Detection and attribution of changes in cultivated land use ecological efficiency: A case study on Yangtze River Economic Belt, China

Yin, Yuqing; Hou, Xianhui; Liu, Jingming; Zhou, Xin; Zhang, Daojun

doi:10.1016/j.ecolind.2022.108753

Cited by 35 publications

(22 citation statements)

References 85 publications

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“…The replanting coefficient, which reflects the intensity of cultivation of arable land, can increase agricultural value added, but can also bring about an increase in the number of tillage and chemical inputs. The replanting coefficient also requires an increase in input intensity, thereby bringing about an increase in undesired agricultural output, which likewise demonstrates the unsustainability of long-term and high-intensity cultivation [ 60 ]. As far as agricultural support policies are concerned, the financial support from the government for agriculture comes mainly in the form of investment and subsidies, which can support the construction of agricultural infrastructure and improve the input structure of agricultural production, thus enhancing AEE.…”

Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

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Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China’s Carbon Neutrality Targets

Cheng

Qiu

et al. 2022

IJERPH

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Agriculture has the dual effect of contributing to both carbon emissions and sequestration, and thus plays a critical role in mitigating global climate change and achieving carbon neutrality. Agricultural eco-efficiency (AEE) is an important measurement through which we can assess the efforts toward reduced emissions and increased sequestration. The purpose of this study was to understand the relationship between China’s target of carbon neutrality and AEE through an evaluative model, so as to improve AEE and ultimately achieve sustainable agricultural development. The Super-SBM model scientifically measures the AEE based on provincial panel data collected between 2000 and 2020. We selected kernel density function and spatial distribution to explore the spatial and temporal evolutionary trends, and used a Tobit model to identify the drivers of AEE. The research shows that (1) China’s agricultural system functions as a net carbon sink, with all provinces’ agricultural carbon sequestration levels recorded as higher than their carbon emissions from 2000 to 2020. (2) Despite sequestration levels, the level of AEE in China is not high enough, and the average efficiency level from 2000 to 2020 is 0.7726, showing an overall trend where AEE decreased at first and then increased. (3) The AEE of each province is clearly polarized; there are obvious core–periphery characteristics and spatial distribution of clustered contiguous areas. Central provinces generally have lower efficiency, eastern and northeastern provinces have higher efficiency, and northeastern provinces always remain in the high-efficiency group. (4) Influencing factors show that urbanization, upgrading of industrial structure, financial support for agriculture, and mechanization have a significant positive impact on AEE. These findings have important implications for the promotion of the low-carbon green development of Chinese agriculture.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China’s Carbon Neutrality Targets

Cheng

Qiu

et al. 2022

IJERPH

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“…In cropping systems with a wide variety of crops, multiple crops also allow for the spread and diversification of risk to suit different growing seasons and different crops for personal use or the markets (Rana et al, 2007;Timo and Anni, 2013;Michalscheck et al, 2018). Studies have shown that China's multi-cropping accounts for 50% of the total farmland, its sown area accounts for about 2/3 of the total sown area, and the crop yield accounts for about 3/4 of the total output (Wang et al, 2022;Yin et al, 2022). Improving the multiple cropping index is one of the important factors for the continuous increase of agricultural production, and has played an important role in ensuring stable agricultural production and reducing poverty (Zhang et al, 2019;Mustafin and Kantarbayeva, 2021;Sun et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of multiple cropping of farmland on the welfare level of farmers: Based on the perspective of poverty vulnerability

Peng

Chen²,

Yu³

et al. 2022

Front. Ecol. Evol.

