Repositorio Nacional

Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel, AK) method, Gaussian kernel (GK) method and the conventional kernel method (Genomic Best Linear Unbiased Predictor, GBLUP, GB). We used two real wheat data sets for the benchmarking of these methods. We found that the GK and deep kernel AK methods outperformed the DL and the conventional GB methods, although the gain in terms of prediction performance of AK and GK was not very large but they have the advantage that no tuning parameters are required. Furthermore, although AK and GK had similar genomic-based performance, deep kernel AK is easier to implement than the GK. For this reason, our results suggest that AK is an alternative to DL models with the advantage that no tuning process is required.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Product development for Eastern Africa: EA-PP1

EA-PP2

Berhanu Tadesse Ertiro Aparna Das Yoseph Beyene Dan Makumbi Manje Gowda Suresh L.M. Anani Bruce Walter Chivasa Vijay Chaikam Juan Burgueño Prasanna Boddupalli (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PRODUCT DEVELOPMENT TESTING DATA MAIZE

Prediction models for canopy hyperspectral reflectance in wheat breeding data

Osval Antonio Montesinos-Lopez Jose Crossa Gustavo de los Campos Gregorio Alvarado Suchismita Mondal Jessica Rutkoski Lorena González Pérez Juan Burgueño (2016)

Vegetation indices (VI) generated by using some bands from hyperspectral cameras are used as predictors of primary traits. This study proposes models that use all available bands as predictors of primary traits. The proposed models were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square (PLS). The results were compared with the OLS performed using as predictors each of the eight VIs individually and combined. The data set comes from CIMMYT’s Global Wheat Program and comprises 1170 genotypes evaluated for grain yield in five environments with the reflectance data measured in 250 discrete narrow bands ranging between 492 and 851 nm. in 9 time-points of the crop cycle. Results show that using all the bands simultaneously produced better predictions than using one VI alone or all the VI together, but when used only the bands with heritabilities > 0.5 in Drought environment, the predictions improved, while in the rest of the environments, using all the bands simultaneously produced slightly better prediction accuracies. The models with highest prediction when using all bands were functional B-spline and Fourier. Time-point 6 gives gave promising prediction accuracies for wheat lines before harvesting.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

CIMMYT Eastern Africa early- to intermediate maturity maize breeding pipelines (EA-PP1)

Yoseph Beyene Andrew Chavangi Manje Gowda Suresh L.M. Vijay Chaikam Anani Bruce Berhanu Tadesse Ertiro Walter Chivasa Aparna Das Juan Burgueño Jose Crossa Prasanna Boddupalli (2023)