CLASSIFICATION OF SPECIALIZED FARMS APPLYING MULTIVARIATE STATISTICAL METHODS
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F17%3A72069" target="_blank" >RIV/60460709:41110/17:72069 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.11118/actaun201765031007" target="_blank" >http://dx.doi.org/10.11118/actaun201765031007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.11118/actaun201765031007" target="_blank" >10.11118/actaun201765031007</a>
Alternative languages
Result language
angličtina
Original language name
CLASSIFICATION OF SPECIALIZED FARMS APPLYING MULTIVARIATE STATISTICAL METHODS
Original language description
The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly), and the Large and Very Large enterprises (100 % filed correctly). The Medium Size farms have been correctly f
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
ISSN
1211-8516
e-ISSN
2464-4781
Volume of the periodical
65
Issue of the periodical within the volume
3
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
8
Pages from-to
1007-1014
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85021791489