Comprehensive Evaluation of Agricultural Modernization Levels

Abstract

Agricultural modernization is the most important task of modernization construction. This study used the multi-index comprehensive measurement method to estimate the agricultural modernization level of Shandong Province from 2010 to 2019. Regional differences were analyzed using the ESDA method, and the main obstacles were diagnosed using the obstacle degree model. This paper constructs an index system, including an agricultural production system, a management system, an industrial system, output benefits, rural social development, and agricultural sustainable development. The results showed that the level of agricultural modernization in Shandong Province was divided into three stages: decelerating ascent (2010–2013), accelerating ascent (2014–2016), and high-level fluctuation (2017–2019). Results found that the scores of the agricultural production system, rural development level, and agricultural industrial system peaked in 2017.The regional difference in the agricultural modernization level of Shandong Province is high in the north and low in the south of China. The main obstacles in the rising stage of agricultural modernization in Shandong Province are the proportion of the added value of agriculture, forestry, fishery, and animal husbandry service; the amount of fertilizer and pesticide used per land area; the total power of units farmland machinery; the high-level fluctuation order are shelterbelt construction rate; the proportion of livestock production value; rural per capita electricity consumption; and rural employment rate. The average arable land per household and the employment rate of the rural population are the key obstacles to high levels of agricultural modernization. Therefore, optimizing industrial structure, improving the input of productive factors, cultivating a high-quality rural labor force, improving the efficiency of agricultural funds use, and awareness of the sustainable development of agriculture are all suggested.

1. Introduction

Agricultural modernization refers to the transformation from traditional agriculture to modern agriculture; the establishment of agricultural production on the basis of modern science; the use of modern science and technology and modern industry to equip agriculture; and the use of modern economic science to manage agriculture and create a high-yield, high-quality, low-consumption agricultural production system and an agricultural ecosystem that rationally utilizes resources, protects the environment, and has high conversion efficiency. The sustainable development of agriculture is an important feature of agricultural modernization, and it is also the fundamental strategy of our country’s agricultural development. The evaluation of agricultural modernization levels promotes the agricultural modernization process in Shandong Province and takes a key step forward in realizing the sustainable development of agriculture. Environmental sustainability in production systems (i.e., agricultural and industrial production systems) is imperative for reducing their negative impact on human health. Agricultural modernization is related to national food security, the supply of high-quality agricultural products, the increase in farmers’ incomes, and the release of rural consumption potential, which are key points for promoting the formation of a new development pattern mainly dominated by domestic cycles and double domestic and international cycles. The No.1 central document (2021) of China proposed the mobilization of the power of the whole party and society to accelerate the modernization of agriculture and rural areas. However, as an important foundation/component of national modernization [1], agricultural modernization lags behind industrialization and urbanization, in China and is the four modernizations’ biggest weakness [2]. Agricultural modernization should be the top priority of current modernization construction if we want to achieve the goal of building a modern and powerful country in the middle of the 21st century [3,4,5].

Shandong Province is a big agricultural province in China. In 2020, the total output value of agriculture, forestry, fishery, and animal husbandry in Shandong Province reached 1019.06 billion yuan, becoming the first province in China to break one trillion yuan and contributing 8% of grain output, 9% of meat output, 12% of fruit output, and 13% of vegetable output with 6% of the country’s arable land and 1% of its freshwater resources. The total export of agricultural products accounted for 24% (Data source: <Shandong: Drawing a “Qilu Model”for Rural Revitalization>of Xinhua News Agency https://baijiahao.baidu.com/s?id=1602070601671827578&wfr=spider&for=pc, accessed on 10 January 2022) of the country’s. At the same time, Shandong Province has made great efforts to improve the ability of sustainable agricultural development. Shandong Province emphasizes the strictest arable land protection system. At the end of the year, the forest greening rate reached 27%. By 2020, 30 provincial-level ecological cycle agriculture demonstration counties will be built. The area of the ecological circular agriculture demonstration base is 30 million mu. All of these are closely related to the agricultural modernization and sustainable development of Shandong Province.

Since the beginning of the new century, quantitative evaluation and analysis of the level of agricultural modernization have become an important part of agricultural modernization research [6,7,8,9]. In developed countries, agricultural modernization started early and developed at a high level. The research on agricultural modernization mainly focuses on the relationship between mechanization, technology, renewable energy technology, sustainable development of the ecological environment, and agricultural modernization [10,11,12,13,14,15,16], so the indices for evaluating the level of agricultural modernization focus on four aspects: economy, environment, pollution, and development [17,18].

