1 Introduction

In the context of the increasingly globalized economy, the scope of development of various industries continues to expand, and the boundaries among industrials are becoming more and more ambiguous. Ambiguous boundaries among industries give rise to industry coupling (Ashouri et al., 2021). For example, the further development of agriculture toward integration with tourism gives births to new crossing types of businesses, such as countryside tourism and sightseeing agriculture (Lu & Li, 2021). In the extensive trend of industrial integration, integrated development of agriculture and tourism is relatively common. The emergence of new formats is precisely to adapt to the development of the economy and society. At the same time, the new formats also promote the sustainable development of the economy and society, and sustainable development continues to promote the continuous transformation of the economy and society. This is a virtuous circle. Therefore, this paper takes the agriculture and tourism industry of Inner Mongolia as the research object, analyzes the coupling coordination degree of these two subsystems, and provides suggestions for the future agriculture and tourism industry of Inner Mongolia, as well as for the agriculture and tourism industry of Southeast Asia.

Many scholars have studied the integrated development of agriculture and relevant industries. Yang et al. (2019) analyzed the coupling relationship between agricultural economy and agricultural ecological environment in semi-arid regions based on a case study of elm forest in China, and constructed a coupling coordination model. The model designed several major categories of indicators such as agricultural input, output, and ecological environment. Based on the elm forest in China from 1997 to 2016, they concluded that the coupling coordination of agricultural economy and agricultural ecological environment grew slowly. Moreover, the worsening agricultural ecological environment hinders the coordinated development of agricultural economy. They believed that it is necessary to increase continuous investment to agricultural ecological environment in order to develop agriculture. Wu et al. (2020) investigated the coupling mechanism and coordinated development mode of rural tourism system, and analyzed the coupling dynamic mechanism based on Kaili City, Guizhou Province, China. The model designed many indicators related to rural revitalization and rural tourism. By collecting data samples from 2010 to 2017, they concluded that the coupling coordination between rural vibration and rural tourism enhanced year by year. Wang et al. (2020) analyzed comprehensive evaluation indicators changes, coupling coordination state and coupling evolution trend of agricultural economy and agricultural economic system in Yan’an City from 2010 to 2018. The indicators designed of this study were basically similar to those designed by Yang et al. (2019). Results demonstrated that both agricultural economy and agricultural economic system presented S-shaped growth and trend, and they have experienced different stages, including recession coupling, coordination coupling, repair coupling and re-coordinated coupling. Cai et al. (2021) discussed the coupling coordination problem between urbanization and agricultural ecological environment, and analyzed the panel data by using the random panel Tobit model. They found that the coupling degree between urbanization and agricultural ecological environment in China ranged between 0.30 and 0.50, indicating that they are in the opposite stage. Moreover, they pointed out that finance supports to agriculture, rural economic development, and environmental pollution control capacity have significant influences on the coupling coordination between urbanization and agricultural ecological environment. Finally, they proposed some policy suggestions to urbanization and agricultural ecological environment. Although the research indicators of the above-mentioned scholars had been selected well enough, they basically did not involve the relevant indicators of the effects brought about by the coupling of subsystems. In this study, indicators of coupling effects were added, for example, two types of indicators, such as rural development level and agricultural ecological level, are added to the agricultural subsystem, and indicators such as tourism effects are added to the tourism subsystem.

