Agricultural Water Efficiency Research Articles

Sensitivity of gross primary production to precipitation and the driving factors in China's agricultural ecosystems

Recent climate warming has significantly affected the sensitivity of Gross Primary Productivity (GPP) to precipitation within China's agricultural ecosystems. Nonetheless, the spatial and temporal nonlinear evolution patterns of GPP-precipitation sensitivity under climate change, as well as the underlying drivers and long-term trends of this sensitivity, are not well understood. This study employs correlation analysis to quantify the sensitivity between GPP and precipitation in China's agricultural ecosystems, and utilizes nonlinear detection algorithms to examine the long-term changes in this sensitivity. Advanced machine learning techniques and frameworks are subsequently applied to analyze the driving factors of GPP-precipitation sensitivity in China's agricultural ecosystems. The findings reveal that approximately 49.00 % of the analyzed pixels exhibit a significant positive correlation between GPP and precipitation. Nonlinear change analysis indicates spatial heterogeneity in GPP-precipitation sensitivity across China's agricultural ecosystems, with patterns showing initial increases followed by decreases accounting for 25.12 %, and patterns of initial decreases followed by increases at 13.27 %. Machine learning analysis identifies temperature, soil moisture, and crop water footprint as the primary factors influencing GPP-precipitation sensitivity in agricultural ecosystems. This study is the first to introduce crop water footprint as a significant factor in the analysis of GPP-precipitation sensitivity. It not only offers new insights into the temporal nonlinear changes and driving factors of GPP-precipitation sensitivity but also underscores the importance of enhancing agricultural water efficiency to maintain agricultural ecosystem health and ensure food security under climate change.

An Overview of Polymeric Hydrogel Applications for Sustainable Agriculture

Agriculture, a vital element of human survival, confronts challenges of meeting rising demand due to population growth and product availability in developing nations. Reliance on pesticides and fertilizers strains natural resources, leading to soil degradation and water scarcity. Addressing these issues necessitates enhancing water efficiency in agriculture. Polymeric hydrogels, with their unique water retention and nutrient-release capabilities, offer promising solutions. These superabsorbent materials form three-dimensional networks retaining substantial amounts of water. Their physicochemical properties suit various applications, including agriculture. Production involves methods like bulk, solution, and suspension polymerization, with cross-linking, essential for hydrogels, achieved through physical or chemical means, each with different advantages. Grafting techniques incorporate functional groups into matrices, while radiation synthesis offers purity and reduced toxicity. Hydrogels provide versatile solutions to tackle water scarcity and soil degradation in agriculture. Recent research explores hydrogel formulations for optimal agricultural performance, enhancing soil water retention and plant growth. This review aims to offer a comprehensive overview of hydrogel technologies as adaptable solutions addressing water scarcity and soil degradation challenges in agriculture, with ongoing research refining hydrogel formulations for optimal agricultural use.

Evaluation of water efficiency in agriculture: The case of the Konya closed basin

AbstractThe main goal in water efficiency in agriculture is to obtain more products with the same amount of water. In this respect, the use of irrigation performance indicators is important for increasing agricultural water efficiency. This study was conducted to evaluate agricultural water efficiency in the Konya closed basin, which is the region most affected by drought in Turkey. For this purpose, performance indicators selected for evaluating agricultural water efficiency were determined in irrigation associations taken as material for the years 2016–2020.In the research area, the following water use efficiency indicators were determined: annual amount of irrigation water distributed 1.750–517.462 million m3 yr⁻¹ (MCM yr⁻¹), annual amount of irrigation water distributed per unit area 0.529–8.688 MCM ha⁻¹, annual amount of irrigation water distributed per unit irrigated area 0.787–33.909 MCM ha⁻¹ and annual water supply ratio ranging between 0.220 and 52.600. The following agricultural water efficiency performance indicators were determined: income obtained for unit irrigation area, 127–5075 US$ ha⁻¹; income obtained per unit irrigated area, 656–12353 US$ ha⁻¹; income obtained per unit irrigation water taken into the network, 0.104–6.771 US$ m⁻3; and income obtained per unit irrigation water consumed, 0.236–37.358 US$ m⁻3. Correlation analysis was carried out to identify the significance of the relationships between the performance indicators, and the results were discussed.