Some domestic experts and scholars have constructed an index system with agricultural input, agricultural output, rural society, and sustainable agricultural development as the first-level indices or have constructed an index system based on the connotations and characteristics of agricultural modernization [19,20] after referring to international standards and summarizing the results of expert discussions and related reference [21]. After the National Agricultural Modernization Testing and Evaluation Index System Scheme was issued in 2016, some scholars constructed an index system including the industrial system, production system, operation system, quality and benefits system, green development, and support and protection to evaluate the development level of China’s agricultural modernization [22]. The quantitative evaluation methods of agricultural modernization mainly include the multi-index comprehensive measurement method, the key parameter comparison method [23], and the DEA combined with entropy value method [24]. The multi-index comprehensive measurement method collects multiple indicators and information describing the object and, after mathematical processing, confirms the process dynamics of the research object as a whole, so it has the characteristics of comprehensiveness. From the perspective of comparative sociology and other disciplines, the parameter comparison method evaluates the development process of agriculture through the comparison of comparability indicators and gives measurement conclusions with reference to historical data and examples of foreign developed countries. This method has the limitations of subjectivity and one-sidedness. The DEA method measures the agricultural modernization process through the comparison of material input and output relative benefits and their respective advantages in the production sector [24]. This method inevitably has correlations in the selection of indicators, resulting in inaccurate evaluation results. Among them, the multi-index comprehensive measurement method is believed to be the most reasonable and most widely used method for evaluating the development level of agricultural modernization [25], but the methods of index weighting are not the same, mainly including the Delphi method and AHP [26], the grey advantage analysis method [27], the entropy method [28], and the coefficient of variation method [29], of which Delphi method and AHP are subjective. The empowerment method and the remaining three are objective empowerment methods.

In summary, the existing research on the evaluation of the development level of agricultural modernization at the national level is relatively mature, but China has a vast territory, and the differences in natural endowments in different regions will affect the development levels of regional agricultural modernization, so it is necessary to conduct empirical research on the agricultural modernization level in different regions. In addition, current research mainly focuses on the measurement of the development level of agricultural modernization, and there are few types of research on the obstacles to the process of agricultural modernization. Therefore, this paper takes Shandong Province as an example to comprehensively evaluate the agricultural modernization levels from 2010 to 2019 by constructing an evaluation index system, analyzing regional differences, and diagnosing the main obstacles to provide a reasonable scientific basis for promoting the process of agricultural modernization. This article consists of five sections. The first section introduces the research background, research purpose, significance, and innovation of the article. The second section expounds the data sources and constructs the evaluation system of the agricultural modernization levels in Shandong Province. The third section introduces the research method used in this paper in detail according to the research purpose. The fourth section discusses the results. The fifth section summarizes the main findings of the study.

2. Materials and Methods

2.1. Data Source

The data used in this paper were collected from the Statistical Yearbook of Shandong Province (2011–2020), municipal statistical yearbook, and statistical bulletin. The graphic data are all from the 1:1,000,000 national basic database of the National Geographic Information Resource Directory Service System. To ensure the authenticity and rationality of the final calculation results, the original data should be standardized. In this paper, the extreme value method was used to deal with the original data and solve the problem of the different indices having different dimensions, units, orders of magnitude, and positive and negative attributes.

Calculation of standardization of index values based on existing reference [2].

For positive indicators, the original data normalization formula can be written as

$$X_{ij}^{\prime }=\frac{X_{ij}-min{X}_j}{max{X}_j-min{X}_j}$$

(1)

For negative indicators, the original data normalization formula can be written as

$$X_{ij}^{\prime }=\frac{max{X}_j-X_{ij}}{max{X}_j-min{X}_j}$$

(2)

In these equations, $X_{ij}^{\prime }$ is the standardized value of the jth index in the ith year; $X_{ij}$ is the original value of the jth index in the ith year; and $max{X}_j/min{X}_j$ refer to the maximum and minimum values of the jth index, respectively.

2.2. Construction of the Evaluation Index System of Agricultural Modernization Levels

Sustainable agricultural development refers to the optimization of the agricultural industry system, the rational use of natural resources, and the protection and improvement of the ecological environment so as to continuously improve the level of agricultural production and farmers’ income, reduce the proportion of the rural poor, and achieve agricultural sustainability. Agricultural modernization is a systematic project. With the improvement of the level of agricultural modernization, the high efficiency of agricultural production, the improvement of the rural environment, the increase in farmers’ income, and the promotion of the sustainable development of agriculture and rural areas have been achieved. This process has systematic and dynamic characteristics. To ensure the rationality of the selection of indices, this paper takes the connotation of agricultural modernization as the fundamental basis and takes the “Construction of Modern Agricultural Industrial Systems, Production Systems and Operation Systems” proposed in 2016 and the “Constructing a Modern Agricultural Industrial System, Production System, and Management Systems” proposed in the report of the 19th National Congress of the Communist Party of China as the reference, 25 relevant schemes and references [1,2,19,21,22,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44]. There are six first-level indices, including the agricultural production system, the agricultural operation system, the agricultural industrial system, the agricultural output benefit, rural social development, and agricultural sustainable development, and 17 secondary indices (

Table 1. Evaluation of the index system of agricultural modernization levels for Shandong Province.