Guo et al. (2021) analyzed the coupling coordination problems between industrialization and green agricultural development in China from 1990 to 2019 and constructed the evaluation indicators system for industrialization and agricultural green development based on panel data of 31 provinces in China. They calculated and checked through coupling coordination model and spatial autocorrelation. Results demonstrated that industrialization generally presented an upward trend, while the green agricultural development decreased and then increased gradually. The study considered that all the designed indicators were equally important, that is, it did not distinguish the importance of each indicator. Zhu et al. (2021) analyzed the relationship between rural transformation and agricultural ecological environment in Zhejiang Province from 2000 to 2018 and studied the relationship between these two subsystems by coupling coordinated model and Kuznets curve analytical method. Results demonstrated that in temporal, the rural transformation level was increasing gradually and the agricultural ecological environment tended to be stable from quick reduction. In spatial, the coupling coordination was characteristic of high in northwest, but low in central and southeast of Zhejiang Province. Dynamic evolutions of rural transformation and agricultural ecological environment presented U-shaped curves. Shuang et al (2021) investigated the coupling degree of Internet development level and agriculture. Based on data of 13 major food production areas in China from 2009 to 2018, research results showed that the average popularity rate of Internet increased from 0.25 in 2008 to 0.54 in 2018. Moreover, the agricultural ecological efficiency of major food production areas increased gradually and the mean increased from 0.45 in 2009 t0 0.79 in 2018. The coordination and coupling between Internet and agricultural ecological efficiency increased year by year. However, there’s some difference among different regions and there’s still a great growth space for the coordination between them. The above-mentioned scholars generally did not distinguish the importance of each indicator when conducting coupling and coordination research on agriculture-related systems. This study believed that the indicators in each subsystem had different degrees of importance, so the weight of each indicator were added in the research to better distinguish the importance of each indicator.

To sum up, many scholars have conducted research on the coupling coordination degree of agriculture-related industries. However, there are few studies concerning the integrated development of agriculture and tourism in Inner Mongolia. Most scholars basically did not involve the indicators related to the development effect after subsystem coupling, and generally believed that the indicators of each subsystem were equally important, that is, they did not distinguish the importance of indicators. Relevant panel data in Inner Mongolia in 19 years from 2001 to 2019 were chosen and an evaluation indicators system for the integrated development between local agriculture and tourism industry was established. The indicators included the development effect indicators after the integration of the two subsystems, such as the level of rural development, the level of agricultural ecology, and the effect of tourism, and the weight value was set for each indicator. Panel data were analyzed by entropy weight method and coupling coordination model, thus getting the coupling coordination between agriculture and tourism industry. Research conclusions provide references for the coordinated development between agriculture and tourism in Inner Mongolia, also provide references for the coordinated development of agriculture and tourism in Southeast Asia.

2 Data source and methodology

2.1 Data source

Data were collected from the statistical yearbook issued by the statistical bureau of Inner Mongolia Autonomous Region and China Statistical Yearbook published by the National Bureau of Statistics. It covered a time span from 2001 to 2019. Since data of different indicators have varying dimensions, data normalization was implemented for the convenience of follow-up empirical study. Let \(a_{j}^{^{\prime}}\). be data of decision-making unit (DMU) in different subsystems and \(a_{j}^{^{\prime}}\). be the normalized data. The specific calculation formulas are shown as follows (Yao, 2019):

$$ a_{j}^{^{\prime}} = \frac{{\left( {a_{j} - {\text{min}}\left( {a_{j} } \right)} \right)}}{{\left( {\max \left( {a_{j} } \right) - {\text{min}}\left( {a_{j} } \right)} \right)}} $$
(1)
$$ a_{j}^{^{\prime}} = \frac{{\left( {{\text{max}}\left( {a_{j} } \right) - a_{j} } \right)}}{{\left( {\max \left( {a_{j} } \right) - {\text{min}}\left( {a_{j} } \right)} \right)}} $$
(2)

where max and min are the maximum and minimum of the same indicators over years. When the indicator is a positive one, it is normalized by Eq. (1); if it is a negative one, it is normalized by Eq. (2).

2.2 Study area

Inner Mongolia is located in the north of China, bordering Heilongjiang, Jilin, Liaoning, and Hebei in the northeast, Shanxi, Shaanxi, and Ningxia in the south, Gansu in the southwest, and Russia and Mongolia in the north.. Inner Mongolia has a total area of 1.183 million square kilometers and governs 12 prefecture-level administrative regions, including 9 prefecture-level cities and 3 leagues. There are 23 municipal districts, 11 county-level cities, 17 counties, and 49 banners. In 2021, the Inner Mongolia Autonomous Region will have a permanent population of 24 million, a total sown area of crops of 8.743 million hectares, a grain output of 38.403 million tons, and a regional GDP of 2.05142 trillion yuan. Inner Mongolia is rich in tourism resources, mainly composed of "six wonders" of grasslands, historical sites, deserts, lakes, forests, and folk customs. The forest landscape is mainly distributed in Daxing'an Mountains; folklore tours mainly include Mongolian singing and dancing, Mongolian "men's three arts", and places of interest include Wuta Temple, Dazhao, and Zhaojun's Tomb in Hohhot.

