Coupling coordination analysis of grain production and economic development in Huang-Huai-Hai region

Zhang, Yifan; Li, Bingjun

doi:10.1007/s10668-022-02605-y

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…Remote Sens. 2024, 16,659 3 of 22 and (3) explore NGPOWW and its associated effects on winter wheat planting behavior spatiotemporally over the past two decades.…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Zhang,

Sun,

Sun

2024

Remote Sensing

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Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

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“…Remote Sens. 2024, 16,659 3 of 22 and (3) explore NGPOWW and its associated effects on winter wheat planting behavior spatiotemporally over the past two decades.…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Zhang,

Sun,

Sun

2024

Remote Sensing

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“…Other relevant studies often focus on environmental pressure (carbon emission) or a certain method of agricultural land resource utilization, but pay less attention to coupling coordination (Jia et al, 2023;Liu et al, 2023). Several scholars have begun to establish a coupling coordination degree model (CCDM) that analyzes the development-oriented relationships between different systems such as fishery systems and environmental quality (Peng et al, 2021); RECC (Chen et al, 2022;Zhang et al, 2022a); agricultural green development and food security (Zhang et al, 2022b;Zhang and Li, 2022); urbanization and the ecological environment (Ariken et al, 2021;Gao et al, 2021;Yang et al, 2022); tourism and the ecological environment (Zhang and Li, 2021;Zhang et al, 2023); and water resource use efficiency and economic development (Dai et al, 2022;Zhang and Li, 2022) (UNFCCC) proposes improving the capacity for coordinated development between production systems and ecosystems to better respond to climate change (Liu F. et al, 2022). The growing consumer demand for meat has stimulated further expansion of livestock production, greatly increasing pressure on resources and the environment (Liang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the coupling coordination of livestock production, residential consumption, and resource and environmental carrying capacity in China

Zhou,

Wu,

et al. 2024

Front. Sustain. Food Syst.

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Increasing demands for livestock products have stimulated rapid increases in the number of livestock and the scale of farming, thus increasing pressure on resources and the environment. Coordinating the development of livestock production (LP) with residential consumption (RC), resources and the environmental carrying capacity (RECC) is important to ensure sustainable development. In this study, the entropy weight method and the improved-coupling coordination degree (CCD) model were used to identify the spatial–temporal coordination development characteristics of livestock production-residential consumption-resource and environmental carrying capacity (LRRE). Furthermore, the spatial autocorrelation model (SAM) and gray model (GM) were used to analyze the spatial aggregation characteristics and future development trends of the CCDs of the LRRE in China. The findings show that the CCDs of the LRRE values in 31 Chinese provinces increased from 2005 to 2020, but no provinces reached a high coordination level. Specifically, the coordinated development level of LRRE is relatively high in the central, eastern and northeastern regions and relatively low in the western region. The spatial autocorrelation analysis confirmed that the high-high (H-H) aggregation areas were mainly distributed in the northeastern, eastern, and central regions, while low-high (L-H) agglomeration was distributed in the western region. This phenomenon is mainly attributed to the continuous expansion of the scale of livestock production in western China. Regions with relatively developed economies have more funds to invest in environmental protection projects. Using GM method, we find that the CCDs of LRRE in 31 provinces in China will increase from 2021 to 2030, and all provinces will reach the basic coordination level. However, most of the western regions will barely reach the basic coordination level. This result indicates that the low level of LRRE development in western China may be difficult to change in the short term. The level of coordinated LRRE development in the relatively developed eastern region is increasing. The spatial layout of China’s livestock industry should be appropriately adjusted, its expansion rate in the western region should be decreased, and the ecological environment of the areas bordering the western and central regions should be improved. These findings have practical implications for other large livestock production countries. Promoting the coordinated development of LRRE is also an important condition for agricultural transformation in developing countries, especially for improving the environment in key areas of the livestock industry.

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Coupling coordination analysis of grain production and economic development in Huang-Huai-Hai region

Cited by 2 publications

References 41 publications

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Analysis of the coupling coordination of livestock production, residential consumption, and resource and environmental carrying capacity in China

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