Person: González Pérez, L.
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González Pérez
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L.
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González Pérez, L.
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0000-0002-5840-0803 3 results
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- Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.)(Public Library of Science, 2019) Trachsel, S.; Dhliwayo, T.; González Pérez, L.; Mendoza Lugo, J.A.; Trachsel, M.High throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding. We aimed to assess whether the combined use of hyperspectral data with modern marker technology could be used to improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted of 97 lines was evaluated in testcross combination under heat stress as well as combined heat and drought stress during the 2014 and 2016 summer season in Ciudad Obregon, Sonora, Mexico (27°20” N, 109°54” W, 38 m asl). Full hyperspectral data, indicative of crop physiological processes at the canopy level, was repeatedly measured throughout the grain filling period and related to grain yield. Partial least squares regression (PLSR), random forest (RF), ridge regression (RR) and Bayesian ridge regression (BayesB) were used to assess prediction accuracies on grain yield within (two-fold cross-validation) and across environments (leave-one-environment-out-cross-validation) using molecular markers (M), hyperspectral data (H) and the combination of both (HM). Highest prediction accuracy for grain yield averaged across within and across location predictions (rGP) were obtained for BayesB followed by RR, RF and PLSR. The combined use of hyperspectral and molecular marker data as input factor on average had higher predictions for grain yield than hyperspectral data or molecular marker data alone. The highest prediction accuracy for grain yield across environments was measured for BayesB when molecular marker data and hyperspectral data were used as input factors, while the highest within environment prediction was obtained when BayesB was used in combination with hyperspectral data. It is discussed how the combined use of hyperspectral data with molecular marker technology could be used to introduce physiological genomic estimated breeding values (PGEBV) as a pre-harvest decision support tool to select genetically superior lines.
Publication - Stay-green and associated vegetative indices to breed maize adapted to heat and combined Heat-Drought Stresses(MDPI, 2017) Cerrudo, D.; González Pérez, L.; Mendoza, A.; Trachsel, S.The objective of this study was to assess the importance of stay-green on grain yield under heat and combined heat and drought stress and to identify the associated vegetative indices allowing higher throughput in order to facilitate the identification of climate resilient germplasm. Hybrids of tropical and subtropical adaptation were evaluated under heat and combined heat and drought stress in 2014 and 2015. Five weekly measurements with an airplane mounted multispectral camera starting at anthesis were used to estimate the area under the curve (AUC) for vegetation indices during that period; the indices were compared to the AUC (AUCSEN) for three visual senescence scores taken two, four, and six weeks after flowering and a novel stay-green trait (AUC for stay-green; AUCSG) derived from AUCSEN by correcting for the flowering date. Heat and combined heat and drought stress reduced grain yield by 53% and 82% (relative to non-stress trials reported elsewhere) for trials carried out in 2014 and 2015, respectively, going along with lower AUCSG in 2014. The AUCSG was consistently correlated with grain yield across trials and years, reaching correlation coefficients of 0.55 and 0.56 for 2014 and 2015, respectively. The AUC for different vegetative indices, AUCNDVI (rgGY = 0.62; rgAUCSG = 0.72), AUCHBSI (rgGY = 0.64; rgAUCSG = 0.71), AUCGRE (rgGY = 0.57; rgAUCSG = 0.61), and AUCCWMI (rgGY = 0.63; rgAUCSG = 0.75), were associated with grain yield and stay-green across experiments and years. Due to its good correlation with grain yield and stay-green across environments, we propose AUCNDVI for use as an indicator for stay-green and a long grain filling. The trait AUCNDVI can be used in addition to grain yield to identify climate-resilient germplasm in tropical and subtropical regions to increase food security in a changing climate
Publication - Use of hyperspectral image data outperforms vegetation indices in prediction of maize yield(Crop Science Society of America (CSSA), 2017) Aguate, F.M.; Trachsel, S.; González Pérez, L.; Burgueño, J.; Crossa, J.; Balzarini, M.; Gouache, D.; Bogard, M.; De Los Campos, G.Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data.
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