EN中文

Journal of Resources and Ecology >

2021 , Vol. 12 >Issue 2: 203 - 213

DOI: https://doi.org/10.5814/j.issn.1674-764x.2021.02.007

Land Use Change and Land Multifunction Tradeoffs

Optimal Land Use Structure for Sustainable Agricultural Development—A Case Study in Changsha County, South Central China

  • LI Hongqing , * ,
  • LI Wenqi ,
  • ZHENG Fei
Expand
  • Department of Land Resources Management, Hohai University, Nanjing 211000, China
*: LI Hongqing, E-mail:

Received date: 2020-10-15

  Accepted date: 2020-12-30

  Online published: 2021-05-30

Supported by

Foundation: The National Natural Science Foundation of China(41801216)

The Fundamental Research Funds for the Central Universities of China (2018B20914).()

Fold

Abstract

Environmental and social problems caused by overfertilization, excessive pesticides, and encroachment on farmland are increasingly serious in agricultural settings, especially in suburban agricultural areas and highly intensive agricultural areas. Hence, modern agriculture not only pursues economic benefits, but it also pays more attention to ecological functions and social stability. This paper proposes a set of methods which are designed to realize optimal agricultural benefits and sustainable development by scientifically adjusting the land use structure. Taking Changsha County in South Central China as a case study, this paper first built an index system and adopted the information entropy-TOPSIS method to assess the economic, social, and ecological benefits of agricultural land use. Next, a coupled coordination model and an obstacle model were chosen to diagnose those factors that remained as obstacles to achieving the sustainable and coordinated development of the benefits of agricultural land use. Finally, based on the analysis of the changes in the benefits and obstacles over time, socio-economic and ecological constraints were established, and the multi-objective linear programming method (MOLP) was used to determine the comprehensive benefits and optimal land use structure. The results indicate that: (1) The agricultural benefits were stably increasing from 0.20 in 1996 to 0.79 in 2016. (2) The economic benefit index is no longer the main obstacle, while the social benefit index, which includes components such as the food security index, has become the principal influencing factor. (3) The optimal land use structure and comprehensive benefits were presented by taking into consideration the economic development, environmental protection, and social needs. This study emphasizes economic development, but it also seeks coordinated development with comprehensive benefits. The results of the study could provide scientific recommendations for optimizing the agricultural land use spatial patterns and sustainable land use.

Key words： land use structure optimization; MOLP method; comprehensive benefits; Changsha County

Cite this article

LI Hongqing , LI Wenqi , ZHENG Fei . Optimal Land Use Structure for Sustainable Agricultural Development—A Case Study in Changsha County, South Central China[J]. Journal of Resources and Ecology, 2021 , 12(2) : 203 -213 . DOI: 10.5814/j.issn.1674-764x.2021.02.007

