The Role of Mulching in Reducing Greenhouse Gas Emissions and Enhancing Soil Health Among Smallholder Farmers in Zambia, Malawi, Kenya, and Tanzania: An AI-Driven Approach
DOI:
https://doi.org/10.62486/latia202575Keywords:
Mulching, Artificial Intelligence (AI), Greenhouse Gas Emissions, Soil Health, Smallholder Farmers, Precision AgricultureAbstract
Mulching is a widely recognized conservation practice that improves soil moisture retention, enhances fertility, and reduces greenhouse gas (GHG) emissions. This study explores the effectiveness of mulching among smallholder farmers in Zambia, Malawi, Kenya, and Tanzania, focusing on its role in mitigating climate change and improving soil health. Additionally, we integrate artificial intelligence (AI) to optimize mulching practices through predictive analytics and real-time monitoring. AI-powered models, utilizing remote sensing data and machine learning algorithms, assess soil conditions, moisture levels, and carbon sequestration potential. These insights enable precision agriculture techniques, helping farmers make data-driven decisions that maximize mulching benefits while minimizing environmental impact. The study also evaluates AI-driven mobile applications and advisory systems that provide tailored recommendations based on localized climate and soil data. By leveraging AI technology, this research aims to enhance the sustainability of mulching practices, improve productivity, and contribute to climate resilience in smallholder farming systems.
References
Kanojia V. Artificial intelligence and smart farming: An overview varsha kanojia, a. subeesh, and NL Kushwaha. Artif Intell Smart Agric Technol Appl. 2024;1. DOI: https://doi.org/10.1007/978-981-97-0341-8_1
Koriyev M, Mirzahmedov I, Boymirzaev K, Juraev Z. Effects of mulching, terracing, and efficient irrigation on soil salinity reduction in Uzbekistan’s Fergana Valley. Cogent Food Agric. 2025;11(1):2449201. DOI: https://doi.org/10.1080/23311932.2024.2449201
Liu X, Dong W, Si P, Zhang Z, Chen B, Yan C, et al. Linkage between soil organic carbon and the utilization of soil microbial carbon under plastic film mulching in a semi-arid agroecosystem in China. Arch Agron Soil Sci. 2019; DOI: https://doi.org/10.1080/03650340.2019.1578346
Akhir MAM, Mustapha M. Formulation of biodegradable plastic mulch film for agriculture crop protection: a review. Polym Rev. 2022;62(4):890–918. DOI: https://doi.org/10.1080/15583724.2022.2041031
Yan W, Jiang J, Zhu L, Zhang L, Li H, Gu J. Straw mulching improves soil fertility and productivity of water spinach (ipomoea aquatica forsk.) under plastic tunnel. Commun Soil Sci Plant Anal. 2021;52(22):2958–70. DOI: https://doi.org/10.1080/00103624.2021.1971696
Fu B, Chen L, Huang H, Qu P, Wei Z. Impacts of crop residues on soil health: A review. Environ Pollut Bioavailab. 2021;33(1):164–73. DOI: https://doi.org/10.1080/26395940.2021.1948354
Naik SK, Jha BK, Singh AK. Drip Fertigated Planting Systems with Polythene Mulching on Cauliflower–Eggplant Cropping Systems in Hot and Subhumid Climate: Impact on Soil Health and Crop Yield. Commun Soil Sci Plant Anal. 2022;53(10):1261–76. DOI: https://doi.org/10.1080/00103624.2022.2046026
Bonhotal J, Schwarz M. Improving Turf and Soil Health, Reducing Energy Use and Assessing Tick Populations by Mulching Leaves in Place. Compost Sci Util. 2024;31(3–4):116–32. DOI: https://doi.org/10.1080/1065657X.2024.2370854
