Classification of regions for greenhouse production using multivariate analysis

Carlos Alberto Garza Alonso, Emilio Olivares Sáenz, Rigoberto Eustacio Vázquez Alvarado, Nora Estela García Treviño


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.


stratification; horticulture; k-means; temperature; greenhouses; crops; protected agriculture; regions; multivariate analysis


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