Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building.

Stacking desirable haplotypes across the genome to develop superior genotypes has been implemented in several crop species. A major challenge in Optimal Haplotype Selection is identifying a set of parents that collectively contain all desirable haplotypes, a complex combinatorial problem with countless possibilities. In this study, we evaluated the performance of metaheuristic search algorithms (MSAs)-genetic algorithm (GA), differential evolution (DE), particle swarm optimisation (PSO), and simulated annealing (SA) for optimising parent selection under two genotype building (GB) objectives: Optimal Haplotype Selection (OHS) and Optimal Population Value (OPV).

PARAMETRA: A transmission modelling database for livestock diseases.

Dynamic modelling of infectious diseases of importance to livestock production is a valuable tool for policy and decision makers. Mathematical and simulation models play an essential role in understanding complex systems, but parameterising these models can be challenging, especially in data-sparse environments. When parameters are unable to be estimated from epidemiological or experimental data, a time-consuming and labour-intensive literature review-to identify suitable literature-informed values-is often necessary.

Improving imputation accuracy across tropically adapted beef cattle: an application for Brahman and Nellore using whole-genome sequencing data.

Combining information of different breeds is a cost-effective strategy to increase the size and genetic diversity of reference populations, which would improve imputation and/or genomic prediction accuracies in comparison with single-breed evaluations. Here, we have evaluated the impact of combining sequence information from two of the most relevant tropically adapted beef cattle breeds (Brahman and Nellore) on imputation accuracies to the sequence level. Whole-genome sequencing data of 279 (128 Brahman and 151 Nellore) animals were used in this study.

Insights into thermal stress effects on performance and behavior of grazing cattle via multimodal sensor monitoring.

Cattle have been observed to change their behavior and location in response to thermal stress. This study employs a multimodal sensor-based approach to assess if the behavior of grazing cattle changed in response to thermal conditions that occurred during two trials conducted in Queensland, Australia, over late spring and early summer. Each trial involved sixty cattle (Brahman and Droughtmaster) fitted with eGrazor collars containing triaxial accelerometer and GNSS sensors.