As a strategy to find efficient lignocellulose degrading enzymes/microorganisms for sugarcane biomass pretreatment purposes, 118 culturable bacterial strains were isolated from intestines of sugarcane-fed larvae of the moth Diatraea saccharalis. All strains were tested for cellulolytic activity using soluble carboxymethyl cellulose (CMC) degrading assays or by growing bacteria on sugarcane biomass as sole carbon sources. Out of the 118 strains isolated thirty eight were found to possess cellulose degrading activity and phylogenetic studies of the 16S rDNA sequence revealed that all cellulolytic strains belonged to the phyla γ-Proteobacteria, Actinobacteria and Firmicutes. Within the three phyla, species belonging to five different genera were identified (Klebsiella, Stenotrophomonas, Microbacterium, Bacillus and Enterococcus). Bacterial growth on sugarcane biomass as well as extracellular endo-glucanase activity induced on soluble cellulose was found to be highest in species belonging to genera Bacillus and Klebsiella. Good cellulolytic activity correlated with high extracellular protein concentrations. In addition, scanning microscopy studies revealed attachment of cellulolytic strains to different sugarcane substrates. The results of this study indicate the possibility to find efficient cellulose degrading enzymes and microorganisms from intestines of insect larvae feeding on sugarcane and their possible application in industrial processing of sugarcane biomass such as second generation biofuel production.

Background: Molecular markers associated with relevant agronomic traits could significantly reduce the time and cost involved in developing new sugarcane varieties. Previous sugarcane genome-wide association analyses (GWAS) have found few molecular markers associated with relevant traits at plant-cane stage. The aim of this study was to establish an appropriate GWAS to find molecular markers associated with yield related traits consistent across harvesting seasons in a breeding population. Sugarcane clones were genotyped with DArT (Diversity Array Technology) and TRAP (Target Region Amplified Polymorphism) markers, and evaluated for cane yield (CY) and sugar content (SC) at two locations during three successive crop cycles. GWAS mapping was applied within a novel mixed-model framework accounting for population structure with Principal Component Analysis scores as random component. Results: A total of 43 markers significantly associated with CY in plant-cane, 42 in first ratoon, and 41 in second ratoon were detected. Out of these markers, 20 were associated with CY in 2 years. Additionally, 38 significant associations for SC were detected in plant-cane, 34 in first ratoon, and 47 in second ratoon. For SC, one marker-trait association was found significant for the 3 years of the study, while twelve markers presented association for 2 years. In the multi-QTL model several markers with large allelic substitution effect were found. Sequences of four DArT markers showed high similitude and e-value with coding sequences of Sorghum bicolor, confirming the high gene microlinearity between sorghum and sugarcane.

Drought is a major limitation to crop yields worldwide. Screening for soybean yield under water deficit is often a bottleneck in breeding programmes. We assessed the validity of a standardized drought tolerance screening method to predict water‐limited field performance of soybean in NW Argentina. First, to determine the phenological period when yield of glasshouse‐grown plants was more sensitive to water deficit, we applied treatments during 21 days in V7, R3 or R5 stages, being the period from R5 to R6 the most critical for yield. Afterwards, two glasshouse experiments were carried out to quantify the tolerance of either eight or four genotypes, respectively, by applying a controlled water deficit of constant intensity during the critical period. Finally, yield data obtained in field trials in Argentina across several locations and seasons classified according to rainfall were analysed. Drought Susceptibility Index was calculated for each experiment and for field data, and rankings of tolerance were similar in all cases. This standardized method, which can be automated for high‐throughput phenotyping, could represent a useful tool in breeding programmes for identifying soybean cultivars with improved performance under drought conditions.