Cultivated land resources are essential for maintaining food security and promoting agricultural economic growth. However, the reduction in cultivated land area and environmental contamination have hindered the sustainable and healthy development of agriculture. Understanding the impact of cultivated land use transformation (CLUT) on agricultural green total factor productivity (AGTFP) is therefore a crucial step toward advancing sustainable agriculture.
Methods
This study utilizes panel data from 62 cities in the Yellow River Basin between 2000 and 2020. We employ the entropy weight method to assess cultivated land use transformation, and the super-efficient non-radial SBM model with the GML index to measure AGTFP. A two-way fixed effects model is established to examine the influence of CLUT on AGTFP.
Results
The findings indicate that: (1) From 2000 to 2020, the CLUT index showed a fluctuating upward trend, peaking in the southeastern regions and being lowest in the central areas, indicating a movement toward regional equalization. (2) AGTFP also exhibited an overall fluctuating rise, with substantial growth concentrated in the middle and lower reaches and the central part of the upstream section. Productivity growth in the upstream and midstream sectors was mainly driven by efficiency change (EC), whereas the downstream sector was advanced by technical change (TC). (3) Cultivated land use transformation significantly contributes to AGTFP, with a stronger effect observed in major grain-producing areas and eastern regions. Both functional and modal transitions of cultivated land use significantly promote AGTFP, with modal transitions being more effective. In major grain-producing areas and production-marketing balance areas, both types of transformations promoted AGTFP, though functional transformation had a greater effect in major grain-producing areas.
Discussion
This paper provides mechanistic and empirical evidence on how cultivated land use transformation affects AGTFP from a spatio-temporal perspective. The results offer a scientific reference for formulating policies to ensure food security and promote high-quality development in the agricultural economy.
cultivated land use transformationagriculture green total factor productivitytwo-way fixed effect modelthe yellow river basinsustainable development goalsThe author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Project on Risk Assessment and Pollution Control of Typical New Pollutants in South China (2022GDASZH-2022010104-2), the Project of Humanities and Social Sciences of the Ministry of Education of China (grant no. 21YJC630174), National Natural Science Foundation of China (grant no. 42201291), Chinese Universities Scientific Fund (grant no. 2452021009), and the Social Science Foundation of Shaanxi Province of China (grant no. 2021R023).section-at-acceptanceLand, Livelihoods and Food SecurityIntroduction
Achieving food security and promoting sustainable agricultural development is one of the most critical of the United Nations Sustainable Development Goals (SDGs) (Pandey and Pandey, 2023). The Food and Agriculture Organization of the United Nations (FAO) has repeatedly emphasized the importance of ecological, organic, and sustainable agriculture in its reports. Countries worldwide are implementing rules and regulations to highlight the several functions of agriculture, incentivize farmers to use eco-friendly production methods, and advance the sustainable growth of agriculture (Byfuglien et al., 2025). Currently, China’s economic progress has entered a new phase characterized by a focus on the quality of economic growth. Over the years, China’s No.1 central document has proposed the green transformation of agricultural development as an important task in China’s agricultural development. Nevertheless, the accelerated progress of China’s agricultural sector has led to excessive exploitation of cultivated land, unbalanced employment of factors, and increasingly inefficient production modes (Zhou et al., 2021). As a result, a considerable quantity of high-quality cultivated land is being lost, environmental pollution has become severe, and the agricultural economy’s contribution to development has progressively declined. This situation hinders the establishment of a sustainable and healthy agricultural society. Thus, effectively managing cultivated land use and balancing its different functions can reduce agricultural environmental pollution and enhance both the quantity and quality of agricultural total factor productivity. Optimizing the input structure of agricultural factors, enhancing allocation efficiency, and fostering the modernization and growth of the agricultural economy can all be accomplished effectively through reasonable cultivated land use transformation. Guiding cultivated land use transformation to a more efficient and environmentally friendly direction is not only an in-depth exploration of the value of land resources but also a concrete practice of the concept of sustainable agricultural development. It creates novel opportunities for the advancement and modernization of the agricultural sector. Therefore, it is critical for the enhancement of the agricultural economy’s quality that cultivated land use transformation be actively directed. In order to rationally and effectively play the role of cultivated land use transformation, there is an urgent need to clarify in depth the theoretical mechanism of the effect of cultivated land use transformation on the efficiency of green agricultural production. This has far-reaching implications for achieving sustainable use of cultivated land, ensuring food security, and promoting high-quality sustainable development of agriculture.
Research on land use transformation mainly includes forest transformation (Mazya et al., 2023), grassland transformation (Yang et al., 2021), and residential land use transformation (Liu et al., 2023), focusing on such elements as spatial and temporal changes in land use patterns (Xiong et al., 2022), impacts on the environment (Dibba et al., 2025), and factors affecting transformation (Prabhakar, 2021). Research on cultivated land use transformation was first expanded from land use transformation. It mainly focuses on conceptual connotations, modeling approaches, and changes in spatiotemporal patterns (Gao et al., 2022a; Li and Song, 2023), driving mechanisms (Buckley Biggs, 2022), influencing factors (Prabhakar, 2021), and functional transformations (Liang and Li, 2020). In terms of research content, existing studies have mainly explored the conceptual connotation of cultivated land use transformation in terms of explicit and implicit morphological transformation, and spatial pattern and functional form transformation (Li et al., 2022a). Most studies primarily concentrate on national, provincial, municipal, or specific regional scales (Wang et al., 2023), with research scales mainly focusing on provinces, municipalities, and counties. There are very few studies that analyze and discuss administrative villages as the research unit. Most scholars measure it from methods such as hierarchical analysis, the entropy method, and the land use transfer matrix (Xie et al., 2019). Combining Geographically and Temporally Weighted Regression (GTWR), Spatial Durbin Model (SDM), and other methods (Chen et al., 2024). Related studies explore the transition patterns, drivers, and transition effects of cultivated land use transformation (Gao et al., 2022b).
Green total factor productivity is a measure of total factor productivity that considers limitations imposed by resources and environmental pollution (Xia and Xu, 2020a). Research projects on AGTFP have yielded significant results, with a primary focus on the conceptual connotation, modelling methodology (Li and Song, 2023), spatiotemporal evolution characteristics (Bao et al., 2023), and driving factors (Huang et al., 2022). In terms of research content, scholars on AGTFP are mainly interested in the definition and treatment of resource and environmental constraints. Studies have utilized agricultural surface pollution and agricultural carbon emissions as restrictions on resources and the environment (Chen et al., 2021). This involves treating environmental elements as input variables and non-desired output variables. From a methodological point of view, the study mainly adopts the growth accounting method, data envelope analysis, stochastic frontier analysis, and applies the SBM function and Malmquist-Luenberger index to measure AGTFP from static and dynamic perspectives (Chen et al., 2021; Song et al., 2022). The majority of research primarily concentrates on national, provincial, and regional levels, with only a few focusing on the primary group of farmers. Investigate the environmental conditions, resource availability, economic status, and other factors influencing AGTFP (Huang et al., 2022). The factors affecting AGTFP include human capital, government expenditure, climate change, carbon emissions and trading, agricultural industrial agglomeration, agricultural mechanization, and technological innovation (Deng et al., 2023; Hong et al., 2023; Liu et al., 2021; Song et al., 2022; Wang et al., 2022a).
