Abbreviations: AMMI, additive main effects and multiplicative interaction; ASV, additive main effects and multiplicative interaction stability value; AVRC, index and the ranks of the mean yields; BLUP, best linear unbiased prediction; EV, averages of the squared eigenvector values; GEI, genotype × environment interaction; HMGV, harmonic mean of genotypic values; HMRPGV, harmonic mean of relative performance of genotypic values; IPCA, interaction principal component axis; LMM, linear mixed-effect model; MET, multi-environment trials; NF, no fungicide; RCBD, randomized complete block design; RMSPD, root mean square prediction difference; SPIC, sums of the absolute value of the IPCA scores; SVD, singular value decomposition; WAASB, weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixedeffect model; WAASBY, weighted average of weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixed-effect model and response variable; WF, with fungicide; Za, absolute value of the relative contribution of interaction principal component axes to the interaction. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

Abbreviations: AMMI, additive main effects and multiplicative interaction; ASV, additive main effects and multiplicative interaction stability value; AUDPC, area under the disease progress curve; BLUP, best linear unbiased prediction; CW, caryopses weight; GEI, genotype × environment interaction; GSI, genotype stability index; GW, grain weight; GWP, grain weight per panicle; GY, grain yield; HI, hulling index; HW, hectoliter weight; IGY, industrial grain yield; IPCA, interaction principal component axis; LMM, linear mixed-effect model; MET, multi-environment trial; MPE, mean performance and stability; MTSI, multi-trait stability index; NEP, number of spikelets per panicle; NG2, number of grains >2 mm; NGP, number of grains per panicle; PL, panicle length; PM, panicle mass; TGW, thousand-grain weight; WAASB, weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixed-effect model; WAASBY, weighted average of WAASB and response variable. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

This study aims to identify correlations between growth variables and the Dickson quality index in seedlings of Eucalyptus grandis and Pinus elliottii var. elliottii. The experiment was conducted in a greenhouse and the following variables were observed: stem base diameter, shoot height, number of leaves, leaf dry matter, stem base dry matter, root dry matter, shoot dry matter, total dry matter, ratio of shoot dry matter to root dry matter and ratio of shoot height to stem base diameter in E. grandis 60, 75, 90, 105 and 120 days after seedling emergence, and in P. elliottii 25, 50, 75, 100, 125, 150 and 175 days after seedling emergence. Using Pearson correlation and also path and regression analyses, correlations were analyzed between observed variables according to day after emergence and the Dickson quality index. Stem base diameter was found to have stronger correlation with days after emergence in comparison to shoot height, in both species. Root dry matter was found to have stronger correlation with the Dickson quality index. Stem base diameter was the most suitable parameter to indicate seedling quality due to its higher correlation level with the Dickson quality index. Shoot height was only effective to indicate seedling quality if analyzed together with stem base diameter. Variables relating to dry matter showed the highest correlations with the Dickson quality index (DQI), followed by stem base diameter. Conversely, number of leaves showed the poorest correlations with DQI, followed by seedling height.Key words: Eucalyptus grandis, Pinus elliottii, path analysis, regression analysis, linear correlation. RELAÇÕES ENTRE VARIÁVEIS DE CRESCIMENTO E O ÍNDICE DE QUALIDADE DE DICKSON EM MUDAS FLORESTAIS RESUMO: Neste estudo, objetivou-se identificar a relação entre variáveis de crescimento e o Índice de Qualidade de Dickson, em mudas de Eucalyptus grandis e Pinus elliottii var. elliottii. O trabalho foi realizado em casa de vegetação, com as observações das variáveis diâmtero de colo, altura da parte aérea, número de folhas, fitomassas secas de folhas, do colo

We proposed a workflow for nonlinear modeling of data from multiple‐harvest crops. We demonstrated why the nonlinearity measures should be used to select nonlinear models. We demonstrated as the critical points describe the multiple‐harvest crops production. Logistic model parameters determine the precocity and the concentration of production. Growth models are alternative to ANOVA in analyzing data from multiple‐harvest crops. Nonlinear growth models have been widely used for analyzing production curves with a sigmoidal pattern; however, all benefits that these models provide are not being fully exploited. Our aim here is to provide a step‐by‐step guide on how to choose a nonlinear model with parameters close to being unbiased, and to show how to estimate and interpret the critical points of a model aimed at determining the precocity and concentration of the production. Data on two uniformity trials conducted with eggplant (Solanum melongena L.) was used for this purpose. The Brody, Gompertz, logistic, and von Bertalanffy models were fitted to predict the number and fresh mass of fruits per plant. The model with lower nonlinearity measures and lower bias of the parameter estimates was selected. All the tested models presented satisfactory goodness‐of‐fit measures, but they differed regarding nonlinearity measures. The logistic model was selected because it had lower intrinsic and parametric nonlinearity and lower bias in parameter estimates. The inflection point and maximum acceleration/deceleration points of this model provide detailed pieces of information of the production through the productive cycle. Finally, using the logistic model as an example, we demonstrate that lower values of β2 are related to an earlier maximum production rate, and higher values of β3 are related to an earlier production that is concentrated in fewer days. The nonlinearity measures were important for the model selection. Thus, it is strongly recommended that nonlinearity is estimated and used to select nonlinear models in future studies.

RESUMO Com objetivo de caracterizar os cultivares de milho