Volume and surface area of Holstein dairy cows calculated from complete 3D shapes acquired using a high-precision scanning system: Interest for body weight estimation

International audienceThree-dimensional (3D) imaging is a solution for monitoring morphology and growth of dairy cows, but it can also estimate indicators such as body volume, surface area and body weight. A 3D full-body scanning device was used to scan 64 lactating Holstein cows from March-June 2018. The cows were individually and automatically weighed at a static weighing station (mean ± standard deviation = 673 ± 65 kg). These measured weights were compared to those predicted from regression models based on volume, area or morphological traits determined from 177 3D images. Since some images were truncated due to cow movement or technical problems, we developed additional regression models to reconstruct total volume or area. The accuracy of volume and area measurements was first tested on an inert cylindrical form (coefficients of variation (CVs) < 0.72%). The CVs for repeatability and reproducibility of the method of calculating volume and area from truncated images were 0.17% and 3.12%, respectively. Cow volume and area ranged from 0.61 to 0.96 m3 and 5.80 to 8.32 m2 respectively. Five regression models were developed to estimate cow body weight. Their coefficients of determination ranged from 0.82 to 0.93 with prediction errors of ca. 3% (20 kg) and 4% (29 kg) as a function of volume and area, respectively. The device and the method, evaluated and validated in this study, offer the possibility to use new indicators such as body volume and area in precision livestock farming

The use of 3-dimensional imaging of Holstein cows to estimate body weight and monitor the composition of body weight change throughout lactation

International audienceThree-dimensional (3D) imaging offers new possibilities in animal phenotyping. Here, we investigated how this technology can be used to study the morphological changes that occur in dairy cows over the course of a single lactation. First, we estimated the individual body weight (BW) of dairy cows using traits measured with 3D images. To improve the quality of prediction, we monitored body growth (via 3D imaging), gut fill (via individual dry matter intake), and body reserves (via body condition score) throughout lactation. A group of 16 Holstein cows—8 in their first lactation, 4 in their second lactation, and 4 in their third or higher lactation—was scanned in 3D once a month for an entire lactation. Values of morphological traits (e.g., chest depth or hip width) increased continuously with parity, but cows in their first lactation experienced the largest increase during the monitoring period. Values of partial volume, estimated from point of shoulder to pin bone, predicted BW with an error of 25.4 kg (R2 = 0.92), which was reduced to 14.3 kg when the individual effect of cows was added to the estimation model. The model was further improved by the addition of partial surface area (from point of shoulder to pin bone), hip width, chest depth, diagonal length, and heart girth, which increased the R2 of BW prediction to 0.94 and decreased root mean square error to 22.1 kg. The different slopes for individual cows were partly explained by body condition score and morphological traits, indicating that they may have reflected differences in body density among animals. Changes in BW over the course of lactation were mostly due to changes in growth, which accounted for around two-thirds of BW gain regardless of parity. Body reserves and gut fill had smaller but still notable effects on body composition, with a higher gain in body reserves and gut fill for cows in their first lactation compared with multiparous cows. This work demonstrated the potential for rapid and low-cost 3D imaging to facilitate the monitoring of several traits of high interest in dairy livestock farming

Productive and reproductive performance and metabolic profiles of ewes supplemented with hydroponically grown green wheat (Triticum aestivum L.)

Inclusion of high levels of concentrate in the diets of late gestation and lactating ewes to improve productive and reproductive performance is a common practice. However cost-effective alternate feeding strategies for small ruminants must be developed and evaluated in order to counteract sustainability issues of feeding them concentrate feeds (Alexandre and Mandonnet, 2005). Hydroponically grown green forages are a potential high feed quality feedstuff in arid and semiarid regions of the world (Al-Faraki and Al-Hashimi, 2012). The nutritive value and fermentative characteristics of hydroponically grown forages positively influenced the performance of late gestation and lactating ewes (Herrera et al., 2010; Gebremedhin, 2015). Earlier investigations emphasized effects of dietary quality on endocrine and metabolic profiles in ewes during pregnancy and lactation (Lemley et al., 2014; Vonnahme et al., 2013). However adequate nutritional status of ewes is associated with favorable productive and reproductive performance whereby blood glucose, non-esterified fatty acids (NEFA) and blood urea nitrogen (BUN) are utilized to sustain a desirable protein and energy balance in ewes during gestation and lactation (Hatfield et al., 1999). Changes in metabolic hormones, such as insulin, play an important role in metabolic adaptation to changes in body weight (BW) and body condition while providing diagnostic information to evaluate ewe nutritional status (Caldeira et al., 2007). Cortisol may be particularly important in this regard as it is the predominant glucocorticoid in sheep blood and has been used as a reliable physiological endpoint to determine ewe responses to a variety of physiological, physical and environmental stress (Moolchandani et al., 2008). A paucity of information is available with respect to the metabolic profile and performance during mating, gestation and lactation of ewes fed diets containing hydroponically grown green wheat (HGW). Thus this experiment was conducted to determine effects of replacement of dry-rolled corn (DRC) and cottonseed meal (CSM) by HGW in an oat hay-based diet on the metabolic profile as well as the productive and reproductive performance of Katahdin female lambs.Twenty six Katahdin ewes (i.e., female lambs from breeding to 2 mo of their 1st lactation) were used in a completely randomized design (13/treatment)to evaluate effects of replacement of dietary dry-rolled corn grain (DRC) and cottonseed meal(CSM) with hydroponically grown whole plant green wheat (HGW; Triticum aestivum L.) on productive parameters and blood metabolites during mating, gestation and lactation, and on body weight (BW) gain of their lambs in their 1st 60 days of age. The gestation diet contained 70% oat hay, 20% rolled corn grain and 10% cottonseed meal, while the lactation diet contained 50% oat hay, 20% DRC and 30% CSM. Treatments consisted of total replacement of DRC and CSM with HGW in the gestation diet, while in the lactation diet HGW replaced 100% of the DRC and 33% of the CSM. There were no diet effects on reproductive parameters, and substitution of DRC and CSM with HGW did not affect dry matter intake during gestation and lactation. The BW gain of the lambs that were fed HGW did not differ from controls in the first 2 months of gestation, while it was lower (P < 0.05) at the last 3 months of gestation. Feeding HGW did not affect birth BW of lambs or subsequent BW gains through 60 days of age. Plasma non-esterified fatty acids (NEFA) were not affected by the diets fed during gestation, but were 56% lower (P < 0.05) at day 60 of lactation. Plasma glucose was only lower (P < 0.05) at day 90 of gestation, and blood urea nitrogen was only lower (P < 0.05) at day 30 of lactation. There were no effects of diets on plasma insulin, cortisol or progesterone during gestation and lactation. Hydroponically grown green wheat is a suitable substitute for a portion of the DRC and CSM in ewes diets during gestation and lactation without negative effects

