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

Predicting grass growth: mhe MoSt GG model

Predicting grass growth: mhe MoSt GG model. Irish dairyin

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)

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

Usefulness of nitrogen application in heavy soils compared to more favourable land in Ireland – utilisation of the Moorepark Grass Growth model

Ireland’s temperate climate is favourable for grass growth, and efficiently grazed grass is the cheapest feed available on most dairy farms. In heavy soils, grass growth conditions and utilisation of grazed grass are less favourable than in free draining soils and will affect the dairy farm performance. The Moorepark Grass Growth model was used to compare the impact of weather, soil type and nitrogen (N) application on grass growth, number of grazing events and N leaching. Weather data (from the year 2015) and soil from two locations, Moorepark (free draining soil) and Athea (heavy soil), were used. Overall, the soil type and weather conditions for the heavy soil farm are limitations to grass growth

Development and evaluation of the herd dynamic milk model with focus on the individual cow component

The herd dynamic milk (HDM) model is a dynamic model capable of simulating the performance of individual dairy animals (from birth to death), with a daily time step. Within this study, the HDM model is described and evaluated in relation to milk production, body condition score (BCS) and BCS change throughout lactation by comparing model simulations against data from published experimental studies. The model's response to variation in genetic potential, herbage allowance and concentrate supplementation was tested in a sensitivity analysis. Data from experiments in Ireland and France over a 3-year period (2009-11) were used to complete the evaluation. The aim of the Irish experiment was to determine the impact of different stocking rates (SRs) (SR1: 3.28 cow/ha, SR2: 2.51 cow/ha) on key physical, biological and economic performance. The aim of the French experiment was to evaluate over a prolonged time period, the ability of two breeds of dairy cows (Holstein and Normande) to produce and to reproduce under two feeding strategies (high level and low level) in the context of compact calving. The model evaluation was conducted at the herd level with separate evaluations for the primiparous and multiparous cows. The evaluation included the two extreme SRs for the Irish experiment, and an evaluation at the overall herd and individual animal level for the different breeds and feeding levels for the French data. The comparison of simulation and experimental data for all scenarios resulted in a relative prediction error, which was consistently <15% across experiments for weekly milk production and BCS. In relation to BCS, the highest root mean square error was 0.27 points of BCS, which arose for Holstein cows in the low feeding group in late lactation. The model responded in a realistic fashion to variation in genetic potential for milk production, herbage allowance and concentrate supplementation

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