https://www.alice.cnptia.embrapa.br/alice/handle/item/902 Artigo em periódico indexado (SAPC) Thu, 25 Jun 2026 18:44:49 GMT 2026-06-25T18:44:49Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187655 Título: Leaf-scale phenotypic plasticity of Coffea arabica progenies under seasonal variation in water availability. Autoria: SILVA, E. A. da; SANTOS, C. S. dos; MATOS, N. M. S. de; PENNACCHI, J. P.; ABRAHÃO, J. C. de R.; CARVALHO, M. A. de F.; TAVARES, M. C. dos S.; CARVALHO, S. P. de; GUIMARÃES, R. J. Conteúdo: Abstract: Climate variability poses major challenges to coffee production, particularly due to the increasing frequency and intensity of drought events. Understanding the physiological acclimation capacity of Coffea arabica genotypes to water deficit is critical for developing resilient cultivars. We hypothesized that progenies with higher multivariate phenotypic plasticity index (MVPi) values would exhibit coordinated morphophysiological traits associated with greater acclimation capacity to seasonal water availability. This study aimed to quantify leaf-scale phenotypic plasticity in 16 C. arabica progenies derived from a plant selected for its large leaves and fruits, which originated from a natural mutation of the Acaiá cultivar. Physiological, anatomical, and biochemical traits were assessed during the dry and rainy seasons, and plasticity was quantified using the MVPi. Principal component analysis revealed substantial variation in plastic responses among genotypes, with M11, L30, and L16 exhibiting the highest MVPi values. These genotypes showed coordinated adjustments in water use efficiency, chlorophyll content, and leaf tissue structure. Although MVPi proved effective in integrating multidimensional trait variation, its interpretation requires caution, as higher plasticity does not necessarily indicate an adaptive advantage. These findings support the integration of multivariate plasticity analysis into breeding programs as a strategy to identify genotypes with superior acclimation potential under water-limited conditions. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187655 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187490 Título: Extreme learning machine for genomic prediction of rust disease resistance in Arabica coffee. Autoria: SILVA, J. T. da; BARRETO, C. A. V.; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; ALMEIDA, D. P. de; CAIXETA, E. T.; TEIXEIRA, F. R. F.; NASCIMENTO, M. Conteúdo: ABSTRACT – The objective of this work was to investigate the use of Extreme Learning Machines (ELM) for the genomic prediction of rust resistance in Coffea arabica. With the objective of identifying an effective predictive model for the selection of resistant genotypes, ELM was compared to Artificial Neural Networks (ANN) and Bayesian Generalized Linear Regression (GBLR) in terms of accuracy measures and computational time. To this end, an F2 population of 245 C. arabica plants genotyped with 137 markers was used to evaluate the application of ELM for the genomic prediction of coffee rust resistance. The results indicate that ELM and ANN show a higher accuracy – on average 15% greater than that of GBLR – in predicting rust resistance. Additionally, ELM proves to be computationally more efficient, with a processing speed 5.5 and 19.45 times slower than that of ANN and BGLR, respectively, making it promising for large-scale analyses. RESUMO – O objetivo deste trabalho foi investigar o uso de Máquinas de Aprendizagem Extrema (ELM) para a predição genômica da resistência à ferrugem em Coffea arabica. Com o objetivo de identificar um modelo preditivo eficaz para a seleção de genótipos resistentes, o ELM foi comparado a Redes Neurais Artificiais (RNA) e Regressão Linear Generalizada Bayesiana (GBLR) em termos de medidas de acurácia e tempo computacional. Para tanto, uma população F2 de 245 plantas de C. arabica genotipadas com 137 marcadores foi utilizada de modo a avaliar a aplicação do ELM na predição genômica da resistência à ferrugem-do-café. Os resultados indicam que o ELM e a RNA apresentam maior acurácia – em média 15% superior ao GBLR – na predição da resistência à ferrugem. Adicionalmente, o ELM se mostra computacionalmente mais eficiente, com velocidades de processamento 5,5 e 19,45 vezes menores que a de RNA e o BGLR, respectivamente, tornando-o promissor para análises de larga escala. