Technical note: using an automated head chamber system to administer an external marker to estimate fecal output by grazing beef cattle.

The objective of this experiment was to determine if titanium dioxide (TiO2) dosed through an automated head chamber system (GreenFeed; C-Lock Inc., Rapid City, SD, USA) is an acceptable method to measure fecal output. The GreenFeed used on this experiment had a 2-hopper bait dispensing system, where hopper 1 contained alfalfa pellets marked with 1% titanium dioxide (TiO2) and hopper 2 contained unmarked alfalfa pellets. Eleven heifers (BW = 394 ± 18.7 kg) grazing a common pasture were stratified by BW and then randomized to either 1) dosed with TiO2-marked pellets by hand feeding (HFD; n = 6) or 2) dosed with TiO2-marked pellets by the GreenFeed (GFFD; n = 5) for 19 d. During the morning (0800), all heifers were offered a pelleted, high-CP supplement at 0.25% of BW in individual feeding stanchions. The HFD heifers also received 32 g of TiO2-marked pellets at morning feeding, whereas the GFFD heifers received 32 g of unmarked pellets. The GFFD heifers received a single aliquot (32 ± 1.6 g; mean ± SD) of marked pellets at their first visit to the GreenFeed each day with all subsequent 32-g aliquots providing unmarked pellets; HFD heifers received only unmarked pellets. Starting on d 15, fecal samples were collected via rectal grab at feeding and every 12 h for 5 d. A two-one sided t-test method was used to determine agreement and it was determined that the fecal output estimates by HFD and GFFD methods were similar (P = 0.04). There was a difference (P < 0.01; Bartlett's test for homogenous variances) in variability between the dosing methods for HFD and GFFD (SD = 0.1 and 0.7, respectively). This difference in fecal output variability may have been due to variability of dosing times-of-day for the GFFD heifers (0615 ± 6.2 h) relative to the constant dosing time-of-day for HFD and constant 0800 and 2000 sampling times-of-day for all animals. This research has highlighted the potential for dosing cattle with an external marker through a GreenFeed configured with two (or more) feed hoppers because estimated fecal output means were similar; however, consideration of the increased variability of the fecal output estimates is needed for future experimental designs.

Estimation of biomass and canopy height in bermudagrass, alfalfa, and wheat using ultrasonic, laser, and spectral sensors.

Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha(-1), respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha(-1)) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha(-1)). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation.

Climate influences the demography of three dominant sagebrush steppe plants.

Climate change could alter the population growth of dominant species, leading to profound effects on community structure and ecosystem dynamics. Understanding the links between historical variation in climate and population vital rates (survival, growth, recruitment) is one way to predict the impact of future climate change. Using a unique, long-term data set from eastern Idaho, USA, we parameterized integral projection models (IPMs) for Pseudoroegneria spicata, Hesperostipa comata, and Artemisia tripartita to identify the demographic rates and climate variables most important for population growth. We described survival, growth, and recruitment as a function of genet size using mixed-effect regression models that incorporated climate variables. Elasticites for the survival + growth portion of the kernel were larger than the recruitment portion for all three species, with survival + growth accounting for 87-95% of the total elasticity. The genet sizes with the highest elasticity values in each species were very close to the genet size threshold where survival approached 100%. We found strong effects of climate on the population growth rate of two of our three species. In H. comata, a 1% decrease in previous year's precipitation would lead to a 0.6% decrease in population growth. In A. tripartita, a 1% increase in summer temperature would result in a 1.3% increase in population growth. In both H. comata and A. tripartita, climate influenced population growth by affecting genet growth more than survival or recruitment. Late-winter snow was the most important climate variable for P. spicata, but its effect on population growth was smaller than the climate effects we found in H. comata or A. tripartita. For all three species, demographic responses lagged climate by at least one year. Our analysis indicates that understanding climate effects on genet growth may be crucial for anticipating future changes in the structure and function of sagebrush steppe vegetation.