Effects of crop planting structure optimization on water-saving, environmental, and economic multibenefits in Northeast China

The complex global environmental situation has intensified the negative impact on the food supply chain and food prices, and food security has become a concern worldwide (Mok et al. 2020; Liu et al. 2023). Crop production consumes large volumes of water, and approximately 70% of global water withdrawals are used for agricultural irrigation (Folberth et al. 2020), which can trigger a series of water issues, such as water scarcity, water environment pollution, and low water resource utilization efficiency (Levidow et al. 2014; Ma et al. 2020a; Yan et al. 2023). The comprehensive influence of resources, the environment, the economy, and society makes water security face great challenges (Yao et al. 2020). Water security is the basis of food security, and efficient utilization of agricultural water resources is the fundamental way to ensure water security and food security (Kang 2014). Optimizing the agricultural planting structure is important for achieving efficient utilization of water resources (Yu et al. 2021) and can enhance economic and environmental benefits.

The water footprint (WF) is a measure of humanity's appropriation of water resources related to both water quantity and quality and includes three components: blue, green, and gray WFs (Hoekstra 2003). The crop WF is the total amount of water required for crop production and is thus a powerful tool for measuring water consumption and diffusing pollution in agricultural water management practices. The crop WF and extension indicators can be employed to measure the relationship between water resources and crop production (Wang et al. 2022). The mitigation of water stress in crop production is critical for alleviating growing global water scarcity, and more attention has been given to studies on blue water scarcity during crop production (Pfster & Bayer 2014; Fu et al. 2019; Huang et al. 2019; Xu et al. 2019). Green water resources are also essential for crop production; thus, by integrating blue and green water resources, a WF scarcity indicator was developed for regional water stress evaluation (Cao et al. 2018).

In addition to water scarcity, one of the most common indicators for measuring the relationship between water resources and crop production is water use efficiency. For example, the WF, water scarcity, and crop water productivity have been studied simultaneously at the county level on the North China Plain, and all the counties faced water scarcity during 1986–2010, even as the average crop water productivity increased from 0.90 to 1.94 kg/m³ (Xu et al. 2019). This study indicated that the enhancement in water use efficiency may be attributed mainly to the improvement in yield rather than to water savings. Economic benefit is the goal of crop production, which plays a critical role in deciding which crop to plant for the government and farmers (Ren et al. 2018). But in actual agricultural production greater, water consumption in crop production does not necessarily correspond to greater economic benefit due to differences in crop types and geographic location (Ma et al. 2020b). Water use efficiency and water use benefit measure the relationship between water resources and crop production from the perspective of the economy. The excessive use of fertilizer in crop production will cause diffuse pollution. Studying water pollution levels based on gray WF (D'Ambrosio et al. 2018; Novoa et al. 2019; Aldaya et al. 2020) is helpful for assessing the severity of water pollution and reflects the relationship between water resources and crop production from the perspective of the environment. In sum, previous studies have focused predominantly on assessing the efficiency and stress of water use during crop production and only paid much attention to one or two aspects of resources, economy, and environment. However, evaluating water consumption in isolation is inadequate for ensuring food security and generating income (Wang et al. 2021), a comprehensive consideration of the benefits of resources, environment, and economy in crop production should be taken simultaneously.

The optimization of crop structure is a key method for balancing food production and water savings (Zhang et al. 2014; Cai & Qian 2017) while simultaneously considering both economic and environmental benefits. Planting structure optimization involves optimizing the planting area of different crops and rationally allocating agricultural resources within a region to improve the regional suitability of crops (Li 2018). According to the resource and environmental characteristics of the study area, the objective function and constraint conditions should be determined first, and then the corresponding optimization model should be built. The traditional optimization algorithms include linear programming models (Heady 1954) and fuzzy compromise programming models (Zhang et al. 2013). New planting structure optimization algorithms, such as the fuzzy optimization algorithm (Biswas & Pal 2005), genetic algorithm (Gao & Zhang 2013), ant colony algorithm (Zhang et al. 2011), and fuzzy optimization particle swarm optimization algorithm (Toyonaga et al. 2005), are constantly emerging. For instance, the planting schemes for sugar beet, maize, and cotton plants were adjusted under multiple water source conditions in Thessaly Plain of Greece by using the objective programming model of fuzzy set theory (Tsakiris & Spiliotis 2006). Although different regions have different geographical environments, crop types, water resources, and economic development conditions, the goals that need to be achieved are similar. The objective functions of minimizing total water consumption and maximizing agricultural total output were set to optimize the planting structures in water-scarce areas such as South Africa (Adeyemo & Otieno 2010), Ethiopia's Koga irrigation district (Birhanu et al. 2015), Liaoning Province (Wang et al. 2021), and the Haihe River Basin (Dai et al. 2021). And environmental benefits are also receiving increasing attention (Guo et al. 2021; Yu et al. 2021). In general, the existing studies have focused on the issues of water-saving, reducing pollution, or increasing economic output after optimization; there are relatively few studies considering the multiple benefits of resource, economy, and environment simultaneously after optimizing planting structure.