2.3 Construction of indicators system

Based on previous research fruits, an indicator system was designed by combining practical situations in the study area. With respect to construction of indicators system, many scholars have designed different indicators from different perspectives. For agricultural subsystems, Han (2018), Wang (2019), Chen (2019) and Zhu et al., (2021) designed indicators of cultivated area, total agricultural machinery power, total fertilizer amount, food yield, per capita income and Engel coefficient. For tourism subsystem, Wang (2019), Geng (2020), Li (2021), Pan (2021) and Zhang et al (2021) designed indicators of global tourism income, tourist trips, number of travel agencies and hotels, etc. With references to previous research fruits during design of subsystem indicators, based on the theory of industrial integration, this study considered development efficiency brought by integration of subsystems, such as ecological development level. In other words, two indicators of rural development level and agricultural ecological level were added in the agricultural subsystem, and some major indicators like tourist effect were added in the tourism subsystem. Details are shown in Table 1.

Table 1 Evaluation indicators system and weights for the integrated development of agriculture and tourism in Inner Mongolia
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2.4 Measurement methods of integrated development

Entropy weight method and coupling coordination model were used for measurement. The main goal of entropy weight is to measure weights of different indicators, while the coupling coordination model is mainly to estimate coupling degree, coordination degree and comprehensive evaluation indicator between the agricultural subsystem and tourism subsystem. They are finished in several steps.

  1. 1.

    Calculation of weights. Entropy weight method is a relatively objective way to determine weights. Firstly, information entropies of different indicators have to be calculated. Their weights are then calculated according to information entropy. Details are shown in Eq. (3) and Eq. (4)[6].

    $$E_{i} = \frac{{ - a_{ij} }}{{{\text{ln}}\left( n \right)}}*\mathop \sum \limits_{j = 1}^{n} \left( {a_{ij} *{\text{ln}}\left( {a_{ij} } \right)} \right).$$
    (3)
    $$ W_{i} = \frac{{1 - E_{i} }}{{m - \mathop \sum \nolimits_{i = 1}^{k} E_{i} }} $$
    (4)

    where \({E}_{i}\) refers to the information entropy. \(a_{ij}\). is the value of DMU j of the index i. n denotes total quantity of DMU. \(W_{i}\) shows weights of the index i. m is the total number of indicators.

  2. 2.

    Calculate the comprehensive evaluation index. The comprehensive evaluation index expresses the comprehensive benefits of different subsystems. There were two subsystems, which were agricultural subsystem and tourism subsystem. Their comprehensive evaluation indicators were expressed by f(a) and g(b). Details are shown in Eqs. (5) and (6) [6].

    $$ f\left( a \right) = \mathop \sum \limits_{j = 1}^{n} W_{i} *a_{ij} $$
    (5)
    $$ g\left( b \right) = \mathop \sum \limits_{j = 1}^{n} W_{i} *b_{ij} $$
    (6)

    \(a_{ij}\) and \(b_{ij}\) are values of DMU j of index i of two subsystems. Numerical values were normalized and n refers to the total quantity of DMU. \(W_{i}\). is the weight of index i of each subsystem.

  3. 3.

    Calculate coupling degree. Coupling degree was used to evaluate interaction level of different subsystems. From the disordered to ordered cooperative capability, the coupling degree ranges from 0 to 1. As it approaches to 1, the coupling degree is higher, the correlation is stronger and it is more recommended to adopt coordinated development. Otherwise, it is not related and shall not adopt the coordinated development. Details are shown in Eq. (7)[6]. f(a) and g(b) are comprehensive evaluation indicators of two subsystems and C refers to the coupling degree.

    $$ C = \sqrt {\frac{f\left( a \right)*g\left( b \right)}{{\left( {f\left( a \right) + g\left( b \right)} \right)^{2} }}} $$
    (7)
  4. 4.