1 Introduction

Land resources are carriers of social and economic activities, and they form the basis of human survival and development. With increasing urbanization, industrialization, and population levels, the scarcity of land resources has led to intensifying conflicts between people and land. Especially in China, a country that is experiencing rapid economic development, the rapid expansion of cities has led to many land use problems. Relevant research areas on this topic include land expansion and driving force research, land use and control performance research, construction land expansion and farmland protection research (Wu et al., 2017; Zhou et al., 2020), and others. As such research has matured, the solution to these problems has been found to lie in how the land resources are properly allocated in the contexts of economic development and social needs, how the benefits of land assets are maximized, and how the sustainable use of land resources is achieved.
The two main purposes of land use structure optimization are to reach certain ecological and economic goals, and also to design, combine, and lay out a land use structure in an area based on the characteristics of the land (Kaim et al., 2018). Optimization of the land use structure is in essence the establishment of proper proportions of various land use types, improvement of the sustainable utilization rate of land resources, and maintenance of a relative balance of the ecosystem in the land environment (Marulla et al., 2010). The traditional land use optimization structure is divided into two sections. The first is the land use spatial evolution simulation, which not only analyzes land use evolution through time series data but also sets certain constraints to simulate a future land space optimization layout, such as CLUEs, DLS, or AMB (Guo et al., 2010; Rasmussen et al., 2012; Wang et al., 2018a). The second is the land optimization model, which optimizes the land use structure using certain mathematical models, such as objective linear programming (Aerts et al., 2003), integer programming (Türk and Zwick, 2019), or the Cellular Automata model (Song et al., 2019). Because of differences in developmental backgrounds or constraints, land use structure optimization can also have different purposes, such as land use optimization research based on the ecological service value (Wu et al., 2018), carbon emissions (Zhang et al., 2018), or economic development (Emerton and Snyder, 2018). Previous studies have solved the problem of land use optimization scientifically using different methods that do not consider the ecological environment or social stability. However, in most developing countries with a rapid urbanization process, the ULUS that gives priority to economic growth will impair the environmental-ecological functions of land resources. In addition, a single-index or single-attribute model is used to determine the design, while ignoring the attributes of economic development and the ecological environment. At the same time, the policy orientation has a great impact on the scenario design or constraint design, and there is no research on uncertain factors, such as government investment behavior or land development capability (Zhou et al., 2015; Luo et al., 2019). Therefore, the ideal land use optimization structure model should be based on the actual development situation, find the reasons that hinder development according to different scenario design goals, use an integrated method to propose an optimization structure that takes into account different relevant aspects, and provide a reasonable scientific reference for decision makers.
In both suburban areas and highly intensive agricultural areas in China, the problem of agricultural land use is more prominent (Dong et al., 2014; Liang et al., 2019). First, the geographical location of a city is special. Due to the low output value per unit area, it is continuously used as a site for urban construction and as industrial land. As a result, agricultural land is continuously shrinking, which has an impact on basic farmland protection and food security (Qi and Dang, 2018). Second, because such areas are in close proximity to the city and suffer massive loss of labor, social phenomena such as land abandonment and land transfer (Yuan et al., 2018; Tan et al., 2020) have an impact on agricultural development and rural stability. Third, most suburban agriculture is very intensive agriculture characterized by high inputs, high yield, and high pollution. Unreasonable farmer behavior and investment lead to environmental problems (Wang et al., 2018b; Wang et al., 2019). In summary, suburban agricultural land use follows a complex path that involves economic, ecological, and social factors. Reasonable suburban agricultural land use is not only conducive to improving agricultural output value but it is also effective for controlling the scale of the city and implementing land use control. With the development of economic considerations, policy makers in some regions have given more importance to protecting ecological and environmental resources through the rational allocation of land resources (Zhang et al., 2014), such as designing and distributing green spaces (Koprowska et al., 2020). Therefore, the expansive effect caused by rapid economic growth, and the repressive effect resulting from ecological environment constraints are the main factors to target in land use structure optimization.
This study takes the urban suburbs and highly intensive agricultural district of Changsha County as an example, and examines the economic, ecological, and social benefits of agricultural land use, with the ecological environment as the main constraint, by using the information entropy-TOPSIS method, coupled coordination model, obstacle model, and the linear programming method. From the scientific research perspective, the optimal structure of suburban agricultural land use for coordinated economic, ecological, and social benefits is determined.
This study has two characteristics. First, it does not overemphasize economic development but seeks coordinated economic, ecological, and social development. Second, it is focused on the analysis of the root causes of the economic, ecological, and social barriers, as well as the constraints of ecological and economic development, and proposes a future optimization structure for agricultural land use. Therefore, the two main innovations of this research are: proposing a set of methods to realize optimal agricultural land structure, and obtaining the maximum agricultural comprehensive benefits based on ecological environmental constrains. The results of this study can provide a reference for decision-makers in developing agricultural land use plans and sustainable land use decisions in suburban areas.

2 Methods

2.1 Framework of agricultural land use structure optimization

Unlike previous studies, this paper combined several different kinds of methods to calculate the land use structure and agricultural benefits. In Fig. 1, the left column shows the research contents and procedures, and the right column presents the adopted models.
Fig. 1 Flowchart of agricultural land use structure optimization
Firstly, this study built an assessment index system with respect to three aspects: economic, social, and ecological benefits. Secondly, this study uses the min-max normalization method, entropy weight method and TOPSIS method to compare and analyze the law governing the evolution of agricultural benefit value, which falls in the range of [0,1]. Next, a coupled coordination model was adopted to analyze the degree of coordination and to check whether coordinated development occurred among the three benefits. Then, an obstacle model was used to diagnose and recognize which indexes showed the greatest impedance to the agricultural development. Finally, according to the above results, the objective function and economic, social, and ecological constraints were established; and the optimal benefits and optimal structure of land use were obtained based on the multi-objective linear programming method.
In this paper the multi-objective linear programming method is the core among the models used. Linear programming is an important branch of operations research. Its theory is mature and it is widely used in agriculture, business, transportation, military operations, economic planning, and management decision-making. It is one of the most important methods used in modern scientific management. The basic model is as follows:
Objective function: max Z=RX
Constraint function: AX≤(=or≥)b
X≥0
In the land use structure optimization model, Xn is a type of land use (such as rice field or tea garden), Rn is the benefit coefficient (the unit is yuan ha-1), amn is the constraint coefficient of the mth constraint function, A is the matrix of the constraint coefficient, and bn is the constraint constant.

2.2 Study area

As a case study area, Changsha County (Fig. 2) is located in the Hunan Province of South-Central China. Changsha County has a subtropical humid climate. The average frost- free period is 270 days. The annual average temperature is 18 ℃, and the annual precipitation is 1877 mm. The population is approximately 946 thousand people. Changsha’s gross agricultural output value is about 7 billion yuan, which accounts for 5.8% of the local GDP, making Changsha among the top 100 counties of China.
Fig. 2 Location of Changsha County
Changsha County is part of the primary grain-producing area of the Dongting Lake Basin. The county covers 175.6 thousand ha, including 51.9 thousand ha of cultivated land (30%) and 77.8 thousand ha of woodland (44%). The main crop is rice, with a total yield of about 5.4862×105 t. Changsha County is adjacent to the provincial capital and is a typical suburban, highly intensive agricultural area, with characteristics of high inputs, high yield, and high environmental risk. The county faces various conflicts between urban development and the protection of cultivated land, between economic development and the improvement of the environment, between the efficiency of agricultural land usage and the loss of farmers, and between incentive policies and food security. Therefore, Changsha County serves as a typical case study for agricultural land management.

