Research on Coupling and Coordination of Agro-Ecological and Agricultural Economic Systems in the Ebinur Lake Basin

Abstract

The Ebinur Lake Basin is an important ecological area in China. The sustainable development of the basin is imperative, particularly the coupling and coordination between the agro-ecological environment and economy. Six counties in the Ebinur Lake Basin were studied and CRITIC—the entropy weight method used in the construction of regional agro-ecosystem and economic evaluation index systems for 2001–2021. The entropy weight method and coupling coordination were used to evaluate and analyze the systems. The results indicate that: (1) The integrated index grew slowly, increasing from 0.15 in 2000 to 0.18 in 2020. The agro-economic integrated index grew rapidly, increasing from 0.08 in 2000 to 0.25 in 2020. (2) High quality coupling was achieved from 2000 to 2010, with 50% superior coupling in 2010, which then decreased to reach 17% in 2020. (3) The agro-ecological–economic system coupling was high at 0.8; however, the coordination degree was low, at 0.36. (4) Most counties suffered from economic lag before 2010, with an average $U_e$/$U_s$ of 0.93 in 2010. Ecological lag has dominated since 2010, and the average $U_e$/$U_s$ value reached 1.48 in 2015. Coupling and coordinating the agro-ecological and economic systems is important for the sustainable development of local agriculture.

1. Introduction

The impact of the epidemic and complex changes in the international environment have led to a recent proposal for a food security strategy in China, with cultivated land as the foundation for national food security and social stability [1]. Food security is inseparable from the protection and utilization of cultivated land and the sustainable development of agriculture [2]. In recent years, the rapid development of the economy, accelerated industrialization, rapidly increasing population, and urbanization in China have resulted in an increasing demand for land resources by both the government and population. These circumstances have led to many problems in terms of land use. A total of 76,173 ha was used as illegal arable land at the end of 2019, with more than one tenth comprising basic farmland [2]. Urbanization has overrun large areas of arable land [3,4], and significant changes have occurred in terms of basic farmland use [5], with a decrease in arable land use and the amount of arable land available per capita. The total arable land was 134,881,200 million ha at the end of 2017, which was a decrease of 0.04 million ha as compared to 2016 [6]. Together with the insufficient amount of arable land reserves available, these problems directly endanger the food security and seriously affect the sustainable development of agriculture in China. With these issues in mind, and under the background of the food security strategy, the policy of increasing cultivated land to 120 million ha has been proposed, with the aim of protecting cultivated land and enhancing food security [6,7]. Many problems and challenges, such as water shortages, sparse surface vegetation, a relatively poor ecological environment, and severe desertification, are inhibiting agricultural development, especially in the arid region of northwest China [8]. The oasis agricultural ecosystem is relatively fragile. Once severely damaged, oases can take long periods to restore, and may never be fully restored [9]. Most of the arid areas in northwestern China are mainly used for agricultural production and business. Factors such as a lack of water resources, poor and unstable cultivated land quality, the use of land for non-agricultural purposes, land pollution, and other risks and challenges also affect this area [10]. Residents therefore continue to cultivate farmland in these areas for economic benefit. Local governments transfer cultivated land through policies such as the “Cultivated land requisition-compensation balance,” and the “Increase and decrease connection of urban and rural construction land use” [11,12]. An inter-provincial transfer of cultivated land between the east and west, which was performed to alleviate poverty in the western counties, has intensified the reclamation of cultivated land [13]. This action is likely to damage the original natural ecosystem and cause problems such as reduced surface vegetation, soil desertification, and salinization [14,15,16]. In addition, the excessive use of pesticides, chemical fertilizers, and plastic film has exacerbated soil pollution and agricultural non-point source pollution [17,18,19]. Arid regions are prone to salinization due to natural factors and climatic characteristics [20]. In order to achieve sustainable agricultural development, several scholars have analyzed the impact of agricultural production from the perspectives of global climate and desertification [21,22,23,24], and proposed that it is necessary to adopt water saving measures such as drip irrigation and biological water saving in the development of precision agriculture [25,26]. Research on analysis of the degree to which coupling occurs between two or three systems from the perspective of system coordination has become popular [27,28,29,30,31], with many researchers analyzing and researching agricultural ecosystems and economic systems [32,33,34,35,36,37]. However, few studies have reported on the coupling coordination in agricultural oasis eco–economic systems in arid areas, rendering it imperative that the current situation in terms of the oasis agricultural eco-environment and economic development is analyzed to solve the contradiction between the two, allowing sustainable development of the agricultural eco-environment while increasing the economic benefits. Although the coupling coordination model has been widely used in many fields, misunderstandings commonly limit the results obtained by the traditional coupling coordination model. Wang Shujia therefore revised and verified the traditional model to generate a revised model that is equally valid [38]. Oases are mostly distributed in the river basins and basin margins and cover approximately 5% of the total area of Xinjiang, which is relatively typical for an oasis arid zone. The cultivated land area of Xinjiang accounts for 2.48% of the total land area of Xinjiang [39]. The Ebinur Lake basin has a fragile ecological environment and low natural resource-carrying capacity due to the arid climatic conditions in the area, with 10.5% of the total land in the basin currently cultivated [40]. Based on Shujia’s revised coupling model, the Ebinur Lake Basin was selected as the study area, and the dynamic coupling relationship between agro-ecological environment and economic interests from 2000 to 2020 were analyzed along with the trend of their coordinated development. The study is expected to promote the sustainable development of agriculture in the Ebinur Lake Basin and alleviate the ecological and economic contradictions associated with agriculture in arid areas in general.

