Does the dynamic adjustment of agricultural water prices drive variation of the agricultural production？

doi:10.21203/rs.3.rs-2470829/v1

Download PDF

Research Article

Does the dynamic adjustment of agricultural water prices drive variation of the agricultural production？

https://doi.org/10.21203/rs.3.rs-2470829/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 03 Jan, 2024

Read the published version in Water Resources Management →

You are reading this latest preprint version

Regarding the relationship between value, resources, and environmental issues, how to use the principle of economic leverage to manage the demand for agricultural water resources has received increasing research attention. Yet, due to the complexity of how the water economy is structured and to uncertainty in how setting an agricultural water price will affect water demand responses, it is still unclear how to determine a reasonable way to charge for agricultural water. This study investigates the impact of water prices on regional cropping structure, irrigation water use, and environmental sustainability under different increasing price scenarios, using a positive mathematical programming (PMP) model based on GAMS software. The model was run and calibrated using 427 field survey data from the pilot areas in the Wei River Basin for the 2022 crop year. These pilot areas have been selected for comprehensive reform with regard to agricultural water pricing. Our results show that increasing the agricultural water price leads to the changing of crop planting structure in the study area and to the increase of water price elasticity. Furthermore, when the water price rose 200% and 400%, the pesticide input in site A decreased by 1.71% and 3.40%, respectively, and the fertilizer input in site B decreased by 1.05% and 2.03%, respectively. Therefore, our results support the positive role of implementing water price reform policies in the Wei River Basin, but additional policies are also needed to improve the motivation of farmers to save water.

Agricultural water pricing

positive mathematical programming model (PMP)

Irrigation water demand

crop water productivity

sustainable development

Balancing limited freshwater resources and the huge demands for it remains one of the world’s greatest social challenges along with the issues of food security, ecological security, and economic security (Grafton et al., 2018; Stitzel & Rogers, 2022). The agricultural industry is the most extensive consumer of global water resources; it makes 70% of all global freshwater withdrawals (FAO, 2021a). While water pollution is beginning to receive the attention it deserves, the contribution of agriculture needs to be given more consideration as current agricultural practices (such as the use of chemical fertilizers and pesticides) have an unprecedented non-point source pollution impact on water quality, which makes the global water crisis and water pollution increasingly worse (Evans et al., 2019). However, agricultural water consumption will have to compete with other users such as those from the industrial sector and from households, which need water to meet human needs (Auci and Vignani, 2021). The Food and Agriculture Organization (FAO) of the United Nations predicts that from 2008 to 2050, agricultural water withdrawals will increase by 5.5% (Water, 2020). In addition, as China’s economy grows and urbanization accelerates, the transfer of rural labor both reduces the labor force for agricultural production and increases the demand for agricultural products (Chou et al., 2021). To address this issue, a specialized policy program should place emphasis on adapting to water scarcity caused by increasing demand through sustainable agricultural water management (Feyen et al., 2020).

Undoubtedly, the economic value of water can be utilized as part of the solution through appropriate pricing that restores the true value of water; doing this is considered to be the most effective means of optimizing water allocation and conservation (Expósito & Berbel, 2017; Zamani et al., 2021). In addition, charging for water is expected to reduce pesticide and fertilizer consumption in response to water pollution and environmental concerns (Khanali et al., 2018). Although market-based mechanisms to manage water use have received attention and are a priority, the view that reasonable pricing signals could be used to diminish a large amount of current water consumption and restrain the waste of water resources lacks evidence-based support and there are conflicting opinions about this approach (Tsur, 2020). Research studies have indicated that current low water prices do not recover irrigation investment and operation costs and this leads to a lack of incentive for farmers to save water. The result is that agricultural water system management remains ineffective and dysfunctional (Schoengold and Sunding, 2014). In contrast, others argue that high water prices may conflict with the alleviation of rural poverty. They argue that using higher water prices to promote water-saving efficiency will not be significant, but will have a negative impact on agricultural production and farmers’ income (Momeni et al., 2019). More importantly, it is still difficult to determine a reasonable agricultural water price because of the uncertainty of the response of agricultural water demand to pricing and the complexity of the water economy system (Zhou et al., 2022). As a consequence, it is of great significance for agricultural water management to reasonably grasp the “degree” of agricultural water price adjustment.

As the largest developed country in the world, China's situation in many aspects exemplifies the global picture, especially the water crisis in arid areas such as the Wei River Basin (WRB) (Mu et al., 2021). The agricultural sector here is suffering from serious water scarcity, and the amount of water resources per hectare of arable land is only 17.7% of the national average. The WRB is one of the developed agricultural areas in China, with a large demand for agricultural water, but relatively low water pricing. Additionally, due to the lack of water meter facilities, agricultural water fees in this area are paid according to the size of irrigation area. Therefore, the low agricultural water prices result in farmers having no water-saving incentives, inadequate maintenance and management of irrigation channels, and serious water seepage (Momeni et al., 2019). The No.1 document issued by the China central government in 2016 (the first document issued by the Central Committee of the Communist Party of China every year) called for the full implementation of the agricultural water price reform programme. It clearly stated that it would take about 10 years for “agricultural water prices to be increased to match the operation and maintenance costs of the water supplier” (Chou et al., 2021). Nevertheless, it is important to carefully determine the degree of increase for agricultural water pricing to ensure affordability while making water supply sustainable, financially and otherwise (Nikouei and Ward, 2013). One of the goals of policy planners and decision makers is to learn how implementing reform measures affects farmers (Sapino et al., 2020). Regarding the WRB, how water prices will affect agricultural production, the irrigation behavior of farmers, and other responses of farmers are largely unexplored.