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This paper aims to explore the impact of multiple cropping on farmers’ welfare level and provide the theoretical and empirical basis for solving relative poverty in rural areas in the future. The paper uses data from the field survey of 1,120 farmers in Hubei in 2018 and uses the Endogenous Transformation Regression Model (ESR) and generalized propensity score matching (GPSM) model to construct a counterfactual framework. The paper analyses the effect of multiple cropping on farmers’ relative poverty and examines its mechanism. The result shows that: Multiple cropping of farmland can reduce the relative poverty of farmers through the mechanism of yield improvement path and factor intensification path. Under the counterfactual hypothesis, the relative poverty of farmers would increase by 28.43% if the farmers who participated in the multiple cropping did not; and that the relative poverty of the farmers would decrease by 29.57% if the farmers who did not participate in the multiple cropping participated. From the perspective of multiple cropping, the poverty reduction effect of multiple cropping in paddy fields is higher than that of dry land. From the perspective of the degree of multiple cropping, the poverty reduction effects of paddy fields and dryland will experience an increasing return to scale as the multiple cropping index increases. When the household equivalent scale adjustment coefficient is not used to eliminate the impact of family population structure on the “family per capita comparable income,” the artificially high results estimated by the model cannot truly reflect the poverty-reducing effect of multi-cropping of farmland. This paper argues that the government can guide farmers to choose the suitable mode of multiple cropping to reduce farmers’ vulnerability to relative poverty.

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“…(2) As for the research methods, the cultivated land utilization efficiency was initially identified through qualitative analyses such as descriptive analyses and trending analyses [ 22 ]. In recent studies, the focus has progressively shifted to empirical research, such as the super-efficient SBM model [ 21 ], comprehensive index evaluation [ 23 ], the SFA model [ 24 ], the three-stage super-efficiency SBM-U model [ 25 ], and the non-radial directional distance function (NDDF) method [ 26 ]. (3) When it comes to the concept of cultivated land utilization efficiency given the specific targets, cultivated land green utilization efficiency (CLGUE) was put forward by taking into account the negative influences of pesticides, fertilizers and carbon emissions on the ecosystem.…”

Section: Introductionmentioning

confidence: 99%

The Spatial and Temporal Evolution of the Coordination Degree in Regard to Farmland Transfer and Cultivated Land Green Utilization Efficiency in China

Zhou

Kuang

2022

IJERPH

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In many parts of the world, the shortage of cultivated land and the food crisis are worsening on a continued basis. Hence, the central and local governments of the PRC have successively issued various related policies to encourage the practice of farmland transfer, promote the eco-friendly utilization of cultivated land, and ameliorate the efficiency of cultivated land utilization. Under the context of large-scale farmland transfer and rural revitalization strategy in China, it is significant to ensure agricultural sustainability through the coordination of farmland transfer and the amelioration of cultivated land green utilization efficiency (CLGUE). In the present study, 30 Chinese provinces were taken as the research object, with the super-efficient SBM model, the coupling coordination degree model and the spatial analysis model applied in combination. Based on the measurement of CLGUE, a thorough analysis was conducted to explore the evolution of coordination degree in regard to farmland transfer and CLGUE in China from both spatial and temporal perspectives. The conclusions drawn from this study are as follows. Firstly, the overall CLGUE exhibited an upward tendency in the PRC, from 0.440 in 2005 to 0.913 in 2019, with a yearly growth rate of 5.47% on average. However, there were significant spatial disparities in CLGUE between different regions and provinces. Secondly, there was a steady increasing trend shown by the level of coordination degree regarding farmland transfer and CLGUE across China. Further, due to the variation in natural and economic conditions, there were significant spatial–temporal disparities in the coordination degree among these 30 provinces. Lastly, there were obvious spatial aggregation patterns at the provincial level regarding the coordination degree in farmland transfer and CLGUE across China. However, there was a declining trend in the level of spatial aggregation patterns for coordination degree.

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Detection and attribution of changes in cultivated land use ecological efficiency: A case study on Yangtze River Economic Belt, China

Cited by 35 publications

References 85 publications

Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China’s Carbon Neutrality Targets

Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China’s Carbon Neutrality Targets

Effects of multiple cropping of farmland on the welfare level of farmers: Based on the perspective of poverty vulnerability

The Spatial and Temporal Evolution of the Coordination Degree in Regard to Farmland Transfer and Cultivated Land Green Utilization Efficiency in China

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