There is limited research both domestically and internationally on the influence of cultivated land use transformation on AGTFP. Most studies focus on the relationship between the multifunctionality of cultivated land use and agricultural economic development (Gao et al., 2022b; Xu et al., 2019a), and the impact of cultivated land use transformation on urbanization. Specifically, several studies have examined the influence of cultivated land production and ecological functions on agricultural economic growth. This includes research on socio-ecological feedback and socio-economic changes resulting from land-use change (Lambin and Meyfroidt, 2010). The correlation between cultivated land area conversion and quality changes with economic growth, the connection between cultivated land turnover and agricultural production potential, and the effects of cultivated land de-agriculturalization on the regional agricultural economy (Li et al., 2023; Ustaoglu and Williams, 2022). An additional aspect of the study investigates the correlation between cultivated land use transformation and county urbanization, technical efficiency of food production, urban–rural integration, and the effect of rural labor force transfer (Dai et al., 2024). The findings indicate that cultivated land use transformation influences and interacts with the scale of agricultural operations, industrial structure upgrading, and county urbanization (Chen et al., 2023; Wang et al., 2022b). Taken together, previous studies have provided extensive theoretical and empirical explorations of cultivated land use transformation and AGTFP, respectively. The existing body of research has predominantly concentrated on examining the effects of cultivated land utilization patterns on both social development and agricultural economic growth. Improvements in agricultural productivity are inevitable in the process of agricultural economic development, but the environmental impacts they generate are also crucial from the perspective of green and sustainable development. Nevertheless, there is a scarcity of research that specifically examines the effects of the cultivated land use transformation process on agricultural green production. Furthermore, the underlying theoretical mechanism remains obscure, which complicates the task of offering scientific guidance to address the green transformation of agricultural development.
The Yellow River Basin in China is a significant region for grain production, encompassing areas like the Huanghuaihai Plain, the Fenwei Plain, and the Hetao Irrigation Area. These areas contribute to one-third of China’s grain output and are essential for ensuring food production security (Wang et al., 2018). China’s annual ecological environment statistics report for 2020 reveals that the Yellow River Basin had a total water pollution emission of 6,940,900 t. Among these, 3,609,400 t were from agricultural surface pollution, making up 52% of the total. This high percentage is attributed to the focus on maximizing agricultural output without considering the adverse environmental effects, leading to a growing and concerning issue. In 2021, the State Council of the Central Committee of the Communist Party of China released the “Yellow River Basin Ecological Protection and High-quality Development Plan Outline,” which emphasizes the need to encourage different types of medium-scale farming based on local circumstances and enhance the overall control of pollution on agricultural land. Therefore, it is crucial to enhance the ecological environment of the Yellow River Basin and advance high-quality regional development by studying how changes in cultivated land use impact the input structure and efficiency of agricultural factors. This research elucidates the logical relationship and theoretical structure linking cultivated land use transition and AGTFP by examining relevant literature. This paper examines 62 cities in the Yellow River Basin to establish an index system for assessing the transformation of cultivated land use and AGTFP. The entropy weight approach, linear weighted sum method, and SBM-GML model were employed to assess the transition of cultivated land use and AGTFP within the Yellow River Basin between 2000 and 2020. Utilizing panel data and employing a bi-directional fixed-effects model to investigate the influence of cultivated land use transformation on AGTFP in the Yellow River Basin. The aim is to elucidate the mechanism and empirical impact of cultivated land use transformation on AGTFP, offering scientific insights for guiding the future direction of cultivated land use transformation in the Yellow River Basin. This research also aims to contribute to the food security, high-quality, and sustainable development of the agricultural economy.
Theoretical frameworkConnotation analysis
Cultivated land undergoes noticeable changes in quantity and spatial arrangement over time and space. With socio-economic progress, the role of cultivated land shifts from primary production to encompassing ecological and social aspects. The management of cultivated land is increasingly focused on intensification, sustainability, and ecological advancement. Land use transition is a shift in land use patterns throughout time that aligns with the region’s socio-economic development stage. Cultivated land use transformation is a significant aspect of land use change, where cultivated land is altered from one form to another over an extended period due to intricate societal issues and genuine needs (Li et al., 2022b). There are two types of land use modification: explicit transformation and implicit transformation. Explicit transformation mainly includes the quantity, space, and landscape pattern of land use, while implicit transformation mainly includes land quality, property rights, and functions (Long and Li, 2012). This paper’s analysis categorizes cultivated land use transformation into spatial transformation, functional transformation, and modal transformation, considering both explicit and implicit forms (Gao et al., 2022b; Li et al., 2022b). Cultivated land undergoes noticeable changes in quantity and spatial arrangement over time and space. With socio-economic progress, the role of cultivated land shifts from primary production to encompassing ecological and social aspects. The management of cultivated land is increasingly focused on intensification, sustainability, and ecological advancement.
Total Factor Productivity is a crucial indicator of economic growth quality. It represents the portion of overall economic growth that excludes the influence of labor and capital inputs on growth (IRMANTO and OKTORA, 2023). Green Total Factor Productivity (GTFP) is calculated by incorporating resource and environmental elements as non-desired outputs alongside conventional input–output indicators like capital, labor, and energy (Liu et al., 2021). AGTFP is a measure of production efficiency that aims to achieve balanced development of the environment, society, and the economy by strategically utilizing production factors in agricultural activities while safeguarding the ecological environment. Its growth can be primarily attributed to improvements in technical efficiency and technological advancements. Technical efficiency is the comparison between the minimum cost needed to produce a specific quantity of a product and the actual cost of production, considering fixed market prices and production technology, indicating how effectively resources are utilized in the production process. Technology advancement is a broad notion that encompasses scientific and technological innovation, managerial optimization, workforce quality enhancement, and economic system change (Sun, 2022).
Mechanism analysis
In the context of agricultural production, cultivated land use transformation reshapes the allocation and efficiency of key production factors—land, labor, technology, and agricultural resources—through spatial restructuring, quantitative change, technological innovation, and factor mobility. These changes affect agricultural green total factor productivity (AGTFP) by influencing both desirable output (total agricultural production) and undesirable output (agricultural carbon emissions). From the economic perspective, transformation of cultivated land use optimizes the allocation of production factors, improves land use efficiency, and promotes economies of scale, thereby reducing production costs and enhancing productivity. From the technological perspective, promoting green agricultural technologies and practices—such as precision farming, organic fertilization, and water-saving irrigation—reduces dependence on chemical fertilizers and pesticides, decreases carbon emissions, and improves AGTFP. From the ecological perspective, however, excessive land conversion, intensive cultivation, and improper use of agrochemicals may lead to ecological degradation, soil quality decline, and environmental pollution, thereby constraining green productivity growth.
Specifically, cultivated land use transformation influences AGTFP through three interconnected dimensions: (1) Spatial transformation focuses on changes in the quantitative morphology and spatial pattern of cultivated land. Optimization of land structure and spatial allocation expands production scale and improves land-use efficiency, thus promoting AGTFP. In contrast, excessive fragmentation and declining land quality can inhibit efficiency and green productivity. (2) Functional transformation emphasizes the evolution of cultivated land from single productive or subsistence functions toward multifunctional systems that integrate ecological and social benefits. Reasonable production structure and resource matching can enhance agricultural value addition and reduce pollution, while resource mismatch and ecosystem damage may restrain AGTFP. (3) Model transformation reflects the transition of land use modes toward green utilization and ecological progress. The rational allocation of resources and innovation in green technologies promote sustainable production, but overexploitation of soil fertility, high input intensity, and inefficient technology application can undermine AGTFP improvement.
