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Leaf scald caused by Xanthomonas albilineans in sugarcane is one of the most important diseases around the world since it causes severe losses in both agricultural and industrial yields. In Cuba, resistance to this disease is considered a major selection criterion within the breeding program. The aim of this work was to detect sugarcane cultivars with stable reaction to X. albilineans by using both additive main effects and multiplicative interaction (AMMI) and linear general models. For this, 16 cultivars planted simultaneously in 2015, 2016, and 2017 in two locations: Jovellanos (Matanzas) and Florida (Camaguey) in Cuba were mechanically inoculated with a bacterial isolate previously characterized by sequencing its DNA fragment. Disease severity was recorded in plant cane and first ratoon, and results were consistent in both analyses and revealed that L55-5 and C323-68 were the most susceptible cultivars and C1051-73 was the most resistant; however, C1051-73, C275-80, C86-12, C88-382, C89-147, My5514, and Ty86-28 were the most stable across the years and localities evaluated. Results will allow adapting the methodology for the evaluation of the reaction to leaf scald of new sugarcane cultivars.

Identifying high-yield genotypes under low water availability is an important goal for soybean breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments using two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), aiming to identify and statistically validate stress tolerance and yield stabilization markers. Firstly, at the R5 critical reproductive stage, the molecular differences between the genotypes responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight and correlation with yield. Here, pubescence, stomatal density and canopy temperature depression emerged as promising markers for early selection of drought-tolerant soybeans.

The incidence and prevalence of strawberry viruses were determined in surveys of randomly selected strawberry plants grown in different regions of Argentina. In 2009 and 2010, 1034 plants from 28 fields and 1060 plants from 33 fields, respectively, were collected from Lules and Coronda. The samples were analysed by double‐antibody sandwich enzyme‐linked immunosorbent assay to detect Strawberry mild yellow edge virus (SMYEV). In 2014, 606 plants from 43 fields in Lules, Coronda and Mar del Plata were analysed by reverse transcription polymerase chain reaction with primers specific for SMYEV, Strawberry crinkle virus (SCV), Strawberry mottle virus (SMoV) and Strawberry polerovirus 1 (SPV1). The SMYEV incidence was 4–35%, while prevalence was 60–100%, depending on the year and region sampled. Meanwhile, SMoV and SPV1 incidences were 8–17%, and prevalences were 46–62%, depending on the virus and region sampled. SCV was observed relatively low (incidence was 0.5–8% and prevalence was 8–50%), although it was more abundant in Mar del Plata than in the other analysed regions. Spearman's correlation analysis indicated that SCV and SMYEV were correlated with disease symptoms (P < 0.005). A principal component analysis revealed a close relationship between SMYEV and SCV in Mar del Plata, in which the lowest temperatures were recorded. Interactions among viruses, regions and climatic conditions will need to be studied in greater detail. Accurately determining the incidence and prevalence of viruses in different regions will improve estimations of possible damages or yield decreases caused by viral infections during strawberry production.

Identifying population genetic structure (PGS) is crucial for breeding and conservation. Several clustering algorithms are available to identify the underlying PGS to be used with genetic data of maize genotypes. In this work, six methods to identify PGS from unlinked molecular marker data were compared using simulated and experimental data consisting of multilocus-biallelic genotypes. Datasets were delineated under different biological scenarios characterized by three levels of genetic divergence among populations (low, medium, and high FST) and two numbers of sub-populations (K=3 and K=5). The relative performance of hierarchical and non-hierarchical clustering, as well as model-based clustering (STRUCTURE) and clustering from neural networks (SOM-RP-Q). We use the clustering error rate of genotypes into discrete sub-populations as comparison criterion. In scenarios with great level of divergence among genotype groups all methods performed well. With moderate level of genetic divergence (FST=0.2), the algorithms SOM-RP-Q and STRUCTURE performed better than hierarchical and non-hierarchical clustering. In all simulated scenarios with low genetic divergence and in the experimental SNP maize panel (largely unlinked), SOM-RP-Q achieved the lowest clustering error rate. The SOM algorithm used here is more effective than other evaluated methods for sparse unlinked genetic data. Fil: Peña Malavera, Andrea Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina Fil: Fernandez, Elmer Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina Fil: Bruno, Cecilia Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Catolica de Córdoba. Facultad de Ingeniería; Argentina Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina