https://repositorio.ufu.br/handle/123456789/30498 Thu, 25 Jun 2026 20:16:50 GMT 2026-06-25T20:16:50Z https://repositorio.ufu.br/handle/123456789/48770 Title: Diversidade espacial e prospecção de fungos fitopatogênicos para o controle biológico do cipó preto (Adenocalymma impressum) Abstract: The black liana (Adenocalymma impressum) poses a significant challenge to comercial Eucalyptus plantations in the eastern Amazon owing to its rapid growth and resistance to conventional herbicide-based control. This study aimed to prospect and characterize phytopathogenic fungi associated with A. impressum for potential use in biological control. Fungal isolates were collected from symptomatic plants in Maranhão, Brazil, resulting in 124 isolates, of which 24 were pathogenic. Among these, Lasiodiplodia theobromae SC-35 exhibited rapid symptom development, extensive tissue colonization, and host specificity, while remaining nonpathogenic to eucalyptus clones. Environmental factors, including temperature and native forest remnants, influenced fungal diversity and aggressiveness. These findings demonstrate the potential of SC-35 as candidate for development of a sustainable and effective bioherbicide for managing black liana, highlighting the value of endemic phytopathogens in integrated forest weed management strategies. Fri, 06 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48770 2026-03-06T00:00:00Z https://repositorio.ufu.br/handle/123456789/48297 Title: Bioestimulante sustentável de folhas de tomateiro anão: avaliação metabolômica e efeito em mudas de alface por imagens RGB Abstract: Plant production practices that reduce the excessive use of chemical inputs are essential for sustainable agriculture. Dwarf tomato leaf extract, characterized by its richness in secondary metabolites, represents a promising sustainable bio-stimulant for vegetable crops. This study aimed to evaluate the potential of leaf extract from the dwarf tomato genotype UFU MC TOM1 as a sustainable biostimulant in lettuce seedlings using RGB image analysis, as well as to identify the metabolites present in the leaves by gas chromatography–mass spectrometry. The experiment was conducted under controlled conditions in a spray test chamber, using lettuce seedlings grown in trays and subjected to three spray applications of leaf extracts from two tomato genotypes (UFU MC TOM1 and Santa Clara) at different concentrations (50%, 75%, and 100%), with distilled water as the control. Evaluations included fresh and dry mass, shoot length, and root length. Application of UFU MC TOM1 leaf extract promoted significant increases in fresh and dry mass, especially at concentrations of 50%, 75%, and 100%. Shoot length was also favored, particularly in the 75% and 100% treatments, while root length and total seedling length did not show consistent gains. Multivariate analysis confirmed the superior performance of UFU MC TOM1 extracts, and RGB image analysis corroborated the manual measurements. Metabolomic characterization revealed higher abundance of amino acids, carbohydrates, and carboxylic acids in UFU MC TOM1 compared to Santa Clara, with emphasis on L-serine, L-erythrulose, adenine, and urea, reinforcing its potential as a natural and sustainable biostimulant for agricultural systems. Wed, 17 Dec 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48297 2025-12-17T00:00:00Z https://repositorio.ufu.br/handle/123456789/48236 Title: Modelagem do crescimento e produção de florestas plantadas usando indicadores de capacidade fotossintética derivados de sensoriamento remoto e aprendizado de máquinas Abstract: Forest growth and yield modeling is fundamental for sustainable planning and management of planted forests. The Clutter model, widely used in Brazil, relies exclusively on conventional dendrometric variables (age, basal area, and site index), without considering physiological factors such as photosynthetic capacity. This study aimed to evaluate the incorporation of proxy variables for photosynthetic capacity, derived from remote sensing, into forest growth models, comparing the performance of the traditional Clutter model, its modified variant with spectral and textural indices, and machine learning algorithms. The research was conducted in commercial eucalyptus plantations in the Vale do Rio Doce region, Minas Gerais, using continuous forest inventory data (2013-2019) provided by CENIBRA. Reflectance values were extracted from cloud-free Landsat 8 images, calculating eight spectral indices (NDVI, GNDVI, SAVI, SR, NBRI, RVI, DVI, EVI) and eight texture indices based on the Gray Level Co-occurrence Matrix (Energy, Entropy, Correlation, IDM, Inertia, CS, CP, HC). Five machine learning algorithms were tested: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), and K-Nearest Neighbors (KNN). Validation was performed through 5-fold cross-validation, using RMSE, RMSE%, Pearson correlation (r), R², and BIAS% metrics. Results demonstrated that the traditional Clutter model exhibited high accuracy, with RMSE% below 10% for volume, R² greater than 0.93, and r = 0.97. The incorporation of spectral and textural indices into the Clutter model promoted modest improvements, reducing RMSE% from 8.51% to 8.46% with SR and NBRI indices. However, machine learning algorithms, particularly SVM, demonstrated superior capability in incorporating these variables, resulting in accuracy gains of up to 0.7% compared to the traditional model. The SR, NBRI, and HC indices stood out as the best predictors. It is concluded that the inclusion of remote sensing variables can discretely enhance the predictive capacity of traditional models, with machine learning algorithms being more efficient in incorporating these variables, indicating a promising path for improving forest growth and yield models. Fri, 12 Dec 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48236 2025-12-12T00:00:00Z https://repositorio.ufu.br/handle/123456789/48115 Title: Distribuição espacial de nematoides entomopatogênicos e sua relação com variáveis edáficas em cafezais sob manejo regenerativo Abstract: Entomopathogenic nematodes (EPNs) are promising allies in the integrated management of coffee pests, but their dynamics in regenerative systems of the Cerrado Mineiro remain poorly understood. We assessed the occurrence of EPNs in the soil and their relationship with edaphic attributes in three commercial plots of Coffea arabica L. under regenerative management. EPNs were isolated using Tenebrio molitor L. (Coleoptera: Tenebrionidae) larvae as bait and the White trap method, and occurrence was modeled using logistic regression at two scales. Soil compaction did not explain the presence of EPNs within plots; between plots, soil moisture showed a consistent positive association, whereas β-glucosidase and compaction were not determinant factors. The models showed good calibration and moderate discrimination.We conclude that differences among plots and higher soil moisture are associated with an increased probability of EPN occurrence, suggesting that monitoring and water management should consider wetter zones as priority areas to enhance biological control. Fri, 19 Dec 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48115 2025-12-19T00:00:00Z