What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Yang, Zhe; Chu, Lin; Wang, Chen; Pan, Yan; Su, Wenxia; Qin, Yulu; Cai, Chongfa

doi:10.1016/j.scitotenv.2023.167810

Cited by 8 publications

(1 citation statement)

References 49 publications

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“…The third category involves capturing the distinctive phenological characteristics of crops to characterize changes in the crop growth cycle and subsequently map cropping patterns. This represents the current mainstream direction in the mapping of cropping patterns [10,21,22]. Currently, research is primarily categorized into two approaches.…”

Section: Jstars-2023-01025mentioning

confidence: 99%

Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Yang,

Wang,

Xin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cropping patterns, especially in agricultural landscape regions. Hence, taking Xinghua County as study area, this paper proposed a novel method for integrating multidimensional feature indices and phenological windows, named PWF (Phenological Window Feature), to achieve efficient and accurate mapping of cropping patterns. In this study, we adopt a two-step approach. Firstly, time-series curves of feature indices were constructed using Sentinel-1/2 satellite data to determine the phenological windows of different cropping patterns and construct PWF sets. Then, the RTM (Ruleset Threshold Method) and RF (Random Forest) algorithms were used to map cropping patterns including Wheat-Rice, Crayfish-Rice, Vegetable-Rice, Rice-Rapeseed, Rapeseed-Vegetable and year-round Vegetables. The results indicate that the phenological windows extracted from the cropping patterns in the study area were 30-120 days, 90-135 day and 200-270 days, respectively. The overall accuracies of RTM and RF, based on PWF, were 85.91% and 89.50%, respectively, and the kappa coefficients for RTM and RF were 0.831 and 0.872, respectively. In terms of classification performance, RF slightly outperformed RTM. The study demonstrates that PWF proposed in this paper can be effectively utilized for mapping cropping patterns in complex agricultural landscape regions.

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Section: Jstars-2023-01025mentioning

confidence: 99%

Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Yang,

Wang,

Xin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Promoting or inhibiting? The impact of farmland use transition on rural household income: Evidence from the Three Northeast Provinces, China

Wang,

Zhao,

Qian

et al. 2025

Habitat International

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How the Soybean Producer Subsidy Policy affects crop planting structure: evidence from Chinese county-level data

Zhang,

Cai,

Liang

et al. 2024

BFJ

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PurposeThe Soybean Producer Subsidy Policy (SPSP), an agricultural support policy enacted in China within the past few years, is designed to optimise crop planting structure. This study analyses the impact of SPSP on the crop planting structure in terms of absolute and comparative incomes and elucidates the mechanisms involved.Design/methodology/approachUtilising balanced county-level panel data from 966 counties in China’s major soybean-producing regions, spanning from 2008 to 2021, we investigate the impacts of SPSP on crop planting structure by applying a difference-in-difference (DID) model.FindingsThe findings reveal several crucial insights. First, SPSP optimises the crop planting structure in Northeast China, primarily through an expansion in the area sown to soybeans and a simultaneous reduction in the area sown to maize. Second, the impacts of SPSP gradually strengthen over time but begin to weaken by 2021. Third, heterogeneity analysis indicates that the effects of SPSP are most pronounced in Eastern Inner Mongolia, followed by Heilongjiang, Jilin, and Liaoning. Finally, SPSP incentivises farmers to expand soybean sown areas by improving absolute rather than comparative incomes from soybean cultivation.Practical implicationsAddressing structural contradictions within China’s food supply chain necessitates the adjustment of support policies for different crops to mitigate market distortions. Establishing a holistic agricultural support system encompassing various crops could promote sustainable agricultural practices in the future.Originality/valueOur findings are valuable for policy makers in China and globally who aim to establish support systems for regional linkages that include a variety of crops.

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What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Cited by 8 publications

References 49 publications

Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Promoting or inhibiting? The impact of farmland use transition on rural household income: Evidence from the Three Northeast Provinces, China

How the Soybean Producer Subsidy Policy affects crop planting structure: evidence from Chinese county-level data

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