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Predicting the seedling emergence time of sugar beet ( Beta vulgaris ) using beta models


Predicting the seedling emergence time of sugar beet ( Beta vulgaris ) using beta models



Physiology and Molecular Biology of Plants: An International Journal of Functional Plant Biology 26(12): 2329-2338



ISSN/ISBN: 0971-5894

PMID: 33424150

DOI: 10.1007/s12298-020-00884-1

Soil temperature, texture, water content and sowing depth are effective factors on the estimation of emergence time. This research aimed to test the Beta model for its adequacy in predicting the time of emergence for sugar beet. The Beta growth model as a phenological model have been used for evaluating the time of seedling emergences under both controlled environments in laboratory and field conditions. An experiment was conducted both in the laboratory with five soil textures, three sowing depths, five soil water contents and ten constant soil temperatures, under field conditions on five sowing dates (20 February, 28 March, 19 April, 10 May, and 31 May) and three sowing depths. The results demonstrated that the Beta model can predict the time of emergence. Based on the root mean square error (RMSE), the time of emergence estimated by the Beta model was in high agreement with the time of emergence measured in the laboratory. Estimation accuracy was reduced slightly by the Beta model under field conditions. The accuracy of the Beta model was influenced by the sowing date under field conditions. So, on the first and second sowing dates (with low air temperature), the estimation of time of emergence by the model was lower and on the fourth and the fifth sowing date (with warmer air temperature), was more than the duration measured. Estimation accuracy was increased by the Beta model under field conditions using soil temperature. In conclusion, the Beta model can predict the time to emergence of sugar beet seedlings in different levels of soil texture and soil water content under field conditions, and with that, the proper planting date for sugar beet seeds to overcome weeds in different soil water content can be predicted.

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Accession: 071087210

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