A presentation about a modelling tool to estimate the economic impact of implementing precision livestock technologies (PLF) on farm. Presented at the EAAP/EU-PLF Conference, 2014, in Copenhagen, Denmark
Can we estimate the economic benefit of precision livestock technologies
1. Economic modelling to evaluate the benefits
of precision livestock farming technologies
C. Kamphuis, W. Steeneveld, and H. Hogeveen
2. Tools that monitor animal production, welfare and health
● Automatically, continuously, and (near) real-time
Support farmers
● Decision-support management
● Reduce dependency on human labour
Emerging research field
● Scientific research:126 studies, 139 technologiesa
Precision Livestock Farming
aRutten et al., 2013
(Inter)national projects International conferences
3. Provides a lot of data but no decision-support informationa
No (perceived) economic benefita
Undesirable / unclear cost-benefit ratioa,b
Clear data on cost-benefit are lacking
● Most important limiting factor for commercialisationc
Slow uptake of PLF tools by farmers
a Russel and Bewley, 2013
b Hogeveen and Steeneveld, 2013
c Banhazi et al., 2012
4. Strong need for economic models to
increase adoption of PLF on farms
Partial Budget (e.g., Jago et al., 2011)
● Better informed purchase decisions
● Only consider cost and benefits that change
● Straightforward and easy to comprehend
Bio-economic simulation (e.g., Bewley et al., 2010; Rutten et al., 2014)
● Accurately estimate economic impact
● Simulating all biological effects
● Complex and only applicable for PLF modelled
5. To be used by suppliers of PLF technologies
Estimate economic impact of PLF at farm level
Generic for any PLF technology
Generic for any country or region
Development of a Value Creation Tool
6. The Value Creation Tool
SCENARIO 1
SCENARIO 2
Labour Intensive
Labour Extensive
Capital Extensive Capital Intensive
SCENARIO 3 SCENARIO 4
A tool for dairy, fattening pig and broiler farms each
Four farm scenarios
Input data include
● technical parameters
● data on investments, prices and costs
8. Average Costs € / cow
Buildings and inventory 800
rearing costs 270
PLF technology 0
Machinery and equipment 276
Land 331
Feed 801
Interest cattle 77
Customer work 200
Fertilizer and pesticides 53
Health care (preventive) 50
Health care (curative) 100
AI and Breeding 80
Miscellaneous costs 200 +
Total costs excluding labour 3,237
Labour 468 +
Total costs including labour 3,705
Output parameters
Average Revenues € / cow
Milk 3,159
Livestock sales 259
Other revenues 166+
Total revenues 3,584
Net Farm Income (NFI) / cow = revenues – costs = €-121
Total NFI = €-9,657
Labour Income (LI) = €27,783
10. Economic benefit automated heat detection
No PLF Heat detection (PLF)
Total NFI -9,657 -2,295
Total LI 27,783 35,145
Economic benefit / year €7,362 / year
Automated heat detection
(Nedap N.V., Groenlo, the Netherlands)
11. The Value Creation Tool for Scenario 1
Labour intensive, capital extensive farming
Adapting default values (no PLF)
● National database
● Assuming same magnitude of effects
No PLF Heat detection (PLF)
Total NFI 3,415 7,230
Total LI 40,855 44,460
Economic benefit €3,815 / year
12. Discussion
A tool for dairy, fattening pig and broiler farms
Made available by EU-PLF
The Value Creation Tool is easy to use by suppliers
but...
Clear view of affected parameters and to what extend
Not accounting for other areas affected by PLF
Thank Chairman and audience.
Start with intro and why adoption rates on farms remain low.
Followed by introducing Value Creation Tool, specifically developed for suppliers to estimate potential economic impact of their product.
Illustrating the tool with example heat detection on dairy farms.
Aware entire session is called ‘Precision Livestock Farming’, so perhaps unnecessary.
But, think of PLF similarly for duration of this presentation.
PLF tools measure ‘something’, for example a cow’s activity, automatically, continuously and (near) real-time.
PLF aims at helping end-users in their decision-taking management processes or at reducing dependency on human labour. Examples, pedometers can aid in insemination decisions, automatic milking replace a significant amount of hard and repetitive labour.
PLF is emerging, supported with 126 publications on 139 PLF technologies past decade. Moreover, national and EU-funded projects that focus on implementation of PLF on-farms (SDF and All Smart Pigs). Finally, emerging international conferences dedicated to PLF (smartagrimatics and PDC in 2016, mentioned by Wilma Steeneveld.
So, a lot is going on in the field of PLF, but....
Uptake of PLF tools by farmers has been disappointingly low. So, what could explain so many parties being interested in PLF but don’t see that enthusiasm when looking at adoption rates.
Most common explanation of low adoption rates is that PLF tools provide data but no decision-support information. Example of automatic weighing of dairy cows: offered by many suppliers, but unclear what to do with this data by farmer or as additional data for detection models.
Other reasons all include economic reasons, confirmed by study Wilma. Famers see or perceive no economic benefit, or there is an undesirable or unclear cost-benefit ratio.
Lack of economic data most important limiting factor for commercialisation of a PLF tool.
