Dependência do crescimento das plantas ao regime de umidade do solo

Plant production is a result of manu integrate physiological processes which occur during growing season. Whathever those processes show considerable individuality with relation to environment. In this work the vegetative growth of potato plants (Solanum tuberosum L.) growing in the field was observed for three soil moisture regimes in three growth stages. The vegetative growth was evaluated through leaf area estimate in weakly basis. The results showed that by the ending of the first growth stage (about 25 days after plant emergency) the vegetative growth in the treatment where soil water matric potential never dropped less than -0.5 bar, exceeded in about 34 percent that observed for plants submitted to moderate water deficits (-1.0 bar), and in about 80 percent for those submitted to more severe water deficits (5.0 bars). However, the inhibition of vegetative growth in the first treatment was partially recovered after the re-establishment of suitable soil water condition. Occuring more severe water deficits in the initial growth stage, recovering of normal rates of vegetative growth did not occur upon rewatering. Intensive soil water depletion after complete vegetative growth hastenned plant senescence when compared with plants growing in well watered soils.A produção de uma planta resulta do desenvolvimento integrado de inúmeros processos fisiológicos que, por sua vez, apresentam considerável individualidade nas interrelações com o meio ambiente. Neste trabalho foi observado o crescimento de plantas de batata (Solanum tuberosum L.), em condições de campo, quando submetidas a três regimes de umidade do solo, individualizados em três estádios fenológicos. O desenvolvimento vegetativo foi avaliado através de estimativas semanais da área foliar. Os resultados mostraram que ao final do primeiro estágio (cerca de 25 dias após a emergência das plantas) o desenvolvimento vegetativo nos tratamentos irrigados quando o potencial matricial da água do solo atingia -0,5 bar, superou, em cerca de 34%, àquele verificado nas plantas submetidas a déficits híbridos moderados (-1,0 bar) e, em aproximadamente 80%, aquelas sujeitas a déficits mais severos (-5,0 bares). Entretanto, a limitação do crescimento no primei 6 ro caso, foi parcialmente recuperada após o restabelecimento de condições mais adequadas de umidade no solo, no estádio intermediário. O mesmo não foi verificado onde ocorreram déficits hídricos mais severos no estádio inicial, quando o crescimento foi quase irreversivelmente reduzido. A intensa desidratação imposta após o completo desenvolvimento vegetativo apressou a senescência das plantas em relação aos tratamentos melhores supridos com água

Study of using marker assisted selection on a beef cattle breeding program by model comparison

[EN] A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.We are grateful to the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Merialilgenity and Conselho Nacional de apoio a Pesquisa (CNPq) for the financial support, to Agro-Pecuaria CFM for data set and the Institut de Investigacion y Tecnologia Agroalimentarias de Cataluña (IRTA) as the host institution for its full backing while preparing the research and the manuscript.Rezende, F.; Ferraz, J.; Eler, J.; Silva, R.; Mattos, E.; Ibáñez-Escriche, N. (2012). Study of using marker assisted selection on a beef cattle breeding program by model comparison. Livestock Science. 147(1-3):40-48. https://doi.org/10.1016/j.livsci.2012.03.017S40481471-