the Local Moran's I have been introduced to measure the agglomeration situation of China's agricultural water use efficiency in neighboring areas. The calculation formula is as follows:
formula
(3)
In the formula, the value range of I is (−1, 1). When I = 0, it means that there is no correlation in the spatial analysis matrix. The larger the absolute value of I, the stronger the spatial correlation. X represents the sample observation value, represents the average value of the sample, represent the agricultural water use efficiency value of the i region and j region, respectively, S2 represents the sample variance, Wij represents the spatial weight matrix. This paper constructs an adjacency matrix, a geographic distance matrix, and an economic geographic distance matrix for III species, which are specifically expressed as follows:
formula
(4)
formula
(5)
formula
(6)

In the above formula, is the geographic distance between regions, represents the elements in the regional GDP matrix.

Spatial panel model

The development level of the economy, society, and environment in a region has not only affected the utilization efficiency of agricultural water resources in the region but also affects the utilization efficiency of agricultural water resources in neighboring regions. Therefore, when discussing the influencing factors of agricultural water resources’ utilization efficiency, select the measurement model in the spatial panel. Generally speaking, spatial econometric models include three types: spatial error model (SEM), spatial lag model (SLM), and spatial Durbin model (SDM). The general spatial panel model is expressed in the following form:
formula
(7)

Yit is the agricultural water use efficiency of the i region in year t; λ and ρ represent spatial regression coefficients; is the spatial lag coefficient of the independent variable; is the spatial weight matrix; is the parameter to be estimated; are items. When , then the model is the SLM, when then the model is the SEM, when , then the model is the spatial Doberman model (SDM); which model should be selected for research needs to be verified by testing. In this study, the LR test and the Wald test were selected to determine which model to choose, and the Hausman test results were used to determine whether to choose a random effect or a fixed effect.

Data sources

The samples studied 30 provinces in Mainland China in this research. Hong Kong, Macao, Taiwan, and Tibet regions were not included in this empirical study due to the extremely special agricultural production conditions. The data collection period of time has been 1998–2018. Based on the principles of the feasibility of index quantification and the availability of data, all kinds of index data involving variables are from the ‘China Statistical Yearbook’, ‘China Rural Statistical Yearbook,’ and the provincial statistical yearbooks, ‘The First National Pollution Source Census’. Bulletin and other officially released statistical data, some missing data have been filled by linear interpolation, and 21 years of panel data of 30 provinces have been obtained after systematic sorting with the help of Excel tools. The data collected variables are shown in Table 1.

Indicator selection

According to the KLEM model proposed by Jorgenson, the use of water resources in the process of economic growth can be divided into four types of inputs: energy, labor, capital, and intermediate inputs (Jun et al., 2004). The three basic indicators of labor input and capital investment have been used as input indicators. Agricultural water resources produce necessities for human survival and promote economic growth in the process of agricultural production, and at the same time, they also harm the ecological environment. Therefore, this paper involves output indicators. Considering two aspects of expected output and undesired output, we choose to take the agricultural economic output as the expected output indicator and agricultural carbon emission as the undesired output indicator.

Among these, the water resources input adopts the agricultural effective irrigation area as the secondary indicator, the labor input selects the primary industry employees as the secondary indicator, and the capital investment adopts the agricultural fixed investment as the secondary indicator. The carbon emission index refers to the ‘China Emissions Trading Network’. The carbon emission (E) of agriculture mainly includes the carbon emission of fertilizer production (Ef), the carbon emission of machinery use (Em), and the carbon emission of irrigation (Ei), where Et = Ef + Em + Ei; Ef = Gf × A. Among them, Gf is the amount of fertilizer applied, A is the coefficient A = 857.54 kgC/t; Em = (Am × B) + (Wm × C), where Am is the planting area of crops, Wm is the total power of agricultural machinery, and B and C are the conversion coefficients, B = 16.47 kgC/hm2, C = 0.18 kgC/Kw; Ei = Ai × D, Ai is irrigation area, D is conversion coefficient, D = 266.48 kgC/hm2. Index selection descriptive statistics are presented in Table 2.

Data selection

To further explore the spatial spillover effect of China's agricultural water use efficiency, this paper selects China's agricultural water use efficiency (e) as the explained variable. According to the actual situation and previous research, the potential influencing factors of agricultural water use efficiency are found from the three subsystems of economy, society, and environment. The economic subsystem includes agricultural fixed asset investment (ai), rural residents’ disposable income (ri); social subsystems include labor transfer (tr) and years of education for rural residents (ey); environmental subsystems include agricultural and forestry water fiscal expenditures (ec) and crop sown area (sf). For farmers, it is an uncontrollable variable, so it is included in the impact of social subsystems as shown in Figure 1. The above index data are all original data. In order to eliminate the heteroskedasticity caused by the different dimensions of the variables and maintain the actual economic significance of the source data, each variable is processed logarithmically.
Table 1

Index system for measuring agricultural water resources’ utilization efficiency.

IndexVariableVariable description
Inputs Water input Total agricultural water use (effective irrigated area)/10 km2 
 Labor input Primary industry employees/10,000 people 
 Capital investment Agricultural fixed investment/100 million yuan 
Expected output Agricultural economic output Gross agricultural output value/100 million yuan 
Undesired output Carbon emission Total agricultural carbon emissions/10,000 tC 
IndexVariableVariable description
Inputs Water input Total agricultural water use (effective irrigated area)/10 km2 
 Labor input Primary industry employees/10,000 people 
 Capital investment Agricultural fixed investment/100 million yuan 
Expected output Agricultural economic output Gross agricultural output value/100 million yuan 
Undesired output Carbon emission Total agricultural carbon emissions/10,000 tC 
Table 2

Descriptive statistics of spatial spillover effect for agricultural water resources’ utilization efficiency in China.

