Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Abstract

Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.

Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data

“…For example, Greenwood, et al [3] proposed simple initial algorithms for predicting pasture intake by individual cattle using sensor data. Other studies (e.g., [4,5]) addressed the scope of developing the support systems that could assist farmers with proper feed allowances, physical activities and behavioural changes, estimation of herbage dry matter and locomotion behaviour of the cattle.…”

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

“…This approach has been implemented, where feeding time correlated with feed intake, which can be useful for estimating intake rates on pasture [ 99 ]. Hills et al [ 100 ] also describe that sensors can potentially give indications to optimize nutritional demands through individualized feeding strategies, and thus improve the management of grazing cows.…”

Short-term feeding behaviour sound classification method for sheep using LSTM networks

“…Given that abnormalities in this rhythm can indicate health disorder, accurate measure of a sheep's daily grass intake can thus be utilized to assess animal's healthy status. Previous studies have reported a good correlation between the grass intake of individual ruminant animals and different short-term feeding behaviours [2][3][4][5][6][7] .…”