scholarly journals Features of recording of meteorological conditions in the data warehouse of qualification examination of plant varieties

2021 ◽  
Vol 17 (3) ◽  
pp. 254-261
Author(s):  
S. I. Melnyk ◽  
N. V. Leschuk ◽  
N. S. Orlenko ◽  
E. M. Starychenko ◽  
K. M. Mazhuha ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Data Storage ◽  
Field Experiments ◽  
Data Entry ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Multidimensional Data ◽  
Phenological Stages ◽  
Agricultural Business ◽  
Meteorological Observations

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.

Development of Online Egg Grading Information Management System with Data Warehouse Technique

2020 ◽  
Vol 36 (4) ◽  
pp. 589-604
Author(s):  
Seung-Chul Yoon ◽  
Tae Sung Shin ◽  
Kurt Lawrence ◽  
Deana R. Jones
Keyword(s):  
Information System ◽  
Data Warehouse ◽  
Information Management ◽  
Data Storage ◽  
Management System ◽  
Web Application ◽  
Data Entry ◽  
Information Management System ◽  
Online Data ◽  
Web Based

Highlights Digital data collection and management system is developed for the USDA-AMS’s shell-egg grading program. Database system consisting of OLTP, data warehouse and OLAP databases enables online data entry and trend reporting. Data and information management is done through web application servers. Users access the databases via web browsers. Abstract . This paper is concerned with development of web-based online data entry and reporting system, capable of centralized data storage and analytics of egg grading records produced by USDA egg graders. The USDA egg grading records are currently managed in paper form. While there is useful information for data-driven knowledge discovery and decision making, the paper-based egg grading record system has fundamental limitations in effective and timely management of such information. Thus, there has been a demand to electronically and digitally store and manage the egg grading records in a database for data analytics and mining, such that the quality trends of eggs observed at various levels (e.g., nation or state) are readily available to decision makers. In this study, we report the design and implementation of a web-based online data entry and reporting information system (called USDA Egg Grading Information Management System, EGIMS), based on a data warehouse framework. The developed information system consisted of web applications for data entry and reporting, and internal databases for data storage, aggregation, and query processing. The internal databases consisted of online transaction processing (OLTP) database for data entry and retrieval, data warehouse (DW) for centralized data storage and online analytical processing (OLAP) database for multidimensional analytical queries. Thus, the key design goal of the system was to build a system platform that could provide the web-based data entry and reporting capabilities while rapidly updating the OLTP, DW and OLAP databases. The developed system was evaluated by a simulation study with statistically-modeled egg grading records of one hypothetical year. The study found that the EGIMS could handle approximately up to 600 concurrent users, 32 data entries per second and 164 report requests per second, on average. The study demonstrated the feasibility of an enterprise-level data warehouse system for the USDA and a potential to provide data analytics and data mining capabilities such that the queries about historical and current trends can be reported. Once fully implemented and tested in the field, the EGIMS is expected to provide a solution to modernize the egg grading practice of the USDA and produce the useful information for timely decisions and new knowledge discovery. Keywords: Data warehouse, Database, OLTP, OLAP, Egg grading, Information management, Web application, Information system, Data.

Identification of coherent links between interannual sedimentary structures and daily meteorological observations in Arctic proglacial lacustrine varves: potentials and limitations

2008 ◽  
Vol 45 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Krystopher J Chutko ◽  
Scott F Lamoureux
Keyword(s):  
Lacustrine Sediments ◽  
Weather Conditions ◽  
High Arctic ◽  
Meteorological Conditions ◽  
Positive Temperature ◽  
Training Set ◽  
Robust Model ◽  
Structure Thickness ◽  
Meteorological Observations

Proglacial lacustrine sediments from High Arctic Lake R (76°17.9′N, 90°59.3′W, unofficial name) are shown to be annually laminated (varved) and contain a variety of subannual structures. The formation of the subannual structures (and overall varve) was controlled by a combination of meteorologic (temperature and rainfall) and geomorphic factors. Using a training set of the ten thickest varves in the 38-year sedimentary record, a heuristic model was developed to link subannual structures with regional meteorological conditions. Within the training set, significant correlations were shown between subannual structure thickness and the magnitude of the corresponding melt event, defined as a period of continuously positive temperature. However, these correlations deteriorated as the varves progressively thinned, and several varves exhibited no relationship between their subannual structures and respective meteorological conditions. Grain size analyses showed that the thin varves were significantly finer than the thick varves and are inferred to reflect changed sediment inflow patterns that altered deposition and reduced the fidelity of the model. Despite these complexities, this study identified the potential to produce long-term, subannual reconstructions of weather conditions. Model results revealed the limitations of simple varve–meteorology relationships, as well as identified necessary environmental and sampling conditions required to produce a more robust model for future applications.

