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.

Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R² = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R² = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R² = 0.62); Conquista Certificada (MCARI, 116 DAS, R² = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R² = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture.

This research analyzes the relationship between the use of precision technologies and the management of agricultural inputs within the framework of sustainability. Structured surveys were conducted with 120 producers, addressing variables such as access to technology, training, technological integration, productivity, and barriers. Multiple linear regression, Spearman correlation, and cluster analysis were applied to identify the factors influencing efficient, environmentally responsible, and socioeconomically viable input management. The findings show that the Productivity and Sustainability dimension has a significant positive effect, while barriers and challenges have a negative influence. The model related to soil health (adjusted R² = 0.304) highlights the potential of precision technologies to enhance yield and reduce environmental impacts. However, structural limitations, such as high costs, insufficient infrastructure, and lack of training, were identified as major obstacles to adoption. The results emphasize the need for public policies, technical training programs, and support strategies to foster more sustainable, resilient, and inclusive agricultural systems aligned with the Sustainable Development Goals.

Soils are highly sensitive to the type of tillage practices used, as these practices influence soil properties and affect crops, the environment, and society. However, research on cassava production under different tillage systems during drought conditions in the Peruvian Amazon has not been reported. The objective of this study was to compare soil properties, cassava physiology, and yield under conservation agriculture (CA) and traditional agriculture (TA) practices, with and without mulch, in a water-scarce environment. Soil moisture, earthworm population (Ew), stomatal conductance, leaf area index, and commercial yield under CA were 5.26% (~105.2 m³ ha⁻¹), 83%, 1.2 times, 1.14 times, and 7.3 t ha⁻¹, respectively, higher than under TA. Hydraulic conductivity (Ks) in TA was 2.1 times higher than that in CA. However, Ks, bulk density, and Ew over time showed a gradual recovery under CA. The mulch factor only affected Ew, which was higher without mulch than with mulch. The results indicate that CA practices were superior to TA practices, improving soil properties, cassava physiology, and yield, and, therefore, offer significant benefits in resource conservation and higher production and profitability in a drought-prone environment.