The consequences of climate change influence the distribution of species, which plays a key role in ecosystems. In this work, the modeling of the current and potential future distribution was carried out under different climate change scenarios of a tree species of high economic and commercial value, Dipteryx spp. This is a hardwood species that plays an important role in carbon sequestration, providing food and nesting for wildlife species, reaching more than 40 m in height with an average diameter of 70 to 150 cm. This species is currently threatened by overexploitation. Thirty-six bioclimatic, topographic and edaphic variables with ~1 km² spatial resolution obtained from the WorldClim, SoilGrids and SRTM databases where used. Highly correlated variables were identified with the MaxEnt software for forecasting how the species distribution will be affected until the year 2100, according to the climate scenarios SPP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5, representing the periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100, respectively. The AUC accuracy value of 0.88 to 0.89 was found for the distribution models and the highest contributing variables used were Bio 5, precipitation, Bio 2, and Bio 14. In the climate scenario SPP1-2.6 (Bio 5, precipitation and Bio 2) in 2061–2080, suitable and very suitable habitats represented 30.69% of the study area (2616 ha and 586.97 ha, respectively) and those increased by 1.75% under current climate conditions, and the suitable and unsuitable habitats represented 69.31% of the total area. The results of this research provide valuable information on the current and future distribution of the species and identify zones that can be used as the basis for the creation of conservation areas, formulation of restoration projects, reforestation and sustainable management to avoid the extinction of the species in the face of the effects of climate change.

Forest plantations can be established in order to restore degraded areas. Acoustic tomography, which is of increasing importance in forest management, was used in the present study to obtain information for managing plantations of Cedrelinga cateniformis Ducke in the Peruvian Amazon. The species is valuable in the timber sector of Peru, but the core wood tends to deteriorate and develop cavities. The main objective of the study was to model wood deterioration in Cedrelinga cateniformis Ducke using the data obtained through acoustic tomography. Eight plantations of varying ages were analyzed using acoustic tomography in order to obtain indicators of wood deterioration. Biometric, climatic, and edaphic data (explanatory variables) were also measured in each plantation. The indicator variables and explanatory variables were compared and evaluated using correlation and principal component analysis. Wood deterioration was modelled using stepwise regression. The indicator variables differed significantly between plantations and were mainly correlated with the biometric variables (age and diameter at breast height). The models explained 81% of the variability of pith rot. The percentage rotten area was minimal in young plantations (1%), and the opposite was observed in mature trees (21.5 to 25.6%). The study findings provide valuable information, enabling foresters to determine the optimal age and diameter for felling Cedrelinga cateniformis in plantations in the Peruvian Amazon.

Tillage conservation practices (CA), traditional agriculture (TA), and liming influence soil properties and crop yield. However, it is essential to demonstrate which tillage and liming practices improve soil properties and forage corn yield. This study compared soil properties and forage corn production in two tillage systems with the addition of dolomite and lime, which formed four treatments. The tillage in the first three days surpassed the TA soil CO₂ emission, with 64.8% more CO₂ than in the CA soil, and the TA hydraulic conductivity and bulk density were more suitable than those in the CA soil. The CA soil had 233 earthworms m⁻² more than in TA. The TA green forage corn yielded 6.45 t ha⁻¹ more than in CA, with a higher P, Ca, and Mg foliar content than in CA, but in the CA, the foliar N and K were higher than in TA. The liming increased soil cations (except K), highlighting the lime on dolomite with—52% Al and + 4.85 t ha⁻¹ of forage corn compared to the control. Soil CO₂ emission was far lower in CA than in TA, with a slightly lower forage yield, and other soil properties were improved, meaning lower land preparation costs and time savings than in TA. Lime improved acidic soil faster than dolomite, generating higher forage yields.

Avocado crop (Persea americana Mill.) is of great commercial importance due to its high profitability. However, it is being affected by various diseases and pests that affect yield and reduce fruit quality. The aim of this research was to develop methodologies for the evaluation of avocado plantations using different non-destructive technologies for rapid phenotyping and early detection of the incidence of diseases or damage due to stress in the stem. A plot of 0.7 ha. was evaluated, with a total of 44 individuals using Field-Map technology (dasometric and morphological characterization), RGB-multispectral images from Remotely Piloted Aircraft System (RPAS) (rapid phenotyping), while 15 individuals were evaluated using tomography (assessment of the internal state of the stem). The results with tomography indicated that there is a tree with wood rot of 14% with a lower acoustic speed with respect to the other trees evaluated. A high correlation was observed between the dasometric variables (r-Pearson from 0.63 to 0.98) estimated with Field-Map [crown base height, crown projection (m2 ) and total height] and with RPAS (height, perimeter and area). The vegetation indices do not have a direct correlation with the dasometric variables; five of the indices have a high contribution to variability except for the Normalized Difference Red Edge (NDRE). It can be concluded that the technologies used in this study would help to perform evaluations with a greater number of reliable and precise data with respect to the information obtained in a traditional way, while they can be replicated in commercial plots or research studies of different perennial crops, generating useful information for management decisions and crop evaluation.

This study addressed multi-product optimization in Cedrelinga cateniformis plantations in the Peruvian Amazon, aiming to maximize volumetric yields of logs and sawn lumber. Data from seven plantations of different ages and types, established on degraded land, were analyzed by using ten stem profile models to predict taper and optimize wood use. In addition, the structure of each plantation was evaluated using diameter distributions and height–diameter ratios; log and sawn timber production was optimized using SigmaE 2.0 software. The Garay model proved most effective, providing high predictive accuracy (adjusted R² values up to 0.963) and biological realism. Marked differences in volumetric yield were observed between plantations: older and more widely spaced plantations produced higher timber volumes. Logs of optimal length (1.83–3.05 m) and larger dimension wood (e.g., 25.40 × 5.08 cm) were identified as key contributors to maximizing volumetric yields. The highest yields were observed in mature plantations, in which the total log volume reached 508.1 m³ha⁻¹ and the sawn lumber volume 333.6 m³ha⁻¹. The findings demonstrate the power of data-driven decision-making in the timber industry. By combining precise modeling and optimization techniques, we developed a framework that enables sawmill operators to maximize log and lumber yields. The insights gained from this research can be used to improve operational efficiency and reduce waste, ultimately leading to increased profitability. These practices promote support for smallholders and the forestry industry while contributing to the long-term development of the Peruvian Amazon.