Multi-trait multi-environment genomic prediction of durum wheat

Osval Antonio Montesinos-Lopez ROBERTO TUBEROSA MARCO MACCAFERRI GIUSEPPE SCIARA Karim Ammar Jose Crossa (2019)

In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also were compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method. All models were implemented with and without the genotype×environment interaction term. We found that the best predictions were observed without the genotype×environment interaction term in the univariate and multivariate deep learning methods, but under the GBLUP method, the best predictions were observed taking into account the interaction term. We also found that in general the best predictions were observed under the GBLUP model but the predictions of the multi-trait deep learning model were very similar to those of the GBLUP model.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Response to heat stress and glutenins allelic variation effects on quality traits in durum wheat

Facundo Tabbita Karim Ammar Maria Itria Ibba MARCO MACCAFERRI ROBERTO TUBEROSA Carlos Guzman (2024)

Artículo

Gluten Quality Sedimentation Volume Wheat Quality CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GLUTEN QUALITY GLUTENINS HEAT STRESS WHEAT