Genetic divergence for adaptability and stability in sugarcane: Proposal for a more accurate evaluation

The best agro-industrial performance presented by a crop genotype in one environment may not be reproduced in another owing to complex edaphoclimatic variations. Therefore, breeding programs are constantly attempting to obtain, through artificial hybridization, novel genotypes with high adaptability and stability potential. The objective of this study was to analyze genetic divergence in sugarcane based on the genotypic values of adaptability and stability. A total of 11 sugarcane genotypes were analyzed for eight agro-industrial traits. The genotypic values of the traits were determined using mixed model methodology, and the genetic divergence based on phenotypic and genotypic values was measured using the Mahalanobis distance. The distance matrices were correlated using the Mantel test, and the genotypes were grouped using the Tocher method. Genetic divergence is more accurate when based on genotypic values free of genotype–environment interactions and will differ from genetic divergence based on phenotypic data, changing the genotype allocations in the groups. The above methodology can be applied to assess genetic divergence to obtain novel sugarcane genotypes with higher productivity that are adapted to intensive agricultural systems using diverse technologies. This methodology can also be tested in other crops to increase accuracy in selecting the parents to be crossed.

Efficiency of selection within sugarcane families via simulated individual BLUP

The aim of this study was to assess the efficiency of the simulated individual BLUP (BLUPIS) method in selecting genotypes within full-sib families of sugarcane in ratoon stage, through comparison with selection using the individual BLUP method. The optimal number of genotypes to be selected in the best families were established for mean stems mass, total soluble solids assay (BRIX), ton of stalks per hectare, and BRIX tons per hectare traits. Seventeen full-sib families were assessed in the Centre for Experimentation in Sugarcane, located in Oratórios, MG, Brazil. Mixed model methodology was used to predict the genotypic effects of each family and the genotypic values of each individual within family. BLUPIS method is efficient for individual selection. The optimal number of genotypes to be selected in the best family for obtaining higher efficiency of the BLUPIS method is 100 in the majority of cases.

Selection of coffee progenies for resistance to nematode Meloidogyne paranaensis in infested area

The purpose of this study was to select Coffea arabica progenies for resistance to M. paranaensis in an infested coffee growing area using Henderson's mixed model methodology. Forty-one genotypes were selected at the Coffee Active Germplasm Bank of Minas Gerais, and evaluated in regard to stem diameter, number of plagiotropic branches, reaction to the nematode, and yield per plant. There was genetic variability among the genotypes studied for all the traits evaluated, and among the populations studied for yield and reaction to the nematode, indicating possibilities for obtaining genetic gains through selection in this population. There was high rate of genotypic association between all the traits studied. Coffee plants of Timor Hybrid UFV408-01 population, and F3 progenies derived from crossing Catuaí Vermelho and Amphillo MR 2161 were the most promising in the area infested by M. paranaensis.

Genetic parameters and simultaneous selection for root yield, adaptability and stability of cassava genotypes

The objective of this work was to estimate genetic parameters and to evaluate simultaneous selection for root yield and for adaptability and stability of cassava genotypes. The effects of genotypes were assumed as fixed and random, and the mixed model methodology (REML/Blup) was used to estimate genetic parameters and the harmonic mean of the relative performance of genotypic values (HMRPGV), for simultaneous selection purposes. Ten genotypes were analyzed in a complete randomized block design, with four replicates. The experiment was carried out in the municipalities of Altamira, Santarém, and Santa Luzia do Pará in the state of Pará, Brazil, in the growing seasons of 2009/2010, 2010/2011, and 2011/2012. Roots were harvested 12 months after planting, in all tested locations. Root yield had low coefficients of genotypic variation (4.25%) and broad-sense heritability of individual plots (0.0424), which resulted in low genetic gain. Due to the low genotypic correlation (0.15), genotype classification as to root yield varied according to the environment. Genotypes CPATU 060, CPATU 229, and CPATU 404 stood out as to their yield, adaptability, and stability.

Comparison of growth curves of two genotypes of dairy goats using nonlinear mixed models

SUMMARYThe objective of the current study was to assess the use of nonlinear mixed model methodology to fit the growth curves (weightv.time) of two dairy goat genotypes (Alpine, +A and Saanen, +S). The nonlinear functions evaluated included Brody, Von Bertalanffy, Richards, Logistic and Gompertz. The growth curve adjustment was performed using two steps. First, random effectsu1,u2andu3were linked to the asymptotic body weight (β1), constant of integration (β2) and rate constant of growth (β3) parameters, respectively. In addition to a traditional fixed-effects model, four combinations of models were evaluated using random variables: all parameters associated with random effects (u1,u2andu3), onlyβ1andβ2(u1andu2), onlyβ1andβ3(u1andu3) and onlyβ1(u1). Second, the fit of the best adjusted model was refined by using the power variance and modelling the error structure. Residual variance ($\sigma _e^2 $) and the Akaike information criterion were used to evaluate the models. After the best fitting model was chosen, the genotype curve parameters were compared. The residual variance was reduced in all scenarios for which random effects were considered. The Richards (u1andu3) function had the best fit to the data. This model was reparameterized using two isotropic error structures for unequally spaced data, and the structure known in the literature as SP(MATERN) proved to be a better fit. The growth curve parameters differed between the two genotypes, with the exception of the constant that determines the proportion of the final size at which the inflection point occurs (β4). The nonlinear mixed model methodology is an efficient tool for evaluating growth curve features, and it is advisable to assign biologically significant parameters with random effects. Moreover, evaluating error structure modelling is recommended to account for possible correlated errors that may be present even when using random effects. Different Richard growth curve parameters should be used for the predominantly Alpine and Saanen genotypes because there are differences in their growth patterns.

Plant selection in F2 segregating populations of papaya from commercial hybrids

The objective of this paper was to estimate the genetic parameters and additive genetic values in segregating populations of papaya using the mixed model methodology. Two F2 populations from Tainung and Calimosa hybrids were evaluated. The experimental design was an augmented block with four replicates, and Golden and Calimosa cultivars were the common treatment. Estimates of individual heritability were high for fruit length (FL) and weight (FW), moderate for fruit diameter (FD), and low for total soluble solids (TSS) and fruit firmness (FF). Considering FF and TSS as main traits for selection, genotypes of Calimosa-F2 population showed better performance to FF, but worse concerning TSS. It was selected 18.3% and 24.6% of plants from Tainung-F2 and Calimosa-F2 populations, respectively. Negative correlation between TSS and FF was not able to reduce the genetic gains. The segregating populations from Calimosa hybrid are more promising for the selection of papaya lines.