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration (ET0) is an important factor in determining DI. ET0 forecasting predicts field water consumption and enables proactive irrigation decisions, offering guidance for water resource management. However, implementation of ET0 forecasting faces challenges due to complex calculations and extensive meteorological data requirements. This project aims to develop a machine learning system for ET0 forecasting. The project involves studying ET0 methods and identifying required meteorological parameters. Historical meteorological data and weather forecasts were obtained from meteorological websites and analyzed for accuracy after preprocessing. A machine learning-based model was created to forecast reference crop evapotranspiration. The model’s input parameters were selected through path analysis before it was optimized using Bayesian optimization to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather forecasts, and one that corrects the weather forecasts. All three models achieved good accuracy, with root mean square errors ranging from 0.52 to 0.81 mm/day. Among them, the model based on weather forecast had the highest accuracy; the RMSE six days before the forecast period was between 0.52 and 0.75 mm/day, and the RMSE on the seventh day of the forecast period was 1.12 mm/day. In summary, this project has established a mathematical model of ET0 prediction based on machine learning, which can achieve more accurate predictions for within a few days.

Optimized water management for precision agriculture using IoT-based smart irrigation system

This project introduces a Smart Irrigation System aimed at enhancing water efficiency in agriculture through the integration of an Arduino Uno micro-controller and a variety of sensors. The system incorporates a soil moisture sensor, a DHT11 sensor for monitoring temperature and humidity, an LCD, a buzzer for alerts, and a relay module to control a pump motor. The main goal is to create an automated irrigation system that adjusts to environmental conditions, thereby optimizing water usage for plant growth. The soil moisture sensor continuously evaluates soil moisture levels, while the DHT11 sensor keeps tabs on temperature and humidity. The Arduino Uno processes this data and displays it on an LCD screen. When the soil moisture falls below a predefined threshold, the system activates a buzzer and triggers the relay to turn on the pump motor. The LCD provides real-time feedback on environmental conditions and the irrigation status. This Smart Irrigation System offers several advantages, including water conservation, energy efficiency, and improved plant health. By automating irrigation based on real sensor data, the system minimizes water wastage and ensures that plants receive optimal moisture levels. Additionally, temperature and humidity monitoring contribute to effective microenvironment management. The project serves as a demonstration of IoT-based solutions for agricultural challenges, featuring a modular design that allows for scalability. Future developments may include incorporating additional sensors, enabling wireless connectivity for remote monitoring, and integration with weather data. In summary, the system provides a practical and cost-effective solution for sustainable agriculture, promoting water conservation and efficient farming practices.

Influence of energy poverty on agricultural water efficiency using a panel data study in China

The research attention is increasingly directed towards the effective integration of the 17 United Nations Sustainable Development Goals (SDGs) within the limitations of the real world and amidst intersectoral conflicts. In light of the inextricable relationship between irrigation and energy, the objective of this study is to identify potential avenues for achieving the SDG6 and SDG7 goals of enhancing water use efficiency in agriculture and eradicating energy poverty, respectively. Utilizing data from 30 Chinese provinces from 2002 to 2017, this study explores the dynamic influence of energy poverty on agricultural water efficiency with a system generalized method of moments methodology. The findings suggest that energy poverty may greatly reduce agricultural water efficiency. The heterogeneity study shows that when agricultural water efficiency grows, the negative impacts of energy poverty continue to fade. Based on an assessment of various processes, results suggest that non-farm employment and cropping structure modification is a prominent conduit via which energy poverty negatively influences agricultural water efficiency.