Over the years, different econometric models have been constructed to process research topics associated with agricultural sector. The positive mathematical programming method (PMP) is considered as a model suitable for policy evaluation and problem-solving at the level of consistency (Lee et al., 2019). As a normative mathematical model proposed by Howitt (2005), the PMP model provides a flexible framework for studying agricultural policies due to its high simulation accuracy and small data requirements (Graveline, 2016; Mérel and Howitt, 2014). The structure of PMP is set to evaluate the possible changes and effects of market conditions, to analyze agriculture policies related to farmers’ planting patterns and water consumption, and to examine the economic impact according to the objective function and all relevant constraints (Asaadi et al., 2019). The main purpose of developing such models is to improve confidentiality by eliminating discrepancies between the current real position and the simulated base position and to reevaluate farmers’ behavior under special circumstances coming from the quantitative data existing for the land (the use of land and level of production) decision-making process (Gharaghani et al., 2009). Our study comprehensively analyzed the multi-dimensional effect of water price reform based on this PMP model by collecting data on individual farms in the WRB of China. Within this framework, the objectives pursued in this paper were twofold: (i) to estimate the social, economic, and environmental impacts of the comprehensive reform of water prices in the WRB; and (ii) to promote the consistency of water price policy objectives and their actual effects by exploring the benefit compensation mechanism and water price sharing mechanism that give consideration to agricultural water conservation and farmers' welfare. Our research provides a theoretical foundation and a policy reference by which to perform comprehensive reform of agricultural water pricing in water resources management.

2.1. Positive mathematical programming (PMP) model

The main challenge for agricultural water planners and decision makers is to assess all possible consequences of decisions on the agricultural sector and to predict the response of farmers (Hassani and Shahdany, 2021). In this regard, the PMP model provides a flexible framework and is verified to be a proper method for studying agricultural water-related issues, playing a crucial part in the assessment of agricultural and water policies in many parts of the globe (Graveline and Mérel, 2014; Laskookalayeh et al., 2022). As the most popular economic calibration model, PMP alleviates the problem of the tendency of over-specialization and can achieve good prediction results even when there is limited and incomplete information (Hassani and Hashemy Shahdany, 2019; Lee et al., 2019). Referring to existing research, we applied the PMP model using the following three-step procedure:

2.1.1 Base linear model

In the first step, we set up a linear programming model to solve the problem of maximizing farmers’ production profits; the model incorporated a series of available resource constraints and calibration constraints. Crop area allocation was selected as the goal to optimize the model to maximize farmer gross profit under specific conditions. The optimization model is expressed as:

$$\underset{{x}_{i}}{M{ax}}Z=\sum _{i}{{P}_{i}Y}_{i}{x}_{i}-\sum _{j}{a}_{i,j}{x}_{i}{C}_{ij}{-CW}_{i} \left(linear objective function\right)$$

$${CW}_{i}={WP}_{i}\bullet {WU}_{i}\bullet {x}_{i} \left( irrigation costs\right)$$

$$\sum _{i}{x}_{i}\le TOL \left(total land\right)$$

$$\sum _{i}{WU}_{i}\bullet {x}_{i}\le TAW \left(annual water\right)$$

$${x}_{i}={x}_{i}^{0}+\epsilon \left({\lambda }_{i}\right) \left(calibration constraints\right)$$

$${x}_{i}\ge 0 \left(\forall i\right)$$

In formula (1), $\underset{{x}_{i}}{M{ax}}Z$represents the linear net profit maximization of farmers’ land use; ${x}_{i}$ is the planting area of major crop $i$ (wheat, corn, winter jujube, pear, grape, and peach) in the research area; ${P}_{i}$ and ${Y}_{i}$are the output price and average yield of crop $i$, respectively; ${a}_{i,j}$and ${C}_{ij}$ represent the Leontief coefficient and the unit input cost of $j$ species of crop $i$ (including labor, machinery, fertilizer, pesticide, etc., but excluding irrigation water charges); and CW is the irrigation cost of crop $i$. In formula (2), ${WP}_{i}$is the irrigation water price of crop $i$, and ${WU}_{i}$ is the irrigation water consumption per unit area of crop$i$. Eq. (3) takes $TOL$ as the constraint on the total planting area of land, and in Eq. (4), we specify the irrigation amount of crop year as the resource constraint of the model. Eq. (5) is the calibration constraint of the model, where ${x}_{i}^{0}$ is the actual planting area the farmer observed in the base period, and the $\epsilon$ used is a disturbance term to ensure that the dual value of the calibration constraint is positive. Within the framework of the linear mathematical programming model, according to the above constraints, the model will be optimized in equations (3) through (6).

2.1.2 Estimate volume and cost parameters

By solving the above linear model, the dual value ${\lambda }_{i}$ (Lagrangian multiplier of the model) corresponding to the correction constraint can be obtained, which indirectly provides cost-related information to reproduce the nonlinear yield or nonlinear cost function of PMP. Because the study areas selected in this paper are more concentrated, with similar agroclimatic and soil conditions, the core difference between farms was cost structure rather than yield. Therefore, here we chose to calibrate the nonlinear cost function. Based on the economic assumption of diminishing marginal returns, our paper specified that the variable cost function is quadratic:

$$T{C}_{i}\left({\alpha }_{i};{\beta }_{i}\right)={\alpha }_{i}{x}_{i}+\left(1/2\right){x}_{i}^{{\prime }}{\gamma }_{i}{x}_{i}+{CW}_{i}$$

where TC is the total cost of agricultural production for crop $i$ (including irrigation water charges), and ${\alpha }_{i}$ and ${\gamma }_{i}$ are linear coefficients and quadratic coefficient parameters of the cost function that needs to be restored. We used the average cost method also used by Moghaddasi et al. (2009) to restore, linking the cost per unit area observation (excluding irrigation water charges) to the model so that the average cost of different crops was equal to the base period cost observation. The variable cost per unit of activity of different crops functions is as follows:

$${C}_{i}\left({x}_{i}\right)={\alpha }_{i}+\left(1/2\right){\gamma }_{i}{x}_{i}$$

With the dual values of the calibration constraints, the parameters can be restored in the following way:

${\gamma }_{i}=2{\lambda }_{i}/{x}_{i}^{0}$(9) ${\alpha }_{i}={C}_{i}^{0}-{\lambda }_{i}$ (10)

where ${C}_{i}^{0}$is the average accounting cost observed in the base period, ${\lambda }_{i}$ represents the dual value of the calibration constraint, and the required parameters of the cost function can be restored.

