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More presentations at: http://www.trufflemedia.com/agmedia/conference/2012-decreasing-resources-increasing-regulation-advance-animal-agriculture
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More presentations at https://www.youtube.com/channel/UCeUDeS810OcOfuEYwj1oHKQ
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This presentation by University of Maryland Extension Sheep & Goat Specialist Susan Schoenian discusses the University of Maryland's meat goat performance testing program.
Field testing—A conceptual framework for innovation platform impact assessmen...ILRI
The document describes a study that tested a conceptual framework for assessing the impact of innovation platforms (IPs) established by the MilkIT project in Tanzania. Data was collected through surveys of IP members and non-members on structure, conduct, and performance variables. Regression analysis found that increased communication quality and information sources related to improved feed availability and access to inputs. Other factors like herd size, land size, and feed storage also influenced performance. While IP members generally had better outcomes, differences from non-members were not statistically significant based on the sample. The framework provided initial insights but could be improved with a larger sample size.
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The document discusses the value of genomic predictions in beef cattle breeding. It provides examples showing how genomic-enhanced EPDs (GE-EPDs) increase the reliability and precision of EPDs by incorporating genomic test results. Using GE-EPDs allows producers to identify genetic differences between animals earlier and reduce selection risk. Simulation studies show the total value of genetic improvement from genomic testing can be $204 to $1,119 per animal. Programs like Show-Me-Select that use GE-EPDs see premiums of $130 to $460 per head for genomically-tested replacement females. The document also discusses how genomic testing can aid in selection for local genetic adaptation across cattle environments and regions.
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The slide pack provides an overview of the Impact Lab itself, as well as presentations on:
- Consumer Perspective of Food (Will Jackson, AHDB)
- Sustainable Diets & Role of Livestock (Professor Michael Lee, Rothamsted Research)
- (contact info for) Linking Ruminant Emissions to Climate Impact, and the Sustainability of Production Systems (Dr. John Lynch, University of Oxford)
- Sustainable Beef Supply Chain (Ian Wheal, Breedr)
- Farmers’ Perspective of Food Sustainability (Patrick Holden, Sustainable Food Trust
- Growth Hub Opportunities for Agritech (David Hynd, Heart of the South West Growth Hub)
The Impact Lab offers free support to businesses looking to create new products and services by capitalising on Big Data and Environmental opportunities. It also helps academics and scientists to commercialise their expertise.
The Impact Lab is part-funded by the European Regional Development Fund. It is a 3-year collaborative project between: University of Exeter, Exeter City Futures, Met Office, University of Plymouth, Plymouth College of Art, Plymouth Marine Laboratory and Rothamsted Research.
For further information and general enquiries, please contact: info@impactlab.org.uk
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The sustainable use of animal genetics in developing countriesILRI
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The document summarizes strategies to increase genetic gains in staple crops through plant breeding. It discusses how the genetic gains initiative aims to shorten breeding cycles from 15 to 5 years while more accurately selecting traits. This would be done by optimizing breeding population management, using genomics tools to aid selection, increasing selection intensity, shortening breeding cycles, and improving delivery of new varieties to farmers. The initiative also aims to increase the rate of varietal replacement in farmers' fields so the average age of varieties is less than 10 years.
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This document summarizes the agenda and goals of a conference on mainstreaming livestock value chains in developing countries. The conference aims to strengthen the linkages between livestock impact assessments, value chain analysis, and economic modeling, and to identify pro-poor livestock policies based on existing analysis. An outline is provided for the conference sessions on livestock's role in development, demand and market trends, policy challenges, and contributions from the International Livestock Research Institute. Input from participants identifies gaps between household and economy-level analysis, a lack of focus on incomes and profitability, and weaknesses across the livestock policy cycle as key problems limiting livestock's development potential.
Food safety challenges in smallholder pig value chains in Vietnam: From an as...ILRI
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This document summarizes organic farming research projects at the University of Nebraska. It discusses three main grants focused on improving organic farming systems across the state. The first grant aims to create certified organic research plots, improve production through research, and extend results to the public. The second grant supports developing wheat cultivars and cropping systems optimized for organic production. The third grant provides research on organic nutrient management, weed control methods, and their impact on biodiversity. Key areas of research discussed include cover crops, weed control through flaming, breeding wheat varieties for organic systems, and assessing biodiversity using the Healthy Farm Index. The document emphasizes developing partnerships with organic organizations and farmers to support the research.
