ALBERT

Description: Eucalyptus grandis and E. dunnii have high productive potential in the South of Brazil, Uruguay, and central Argentina. This is based on the similarity of the climate and soil of these areas, which form an eco-region called Campos. However, previous results show that these species have differences in their distribution caused by the prioritization of Uruguayan soils for forestry, explained by the particular conditions of each site. In this study, the site variables (climate, soil, and topography) that better explain the distribution of both species were identified, and prediction models of current and future distribution were adjusted for different climate change scenarios (years 2050 and 2070). The distribution of E. grandis was associated with soil parameters, whereas for E. dunnii a greater effect of the climatic variables was observed. The ensemble biomod2 model was the most precise with regard to predicting the habitat for both species with respect to the simple models evaluated. For E. dunnii, the average values of the AUC, Kappa, and TSS index were 0.98, 0.88, and 0.77, respectively. For E. grandis, their values were 0.97, 0.86, and 0.80, respectively. In the projections of climatic change, the distribution of E. grandis occurrence remains practically unchanged, even in the scenarios of temperature increase. However, current distribution of E. dunnii shows high susceptibility in a scenario of increased temperature, to the point that most of the area currently planted may be at risk. Our results might be useful to political government and foresters for decision making in terms of future planted areas.

Description: Background: Hybrid mensurational and physiological models seek to combine precision, process explanation, simplicity in parameter definition, and ability to estimate wood products. The aim of this study was to assess the suitability and the advantages of the hybrid mensurational-physiological approach where time has been substituted for light sums in growth equations, to replace traditional time-based models in forecasting systems for Eucalyptus grandis W.Hill and Pinus taeda L. Methods: Using 974 permanent sample plots from plantations in Uruguay, we adjusted growth equations to project dominant height, net basal area, maximum diameter breast height, and standard deviation of diameters as a function of accumulated light restricted by modifiers that account for principal physiological limitations on photosynthesis. We analysed: i) the inclusion of terrain aspect and slope information for computing radiation; ii) the use of modifiers for temperature, vapour pressure deficit and water balance; iii) bias and precision of hybrid models with respect to time-based equations. Results: Growth equations showed a good fit for both species when modelled as a function of light sums modified by vapor pressure deficit, air temperature, and water balance. Accounting for slope orientation when computing light sums did not increase precision. Compared to time-based formulations, hybrid models presented a root mean squared error reduction of 10.7% and 4.5% on average for Eucalyptus grandis and for Pinus taeda, respectively, and the relationship between growth and resource availability was consistent with eco-physiological principles for both species. Conclusions: The hybrid methodology can be applied as a basis of forecasting systems for the species studies with significant advantages over time-based models, such as: (i) an increase in precision; (ii) an increase in spatial and time resolution; and (iii) the possibility of simulating the effect of changes in air temperature and water availability on tree growth.

Description: This study presents a yield model for aboveground biomass production from three species the Eucalyptus in northern and western regions of Uruguay, based on sampling records from intensive crop plantations. High-density eucalyptus plantations represent a forestry alternative for the production of forest biomass. This work assessed the survival and growth of three eucalyptus species (Eucalyptus benthamii Maiden & Cambage, E. dunnii Maiden and E. grandis Hill ex Maiden) planted at densities of 2220, 3330, 4440 and 6660 trees ha−1, for a period of 57 months in northern (Tacuarembó) and western (Paysandú) regions of Uruguay. Linear and logarithmic equations of individual volume were fitted by site and species. The survival of E. grandis, E. benthamii and E. dunnii was not related to planting density, and the highest mortality values occurred in Tacuarembó. The effects of competition among trees were more evident at the highest planting density for E. grandis. In all species, the reduction in diameter was more marked than that of height, as planting density increased. Tree volume showed the same trend, and this was higher with higher planting densities. At Tacuarembó, the volume was the highest with E. benthamii at 6660 trees ha−1 (416.4 m3 ha−1), and, at Paysandú, the highest production was obtained with E. grandis (370.7 m3 ha−1) and with the densities of 4440 and 6660 trees ha−1 (305.9 and 315.3 m3 ha−1, respectively). With all species and planting densities, there was an increase in the accumulated volume during the 57-month study period; however, growth curves indicate that the maximum production per unit time and, therefore, the optimum harvest time occurred at 48 months. In this work, it has been shown that the use of intensive short-rotation plantations of eucalyptus for the production of biomass in Uruguay is suitable in soils prioritized for forestry.

Description: Airborne lidar scanner (ALS) technology is used in a variety of applications, including forestry. ALS has enormous potential for the estimation of relevant biometric parameters in forest plantations. This study investigates the use of an object-oriented semi-automated segmentation algorithm for stands delineation, based on modeling ALS data, in plantations of Eucalyptus grandis and E. dunnii in Uruguay. The results show that non-parametric methods delivered more accurate and less biased results for total volume (TV) with R2 0.93, RMSE 20.04 m3 h−1 for E. grandis and R2 0.93, RMSE 18.43 m3 h−1 for E.dunnii; and above ground biomass (AGB) with R2 0.95, RMSE 70.2 kg h−1 for E. grandis and R2 0.96, RMSE: 71.2 Kg h−1 for E. dunnii. Parametric methods performed better for dominant height (Ho) with R2 0.98, RMSE 0.67 m and R2: 0.96, RMSE: 0.8 m for E. grandis and E. dunnii, respectively. The most informative ALS metrics for the estimation of AGB and TV were metrics related to the elevation in parametric models (Elev.70 and Elev.75), while for the non-parametric models (k-NN) they were Elev.75 and canopy density. For Ho, the ALS metrics selected were also related to elevation both in the parametric (Elev.90 and Elev.99) and random forest models (Elev.max and Elev.75). The segmentation methodology proposed here matched closely the segments delineated by human operators, and provides a low-cost, cost-effective, easy to apply and update model aimed at generating AGB or TV maps for harvest tasks, based on rasters derived from ALS metrics. The present research shows the capacity of ALS metrics to improve extensive strategic inventories; validating and promoting the adoption of ALS technology for inventory forest stands of Eucalyptus spp. in Uruguay.