How is agricultural water efficiency affected by the digital economy? Insights from China

Abstract With the continued advancement of digital technology, the digital economy will gradually become the primary economic form in the future, having a profound impact on a variety of industries, including agriculture. Agriculture is a major source of global water use, and efficient water use in agriculture is critical to coping with water scarcity and ensuring food security. This study used publicly available data from 30 Chinese provinces from 2006 to 2017 to estimate the relationship between the digital economy and agricultural water use efficiency using the systematic generalized method of moments technique. According to the findings, a 1% increase in the digital economy indicator is associated with a 0.053% increase in agricultural water use efficiency. In addition, the digital economy improves agricultural water usage efficiency through three mediating channels: structural effect, scale effect, and spillover effect. For the digital economy and agricultural water use efficiency to develop in tandem, the Chinese government should strive to strengthen the development of the digital economy and work on the intermediate channels demonstrated in this study.

Pengembangan Sistem Pengontrolan Irigasi Cerdas dengan Teknologi Internet of Things (IoT)

Agriculture plays a pivotal role in addressing global food demands, with efficient resource utilization, particularly water, being crucial for sustainability. In this context, the Internet of Things (IoT) has emerged as a potent tool for optimizing irrigation water usage. This study centers on developing an IoT-based smart irrigation control system to enhance agricultural water efficiency. The system integrates soil moisture sensors, water pumps, and an IoT platform for autonomous plant irrigation management. Real-time sensor data is collected and processed to make intelligent irrigation decisions, reducing water consumption by up to 30% compared to traditional methods, as demonstrated in field trials. Beyond water efficiency, the system empowers farmers to remotely access and manage irrigation through a web or mobile app, offering resource management flexibility and improved adaptability to changing weather conditions. This research underscores IoT's substantial potential in advancing agricultural irrigation efficiency, curbing water use, and promoting economically and environmentally sustainable farming. These findings serve as a blueprint for future agricultural solutions in the IoT-driven sector.

Canal water distribution optimization model based on water supply conditions

The optimization of canal water distribution is vital in irrigation areas because a reasonable distribution is required to improve the water distribution efficiency and maximize the benefits of water resources. In this paper, under the condition that the total volume and flow rate of water supply in the primary canal have been determined, the water supply is compared with crop water demand, and three optimal models of canal water distribution are established. In situation 1, the total volume of water supply is sufficient to meet the crop water demand during the growing period. In situation 2, the water supply is insufficient to meet crop water demand during their growth period; however, this problem is resolved by extending the water supply period. In situation 3, the water supply cannot meet the crop water demands during the growth period, even after adjusting the water supply period. Because the proposed water distribution optimization model is based on water supply conditions in irrigation areas, the irrigation area managers can easily develop their operation water distribution schemes. The model was validated using the Bojili irrigation area. Results showed that the model’s calculated variation trend of the main-canal flow rate was similar to the actual flow rate, and irrigation area managers could meet the model requirements with a small adjustment in the actual flow rate. The model also considered canal leakage loss, timely water distribution, water distribution fairness, and other goals, potentially improving agricultural water efficiency and water distribution satisfaction as well as promoting sustainable agricultural development. The model presented here is universal and can be used in other irrigation areas.

Agricultural water use efficiency and spatial spillover effect considering undesired output in China

Abstract 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.

Developing an agricultural water pricing model considering both physical and virtual water: A case study of an irrigation district in China

