Mathematical Modelling in Crop Production to Predict Crop Yields

In this study, for remote recognition of crops of agroecosystems in Kazakhstan by methods of comparative and historical analogy with the active use of mathematical modelling, the yield indicator of agricultural crops was determined, their dynamic characteristics were studied to predict productivity....

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Autores principales:	Marzhan Anuarbekovna Sadenova, Nail Alikuly Beisekenov, Marzhan Y. Rakhymberdina, Petar Sabev Varbanov, Jirí Jaromír Klemeš
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/05596165a2b24e3ca0db81243959bd34
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spelling	oai:doaj.org-article:05596165a2b24e3ca0db81243959bd342021-11-15T21:46:57ZMathematical Modelling in Crop Production to Predict Crop Yields10.3303/CET21882042283-9216https://doaj.org/article/05596165a2b24e3ca0db81243959bd342021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11997https://doaj.org/toc/2283-9216In this study, for remote recognition of crops of agroecosystems in Kazakhstan by methods of comparative and historical analogy with the active use of mathematical modelling, the yield indicator of agricultural crops was determined, their dynamic characteristics were studied to predict productivity. The parameters of the dynamic-statistical biomass model were determined separately for each region of the Republic of Kazakhstan based on training data for 21 y (2000 – 2021). The correlation coefficient between the calculated yield values and the official statistics is 0.84. According to the results of cross-validation, the correlation coefficient between the actual and predicted yield of spring wheat was ~ 0.70, which indicates a sufficient resistance of the model to the variability of meteorological conditions for the formation of the crop.Marzhan Anuarbekovna SadenovaNail Alikuly BeisekenovMarzhan Y. RakhymberdinaPetar Sabev VarbanovJirí Jaromír KlemešAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Marzhan Anuarbekovna Sadenova Nail Alikuly Beisekenov Marzhan Y. Rakhymberdina Petar Sabev Varbanov Jirí Jaromír Klemeš Mathematical Modelling in Crop Production to Predict Crop Yields
description	In this study, for remote recognition of crops of agroecosystems in Kazakhstan by methods of comparative and historical analogy with the active use of mathematical modelling, the yield indicator of agricultural crops was determined, their dynamic characteristics were studied to predict productivity. The parameters of the dynamic-statistical biomass model were determined separately for each region of the Republic of Kazakhstan based on training data for 21 y (2000 – 2021). The correlation coefficient between the calculated yield values and the official statistics is 0.84. According to the results of cross-validation, the correlation coefficient between the actual and predicted yield of spring wheat was ~ 0.70, which indicates a sufficient resistance of the model to the variability of meteorological conditions for the formation of the crop.
format	article
author	Marzhan Anuarbekovna Sadenova Nail Alikuly Beisekenov Marzhan Y. Rakhymberdina Petar Sabev Varbanov Jirí Jaromír Klemeš
author_facet	Marzhan Anuarbekovna Sadenova Nail Alikuly Beisekenov Marzhan Y. Rakhymberdina Petar Sabev Varbanov Jirí Jaromír Klemeš
author_sort	Marzhan Anuarbekovna Sadenova
title	Mathematical Modelling in Crop Production to Predict Crop Yields
title_short	Mathematical Modelling in Crop Production to Predict Crop Yields
title_full	Mathematical Modelling in Crop Production to Predict Crop Yields
title_fullStr	Mathematical Modelling in Crop Production to Predict Crop Yields
title_full_unstemmed	Mathematical Modelling in Crop Production to Predict Crop Yields
title_sort	mathematical modelling in crop production to predict crop yields
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/05596165a2b24e3ca0db81243959bd34
work_keys_str_mv	AT marzhananuarbekovnasadenova mathematicalmodellingincropproductiontopredictcropyields AT nailalikulybeisekenov mathematicalmodellingincropproductiontopredictcropyields AT marzhanyrakhymberdina mathematicalmodellingincropproductiontopredictcropyields AT petarsabevvarbanov mathematicalmodellingincropproductiontopredictcropyields AT jirijaromirklemes mathematicalmodellingincropproductiontopredictcropyields
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Mathematical Modelling in Crop Production to Predict Crop Yields

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