https://www.alice.cnptia.embrapa.br/alice/handle/item/178 Artigo em periódico indexado (CNPH) Thu, 13 Aug 2026 05:16:23 GMT 2026-08-13T05:16:23Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1189119 Título: The unfolding desert: observed structural drying and projected aridity risks on a Brazilian family farm in a citizenship territory. Autoria: LIMA, C. E. P.; AMORIM, J. R. A. de; CRUZ, M. A. S.; FONTENELLE, M. R. Conteúdo: Aridification in semi-arid regions is a recurring theme in Intergovernmental Panel on Climate Change (IPCC) reports and has been documented in Northeast Brazil. This study analyzes the spatiotemporal dynamics of aridification in the Lower São Francisco "Territory of Citizenship" (Sergipe and Alagoas) at high spatial resolution (~1 km). Regional bias correction was performed using historical meteorological data and WorldClim v2.1 raster imagery. Model performance was evaluated by comparing historical and projected trends for monthly maximum and minimum temperatures and precipitation across four Shared Socioeconomic Pathways (SSPs) and time periods (2021–2100). A coupled regional ETA-HadGEM2-ES model highlighted limitations in detecting aridification at different spatial scales, with coarser models potentially underestimating aridity. Downscaling to 1 km revealed trends toward cooler temperatures and drier precipitation conditions, which were corrected using weather station data. Historically, the region has experienced rapid warming (+0.41°C per decade for Tmax), with projected trends most closely resembling the pessimistic SSP3-7.0 scenario (+0.455°C per decade). The semi-arid and *Agreste* zones showed the most rapid changes. Aridification processes and declining minimum temperatures indicate ongoing aridification. Evidence suggests that by the end of the century, the territory will shift from a sub-humid regime to a predominantly semi-arid one, with at least three months per year reaching arid conditions, thereby threatening water resources and agricultural productivity. These results emphasize the urgent need for adaptation strategies and public policy interventions. The predominance of family farming and low levels of human development—along with the Development Index and socioeconomic vulnerability within the Citizenship Territory—further justify alignment with IPCC principles, specifically those regarding climate justice and a just transition. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1189119 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1189096 Título: A hyperspctral imaging and machine learning approach for rapid and non-invasive diagnosis of cassava bacterial blight. Autoria: CARVALHO, I. C. B. de; FERREIRA, L. da C.; NEVES, A. R. de M.; CARVALHO, A. M. S.; ROSSATO, M. Conteúdo: This study explores the use of hyperspectral imaging (HSI) combined with machine learning to detect physiological alterations in cassava leaves caused by Xanthomonas phaseoli pv. manihotis (Xpm), a bacterial plant disease that causes significant yield losses worldwide. Therefore, the use of hyperspectral images associated with machine learning can provide information rapidly and accurately, aiming to support decision-making. HSI captures spectral data that reflects biochemical changes in infected plant tissues. An image set of cassava healthy and symptomatic leaves (402 and 450, respectively) were imaged using a hyperspectral camera across wavelengths from 400 to 1000 nm, with image calibration and spectral normalization to improve data quality. Spectral parameters, such as mean reflectance and spectral differences (healthy vs. infected), were analyzed. Six machine learning models were tested for classification: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). SVM performed best, achieving the highest accuracy (91.41%), followed by MLP (87.89%), XGBoost (79.69%), and RF (77.34%). DT and KNN had the lowest accuracy (71.88% and 70.31%, respectively). The results suggest that HSI, particularly when combined with SVM, offers a rapid and accurate method for diagnosing cassava bacterial blight, with potential for large-scale field applications. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1189096 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1189095 Título: Erwinia psidii, an emergent pathogen in South America. Autoria: CARVALHO, A. M. S.; CARVALHO, I. C. B. de; HERMENEGILDO, P. da S.; FERREIRA, M. A. da S. V.; ROSSATO, M. Conteúdo: Erwinia psidii is an emerging bacterial phytopathogen of increasing concern in South America due to its ability to infect economically important hosts such as guava (Psidium guajava) and eucalyptus (Eucalyptus spp.). First reported in Brazil in the 1980s, affecting guava trees, the pathogen later emerged in commercial eucalyptus plantations, suggesting a recent host shift within the Myrtaceae family. This emergence may be attributed to the expansion of acreage into warmer and more humid regions, as these conditions favor novel pathogen-host interactions. Studies present contrasting perspectives regarding the genetic diversity of E. psidii and the hypotheses about how this expansion in host range occurred. Nevertheless, there is consensus that E. psidii exhibits cross-pathogenicity between guava and eucalyptus, with no evidence of host specialization or differentiation among isolates from different geographic origins. The pathogen’s ability to survive on plant surfaces and debris, along with its dissemination through asymptomatic propagative material, highlights the importance of understanding disease epidemiology for effective monitoring and management. Advances in molecular diagnostics, including qPCR assays, have significantly improved early detection and surveillance. Currently, management relies on exclusion practices and cul- tural measures, although their effectiveness remains limited in large-scale forestry plantations. The identification of resistant genotypes, such as clones of Eucalyptus urophylla × E. maidenii and resistant guava varieties, offers potential strategies for control. The emergence of E. psidii as a multi-host pathogen highlights the importance of integrated strategies that combine surveillance, resistant germplasm, and diagnostic tools. Understanding the ecological and evolutionary mechanisms under- lying host shifts is essential for anticipating disease dynamics and safeguarding guava and eucalyptus production systems. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1189095 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1188980 Título: Caracterização dos polos de produção e de produtores de cenoura no Brasil. Autoria: PEDROSO, M. T. M.; FERREIRA, Z. R. Conteúdo: Tem por objetivo caracterizar os polos de produção e de produtores de cenoura no Brasil para orientar políticas públicas e pesquisas. Como metodologia analisou os dados do Censo Agropecuário 2017, do IBGE, para mapear o perfil tecnológico regional. Obteve um retrato detalhado do setor hortícola, identificando gargalos e disparidades entre os polos. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1188980 2026-01-01T00:00:00Z