Linkage between predictive transmitting ability of a genetic index, potential milk production, and a dynamic model

peer-reviewedWith the increased use of information and communication technology–based tools and devices across traditional desktop computers and smartphones, models and decision-support systems are becoming more accessible for farmers to improve the decision-making process at the farm level. However, despite the focus of research and industry providers to develop tools that are easy to adopt by the end user, milk-production prediction models require substantial parameterization information for accurate milk production simulations. For these models to be useful at an individual animal level, they require the potential milk yield of the individual animals (and possibly potential fat and protein yields) to be captured and parameterized within the model to allow accurate simulations of the interaction of the animal with the system. The focus of this study was to link 3 predicted transmitting ability (PTA) traits from the Economic Breeding Index (PTA for milk yield, fat, and protein) with potential index parameters for milk, fat, and protein required as inputs to a herd-based dynamic milk model. We compiled a data set of 1,904 lactations that included different experiments conducted at 2 closed sites during a 14-yr period (2003–2016). The treatments implied different stocking rates, concentrate supplementation levels, calving dates, and genetic potential. The first step, using 75% of the data randomly selected, was to link the milk, fat, and protein yields achieved within each lactation to their respective PTA value, stocking rate, parity, and concentrate supplementation level. The equations generated were transformed to correspond to inputs to the pasture-based herd dynamic milk model. The equations created were used in conjunction with the model to predict milk, fat, and protein production. Then, using the remaining 25% data of the data set, the simulations were compared against the actual milk produced during the experiments. When the model was tested, it was capable of predicting the lactation milk, fat, and protein yield with a relative prediction error of <10% at the herd level and <13% at the individual animal level

Using models to establish the financially optimum strategy for Irish dairy farms

peer-reviewedDetermining the effect of a change in management on farm with differing characteristics is a significant challenge in the evaluation of dairy systems due to the interacting components of complex biological systems. In Ireland, milk production is increasing substantially following the abolition of the European Union milk quota regime in 2015. There are 2 main ways to increase the milk production on farm (within a fixed land base): either increase the number of animals (thus increasing the stocking rate) or increase the milk production per animal through increased feeding or increased lactation length. In this study, the effect of increased concentrate feeding or an increase in grazing intensity was simulated to determine the effect on the farm system and its economic performance. Four stocking rates (2.3, 2.6, 2.9, and 3.2 cow/ha) and 5 different concentrate supplementation strategies (0, 180, 360, 600, and 900 kg of dry matter/lactation) resulting in 20 different scenarios were evaluated across different milk, concentrate, and silage purchase prices. Each simulation was run across 10 yr of meteorological data, which had been recorded over the period 2004 to 2013. Three models—the Moorepark and St Gilles grass growth model, the pasture-based herd dynamic milk model, and the Moorepark dairy systems model—were integrated and applied to simulate the different scenarios. Overall, this study has demonstrated that the most profitable scenario was a stocking rate of 2.6 cow/ha with a concentrate supplementation of 600 kg of dry matter/cow. The factor that had the greatest influence on profitability was variability of milk price.The authors acknowledge the financial support of the FP7 GreenHouseMilk Marie Curie project and the funding from the Research Stimulus Fund 2011 administered by the Department of Agriculture, Fisheries and Food (Dublin, Ireland; project 11/S/132)

PastureBase Ireland: A grassland decision support system and national database

peer-reviewedPastureBase Ireland (PBI) is a web-based grassland management application incorporating a dual function of grassland decision support and a centralized national database to collate commercial farm grassland data. This database facilitates the collection and storage of vast quantities of grassland data from grassland farmers. The database spans across ruminant grassland enterprises – dairy, beef and sheep. To help farmers determine appropriate actions around grassland management, we have developed this data informed decision support tool to function at the paddock level. Individual farmers enter data through the completion of regular pasture cover estimations across the farm, allowing the performance of individual paddocks to be evaluated within and across years. To evaluate the PBI system, we compared actual pasture cut experimental data (Etesia cuts) to PBI calculated outputs. We examined three comparisons, comparing PBI outputs to actual pasture cut data, for individual DM yields at defoliation (Comparison 1), for cumulative annual DM yields including silage data (Comparison 2) and, for cumulative annual DM yields excluding silage data (Comparison 3). We found an acceptable accuracy between PBI outputs and pasture cut data when statistically analyzed using relative prediction error and concordance correlation coefficients for the measurement of total annual DM yield (Comparison 2), with a relative prediction error of 15.4% and a concordance correlation coefficient of 0.85. We demonstrated an application of the PBI system through analysis of commercial farm data across two years (2014–2015) for 75 commercial farms who actively use the system. The analysis showed there was a significant increase in DM yield from 2014 to 2015. The results indicated a greater variation in pasture growth across paddocks within farms than across farms