Palsaniya DR, Kumar TK, Chaudhary M, Choudhary M. Effect of reduced tillage and mulching on soil health in Sesbania alley cropping based rainfed food-fodder systems. Arch Agron Soil Sci. 2023;69(10):1750–64. DOI: https://doi.org/10.1080/03650340.2022.2111025
Li M, Zhang Q, Wei S, Liu Z, Zong R, Jin T, et al. Biodegradable film mulching promotes better soil quality and increases summer maize grain yield in North China Plain. Arch Agron Soil Sci. 2023;69(13):2493–509. DOI: https://doi.org/10.1080/03650340.2022.2158327
Cao Q, Li G, Yang F, Kong F, Cui Z, Jiang X, et al. Eleven-year mulching and tillage practices alter the soil quality and bacterial community composition in Northeast China. Arch Agron Soil Sci. 2022;68(9):1274–89. DOI: https://doi.org/10.1080/03650340.2021.1890719
Capetz M, Sharma S, Padilha R, Olsen P, Kiciman E, Chandra R. Enabling Adoption of Regenerative Agriculture through Soil Carbon Copilots. arXiv Prepr arXiv241116872. 2024;
Kassam A, Friedrich T, Derpsch R. Global spread of conservation agriculture. Int J Environ Stud. 2019;76(1):29–51. DOI: https://doi.org/10.1080/00207233.2018.1494927
Magesh S. A convolutional neural network model and algorithm driven prototype for sustainable tilling and fertilizer optimization. npj Sustain Agric. 2025;3(1):5. DOI: https://doi.org/10.1038/s44264-024-00046-w
Shalini VT, Neware R, Kumari T, Kumar M. Soil Health Management Using Artificial Intelligence for Smart Agriculture Systems. Sep; 2024. DOI: https://doi.org/10.20944/preprints202409.0605.v1
Rodenburg J, Büchi L, Haggar J. Adoption by adaptation: Moving from conservation agriculture to conservation practices. Int J Agric Sustain. 2021;19(5–6):437–55. DOI: https://doi.org/10.1080/14735903.2020.1785734
Wekesah FM, Mutua EN, Izugbara CO. Gender and conservation agriculture in sub-Saharan Africa: a systematic review. Int J Agric Sustain. 2019;17(1):78–91. DOI: https://doi.org/10.1080/14735903.2019.1567245
Asule PA, Musafiri CM, Nyabuga G, Kiai W, Ngetich FK, Spurk C. Determinants of soil fertility information needs and access among smallholder farmers in the central highlands of Kenya. Commun Soil Sci Plant Anal. 2022;53(15):1979–98. DOI: https://doi.org/10.1080/00103624.2022.2070190
Madamombe SM, Ng’ang’a SK, Öborn I, Nyamadzawo G, Chirinda N, Kihara J, et al. Climate change awareness and adaptation strategies by smallholder farmers in semi-arid areas of Zimbabwe. Int J Agric Sustain. 2024;22(1):2293588. DOI: https://doi.org/10.1080/14735903.2023.2293588
Tumbure A, Dera J, Kunjeku TC, Nyamangara J. Contextualising smallholder organic agriculture in Zimbabwe and other sub-Saharan African countries: a review of challenges and opportunities. Acta Agric Scand Sect B—Soil Plant Sci. 2022;72(1):1020–35. DOI: https://doi.org/10.1080/09064710.2022.2142657
Waaswa A, Oywaya Nkurumwa A, Mwangi Kibe A, Ngeno Kipkemoi J. Climate-Smart agriculture and potato production in Kenya: review of the determinants of practice. Clim Dev. 2022;14(1):75–90. DOI: https://doi.org/10.1080/17565529.2021.1885336
Mukarumbwa P, Taruvinga A. Landrace and GM maize cultivars’ selection choices among rural farming households in the Eastern Cape Province, South Africa. GM Crops Food. 2023;14(1):1–15. DOI: https://doi.org/10.1080/21645698.2023.2215146
Nyberg Y, Wetterlind J, Jonsson M, Öborn I. Factors affecting smallholder adoption of adaptation and coping measures to deal with rainfall variability. Int J Agric Sustain. 2021;19(2):175–98. DOI: https://doi.org/10.1080/14735903.2021.1895574