2.3 Data sources

The data were obtained from the statistical yearbook of Changsha County’s economic and social development (1996-2016), which outlines the 12th and 13th five-year plans for Changsha County’s economic and social development. We also conducted a field survey and held face-to- face interviews with farmers in 2010, 2012, 2015, and 2017, to obtain basic information on agriculture and farming, such as farmer’s net income, farmer’s behavior, and the amount of fertilizer applied. This survey was very useful in providing a thorough understanding of the practical issues and evidence for policy-making.

3 Analysis of agricultural land use benefits based on the TOPSIS method

3.1 Assessment of the comprehensive benefit of agricultural land use

The utilization benefit of agricultural land can be defined as a combination of the direct and indirect benefits that are produced during the service function of the land (Karp et al., 2015). For example, the economic output of agriculture is easy to measure, and the indirect ecosystem service value can be estimated by an alternative market value method. However, the “loss” due to agricultural pollution and the monetization of social benefits are difficult to quantify. Hence, this paper adopted an evaluation index method. The evaluation index of the comprehensive benefit is given by the first-grade indices for three aspects: economic, social, and ecological benefits.
3.1.1 Economic benefit
The economic benefit mainly comprises monetary profits and material outputs under certain conditions of investment and market requirements. The economic benefit is derived from food crops, cash crops, and fiber material. The index of economic benefit contains two second-grade indices: agricultural production value, which reflects the direct economic profit of agriculture; and agricultural land use efficiency, which expresses agricultural input efficiency. In total, there are twelve specific indices for the economic benefit (Table 1).
3.1.2 Social benefit
The social benefit has a non-physical form, and it includes two main aspects in our study. One aspect is the individual farmer’s psychological characteristics. However, the perception of happiness and personal consciousness of farming are very difficult to express accurately and quantitatively. Therefore, this paper chooses farmers’ living level and labor force quality to reflect the overall psychological aspects. The other aspect is regional food security, which involves a national safety strategy. Changsha County is a major grain-exporting area, so guaranteeing food security is a key criterion in the assessment system. There are nine specific indices in the social benefit index system (Table 1).
3.1.3 Ecological benefit
The ecological benefit is expressed as the various benefits of the agricultural land for the ecological system, which are regulated and controlled by humans. These benefits would arise without direct human intervention, but they are influenced by human behavior (Chen et al., 2016). On the one hand, there is the ecological function of an agricultural ecosystem; for example, a commercial forest ecosystem provides purified air and is important for water conservation, among other functions. On the other hand, agricultural land use has certain effects on the environment; for example, agricultural non-point source pollution causes soil heavy metal pollution and river eutrophication. Therefore, the ecological impacts of agricultural land use can be both positive and negative. In this paper, we consider ecological benefit based on two aspects: the ecological condition and environmental quality. There are ten specific indices in the ecological benefit index system (Table 1).

3.2 Analysis of agricultural land use benefits

The results of the index weights are shown in Table 1. Figure 3 displays the total benefit and the three component benefit values for each year from 1996 to 2016. We should clarify that the benefit value (on the Y-axis) does not imply real economic value but the variation tendency, and the comprehensive benefit does not specifically mean the sum of the three individual benefits.
Table 1 The index system for the comprehensive benefit of agricultural land use
First-grade indices Second-grade indices Specific indices Unit Weight
Economic benefit Agricultural
production value		 Gross agricultural output value per hectare C1 yuan ha-1 0.252
Garden production value per hectare C2 yuan ha-1 0.165
Forestry production value per hectare C3 yuan ha-1 0.328
Husbandry value per hectare C4 yuan ha-1 0.255
Grain yield per hectare C5 kg ha-1 0.041
Agricultural land use efficiency Labor productivity C6 yuan person-1 0.228
Grain yield per labor force C7 kg person-1 0.094
Agricultural GDP per capita C8 yuan person-1 0.167
Increase in the gross agricultural output value C9 % 0.040
Technical efficiency C10 yuan (kWh) -1 0.548
Agricultural mechanization degree C11 kWh ha-1 0.452
Employees per hectare C12 person ha-1 0.315
Social benefit Rural living condition Rural per capita net income C13 yuan 0.404
Rural Engel coefficient C14 % 0.171
Rural medical level C15 % 0.425
Rural education level C16 % 0.396
Rural population density C17 person ha-1 0.224
Rural labor transfer index C18 % 0.242
Food security Food security index C19 % 0.257
Food production per capita C20 kg person-1 0.257
Farmland area per capita C21 ha person-1 0.176
Ecological benefit Ecological condition Forest cover rate C22 % 0.144
Cultivated land load C23 % 0.101
Stable yields area rate C24 % 0.145
Multiple cropping index C25 % 0.130
Cultivated land utilization rate C26 % 0.389
Irrigated area rate C27 % 0.092
Environmental quality Energy consumption per 10000 yuan C28 kWh (104 yuan) -1 0.167
Pesticide use rate C29 kg ha-1 0.175
Fertilizer use rate C30 kg ha-1 0.404
Agricultural plastic film use rate C31 kg ha-1 0.253
Figure 3 indicates that all the benefits show increasing trends year by year. The total benefit (yellow line) increases from 0.201 to 0.792 overall, but it changed twice, notably in 2003 and 2010. The increasing rate of economic benefit (blue line) is the most rapid of the four. In recent years, economic benefit has the largest value, followed by comprehensive benefit, ecological benefit (gray line), and social benefit (orange line).
Fig. 3 The values of agricultural land use benefits
The economic benefit rose steadily, and its average annual growth rate was 36.48%. It has the lowest value of the four until 2009, but it then grew rapidly over the next 2 years, becoming the highest benefit after 2011. The gross agricultural output value was high, at 11.88 billion yuan in 2016. Over the past two decades, the measures of suburban high-value agriculture extension, agricultural mechanization improvement, and implementation of agricultural incentive policies helped to ensure the continuous and steady development of Changsha County’s agriculture.
The social benefit also shows a growing tendency overall. However, the most severe flood disasters in history hit China from 1998 to 2003 and caused a substantial reduction in grain yield, resulting in the social benefit reaching its lowest value in 2003. In 2010, in conjunction with urbanization, many farmers left their farmland to work in the cities, and about 31.2% of cultivated land was transferred in just one year. These events caused the distinctive fluctuations in the social benefit.
The tendency of the ecological benefit was closely related to that of the social benefit. Because of natural disasters in the period from 1998 to 2003, the ecological benefit declined to its lowest value and then maintained steady growth thereafter. In our interview survey, we found that the fertilizer and pesticide input levels were usually stable, as the amounts of fertilizers and pesticides used are mainly determined by a farmer’s individual farming experience, which is unlikely to change. The average amount of pure nitrogen is about 285 kg ha-1, and pesticides are typically applied four times in one season. In addition, the Grain for Green Project and the ecological compensation policy stimulated the transfer of poor quality cultivated land to woodland, which is the major contribution to the ecological benefit.