2.1.3 Nonlinear calibrated model

The final step involved setting a calibrated irrigation area PMP model using cost function parameters (${\alpha }_{i}$ and ${\gamma }_{i}$) and all crop base period data. First, the cost function was substituted into the original linear objective function, and then calibrated with the base period observation data. The complete nonlinear objective function and its constraints can be defined as:

$$\underset{{x}_{i}}{M{ax}}Z=\sum _{i}{{P}_{i}Y}_{i}{x}_{i}-\sum _{j}{a}_{i,j}{x}_{i}\left({\alpha }_{i}{x}_{i}+\left(\frac{1}{2}\right){x}_{i}^{{\prime }}{\gamma }_{i}{x}_{i}\right)-{CW}_{i}$$

$${CW}_{i}={WP}_{i}\bullet {WU}_{i}\bullet {x}_{i} \left( irrigation costs\right)$$

$$\sum _{i}{x}_{i}\le TOL \left(totoal land\right)$$

$$\sum _{i}{WU}_{i}\bullet {x}_{i}\le TAW \left(annual water\right)$$

$${D}_{i}\bullet TP\le \sum _{i}{Y}_{i}\bullet {x}_{i} \left(annual food demand\right)$$

$${x}_{i}\ge 0 \left(\forall i\right)$$

The parameters in equations (11) through (16) are the same as defined above, and the calibrated model of the input data can perfectly reproduce the behavior observed during the base period and can simulate hypothetical policy scenarios as the final PMP model. The model's input data, including crop prices, crop yields, input demand, input prices, and current land use were collected by surveys of local farmers in irrigation districts. The data and parameters collected included the element demand and input cost of all crops, such as crop water demand per mu, labor use, fertilizer and pesticide use per mu, etc. The above analysis was run with the help of a universal algebra modeling system (GAMS software).

2.2. Study area description

The study was conducted in the Wei River Basin (WRB), which is one of the most important river basins in northwest China and the “mother river” of the Guanzhong region. The WRB (Fig. 1) originates from the Niushu Mountains in the loess hilly region and flows through three provinces and 26 counties in Gansu, Ningxia, and Shaanxi (Deng et al., 2020). The Basin has a temperate continental monsoon climate, consisting of arid to semi-humid areas, with an average annual temperature of 7.8°C–13.5°C. The total length of the main stream is 818 km, and the basin area is 1.35×10⁵ km². The average annual rainfall is 600–800 mm, and the precipitation is concentrated from July to October (Song et al., 2018). As the largest first tributary of the Yellow River, the WRB not only provides a large amount of irrigation water to the downstream Guanzhong Plain, but also plays a role in sustaining the economic and social development of the northwest region. The two irrigated areas cover about 98,666 ha and 84,210 ha of irrigated agricultural area, respectively, and the main soil type is cultivated yellow soil (Zhao et al., 2016). Irrigation in these areas is mainly derived from surface water, occasionally supplemented by groundwater, and the type of crop is not significantly related to the distance from the source of the diversion. The main water-saving irrigation methods include sprinkler irrigation and drip irrigation, and the traditional irrigation methods use ditch irrigation and flood irrigation. The price of water is mostly calculated by volume, and the average level is about 0.53 yuan /m^− 3–0.71 yuan /m^− 3. Each farm installs a water meter, and the manager responsible for water supply records the amount of water used by individuals.

In recent years, the local government has attached great importance to the agricultural environment development in the WRB. Basically, relevant agricultural water price adjustment will be introduced every year. For example, in 2017, the National Development and Reform Commission and other five departments jointly issued The Notice on Solid Promotion of Agricultural Water Price Reform. Then, in 2018, the National Development and Reform Commission, the Ministry of Water Resources, the Ministry of Finance, and the Ministry of Agriculture and Rural Affairs jointly issued The Notice on Increasing Efforts to Promote the Comprehensive Reform of Agricultural Water Prices, which presented an annual adjustment pattern. The WRB is the key pilot area of agricultural water price reform. Donglei Irrigation District (site A) and Jiaokou Irrigation District (site B) (Fig. 1) represent the core system of agricultural irrigation in the WRB, and the versatility of irrigated agriculture is more obvious, which is conducive to our exploration of the results of water price reform. Therefore, the two sites were chosen to locate our sampling survey monitoring points to simulate the effect of agricultural water price reform in the WRB.

