Browsing by Author "Bwambale, Erion"

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    Assessment of irrigation water distribution using remotely sensed indicators: A case study of Doho Rice Irrigation Scheme, Uganda
    (Smart Agricultural Technology, 2023) Wamala, Fawaz; Gidudu, Anthony; Wanyama, Joshua; Nakawuka, Prossie; Bwambale, Erion; Chukalla, Abebe D.
    The rising competition for scarce land and water resources and the need to satisfy the global food demand from an ever-growing population necessitates novel methods to monitor irrigation scheme performance for improved water use efficiency. The traditional methods employed in sub-Saharan Africa to assess irrigation performance are point-based, expensive, and time-consuming, making monitoring and evaluation of these capital-intensive projects difficult. This study aimed at employing satellite data with high spatial and temporal resolution in assessing the performance of Doho Rice Irrigation Scheme through estimations of actual evapotranspiration. Actual evapotranspiration (ETa) was modelled from Landsat 7 imagery using the surface energy balance system algorithm on five clear days between January and April 2020. Using equity and adequacy metrics, the derived ETa was used to assess the irrigation performance of the scheme. Results showed that the equity indicator was generally fair, with the coefficient of variation between 0.11 and 0.08, close to the 0.10 threshold implying irrigation water is fairly distributed within the scheme. The average adequacy was 0.87, above the 0.65 threshold, indicating adequate water supply throughout the scheme. The study’s findings can be used in future research and benchmarking with other irrigation schemes to address the country’s water resource management challenges.
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    Data-driven model predictive control for precision irrigation management
    (Smart Agricultural Technology, 2023) Bwambale, Erion; Abagale, Felix K.; Anornu, Geophrey K.
    The future of agriculture faces a threat from a changing climate and a rapidly growing population. This has put enormous pressure on water and land resources as more food is expected from less inputs. Advancement in smart agriculture through the use of the Internet of Things and improvement in computational power has enabled extensive data collection from agricultural ecosystems. This review introduces model predictive control and describes its application in precision irrigation. An overview of the application of data-driven modelling and model predictive control for precision irrigation management is presented. Model predictive control has been applied in irrigation canal control, irrigation scheduling, stem water potential regulation, soil moisture regulation and prediction of plant disturbances. Finally, the benefits, challenges, and future perspectives of data-driven model predictive control in the context of irrigation scheduling are presented. This review provides useful information to researchers and agriculturalists to appreciate and use data collected in real-time to learn the dynamics of agricultural systems.
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    Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling
    (Smart Agricultural Technology, 2023) Bwambale, Erion; Abagale, Felix K.; Anornu, Geophrey K.
    In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.
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    Development of a Water Allocation Model for Equitable Water Distribution at Doho Rice Irrigation Scheme, Uganda
    (Hydrology, 2019) Bwambale, Erion; Gathogo Home, Patrick; Messo Raude, James; Wanyama, Joshua
    The shortage of irrigation water at the tail reaches of Doho Rice Irrigation Scheme especially in the dry season has resulted in decreased rice production. At Doho Rice Irrigation Scheme, water distribution follows a predetermined rotation system where allocated supplies are unknown and the farmer decides how much water they divert to their plot. This has resulted in water inequalities between the tail end and head end blocks of the scheme. The main objective of the study was to develop a water allocation model for equitable distribution of irrigation water taking into account crop growth stages. This study developed an equitable water allocation model by ensuring that the crop water requirements per block are matched with the available supply in the main canal. A water delivery scheduling model was developed to help deliver the decadal irrigation water requirements by grouping different blocks together. The water allocation model gives required irrigation supplies and recommended supplies depending on the crop growth stage and acreage per block. The results from the water delivery scheduling model suggest a 2 days of irrigation per week for the land preparation, development and late seasons and 3 days of irrigation per decade during the high consumptive stages of initial and midseason stages of rice development.
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    Hydraulic performance evaluation of the water conveyance system of Doho Rice Irrigation Scheme in Uganda
    (Journal of Sustainable Research in Engineering, 2019) Bwambale, Erion; Home, Patrick G.; Raude, James M.; Wanyama, Joshua
    Poor water distribution is a major problem in many surface irrigation schemes in Uganda, especially at the tail reaches. This has led to reduced crop yield from these water-stressed fields. This study reports the results of evaluating the hydraulic performance of the water conveyance system of Doho Rice Irrigation Scheme for one cropping season for the first quarter of 2019. For the main canal, the conveyance efficiency indicator was used while for the lateral canals indicators of adequacy, efficiency, dependability, equity, and the equity ratio of head to tail were used to evaluate the hydraulic performance. Performance indicators were computed at the head and tail ends of the canals thus comparing the inlet and distribution processes. Field measurements coupled with simulation techniques were used to obtain the delivered and required flows. It was found that the acceptable average hydraulic performance indicators of the scheme were 0.84, 0.79, 0.07 and 0.26 for adequacy, efficiency, dependability, and equity respectively, the tail reaches suffer in performance with the adequacy, dependability, and equity ratio at 0.68, 0.12, and 3.13 respectively. Improving hydraulic performance of the scheme necessitates reduction of water conveyance losses, adherence to distribution plans and monitoring of diversions to the canals
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    Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies
    (Cogent Engineering, 2022) Bwambale, Erion; Naangmenyele, Zita; Iradukunda, Parfait; Akansake, Daniel A.; Bisa, Michael E.; Hakizayezu, Joseph; Onofua, Oluwaseun Elijah; Chikabvumbwa, Sylvester R.
    Irrigation is the artificial application of water to crops to supply moisture. With rising drought indices and rapid population expansion, the need for irrigation water for food production is growing. Nonetheless, irrigated agriculture is battling several issues that have resulted in poor performance, inefficient water usage, and low crop water production. Improving water use efficiency in irrigated agriculture necessitates using technology that decreases water losses, matches available supplies to demand, and tracks performance. Geographical Information Systems (GIS) and Remote Sensing (RS) allow for effectively managing water and land resources for irrigation. Current GIS and RS uses in irrigation systems are covered in this study, covering land suitability for irrigation, crop water needs, irrigation scheduling, performance evaluation, and other related applications. The future potential of GIS and RS applications for sustainable irrigation water management are highlighted. This paper offers relevant information for researchers, irrigators, and policymakers on using GIS and RS in irrigation water management and how technological improvements will change irrigation water management to enhance water usage efficiency.