Publication detail
Mathematical Modelling in Crop Production to Predict Crop Yields
Sadenova, M.A. Beisekenov, N.A. Rakhymberdina, M. Varbanov, P.S. Klemeš, J.J.
English title
Mathematical Modelling in Crop Production to Predict Crop Yields
Type
journal article in Scopus
Language
en
Original abstract
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 dynamicstatistical 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. © 2021, AIDIC Servizi S.r.l.
English abstract
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 dynamicstatistical 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. © 2021, AIDIC Servizi S.r.l.
Keywords in English
mathematical; modelling; crop; production; predict; crop yields
Released
15.11.2021
Publisher
Italian Association of Chemical Engineering - AIDIC
ISSN
2283-9216
Number
88
Pages from–to
1225–1230
Pages count
6
BIBTEX
@article{BUT175971,
author="Petar Sabev {Varbanov} and Jiří {Klemeš},
title="Mathematical Modelling in Crop Production to Predict Crop Yields",
year="2021",
number="88",
month="November",
pages="1225--1230",
publisher="Italian Association of Chemical Engineering - AIDIC",
issn="2283-9216"
}