So, strong need for economic models that shed some light in cost-benefits of PLF tools when implemented on farm.
Straightforward models, like the one developed by Jago in 2011. Used partial budget approach to help farmers making better decision for investing in automated heat detection. Partial budgets are simple models that only consider costs and benefits that change. Partial budgets are easy to understand and to work with, but in case of Jago, the information should be already available for farmers thinking of investing. Economic data already available at suppliers end.
Bewley and Rutten, both in audience, developed bio-economic simulation models. Very accurate simulating all biological effects at herd level to estimate economic effect. But, very complex and only applicable to the PLF tool that is modelled under the simulated conditions.
So, more economic data slowly becomes available. We believe, however that economic data should already be available at the suppliers’ end and thus that a model should provide suppliers insight in economic potential of their tool at the farm level.
Second part of the presentation, introduction to Value Creation Tool.
Development of this tool is part of EU-PLF project, Smart Farming for Europe. EU-PLF develops blue print for current and emerging suppliers to aid with development of PLF concepts that add value to decision-making processes on farm. The value creation tool is part of that blueprint.
Tool to estimate economic impact of implementing a PLF product at farm level. The tool should be easy to use by suppliers, should provide an accurate enough estimate of the potential economic impact, applicable for multiple PLF concepts, irrespective for a country and or region.
EU-PLF focusses on three type of farms, dairy, fattening pigs and broiler farms. Because these farms differ in many aspects, a separate tool developed for each of these farm types.
In addition, four default scenarios for each farm type defined, based on two economic parameters. The four default situations describe a wide range of farming situations and can provide insight for suppliers what farming situation can benefit most of PLF tools.
Each default situations has technical parameters and data on investments, prices and costs that are used as input by value creation tool. Information retrieved from accountancy data, farmers, national databases reporting on official year numbers.
Illustrate the tool using a labour intensive and capital intensive dairy farm example. These farms can be found in countries like the Netherlands. Default values for this scenario in this presentation therefore derived from a national database LEI, an agricultural economic institute.
Revenues per cow by adding up all revenues from milk production and sales of livestock and e.g., roughage.
Costs per dairy cow
Revenues and costs to compute net farm income per cow by subtracting costs per cow from revenues per cow. In this case, minus 121 euro per cow. The total net farm income is calculated by multiplying NFI per cow with herd size which was 80. Total net farm income for default labour and capital intensive farm is therefore minus 10.000 euro.
The tool also computes Labour income, which adjusts the net farm income for the costs of own labour. In this case, labour income is almost 28000 euro per annum. This could be seen as the farmer’s yearly income
To illustrate how PLF could affect farm economics I use automated heat detection. Meeting with Dutch supplier, presented tool with default values for scenario 2, and discussed input parameters affected by their tool and with what magnitude.
Parameters influenced by their PLF tool were reduction percent replacement heifers from 38 to 30%, an increase in milk production from 8100 to 8200. Furthermore, feed costs increase from 600 to 690 E/c/y, costs for AI decrease from 80 to 70 E/c/y. Feed costs per cow will increase and heat detection improves so fewer inseminations necessary, reducing AI costs from 80 to 70 E/c/y.
Additionally, costs are made because farmers invests in automated heat detection. For 80 cows, costs for this PLF tool are 10,000, with a depreciation of 10% and maintenance costs of 1% of the investment per year.
Input parameter values were adapted according to their views and alternative NFI and LI were computed.
Where total NFI was minus 10000 euro and LI was 28000 euro for a labour and capital intensive farm without automated heat detection, investment in automated heat detection resulted in NFI of minus 2200 Euro and LI of 35000 euro.
This is a positive economic benefit of 7000 euro’s per year when a labour and capital intensive farm would invest in automated heat detection.
What would be the impact if automated heat detection would be used on a labour intensive and capital extensive farm? These farms can be found in regions like Ireland, so I used as much information I could find from that region. Adapted default values of the tool to calculate default NFI and LI for this scenario. Assumed same effect and magnitude of effects as used in the previous example.
Using this new information the tool calculated NFI and LI for the default and PLF situation. Again, there is a positive economic benefit when automated heat detection is applied, but magnitude of this affect is less than for labour and capital intensive farm.
Some final notes: value creation tools are developed for dairy, fattening pig and broiler farms to estimate impact of PLF concepts on farm economics. I used the dairy farm as an example but tools for the other animal groups work similarly. Tools will be available in the future.
The exercise with NEDAP demonstrated that the Tool was very easy to use for suppliers but
Suppliers need to have a clear view which parameters are affected and to what extend. Moreover, this tool does not account for other areas that may be affected by PLF tools. These affects should not be underestimated! Automatic milking systems are a very clear example of that. Despite studies demonstrating that these systems have no economic benefit at farm level, their social impact is huge. It is this social impact that made these systems the most successful PLF concepts of the past 20 years.
To finish this presentation with some acknowledgements,
Thank EU-PLF for funding this project
Members of the Value Creation Work Package within EU-PLF study for their input on the development of the value creation tool.
Finally, Rudie Lammers from Nedap NV, who kindly contributed Nedaps view on which parameters were affected and in improving the value creation tool along the way.