2.3. Data collection and analysis

In order to achieve the research purpose, after pre-testing in July and August in 2021, formal research was conducted from June to July in 2022 in the study area. We used a combination of “county-township-village-farmers” stratification and random sampling to finally select eight townships and 32 villages for face-to-face surveys. The survey was conducted by 12 trained numerators. The technical and economic data was collected using multi-stage sampling techniques. First of all, four towns and three towns were selected purposefully from site A and site B, respectively, which were the promotion zones of the comprehensive reform of regional agricultural water pricing. After that, four villages were randomly selected from each chosen town. Then, we randomly selected about 20 family farms from each village to conduct a questionnaire survey to reveal the micro-relationship between water price, water demand, and planting income. Eventually, a total of 461 farmers were interviewed, yielding 427 valid observations that could be analysed using GAMS software, including 248 farms in site A and 179 farms in site B, with a total planting area of 400.19 hectares, reflecting the typical situation of agricultural planting and irrigation in the WRB agricultural area. The questionnaire was based on previous efforts published in the literature and was divided into four thematic sections (Grilli and Notaro, 2019). The first part of the questionnaire contained questions about farmers’ personal socioeconomic characteristics. The second part of the questionnaire was about the basic planting situation for the farmers. The third section was about the input and output of the farming household, including the changes in planting structure and the amount of water consumption in the growing season, etc.

Table 1 provides a descriptive list of crops by irrigation area, yield, irrigation water consumption, and output price. The data set showed that the main irrigation crops in the two irrigation areas included wheat (40.51%), maize (40.17%), winter dates (8.64%), pears (3.72%), grapes (2.55%), and peaches (4.41%). Food crops occupy the main land area in these two irrigation areas. In terms of irrigation water requirements of different crops, wheat consumed the least water (2827.5 m³/ha year^− 1), while grapes consumed the most water (9660.0 m³/ha year^− 1). In terms of crop water yield, the highest water yield among the base crops was peach 7.64 kg/m³, followed by pear 7.19 kg/m³. At the same time, we observed that there were certain differences in planting patterns and water consumption in the study areas. To observe these subtle differences, the sample irrigation area was classified in the study.

Table 1

Area allocation to different crops, yield, irrigation water requirements and crop prices
Crops	Area (ha)	Yield (kg/ha)	Irrigation (m³/ha year^− 1)	Price (RMB/kg)	Revenue = Yield*Price (RMB/ha)
Dong Lei irrigation area
Wheat	95.31	6,465	2,775	2.63	17,003
Corn	94.21	6,320	3,060	2.74	17,317
Winter jujube (outdoors)	13.39	27,150	6,840	7.53	204,440
Pear	11.25	34,980	4,896	2.46	86,051
Grapes	6.83	22,665	9,870	5.96	135,083
Peach	4.79	35,130	4,530	2.14	7,5178
Jiao Kou irrigation area
Wheat	66.81	6,525	2,880	2.67	17,422
Corn	66.55	6,405	3,240	2.81	17,998
Winter jujube (Cold shed)	21.16	25,800	6,750	17.23	315,534
Pear	3.65	37,815	5,220	2.51	94,916
Grapes	3.38	24,345	9,450	5.79	140,958
Peach	12.85	36,120	4,800	2.36	85,243
Data Source: field survey data

Table 2 details cost statistics per unit input and output for various crops, including basic data for seeds, irrigation, fertilizers, pesticides, machinery, and other variable costs (labor, machinery, etc.). According to the cost of water consumed per unit crop, the cost of water required by cash crops is generally higher. For example, the water cost per hectare input in the site A was the highest for grapes (5,359 yuan/ha^− 1), followed by winter dates (3,714 yuan/ha^− 1), pears (2,659 yuan/ha^− 1), and peaches (2,460 yuan/ha^− 1). For food crops, the operating costs of water and fertilizers account for about 60% of the total cost. For economic crops such as dates and grapes, the cost of labor and fertilizer inputs is far greater than that of food crops. In addition, even for the same crops, the crop operating costs of the two areas are quite different. Our survey data results show that the input costs of irrigation water and fertilizers for crops are mostly higher in site A than in site B. Using the data on cultivated land allocation and the input-output of six crops in the two irrigation areas, we calibrated the model to fit the situation before the simulation.

Table 2

Input cost for different irrigation districts operation in RMB ha^− 1
Crops	Wheat	Corn	Winter jujube (outdoors/shed)	Pear	Grapes	Peach
Dong Lei irrigation area
Seeds	773	638	815	469	416	236
Water applied	1,379	1,521	3,714	2,659	5,359	2,460
Fertilizer	4,275	2,964	14,508	11,291	18,720	9,185
Chemical	288	363	3,701	2,295	4,478	2,295
Machinery	3,090	2,317	713	570	428	414
Remaining variable costs	325	316	29,123	10,683	21,325	913
Jiao Kou irrigation area
Seeds	805.8	715.32	2,252	413	558	242
Water applied	1,930	2,171	4,941	3,821	6,917	3,514
Fertilizer	4,332	3,021	16,482	11,760	19,619	9,780
Chemical	297	375	3,795	2,374	4,541	2,374
Machinery	3,114	2,335	735	588	441	419
Remaining variable costs	331	322	45,137	14,295	23,947	976
Data source: field survey data

This section defines different price scenarios to assess the response to water price adjustments. The simulated potential changes were made with 20%, 40%, 60%, 80%, 100%, 200%, and 400% increases in agricultural water prices relative to the base year. Most studies show that farmers respond effectively to price increases only when they exceed the value of water (Aregay et al., 2013). Therefore, it is reasonable to consider an irrigation price increment of up to 400% from the base year price of 0.52 yuan /m³ (site A) and 0.70 yuan /m³ (site B).

3.1. Impact of agricultural water pricing on planting structure

The changes in the planting structure of farmers in site A under different water price scenarios is shown in Fig. 2. We observed that the acreage of all crops changed to some extent as water prices increased. As an extremely water-consuming economic crop, the planting area of grapes began to show a significant downward trend when the water price rose by 40%. When the water price rose to 400%, the grape planting area decreased by about 54.74% compared with the base period. The planting area of peaches also showed a decline as the water price increased, but this change was relatively weak. Even when the water price rose to 400%, the planting area only decreased by 4.57%. It should be noted that when the water price increased to 200%, the area allocated for winter jujube and pear increased by 8.30% and 6.02%, respectively. Meanwhile, the planting area of wheat and corn is relatively stable when the water price rises 20–100%, but this situation changes when the water price rises by more than 200% (1.04 yuan/m³). At this time, the acreage of wheat increased by 4.28% and that of corn decreased by 4.23%. This indicated that in order to cope with the increase of water price, farmers in this area prioritized reducing the planting area of crops with high water consumption before they considered reducing the planting area of grain rations.