4 Determining which factors are obstacles based on the coordination degree and obstacle factor methods

4.1 Analysis of the degree of coordination among the three benefits

The degree of coordination can be measured by either the interactions among the different factors or the level of coordinated development. A high degree of coordination means that the system can be healthy and stable.
Although agricultural land use has sustained fast growth overall, whether the economic, social, and ecological benefits are in a state of coordination should be explored. We adopted the coupled coordination model to test the degree of coordination among these three benefits. Figure 4 shows the results.
Fig. 4 The degree of coordination between the economic, social, and ecological benefits
From 1996 to 2003, the degree of coordination fluctuated only slightly (Fig. 4), which indicates that the development of the three benefits was incongruous or insufficiently coupled. As mentioned above, the occurrence of natural disasters during this period was the main reason for this lack of coordination. Between 2003 and 2010, the degree of coordination increased at a stable rate, and the interactive relationships among the benefits had gradually improved. After 2010, the speed of growth was even higher, which implies that as a result of the direct economic boost, the social and ecological benefits also increased.
Overall, the degree of coordination between the economic, social, and ecological benefits is getting better; however, compared with other studies (Yang et al., 2018; Fan et al., 2019; Yang and Hu, 2019), the coordination is still mildly disordered. Hence, there is still room for improvement in the future.

4.2 Diagnosis of the factors which act as obstacles

The main aim of this part of the study was to determine which factors influence the degree of coordination among agricultural land use benefits and how these factors exert their influences. First, we applied the obstacle model to test the degree of influence of the obstacles to the three benefits. The result showed that the pressure increased between the economic, social, and ecological aspects of agricultural land use.
Figure 5 illustrates that economic pressure (blue line) decreased continuously, and especially at a fast rate after 2010. In other words, the contribution of the economic benefit to the comprehensive benefit of agricultural land use had gradually weakened. In contrast, the degree to which social benefit acts as an obstacle increased over the study period. It exceeded the degree of economic benefit and became the biggest obstacle among the factors in 2010. Its value increased to 65% in 2016, meaning that the contribution of social benefit to the comprehensive benefit increased by 65%. The degree to which the ecological benefit acts as an obstacle was moderate, because natural resources and farming practices rarely change very dramatically in general. Therefore, the turning point was the year 2010, with the contribution of social benefit becoming increasingly important and the economic function waning year by year thereafter. Thus, if we want to realize healthy agricultural development, the social benefit index must be considered carefully.
Fig. 5 The degree of influence of the obstacles of the economic, social, and ecological benefit.
In order to determine the specific index that most strongly constraints the development of the agricultural benefit, we used an improved obstacle model at the index level. The top seven indices are displayed in Table 2.
Table 2 The top seven factors which act as obstacles to agricultural benefit
Year 1 2 3 4 5 6 7
2010 C16 C3 C13 C15 C19 C20 C30
2011 C16 C15 C30 C3 C19 C20 C13
2012 C16 C20 C19 C15 C30 C3 C13
2013 C20 C19 C16 C14 C30 C21 C3
2014 C20 C19 C17 C16 C14 C21 C30
2015 C20 C19 C14 C12 C21 C31 C30
2016 C20 C19 C14 C21 C12 C18 C31

Note: See Table 1 for the abbreviations for the factors (e.g., C3 is “forestry production value per hectare”).

