DSpace Coleção: Artigo em periódico indexado (CNPTIA)

Disponibilidade hídrica em solos com condições climáticas contrastantes cultivados com Eucalyptus sp.

2023-01-01T00:00:00Z

Título: Disponibilidade hídrica em solos com condições climáticas contrastantes cultivados com Eucalyptus sp. Autoria: BASÍLIO, J. J. N.; COLMANETTI, M. A. A.; CUADRA, S. V.; CUNHA, F. L.; GONÇALVES, A. F. A.; CAMPOE, O. C. Conteúdo: RESUMO: O monitoramento do teor de água do solo pode ser realizado de forma direta por meio de sensores, principalmente sondas de capacitância FDR e TDR e de forma indireta a partir da parametrização de modelos baseados em processos. Diante do exposto, este trabalho teve como objetivo simular a disponibilidade de água em solos com condições climáticas contrastantes cultivados com Eucalyptus sp. O modelo G’Day para a cultura do Eucalyptus sp se encontra implementado junto à plataforma ECOSMOS. O fluxo de água é calculado em escala horária em função do número de camadas do solo e de suas propriedades hidráulicas. A parametrização e avaliação dos parâmetros do modelo ECOSMOS foi realizada a partir de três experimentos micrometeorológicos e quatros sites situados em condições climáticas contrastantes considerando um genótipo genérico de Eucalyptus. O teor de água simulado pelo modelo é próximo ao observado em campo pelas sondas TDR, demonstrando assim a eficiência dos processos hidrológicos dentro do framework ECOSMOS. Além de ser variável entre os sítios, a disponibilidade de água medida pelas sondas TDR também apresentou comportamentos distintos para os quatros clones em estudo. Os resultados encontrados indicam a necessidade de calibração baseada em genótipos específicos ou até mesmo em grupos de genótipos.

Estimating Arabica coffee productivity using Planetscope images and artificial neural networks.

2026-01-01T00:00:00Z

Título: Estimating Arabica coffee productivity using Planetscope images and artificial neural networks. Autoria: FAGUNDES, R. B.; KAYSER, L. P.; BENEDETTI, A. C. P.; AMARAL, L. de P.; BOLFE, E. L.; LEMOS, O. L. Conteúdo: Research on orbital remote sensing and artificial neural networks (ANN) for estimating coffee productivity has advanced in Brazil, but applications remain scarce in states such as Bahia. This study aimed to develop and validate a model to estimate Arabica coffee (Coffea arabica L.) yield using PlanetScope images and ANN, based on two commercial areas with contrasting production systems (rainfed and irrigated). Georeferenced productivity samples (2 per hectare) were collected during the 2024 harvest, and PlanetScope images were analyzed from August 2023 to May 2024. In R software, vegetation indices related to vigor, nutritional status, and water stress were calculated, and their minimum, mean, and maximum values were extracted. Most variables followed a normal distribution (Shapiro–Wilk test, p < 0.05), enabling Pearson’s correlation analysis. The best correlation was observed in Santa Vera, where the CCCI index of 08/20/2023 showed a moderate positive correlation with yield (r = 0.66; p = 0.001). After ANN training in SPSS software, the model achieved very high predictive performance in both areas, with R² and adjusted R² above 0.99, low errors (MAE = 0.031–0.072; RMSE = 0.042–0.087; relative RMSE = 0.029–0.043), and almost no bias (BIAS = 0.001; relative BIAS = 0). Among the vegetation indices, CCCI was the most relevant, followed by NDRE, NDVI, NDWI, and EVI. Overall, the findings demonstrate that combining PlanetScope imagery with ANN provides a highly accurate approach for estimating Arabica coffee productivity under different production systems in Bahia.

Calibration and evaluation of a carbon net primary productivity module based on the SIMPLE model.

2026-01-01T00:00:00Z

Título: Calibration and evaluation of a carbon net primary productivity module based on the SIMPLE model. Autoria: COLMANETTI, M. A. A.; OLDONI, H.; ANNIBAL, L.; WOLFF, W.; REJAILI, R. P. A.; BARIONI, L. G.; ALMEIDA, I. R. de; EWING, R. P.; CUADRA, S. V. Conteúdo: This study presents the carbon net primary productivity (CNPP) module, an imple-mentation of the simple generic crop model (SIMPLE model) that we have extendedto predict aboveground and belowground biomass production and carbon assim-ilation. The goal for this module is to predict biomass inputs at and below thesoil surface, during and at the end of crop cycles, while integrated into a broaderenvironmental carbon simulation framework, ProCarbon-Soil. CNPP was initiallyparameterized for soybean [Glycine max (L.) Merr.] and maize (Zea mays L.) usingmicrometeorological data, and for wheat (Triticum aestivum L.), bean (Phaseolusvulgaris L.), and perennial forage [Urochloa (syn. Brachiaria) brizantha (Hochstex A. Rich.) Stapf cv. Marandu] using agrometeorological experimental data and/ordata from the literature. Subsequently, calibrations for reference cultivars were per-formed, grouping cultivars by crop phenological characteristics and edaphoclimaticregions using farm-level data. CNPP accurately simulated leaf area index, evapotran-spiration, and biomass dry matter production and allocation for soybean and maizewhen evaluated at the sites with micrometeorological data (R2 > 0.76, Nash–Sutcliffeefficiency > 0.56, and relative root mean square error < 38% for all variables).Simulations for wheat, bean, and perennial forage exhibited lower performanceowing to lower availability of yield data. Nonetheless, the resulting statistics supportthis module’s efficacy in predicting crop productivity in major Brazilian agricul-tural areas. By employing a reduced and efficient parameter set, the CNPP moduleachieves enhanced performance and enables robust calibration across diverse crops,genotypes, and management schemes in multiple regions.

Identification of Leucaena leucocephala in urban landscapes using Street-level images and deep learning.

2026-01-01T00:00:00Z

Título: Identification of Leucaena leucocephala in urban landscapes using Street-level images and deep learning. Autoria: FURUYA, D. E. G.; MARRAFON, G.; LEMOS, E. L. de; FURUYA, M. T. G.; GONÇALVES, R. D. S.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; BOLFE, E. L.; LIESENBERG, V.; OSCO, L. P.; RAMOS, A. P. M. Conteúdo: Mapping urban tree species supports green infrastructure planning. An essential issue refers to the monitoring of exotic species that may become invasive. Street-level imagery provides a complementary perspective to aerial images for species identification that are difficult to distinguish from above. In this context, our study aimed to evaluate deep learning-based object detection and image segmentation approaches to identify a potentially invasive tree species known as Leucaena leucocephala in an urban environment in Brazil, using 422 street-level images acquired from Google Street View (SV) and mobile phones (MPs). Object detection models (YOLOv8 and DETR) and a foundation segmentation model (SAM, zero-shot) were applied to assess how deep learning paradigms perform under heterogeneous urban imaging conditions. YOLOv8 achieved detection performance with mAP50 above 0.83 and recall up to 0.76. DETR showed domain sensitivity, with mAP50 of 0.45 in SV images and 0.84 in MP imagery. For segmentation, SAM zero-shot achieved 0.92 accuracy and 0.93 F1-score in SV images, decreasing to 0.63 accuracy and 0.66 F1-score in MP images. Overall, this study demonstrates that combining detection and segmentation approaches provides complementary information for urban vegetation monitoring, supporting decision-making related to invasive species management and sustainable urban landscape planning.