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Using agrometeorological data to assist irrigation management in oil palm crops: A decision support method and results from crop model simulation

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  • Culman, María
  • de Farias, Claudio M.
  • Bayona, Cristihian
  • Cabrera Cruz, José Daniel

Abstract

In order to achieve optimum yields in oil palm, management practices should be tailored to the crop site agro-ecological conditions. Nevertheless, oil palm farmers often have to make decisions based on a limited knowledge base. Considering that water management is a critical aspect of oil palm crops, this paper describes an inference method for irrigation decision-making in oil palm supported on soil moisture and vapor pressure deficit data. Under an ideal scenario where this agrometeorological data is available through a Wireless Sensor Network (WSN) at a crop plot resolution, we formulated a method to prevent oil palm farmers to submit their crops to water deficit stress. The inference method was based on a Data Fusion technique called Dempster-Shafer Inference, which is convenient for the use of uncertain data with distinct levels of detail such as those present in a WSN. The outcome of fusing soil moisture and vapor pressure data was inferring the crop state, regarding soil and plant water status, following the concept of Site-specific Agriculture. To evaluate the impact of the method on crop yield, we carried out two simulations. The first one on a WSNs simulator, Castalia, to generate the irrigation decisions according to the site-specific agrometeorological data collected from the WSN. The second one on a crop simulation model, APSIM (Agricultural Production Systems Simulator), to simulate the oil palm plot at the study site under two treatments: plot with irrigation managed by the inference method and plot without irrigation. Results from oil palm crop simulation showed a 27% increase in the production of bunches of fresh fruit between 2016 and 2017 in the treatment with irrigation. The method has the potential for contributing to irrigation decision-support systems and for being useful in yield-intensification rather than crop-extension politics for oil palm and other crops.

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  • Culman, María & de Farias, Claudio M. & Bayona, Cristihian & Cabrera Cruz, José Daniel, 2019. "Using agrometeorological data to assist irrigation management in oil palm crops: A decision support method and results from crop model simulation," Agricultural Water Management, Elsevier, vol. 213(C), pages 1047-1062.
    • Handle: RePEc:eee:agiwat:v:213:y:2019:i:c:p:1047-1062
    DOI: 10.1016/j.agwat.2018.09.052
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    1. Akram, Humayoun & Levia, Delphis F. & Herrick, Jeffrey E. & Lydiasari, Henny & Schütze, Niels, 2022. "Water requirements for oil palm grown on marginal lands: A simulation approach," Agricultural Water Management, Elsevier, vol. 260(C).
    2. Ruoyu Xiong & Huan Peng & Xi Chen & Chenyang Shuai, 2024. "Machine learning-enhanced evaluation of food security across 169 economies," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26971-27000, October.
    3. Brum, Mauro & Oliveira, Rafael S. & López, Jose Gutiérrez & Licata, Julian & Pypker, Thomas & Chia, Gilson Sanchez & Tinôco, Ricardo Salles & Asbjornsen, Heidi, 2021. "Effects of irrigation on oil palm transpiration during ENSO-induced drought in the Brazilian Eastern Amazon," Agricultural Water Management, Elsevier, vol. 245(C).
    4. Nuzhat Khan & Mohamad Anuar Kamaruddin & Usman Ullah Sheikh & Yusri Yusup & Muhammad Paend Bakht, 2021. "Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps," Agriculture, MDPI, vol. 11(9), pages 1-26, August.

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