Álvaro García
The primary objective in raising dairy heifers is achieving the appropriate weight and stature at calving, typically at around 23 months of age, which is correlated with optimal profitability in Holstein replacement programs. Present-day Holstein heifers exhibit larger and taller dimensions at the withers compared to standards from three to five decades ago. Therefore, it is imperative to evaluate heifer performance and juxtapose it with established benchmarks. This information serves as a cornerstone for developing precise nutritional management plans, assessing feed efficiency, ensuring accurate drug dosages, and maintaining overall animal health. Furthermore, body weight data plays a pivotal role in evaluating the value of culled animals and the efficiency of raising replacement heifers. The established benchmarks for assessing dairy heifer growth, as proposed by M. J. VandeHaar of Michigan State University, include the following parameters:
- Age at First Calving: Ideally, between 22 to 24 months.
- Body Weight After Calving: Targeting a minimum of 1,250 pounds.
- Height at Calving: Should be at least 54 inches at the withers.
- Body Condition Score at Calving: Aim for around 3.5.
- Growth Rate from 3 to 10 Months of Age: Ideally, between 1.7 to 2.0 pounds per day.
Various methods have been employed to accurately measure or estimate the body weight of Holstein heifers. The most precise approach involves individually weighing animals using traditional or electronic scales. Regrettably, these current evaluation methods can be resource-intensive, time-consuming, and potentially hazardous due to the need for physical handling of the animals. Alternative indirect methods for estimating body weight have also been developed. The most common and cost-effective indirect method for estimating the body weight of dairy heifers involves utilizing a heart girth tape. This method entails measuring the circumference of the animal just behind the withers with a tape. The heart girth circumference corresponds to an estimated body weight, a relationship established by Heinrichs and Hargrove in 1987.
Later studies explored relationships between various growth measurements in dairy cattle to predict body weight. Researchers such as Heinrichs et al. (1992) and Enevoldsen and Kristensen (1997) have utilized linear regression models to identify hip width as a skeletal measurement strongly correlated with body weight and minimally influenced by body condition. More recently, A. Skidmore collaborated with Dairy Innovations in 2001 to create the hipometer, a specialized tool primarily used in the field of dairy farming to estimate body weight. The device measures the width between the hip joints at the greater trochanters of the femurs (bony protrusions on the tops of the hind legs). It typically consists of two arms or calipers that are placed around the greater trochanters of the cow’s hind legs. According to research conducted by the University of Applied Sciences Bingen, the hipometer has proven to be a highly accurate tool for estimating the weight of heifers and cows, showing a strong correlation of 0.88.
At the present time, all five benchmarks for heifer growth established by Michigan State University, as highlighted above, can be accomplished using a single artificial intelligence device equipped with three-dimensional (3D) imaging technology. Next-generation 3D cameras are advanced devices with improved depth sensing, higher resolution, wider field of view, real-time processing, low-light performance, reduced size, and lower power consumption. They often incorporate multiple sensing modalities and integrate machine learning. These cameras find applications in robotics, autonomous vehicles, healthcare, augmented reality, and manufacturing.
Oron Nir et al. (2018) implemented a 3D computer-vision system for automatically estimating Holstein heifer height and body mass. This 3D system was found to be cost-effective and accurate (R2: 95.2%, 98.5%, and 94.6% for hip height, withers height, and body mass, respectively), while minimizing the animal-farmer interaction. Another more recent experiment conducted by Le Cozler et al. (2022) employed an imaging approach on growing Holstein heifers previously validated for adult cows. They tracked the animals from 5 weeks of age until the conclusion of their first gestation weighing them monthly and recording their 3D image. These images were then employed to estimate growth trends in various morphological traits, including heart girth and withers height. Additionally, other traits such as body surface area and volume were used to estimate body weight.
In summary, three-dimensional imaging technology has proven to be a user-friendly tool for monitoring the growth and management of heifers, with the potential for increasing accuracy as more data is accumulated for this specific animal group. The integration of 3D imaging technology has shown great promise in enhancing precision, accuracy, and efficiency in monitoring growth of dairy heifers. For instance, when three independent observers assess the body condition of the same heifer, random errors between evaluators may introduce slight variations in their individual scores. This type of error is eliminated when employing a 3D camera to characterize heifers for various bodily measurements such as weight, height, and body condition.
One of the key advantages of 3D cameras is their ability to measure cattle parameters frequently, provided the animals are within the camera’s range. In the case of milking dairy cows, this can occur multiple times a day as they walk to and from the milking parlor. However, when it comes to growing dairy heifers, which don’t follow a similar daily routine, farmers need to establish a schedule for them to pass through a chute or alley at specific intervals. Installing a 3D camera and having them pass through it only once a month negates the camera’s primary advantage, which is the ability to regularly measure growth and act on it. The true value of this device for growing heifers lies in its capacity to track growth dynamics consistently. To achieve this, a camera should be installed so that individual animals walk close to it, ideally at least once a week. Using such a valuable tool only once a month would be inefficient and undermine the potential for savings in feed costs by adjusting their diet based on regular growth measurements.
As technology continues to evolve and more data becomes available for specific populations of young heifers, we can anticipate even greater accuracy and efficiency in managing their growth. The application of three-dimensional imaging technology, along with refined predictive models, holds significant promise for transforming the way we raise and care for dairy heifers, ultimately leading to improved animal well-being and health, profitability, and sustainable dairy farming practices. This progress underscores the essential role that innovation and technology play in the modern dairy industry’s pursuit of excellence.
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