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Delineating Soil Management Zones for Site-Specific Nutrient Management in Cocoa Cultivation Areas with a Long History of Pesticide Usage

Author

Listed:
  • Isong Abraham Isong

    (Department of Soil Science, University of Calabar, Calabar 540271, Nigeria)

  • Denis Michael Olim

    (Department of Soil Science, University of Calabar, Calabar 540271, Nigeria)

  • Olayinka Ibiwumi Nwachukwu

    (Department of Soil Science and Land Resources Management, Michael Okpara University of Agriculture Umudike, Umuahia P.O. Box 7262, Nigeria)

  • Mabel Ifeoma Onwuka

    (Department of Soil Science and Land Resources Management, Michael Okpara University of Agriculture Umudike, Umuahia P.O. Box 7262, Nigeria)

  • Sunday Marcus Afu

    (Department of Soil Science, University of Calabar, Calabar 540271, Nigeria)

  • Victoria Oko Otie

    (Department of Soil Science, University of Calabar, Calabar 540271, Nigeria)

  • Peter Ereh Oko

    (Department of Environmental Resources Management, University of Calabar, Calabar 540271, Nigeria)

  • Brandon Heung

    (Department of Plant, Food, and Environmental Science, Dalhousie University, Truro, NS B3H 4R2, Canada)

  • Kingsley John

    (Department of Plant, Food, and Environmental Science, Dalhousie University, Truro, NS B3H 4R2, Canada)

Abstract

Delineating soil management zones in cocoa cultivation areas can help optimize production and minimize ecological and environmental risks. This research assessed the spatial distribution of heavy metal concentration and soil fertility indicators in Cross River State, Nigeria, to delineate soil management zones (MZs). A total of n = 63 georeferenced, composite soil samples were collected at the 0–30 cm depth increment, air-dried, and subjected to physicochemical analysis. The soil data were subjected to principal component analysis (PCA), and the selected principal components (PCs) were used for fuzzy c -means clustering analysis to delineate the MZs. The result indicated that soil pH varied from 4.8 (strongly acidic) to 6.3 (slightly acidic), with high average organic carbon contents. The degree of contamination was low, while the ecological risk indicator (RI) of the environment under cocoa cultivation ranged from low risk (RI = 18.24) to moderate risk (RI = 287.15), with moderate risk areas mostly found in patches around the central and upper regions. Higher pH was associated with increased levels of exchangeable Ca, Mg, and K, and TN and OC. Strong spatial dependence was observed for silt, pH, OC, Mg, Zn, Cu, Pb, Cd, Cr, and DC. The result showed the first six principal components (PCs) with eigenvalues >1 accounting for 83.33% of the cumulative variance, and three MZs were derived via the selected six PCs using fuzzy c -means clustering analysis. The results of this study further indicated that MZ3 had the highest pH (6.06), TN (0.24%), OC (2.79%), exchangeable Ca (10.62 cmol/kg), Mg (4.01 cmol/kg), and K (0.12 cmol/kg). These were significantly ( p < 0.05) higher than those observed in MZ2 and MZ1, and they represent the most fertile parts of the study area. Furthermore, 40.6% of the study area had marginal soil (i.e., soil under MZ2).

Suggested Citation

  • Isong Abraham Isong & Denis Michael Olim & Olayinka Ibiwumi Nwachukwu & Mabel Ifeoma Onwuka & Sunday Marcus Afu & Victoria Oko Otie & Peter Ereh Oko & Brandon Heung & Kingsley John, 2025. "Delineating Soil Management Zones for Site-Specific Nutrient Management in Cocoa Cultivation Areas with a Long History of Pesticide Usage," Land, MDPI, vol. 14(7), pages 1-22, June.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1366-:d:1689960
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    References listed on IDEAS

    as
    1. Margherita Del Prete & Antonella Samoggia, 2020. "Chocolate Consumption and Purchasing Behaviour Review: Research Issues and Insights for Future Research," Sustainability, MDPI, vol. 12(14), pages 1-18, July.
    2. Sakiru O. Akinbode & Olusegun Folorunso & Taiwo S. Olutoberu & Florence A. Olowokere & Muftau Adebayo & Sodeeq O. Azeez & Sarafadeen G. Hammed & Mutiu A. Busari, 2024. "Farmers’ Perception and Practice of Soil Fertility Management and Conservation in the Era of Digital Soil Information Systems in Southwest Nigeria," Agriculture, MDPI, vol. 14(7), pages 1-18, July.
    3. Kingsley JOHN & Isong Abraham Isong & Ndiye Michael Kebonye & Esther Okon Ayito & Prince Chapman Agyeman & Sunday Marcus Afu, 2020. "Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil," Land, MDPI, vol. 9(12), pages 1-20, December.
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