A Method of Constructing Electric Power Data Warehouse Based on Cloud Computing

2012 ◽  
Vol 591-593 ◽  
pp. 1766-1769
Author(s):  
De Wen Wang ◽  
Kai Xiao
Keyword(s):  
Cloud Computing ◽  
Electric Power ◽  
Data Warehouse ◽  
Data Storage ◽  
Multidimensional Data ◽  
Data Set ◽  
Integration Architecture ◽  
Combining Data ◽  
Hadoop Cluster ◽  
Multidimensional Data Set

Hive is a data warehouse architecture in cloud computing. In order to solve the inadequate of massive data storage, query, and computing power in current electric power data warehouse, a method of electric power data warehouse based on Hive is proposed. Combining data analysis demands of electric power entreprises, the integration architecture between Hive and column-oriented storage is designed in electric power data warehouse, and the process of which is also given. At last, electric power equipment condition data is used for experiment on Hadoop cluster, results show that this method can quickly achieve query and analysis in massive multidimensional data set.

scholarly journals A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

2013 ◽  
Vol 4 (1) ◽  
pp. 146-150
Author(s):  
Lax Maiah ◽  
DR.A.GOVARDHAN DR.A.GOVARDHAN ◽  
DR. C.SUNIL KUMAR
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Warehouse ◽  
Data Storage ◽  
Multidimensional Data ◽  
Metadata Management ◽  
Temporal Data ◽  
Application Layer ◽  
Data Process ◽  
Spatio Temporal

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

Optimizing genotype-environment-management interactions to ensure silage maize production in the Chinese Maize Belt

2020 ◽  
Vol 80 (2) ◽  
pp. 133-146
Author(s):  
L Zhang ◽  
Z Zhang ◽  
J Cao ◽  
Y Luo ◽  
Z Li
Keyword(s):  
Field Experiments ◽  
Weather Conditions ◽  
Growth Cycle ◽  
High Energy ◽  
Risk Level ◽  
Energy Yield ◽  
Maize Production ◽  
Early Date ◽  
Environment Management ◽  
Silage Maize

Grain maize production exceeds the demand for grain maize in China. Methods for harvesting good-quality silage maize urgently need a theoretical basis and reference data in order to ensure its benefits to farmers. However, research on silage maize is limited, and very few studies have focused on its energetic value and quality. Here, we calibrated the CERES-Maize model for 24 cultivars with 93 field experiments and then performed a long-term (1980-2017) simulation to optimize genotype-environment-management (G-E-M) interactions in the 4 main agroecological zones across China. We found that CERES-Maize could reproduce the growth and development of maize well under various management and weather conditions with a phenology bias of <5 d and biomass relative root mean square error values of <5%. The simulated results showed that sowing long-growth-cycle cultivars approximately 10 d in advance could yield good-quality silage. The optimal sowing dates (from late May to July) and harvest dates (from early October to mid-November) gradually became later from north to south. A high-energy yield was expected when sowing at an early date and/or with late-maturing cultivars. We found that Northeast China and the North China Plain were potential silage maize growing areas, although these areas experienced a medium or even high frost risk. Southwestern maize experienced a low risk level, but the low soil fertility limited the attainable yield. The results of this paper provide information for designing an optimal G×E×M strategy to ensure silage maize production in the Chinese Maize Belt.

The questions of methodology of field experiments in agriculture and crop production

2018 ◽  
Vol 1 (94) ◽  
pp. 38-44
Author(s):  
А.M. Malienkо ◽  
N.E. Borуs ◽  
N.G. Buslaeva
Keyword(s):  
Labor Productivity ◽  
Crop Production ◽  
Field Experiments ◽  
Weather Conditions ◽  
High Accuracy ◽  
Experimental Plot ◽  
Increase Labor Productivity ◽  
Biometric Parameters ◽  
Field Crops ◽  
High Condition

In the article, the results of research on the methodology for conducting studies with corn culture under various methods of sowing and weather conditions. The aim of the research was to establish and evaluate the reliability and high accuracy of the experiment, with a decrease in the area's acreage and taking one plant per repetition. Based on the results of the analysis of biometric parameters and yields, the possibility of sampling from 5 to 108 plants was established statistically and mathematically to establish the accuracy of the experiment. The established parameters of sites in experiments with maize indicate the possibility of obtaining much more information from a smaller unit of area, that is, to increase labor productivity not only with tilled crops. This is the goal of further scientific research with other field crops taking 1 plant of repetitions, observing the conditions of leveling the experimental plot according to the fertility of the soil and sowing seeds with high condition. The data obtained give grounds for continuing research on the minimum space required and the sample in the experiments.

