The usefulness of genomic selection (GS) for improvement of complex traits has been demonstrated in plant and animal breeding. While fully adopted in the livestock sector and commercial plant breeding, the implementation of GS in public-sector plant breeding programs lags. The goal of this review is to discuss advancements in GS implementation and opportunities for near-term and long-term adoption in public-sector plant breeding programs. We also highlighted specific applications of GS for cultivar development ordered by what we believe to be their feasibility, where feasibility is defined by cost, disruption to current breeding practices, and risk.
The influence of the basic experimental unit size on the plot size estimation determined by the method of maximum curvature of the coefficient of variation model is unknown in sunn hemp. This study aimed to verify the influence of the basic experimental unit (BEU) size in the estimate of the optimum plot size obtained by the method of maximum curvature of the coefficient of variation model for the evaluation of fresh matter of sunn hemp (Crotalaria juncea L.). Fresh matter of sunn hemp at the flowering was evaluated in uniformity trials in two sowing dates. In each sowing date, 4,608 BEU of 0.5 × 0.5 m (0.25 m 2 ) were evaluated and 64 BEU plans were formed with sizes from 0.25 to 64 m 2 . In each evaluation period for each BEU plan, the first order spatial autocorrelation coefficient, variance, standard deviation, mean, coefficient of variation of the trial and the plot size were determined with the fresh matter data. For each BEU plan, the optimum plot size was determined by the method of maximum curvature of the coefficient of variation model. The estimate of optimum plot size depends on the basic experimental unit size. Determining the plot size to assess the fresh matter in basic experimental units as small as possible is recommended in order to prevent overestimation of the plot size and to contemplate all existing variability.Key words: Crotalaria juncea L., experimental design, basic experimental unit. INTRODUCTIONThe sunn hemp (Crotalaria juncea L.) is a cover crop option for soil protection due to its hardiness, high dry matter production and nitrogen fixation (Silva and Menezes, 2007), improving and maintaining soil quality, raising to considerable levels of soil organic matter and nutrients (Leite et al., 2010). The crop rapid development enables the use of sunn hemp in cropping systems with rotation and crop succession. It is the legume with greatest dry matter production in comparison with gray velvet bean (Mucuna nivea), jack bean (Canavalia ensiformis), velvet bean (Mucuna aterrina), lab-lab (Dolichos lablab), showy crotalaria (Crotalaria spectabilis), and dwarf pigeon pea (Cajanus cajan) (Teodoro et al., 2011); in a study carried out by Andrade Neto et al. (2010), the fresh matter of aerial part values of sunn hemp were 13.9 t ha -1 . One aspect to be considered is the inferences made in agricultural research representing experimental reality which is the use of an optimum plot size to minimize the experimental error. The optimum plot size can be calculated based on data obtained from uniformity trials in which treatments are not applied (Ramalho et al., 2012;Storck et al., 2016). In order to evaluate traits of the studied crop, the experimental area is divided into basic experimental units (BEU) with the smallest possible size. Therefore, based on this information, the plot size is determined.The influence of the BEU size in estimating the optimum plot size is still an area with few studies but Oliveira et al. (2005) verified in potato (Solanum tuberosum L.) the BEU size effect on the optimum p...
ABSTRACT. The State of Rio Grande do Sul (RS) stands out as the largest wheat producer in Brazil. Wheat is the most emphasized winter cereal in RS, attracting public and private investments directed to wheat genetic breeding. The study of genetic progress should be performed routinely at breeding programs to study the behavior of cultivars developed for homogeneous regions of cultivation. The objectives of this study were: 1) to evaluate the genetic progress of wheat grain yield in RS; 2) to evaluate the influence of cultivar competition trial stratification in homogeneous regions of cultivation on the study of genetic progress. Grain yield data of 122 wheat cultivars evaluated in 137 trials arranged in randomized block design with three or four replications were used. Field trials were carried out in 23 locations in RS divided into two homogeneous regions during the period from 2002 to 2013. Genetic progress for RS and homogeneous regions was studied utilizing the method proposed by Vencovsky. Annual genetic progress for wheat grain yield during the period of 12 years in the State of RS was 2.86%, oscillating between homogeneous regions of cultivation. The difference of annual genetic progress in region 1 (1.82%) in relation to region 2 (4.38%) justifies the study of genetic progress by homogeneous regions of cultivation.
ABSTRACT:In experiments, it is important to evaluate sufficient number of plants, so that inferences have the desired precision. The objective of this research was to determine the sample size (i.e., number of plants) required to estimate the mean of jack bean traits (Canavalia ensiformis) with precision levels. In experimental area of 10 × 16 m (160 m 2 ), 194 plants were collected randomly at 202 days after sowing. The morphological (plant height, stem diameter, number of nodes, and number of leaves) and productive traits (number of pods, fresh matter of pods, fresh matter of aerial part without pods, fresh matter of aerial part, dry matter of pods, dry matter of aerial part without pods, and dry matter of aerial part) were measured in each plant. Measures of central tendency, variability, skewness, and kurtosis were calculated for each trait. The sample size was determined by resampling with replacement of 10,000 resamples. In order to estimate the mean of morphological and productive traits of jack bean with the amplitude of the confidence interval of 95% equal to 40% of the estimated mean, 114 plants are required.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.