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Climate Forecast Tools for Livestock Producers

Explore climate-based decision support systems for managing livestock in Florida, tackling challenges like drought and flood. Learn from case studies and expected outcomes of social learning processes.

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Climate Forecast Tools for Livestock Producers

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  1. Climate Forecast Tools for Livestock Producers Norman Breuer and Kenny Broad (UM) Carla Roncoli and Todd Crane (UGA) Clyde Fraisse and Peter E. Hildebrand (UF) Victor Cabrera (NMSU) Presented at the NOAA CDWS Tallahassee October 2007

  2. Outline • Three Case Studies • Beef Cattle in NC Florida • Dairy DSS Process • Beef Cattle and Forage DSS Process • Lessons • Challenges

  3. Livestock and Forage in Florida 1.7 million head of cattle and calves Cattle and calves 330M $ Milk and milk products 371M $ Forage and hay 516M $ Total 1.22B $

  4. Livestock and Forage in Florida • Frequent drought conditions • Severe drought in 2006 and 2007 caused pasture and hay shortage • 2006 hay production was record low for the past 20 years • Most cattlemen were forced to feed supplements • Hay farmers experiencing high fertilizer prices • To cope with drought, ranchers were weaning early or selling off some animals at lower than average weights

  5. Research Sites Case Studies Dairy Beef Cattle Cow-calf Map: Google Earth

  6. Methodology

  7. Case study 1 Beef Cattle North Central Florida • Four Sondeos (multidisciplinary, conversational, rapid team surveys) conducted, 1999-2001 • Linear program developed from secondary data, Sondeo information, and interviews • Models calibrated using Participatory Linear Programming (PLP) • DSSAT and Ceres used for summer and winter pasture crop models, respectively, and connected to the model through stocking rate

  8. Case study 2 Steps in Participatory DSS Building • Identify needs through initial contacts with producers • Create or adapt models • Develop prototype DSS using secondary data • Discuss model with stakeholders Cabrera, Breuer et al. 2006, submitted

  9. Case study 2 Steps in Participatory DSS Building • Include farmers, consultants, extension agents, government officials, and university faculty in process • Elicit input on structure and function of systems, coefficients, parameters, and individual modules • Develop final DSS and return to stakeholders for validation Cabrera, Breuer et al. 2006, submitted

  10. Case study 2 Dairy DS participation in North Florida • We conducted: • 26 separate stakeholder interactions of • different types (Sondeos, focus groups, • meetings) • 90 persons of which • 38 were farmers or farm managers

  11. Case study 3 Social Learning • Collective action rather than individual • Issues and activities of central concern to community members • Scientists, practitioners, and producers co-develop technologies • Increased likelihood of adaptation through adoption of climate-based DSS

  12. Case study 3 Background • Declining profits • Increased cost of energy (pumping) • Increased transportation cost • Pressure from increasing land prices • Variable climate • Frequent drought or flood

  13. Case study 3 Learning Community • Buck Island Ranch - MacArthur Agro-ecology Research Center • Ona Range Cattle Research and Education Center • Private cattle ranchers in the vicinity

  14. Case study 3 Learning Community • Objective • Develop methods to help cattle ranching remain viable in South Florida with climate-based DSS • For this: strong climate component needed

  15. Case study 3 Cow-Calf Production in SFL:Drought and Flood An old-timer saying goes “In SFL you are never more than 3 days away from a flood or 3 weeks away from a drought.” Research needed: • Climate-production relationships • Climate and ranch economics • Potential for hay production • Sod production • Sale of water retention or release rights to SFWMD

  16. Case study 3 Framework for Social Learning Process Adapted from Christensen and Sriskandarajah 2006

  17. Case study 3 Expected Results of Social Learning • Development of local capacity • Increased understanding of relationship between climate and rates of conception in cows and growth rate of calves • Operational, open access DSS • Scientific papers • Sustainability of industry (label of origin)

  18. Lessons • Participation is a good beginning • Social learning is a logical progression from past research • Potential adaptations exist • Co-development aids adoption • Value of climate information may be easier to measure community level where adaptations can be well documented

  19. Challenges and Opportunities • Cow-calf is different from single crop • Multiple variables require better statistical methods • Climate plays an important role • Users are intensely interested • A multidisciplinary approach is needed • Learning communities must be facilitated in order to develop

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