At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F 2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; ) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of . Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

Estimating the date of maturity of soybean breeding field plots is necessary for breeding line characterization and for informing yield comparisons among varieties. The main drawback of visually dating soybean maturity is the sheer scale of note recording entailed and the frequency at which these notes need to be taken. The overall aim of this study was to build upon prior work in using low-cost UAS-based RGB cameras to estimate soybean maturity date by examining the effect of vegetation index, summary statistic of the pixel values from each region of interest (plot), statistical model, and flight frequency. Maturity dates collected from five environments with 53 experimental trials (4,415 plots) were both visually dated and imaged using a RGB camera carried by a UAS. Using the mean greenness leaf index on each plot combined with LOESS regression, we achieved high correlations between ground and UAS-based estimates (r = 0.84-0.97). Precision, quantified by broad-sense heritability estimates, was greater for UAS-based dates in 29 of 53 field trials, and nearly equivalent in 11 more field trials. We found that 54% of the significant deviations between ground and UAS-based estimates were caused by inaccurate UAS-based estimates, while errors in the ground-based estimates accounted for 46% of the deviations. Reasons for these inaccurate estimates were attributed to lodging, presence of weeds, low germination, and within-line genetic heterogeneity in the plots. A detailed description of the analysis pipeline, a user-friendly R script, and all of the images and ground data have been made publicly available to help other researchers and breeders test and adopt these methods.

To meet the growing demand of the soybean consumer market, cultivars increasingly early, productive and resistant to biotic and abiotic stress are sought. Several populations are obtained in soybean breeding programmes, but progeny are selected without being weighted for their respective population effect. As a consequence, progeny originating from high-merit populations may be discarded too early. Given this scenario, this study proposes to employ the selection index with progeny and population effect via best linear unbiased prediction (SIPP-BLUP) for the genetic selection of early and productive soybean progeny. A total of 180 progeny derived from three populations were evaluated for yield-related traits. Genetic gains from selection, Spearman correlation and coincidence index were used to check the efficiency of the models with and without the population effect. The SIPP-BLUP index achieved greater selection accuracy and was efficient in the identification and future selection of early soybean progeny. Therefore, this study demonstrates that soybean breeding programmes should consider the population effect via SIPP-BLUP in progeny selection to obtain future lines that really contribute to genetic gain. K E Y W O R D Saccuracy, breeding values, earlier-maturing progeny, genetic gain, genotype × environment interaction, mixed models, soybean seed yield

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.