First-Level Index	Weights	Second-Level Index	Formula	Weights	Attribute
Agricultural production system	0.2189	c1Total power of arable machinery per unit	Total mechanical power/Total area of arable land	0.0707	+
		c2 Effective irrigation rate	Effective irrigated area/Total cultivated area	0.0483	+
		c3 Electricity consumption per capita in rural areas	Total rural electricity consumption/Rural population	0.0586	+
		c4 Results of expenditure on agriculture, forestry, and water affairs	Expenditure on agriculture, forestry, and water services/Value added of agriculture, forestry, fishery, and animal husbandry	0.0414	+
Agricultural management system	0.1830	c5 Area of arable land per household	Total area of cultivated land/Number of rural households	0.0545	+
		c6 Proportion of the added value of forestry, fishery, and animal husbandry in the added value of agriculture	Added value of agriculture, forestry, animal husbandry, and fishery services/Added value of agriculture, forestry, animal husbandry, and fishery	0.1285	+
Agricultural industry system	0.1068	c7 Proportion of livestock production value in total agricultural output value	(Animal husbandry output value + fishery output value)/Total output value of agriculture, forestry, animal husbandry, and fishery	0.0512	+
		c8 Proportion of output value of primary industry in total output value	Obtained directly	0.0556	-
Agricultural output benefit	0.1396	c9 Grain production per unit area	Total output of grain/Total area cultivated	0.0459	+
		c10 Land productivity	Value added of agriculture, forestry, animal husbandry, and fishery/Total area of cultivated land	0.0453	+
		c11 Farmer net income per capita	Obtained directly	0.0484	+
Rural social development	0.1340	c12 Employment rate of rural population	Rural population from agriculture/Rural population	0.0340	+
		c13 Urbanization rate	Urban population/Total population	0.0599	+
		c14 Rural Engel coefficient	Food consumption of farmers/Total consumption of farmers	0.0401	-
Sustainable agriculture	0.2177	c15 Shelter forest construction rate	Shelterbelt construction area/Afforestation area	0.0566	+
		c16 Pesticide usage per land area	Total pesticide use/Total arable land area	0.0858	-
		c17 Fertilizer usage per land area	Total amount of fertilizer used/Total area of cultivated land	0.0752	-

) in the agricultural modernization index system of Shandong Province were constructed according to the principles of availability, comparability, and systematics.

For the construction of a comprehensive evaluation index system for the development level of agricultural modernization, we consider factors such as agricultural input, management, output, social development, and sustainable development. Agricultural production systems, that is, agricultural production indicators, refers to agricultural production factors inputs, such as machinery, irrigation, electricity consumption, etc. [2,10,12]. The agricultural management system, that is, the agricultural management index, refers to the indicators of the levels of agricultural management, such as the area of cultivated land, etc. [12,14,15,18]. The agricultural industry system, that is, the agricultural industry index, is mainly aimed at the evaluation of the agricultural economic structure and the output values of different agricultural industries, such as the proportion of aquaculture, etc. [2,10,22]. Agricultural output efficiency refers to the agricultural output efficiency index. Compared to traditional agriculture, modern agriculture has a significantly higher level of agricultural output, such as secondary indicators: land productivity, grain yield per unit area, etc. [10,15,18]. Rural social development refers to the rural social development index. The sustainable development of modern agriculture not only includes the progress of agricultural production but also pays more attention to the improvement of rural development, social progress, and farmers’ income growth [10,12,18]. Agricultural sustainable systems are indicators of sustainable agricultural development. Sustainable development indicators mainly refer to the indicators of protecting natural resources and the environment and reducing land pollution [10,18,33].

3. Analytical Framework

3.1. Multi-Objective Comprehensive Measurement Method

3.1.1. The Entropy Method

The entropy method is an objective weight assignment method. It calculates the information entropy of the index and determines the weight of the indicator according to the influence of the relative change degree of the indicator on the whole system. The index with a large relative change degree has a large weight. Referring to the calculation of the entropy value method from the existing research [32], the specific calculation steps are given as follows.

Calculate the proportion of second-level indicators:

$$p_{ij}=\frac{X_{ij}}{{{\displaystyle \sum }}_{i=1}^n{X}_{ij}}$$

(3)

where 0 ≤ $p_{ij}$ ≤ 1.

By taking the logarithm of $p_{ij}$

$$d_{ij}=ln{p}_{ij}$$

(4)

when $p_{ij}$ = 0, let $d_{ij}$ = 0.

Calculate the entropy value of the jth secondary index:

$$s_j=-k{{\displaystyle \sum }}_{i=1}^n{p}_{ij}·d_{ij}$$

(5)

where k is a constant and k > 0, the value is $\frac{1}{lnn}$.

Calculate the utility value $z_j$ of the jth secondary index:

$$z_j=1-s_j.$$

(6)

Calculate the weight of the jth secondary index:

$$w_j=\frac{1-s_j}{{{\displaystyle \sum }}_{i=1}^{n}1-s_j}.$$

(7)

3.1.2. Multi-Objective Linear Weighting Function Method

The multi-objective linear weighting function method converts the multi-objective optimization problem into a single objective nonlinear programming problem by using the linear weighted sum method.

