Features of recording of meteorological conditions in the data warehouse of qualification examination of plant varieties
DOI:
https://doi.org/10.21498/2518-1017.17.3.2021.242980Keywords:
meteorological data, phenological stages, dimensions, facts, attributes, growing season, interphase periodAbstract
Purpose. To develop a multidimensional model of the data storage for the qualification examination of plant varieties for fixing meteorological conditions in conjunction with the phenological stages of development of varieties that undergo DUS and SVD expertise.
Methods. To conduct research with the establishment of the main structural elements of a multidimensional data warehouse, methods of induction, deduction, analysis and synthesis were used. In the design process of the storage facility, W. H. Inmon’s concept was applied, adapted for the agricultural and agricultural business.
Results. The stages of qualification examination of plant varieties were analyzed and methodological approaches to the creation of a multidimensional data warehouse model were considered. The features of the use of data storages for storing the results of qualification examination of plant varieties for distinctness, uniformity and stability (DUS) and suitability of a variety for dissemination in Ukraine (SVD) were highlighted. Particular attention was paid to the implementation of the interconnection between the results of the qualification examination of plant varieties with the data of meteorological observations at various phenological stages of plant growth and development, according to the records in the electronic field journal. The logical data model of the data warehouse was designed and implemented in the MS SQL Server environment.
Conclusions. Sources of data entry into data warehouses were determined and a multidimensional data warehouse model was implemented according to the “snowflake” scheme. The diagram of the data warehouse was presented, which provided a link between the meteorological conditions of the field experiments and the initial data of the qualification examination, and had four tables of measurements. For each dimension table and fact table, an attribute composition of the data was defined. The data warehouse was practically used to analyze the influence of weather conditions on the indicators of DUS and SVD examinations.
Downloads
References
Anderson, P. K., Cunningham, A. A., Patel, N. G., Morales, F. J., Epstein, P. R., & Daszak, P. (2004). Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol., 19(10), 535–544. doi: 10.1016/j.tree.2004.07.021
Audsley, E., Pearn, K. R., Simota, C., Cojocaru, G., Koutsidou, E., Rounsevell, M. D. A., Trnka, M., & Alexandrov, V. (2006). What can scenario modelling tell us about future European scale agricultural and use, and what not? Environ. Sci. Pol., 9(2), 148–168. doi: 10.1016/j.envsci.2005.11.008
Baker, R. H. A., Sansford, C. E., Jarvis, C. H., Cannon, R. J., MacLeod, A., & Walters, K. F. A. (2000). The role of climatic mapping in predicting the potential distribution of non-indigenouspests under current and future climates. Agric. Ecosyst. Environ., 82(1–3), 57–71. doi: 10.1016/s0167-8809(00)00216-4
Bale, J. S., Masters, G. J., Hodkinson, I. D., Awmack, C., Bezemer, M. T., Brown, V. K, … Whittaker, J. B. (2002). Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Global Change Biol., 8(1), 1–16. doi: 10.1046/j.1365-2486.2002.00451.x
Chloupek, O., Hrstkova, P., & Schweigert, P. (2004). Yield and its stability, crop diversity, adaptability and response to climate change, weather and fertilisation over 75 years in the Czech Republic in comparison to some European countries. Field Crops Res., 85(2–3), 167–190. doi: 10.1016/S0378-4290(03)00162-X
Leschuk, N. V., Orlenko, N. S., Khareba, O. V., & Dydiv, O. J. (2020). The use of grouping morphological characteristics of Lettuce varieties L. var. capitata for the difference test in Ukraine. Int. J. Bot. Stud., 5(6), 516–522.
Leschuk, N. V., Mazhuha, K. M., Orlenko, N. S., Starychenko, Ye. M., & Shkapenko, Y. A. (2017). Comparative analysis of statistical software products for the qualification examination of plant varieties for suitability for distribution. Plant Var. Stud. Prot., 13(4), 429–435. doi: 10.21498/2518-1017.13.4.2017.117757 [in Ukrainian]
Inmon, W. H. (2002). Building the Data Warehouse (3rd ed.). New York, NY : John Wiley & Sons.
Kimball, R., & Ross, M. (2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. New York, NY : John Wiley & Sons.
Shakhovska, N. B., & Pasichnyk, V. V. (2009). Skhovyshcha ta prostory danykh [Data warehouses and spaces]. Lviv: Lvivska politekhnika. [in Ukrainian]
Cooper, B. L., Watson, H. J., Wixom, B. H., & Goodhue, D. L. (2000). Data Warehousing supports corporate strategy at First American Corporation. MIS Quarterly, 24(4), 547–567. doi: 10.2307/3250947
Tumanov, V. E. (2016). Proektirovanie khranilishch dannykh dlya prilozheniy sistem delovoy osvedomlennosti (Business Intelligence Systems) [Designing Data Warehouses for Business Intelligence Systems Applications (Business Intelligence Systems)] (2nd ed.). Moscow: Natsional’nyy Otkrytyy Universitet “INTUIT” [in Russian]
Abdullah, A., Brobst, S., Pervaiz, I., Umer, M., & Nisar, A. (2003). Agri Data Mining/Warehousing: Innovative tools for analysis of integrated agricultural and meteorological data: to appear in proceedings of International Workshop on Frontiers of Information Technology, COMSATS (CIIT), Islamabad, Pakistan. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.9159&rep=rep1&type=pdf
Jindal, R., & Taneja, S. (2011). Comparative study of data warehouse design approaches: A survey. Int. J. Database Manag. Syst., 4(1), 205–210. doi: 10.5121/ijdms.2012.4104
Agrawal, R. R., Gupta, A., & Sarawagi, S. (1997). Modelling Multidimensional Databases. Proc. 13th Int. Conf. on Data Engineering (pp. 232–243). doi: 10.1109/ICDE.1997.581777
Sharma, L., & Mehta, N. (2012). Data Mining Techniques: A Tool for Knowledge Management System in Agriculture. Int. J. Sci. Technol. Res., 1(5), 67–73.
Gupta, A. K., Mazumdar, B. D. (2013). Multidimensional schema for agricultural. Int. J. Res. Eng. Technol., 2(3), 245–253. doi: 10.15623/IJRET.2013.0203006
Yost, M., & Nealon, J. (1999). Using a dimensional data warehouse to standardize survey and census metadata. National Agricultural Statistics Service, U.S. Department of Agriculture. Retrieved from https://nces.ed.gov/FCSM/pdf/II_B_Yost_FCSM1999.pdf
Sharma, S. D., Singh, R., & Rai A. (2000). Integrated National Agricultural Resources Information System (INARIS). New Delhi: Indian Agricultural Statistics Research Institute. Retrieved from https://www.geospatialworld.net/article/integrated-national-agricultural-resources-information-system-inaris/
Abdullah, A., Brobst, S., Pervaiz, I., Umer, M., & Nisar, A. (2003). Agri Data Mining/Warehousing: Innovative tools for analysis of integrated agricultural and meteorological data: to appear in proceedings of International Workshop on Frontiers of Information Technology, COMSATS (CIIT). Islamabad, Pakistan. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.9159&rep=rep1&type=pdf
Nilakanta, S., Scheibe, K., & Rai, A. (2008). Dimensional issues in agricultural data warehouse designs. Comput. Electron. Agric., 60(2), 263–278. doi: 10.1016/j.compag.2007.09.009
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 С. І. Мельник
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Our journal abides by the CREATIVE COMMONS copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons Attribution License, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.