Substantial attention has been given to achieving planting structure optimization, while further resource, environmental, and economic effects after optimization also need to be thoroughly and extensively identified, and the differences in resource, environmental, and economic effects among regions need to be further explored. Therefore, exploring the extent of water scarcity and water pollution mitigated, and the degree of economic benefit increased, became the key scientific issues and should be addressed. The WF theory and method can aid in understanding water savings, water environment protection, and yield increases under the adjustment of planting structure. In this study, the WF was used as a measurement index to explore the agricultural water-saving effect and the changes in environmental and economic benefits after the optimization of the planting structure. A multiobjective planting structure optimization model for Northeast China was established to provide multiple benefits, including those related to water savings, the environment, and the economy. The aim of this paper was to clarify whether optimizing the crop planting structure is an effective measure for realizing the sustainable utilization of water resources and high-quality agricultural development.

Northeast China is the main grain-producing area and the main supply area of commodity grain; the grain yield per unit area is high, and the potential for increasing production is large. The total grain yield accounted for approximately 20% of the country's yield in 2017, and this proportion has been increasing, indicating an important strategic position for ensuring national food security. Large amounts of water resources are consumed for crop production; however, the water resources per unit of sown area in Northeast China account for less than half of that of the country, and the regional differences are large. Furthermore, due to the differences in geographical conditions, production inputs, and agricultural management levels among regions, crop productivity and economic and environmental benefits also differed.

Considering the multiple resource-related, environmental, and economic aspects related to agricultural water use, green water scarcity, blue water scarcity, water pollution level, water use efficiency ratio, and water use benefit ratio were selected to characterize the water savings, environmental and economic benefits of crop production before and after the adjustment of the planting structure in this study (Table 1). The mitigation degree of green water scarcity and blue water scarcity can reflect the water-saving benefit of crop production, the water pollution level can reflect the environmental benefit of crop production, and the water use efficiency ratio and water use benefit ratio can reflect the economic benefit of crop production. The calculations of crop WFs and the above indices were performed according to previous methods (Wang et al. 2022).

Table 1

Index system of water-saving, environmental, and economic benefits in crop production

.	Index layer .	Indicator description .	Calculation formula .
Extension indicators system of crop WF	Resource	Green water scarcity (GWS)
		Blue water scarcity (BWS)
	Environment	Water pollution level (WPL)
	Economy	Water use efficiency ratio (WUER)
		Water use benefit ratio (WUBR)

.	Index layer .	Indicator description .	Calculation formula .
Extension indicators system of crop WF	Resource	Green water scarcity (GWS)
		Blue water scarcity (BWS)
	Environment	Water pollution level (WPL)
	Economy	Water use efficiency ratio (WUER)
		Water use benefit ratio (WUBR)

Abbreviations: green WF of the crop (CWF_green), green water availability (GWA), the blue WF of the crop (CWF_blue), blue water availability (BWA), environmental flow requirements (EFR), irrigation water use (IWU), regional total water use (TWU), the gray WF of crop (CWF_gray), actual runoff from the catchment for crop production (R_act,crop), national average water use efficiency (WUE_average), water use efficiency of the ith prefecture (WUE_i), the ratio of water use efficiency between national average and the ith prefecture (WUER_i), national average water use benefit (WUB_average), water use benefit of the ith prefecture (WUB_i), and the ratio of water use benefit between national average and the ith prefecture (WUBR_i).

