Oat being a rabi/winter crop in Kashmir, experiences extremely low temperatures which has detrimental effects on its growth and development. Therefore, this study was designed to evaluate a set of 130 oat genotypes in multi-location trials across temperate conditions of Kashmir valley from 2018 to 2022. From the preliminary data of 56 genotypes, including five checks, were selected and evaluated for nutritional and yield attributing traits under cold stress conditions at two locations. The results demonstrated significant genetic variation and high heritability for majority of traits, except for days to 50% flowering, days to maturity and dry fodder. Positive correlations were observed between green fodder yield and other traits, indicating their potential for enhancing yield. Principal component analysis identified four principal components that accounted for 69.87% of the total variation. Cluster analysis categorized the genotypes into two main clusters and six sub-clusters. Frost damage assessment was conducted at tillering stage after the snow melted in late January 2021 and 2022 using cold tolerance rating scale and subsequently tested for chilling injury through an electrolyte leakage test. From field and lab data analysis, five most promising cold tolerant, nutritious and high-yielding genotypes were identified. These genotypes have significant potential for utilization in future breeding programmes to improve cold tolerance in cultivated oats within the Kashmir valley thus promoting agricultural productivity and sustainability. The outcomes also provide valuable insights into the genetic variation, heritability, genotype-by-environment interactions, correlations and cold tolerance of oat genotypes in Kashmir.

Factor analysis was applied to individual type traits (TT) scored in primiparous cows belonging to two dual purpose Italian breeds, Rendena (REN; 20 individual type traits evaluated on 11 399 first parity cows), and Aosta Red Pied (ARP; 22 individual type traits evaluated on 36 168 primiparous cows). Six common latent factors (F1 to F6; eigenvalues ⩾1) which explained 63% (REN) and 58% (ARP) of the total variance were obtained. F1 included TT mainly related to muscularity, and F2 to body size. The F3 and F4 accounted for udder size and conformation, respectively. F5 included rear legs and feet. Biological significance for F6 was not readily obtained. Moderate to low heritability were estimated through REML single-trait analysis from factor scores (from 0.22 to 0.52 in REN, and from 0.08 to 0.37 in ARP). The greatest heritability values were estimated for body size and muscularity (0.52 and 0.37 for body size; and 0.40 and 0.32 for muscularity in REN and ARP, respectively). As expected, rank correlations, obtained considering estimated breeding values derived from best linear unbiased prediction analysis on the individual TT and factor score, showed similar coefficients to those observed in the factor analysis following loading of TT within each latent factor. These results suggest the possibility to implement the factor analysis in the morphological evaluation, simplifying the information given by the type traits into new variables useful for the genetic improvement of dual purpose cattle.

Dual-purpose winter wheat production is an important economic enterprise in the southern Great Plains of the United States. Because of the complex interactions involved in producing wheat grain and beef gain from a single crop, stocking density is an important decision. The objective of the research is to determine the stocking density that maximizes expected net returns from dual-purpose winter wheat production. Statistical tests rejected a conventional linear-response plateau function in favor of a linear-response stochastic plateau function. The optimal stocking density of 1.48 steers/ha (0.60 steers/acre) is 19% greater with a stochastic than with a nonstochastic plateau.