Predicting Tillering of Diverse Sorghum Germplasm across Environments. Issue 1 (12th October 2016)

• Record Type: Journal Article
• Authors: Alam, Mohammad Mobashwer; van Oosterom, Erik J.; Cruickshank, Alan W.; Jordan, David R.; Hammer, Graeme L.
• Is Part Of: Crop science. Volume 57:Issue 1(2017)
• Page Start: 78
• Page End: 87
• Publication Date: 2016-10-12
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.2135/cropsci2016.04.0262
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract : Prediction of fertile tiller number (FTN) is important for predicting crop leaf area development and provides an avenue to identify genotypes with specific adaptation to variable environments. However, previous tillering prediction models in sorghum [ Sorghum bicolor (L.) Moench] were limited to only a few genotypes. This study aimed to develop an approach to predict FTN for a large number of genotypes grown in multiple environments. A set of 756 genotypes from 17 diverse families of a backcross‐derived, sorghum nested association mapping population were evaluated in test cross combinations with a single female tester. Plants were grown in five environments, but not all genotypes were included in all environments. One of the environments was space planted for expression of tillering propensity. Three predictors of tillering were considered: tillering propensity, incident radiation per unit thermal time during the tillering stage, and plant density. These represent the genotypic, environmental, and management effects on FTN, respectively. Based on these predictors, a robust model was developed for 125 genotypes grown in the spaced planting and all four test environments ( R 2 = 0.85, n = 500). For the independent set of remaining genotypes, the model predicted FTN in each of the four test environments with an accuracy and precision close to that for the training set ( R 2 = 0.69–0.74). The implications for crop improvement of this predictive capability of FTN are discussed in relation to the opportunities for assessing genetic and management options for specific adaptation, and for removing confounding effects in the analysis of breeding trials.