Examinando por Materia "Phenotyping"

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Methodology for avocado (Persea americana Mill.) orchard evaluation using different measurement technologies
(Universidad de Concepción, 2022-12-27) Chumbimune Vivanco, Sheyla Yanett; Cárdenas Rengifo, Gloria Patricia; Saravia Navarro, David; Valqui Valqui, Lamberto; Salazar Coronel, Wilian; Arbizu Berrocal, Carlos Irvin
Avocado crop (Persea americana Mill.) is of great commercial importance due to its high profitability. However, it is being affected by various diseases and pests that affect yield and reduce fruit quality. The aim of this research was to develop methodologies for the evaluation of avocado plantations using different non-destructive technologies for rapid phenotyping and early detection of the incidence of diseases or damage due to stress in the stem. A plot of 0.7 ha. was evaluated, with a total of 44 individuals using Field-Map technology (dasometric and morphological characterization), RGB-multispectral images from Remotely Piloted Aircraft System (RPAS) (rapid phenotyping), while 15 individuals were evaluated using tomography (assessment of the internal state of the stem). The results with tomography indicated that there is a tree with wood rot of 14% with a lower acoustic speed with respect to the other trees evaluated. A high correlation was observed between the dasometric variables (r-Pearson from 0.63 to 0.98) estimated with Field-Map [crown base height, crown projection (m2) and total height] and with RPAS (height, perimeter and area). The vegetation indices do not have a direct correlation with the dasometric variables; five of the indices have a high contribution to variability except for the Normalized Difference Red Edge (NDRE). It can be concluded that the technologies used in this study would help to perform evaluations with a greater number of reliable and precise data with respect to the information obtained in a traditional way, while they can be replicated in commercial plots or research studies of different perennial crops, generating useful information for management decisions and crop evaluation.
Selection in guinea pigs: I. Estimation of phenotypic and genetic parameters for litter size and body weight get access arrow
(American Society of Animal Science, 1983-04-01) Quijandria, B.; Chauca Francia, Lilia Janine; Robison, O. W.
Data on 202 sires, 718 dams and 3,192 progeny from a selection experiment were used to estimate phenotypic and genetic parameters for litter size and body weight in guinea pigs. Effects of sex and parity were estimated. Heritability estimates were obtained from offspring-parent regression and from intraclass correlation of paternal and maternal half-sibs. Genetic and phenotypic correlations also were estimated. Parity effects were significant only for weight traits. Sex effects were significant for weights at several ages. Significant negative linear effects of number born alive were found for birth, weaning and 13-wk weights. Heritability estimates from daughter-dam regression were .10 ± .05, .06 ± .02 and .08 ± .02 for number born, number born alive and number weaned and .12 ± .03, -.13 ± .03 and .12 ± .02 for birth, weaning and 13-wk weights, respectively. Paternal half-sib heritability estimates were .02 ± .04, .10 ± .04 and .17 ± .05 for birth, weaning and 13-wk weights. Heritability values from components for maternal half-sibs were .30 ±.30, .16 ±.31 and .15 ± .31 for number born, number born alive and number weaned, respectively. Genetic correlations among weights at different ages were .24 to 1.2 and among litter size traits were .51 to .77. Genetic correlations between litter size traits and birth and weaning weights ranged from -.61 to -.97; whereas correlations of litter size traits with 13-wk weight were .31 to .39. Genetic parameters estimated from similarity among relatives agreed very well with realized heritabilities and genetic correlations obtained from selection.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru
(MDPI, 2022-05-17) Saravia Navarro, David; Salazar Coronel, Wilian; Valqui Valqui, Lamberto; Quille Mamani, Javier Alvaro; Porras Jorge, Rossana; Corredor Arizapana, Flor Anita; Barboza Castillo, Elgar; Vásquez Pérez, Héctor Vladimir; Arbizu Berrocal, Carlos Irvin
Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from UAV in the Coast of Peru
(MDPI, 2022-10-26) Saravia Navarro, David; Salazar Coronel, Wilian; Valqui Valqui, Lamberto; Quille Mamani, Javier Alvaro; Porras Jorge, Zenaida Rossana; Corredor Arizapana, Flor Anita; Barboza Castillo, Elgar; Vásquez Pérez, Héctor Vladimir; Casas Diaz, Andrés V.; Arbizu Berrocal, Carlos Irvin
Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.