Therefore, cultivated land use transformation exerts a dual effect on AGTFP: it can promote efficiency and sustainability through structural optimization, technological innovation, and ecological enhancement, or inhibit them through resource misallocation and environmental degradation. Based on this theoretical framework, this study proposes the following hypotheses for empirical testing (Figure 1).
Mechanism of cultivated land use transformation on AGTFP.
Flowchart of Cultivated Land Use Transformation affecting Agricultural Green Total Factor Productivity. It includes three transformations: Spatial, Functional, and Model, each leading to various promoting and inhibiting factors like increased land inputs, pollution waste reduction, and ecosystem destruction, ultimately influencing productivity.
H1a: Cultivated land use transformation promotes increased AGTFP.
H1b: Cultivated land use transformation inhibits increased AGTFP.
The spatial transformation of cultivated land use alters both the quantity and spatial configuration of farmland, thereby influencing agricultural total factor productivity (AGTFP). Specifically, rational land transfer and integrated land improvement optimize the utilization structure and spatial layout of farmland, increasing land-use efficiency and the desirable output of agriculture, thus promoting AGTFP. Conversely, ineffective integration of land resources can cause land fragmentation, weaken scale effects, raise management costs, and reduce production efficiency. Moreover, unreasonable spatial transformation may lead to ecological degradation—such as soil erosion and fertility loss—further diminishing farmland quality and green productivity. Based on these mechanisms, this study proposes the following hypotheses.
H2a: Cultivated land use spatial transformation promotes increased AGTFP.
H2b: Cultivated land use spatial transformation inhibits increased AGTFP.
The functional transformation of cultivated land use reshapes agricultural factor flows, production modes, and the agro-ecological environment, thereby influencing agricultural green total factor productivity (AGTFP). Proper functional transformation optimizes production structures, promotes rational allocation of labor and capital, and enhances the integration of agriculture with other industries. These adjustments improve resource efficiency, reduce pollution and waste, and strengthen the value and competitiveness of agricultural output, ultimately boosting AGTFP. However, improper transformation may lead to resource misallocation, land conversion from food to non-food uses, and declining land-use efficiency. In addition, infrastructure expansion and inadequate ecological compensation mechanisms can cause ecological degradation and discourage farmers from engaging in conservation practices. Overemphasis on ecological protection at the expense of production needs can also constrain agricultural productivity. Based on these mechanisms, this study proposes the following hypotheses.
H3a: Cultivated land use functional transformation promotes increased AGTFP.
H3b: Cultivated land use functional transformation inhibits increased AGTFP.
The transformation of cultivated land use patterns influences AGTFP by reshaping resource utilization efficiency, production methods, and the ecological environment. Proper model transformation promotes intensive, green, and ecologically oriented land use, optimizes the allocation of agricultural resources, and facilitates green technological innovation and industrial upgrading. These changes enhance agricultural competitiveness, improve resource efficiency, and reduce environmental impacts, thereby advancing AGTFP. However, excessive pursuit of high yields per unit area may neglect land conservation and soil restoration, leading to fertility degradation and reduced long-term productivity. In addition, the high cost and limited adaptability of green technologies may discourage farmers from adopting sustainable practices, reducing their short-term willingness to engage in green production. Ecological production models also require long-term accumulation and policy support; inadequate implementation may weaken their effectiveness and hinder improvements in AGTFP. Based on these mechanisms, this study proposes the following hypotheses.
H4a: Cultivated land use model transformation promotes increased AGTFP.
H4b: Cultivated land use model transformation inhibits increased AGTFP.
Methodology and data sources
This paper constructs an evaluation index system for cultivated land use transformation and AGTFP based on scientific principles, operability, and comprehensiveness. The entropy value method was utilized to calculate the weights of each index of cultivated land use transformation. The cultivated land use transformation index was assessed through weighted summation. AGTFP was efficiently measured and de-composed using the non-expected SBM model and GML index.
VariablesCultivated land use transformation measurement variables
Based on the connotation of cultivated land use transformation and data availability, three target layers, six-factor layers, and 12 indicator layers are constructed. Cultivated land is usually transformed to varying degrees in both explicit and implicit forms during the transformation process (Table 1). Explicit morphological changes mostly involve the spatial transformation of cultivated land, while implicit morphological changes generally involve changes in mode and function. The significant disparity in resource distribution among cities in the Yellow River Basin has a substantial influence on the spatial heterogeneity mechanism, particularly affecting the quantitative distribution of cultivated land. Thus, the amount of cultivated land and the area of cultivated land are the direct drivers of the spatial transformation of cultivated land use, as measured mainly by the area of cultivated land per capita, the ratio of food crops sown, and the rate of land resettlement (Lv et al., 2022). In terms of the utilization pattern of cultivated land, the transformation has been carried out through changes in factor inputs and agricultural technology inputs, both in terms of intensive utilization and technological progress. It is mainly characterized by the proportion of facility agriculture, grain yield, and total agricultural machinery power per capita (Li et al., 2022c). The main functions provided by cultivated land in daily life are manifested in the three areas of production, subsistence, and ecology. The production function is the material supply capacity of cultivated land, selected to characterize the unit grain yield and agricultural output per unit area; the subsistence function refers to the livelihood security capacity of cultivated land utilization; the per capita food guarantee and the proportion of agricultural employment were selected to characterize; and the ecological maintenance capacity of cultivated land use, which was chosen to be assessed through the intensity of fertilizer and pesticide application, is referred to as the ecological function (Gao et al., 2022b).
The indicator system of cultivated land use transformation.
Transformation
Weight
Indicator’s type
Indicator’s name
Definitions
Trend
Indicator weight
Spatial Transformation
0.2646
Quantitative Morphology
Per capita cultivated land area
Cultivated land area/Total regional population (ha)
+
0.0935
Ratio of food crops sown
The area under food crops/Total arable land/Ripening system
+
0.0592
Cultivated land reclamation rate
Cultivated land area/Total land area (%)
+
0.1118
FunctionalTransformation
0.3401
Intensive Utilization
Percentage of agricultural area in facilities
Area of agricultural land used for facilities/Area of cultivated land
+
0.1912
Grain production per unit area
Total grain production/Area sown with grain crops (tons/HA)
+
0.0770
Technological Advancement
Total agricultural machinery power per capita
Total power of agricultural machinery/Total regional population (kW)
+
0.0719
Model Transformation
0.3953
Productive function
Grain production per unit area
Total grain production/Area sown with grain crops (tons/HA)
+
0.0728
Agricultural production per unit area
Gross agricultural output/Cultivated land area (CNY 10,000/HA)
+
0.1510
Subsistence function
Per capita food security
Food production/Total regional population (tons/person)
+
0.0684
Proportion of employment in agriculture
Number of people working in agriculture/Total employment
+
0.0465
Ecological function
Fertilizer application intensity
Total fertilizer application/Cultivated land area (tons/HA)
−
0.0458
Pesticide application intensity
Total pesticide application/ Cultivated land area (tons/HA)
−
0.0108
AGTFP measurement variables
The main purpose of the construction of the AGTFP evaluation system is to judge whether the socio-economic efficiency and ecological efficiency in the production process of crops have achieved a win-win situation. The input–output indicator system (Table 2) is developed in accordance with the fundamental requirements of “reasonable input, low energy consumption, and low pollution emission” in the agricultural production process. Employing labor, land, and agricultural resources as input variables, factors such as the number of agricultural workers, crop area, agricultural machinery power, water usage, pesticide and fertilizer application, and agricultural film usage were chosen to represent them. The total agricultural output was considered the desired output. The main carbon sources considered were fertilizers, pesticides, agricultural films, and the sown area of crops. Total agricultural carbon emissions were computed as undesired outputs using carbon emission measuring techniques (Xu et al., 2019b).