The adjustment of the planting structure of farmers in site B under different water price scenarios is presented in Fig. 3. Generally speaking, when water price is increasing, the planting areas of pears and wheat show an upward trend, while the planting areas of corn and peaches show a downward trend, and the planting areas of grapes and winter jujube fluctuate very little. Compared with the base period, when the water price increased by 200% and 400%, the land area allocated to pears increased by 5.94% and 11.88%, respectively, and this crop has higher water efficiency. However, the adjustment of the water price had little impact on grapes and winter jujube. When the water price increased by 400%, the area of grapes planted only increased by 0.20%, and the area of winter jujubes decreased by 0.79%. In addition, compared with site A, the fluctuation of wheat and corn planting area is more significant in site B. When the water price increased by 200%, the wheat planting area increased by 20.03%, while the corn planting area decreased by 19.83%. When the water price increased by 200% and 400%, the peaches planted area decreased by 2.29% and 4.57%, respectively.

3.2. Influence of agricultural water pricing on irrigation requirement

By simulating different water price scenarios, we generated water demand function diagrams for the two irrigation areas (Fig. 4), reflecting the response of crops to irrigation water demand under different price scenarios. The trend results showed that when the water price gradually increased, the water consumption in both irrigation districts decreased. But in most cases, the elasticity of demand for water was relatively low. It was revealed that before the water price in site A reached 0.94 yuan/m³, the elasticity of demand was small (Fig. 4a). When the price of water exceeded 1.04 yuan/m³, the relative base period of water consumption decreased by 8.5%, and the price elasticity of demand also increased. However, when the water price exceeded 1.56 yuan/m³, the demand started to lack price elasticity, and the water quantity decreased by 12.30% compared with the base period.

In site B, before the water price rose from 0.70 yuan/m³ to 1.40 yuan/m³, the response to the price elasticity of demand for water was low (Fig. 4b). Although the data showed that the base annual water price of site B was higher than that of site A, the adjustment incentive of farmers to crop planting and water consumption was still weak before the increase of water price reached 100%. The results showed that when the water price rose to 1.40 yuan/m³, the water demand tended to be price elastic, and the water quantity decreased by 6.85% compared with the base period. When the water price increased by 200%, irrigation water consumption decreased by 9.98% compared with the base period, and then the demand elasticity showed a downward trend.

3.3. Effect of agricultural water pricing on water productivity

In order to understand the changes in agricultural production caused by water price changes, we considered the impact of agricultural water price changes on water productivity. For the calculation of water productivity, we referred to the method of Zamani et al. (2021), which used the net revenue generated per unit of water consumption to measure water productivity. The water productivity of various crops is simulated by Eq. (17), and its magnitude depends on changes in water consumption, yield, and input costs.

$$PW=({P}_{i}\times {Y}_{i}-{C}_{i})/{-CW}_{i}$$

By observing the simulation results (Table 3), we can draw the two following conclusions. First, the implementation of water-pricing policy will improve the water productivity of all indicators. Regardless of the degree of water price increase, the water productivity of all crops in the two irrigated areas will show different degrees of increase. Therefore, the implementation of water pricing will lead to the redistribution of land and water and motivate farmers to make more reasonable water-use decisions to achieve higher water benefits. Second, crops with a faster growth rate of water productivity have obvious advantages in land area allocation, which can be observed from the comparison of crop planting structure and the growth trend of water productivity. When the water price increased by 400% in site A, the water productivity of winter jujube and pear increased to 43.04 yuan/m³ and 21.87 yuan/m³, respectively, while the water productivity of grapes, peaches, and other crops only increased slightly. Meanwhile, the water productivity of peach was higher than that of pear in the basal period, but this situation changed when the water price increased by 60%, which was indirectly reflected in the distribution of planting area of farmers. In site B, the water productivity of pear and wheat increased greatly due to the increase of water price, while the water productivity of grape, peach, and other crops increased slightly. Correspondingly, the area of pear and wheat increased significantly during land allocation, indicating that the adjustment of water price would encourage farmers to reallocate water to more efficient crops or reduce farmland area.

Table 3

Response of different crop water productivity to water price increasing
(yuan/m³)	Dong Lei irrigation area						Jiao Kou irrigation area
(yuan/m³)	Wheat	Corn	Winter jujube	Pear	Grapes	Peach	Wheat	Corn	Winter jujube	Pear	Grapes	Peach
0%	2.477	3.006	22.203	11.864	8.547	13.173	2.296	2.796	35.880	11.813	8.988	14.154
20%	2.505	3.025	22.952	12.111	8.558	13.182	2.432	2.810	36.102	12.992	9.111	14.246
40%	2.566	3.044	23.861	12.787	8.562	13.214	2.486	2.823	36.542	13.284	9.199	14.577
60%	2.580	3.059	25.634	13.505	8.572	13.261	2.531	2.837	36.991	13.986	9.288	14.679
80%	2.618	3.096	27.046	14.191	8.576	13.241	2.614	2.864	37.735	14.592	9.436	14.848
100%	2.685	3.110	30.962	15.849	8.582	14.121	2.668	2.891	38.752	15.798	9.737	15.076
200%	2.806	3.176	36.301	18.904	8.597	14.357	2.753	2.959	39.801	17.257	10.131	15.332
400%	3.256	3.222	43.038	21.866	8.599	15.117	2.907	2.987	40.380	19.934	10.438	15.611