The selected indices show rank changes only between 1996 and 2007. We defined 2008-2012 as a chaotic period, with new factors substituting for the previous top index factors; for example, rural education level replaced forestry production value per hectare. In these two periods, the index of forestry production value per hectare (factor C3 in Table 1) had the highest frequency, which lasted for 18 years. The rural per capita net income (C13) and rural medical level (C15) were also among the main factors acting as agricultural land use benefit obstacles, but they both disappeared in 2012. Cultivated land utilization rate (C26) ranked fourth from the top but vanished suddenly in 2007. In general, during the period of 1996 to 2012, the sole purpose of agricultural development was to make large profits and raise farmers’ income; hence, the economic benefit indices played a leading role. After 2012, some of the economic indices were replaced by social indices, a phenomenon that obeys the law of diminishing returns in land, meaning that as the input increases, the rate at which the agricultural output increases becomes lower.
Along with the development of society, the food security index (C19), food production per capita (C20), farmland area per capita (C21), and rural Engel coefficient (C14) entered the top four after 2010. Obviously, each of the food security indices under the social benefit started to play a dominant role in affecting agricultural land use benefit and coordination. Only fertilizer use rate (C30) of the ecological benefit appeared in the obstacle factor group from 2009 to 2015.
Based on the coordination degree and obstacle factor methods, the main obstacles that hinder the improvement of agricultural land-use efficiency were determined. The results show that population, grain yield, cultivated land area, woodland area, rural per capita net income, and fertilization amount are the main ones involved, which provide a reasonable basis for making constraints.

5 Designing the optimal land-use structure based on multi-objective linear program

5.1 Scenario design and constraint establishment

This study sets the scenario to 2025, which requires ensuring food security, increasing farmers’ income, and increasing the agricultural output value to meet the relevant requirements of the outline of the 13th five-year plan for national economic and social development of Changsha County. Under the premise of meeting the national standards for soil pollutants and surface water quality, the comprehensive economic benefits of the agricultural economy are the largest, and relevant constraints are established based on the government’s future planning and development requirements. The land-use type (x1), garden (x2), forest land (x3), and grassland (x4) were chosen as the decision variables.
5.1.1 Land suitability constraints
The land-use area is an important constraint condition related to agricultural value per unit, land area per capita, and other key metrics. Based on the soil type, topography, and socio-economic factors, the land suitability evaluation is completed which sets the main constraint conditions. In combination with the planning requirements, various types of land area constraints are proposed.
(1) Cultivated land area
Cultivated land is the foundation of agricultural development, and it is connected to people’s basic livelihood issues. It is important that it meets certain conditions. First, its value cannot be lower than the minimum quantity required in government planning, namely, 51117 ha. Second, due to the constraints of natural resources, its increase in terms of area has an upper limit.
51117 ≤ x1 ≤ 66500
(2) Garden, woodland, and grassland areas, and other
Gardens, which include tea gardens and orchards, are important sources for an agricultural economy. They yield many benefits and are important sources of income for farmers. Woodlands are important for improving the ecological environment function and could have a certain economic value. Generally, local governments require that forestlands expand in certain areas every year. Grasslands also have ecological functions. However, grassland resources in Changsha County are not very rich and are experiencing a decreasing trend. Therefore, garden, woodland, and grassland areas should not be smaller than their areas in the base year, and the values of the maximum area should not exceed the land suitability evaluation number.
Garden area: 2183 ≤ x2 ≤ 2672
Woodland area: 83654 ≤ x3 ≤ 96200
Grassland area: 528 ≤ x4 ≤ 7914
Other constraint condition: xi ≥ 0
5.1.2 Ecological environment constraints
Improving agricultural land use ecological functioning and environmental quality is the main goal of land use structure optimization and an important part of sustainable land development. According to an analysis of the factors acting as obstacles, after 2010, the values and areas indicate that forest real estate, plastic films, fertilizers and other ecological environment indicators have had an increasing impact on comprehensive agricultural benefits and coupling coordination. Therefore, the ecological environment constraints are mainly reflected by forestland-related indicators, agricultural inputs, biodiversity, and soil and water pollution. Quality control in the soil and water environments is based on the relevant national requirements specified in “Soil environmental quality risk control standard for soil contamination of agricultural land” (GB15618-2018) and “Environmental quality standards for surface water” (GB 3838-2002).
(1) Forest and grass coverage
Forest and grass coverage is a common indicator. Many urban development evaluations and honorary titles include this indicator, for example, the Livable Cities assessment index. According to Changsha County planning requirements, this indicator’s value is higher than 51%.
(x2 + x3)/175600 ≥ 0.51
(2) Green equivalent
Green equivalent is an important indicator used to determine whether the ecology of a region meets its development needs. Based on the land use situation and the results of a field investigation for Changsha County, the value of a forestland’s ecological function service under the premise that the same area is gauged for an entire year is 174.03, while a cultivated land’s value is 120.84, a garden’s value is 124.53, and a grassland’s value is 121.86. For this study, the green equivalent of forestland was set to 1, and the growth factor of a subtropical zone was set to 0.67. After optimization, the green equivalents for various types of land were 0.47 for cultivated land, 0.48 for garden land, 0.47 for grassland, and 1.00 for forestland. That is, when the green equivalent is >1, the ecological compliance of a region is determined.
(0.47x1 + 0.48x2 + x3 + 0.47x4)/89566 > 1
(3) Fertilizer application
Fertilizer is the main agricultural nonpoint source pollutant and has a great impact on the ecological environment. According to published research, the amount of chemical fertilizer used mainly depends on farmers’ behavior, which is greatly influenced by recommendations from the Agricultural Technology Promotion Station. According to planning requirements, the amount of fertilizer used in the future needs to be reduced by 90 kg ha-1. In this study, even higher environmental standards were adopted; that is, testing soil fertilization requirement data were used as a constraint basis.
1.04×108/x1 ≤ 410
(4) Soil and surface water quality
According to the statistical yearbook and related literature, there is only a slight amount of soil pollution in a small section of Changsha County, and the quality of the river water meets relevant national standards. In 2011, the water quality monitoring of Jinjing Town in Changsha County also revealed that, except for certain rivers during winter, the water quality met national standards in other periods (Li et al., 2015).
Soil pollutant concentration: Csoil ≤ Cstandard
Surface water pollutant concentration: Cwater ≤ Cstandard
Since there is no data support for the concentrations of soil pollutants and surface water pollutants, this study does not consider them as constraints.
5.1.3 Social factor constraints
(1) Grain yield
Food security is a major strategic issue in China that is related to social stability. According to the assessment results, all food security indicators are among the factors acting as obstacles and they are at the forefront after 2010. This study mainly considers the demand for cereal grain, but disregards the demand for meat, eggs, and milk; and adopts a high consumption value of 144 kg per person and a normal level of grain yield, with a multiple cropping index of 1.6.
1.6×6.6x1 ≥ 17280
(2) Rural population density
There are many indicators related to the rural population, especially in terms of food security. The resident population of Changsha County in 2025 is predicted to be 1.2 million, of which the rural population will be 0.3576 million. The data show a downward trend in the rural population. This rapid rural population decrease will have serious adverse effects on agricultural development and food security. Based on the GM(1,1) simulation, in 2025 the rural population density will be 2.43 persons ha-1. Therefore, we set it to be no lower than this value, so the constraints are as follows:
3576000/(x1 + x2 + x3 + x4) ≥ 2.43

