A grande maioria dos solos brasileiros são ácidos, com baixa fertilidade e elevada capacidade de retenção de fósforo, o que leva à necessidade de aplicação de elevadas doses de fosfatos, contribuindo para o aumento nos custos de produção, além de reduzir os recursos naturais não renováveis que originam esses insumos. Assim, tem-se intensificado a busca para o aproveitamento do potencial adaptativo de genótipos às condições adversas de fertilidade do solo, por meio do melhoramento gené-tico, ou seja, cultivares que apresentam maiores eficiência nutricional.Inúmeros conceitos de eficiência nutricional, têm sido relatados na lite- RESUMOForam avaliadas dez linhagens de pimentão, quanto à eficiência nutricional para fósforo. O experimento foi conduzido em casa de vegetação, em vasos contendo 5 dm 3 de amostra de um latossolo com textura muito argilosa. Os tratamentos foram distribuídos em arranjo fatorial (10 x 5) x 4, sendo dez linhagens de pimentão, cinco doses de fósforo (0; 250; 500; 750 e 1.000 mg de P/kg de solo) e quatro repetições, com delineamento em blocos casualizados. Constatou-se ampla variabilidade genética entre as linhagens quanto à eficiência nutricional para fósforo (P), a qual foi decorrente, principalmente das variações na eficiência de enraizamento, de absorção e de utilização do elemento para produção de matéria seca de parte aérea, uma vez que observou-se poucas variações na eficiência de translocação do P. Maiores eficiências de enraizamento não refletiram em maiores aquisições de P do solo, sugerindo que a absorção do elemento foi influenciada por características morfológicas e fisiológicas do sistema radicular. Dentre as linhagens estudadas a L10 foi a mais eficiente na absorção e utilização do P. Também a L8 mostrou bom comportamento. Já as linhagens L1, L2 e L6 mostraram-se menos eficientes na utilização do P. As outras linhagens apresentaram comportamentos variáveis em relação aos índices de eficiência avaliados. Essa ampla variabilidade observada pode ser explorada em programas de melhoramento genético visando maior eficiência nutricional para P. Palavras-chave:Capsicum annuum, fosfato, enraizamento, absorção, translocação, utilização. ABSTRACT Phosphorus efficiency of sweet pepper lines.Ten sweet pepper lines were screened for phosphorus efficiency. An experiment was carried out in the greenhouse in pots filled with 5 dm 3 of clayey latosol soil samples. The treatments followed a randomized complete block design, in a factorial layout (10 x 5 x 4), comprising ten lines, five doses of P (0; 250; 500; 750 and 1,000 mg of P/kg of soil) and four replications. Results showed genetic variability among lines for P-efficiency, characterized mainly by rooting efficiency, P-uptake and P-use efficiency. Low differences in the P-translocation efficiency were observed. Higher rooting efficiency was not translated into higher acquisition of P from the soil, which suggests that P-absorption was influenced by morphologic and physiologic characteristics of the root system. The L10 line showed high P-e...

ABSTRACT. Genetic parameters and associations between morphoagronomic traits and nutritional efficiencies of arabica coffee cultivars were estimated to identify promising traits to assist in the selection of coffee genotypes efficient in potassium use, under limiting conditions of this nutrient. The experiment was conducted in a greenhouse with 20 arabica coffee cultivars grown in nutrient solution with a low potassium level (1.5 mM), using a randomized block design with three replicates. The traits evaluated were plant height, number of leaves, number of nodes, internode length, stem diameter, leaf area, rooting efficiency, potassium absorption efficiency, potassium translocation efficiency, biomass production efficiency, and potassium use efficiency. Genetic variability among coffee cultivars for all the evaluated traits was found. The phenotypic variance for all traits showed a higher contribution of genetic variance compared to environmental variance. Plant height, internode length, stem diameter, leaf area, biomass production efficiency, and potassium use efficiency had a genotypic determination coefficient (H 2 ) above 80% and variation index greater than one. Leaf area and stem diameter had significant and positive genetic correlations with rooting, biomass production, and potassium use efficiencies. Stem diameter has great potential for use in breeding programs with a goal of indirect selection of cultivars that have greater potassium use efficiency in environments with restrictions of this nutrient.

Extensive use of nitrogen fertilizers in coffee crops increases production costs and environmental pollution. Developing cultivars more efficient in nitrogen (N) utilization could contribute to reduce the need for N fertilization and promote sustainable production. We evaluated the variation in production characteristics among 20 coffee cultivars grown in nutrient solution with low N concentration (1.0 mmol.L -1 ), aiming to identify combinations to compose future populations to be used in breeding programs and obtain cultivars more efficient in N utilization. Morpho-agronomic traits and rooting, absorption, translocation, biomass production, and N utilization efficiencies were evaluated. The clustering methods Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and canonical variables were employed. Cultivars presented differentiated responses at low N concentrations, except for nitrogen absorption efficiency. The UPGMA and canonical variables methods presented similar results, forming five cultivar clusters. Total dry mass contributed the most in the total dissimilarity. Significant genetic variability was detected among coffee cultivars grown at low N availability. Hybrids generated from cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, and Acaiá Cerrado MG 1474 were found to be ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (2): gmr18175 W.M. Moura et al. 2 the most promising to obtain a segregating population or heterotic hybrids, aiming at greater N utilization efficiency.

Machine learning and computational intelligence are rapidly emerging in plant breeding, allowing the exploration of big data concepts and predicting the importance of predictors. In this context, the main challenges are how to analyze datasets and extract new knowledge at all levels of research.  Predicting the importance of variables in genetic improvement programs allows for faster progress, carrying out an extensive phenotypic evaluation of the germplasm, and selecting and predicting traits that present low heritability and/or measurement difficulties. Although, simultaneous evaluation of traits provides a wide variety of information, identifying which predictor variable is most important is a challenge for the breeder. The traditional approach to variable selection is based on multiple linear regression. It evaluates the relationship between a response variable and two or more independent variables.  However, this approach has limitations regarding its ability to analyze high-dimensional data and not capture complex and multivariate relationships between traits. In summary, machine learning and computational intelligence approaches allow inferences about complex interactions in plant breeding. Given this, a systematic review to disentangle machine learning and computational intelligence approaches is relevant to breeders and was considered in this review. We present the main steps for developing each strategy (from data selection to evaluating classification/prediction models and quantifying the best predictor).