Resumo
Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental results, mainly because much of the existing software perform this analysis and a lack of knowledge of other models. On the other hand, many of the natural phenomena do not present such behavior; nevertheless, the use of non-linear models is costly and requires advanced knowledge of language programming such as R. Thus, this work presents several regression models found in scientific studies, implementing them in the form of an R package called AgroReg. The package comprises 44 analysis functions with 66 regression models such as polynomial, non-parametric (loess), segmented, logistic, exponential, and logarithmic, among others. The functions provide the coefficient of determination (R2), model coefficients and the respective p-values from the t-test, root mean square error (RMSE), Akaike's information criterion (AIC), Bayesian information criterion (BIC), maximum and minimum predicted values, and the regression plot. Furthermore, other measures of model quality and graphical analysis of residuals are also included. The package can be downloaded from the CRAN repository using the command: install.packages("AgroReg"). AgroReg is a promising analysis tool in agricultural research on account of its user-friendly and straightforward functions that allow for fast and efficient data processing with greater reliability and relevant information.
Assuntos
Pesquisa , Análise de Regressão , Ciências AgráriasResumo
Sustainability - the new hype of the 21st century has brought discomfort for the government and society. Sustainable agriculture is essential to face our most concerning challenges: climate change, food security, and the environmental footprint, all of which add to consumers' opinions and choices. Improvements in reproductive indexes can enhance animal production and efficiency, guaranteeing profit and sustainability. Estrus detection, artificial insemination (AI), embryo transfer (ET), estrus synchronization (ES), and multiple ovulations are some strategies used to improve animal reproduction. This review highlights how reproductive strategies and genetic selection can contribute to sustainable ruminant production. Improved reproductive indices can reduce the number of nonproductive cows in the herd, reducing methane emissions and land use for production while preserving natural resources.(AU)
Assuntos
Bovinos/fisiologia , Inseminação Artificial/veterinária , Indústria de Laticínios/métodos , Fertilidade , Seleção Genética , Metano/análiseResumo
Genomic selection has transformed the livestock industry, enabling early-life selection of animals. Biopsy sampling of pre-implantation embryos has been described since 1968. However, it was only after 2010, with the advancement of molecular biology techniques such as whole genomic amplification and SNP Chips, that next-generation sequencing became commercially available for bovine embryos. It is now possible to make decisions about which embryos to transfer not only based on recipients' availability or embryo morphology but also on genomic estimates. This technology can be implemented for a wide spectrum of applications in livestock. In this review, we discuss the use of embryo biopsy for genomic selection and share our experience with Gir and Girolando Brazilian breeding programs, as well as future goals for implementing it in Brazilian bovine in vitro embryo production practices.(AU)
Assuntos
Animais , Feminino , Biópsia/veterinária , Bovinos/embriologia , Seleção Genética , Melhoramento Genético/métodosResumo
ABSTRACT: Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.
RESUMO: Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.
Resumo
Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.
Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.
Assuntos
Seleção Genética , Genoma , Genômica , Epistasia Genética , Algoritmo Florestas AleatóriasResumo
ABSTRACT: The development process of a new wheat cultivar requires time between obtaining the base population and selecting the most promising line. Estimating genetic parameters more accurately in early generations with a view to anticipating selection means important advances for wheat breeding programs. Thus, the present study estimated the genetic parameters of F2 populations of tropical wheat and the genetic gain from selection via the Bayesian approach. To this end, the authors assessed the grain yield per plot of 34 F2 populations of tropical wheat. The Bayesian approach provided an adequate fit to the model, estimating genetic parameters within the parametric space. Heritability (h2) was 0.51. Among those selected, 11 F2 populations performed better than the control cultivars, with genetic gain of 7.80%. The following populations were the most promising: TbioSossego/CD 1303, CD 1303/TbioPonteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton, and Tbio Aton/CD 1303. Bayesian inference can be used to significantly improve tropical wheat breeding programs.
