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We address the hypothesis that individual cow feed intake can be measured in commercial farms through the use of a photogrammetry method. Feed intake and feed efficiency have a significant economic value for the farmer. A common method for measuring feed mass in research is a feed mass weighing system, which is excessively expensive for commercial farms. However, feed mass can be estimated by its volume, which can be measured by photogrammetry. Photogrammetry applies cameras along the feed-lane, photographing the feed before and after the cow visits the feed-lane, and calculating the feed volume. In this study, the precision of estimating feed mass by its volume was tested by comparing measured mass and calculated volume of feed heaps. The following principal factors had an impact on the precision of this method: camera quality, lighting conditions, image resolution, number of images, and feed density. Under laboratory conditions, the feed mass estimation error was 0·483 kg for heaps up to 7 kg, while in the cowshed the estimation error was 1·32 kg for up to 40 kg. A complementary experiment showed that the natural feed compressibility causes about 85% of uncertainty in the mass estimation error.

Body condition scoring (BCS) is a farm-management tool for estimating dairy cows' energy reserves. Today, BCS is performed manually by experts. This paper presents a 3-dimensional algorithm that provides a topographical understanding of the cow's body to estimate BCS. An automatic BCS system consisting of a Kinect camera (Microsoft Corp., Redmond, WA) triggered by a passive infrared motion detector was designed and implemented. Image processing and regression algorithms were developed and included the following steps: (1) image restoration, the removal of noise; (2) object recognition and separation, identification and separation of the cows; (3) movie and image selection, selection of movies and frames that include the relevant data; (4) image rotation, alignment of the cow parallel to the x-axis; and (5) image cropping and normalization, removal of irrelevant data, setting the image size to 150×200 pixels, and normalizing image values. All steps were performed automatically, including image selection and classification. Fourteen individual features per cow, derived from the cows' topography, were automatically extracted from the movies and from the farm's herd-management records. These features appear to be measurable in a commercial farm. Manual BCS was performed by a trained expert and compared with the output of the training set. A regression model was developed, correlating the features with the manual BCS references. Data were acquired for 4 d, resulting in a database of 422 movies of 101 cows. Movies containing cows' back ends were automatically selected (389 movies). The data were divided into a training set of 81 cows and a test set of 20 cows; both sets included the identical full range of BCS classes. Accuracy tests gave a mean absolute error of 0.26, median absolute error of 0.19, and coefficient of determination of 0.75, with 100% correct classification within 1 step and 91% correct classification within a half step for BCS classes. Results indicated good repeatability, with all standard deviations under 0.33. The algorithm is independent of the background and requires 10 cows for training with approximately 30 movies of 4 s each., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Lameness is still an important problem in modern dairy farming. Human observation of locomotion, by looking at different traits in one go, is used in practice to assess locomotion. The objectives of this article were to determine which individual locomotion traits are most related to locomotion scores in dairy cows, and whether experienced raters are capable of scoring these individual traits consistently. Locomotion and 5 individual locomotion traits (arched back, asymmetric gait, head bobbing, reluctance to bear weight, and tracking up) were scored independently on a 5-level scale for 58 videos of different cows. Videos were shown to 10 experienced raters in 2 different scoring sessions. Relations between locomotion score and traits were estimated by 3 logistic regression models aiming to calculate the size of the fixed effects on the probability of scoring a cow in 1 of the 5 levels of the scale (model 1) and the probability of classifying a cow as lame (locomotion score ≥3; model 2) or as severely lame (locomotion score ≥4; model 3). Fixed effects were rater, session, traits, and interactions among fixed effects. Odds ratios were calculated to estimate the relative probability to classify a cow as lame when an altered (trait score ≥3) or severely altered trait (trait score ≥4) was present. Overall intrarater and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement, respectively. Specific intrarater and interrater agreement for individual levels within a 5-level scale were calculated. All traits were significantly related to the locomotion score when scored with a 5-level scale and when classified as (severely) lame or nonlame. Odds ratios for altered and severely altered traits were 10.8 and 14.5 for reluctance to bear weight, 6.5 and 7.2 for asymmetric gait, and 4.8 and 3.2 for arched back, respectively. Raters showed substantial variation in reliability and agreement values when scoring traits. The acceptance threshold for overall intrarater reliability (κw ≥0.60) was exceeded by locomotion scoring and all traits. Overall interrater reliability values ranged from κw=0.53 for tracking up to κw=0.61 for reluctance to bear weight. Intrarater and interrater agreement were below the acceptance threshold (percentage of agreement <75%). Most traits tended to have lower specific intrarater and interrater agreement in level 3 and 5 of the scale. In conclusion, raters had difficulties in scoring locomotion traits consistently, especially slight alterations were difficult to detect by experienced raters. Yet, the locomotion traits reluctance to bear weight, asymmetric gait, and arched back had the strongest relation with the locomotion score. These traits should have priority in locomotion-scoring-system guidelines and are the best to be used for the development of automated locomotion scoring systems., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Locomotion scores are used for lameness detection in dairy cows. In research, locomotion scores with 5 levels are used most often. Analysis of scores, however, is done after transformation of the original 5-level scale into a 4-, 3-, or 2-level scale to improve reliability and agreement. The objective of this study was to evaluate different ways of merging levels to optimize resolution, reliability, and agreement of locomotion scores for dairy cows. Locomotion scoring was done by using a 5-level scale and 10 experienced raters in 2 different scoring sessions from videos from 58 cows. Intra- and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement (PA), respectively. Overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were determined for the 5-level scale and after transformation into 4-, 3-, and 2-level scales by merging different combinations of adjacent levels. Intrarater reliability (κw) ranged from 0.63 to 0.86, whereas intrarater agreement (PA) ranged from 60.3 to 82.8% for the 5-level scale. Interrater κw=0.28 to 0.84 and interrater PA=22.6 to 81.8% for the 5-level scale. The specific intrarater agreement was 76.4% for locomotion level 1, 68.5% for level 2, 65% for level 3, 77.2% for level 4, and 80% for level 5. Specific interrater agreement was 64.7% for locomotion level 1, 57.5% for level 2, 50.8% for level 3, 60% for level 4, and 45.2% for level 5. Specific intra- and interrater agreement suggested that levels 2 and 3 were more difficult to score consistently compared with other levels in the 5-level scale. The acceptance threshold for overall intra- and interrater reliability (κw and κ ≥0.6) and agreement (PA ≥75%) and specific intra- and interrater agreement (≥75% for all levels within locomotion score) was exceeded only for the 2-level scale when the 5 levels were merged as (12)(345) or (123)(45). In conclusion, when locomotion scoring is performed by experienced raters without further training together, the lowest specific intra- and interrater agreement was obtained in levels 2 and 3 of the 5-level scale. Acceptance thresholds for overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were exceeded only in the 2-level scale., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)