Such improvements have great importance and can increase confidence and reliability when simulating the response of plants, for example, to climate change and temperature, to better plan agronomic activities to maximize crop yield while reducing the risks.
More specifically, studies have highlighted that robust phenological submodels are crucial tools to improve the accuracy of crop models, given the importance of the timing of anthesis and crop duration in crop yield determination. Suggestions for identifying and improving the prediction accuracy of various processes have been an important part of several recent crop model ensembles and inter-comparison studies. Since the development of the models, improving their prediction accuracy has been the main emphasis to better adapt them for new environments and future research questions. Most of the commonly and widely used crop models (e.g., DSSAT, APSIM, CROPSYST, EPIC, STICS, WOFOST, DAISY, ORYZA, GLAM, and INFOCROP) were constructed at least two decades ago using crop data obtained under controlled growing conditions and limited datasets. Since crop models are being extensively applied to a wide range of agricultural research questions and hypothesis testing, such as assessments of climate change effects, decision making and planning, farmer advisory, crop–livestock systems, agronomic management, physiological mechanisms and traits, linking phenotype to genotype, and plant breeding, it is critical to find ways to reduce the uncertainties. Model algorithms and parameters are still simplifications of real systems, which makes crop models contain unavoidable systematic errors.Īdditionally, at the user level, the quality of input data and the choice of model parameterization approach create further uncertainties. The processes related to phenology, dry matter accumulation and partitioning, and soil hydrology and chemistry are simulated with the various algorithms and parameters. Various algorithms and parameters in crop models simulate different plant and soil processes on several interactions and linkages. Process-based crop models simulate dynamic and complex interactions between environment, genotype, and management factors. For a robust phenology prediction at high latitudes with APSIM-NG, more research on the conception of thermal time computation and implementation is suggested. The differences between the models are possibly due to slower thermal time accumulation mechanism, with higher cardinal temperatures in APSIM-NG. However, in the evaluation, APSIM-NG showed an inclination to overestimate days to physiological maturity. The calibration performance for both growth stages of APSIM-NG was better compared to APSIM 7.9. The calibration was performed separately for days to anthesis and physiological maturity, and evaluations for the calibrations were done with independent datasets. The models have different mechanisms to simulate days to anthesis. A factorial-based calibration approach provided within APSIM-NG was performed to calibrate both models.
Phenological data of twelve spring barley varieties were used for the 2014–2018 cropping seasons from northern Sweden and Finland.
In this study, the phenology simulation algorithms in APSIM classical (APSIM 7.9) and APSIM next generation (APSIM-NG) were compared for spring barley models at high latitudes. Phenology algorithms in crop growth models have inevitable systematic errors and uncertainties.
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