RESUMO: O processo de desenvolvimento de uma nova cultivar de trigo requer tempo entre a obtenção da população base e a seleção da linhagem mais promissora. Estimar parâmetros genéticos com mais precisão nas primeiras gerações com vistas a antecipar a seleção significa avanços importantes para os programas de melhoramento de trigo. Assim, o presente estudo estima os parâmetros genéticos de populações F2 de trigo tropical e o ganho genético da seleção via abordagem Bayesiana. Para tanto, os autores avaliaram a produtividade de grãos por parcela de 34 populações F2 de trigo tropical. A abordagem Bayesiana proporcionou um ajuste adequado ao modelo, estimando parâmetros genéticos dentro do espaço paramétrico. A herdabilidade (h2) foi de 0,51. Dentre as selecionadas, 11 populações F2 obtiveram desempenho superior às cultivares controle, com ganho genético de seleção de 7,80%. As seguintes populações foram as mais promissoras: Tbio Sossego/CD 1303, CD 1303/Tbio Ponteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton e Tbio Aton/CD 1303. A inferência Bayesiana pode ser usada para melhorar significativamente programas de melhoramento de trigo tropical.
Resumo
The aim was to estimate the genetic correlations between residual feed intake (RFI) and dry matter intake (DMI) with carcass finish (CF), rib eye area (REA), and marbling (MAR) of Nellore cattle. Data from 7,808 animals were considered. In addition, data from 2,261 females included in the complete database were also considered. Estimates of variance and covariance components, as well as heritabilities and genetic correlations were obtained by means of two-character analysis under animal model. Heritability estimates were found to be moderate for the RFI (0.22) and DMI (0.29) traits. It was observed that genetic correlation was close to zero for all traits, except between RFI and REA (-0.11). However, considering the female population, there was an increase in the estimated genetic correlation between RFI and DMI, although still a favorable genetic association of low magnitude (-0.30). There was also an increase in the genetic association of REA with RFI (-0.21). It can be concluded that the direct selection for RFI and DMI will not influence the CF, MAR, or REA of Nellore cattle. However, this selection may generate some favorable responses in MAR and REA in Nellore females.
Objetivou-se estimar as correlações genéticas entre consumo alimentar residual (CAR) e ingestão de matéria seca com acabamento de carcaça (ACAB), área de olho de lombo (AOL) e marmoreio (MAR) para bovinos da raça Nelore. Foram consideradas informações de 7.808 animais. Além disso foram consideradas informações de 2.261 animais fêmeas que compunham o banco de dados completo. As estimativas dos componentes de variâncias e covariâncias, bem como das herdabilidades e correlações genéticas foram obtidas por meio de análises bicaracterísticas sob modelo animal. Verificou-se que as estimativas de herdabilidade foram moderadas para as características de CAR (0,22) e IMS (0,29). Observou-se que as estimativas de correlação genética foram próximos a zero para todas as Características, exceto entre CAR e AOL (-0,11). No entanto, considerando a população de fêmeas, houve um aumento na estimativa de correlação genética com CAR e IMS, apesar de ainda ser uma associação genética favorável de baixa magnitude (-0,30). Também houve um aumento na associação genética da AOL com o CAR (-0,21). Conclui-se, assim, que a seleção direta para o CAR e IMS não influenciará no ACAB, MAR e AOL de bovinos da raça Nelore. No entanto, essa seleção poderá gerar alguma resposta favorável em MAR e AOL em fêmeas Nelore.
Assuntos
Animais , Bovinos , Bovinos/genética , Ingestão de AlimentosResumo
ABSTRACT: Canonical correlation analysis based on genotypic correlations allows determining the associations between groups of traits and carrying out the direct or indirect selection of superior genotypes. This study investigated the existence of linear and multivariate relationships between high and low heritability traits via canonical correlation analysis based on genotypic correlations. The experiment was conducted at the Professor Diogo Alves de Melo Experimental Field at the Universidade Federal de Viçosa, in Viçosa, MG. 90 wheat cultivars were evaluated under a 9 × 10 alpha-lattice design, with three replications and plots consisting of four rows of three meters spaced at 0.20 meters. Canonical groups were established between spike height and plant height, days for heading, number of spikelets per spike, and number of grains per spike (Group I) and, spike weight, spike grain mass, 100-grain mass, hectoliter weight, and grain yield (Group II). There was dependence between the established groups, which allowed the investigation of the relationships between traits based on their genotypic values. The traits cycle and plant height can be used for indirect selection of genotypes superior in hectoliter weight and grain yield, which are important factors for industries and farmers.
