Browsing by Author "Tedeschi, Luis O."
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Item A practical method to account for outliers in simple linear regression using the median of slopes(2024) Tedeschi, Luis O.; Galyean, Michael L. (TTU)The ordinary least squares (OLS) can be affected by errors associated with heteroscedasticity and outliers, and extreme points can influence the regression parameters. Methods based on the median rather than on the mean and variance are more resistant to outliers and extreme points. These methods could be used to obtain regression parameter estimates that reflect more accurately the genuine relationship between the Y and X variables, leading to better identification of outliers and extreme points by comparing the slopes and intercepts of both methods. The Theil-Sen (TS) regression computes all possible pairwise slopes and determines the median of slopes as the regression slope. Here, we illustrated the potential use of TS and frequently used robust regression (RR) techniques to single linear regression using synthetic datasets and a practical problem in animal science. Three synthetic datasets were created assuming the normal distribution of Y and X values: one was free of outliers, while the other two had one or two clusters of outliers but the same X values. The TS, OLS, and RR had nearly identical regression parameter estimates for the dataset without synthetic outliers. However, the intercept and slope estimates by the OLS method differed considerably from the TS and RR methods when one or two clusters of outliers were included. The TS approach could be used to indirectly determine the presence of outliers or extreme points by comparing the 95 % confidence interval of the TS and OLS parameter estimates.Item Nutritional Aspects of Ecologically Relevant Phytochemicals in Ruminant Production(2021) Tedeschi, Luis O.; Muir, James P.; Naumann, Harley D.; Norris, Aaron B. (TTU); Ramírez-Restrepo, Carlos A.; Mertens-Talcott, Susanne U.This review provides an update of ecologically relevant phytochemicals for ruminant production, focusing on their contribution to advancing nutrition. Phytochemicals embody a broad spectrum of chemical components that influence resource competence and biological advantage in determining plant species' distribution and density in different ecosystems. These natural compounds also often act as plant defensive chemicals against predatorial microbes, insects, and herbivores. They may modulate or exacerbate microbial transactions in the gastrointestinal tract and physiological responses in ruminant microbiomes. To harness their production-enhancing characteristics, phytochemicals have been actively researched as feed additives to manipulate ruminal fermentation and establish other phytochemoprophylactic (prevent animal diseases) and phytochemotherapeutic (treat animal diseases) roles. However, phytochemical-host interactions, the exact mechanism of action, and their effects require more profound elucidation to provide definitive recommendations for ruminant production. The majority of phytochemicals of nutritional and pharmacological interest are typically classified as flavonoids (9%), terpenoids (55%), and alkaloids (36%). Within flavonoids, polyphenolics (e.g., hydrolyzable and condensed tannins) have many benefits to ruminants, including reducing methane (CH4) emission, gastrointestinal nematode parasitism, and ruminal proteolysis. Within terpenoids, saponins and essential oils also mitigate CH4 emission, but triterpenoid saponins have rich biochemical structures with many clinical benefits in humans. The anti-methanogenic property in ruminants is variable because of the simultaneous targeting of several physiological pathways. This may explain saponin-containing forages' relative safety for long-term use and describe associated molecular interactions on all ruminant metabolism phases. Alkaloids are N-containing compounds with vast pharmacological properties currently used to treat humans, but their phytochemical usage as feed additives in ruminants has yet to be exploited as they may act as ghost compounds alongside other phytochemicals of known importance. We discussed strategic recommendations for phytochemicals to support sustainable ruminant production, such as replacements for antibiotics and anthelmintics. Topics that merit further examination are discussed and include the role of fresh forages vis-à-vis processed feeds in confined ruminant operations. Applications and benefits of phytochemicals to humankind are yet to be fully understood or utilized. Scientific explorations have provided promising results, pending thorough vetting before primetime use, such that academic and commercial interests in the technology are fully adopted.Item Predicting metabolizable energy from digestible energy for growing and finishing beef cattle and relationships to the prediction of methane(2022) Hales, Kristin E. (TTU); Coppin, Carley A. (TTU); Smith, Zachary K.; McDaniel, Zach S. (TTU); Tedeschi, Luis O.; Cole, N. Andy; Galyean, Michael L. (TTU)Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE - 0.3926; r2 = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = -0.567 to -0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE - 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH4, Mcal/kg = 0.3344 - 0.05639 × multiple of maintenance; r2 = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.