The Effect of Farmland Transfer on Agricultural Green Total Factor Productivity: Evidence from Rural China

Ma, Guoqun; Dai, Xiaopeng; Luo, Yuxi

doi:10.3390/ijerph20032130

Cited by 13 publications

(6 citation statements)

References 67 publications

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“…The degree of agricultural mechanization significantly inhibits AGTFP. Possible explanations include the following: On the one hand, the development of new ecological agriculture and the optimization of traditional farming practices are facilitated by the increase in agricultural mechanization [8,50,51]. On the other hand, because agricultural mechanization has the same characteristics as petroleum agriculture, its excessive use will result in the wasting of petroleum resources and an increase in carbon emissions [34,52].…”

Section: Analysis Of Empirical Resultsmentioning

confidence: 99%

Research on Spatial-Temporal Characteristics and Affecting Factors of Agricultural Green Total Factor Productivity in Jiangxi Province

et al. 2023

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Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries, with the impact effect values of 0.581, 0.647, −0.126, −0.729, −0.326, and −0.559, respectively. As a result, it is suggested that in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

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Section: Analysis Of Empirical Resultsmentioning

confidence: 99%

Research on Spatial-Temporal Characteristics and Affecting Factors of Agricultural Green Total Factor Productivity in Jiangxi Province

et al. 2023

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“…Academic explorations of farmers' choices of technology and the influencing factors generally unfold along four main dimensions: (1) A comprehensive analysis of the various factors: research undertaking a quantitative analysis of the diverse factors influencing farmers' choices of technology, considering both internal and external factors [12] as well as the perspectives of micro-individuals and macropolicies [13][14][15]. (2) A focus on specific factors: Some studies have honed in on specific factors affecting farmers' decisions concerning technology, such as technological awareness [16][17][18][19], technological environment [20], risk preferences [21,22], migration for work [23], market conditions [24], policy incentives, and others [25][26][27]. (3) Classification based on technological attributes: research has categorized the impact of different technological attributes on farmers' technology choice, including single attribute characteristics [28] and multi-attribute features [29].…”

Section: Timesmentioning

confidence: 99%

Impacts of Capital Endowment on Farmers’ Choices in Fertilizer-Reduction and Efficiency-Increasing Technologies (Preferences, Influences, and Mechanisms): A Case Study of Apple Farmers in the Provinces of Shaanxi and Gansu, China

Chen,

Xiang,

Zhao

2024

Agriculture

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On the basis of data collected from 1208 apple farmers in the provinces of Shaanxi and Gansu, this study utilizes the weighted-frequency method to investigate the priority sequence of farmers’ preferences in choosing fertilizer-reduction and efficiency-increasing technologies. Subsequently, ordered-probit models, a mediating-effect model, and a moderating-effect model are employed to empirically analyze the influence of capital endowment on farmers’ choices related to fertilizer-reduction and efficiency-increasing technologies and their underlying mechanisms. The study further examines how agricultural-technology extension moderates these mechanisms. The main findings are: (1) The priority sequence of farmers’ choices concerning fertilizer-reduction and efficiency-increasing technologies is as follows: organic fertilizer substitution, new efficient fertilizers, soil testing and formula fertilization, green manure cultivation, straw mulching, fertilizer-reduction application, and deep mechanical application. (2) Capital endowment significantly enhances farmers’ choices in fertilizer-reduction and efficiency-increasing technologies. (3) The mechanism analyses indicate that capital endowment can promote farmers’ choices in fertilizer-reduction and efficiency-increasing technologies by improving their information-acquisition capabilities. (4) Moderation effects reveal that agricultural-technology extension methods, such as technical training, financial subsidies, and government publicity, significantly and positively moderate the relationship between information-acquisition capabilities and farmers’ choices in fertilizer-reduction and efficiency-increasing technologies. The moderating effects of educational attainment and generational differences on different agricultural-technology extension methods are heterogeneous. Technical training, financial subsidies, and government publicity can effectively enhance the positive impact of information-acquisition capabilities on farmers with a higher educational attainment. Financial subsidies can effectively strengthen the positive impact of information-acquisition capabilities on the older generation of farmers. Therefore, it is recommended to prioritize the accumulation of farmers’ capital endowment, improve their information-acquisition capabilities, and intensify agricultural-technology extension efforts, especially taking into account farmers’ educational attainment and generational differences.

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“…Therefore, finding new driving forces to promote rural land transfer is the main focus of future research. Existing scholars have studied various aspects of rural land transfer, including the willingness to grow nonfood crops [5], property rights systems [6], agricultural green total factor productivity [7], agricultural labor [8], agricultural socialized services [9], agricultural resource endowments [10], land tenure clarification [11], financial literacy [12], subsidy policies [13], land lease agreements [14], agriculture-related loans [15], and agricultural household start-ups [16]. In fact, the most important factor limiting farmland transfer is the shortage of disposable funds for farmers.…”

Section: Introductionmentioning

confidence: 99%

Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China

Xu,

Niu,

Wei

et al. 2024

Sustainability

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Land use efficiency is primarily limited by the fragmentation of land management. China’s fragmented farmland poses a significant threat to the country’s food security and rural revitalization. Therefore, promoting land transfer to establish large-scale operations is a significant solution. With digital technology’s advancements, digital inclusive finance (DIF) has permeated rural regions to provide financial assistance for farmers’ livelihood and rural development. However, it remains unclear if and how DIF can incentivize land transfer. Therefore, this paper aims to establish an econometric model to analyze the impact of digital inclusive finance on land transfer. Additionally, a chain mediation effect model is established to analyze how DIF affects land transfer through an exploration of the mechanisms of farmers’ livelihood capital and the use of digital information. Therefore, the findings from the analysis of data from 3165 farmers demonstrate that DIF has the potential to notably facilitate land transfer and work through the chain mediation channel. Moreover, the impact of DIF on land transfer is even more pronounced in economically developed regions. Consequently, this paper’s results hold the potential to inform policy making by offering insight into three viable paths—digital inclusive financial support, livelihood capital, and digital information—as means to promote land transfer.

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The Effect of Farmland Transfer on Agricultural Green Total Factor Productivity: Evidence from Rural China

Cited by 13 publications

References 67 publications

Research on Spatial-Temporal Characteristics and Affecting Factors of Agricultural Green Total Factor Productivity in Jiangxi Province

Research on Spatial-Temporal Characteristics and Affecting Factors of Agricultural Green Total Factor Productivity in Jiangxi Province

Impacts of Capital Endowment on Farmers’ Choices in Fertilizer-Reduction and Efficiency-Increasing Technologies (Preferences, Influences, and Mechanisms): A Case Study of Apple Farmers in the Provinces of Shaanxi and Gansu, China

Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China

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