According to the existing reference, the calculation of the multi-objective linear weighting function method must be conducted [2].

The formula for calculating the score of the sth first-level index is given as

$$Q_s={\displaystyle \sum }_{j=1}^{m}X{\prime }_{ij}·w_j$$

(8)

where $Q_{\mathrm{s}}$ is the score of the sth first-level index, and m is the total number of indices contained in the first-level index layer.

The formula for calculating the total score of agricultural modernization levels is given as

$$\mathrm{G}={\displaystyle \sum }_{s=1}^6{Q}_{is}$$

(9)

where $\mathrm{G}$ is the total score of the agricultural modernization level and $s$ is the first-level index with a total of six items.

3.2. Exploratory Spatial Data Analysis

To explore the spatial distribution and evolution trend of agricultural modernization in Shandong Province, ESDA was used to analyze the spatial distribution characteristics of data through visualization methods. ESDA is a collection of a series of spatial data analysis methods and techniques. In this article, the Natural Break method in ArcGIS10.8 software is used to divide the agricultural modernization level of Shandong Province into different regions according to the principle of maximum inter-group variance and minimum intra-group variance.

3.3. Obstacle Degree Model

We used the calculation of the obstacle degree model by referring to the existing reference [1]. The agricultural modernization development obstacle factor diagnosis model adopts the factor contribution degree, index deviation degree, and obstacle degree to analyze and diagnose the main obstacles to agricultural modernization development. Among them, the factor contribution degree $u_j$ represents the influence degree of the j-th index on the overall target, that is, the weight $w_j$ of a single factor to the overall target. The formula can be written as

$$u_j=w_j.$$

(10)

The index deviation degree $I_{ij}$ represents the gap between the secondary index and the modern agricultural development index, which is the difference between the standardized value $X{\prime }_{ij}$ and 100% of the single index calculated by the extreme value method. The formula can be written as

$$I_{ij}=1-X{\prime }_{ij}.$$

(11)

The obstacle degree $O_{ij}$ represents the impact value of the secondary index on the development of agricultural and rural modernization, namely the one-way index obstacle degree. The ranking of this value can determine the primary and secondary relationship of the obstacles in the development of agricultural modernization. The formula of the obstacle degree can be written as

$$O_{ij}=\frac{I_{ij}·w_j}{{{\displaystyle \sum }}_{j=1}^n{I}_{ij}·w_j}.$$

(12)

4. Results and Discussion

4.1. Comprehensive Evaluation of Agricultural Modernization Levels in Shandong Province

4.1.1. The Changing Trend of Agricultural Modernization Levels in Shandong Province

First, the original data were standardized and the agricultural modernization level score of Shandong Province from 2010 to 2019 was calculated. On this basis, the annual growth rate of the agricultural modernization levels is obtained (Figure 1).

The score of the agricultural modernization levels in Shandong Province showed an overall upward trend, rising from 0.1408 in 2010 to 0.7872 in 2019, with an average annual growth rate of 18.78%. The annual growth rate showed a “wavy” change, reaching the local highest (low) point in 2011 (57.51%), 2014 (4.78%), 2016 (32.69%), 2018 (−0.46%), and 2019 (11.53%). Therefore, the development process of the agricultural modernization levels in Shandong Province can be divided into three stages: decelerating ascent (2010–2013), accelerating ascent (2014–2016), and high-level fluctuation (2017–2019). In the rising with decreasing speed stage, the growth rate had been declining but always positive, and the level of agricultural modernization keeps rising; 2014 is the turning point in the stage of increasing growth, and the annual growth rate has gradually increased to 32.69% in 2016, accelerating the improvement of agricultural modernization. After the development of the first two stages, the agricultural modernization level of Shandong Province exceeded 0.7 in 2017 and entered the high-level stage. However, the growth rate in 2018 was negative compared to the previous year (−0.46%), which made the trend of this stage “low-lying”.

4.1.2. Internal Structural Characteristics of Agricultural Modernization Levels in Shandong Province

Six first-level index scores of the agricultural production system, agricultural operation system, agricultural industrial system, agricultural output, benefits system, and rural levels from 2010 to 2019 were calculated (Figure 2). The increased range of the first-level indices in the evaluation of agricultural modernization levels is as follows: agricultural operation system > agricultural output benefit > agricultural production system > agricultural sustainable development > rural development level > agricultural industrial system. The specific evaluation results are as follows:

Agricultural production system: The level of the agricultural production system was on the rise from 2010 to 2017, and the growth rate from 2015 to 2016 was as high as 78.05%; it reached its peak (0.1936) in 2017, and the score of the agricultural production system in 2018 decreased by 28.51% compared to 2017. The score has picked up again in 2019. Further investigation shows that the scores of effective irrigation rate, rural per capita electricity consumption and expenditure on agriculture, forestry, and water affairs all increased year by year before 2017. In 2018, the significant decline in rural per capita electricity consumption and the continuous decline in the total power score of unit farmland machinery were the main reasons for the obvious decline in the score of the agricultural production system. Rural per capita electricity consumption in 2018 (610.59 kWh/person) decreased by 13.4% from 2017 (705.17 kWH/person). The total mechanical power score per unit area continued to decline from 0.0678 kWh/ha in 2010 to the lowest in 10 years (0.0570 kWh/ha) in 2015 and then rose to 0.0777 kWh/ha in 2016 and continued to decline, which led to the weak improvement in the agricultural production system score in the early stage and unstable development in the later stage.