Variable nameVariable symbolMeanStandard deviationMinimum valueMaximum valueSample size
Agricultural water use efficiency logarithm e 0.551 0.311 0.112 1.112 630 
Agricultural fixed asset investment logarithm lnai 3.529 1.273 − 2.303 3.239 630 
Labor transfer logarithm lntr 6.876 0.964 4.015 8.487 630 
Fiscal expenditure on agriculture, forestry, and water affairs logarithm lnec 4.821 1.287 1.341 7.178 630 
Years of education of rural residents’ logarithm lney 1.966 0.128 1.485 2.386 630 
Disposable income of rural residents’ logarithm lnri 8.530 0.751 7.154 11.635 630 
Crop sown area logarithm lnsf 8.198 1.057 4.642 9.601 630 
Variable nameVariable symbolMeanStandard deviationMinimum valueMaximum valueSample size
Agricultural water use efficiency logarithm e 0.551 0.311 0.112 1.112 630 
Agricultural fixed asset investment logarithm lnai 3.529 1.273 − 2.303 3.239 630 
Labor transfer logarithm lntr 6.876 0.964 4.015 8.487 630 
Fiscal expenditure on agriculture, forestry, and water affairs logarithm lnec 4.821 1.287 1.341 7.178 630 
Years of education of rural residents’ logarithm lney 1.966 0.128 1.485 2.386 630 
Disposable income of rural residents’ logarithm lnri 8.530 0.751 7.154 11.635 630 
Crop sown area logarithm lnsf 8.198 1.057 4.642 9.601 630 
Fig. 1

Agricultural water use efficiency impact indicators.

Fig. 1

Agricultural water use efficiency impact indicators.

Close modal
Fig. 2

Radar map of agricultural water use efficiency in 30 provinces in China.

Fig. 2

Radar map of agricultural water use efficiency in 30 provinces in China.

Close modal

Agricultural water use efficiency in China

The Super-SBM model of undesired output, based on the input-oriented perspective, Maxdea ultra 7.12, has been used to calculate the agricultural water resource utilization efficiency of 30 provinces in China from 1998 to 2018. To analyze its evolution process from a more comprehensive and detailed time perspective, the whole research period has been divided into two time periods: 1998–2008 and 2008–2018. Table 3 lists the efficiency values of 1998, 2008, and 2018, and measures the scale efficiency changes of 30 provinces in the two decades. It is divided into three different space types: efficiency increase area, efficiency decrease area, and efficiency stabilization area.

Table 3

Utilization efficiency of agricultural water resources in China.

