The evolution of cultivated land utilization efficiency and its influencing factors in Nanyang Basin

Ji, Zhengxin; Wang, Xiuli; Li, Ling; Xiaoke, Guan; Lin, Yu; Xu, Yao

doi:10.31497/zrzyxb.20210312

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…As shown in Table 7, PIR has significant positive effect on EALU in MGPAs, MGMAs and GPMBAs, with each 1% increase in PIR improving EALU by 0.2323%, 0.3379% and 0.1929%, respectively. With the improvement of farmers' living standards, farmers are relatively more willing to use arable land in a low-carbon way, which in turn improves the level of sustainable utilization of arable land [63,68].…”

Section: Influencing Factors Of Ealumentioning

confidence: 99%

Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Zhang

et al. 2022

Land

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Eco-efficiency of arable land utilization (EALU) emphasizes efficient coordination between land use systems and ecosystems. It is therefore of great significance for agricultural sustainability based on the systematic assessment of EALU. This study took carbon emissions and non-point source pollution resulting from arable land utilization into the measurement system of EALU, and a super-SBM model, kernel density estimation and Tobit regression model were used to analyze regional differences and influencing factors of EALU for 31 provinces in China from 2000 to 2019. The results showed that there was an upward trend in EALU in China from 0.4393 in 2000 to 0.8929 in 2019, with an average annual growth rate of 4.01%. At the regional level, the EALU of three categories of grain functional areas generally maintains an increasing trend, with the highest average value of EALU in main grain marketing areas (MGMAs), followed by grain producing and marketing balance areas (GPMBAs) and main grain producing areas (MGPAs). There are obvious differences in EALU among provinces, and the number of provinces with high eco-efficiency has increased significantly, showing a spatial distribution pattern of “block” clustering. In terms of dynamic evolution, kernel density curves reflect the evolution of EALU in China and grain functional areas with different degrees of polarization characteristics. The results of Tobit regression show that natural conditions, financial support for agriculture, science and technology inputs, level of industrialization, agricultural mechanization, and the living standards of farmers are significant factors resulting in regional disparities of EALU. Therefore, this study proposes the implementation of differentiated arable land use/agricultural management strategies to improve the sustainable utilization of arable land.

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Section: Influencing Factors Of Ealumentioning

confidence: 99%

Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Zhang

et al. 2022

Land

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“…Agricultural machinery can perform the functions of levelling, land preparation, deep turning and deep scarification (Aslan et al, 2007), which can improve land quality better than the traditional manual and livestock operation methods, especially in the transformation of medium-and low-yield fields (Zhou et al, 2019;. Agricultural machinery can increase the degree of multiple cropping of cultivated land to provide the potential for multiple crop cycles per year, thus improving production capacity and land output rates Ji et al, 2021). Mechanical irrigation and drainage, dry farming machinery and mechanical spraying can effectively mitigate risks such as drought, floods, weeds and insect pests (Berdnikova, 2018).…”

Section: Related Literature Reviewmentioning

confidence: 99%

“…and Zhang L.G, 2020). The function of scrambling for planting and harvesting of agricultural machinery will promote the repeated planting of cultivated land and improve the comprehensive agricultural production capacity and land yield rate (Ji et al, 2021). Furthermore, agricultural machinery has the function of resisting disasters such as droughts and floods, weeds and pests, and mechanical seeding and field management can make crops evenly distributed and grow well (Hu and Zhang, 2018).…”

Section: Quality Improvement Pathmentioning

confidence: 99%

Impact of Agricultural Mechanization on Agricultural Production, Income, and Mechanism: Evidence From Hubei Province, China

Peng

Zhao

Liu

2022

Front. Environ. Sci.

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Increasing agricultural operating income is not only an important step in improving agricultural work for farmers in the new era, but is also a powerful way to promote rural revitalization. To improve our understanding of the high-quality development of agriculture in China, the factors limiting agricultural income and the impact of the level of agricultural mechanization on agricultural production and income and its mechanism were analysed. Based on field survey data on farmers, this study analysed the influence of agricultural mechanization level on agricultural production and income by utilizing a sample-modified endogenous merging model and a threshold effect model. The level of mechanization has a significant positive impact on the cost, output value, income and return rate of all types of crops. For every 1% increase in the level of mechanization, the yields of all crops, grain crops and cash crops increase by 1.2151, 1.5941 and 0.4351%, respectively. Heterogeneity analysis shows that the level of mechanization has a certain threshold effect on income, with a greater effect occurring after the threshold. A test of action mechanism shows that the mechanization level can increase income via a factor intensification path and quality improvement path, with the partial mediation effects of the two paths being 28.8 and 27.4%, respectively. It is recommended to increase subsidies to purchase agricultural machinery, research and promote machinery suitable for cash crops, increase the level of socialized agricultural services, and improve the ability of farmers to apply novel agricultural machinery and tools so as to increase their operating profits.

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“…The center of gravity of wheat production in 2010 and 2020 is located in Henan province, and similar conclusions were reached by Li et al [ 49 ]. Ji et al also considered Henan province an important national grain production base, and the stability of wheat production in Henan province is of great significance to guarantee grain production in the central plains [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

Zhang

2022

Foods

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The stability of wheat production is closely related to national food security and agricultural sustainable development, and it has been a major policy concern for China. By analyzing the spatiotemporal factors and causes of wheat production, we can grasp the spatiotemporal distribution law of wheat production to rationally allocate agricultural resources. To this end, this study first conducted a quantitative analysis of the yield differentiation patterns in Huang-Huai-Hai (HHH) wheat based on the 2010–2020 wheat agricultural data, comprehensively using the Theil index and exploratory spatial data analysis. Second, to eliminate the spatial heterogeneity and multicollinearity of the modeling variables, a local model of SCA-GWR combining Spearman correlation analysis (SCA) and geographically weighted regression (GWR) was established. Compared with the traditional global regression model, the superiority and applicability of the SCA-GWR model are proved, and it is a simple and effective new method to detect spatial data nonstationarity. Finally, the factors influencing wheat production in the HHH region were detected based on the SCA-GWR local model, and relevant policy recommendations were put forward. The results show that: (1) The yield difference in different farming areas gradually narrowed, and the wheat production had a significant High-High aggregation trend. The center of gravity for wheat production lies in the southwest of the HHH region. (2) Wheat production still has a strong dependence on irrigation and fertilizer. Effective irrigated areas and temperature are the main driving forces for its production. The inhibitory effect of the proportion of nonagricultural employment on wheat production gradually weakened. Radiation and rainfall were only significantly positively correlated with wheat production in the central and southern HHH region. In response to the findings of the study, corresponding policy recommendations are made in terms of optimizing the allocation of resources, increasing investment in agricultural infrastructure, and vigorously researching and developing agricultural science and technology, and the results of the study can provide a basis for decision-making and management by relevant departments.

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The evolution of cultivated land utilization efficiency and its influencing factors in Nanyang Basin

Cited by 6 publications

References 6 publications

Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Impact of Agricultural Mechanization on Agricultural Production, Income, and Mechanism: Evidence From Hubei Province, China

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

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