Жарияланымдар / Публикации

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Assessment of Vegetation and Soil Characteristics of Calligonum Leucocladum Populations in the Balkhash Region, Kazakhstan
(2025-02-10)
Alibek Ydyrys
;
Мaral Mussina
;
Saltanat Nazarbekova
;
Bektemir B. Osmonali
;
Raushan Dossymbekova
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Effect of fertilizer application on winter wheat productivity under precision agriculture in Kazakhstan
(MBI & UNS Solo, 2021-03-04)
Alibek Baimuratov
;
Sholpan O. Bastaubayeva
;
Mehmet Arslan
;
Zhanar Muratovna Yeraliyeva
Abstract. Baimuratov, A, Bastaubayeva S, Arslan M, Yeraliyeva Z. 2021. Effect of fertilizer application on winter wheat productivity under precision agriculture in Kazakhstan. Biodiversitas 22: 1558-1563. The effect of mineral fertilization of light chestnut soils of Almalybak, Kazakhstan on the three winter wheat varieties grain yields and NDVI was carried out in 2017/18, 2018/2019 as well as 2019/2020 growing seasons using precision agriculture technologies. The experiments were set up in an incomplete factorial design with rates of NPK N90P60K45, N120P90K60, and N150P120K90. Agrochemical cartograms were ordered for the substance of effectively hydrolyzable nitrogen, extractable phosphorus, and exchangeable potassium. The total variation of NDVI was from 0.62 in the minimum (Bezostaya 100 variety, 2017/18) to 0.88 in the maximum (Aliya variety, 2019/20) in the flowering phase of wheat plants. The highest index of NDVI was observed in the N150P120K90 in the three winter wheat varieties for the three seasons. Studies directed in multivariate analyses built up the connection between fertilizer application, soil fruitfulness, and grain yield. As per the genuine heterogeneity of the fields, standards were determined and differential use of the fundamental phosphorus fertilizer was completed. The yield for the winter wheat varieties varied between 5.6-5.9 t/ha for Aliya variety, 6.5-7.0 t/ha for Grom, and 6.2- 6.7 t/ha for Bezostaya 100, which presents that P120 doses were more suitable in experimental plots for winter wheat varieties. The grain yield on mineral fertilization of NPK for the studied varieties varied from 4.2 to 7.6 t/ha for Aliya variety, from 4.0 to 6.8 t/ha for the Bezostaya 100, and from 4.5 to 7.9 t/ha for Grom. The highest grain yield was observed in the N150P120K90 for three winter wheat varieties in all three seasons.
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Integrating satellite data and machine learning algorithms into a flood prediction system
(2025-07-19)
Gulzat Z. Ziyatbekova
;
Dauren Darkenbayev
;
Shyraigul Shekerbayeva
;
Aisulu Zhaksymbet
The objective of this study is to develop an intelligent flood forecasting system based on the integration of satellite and ground-based hydrometeorological data using machine learning algorithms in the Orange visual analytical environment. The aim of the article is to improve the accuracy and efficiency of forecasting extreme hydrological events, as well as to simplify the process of building forecasting models through the use of an interface that does not require programming. The methodological basis of the study is the formation of a synthetic multivariate dataset combining satellite vegetation indices NDVI and LST temperature with meteorological parameters such as precipitation, temperature, humidity, and water level. Random forest, gradient boosting (XGBoost), and multilayer perceptron (MLP) algorithms were used to build and validate the models. All stages from data loading and pre-processing to visualization and interpretation of results are implemented in the Orange environment using cross-validation and feature significance assessment. The results obtained demonstrated high forecast accuracy (up to 94%), especially when using ensemble and deep models. The significance of satellite data is confirmed by analyzing the contribution of features to the final classification. The developed forecasting model can be adapted to various geographical conditions and integrated into existing monitoring and early warning platforms. The proposed approach has high applied value and demonstrates the potential for using modern big data analysis technologies and artificial intelligence methods in the tasks of reducing the risk of natural floods.
Цитирования (OpenAlex) 1Цитирования (OpenCitations) 1
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Идентификация перспективных линий пшеницы устойчивых к пиренофорозу (Pyrenophora tritici-repentis)
(2026-03-30)
Бактигул Раимбекова
;
Л. Ануарова
;
Elzira Kyrbassova
;
Elmira Imаnova
;
Акмарал Сартаева
Пиренофороз пшеницы (желтая пятнистость) — одно из наиболее распространённых и опасных грибковых заболеваний пшеницы, вызываемое фитопатогенным грибом Pyrenophora tritici-repentis. Целью исследования является идентификация устойчивости к пиренофорозу перспективных линий пшеницы на основе фенотипических показателей и молекулярного скрининга. Проведена комплексная оценка образцов пшеницы по индексу биомассы (NDVI), устойчивости к патогенам (PTR, AUDPC) и молекулярным маркерам. Установлено, что NDVI варьирует от 65 до 82, при этом высокие значения (>75) коррелируют с устойчивостью (R²=0,652), а низкие (50 %) и экстремальные значения (до 165). Статистический анализ подтвердил стабильность NDVI (CV=6,5–6,7 %) и значительную изменчивость PTR/AUDPC, отражающую смешанную устойчивость. Результаты подчеркивают необходимость углубленного изучения связи молекулярных маркеров с устойчивостью к пиренофорозу.

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