Associations between paratuberculosis ELISA results and test-day records of cows enrolled in the Irish Johne's Disease Control Program

peer-reviewedThe effect of the Mycobacterium avium ssp. paratuberculosis (MAP) ELISA status on test-day milk performance of cows from Irish herds enrolled in the pilot national voluntary Johne's disease control program during 2013 to 2015 was estimated. A data set comprising 92,854 cows and 592,623 complete test-day records distributed across 1,700 herds was used in this study. The resulting ELISA outcome (negative, inconclusive, and positive) of each cow within each year of the program was used to allocate the cow into different scenarios representing the MAP status. At MAPscenario1, all cows testing ELISA nonnegative (i.e., inconclusive and positive) were assigned a MAP-positive status; at MAPscenario2 only cows testing ELISA-positive were assigned a MAP-positive status; at MAPscenario3 only cows testing ELISA nonnegative (inconclusive or positive) and gathered exclusively from herds where at least 2 further ELISA nonnegative (inconclusive or positive) cows were found were assigned a MAP-positive status; at MAPscenario4 only cows testing ELISA-positive that were gathered exclusively from herds where at least 2 further ELISA-positive cows were found were assigned a MAP-positive status. Milk outputs based on test-day records were standardized for fat and protein contents (SMY) and the effect of MAP ELISA status on the SMY was estimated by a linear mixed effects model structure. The SMY mean difference recorded at test day between cows with a MAP-positive status and those with a MAP-negative status within MAPscenario1 was estimated at −0.182 kg/test day; the mean difference was −0.297 kg/test day for MAPscenario2; for MAPscenario3 mean difference between MAP-positive status and MAP test-negative cows was −0.209 kg/test day, and for MAPscenario4, the difference was −0.326 kg/test day.This study was carried out as part of the ICONMAP (Improved Control of Mycobacterium avium ssp. paratuberculosis) multidisciplinary research program, funded by the Research Stimulus Fund 2011, administered by the Irish Department of Agriculture, Food and the Marine

Cow and herd-level risk factors associated with mobility scores in pasture-based dairy cows

peer-reviewedLameness in dairy cows is an area of concern from an economic, environmental and animal welfare point of view. While the potential risk factors associated with suboptimal mobility in non-pasture-based systems are evident throughout the literature, the same information is less abundant for pasture-based systems specifically those coupled with seasonal calving, like those in Ireland. Therefore, the objective of this study was to determine the potential risk factors associated with specific mobility scores (0 = good, 1 = imperfect, 2 = impaired, and 3 = severely impaired mobility) for pasture-based dairy cows. Various cow and herd-level potential risk factors from Irish pasture-based systems were collected and analyzed for their association with suboptimal mobility, whereby a mobility score of 0 refers to cows with optimal mobility and a mobility score ≥ 1 refers to a cow with some form of suboptimal mobility. Combined cow and herd-level statistical models were used to determine the increased or decreased risk for mobility score 1, 2, and 3 (any form of suboptimal mobility) compared to the risk for mobility score 0 (optimal mobility), as the outcome variable and the various potential risk factors at both the cow and herd-level were included as predictor type variables. Cow-level variables included body condition score, milk yield, genetic predicted transmitting ability for ‘lameness’, somatic cell score, calving month and cow breed. Herd-level variables included various environmental and management practices on farm. These analyses have identified several cow-level potential risk factors (including low body condition score, high milk yield, elevated somatic cell count, stage of lactation, calving month, and certain breed types), as well as various herd-level potential risk factors (including the amount of time taken to complete the milking process, claw trimmer training, farm layout factors and foot bathing practices) which are associated with suboptimal mobility. The results of this study should be considered by farm advisors when advising and implementing a cow/herd health program for dairy cows in pasture-based systems

