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.
The global optimization of parameters in process-based crop models is often considered computationally expensive. Gaussian process (GP) emulation is a widely used method for reducing the computational burden of the optimization process. Total above-ground biomass and cane dry weight of three Thai sugarcane cultivars (KK3, LK92-11 and 02-2-058) collected under rainfed and irrigated conditions were used to optimize cultivar-specific parameters in the Agricultural Production Systems sIMulator (APSIM)-Sugarcane crop model through a GP emulation. GP emulators were trained and validated to approximate APSIM-Sugarcane model and then used for optimizing the cultivar-specific parameters through the differential evolution algorithm. Resulting optimized parameters allowed to obtain simulations that quite well approximated the observed biomass and CDW (validation results between simulated and observed yields: R2 0.93–0.98; normalized root mean squared error: 5–22%; Willmott’s agreement index: 0.87–0.99). The best parametrization was obtained under the lowest water stressed conditions. Based on these results, we suggest that GP emulation can be efficiently implemented for the parameterization of computationally expensive simulators.
Northeastern Thailand has little rainfall and requires efficient irrigation development to enhance stable sugarcane production. However, identifying the highest priority areas for irrigation development is complex because the benefit derived from irrigation development depends on rainfall, available irrigation water, and soil characteristics. We used the CANEGRO model to simulate the sugarcane yield of existing cultivation areas under rainfed and irrigated conditions, taking into account actual weather and soil type. We then calculated the benefit of the irrigation development using the simulation results and actual data for groundwater well capacities, sugarcane prices, and irrigation development and running costs. We then analysed the results of the benefit calculation by ABC analysis and the decision tree method. The decision tree analysis confirmed that well capacity most influenced benefit. Areas with higher rainfall had high yields under rainfed condition, so the benefit from irrigation was small (or even negative as the cost of irrigation exceeded the increased income). A notable finding was that low soil available water content resulted in low yields in both rainfed and irrigated conditions, and high available water content resulted in high yields under rainfed conditions; therefore, both low and high available water content resulted in low benefit from irrigation development.
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