2. Materials and Methods

2.1. Overview of the Study Area

The Ebinur Lake Basin (43°38′ N–46°39′ N and 79°89′ E–85°38′ E) is located in the northwest of the Xinjiang, west of Boertala Valley, south of Jinghe alluvial fan, and east of the Gobi Desert in the lower reaches of the Kuitun River. As shown in Figure 1, the study area covers a total of 60,452 km², and includes the entire Toli County, while excluding the Dushanzi District, Alashankou City, and the Corps. The north, west, and south of the basin are blocked by high mountains, and the climate is extremely dry. The area is affected by westerly flow, the Mongolian high-pressure system, and cold air from Siberia, and has a typical temperate continental climate that is characterized by aridity, little rainfall, high evaporation, and drastic climate changes. The average annual precipitation is 100–200 mm and the annual potential evaporation is 1500–2000 mm. The average temperature in July is 27 °C, the average temperature in January is −17 °C, the daily average temperature is 6–8 °C, and the annual sunshine hours are ~2800 h. The region is typically subjected to strong winds that exceed 8 m/s on an average of 164 d per y [41,42]. The basin includes three systems: a mountain ecological subsystem, alluvial plain ecological subsystem, and social ecosystem subsystem. The plains cover 25,762 km², accounting for 50.90% of the total basin, while the arable land accounts for 10.5% of the basin [40]. Industry in the Ebinur Lake Basin is dominated by agriculture, with cotton, corn, sugar beet, rape, and wheat being common in the area. The basin serves as a major export base for high-quality cotton from Xinjiang. The total agricultural output value reached 131.2911 × 10⁸ CNY and the total sown area was 375,130 ha by the end of 2020, with 4143.141 × 10⁵ ha of commonly used arable land, total grain production of 10.27 × 10⁵ tons, oil production of 3312 t, and a livestock volume of 226.566 × 10⁴ head. The reservoirs and lakes in the Ebinur Lake Basin are mostly recharged by alpine snow, ice melt, and groundwater recharge, allowing for irrigation [43]. However, drought has restricted agricultural development, and cultivated land reclamation has undoubtedly exacerbated the shortage of water resources. Simultaneously, animal husbandry and sheep farming have also undergone rapid development, resulting in destruction of the surface vegetation, severe desertification, and deterioration of the agricultural ecological environment. Under these circumstances, the degree of coupling and coordination of agro-ecological economic development in the Ebinur Lake Basin should be urgently studied to maintain the development of agriculture in a sustainable manner can be obtained.

2.2. Data and Data Normalization

The data required for this study were obtained from the Xinjiang Statistical Yearbook (2001–2021), Bortala Statistical Yearbook (2011–2019), Ili Statistical Yearbook (2009–2018), and national economic and the statistical bulletin for the national economic and social development of counties (cities). The annual rainfall and annual average temperature data were obtained from the Resource and Environmental Science and Data Center at the Chinese Academy of Sciences (https://www.resdc.cn (accessed on 15 April 2022)), and the forest cover data were obtained from the land cover classification product by Yang Jie et al. [44] (https://zenodo.org/record/4417810#.YrL2j0ZByUk (accessed on 20 April 2022)). The vegetation coverage data were calculated using the MODISNDVI product (https://doi.org/10.5067/MODIS/MOD13Q1.061 (accessed on 25 April 2022)). The per capita net income of rural residents was replaced by the per capita net income of rural residents in Xinjiang in years for which data were unavailable.