5.2 Building the objective function equation

An indicator system was established from the three aspects of economy, society, and ecology, and land-use benefit evaluation and analysis were conducted to prove that a correlation exists between each indicator and the output value. Therefore, the objective functional equations were also developed from the economic, social, and ecological aspects.
5.2.1 Economic benefit function
Based on the various agricultural property values and land-use change data from 1996 to 2016, the unit area benefits of different land-use types were calculated, and the GM(1,1) model was used to predict the 2020 agricultural land benefit coefficients. The coefficients of cultivated land, garden land, forest land, and grassland are 77569 yuan ha-1, 77569 yuan ha-1, 34694 yuan ha-1, and 31611 yuan ha-1, respectively. Since social and ecological benefits are difficult to specify, the economic benefit evaluation uses the entropy weight method to calculate the relative benefit coefficients. This calculation yields values of 0.47, 0.22, 0.17, 0.14 for cultivated land, garden land, forest land, and grassland, respectively. The resulting objective function is as follows:
V(x) = 36767x1 + 16755x2 + 58633x3 + 4489x4
5.2.2 Social benefit function
This study quantifies the social benefits from the perspective of resource value. Therefore, the social values of various agricultural lands were quantitatively calculated from the aspects of food security, employment security, social stability, and social security in Changsha County’s cultivated land, garden land, forest land, and grassland. The expression is as follows:
S(x) = 20742x1 + 1270x2 + 1270x3 + 4058x4
5.2.3 Ecological benefit function
Eco-efficiency establishes correlation functions from the perspective of the value of ecosystem function services. Costanza et al. (1998) estimated the value of functional services such as gas regulation and water conservation in different ecosystems on a global scale in 1997. Xie et al. (2015) revised the unit value equivalent according to the specific characteristics of China’s ecosystem and economic development. With reference to relevant research results, the benefit coefficients for the various types of agricultural land ecological services were determined, namely:
E(x) = 16769x1 + 35285x2 + 39046x3 + 17569x4
5.2.4 The objective function equation
The aim of this study is to achieve the highest comprehensive benefits and optimal structure of land use under the premise of economic, social and ecological constraints. The objective function is as follows:
max Z(x) = V(x) + E(x) + S(x)