Low agricultural water prices have led to waste of agricultural water and the rapid conversion of agricultural water to other industrial uses. In recent years, the Chinese government has implemented a comprehensive reform of agricultural water prices to reduce agricultural water waste. However, it is still difficult to achieve optimal allocation of saved agricultural water among different industries with the current reform, and the relationship between agricultural water savings and agricultural water efficiency is not closely related. In this study, an agricultural water pricing model was developed considering both physical and virtual water. Based on increasing agricultural water price, the model comprehensively considered compensation for agricultural water price, water rights transfer and virtual water export. The model was applied in the Hetao irrigation district in China for a case study to determine the prices of agricultural water and the related compensation in 2017, 2025 and 2035. The results showed that the agricultural water price, the price of agricultural water compensation and the virtual water compensation price were 0.40 CNY/m³, 0.15 CNY/m³, and 0.25 CNY/m³, respectively. Compared with the original reform policy, the income of irrigation district administration and agricultural water users increased by 9.3% and 13.5%, respectively. The agricultural water pricing model developed in this study could help to improve the income of participants, avoid behaviors of not irrigating or irrigating less, promote optimal allocation of water resources among industries and regulate virtual water flow.

Understanding the Mechanism of Urbanization Affect Agricultural Water Efficiency: Evidence from China

Concerns regarding food security and sustainable development have been highlighted as a result of water scarcity and growing urbanization. It is imperative to look into their relationship. This study examines the impact of urbanization on agricultural water efficiency (AWE) in China utilizing China province-level panel data from 2002 to 2019. The findings indicate that urbanization has a U-shaped relationship with AWE, meaning that urbanization first had a detrimental effect on AWE before reversing course. These findings are robust to the inclusion of three measures of urbanization and the estimation of the instrumental variable method. Structural equation modeling of the underlying mechanisms demonstrates that, at higher levels of urbanization, planting structure and irrigation facilities partially mediate the urbanization-AWE relationship; the mediate effects account for between 27.3% and 100% of total effects, depending on the urbanization measurement used. China should continue investing in rural irrigation infrastructure as it urbanizes, as this would improve water efficiency.

Assessment of Economic Efficiency of Water Use through a Household Farmer Survey in North China

Water use efficiency (WUE) is one of the most widely used indicators in agricultural water management. Although this indicator has obvious advantages, it is limited to measuring the relationship between crop yield and corresponding water use. In recent years, many researchers have noted that understanding the economic efficiency of water use (EEWU) could have great water-saving potential, while it has been poorly investigated with respect to China’s agricultural water management. This paper assesses EEWU through a household farmer-level survey in the piedmont region of North China. First, EEWU of crops are estimated based on agricultural water consumption (including irrigation water and effective precipitation) and profit (including gross profit and net profit); Second, the impact of monthly price changes in 2019 and annual price changes in 2014–2019 on EEWU is analysed. Main conclusions are as follows: (1) EEWU values of cash crops such as apple and cauliflower are much higher than those of grain crops such as wheat and maize; (2) For different crops, the median economic efficiency of irrigation water (EEWiU) and total water (EEWtU) range from 31.71 to 99.54 ¥/m3 and 11.31 to 44.05 ¥/m3, respectively; (3) The multi-year average EEWiU and EEWtU ranged from 4.75 to 63.99 ¥/m3 and from 2.67 to 31.71 ¥/m3, respectively. Economic efficiency of water use shows a slightly downward trend in the period of study, which would contradict the trend towards the use of more water-efficient technologies and shows an even larger margin of improvement in the domain of agricultural water efficiency. The results provide a powerful reference for the management of agricultural water use through economic leverage.

The effect of drip irrigation under mulch on groundwater infiltration and recharge in a semi-arid agricultural region in China

Abstract The wide application of drip irrigation under mulch in semi-arid agricultural regions in China not only improves agricultural water efficiency, but also affects formation of groundwater and the mechanism of water infiltration to a certain extent. This paper takes the typical semi-arid agricultural region in China as the research object. The movement of soil water under the three types of underlying surface was simulated by the Hydrus-2D model for the quantitative analysis of groundwater recharge. The influence of drip irrigation under mulch on groundwater infiltration depth and cumulative infiltration amount under different level years was simulated. Taking a normal flow year as an example, the simulated results showed that the maximum infiltration depth of drip irrigation under mulch reached 250 cm, which was greater than that of border irrigation (138 cm) and bare area (158 cm). The cumulative infiltration amounts of drip irrigation under mulch at 80, 120, 140 and 200 cm were respectively 1,484.8 m3/hm2, 686.3 m3/hm2, 554.1 m3/hm2 and 238.1 m3/hm2, which were greater than that of border irrigation and bare land at the same depth. The results proved that drip irrigation under mulch could increase the infiltration depth and cumulative infiltration amount, which is beneficial to groundwater recharge in semi-arid agricultural regions of China.

