Improving water efficiency and crop yield on a sugarcane plantation in Xinavane, Mozambique

Improving water efficiency and crop yield on a sugarcane plantation in Xinavane, Mozambique: An analysis of irrigation practices, yield variability and the potential of a decision support system

Koenders, David (TU Delft Civil Engineering and Geosciences)
van den Munckhof, Gijs (TU Delft Civil Engineering and Geosciences)
van Egmond, Jessie Lynn (TU Delft Civil Engineering and Geosciences)
Brummelkamp, Judith (TU Delft Civil Engineering and Geosciences)
van Dongen, Kirsten (TU Delft Civil Engineering and Geosciences)
Schurer, Sten (TU Delft Civil Engineering and Geosciences)

Ertsen, M.W. (mentor)
van Breukelen, B.M. (mentor)
Lhermitte, S.L.M. (mentor)

Delft University of Technology

MP277

2019-05-10

This report contains the findings of a multidisciplinary project in Mozambique which ran from mid-November 2018 to mid-January 2019. This study is part of the IWACA-TECH project, which is an abbreviation for “Improved Water efficiency Control based on remote sensing TECHnologies. The goal is to increase water efficiency and crop yield without increasing the consumptive use of water, using remote sensing and Model Predictive Control (MPC). The structural water scarcity in the region points out the relevance of the IWACATECH project and with that this study. The research, carried out by students of Delft University of Technology, is of importance for both the company Tongaat Hulett and all inhabitants who are direct or indirect dependent on the water of the Incomati river. The study area on the plantation in Xinavane copes not only with inefficient irrigation water use but also suffers a sugarcane yield decline in recent years ??. Therefore, the overall aim of this report is to improve water efficiency and crop yield within Tongaat Hulett. To achieve this from a multidisciplinary perspective several research questions have been formulated. Although they all contribute to the overall aim, they do so from different angles and in varying degrees. Therefore, to increase the readability of the report, the research questions have been divided into three sections: (1) Irrigation Practices, (2) Field Assessment on Yield Variability and (3) Decision Support System. Fieldwork was conducted over a five week period in order to gather data. Groundwater levels were measured, water quality of irrigation, ground- and precipitation water samples was analysed and soil moisture content was measured. This led to further research of soil types and quality. Soil profiles along the edges of both fields were made revealing a shallow aquifer in the bad-performing field. Irrigation water quality seems to form no hazard, but ground water quality analyses revealed significantly high electrical conductivity levels and a high sodium adsorption ratio in areas without growth. These findings, combined with an analysis of the digital elevation map and socio-technical data revealed that evaporation of irrigation water seems to be a large contributor to the impaired crop growth. When excess water cannot run off, puddles are formed. When these puddles evaporate, salts can be taken up by the soil once the thickened irrigation water infiltrates. This process is strengthened by the clay soil layer and the shallow aquifer, which prevent water from infiltrating deeper into the ground. Results concerning remote sensing prove the relationship between soil moisture content and precipitation for meteo-station XNA-20. Combining spatial and temporal variability of soil moisture content with remote sensing can play an essential role in managing irrigation practices. For the decision support system, and in specific the controller part of the system, measurements have been done. It can be concluded that storage area and delay times can be considered insignificant, and that canals do not have to be modelled. This makes the controller significantly easier and thus more time and effort can be spend on other aspects of the controller.

sugarcane
water efficiency
over-irrigation
soil quality
remote sensing
decision support system

http://resolver.tudelft.nl/uuid:dd56f7e2-8e70-41d2-8ccb-cd690b96c983

2021-05-10

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© 2019 David Koenders, Gijs van den Munckhof, Jessie Lynn van Egmond, Judith Brummelkamp, Kirsten van Dongen, Sten Schurer