Margarita Igorevna Ivanova, V. Ivanchenko, Dmitry Valerievich Potanin
{"title":"开发用于预测技术工艺有效性的葡萄品种数字模型的前景","authors":"Margarita Igorevna Ivanova, V. Ivanchenko, Dmitry Valerievich Potanin","doi":"10.30679/2219-5335-2024-1-85-157-173","DOIUrl":null,"url":null,"abstract":"At the present stage, with an increase in the volume of consumption of grape-growing products, it is necessary to carry out a monitoring forecast of the possibility of its production for each individual variety or scion-rootstock combination, depending on edaphoclimatic conditions and cultivation technology. This is possible only if predictive models of the behavior of the grape variety or its scion-rootstock combination are developed in the grafted culture in various ecoagrobiocenoses. The purpose of the study was to consider methodological approaches to the creation of mathematical models for predicting the behavior of an individual variety or groups of grape varieties, depending on the abiotic and agrotechnological characteristics of cultivation. To achieve this goal, a previously created database was used, obtained during an experiment conducted on the basis of uterine plantations and an open grape school of the Institute \"Agrotechnological Academy\" of the V.I. Vernadsky Crimean Federal University, collected in the period from 2018 to 2021 and subjected to multidimensional regression analysis using the developed program. The total number of items included in the database is 1,860. (31 parameters). The research proved the possibility of developing regression models for predicting productivity using nonparametric digital introduction of varieties, as well as environmental factors. It is established that regression models characterizing the quality of the vine, taking into account varietal characteristics and weather conditions, can vary depending on a particular variety. Thus, a similar model for the Cabernet Sauvignon variety is fundamental with a multiple correlation coefficient of R = 0.9866, and for the Syrah variety it is logarithmic at R= 1.0000. Promising possibilities and ways of developing digital (mathematical) models characterizing individual varieties or groups of varieties by origin according to their productivity, depending on edaphoclimatic conditions, production technology, as well as quality parameters of manufactured products are considered","PeriodicalId":504482,"journal":{"name":"Fruit growing and viticulture of South Russia","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PROSPECTS FOR THE DEVELOPMENT OF DIGITAL MODELS OF GRAPE VARIETIES FOR PREDICTING THE EFFECTIVENESS OF TECHNOLOGICAL PROCESSES\",\"authors\":\"Margarita Igorevna Ivanova, V. Ivanchenko, Dmitry Valerievich Potanin\",\"doi\":\"10.30679/2219-5335-2024-1-85-157-173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At the present stage, with an increase in the volume of consumption of grape-growing products, it is necessary to carry out a monitoring forecast of the possibility of its production for each individual variety or scion-rootstock combination, depending on edaphoclimatic conditions and cultivation technology. This is possible only if predictive models of the behavior of the grape variety or its scion-rootstock combination are developed in the grafted culture in various ecoagrobiocenoses. The purpose of the study was to consider methodological approaches to the creation of mathematical models for predicting the behavior of an individual variety or groups of grape varieties, depending on the abiotic and agrotechnological characteristics of cultivation. To achieve this goal, a previously created database was used, obtained during an experiment conducted on the basis of uterine plantations and an open grape school of the Institute \\\"Agrotechnological Academy\\\" of the V.I. Vernadsky Crimean Federal University, collected in the period from 2018 to 2021 and subjected to multidimensional regression analysis using the developed program. The total number of items included in the database is 1,860. (31 parameters). The research proved the possibility of developing regression models for predicting productivity using nonparametric digital introduction of varieties, as well as environmental factors. It is established that regression models characterizing the quality of the vine, taking into account varietal characteristics and weather conditions, can vary depending on a particular variety. Thus, a similar model for the Cabernet Sauvignon variety is fundamental with a multiple correlation coefficient of R = 0.9866, and for the Syrah variety it is logarithmic at R= 1.0000. Promising possibilities and ways of developing digital (mathematical) models characterizing individual varieties or groups of varieties by origin according to their productivity, depending on edaphoclimatic conditions, production technology, as well as quality parameters of manufactured products are considered\",\"PeriodicalId\":504482,\"journal\":{\"name\":\"Fruit growing and viticulture of South Russia\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fruit growing and viticulture of South Russia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30679/2219-5335-2024-1-85-157-173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fruit growing and viticulture of South Russia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30679/2219-5335-2024-1-85-157-173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PROSPECTS FOR THE DEVELOPMENT OF DIGITAL MODELS OF GRAPE VARIETIES FOR PREDICTING THE EFFECTIVENESS OF TECHNOLOGICAL PROCESSES
At the present stage, with an increase in the volume of consumption of grape-growing products, it is necessary to carry out a monitoring forecast of the possibility of its production for each individual variety or scion-rootstock combination, depending on edaphoclimatic conditions and cultivation technology. This is possible only if predictive models of the behavior of the grape variety or its scion-rootstock combination are developed in the grafted culture in various ecoagrobiocenoses. The purpose of the study was to consider methodological approaches to the creation of mathematical models for predicting the behavior of an individual variety or groups of grape varieties, depending on the abiotic and agrotechnological characteristics of cultivation. To achieve this goal, a previously created database was used, obtained during an experiment conducted on the basis of uterine plantations and an open grape school of the Institute "Agrotechnological Academy" of the V.I. Vernadsky Crimean Federal University, collected in the period from 2018 to 2021 and subjected to multidimensional regression analysis using the developed program. The total number of items included in the database is 1,860. (31 parameters). The research proved the possibility of developing regression models for predicting productivity using nonparametric digital introduction of varieties, as well as environmental factors. It is established that regression models characterizing the quality of the vine, taking into account varietal characteristics and weather conditions, can vary depending on a particular variety. Thus, a similar model for the Cabernet Sauvignon variety is fundamental with a multiple correlation coefficient of R = 0.9866, and for the Syrah variety it is logarithmic at R= 1.0000. Promising possibilities and ways of developing digital (mathematical) models characterizing individual varieties or groups of varieties by origin according to their productivity, depending on edaphoclimatic conditions, production technology, as well as quality parameters of manufactured products are considered