Evaluating the Performance of AquaCrop Model for Potato Production Under Deficit Irrigation

Crop modeling is a powerful tool for estimating yield and water use efficiency, and it plays an important role in determining water management strategies. Under the condition of scarce water supply and drought, deficit irrigation can lead to greater economic gains by maximizing yield per unit of water. Studies have shown that deficit irrigation significantly increased yield, crop evapotranspiration, and water use efficiency as compared to full irrigation requirement. However, this approach requires precise knowledge of crop response to water as drought tolerance varies considerably by growth stage, species and cultivars. This study was conducted in Lasta district, for two successive years to evaluate the effects of water shortage on potato production and water use efficiency, as well as to test the AquaCrop model for potato-producing areas. The irrigation water levels for potatoes were 100%, 75%, and 50% of crop evapotranspiration (ETc). Six treatments were arranged using a randomized complete block design. Climate, soil, and crop data were calibrated using observed weather parameters, and measured crop parameters conducted in the 2018/19 growing season. The model was validated using the observed data conducted in the 2019/20 growing season. The calibration of the model revealed a good fit for canopy cover (CC) with a coefficient of determination ( R2) = .98, Root mean square error (RMSE) = 9.6%, Nash-Sutcliffe efficiency ( E) = 0.92, index of agreement ( d) = 0.98, and coefficient of residual moss (CRM) = −0.07, and good prediction for biomass ( R2 = .98, RMSE = 1.8 t ha−1, E = 0.96, d = 0.99, CRM = −0.13). Similarly, the validation result showed good fit for CC by 100% water application at development and mid growth season and a 75% water applied at the other stages ( R2 = .98, RMSE = 9.4%, E = 0.94, d = 0.98, CRM = −0.12). The AquaCrop model is simple to use, requires fewer input data, and has a high level of simulation precision, making it a useful tool for forecasting crop yield under deficit irrigation and water management to increase agricultural water efficiency in data-scarce areas.

Ecological balance model of effective utilization of agricultural water resources based on fractional differential equations

Abstract Water is a key factor that controls the evolution of the ecosystem and restricts social and economic development. The rational allocation of water resources is the basis for the basin's sustainable use of water resources. The article uses fractional differential equations to discuss the increasingly prominent contradictions between supply and demand of agricultural water resources, serious pollution and overexploitation, the decline in groundwater level and water quality, low agricultural water efficiency, and serious waste from the perspective of sustainable development. Second, the article classifies the index system of the water resources carrying capacity evaluation model. Finally, model the resource balance of water supply and water demand in the irrigation area. The study found that the water quantity analysis and determination of the irrigation area will have important guiding significance for implementing the irrigation area's continued construction and water-saving transformation. The allocation and utilization of water resources in farmland irrigation areas will tend to be rationalized. This will greatly improve the water-saving efficiency of irrigation districts.

Analysis on regional differences of water use efficiency rigid constraints and countermeasures in Zhejiang Province

With the implementation of the most stringent water resources management system, rigid constraints on water resources have gradually increased, the total amount of water used has been effectively controlled. In order to quantify the strength of rigid constraints on water resources, the concept of rigid constraints on water efficiency was proposed, and the constraints on industrial and agricultural water efficiency targets and regional differences in 11 districted cities in Zhejiang Province were analyzed, and the target constraints in the industrial and agricultural water use were comprehensively considered through scenario analysis. Seven types of areas, including industrial and agricultural strong control, agricultural main control, and industrial main control are drawn, and countermeasures for optimizing water resources and water-saving management for different types of areas are put forward to further improve the level of water resources management in Zhejiang Province.