RESUMO: As análises de correlações canônicas baseadas nas correlações genotípicas, permitem determinar associações entre grupos de caracteres e realizar a seleção direta ou indireta de genótipos superiores. Objetivou-se com este trabalho investigar a existência de relações lineares e multivariadas entre caracteres de alta e baixa herdabilidade via análise de correlações canônicas com base nas correlações genotípicas. O experimento foi conduzido no Campo Experimental Professor Diogo Alves de Melo da Universidade Federal de Viçosa, em Viçosa, Minas Gerais. 90 cultivares de trigo foram avaliadas sob o delineamento alpha-lattice 9 × 10, com três repetições e parcelas constituídas por quatro linhas de três metros espaçadas a 0.20 metros. Os grupos canônicos foram estabelecidos entre altura de espiga e planta, dias para o espigamento, número de espiguetas por espiga e número de grãos por espiga (Grupo I) e, peso de espiga, massa de grãos da espiga, massa de 100 grãos, peso hectolitro e produtividade de grãos (Grupo II). Houve dependência entre os grupos estabelecidos, o que permitiu a investigação das relações entre os caracteres com base em seus valores genotípicos. Os caracteres ciclo e altura de plantas podem ser utilizados para a seleção indireta de genótipos superiores em peso hectolitro e produtividade, fatores estes importantes para indústrias e produtores.
Resumo
ABSTRACT: The improper disposal of pesticide packaging wastes (PPW) has posed serious harm to the environment, including groundwater and soil pollution and even health concerns to the public. To address the environmental concerns and public health issues, there is a need to recycle the pesticides packaging waste (RPPW). Though small farmers in many developing countries have joined the cooperatives to reduce the production costs and increase the product premium, how these cooperatives improve farmers' RPPW behaviors is still sparse. The current study used data collected from 725 apple farmers in Shaanxi and Gansu provinces of China to explore the phenomenon empirically. Recycling decisions and degree are used to portray the farmers' RPPW behaviors. Firstly, the Logit model was used to analyze the effect of joining cooperatives on farmers' recycling decisions. Further, to address the sample selection bias, the present study employed the propensity score matching (PSM) method for empirical analysis concerning the effect of joining cooperatives on farmers' recycling degree. Results showed that joining cooperatives positively and significantly influences farmers' recycling decisions. If farmers join a cooperative, the probability of the recycling decisions and degree will increase by 20.30% and 27.50%, respectively. Moreover, it is also found that some other factors such as education level, environmental and public health risk perception, peer effect, and relationship network also significantly influence farmers' recycling decisions. Moreover, considering the differences in farmers' gender, age, and educational attainment, the study unveiled the heterogeneous effects of joining cooperatives on farmers' RPPW behaviors. The findings revealed that gender and age variables have noticeable masking effects while education level has a typical threshold effect. The overall findings provided insights for policymakers to emphasize the development of agricultural cooperatives, improve the risk and interest linkage mechanism, and build the RPPW system. These implications are also supportive for policymakers in other developing countries.
