https://ri.ufs.br/jspui/handle/riufs/2408 Sat, 05 Sep 2026 09:44:21 GMT 2026-09-05T09:44:21Z http://ri.ufs.br:80/retrieve/a15346f0-3c11-45f7-997f-dac4b3269660/Logomarca PPGAGRI Fundo Branco v2 (1).jpg https://ri.ufs.br/jspui/handle/riufs/2408 https://ri.ufs.br/jspui/handle/riufs/25924 Título: Enviromics e modelagem preditiva aplicadas ao melhoramento de milho (Zea mays L.) no semiárido brasileiro Autor(es): Martins, Gabriel Oliveira Abstract: Maize (Zea mays L.) is a strategic crop for food security, forage production, and income generation in the Brazilian semiarid region. However, high climatic variability and edaphic constraints result in low average productivity and elevated risk of crop failure, while also hindering the recommendation of adapted cultivars. In this dissertation, we propose an integrated framework that combines envirotyping, environmental stratification, geospatial delineation of the target population of environments (TPE), and the multi-trait mean performance and stability index (MTMPS) to support maize breeding and cultivar recommendation in the semiarid region. In the first study, the envirotyping approach was applied across selected states of Northeastern Brazil, delineating four mega-environments (MEs) based on 19 environmental covariates, ten experimental sites, and a 23-year climatological series. Across the ten environments, 20 maize hybrids were evaluated over two growing seasons, and the MTMPS was employed to select superior genotypes, simultaneously assessing grain yield (GY), plant height (PH), ear height (EH), plant density (PD), and number of ears (NE). This approach enabled the identification of productive hybrids combining high mean performance and greater consistency across the two growing seasons per ME, with G03 and G04 standing out as selected across three of the four MEs. The integration of envirotyping and MTMPS enabled the simultaneous assessment of mean performance and stability across multiple traits and may support the future rationalization of multi-location trial networks. In the second study, environmental covariables were used – through the EnvRtype package in R – to segment the Alto Sertão Sergipano into macroenvironments (MEs) based on similarities among environmental and ecophysiologically relevant covariates. Cluster analysis revealed four major environmental groups, showing that neighboring municipalities may belong to distinct climatic zones, while geographically distant areas may share similar environmental profiles. This stratification highlighted the possibility of reducing the number of testing sites by concentrating trials in more representative environments, thereby providing a basis for future environmentally informed cultivar evaluations for the region. In the third study, we developed a georeferenced TPE for maize in the Alto Sertão Sergipano using geographic information system (GIS) tools in R, including the TPEmap function. Based on the coordinates of 21 georeferenced locations, concave polygons were built, rasterized, and integrated with climatic and soil covariables (NASA POWER and SoilGrids v2.0). Cluster analysis, the environmental similarity matrix, and principal component analysis revealed that the TPE is structurally heterogeneous and composed of six edaphoclimatic sub-environments, reflecting gradients of aridity, temperature, and water availability. Using grain yield data from 352 genotypes evaluated at CEUFS (2019– 2024), mixed models (REML) were fitted to estimate BLUPs and rank genotypes for productivity and stability. Cultivars AGR Vereda, NS80VIP3, GNZ59, GNZ15, and AG8780 comprised the elite group, with high predicted performance and temporal consistency at CEUFS, which belongs to the central environmental cluster of the TPE. Taken together, the results indicate that integrating envirotyping, environmental stratification, geospatial TPE mapping, and MTMPS provides an environmentally informed basis for redesigning experimental networks, guiding breeding programs, and the future evaluation and recommendation of maize cultivars for the Brazilian semiarid region, with the potential to contribute to productivity, resilience, and food security following multi-location validation in the Alto Sertão Sergipano and similar regions. Tue, 14 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25924 2026-07-14T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25921 Título: Filmes de partículas de óxido de cálcio e silício em campo e casa de vegetação, na avaliação de déficit hídrico e aprendizagem de máquinas na predição da produtividade de abóbora “mini jack” Autor(es): Graça, Genilza Almeida da Abstract: Climate change has intensified the frequency and severity of drought periods, increasing risks to agricultural production and the need for strategies capable of enhancing crop tolerance to water deficit. In this context, calcium oxide (CaO)-based particle films and silicon (Si) have the potential to modulate physiological responses and alleviate the effects of stress, while machine learning enables the integration of biological and agronomic information for predicting crop productivity. This study evaluated the effects of CaO and Si on mitigating water deficit in ‘Mini Jack’ pumpkin (Cucurbita moschata) plants, considering physiological, biochemical, growth, and productivity responses, as well as the potential of machine learning algorithms for productivity prediction. The study was structured into three articles. In the first article, different CaO concentrations (0, 2.5, 5, and 7.5%) were evaluated under two irrigation regimes (66 and 100% of crop water requirements). CaO application modulated photochemical functioning and productivity performance, with the 5% concentration promoting electron transport, preserving photochemical efficiency, and improving yield. In contrast, the 7.5% concentration resulted in less favorable responses, highlighting the importance of optimizing particle film concentration. In the second article, the individual and combined applications of Si and CaO were evaluated under different water regimes. Water deficit impaired plant growth and altered photosynthetic pigments, the chlorophyll a fluorescence transient, and osmotic and antioxidant responses. CaO alone contributed to maintaining leaf area under water deficit, whereas the