Abstract
The need for techniques and instruments that enable rapid soil testing has gained attention in the face of climate change and environmental degradation. This could improve efficiency and productivity by providing real-time, high-quality, and accessible data for decision-making. This study used GPS tools to visualize, analyze, and gather essential field information and applied Near Infra-Red Spectrometry to assess soil parameters and recommend corrective action for sustainable livelihood in five Counties in North Rift Kenya. Soil reaction varied from 5.5 in Kaptega, Transnzoia, to 7.8 in Kospir, Turkana counties. Low soil pH and CEC were recorded in parts of Nandi and Transnzoia counties. Soils from the dryland ecologies in Turkana, W. Pokot, and Samburu were predominantly alkaline. Total organic Carbon was generally low in the dryland ecologies of Samburu and Turkana. Low soil fertility was generally indicated in Samburu, Turkana, and W. Pokot. This was attributable to the low organic carbon levels and low precipitation, which may have negatively influenced soil microbial activity. Sustainable farming practices such as crop rotation, mulching, mixed farming, cover cropping, and minimum/conservation tillage are recommended in areas where crop cultivation is feasible. Amelioration of soils with agricultural lime and organic matter is highly recommended in the affected areas within the agropastoral counties for improved production to guarantee food security and sustainable livelihoods.
Keywords
References
- Abbasi, H., Jamil, M., Haq, A., Ali, S., Ahmad, R., & Malik, Z. (2016). Salt stress manifestation on plants mechanism of salt tolerance and potassium role in alleviating it: a review. Zemdirbyste, 103, 229–238. [Google Scholar]
- Akuja, T. E., & Kandagor, J. (2019). A review of policies and agricultural productivity in the arid and semi-arid lands (ASALS), Kenya: the case of Turkana County. Journal of Applied Biosciences, 140, 14304-14315. https://dx.doi.org/10.4314/jab.v140i1.9 [Google Scholar]
- Chianu, J. N., Chianu, J. N., & Mairura, F. (2012). Mineral fertilizers in the farming systems of sub-Saharan Africa. A review. Agronomy for Sustainable Development, 32, 545-566. [Google Scholar]
- FAO (2021). Food and Agriculture Organization of the United Nations. [Google Scholar]
- Farmaha, B. S., Caughman, W., & Park, D. (2020). Precision Agriculture based Soil Sampling Strategies. Clemson (Sc). Available online: https://lgpress.clemson.edu/publication/precision-agriculture-based-soil-sampling-strategies/ (accessed on 10 December 2024). [Google Scholar]
- Jaetzold, R., Hornetz, B., Shisanya, C. A., & Schmidt, H. (2012). Management Handbook of Kenya.-Vol. I-IV (Western, Central, Eastern, Nyanza, Southern Rift Valley, Northern Rift Valley, Coast), Nairobi. Farm Management of Kenya, Vol. 11C: National Conditions and Farm Management Information. East Kenya (Eastern and Coast Provinces). Farm Management Branch, Ministry of Agriculture, Nairobi, Kenya [Google Scholar]
- Jin, X., Li, S., Zhang, W., Zhu, J., & Sun, J. (2020). Prediction of soil-available potassium content with visible near-infrared ray spectroscopy of different pretreatment transformations by the boosting algorithms. Applied Sciences, 10(4), 1520. [Google Scholar]
- KNBS (2020). Kenya County Profile Report. CHM Network. Available online: https://ke.chm-cbd.net (accessed on 10 December 2024). [Google Scholar]
- Ma, D., He, Z., Zhao, W., Li, R., Sun, W., Wang, W., & Ju, W. (2024). Long-term effects of conventional cultivation on soil cation exchange capacity and base saturation in an arid desert region. Science of The Total Environment, 949, 175075., https://doi.org/10.1016/j.scitotenv.2024.175075 [Google Scholar]
- Nocita, M., Stevens, A., Noon, C., & van Wesemael, B. (2013). Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 199, 37-42. [Google Scholar]
- Shao, Y., & He, Y. (2011). Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy. Soil Research, 49(2), 166-172. [Google Scholar]
- Ulusoy, Y., Tekin, Y., Tümsavaş, Z., & Mouazen, A. M. (2016). Prediction of soil cation exchange capacity using visible and near infrared spectroscopy. Biosystems Engineering, 152, 79-93. [Google Scholar]
- Uwiragiye, Y., Ngaba, M. J. Y., Yang, M., Elrys, A. S., Chen, Z., & Zhou, J. (2023). Spatially explicit soil acidification under optimized fertilizer use in Sub-Saharan Africa. Agronomy, 13(3), 632. https://doi.org/10.3390/agronomy13030632 [Google Scholar]
- Van Antwerpen, T., Van Antwerpen, R., Meyer, J. H., Naidoo, P., Berry, S., Spaull, V. W., & Laing, M. (2007). Factors associated with a healthy soil in sugarcane production in KwaZulu-Natal. Proceedings of the International Society of Sugar Cane Technologists, 26, 273-281. [Google Scholar]
- Wang, P., Xu, D., Lakshmanan, P., Deng, Y., Zhu, Q., & Zhang, F. (2024). Mitigation strategies for soil acidification based on optimal nitrogen management. Frontiers of Agricultural Science and Engineering, 11(2), 229-242. [Google Scholar]
- World Bank (2021). Climate Risk Profile: Kenya (2021): The World Bank Group. Available online: https://climateknowledgeportal.worldbank.org (accessed on 10 December 2024). [Google Scholar]
- Xu, S., Zhao, Y., Wang, M., & Shi, X. (2018). Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma, 310, 29-43. [Google Scholar]
- Zingore, S., Mutegi, J. K., Agesa, B., Tamene, L., & Kihara, J. (2015). Soil Degradation in Sub-Saharan Africa and Crop Production Options for Soil Rehabilitation. Better Crops, 99, 24–26. [Google Scholar]