Province1998
2008
2018
ITEPTESERSITEPTESERSITEPTESERS
Beijing 0.953 1.126 0.844 1.043 1.208 0.864 1.056 1.725 0.635 
Tianjin 0.877 1.154 0.762 0.598 0.750 0.826 0.505 1.026 0.493 
Hebei 0.394 0.683 0.576 − 0.512 0.646 0.789 − 0.484 0.585 0.825 − 
Shanxi 0.231 0.260 0.887 − 0.319 0.342 0.927 0.322 0.337 0.953 + / − 
Inner Mongolia 0.292 0.375 0.776 − 0.351 0.378 0.927 − 0.337 0.352 0.965 + / − 
Liaoning 0.764 1.037 0.734 − 1.021 1.044 0.977 − 0.956 0.967 0.990 − 
Jilin 0.399 0.642 0.663 − 0.430 0.477 0.905 + / − 0.303 0.312 0.971 + / − 
Heilongjiang 0.255 0.349 0.731 − 0.343 0.388 0.884 + / − 0.600 1.021 0.588 − 
Shanghai 1.090 1.677 0.762 1.007 2.592 0.434 0.577 1.697 0.355 
Jiangsu 0.533 1.073 0.496 − 0.894 1.084 0.824 − 0.980 1.080 0.903 − 
Zhejiang 0.837 1.059 0.790 − 1.020 1.052 0.970 − 1.044 1.048 0.996 + / − 
Anhui 0.307 0.465 0.661 − 0.403 0.480 0.839 − 0.386 0.427 0.905 − 
Fujian 0.623 1.032 0.604 − 0.897 1.029 0.871 − 1.039 1.053 0.986 − 
Jiangxi 0.278 0.378 0.736 − 0.344 0.393 0.878 + / − 0.325 0.331 0.983 + / − 
Shandong 0.641 1.038 0.615 − 1.033 1.060 0.975 − 1.029 1.060 0.971 − 
Henan 0.748 1.027 0.727 − 0.727 0.943 0.785 − 0.537 0.586 0.945 + / − 
Hubei 0.325 0.486 0.668 − 0.448 0.542 0.821 − 0.498 0.664 0.779 − 
Hunan 0.286 0.445 0.664 − 0.422 0.543 0.776 − 0.394 0.491 0.807 − 
Guangdong 0.491 1.036 0.473 − 0.601 1.022 0.588 − 0.527 1.006 0.524 − 
Guangxi 0.230 0.313 0.734 − 0.349 0.420 0.830 − 0.356 0.392 0.909 − 
Hainan 0.967 1.037 0.931 − 0.977 1.034 0.944 + / − 1.041 1.075 0.968 
Chongqing 0.953 0.989 0.947 1.041 1.068 0.975 1.019 1.026 0.993 + / − 
Sichuan 0.378 0.818 0.496 − 0.688 0.889 0.790 − 0.459 0.958 0.493 − 
Guizhou 0.247 0.284 0.866 − 0.246 0.264 0.928 + / − 0.590 0.674 0.884 + / − 
Yunnan 0.263 0.328 0.797 − 0.285 0.320 0.891 + / − 0.320 0.335 0.959 + / − 
Shaanxi 0.241 0.290 0.830 − 0.398 0.442 0.889 − 0.547 0.574 0.957 + / − 
Gansu 0.188 0.220 0.857 − 0.229 0.250 0.913 + / − 0.265 0.276 0.958 + / − 
Qinghai 0.229 0.897 0.263 0.379 0.993 0.391 0.637 0.816 0.758 
Ningxia 0.142 0.336 0.426 0.191 0.254 0.751 0.222 0.309 0.736 
Xinjiang 0.393 0.813 0.537 − 0.277 0.315 0.875 + / − 0.280 0.288 0.977 + / − 
Average 0.496 0.766 0.685  0.572 0.774 0.812  0.657 0.768 0.818  
Province1998
2008
2018
ITEPTESERSITEPTESERSITEPTESERS
Beijing 0.953 1.126 0.844 1.043 1.208 0.864 1.056 1.725 0.635 
Tianjin 0.877 1.154 0.762 0.598 0.750 0.826 0.505 1.026 0.493 
Hebei 0.394 0.683 0.576 − 0.512 0.646 0.789 − 0.484 0.585 0.825 − 
Shanxi 0.231 0.260 0.887 − 0.319 0.342 0.927 0.322 0.337 0.953 + / − 
Inner Mongolia 0.292 0.375 0.776 − 0.351 0.378 0.927 − 0.337 0.352 0.965 + / − 
Liaoning 0.764 1.037 0.734 − 1.021 1.044 0.977 − 0.956 0.967 0.990 − 
Jilin 0.399 0.642 0.663 − 0.430 0.477 0.905 + / − 0.303 0.312 0.971 + / − 
Heilongjiang 0.255 0.349 0.731 − 0.343 0.388 0.884 + / − 0.600 1.021 0.588 − 
Shanghai 1.090 1.677 0.762 1.007 2.592 0.434 0.577 1.697 0.355 
Jiangsu 0.533 1.073 0.496 − 0.894 1.084 0.824 − 0.980 1.080 0.903 − 
Zhejiang 0.837 1.059 0.790 − 1.020 1.052 0.970 − 1.044 1.048 0.996 + / − 
Anhui 0.307 0.465 0.661 − 0.403 0.480 0.839 − 0.386 0.427 0.905 − 
Fujian 0.623 1.032 0.604 − 0.897 1.029 0.871 − 1.039 1.053 0.986 − 
Jiangxi 0.278 0.378 0.736 − 0.344 0.393 0.878 + / − 0.325 0.331 0.983 + / − 
Shandong 0.641 1.038 0.615 − 1.033 1.060 0.975 − 1.029 1.060 0.971 − 
Henan 0.748 1.027 0.727 − 0.727 0.943 0.785 − 0.537 0.586 0.945 + / − 
Hubei 0.325 0.486 0.668 − 0.448 0.542 0.821 − 0.498 0.664 0.779 − 
Hunan 0.286 0.445 0.664 − 0.422 0.543 0.776 − 0.394 0.491 0.807 − 
Guangdong 0.491 1.036 0.473 − 0.601 1.022 0.588 − 0.527 1.006 0.524 − 
Guangxi 0.230 0.313 0.734 − 0.349 0.420 0.830 − 0.356 0.392 0.909 − 
Hainan 0.967 1.037 0.931 − 0.977 1.034 0.944 + / − 1.041 1.075 0.968 
Chongqing 0.953 0.989 0.947 1.041 1.068 0.975 1.019 1.026 0.993 + / − 
Sichuan 0.378 0.818 0.496 − 0.688 0.889 0.790 − 0.459 0.958 0.493 − 
Guizhou 0.247 0.284 0.866 − 0.246 0.264 0.928 + / − 0.590 0.674 0.884 + / − 
Yunnan 0.263 0.328 0.797 − 0.285 0.320 0.891 + / − 0.320 0.335 0.959 + / − 
Shaanxi 0.241 0.290 0.830 − 0.398 0.442 0.889 − 0.547 0.574 0.957 + / − 
Gansu 0.188 0.220 0.857 − 0.229 0.250 0.913 + / − 0.265 0.276 0.958 + / − 
Qinghai 0.229 0.897 0.263 0.379 0.993 0.391 0.637 0.816 0.758 
Ningxia 0.142 0.336 0.426 0.191 0.254 0.751 0.222 0.309 0.736 
Xinjiang 0.393 0.813 0.537 − 0.277 0.315 0.875 + / − 0.280 0.288 0.977 + / − 
Average 0.496 0.766 0.685  0.572 0.774 0.812  0.657 0.768 0.818  

Note: ITE shows comprehensive technical efficiency and PTE shows pure technical efficiency. SE and RS represent scale efficiency and scale returns, respectively; +/ − , +, and − represent constant returns to scale, increasing returns to scale, and decreasing returns to scale, respectively.

Table 3 shows that the utilization efficiency of agricultural water resources in China remains in a low state. In the three listed years, the average efficiency values remain at 0.496, 0.572, and 0.657, respectively. Although the values are low, they are in a state of slow rise. It can be seen that this is mainly caused by low technical efficiency. In Figure 1, we can see that interprovincial agricultural water resources’ utilization efficiency has many differences. Beijing, Hainan, Chongqing, Shandong, Zhejiang, Shanghai, and other provinces have relatively high utilization efficiency of agricultural water resources. It can be seen that these provinces belong to the forefront of China's economic development, have a good economic foundation and solid technical support in agricultural infrastructure, and some provinces are located in coastal areas, indicating the water resources endowment and economic development; all these situations have an important impact on the efficiency of agricultural water use. Some provinces with low efficiency, especially those in the lower ranks from 1998 to 2018, need more policy reforms and agricultural subsidies for resource configuration and facility construction.

