Jehan Frans Ettema

Modelling the economic impact of three lameness causing diseases using herd and cow level evidence.

Diseases to the cows hoof, interdigital skin and legs are highly prevalent and of large economic impact in modern dairy farming. In order to support farmers decisions on preventing and treating lameness and its underlying causes, decision support models can be used to predict the economic profitability of such actions. An existing approach of modelling lameness as one health disorder in a dynamic, stochastic and mechanistic simulation model has been improved in two ways. First of all, three underlying diseases causing lameness were modelled: digital dermatitis, interdigital hyperplasia and claw horn diseases. Secondly, the existing simulation model was set-up in way that it uses hyper-distributions describing diseases risk of the three lameness causing diseases. By combining information on herd level risk factors with prevalence of lameness or prevalence of underlying diseases among cows, marginal posterior probability distributions for disease prevalence in the specific herd are created in a Bayesian network. Random draws from these distributions are used by the simulation model to describe disease risk. Hereby field data on prevalence is used systematically and uncertainty around herd specific risk is represented. Besides the fact that estimated profitability of halving disease risk depended on the hyper-distributions used, the estimates differed for herds with different levels of diseases risk and reproductive efficiency.

Effect of including genetic progress in milk yield on evaluating the use of sexed semen and other reproduction strategies in a dairy herd

The objective of this study was to explore the importance of including genetic progress in milk yield when evaluating different reproductive strategies in a dairy herd by simulation modeling. The model used in this study was SimHerd V, a dynamic and mechanistic Monte Carlo simulation model of a dairy herd including young stock. A daily increasing trend describing genetic milk yield potential of the sire population was included in the model. The inaccuracy of assuming that replacement heifers have the same (milk yield) potential as the cows present in the herd was hereby dealt with. Improving estrus detection rate from 0.45 to 0.80 increased gross margin (GM) per cow-year by €20 when genetic progress was not included in the model. When genetic progress was included in the model, then the same improvement in estrus detection decreased the GM per cow-year by €7.4. This reduced effect was explained by a lower replacement rate in consequence of the improved estrus detection and thereby a slower genetic progress in the herd. There was a reduced effect of including genetic progress on GM when surplus heifers were sold selectively based on breeding values. Repeated insemination with sexed semen on the superior half of all heifers reduced GM by €8 per cow-year when genetic progress was not included and increased the GM by €16 per cow-year when genetic progress was included in the model. Including genetic progress reduced the losses caused by lower conception and estrus detection rates and had a minimal effect with regard to postponing first insemination. This study has proven that it is important to include genetic progress in decisions on reproduction strategies in a dairy herd.

Genomic testing interacts with reproductive surplus in reducing genetic lag and increasing economic net return

Until now, genomic information has mainly been used to improve the accuracy of genomic breeding values for breeding animals at a population level. However, we hypothesize that the use of information from genotyped females also opens up the possibility of reducing genetic lag in a dairy herd, especially if genomic tests are used in combination with sexed semen or a high management level for reproductive performance, because both factors provide the opportunity for generating a reproductive surplus in the herd. In this study, sexed semen is used in combination with beef semen to produce high-value crossbred beef calves. Thus, on average there is no surplus of and selection among replacement heifers whether to go into the herd or to be sold. In this situation, the selection opportunities arise when deciding which cows to inseminate with sexed semen, conventional semen, or beef semen. We tested the hypothesis by combining the results of 2 stochastic simulation programs, SimHerd and ADAM. SimHerd estimates the economic effect of different strategies for use of sexed semen and beef semen at 3 levels of reproductive performance in a dairy herd. Besides simulating the operational return, SimHerd also simulates the parity distribution of the dams of heifer calves. The ADAM program estimates genetic merit per year in a herd under different strategies for use of sexed semen and genomic tests. The annual net return per slot was calculated as the sum of operational return and value of genetic lag minus costs of genomic tests divided by the total number of slots. Our results showed that the use of genomic tests for decision making decreases genetic lag by as much as 0.14 genetic standard deviation units of the breeding goal and that genetic lag decreases even more (up to 0.30 genetic standard deviation units) when genomic tests are used in combination with strategies for increasing and using a reproductive surplus. Thus, our hypothesis was supported. We also observed that genomic tests are used most efficiently to decrease genetic lag when the genomic information is used more than once in the lifetime of an animal and when as many selection decisions as possible are based on genomic information. However, all breakeven prices were lower than or equal to €50, which is the current price of low-density chip genotyping in Denmark, Finland, and Sweden, so in the vast majority of cases, it is not profitable to genotype routinely for management purposes under the present price assumptions.

Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.

Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.

Short communication: Economics of sex-biased milk production

In a recent data study using 2.4 million lactations of 1.5 million cows, it was reported that gestation of a female calf in the first parity increases cumulative milk production by approximately 445kg over the first 2 lactations. The reported effect in this study is large and remarkable because it has not been found before. To our knowledge, the economic implications of this or any other sex bias have not been studied. The objective of the current study was to quantify the reported influence of fetal sex across lactations by using a simulation model of a dairy herd including youngstock. Two scenarios were evaluated and compared with a scenario in which cows and heifers were exclusively bred with conventional (nonsexed) semen. In the first scenario, sexed semen was used moderately-on 30% of all heifers and 30% of the first parity cows. A second scenario was studied in which sexed semen was used intensively-on all heifers and 50% of the first-parity cows. The simulated proportion of cows giving birth to 2 consecutive heifers increased from 23% when using exclusively conventional semen up to 31 and 48% when using sexed semen moderately and intensively, respectively. The proportion of cows having 2 consecutive bulls decreased from 27% (conventional semen only) to 20 and 8% when using sexed semen moderately and intensively, respectively. When incorporating the sex bias in the simulation model, the simulated milk yield in the scenario in which sexed semen was used moderately increased by 48kg of energy-corrected milk (ECM) per cow/yr, compared with only 36kg of ECM when not incorporating the sex bias in the model. For the scenario in which sexed semen was used intensively, milk yield increased by 66 and 99kg of ECM when excluding and including the sex bias, respectively. The economic implications of the assumed sex bias were €4.0 and €9.9 per cow/yr, in the scenarios in which sexed semen was used moderately and intensively, respectively.