RESUMO: O descarte inadequado de resíduos de embalagens de pesticidas (PPW) tem causado sérios danos ao meio ambiente, incluindo a poluição das águas subterrâneas e do solo e até mesmo problemas de saúde pública. Para abordar as preocupações ambientais e questões de saúde pública, há a necessidade de reciclar os resíduos de embalagens de pesticidas (RPPW). Embora pequenos agricultores, em muitos países em desenvolvimento, tenham se unido às cooperativas para reduzir os custos de produção e aumentar o prêmio do produto, ainda é escassa a forma como essas cooperativas melhoram os comportamentos de RPPW dos agricultores. O estudo atual usou dados coletados de 725 produtores de maçã nas províncias de Shaanxi e Gansu da China para explorar o fenômeno empiricamente. Decisões e grau de reciclagem são usados para retratar os comportamentos de RPPW dos agricultores. Primeiramente, o modelo Logit foi utilizado para analisar o efeito da adesão às cooperativas nas decisões de reciclagem dos agricultores. Além disso, para abordar o viés de seleção da amostra, o presente estudo empregou o método Propensity Score Matching (PSM) para análise empírica sobre o efeito da associação de cooperativas no grau de reciclagem dos agricultores. Os resultados mostraram que a adesão às cooperativas influencia positiva e significativamente as decisões de reciclagem dos agricultores. Se os agricultores aderirem a uma cooperativa, a probabilidade das decisões de reciclagem aumentará em 20,30%, e o grau de reciclagem aumentará em 27,50%. Além disso, também se constata que alguns outros fatores como nível de escolaridade, percepção de risco ambiental e de saúde pública, efeito de pares e rede de relacionamento também influenciam significativamente as decisões de reciclagem dos agricultores. Além disso, considerando as diferenças de gênero, idade e escolaridade dos agricultores, o estudo também revelou os efeitos heterogêneos da adesão às cooperativas sobre os comportamentos de RPPW dos agricultores. Os resultados revelaram que as variáveis de gênero e idade têm efeitos de mascaramento perceptíveis, enquanto o nível de escolaridade tem um efeito limiar típico. As descobertas gerais fornecem insights para os formuladores de políticas enfatizarem o desenvolvimento de cooperativas agrícolas, melhorar o mecanismo de vinculação de risco e interesse e construir o sistema RPPW. Essas implicações também são favoráveisaos formuladores de políticas em outros países em desenvolvimento.
Resumo
Canonical correlation analysis based on genotypic correlations allows determining the associations between groups of traits and carrying out the direct or indirect selection of superior genotypes. This study investigated the existence of linear and multivariate relationships between high and low heritability traits via canonical correlation analysis based on genotypic correlations. The experiment was conducted at the Professor Diogo Alves de Melo Experimental Field at the Universidade Federal de Viçosa, in Viçosa, MG. 90 wheat cultivars were evaluated under a 9 × 10 alpha-lattice design, with three replications and plots consisting of four rows of three meters spaced at 0.20 meters. Canonical groups were established between spike height and plant height, days for heading, number of spikelets per spike, and number of grains per spike (Group I) and, spike weight, spike grain mass, 100-grain mass, hectoliter weight, and grain yield (Group II). There was dependence between the established groups, which allowed the investigation of the relationships between traits based on their genotypic values. The traits cycle and plant height can be used for indirect selection of genotypes superior in hectoliter weight and grain yield, which are important factors for industries and farmers.
As análises de correlações canônicas baseadas nas correlações genotípicas, permitem determinar associações entre grupos de caracteres e realizar a seleção direta ou indireta de genótipos superiores. Objetivou-se com este trabalho investigar a existência de relações lineares e multivariadas entre caracteres de alta e baixa herdabilidade via análise de correlações canônicas com base nas correlações genotípicas. O experimento foi conduzido no Campo Experimental Professor Diogo Alves de Melo da Universidade Federal de Viçosa, em Viçosa, Minas Gerais. 90 cultivares de trigo foram avaliadas sob o delineamento alpha-lattice 9 × 10, com três repetições e parcelas constituídas por quatro linhas de três metros espaçadas a 0.20 metros. Os grupos canônicos foram estabelecidos entre altura de espiga e planta, dias para o espigamento, número de espiguetas por espiga e número de grãos por espiga (Grupo I) e, peso de espiga, massa de grãos da espiga, massa de 100 grãos, peso hectolitro e produtividade de grãos (Grupo II). Houve dependência entre os grupos estabelecidos, o que permitiu a investigação das relações entre os caracteres com base em seus valores genotípicos. Os caracteres ciclo e altura de plantas podem ser utilizados para a seleção indireta de genótipos superiores em peso hectolitro e produtividade, fatores estes importantes para indústrias e produtores.
