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A calibration procedure to improve global rice yield simulations with EPIC

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  • Xiong, Wei
  • Balkovič, Juraj
  • van der Velde, Marijn
  • Zhang, Xuesong
  • Izaurralde, R. César
  • Skalský, Rastislav
  • Lin, Erda
  • Mueller, Nathan
  • Obersteiner, Michael

Abstract

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER) and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.

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  • Xiong, Wei & Balkovič, Juraj & van der Velde, Marijn & Zhang, Xuesong & Izaurralde, R. César & Skalský, Rastislav & Lin, Erda & Mueller, Nathan & Obersteiner, Michael, 2014. "A calibration procedure to improve global rice yield simulations with EPIC," Ecological Modelling, Elsevier, vol. 273(C), pages 128-139.
    • Handle: RePEc:eee:ecomod:v:273:y:2014:i:c:p:128-139
    DOI: 10.1016/j.ecolmodel.2013.10.026
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    6. Dennis Junior Choruma & Frank Chukwuzuoke Akamagwuna & Nelson Oghenekaro Odume, 2022. "Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa," Agriculture, MDPI, vol. 12(6), pages 1-24, May.
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    8. Chul-Hee Lim & Seung Hee Kim & Yuyoung Choi & Menas C. Kafatos & Woo-Kyun Lee, 2017. "Estimation of the Virtual Water Content of Main Crops on the Korean Peninsula Using Multiple Regional Climate Models and Evapotranspiration Methods," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    9. Food and Agricultural Organization [FAO], 2016. "Climate Change and Food Systems: Global Assessments and Implications for Food Security and Trade," Working Papers id:8512, eSocialSciences.
    10. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    11. Arunrat, Noppol & Pumijumnong, Nathsuda & Hatano, Ryusuke, 2018. "Predicting local-scale impact of climate change on rice yield and soil organic carbon sequestration: A case study in Roi Et Province, Northeast Thailand," Agricultural Systems, Elsevier, vol. 164(C), pages 58-70.
    12. Kieu N. Le & Manoj K. Jha & Jaehak Jeong & Philip W. Gassman & Manuel R. Reyes & Luca Doro & Dat Q. Tran & Lyda Hok, 2018. "Evaluation of Long-Term SOC and Crop Productivity within Conservation Systems Using GFDL CM2.1 and EPIC," Sustainability, MDPI, vol. 10(8), pages 1-17, July.
    13. Qiao, Jianmin & Cao, Qian & Liu, Yupeng & Wu, Quanyuan, 2018. "Scale dependence and parameter sensitivity of the EPIC model in the agro-pastoral transitional zone of north China," Ecological Modelling, Elsevier, vol. 390(C), pages 51-61.
    14. Zhang, Yuanhong & Peng, Xingxing & Ning, Fang & Dong, Zhaoyang & Wang, Rui & Li, Jun, 2022. "Assessing the response of orchard productivity to soil water depletion using field sampling and modeling methods," Agricultural Water Management, Elsevier, vol. 273(C).
    15. Wang, Xuechun & Samo, Naseem & Wang, Mengran & Qadir, Muslim & Yang, Guotao & Hu, Yungao & Ali, Kawsar, 2019. "Dynamic changing of soil water in artificial ryegrass land in the hilly regions of Sichuan Basin area," Agricultural Water Management, Elsevier, vol. 221(C), pages 99-108.

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