Estimation and influencing factors of agricultural water efficiency in the Yellow River basin, China

As an important grain production base in China, the Yellow River basin (YRB) plays an important role in China's economic development and ecological security. However, with increasing agricultural water environmental problems and deteriorating water quality, the agricultural water situation in the YRB is grim. Under the background of comprehensively promoting the high-quality development of the YRB, improving agricultural water efficiency can reduce the constraint effect of insufficient water resources on agriculture and improve the water ecological environment, which will help achieve coordinated social, economic and environmental development. In this study, agricultural water efficiency of nine provinces in the YRB from 2008 to 2017 was measured by the super-efficient slack-based measured Data Envelopment Analysis (SBM-DEA) method with unexpected outputs, spatial autocorrelation analysis and the Malmquist index, and the key influencing factors were identified by the spatial Tobit regression model. The results showed that the agricultural water efficiency of nine provinces in the YRB was increasing, with large differences among provinces and little spatial correlation, presenting a spatial distribution with the lower reaches higher than the middle reaches and the middle reaches higher than the upper reaches; the change in the Malmquist index of agricultural water showed an increasing trend, which was mainly determined by the technical progress. Additionally, the economic development level and water resource endowment had positive effects on agricultural water efficiency, while government expenditure and urbanization level had significant negative correlation with agricultural water efficiency.

Has the water rights system reform restrained the water rebound effect? Empirical analysis from China's agricultural sector

Improving water-saving irrigation technology and water use efficiency are often regarded as effective ways to control the amount of agricultural water consumption, but the rebound effect may offset this effort in practice. Based on the sample of 30 provinces in China from 2000 to 2017, this paper applies the meta frontier data envelopment analysis method to measure agricultural water efficiency, and then estimates the rebound rate of agricultural water resource applying difference-generalized method of moments and elasticity analysis formula. Further, the difference-in-difference model is used to test the effectiveness of China's water rights reform on China’s agricultural water rebound rate in recent years. The results show that: (1) Within the survey period, the average agricultural water rebound rate of China in short-term and long-term were 0.49 and 0.66 respectively, and the water rebound rate in all provinces were positive, indicating that water rebound effect was widespread in China’s agricultural water market; (2) Agricultural water rebound rates were significantly different among regions, which was higher in western region while were comparatively smaller in the eastern and central regions; (3) The water rights pilot project failed to restrain the rebound of agricultural water use, but promoted it, which may be resulted from the market frictions of water trade. According to the results, recommendations are put forward for the governments to reinforce management of water, improve water rights system and reduce trade costs, in order to reduce the friction of agricultural water market and the effective use of water resource.

Can Water Rights Trading Scheme Promote Regional Water Conservation in China? Evidence from a Time-Varying DID Analysis

Using the panel data of 30 provinces in China from 1998 to 2017, we adopt a time-varying difference-in-differences (time-varying DID) model to estimate the impact of water rights trading scheme on regional water consumption. The results show that water rights trading can significantly promote water conservation in the pilot regions by 3.1% compared to that in the non-pilot regions, and a series of robustness tests show consistent results. Policy effects are mainly driven by improving water-use efficiency and adjusting water structure; that is, by transferring water resources from the agricultural sector to the other sectors, agricultural water efficiency is improved and water conflict among sectors is alleviated; thus, water saving is achieved. In addition, by constructing two indexes of regional water pressure and tradable water resources, our heterogeneity analysis shows that water rights trading performs better in areas with high water pressure and large tradable water resources. Under the high pressure of large water use and low water endowment, water rights trading will evidently reduce water consumption more so than in the low-pressure regions, and with water rights trading, it is hard to achieve a policy effect in regions without sufficient tradable water resources. This paper provides important policy implications for China for further promoting the water rights trading scheme in the field of resource conservation.