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Dr. Rod Hill - Controlling the Cost of Beef Production Through Improving Feed Efficiency
1. Controlling the Cost of Beef
Production through
Improving Feed Efficiency
Rod Hill1*, Cassie M. Welch1,
J.D. Wulfhorst2, Stephanie Kane2
Larry D. Keenan3 and Jason K. Ahola4
1Department of Animal and Veterinary Science, and
2Social Sciences Research Unit, University of Idaho
3Red Angus Association of America and
4Department of Animal Science, Colorado State University
2. Introduction
• Who cares about Feed Efficiency?
• What about Feed Efficiency in the
feedlot?
• What about Cowherd efficiency?
3. Part 1
A time of increasing costs and cost
volatility
6. U.S. average annual prices for
alfalfa and other hay
(source: USDA-NASS Monthly Agricultural Prices summarized by LMIC)
7. U.S. average annual price for corn
(source: USDA-NASS Monthly Agricultural Prices summarized by LMIC)
8. Cow-calf profit?
• Standardized performance analysis
(SPA) – IA and IL – 225 producers
Miller et al., 2001
• Feed cost largest of 12 factors
• 52 – 57% of variation in profit
9. DISCUSSION
• What do you control?
• Price paid for product?
• Investment in feed and other inputs?
• What percentage of total cost of beef
production is carried in Cow-calf
sector?
11. Measuring Efficiency
Gross Feed efficiency (feed-to-gain ratio)
feed consumed
weight gain
Limitations
Highly correlated with growth rate
Confounded with: maturity pattern, size,
appetite, composition of gain, heifer/cow
size
Favors genetically-larger cattle
= gross feed efficiency
12. Residual Feed Intake (RFI)
Actual vs. predicted feed intake (h2 = 0.16 - 0.43)
(prediction is based on: gain and body weight)
Independent of growth and mature patterns
Interpretation:
+ RFI: Intake higher than requirements
– RFI: Eat less and produce same weight gain
New Measure of Efficiency
Herd et al., 2003; Johnson et al., 2003; Richardson et al., 2001
14. Residual Feed Intake (RFI)
Baker et al., 2006
Circled points = 3.2 lbs/d ADG with 40.7 vs. 56.1 lbs/d feed intake
RFI value = difference between the line and each data point
15. • Consume less feed but weigh the same at
harvest vs. high RFI progeny
• Reduction of feed costs w/o negatively affecting
growth, reproduction, carcass, or meat quality
• Environmental outcomes
• reduced impact on rangelands
• reduction of methane, manure, nitrogen
Value of RFI
16. Feeding Patterns
(2%)
Protein Turnover
Tissue Metabolism and
Stress
(37%)
Body Composition
(5%)
Other
(27%)
Activity
(10%)
Digestibility
(10%) Heat Increment of
Fermentation
(9%)
Contributions of biological mechanisms to variation in residual feed intake as determined from
experiments on divergently selected cattle (from Richardson and Herd, 2004).
Contributing Mechanisms to
Variation in RFI
17. What About Correlations with
Other Traits?
•Production and reproduction?
•Birth weight
•Fertility
•Pasture grazing?
•Heifers and steers
•Mature cows
• (impact on rangelands)
• (major contributor to cost of production)
•End product?
•Carcass yield
•Palatability
18. DISCUSSION
• Why does a negative value indicate
superior performance?
• -ve RFI is superior to +ve RFI
• What is the problem with using
feed conversion ratio?
19. Part 3
What do we know about producer
understanding and adoption of RFI?
21. Design Summary
Bulls: High vs Low ME EPD
• Three cohorts
• Approximately 300 calves
•Steer progeny followed through slaughter product
quality
• Physiological indicators (plasma IGF-1)
•
23. Goal: Find livestock superior for RFI
Traditional test
70-day period, individual intake
Alternative tests
Gene markers
Physiological tests
Need extensive RFI testing to validate
marker technologies.
New Physiological Tests
25. Factors affecting beef cattle
producer perspectives on feed
efficiency
J.D. Wulfhorst J.K. Ahola, S.L.
Kane, L.D. Keenan and R.A. Hill
Journal of Animal Science. 88: 3749-3758.
