ABSTRACT. The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.

Abstract: Enhancing the rate of genetic gain in plant breeding program is critical to address global food security in the face of climate change and a growing population. Rapid cycling genomic selection offers a powerful breeding strategy to reduce the breeding cycle and obtain rapid genetic gains in plant breeding programs. In this paper, we discuss theoretical and empirical approaches to deploy and optimize rapid cycling genomic selection in crop improvement programs. We highlight major advantages and challenges associated with rapid cycling genomic selection and provide example to overcome these issues. Finally, we discuss the trends and general conclusion on this breeding strategy and provide recommendations for future discussions and continued adoption in plant breeding programs.

Abstract Aims: This study aimed to measure light-intensity physical activity (LPA) patterns in adolescents according to weekdays and school period and investigated associations between LPA patterns and associated factors. Methods: This is a cross-sectional study with 388 students of both genders, selected from public schools from Londrina city, Parana state. LPA was measured using accelerometry for seven consecutive days and was analyzed by the time spent in very short (15 to 30 s), short (30 to 60 s), intermediate (1 to 3 min), and long bouts (≥ 3 min). Results: Approximately 22% of the total daily time was spent in LPA. The time in LPA accumulated in short or intermediate bouts was higher than the time accumulated in very short or long bouts (P < 0.05). Gender was associated with short (β= 2.180; 95% confidence interval [CI], 0.357 to 4.003) and intermediate bouts (β = 2.822; 95% CI, 0.681 to 4.964), girls presented higher daily time. Age was inversely associated with very short (β = −0.925; 95% CI, −1.676 to −0.175) and intermediate bouts (β = −1.985; 95% CI, −3.579 to −0.390). The total time in LPA and time accumulated in very short, short, and intermediate bouts was higher in the school period when compared to the non-school period (P < 0.05). Conclusion: It could be concluded that the LPA patterns are predominantly composed of time spent in bouts between 30 seconds and three minutes. Gender, age, and school period appear to influence the LPA pattern in adolescents.

Abstract Phytophthora root and stem rot is one of the most aggressive diseases in soybean crop. The use of resistant cultivars is the main strategy to reduce losses caused by the pathogen. This study aims to identify SNP markers associated with genes or QTLs that provide soybean with partial resistance to Phytophthora sojae. A total of 169 soybean cultivars were inoculated with Phytophthora sojae and genotyped with 3,807 SNP markers. Genome-wide association analysis was carried out via multiple linear models, followed by multiple linear regression and linkage disequilibrium analysis. Four QTLs associated with the characteristic were identified: two on chromosome 3 and two on chromosome 15. The regions containing these QTLs contain genes already annotated as providers of resistance to pathogens, in plants. The use of those markers in the selection of resistant plants can increase the efficiency of breeding programs in the development of soybean varieties resistant to P. sojae.