Assuntos
Triticum , Análise de Correlação Canônica , GenótipoResumo
Simultaneous selection for various agronomic traits, cooking time and mineral concentration are major challenges for common-bean (Phaseolus vulgaris L.) breeding programs. The authors of this study proposed to analyze genetic gain estimates obtained by direct and indirect selection using selection indices and economic weights for 13 traits, and to determine the most efficient selection strategy for the simultaneous selection of fast cooking, mineral-biofortified common bean cultivars with high agronomic performance. For this purpose, three experiments were carried out in different growing seasons to evaluate 49 common bean cultivars of different grain types. Agronomic performance was evaluated based on six traits; cooking time was determined using a Mattson cooker; and the concentration of six minerals was analyzed in samples of raw grains. Significant genotype × environment interaction or genotype effects were observed for all traits, indicating the existence of genetic variability. Direct selection resulted in high genetic gain estimates for individual traits, but caused undesirable changes in one or more of the traits under selection. The classic, base, desired-gains and rank-sum selection indices tested with six economic weights do not provide genetic gain estimates favorable to the selection of all traits. The multiplicative index is the best selection strategy for use in the breeding program when aiming at the simultaneous selection of fast cooking, mineral-biofortified common bean cultivars with high agronomic performance.
Assuntos
Phaseolus/genética , Melhoramento Vegetal/economia , Mutação com Ganho de FunçãoResumo
This study aimed to introduce R package pedSimulate, which was built to simulate pedigree, genetic merit, phenotype, and genotype data. These are amongst the most important data types that animal breeders and quantitative geneticists deal with. Twenty pedigrees with ten generations were simulated applying different combinations of three parameters: genetic variance (10 vs. 20), proportion of males selected (10 vs. 20%), and the pattern for selecting females (random, positively, or negatively based on own phenotype or parent average). Males were selected positively based on parent average. Consequently, assortative mating was applied to the pedigrees in which females were positively selected based on their own phenotype or parent average. Disassortative mating was applied to the pedigrees in which females were selected negatively based on phenotype or parent averages. Genetic gain and response to selection over generations were positive for all the pedigrees due to high selection intensity on males, mating each male with multiple females, and moderate to high heritability (0.25 and 0.40 for genetic variances 10 and 20, and the residual variance of 30). Genetic variance showed a slightly increasing trend over generations by assortative mating and lower selection intensity on males. Selection intensity on females was the same in all the pedigrees. This study provided examples of how R package pedSimulate can be adopted for pedigree, genetic merit, phenotype, and genotype data simulation in animal breeding studies. By using different functions and combining different parameters for their arguments, many scenarios can be simulated by R package pedSimulate.(AU)
Assuntos
Animais , Masculino , Feminino , Simulação por Computador , Cães/genética , Variação GenéticaResumo
Cryopreservation of equine semen is crucial to semen commercialization. However, it reduces sperm motility and longevity. Thus, sperm selection methods and addition of motility-activating substances to sperm, such as caffeine, may improve sperm quality of equine frozen semen. The objective of the current work was to evaluate the effects of caffeine on recovery and quality parameters of frozen-thawed sperm subjected to swim-up selection to be used in intracytoplasmic sperm injection (ICSI) in assisted reproductive techniques. Stallion semen were frozen and after thawing different caffeine concentrations were added to the samples performing four treatments control (no caffeine), 3, 5, and 7.5 mM caffeine. Sperm kinematic and motility were assessed by computer-assisted sperm analysis (CASA). Then, the four treated samples were submitted to the swim-up sperm selection, and the number of recovered sperm and morphology were evaluated at four times 20, 40, 60, and 80 min. The swim-up increased the recovery proportion of normal morphology sperm without (80.1±1%) or with caffeine addition (3mM: 81.2±1%, 5mM: 79.9±1% and 7.5 mM 78.9±1%) compared to the thawed semen (70±2%). However, the addition of 5 mM caffeine induced an increase in sperm motility (38.9±2.8 vs. 32.6±3.4%, P<0.05), and sperm recovery after swim-up (7.9x106 vs. 3.4x106 sperm/ml, P<0.05) compared to the control. The addition of 5 mM caffeine to frozen-thawed equine semen before swim-up selection improved sperm motility and increased the sperm recovery rate while not decreasing the percentage of morphologically normal sperm. Thus, caffeine addition to frozen-thawed equine semen before swim-up selection has potential clinical application in improving sperm quality for use in ICSI.(AU)
Assuntos
Animais , Masculino , Sêmen/efeitos dos fármacos , Cafeína/efeitos adversos , Criopreservação/métodos , Análise do Sêmen/métodos , Cavalos/fisiologiaResumo
Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called Fscore, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables.