The input–output indicators system of AGTFP.
indicator’s type
Indicator’s name
Definitions
Input
Labor
The number of agricultural employees(10,000 people)
Land
The total planting area of crops(1,000 Ha)
The total power of agricultural machinery(10,000 kw)
Agricultural fertilizer application (t)
Agricultural resources
The amount of pesticide (t)
Total water used in agriculture (10,000 m3)
Consumption of agricultural plastic film (t)
Desirable output
Total agricultural production (10,000 t)
Undesirable output
Agricultural total carbon emissions (t)
The control variables
According to the actual situation of agricultural economic development, this paper selects the following control variables from social, economic, and natural aspects (Table 3): (1) Regional industrial structure (Stru). Generally speaking, the proportion of agricultural output value is closely related to the degree of agglomeration of agricultural production, which has a certain impact on AGTFP, expressed as the proportion of total agricultural output value to GDP. (2) Technical fiscal expenditure (Fin). An increase in agricultural mechanization boosts production but also leads to higher carbon emissions per capita, with uncertain consequences. Expressed in terms of total agricultural machinery power per capita. (3) Educational attainment of farmers (Edu). The level of education of agricultural producers has a strong correlation with the mastery of production technology and the rational use of fertilizers, pesticides, and other factors of production, which theoretically affect AGTFP. We have chosen to characterize the number of years of education attained by each cultivator in this article. (4) Agricultural labor inputs (Lab). As the primary resource factor, the labor force has a significant impact on the growth of the agricultural economy, and the proportion of agricultural employment is utilized to illustrate this. (5) Technical fiscal expenditure (Fin). Government financial investment in science and technology has a substantial influence on the advancement of green technology in agriculture and serves as a crucial guarantee for fostering scientific and technological talent, promoting production and technological innovation, and fostering scientific and technological innovation. Science and technology expenditures as a percentage of the local general budget were chosen to characterize this. (6) Land productive capacity (Prod). The fundamental level of agricultural production is directly influenced by land output capacity, which also affects the structure and efficiency of agricultural resource allocation and, to some extent, the total factor productivity of agriculture. This paper characterizes grain production per unit area.
Indicator system of control variables.
Control variables
Definitions
Industrial structure (Stru)
The proportion of gross agricultural production in GDP
Technical fiscal expenditure (Fin)
The proportion of technical fiscal expenditure in the local general budget
Educational attainment of farmers (Edu)
Average years of education of farmers
Land productive capacity (Prod)
Grain production per unit area (tons per hectare)
Agricultural labor inputs (Lab)
The proportion of agricultural employment in total employment
Agricultural mechanization level (Mach)
Total agricultural machinery power per capita (Kilowatts per person)
ModelCultivated land use transformation measurement
The mathematical technique utilized to ascertain the degree of dispersion of an indicator is known as the entropy method. As the degree of dispersion increases, so does the magnitude of the indicator’s influence on the overall evaluation. Therefore, the weight of each indicator can be calculated using information entropy according to the degree of variation of each indicator, which provides a basis for the comprehensive evaluation of multiple indicators (Chen et al., 2022). After determining the weights, a linear weighted sum method was applied to determine the cultivated land use transformation index (Zhou et al., 2023) (Equation 1). The linear weighted sum method is an evaluation function method that solves multi-objective planning problems through the assignment of weight coefficients that correspond to the importance of each objective (Lu et al., 2020). Subsequently, the method determines the linear combination of each objective that is optimal.
S=∑j=1nWjRj
Where S is the comprehensive score value of cultivated land utilization pattern, W is the weight of indicators, R is the standardized value of indicators, and n is the number of indicators.
AGTFP measurementSuper-efficient non-expected SBM model
The super-efficient SBM model is constructed by eliminating the evaluated unit from the set of production possibilities, implying that the efficiency value of the evaluated unit is obtained with reference to the production frontier composed of units other than itself. The projection point improved by the model is the point closest to the frontier surface within the set of production possibilities (Tone, 2002). In agricultural production, n decision-making units (DMU) are considered, with x∈Rm, y∈Rh, and b∈Rq representing the inputs, desired outputs, and non-desired outputs of each DMU. The quantities m, h, and q represent the number of variables associated with the inputs, desired outputs, and non-desired outputs. For the kth DMU, the set of production possibilities for period t can be expressed as (Equation 2):
Therefore, the super-efficient SBM model, including non-desired outputs, is constructed. λi denotes the weight of each evaluation unit in constructing the production reference set, Snx, Smyand Sibare the slack variables of inputs, desired outputs, and non-desired outputs, respectively; 1N∑n=1NSnxxn0stands for the average level of input inefficiency, while 1M+I(∑m=1MSmyym0+∑i=1ISibbi0) represents the average level of output inefficiency; and ρ0 denotes the efficiency value of the evaluated unit K (Equations 3, 4).
ρ0=min1−1N∑n=1NSnxxn01+1M+I(∑m=1MSmyym0+∑i=1ISibbi0)s.t.{∑K=1Kkxnk+snx=xn0(n=1,2……N)∑K=1Kkymk+smy=ym0(m=1,2……N)∑K=1Kkbmk+sib=bi0(i=1,2……N)k≥0,snx≥0,smy≥0,sib≥0Global-Malmquist-Luenberger index and its decomposition
The Malmquist-Luenberger productivity index is able to analyze the relative position (efficiency change) and movement (technological progress) of each municipality with respect to the production boundary. To solve the problem of linear programming without feasible solutions efficiently, scholars constructed the Global-Malmquist-Luenberger index by including the production units in the global reference set (Oh, 2010), defining the GML index as (Equations 5–7):
GMLtt+1 represents the AGTFP from period t to t + 1. GML larger than 1 signifies a decrease in non-wanted production, an increase in desired output, and a rise in AGTFP; conversely, it implies a drop in AGTFP. GML can be further decomposed into changes in technical efficiency (EC) and changes in technical progress (TC), and GECtt+1 and GTCtt+1 in Equations 6, 7 denote the agricultural green technical progress and agricultural green technical efficiency of EC and TC, respectively, in the period t to t + 1. Values greater than 1 (less than 1) indicate technical efficiency improvement (deterioration) and technical progress enhancement (degradation), respectively.
Regression model
To solve the problem of omitted variables, and taking into account the individual effect and time effect, this paper establishes the two-way fixed effect model to study the impact of cultivated land use transformation on AGTFP, and the specific model is set as follows:
AGTFPit=α0+α1∗CLUTit+∑j=2nβj∗Controlit+γt+μi+εit
The analysis of this paper shows that cultivated land use transformation includes spatial transformation, functional transformation, and model transformation, and that different types of transformation have different impacts on AGTFP. In view of this, this paper further explores the role of spatial transformation, functional transformation, and model transformation in cultivated land use transformation affecting AGTFP enhancement through empirical analysis. The two-way fixed effect model is used for empirical investigation, and the specific model is set as follows:
Where the explanatory variable AGTFPit denotes the AGTFP in year t of the ith city, the core explanatory variable CLUTit denotes the cultivated land use transformation index in year t of the ith city. CLUSTit,CLUFTit,CLUMTitdenote the spatial transformation, functional transformation, and model transformation indices of cultivated land use transformation of the ith city in the tth year. The Controlit denotes a set of control variables. γt and μi denote individual fixed effects and time fixed effects, and εit is the random error term.