2.3. Methods

This research mainly comprised three steps: (1) constructing the evaluation index system; (2) using the CRITIC–entropy combination weight method to assign weights to the indicators; and (3) using the coupling coordination model to evaluate the coupling coordination degree of the agro-ecological economic system. Figure 2 consists of a flow chart describing the study:

2.3.1. Construction of the Evaluation Index System

The frequency statistics method was used in combination with the actual local situation to construct two index systems: the agricultural ecosystem and the agricultural economic system. The agricultural ecosystem includes two first-level indicators and 10-s-level indicators describing the natural ecological conditions and agricultural resources, while the agricultural economic system includes two first-level indicators and 10-s-level indicators for industrial development and economic benefit, as shown in

Table 1. Indicators for the agricultural ecosystem and agricultural economic system.

Indicator System	First-Class Indicators	Codes	Secondary Indicators	Indicator Types
Agro-ecosystem	Natural ecological conditions	X1	Annual precipitation (mm)	+
		X2	Average annual temperature (°C)	+
		X3	Forest cover rate (%)	+
		X4	Vegetation coverage rate (%)	+
		X5	Crop effective irrigation rate (%)	+
	Agricultural resources	X6	Per capita arable land area (m2/person)	+
		X7	Area of grassland per capita (m2/person)	+
		X8	Multiple cropping index (%)	+
		X9	Consumption of chemical fertilizers (kg/ha2)	−
		X10	Proportion of agricultural population (%)	−
Agricultural economic system	Industrial development	X11	Per capita grain output of rural residents (kg/person)	+
		X12	Vegetable production per capita (kg/person)	+
		X13	Number of livestock per capita (only/person)	+
		X14	Meat production per capita (kg/person)	+
	Economic benefits	X15	Per capita agricultural output value (CNY/person)	+
		X16	Per capita net income of rural residents (CNY/person)	+
		X17	Gross output farming, forestry, animal husbandry, and fishery value per capita (CNY/person)	+
		X18	Total power used for agricultural machinery per capita (kw/person)	+
		X19	Electricity consumption per capita (kw/person)	+
		X20	Proportion of primary industry (%)	−

“+” represents positive correlation; ”−” represents negative correlation.

.

2.3.2. CRITIC—The Entropy Weight Combination Method

Referring to the treatment method of Fu Weizhong and Chu Liuping [45], we adopted the CRITIC–entropy weight combination method to determine the index weight, rendering the weighting process more objective, fair, and scientific, and avoiding the bias caused by subjective weights. The CRITIC method is an objective weighting method that was proposed by Diakoulaki et al. in 1995, in which the objective weight of indicators is measured by evaluating the contrast strength and conflict of the included indicators. The objectivity of the entropy weight method allows determination of the index weight according to the degree of dispersion between the indexes. The indication-entropy weight model can not only fully consider the comparison strength and conflict between indicators, but also the dispersion degree between indicators, meaning that the indicator information can be fully expressed, reflecting the weight of the indicators with improved accuracy [46]. The weights of the agro-ecological economic system indicators are shown in

Table 2. Indicator weights in the agro-ecological economic system.

Indicator System	First-Class Indicators	Codes	CRITIC Weights	Entropy Weight Method Weight	CRITIC–Entropy Weight Method Weight
Agro-ecosystem	Natural ecological conditions	X1	0.0431	0.0364	0.0397
		X2	0.0645	0.0235	0.0440
		X3	0.0552	0.0481	0.0517
		X4	0.0513	0.0190	0.0351
		X5	0.0544	0.0095	0.0319
	Agricultural resources	X6	0.0353	0.0277	0.0315
		X7	0.0547	0.0637	0.0592
		X8	0.0379	0.0565	0.0472
		X9	0.0503	0.0090	0.0296
		X10	0.0539	0.0265	0.0402
Agricultural economic system	Industrial development	X11	0.0407	0.0767	0.0587
		X12	0.0463	0.1798	0.1131
		X13	0.0626	0.0407	0.0516
		X14	0.0506	0.0369	0.0438
	Economic benefits	X15	0.0514	0.0707	0.0610
		X16	0.0521	0.0492	0.0506
		X17	0.0453	0.0559	0.0506
		X18	0.0395	0.0429	0.0412
		X19	0.0432	0.1012	0.0722
		X20	0.0679	0.0262	0.0470

and the coupling coordination and development type are given in

Table 3. Coupling coordination and development type.

