Visualization of uncertain leaching fraction and drought exposure as a function of irrigation dosage and frequency

F.D. Mondaca-Duarte, D. Reyes-Lastiri, M. Heinen, E.J. van Henten and S. van Mourik

Agricultural Water Management, 2023, vol. 283, issue C

Abstract: There is a need for precise irrigation strategies to reduce leaching and water use, whilst avoiding drought exposure. A model-based prediction and visualization method is proposed to help estimate the precise required irrigation dosage and frequency to achieve desired leaching fraction and drought exposure as a multi-objective optimization problem. The method produces a contour plot that shows the estimated leaching and drought exposure under uncertain evapotranspiration. Three sandy soils and three clay soils were examined for a total of 6 different soils, which included a clay soil case with protected Chrysanthemum cultivation. The results illustrate the usefulness of this type of visualization in decision support. Predictions indicate that compared to a conventional irrigation strategy, leaching could be reduced by 40% and water use by 9% whilst maintaining a low drought exposure (below 1%). Furthermore, predictions indicate that reducing evapotranspiration uncertainty would require an irrigation frequency increase (∼20%) or a total irrigation dosage increase (∼10%). Compared to clay soils, sandy soils require a higher frequency irrigation (more than once per 2 days) to prevent drought exposure. The proposed model-based framework and visualization method provides a useful understanding of low-risk irrigation strategies that account for evapotranspiration uncertainty, while reducing water use and minimizing leaching, giving valuable insights to agricultural professionals and policymakers alike. Additionally, the developed framework has the potential to be used as a core for decision and control tools developed for sustainable agriculture and water resource management.

Keywords: Performance framework; Model-based irrigation; Drought exposure; Leaching fraction; Monte Carlo; Decision support (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:283:y:2023:i:c:s037837742300166x

DOI: 10.1016/j.agwat.2023.108301

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