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The solution to the multiobjective optimization problem involves obtaining a set of optimal solutions. The fuzzy compromise programming method is an effective method for solving multiobjective optimization problems. The distance between the feasible solution set and the ideal solution is calculated, and the solution closest to the ideal solution is used to determine the final solution to achieve overall optimization (Li 2018).

Representative crops in Northeast China were selected for the optimization simulation. The planting areas x_ij (i = 1, 2, 3,…, 40；j = 1, 2, 3,…10) of different crops in each prefecture (j) were the decision variables of the model.

The representative crops in Heilongjiang Province include maize, rice, soybean, tobacco, potato, vegetables, sunflower, sorghum, millet, and wheat. The representative crops in Jilin Province include maize, rice, soybean, tobacco, potato, vegetables, and sunflower. The representative crops in Liaoning Province include maize, rice, soybean, tobacco, potato, vegetables, sunflower, sorghum, millet, and peanut plants. The representative crops in the eastern Inner Mongolia Autonomous Region include maize, soybean, wheat, potato, and sunflower.

From the three aspects of resources, the environment, and the economy, the following three objective functions were constructed.

• (1) Blue WF

  

  (3)

  where f₁ is the blue WF, m³; i is the prefecture of Northeast China; j is the crop type; BWF_ij is the blue WF per unit area of the jth crop in the ith prefecture, m³/ha; and x_ij is the planting area of the jth crop in the ith prefecture, ha.
• (2) Chemical fertilizer application

  

  (4)

  where f₂ is the chemical fertilizer application, kg; and CFA_ij is the chemical fertilizer application per unit area of crop j in prefecture i, kg/ha.
• (3) Economic benefit

  

  (5)

  where f₃ is the economic benefit, yuan; P_ij is the average selling price of crop j in prefecture i, yuan/kg; and Y_ij is the yield per unit area of crop j in prefecture i, kg/ha.

• (1) Planting area constraint

The parameters of the planting structure adjustment model were determined based on the objective function and constraint conditions, and the multiobjective optimization algorithm was implemented in MATLAB (2018a). The model was used to obtain the optimized planting area of each crop, after which the changes in the crop's WF and its expansion index were analyzed.

Sources of meteorological, agricultural, and water resource data are shown in Table 2.

The relevant data, including the planting area, blue WF per unit area, chemical fertilizer application per unit area, yields per unit area, and average selling prices of crops, are summarized in Tables S1–S5.

The decreases in WFs among regions were different owing to the differences in the crop type and acreage after the optimization of the crop planting structure. The crop planting area in eastern Northeast China was the smallest, and the reduction in WFs is minimal. The reduction of green, blue, gray, and total WFs in eastern Northeast China was 1.12 × 10⁸, 1.26 × 10⁸, 1.28 × 10⁸, and 3.66 × 10⁸ m³, those of green, blue, gray, and total WFs in middle Northeast China were 3.12 × 10⁸, 0.82 × 10⁸, 3.08 × 10⁸, and 7.02 × 10⁸ m³, respectively, and those of green, blue, gray, and total WFs in western Northeast China were 1.42 × 10⁸, 1.61 × 10⁸, 3.77 × 10⁸, and 6.80 × 10⁸ m³, respectively. The blue and gray WFs decreased the most in western Northeast China, which indicated that the adjustment of the planting structure can alleviate water scarcity and reduce the negative effects on the environment in western Northeast China to a greater extent. Among the 40 prefectures, the green WF of Harbin decreased the most (1.48 × 10⁸ m³). The green WFs of Daxing'anling, Jixi, and the eastern Inner Mongolia Autonomous Region increased. The planting area of maize expanded in Daxing'anling, and the areas of soybeans, wheat, and other cereals expanded in Jixi and the eastern Inner Mongolia Autonomous Region. Moreover, the unit area of the green WF of maize, soybeans, and wheat was greater than that of the other crops, resulting in an increase in the green WF.

In Northeast China, the crop WF decreased, and the crop output increased, which indicated that the output values of tobacco, potatos, vegetables, sunflowers, peanuts, and other cash crops were high, while the water consumption was low. A greater economic benefit could be obtained with less water consumption after optimization, and the efficiency of water resource utilization in Northeast China improved. Therefore, under the premise of ensuring food security needs, increasing the planting area of cash crops was conducive to improving agricultural output and increasing the rational use of limited water resources.