A review of precision technologies in pasture-based dairying systems

peer-reviewedGrassland-based dairy production provides multiple benefits to farmers and to the wider society, but the European grassland area has been significantly reduced during the last decades. This paper aims to explore societal and economic options to support grassland-based dairy production in Europe. In the recent past, several societal initiatives have emerged to stimulate grassland-based dairy production: treaties, premiums and market concepts. When developing stimulating initiatives, the mindset of the farmer should be taken into account. Farmers are key actors when it comes to maintaining and improving grassland-based dairy production systems since they decide on the day-to-day management of the farm. To maintain grassland-based dairy production and to preserve the associated ecosystem services, it is, therefore, necessary to clearly show the importance of this production system for society to the farmers (show the customer perspective) and to support this by valuing the products from these systems accordingly. “New” business models should financially reward farmers for their added value contributions in delivering ecosystem services

Pertinence du modèle Moorepark-St Gilles Grass Growth dans les conditions climatiques de l'Ouest de la France

Pertinence du modèle Moorepark-St Gilles Grass Growth dans les conditions climatiques de l'Ouest de la France. Journées de l'Association Française pour la Production Fourragère (AFPF

Achieving high milk production performance at grass with minimal concentrate supplementation with spring-calving dairy cows: actual performance compared to simulated performance

The aim of high-profitability grazing systems is to produce milk efficiency from grazed pasture. Thereis very limited information available on the milk production capacity of dairy cows offered a grass-onlydiet for the main part of her lactation. In this study, spring-calving dairy cows were managed to achievehigh milk production levels throughout the grazing season without supplementation. The calving dateof the herd was 12 April; the herd had access to grass as they calved and remained full-time at grass until20 November. During this period the herd produced 5,513 kg milk, while receiving 130 kg concentratesupplementation. The herbage mass offered was maintained at 1,490 kg dry matter ha‑1 (>3.5 cm) andthe herd grazed to 4.5 cm across the grazing season. The weekly milk production performance achievedwas then compared to the Herd Dynamic Milk model. The root mean square error (RMSE) and relativepredicted error (RPE) for milk yield (as expressed weekly across lactation) was 1.47% and 6.09%,respectively, for body condition score the RMSE and RPE were 0.093% and 4.14% respectively. Offeringspring-calving cows high levels of high quality grass resulted in excellent animal performance, however,this can be achieved with very good daily grazing management

Using models to establish the financially optimum strategy for Irish dairy farms

Determining the effect of a change in management on farm with differing characteristics is a significant challenge in the evaluation of dairy systems due to the interacting components of complex biological systems. In Ireland, milk production is increasing substantially following the abolition of the European Union milk quota regime in 2015. There are 2 main ways to increase the milk production on farm (within a fixed land base): either increase the number of animals (thus increasing the stocking rate) or increase the milk production per animal through increased feeding or increased lactation length. In this study, the effect of increased concentrate feeding or an increase in grazing intensity was simulated to determine the effect on the farm system and its economic performance. Four stocking rates (2.3, 2.6, 2.9, and 3.2 cow/ha) and 5 different concentrate supplementation strategies (0, 180, 360, 600, and 900 kg of dry matter/lactation) resulting in 20 different scenarios were evaluated across different milk, concentrate, and silage purchase prices. Each simulation was run across 10 yr of meteorological data, which had been recorded over the period 2004 to 2013. Three models—the Moorepark and St Gilles grass growth model, the pasture-based herd dynamic milk model, and the Moorepark dairy systems model—were integrated and applied to simulate the different scenarios. Overall, this study has demonstrated that the most profitable scenario was a stocking rate of 2.6 cow/ha with a concentrate supplementation of 600 kg of dry matter/cow. The factor that had the greatest influence on profitability was variability of milk price.The authors acknowledge the financial support of the FP7 GreenHouseMilk Marie Curie project and the funding from the Research Stimulus Fund 2011 administered by the Department of Agriculture, Fisheries and Food (Dublin, Ireland; project 11/S/132)

Grass growth prediction in Ireland to improve grazing management practice

International audienceIn pasture-based systems, farmers need to make daily management decisions to ensure that their livestock have enough feed and that it is of high quality, both during the grazing season and during the housing period. Being able to predict grass growth for the following week at farm level helps farmers to betteranticipate variations in grass growth. The Moorepark St Gilles Grass Growth (MoSt GG) model works at the paddock and farm level. The model is currently used on 78 farms across Ireland. Each Tuesday, grass growth predictions for the following week are communicated to the farmers involved in the project as well as the grassland industry. The predictions are published in a weekly newsletter and other media sources. While only 78 farms are currently involved in predictions, the model will soon be incorporated into PastureBase Ireland (PBI) to predict specific farm growth for all PBI users