An accurate prediction of maize crosses using diallel analysis and best linear unbiased predictor (BLUP)

Abstract

A major obstacle in corn (Zea mays L.) breeding programs is how to obtain strains which, when combined, ensure increased productivity in all crop characteristics. Therefore, the objective of this study was to evaluate the prediction efficiency of hybrid maize using diallel analysis and the best linear unbiased predictor (BLUP). Eight synthetic varieties were sown in a diallel scheme and the hybrid and their parents were then evaluated in three separate environments; both combining ability and predicted breeding values (BLUPs) were estimated. Correlations between the BLUP and combining abilities were also obtained. Combining ability analysis revealed that both additive and non-additive types of gene action were important in the studied traits. There was a moderate to high correlation between the mean square of the combining ability and the predicted breeding values. This shows that BLUP can be used to select the best parents for different traits, especially for ear height and ear position, which had the highest correlations. The parent VAR-01 had significant general combining ability (GCA) values, and it can be used in crosses to reduce plant height, ear height, and ear position while the parent VAR-06 can participate in crosses to increase ear height, plant height, and ear position. The result obtained for combining ability was moderately to highly consistent with BLUP results, but selection must be carefully undertaken.

Diallel analysis and prediction of untested maize single cross hybrids

“…Considering GY and PE, the commercial checks obtained average predicted values of 3.42 t ha -1 and 27.18 mL g -1 , whilst the single hybrid were superiors for GY (3.68 t ha -1 ) but presented lower values for PE (22.71 L g -1 ). For GY, these values can be considered superior when compared with Vieira et al (2011) and Silva et al (2013), although, the values obtained for PE are below than those observed by Arnhold et al (2010) and Rossato Júnior et al 2013, but very similar from the data observed by authors with experimental (VIEIRA et al, 2011) and commercial (LEONELLO et al, 2009) single hybrids. The predicted values for PH and EH were similar between the single hybrids and the commercial checks, which are already established in the popcorn market.…”

“…Popcorn (Zea mays everta) is a popular snack food and it is very appreciated in Brazil, presenting higher aggregate value when compared with field corn. Popping expansion is conferred by pericarp resistance and grain moisture, differing from the field corn and being classified as specialty corn, although both belong to the same genus and species (LEONELLO et al, 2009;DE CARVALHO et al, 2013). When popcorn grains are exposed under heat, the oil and moisture content make pressure over pericarp, bursting it and exposing the endosperm, the so-called "popcorn flower".…”

Combining ability for grain yield and silking of maize inbred lines derived from three open pollinated varieties released for mid altitudes of Rwanda: Comparison of Diallel and North Carolina Design II

“…The lines with best GCA effects and crosses with best SCA effects can be used in predicting the best performing cultivars (Technow et al, 2012;Oliveira et al, 2016). Lines with best GCAs effects are best to predict synthetics; while the crosses with best SCAs are best for predicting hybrid varieties (Malik et al, 2004).…”

“…The inbred lines with best GCAs for grain yield and or silking will be utilised in predicting and generating the best maize synthetic varieties (Technow et al, 2012;Oliveira et al, 2016); while the crosses with best SCAs will be used in maize hybrid development programs aiming at high yield and small silking period (Ndhlela et al, 2015).…”

“…The four Griffing's diallels methods have so far been extensively used in several studies involving model I as well model II on maize crop, for estimating the combining abilities of parents for use in hybrid development (Karaya et al, 2009;Devi and Singh, 2011;Gakunga et al, 2012;Cabral et al, 2015;Al-Naggar et al, 2016) or estimating the components of variance (Technow et al, 2012;Chukwu et al, 2016;Nardino et al, 2016;Oliveira et al, 2016) of key agronomic traits. Furthermore, they have been widely applied to other crops such as rice, wheat and potato (Asfaliza et al, 2012;Kumar et al, 2017;Terres et al, 2017).…”