Climate Prediction and Agriculture

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

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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

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Including contemporary IFPRI work

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Past endeavor on CGIAR, Bank-supported research & extension

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Impact of impact studies?

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Semantic Issues Persist

Weather, Climate, Climate Change Timescales critical but open to opinion But let me commend the paper of Holger Meinke!

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“Forecast” covers many interpretations Categoric vs Probabilistic Concrete/specific vs descriptive

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Not that this is the only field with such semantic issues, e.g., “Risk”

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Uncertainty and Climate Change John Zillman, Warwick McKibbin, Aynsley Kellow www.ASSA.edu.au Policy Paper #3

Ex Ante or Ex Post

A

(prior beliefs) Receive Forecast Signal

B

Realized Climate Outcome (update beliefs) (model possible response)

C

(observe outcome of event) (also observe agent’s actions)

Ex Ante Ex Post

Modeled behavior Simulated Benefit Measured behavior Realized Benefit

Measuring Forecast Value

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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?

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

Forecasting in an Uncertain World

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Priors represent uncertainty held before a forecast

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Forecast information is captured in likelihood probabilities

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Posterior probabilities come from combining these

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Such revision cycles can be treated sequentially, dynamically

Towards an analytic approach

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Take a multi-enterprise production function Often estimated pragmatically, simplistically, badly But if done right, provides a framework worthy of our attention

Ag. Output

Q

t



f

(

X

t

,

Z

t

,

K

t

,

U

t

) Conventional Inputs (e.g. land) Unconventional Inputs (e.g. infrastructure) Technical knowledge (e.g. R&D investment) Uncontrollable factors (e.g. weather)

Mark’s Pragmatic Reduced Form

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Relationship tying farm profits (P) to climate information (K) and other on farm characteristics

P

t



t

,

t

,

t

,

t

) Conventional Inputs (e.g. land) Unconventional Inputs (e.g. infrastructure) Climate Information (knowledge sources) Uncontrollable factors (e.g. weather)

Behavioral Factors

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Representing preferences is a possibly significant challenge…Risk-averse?

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Ability of farmers to adjust should be accounted: Representing constraints?

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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?

All easier said than done

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Estimation is “demanding”

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Of conceptualization, incl dynamics & participatory insights

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Of data, especially in LDCs Of “estimational” /“modeling” skills Of optimization skills Of interpretation skills

Challenges of Assessment

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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”!

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So, much research, intrinsically multi disciplinary, is seemingly needed

Ex Post Assessment in Ag Research

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Mark spoke on this extensive (competitor) literature…and I can not get into it here, except to raise it as a “problem”

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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)

Wider Cogent Research Themes

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Understanding the mechanisms diverse rural communities use for

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Managing risk e.g., borrowing, selling,..

Coping with risk e.g., calling on rellies Shifting from risk e.g., migrating

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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….

Some Policy Dimensions

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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,

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Emergency policy and intervention history, safety net processes, etc.

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Climate policy? Informed by climate research? Understanding & prediction!

Risk transfer for market premium GIIF

Reinsurance and Capital markets

EC Co-financing to cover Transaction Costs Co finances premium (Re)insurance contract based on risk Index Government pays true risk cost Premium Government Bank Primary Insurer Borrowers Payout according to index trigger