Identifying cogs in the adoption wheel: opportunities to target extension
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Identifying cogs in the adoption wheel: opportunities to target extension



by Dr Rick Llwellyn

by Dr Rick Llwellyn
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Identifying cogs in the adoption wheel: opportunities to target extension Identifying cogs in the adoption wheel: opportunities to target extension Presentation Transcript

  • Identifying cogs in the adoption wheel - opportunities to target extension Rick Llewellyn CSIRO Sustainable Ecosystems Adelaide, SA
    • Initially decision makers do not know everything that matters
    • Hiebert
    Adoption as a learning process Learning of relative advantage An economic basis
  • Targeting information and extension
    • Many projects have aims of extensive adoption and impact
    • Most projects don’t have close personal contact with all potential adopters
    • Many projects involve practices that aren’t ‘new’
    • Many projects have information and learning as their primary tool
  • Key Points
    • Understanding adoption decisions so that you can see where you can make a difference
    • You can identify and quantify the key (non) adoption drivers
    • You can identify where information and learning can be most effective
    • You can target extension and consider information effectiveness
  • Identifying common drivers
  • Steps to more effective targeting of information
    • Explore factors that may influence the relative value of the innovation for individual growers on their farm
    • Collect quantitative data from a representative sample of growers, including perceptions
    • Identify inconsistencies, high uncertainties and likely misperceptions
    • Conduct analyses to identify variables that have the biggest influence on the likelihood of adoption
    • Identify what influential variables can be influenced by learning (if any) and targeted R, D and/or E
  • Adoption of no-till and conservation farming practices
    • Public good aim of reduced soil erosion
    • Still the focus of major public investment
  • Adoption of no-till cropping practices % farmers using some no-till GRDC, SANTFA, DAFF (2008)
  • Factors influencing no-till adoption 82% of decisions correctly predicted (logit and duration analysis) Source: D’ Emden et al. 2007 (SA, Vic, NSW, WA 2003)
    • Location (region/state) and average rainfall
    • Perceived soil moisture conserving benefits and improved seeding timeliness
    • Relative price of glyphosate herbicide
    • Prior year much drier than average
    • Higher education
    • Perceived relative effectiveness of pre-emergent weed control
    • Higher participation in extension activities
    • Use of directly paid consultant
    • Years since first awareness of nearby no-till adopter
  • No-till adoption strategies
    • Early-season weed control
    • Water use efficiency benefits
    • Benefits of ability to seed on time
    • Ability of no-till to reduce erosion
  • Where to now? % farmers using some no-till Llewellyn & D’Emden and 2008
  • Extent of no-till use
    • % using no-till % users using all NT
    • Year 03 08 13* 08
    • Vic Mallee 40 68 88 44
    • WA Northern 84 92 96 64
    • SA Western EP 48 55 73 30
    • Adoption AND probably extent of use will have lower peak in some districts
    • Some disadoption (5% disadopted; 10% reduced area)
    • Still some opportunities to target some information
    • But most farmers now have some on-farm experience
    • Only 20% of non-adopters use a paid cropping adviser
    • Options for lower-cost ‘farm-specific’ information e.g. local clusters with shared agronomy consultant?
  • Location, location, location Walker et al. 2005. Distance from trial (km) Willingness to pay ($) The value placed on trial report information by WA growers
  • Considering learning and information quality Relative Advantage -> Prob. Actual Average Advantage of Practice Actual Variance Of Practice Grower perception of practice
  • Considering information quality Perception of trial results when considering own farm Perception of trial results when considering own farm Relative Advantage -> Prob. Observed trial
  • Factors associated with adoption of multiple integrated weed management practices 86% of decisions correctly predicted (logit)
    • Perception of higher ryegrass control (efficacy)
    • Perception of higher economic value of practices in farming system
    • Perception of a longer time until effective new herbicide
    • Level of uncertainty of when a new herbicide will become available
    • Higher use of information/extension
    • Higher education
    • Higher proportion of the farm cropped
    • Lower discount rate for future returns
    • The resistance status of the farm
  • 0 20 40 60 80 100 Efficacy (% weed reduction) Probability Perceived efficacy of weed seed capture
  • 0 20 40 60 80 100 Probability Perceived efficacy of weed seed capture Efficacy (% weed reduction) Non-adopters Adopters
  • Factors associated with first use of durum wheat in WA 84% of decisions correctly predicted (logit) Source: Nguyen et al. 2007 Past durum growing was not a significant predictor of future durum growing
    • Larger farm size
    • Involvement in cropping extension events
    • Perception of higher rust resistance of durum
    • Higher expected durum: bread wheat yield ratio
  • Recognising information and learning costs
    • “ .. a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it"
    • Herbert Simon
  • Recognising the appeal of ‘convenience agriculture’
    • Less managers per hectare ; More management demands
    • ‘ Attention’ is scarce and valuable
    • The challenge for ‘inconvenient’ agricultural & NRM practices
  •           The future of Mixed-Farming Agriculture A livestock management service for time pressured farmers
    • It is possible to:
    • Predict a high proportion of adoption decisions for a particular practice
    • Identify the common factors driving adoption decisions for a particular practice – including perceptions
    • Target R,D & E towards factors that are influential and can be influenced to improve decision making
    • Identify where further information is unlikely to have any impact on the adoption decision
    • Consider information quality and effectiveness
    • Novel ways to address ‘attention’ scarcity
  • Acknowledgements: GRDC, SANTFA, DAFF, Mallee Sustainable Farming Faculty of Natural and Agricultural Sciences, University of Western Australia CRC Australian Weed Management CRC Future Farm Industries Frank D’Emden, Michael Burton, Bob Lindner, David Pannell, Steve Powles, Sally Marsh, Amir Abadi, Tracey Gianatti, Bob McCown, Peter Carberry, Shahajahan Miyan, Ellen Walker, Van Nguyen, Thank You