Table 4 lists three different spatial evolution trends. Among them, Beijing, Tianjin, Shanghai, and Hainan have become areas for increasing agricultural water use efficiency due to their sound agricultural development foundation and abundant resource stock. Qinghai and Ningxia also have good policy protection and are in a state of efficiency improvement. However, Guangxi is in the efficiency reduction zone due to its fragile ecological environment and backward technological and economic levels. Although Guizhou, Yunnan, Gansu, Xinjiang, and other provinces are in the efficiency stability zone, it can be seen that the efficiency values in the two time periods have been extremely low, which is also due to their relatively backward economic level. At the same time, it is worth noting the agricultural industries such as Hebei, Jiangsu, Shandong, Anhui, Hubei, Hunan, Liaoning, and Heilongjiang. The efficiency of the main production areas is also in the efficiency reduction area.

Table 4

The spatial evolution trend of utilization efficiency of agricultural water resources in China.

Types of efficiencyProvince
Efficiency increase area Beijing, Tianjin, Shanghai, Hainan, Ningxia, and Qinghai 
Efficiency reduction zone Hebei, Liaoning, Jiangsu, Anhui, Fujian, Shandong, Hubei, Hunan, Guangdong, Guangxi, Sichuan, and Heilongjiang 
Efficiency stable zone Chongqing, Shanxi, Inner Mongolia, Zhejiang, Henan, Shaanxi, Jilin, Jiangxi, Guizhou, Yunnan, Gansu, and Xinjiang 
Types of efficiencyProvince
Efficiency increase area Beijing, Tianjin, Shanghai, Hainan, Ningxia, and Qinghai 
Efficiency reduction zone Hebei, Liaoning, Jiangsu, Anhui, Fujian, Shandong, Hubei, Hunan, Guangdong, Guangxi, Sichuan, and Heilongjiang 
Efficiency stable zone Chongqing, Shanxi, Inner Mongolia, Zhejiang, Henan, Shaanxi, Jilin, Jiangxi, Guizhou, Yunnan, Gansu, and Xinjiang 

Spatial spillover effect

The level of agricultural water resources’ utilization efficiency not only depends on the province's measured own water resources’ utilization and agricultural economic development, but is also affected by the other regions and provinces, especially neighboring regions and provinces with convenient communication. The previous article has studied the changing trend of China's agricultural water use efficiency in the time dimension. On this basis, this paper will use the spatial Durbin model to continue to analyze its spillover effect in the spatial perspective.

Spatial autocorrelation test

Before carrying out the regression analysis of the SDM, the spatial autocorrelation test has been carried out on the utilization efficiency of agricultural water resources in China through the global Moran's I and the local Moran scatter plot. The results are shown in Table 5. It is shown in Table 5 that the estimation results under the three matrices are the same. Except for 2016, 2017, and 2018, the Moran values are positive numbers, and most of the Moran's P values are less than 0.1, indicating that China's Agricultural water use efficiency showed a significant correlation at the 10% level, and some of them have less than 0.05 and passed the significance test at the 5% level, indicating that the spatial correlation of agricultural water use efficiency from a spatial perspective is considered very necessary.

Table 5

Global spatial autocorrelation test of the utilization efficiency of agricultural water resources in China from 1998 to 2018.

YearsMoran's I valueP-valueYearsMoran's I valueP-valueYearsMoran's I valueP-value
Adjacency matrix 1998 0.103 0.136 2005 0.146 0.073 2012 0.298 0.004 
1999 0.389 0.000 2006 0.152 0.067 2013 0.140 0.082 
2000 0.235 0.013 2007 0.226 0.019 2014 0.108 0.128 
2001 0.333 0.001 2008 0.243 0.014 2015 0.118 0.110 
2002 0.168 0.052 2009 0.323 0.002 2016 −0.017 0.444 
2003 0.195 0.031 2010 0.330 0.002 2017 −0.075 0.374 
2004 0.080 0.177 2011 0.130 0.094 2018 −0.092 0.323 
Geographic distance matrix 1998 0.046 0.015 2005 0.037 0.026 2012 0.037 0.027 
1999 0.052 0.009 2006 0.022 0.062 2013 0.001 0.169 
2000 0.028 0.040 2007 0.028 0.045 2014 0.007 0.099 
2001 0.083 0.001 2008 0.032 0.036 2015 0.022 0.062 
2002 0.024 0.055 2009 0.043 0.017 2016 −0.071 0.158 
2003 0.038 0.023 2010 0.043 0.018 2017 −0.080 0.109 
2004 0.013 0.097 2011 0.035 0.027 2018 −0.078 0.118 
Economic geographic distance matrix 1998 0.057 0.010 2005 0.042 0.025 2012 0.044 0.023 
1999 0.074 0.003 2006 0.026 0.059 2013 0.034 0.030 
2000 0.042 0.024 2007 0.038 0.033 2014 0.013 0.109 
2001 0.106 0.000 2008 0.040 0.028 2015 0.030 0.056 
2002 0.037 0.033 2009 0.051 0.015 2016 −0.074 0.156 
2003 0.050 0.014 2010 0.050 0.016 2017 −0.084 0.105 
2004 0.013 0.109 2011 0.027 0.059 2018 −0.083 0.109 
YearsMoran's I valueP-valueYearsMoran's I valueP-valueYearsMoran's I valueP-value
Adjacency matrix 1998 0.103 0.136 2005 0.146 0.073 2012 0.298 0.004 
1999 0.389 0.000 2006 0.152 0.067 2013 0.140 0.082 
2000 0.235 0.013 2007 0.226 0.019 2014 0.108 0.128 
2001 0.333 0.001 2008 0.243 0.014 2015 0.118 0.110 
2002 0.168 0.052 2009 0.323 0.002 2016 −0.017 0.444 
2003 0.195 0.031 2010 0.330 0.002 2017 −0.075 0.374 
2004 0.080 0.177 2011 0.130 0.094 2018 −0.092 0.323 
Geographic distance matrix 1998 0.046 0.015 2005 0.037 0.026 2012 0.037 0.027 
1999 0.052 0.009 2006 0.022 0.062 2013 0.001 0.169 
2000 0.028 0.040 2007 0.028 0.045 2014 0.007 0.099 
2001 0.083 0.001 2008 0.032 0.036 2015 0.022 0.062 
2002 0.024 0.055 2009 0.043 0.017 2016 −0.071 0.158 
2003 0.038 0.023 2010 0.043 0.018 2017 −0.080 0.109 
2004 0.013 0.097 2011 0.035 0.027 2018 −0.078 0.118 
Economic geographic distance matrix 1998 0.057 0.010 2005 0.042 0.025 2012 0.044 0.023 
1999 0.074 0.003 2006 0.026 0.059 2013 0.034 0.030 
2000 0.042 0.024 2007 0.038 0.033 2014 0.013 0.109 
2001 0.106 0.000 2008 0.040 0.028 2015 0.030 0.056 
2002 0.037 0.033 2009 0.051 0.015 2016 −0.074 0.156 
2003 0.050 0.014 2010 0.050 0.016 2017 −0.084 0.105 
2004 0.013 0.109 2011 0.027 0.059 2018 −0.083 0.109 