The reduction in the water use benefit ratio signifies the increase in economic benefits achieved through the adjustment of planting structures. The water use benefit ratio in eastern, middle, and western Northeast China decreased by 5.48, 3.68, and 1.44%, respectively (Figure 6(a)), and the largest decrease was in Fushun (12.04%). The decrease in the water use benefit ratio in middle and western Northeast China was less than that in eastern Northeast China, which indicated that the increase in the water use benefit in eastern Northeast China was larger than that in middle and western Northeast China. The water use benefit was determined by crop output and crop WF. Although the crop output in middle Northeast China was the highest, the crop WF in middle Northeast China was also the largest; thus, the increase in the water use benefit was not the largest. The increase in the water use benefit was the smallest in western Northeast China due to the increase in the green WF, as observed in the Daxing'anling and eastern Inner Mongolia Autonomous Regions.

The reduction in the water pollution level signifies the increase in environmental benefits achieved through the adjustment of planting structures. The water pollution levels in middle and western Northeast China decreased by 3.25 and 4.53%, respectively, with Jinzhou dropping the most (14.68%), and in eastern Northeast China, the water pollution decreased by 1.85%, with the largest decrease occurring in Yingkou (7.66%) (Figure 6(b)). The optimization of the planting structure had a greater mitigating effect on the water pollution level in the prefectures whose percentage of decrease was greater than the average level. The environmental benefit of planting structure adjustment in western Northeast China was greater than that in middle and eastern Northeast China, possibly because the original water pollution level was high and the potential for decline was great. The gray WF of crops in western Northeast China was large while the actual runoff from the catchment for crop production in western Northeast China was much less than that in middle and eastern Northeast China. Therefore, the water pollution level in western Northeast China was the highest. The water pollution level in eastern Northeast China was the lowest due to abundant water resources.

A decrease in blue and green water scarcity values indicates an improvement in the water-saving benefit when adjusting the planting structure. There was a large difference in the variation in blue water scarcity among regions (Figure 6(c)), and the percentage of decline was western > middle > eastern Northeast China. The blue water scarcity in eastern Northeast China decreased by 1.82%, with the largest decrease occurring in Jixi (4.96%); in middle Northeast China, it decreased by 1.94%, with Heihe dropping the most (9.61%); and in western Northeast China, it decreased by 3.96%, which was greater than that in eastern and middle Northeast China. Especially in the eastern Inner Mongolia Autonomous Region, the largest decrease reached 11.36%. Although both blue water scarcity and water pollution levels were limited by locally available fresh water and the downward trends were consistent, the difference in the variation in water pollution levels among regions was more evident than that in blue water scarcity.

Green water scarcity varied within 2% in each prefecture (Figure 6(d)), and the percentage of decline was middle > eastern > western Northeast China. The green water scarcity in eastern Northeast China decreased by 0.57%, with Fushun dropping the most (1.68%); in middle Northeast China, it decreased by 0.71%, with the largest decrease occurring in Harbin (1.93%); and in western Northeast China, it decreased by 0.32%, with the largest decrease occurring in Qiqihar (1.65%). Due to the increase in the green WF in some prefectures, green water scarcity has increased in these areas. As a result, the decrease in green water scarcity in Northeast China was small.

The water use efficiency ratio and water use benefit ratio together indicated the economic benefit of adjusting the planting structure. The water use efficiency ratio in eastern, middle, and western Northeast China decreased by 3.03, 2.65, and 0.87%, respectively (Figure 6(e)), with the largest decrease occurring in Fushun (9.92%). The decrease in the water use efficiency ratio in middle and western Northeast China was less than that in eastern Northeast China. The decrease in proportion was the smallest in western Northeast China because the yield decreased and the green WF increased in some western prefectures.