5.3 Result of optimal land-use structure

Linear programming can serve as an important mathematical model to solve the rational allocation of resources, providing optimal decision-making and a scientific basis for management and the economy. Based on the scenario design requirements, the optimal agricultural land comprehensive benefits were obtained under certain ecological environmental constraints. Based on the objective function and constraints, MATLAB was used to optimize the analysis of agricultural land-use structure and solve the optimal plan of land-use structure, as shown in Table 3.
Table 3 Optimal agricultural land-use structure in Changsha County
Land type and benefit Base year (2016) Target year (2025) Change rate (%)
Arable land (ha) 51866 53213 2.60
Garden land (ha) 2450 2533 3.39
Woodland (ha) 77809 89260 14.72
Grassland (ha) 528 528 0
Economic benefit (×106 yuan) 5559 7235 30.15
Social benefit (×106 yuan) 1007 1222 21.38
Ecological benefit (×106 yuan) 3417 4476 30.99
Overall benefit (×106 yuan) 9983 12934 29.56
Table 3 shows that the optimized land-use structure has the largest increase in forest land area, followed by garden land and cultivated land, and the grassland has not changed at all. The comprehensive benefit has increased from 9983×106 yuan in 2016 to 12934×106 yuan in 2025, an increase of 29.56%, and the comprehensive benefits have significantly improved. The highest growth rate was 30.99% for ecological benefit, and the rate for social benefit was the lowest, although it also increased by 21.38%. Under this land-use structure, the ecological environment and food security requirements will be met, and the economic-social-ecological three-pronged coupling and coordinated development, as well as the optimal comprehensive benefit output, will be achieved. The results of this study can provide a scientific basis for land-use planning or adjustment, and they can benefit future agricultural development proposals based on the analysis process and results.

5.4 Implications for agricultural land use policy

In order to obtain higher agricultural comprehensive benefits and maintain sustainable land use, according to the results, we propose four main suggestions for the future.
(1) The results of this research indicate that the food security index has become the principal factor for influencing agricultural benefit in the future. Therefore, the government should ensure food security and meet the regional grain supply through a stable agricultural output. Achieving this goal means that the grain yield per hectare cannot decrease and the area of agricultural land must increase. The strict control of land use, reasonable guidance of land transfer and control of the population are the most efficient methods for improving the comprehensive effects of agricultural land use.
(2) The value of the ecological benefit will increase by 30.99% in 2025 by controlling agricultural input applications and increasing forest area. We suggest that efforts be made to properly reduce the amount of fertilizer and pesticide, to moderate the pollution of the agricultural environment, and to increase the woodland area.
(3) Loss of the farmer population is a serious issue for China. This study also proves that the rural population can impact agricultural economic and social benefits. We propose three measures to maintain the rural labor-force resource by promoting the interests of farmers, i.e., raising farmers’ net income from agriculture, enhancing their education and medical levels, and upgrading their professional farming skills.
(4) At an official level, we propose that the government should formulate an appropriate land use policy that emphasizes the coordinated development of the three benefits, rather than sacrificing either ecological or social benefits for economic development.

6 Conclusions

This study takes Changsha County, a suburban agricultural area, as an example for categorizing the agricultural land use benefits into economic, ecological, and social benefits. Based on the assessment of comprehensive benefits from 1996 to 2006, the law governing the evolution of benefits and the coupling coordination degree are analyzed, and the factors that hinder the development and coordination of comprehensive benefits at different stages are determined. Based on the economic development, government planning, and ecological environment protection, the optimal land use structure and its efficiency in Changsha County were calculated using the MOLP method.
This research method combined index analysis and MOLP to analyze the optimal structure of land use, although other land-use structure models can also be obtained by modifying and providing different constraints. The focus of this study is not on specific government policies, but on the analysis of the impacts of the economic, ecological, and social benefits of agricultural land use, taking into consideration the planning and ecological environment protection, to obtain the optimal land use benefits. It offers certain advancements in research methods and research ideas. This study utilizes mathematical models to optimize the land use structure, and while it does not involve a spatial structure, the research results could provide a strong basis for land use spatial structure optimization. Subsequent research can use the powerful spatial analysis capabilities of GIS, combined with land suitability maps, population prediction maps, and land use maps, among others, and employ the CA-Markov chain or grey forecast model to propose spatial pattern optimization.

References

Publishing order | Descend order by publishing year | Descend order by cited within
1
Aerts J C J H, Eisinger E, Heuvelink G B M , et al. 2003. Using linear integer programming for multi-site land-use allocation. Geographical Analysis, 35(2): 148‒169.

2
Chen L, Sui X, Wang D S , et al. 2016. The ecological benefit-loss evaluation in a riverine wetland for hydropower projects—A case study of Xiaolangdi Reservoir in the Yellow River, China. Ecological Engineering, 96(1): 34‒44.

3
Costanza R, d’Arge R, de Groot R , et al. 1998. The value of the world’s ecosystem services and natural capital. Ecological Economics, 25(1):3-15.

4
Dong Y, Liu Y, Chen J N . 2014. Will urban expansion lead to an increase in future water pollution loads?—A preliminary investigation of the Haihe River Basin in northeastern China. Environmental Science and Pollution Research, 21(11): 7024‒7034.

5
Emerton L, Snyder K A . 2018. Rethinking sustainable land management planning: Understanding the social and economic drivers of farmer decision-making in Africa. Land Use Policy, 79(1): 684‒694.

6
Fan Y P, Fang C L, Zhang Q . 2019. Coupling coordinated development between social economy and ecological environment in Chinese provincial capital cities-assessment and policy implications. Journal of Cleaner Production, 229(1): 289‒298.

7
Kaim A, Cord A F, Volk M . 2018. A review of multi-criteria optimization techniques for agricultural land use allocation. Environmental Modelling & Software, 105(1): 79‒93.