Cishahayo L, Zhu Y, Wang F. Land fragmentation, adoption intensity of climate-smart agricultural practices, and economic performance of banana farmers in China. Clim Dev. 2024;1–14. DOI: https://doi.org/10.1080/17565529.2024.2407357
Waaswa A, Oywaya Nkurumwa A, Mwangi Kibe A, Ng’eno Kipkemoi J. Adapting agriculture to climate change: institutional determinants of adoption of climate-smart agriculture among smallholder farmers in Kenya. Cogent Food Agric. 2024;10(1):2294547. DOI: https://doi.org/10.1080/23311932.2023.2294547
Dewi RK, Fukuda M, Takashima N, Yagioka A, Komatsuzaki M. Soil carbon sequestration and soil quality change between no-tillage and conventional tillage soil management after 3 and 11 years of organic farming. Soil Sci Plant Nutr. 2022;68(1):133–48. DOI: https://doi.org/10.1080/00380768.2021.1997552
Aryal S, Shrestha S, Maraseni T, Wagle PC, Gaire NP. Carbon stock and its relationships with tree diversity and density in community forests in Nepal. Int For Rev. 2018;20(3):263–73. DOI: https://doi.org/10.1505/146554818824063069
Ouyang X, Lee SY. Improved estimates on global carbon stock and carbon pools in tidal wetlands. Nat Commun. 2020;11(1):317. DOI: https://doi.org/10.1038/s41467-019-14120-2
Dibaba A, Soromessa T, Workineh B. Carbon stock of the various carbon pools in Gerba-Dima moist Afromontane forest, South-western Ethiopia. Carbon Balance Manag. 2019;14:1–10. DOI: https://doi.org/10.1186/s13021-019-0116-x
Raihan A, Begum RA, Said MNM. A meta-analysis of the economic value of forest carbon stock. Geografia. 2021;17(4):321–38. DOI: https://doi.org/10.17576/geo-2021-1704-22
Umar Y, Chavula P, Abdi E, Ahmed S. Small - Scale Irrigation Farming as a Climate - Smart Agriculture Practice : Its Adoption and Impact on Food Security for Ethiopian Smallholder Farmers : A Review. 2024;6(1):163–80.
Chavula P, Mambwe H, Mume AA, Umer Y. East African Journal of Forestry & Agroforestry Impact of Agroforestry Adoption among Smallholder Households in Zambia : An Expenditure Approach Farmers ’. 2023;6(1):309–28. DOI: https://doi.org/10.37284/eajfa.6.1.1474
Umer Y, Chavula P, Abdi E, Ahamad S, Lungu G, Abdula H, et al. Small-scale irrigation farming as a climate-smart agriculture practice; its adoption and impact on food security for Ethiopian smallholder farmers: a review. Asian Res J Curr Sci. 2024;6(1):163–80.
Samsudin YB, Puspitaloka D, Rahman SA, Chandran A, Baral H. Community-Based Peat Swamp Restoration Through Agroforestry in Indonesia. Vol. 1, Agroforestry for Degraded Landscapes: Recent Advances and Emerging Challenges-Vol.1. 2020. 349–365 p. DOI: https://doi.org/10.1007/978-981-15-4136-0_12
Kayusi F, Kasulla S, Malik SJ. Climate Information Services ( CIS ): A Vital Tool for Africa ’ s Climate Resilience. 2024;18(10):108–17. DOI: https://doi.org/10.9734/ajarr/2024/v18i10759
Šūmane S, Kunda I, Knickel K, Strauss A, Tisenkopfs T, des Ios Rios I, et al. Local and farmers’ knowledge matters! How integrating informal and formal knowledge enhances sustainable and resilient agriculture. J Rural Stud. 2018;59:232–41. DOI: https://doi.org/10.1016/j.jrurstud.2017.01.020
Damba O, Kizito F, Bonilla-Findji O, S. Y, Oppong-Mensah B, Clottey V, et al. Climate Smart Agriculture (CSA)- Climate Information Services (CIS) Prioritization in Ghana: Smartness Assessments and Outcomes. AICCRA Ghana Clust Reports. 2021;18pp.
Tumwesigye W, Tefera TL, Bedadi B, Mwanjalolo M, Chavula P, Conservation B, et al. Chelonian Conservation And Biology APPLICATION OF INDIGENOUS KNOWLEDGE SYSTEMS IN CLIMATE SMART. 2023;18(2):1785–800.