In summary, the multibenefits of water savings, the environment, and the economy improved after the optimization of the planting structure, while there were differences among regions. The difference in green water scarcity in western, middle, and eastern Northeast China was small. The decreases in blue water scarcity and water pollution levels in middle and western Northeast China were greater than those in eastern Northeast China, indicating that the adjustment of the planting structure had a more prominent effect on the improvement of water savings and environmental benefits in middle and western Northeast China. The decreases in the water use efficiency ratio and water use benefit ratio in middle and western Northeast China were less than those in eastern Northeast China, indicating that the adjustment of the planting structure had a more prominent effect on the improvement of economic benefits in eastern Northeast China.

The results of the planting structure adjustment in this study were basically consistent with the direction of the national adjustment of the planting structure in Northeast China: stabilization of the area of rice, reduction of the area of maize, and expansion of the area of soybean, miscellaneous grain and tubers (NPSAP 2016–2020). Reducing the cultivation area of food crops and increasing the cultivation area of cash crops can achieve the goals of increasing economic income, improving water resource utilization efficiency, and reducing the negative environmental impacts caused by food production (Guo et al. 2021), which were similar to the results in this paper. The maize area has been reduced in most prefectures and the cultivation area of cash crops increased in Northeast China. Rice is a highly water-consuming crop. To stabilize the rice area without increasing water consumption, it is necessary to accelerate the construction of water-saving auxiliary facilities in large and medium-sized irrigation areas and to improve rice irrigation methods. Maize is the crop with the largest planting area in Northeast China. According to the results of the planting structure adjustment, it is necessary to control maize acreage, and the reduced maize acreage was replanted with other crops. Spring wheat should be properly restored to ensure food security, especially for high-yield, high-quality, stress-resistant, and water-saving varieties. With the improvements in people's living standards and health awareness, the demand for various cash crops and multigrain crops has substantially increased, resulting in a mismatch between supply and demand. Reduced maize acreage was used to plant multigrain crops, which is also in line with market demand. Of course, the increase in the area of cash crops and multigrain crops is not blind and must be adapted to local conditions. For instance, the planting area of peanuts can be appropriately expanded in the agricultural and animal husbandry zones of Jilin Province and Liaoning Province.

Agriculture has always been a major water user. Adjusting planting structure is an important way to realize the sustainable utilization of water resources, which can improve the utilization efficiency of water resources and alleviate water scarcity (Ma et al. 2015). Water quality issues also affect the sustainable utilization of water resources (Ma et al. 2020a). A study in the Hetao Irrigation District indicated that the WF has been reduced by 9.0 × 10⁷ m³ after optimization, there has been a certain degree of improvement in water use efficiency (Guo et al. 2021), which supported the results of this paper, the WF decreased after optimization and the water use efficiency in most prefectures was improved. Therefore, the optimization of planting structures has a positive effect on the sustainable utilization of water resources. While considering the sustainable utilization of water resources, it is also necessary to take into account the economic benefits of agriculture. After adjusting the planting structure of the People's Shengli Canal irrigation district, the economic benefit was increased by 1.80 × 10¹⁰ CNY and 63.71 × 10⁶ m³ of irrigation water was saved (Ma et al. 2016). Our study results proved that the planting structure optimization can alleviate blue and green water scarcity, reduce diffuse pollution, and increase the output value of crop production in Northeast China to some extent. Several scholars have also affirmed the effect of planting structure adjustment on water scarcity mitigation (Chouchane et al. 2020), pollutant reduction (Yu et al. 2021), and economic benefit improvement (Fu & An 2021). In summary, the above studies focused only on one or two aspects of resources, the environment, and the economy, while the development of the planting industry faces many problems and challenges. The total amount of water resources is insufficient, the utilization rate is not high, and a serious mismatch between land and water resources still occurs. Under the influence of global climate change, extreme weather has increased (Shen et al. 2018; Clarke et al. 2022). Extreme disasters have appeared unexpectedly, and the impact on agriculture, especially food production, has increased. The unscientific and irrational use of chemical fertilizers and pesticides in some areas is still a prominent problem. To address the above challenges, higher requirements have been proposed for improving planting benefits (NPIDP 2021). Therefore, comprehensive attention should be given to the unity of the multibenefits of resources, the environment, and the economy to cope with complex situations and severe challenges and ensure food security and water security.