Coupling Coordinated Degree Interval	Development Types for Coupling Degree C	Development Types for Coordination Degree D	${\mathit{U}}_{\mathit{s}}$$\mathbf{and} {\mathit{U}}_{\mathit{e}} \mathbf{Comparison} \mathbf{Relationship} \mathbf{and} \mathbf{Development} \mathbf{Types}$
[0, 0.1)	Extreme imbalance	Extreme imbalance	(1) $U_s$ > $U_e$ indicates agricultural economic lag: $U_e/U_s$ > 0.8 indicates relative lag; 0.6 < $U_e/U_s$ ≤ 0.8 indicates serious lag, and; 0 < $U_e/U_s$ ≤ 0.6 indicates extreme lag in agricultural economy. (2) $U_s$ < $U_e$ is agro-ecological lag: $U_s/U_e$ < 0.8 indicates relative lag; 0.6 < $U_s/U_e$ ≤ 0.8 indicates serious lag, and; 0 < $U_s/U_e$ ≤ 0.6 extreme lag in the agricultural ecology (3) $U_s$$=U_e$ indicates economic and ecological synchronization.
[0.1, 0.2)	Serious imbalance	Serious imbalance
[0.2, 0.3)	Moderate imbalance	Moderate imbalance
[0.3, 0.4)	Slight imbalance	Slight imbalance
[0.4, 0.5)	Near imbalance	Near imbalance
[0.5, 0.6)	Barely coupled	Barely coordinated
[0.6, 0.7)	Primary coupling	Primary coordination
[0.7, 0.8)	Intermediate coupling	Intermediate coordination
[0.8, 0.9)	Good coupling	Good coordination
[0.9, 1]	Superior coupling	Superior coordination

$U_s$: Comprehensive agro-ecosystem index; $U_e$: Comprehensive agro-economical index.

.

With m evaluation objects and n evaluation indicators, the original data is $X_{ij}$, i = 1, …, m; j = 1, …, n, with dimensionless processing first performed.

To eliminate the dimensional differences of the indicators; it is necessary to standardize the indicator data:

$$\mathrm{Positive} \mathrm{indicators}: x_{ij}=(X_{ij}-X_{min})/(X_{max}-X_{min})$$

(1)

$$\mathrm{Negative} \mathrm{indicators}: x_{ij}=(X_{max}-X_{ij})/(X_{max}-X_{min})$$

(2)

where $x_{ij}$ is the processed data and $X_{max}$ and $X_{min}$ are the maximum and minimum values of the j-th index, respectively.

To render the standardized data more meaningful, 0.00001 was added to the processed index data:

$$Q_{ij}=x_{ij}+0.00001$$

(3)

The weight was calculated according to the CRITIC method and the information amount of the j-th indicator was calculated using:

$$c_j={\sigma }_j{\displaystyle \sum }_{i=1}^m\left(1-r_{ij}\right)$$

(4)

where σ is the standard deviation of the j-th indicator, and $r_{ij}$ is the correlation coefficient between the i-th and the j-th indicators.

The weight of the j-th indicator was found using:

$$w_1=\frac{c_j}{{{\displaystyle \sum }}_{j=1}^n{c}_j}$$

(5)

The weight was then calculated according to the entropy weight method, and the probability of the occurrence of the j-th index of the i-th evaluation object as calculated using:

$$P_{ij}=\frac{Q_{ij}}{{{\displaystyle \sum }}_{i=1}^m{Q}_{ij}}$$

(6)

The information entropy of the j-th index was calculated using:

$$e_j=-\frac{1}{\ln m}{\displaystyle \sum }_{i=1}^m{P}_{ij}\ln P_{ij}$$

(7)

The information entropy redundancy of the j-th index is:

$$D_j=1-e_j$$

(8)

The weight of the j-th indicator is:

$$w_2=\frac{D_j}{{{\displaystyle \sum }}_{j=1}^n{D}_j}$$

(9)

The combined weight of the j-th indicator was then obtained using:

$$w_j=\beta w_1+\left(1-\beta \right)w_2$$

(10)

According to previous research, the two weighting methods are considered be equally important; we therefore assumed that β = 0.5 [42].

