Grateful to my supervisors and co-authors @annalintern.bsky.social, @perrancook.bsky.social, Andrew Western, Robert Sargent, and Wei Wen Wong, and everyone who supported this work!

• The proximity of effluent ponds to the stream network was identified as an important predictor of stream nutrient concentrations during dry weather conditions.
• Deep learning was used to estimate the stocking rate from high-resolution aerial imagery.

• The study focused on 10 subcatchments in the Macalister Irrigation District (MID), Victoria, Australia.
• Effluent pond density, dairy shed density, and stocking rate were identified as key drivers of stream nutrient concentrations, particularly during wet periods.

Summary and key takeaways:

• We investigated how dairy farm infrastructure and stocking rates – along with other catchment characteristics such as climate, hydrology, land use, land cover, etc. – influence nutrient concentrations (NH₄⁺, FRP, NOₓ, TP, TN) in streams.

Our team’s latest paper, “Correlations between catchment-scale farm infrastructure densities and stocking rate to stream nutrient concentrations in dairy-dominant catchments,” has been published in the Journal of Environmental Management.

You may read the full paper here: doi.org/10.1016/j.je...

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