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187490 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186532 Título: Agronomic performance of irrigated and rainfed arabica coffee cultivars in the cerrado mineiro region. Autoria: VOLTOLINI, G. B.; CARVALHO, G. R.; ANDRADE, V. T.; FERREIRA, A. D.; RAPOSO, F. V.; CARVALHO, J. P. F.; VILELA, D. J. M.; SILVA, C. A. da; COSTA, J. de O.; ABREU, G. B.; ABRAHÃO, J. C. de R.; BOTELHO, C. E.; NADALETI, D. H. S.; MALTA, M. R.; SILVA, V. A.; SALGADO, S. M. de L.; FARIA, R. S. de; OLIVEIRA, A. C. B. de; PEREIRA, A. A. Conteúdo: Coffee genetic improvement programs have been evolving very quickly, with frequent launches of new cultivars. The adoption of these new genetic materials by rural producers requires knowledge of agronomic performance in different production systems. Thus, this research aimed to evaluate the agronomic performance of irrigated and rainfed Arabica coffee (Coffea arabica L.) cultivars in the Cerrado Mineiro region. Evaluations were conducted in experimental fields across 22 farms of Arabica coffee producers, and 11 used an irrigated production system and 11 used a rainfed system. Twelve cultivars were evaluated as follows: Catuaí Vermelho IAC 144, Bourbon Amarelo IAC J10, Topázio MG 1190, MGS Epamig 1194, Catiguá MG2, MGS Catiguá 3, MGS Ametista, Pau Brasil MG1, MGS Paraíso 2, MGS Aranãs, Sarchimor MG 8840, and IAC 125 RN. Based on grain yield, processing yield, seed density, grain size, and cup quality, agronomic performance evaluations were conducted annually for the 2019, 2020, 2021, and 2022 harvests. The results showed that the grain yield was higher in the irrigated system compared to the rainfed system. In irrigated fields, the average increases in grain yield were 38%. Irrigation improved the performance of the cultivars in terms of processing yield, although it reduced cup quality. MGS Paraíso 2 cultivar showed the best productive performance, with an average of over four harvests of 52 and 42 bags ha−1 (1 bag = 60 kg) in irrigated and rainfed systems, respectively. The cultivars least responsive to irrigation were IAC 125 RN, MGS Catiguá 3, MGS Ametista, and MGS Paraíso 2, with grain yield increases of 24%, 26%, 27%, and 28%, respectively. The most responsive cultivars were MGS EPAMIG 1194, Sarchimor MG 8840, and Pau Brasil MG1, with grain yield increases of 33%, 35%, and 39%, respectively. The agronomic performance results of coffee cultivars in irrigated and rainfed production systems will allow rural producers to adopt cultivars that are more suitable for the Cerrado Mineiro region. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186532 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186531 Título: Initial vegetative vigor of Coffea canephora genotypes grown in the western amazon. Autoria: KOLLN, A. M.; ESPINDULA, M. C.; ROCHA, R. B.; ARAÚJO, L. F. B. de; TEIXEIRA, A. L.; LOPES JUNIOR, H. Conteúdo: Understanding the vegetative vigor of clonal genotypes under nursery conditions is essential for efficient seedling production and the development of new cultivars. This study aimed to evaluate the initial vegetative vigor of Coffea canephora genotypes cultivated in the Western Amazon. Twenty-one genotypes from the Embrapa Germplasm Bank, four registered cultivars, and three publicly available clones were assessed in a completely randomized design with four replicates and six plants per plot. Vegetative traits were evaluated 128 days after planting the cuttings and included: shoot length (SL), shoot diameter (SD), number of roots (NR), root volume (RV), shoot dry mass (SDM), root dry mass (RDM), total dry mass (TDM), leaf area (LA), the SDM/RDM ratio, and the Dickson Quality Index (DQI). Genotype performance and experimental accuracy were analyzed using genetic parameter estimates and genotype divergence. All commercial genotypes, except GJ25 and AS2, exhibited superior vegetative vigor during the seedling stage. Genotype BAG15 was initially the most vigorous. Genotypes BAG30, BAG31, BAG21, BAG29, BAG19, BAG26, BAG24, BAG28, BAG39, BAG41, BAG33, BAG38, BAG43, BAG23, BAG34, and BAG32 were considered promising due to their high RDM/SDM ratio and/or proximity to ideotype III, which was characterized by greater root development a trait likely to enhance survival following transplanting in the field. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186531 2025-01-01T00:00:00Z