8
Karp D S, Tallis H, Sachse R , et al. 2015. National indicators for observing ecosystem service change. Global Environmental Change, 35(6):12-21.

9
Koprowska K, Łaszkiewicz E, Kronenberg J . 2020. Is urban sprawl linked to green space availability? Ecological Indicators, 108(1):105723. DOI: 10.1016/j.ecolind.2019.105723.

10
Li H Q, Liu L M, Ji X . 2015. Modeling the relationship between landscape characteristics and water quality in a typical highly intensive agricultural small watershed, Dongting Lake basin, south central China. Environmental Monitoring and Assessment, 187(3):129. DOI: 10.1007/s10661-015-4349-1.

11
Liang L W, Wang Z B, Li J X . 2019. The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. Journal of Cleaner Production, 237(32):117649. DOI: 10.1016/j.jclepro.2019.117649.

12
Luo G P, Yin C Y, Chen X , et al. 2010. Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: A case study of Sangong Watershed in Xinjiang, China. Ecological Complexity, 7(2): 198‒207.

13
Luo X, Lu X H, Jin G , et al. 2019. Optimization of urban land-use structure in China’s rapidly developing regions with eco-environmental constraints. Physics and Chemistry of the Earth, Parts A/B/C, 110(1): 8‒13.

14
Marull J, Pino J, Tello E , et al. 2010. Social metabolism, landscape change and land-use planning in the Barcelona Metropolitan Region. Land Use Policy, 27(2): 497‒510.

15
Qi X X, Dang H P . 2018. Addressing the dual challenges of food security and environmental sustainability during rural livelihood transitions in China. Land Use Policy, 77(1): 199‒208.

16
Rasmussen L V, Rasmussen K, Reenberg A , et al. 2012. A system dynamics approach to land use changes in agro-pastoral systems on the desert margins of Sahel. Agricultural Systems, 107(1): 56‒64.

17
Song X D, Yang J Y, Zhao M S , et al. 2019. Heuristic cellular automaton model for simulating soil organic carbon under land use and climate change: A case study in eastern China. Agriculture, Ecosystems & Environment, 269(1): 156‒166.

18
Tan R, Wang R Y, Heerink N . 2020. Liberalizing rural-to-urban construction land transfers in China: Distribution effects. China Economic Review, 60(1):101147. DOI: 10.1016/j.chieco.2018.01.001.

19
Türk E, Zwick P D . 2019. Optimization of land use decisions using binary integer programming: The case of Hillsborough County, Florida, USA. Journal of Environmental Management, 235(1): 240‒249.

20
Wang Q R, Liu R M, Men C . 2018 a. Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT. Journal of Hydrology, 560(1): 86‒96.

21
Wang Y D, Liang J P, Yang J , et al. 2019. Analysis of the environmental behavior of farmers for non-point source pollution control and management: An integration of the theory of planned behavior and the protection motivation theory. Journal of Environmental Management, 237(1): 15‒23.

22
Wang Y D, Yang J, Liang J P , et al. 2018b. Analysis of the environmental behavior of farmers for non-point source pollution control and management in a water source protection area in China. Science of the Total Environment, 633(1): 1126‒1135.

23
Wu X T, Wang S, Fu B J , et al. 2018. Land use optimization based on ecosystem service assessment: A case study in the Yanhe Watershed. Land Use Policy, 72(1): 303‒312.

24
Wu Y Z, Shan L P, Guo Z , et al. 2017. Cultivated land protection policies in China facing 2030: Dynamic balance system versus basic farmland zoning. Habitat International, 69(1): 126‒138.

25
Xie G, Zhang C, Zhang C , et al. 2015. The value of ecosystem services in China. Resources Science, 37(9): 1740‒1746. (in Chinese).

26
Yang W C, Xu K, Lian J J , et al. 2018. Multiple flood vulnerability assessment approach based on fuzzy comprehensive evaluation method and coordinated development degree model. Journal of Environmental Management, 213(1): 440‒450.

27
Yang Y, Hu N . 2019. The spatial and temporal evolution of coordinated ecological and socioeconomic development in the provinces along the Silk Road Economic Belt in China. Sustainable Cities and Society, 47(1):101466. DOI: 10.1016/j.scs.2019.101466.

28
Yuan X F, Du W P, Wei X D , et al. 2018. Quantitative analysis of research on China’s transfer system. Land Use Policy, 74(1): 301‒308.

29
Zhang J J, Fu M C, Zhang Z Y , et al. 2014. A trade-off approach of optimal land allocation between socio-economic development and ecological stability. Ecological Modelling, 272(1): 175‒187.

30
Zhang R S, Matsushima K, Kobayashi K . 2018. Can land use planning help mitigate transport-related carbon emissions? A case of Changzhou. Land Use Policy, 74(1): 32‒40.

31
Zhou M, Cai Y L, Guan X L , et al. 2015. A hybrid inexact optimization model for land-use allocation of China. Chinese Geographical Science, 25(1): 62‒73.

32
Zhou Y, Li X H, Liu Y S . 2020. Land use change and driving factors in rural China during the period 1995-2015. Land Use Policy, 99(1):105048. DOI: 10.1016/j.landusepol.2020.105048.

Options
Outlines

/

〈 〉