Choosing the ideal number of rotations of planted forests under a silvicultural management regime results in uncertainties in the cash flows of forest investment projects. We verified if there is parity in the Eucalyptus wood price modeling through fractional Brownian motion and geometric Brownian motion to incorporate managerial flexibilities into investment projects in planted forests. We use empirical data from three production cycles of forests planted with Eucalyptus grandis × E. urophylla in the projection of discounted cash flows. The Eucalyptus wood price, assumed as uncertainty, was modeled using fractional and geometric Brownian motion. The discrete-time pricing of European options was obtained using the Monte Carlo method. The root mean square error of fractional and geometric Brownian motions was USD 1.4 and USD 2.2, respectively. The real options approach gave the investment projects, with fractional and geometric Brownian motion, an expanded present value of USD 8,157,706 and USD 9,162,202, respectively. Furthermore, in both models, the optimal harvest ages execution was three rotations. Thus, with an indication of overvaluation of 4.9% when assimilating the geometric Brownian motion, there is no parity between stochastic processes, and three production cycles of Eucalyptus planted forests are economically viable.

In forest nurseries, irrigation management becomes more complex as different seedlings of tropical species, with different architectures, are grown close to each other. In this context, there are gaps in knowledge about the physiological responses of species with different mean leaf angles when subjected to different irrigation depths. Thus, this work aimed to analyze whether mean leaf angles affect irrigation efficiency and, consequently, physiological responses of tree seedlings. Six species with different mean leaf angles were submitted to three irrigation depths (6, 9, and 12 mm) applied daily by micro-sprinklers in a completely randomized design in a split plot scheme. The following variables were evaluated: leaf water potential, stomatal conductance, relative water content in the leaf, daily transpiration, leaching fraction, and total dry mass. In tree species seedlings with positive mean leaf angles, smaller irrigation depths are already able to increase leaf water potential, stomatal conductance, leaf relative water content, and transpiration efficiency. In contrast, when the mean leaf angles are negative, it is necessary to apply larger irrigation depths so that seedling physiological responses do not reduce the production of total dry mass.

The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.

Background: The commonly used methods for the financial evaluation of plantation forest investment projects do not incorporate uncertainties and ignore the value related to flexibility. The real options analysis makes it possible to capture these values in investment projects, increasing their value and return. Despite this, studies involving real options in forest investment projects are scarce, specifically those related to Pinus spp. Therefore, this study aimed to: (a) analyze whether the real options analysis adds value to investment projects of Pinus elliottii Engelm. plantations; and (b) make the real options analysis more accessible to forest managers and potentially increase its use in the investment projects of Pinus spp. plantations. Methods: We evaluated two investment projects in P. elliottii plantations in southern Brazil, which differed in the way of obtaining the land for planting: with lease or purchase of land on a planning horizon of 21 years. In the real options analysis, we used deferral, expansion, and abandonment. Results: Individually, the deferral, expansion, and abandonment options add value to investment projects in Pinus elliottii plantations. The option to expand the forested area is one that adds the most value to the investment project with land lease. In the investment project with land purchase, it is abandonment. Conclusions: Investment projects in Pinus elliotti plantations that contemplate the land purchase analyzed through the real options analysis present higher financial returns than those that consider land lease, inverting the result provided by the traditional analysis.

Seedling species with different architectures, e.g., mean leaf angles, are often subjected to the same irrigation management in forest nurseries, resulting in wasted water and fertilizer and reduced seedling quality. We aimed to evaluate whether irrigation volumes applied to tree seedling species with different leaf angles affect the physiological quality in forest nurseries and, consequently, performance after potting. We submitted nine seedling species with different mean leaf angles to four daily water regimes (8, 10, 12, and 14 mm). In the nursery, the following physiological attributes were considered to assess seedling quality: leaf water potential, daily transpiration rate, SPAD value, chlorophyll a and b, anthocyanins, carotenoids, and total nutrient content. After potting, we evaluated height and stem diameter over 120 days. Leaf angle can be used as a criterion for optimizing irrigation in forest nurseries, avoiding water and fertilizer wastage, and increasing physiological seedling quality. Leaf angle measurements combined with concurrent assessments of leaf traits are helpful in further understanding the effects of leaf angle variation and water regime on seedling quality. For positive leaf angles, an irrigation volume of 8 mm is sufficient to increase physiological seedling quality. Conversely, seedlings with negative leaf angles show the opposite response, requiring the largest irrigation volume (14 mm) to increase physiological seedling quality, except when the mean leaf area is small and concentrated in the upper half of the stem, which facilitates the access of irrigation water to the substrate and thus satisfies seedling water requirements. For all species, up to 120 days after planting in pots, the effect of the irrigation volume that provides greater growth and physiological quality at the end of the nursery phase is not overcome by other irrigation volumes applied.