The duration of the dormancy period in tubers is a determining factor in the planning of potato planting and production. The effect of two plant growth regulators on the dormancy period and sprouting of cv. Diacol Capiro tubers was evaluated in this study. The experiment was carried out under storage conditions (15°C and 75% RH) using a completely randomized block design with 3×3×3 factorial arrangement. The factors were: gibberellic acid-3 (GA3) and 6-benzylaminopurine (6BAP) (0, 25, and 50 mg L-1) and the immersion time (iT) (10, 60, and 120 min). The application of GA3 and iT had an effect on dormancy breakage; the treatments with 25 mg L-1 GA3 and 60 min of immersion were enough to reduce dormancy by 18 d (35%) compared to untreated tubers. The factor GA3 increased tuber weight loss (10.2%), generated sprouts with higher weight (25.6-28.4%), higher length growth rate (42.3%), and lower dry matter content (21.8-28.4%), and it increased secondary sprouting (36.2-57.9%) in comparison with untreated tubers. This way, despite the treatments with 25 mg L-1 GA3 reduced the dormancy period, this dose generated sprouts more susceptible to mechanical damage. The treatments with 6BAP did not significantly affect the evaluated variables.
A model for cross-over designs with repeated measures within each period was developed. It was obtained using an extension of generalized estimating equations that includes a parametric component to model treatment effects and a non-parametric component to model time and carry-over effects; the estimation approach for the non-parametric component is based on splines. A simulation study was carried out to explore the model properties. Thus, when there is a carry-over effect or a functional temporal effect, the proposed model presents better results than the standard models. Among the theoretical properties, the solution is found to be analogous to weighted least squares. Therefore, model diagnostics can be made by adapting the results from a multiple regression. The proposed methodology was implemented in the data sets of the cross-over experiments that motivated the approach of this work: systolic blood pressure and insulin in rabbits.
This paper presents an experimental cross‐over design whose response variable is a count that belongs to the Poisson distribution. The methodology is extended to data with overdispersion or subdispersion. We present the theoretical development for analysis of cases with few treatments and a few periods. In this case, we consider the log‐linear link for estimation effects and the Delta method for the asymptotic inference of the estimators. When the number of periods and sequences increases, we propose an extension of the previous methodology, using the generalized linear models. In this extension, cross‐over designs for count data include treatments, sequences, time effects, covariables, and any correlation structure. The most important result of the methodology is that it allows the detection of significant factors within the cross‐over design when the response variable belongs to the exponential family, especially the treatment effects. Finally, we present the analysis of data obtained in a student hydration study and a simulation study. We show a comparison between the usual methods of analysis and those obtained in the present work, demonstrating the advantage over the usual methods in situations with carry‐over presence.
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