Agricultural management system: Before 2015, the score of the agricultural management system fluctuated at a low level between 0 and 0.05; since 2015, the level of the agricultural management system has risen from 0.025 to 0.1830 in 2019, with an average annual growth rate of 64.31%.The reason is that the area of arable land per household and the proportion of the added value of the agriculture, forestry, animal husbandry, and fishery service industry have increased to varying degrees in the past 10 years. In particular, the proportion of the added value of the agriculture, forestry, animal husbandry, and fishery service industry has increased year after year since 2015 (0.0141), with a score of 0.1285 in 2019, with an average annual growth rate of 73.91%.

Agricultural industrial system: The trend of the score of the agricultural industrial system from 2010 to 2019 showed a “hump” shape, reaching the first peak in 2012 (0.0635) and then dropping by 30.23% in 2013 compared with 2012. Since 2014, the score of the agricultural industrial system has been rising slowly and reached the second peak in 2017. It also peaked during the past 10 years at 0.0921. The reason is that the secondary indicators of the proportion of aquaculture output value in the total agricultural output value reached the local highest value in 2012 (0.0512) and 2017 (0.0334) respectively, which is consistent with the trend of the agricultural industrial system score, and the score declined sharply after 2017. The proportion of the primary industry in the total output value took a turn in 2018, which was in a state of fluctuation before, and then showed a downward trend.

Agricultural output benefit system: Compared with other first-level indicators of agricultural modernization development in Shandong Province, agricultural output benefit has been increasing and steadily improving in the past 10 years. The overall agricultural output benefit level rose from the sixth in the six first-level indicators in 2010 to the fourth in 2019. Among the secondary indicators, land productivity and per capita net income of farmers increased year by year, while grain yield per unit area was unstable. By observing the original data, it can be seen that grain yield per unit area decreased from 6208 kg/ha in 2013 to 6290 kg/ha in 2014. It also decreased from 6290 kg/ha to 6258 kg/ha.

Level of rural social development: From 2010 to 2013, the level of rural development increased from 0.026 to 0.092. Since then, the score of the rural development level fluctuated around 0.1, and the score of rural development level was relatively stable throughout the decade. By observing the original data of the secondary indicators, it can be seen that the urbanization rate has been steadily increasing at an average annual growth rate of 5.08%, and the rural Engel coefficient has been decreasing at an average annual rate of 3.60%. However, the employment rate score of the rural population has been declining since 2014. According to the original data, the employment rate of the rural population decreased by 0.84% annually from 2014 to 2019 and reached the lowest value in 10 years in 2019 (54.08%).

Sustainable agricultural development level: The score of the agricultural sustainable development level increased slightly from 2010 to 2012, and then remained at about 0.075. Since 2017, the score of agricultural sustainable development level has increased rapidly, with an annual growth rate of 25.81%. The reason is that the shelterbelt construction rate decreased from 62.06% in 2010 to 30.04% in 2017, hindering the improvement of the score of the agricultural sustainable development level, but the use rate of fertilizer and pesticides on average decreased at an annual rate of 1.93% and 3.18%, respectively. Until 2018, the shelterbelt construction rate rose slightly to 33%, and the score of the agricultural sustainable development level began to rise.

4.2. Obstacles Analysis at Different Stages of Agricultural Modernization in Shandong Province

The agricultural modernization obstacle value of each secondary index in Shandong Province from 2010 to 2019 is calculated, and results are given in

Table 2. The values of obstacles to various indicators of agricultural modernization in Shandong Province from 2010 to 2019.

Index	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
c1	5%	7%	8%	9%	11%	12%	0%	3%	6%	11%
c2	5%	4%	7%	6%	5%	4%	4%	3%	0%	0%
c3	7%	6%	5%	4%	3%	4%	4%	0%	20%	19%
c4	5%	4%	4%	4%	4%	1%	2%	2%	2%	0%
c5	4%	5%	4%	5%	7%	8%	13%	4%	0%	0%
c6	15%	17%	18%	19%	18%	20%	19%	16%	7%	0%
c7	6%	2%	0%	3%	4%	4%	3%	4%	12%	24%
c8	6%	6%	6%	7%	5%	5%	3%	1%	0%	7%
c9	5%	5%	4%	5%	6%	4%	6%	4%	6%	0%
c10	5%	5%	5%	4%	3%	3%	3%	3%	2%	0%
c11	6%	5%	5%	5%	4%	4%	4%	4%	2%	0%
c12	1%	1%	1%	0%	1%	2%	3%	5%	10%	16%
c13	7%	7%	8%	3%	3%	2%	2%	1%	0%	0%
c14	5%	4%	4%	3%	3%	3%	3%	3%	2%	0%
c15	0%	1%	0%	2%	3%	4%	9%	20%	17%	24%
c16	10%	11%	11%	11%	10%	11%	12%	13%	6%	0%
c17	9%	9%	9%	9%	9%	10%	12%	14%	8%	0%