    Calculate comprehensive coordinated index. The comprehensive coordinated index is used to evaluate coordination of two subsystems and it values between 0 and 1. If it approaches to 1, the coordination between two subsystems is the better; otherwise, the coordination is poorer. Details are shown in Eq. (8)[6], where \(\alpha\) and \(\beta\). are importance of two subsystems and they are usually determined by the equalization method. For example, there’s \(\alpha = \beta = 0.5\) for two subsystems. f(a) and g(b) are comprehensive evaluation indicators of two subsystems. T denotes the comprehensive coordination index.

    $$ T = \alpha *f\left( a \right) + \beta *f\left( b \right) $$
    (8)
  5. 5.

    Calculate coordination degree. Coordination degree used to evaluate comprehensive interaction level between subsystems, that is, the comprehensive interaction level between agricultural subsystem and tourism subsystem. It is between 0 and 1. If it approaches to 1, the coordination is better; otherwise, it is the poorer. Details are shown in Eq. (9)[6]. D refers to the coordination degree, C is the coupling degree and T is the comprehensive coordination index.

    $$ D = \sqrt {C*T} . $$
    (9)

2.5 Evaluation criteria

For better evaluation of integrated development between agriculture and tourism in Inner Mongolia, an objective evaluation on the measurement results was needed. Specific evaluation criteria are introduced as follows.

  1. 1.

    Evaluation criteria of comprehensive evaluation indicator: after comprehensive evaluation indicators of two subsystems are estimated, the subsystem b lags behind if f(a) > f(b); otherwise, the subsystem a lags behind.

  2. 2.

    Evaluation criteria of coupling degree: previous scholars have carried out a lot of things. With references to evaluation standards of Mao (2020), the evaluation criteria of coupling degree are listed in Table 2.

  3. 3.

    Evaluation criteria of coordination degree: many associated studies have been reported. With references to Mao (2020)[20], the evaluation criteria of coordination degree are listed in Table 3.

Table 2 Evaluation criteria for coupling degree
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Table 3 Evaluation criteria for coordination degree
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3 Empirical analysis

According to the integrated development measurement method, the collected data were processed, thus getting evaluation results of integrated development of agriculture and tourism in Inner Mongolia (Table 4).

Table 4 Evaluation of the integrated development of agriculture and tourism in Inner Mongolia
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3.1 Coupling degree

According to the coupling degree of agriculture and tourism in Inner Mongolia from 2001 to 2019 (Table 4), the coupling degree was 0.34944 in 2001 and then it increased year by year to 0.99573 in 2019. The broken line is shown in Fig. 1. Obviously, it began to be high since 2004, close to 0.9. The coupling degree was higher than 0.9 after 2004. The coupling degree between agriculture and tourism was in the “climbing stage” (antagonism stage and running-in stage) from 2001 to 2004. It “climbed” quickly in first years and there’s a large slope of the curve, indicating that two subsystems were establishing a coupling system gradually. The curve became relatively stable after 2007 and the coupling degree was higher than 0.98. In 13 years from 2007 to 2019, the agriculture and tourism were in the high-coupling stage in Inner Mongolia and there’s relatively good integrated development.

Fig. 1
figure 1

Coupling degree of agriculture and tourism in Inner Mongolia

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3.2 Comprehensive coordination index

It can be seen from Table 4 that the comprehensive coordination index of agriculture and tourism in Inner Mongolia was 0.11118 in 2001 and it kept increasing in subsequent years. It reached 0.85811 in 2019. The broken line is shown in Fig. 2. Clearly, the comprehensive coordination index fluctuated very small and it was almost a straight line, indicating that the comprehensive coordination between agriculture and tourism in Inner Mongolia was relatively stable and continued to be improved from 2001 to 2019. However, the comprehensive coordination index was 0.85811 in 2019, which had some distance to 1. This reveals that the coordination between two subsystems is not perfect and there’s still some room for improvement.

