AccScience Publishing / EER / Online First / DOI: 10.36922/eer025040005
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ORIGINAL RESEARCH ARTICLE

Pilot climate suitability investigation of alternate wetting and drying irrigation practice for improving water management in paddy rice fields in Uganda

Edson Bagamba1 ,  Denis Bwire1,2,3* ,  Victo Nabunya1 ,  Daniel Otim1
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1 Department of Agricultural Mechanization and Irrigation Engineering, Busitema University, Tororo, Uganda
2 United Graduate School of Agricultural Sciences, Tokyo University of Agriculture and Technology, 3-8-1, Harumicho, Fuchu, Tokyo, Japan
3 RD and Business Development, Saerd-tech Consultants Limited, Kampala, Uganda
EER, 025040005 https://doi.org/10.36922/eer025040005
Submitted: 22 January 2025 | Revised: 14 March 2025 | Accepted: 17 March 2025 | Published: 28 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Although susceptible to the negative consequence of climate change, rice production using continuous flooding in paddy fields contributes significantly to food security in Uganda. Alternate wetting and drying practice (AWD) is a sustainable irrigation technique with water-saving potential without reducing yields. Adopting this technique requires technical knowledge and understanding of AWD’s influence and its interaction with hydrological conditions – components of water balance including percolation and precipitation in paddy fields. This study investigated the climate suitability of AWD for improving water management with paddy rice farming in Uganda. The climate suitability analysis was conducted for Eastern Uganda considering the rainy (March – May) and dry seasons (June – July, November – February). First, we conducted a field survey from Kibimba and Doho, major rice growing schemes, and obtained FAO-WaPOR climatic data for climate suitability analysis. Ecological niche modeling in QGIS and Maximum Entropy (MaxEnt), a machine learning model, was used to evaluate AWD viability in paddy fields. Then, a hydrological assessment of paddy fields was performed using a water balance equation considering the ecological requirements of the paddy rice, climatic conditions, and soil properties. Results from the MaxEnt and Jackknife tests gave >92% and 90% high-performing metrics of area under the curves and percentage correctly classified, respectively. The significant environmental predictors identified include organic carbon stock (OCS) and available water, with OCS as the most influential factor. The percolation rate of 1 – 5 mm/day was unsuitable for AWD during the rainy season (when precipitation >20 mm/day since the increase in precipitation decreases percolation rates). Otherwise, AWD was suitable in dry and rainy seasons if the precipitation was <20 mm/day for all percolation rates, and over 70% of significant areas in Eastern Uganda favor AWD practice. These findings provide valuable quantitative insights based on climate suitability evaluation of AWD irrigation in Uganda to (i) upscale AWD technique for improving water management in paddy fields and (ii) support Uganda’s rice production goal.

Graphical abstract
Keywords
Paddy rice
Machine learning in agriculture
Ecological niche modeling
Sustainable irrigation
Food security
Climate adaptation
Funding
None.
Conflict of interest
There are no conflicts of interest among the authors.
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