3.4. Impact of agricultural water pricing on agricultural green production

In this paper, the consumption of fertilizers and pesticides by farmers during planting is used to measure the impact of different water prices on the farmland soil environment. On one hand, the simulation shows that in both study sites, when the water price increased, the amount of fertilizer used by farmers during planting decreased slightly (Table 4). As is shown in Table 4, when the water price increased by 200% and 400%, the fertilizer input in site A decreased by 5.99% and 8.45%, respectively, and the fertilizer input in site B decreased by 2.65% and 4.91%, respectively. Meanwhile, the reduction degree and speed of chemical fertilizer input in site A was obviously faster than that in site B. This can be explained to some extent from the adjustment of the planting area; site A had greatly reduced the planting area of grapes (which are fertilizer-intensive crops).

Table 4

Percentage change in fertilizer and pesticide consumption for different water price increment scenarios
Irrigation area	Resource	20%↑	40%↑	60%↑	80%↑	100%↑	200%↑	400%↑
Dong Lei	Fertilizer	-0.12%	-0.39%	-0.58%	-0.86%	-3.67%	-5.99%	-8.45%
Dong Lei	Pesticide	-0.18%	-0.38%	-0.52%	-0.69%	-0.86%	-1.71%	-3.40%
Jiao Kou	Fertilizer	-0.10%	-0.27%	-0.49%	-0.74%	-1.43%	-2.65%	-4.91%
Jiao Kou	Pesticide	-0.16%	-0.25%	-0.34%	-0.43%	-0.52%	-1.05%	-2.03%
Note:↑= water price increment.

On the other hand, when the water price rose, pesticide input also decreased slightly, but the rate and degree of decline was significantly slower than that of fertilizer. When the water price rose 200% and 400%, the pesticide input in site A decreased by 1.71% and 3.40%, respectively, and the fertilizer input in site B decreased by 1.05% and 2.03%, respectively. The reduction of chemical fertilizers and pesticides in the process of planting will help improve the soil environment and restore the ecological environment of farmland. Therefore, according to the results, we believe that the adjustment of local water prices may help maintain the stability of farmland soil.

In this paper, a series of responses to irrigation water price changes in the WRB were simulated, including planting structure, water consumption, water productivity, and farmland soil environment. The main purpose was to analyze whether water-pricing policy could be regarded as a qualified water management tool, so as to provide a theoretical reference for the next comprehensive reform of agricultural water pricing in the study area. By collecting the data of 427 family farms in the WRB, this study used the PMP model of gross margin maximization objective function for analysis. There were three main conclusions of our study.

First, within the limits of our case study, different water price scenarios changed the planting patterns of the irrigated areas to some extent. Our results demonstrated that the rise of water prices in site A will be accompanied by the growth of planting area of winter jujube, pear, and other crops and there will be a decline of planting area of grapes and pears. Among these, the grape-water intensive degree is larger, and the impact of water price on its degree can be said to be drastic. When the water price changed, crops were also adjusted in site B, but the changes in soil area of most crops were relatively small except for corn and pear. Due to regional natural endowments, market conditions, and farmers’ preferences, the responses of planting structure to water price adjustments have been different in the existing literature. For example, Iglesias and Blanco (2008) confirmed that rising water prices would encourage farmers to switch to crops that consume less water and switch from surface irrigation to sprinkler irrigation and drip irrigation. Sapino et al. (2020) evaluated the irrigation water prices in Piedmont, Italy, and showed that the rice planting area would decrease rapidly within the price of 0.012–0.074 euros /m³.

Second, farmers’ demand for irrigation water is not price elastic in these two irrigated areas. Farmers will reduce water consumption to some extent by adjusting the area planted by water-intensive crops. However, this situation will only occur when the price of water increases to a point that causes a significant decrease in the net income of planting. When the price is significantly out of the range of farmers, the demand elasticity of water price tends to be stable.

In site A and site B, water demand started to become more elastic when the water price reached 0.94 yuan /m³ and 1.40 yuan /m³, respectively. As documented in some literature, significant reductions in water consumption occur only when water prices reach a certain level, that is, irrigation water has a low elasticity of demand (Chebil et al., 2010; Fragoso et al., 2011; Gómez-Limón and Riesgo, 2004; Shi et al., 2014).

Third, the adjustment of water price policy has changed the water productivity of crops, which may be an important reason for farmers to change the planting structure. The simulation showed that the increase of water price in site A promoted the general improvement of crop water productivity. In particular, water productivity of winter jujube and pear increased rapidly, and farmers’ willingness to allocate land for these two crops increased. The changing trend of crop water productivity in site B was similar to that in site A. The difference was that the water productivity of winter jujube and grape did not fluctuate greatly in site B, while the water productivity of pear increased the most in this area. Overall, our research on water productivity is in line with the literature. Zamani et al. (2021) also verified that water-pricing policies can increase water productivity and reduce water use.

The inefficient utilization of irrigation water and excessive use of chemical inputs in the WRB have greatly affected the sustainability of local agricultural water resources and the environment. After verification, we found that the increase of water prices can reduce the use of fertilizers and pesticides by farmers in their planting strategies. This change may come from farmers seeing a trade-off between the high cost of water price and the adjustment of the planting area. Of course, even if the effect is weak before the water price rises, it can have a beneficial effect on the soil environment. Some literature studies have concluded that by changing cropping patterns and irrigation methods, and by adopting new technologies aimed at reducing irrigation volume, increasing irrigation prices is expected to reduce the use of pesticides and fertilizers (Bartolini et al., 2010; Khanali et al., 2018; Moghaddasi et al., 2009), and this is consistent with our verified results.

