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Application of the source–sink landscape method in the evaluation of agricultural non-point source pollution: First estimation of an orchard-dominated area in China

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  • Wan, Wei
  • Han, Yiwen
  • Wu, Hanqing
  • Liu, Fan
  • Liu, Zhong

Abstract

Agricultural non-point source pollution (ANPSP) is a critical cause of global environmental problems. However, the estimation of ANPSP in typical orchard-dominated areas is lacking. In this study, the site conditions and fertilizer application patterns of apple orchard-dominated landscapes in Qixia, north China, were investigated, along with the spatial distribution of land use types, which were obtained through field survey and remote sensing interpretation. Combined with rainfall runoff experiments and an analysis of soil samples and statistical yearbook data, the results revealed that the total N (P) export coefficients of apple orchards, cropland, and built-up land were 68.90 (2.79), 41.58 (1.87), and 4.19 (0.47) kg ha–1 a–1, respectively. Moreover, the areas of extremely low, low, medium, high, and extremely high risks for ANPSP in Qixia were 35,232, 56,514, 62,106, 29,331, and 18 450 ha, respectively, under the current fertilizer application rates of N 545 kg ha–1 a–1 and P2O5 569 kg ha–1 a–1 in apple orchard areas. Two fertilization scenarios were simulated according to local standards; the reduction of the extremely high-risk ANPSP area reached 73.39% in scenario I and 100% in scenario II. In contrast, extremely low-risk areas increased by 14.88% (scenario I) and 218.15% (scenario II). Eventually, a correlation analysis between the source, sink, and current status of ANPSP risks and various environmental factors was conducted. The results showed that ANPSP was significantly positively correlated with the drainage density, water erosion intensity, clay content, and SOC, while it was significantly negatively correlated with the slope, altitude, and the sand content, which indicates the good performance of the source–sink landscape method. Therefore, this method at a source–sink landscape scale may facilitate the evaluation of ANPSP in areas both regionally and worldwide.

Suggested Citation

  • Wan, Wei & Han, Yiwen & Wu, Hanqing & Liu, Fan & Liu, Zhong, 2021. "Application of the source–sink landscape method in the evaluation of agricultural non-point source pollution: First estimation of an orchard-dominated area in China," Agricultural Water Management, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:agiwat:v:252:y:2021:i:c:s037837742100175x
    DOI: 10.1016/j.agwat.2021.106910
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    References listed on IDEAS

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    1. Wan, Wei & Liu, Zhong & Li, Kejiang & Wang, Guiman & Wu, Hanqing & Wang, Qingyun, 2021. "Drought monitoring of the maize planting areas in Northeast and North China Plain," Agricultural Water Management, Elsevier, vol. 245(C).
    2. Xuejun Liu & Ying Zhang & Wenxuan Han & Aohan Tang & Jianlin Shen & Zhenling Cui & Peter Vitousek & Jan Willem Erisman & Keith Goulding & Peter Christie & Andreas Fangmeier & Fusuo Zhang, 2013. "Enhanced nitrogen deposition over China," Nature, Nature, vol. 494(7438), pages 459-462, February.
    3. Ma, Wanglin & Renwick, Alan & Yuan, Peng & Ratna, Nazmun, 2018. "Agricultural cooperative membership and technical efficiency of apple farmers in China: An analysis accounting for selectivity bias," Food Policy, Elsevier, vol. 81(C), pages 122-132.
    4. Ma, Wanglin & Abdulai, Awudu, 2016. "Does cooperative membership improve household welfare? Evidence from apple farmers in China," Food Policy, Elsevier, vol. 58(C), pages 94-102.
    5. Risal, Avay & Parajuli, Prem B. & Dash, Padmanava & Ouyang, Ying & Linhoss, Anna, 2020. "Sensitivity of hydrology and water quality to variation in land use and land cover data," Agricultural Water Management, Elsevier, vol. 241(C).
    6. Chen, Han & Huang, Jinhui Jeanne & McBean, Edward, 2020. "Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland," Agricultural Water Management, Elsevier, vol. 228(C).
    7. Huang, Ning-bo & Su, Bao-lin & Li, Rui-rui & Yang, Wu-zhi & Shen, Meng-meng, 2014. "A field-scale observation method for non-point source pollution of paddy fields," Agricultural Water Management, Elsevier, vol. 146(C), pages 305-313.
    8. Grunwald, S. & Norton, L. D., 2000. "Calibration and validation of a non-point source pollution model," Agricultural Water Management, Elsevier, vol. 45(1), pages 17-39, June.
    9. Villamizar, Martha L. & Brown, Colin D., 2016. "Modelling triazines in the valley of the River Cauca, Colombia, using the annualized agricultural non-point source pollution model," Agricultural Water Management, Elsevier, vol. 177(C), pages 24-36.
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    2. Kanthilanka, H. & Ramilan, T. & Farquharson, R.J. & Weerahewa, J., 2023. "Optimal nitrogen fertilizer decisions for rice farming in a cascaded tank system in Sri Lanka: An analysis using an integrated crop, hydro-nutrient and economic model," Agricultural Systems, Elsevier, vol. 207(C).
    3. Yuchen Guo & Liusheng Han & Dafu Zhang & Guangwei Sun & Junfu Fan & Xiaoyu Ren, 2023. "The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    4. Lingyan Xu & Jing Jiang & Mengyi Lu & Jianguo Du, 2022. "Spatial-Temporal Evolution Characteristics of Agricultural Intensive Management and Its Influence on Agricultural Non-Point Source Pollution in China," Sustainability, MDPI, vol. 15(1), pages 1-22, December.
    5. Qiongrui Zhang & Tao Huang & Songjun Xu, 2023. "Assessment of Urban Ecological Resilience Based on PSR Framework in the Pearl River Delta Urban Agglomeration, China," Land, MDPI, vol. 12(5), pages 1-13, May.
    6. Liping Tian & Baixing Yan & Yang Ou & Huiping Liu & Lei Cheng & Peng Jiao, 2022. "Effectiveness of Exogenous Fe 2+ on Nutrient Removal in Gravel-Based Constructed Wetlands," IJERPH, MDPI, vol. 19(3), pages 1-16, January.
    7. Xuekai Chen & Guojian He & Xiaobo Liu & Bogen Li & Wenqi Peng & Fei Dong & Aiping Huang & Weijie Wang & Qiuyue Lian, 2021. "Sub-Watershed Parameter Transplantation Method for Non-Point Source Pollution Estimation in Complex Underlying Surface Environment," Land, MDPI, vol. 10(12), pages 1-25, December.

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