After planting structure optimization, the multibenefits of water savings, the environment, and the economy improved; among them, the economic benefits improved the most. The degree of decline was ranked as follows: water use benefit ratio > water pollution level > blue water scarcity > water use efficiency ratio > green water scarcity in Northeast China. However, the multibenefits of water savings, the environment, and the economy were not achieved in all prefectures, and there were obvious regional differences. Green water scarcity, the water use efficiency ratio, and the water use benefit ratio in Daxing'anling and the eastern Inner Mongolia Autonomous Region have increased rather than decreased due to spatial differences in crop suitability. The Greater Khingan Mountains region is a forest area, and eastern Inner Mongolia is a semiarid region that has great differences in natural conditions from the plains of middle and eastern Northeast China. Additionally, the interannual and intrayear change rates of precipitation are large, which introduces great uncertainty in the realization of multiple benefits. Land resources are also an important factor. The quality of black soils is being increasingly degraded by long-term intensive use (Li et al. 2021), and changes in soil parameters cause uncertainties in both green and gray WFs. Considering the spatiotemporal heterogeneity of the above natural conditions, the optimization of crop planting structure could provide more accurate policy recommendations (Wang et al. 2021) than the current study. In addition to natural factors, human factors can also affect the planting structure. The demand for food will increase in the future due to population growth and climate change. Future research should consider the impact of the above factors, which will affect crop production in the future.

The adjustment of the planting structure is a systematic process and should provide planning guidance, strengthen project support and policy support, and mobilize the enthusiasm of local governments and farmers. The following measures were proposed to promote the adjustment of the planting structure. (1) According to the dynamics of the food market, supply and demand information is mined to guide farmers to adjust the planting structure (Li 2018). (2) The crop rotation system, such as grain and bean rotation, grain and warp rotation, and grain and feed rotation, should be gradually established according to local conditions to promote sustainable agricultural development (Luo et al. 2018). (3) High-tech cultivation like drip irrigation, water and fertilizer integration, and better breeding would also contribute to the planting structure optimization (Wang et al. 2021; NPIDP 2021).

However, there are some limitations and uncertainties of data and methods in this study. The statistical data are based on administrative divisions and have low spatial resolution, and the remote sensing data should be used to improve spatial resolution in the future. In the optimization, the planting area, blue WF per unit area, chemical fertilizer application per unit area, yields per unit area, and average selling prices of crops are for 1 year, studies in the future need to be explored based on long time series. The results of this study verified the feasibility of the multiobjective optimization model, even so, this study took little consideration of the practical uncertainty factors, and it is necessary to introduce uncertainty methods to further improve the model in the future.

The adjustment of crop planting structure and the change in planting area of different crops in Northeast China would have a great impact on the efficient production of grain and the stable development of agriculture in China. In this paper, by constructing and implementing a multiobjective optimization model, the optimized planting area, WF, yield, and output value in Northeast China were obtained, and the multibenefits of water savings, the environment, and the economy of planting structure optimization were quantitatively analyzed.

After planting structure optimization, the planting area of maize decreased, that of rice remained basically stable and decreased slightly, and that of other crops increased to varying degrees in prefectures of Northeast China; these results were basically consistent with the direction of the state's adjustment of the planting structure in Northeast China. The adjustment of the planting structure can alleviate blue and green water scarcity, reduce diffuse pollution, and increase the output value of crop production in Northeast China to some extent. Overall, the multibenefits of water savings, the environment, and the economy improved after the optimization of the planting structure, while there were differences among regions. The improvements in water savings and environmental benefits in middle and Western Northeast China were greater than those in eastern Northeast China, while the improvements in economic benefits in middle and western Northeast China were less than those in eastern Northeast China.

In the future, not only the the inter-regional but also intra-regional differences of multibenefits from planting structure optimization should be explored; therefore, the research scale needs to be further refined, and the remote sensing data and long time series will be the focus.

J.Q.W. and L.J.Q conceptualized the process and developed the methodology; J.Q.W. and L.J.Q rendered support in formal analysis and investigated the project; J.Q.W. rendered support in data curation; J.Q.W. wrote the original draft preparation; J.Q.W. and L.J.Q. wrote the reviewed and edited the article. All authors have read and agreed to the final version of the manuscript.

This work was funded by the Tangshan Normal University Doctoral Fund (2023B03), the National Natural Science Foundation of China (42471289), and the National Key Research & Development Program of China (2019YFC0409101).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.