In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

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BackgroundWhole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.ResultsIn this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available.ConclusionsPoorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3120-5) contains supplementary material, which is available to authorized users.

Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.Additional keywords: genotype by environment interaction; multi-environment trials; sunflower; network efficiency; yield stability. Abbreviations used: CPD (critical percentage difference); GEI (genotype by environment interaction); GGE (genotype plus genotype by environment); LE (La Estanzuela); MET (multi-environment trial); NENSU (National evaluation network of sunflower cultivars of Uruguay); PLS (partial least squares); RCBD (randomized complete block design); YG (Young).

Weather conditions determine seasonal forage production. Air temperature, solar radiation, and soil water availability are the main variables affecting alfalfa growth. This study analyzed the relationship between alfalfa growth (Medicago sativa L.) and some climatic variables along 15 years (1997 to 2011) of production and climate data, collected in the southwest of Uruguay. The results highlighted that alfalfa growth rate (GR) presented significant differences among seasons and varied with pasture age. The alfalfa growth rate increased in autumn when the accumulated radiation was less than or equal to 1095 MJ m-2 period-1 and the difference between atmospheric demand and rainfall (cWB) was close to 0 mm. In winter, the GR increased with minimum temperatures up to 8.4 °C and daily average radiation higher than 11 MJ m-2 day-1. In spring the GR was higher during the years with daily radiation higher than 16 MJ m-2 day-1. Maximum air temperatures above 27.5 °C affected negatively summer GR. The highest GR (62.5 kg ha-1 day-1) was achieved in summer when the ETa:ETm ratio was close to one. This result suggests the implementation of field techniques that increase water-use efficiency, as well as summer irrigation as a management practice to achieve alfalfa forage potential.

Crop cycles in spring canola (Brassica napus L.) and carinata (Brassica carinata A. Braun) are controlled by temperature and photoperiod. In the central region of South America, which accounts for 90% of South American rapeseed crop production, farmers seek to sow as early as possible to maximize yield even though early sowing dates expose crops to a higher probability of frost occurrence that had not been quantified before. Our objective was to model phenology for one spring cultivar of canola ('Rivette') and one of carinata ('Avanza 641') and assess the probability of frost occurrence during flowering and grain filling in the central region of South America. For the estimation of frost risk, we modeled phenology resulting from 15 April to 15 August sowing dates at 12 locations (from 31.16˚to 34.20˚S latitude). We assumed that the critical period spans from 100 to 500 growing degree days (GDD) after flowering. Under early sowing dates, carinata tends to have a longer cycle (200 GDD more than canola) although both crops had a similar cycle length under a late sowing date. Predicted and observed flowering dates showed an average difference of 1.6 and 0.95 d for canola and carinata, respectively. Carinata has a lower probability of suffering frost events, and early sowing dates result in a greater exposure to frost damage. The results of this work will enable farmers and stakeholders to know the potential frost risk when defining a sowing date for canola and carinata. INTRODUCTIONRapeseed crops are among the most important commodities, with an annual worldwide production of 723 million t in 2019-2020. Rapeseed ranks fourth among the most important oilseed crops produced globally, accounting for 6% of a world oil crop market largely led by oil palm (Elaeis guineensis Jacq.) and soybean [Glycine max (L.) Merr.] (FAOSTAT, 2020). Rapeseeds include canola (Brassica napus L.), an edible oil crop with low erucic acid and glucosinolates content Abbreviations: CCC, concordance correlation coefficient; GDD, growing degree days.

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