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Article

A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand

1
Faculty of Agriculture, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Okinawa 903-0213, Japan
2
Department of Agricultural Engineering, Faculty of Agriculture, University of Ruhuna, Kamburupitiya 81100, Sri Lanka
3
United Graduate School of Agricultural Sciences, Kagoshima University, 1-21-24 Korimoto, Kagoshima-shi, Kagoshima 890-0065, Japan
4
Nakhon Sawan Agricultural Research and Development Center, Moo 2, Udomthanya, Takfa 60190, Thailand
*
Authors to whom correspondence should be addressed.
Agronomy 2020, 10(7), 984; https://doi.org/10.3390/agronomy10070984
Received: 22 June 2020 / Revised: 2 July 2020 / Accepted: 7 July 2020 / Published: 9 July 2020
Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK. View Full-Text
Keywords: APSIM; Gaussian process emulation; global sensitivity analysis; sugarcane APSIM; Gaussian process emulation; global sensitivity analysis; sugarcane
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MDPI and ACS Style

Bandara, W.B.M.A.C.; Sakai, K.; Nakandakari, T.; Kapetch, P.; Rathnappriya, R.H.K. A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand. Agronomy 2020, 10, 984. https://doi.org/10.3390/agronomy10070984

AMA Style

Bandara WBMAC, Sakai K, Nakandakari T, Kapetch P, Rathnappriya RHK. A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand. Agronomy. 2020; 10(7):984. https://doi.org/10.3390/agronomy10070984

Chicago/Turabian Style

Bandara, W. B.M.A.C., Kazuhito Sakai, Tamotsu Nakandakari, Preecha Kapetch, and R. H.K. Rathnappriya 2020. "A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand" Agronomy 10, no. 7: 984. https://doi.org/10.3390/agronomy10070984

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