Study area
The Yellow River Basin is situated between longitude 95°58′ ~ 119°05′E and latitude 32°10′ ~ 41°50′N (Figure 2). It features a topography that is elevated in the west and lower in the east, encompassing four distinct geomorphologic units: the Tibetan Plateau, the Inner Mongolian Plateau, the Loess Plateau, and the Yellow-Huai-Hai Plain from west to east. The basin is divided into the upper, middle, and lower reaches by the cities of Haikou and the old Mengjin. The Yellow River Basin is a vital region for agricultural production in China and is essential for sustaining the agricultural economy’s development. However, in order to increase the value of agricultural output and the yield of agricultural products, elements such as chemical fertilizers, pesticides, and machinery are blindly increased in the process of cultivated land utilization, which generates problems such as agroecological environment pollution and inappropriate production methods. There is an urgent need to address the contradiction between the “quantity” and “quality” of agricultural productivity and to conduct research with a view to providing practical lessons for the realization of high-quality synergistic development of agricultural regions. Considering the availability of data, 62 prefecture-level cities through which the Yellow River flows are selected to represent the Yellow River Basin in this paper.
Map of the study area.
Topographic map of the Yellow River Basin in China showing elevation in meters with a color gradient from green (low) to brown (high). The basin is outlined in blue. An inset map shows the basin’s location within China. The map includes latitude and longitude markers and a scale bar.Data description
In this paper, data from 62 cities in the Yellow River Basin are selected, and the examination period is 2000–2022. Geospatial data from the Ministry of Natural Resources and the Center for Resource and Environmental Science and Data of the Chinese Academy of Sciences.1 Economic and social data from 2000 to 2020 China Statistical Yearbook, China Agricultural Statistical Yearbook, China Rural Statistical Yearbook, China Environmental Statistical Yearbook, and city statistical yearbooks, as well as the website of the National Bureau of Statistics, the ESP database, and the database of the State Administration of Statistics. To mitigate the limitations arising from incomplete data, missing values were addressed via linear interpolation. This approach is justified, as the proportion of missing observations is minimal and temporally evenly distributed, thereby preserving the inherent temporal trends without imposing strong assumptions.
ResultsSpatial and temporal patterns of cultivated land use transformation
From Figure 3, the overall trend of the cultivated land use transformation index in the Yellow River Basin from 2000 to 2020 fluctuated and increased, from 0.7956 to 1.7680. Among them, the period 2000–2004 witnessed a substantial increase, with a 65% growth rate from 0.7956 to 1.3196; the cultivated land use transformation index showed fluctuating changes from 2004 to 2010, with a large rate of change but not a clear trend of transformation; the period 2010–2020 shows a significant increase from 1.3197 to 1.7680, with a growth rate of 33 percent.
Changes in the cultivated land use transformation index in the Yellow River Basin.
A line graph shows the Cultivated Land Use Transformation Index from 2000 to 2024. The index exhibits an upward trend with fluctuations. The trend line equation is y = -79.0419 + 0.0399x, with an R-squared value of 0.8169, indicating a strong correlation.
Using the quartile classification method, based on the measurement results of cultivated land use transformation, this paper classifies cultivated land use transformation into four categories: significantly decline, slightly decline, slightly increase, and significantly increase. From Figure 4, it can be seen that the index of cultivated land use transformation in the Yellow River Basin experienced a fluctuating upward evolution from 2000 to 2020, and showed an increasingly balanced spatial distribution. In 2000, the average transformation index value was 0.7956. Regions such as Golo Tibetan Autonomous Prefecture, Jinan City, and Zibo City experienced significant or slight increases in transformation. In the Yellow River Basin, most regions saw slight decreases or significant decreases in cultivated land use transformation. 22.5% of regions had increasing transformation indexes, while 77.5% had decreasing indexes. In 2005, the average transformation index value was 1.2077. The number of regions experiencing a significant increase in cultivated land use transformation from 2000 to 2005 rose, with 51.6% of regions showing an increase in the transformation index. Conversely, the number of regions with significant declines decreased, with 14.5% of regions transitioning from significant declines to slight declines. This shift included Baotou City, Yulin City, Qingyang City, and the Tibetan Autonomous Prefecture of Gannan. In 2010, the transformation index had an average value of 1.3197. The percentage of regions seeing a slight increase in agricultural land use transformation rose from 20.9% in 2005 to 27.4% in 2010, primarily in Qingyang City, Shangluo City, and other areas. In 2015, the average transformation index value was 1.5464. The percentage of regions seeing a significant increase in agricultural land use transformation from 2010 to 2015 rose from 33.8 to 53.2%, encompassing areas such as Bayannur, Weinan, Jinzhong, and Sanmenxia. In 2020, the average transformation index value was 1.7680. The number of regions with significantly increased and slightly increased cultivated land use transformation rose from 2015 to 2020. The index increased significantly in more regions than it declined, with 88.7% showing an increase and 11.3% showing a decrease. In summary, from the perspective of spatial and temporal patterns, the cultivated land use transformation index of the Huanghuaihai Plain and the Hetao Plain in the middle and lower reaches of the Yellow River Basin takes the lead in showing an increasing trend. Along the cities on both sides of the Yellow River, the transition from a significant decline to a slight increase or a significant increase is gradually realized from the northwestern, southeastern, and southern regions.
Spatial pattern of cultivated land use transformation in the Yellow River basin.
A map displaying regions with varying levels of growth and decline. Areas are color-coded: blue for significant decline, light blue for slight decline, yellow for slight increase, and orange for significant increase. Circles indicate growth magnitude: small for slight increase, medium for steady increase, larger for significant increase, and largest for dramatic increase. A compass and scale bar are included.Spatial and temporal patterns of AGTFP
The GML index is calculated using the SBM-GML model and the cumulative value of the AGTFP is obtained by cumulation. As can be seen from Figure 5, the overall trend of AGTFP in the Yellow River Basin from 2000 to 2020 fluctuated upward, from 1 to 1.3623. Among them, the period 2000–2008 showed a small increase, with a faster growth rate, from 1 to 1.1218, a growth rate of 12%. AGTFP showed a slow downward trend in 2008–2013, from 1.1218 to 1.1080, with a slow rate of change of 1.2 percent. AGTFP increases dramatically from 2013 to 2020, rising from 1.1080 to 1.3623, showing a significant increase, with a growth rate of 22%.
Changes in AGTFP in the Yellow River Basin.
Map showing geographical regions with color codes and circles indicating changes. Blue areas signify a significant decline, light green for slight increase, beige for steady increase, and orange for significant increase. Circle size represents the level of increase: small for slight, medium for steady, large for significant, and very large for dramatic. A scale bar indicates distances in kilometers.