To observe the changing trend of the spatial correlation of China's agricultural water use efficiency in the time dimension more comprehensively and intuitively, the spatial local Moran test was carried out on the agricultural water use efficiency in China in 1998 and 2018. The economic geographic distance matrix is more significant; therefore, the economic geographic distance matrix is used for testing.

Table 6 shows the specific distribution of Moran's I in each province at two time points. In the spatial local autocorrelation test, the first quadrant is ‘High-high agglomeration type’ (HH), which means that its agricultural water resources’ use efficiency value is high, and the adjacent agricultural water resources’ use efficiency value is also high; the second quadrant is ‘low-high agglomeration type’ (LH), indicating that its agricultural water use efficiency value is low but surrounded by provinces with high agricultural water resources’ use efficiency value; the third quadrant is ‘low-low agglomeration type’ (LL), indicating that its agricultural water resources’ use efficiency value is low and surrounded by the surrounding areas with the low value of agricultural water use efficiency; the fourth quadrant is ‘high-low agglomeration type’ (HL), which means that the value of its agricultural water use efficiency is high but the surrounding agricultural water use efficiency value is lower. Surrounded by regions, among the provinces in the four quadrants, the first and third quadrants indicate that there is a positive correlation between the observation object and its related regions, whereas the regions located in the second and fourth quadrants have a negative correlation with their related provinces. Table 6 indicates the local spatial autocorrelation test of China's agricultural water resources’ use efficiency; it can be found that during the period time from 1998 to 2018, it remained in the first place. Provinces in the first and third quadrants decreased, and moved to the second and fourth quadrants, indicating that the positive correlations of adjacent provinces tended to decrease while the negative correlations tended to increase.

Table 6

Local spatial autocorrelation test of agricultural ecological efficiency in China from 1998 to 2018.

Quadrant19982018
First quadrant (HH) Beijing, Shanghai, Fujian, Tianjin, Zhejiang, Shandong, Jiangsu, Hainan, and Guangdong Jiangsu, Fujian, Chongqing, and Zhejiang 
Second quadrant (LH) Hebei, Anhui, Hubei, Jiangxi, Shanxi, and Jilin Shanghai, Tianjin, Anhui, Sichuan, Guangdong, Jiangxi, Hunan, Hebei, Guangxi, Hubei, and Yunnan 
Third quadrant (LL) Hunan, Qinghai, Shaanxi, Guizhou, Heilongjiang, Gansu, Inner Mongolia, Sichuan, Guangxi, Yunnan, and Ningxia Heilongjiang, Inner Mongolia, Ningxia, Jilin, Shanxi, Gansu, Xinjiang, and Henan 
Fourth quadrant (HL) Henan, Liaoning, Chongqing, and Xinjiang Guizhou, Beijing, Hainan, Liaoning, Henan, Shaanxi, and Shandong 
Quadrant19982018
First quadrant (HH) Beijing, Shanghai, Fujian, Tianjin, Zhejiang, Shandong, Jiangsu, Hainan, and Guangdong Jiangsu, Fujian, Chongqing, and Zhejiang 
Second quadrant (LH) Hebei, Anhui, Hubei, Jiangxi, Shanxi, and Jilin Shanghai, Tianjin, Anhui, Sichuan, Guangdong, Jiangxi, Hunan, Hebei, Guangxi, Hubei, and Yunnan 
Third quadrant (LL) Hunan, Qinghai, Shaanxi, Guizhou, Heilongjiang, Gansu, Inner Mongolia, Sichuan, Guangxi, Yunnan, and Ningxia Heilongjiang, Inner Mongolia, Ningxia, Jilin, Shanxi, Gansu, Xinjiang, and Henan 
Fourth quadrant (HL) Henan, Liaoning, Chongqing, and Xinjiang Guizhou, Beijing, Hainan, Liaoning, Henan, Shaanxi, and Shandong 

Analysis of SDM

According to the theoretical basis of the previous model selection, the LR test and Wald test have been carried out on the utilization efficiency of agricultural water resources in China. Since the test results rejected both Hypothesis 1 and Hypothesis 2, the SDM has been selected for spatial effect analysis. On this basis, according to the results of the Hausman test, the χ2 value is 24.41, and the Prob > χ2 value is 0.0010. Therefore, the fixed effect is selected to estimate the SDM. In order to ensure the accuracy of the estimation results, this paper reports the estimation results under the geographic distance matrix and the economic geographic matrix at the same time, as shown in Table 7.

Table 7

Durbin model estimation results under fixed effects.