. The larger the percentage value of the barrier value, the higher the degree of restriction of the index on the agricultural modernization level of Shandong Province in that year. The percentage value of 0 indicates that the degree of restriction is the smallest among the selected indicators, which does not mean that this index does not affect the overall agricultural modernization level of Shandong Province.

According to Table 2, the obstacles at the level of agricultural modernization in Shandong Province gradually show a two-level differentiation state, the high-value obstacles are more and more concentrated, and the obstacle degrees of other indicators are close to or equal to 0. To clarify the main contradictions that restrict the level of agricultural modernization in Shandong Province, this paper ranks the above obstacles from high to low and selects the top five key obstacles (

Table 3. The top five obstacles of secondary indicators in different stages of agricultural modernization.

Ranking	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
1	c6	c6	c6	c6	c6	c6	c6	c15	c3	c7
2	c16	c16	c16	c16	c1	c1	c5	c6	c15	c15
3	c17	c17	c17	c1	c16	c16	c16	c17	c7	c3
4	c13	c13	c13	c17	c17	c17	c17	c16	c12	c12
5	c3	c1	c1	c8	c5	c5	c15	c12	c17	c1

).

At the stage of decelerating ascent (2010–2013), the proportion of the added value of agriculture, forestry, fishery, and animal husbandry in the added value of agriculture (c6), pesticide usage per land area (c16), and fertilizer usage per land area (c17) were important factors that restricted the development of the overall agricultural modernization of Shandong Province. The restriction degree of the total power of arable machinery per unit (c1) has risen since 2011. Although the urbanization rate (c13) ranked fourth in terms of constraint from 2010 to 2012, it has not been a major constraint since 2013; it benefits from China’s new urbanization strategy. At this stage, the improvement rate of agricultural modernization was slowed down by the imperfect and low efficiency of agricultural socialized service systems; inappropriate use of pesticides and fertilizers; and insufficient agricultural investment in agricultural machinery, electricity, and other factors.

At the stage of accelerating ascent (2014–2016), the proportion of the added value of agriculture, forestry, fishery, and animal husbandry in the added value of agriculture (c6), pesticide usage per land area (c16), fertilizer usage per land area (c17), and total power of arable machinery per unit (c1) were still the main factors restricting the development of agricultural modernization in Shandong Province. The state promotes land circulation and encourages moderate scale management and expects to reduce the cost by increasing the degree of the land scale. However, as China’s second-most populous province, Shandong Province is a typical area on the east coast. With more people and less land, the implementation of the household contract responsibility system in the early stages improved farmers’ production enthusiasm and at the same time led to fine land. In addition, due to the lack of guidance from the government and social service organizations in the later period, farmers regard land as guaranteed wealth and refuse to transfer it effectively, and the arable land area per household (c5) has become an obstacle with a high degree of restriction. There is little difference between this stage and the rising with decreasing speed stage in terms of obstacle presentation.

During the high-level fluctuation stage (2017–2019), the shelterbelt construction rate (c15) and the proportion of livestock production value in the total agricultural output value (c7) have become important factors that restrict the development of agricultural modernization in Shandong Province. The area of arable land per household has not appeared in the top five constraints since 2017. With the improvement in land scale, the agricultural surplus labor force was separated from the agricultural sector, and the employment rate of the rural population (c12) became the main constraint factor in this period. The electricity consumption per capita in rural areas (c3) and total power of arable machinery per unit (c1) re-appeared in the top five constraints after 2018, indicating that the development of agricultural modernization in the new era cannot slack on the basic productive input, and large-scale land needs mechanization and electrification to complete the task of efficient production.

4.3. Regional Differences in the Level of Agricultural Modernization in Shandong Province

4.3.1. Spatial Distribution of Agricultural Modernization Level in Shandong Province

We calculated the agricultural modernization level of 17 (16) cities in Shandong Province at three time points in 2010, 2014, and 2019. The ESDA method and ArcGIS10.8 software were used to divide the agricultural modernization levels of cities in Shandong Province from low to high into four grades: low value, lower value, high value, and higher value, also making visual processing (Figure 3).