scholarly journals Influence of Meteorological Conditions and Aerosol Properties on the COVID-19 Contamination of the Population in Coastal and Continental Areas in France: Study of Offshore and Onshore Winds

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 523
Author(s):  
Jacques Piazzola ◽  
William Bruch ◽  
Christelle Desnues ◽  
Philippe Parent ◽  
Christophe Yohia ◽  
...  
Keyword(s):  
Atmospheric Transport ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Sea Spray ◽  
Atmospheric Conditions ◽  
Virus Transport ◽  
Virus Propagation ◽  
Viral Particles ◽  
The Social ◽  
Aerosol Properties

Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols—and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)—on the spread of the epidemic remains an open and still debated question. This work aims to study whether or not the meteorological conditions related to the different aerosol properties in continental and coastal urbanized areas might influence the atmospheric transport of the SARS-Cov-2 virus. Our analysis focuses on the lockdown period to reduce the differences in the social behavior and highlight those of the weather conditions. As an example, we investigated the contamination cases during March 2020 in two specific French areas located in both continental and coastal areas with regard to the meteorological conditions and the corresponding aerosol properties, the optical depth (AOD) and the Angstrom exponent provided by the AERONET network. The results show that the analysis of aerosol ground-based data can be of interest to assess a virus survey. We found that moderate to strong onshore winds occurring in coastal regions and inducing humid environment and large sea-spray production episodes coincides with smaller COVID-19 contamination rates. We assume that the coagulation of SARS-Cov-2 viral particles with hygroscopic salty sea-spray aerosols might tend to inhibit its viral infectivity via possible reaction with NaCl, especially in high relative humidity environments typical of maritime sites.

AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

2001 ◽  
Vol 10 (03) ◽  
pp. 377-397 ◽  
Author(s):  
LUCA CABIBBO ◽  
RICCARDO TORLONE
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Multidimensional Data ◽  
Multidimensional Databases ◽  
Level Of Aggregation ◽  
High Level ◽  
Data Independence ◽  
Logical Architecture

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.

scholarly journals Сортоиспытание моркови столовой в условиях Московской и Ростовской областей

2019 ◽  
Author(s):  
L.A. Yusupova ◽  
L.M. Sokolova ◽  
A.V. Kornev ◽  
A.N. Khovrin
Keyword(s):  
Field Experiments ◽  
Meteorological Conditions ◽  
Moscow Region ◽  
Qualitative And Quantitative ◽  
Rostov Region ◽  
Opposite Trend ◽  
Quantitative Characteristics ◽  
Variety Testing ◽  
Domestic Breeding ◽  
Qualitative And Quantitative Characteristics

