SciELO - Scientific Electronic Library Online

 
vol.12 número24Capacidad antirradical y quelante de (+)-catequina, procianidina B1 y una fracción rica en procianidinas, aislada del salvado de sorgo caféPromoción de crecimiento en trigo (Triticum turgidum L. subsp. durum) por la co-inoculación de cepas nativas de Bacillus aisladas del Valle del Yaqui, México índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Nova scientia

versão On-line ISSN 2007-0705

Resumo

GARZA-ALONSO, Carlos A.; OLIVARES-SAENZ, Emilio; VAZQUEZ-ALVARADO, Rigoberto E.  e  GARCIA-TREVINO, Nora E.. Classification of regions for greenhouse production using multivariate analysis. Nova scientia [online]. 2020, vol.12, n.24.  Epub 02-Jul-2020. ISSN 2007-0705.  https://doi.org/10.21640/ns.v12i24.2125.

Introduction:

Protected agriculture has grown considerably in recent years. Temperature is a very important factor that must be considered in greenhouse crop production. The high and low temperatures influence the crops production. Therefore, the temperature is the main climate variable to select an area to install a greenhouse.

Method:

Regions classification was carried out with data of 22 locations in the State of Nuevo Leon using two methodologies, one considering the mean monthly temperature external to the greenhouse and another method of classification using multivariate analysis. The criterion of classification using monthly temperatures was: when average monthly external temperatures were below 15 °C it was classified as “cold month”; Temperatures in the range of 15 to 22 °C were classified as "optimal"; Temperatures in the range of 22 to 27 °C were classified as “hot” and temperatures above 27 °C were classified as ”very hot”. A stratification of the localities was also carried out using the multivariate model of cluster analysis with the height above sea level and the annual average temperature as variables. In addition, a correlation analysis was performed between the height average and monthly temperatures.

Results:

Results showed that the methodology used allows to classify different regions according to the average monthly temperatures. Regarding the classification analysis using multivariate statistics, the k-means analysis for k = 4 identified four groups, of which the one and two belong to the high localities and the three and four groups to the lower localities. Considering both classification methods, it was concluded that the high region presents better probabilities of success for the greenhouse production and within this region, the lower asnm localities are more suitable and in the lower region the higher asnm localities are preferable. Considering the correlation analyzes of Pearson and Sperman, it was found that the asnm can be an indicator to define suitable regions to produce in the greenhouse.

Conclusion:

The multivariate analysis of k-means to classify data sets according to a predetermined number of groups was an adequate strategy to classify regions to install greenhouses, together with other methodologies such as the one based on monthly average temperatures, which is described in the present publication.

Palavras-chave : stratification; horticulture; k-means; temperature; greenhouses; crops.

        · resumo em Espanhol     · texto em Espanhol     · Espanhol ( pdf )