Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning

Panjala, P and Gumma, M K and Ajeigbe, H A and Badamasi, M M and Deevi, K C and Tabo, R (2022) Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning. International Journal o f Geo-Information, 11. 01-17. ISSN 2220-9964

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Abstract

Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid agricultural planning. Our study included various critical thematic layers such as precipitation, temperature, slope, land-use/land-cover (LULC), soil texture, soil depth, and length of growing period, prepared and modeled on the Google Earth Engine (GEE) platform. Using expert knowledge, scores were assigned to these thematic layers, and a priority map was prepared based on the combined weighted average score. We also validated priority watersheds. For this, the study area was classified into three priority zones ranging from ‘high’ to ‘low’. Of the 277 watersheds identified, 57 fell in the high priority category, implying that they are highly favorable for interventions. This would be useful for regional-scale water resource planning for agricultural landscape development.

Item Type:	Article
Divisions:	Global Research Program - Enabling Systems Transformation Research Program : West & Central Africa
CRP:	UNSPECIFIED
Uncontrolled Keywords:	water, watershed proritization, agriculture, dryland, Google Earth Engine
Subjects:	Others > Agriculture Others > Nigeria Others > Water Resources Others > Drylands
Depositing User:	Mr Nagaraju T
Date Deposited:	19 Jul 2023 08:59
Last Modified:	19 Jul 2023 09:01
URI:	http://oar.icrisat.org/id/eprint/12133
Official URL:	https://www.mdpi.com/2220-9964/11/8/416
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	The authors would like to thank the International Crops Research Institute for The Semi-Arid Tropic (ICRISAT), Centre for Dryland Agriculture Bayero University Kano, Nigeria for providing institutional funding to conduct the experimental work. We are grateful for the help of the field and the staff of the Agronomy Unit at ICRISAT Kano for assisting with field operations.
Links:	Google Scholar

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