Geographic distance matrix
Economic geographic distance matrix
VariableTime fixed effectsSpatial fixed effectsTwo-way fixed effectsTime fixed effectsSpatial fixed effectsTwo-way fixed effects
ai 0.0825*** 0.0588*** 0.0564*** 0.0808*** 0.0619*** 0.0556*** 
tr 0.0274 − 0.0827*** − 0.0939*** 0.0351* − 0.0834*** − 0.0904*** 
ec 0.0489* 0.0281 0.0271 0.0476* 0.0412* 0.0336 
ey 0.1022 0.4638*** 0.4412*** 0.0491 0.5208*** 0.4484*** 
ri 0.3906*** − 0.0823 − 0.0667 0.3617*** − 0.0863* − 0.0681 
sf − 0.1611*** − 0.0939* − 0.0476 − 0.1712*** − 0.0908* − 0.0185 
W* ai − 0.2519*** 0.0094 0.0051 − 0.2746*** 0.0037 − 0.0456 
W*tr 0.0963 0.0689 − 0.1433 0.0883 0.0292 − 0.1383 
W* ec 0.2107 0.0077 − 0.0633 0.0194 − 0.0203 − 0.1488 
W* ey − 0.7401 − 0.6847*** − 0.6026 − 0.3222 − 0.6902*** − 1.0857* 
W* ri 0.1297 0.0940 0.2468 0.2188 0.1247* 0.2539 
W* sf 0.2784* 0.7099*** 1.0169*** 0.3029*** 0.5936*** 0.8754*** 
Spatial rho − 0.9174*** 0.0159 − 0.6294*** − 0.1516*** − 0.1605** − 0.0819** 
observation 630 630 630 630 630 630 
R2 0.561 0.523 0.741 0.549 0.463 0.667 
LogLik 34.073 340.601 363.023 21.101 343.247 364.302 
AIC − 40.145 − 653.203 − 698.046 − 14.201 − 658.494 − 700.603 
BIC 22.095 − 590.962 − 635.806 48.039 − 596.254 − 638.363 
Geographic distance matrix
Economic geographic distance matrix
VariableTime fixed effectsSpatial fixed effectsTwo-way fixed effectsTime fixed effectsSpatial fixed effectsTwo-way fixed effects
ai 0.0825*** 0.0588*** 0.0564*** 0.0808*** 0.0619*** 0.0556*** 
tr 0.0274 − 0.0827*** − 0.0939*** 0.0351* − 0.0834*** − 0.0904*** 
ec 0.0489* 0.0281 0.0271 0.0476* 0.0412* 0.0336 
ey 0.1022 0.4638*** 0.4412*** 0.0491 0.5208*** 0.4484*** 
ri 0.3906*** − 0.0823 − 0.0667 0.3617*** − 0.0863* − 0.0681 
sf − 0.1611*** − 0.0939* − 0.0476 − 0.1712*** − 0.0908* − 0.0185 
W* ai − 0.2519*** 0.0094 0.0051 − 0.2746*** 0.0037 − 0.0456 
W*tr 0.0963 0.0689 − 0.1433 0.0883 0.0292 − 0.1383 
W* ec 0.2107 0.0077 − 0.0633 0.0194 − 0.0203 − 0.1488 
W* ey − 0.7401 − 0.6847*** − 0.6026 − 0.3222 − 0.6902*** − 1.0857* 
W* ri 0.1297 0.0940 0.2468 0.2188 0.1247* 0.2539 
W* sf 0.2784* 0.7099*** 1.0169*** 0.3029*** 0.5936*** 0.8754*** 
Spatial rho − 0.9174*** 0.0159 − 0.6294*** − 0.1516*** − 0.1605** − 0.0819** 
observation 630 630 630 630 630 630 
R2 0.561 0.523 0.741 0.549 0.463 0.667 
LogLik 34.073 340.601 363.023 21.101 343.247 364.302 
AIC − 40.145 − 653.203 − 698.046 − 14.201 − 658.494 − 700.603 
BIC 22.095 − 590.962 − 635.806 48.039 − 596.254 − 638.363 

Note: z-statistic value. The ‘ai’ stands for Agricultural fixed asset investment; the ‘tr’ stands for Labor transfer; the ‘ec’ stands for Fiscal expenditure on agriculture, forestry, and water affairs; the ‘ey’ stands for Years of education of rural residents; the ‘ri’ stands for Disposable income of rural residents; the ‘sf’ stands for Crop sown area.

***, **, * indicate significance at the 1, 5, and 10% levels, respectively.

Table 7 shows the estimation results of the geographic distance matrix and the economic geographic distance matrix under the fixed effect of the SDM of agricultural water resource utilization efficiency in China. The spatial ρ values were −0.6294 and −0.0819 under the two-way fixed effects, respectively. Except for the spatial fixed effect under the geographic distance matrix, the spatial ρ value is significant at the 5% test level and is negative, indicating that China's agricultural water resources’ utilization efficiency on the interprovincial level shows a negative spillover effect. The improvement of agricultural water use efficiency in neighboring provinces has not been conducive to the improvement of agricultural water use efficiency in this province.

In addition, it can be seen from Table 7 that in the fixed-effect regression of the Durbin model under the two distance matrices, the LogLik values of the time fixed effects are 34.073 and 21.101, respectively, and the LogLik values of the spatial fixed effect are 340.601 and 343.247, respectively, and the LogLik values of the two-way fixed effect are, respectively, 34.073 and 21.101, and 363.023 and 364.302, and the two-way fixed effect LogLik value is the largest; on this basis, in the geographic distance matrix, the AIC and BIC values under the time fixed effect are −40.145 and 22.095, respectively, and the AIC and BIC values under the space fixed effect are −653.203 and −590.962, the AIC and BIC values under the two-way fixed effect are −698.046 and −635.806, respectively; in the economic geographic distance matrix, the AIC and BIC values under the time fixed effect are −14.201 and 48.039, and −658.494 and −596.254, respectively, and the AIC and BIC values under the two-way fixed effects are −700.603 and −638.363, respectively; whether it is a geographic distance matrix or an economic geographic distance matrix, the two information criteria of the two-way fixed effect, AIC and BIC, have the smallest. Therefore, the two-way fixed effect in the Durbin model is the best choice. To further analyze the spatial spillover effect of China's agricultural water use efficiency, the direct, indirect, and total effects of the explanatory variables on the explained variables are estimated. The direct effect represents the spatial spillover effect of the explanatory variable in the province on the explained variable in the province, the indirect effect represents the spatial spillover effect of the explanatory variable in the neighboring province on the explained variable in the province, and the total effect is the sum of the two. The specific results under the geographic distance matrix and the economic geographic distance matrix are shown in Table 8.