L'imagerie 3D pour la modélisation complète de bovins laitiers: vers de nouvelles données morphologiques disponibles à haut débit (surface, volume, poids vif)

Les suivis de variation de poids, d'état d'engraissement et/ou de morphologie peuvent contribuer à un meilleurpilotage de l'élevage des animaux laitiers. Cependant, en raison des difficultés de mise en oeuvre, ces suivis sontpeu réalisés en élevage. Les technologies basées sur l'imagerie en trois dimensions (3D) peuvent être une solution intéressante. A cet effet, un dispositif d'acquisition de formes en 3D (Morpho3D) a été mis au point dans le cadre des projets CAS DAR Morpho3D et ANR APIS-GENE DEFFILAIT, avec l'appui au départ d'un crédit incitatif du département PHASE. Pour évaluer les performances de cet outil et commencer à interpréter les premières mesures, différents essais ont été réalisés:1. Des mesures manuelles de morphologie sur 30 vaches ont été comparées à celles acquises à partir du dispositif Morpho3D. Les corrélations entre les mesures manuelles et Morpho3D variaient de 0,62 à 0,89 suivant le type de mesure. Le système Morpho3D a permis de réaliser des mesures avec un coefficient de variation (CV) de répétabilité allant de 0,26 à 9,81% et de reproductibilité allant de 0,94 à 7,34 %. Ces valeurs sont proches de celles obtenues avec des mesures manuelles (respectivement de 0,09 à 9,81 et 0,42 à 4,46) La répétabilité permet de qualifier l'erreur réalisée à chaque mesure liée au pointage sur les images 3D. La reproductibilité permet de qualifier l'erreur réalisée à chaque mesure, liée au système d'acquisition2. Les mesures de volume et de surface réalisées sur une forme inerte cylindrique sont très précises, avec descoefficients de variation inférieurs à 0,72%. Les volumes et surfaces moyens obtenus chez les vaches pesant de539 à 871 kg sont de 0,76 m3 (± 0,07) et 5,53 m2 (± 0,39). Les variables morphologiques et les volumes et surfaces ont été utilisés pour prédire le poids vif des vaches. Ces modèles décrivaient entre 82 et 93% de la variabilité observée des indicateurs à prédire et ont permis de prédire le poids vif avec une erreur de l'ordre de 3% (20-25 kg) avec le volume comme seule variable et 4% (29 kg) avec la surface comme seule variable.3. Un suivi de croissance de 16 vaches a été réalisé au cours d'une lactation par le suivi du poids vif quotidien etdes mesures corporelles mensuelles par imagerie 3D. L'évolution des indicateurs morphologiques reflète unecroissance continue sur au moins 3 lactations, avec une croissance plus importante pour les primipares. Le volume pris entre les pointes des épaules et le postérieur est un bon prédicteur du poids vif (R2 = 0,92), avec une erreur de prédiction de 25,8 kg. Cette erreur est réduite à 13,7 kg si la prédiction est faite intra-vache. Cette amélioration peut traduire des évolutions différentes des densités corporelles, qu'il reste à mieux appréhender.L'imagerie 3D permet de revisiter le phénotypage de la morphologie avec des mesures précises et présente déjàun intérêt pour la recherche. Les futurs développements qui pourraient découler de l'interprétation de cesphénotypes morphologiques sont susceptibles d'apporter de nouvelles solutions pour l'élevage, comme la peséesans contention, l'estimation des valeurs bouchères, la détection de problèmes de postures ou encore lescapacités digestives des animaux