Effect of supplemental niacin or niacinamide and soybean source on ruminal bacterial fermentation in continuous culture

Effects of niacin or niacinamide in diets containing either soybean meal, raw whole soybeans or whole soybeans extruded at 132 and 149 C on ruminal bacterial fermentation were examined with a dual-flow continuous culture system. In Exp. 1, soybean sources each provided 50% of total crude protein in diets comprised of 52% concentrate mix, 36% corn silage and 12% alfalfa hay (dry-matter basis). Each diet was supplemented with 0 or 100 mg/kg niacin. Niacin supplementation increased (P less than .05) total nonstructural carbohydrate digestibility and lowered (P less than .05) butyrate concentration. There was also an increase (P less than .10) in amino acid effluent flow from 8,413.3 to 8,665.3 mg/d with addition of niacin to the diet. In Exp. 2, diets were supplemented with 0 or 100 mg/kg of niacin or niacinamide. The total mixed diet was comprised of 60% concentrate mix, 20% corn silage and 20% alfalfa hay (dry matter basis). Acid detergent fiber and cellulose digestibilities and total amino acid effluent flow were higher (P less than .10) with niacinamide supplementation. Niacin or niacinamide had no effect on dry matter and organic matter digestibilities, ammonia-N, total VFA concentration or crude protein degradation. Contrary to results found in other studies, niacin or niacinamide supplementation had no effect on bacterial protein synthesis.     

Potential futures for road transportation CO2 emissions in the Asia Pacific

Abstract: Will future transportation carbon dioxide (CO₂) emissions per capita in Asia Pacific economies follow historical trends of the now developed world? Evidence to date is inconclusive. A comparison at similar income levels (purchasing power parity) between recent emissions in Asia Pacific countries and historical emissions in developed countries suggests diverging patterns. (A) High‐income Asia economies (Japan, Hong Kong and Singapore –‘low emitters’) exhibit lower emissions than a selected sample of seven developed countries (United States, Australia, France, the Netherlands, Italy, Sweden and United Kingdom). (B) Another set of Asian countries (South Korea and Taiwan –‘medium emitters’) follow the emissions trends of European countries, which are lower than those of Australia and the United States. (C) A third Asian group (Malaysia and Thailand –‘high emitters’) exhibit emission trends comparable to that of Australia. We describe these trends, examine their causes and extrapolate likely futures for emissions in low‐income Asia Pacific economies (China, Indonesia, Philippines and Vietnam). Although such predictions are speculative, the available evidence suggests that road CO₂ emissions for Indonesia, the Philippines and Vietnam will follow those of the third group (high emitters), while those for China may follow either Group B or Group C.     

Direct inhibition of maintenance respiration in western hemlock roots exposed to ambient soil carbon dioxide concentrations

Root respiration often exhibits a direct and immediate decline with increasing concentrations of ambient soil carbon dioxide concentration ([CO(2)]), and recent evidence suggests this decline may be attributable to a decline in maintenance respiration within the root. If true, this concept could provide a clue to the biochemical process underlying respiratory inhibition as well as improve our knowledge of the timing and degree to which this inhibition occurs in nature. To test the hypothesis that maintenance respiration exhibits a direct, negative response to increasing [CO(2)], we measured total respiration in intact root systems of western hemlock (Tsuga heterophylla (Raf.) Sarg.) seedlings grown at different relative growth rates and exposed to soil [CO(2)]s ranging from 91 to 7008 &mgr;mol mol(-1). Analysis of covariance was used to separate maintenance from total respiration. Total respiration declined exponentially with increasing [CO(2)]. Maintenance respiration, which comprised 85% of total respiration over all treatments, also declined exponentially with increasing [CO(2)]. Growth respiration was not inhibited at any [CO(2)]. These findings may explain why roots of some fast-growing species do not show [CO(2)] inhibition.     

A regression-based equivalence test for model validation: shifting the burden of proof

Model validation is often realized as a test of how well model predictions match a set of independent observations. One would think that the burden of proof should rest with the model, to force it to show that it can make accurate predictions. Further, one would think that increasing the sample size ought to increase the model's ability to demonstrate its utility. Traditional statistical tools are inappropriate for this because they default to the case that the model and the data are no different, and their ability to detect differences increases with the sample size. These traditional tools are optimized to detect differences, rather than similarities. We present an alternative strategy for model validation that is based on regression and statistical tests of equivalence. Equivalence tests reverse the usual null hypothesis: they posit that the populations being compared are different and use the data to prove otherwise. In this sense, equivalence tests are lumping tests, whereas the traditional statistical tests are splitting tests. To date, model validation with equivalence tests has focused on comparisons of means. Our proposed test checks not only for similarity of means, but also for similarity between individual predictions and observations. The strategy is demonstrated using three case studies that differ in their modeling objectives, and for varied sample sizes. The proposed strategy provides a formal means of model validation that is superior to traditional statistical tests in each case.     

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.