Spatio-Temporal Modeling and Competition Dynamics in Forest Tillage Experiments on Early Growth of Eucalyptus grandis L. Issue 5 (28th March 2020)

• Record Type: Journal Article
• Authors: González Barrios, Pablo; Borges, Alejandra; Terra, José; Pérez Bidegain, Mario; Gutiérrez, Lucía
• Is Part Of: Forest science. Volume 66:Issue 5(2020)
• Page Start: 526
• Page End: 536
• Publication Date: 2020-03-28
• Subjects: site preparation -- tillage intensity -- spatio-temporal variability -- growth curves; Forests and forestry -- Periodicals; Forest management -- Periodicals; Forest policy -- United States -- Periodicals; Forest management; Forest policy; Forests and forestry; United States; Electronic journals; Periodicals; 634.9
• DOI: 10.1093/forsci/fxaa007
• Languages: English
• ISSNs: 0015-749X

Abstract

Abstract: Forest tillage experiments regularly use long-term evaluations of large plots creating temporal and/or spatial correlations among observations. Not modeling these correlations could compromise treatment comparisons. The aim of this study was to evaluate the effect of modeling spatio-temporal (ST) variability in forest tillage experiments. We used different strategies that incorporate spatial and/or temporal correlations in the evaluation of tillage intensity effect in initial Eucalyptus growth as well as evaluate the effect of intraplot mortality and competition dynamics. Three tillage intensities in two contrasting soil conditions were compared for tree height and wood volume. Additionally, we compared the use of three individual growth curves for plant height to evaluate the time needed to reach 2 m in height ( T 2m ). We modeled the spatial correlation of T 2m using mixed models. In both sites, ST models were superior for plant height and wood volume per hectare, whereas for individual-tree wood volume, temporal models were superior. Pit planting always had a lower performance than disk harrowing and subsoiler, which behaved similarly. The competition dynamics within the plot because of tree mortality was affected by treatments and site. Modeling ST variability is key to improving treatment comparisons in forest experiments.