Dual purpose wheat could be a good alternative for helping overcome the need to import this cereal in Brazil. To achieve this, development of cultivars with high yield is necessary. The contribution of genetics in defining traits is very important for directing breeding programs for the development of cultivars that provide the desired agronomic ideotype. We estimated heritability for 36 characters of agronomic importance in dual-purpose wheat. The inheritable genetic patterns were examined using linear trends, a Euclidean algorithm, factor analysis and artificial neural networks. The study was carried out during the crop seasons of 2011, 2012 and 2013. The experimental design was randomized block, arranged in a factorial scheme with three growing seasons (2011, 2012 and 2013) and five dual-purpose wheat genotypes (BRS Tarumã, BRS Umbu, BRS Figueira, BRS Guatambu and BRS 277) x three cuttings (first cutting, second cutting and third cutting), with three replicates. Deviance analysis or maximum likelihood was significant for the 36 characters. The length of the head of the main plant, plant height ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (3): gmr18266 I.R. Carvalho et al. 2 before the first second cutting and dry mass of the seedlings showed high variability. The 36 characters expressed linear genetic dependence based on the Euclidean Algorithm; similar to what was found with the Tocher Optimized Clustering and Artificial Neural Networks K-means methods. Similar genetic trends for heritability profiles were obtained with factor analysis and Artificial Neural Networks by the Kohonem method. The use of Artificial Neural Networks through the Kohonem method gave the greatest efficacy in the definition of the genetic profiles needed to develop the recommended agronomic ideotype for the improvement of dualpurpose wheat.
The internal points method (IPM-Carvalho), with regression analysis, can generate an efficient hybrid model for estimating oat grain productivity. We tested a combination of the internal points method and regression to estimate straw productivity. We also applied this methodology to forecast a harvest index in the elaboration of a hybrid model to estimate oat grain productivity, taking into account nitrogen management and growth regulator use, with biological and environmental indicators. Simulation of oat yield as a function of nitrogen and growth regulator applications, with biological and environmental inputs, can assist in the development of more efficient and sustainable management for this crop. Two experiments were conducted during 2013, 2014, and 2015; one was used to quantify biomass yield and the other to determine grain yield and plant lodging. The experimental design was randomized blocks with four replications in a 4 x 3 factorial scheme in the sources of variation, which were growth regulator (0, 200, 400 and 600 mL ha -1 ) and nitrogen (30, 90 and 150 kg ha applications. The environmental parameters that were included were rainfall and maximum air temperature. The nitrogen was applied as urea at the expanded fourth leaf stage. The growth regulator was trinexapac-ethyl applied at the ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (2): gmr18756 A. Marolli et al. 2 stage between the 1st and 2nd visible stem node. Straw productivity was obtained by the IPM model with nitrogen dose and rainfall inputs. The harvest index was obtained by regression as a function of the growth regulator doses. The combination of the internal points method to estimate straw productivity with the use of regression in the forecast of the harvest index proved to be a useful model for estimating oat grain productivity based on biological and environmental parameters, together with nitrogen and growth regulator applications.
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