The higher-value areas of agricultural modernization are mainly distributed in the coastal area of eastern Shandong and the central area of Shandong, including Qingdao, Weihai, Dongying, and Zibo. Cities in higher-value areas have one or several absolute advantages compared with other cities. For example, Qingdao and Weihai, as coastal port cities, rank at the forefront of Shandong Province in terms of proportion of aquaculture, per capita net income of farmers, and urbanization rate. Located at Yellow River estuary, Dongying is rich in oil and has developed its fishing industry. Its score of arable land per household, employment rate of the rural population, and proportion of the breeding industry are at the forefront in Shandong Province. Zibo is located in the middle of Shandong Province with convenient transportation and developed industry. The per capita electricity consumption in rural areas is higher than in other areas. In 2010, Yantai was a high-value area, but in 2019, it became a “hollow area” of agricultural modernization development in the eastern coastal areas due to the large decrease in rural per capita electricity consumption and per household cultivated land area.

The area with a high agricultural modernization level is located in the Northwest region of Shandong and the middle region of Shandong. It mainly includes Jinan, Binzhou and Dezhou, bordering Jinan and Weifang, which is famous for its vegetables. Cities belonging to regions with high values also have their top-ranking secondary indicators, but there are not many of them, or there is no absolute advantage over the high-value areas or even an absolute disadvantage that lowers the overall score. For example, the proportion of aquaculture industry in Jinan; the rural per capita net income in Dezhou; the total power score of unit farmland machinery in Binzhou; and the expenditure efficiency scores of agriculture, forestry, and water affairs in Weifang are the lowest in the province.

The areas with lower and low agricultural modernization levels are located in the Southern Shandong region, including Jining, Heze, Linyi, Rizhao, and so on. Agricultural output value accounts for a high proportion of total output value, but agricultural productivity is not. In 2019, only the land productivity of Tai’an city (86,100 yuan/ha) was slightly higher than the average land productivity of Shandong Province (79,800 yuan/ha), and other cities were far below the average.

4.3.2. Diagnosis of Obstacles of Agricultural Modernization Levels in Various Regions of Shandong Province

The obstacles to agricultural modernization in various cities of Shandong Province in 2010, 2014, and 2019 were calculated and ranked, as were the top two key obstacles of each city (

Table 4. Key obstacles of individual indicators in Shandong Province.

City/Year		2010		2014		2019
Higher value area	Dongying City	c1	c3	c1	c3	c1	c3
		20%	26%	22%	28%	15%	21%
	Weihai City	c4	c5	c3	c5	c4	c12
		15%	16%	15%	15%	14%	14%
	Qingdao City	c3	c4	c3	c4	c7	c12
		15%	13%	16%	17%	12%	12%
	Zibo City	c5	c7	c5	c7	c5	c7
		16%	16%	18%	19%	18%	20%
High value area	Jinan City	c3	c4	c3	c4	c5	c7
		16%	13%	13%	16%	15%	17%
	Dezhou City	c3	c4	c3	c4	c3	c7
		21%	12%	19%	15%	15%	13%
	Bingzhou City	c1	c3	c1	c3	c1	c3
		13%	20%	12%	20%	12%	16%
	Weifang City	c3	c4	c3	c4	c4	c7
		14%	15%	13%	15%	13%	13%
	Yantai City	c4	c5	c4	c5	c4	c5
		13%	14%	12%	14%	13%	14%
Lower value area and low-value area	Zaozhuang City	c1	c4	c4	c7	c5	c7
		13%	12%	12%	12%	12%	15%
	Jining City	c3	c4	c3	c4	c3	c4
		18%	12%	17%	14%	16%	12%
	Tai’an City	c1	c3	c3	c4	c3	c7
		11%	19%	17%	13%	15%	13%
	Rizhao City	c1	c3	c1	c3	c3	c5
		11%	18%	10%	14%	13%	12%
	Linyi City	c1	c3	c3	c4	c3	c7
		12%	15%	14%	10%	12%	10%
	Liaocheng City	c3	c4	c3	c4	c3	c7
		17%	12%	16%	13%	14%	13%
	Heze City	c3	c7	c3	c7	c3	c7
		14%	8%	13%	10%	11%	12%
	Laiwu City	c1	c5	c3	c5	/	/
		11%	13%	11%	12%	/	/

).

In 2010 and 2014, the main obstacles in higher-value regions included total power of arable machinery per unit (c1); electricity consumption per capita in rural areas (c3); results of expenditure on agriculture, forestry and water affairs (c4); area of arable land per household (c5); and proportion of livestock production value in total agricultural output value (c7). As higher-value areas tend to develop knowledge-based, information-based, and technology-based agriculture, the requirements for the quality of the agricultural labor force are becoming higher and higher. The quality of agricultural surplus labor liberated by land transfer cannot meet the current industry needs. Therefore, the rural employment rate of the rural population (c12) became one of the main obstacles of the higher value area in 2019.

In the regions with high values, the main obstacles in 2010 and 2014 were total power of arable machinery per unit (c1), electricity consumption per capita in rural areas (c3), results of expenditure on agriculture, forestry and water affairs (c4), and area of arable land per household (c5). In 2019, the main obstacle factor in some regions became the proportion of livestock production value in total agricultural output value (c7), indicating that the adjustment and optimization of agricultural internal structure is an urgent problem to be solved in regions with high agricultural value.

