GIS-based approach applied to optimizing recommendations of Eucalyptus genotypes

Abstract

The study of the genotype×environment interaction is a prominent issue, requiring care for recommending improved superior genotypes to certain areas. Experimentally, it is possible to infer that the puzzle of genotype recommendations relies on edaphic and climatic changes over different terrain latitudes and longitudes, and further fluctuates in microenvironments as a result of site variations. Different gene expression is activated or suppressed in accordance with environmental requirements, resulting in phenotypic plasticity of cultivars. The goal of this study was to generate an optimal recommendation of eucalyptus genotypes in a 6846.0km2 using climatic and geographical information. For this purpose, we used 24 clones unevenly distributed in 988 plots over the area, with planting ages between 2.5 and 6.5years. The evaluated trait was production, measured in cubic meters of wood per hectare. Our study area typically has a mid-to-low altitude range (0–390m), rainfall between 599 and 1749mm, temperature from 22 to 25°C, and a series of other bioclimatic variables. For statistical analysis, we used the random regression via mixed-effects models (REML/BLUP) combined with logistic growth models. Among the total number of clones, six proved to be the most suitable to maximize volumetric production in the work area. In addition, a change in the recommendation was verified for clones between 2.5 and 6.5years of age. This study presents an alternate concept of environmental stratification, which to date has been made categorically by evaluating a number of locations. Our study proposes a way to perform quantitative stratum over an entire area, according to the environmental gradient. The optimal selection of genotypes promotes increased wood production without necessarily increasing the planting area, which is extremely desirable because land is a resource that is becoming increasingly scarce. Furthermore, eucalyptus productivity represents an important part of the costs and revenues of the forest enterprise, such that minimal improvements can significantly affect profit margins. The proposed method is easily adapted for use for other crops and domestic animal production.