M.H.A. de Haan

Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection.

Mitigation of enteric methane (CH₄) emission in ruminants has become an important area of research because accumulation of CH₄ is linked to global warming. Nutritional and microbial opportunities to reduce CH₄ emissions have been extensively researched, but little is known about using natural variation to breed animals with lower CH₄ yield. Measuring CH₄ emission rates directly from animals is difficult and hinders direct selection on reduced CH₄ emission. However, improvements can be made through selection on associated traits (e.g., residual feed intake, RFI) or through selection on CH₄ predicted from feed intake and diet composition. The objective was to establish phenotypic and genetic variation in predicted CH₄ output, and to determine the potential of genetics to reduce methane emissions in dairy cattle. Experimental data were used and records on daily feed intake, weekly body weights, and weekly milk production were available from 548 heifers. Residual feed intake (MJ/d) is the difference between net energy intake and calculated net energy requirements for maintenance as a function of body weight and for fat- and protein-corrected milk production. Predicted methane emission (PME; g/d) is 6% of gross energy intake (Intergovernmental Panel on Climate Change methodology) corrected for energy content of methane (55.65 kJ/g). The estimated heritabilities for PME and RFI were 0.35 and 0.40, respectively. The positive genetic correlation between RFI and PME indicated that cows with lower RFI have lower PME (estimates ranging from 0.18 to 0.84). Hence, it is possible to decrease the methane production of a cow by selecting more-efficient cows, and the genetic variation suggests that reductions in the order of 11 to 26% in 10 yr are theoretically possible, and could be even higher in a genomic selection program. However, several uncertainties are discussed; for example, the lack of true methane measurements (and the key assumption that methane produced per unit feed is not affected by RFI level), as well as the limitations of predicting the biological consequences of selection. To overcome these limitations, an international effort is required to bring together data on feed intake and methane emissions of dairy cows.

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.

The total cost of rearing a heifer on Dutch dairy farms: calculated versus perceived cost.

BackgroundAs farmers do not often keep a record of the expenditures for rearing, an economic tool that provides insight into the cost of rearing is useful. In the Netherlands, an economic tool (Jonkos) has been developed that can be used by farmers to obtain insight into the cost of rearing on their farm. The first objective of this study is to calculate the total cost of rearing young stock in Dutch dairy herds using Jonkos. The second objective is to compare the calculated total cost of rearing with the farmers’ own estimation of the cost of rearing (the perceived cost).FindingsInformation was available for 75 herds that reared their own young stock and who had used the Jonkos tool. The perceived cost of rearing young stock was only available for 36 herds. In the 75 herds, the average herd size was 100 dairy cows. The average calculated total cost of rearing a heifer was €1,790. The average perceived total cost of rearing a heifer (including labour and housing costs) was €1,030.ConclusionMost Dutch farmers in the study underestimated the total cost of rearing. The Jonkos economic tool has the advantage that herd-specific information can be entered as input values. The output of the tool can improve the awareness of farmers about the total costs of rearing. This awareness can lead to a higher priority of young stock rearing and consequently to an improved quality of young stock rearing.