Body condition scoring, an indirect measure of the level of subcutaneous fat in dairy cattle, has been widely adopted for research and field assessment or for management purposes on farms. The feasibility of utilizing digital images to determine body condition score (BCS) was assessed for lactating dairy cows at the Scottish Agricultural College Crichton Royal Farm. Two measures of BCS were obtained by using the primary systems utilized in the United Kingdom (UK-BCS) and the United States (USBCS). Means were 2.12 (+/-0.35) and 2.89 (+/-0.40), modes were 2.25 and 2.75, and ranges were 1.0 to 3.5 and 1.5 to 4.5 for the UKBCS (n = 2,346) and USBCS (n = 2,571), respectively. Up to 23 anatomical points were manually identified on images captured automatically as cows passed through a weigh station. Points around the hooks were easier to identify on images than points around pins and the tailhead. All identifiable points were used to define and formulate measures describing the cow's contour. For both BCS systems, hook angle, posterior hook angle, and tailhead depression were significant predictors of BCS. When the full data set testing only the angles around the hooks was used, 100% of predicted BCS were within 0.50 points of actual USBCS and 92.79% were within 0.25 points; and 99.87% of predicted BCS were within 0.50 points of actual UKBCS and 89.95% were within 0.25 points. In a reduced data set considering only observations in which the tailhead depression angle was available, adding the tailhead depression to models did not improve model predictions. The relationships of the calculated angles with USBCS were stronger than those with UKBCS. This research demonstrates the potential for using digital images for assessing BCS. Future efforts should explore ways to automate this process by using a larger number of animals to predict scores accurately for cows across all levels of body condition.

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This paper expands on previous work considering methods of stratifying property data in order to enhance its susceptibility to modelling for mortgage value estimation. Previous work [1] considered a clustering approach using a Kohonen Self-Organising Map (SOM) to stratify the training data prior to training a suite of MLPs. Although the results were encouraging, the approach suffers from its estimation of trainability post-clustering. The following method ameliorates the approach by replacing the static clustering step with a dynamic genetic algorithm implementation. The results show a healthy improvement in accuracy over the non-stratified approach, and a more consistent level of accuracy compared with the Kohonen SOM approach. The paper concludes by analysing the underlying content of the derived stratas, thus providing a 'human readable' element to the approach that enhances its potential for acceptance by valuation institutions for as a complementary technique to traditional valuation methods.

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