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Jock Anderson Climate Prediction and Agriculture Lessons Learned and Future Challenges from an Agriculture Development Perspective.

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Presentation on theme: "Jock Anderson Climate Prediction and Agriculture Lessons Learned and Future Challenges from an Agriculture Development Perspective."— Presentation transcript:

1 Jock Anderson Climate Prediction and Agriculture Lessons Learned and Future Challenges from an Agriculture Development Perspective

2 Why this outsider speaker?  Queensland farmer/drought manager  Decision analysis background  Early interest in climate  Risk management as a way of life  Decades on agricultural development  Impact assessment as major hobby  Including contemporary IFPRI work  Past endeavor on CGIAR, Bank-supported research & extension  Impact of impact studies?

3 Semantic Issues Persist  Weather, Climate, Climate Change  Timescales critical but open to opinion  But let me commend the paper of Holger Meinke!  “Forecast” covers many interpretations  Categoric vs Probabilistic  Concrete/specific vs descriptive  Not that this is the only field with such semantic issues, e.g., “Risk”  Uncertainty and Climate Change  John Zillman, Warwick McKibbin, Aynsley Kellow Policy Paper #3

4 Ex Ante or Ex Post A (prior beliefs) B (update beliefs) (model possible response) C (observe outcome of event) (also observe agent’s actions) Receive Forecast Signal Realized Climate Outcome Ex AnteEx Post Modeled behavior Simulated Benefit Measured behavior Realized Benefit

5 Measuring Forecast Value  Information has value when it can influence behavior  It usually also has a cost  So, whether it has +ve net value is an empirical question Evidence on this has been sparse in this Workshop: should be key item! Indeed, has CLIMAG been worthy?

6 One user-friendly Bayesian manual COPING WITH RISK IN AGRICULTURE Second Edition J. Brian Hardaker, Ruud B.M. Huirne, Jock R. Anderson and Gudbrand Lien CABI Publishing, Wallingford 2004

7 Forecasting in an Uncertain World  Priors represent uncertainty held before a forecast  Forecast information is captured in likelihood probabilities  Posterior probabilities come from combining these  Such revision cycles can be treated sequentially, dynamically

8 Towards an analytic approach  Take a multi-enterprise production function  Often estimated pragmatically, simplistically, badly  But if done right, provides a framework worthy of our attention Conventional Inputs (e.g. land) Unconventional Inputs (e.g. infrastructure) Technical knowledge (e.g. R&D investment) Uncontrollable factors (e.g. weather) Ag. Output

9 Mark’s Pragmatic Reduced Form  Relationship tying farm profits (P) to climate information (K) and other on- farm characteristics Conventional Inputs (e.g. land) Unconventional Inputs (e.g. infrastructure) Climate Information (knowledge sources) Uncontrollable factors (e.g. weather)

10 Behavioral Factors  Representing preferences is a possibly significant challenge…Risk-averse?  Ability of farmers to adjust should be accounted: Representing constraints?  Farmers and others are all swimming in the stormy seas of risk, with and without formal climate forecasts… Are such forecasts a marginal part of the picture?

11 All easier said than done  Estimation is “demanding”  Of conceptualization, incl dynamics & participatory insights  Of data, especially in LDCs  Of “estimational” /“modeling” skills  Of optimization skills  Of interpretation skills

12 Challenges of Assessment  Many challenges, even if one can borrow or adapt existing models, such as the now-popular crop growth models  Recall Mark noting that “Dis-entangling the underlying structural relationships is non-trivial”!  So, much research, intrinsically multi- disciplinary, is seemingly needed

13 Ex Post Assessment in Ag Research  Mark spoke on this extensive (competitor) literature…and I can not get into it here, except to raise it as a “problem”  But some of the research products that will have potentially high payoffs in responding to climate predictions present new evaluation tasks (e.g., short-cycle varieties that can “escape” or better “endure” some droughts)

14 Wider Cogent Research Themes  Understanding the mechanisms diverse rural communities use for  Managing risk e.g., borrowing, selling,..  Coping with risk e.g., calling on rellies  Shifting from risk e.g., migrating  Agro-meteorologists may not have spent much time grappling with rural financial systems, futures markets etc. but maybe they will have to? Or work more with….

15 Some Policy Dimensions  A few selective aspects of farmer risk management to illustrate a widened agenda  Property rights (especially land)  Other enabling aspects such as PSD (incl index insurance), investment climate,  Emergency policy and intervention history, safety net processes, etc.  Climate policy? Informed by climate research? Understanding & prediction!

16 Co- finances premium Government pays true risk cost Premium GIIF EC Co-financing to cover Transaction Costs Payout according to index trigger Reinsurance and Capital markets Risk transfer for market premium Government Bank Primary Insurer (Re)insurance contract based on risk Index Borrowers


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