Work package 4

Work package 4. Economic assessment of integrated grazing and AM technologies

Objectives

  • Assess the economic implications of integrating grazing with AM systems
  • Assess the interaction between capital investment, labour requirements and running costs for
  • integrated grazing and AM technologies
  • Optimise the impact of a variety of innovative feeding and grazing technologies on dairy farm
  • efficiency
  • Develop a decision support tool to assist dairy farmers to economically optimize their production
  • system integrating grazing and AM for different regions in the EU


WP Leader: TEAGASC, Dr. Pat Dillon

Description of work

Investments required for AM systems are higher than for conventional milking systems and thus fixed costs are higher. However more milk with less labour means that the costs of milking per kg of milk will decrease. In this WP, firstly, the economic implications of integrating grazing with AM on farm profitability will be established in different participating countries (source of data: accountancy networks). Secondly, an economic assessment will be undertaken to determine the implications of integrating cow grazing with AM and the interaction between capital investment and operating costs (including labour costs). The data source for this economic assessment will come from WPs 2 and 3 as well as data sources such as the International Farm Comparison Network IFCN (www.ifcnnetwork.org). Thirdly, selected model/models will be used to model different scenario combinations of AM technologies and cow management, where the proportion of grazed grass in the cows’ diet varies from 90 to 10%, with the aim of optimizing the integrated production system in different partner countries. Finally a Decision Support Tool will be developed to allow farmers to make informed, economically sensible decisions on the management of their integrated pasture and AM production system, irrespective of the participant country they reside in.

Tasks

Task 4.1 Economic implications of integrated grazing and AMS

Leader: AU, Frank Oudshoorn

Involved partners: TEAGASC, WLR, AU, LTO, CNIEL

An economic comparison of dairy farms using AM with and without grazing (and using similar milking plant design) will be completed using on-farm data. These on-farm data will be obtained from accountancy networks. Farms from France, the Netherlands and Denmark will be included. The analysis will be completed using a number of years’ data with differing proportions of grazed grass in the diet as an indicator of the grazing proportion. The analysis will be completed for a number of key benchmark criteria, including variable costs, total costs and net profit. The comparisons will be completed on a per litre of milk produced, per hectare of land farmed and per farm basis.

Task 4.2 Interaction between capital investment, labour requirement and running costs

Leader: TEAGASC, Laurence Shalloo

Involved partners: TEAGASC, WLR

An economic assessment will be undertaken to determine the implications of integrating automatic milking technologies with cow grazing systems as influenced by the interaction between capital investment and operating costs (including labour costs). The data source for this economic assessment will come from WP 2 and WP 3 (’Monitor Farms’) as well as data sources such as the IFCN. The timeframe under which the different systems will be evaluated will be decided and all of the available data sources within the consortium countries will be compiled. The analysis will compare the introduction of AM technologies to farming systems with different capital infrastructure components. Capital costs specific to the consortium country will be included as well as differing regulations around tax and if there is any capital grants available in one country or another. Labour information will be ascertained from experimental evidence and detailed surveys in specific countries (this will be influenced by grazing component). Information from WP 3 with the ‘Monitor Farms’ will also be used. The models will be set up including the capital costs, all of the running costs associated with AM and all of the labour costs. For each dataset, the AM technology will be compared to conventional milking technology. The interaction between age of current milking facility and investment in the new technology will be investigated in each of the analyses completed. Stochastic analysis will be completed where possible, with country specific probability distributions for key input and output variables for each of the analyses completed.

Task 4.3 Optimising integration of grazing with AMS

Leader: TEAGASC, Laurence Shalloo

Involved partners: TEAGASC, WLR, AU, ULg

A number of farm bio-economic models have been developed within the consortium, e.g. DairyWise – WLR (Schils et al., 2007); Moorepark Dairy System Model – TEAGASC (Shalloo et al., 2004). A detailed analysis of the concepts, structure, capabilities and interface of the different models will be carried out in order to identify the strengths and weaknesses of each of them for use in AUTOGRASSMILK. The most appropriate model/models within the context of a country or region will be used to evaluate the impacts of a variety of innovative feeding and grazing technologies identified in WP 1 on dairy farm efficiency. The model/models will be the basis for the development of decision support tools to help farmers achieve greater integration of grazing with AM. The models will be used to assess the effects of optimising various components of the production system and AM technologies. The factors such as the feeding strategy, milking frequency, type of cow, number of units and grassland management will be optimised in the models with the objective to maximise the overall profitability of the production systems. It is anticipated that the optimised systems will be different across the consortium countries examined as there are substantial differences in feed prices and milk price structures. Therefore, for example, the optimum milking frequency (influenced by type of cow and AM technology) may be different by country and system.

Task 4.4 A Decision Support Tool to quantify interaction of feed system and AM system

Leader: WLR, Agnes van den Pol-van Dasselaar

Involved partners: TEAGASC, WLR, LTO, AU, CNIEL, ULg, CDL, SDA,

An interactive web based decision support tool will be developed for farmers that can be used around grazing and AM based decisions. The objective of this simple tool will be to increase the profitability of integrated cow grazing and AM for farmers within the EU. The tool will test the effect of different sized grazing components of the cows’ diet, allowing more informed decisions to be taken by farmers. The tool will also be designed to take farm fragmentation into account. It will be capable of informing farmers in north-western Europe on the decision-making process around the introduction of an AM system from an economic perspective. Outputs from Tasks 4.1, 4.2, 4.3 and Work Packages 1, 2 and 3 will provide data for this task.