Guardian dogs protect sheep by guarding sheep, not by establishing territories and excluding predators

Guardian animals have been a common non-lethal method for reducing predator impacts on livestock for centuries in Europe. But elsewhere, livestock producers sometimes doubt whether such methods work or are compatible with modern livestock husbandry practices in extensive grazing systems. In this study we evaluate the hypothesis that guardian dogs primarily ‘work’ by establishing and defending territories from which canid predators are excluded. Eight maremmas and six free-ranging wild dogs of different sexes were fitted with GPS collars and monitored for 7 months on a large sheep property in north Queensland, Australia. Wild dog incursions into the territories of adjacent wild dogs and maremmas were recorded. Wild dog territories never overlapped and their home ranges infrequently overlapped. In contrast, 713 hourly locations from 120 wild dog incursions into maremma territories were recorded, mostly from three wild dogs. These three wild dogs spent a mean of 2.5–5.9 h inside maremma territories during incursions. At this location, maremmas worked by guarding sheep and prohibiting fine-scale interaction between wild dogs and sheep, not by establishing a territory respected by wild dogs. We conclude that shepherding behaviour and boisterous vocalisations of guardian dogs combined with the flocking behaviour of sheep circumvents attacks on sheep but does not prevent nor discourage wild dogs from foraging in close proximity. Certain husbandry practices and the behaviour of sheep at parturition may incur greater predation risk.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.