Statistical analysis

Data were analysed by ANOVA for each of the four experiments independently and are presented in Appendices 6.1 and 6.2. Feed intake was the principal treatment effect and before data from all four experiments were combined, a single regression examining the slope and intercept between DMI and CH4 yield for white clover and ryegrass forages

were compared (Figure 6.1). This was to confirm that there was no effect of diet type on CH4 emissions. Because of the similar responses for both white clover and ryegrass

forages, and similar experimental structure, a combined analysis of data from all four experiments could be undertaken. Interpretation of the white clover and ryegrass chemical composition was based on the range and average of values for each component concentration (g/kg DM) and intake (kg/d) (Table 6.3).

For Experiments 2, 3, 4 and 5, to better understand the effect of DMI on CH4 emission,

DMI was related to the following parameters:

 digestibility: digestible DM, DDM; digestible OM, DOM; digestible neutral detergent fibre, DNDF;

 emissions of CH4: g CH4/d; g CH4/kg DMI, g CH4/kg OMI; g CH4/kg DDMI; g

CH4/kg DOMI; and CH4 energy (CH4-E) in relation to gross energy intake

(GEI), CH4-E/GEI;

 emissions of H2: g H2/d, g H2/kg DMI;  emissions of CO2: g CO2/d;

 rumen parameters: total VFAs; and individual VFA molar proportions.

Because DMI is a covariate, in order to determine the effect of DMI on the above parameters, an analysis using the REML method in GenStat software (Payne et al., 2010) was used. Set DMI values (0.4, 0.6, 0.8, 1.0, 1.2, 1.4, and 1.6 kg/d) were used to predict values of the response variables, based on data from all four experiments. The prediction responses are given in Table 6.3. By using REML for a combined analysis of several related experiments, the ‘best’ estimate (based on data from all experiments) of DMI on the effect of the variables was obtained. The REML model takes into account the effect of the experiment and produces one overall test.

The fixed REML model was expressed as:

Variable = C + DMI

and the random model as:

Variable = Expt. + Expt. x DMI

Where: C, is a constant; Variable, is the variable of interest (i.e. digestibility, digestible intake, gas emissions, and rumen parameters); DMI, is dry matter intake (kg/d); and Expt., is Experiments 2, 3, 4 or 5.

The random model included ‘experiment’ and interactions between experiment and treatment terms (e.g. Expt. x DMI). Thus, in effect, each treatment term was compared against its interaction within the experiment, and a significant treatment effect implied that the effect was consistent and large compared with its variation across experiments. Because REML used set DMI values (0.4, 0.6, 0.8, 1.0, 1.2, 1.4, and 1.6 kg/d) to predict values of the response variables, the prediction model is expressed as:

CHAPTER 6: Effect of feed intake on methane emissions from sheep 135 where: C, is a constant; Response variate, is the prediction value of the variable of interest (i.e. digestibility, digestible intake, gas emissions, and rumen parameters) at each set DMI value; Diet, is white clover or ryegrass; DMI, is dry matter intake (kg/d); and Expt., is Experiments 2, 3, 4 or 5.

The results of the REML analysis are expressed as prediction means ± standard errors of the difference of the mean (SEM), and p-values (Table 6.3).

The different experiments are likely to have different variability and these are estimated in the separate residual terms for each experiment to give an overall feed intake effect which is largely based on the consistency of the effect across all experiments. In Experiments 2, 3 and 4, sheep were fed white clover and ryegrass but because there were no differences in CH4 emissions between the two diets (Chapter 5), data were

combined for both diets. Both the individual experiment analysis by ANOVA and the REML analysis of predicted responses used data from Experiments 2, 3 and 4 that were adjusted to remove the overall effect of diet. Additional adjustments were made to Experiment 2 data to remove the effect of animal fistulation, and to Experiment 4 data for the effects of measurement period (1 vs. 2) and rumen water balloon treatment (Balloon vs. Control) because these variables did not affect DMI (Chapter 7). No adjustments were made to data from Experiment 5 (no fistulated sheep and only ryegrass forages fed).

Data adjustments to each experimental dataset were done by performing an ANOVA which incorporated only the term(s) whose effect was to be removed (i.e. fistulation and diet for Experiment 2; diet for Experiment 3; and period, treatment and diet for Experiment 4). Residuals were obtained for each of the variables (e.g. digestibility, digestible intake, gas emissions, and rumen parameters) and added to a grand mean to give an adjusted mean value for each variable.

For individual experiment analyses, data can be located at the end of this Chapter in Appendices 6.1 and 6.2. Data from both the individual experiments and combined REML predictions were generated from individual animals and the means presented within tables will not always appear compatible.

Single and multiple regression analyses were conducted on data from all four experiments (Appendices 6.1 and 6.2) (Payne et al., 2010) to investigate the relationship

between CH4 emissions (g/d and g/kg DMI) and diet composition, feed intake,

digestibility, digestible intake, and rumen VFAs (Tables 6.5 and 6.6). All subsets of up to 13 variables were assessed by multiple regression for their ability to predict CH4

production or yield. The ‘all subsets regression’ procedure in GenStat software, version 10.2 (Payne et al., 2010), was used for analysis and the model which predicted the most variation was identified.

6.3

RESULTS