The best way to balance supply and demand for agricultural water is to introduce reasonable water charges. However, the willingness of farmers to accept and adapt largely determines the success of water management practices (Boazar et al., 2019). Therefore, the response of farmers to different water price scenarios should be considered in policy evaluation. Through analysis, this paper concludes that irrigation water-pricing policies will encourage farmers to adjust their planting structures, push irrigators to improve water production efficiency, and reduce the input of pesticides and fertilizers. However, the effect of water price increases in promoting water saving is not significant and may cause adverse effects on the welfare of farmers. Therefore, in addition to water pricing, we propose introducing additional subsidy policies to improve irrigation water efficiency. In conclusion, this study provides a good framework for the Chinese government to accelerate the implementation of comprehensive agricultural water price reform in the WRB. This framework will not only help guide the local reasonable allocation of water to meet demand and improve the current situation of regional water resources management, but also will provide reference for other regions to explore the value of water and the impact of managing water pricing to achieve sustainable use of limited water resources.

Ethical Approval (Not applicable)

Consent to Participate ((Not applicable)

Consent to Publish (Not applicable)

Authors Contributions

L., Mu- Conceptualization, Formal analysis and software, Funding acquisition

Y., Wang- Methodology, Visualization, Data curation, Original draft preparation

B.R., Xue-Validation, Review and editing, supervision

All authors read and approved the final manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China (42001221); Shaanxi Provincial Innovation Capability Support Program (2022KRM067); Shaanxi Social Science Fund (2022D049); the Fundamental Research Funds for the Central University of Shaanxi Normal University (2022ZYYB25).

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All data generated or analysed during this study are included in this published article

