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

• Published in: Agronomy (Basel) 2020-07, Vol.10 (7), p.984
• Main Authors: Bandara, W. B. M. A. C., Sakai, Kazuhito, Nakandakari, Tamotsu, Kapetch, Preecha, Rathnappriya, R. H. K.
• Format: Article
• Language: English
• Subjects: Agricultural management; Agricultural production; APSIM; Biomass; Climate change; Computer simulation; Cultivars; Emulators; Environmental management; Gaussian process; Gaussian process emulation; Global optimization; global sensitivity analysis; Mathematical models; Model accuracy; Parameter identification; Parameter sensitivity; Sensitivity analysis; Simulation; Soil investigations; Soil types; Soils; Studies; Sucrose; Sugar; Sugarcane; Variability; Water shortages; Water stress; Water supply
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy10070984

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.