Author: Lorena González Pérez
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
MYRNA LORENA PEREZ GONZALEZ (2010)
La creciente urbanización ha transformado y alterado el medio ambiente y los ecosistemas. Los techos verdes son una forma sostenible para devolver parte de la esencia única de la naturaleza. El objetivo de este estudio fue implementar y evaluar los techos verdes como una alternativa sostenible para regular la temperatura dentro de un edificio en la zona urbana de Querétaro, así como evaluar el efecto en el escurrimiento generado por las eco-estructuras. El experimento se realizó en las aulas de posgrado de la Facultad de Ingeniería de la Universidad Autónoma de Querétaro (UAQ) en los 20°35'27" de latitud Norte y 100°24'47" de altitud oeste. El edificio experimental se dividió en seis partes tres de ellas cubiertas por un pasto Pennisetum Clandestinum (Kikuyo) sobre una capa de 10 cm de suelo Vertisol. Un techo sin cubierta vegetal entre dos de los techos verdes se utilizó como control contiguo y otro control, en condiciones similares, fue colocado en un edificio anexo sin techo verde. Para evaluar el confort térmico interno, la temperatura del aire se registró cada 10 minutos desde abril 2009 hasta abril 2010 utilizando un sensor WatchDog marca Spectrum. Para apreciar la respuesta hidrológica de los techos verdes se evaluó la precipitación y el volumen de escurrimiento durante el periodo de lluvias y se determinaron los hidrogramas para eventos específicos. Los resultados térmicos demostraron que los techos verdes son una alternativa eficaz para regular la temperatura y lograr confort disminuyéndola temperatura interna con respecto al control en aproximadamente 6°C en temporada cálida y aumentándola alrededor de 2°C en temporada fría. El estudio hidrológico demostró que los techos verdes retrasaron el tiempo de inicio y volumen de escurrimiento al aumentar la infiltración y almacenamiento de las aguas pluviales. Los datos mostraron una reducción de la escorrentía media del 85% y un retraso del tiempo de escurrimiento de 10 a 15 minutos. Con base en estos resultados, se ha demostrado que los techos verdes son una buena estrategia ambiental para lograr un mejor confort térmico y reducir los escurrimientos generados en edificios de regiones urbanas semiáridas.
The growing urbanization has transformed the environment and altered the ecosystems. Green roofs are a sustainable way to get back part of nature unique essence. The aim of this study was to implement and evaluate green roofs as a sustainable alternative to regulate the temperature inside a building in the urban area of Queretaro, and to assess the effect of the runoff generated by the eco-structures. The experiment was done in the graduate classrooms of the Engineering Faculty of the Universidad Autónoma de Querétaro (UAQ) at the 20°35'27" north latitude and 100°24'47" west altitude. The experimental building was divided into six parts three of them covered by a Pennisetum Clandestinum grass (Kikuyu grass) over a 10 cm layer of Vertisol soil. A roof without vegetation between two of the green roofs was used as control and another control, in similar conditions, was placed in an annex building without green roof. To evaluate the internal thermal comfort, air temperature was recorded every 10 minutes from April 2009 to April 2010 using a WatchDog sensor from the Spectrum Company. To assess the hydrological response of green roofs the rainfall volume and runoff were evaluated during the rainy season and the hydrographs for specific events were determined. The thermal results showed that green roofs are an effective alternative to regulate temperature and bring comfort by reducing the internal temperature in about 6 °C for warm season and increasing it in about 2 °C for cold season with respect to the control. The hydrological study showed that the green roofs delayed the starting time and runoff volume by increasing rainwater infiltration and storage. Data showed an average runoff reduction of 85% and runoff time lag of 10 to 15 min. Based on these results, it has been shown that green roofs are a good environmental strategy to achieve a better thermal comfort and reduce the runoff generated by buildings in semiarid urban regions.
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.
Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1–8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1–23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information.
Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For fieldbased high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. Results: We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.
Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT’s global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
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.
Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.
Philomin Juliana Osval Antonio Montesinos-Lopez Jose Crossa Suchismita Mondal Lorena González Pérez Jesse Poland JULIO HUERTA-ESPINO Leonardo Abdiel Crespo Herrera Govindan Velu Susanne Dreisigacker Paulino Pérez-Rodríguez Francisco Pinto Ravi Singh (2019)
Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center’s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress–resilience within years.
Genomics Phenotypes Breeding Climatic factors Soft wheat Genomic Selection Phenotyping WHEAT CLIMATE CHANGE RESILIENCE AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING NURSERY CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
El capítulo del libro forma parte de la tesis de Doctorado en Ciencias de la Salud, de mi alumna la Dra. Lorena Toribio Pérez, quien se graduo el 7 de nov de 2017.
La percepción de la salud constituye un indicador importante de bienestar, ya que permite una valoración integral del individuo de su estado de bienestar físico, psíquico y social. En la actualidad, se está analizando su relación con variables salutógenas como lo es la salud mental positiva, así como la diferenciación que se presenta en torno al sexo. El objetivo de la presente investigación es analizar la relación entre percepción de la salud y salud mental positiva, asimismo analizar si existen diferencias en la percepción de la salud en hombres y mujeres. Se evaluaron un total de 533 adolescentes entre los 14 y 20 años de edad, 237 hombres y 296 mujeres del turno matutino de cinco escuelas públicas de la ciudad de Toluca (México). Los instrumentos que se aplicaron fueron el Cuestionario de Percepción de la Salud (Ware y Sherbourne, 1992) y la Escala de Salud Mental Positiva (Lluch, 1999). De acuerdo con los resultados, la muestra obtuvo altos niveles de percepción de la salud y salud mental positiva, presentándose correlaciones positivas y significativas entre ambas. Se muestran diferencias estadísticamente significativas a favor de los hombres en las dimensiones: salud general, salud mental, ausencia de dolor corporal, vitalidad y rol emocional. Se concluye que se confirma la correlación entre percepción de la salud y salud mental positiva, de igual forma se corrobora parcialmente el supuesto de las diferencias por sexo, ya que se encontraron discrepancias en 5 de las 8 dimensiones de percepción de la salud. Se sugiere ampliar las líneas de investigación para indagar sobre otros factores que pudieran estar asociados a la percepción de la salud con la finalidad de obtener mayor evidencia científica al respecto.