26. Overview
• Conduct social assessment
– Willingness & barriers to adoption of RFI
– National scope – but focus groups – Idaho &
RAAA
• Will address some key responses
26
27. Approach
• Survey to 1,888 producers
– Stratified random sample
• Idaho Cattle Association (n = 488)
• Red Angus Association of America (RAAA)
members (n = 2208 / 700)
• RAAA bull buyers (n = 5,325 / 700)
– 35 Questions
27
28. Survey Response Rates / Categories
• 49.9% (902) - 57%, 50%, and 45%.- responded
– 59% commercial cow-calf producers
– 41% seedstock or combined operations
• Mean (± SD) respondent age / experience
– seedstock producers, 52.6 ± 0.8 yr, with 25.3 ± 0.9
yr of experience
– commercial producers, 56.1 ± 0.7 yr, with 30.7 ± 0.8
yr experience
• Regions based on NCBA (pooled)
28
30. Respondents’ – Cattle Operation
description
• 77% - British breeds exclusively
• 19% - Combination of British and
Continental breeds
30
31. Commercial
Producers
Seedstock
Producers
Variable Mean Mean
No. of cows 223.4 205.8
No. of bulls 12.8 23.2
Calves sold at weaning, % 49.8 38.2
Calves sold at yearling, % 34.3 40.1
Calves retained through harvest,
%
16.4 21.2
No. of cows marketed/yr 25.9 21.0
No. of calves marketed/yr 209.7 147.5
No. of bulls purchased/yr 2.6 1.8
Price per bull, $/animal 2,325.6 3,097.1
Characteristics of beef cattle operations
Source: Social survey of commercial and seedstock cattle producers, 2008, administered by University of Idaho,
Social Science Research Unit (SSRU).
32. Sources of having heard about RFI
Category No (%) Yes (%) SEM
Breed association magazine 46.3 53.7 3.2
Scientific journal 90.5 9.5 1.8
Weekly livestock newspaper 69.3 30.7 3.0
University Extension 72.3 27.7 2.8
Beef Improvement Federation 88.3 11.7 1.9
Veterinarian 94.3 5.7 1.6
Websites 95.8 4.2 1.3
From a neighbor, friend, or
colleague
83.7 16.2 2.7
Source: Social survey of commercial and seedstock cattle producers, 2008, administered by University of
Idaho, Social Science Research Unit (SSRU).
Note: Producer responses to the question “Where did you hear about RFI? Please mark ALL that apply.”
33. Level of knowledge about the use of
RFI as a measure of genetic value
Category % SEM
No knowledge 23.6 2.8
Limited knowledge 60.8 3.2
Knowledgeable 14.3 2.0
Very knowledgeable 1.3 1.3
Source: Social survey of commercial and seedstock cattle producers, 2008, administered by University of
Idaho, Social Science Research Unit (SSRU).
Note: Producer responses to the question “How knowledgeable are you with the use of RFI as a measure of
genetic value?”
34. Perceived willingness-to-pay to have
bulls evaluated for RFI
(seedstock producers only)
Category % SEM
$0 more / head 28.1 2.6
$1 – 100 more / head 51.1 2.7
$101 – 200 more / head 13.1 1.9
$201 – 300 more / head 3.9 1.1
$301 – 450 more / head 1.2 0.7
More than $450 more / head 2.5 0.8
Source: Social survey of commercial and seedstock cattle producers, 2008, administered by University of Idaho,
Social Science Research Unit (SSRU).
Note: Seedstock producer responses to the question “If the equipment to collect individual feed intake data were
available to you, how much more would you be willing to pay on a per-head basis to have your bulls evaluated for
RFI?”.
35. Perception of whether bulls are
worth more if evaluated for RFI and
found to be more efficient
Category % SEM
No 15.9 1.4
Yes 75.9 1.7
Don’t know 8.2 1.1
Source: Social survey of commercial and seedstock cattle producers, 2011, administered by University of
Idaho, Social Science Research Unit (SSRU).
Note: Producer responses to the question “Do you think bulls (and/or their semen) that were evaluated for
RFI and found to be more efficient should be worth more?”
36. Perception of demand among
bull customers for feed efficiency or
RFI data
Category % SEM
No demand 3.2 0.7
Little demand 12.9 1.3
Moderate demand 38.8 1.9
Great deal of demand 41.2 1.9
Don’t know 3.9 0.8
Source: Social survey of commercial and seedstock cattle producers, 2011, administered by University of Idaho,
Social Science Research Unit (SSRU).
Note: Seedstock producer responses to the question “How much demand is there among your bull customers
for feed efficiency or RFI data?”
37. Perceived willingness-to-pay
among buyers for bulls evaluated
for feed efficiency
Category % SEM
0% (no additional value) 17.9 2.0
1-10% 45.0 2.6
11-25% 19.5 2.0
26-50% 3.8 1.0
Greater than 50% 1.1 0.6
Don’t know 12.6 1.7
Source: Social survey of commercial and seedstock cattle producers, 2011, administered by University of
Idaho, Social Science Research Unit (SSRU).
