Selection of climate variables by statistical-computational modeling in forest seedlings treated with growth biopromoter

Abstract

Plant production requires improvement in nursery-field techniques to increase yield and economic gains. Manipulating the interaction between biotic and abiotic factors in the production of forest seedlings can improve productivity. In this context, we examined and selected the climatic variables that influence the yield of forest seedlings treated with different dosages of growth biopromoter Trichoderma sp. Climatic data were obtained from the NASA Power platform and correlated with growth data from forest seedlings treated with Trichoderma sp. The growth variables used were base diameter, height, and north-south and east-west canopy diameters. Climatic variables were selected by multiple regression models via forward-backward stepwise regression, and artificial intelligence via random forest (RF) method. The climatic variables that most influenced the growth of plants treated with Trichoderma sp. were the incidence of shortwave insolation on the horizontal surface, longwave thermal infrared radiative flux, relative air humidity, temperature, wind speed, and precipitation. Wind speed influenced only seedling height, while temperature, precipitation, and radiative flux interfered with all growth variables. Both the multiple regression and RF methods similarly describe the interaction of fungus × plant × environment. Most of the climatic variables detected via multiple regression coincided with the RF procedure. Future studies on the application of Trichoderma sp. as a biopromoter of growth should consider the identified climatic variables to optimize the production of forest seedlings in nurseries and in the field.