Estimating net energy for activity for grazing beef cattle by integrating GPS tracking data, in-pasture weighing technology, and animal nutrition models

Abstract

Beef cattle production is largely dependent on rangelands for cattle to convert unusable plant-based fibers into an animal-based protein source for human consumption. Solutions are needed to meet both the growing demand for animal-based proteins and the desire of managers to produce energy-efficient cattle. Animal energetics has largely focused on beef cattle within confined systems such as feedlots. However, beef cattle grazing in extensive rangelands likely have a higher energetic requirement due to the need to forage across heterogeneous landscapes. In this study, we created a precision system model to account for net energy for activity of beef cattle on extensive rangeland systems by integrating in-pasture weighing technology, Global Positioning System (GPS) data, and animal nutrition models. The results from the mixed model analysis of variance (ANOVA) for net energy for maintenance activity (Nemr_act) indicated a significant main effect of treatment (P < 0.0001) and stocking rate (P < 0.0001), but there was no significant interaction (P = 0.705). These results indicate that, although the overall energetic expenditure may be similar, individual pasture effects may impact the proportional cost of physical activity partitioned between Resting, Flat, and Ascending energetic expenditures, as animals utilize diverse landscapes. Cattle grazing on extensive rangelands within the intermountain west with greater variations in both topography and slope will likely impact energetics to a greater extent. As the rates of precision technology and virtual fencing are adopted, the applications of the algorithm developed in this study may be used to quantify these differences at larger landscape scales across western rangelands.

Keywords: animal nutrition; data integration; modeling; precision livestock; rangelands.

Figure 1

Least square means (kcal/BW^0.75/d) with standard error bars for Flat Energetic Expenditure (Flat EE) and Ascending Energetic Expenditure (Ascending EE) for steers grazing in the Northern Great Plains rangelands under three stocking rate intensities and two grazing treatments (continuous and virtual rotation). Energetic expenditure was calculated by integrating GPS tracking data and in-pasture weighing technology with animal nutrition models.

Figure 2

Sensitivity analysis of herd average Net Energy for Maintenance Required Activity (Mcal/d) over a 60-day period. Colors represent percentiles (yellow = 50%, green = 75%, blue = 95%, and gray = 100%), and the blue line represents the base run (i.e., model parameter values from the observed data).

Figure 3

Regression of observed body weight (kg/hd/d) and predicted body weight (kg/hd/d, Adjusted R² = 0.98).