This project aimed to improve the livelihoods of smallholder goat farmers in Lao PDR and Vietnam by enhancing their production systems and marketing opportunities. Some of the major outcomes of the project were improved goat health and nutrition, increased goat productivity and profitability, and enhanced goat market access and value chain development.

Accurate and timely sugarcane yield prediction is critical for optimizing agricultural resource management, informing policy decisions, and ensuring food and energy security in sugarcane-producing regions. This study presents a novel Geo-Climate Intelligence (GCI) model for regional-scale sugarcane yield prediction across the sugar growing regions of Condong, Broadwater and Harwood, New South Wales (NSW), Australia, integrating multi-temporal satellite-derived variables with key climate indicators to enhance predictive accuracy and spatial transferability. The proposed model leverages high-resolution satellite imagery from Sentinel-2 and Sentinel 1 platforms, incorporating 30 different vegetation and structural indices, alongside 10 different climate variables. These multisource inputs were systematically integrated within a machine learning framework to capture the complex non-linear relationships between crop growth dynamics and environmental drivers across diverse agro-climatic zones in NSW. Model performance was rigorously evaluated using Leave One Year Out (LOYO) cross-validation across three NSW sugar growing regions. The GCI model demonstrated strong predictive performance with a coefficient of determination (R^²) ranging from 0.87 to 0.93, indicating that the model explained up to 93% of the observed yield variability. The Root Mean Square Error (RMSE) ranged from 6.9 to 9.8 tonnes per hectare (T/ha), reflecting robust and consistent accuracy across heterogeneous growing conditions and inter-annual climate variability. These findings confirm that the integration of satellite-derived crop phenological signals and regional climate variables significantly improves yield prediction accuracy compared to conventional agronomic models. The GCI model provides a scalable, cost-effective, and operationally viable framework for in-season and pre-harvest sugarcane yield forecasting, offering substantial value to growers, mills, and agricultural agencies across NSW. Future work will explore deep learning architectures and incorporation of agronomic variable to further refine predictive capabilities.

Social media can be a powerful tool for adding value for members and fostering community alongside traditional events (Hertel et al. 2026). In 2024, the Australian Association of Animal Sciences (AAAS) established a social media working group, which has since evolved into a Communications sub-committee that oversees online communication platforms for AAAS. In mid-2024, a streamlined online submission process via Google Forms was introduced for members and branches to share and promote AAAS opportunities and other information relevant to AAAS members. The aim of this analysis of 12 months of LinkedIn analytics was to get insight into: engagement and growth of the AAAS LinkedIn page as a communication channel, and the content that members most engage with.

Accurate genetic evaluation of flystrike resistance in Merino sheep is limited by low heritability and recording challenges. Combining information across related traits and time points, such as breech and body strike at early (EBRS and EBDS) and late (LBRS and LBDS) age stages, can improve prediction accuracy by exploiting genetic correlations and increasing the amount of information available for each trait. Therefore, a multi-trait approach is expected to enhance precision in genetic evaluation and support sustainable improvement in strike resistance. This study compares single-trait and multi-trait analyses (EBRS, LBRS, EBDS and LBDS) using flystrike records to assess their impact on prediction accuracy and bias.