2.3.3. Coupling Coordination Model

The comprehensive development index of the agro-ecological system and the agricultural economic system is calculated using the weights of each index within the two subsystems, which respectively indicate the degree of influence that each subsystem has on the agro-ecological economic system. $U_s$ represents the comprehensive development index value of the agricultural ecological subsystem to the agricultural ecological economic system, and $U_e$ represents the comprehensive development index value of the agricultural economic subsystem to the agricultural ecological economic system. Tables S1 and S2 in the Supplementary Material show the original data of agroeco-economic system indicators in Ebinur Lake Basin.

The comprehensive development index of the two subsystems is calculated by the weighted method, using the formula:

$$U_i={\displaystyle \sum }_{j=1}^m{w}_j{x}_{ij}$$

(11)

where $U_i$ is the comprehensive development index value of the agricultural ecosystem or agricultural economic system in the i-th year. The larger the value, the higher the development level of the associated system in that year.

In this study, according to the second scheme in which the coupling coordination degree model proposed by Wang Shujia and Kong Wei [38] is revised, the coupling degree C is distributed between [0, 1] as much as possible, and each stage corresponds to a different development type of coupling degree C and coordination degree D. According to the classification criteria for the relative relationship between two systems by Gao Jing [47] et al., the comparative relationship and basic types of the comprehensive development of agricultural ecosystems and agricultural economic systems are divided into seven basic types.

$$C=\sqrt{\left[1-\frac{{{\displaystyle \sum }}_{i>j,j=1}^n\sqrt{{\left(U_i-U_j\right)}^2}}{{{\displaystyle \sum }}_{m=1}^{n-1}m}\right]\times {\left(\left|{{\displaystyle \prod }}_{i=1}^n\frac{U_i}{max{U}_i}\right|\right)}^{\frac{1}{n-1}}}$$

(12)

$$T={\displaystyle \sum }_{i=1}^n{\alpha }_i\times U_i,{\displaystyle \sum }_{i=1}^n{\alpha }_i=1$$

(13)

$$D=\sqrt{C\times T}$$

(14)

In the formula, $U_i$ ∈ [0, 1], C ∈ [0, 1]; when each subsystem is more discrete, the value of C is lower; otherwise, the value of C is higher.

3. Results

The development in each county (city) over the past 20 y from the perspective of the comprehensive index value, growth rate, coupling degree, coordination degree, and development types for the agricultural ecosystem and agricultural economic system were analyzed in this study.

3.1. Agro-Ecosystem Development

As seen in Figure 3, the development of the agro-ecosystem in the six counties (cities) of the Ebinur Lake Basin shows an overall fluctuating upward trend, indicating that the protection and governance of the agro-ecological aspects in the study area are increasing. The slight downward trend in Bole from 2010 to 2015 was due to a decrease in the annual rainfall and an increase in the application of agricultural chemical fertilizers. The growth rate in this county was −0.00073 and the growth percentage was −0.37% in 2015.

The slight downward trend that is observed in Wusu from 2010 to 2015 was due to a reduction in the per capita area of grassland and an increase in the application of agricultural chemical fertilizers per unit area. The growth rate of −0.00043 and the growth percentage of −0.27% in 2015 were in contrast with the upward trend observed during other periods. The downward trend that occurred between 2005 and 2015 in the Jinghe County was due to a decrease in the grassland area per capita and an increase in the amount of fertilizer applied per unit area. The growth rate of −0.0016 and growth percentage of −1.02% in 2010 deteriorated by 2015, when the growth rate decreased to −0.0052 and the growth percentage was −3.35%. Compared with 2010, the annual rainfall in Wenquan Xian decreased by 87.38 mm in 2015, the consumption of chemical fertilizers increased by 79.26 kg/ha², and the proportion of agricultural population increased by 24%. These three significant changes led to a significant decline in Wenquan Xian between 2010 and 2015, with a growth rate of −0.0114 in 2015 and a growth percentage of −5.97%. The downward trend that is observed in Kuitun from 2000 to 2005 was due to a decrease in the sown area and an increase in the application of chemical fertilizer. The decrease in forest coverage and grassland led to a downward trend in this area from 2010 to 2015; with a decrease in the growth rate of −0.01114 and the growth percentage of −7.24% in 2005 to −0.009 and 5.85% in 2015, respectively. The downward trend in Toli County that occurred between 2010 and 2015 was due to a decrease in annual rainfall and vegetation coverage, which led to a growth rate of −0.00645 and a growth percentage of −3.74% in 2015. As decrease in annual rainfall, vegetation coverage and effective irrigation area in the county, the decreasing trend continued from 2015 to 2020, when the growth rate reached −0.029 and the growth percentage reached −1.72%.