Fig. 2
figure 2

Comprehensive coordination index for agriculture and tourism in Inner Mongolia

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3.3 Coordination degree

It can be seen from Table 4 that the coordination degree of agriculture and tourism in Inner Mongolia was 0.19710 in 2001 and 0.92436 in 2019. The broken line is shown in Fig. 3. Clearly, the coordination degree increased year by year. It was 0.54455in 2007, which was slightly higher than 0.5 and in the managed balance state. It was relatively low before and in the state of “imbalance”. Subsequently, it kept increasing and reached to 0.92436 in 2019, just reaching “excellent balance” (Table 3). However, the coordination degree was 0.9 and 1 in the stage of “excellent balance”, indicating that there’s some room for improvement of coordination between two subsystems.

Fig. 3
figure 3

Degree of coordination between agriculture and tourism in Inner Mongolia

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3.4 Subsystem development

According to Eqs. (5) and (6), the comprehensive evaluation indicators of two subsystems were gained (Table 4). The relevant curves are shown in Fig. 4. Clearly, the curve of tourism was below that of agriculture before 2012, indicating that the tourism development lags behind the agriculture. After 2012, the curve of tourism was above that of agriculture, indicating that the agricultural development lags behind the tourism. If two subsystems are in complete coordinated development, two curves overlap fully. It can be seen from Fig. 4 that there’s some space between two curves, indicating that it still has some way to complete coordination. However, the space between two curves is not too large, indicating the good coordination.

Fig. 4
figure 4

Subsystem development

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3.5 Comprehensive analysis

Generally speaking, agriculture and tourism in Inner Mongolia has keeping a high coupling since 2004, indicating the strong correlation. In the beginning, the coordination was not good and a managed coordination was achieved after 2007. Subsequently, the coordination was improving gradually until reaching the excellent coordination in 2019. The tourism development lagged behind agricultural development from 2001 to 2011, but it turned out the opposite after 2012.

4 Suggestions for integrated development of agriculture and tourism

4.1 Tourism promotes agricultural development

Agricultural development in Inner Mongolia began to lag behind tourism since 2012. It can be seen from Fig. 4 that the space between two curve increased gradually after 2012, indicating that the lagging behind was intensifying year by year. Hence, it is very necessary to drive agricultural development by tourism. Firstly, tourism shall develop more products related with agriculture and mine tourism attributes of agriculture fully, such as developing rural tourism and promoting development of participatory tourism agriculture, sightseeing agriculture, leisure agriculture, leisure farms and agritainment. Secondly, marketing of tourism products shall be inclined to agriculture consciously. On one hand, it shall attract tourists. On the other hand, it shall promote agricultural development. Wang (2019) put forward a similar proposal that encourages urban residents to travel to rural areas, on the one hand to respond to the "low-carbon life", on the other hand to promote the development of local agriculture. Mao (2020) believed that tourism should play a leading role in promoting the coordinated development of modern agriculture and tourism.

4.2 Modern agriculture promotes tourism development

The development of traditional agriculture is facing with bottlenecks and it is necessary to develop modern agriculture and thereby promote further development of tourism. Firstly, it is recommended to train new agricultural households, support development of large and medium-sized farms, increase agricultural productivity through scaled effect and establish more brands of agricultural products. Secondly, it is suggested to promote green agriculture, industrial three-dimensional agricultural development. The modern agriculture shall be developed and integrated with tourism deeply, thus promoting development of tourism. Geng (2020) put forward similar suggestions and believed that villages should make full use of the air environment to explore new tourism resources. Mao (2020) proposed to accelerate the construction of modern agricultural industrial system and attract more rural tourism through modern agriculture.

5 Conclusions

This article took the agriculture and tourism industry of Inner Mongolia as the research object, and based on the data from 2001 to 2019, designed the research indicators of two subsystems, and analyzed the data by using the entropy weight method and the coupling coordination degree model. The research results show that the development of agriculture and tourism in Inner Mongolia has been highly coupled since 2004; it has been reluctantly coordinated from 2007 until 2019 to achieve high-quality coordination; of agriculture, while after 2012, the development of agriculture has been lagging behind the tourism industry. This article puts forward two suggestions on promoting agricultural development through tourism and promoting tourism development through modern agriculture. The research results and the proposed suggestions provide a basis for improving the coupled development of agriculture and tourism in Inner Mongolia, and also provide references for the coupled development of agriculture and tourism in Southeast Asia.