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Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling

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  • Gao, Zitian
  • Guo, Danlu
  • Ryu, Dongryeol
  • Western, Andrew W.

Abstract

Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage.

Suggested Citation

  • Gao, Zitian & Guo, Danlu & Ryu, Dongryeol & Western, Andrew W., 2024. "Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling," Agricultural Water Management, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:agiwat:v:294:y:2024:i:c:s0378377424000507
    DOI: 10.1016/j.agwat.2024.108715
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    References listed on IDEAS

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    1. Gibson, Katherine E.B. & Yang, Haishun S. & Franz, Trenton & Eisenhauer, Dean & Gates, John B. & Nasta, Paolo & Farmaha, Bhupinder S. & Grassini, Patricio, 2018. "Assessing explanatory factors for variation in on-farm irrigation in US maize-soybean systems," Agricultural Water Management, Elsevier, vol. 197(C), pages 34-40.
    2. Popova, Zornitsa & Pereira, Luis S., 2011. "Modelling for maize irrigation scheduling using long term experimental data from Plovdiv region, Bulgaria," Agricultural Water Management, Elsevier, vol. 98(4), pages 675-683, February.
    3. Rijsberman, Frank R., 2006. "Water scarcity: Fact or fiction?," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 5-22, February.
    4. Monaco, Federica & Sali, Guido, 2018. "How water amounts and management options drive Irrigation Water Productivity of rice. A multivariate analysis based on field experiment data," Agricultural Water Management, Elsevier, vol. 195(C), pages 47-57.
    5. Chen, Mengting & Luo, Yufeng & Shen, Yingying & Han, Zhenzhong & Cui, Yuanlai, 2020. "Driving force analysis of irrigation water consumption using principal component regression analysis," Agricultural Water Management, Elsevier, vol. 234(C).
    6. J. Akhter & K. Mahmood & K.A. Malik & A. Mardan & M. Ahmad & M.M. Iqbal, 2004. "Effects of hydrogel amendment on water storage of sandy loam and loam soils and seedling growth of barley, wheat and chickpea," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 50(10), pages 463-469.
    7. Li-Wei Liu & Chun-Tang Lu & Yu-Min Wang & Kuan-Hui Lin & Xingmao Ma & Wen-Shin Lin, 2022. "Rice ( Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms," Agriculture, MDPI, vol. 12(1), pages 1-11, January.
    8. Walaa Elnashar & Ahmed Elyamany, 2023. "Managing Risks of Climate Change on Irrigation Water in Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2429-2446, May.
    9. Chen, Mengting & Cui, Yuanlai & Wang, Xiaonan & Xie, Hengwang & Liu, Fangping & Luo, Tongyuan & Zheng, Shizong & Luo, Yufeng, 2021. "A reinforcement learning approach to irrigation decision-making for rice using weather forecasts," Agricultural Water Management, Elsevier, vol. 250(C).
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