3.2. Spatiotemporal Change Analysis of the Agricultural Economic System

Figure 4 indicates an overall upward trend in the development of the agricultural economic system in the study area, with rapid development observed in all six counties in terms of the agricultural economy. The rapid growth rate was particularly significant during the period 2005–2015. A slow growth trend occurred in Kuitun from 2000 to 2020. Compared with 2015, the per capita grain output of rural residents in Wusu decreased by 1801.7 kg (87%), while the per capita vegetable output decreased by 1005.1 kg (32%), the per capita livestock production decreased by 0.9 (24%), and the per capita meat output decreased by 125.1 kg (71%). The drastic reduction in agricultural output, combined with the decrease in the agricultural output value, meant that Wusu showed a drastic downward trend from 2015 to 2020, with a growth rate of −0.071 and a growth percentage of −17.30%. A downward trend in Toli County from 2015 to 2020 is indicated by a growth rate of −0.0147 and a growth percentage of −5.62% in 2020, whereas a growth trend was observed in the other counties (cities) during this period. The fastest growth and the largest increment were observed in the Jinghe County from 2000 to 2020. The $U_e$ value in 2020 was 550.83% that in 2000, with an increment of 0.247, which was the slowest growth overall. The $U_e$ value in 2020 was 224.35% of that in 2000, with the slowest increment of 0.082 in Kuitun City.

3.3. Coupling Coordination Analysis

The coupling degree, coordination degree, and development type of the agro-ecological economic system in the six counties (cities) in the Ebinur Lake Basin from 2000 to 2020 are shown in Figure 5 and Figure 6.

3.3.1. Coupling Degree Analysis

Figure 5a and Figure 6a indicate that overall, the coupling development status of the agro-ecological economic system in the six counties (cities) exhibited a trend of improvement followed by deterioration. During the period from 2000 to 2010, the degree of coupling and coordination in the study improved continuously and gradually developed towards good or superior coupling. Superior coupling accounted for 50% of the total in 2010, indicating that the agricultural ecosystem and agricultural economic system in these counties (cities) gradually developed towards balance over the previous decade. The main reasons for this change were the development of agricultural economy, the gradual achievement of an agricultural ecological environment capacity, and the change to an agricultural ecological environment (in 2000, Bole and Jinghe County were in a primary coupling state, Wusu and Toli County were in a good coupling state, Wenquan Xian was in an intermediate coupling state, and Kuitun was barely coupled). The coupling degree of Bole increased from 0.85 to 0.97, while that of Kuitun increased from 0.69 to 0.89 during the period 2010–2020, indicating that the coupling development state of the two cities was constantly improving. However, the coupling status of the other counties (cities) continued to decrease to varying degrees, and the proportion of superior coupling reached only 17% in 2020, decreasing by 33% as compared to 2010. These results indicate that the agricultural ecosystems and agricultural economic systems of the Jinghe County, Wenquan Xian, Wusu, and Toli County increased at different rates and developed in an unbalanced direction. The main reason for this was the continuous strengthening of the agricultural economy, and the expansion of agricultural income means that the development of the agricultural economic system exceeded the maximum amount that the associated ecological environment could accommodate; it also exceeded the carrying capacity of the ecological environment.

3.3.2. Coordination Degree Analysis

Figure 5b and Figure 6b indicate that the coordinated development of the agro-ecological economic system in the six counties (cities) shows a trend of gradually developing in a coordinated direction; however, the coordinated development process was slow, and the overall coordination degree was low. Most of the counties (cities) reached a state of near imbalance from moderate or slight imbalance during the period 2000–2020, with near imbalance accounting for 83% in 2020. The overall degree of coordination in all the counties (cities) was between 0.2 and 0.3 in 2000, and the Ebinur Lake Basin was in only moderate or slight imbalance. In 2020, the overall coordination degree in all counties (cities) of between 0.3 and 0.4 indicates that the Ebinur Lake Basin reached slight or near imbalance during this period. These results indicate that the development coordination of the agro-ecological economic systems in the six counties (cities) was low over the past 20 y and did not reach the bare coordination. So far, the best state was near imbalance. The main reasons for this were the slow development of the two systems before 2010 and the low comprehensive index followed by rapid development of the agricultural economy accompanied by insufficient development in the agricultural ecology after 2010, which led to the serious lagging of the agricultural ecosystem as compared to the agricultural economic system, which led to decreased coordinated development of the two systems.