Aregay, F.A., Minjuan, Z., Bhutta, Z.M., 2013. Irrigation water pricing policy for water demand and environmental management: a case study in the Weihe River basin. Water Policy. 15, 816-829.
Asaadi, M.A., Mortazavi, S.A., Zamani, O., Najafi, G.H., Yusaf, T., Hoseini, S.S., 2019. The impacts of water pricing and non-pricing policies on sustainable water resources management: A case of Ghorveh plain at Kurdistan province, Iran. Energies 12, 2667.
Auci, S., Vignani, D., 2021. Irrigation water intensity and climate variability: an agricultural crops analysis of Italian regions. Environmental Science and Pollution Research. 28, 63794-63814.
Bartolini, F., Gallerani, V., Raggi, M., Viaggi, D., 2010. Water management and irrigated agriculture in Italy: multicriteria analysis of alternative policy scenarios. Water Policy. 12, 135-147.
Boazar, M., Yazdanpanah, M., Abdeshahi, A., 2019. Response to water crisis: How do Iranian farmers think about and intent in relation to switching from rice to less water-dependent crops? Journal of hydrology. 570, 523-530.
Chebil, A., Frija, A., Thabet, C., 2010. Irrigation water pricing between governmental policies and farmers’ perception: Implications for green-houses horticultural production in Teboulba (Tunisia). Agricultural Economics Review. 11, 44-54.
Chou, L., Dai, J., Qian, X., Karimipour, A., Zheng, X., 2021. Achieving sustainable soil and water protection: The perspective of agricultural water price regulation on environmental protection. Agricultural Water Management. 245, 106583.
Deng, W., Song, J., Sun, H., Cheng, D., Zhang, X., Liu, J., Kong, F., Wang, H., Khan, A.J., 2020. Isolating of climate and land surface contribution to basin runoff variability: A case study from the Weihe River Basin, China. Ecological Engineering. 153, 105904.
Evans, A.E., Mateo-Sagasta, J., Qadir, M., Boelee, E., Ippolito, A., 2019. Agricultural water pollution: key knowledge gaps and research needs. Current opinion in environmental sustainability. 36, 20-27.
Expósito, A., Berbel, J., 2017. Why is water pricing ineffective for deficit irrigation schemes? a case study in Southern Spain. Water Resources Management. 31, 1047-1059.
FAO, F., 2021a. Food and agriculture organization of the United Nations. Rome, URL: http://faostat. fao. org.
Feyen, L., Ciscar Martinez, J.C., Gosling, S., Ibarreta Ruiz, D., Soria Ramirez, A., Dosio, A., Naumann, G., Russo, S., Formetta, G., Forzieri, G., 2020. Climate change impacts and adaptation in Europe. JRC PESETA IV final report. Joint Research Centre (Seville site).
Fragoso, R., Marques, C., Lucas, M.R., Martins, M.d.B., Jorge, R., 2011. The economic effects of common agricultural policy on Mediterranean montado/dehesa ecosystem. Journal of Policy Modeling. 33, 311-327.
Gharaghani, F., Boostani, F., Soltani, G., 2009. Assessing the impact of reducing irrigation water and increasing its price on cropping pattern by positive mathematical programming model: A case study of Eghlid in Fars Province.
Gómez‐Limón, J.A., Riesgo, L., 2004. Irrigation water pricing: differential impacts on irrigated farms. Agricultural economics. 31, 47-66.
Grafton, R.Q., Williams, J., Perry, C.J., Molle, F., Ringler, C., Steduto, P., Udall, B., Wheeler, S., Wang, Y., Garrick, D., 2018. The paradox of irrigation efficiency. Science. 361, 748-750.
Graveline, N., 2016. Economic calibrated models for water allocation in agricultural production: A review. Environmental Modelling & Software. 81, 12-25.
Graveline, N., Mérel, P., 2014. Intensive and extensive margin adjustments to water scarcity in France's Cereal Belt. European Review of Agricultural Economics. 41, 707-743.
Grilli, G., Notaro, S., 2019. Exploring the influence of an extended theory of planned behaviour on preferences and willingness to pay for participatory natural resources management. Journal of Environmental Management. 232, 902–909.
Hassani, Y., Hashemy Shahdany, S.M., 2019. Agricultural water distribution under drought conditions based on economic priorities: Case study of Qazvin Irrigation District. Irrigation and Drainage. 68, 443-451.
Hassani, Y., Shahdany, S.M.H., 2021. Implementing agricultural water pricing policy in irrigation districts without a market mechanism: comparing the conventional and automatic water distribution systems. Computers and Electronics in Agriculture. 185, 106121.
Howitt, R.E., 2005. Agricultural and environmental policy models: Calibration, estimation and optimization. Davis: University of California, Davis. Available online at< http://www. agecon. ucdavis. edu/people/faculty/facultydocs/howitt/master. pdf.
Iglesias, E., Blanco, M., 2008. New directions in water resources management: The role of water pricing policies. Water Resources Research. 44.
Khanali, M., Mousavi, S.A., Sharifi, M., Nasab, F.K., Chau, K.-w., 2018. Life cycle assessment of canola edible oil production in Iran: a case study in Isfahan province. Journal of Cleaner Production. 196, 714-725.
Laskookalayeh, S.S., Najafabadi, M.M., Shahnazari, A., 2022. Investigating the effects of management of irrigation water distribution on farmers' gross profit under uncertainty: A new positive mathematical programming model. Journal of Cleaner Production. 351, 131277.
Lee, H., Eom, J., Cho, C., Koo, Y., 2019. A bottom-up model of industrial energy system with positive mathematical programming. Energy. 173, 679-690.
Mérel, P., Howitt, R., 2014. Theory and application of positive mathematical programming in agriculture and the environment. Annu. Rev. Resour. Econ. 6, 451-470.
Moghaddasi, R., Bakhshi, A., Kakhki, M.D., 2009. Analyzing the effects of water and agriculture policy strategies: an Iranian experience. American Journal of Agricultural and Biological Sciences. 4, 206-214.
Momeni, M., Zakeri, Z., Esfandiari, M., Behzadian, K., Zahedi, S., Razavi, V., 2019. Comparative analysis of agricultural water pricing between Azarbaijan Provinces in Iran and the state of California in the US: A hydro-economic approach. Agricultural Water Management. 223, 105724.
Mu, L., Fang, L., Dou, W., Wang, C., Qu, X., Yu, Y., 2021. Urbanization-induced spatio-temporal variation of water resources utilization in northwestern China: A spatial panel model based approach. Ecological Indicators. 125, 107457.
Nikouei, A., Ward, F.A., 2013. Pricing irrigation water for drought adaptation in Iran. Journal of Hydrology. 503, 29-46.
Sapino, F., Pérez-Blanco, C.D., Gutiérrez-Martín, C., Frontuto, V., 2020. An ensemble experiment of mathematical programming models to assess socio-economic effects of agricultural water pricing reform in the Piedmont Region, Italy. Journal of Environmental Management. 267, 110645.
Schoengold, K., Sunding, D.L., 2014. The impact of water price uncertainty on the adoption of precision irrigation systems. Agricultural Economics. 45, 729-743.
Shi, M., Wang, X., Yang, H., Wang, T., 2014. Pricing or quota? A solution to water scarcity in oasis regions in China: a case study in the Heihe River Basin. Sustainability. 6, 7601-7620.
Song, J., Tang, B., Zhang, J., Dou, X., Liu, Q., Shen, W., 2018. System dynamics simulation for optimal stream flow regulations under consideration of coordinated development of ecology and socio-economy in the Weihe River Basin, China. Ecological Engineering. 124, 51-68.
Stitzel, B., Rogers, C.L., 2022. Residential water demand under increasing block rate structure: conservation conundrum? Water Resources Management. 36, 203-218.
Tsur, Y., 2020. Optimal water pricing: Accounting for environmental externalities. Ecological Economics. 170, 106429.
Water, U., 2020. Water and climate change. The United Nations World Water Development Report; UNESCO: Paris, France.
Zamani, O., Azadi, H., Mortazavi, S.A., Balali, H., Moghaddam, S.M., Jurik, L., 2021. The impact of water-pricing policies on water productivity: Evidence of agriculture sector in Iran. Agricultural Water Management. 245, 106548.
Zhao, A., Zhu, X., Liu, X., Pan, Y., Zuo, D., 2016. Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of northwest China. Catena. 137, 318-327.
Zhou, Q., Zhang, Y., Wu, F., 2022. Can Water Price Improve Water Productivity? A Water-Economic-Model-Based Study in Heihe River Basin, China. Sustainability. 14, 6224.

Download PDF

Journal Publication

published 03 Jan, 2024

Read the published version in Water Resources Management →

Editorial decision: Major revisions
13 Oct, 2023
Reviewers agreed at journal
08 Feb, 2023
Reviewers invited by journal
30 Jan, 2023
Editor assigned by journal
15 Jan, 2023
First submitted to journal
13 Jan, 2023

You are reading this latest preprint version

Does the dynamic adjustment of agricultural water prices drive variation of the agricultural production？

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1. Positive mathematical programming (PMP) model

2.1.1 Base linear model

2.1.2 Estimate volume and cost parameters

2.1.3 Nonlinear calibrated model

2.2. Study area description

2.3. Data collection and analysis

3. Results

3.1. Impact of agricultural water pricing on planting structure

3.2. Influence of agricultural water pricing on irrigation requirement

3.3. Effect of agricultural water pricing on water productivity

3.4. Impact of agricultural water pricing on agricultural green production

4. Conclusion And Discussion

Declarations

References

Status:

Journal Publication

Version 1