CALIBRATION AND PARAMETERISATION OF THE AQUACROP MODEL FOR GROWING WINTER WHEAT IN BULGARIA
D. Atanassov, V. Georgieva, V. Kazandjiev
Strony: 176-186
Opublikowano: 13 Nov 2023
DOI: 10.62991/AF1996296060
Wyświetlenia: 1,281
Pobrania: 126
Streszczenie: The sustainable development of modern agriculture and the manufacturing of quality crop production requires a precise assessment and management of the conditions and potential of the available resources. The use of numerical models in operational practice gives insight into the effective utilization of water resources. Numerical modeling methods can be used to assess of the extent and negative impact of agrometeorological drought. It is an increasingly common phenomenon and affects the quantity and quality of the harvest. The AquaCrop model is suitable for estimating the growth and productivity of agricultural crops in areas with insufficient moisture. Our research aims to calibrate and optimize the parameters of the AquaCrop model for winter wheat under the conditions of the country. This will be achieved through conducting a field experiment. The collection of observed and measured data will enable their comparison with the results of the model simulation. Through the AquaCrop model, water effectivity and yield sensitivity to available water reserves will be estimated. In the process of parameterization of the model, the values of the elements of the meteorological, biological, and soil block of the model affecting the soil water balance and natural fertility will be optimized. The obtained results will be widely used in the compilation of agrometeorological forecasts and determination dates for the onset of drought in different regions of the country.
Słowa kluczowe: winter wheat, calibration, aquacrop, parameterization, field experiment, yield simulation
Cytowanie artykułu: D. Atanassov, V. Georgieva, V. Kazandjiev. CALIBRATION AND PARAMETERISATION OF THE AQUACROP MODEL FOR GROWING WINTER WHEAT IN BULGARIA. Journal of International Scientific Publications: Agriculture & Food 11, 176-186 (2023). https://doi.org/10.62991/AF1996296060
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