3.3.3. Types of Agro-Ecological Economic System Development

The ratio $U_e$/$U_s$ was used to obtain the results given in

Table 4. Agricultural ecological economy system development type.

County	2000	2005	2010	2015	2020
Bole	0.43	0.44	0.68	1.01	1.05
Jinghe	0.40	0.55	0.96	1.81	1.73
Wenquan	0.64	0.63	0.81	1.37	1.28
Kuitun	0.30	0.35	0.44	0.68	0.77
Wusu	0.77	1.04	1.77	2.54	1.81
Toli	0.72	0.92	1.02	1.56	1.50

. Figure 5c and Table 4 together indicate that the development type of the agro-ecological economic system in the study area has generally changed from agricultural economic lag to agro-ecological lag. The overall degree of lagging in the agricultural economy improved from 2000 to 2010, with the extremely lagging agricultural economy changing to a relatively lagging agricultural economy. From 2010 to 2020, the degree of agro-ecology lag deteriorated from relative to extreme. The $U_e$/$U_s$ of 1.04 in Wusu in 2005 indicates a change from economic to agricultural lag in the county. Toli County, which switched from economic to agricultural lag in 2010, exhibited a $U_e$/$U_s$ of 1.02. The $U_e$/$U_s$ values of Bole, Jinghe County, and Wenquan Xian reached > 1 in 2015, indicating slow agricultural economic development in these three counties (cities). The $U_e$/$U_s$ of 0.77 for Kuitun in 2020 is similar to that previously; Kuitun has always suffered from agricultural economic lag, indicating weak agricultural economic development. The main reason is that the jurisdiction of Kuitun is small, with less cultivated land, resulting in greater resistance to the development of an agricultural economy. On the contrary, the rapid development of an agricultural economy in other counties (cities) has led to increasing lag in their agricultural ecology.

4. Discussion

The comprehensive index for the agro-ecosystem in the Ebinur Lake Basin was larger than that of the agricultural economic system, and the region was in economic lag in 2000. By 2015, the comprehensive index for the agricultural economic system in most counties and cities was larger than the comprehensive index for the agricultural ecological system and the area was in ecological lag, which is consistent with the research results of Zhang Cuiyan et al. [39]. The minimal difference between the calculated values for coupling and coordination and the fact that the coupling degree was slightly higher than the coordination degree in 2010 indicate economic lag, which is consistent with the results obtained by Xiang Li [48]. The average coupling degree of 0.8 indicates good coupling over the past 20 y; however, the average coordination degree of 0.36 suggests the presence of a slightly unbalanced state with overall high coupling and low coordination, which is consistent with the research results of Yang Yanfeng and Jing Li [8]. When the coupling and coordination in an agro-ecological economic system are good or superior, coordinated and sustainable development can be achieved. However, the continuous and rapid development of the agricultural economic system has impacted the agricultural ecological environment, making both systems regress. This situation could lead to decoupling of the systems, which is consistent with the prediction conclusion of Zheng Bofu [33].

The use of the optimized coupling coordination model and the refined division of coupling and coupling coordination points to obvious differences in the results for each county and city, rendering the results more accurate with improved validity. However, different scholars use different criteria to classify the degree of coupling and coordination, which will undoubtedly lead to bias. Meanwhile, the coupling and coordination model itself is highly susceptible to influence from factors such as the scope of the study area and the subjectivity of the indicators used, which can lead to confidence problems in terms of the volatility and non-comparability of the coupling results [38,49]. Nevertheless, the results are generally consistent, and can be used to analyze the process and degree to which the agro-ecological and economic systems in the six counties and cities in the Ebinur Lake Basin are coupled based on the coupled coordination model. Revealing the internal drivers of these factors is a focus for future research.

