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Canada is committed to reducing its greenhouse gas emissions to 6% below 1990 amounts between 2008 and 2012, and methane is one of several greenhouse gases being targeted for reduction. Methane production from ruminants is one area in which the agriculture sector can contribute to reducing our global impact. Through mathematical modeling, we can further our understanding of factors that control methane production, improve national or global greenhouse gas inventories, and investigate mitigation strategies to reduce overall emissions. The purpose of this study was to compile an extensive database of methane production values measured on beef cattle, and to generate linear and nonlinear equations to predict methane production from variables that describe the diet. Extant methane prediction equations were also evaluated. The linear equation developed with the smallest root mean square prediction error (RMSPE, % observed mean) and residual variance (RV) was Eq. I: CH4, MJ/d = 2.72 (±0.543) + [0.0937 (+0.0117) × ME intake, MJ/dJ + [4.31 (±0.215) × Cellulose, kg/dj - [6.49 (±0.800) × Hemicellulose, kg/d] - [7.44 (±0.521) × Fat, kg/dI [RMSPE = 26.9%, with 94% of mean square prediction error (MSPE) being random error; RV = 1.13]. Equa- tions based on ratios of one diet variable to another were also generated, and Eq. P, CH4, MJ/d = 2.50 (±0.649) - [0.367 (±0.0191) × (Starch:ADF)] + [0.766 (±0.116) × DM1, kg/d], resulted in the smallest RMSPE values among these equations (RMSPE = 28.6%, with 93.6% of MSPE from random error; RV = 1.35). Among the nonlinear equations developed, Eq. W, CH4, MJ/d = 10.8 (±1.45) × (1 - e{-0.141 (±0.0381) × DMI, kg/d}) performed well (RMSPE = 29.0%, with 93.6% of MSPE from random error; RV = 3.06) as did Eq. W3, CH4, MJ/d = 10.8 (±1.45) × (1 - e.{{-|-0.034 × (NFC/NDF) + 0.228] × DM1, kg/d}] (RMSPE = 28.0%, with 95% of MSPE from random error). Extant equations from a previous publication by the authors performed comparably with, if not better than in some cases, the newly developed equations. Equation selection by users should be based on RV and RMSPE analysis, input variables available to the user, and the diet fed, because the equation selected must account for divergence from a "normal" diet (e.g., high-concentrate diets, high-fat diets). [ABSTRACT FROM AUTHOR]

The detection and mapping of genetic markers linked to quantitative trait loci (QTL) can be utilized to enhance genetic improvement of livestock populations. With the completion of the bovine genome sequence assembly, single nucleotide polymorphisms (SNP) assays spanning the whole bovine genome and research work on large scale identification, validation and analysis of genotypic variation in cattle has become possible. The objective of the present study was to perform a whole genome scan to identify and map QTL affecting milk production traits and somatic cell scores using linkage disequilibrium (LD) regression and 1536 SNP markers. Three and 18 SNP were found to be associated with only milk yield (MY) at a genome and chromosome wise significance (p < 0.05) level respectively. Among the 21 significant SNP, 16 were in a region reported to have QTL for MY in other dairy cattle populations and while the rest five were new QTL finding. Four SNP out of 21 are significant for the milk production traits (MY, fat yield, protein yield (PY), and milk contents) in the present study. Six and nine SNP were associated with PY at a genome and chromosome wise significant (p < 0.05) level respectively. Three and 17 SNP were found to be associated with FY at a genome and chromosome wise significant (p < 0.05) level. Five and seven SNP were mapped with somatic cell score at a genome and chromosome wise significant (p < 0.05) level respectively. The results of this study have revealed QTL for MY, PY, protein percentage, FY, fat percentage, somatic cell score and persistency of milk in the Canadian dairy cattle population. The chromosome regions identified in this study should be further investigated to potentially identify the causative mutations underlying the QTL.