Assuntos
Lactuca/crescimento & desenvolvimento , Análise do Solo , Mineração de Dados/métodos , Química do Solo/análise , Abastecimento de AlimentosResumo
Studies on aggressiveness of parasitoids, as assessed by their parasitism against pests, used in biological-control programs are highly important to select the most suitable species and/or strain to control insect pests. The present study investigated whether the egg parasitoid Trichogramma galloi Zucchi, an efficient control agent for sugarcane borer Diatraea saccharalis (Fabricius) in Brazil, could be replaced by Trichogramma atopovirilia Oatman & Platner, a parasitoid easier to mass-produce, since it has been found parasitizing D. saccharalis eggs in the warmest region of Brazil and Argentina. Three strains of the genus Trichogramma were compared: T. atopovirilia (ATP strain) reared on a factitious host Anagasta kuehniella (Zeller); T. atopovirilia isoline ATP-I, reared on D. saccharalis eggs for six generations; and T. galloi, reared on A. kuehniella eggs. We measured parasitism of each strain for 72 h and for the entire life span, parasitism rate per cluster of D. saccharalis eggs, number of parasitoids emerged (parasitism viability), and parasitoid life span. The results confirmed that T. galloi is the best species for D. saccharalis control, showing higher control potential, since parasitism and emergence rate were higher for this species. Although T. atopovirilia ATP-I performed reliably in all parameters, T. galloi exceeded and was the most indicated for mass-rearing in control programs for sugarcane borer.
Assuntos
Vespas , Controle Biológico de Vetores , Agentes de Controle Biológico , Mariposas , SaccharumResumo
The Fisher's infinitesimal model is traditionally used in quantitative genetics and genomic selection, and it attributes most genetic variance to additive variance. Recently, the dominance maximization model was proposed and it prioritizes the dominance variance based on alternative parameterizations. In this model, the additive effects at the locus level are introduced into the model after the dominance variance is maximized. In this study, the new parameterizations of additive and dominance effects on quantitative genetics and genomic selection were evaluated and compared with the parameterizations traditionally applied using the genomic best linear unbiased prediction method. As the parametric relative magnitude of the additive and dominance effects vary with allelic frequencies of populations, we considered different minor allele frequencies to compare the relative magnitudes. We also proposed and evaluated two indices that combine the additive and dominance variances estimated by both models. The dominance maximization model, along with the two indices, offers alternatives to improve the estimates of additive and dominance variances and their respective proportions and can be successfully used in genetic evaluation.