Table 8

The direct and indirect effects of SDM coefficient estimation.

Geographic distance matrix
Economic geographic distance matrix
VariableDirect effectIndirect effectTotal effectDirect effectIndirect effectTotal effect
ai 0.0577*** − 0.0198 0.0379** 0.0577*** − 0.0508 0.0069 
tr − 0.0925*** − 0.0570 − 0.1496** − 0.0891*** − 0.0663 − 0.1554*** 
ec 0.0321 − 0.0495 − 0.0175** 0.0402* − 0.1119 − 0.0718 
ey 0.4629*** 0.5440** − 0.0811* 0.4778*** 0.8851** − 0.4073 
ri − 0.0739* 0.1827 0.1089 − 0.0745* 0.1969 0.1224 
sf 0.0733* 0.6637*** 0.5904*** 0.0379 0.6053*** 0.5674*** 
Geographic distance matrix
Economic geographic distance matrix
VariableDirect effectIndirect effectTotal effectDirect effectIndirect effectTotal effect
ai 0.0577*** − 0.0198 0.0379** 0.0577*** − 0.0508 0.0069 
tr − 0.0925*** − 0.0570 − 0.1496** − 0.0891*** − 0.0663 − 0.1554*** 
ec 0.0321 − 0.0495 − 0.0175** 0.0402* − 0.1119 − 0.0718 
ey 0.4629*** 0.5440** − 0.0811* 0.4778*** 0.8851** − 0.4073 
ri − 0.0739* 0.1827 0.1089 − 0.0745* 0.1969 0.1224 
sf 0.0733* 0.6637*** 0.5904*** 0.0379 0.6053*** 0.5674*** 

Note: z-statistic value. The ‘ai’ stands for Agricultural fixed asset investment; the ‘tr’ stands for labor transfer; the ‘ec’ stands for Fiscal expenditure on agriculture, forestry, and water affairs; the ‘ey’ stands for years of education of rural residents; the ‘ri’ stands for disposable income of rural residents; the ‘sf’ stands for crop sown area.

***, **, * indicate significance at the 1, 5, and 10% levels, respectively.

Table 8 shows the direct effect, indirect effect, and total effect results under the two distance matrices. The estimated results under the two matrices are the same. Among the direct effects, fixed investment in agriculture, fiscal expenditure in agriculture, forestry and water affairs, years of education of rural residents, and crop planting area are positively correlated with agricultural water use efficiency. Among them, the coefficient estimation of agricultural fixed investment is 0.0577, and the coefficient estimation of rural residents’ years of education is 0.4629 (0.4778), both of which are significant at the 1% test level. When agricultural fixed investment and rural residents’ years of education increase, agricultural water use efficiency will increase accordingly, within the range of cognitive knowledge. On the contrary, the estimated value of the labor transfer coefficient is −0.0925 (−0.0891), and the estimated value of the disposable income coefficient of rural residents is −0.0739 (−0.0745), which is negatively correlated with agricultural water use efficiency in theory. Among them, the labor transfer index is significant at the 1% test level.

Concerning the indirect effect, it can be seen that the estimated values of agricultural fixed investment, labor transfer, and fiscal expenditure on agriculture, forestry, and water affairs are −0.0198 (−0.0508), −0.0570 (−0.0663) and −0.0495 (−0.1119), respectively, and the coefficient sign is negative. It means that the surrounding provinces’ agricultural fixed investment, labor transfer, and financial expenditure in agriculture, forestry, and water affairs increase, and reduce the efficiency of agricultural water use in this province. In Table 8, the estimated coefficient of rural residents’ years of education is 0.5440 (0.8851), and the estimated coefficient of rural residents’ disposable income is 0.1827 (0.1969). The estimated coefficient of crop sown area was 0.6637 (0.6053). With the increase of years of education, the increase of disposable income of rural residents, and the increase of crop sown area in neighboring provinces, the utilization efficiency of agricultural water resources in this province was improved. The radar map of agriculture water use efficiency can be seen in Figure 2.

The study measures the utilization efficiency of agricultural water resources in China based on the perspective of unexpected output. The results show that the utilization efficiency of agricultural water resources in China always remained at a low level but in a state of slow rise. As is indicated from measured data results, it is mainly caused by the low technical efficiency which has been mainly affected by the input factor and management system, and the low technical efficiency of agricultural water resources’ utilization in China is mainly caused by the loss of water input factors. The comparative benefits have been relatively low, especially in the planting industry. Therefore, farmers, especially those in water-scarce areas, have invested water resources in aquaculture and other industries with higher returns. On the other hand, the construction of irrigation and water conservancy facilities is the key to the effective use of agricultural water resources. China's water conservancy infrastructure construction is weak, especially for irrigated agriculture in the central and western regions. It is affected by the climate, terrain, and water scarcity; irrigation technology has not been mature; and agricultural water use efficiency has a low trend. In addition, Shicheng (2011) believes that there are still some problems in China's water conservancy facilities, such as inadequate market-oriented reform, unreasonable proportion in the structure of the water conservancy facilities system, and lagging reform of management system and backward irrigation technology.