Si + CaO combination increased chlorophyll indices, preserved photochemical functioning, and reduced proline accumulation As well as CAT and APX activities, indicating lower oxidative stress and a predominantly preventive strategy for protecting the photosynthetic apparatus. In the third article, Linear Regression, Random Forest, and SMOreg algorithms were evaluated for productivity prediction using progressively integrated datasets comprising pigments, OJIP parameters, biometric traits, and management variables. Pigments alone showed low predictive capacity, whereas the incorporation of OJIP parameters improved model performance, with Random Forest showing the best results. The integration of photochemical, pigment-related, and biometric information provided the highest predictive performance, with SMOreg outperforming the other algorithms, demonstrating that biological complementarity among predictors was critical for accurately representing productivity. Overall, the results demonstrate that CaO and its combination with Si are promising strategies for modulating the responses of ‘Mini Jack’ pumpkin plants to water deficit, while the integration of plant ecophysiology and machine learning enhances the ability to relate crop management, photosynthetic functioning, growth, and productivity. Fri, 24 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25921 2026-07-24T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25788 Título: Microrganismos da rizosfera de mangabeiras em reserva extrativista urbana: bioprospecção e aplicação Autor(es): Matos, Ana Paula Alves Abstract: The Mangabeiras Extractive Reserve Uilson de Sá, located in Aracaju, Sergipe, hosts native populations of Hancornia speciosa Gomes (mangabeira), a species threatened by urban expansion and the consequent reduction of its natural habitats. This study aims to investigate the genetic, ecological, and biotechnological potential of the mangabeira rhizosphere microbiota, with emphasis on plant growth-promoting microorganisms. The research was organized into three main axes. First, a systematic review was conducted, covering scientific literature and patent documents on species of the genus Bacillus associated with plant growth promotion, aiming to map metabolic pathways and genes involved in the synthesis and regulation of hormones and bioactive metabolites. Second, the obtained diazotrophic isolates were phenotypically characterized and evaluated for their plant growth-promoting potential in seedlings, as well as for strain compatibility. Third, rhizospheric soil samples collected in the reserve were analyzed through metagenomics, using amplicon sequencing for taxonomic and functional characterization of the microbial community. The results revealed relevant microbial diversity associated with the rhizosphere, including the presence of functional groups linked to soil fertility and isolates capable of stimulating germination and early growth of lettuce seedlings. The rhizosphere of H. speciosa constitutes a reservoir of microorganisms with potential applications in local biodiversity conservation and in the development of bioproducts aimed at species management. Thu, 12 Feb 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25788 2026-02-12T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25787 Título: Índice MGIDI na seleção de genótipos de milho com melhor eficiência no uso de nitrogênio e tolerância à salinidade Autor(es): Aragão, Nartênia Susane Costa Abstract: Maize (Zea mays L.) plays a fundamental role in the economy and society, being widely used for human consumption and animal feed. In semi-arid regions, high evapotranspiration rates favor salt accumulation in the soil, making salinity a recurrent problem and one of the main limiting factors for agricultural production. This stress negatively affects nutrient uptake and assimilation by plants, especially nitrate, the main form of nitrogen available in the soil, compromising maize growth, development, and productivity. Despite the relevance of this topic, studies aimed at identifying genotypes that combine nitrogen use efficiency and salinity tolerance, particularly under semi-arid conditions, are still scarce. In this context, the objective of this study was to select maize half-sib progenies with superior performance regarding nitrogen use efficiency and salinity tolerance using the MGIDI index (Multi-Trait Genotype-Ideotype Distance Index). The experiments were conducted during the 2024 growing season at the Experimental Farm of the Federal University of Sergipe (UFS), Sertão Campus, under two experimental conditions: irrigated environment (greenhouse) and rainfed condition (field). In the greenhouse experiment, a randomized block design was adopted in a 21 × 2 factorial scheme (genotypes × salinity levels), with three replications. In the field experiment, a randomized block design with split plots was used in a 35 × 2 factorial scheme (genotypes × nitrogen levels), also with three replications. In the greenhouse, morphological and physiological traits were evaluated, while in the field, morphophysiological and postharvest traits were analyzed. The data were subjected to analysis of variance, factor analysis, and application of the MGIDI index. The analysis of variance indicated significant differences for the interaction between genotypes and salinity levels in the greenhouse experiment, as well as for the sources of variation genotype and nitrogen levels in the field experiment. Factor analysis grouped the variables into two factors for the greenhouse experiment and three factors for the field experiment, explaining 82.80% and 80.60% of the total data variance, respectively. In the greenhouse experiment, genotype G9_C2 stood out for presenting greater physiological tolerance to salinity. In the field, genotypes G27, G4, G35, and G31 showed high productive performance and greater agronomic nitrogen use efficiency under contrasting nitrogen availability conditions. Multivariate selection using the MGIDI index proved to be efficient in identifying superior genotypes, highlighting the potential of the selected genotypes for maize breeding programs focused on nitrogen use efficiency and salinity tolerance. Fri, 20 Feb 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25787 2026-02-20T00:00:00Z