The cultivated area in the Ebinur Lake Basin has increased over the past 20 y. At the same time, the high economic benefits of crops and a high agricultural income has led to local farmers concentrating on agricultural cultivation. One of the reasons for the rapid development of the agricultural economy in the Ebinur Lake Basin is that although the agricultural economy is developing rapidly, the agro-ecological development is currently relatively backwards, rendering it necessary to increase agricultural technology innovation, continuously promote agricultural mechanization, adhere to technologies such as drip irrigation and water-saving irrigation, further optimize the structure of agricultural production, improve the utilization of water resources, and promote agricultural modernization [8], while protecting and managing the agricultural ecological environment with reasonable and moderate farmland reclamation, minimizing the use of agricultural fertilizers and other pollutants, and establishing a good agricultural ecosystem that can ensure that agricultural development is established under the maximum capacity of the agricultural ecological environment. Such actions are more conducive to the efficient development of the agricultural economy and the sustainable and coordinated development of ecology and the environment. It should be noted that several of the indicators that could additionally be utilized in evaluating the agro-ecological–economic coupling coordination of the Ebinur Lake Basin were not included in this study, including data concerning crop film and pesticide use, meaning that further evaluation may be necessary. It is hoped that subsequent research will add other representative indicators that can improve the accuracy of the obtained results.

5. Conclusions

In this paper, an evaluation index of the agro-ecological economic system in six counties (cities) of the Ebinur Lake Basin was developed, and the comprehensive index, growth rate, coupling degree, coordination degree and development type calculated, with the following conclusions:

(1)

From the perspective of the comprehensive index, the agro-ecosystem has developed slowly with little change over time, and the differences between counties (cities) are small. By 2020, the highest comprehensive index for the agro-ecosystem was obtained in Bole, at 0.216, with the lowest in Toli County, at 0.165. However, the agricultural economic system is developing rapidly, with large differences between the counties (cities). By 2020, the highest comprehensive index of agricultural economic system of 0.339 was obtained in Wusu, with the lowest of 0.126 in Kuitun; however, both counties show an upward process;

(2)

Judging from the results, the coupling degree of most counties (cities) developed towards good and superior coupling between 2000 and 2010, and reversed towards intermediate, primary, and even barely coupled systems after 2010, indicating that the gap between agricultural ecology and agricultural economy has increased. This change has been accompanied by an increase in the possibility of decoupling and serious imbalance, which may hinder sustainable agricultural development;

(3)

The degree of coordination from 2000 to 2020, suggests that the agro-ecological and agricultural economic systems in the six counties (cities) of the Ebinur Lake Basin were generally in a state of imbalance in terms of coordination, and that the development of coordination toward good and superior coordination was extremely slow, further illustrating the serious incompatibility between the two systems. However, the development process is evolving. By optimizing and adjusting agro-ecology and agricultural economy, the systems could continue to develop towards coordination and may even eventually reach good or superior coordination;

(4)

From the perspective of development type, most of the counties (cities) were suffering from agricultural economic lag before 2010, indicating that the agricultural ecosystem was better than the agricultural economic system at this stage, whereas most counties (cities) transformed to agro-ecological lag after 2010, indicating rapid development in the agricultural economy in this stage, rendering the agricultural economic system much better than the agricultural ecosystem. These results also directly reflect the rapid development of the agricultural economy in the region. Over the past two decades, the agricultural economy has continued to strengthen and grow rapidly in the region, and surpassed agro-ecology after 2010, widening the gap. As a result, the agricultural ecology is in relative lag, which could lead to damage in terms of the agricultural ecology.

The research on the coupling and coordination of the agro-ecological economic system in the Ebinur Lake Basin led to the following suggestions: First, the government should publicize the concept of agricultural protection, strictly implement the returning of farmland to forests and grasslands with effective supervision, and guide local people to protect the agricultural ecological and natural ecological environments while improving the carrying capacity of the ecological environment. Second, rational allocation of the agricultural and animal husbandry production structure, together with reasonable assessment of cultivated land reclamation, could reduce the damage to the surface vegetation that results from blind reclamation and land use development. Third, precision agriculture should be implemented, with fine agricultural management, the rational and efficient use of water resources, increased investment in agricultural science and technology, and reduced agricultural non-point source pollution. Fourth, agro-ecological protection needs to be coordinated to work alongside steady economic growth, with priority given to the governance and protection of agro-ecological environment, so that agriculture can develop in a sustainable manner.