Thermistors were implanted in the right front quarter of udder and peritoneal cavity of six lactating Holstein cows to investigate whether udder temperature is regulated independently of deep body temperature. Sequential measurements of udder, body, chamber, and outdoor temperatures were every 1.4 min (1024 readings/probe per 24 h) by digital computer. Cows were housed (except for short exercise periods) in a chamber at 16.7 +/- .3 degrees C, lights on 0730 to 1630 and 2100 to 0200 h. Temperature was monitored continuously for 5 days in three cows in early lactation and in three cows in late lactation. Udder temperature was closely correlated with body temperature (body and udder temperatures were 38.8 +/- .1 degree C). Five of six cows showed two patterns of temperature variation: a 24-h pattern with two troughs each day--minimum at 0930 to 1100 h, increase 1.0 degree C by 1200 to 1300 h, decline 1 degree C from 2000 to 2200 h, second minimum by 2100 to 2200 h, and constant elevation from 2300 to 0800 h (peak to trough, 1.23 +/- .09 degrees C); and superimposed upon the 24-h rhythm was an ultradian rhythm with an approximate 90 min period (peak to trough, .5 +/- .03 degrees C). Rhythmicity of udder and body temperatures should be considered in research on the chronobiology of milk secretion and mastitis.
Development of cost-effective amendments for treating dairy slurry has become a critical problem as the number of cows on farms continues to grow and the acreage available for manure spreading continues to shrink. To determine effectiveness and optimal rates of addition of either alum or zeolite to dairy slurry, we measured ammonia emissions and resulting chemical changes in the slurry in response to the addition of amendments at 0.4, 1.0, 2.5, and 6.25% by weight. Ammonia volatilization over 96 h was measured with six small wind tunnels with gas scrubbing bottles at the inlets and outlets. Manure samples from the start and end of trials were analyzed for total nitrogen and phosphorus, and were extracted with 0.01 M CaCl2, 1.0 M KCl, and water with the extracts analyzed for ammonium nitrogen, phosphorous, aluminum, and pH. The addition of 6.25% zeolite or 2.5% alum to dairy slurry reduced ammonia emissions by nearly 50 and 60%, respectively. Alum treatment retained ammonia by reducing the slurry pH to 5 or less. In contrast, zeolite, being a cation exchange medium, adsorbed ammonium and reduced dissolved ammonia gas. In addition, alum essentially eliminated soluble phosphorous. Zeolite also reduced soluble phosphorous by over half, but the mechanism for this reduction is unclear. Alum must be carefully added to slurry to avoid effervescence and excess additions, which can increase soluble aluminum in the slurry. The use of alum or zeolites as on-farm amendment to dairy slurry offers the potential for reducing ammonia emissions and soluble phosphorus in dairy slurry.
High−resolution hyperspectral imaging (HSI) provides an abundance of spectral data for feature analysis in image processing. Usually, the amount of information contained in hyperspectral images is excessive and redundant, and data mining for waveband selection is needed. In applications such as fruit and vegetable defect inspections, effective spectral combination and data fusing methods are required in order to select a few optimal wavelengths without losing the crucial information in the original hyperspectral data. In this article, we present a novel method that combines principal component analysis (PCA) and Fisher's linear discriminant (FLD) method to show that the hybrid PCA−FLD method maximizes the representation and classification effects on the extracted new feature bands. The method is applied to the detection of chilling injury on cucumbers. Based on tests on different types of samples, results show that this new integrated PCA−FLD method outperforms the PCA and FLD methods when they are used separately for classifications. This method adds a new tool for the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications for fruit and vegetable safety and quality inspections.
Bovine lameness results in pain and suffering in cattle and economic loss for producers. A system for automatically detecting lame cows was developed recently that measures vertical force components attributable to individual limbs. These measurements can be used to calculate a number of limb movement variables. The objective of this investigation was to explore whether gait scores, lesion scores, or combined gait and lesion scores were more effectively captured by a set of 5 limb movement variables. A set of 700 hind limb examinations was used to create gait-based, lesion-based, and combined (gait- and lesion-based) models. Logistic regression models were constructed using 1, 2, or 3 d of measurements. Resulting models were tested on cows not used in modeling. The accuracy of lesion-score models was superior to that of gait-score models; lesion-based models generated greater values of areas under the receiving operating characteristic curves (range 0.75 to 0.84) and lower mean-squared errors (0.13 to 0.16) compared with corresponding values for the gait-based models (0.63 to 0.73 and 0.26 to 0.31 for receiving operating characteristic and mean-squared errors, respectively). These results indicate that further model development and investigation could generate automated and objective methods of lameness detection in dairy cattle.
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