BackgroundDrought stress is one of the most important abiotic stresses and the main constraint to rice agriculture. MicroRNA-mediated post-transcriptional gene regulation is one of the ways to establish drought stress tolerance in plants. MiRNAs are 20–24-nt regulatory RNAs that play an important role in regulating plant gene expression upon exposure to biotic and abiotic stresses.Methodology/Principal FindingsIn this study, we applied a partial root drying system as well as a complete root drying system to identify miRNAs involved in conditions of drought stress, drought signaling and wet signaling using high-throughput sequencing. To this end, we produced four small RNA libraries: (1) fully-watered (WW), (2) fully-droughted (WD), and split-root systems where (3) one-half was well watered (SpWW) and (4) the other half was water-deprived (SpWD). Our analysis revealed 10,671 and 783 unique known and novel miRNA reads in all libraries, respectively. We identified, 65 (52 known + 13 novel), 72 (61 known + 11 novel) and 51 (38 known + 13 novel) miRNAs that showed differential expression under conditions of drought stress, drought signaling and wet signaling, respectively. The results of quantitative real-time PCR showed expression patterns similar to the high-throughput sequencing results. Furthermore, our target prediction led to the identification of 244, 341 and 239 unique target genes for drought-stress-, drought-signaling- and wet-signaling-responsive miRNAs, respectively.Conclusions/SignificanceOur results suggest that miRNAs that are responsive under different conditions could play different roles in the regulation of abscisic acid signaling, calcium signaling, detoxification and lateral root formation.

In order to assess the genotype by environment interaction (GE) and select genotypes to exploit narrow and broad adaptations, twenty-two spring oilseed rape genotypes were subjected to field surveys at five experimental sites in the 2015-16 and 2016-17 growing seasons. Plant materials were sown in the form of a randomized complete block design with three replicates. The additive main effects and multiplicative interaction (AMMI) model was used to determine the genotype, environment, and GE effects. The sum of squares (SS) for the first three interaction principal components was very close to the SS for the GE signal; therefore, AMMI3 was diagnosed as the most accurate model to optimize predictive accuracy. Hyola 401 had the highest broad adaptability. In total, the chances of increasing yield were 55.

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Thirteen stability statistics were used to analyze genotype × environment (GE) interaction of 36 canola genotypes. Combined analysis of variance indicated that GE interaction significantly influenced seed yield performance. According to Type I stability concept (environmental variance, coefficient of variation and stability variance) genotypes G7, G9 and G13 were the most stable genotypes, while based on the Type II concept (coefficients of three linear regres­sion models), genotypes G33, G27 and G29 could be selected as the most favorable genotypes. Also, genotype G7 was the most favorable genotype according to Type III stability concept (deviation from linear regression method). Genotypes clustering based on stability properties and mean yield grouped them into three distinct classes. Coefficient of determination for the canola genotypes indicated that genotypes G27 and G33 were the most stable genotypes but the genotypes G1, G10 and G25 had the highest desirability index and were the most stable ones. The plot of principal component analysis was used for graphic display of the relationships among statistics and the first axis distinguished the Type II of stability concept from other types and mean yield groups near this stability type. However, based on most statistics and mean yield performance, genotypes G9 or Fanaei‑6 (2592.47 kg ha‑1), G11 or Fanaei‑14 (2592.47 kg ha−1), G12 Fanaei‑15 or (2592.47 kg ha‑1) and G19 or Dez‑7169 (2592.47 kg ha‑1) were the most stable and favorable genotypes and are recommended for national release Iran.

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