Alamu EO, Adesokan M, Fawole S, Maziya-Dixon B, Mehreteab T, Chikoye D. Gliricidia sepium (Jacq.) Walp Applications for Enhancing Soil Fertility and Crop Nutritional Qualities: A Review. Forests. 2023;14(3):1–13. DOI: https://doi.org/10.3390/f14030635
Mthembu BE, Everson TM, Everson CS. Intercropping for enhancement and provisioning of ecosystem services in smallholder, rural farming systems in KwaZulu-Natal Province, South Africa: a review. J Crop Improv. 2019;33(2):145–76. DOI: https://doi.org/10.1080/15427528.2018.1547806
Muchuru S, Nhamo G. A review of climate change adaptation measures in the African crop sector. Clim Dev. 2019;11(10):873–85. DOI: https://doi.org/10.1080/17565529.2019.1585319
Mmbando GS. Harnessing artificial intelligence and remote sensing in climate-smart agriculture: the current strategies needed for enhancing global food security. Cogent Food Agric. 2025;11(1):2454354. DOI: https://doi.org/10.1080/23311932.2025.2454354
Sahoo S, Singha C, Govind A. Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India. J Agric Food Res. 2024;18:101424. DOI: https://doi.org/10.1016/j.jafr.2024.101424
Teng J, Jakeman AJ, Vaze J, Croke BFW, Dutta D, Kim S. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environ Model Softw. 2017;90:201–16. DOI: https://doi.org/10.1016/j.envsoft.2017.01.006
Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD. A review of machine learning applications in wildfire science and management. Environ Rev. 2020;28(4):478–505. DOI: https://doi.org/10.1139/er-2020-0019
Bastin L, Cornford D, Jones R, Heuvelink GBM, Pebesma E, Stasch C, et al. Managing uncertainty in integrated environmental modelling: The UncertWeb framework. Environ Model Softw. 2013;39:116–34. DOI: https://doi.org/10.1016/j.envsoft.2012.02.008
Nuwarapaksha TD, Udumann SS, Dissanayaka NS, Dilshan RMN, Atapattu AJ. AI-Driven Solutions for Sustainable Irrigation: Exploring Smart Technologies to Enhance Conservation and Efficiency. In: Integrating Agriculture, Green Marketing Strategies, and Artificial Intelligence. IGI Global Scientific Publishing; 2025. p. 1–32. DOI: https://doi.org/10.4018/979-8-3693-6468-0.ch001
Sakhong R, Jaiswal P, Yosung L, Shukla YK, Kumari S. Technology Innovation: A Green Approach to Soil Health. Int J Plant Soil Sci. 2024;36(12):613–22. DOI: https://doi.org/10.9734/ijpss/2024/v36i125235
Feyissa S, Sileshi M, Shepande C. Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District , Zambia Chavula Petros. 2024;1–24.
Kayusi F, Kasulla S, Malik SJ, Wasike JA, Lungu G, Mambwe H, et al. Advanced AI , Machine Learning and Deep Learning Techniques for Climate Change Studies : A Review. 2024;4010(6):101–8. DOI: https://doi.org/10.36346/sarjaf.2024.v06i06.001
Chavula P. A Review between Climate Smart Agriculture Technology Objectives’ Synergies and Tradeoffs. Int J Food Sci Agric. 2021;5(4):748–53. DOI: https://doi.org/10.26855/ijfsa.2021.12.023
Micheli L, Smestad GP, Bessa JG, Muller M, Fernández EF, Almonacid F. Tracking soiling losses: Assessment, Uncertainty, and challenges in mapping. IEEE J Photovoltaics. 2021;12(1):114–8. DOI: https://doi.org/10.1109/JPHOTOV.2021.3113858
Petros C, Feyissa S, Sileshi M, Shepande C. Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia. F1000Research. 2024;13:815. DOI: https://doi.org/10.12688/f1000research.144332.2
Si P, Liu E, He W, Sun Z, Dong W, Yan C, et al. Effect of no-tillage with straw mulch and conventional tillage on soil organic carbon pools in Northern China. Arch Agron Soil Sci. 2018;64(3):398–408. DOI: https://doi.org/10.1080/03650340.2017.1359410
Published
Issue
Section
License
Copyright (c) 2025 Fredrick Kayusi, James Wasike, Petros Chavula (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