Представлены результаты испытаний образцов моркови столовой в условиях Московской и Ростовской областей. Цель исследований – провести сортоиспытание моркови столовой в двух эколого-географических зонах и выявить различия по отдельным качественным и количественным признакам. Исследования проведены в 2017-2018 годах. Метеорологические условия 2017-2018 годов в Московской области (МО) складывались неблагоприятно для развития моркови в фазу «вилочки» и начала формирования корнеплодов. В Ростовской области жаркая и сухая погода в июле-августе отрицательно влияла на рост развитие корнеплодов. Материалом для исследований служили 3 сорта и 3 гибрида моркови столовой отечественной селекции: Корсар, Шантенэ королевская, Нанте, F1 Таврида, F1 Поиск 32, F1 Поиск 41. Полевые опыты были заложены согласно общепринятым методикам. Образцы значительно отличались по длине корнеплода: корнеплоды, выращенные в Ростовской области, имели большую длину, чем в Московской области, за исключением сорта Шантенэ королевская (11,9 и 13,2 см соответственно) и гибрида F1 Поиск 32 (18,4 и 15,8 см соответственно), у которых наблюдали обратную тенденцию. По урожайности сорта и гибриды, полученные в МО, значительно превосходили аналогичные образцы, выращенные в Ростовской области. На юге в течение двух лет испытаний лучше всех показал себя сорт Шантенэ королевская (55,0 и 54,9 т/га). В Московской области наибольшую урожайность показывал сорт Шантенэ королевская (75,0 и 69,5 т/га соответственно), гибриды F1 Поиск 32 (73,2 и 69,0 т/га), F1 Поиск 41 (69,0 и 64,7 т/га). Распространение листовых болезней в условиях Московской и Ростовской областей сводилось к тому, что в 2018 году по сравнению с 2017 годом образцы были более устойчивы, кроме гибридов F1 Таврида и F1 Поиск 41.The results of testing samples of carrots in the conditions of Moscow and Rostov regions are presented. The purpose of the research is to carry out a variety testing of carrots in two ecological-geographical zones and to identify differences in individual qualitative and quantitative characteristics. Research conducted in 2017-2018. The meteorological conditions of 2017–2018 in the Moscow Region (MO) were unfavorable for the development of carrots in the “fork” phase and the beginning of the formation of roots. In the Rostov region, hot and dry weather in July and August negatively influenced the growth of the development of roots. The material for research was 3 varieties and 3 carrot hybrids of the domestic breeding: Corsar, Shantene korolevskaya, Nante, F1 Tavrida, F1 Poisk 32, F1 Poisk 41. Field experiments were established according to generally accepted methods. The samples differed significantly in the length of the roots: roots grown in the Rostov region had a greater length than in the Moscow region, with the exception of the Shantene korolevskaya variety (11.9 and 13.2 cm, respectively) and the hybrid F1 (18.4 and 15.8 cm, respectively), which observed the opposite trend. In terms of yield, the varieties and hybrids obtained in the MO were significantly superior to similar samples grown in the Rostov region. In the south, during two years of testing, the Shantene korolevskaya variety (55.0 and 54.9 t/ha) performed best of all. In the Moscow region, the highest yield was shown by the variety Shantene korolevskaya (75.0 and 69.5 t/ha, respectively), hybrids F1 (73.2 and 69.0 t ha), F1 Poisk 41 (69.0 and 64. 7 t/ha). The spread of leaf diseases in the conditions of Moscow and Rostov regions was reduced to the fact that in 2018 compared to 2017, the samples were more stable, except for the F1 Tavrida and F1 Poisk 41.

scholarly journals Influence of the growth regulator “Regoplant” and microfertilizer “Ultramag Kombi” on photosynthetic activity of potatoes in the forest-steppe zone Republic of North Ossetia-Alania

2021 ◽  
Vol 210 (07) ◽  
pp. 55-65
Author(s):  
Larisa Ikoeva ◽  
Oksana Haeva
Keyword(s):  
Growth Regulator ◽  
Field Experiments ◽  
Photosynthetic Activity ◽  
Low Cost ◽  
Weather Conditions ◽  
Steppe Zone ◽  
Practical Significance ◽  
Forest Steppe ◽  
Photosynthetic Productivity ◽  
North Ossetia

Abstract. The purpose of the work is to study the influence of the growth regulator “Regoplant” and microfertilizer “Ultramag Kombi” on the photosynthetic productivity of the potatoes of the Barna variety based on the results of field experiments in the forest-steppe zone Republic of North Ossetia-Alania. Methods. Studies on the tasks were carried out in 2018–2020. at the experimental site of the NCRIMFA branch of the VSC of RAS in the conditions of the forest-steppe zone Republic of North Ossetia-Alania according to generally accepted methods. The soil of the experimental plot is medium-power heavy loamy leached chernozem, lined with pebbles. Results. It is established, that biological products under identical soil and weather conditions assisted different progress of plants and approach of phases of vegetation. For all variants of the experiment, the number of stems increased by 0,3–0,7 pcs., the height of the stems of potato plants – by 3,8–4,9 cm in comparison with the control. An intense increase in the mass of tops occurred when using a tank mixture (growth regulator “Regoplant” (25 ml/ha) + microfertilizer “Ultramag Combi” (0,75 l/ha)) – by 74 g/bush, or 15.5 % compared with the control variant. During the growing season, the sum of the photosynthetic potential (FP) was 1,070 thousand m2 •days/ha in the control, and on average 1198–1406 thousand m2•days/ha in the experimental variants. The greatest accumulation of dry matter was noted when using a tank mixture – 917 g/m2. The maximum pure photosynthetic productivity was observed in experimental variant IV – 6,52 g/m2•day compared to the control option. Scientific novelty. For the first time in the forest-steppe zone Republic of North Ossetia-Alania the effect of the growth regulator “Regoplant” and microfertilizer “Ultramag Kombi” on photosynthetic activity of potatoes was studied. Practical significance. The studies carried out make it possible to recommend in potato production the use of a tank mixture of an effective growth regulator “Regoplant” at a dose of 25 ml/ha and microfertilizer “Ultramag Combi” at a dose of 0,75 l/ha, as an ecologically safe and low-cost agricultural method when processing vegetative plants, providing an increase in yield and quality of tubers.

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