In terms of method selection, the traditional DEA model does not consider the problem of slack variables and cannot measure the existence of undesired output, and the measurement of efficiency will be biased. Therefore, this study selects the SBM model that can measure the undesired output. Just using the ordinary SBM model, there will be cases where the efficiency is all 1 and there is no obvious fluctuation. In this study, the Super-SBM model was finally selected to measure the agricultural water use efficiency, but this is not perfect. This study assumes that all inputs have equal weights. Subsequent research needs to weigh the subjective and objective weights scientifically and seek greater breakthroughs in the model.

From the perspective of the measurement index system, in the past, in the measurement of agricultural water use efficiency, the focus was on the expected agricultural output, and the undesired output was ignored. For example, Guoji et al. (2020) did not consider undesired output to measure agricultural water use efficiency and concluded that the efficiency value was high, the efficiency value was higher than 1 in most provinces, and the conclusions were significantly different. In consideration of ecology and low carbon, the results obtained by incorporating carbon emissions into the output perspective to measure agricultural water use efficiency are more accurate. The differences in research results are particularly prominent in Beijing, Shanghai, and other provinces. These provinces have more developed agricultural technologies and more significant carbon emissions. If the undesired output of carbon emissions is not considered, the efficiency obtained is higher than the actual level. Therefore, this paper incorporates carbon emissions as an expected output in the analysis, which will make the results more reasonable.

In terms of spatial correlation, the results show that the positive correlation among the Chinese provinces shows a weaker trend, while the negative correlation shows a strengthening trend. This shows that there are few exchanges between provinces in China in terms of agricultural water use efficiency. The reason may be that the unique characteristics of water resources are easy to flow and difficult to transport which makes cross-regional exchanges more complicated. For example, the South-to-North Water Diversion Project is to improve economic development and the optimal allocation of water resources in China. The improvement of water use efficiency has made outstanding contributions. But its implementation, from approval to trial operation, took more than 10 years, and the time cost is quite large. In addition, in the South-to-North Water Diversion Project, the implementation of water resources has a great impact on the ecological environment of the Yangtze River Basin. This also reflects to a certain extent that China's agricultural water resources’ utilization efficiency has difficulties with spatial communication.

When exploring the factors affecting agricultural water use efficiency, the Tobit model was used for correlation analysis in the past, but this method has a strong dependence on the distribution of data, and the results are not robust enough; it can only analyze the influencing relationship of the province and cannot explore the relationship between provinces. The spatial correlation of the obtained results cannot be compared horizontally. For example, in the analysis of factors affecting China's agricultural total factor water use efficiency, Tong Jinping et al. used the Tobit model to analyze the influencing factors of 30 provinces, which lacked spatial connectivity. This study adopts the spatial spillover effect analysis method. On the one hand, it can explore how the economic, social, and environmental perspectives have an impact on agricultural water use efficiency and then propose ways to improve efficiency; on the other hand, it can discover the mutual influence mechanism of neighboring provinces through indirect effects, strengthen cooperation in surrounding areas, and ensure the comprehensiveness of research. Of course, this method still has some limitations. When selecting explanatory variables, this study comprehensively considered and selected impact indicators from multiple perspectives. However, due to the complexity of agriculture, there are many factors affecting agricultural water use efficiency, practice-based maneuverability cannot incorporate all indicators into the study, and follow-up studies can be more detailed.

The study concluded that the comprehensive technical efficiency of China's agricultural water resources’ utilization efficiency has not been high in the past 21 years, but it is in a state of slow rise, and the low-efficiency state is mainly caused by the low pure technical efficiency. China's agricultural water use efficiency is in a state of spatial agglomeration as a whole, but there is a trend of positive correlation to the negative correlation between neighboring provinces, and China's agricultural water use efficiency shows a negative spatial spillover effect in terms of spatial spillover effect. From the perspective of the direct effect of agricultural water use efficiency, agricultural fixed asset investment, agricultural, forestry, and water affairs fiscal expenditure, rural residents, the increase in the number of years of education, and the sown area of crops have a positive impact on the efficiency of agricultural water use in the province. Meanwhile, the transfer of labor and the increase in the disposable income of rural residents has reduced the efficiency of agricultural water use in the province. From the perspective of indirect effects, the years of education, disposable income of rural residents, and crop sown area of rural residents in neighboring provinces have a positive correlation with the efficiency of agricultural water use in the province, which is manifested in the spatial spillover effect. Transfer and fiscal expenditure on agriculture, forestry, and water affairs will have a negative correlation, which is manifested as a negative spatial spillover effect. Although the overall water resources’ utilization efficiency has a negative spillover effect from a spatial perspective, it can be seen that the reason for the low water resources’ utilization efficiency in China is purely technical efficiency, and the construction of agricultural infrastructure, especially water conservancy facilities, is particularly important. On the other hand, the low-income problem caused by the natural characteristics of agriculture is also the reason for the low water efficiency. This problem is still the top priority of researchers, and it is also the fundamental reason to improve the utilization efficiency of agricultural water resources in China.

According to the above conclusions, the following inspirations are obtained: (1) The improvement of agricultural water resources’ utilization efficiency mainly depends on water resources endowment and infrastructure construction. (2) Increase investment in education, strengthen the supervision system for the implementation of 9-year compulsory education, comprehensively improve the quality of agricultural producers, and on this basis, focus on the promotion and application of agricultural technology, especially irrigation technology. (3) The reform of agricultural supply side can turn agricultural development into green and healthy development, and accelerate the pace of ecological civilization construction. (4) Starting from the top-level design, interprovincial exchanges and cooperation in agricultural water resources’ utilization technologies, and rationally optimize water resources allocation. (5) Reasonably set water prices to reduce waste caused by large-scale flood irrigation and the loss of agricultural water resources.

This research work was supported by the National Social Science Foundation Project ‘Study on the Transformation of Ecological Modernization in Rocky Desert areas of Western China’ (Grant No.: 18CSH036).

We declared that we have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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