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March 14, 20260 citations

Projecting the Effects of Climate Change on Water irrigation needs for Maize Production Systems Using the LARS-WG and Hargreaves Method

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IAIdah AndriyaniSWSri WahyuningsihSWSuwimon Wicharuck

Key Points

  • The research aims to project how climate change impacts irrigation needs for maize across three regions in Indonesia over the next two decades.
  • Generated future climate scenarios using the LARS-WG model and HadGEM3-GC31-LL model with CMIP6 pathways.
  • Applied the Hargreaves method for calculating reference evapotranspiration and crop irrigation requirements.
  • Analyzed historical climate data (2004–2023) with Kolmogorov-Smirnov, t-test, and f-test for validation.
  • Conducted model calibration and evaluation using R², MSE, and RMSE metrics.
  • LARS-WG accurately simulated local climate variables, with consistent evapotranspiration estimates.
  • Temperature showed a positive correlation with irrigation demand, while effective precipitation had a negative correlation.
  • Mean temperature and effective precipitation exhibited no significant direct effect on maize yields, though extreme temperatures had minor impacts.
  • Future climate scenarios are projected to increase irrigation needs, necessitating improved water resource management strategies.

Abstract

The production of food crops is severely hampered by climate change, which is characterized by rising temperatures and changes in precipitation. This is especially true for maize, a cornerstone of Indonesia's food security. This study aims to forecast future climatic conditions over the next two decades and estimate the resulting irrigation water demands for maize cultivation in East Java, West Sumatra, and North Maluku. Future climate scenarios were generated using the LARS-WG model, which incorporated the HadGEM3-GC31-LL General Circulation Model and three CMIP6 pathways (SSP126, SSP245, and SSP585). The Hargreaves method was then applied to calculate reference evapotranspiration and, subsequently, crop irrigation requirements. To validate the model's reliability, historical climate data (2004–2023) was analyzed using the Kolmogorov-Smirnov, t-test, and f-test at an α = 0.05 significance level. Model calibration and evaluation were conducted using the R², MSE, and RMSE metrics. The results show that LARS-WG effectively simulated local climate variables, and the evapotranspiration estimates were consistent with regional characteristics. The analysis revealed that while temperature has a positive correlation with irrigation demand, effective precipitation has a negative one. Furthermore, mean temperature and effective precipitation showed no significant direct effect on maize yields, whereas extreme temperatures had a minor impact. These findings suggest that future climate scenarios could increase irrigation needs, highlighting the necessity for adaptive management of water resources strategies.

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Cite This Study

Andriyani et al. (2026) studied this question.

synapsesocial.com/papers/69b4fc1fb39f7826a300cd5dhttps://doi.org/10.1051/bioconf/202622702004/pdf
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