Using the quartile classification method, based on the GML index and the results of the index decomposition, this paper divides the GML index into four categories: slightly declining, steadily increasing, significantly increasing, and dramatically increasing; the technical efficiency (EC) into four categories: significantly declining, slightly declining, slightly increasing, and significantly increasing; and the technical progress (TC) into four categories: slightly declining, slightly increasing, steadily increasing, and significantly increasing. The GML index compared to 1 can indicate a rise (greater than 1) or a fall (less than 1) in AGTFP, with the magnitude of the index value indicating the speed of the rate of change in AGTFP. As can be seen from Figure 6, 7, the average value of the GML index of the 62 cities in the Yellow River Basin from 2000 to 2020 is greater than 1, indicating that the average level of AGTFP in the region during this period shows a trend of growth. Figure 6 illustrates that the majority of the region’s technical efficiencies have either slightly improved or significantly decreased. Specifically, 72.5% of regions experienced an increase in technical efficiency, while 27.4% saw a decline. Notably, regions such as Erdos City, Qingyang City, Dingxi City, and Huangnan Tibetan Autonomous Prefecture showed significant improvements in technical efficiency. Figure 7 shows that technological progress in the Yellow River Basin has mostly slightly increased or significantly declined. Specifically, 87.1% of the areas experienced slight increases, while 12.9% saw significant declines. Notably, areas such as Jinan, Heze, Yinchuan, Jiaozuo, and Gannanzhou showed significant increases in technological progress. AGTFP significantly increases along both sides of the Yellow River, with the fastest growth rate, and cities far away from both sides of the Yellow River have a lower growth rate. Which significantly increasing areas are mainly clustered in the middle and lower reaches, and the middle part of the upper reaches of the river. Comparison of Figures 6, 7 shows that in the upper and middle reaches of the Yellow River Basin, significant or marked increases in AGTFP are mainly caused by increases in technical efficiency, while increases in efficiency in slightly increasing areas are provided by technical progress. The increase in AGTFP in the lower reaches of the Yellow River Basin is mainly provided by the rise in technical progress, and the slower increase in efficiency in the slightly increasing areas is mainly due to the decrease in technical efficiency.
Distribution of GML index and technical efficiency in the Yellow River Basin.
Five maps showing cultivated land use transformation over the years 2000, 2005, 2010, 2015, and 2020. Colors indicate changes: yellow for significant decline, light orange for slight decline, dark orange for slight increase, and brown for significant increase. A north arrow and scale bar are included.
Distribution of GML index and technological progress in the Yellow River Basin.
Line graph showing Agricultural Green Total Factor Productivity from 2000 to 2022. The trend line equation is y = -29.6009 + 0.0153x with R² = 0.9296, indicating strong growth with fluctuations, especially a sharp increase after 2020.Impact of cultivated land use transformation on AGTFPBenchmark regression
Based on the results of the Fisher test, Lagrange Multiplier test, and Hausman specification test, this paper selects the fixed effects model. In order to ensure the accuracy of the results, this paper chooses a two-way fixed effects model to regress the linear model of Equation 8 to verify the relationship between cultivated land use transformation and AGTFP. Table 4 presents the outcomes of the regression analysis. The estimation results indicate that, with a 1% level of confidence, the cultivated land use transformation index is significantly positive. This suggests that the transformation of cultivated land use in the Yellow River Basin contributes significantly to the enhancement of the AGTFP level. The impact of control variables on AGTFP was examined by means of a stepwise regression methodology. At the 1 and 5% confidence levels, the regression results indicate that regional industrial structure, government support for science and technology, agricultural labor input, agricultural mechanization, and agricultural mechanization all have a significant inhibitory effect on AGTFP. At the 5 and 10% confidence levels, food production per unit area and the level of education of producers, on the other hand, make substantial contributions to AGTFP. AGTFP and cultivated land use transformation remain substantially and positively correlated at the 1% confidence level, even after control variables are incorporated; this confirms that cultivated land use transformation contributes to AGTFP. Select the two-way fixed effect model to regress the linear models of Equations 9–11. The estimation results indicate that the spatial transformation fails to pass the significance test and does not verify that the spatial transformation of cultivated land use in the Yellow River Basin significantly contributes to the increase in AGTFP level. The regression results are presented in Table 5. Functional transformation and model transformation are significantly positive at the 1% confidence level, indicating that functional transformation and model transformation of cultivated land use in the Yellow River Basin significantly contribute to the increase of AGTFP level. The regression coefficients for cultivated land use transformation and pattern transformation are 0.0501 and 0.1941, respectively. This suggests that the transformation of cultivated land use patterns in the Yellow River Basin has a greater ability to enhance the increase of AGTFP. In summary, the above results validate the basic hypotheses H1a, H3a, and H4a of the study.
To verify the robustness of the benchmark regression results, two methods are used in this paper. First, regressions are performed using the robust standard error method to avoid the endogeneity problem that exists; second, the explanatory variable AGTFP in this paper is subjected to bilateral 5% shrinkage (the explanatory variables are subjected to 1 and 2% shrinkage with equally robust results) to avoid outliers from affecting the regression results. The robustness results are shown in Tables 6, 7. In conclusion, regardless of whether the explanatory variables are reduced-tailed or the regression is performed utilizing the robust standard error method, there is no substantial difference in the obtained results with respect to coefficients and significance when compared to the baseline regression. At a 1% confidence level, the impact of cultivated land use transformation on AGTFP is statistically significant. This suggests that the findings of the benchmark regression presented in this article are reliable and that cultivated land use transformation significantly enhances AGTFP. The results of the regressions of spatial, functional, and model transformation on AGTFP do not differ significantly in terms of coefficients and significance either. Spatial transformation fails the significance test at the 10% confidence level, and the effects of functional transformation and model transformation on AGTFP are significant at the 1% confidence level. Taken together, the results of these tests indicate that the basic conclusions of the study are robust.
Due to the numerous influences of economic structure, topography, and climate, the degree of cultivated land use transformation in each city in China varies with the degree of regional economic and social development across the country’s vast land area. The division of major grain-producing areas has led to regional differences in terms of agricultural development conditions and policy support (Table 8). There is a wide gap between the eastern, central, and western regions in terms of economic and social development and agricultural production conditions. Therefore, this paper studies the impact of cultivated land use transformation on AGTFP in different regions based on production conditions and regional differences. According to China’s regional division standards, the study area was divided into major grain-producing areas and production and marketing balance areas, using grain production, per capita possession, and commercial grain stocks as indicators. The balance of production and marketing areas consists of 31 cities in Shaanxi Province, Gansu Province, Qinghai Province, Shanxi Province, and Ningxia Hui Autonomous Region. The principal grain-producing regions comprise 31 cities in Shandong Province, Henan Province, Sichuan Province, and Inner Mongolia Autonomous Region. The categorization of cities into distinct regions is determined by geographic location and regional economic development. Nine cities are classified as eastern regions in Shandong Province, while 28 cities are classified as central regions in Shanxi Province and Henan Province. The remaining cities are classified as western regions.
Cultivated land use transformation significantly enhances AGTFP growth in major grain-producing areas and production and marketing balance areas. The regression coefficient for major grain-producing areas is 0.128, and for production and marketing balance areas, it is 0.044. The analysis indicates that cultivated land use transformation is more effective in boosting AGTFP in major grain-producing areas. Cultivated land use transformation in the east, center, and west regions all contribute to the growth of AGTFP. The regression coefficients for these regions are 0.240, 0.074, and 0.049, respectively. These coefficients are statistically significant at the 1 and 5% confidence levels. The east region has the largest regression coefficient, suggesting that cultivated land use transformation in this region has the greatest impact on improving AGTFP. As can be seen from the comparison in Table 9, functional transformation significantly contributes to the growth of AGTFP at the 1% confidence level in major grain-producing areas and balanced production and marketing areas. The regression coefficient for the major grain-producing area is 0.0838, while the coefficient for the balance of production and marketing area is 0.0352, which shows that the functional transformation is more able to promote the growth of AGTFP in the major grain-producing area. The model transformation significantly promotes the growth of AGTFP at a 1% confidence level. The regression coefficients are basically consistent, indicating that the model transformation has basically the same effect on promoting AGTFP growth in major grain-producing areas and areas with balanced production and marketing. Functional transformation in the eastern and central regions promotes the growth of AGTFP and is significant at 5 and 1% confidence levels with coefficients of 0.1784 and 0.0562. This indicates that the cultivated land use model transformation promotes the growth of AGTFP more in the eastern region. Cultivated land use transformation in the western region significantly contributes to AGTFP growth at the 5% confidence level.