Note: Seedstock producer responses to the question “How much more are your buyers willing to pay for bulls
evaluated for feed efficiency?”
38. DISCUSSION
• What is the cost to RFI-test a bull?
• Should an RFI-tested bull be worth
more?
• Should we continue to research
RFI?
39. Acknowledgements
This work was supported by National Research Initiative
Competitive Grant no. 2008-55206-18812 from the USDA
Cooperative State Research, Education, and Extension Service
&
by the National Science Foundation and
Idaho EPSCoR under award number EPS 0447689.
41. Project Key-points:
• Studies on the progeny of Red Angus bulls
divergent for Maintenance Energy EPD.
• Begin to characterize these Red Angus bulls for
Residual Feed Intake
• Study the relationships between Maintenance
Energy EPD, RFI and other production traits
• Study the underlying physiological drivers of
variation in RFI.
42. Project Objectives (1-2):
• Research Objective 1: Evaluate and quantify the
relationships between RFI and product quality in progeny
of Red Angus sires divergent for maintenance energy
EPD.
• Research Objective 2: Evaluate and quantify the
relationships between RFI and finishing phase FE in the
steer progeny of Red Angus sires divergent for
maintenance energy EPD.
43. Project Objectives (3-5):
• Research Objective 3: Determine the relationship
between plasma IGF-1 at weaning and RFI in progeny of
Red Angus sires divergent for maintenance energy EPD.
• Research Objective 4: Determine the relationship
between ME EPD and RFI EPD in Red Angus bulls
divergent for ME EPD.
• Research Objective 5: Establish baseline and follow-up
measures of producer perceptions about the perceived
unique benefits and/or costs associated with RFI, as well
as the efficacy of outreach programs in conveying this
information.
44. Project Outreach Objectives:
Outreach Objective 1: Develop outreach materials using
research results that are suitable for delivery to all levels
within the industry via several methods including field days
and symposia, train-the-trainer events, popular press and
scientific journal articles, final reports, and other outreach
methods via partnerships with industry.
Outreach Objective 2: Disseminate information to
producers via internet-based outreach, primarily through
the recently-created Beef Cattle Community of Practice with
eXtension.
45. Design Summary
Bulls: High vs Low ME EPD
CED BW WW YW Milk TM ME MEacc HPG CET ST Marb REA Fat
HIGH Average 7.00 -0.13 32.50 61.83 16.33 32.67 14.00 55.67 11.67 8.17 12.17 0.00 -0.02 0.01
Max 10.00 1.90 39.00 68.00 25.00 41.00 20.00 60.00 13.00 13.00 17.00 0.14 0.30 0.02
Min 2.00 -1.50 26.00 54.00 9.00 26.00 12.00 51.00 9.00 2.00 5.00 -0.12 -0.27 -0.01
CV 0.50 -9.08 0.13 0.09 0.34 0.16 0.23 0.06 0.15 0.50 0.33 - -9.86 2.10
LOW Average 13.83 -3.73 28.33 55.00 16.00 30.33 -8.83 53.67 7.17 2.83 12.33 0.08 0.12 0.00
Max 19.00 1.60 37.00 62.00 29.00 46.00 -4.00 58.00 10.00 8.00 17.00 0.15 0.52 0.03
Min 2.00 -7.40 17.00 46.00 10.00 19.00 -13.00 49.00 5.00 -5.00 8.00 -0.03 -0.05 -0.04
CV 0.47 -0.89 0.28 0.13 0.50 0.34 -0.39 0.07 0.30 1.72 0.25 0.96 1.75 -
47. Results
Relationship between ADG and DMI of Red Angus-sired steers
and heifers measured for RFI
Average Daily Gain, ADG (lb/d)
1.0 1.5 2.0 2.5 3.0 3.5
DryMatterIntake,DMI(lb/d)
12
14
16
18
20
22
24
26
A (ME = -11)
B (ME = -7)
C (ME = 1)
D (ME = 11)
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48. Discussion
• Mean RFI value is 0.0
– Range typical
• Correlations typical with other studies
– Carcass & end-product traits not yet evaluated
• No relationship between RFI and ADG
– RFI independent of growth rate
49. Discussion
• RFI correlated with DMI
• Measuring RFI – no sex differences detected
• Progeny RFI range is large
– 35% variation in DMI for same growth rate
50. Summary and Next Steps
• Sire ME and progeny RFI
– No formal conclusions
– Data obtained from small population
• Data provide preliminary indications
• RFI and ME EPD relationship will become clearer
• A need to generate more data on mature cows