A5047937656- Genetics and Plant Breeding
- Remote Sensing in Agriculture
- Genetic Mapping and Diversity in Plants and Animals
- Genetic and phenotypic traits in livestock
- Crop Yield and Soil Fertility
- Leaf Properties and Growth Measurement
- Spectroscopy and Chemometric Analyses
- Remote Sensing and LiDAR Applications
- Smart Agriculture and AI
- Wheat and Barley Genetics and Pathology
- Climate change impacts on agriculture
- 3D Surveying and Cultural Heritage
Centro Internacional de Mejoramiento de Maíz Y Trigo
2016-2022
Sonora Institute of Technology
2017
Abstract Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could useful for improving pedigree genomic prediction model accuracies traits not yet possible phenotype directly. We tested if aerial measurements canopy temperature, green red normalized difference vegetation index as secondary in best linear...
Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context plant breeding and genetics, current approaches phenotyping a large number lines under field conditions require substantial investments time, cost, labor. For field-based high-throughput (HTP), UAS platforms can provide high-resolution small plot research, while enabling assessment tens-of-thousands plots. The objective this study was to complete baseline...
Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands cover the whole visible light spectrum and part infrared ultraviolet spectra, depending on camera. This information is used construct vegetation indices (VI) (e.g., green normalized difference index or GNDVI, simple ratio SRa, etc.) which are for prediction primary traits biomass). However, these only use some cultivar-specific; therefore they lose considerable not robust...
Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. cameras quantify canopy across a wide range of wavelengths associated with numerous biophysical biochemical processes in plants. Genomic models utilize genome-wide marker or pedigree information predict genetic values lines. In this study, we propose multi-kernel GBLUP approach uses marker-,...
Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding climate-resilient wheat varieties. Hence, our objective was evaluate them predicting grain yield (GY) in drought-stressed (DS) late-sown heat-stressed (HS) environments of the International maize improvement center's elite trial nurseries. We observed that average genomic prediction accuracies using fivefold cross-validations were 0.50 0.51 DS HS environments,...
Plant height (PH) is an essential trait in the screening of most crops. While crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH easy measure manually, although it can be labor-intense depending on number plots. There increasing demand for alternative approaches estimate a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could used PH. This study...
Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the generated by hyperspectral richer than what summarized in a VI. Therefore, this study, we examined whether prediction equations using image lead better predictive performance for grain yield achieved VIs. For equations, considered three estimation methods: ordinary least squares,...
Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These often cover the whole visible light spectrum and part infrared ultraviolet spectra. With bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield biomass. However, since only use some wavelengths (referred to bands), we propose using all simultaneously predictor variables primary trait yield; results...
Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in parameter prediction and application to nitrogen (N) fertilization remain open. The objective this study was optimize remote spectral information for its grain yield (GY), biomass, N concentration (GNC), output assessment, decision making on spring wheat fertilization. Spring (Triticum turgidum L.) field experiments testing two tillage treatments, irrigation levels six...
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 assess whether combined hyperspectral data with could be used improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted 97 lines was evaluated testcross combination under heat stress as well and drought...
The objective of this study was to assess the importance stay-green on grain yield under heat and combined drought stress identify associated vegetative indices allowing higher throughput in order facilitate identification climate resilient germplasm. Hybrids tropical subtropical adaptation were evaluated 2014 2015. Five weekly measurements with an airplane mounted multispectral camera starting at anthesis used estimate area curve (AUC) for vegetation during that period; compared AUC...
ABSTRACT Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. cameras quantify canopy across a wide range of wavelengths associated with numerous biophysical biochemical processes in plants. Genomic models utilize genome-wide marker or pedigree information predict genetic values lines. In this study, we propose multi-kernel GBLUP approach uses...
Heat and drought stresses negatively affect maize (Zea mays L.) productivity. We aimed to identify the genetic basis of tolerance heat stress (HS) combined (HS+DS) compare how QTL whole genome selection (GS) could be leveraged improve both stresses. A set 97 testcross hybrids derived from a bi-parental doubled-haploid population was evaluated during summer seasons 2014, 2015, 2016 in Ciudad Obregon, Sonora, Mexico, under HS HS+DS. Grain yield (GY) reached 5.7 t ha−1 3.0 Twenty-six were...