Regional heterogeneity in spatial, functional, and model transformation on AGTFP.
DiscussionCultivated land use transformation significantly contributes to AGTFP
The results of the study show a significantly increasing trend in the cultivated land use transformation index in the Yellow River Basin from 2000 to 2020. In the early 21st century, rapid population and economic expansion led to an increased demand for cultivated land output, which resulted in a shift towards non-food cultivation. Urbanization has attracted numerous laborers to cities, resulting in a significant increase in cultivated land abandonment and causing fluctuating shifts in cultivated land use transformation (Qu et al., 2024). Recently, the government has implemented policies to restrict the non-agricultural use of farmland, improve the quality of farmland, and enhance the versatility of farmland, creating a conducive environment for transforming farmland use. The study identified that places experiencing positive changes are primarily located in the northwest, southeast, and southern regions due to their advanced economic status, increased societal needs, and favorable production characteristics, including topography and climate (Lv et al., 2022). The central region is impacted by the natural environment, experiencing severe soil erosion and land salinization. The change in cultivated land use patterns is greatly influenced by natural environmental obstacles (Niu et al., 2022). Influenced by the ecological protection of the Yellow River Basin, regional high-quality coordinated development, and other policies, the negative change area gradually shrinks from the scope along the upper and middle reaches of the Yellow River, and the spatial agglomeration degree of the highly transformed area decreases in the process of transformation. The degree of spatial differentiation within the whole region has weakened, and the cultivated land use transformation index has developed towards equalization within the Yellow River Basin.
Meanwhile, AGTFP in the Yellow River Basin showed a significantly increasing trend from 2000 to 2020. Since the year 2000, the government has consistently implemented policies that are advantageous to farmers, including the elimination of agricultural taxes, the augmentation of direct subsidies for grain planting, subsidies for agricultural apparatus, and comprehensive agricultural subsidies. This has increased the incentives for farmers to cultivate, resulting in a moderate increase in AGTFP. However, with the development of the agricultural industry, the improper use of pesticides and chemical fertilizers has caused serious pollution problems, leading to a slow decline in AGTFP (Xu et al., 2019b). The government has implemented a series of environmental protection measures for agricultural production in recent years in an effort to promote the growth of ecological recycling agriculture; these measures have contributed to the exponential increase in AGTFP. This research reveals that productivity growth is notably higher along both sides of the Yellow River, particularly concentrated in the middle and lower reaches, as well as the middle section of the upper reaches. This is primarily due to the favorable natural conditions and a weak binding force on resources and the environment in these areas. Regional disparities in the sources of AGTFP growth are evident, as more developed downstream regions, early marketization of agriculture, and advancements in technology contribute to the rise in AGTFP. In the upstream and midstream regions, the diffusion of modern agricultural technologies is more difficult, and technological progress plays a lesser role. However, due to the low level of marketization and scale in agriculture, the growth of technical efficiency is significantly faster, and the growth of AGTFP is mainly provided by the increase in technical efficiency.
Cultivated land use transformation contributes significantly to AGTFP, with a more pronounced effect in major grain-producing areas and eastern regions. The spatial transformation of the Yellow River Basin does not contribute significantly to AGTFP. Spatial transformation usually involves a number of aspects, such as the reconfiguration of land resources, the construction of infrastructure, and the rearranging of agricultural production. It is difficult to implement and takes a long time to show its contribution to AGTFP. Functional and model transformation play a significant role in promoting AGTFP, and the effect of model transformation in promoting AGTFP is more obvious. Although functional transformation can promote the development of agriculture in the direction of diversification and integration, its main concern is the expansion of agricultural functions and the integration of agriculture with secondary and tertiary industries. The model transformation not only involves comprehensive innovation in agricultural production methods, business models, and management mechanisms, but also pays more attention to the efficient utilization of resources and environmentally friendly development. In the Yellow River Basin, a region with limited water resources and a delicate ecological environment, the model transformation incorporates innovative agricultural production technology and management practices by promoting green agricultural technologies like water-saving irrigation and precision fertilization. It has the potential to significantly alter the conventional agricultural production methods and enhance the progress of agricultural production towards a more efficient and environmentally sustainable path. In the major grain-producing area and the balance-of-production and marketing area, both functional transformation and model transformation are responsible for promoting AGTFP. And the functional transformation is more effective in major grain-producing areas than in balanced production and marketing areas. Functional transformation involves the optimization and upgrading of the structure of the agricultural industry, as well as innovations in agricultural production methods. As the core area of agricultural production in the Yellow River Basin, the major grain-producing area has a larger scale of agricultural production, and the inputs of production factors such as land and labor are more concentrated, so that the transformation measures can better exert the scale effect. In contrast, the scale of agricultural production in the balance of production and marketing areas is relatively small, and the production structure is more diversified, including not only food crops, but also cash crops and so on. The scale effects of transformation measures may not be as pronounced as in major grain-producing areas, and diversified production structures make functional transformation relatively difficult and complex to implement in production and marketing balance areas. In the eastern and central regions, functional transformation was the main contributor to the increase in AGTFP, with the effect being more pronounced in the eastern region. The increase in AGTFP in the western region is mainly provided by a model transformation. This is mainly because the eastern region has a good agricultural base and a developed economy, and the expansion of agricultural functions and industrial chains plays a greater role in promoting AGTFP. As a result of less favorable natural conditions, the western region possesses a greater capacity to optimize agricultural production methods, enhance resource use efficiency, and improve AGTFP through model transformation. The economic factors, including an uncoordinated industrial structure and inadequate government support, in the Yellow River Basin hinder the improvement of AGTFP. Inappropriate use of agricultural machinery and labor force loss also contribute to this inhibition. Agricultural producers with greater education levels demonstrate more logical utilization of production technologies, fertilizers, insecticides, and other production elements, hence enhancing AGTFP.
Comparison with previous studies
Previous studies have focused on implicit and explicit, or spatial and functional unilateral studies. This paper analyzes the spatial, functional, and pattern aspects in the study of cultivated land use transformation, and synthesizes the index system of related studies to make up for the single index (Li et al., 2022b). Considering that AGTFP is closely related to technical efficiency and technological progress, it is analyzed from the perspective of spatio-temporal patterns, which is consistent with previous studies (Peng et al., 2024). The extensive transformation of cultivated land function in China and the progressive increase in the overall value of the functional supply of cultivated land align with the findings of this study (Song et al., 2015). The cultivated land use transformation index in Huaibei, Jiangsu Province, shows a trend of first decline and then increase, which is somewhat different from the study in this paper. Mainly due to geographical differences resulting in different climates, cultivated land in Huaibei is susceptible to flooding, resulting in a downward trend in the cultivated land utilization index (Li et al., 2022b). It has been concluded that China’s AGTFP in general shows a fluctuating growth trend, and there are regional differences. Provinces with higher average annual AGTFP are mainly located in the eastern region, and their provincial differences tend to increase (Liu et al., 2021; Xia and Xu, 2020b). The general technical efficiency is below 1, suggesting a decline in green efficiency. The increase in AGTFP is mostly driven by technology advancements, with the most rapid rise observed in the east, followed by the center, and the slowest in the west. Regional disparities in the sources of AGTFP growth are evident, with the limited growth potential of technical advancements in the western region being the primary cause of the regional gap, aligning closely with the findings of this study (Xu et al., 2019c).
