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Title: FORECASTING WEEKLY COW MILK PRODUCTION USING A MULTIVARIATE TIME SERIES APPROACH
FORECASTING WEEKLY COW MILK PRODUCTION USING A MULTIVARIATE TIME SERIES APPROACH

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Kristina Dineva; Tatiana Atanasova
10.5593/sgem2023V/6.2/s25.29
10.5593/sgem2023v/6.2
1314-2704
978-619-7603-66-8
English
23
6.2
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Advances in Biotechnology
The continuous rise in global population necessitates the prediction of food resources, among which milk stands out as one of the staples. Dairy farms and processing plants need to know how much milk they will have available to manage their storage and processing capacities efficiently. Overestimating production can lead to wastage, while underestimating can lead to shortages. Knowing the expected milk quantity helps in planning the transportation, processing, and distribution of milk and milk products. This study presents a cutting-edge method for forecasting weekly cow milk production using a multivariate time series approach. Leveraging collected data from IoT devices for environmental variables, genetic data from health diaries, and health metrics alongside traditional temporal data, this research aims to provide a comprehensive model for predicting milk yields. А multivariate time series model SARIMAX was trained, tested and evaluated, focusing on ensuring both accuracy and robustness. The findings demonstrate that by integrating multiple data streams, significant improvements in forecast precision can be achieved. Furthermore, this integrated approach provides insights into the key factors influencing weekly milk production, paving the way for informed strategies in dairy management. The proposed model showcases the potential for scalability across different dairy operations and regions, contributing to the global effort towards food security and sustainable agriculture practices.
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The research leading to these results received funding from the Ministry of Educationand Science of the Republic Bulgaria under the National Science ProgramINTELLIGENT ANIMAL HUSBANDRY, grant agreement No. Д01-62/18.03.2021/.
conference
https://doi.org/10.5593/sgem2023V/6.2/s25.29
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Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 28-30 November, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference-SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
231-238
28-30 November, 2023
website
9604
Weekly Milk Production, Multivariate time series forecasting, SARIMAX, endogenous and exogenous features