Assuntos
Seleção Genética , Melhoramento Vegetal/métodos , Genes Dominantes , Eucalyptus/genéticaResumo
Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called Fscore, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables.(AU)
Assuntos
Lactuca/fisiologia , Características do Solo , Programas de Rastreamento/métodos , MineraçãoResumo
The objective of this study was to estimate (co)variances and genetic parameters and to predict genetic trends for weight at 120 (W120), 210 (W210), 365 (W365), and 450 (W450) days of age in Nelore cattle raised in the northern region of Brazil. The database comprised records of 30,387 animals born between 2000 and 2013 in the Brazilian North. Estimates were calculated by the Restricted Maximum Likelihood (REML) method, in single- and multi-trait analyses in an animal model. Heritability as obtained using single- and multi-trait models for W120 (0.22 and 0.31), W210 (0.20 and 0.34), W365 (0.51 and 0.51), and W450 (0.49 and 0.51) indicated moderate to high magnitudes, with the possibility of genetic selection and incorporation into the herd. Genetic correlations between growth traits were favorable, ranging from 0.78 to 0.96. Genetic trends for W120 and W210 varied largely, from -0.31 to 4.68 and -0.53 to 7.62 kg/year, respectively. Smaller fluctuations were observed in genetic trends for W365 and W450, which ranged from -1.08 to 10.90 and -1.29 to 12.51 kg/year, respectively. Selection for W365 and W450 proved to be the criterion of choice for Nelore herds raised in the region; however, it may compromise adult performance because of higher costs and time for production. A thorough analysis of mattings is recommended to allow the selection of earlier-developing animals.(AU)
O presente trabalho foi delineado para estimar as (co) variâncias, parâmetros genéticos e de predizer as tendências genéticas para o peso aos 120 (W120), 210 (W210), 365 (W365) e 450 (W450) dias de idade de gado Nelore criado na região norte do Brasil. A base de dados foi constituituída por registro de 30387 animais, nascidos entre 2000 e 2013 no norte do Brasil. As estimativas foram calculadas pelo método de máxima restrição de probabilidade (REML) em um modelo animal com análises isoladas e multi variadas. A herdabilidade obtida para os modelos utilizados foi: W120 (0,22 e 0,31); W210 (0,20 e 0,34); W365 (0,51 e 0,51) e W450 (0,49 e 0,51), indicando moderada e alta magnitude com a possibilidade de seleção genética e incorporação no rebanho. As correlações genéticas entre grupos de tendências foram favoráveis variando de 0,78 a 0,96. As tendências genéticas para W120 e W210 apresentaram ampla variação de -0,31 a 4,68 e -0,53 a 7,62 kg/ano, respectivamente. Menores flutuações foram observadas nas tendências genéticas para W365 e W450, as quais variaram de -1,08 a 10,90 e -1,29 a12,51 kg/ano, respectivamente. Foi constatado que a seleção para W365 e W450 deve ser um critério de escolha para os rebanhos Nelore criados na região; contudo ela pode comprometer a performance dos adultos devido aos elevados custos e da duração da produção. Uma completa análise de cruzamentos é recomendada para possibilitar a seleção de animais jovens em desenvolvimento.(AU)
Assuntos
Animais , Fenômenos Genéticos/fisiologia , Gado/genética , Crescimento/genética , BrasilResumo
ABSTRACT Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called Fscore, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables.
Resumo
Common bean is a worldwide important crop. The development of varieties with durable resistance to diseases is a major challenge in common bean breeding. The present study aimed at evaluating the phenotypic and molecular selection of anthracnose resistance in a population obtained by assisted backcrossing from IAC Formoso (resistant, donor parent) × BRS Pérola (susceptible, recurrent parent). Nine microsatellites (SSRs) and one Sequence Tagged Sites (STS) markers previously linked to ANT resistance were used to genotype this progeny, and the results showed that the selection of the genotypes closest to the donor parent in the BC1F1 population decreased the number of backcrossing cycles necessary to obtain advanced isogenic lines, potentiating the use of this tool for early selection of resistant cultivars. A total of 31 % of the BC1F1 progeny was selected and backcrossed again. The progeny derived from the second backcross (BC2F3) was selected for the Carioca grain ideotype, and 42 % of the genotypes showed high resistance to anthracnose under controlled conditions of infection for races 65 and 81. Superior resistant plants were selected and evaluated under natural conditions of infection to fusarium wilt and angular leaf spot, allowing the selection of two inbred lines with higher resistance to anthracnose, fusarium wilt, angular leaf spot and postharvest quality traits such as yield, 100 seed weight, L value at seed harvest grain darkening and cooking time. The approach outlined in this paper proved to be effective to simultaneously select for disease resistance without losing technological quality aspects of the bean.