The main obstacles of lower- value and low-value regions in 2010 and 2014 included total power of arable machinery per unit (c1); electricity consumption per capita in rural areas (c3); results of expenditure on agriculture, forestry, and water affairs (c4); and proportion of livestock production value in total agricultural output value (c7). With the promotion of mechanized forms, such as cross-regional combined harvesting in the main planting areas in southwest Shandong, the total power of arable machinery per unit (c1) in 2019 was no longer the main obstacle in such areas and was replaced by the results of expenditure on agriculture, forestry, and water affairs (c4).

5. Conclusion and Policy Implications

By constructing the evaluation index system of agricultural modernization levels, this paper comprehensively evaluates the development level of agricultural modernization in Shandong Province by using the multi-index comprehensive measurement method, analyzing its development trends and internal structure changes, using the natural breakpoint method to analyze the regional difference of agricultural modernization levels in Shandong Province, and using a degree model diagnosis to determine obstacles restricting agricultural modernization. The level of agricultural modernization in Shandong Province can be divided into three stages: decelerating ascent (2010–2013), accelerated ascent (2014–2016), and high-level fluctuation (2017–2019). The increased range of the first-level indices in the evaluation of agricultural modernization levels is as follows: agricultural operation system > agricultural output benefit > agricultural production system > agricultural sustainable development > rural development level > agricultural industrial system. Among them, the agricultural production system, rural development level, and agricultural industrial system all reached their peaks in 2017. The level of agricultural modernization in Shandong Province presents a spatial differentiation of high in the north and low in the south. The higher-value areas are distributed in the eastern coastal areas, and most of the secondary indicators are ranked at the forefront of the province; the regions with high values were distributed in the northwest and middle of Shandong Province, and the scores of some secondary indices were high, but they also had absolute disadvantages. The areas with lower and low values are distributed in the south of Shandong Province, and almost all the scores of secondary indices are located in the middle and lower reaches of the province. In the stage of rising with the decreasing speed and rising with the increasing speed of agricultural modernization in Shandong Province, the proportion of the added value of agriculture, forestry, fishery, and animal husbandry service industry; the number of fertilizers and pesticides used on the land; the total power of machinery per unit of cultivated land; and the area of cultivated land per household are the main obstacles. The main obstacles in the high-level fluctuation stage are the construction rate of shelterbelts, the proportion of the output value of aquaculture, the employment rate of the rural population, and the electricity consumption per capita in rural areas. The total mechanical power per unit area; the rural per capita electricity consumption, the proportion of aquaculture output value; and the expenditure efficiency of agriculture, forestry, and water affairs are the key obstacles in all regions. The average arable land area per household and the employment rate of the rural population are the key obstacles unique to high and high-value regions. For the improvement of the level of agricultural modernization in Shandong Province and the achievement of regional coordinated development, the following policy implications should be followed.

• Optimize the industrial structure according to local conditions. Increase the construction and support of high-quality industries with regional characteristics, develop suburban agriculture and commercial crop planting in the eastern coastal areas and central cities of Shandong, and promote large-scale planting and livestock breeding in the southwestern plains of Shandong. It is also necessary to break the traditional single model of agriculture and fully develop innovative agriculture that combines science and technology, culture, and human creativity with agriculture, precision agriculture that combines big data collection and analysis, and ecological agriculture that emphasizes green and sustainable development.
• Improve the input of agricultural productive factors. Selecting suitable agricultural machinery according to the area of the mechanical work area to achieve the optimal configuration of land scale-mechanical operation is recommended. The plain area of Shandong Province can implement cross-regional work with a large combine harvester, the central area of Shandong can implement economic crop planting areas, and the eastern coastal are of Shandong should promote more sophisticated small- and medium-sized agricultural machinery and equipment. Then, the construction of power supply, mechanical power stations, and other infrastructure in rural areas should be promoted to ensure that there are power and mechanical maintenance stations that can radiate and cover all rural areas.
• Cultivate a high-quality rural labor force to meet market demand. The fundamental way to solve the declining employment rate of the rural population in areas with a high level of agricultural modernization development is to adapt the labor force to the labor market, improve the education and training of new professional farmers, transfer rural personnel engaged in non-agricultural occupations, broaden the channels of investment in education and training funds by the government and social forces, and input preferential policies to attract more professionals and organizations to participate in the education and training of the rural labor force.
• Improve the use efficiency of agricultural support and protection funds. The expenditure efficiency of agriculture, forestry, and water affairs has become the main obstacle factor restricting more than half of the cities in Shandong Province, and the use efficiency of agricultural fiscal expenditure needs to be improved urgently. For one thing, the government should establish the management criteria and mechanism of financial funds for agricultural support, simplify the distribution of agricultural support and protection funds, and avoid funds being controlled by multiple parties. For another, a supervision organization composed of relevant government departments, farmers, and a third party should be set up to supervise the work efficiency of government departments upward and the use of farmers’ funds downward to provide real information for the rational allocation of funds.