Policy implications
The current policies for the Yellow River Basin prioritize cultivated land protection and agricultural development, addressing several elements such as water resource management, ecological restoration, and industrial restructuring. Due to the extensive size of the Yellow River Basin and the notable variations in natural environment, economic conditions, and social development levels among areas, it is crucial to provide specific policy recommendations tailored to each region. Based on the differences in the Yellow River Basin region, this paper proposes the following targeted policy recommendations: (1) The center region of the Yellow River Basin should prioritize protecting cultivated land from ecological harm and enhancing ecological restoration and management due to environmental limitations. The center region features a loess plateau landform with gullies, undulations, susceptibility to landslides and mudslides, substantial soil erosion, and a fragile natural habitat. The government should allocate resources and personnel to soil and water conservation initiatives, promote the adoption of advanced agricultural practices among farmers, enhance disaster early warning systems, and utilize modern technology for real-time monitoring of soil erosion, landslides, mudslides, and other disasters. Government departments must implement a variety of steps to effectively protect the quantity of farmed land and reduce the influence of uncontrollable factors on agricultural production. (2) Cities on both sides of the Yellow River Basin should enhance agricultural management, upgrade agricultural infrastructure, promote crop diversification based on regional characteristics, and improve the economic and ecological roles of cultivated land. Government departments need to enhance resource conservation efforts, further support initiatives to decrease waste, recycle pharmaceuticals and agricultural film, optimize the distribution of agricultural resources, and enhance agricultural technological innovation. Cities should develop resource-saving and environmentally friendly agriculture and realize the shift from factor-driven to innovation-driven. (3) Strengthening the promotion of modern agricultural technology in the upstream and midstream areas and carrying out various forms of publicity activities to enhance farmers’ awareness of green and ecological agriculture. To help most farmers understand the profound importance of green ecological agriculture in protecting the environment, enhancing the quality of agricultural goods, and boosting farmers’ income. Organizing the teaching of agricultural technology by professionals to enhance the efficiency and specialization of agricultural production and promote sustainable agricultural growth in local contexts. (4) Municipalities in the southern region must implement strategies, including land remediation and land reclamation, to consolidate scattered cultivated land and enhance the spatial distribution of cultivated land through agglomeration and rationalization. It is recommended that municipalities elucidate the correlation between resource property rights and large-scale agricultural management, advocate for the integration of mechanized farming practices, and improve the overall landscape and management structure of cultivated land. A cooperative and intensive approach to agricultural development continues to advance agricultural marketization and scale. While ensuring the effective amount of cultivated land, go on to improve the spatial distribution of cultivated land in terms of agglomeration and rationality. Realize the improvement of green efficiency based on maintaining the progress of green technology, and promote the steady improvement of green production efficiency in agriculture.
Limitations
This article conducts empirical research and mechanism analysis regarding the impact of cultivated land use transformation on AGTFP, utilizing a regression model and spatiotemporal pattern. On the one hand, the evaluation system for cultivated land use transformation is not comprehensive enough. Cultivated land use transformation is a complex process that involves the transformation of material elements as well as the transformation of farmers’ concepts and decision-making behaviors. Nevertheless, due to the lack of data availability, non-physical factors are not accounted for in the indicator system. In order to enhance the comprehensiveness of the cultivated land use transformation index, further investigations may incorporate these factors. On the other hand, this paper examines fertilizers, pesticides, agricultural machinery inputs, and the use of agricultural films as unwanted outputs in AGTFP measurements. It excludes factors like straw burning and livestock and poultry farming, which may lead to discrepancies between calculated and actual values. The data processing process did not completely consider regional spillover effects, natural environmental factors, policy considerations, and other influences due to challenges in quantitatively assessing these elements. The study should investigate in more depth the mechanisms behind AGTFP, its development, and its opposing impacts on changes in cultivated land use. It is yet to be determined if the value of the consequences researched can be applied to other places, considering variations in natural circumstances, economic development levels, and regulatory influences.
Conclusion
This paper calculates the cultivated land use transformation index by analyzing panel data from 62 cities in the Yellow River Basin between 2000 and 2020, utilizing the entropy weight method and linear weighted sum. The unanticipated highly efficient SBM model was utilized in conjunction with the GML index and index decomposition to assess AGTFP. A spatio-temporal pattern analysis was conducted using ArcGIS software. Studying how changes in cultivated land use impact AGTFP using the two-way fixed effect model. Summarizing the above studies, the following conclusions were drawn: In terms of cultivated land use transformation, the overall trend of the cultivated land use transformation index in the Yellow River Basin from 2000 to 2020 was fluctuating and increasing (from 0.7956 to 1.7680). The geographical pattern shows high in the southeast and low in the center. The region experiencing rising transformation increased from 22.5 to 88.7% of the share, and the development of cultivated land use transformation in the Yellow River Basin towards regional equalization. In terms of AGTFP, the overall trend of AGTFP in the Yellow River Basin from 2000 to 2020 shows a fluctuating upward trend (from 1 to 1.3623). The proportion of regions with rising and falling average values of technical efficiency was 72.5 percent and 27.4 percent, while the proportion of regions with rising and falling average values of technical progress was 87.1 percent and 12.9 percent. The two banks of the Yellow River exhibit spatial and temporal patterns predominantly concentrated in the middle and lower reaches, as well as the central part of the upper reaches, where the AGTFP growth rate is notably higher. Productivity growth in the upstream and midstream sectors is mostly driven by enhanced technical efficiency, whereas the downstream sector is powered by advancements in technology. The cultivated land use transformation index has a significantly positive impact at the 1% confidence level, as shown by the regression results of the fixed-effects model. Cultivated land use transformation in the Yellow River Basin notably enhances the AGTFP level, particularly in the major grain-producing area and the eastern part of the country. The spatial transformation of the Yellow River Basin does not contribute significantly to AGTFP at the 10% confidence level. Functional transformation and model transformation play a significant role in promoting AGTFP at the 1% confidence level, and the effect of model transformation in promoting AGTFP is more obvious. Both functional transformation and modal transformation contributed to the promotion of AGTFP in the balance-of-production and marketing area and the major grain-producing area. Notably, functional transformation exerted a more pronounced promotional impact in the major grain-producing area compared to the balance-of-production and marketing area. The increase in AGTFP was primarily facilitated by functional transformation in the eastern and central regions, with the effect being more pronounced in the eastern region. In contrast, modal transformation is primarily responsible for the increase in AGTFP in the western region. Agricultural mechanization and agricultural labor input negatively impact AGTFP significantly at a 5% confidence level, whereas the education level of agricultural producers positively influences it at a 10% confidence level.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
The author gratefully acknowledges the time, administrative, and logistical support provided by the Bern University of Applied Sciences, School of Agricultural, Forest, and Food Sciences. The author further extends sincere thanks to the data collectors and the staff and projects of Swiss, Austrian, German, Dutch, and American non-governmental organizations, as well as research and universities whose work across agribusiness and rural development projects in the three countries enriched the contextual understanding and field access necessary for this study. Their insights and long-standing engagement with local systems were instrumental in shaping the research. Gratitude is also owed to the many key informants, including farmers, entrepreneurs, extension officers, cooperative leaders, and public sector representatives, who generously shared their time, experiences, and perspectives. Anonymity has been maintained for all respondents under ethical re-search standards and to protect business confidentiality.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Edited by: Liye Wang,Shandong University of Finance and Economics, China
Reviewed by: Zhang Yue, Henan Agricultural University, China