Abstract
As climate adaptation strategies gain importance, mitigating heat stress in livestock, particularly in New Zealand's dominant pasture-based farming system, is crucial. Studies have documented the positive effects of shade provision in mitigating heat stress, leading to increased rumination, milk production, and improved welfare in dairy cows (Blackshaw & Blackshaw, 1994; Bloomberg & Bywater, 2007; Fisher et al., 2008). Our project, "Shade from Space," employs high-resolution (30cm) Maxar Satellite 4-band imagery and deep learning algorithms to automate shelter identification and shade quantification on dairy farms. The overarching goal is to optimize shelter and shade provision on a national scale, ensuring maximum accessibility for livestock during peak heat loading times.
Our pixel-classification model identifies trees and categorizes them into broad classifications: individual deciduous and coniferous trees, riparian, NZ native bush, grouped trees, and shelterbelts. We isolate each tree crown in grouped trees using individual tree crown techniques. The model generates data on tree location, classification, average canopy diameter, and footprint raster. Tree heights are inferred from regional growth curves for each classification. We then apply a ray-shading model to estimate the projected shade based on the position and angle of the sun at different times of the year for each tree.
To validate our model, we collected field data from eight farms, covering 239 trees. Technicians logged tree locations, delineated the shade extent using GPS, and captured images for future model refinement through enhanced speciation/classification. The ray-shading model was applied concurrently to assess our shade coverage model's predictive accuracy at the date and time of field capture.
Looking to the future, we aim to refine our model using field data to improve classification granularity, extend our shelter identification to man-made shelters, and optimize shade estimations considering factors like terrain and paddock use. Alongside these goals, we face challenges such as streamlining workflows for efficient, cost-effective satellite imagery acquisition and managing computing requirements for a larger-scale rollout. We also plan to evaluate the cost-benefit trade-off of lower-resolution, cheaper imagery against model accuracy and effectiveness. Despite these challenges, we are confident that our innovative methodology will significantly enhance our understanding and management of heat stress in livestock farming in NZ amid evolving climatic conditions.
References
Blackshaw, J.K. & Blackshaw, A.W. (1994). Heat-stress in cattle and the effect of shade on production and behavior. Animal Production science, 34(2), 285-295. DOI: 10.1071/ea9940285.
Bloomberg, M. & Bywater, A. (2007). Estimating the effect of shade on heat stress in New Zealand dairy cows using two published models.
Fisher, A., Roberts, N., Bluett, S., Verkerk, G. & Matthews, L. (2008). Effects of shade provision on the behaviour, body temperature and milk production of grazing dairy cows during a New Zealand summer. New Zealand Journal of Agricultural Research 51, 99-105