In  previous  sessions,  we  learned  about  how  to  iden4fy  and  assess  risks  and  quan4fy  their  impacts.  We  learned  various  approaches  on  assessing  the  capacity  of  stakeholders’  to  manage  different  risks.  In  this  session,  we’ll  learn  approaches  to  priori4zing  risks.  Again,  why  do  we  priori4ze?  So  that  we  can  op4mize  available  resources  to  beDer  manage  those  risks  that  are  having  the  biggest  adverse  impacts  on  incomes  and  livelihoods,  and  sector  growth.    

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At   this  point  we  need   to  understand  which   risks  are  more   likely   to   cause   the  most  adverse  shocks  to  specific  stakeholder  groups,  target  sub-­‐sectors  and  the  sector  as  a  whole.    The  primary  criteria  used  to  priori4ze  risk  are:  (1)  probability  of  event  (or  frequency  of  occurrence),  and  (2)  severity  of  impact.  However,  lack  of  clarity  about  the  meaning  of  different  terms  might  introduce  undesired  bias.      Strategic   Priori,es-­‐   Certain   stakeholders,   objec4ves   or   regions   may   be   priori4zed  based  on  sub-­‐sector  context  or  client  preferences.    Regional  varia,ons  -­‐  There  may  be  a  need  to  account   for  different  regional  or  crop  risk   profiles   given   the   varia4ons   in   climate,   agro-­‐ecological   and   socio-­‐economic  condi4ons.    Recovery  period  -­‐  The  amount  of  4me  it  takes  stakeholders  to  recover  could  be  used  for  priori4za4on.  For  example:    

Short  term.  Single  produc4on  season/year.  Medium  term.  Impact  lasts  a  three  to  five  seasons  /  years.    Long  term.  Event  permanently  cripples  an  industry.    

 

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Classifica4on  is  based  on  the  occurrence  of  historical  events  that  have  records  and  their  impact  is  known.    But  in  some  cases,  historical  records  might  not  be  available.    In  such  cases,  interviews  with  the  stakeholders  and  key  informants  and  their  individual  (subjec4ve)  experiences  about  the  frequency  of  occurrences  can  help  classify  the  risk  in  an  appropriate  category.    

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Historical  occurrence  of  events  is  the  best  way  to  determine  the  probability  of  events  occurring  in  the  future.  Frequency  of  occurrence  of  risk  events  can  be  es4mated  from  news,   databases   and   stakeholder   input.   To   reduce   subjec4ve   bias,   define   clear,  measurable  terms  such  as  those  in  this  table.      

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The  magnitude  of  losses  from  each  risk  reflects  the  severity  of  impacts.  This  in  turn  is  a  func4on  of  two  different  factors:  1)  frequency  of  the  risk  event  and  2)  likelihood  (or  probability)  of  loss  from  a  risk  event.  This  o\en  can  be  calculated  by  mul4plying  the  financial  loss  sustained  by  an  actor  by  the  frequency/spread  of  the  event).  Since  the  objec4ve   of   the   assessment   is   to   provide   a   na4onal   snapshot,   losses   should   be  assessed  at  na4onal  level,  using  disaggregated  regional  and  district  level  data,  where  available.        

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How  to  summarize  (illustrate)  priori4za4on  factors      

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There  are  different  approaches  to  visualizing  scope  and  frequency  of  vola4lity  and  risk  events.  The  first  step  is  mapping  via  a  visual  4meline  of  loss  events.  This  will  provide  insights  into  what  are  the  principal  causes  of  observed  vola4lity  and  resul4ng  losses.  Time-­‐series  FAOSTA  Produc4on  indices  (e.g.,  crops,  good,  livestock)  can  be  useful  or  Agricultural  GDP  growth.    

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As  we  saw  earlier,  iden4fying  the  specific  sources  of  risks  that  may  have  contributed  to  observed  shocks  based  on  best  available  primary  and  secondary  sources  allows  us  to  ascribe  aDribu4on  

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Another  tool  leverages  the  results  of  the  crop  loss  analysis  to  compare  scope  and  frequency  of  losses  across  commodi4es  to  see  which  are  most  impacted,  in  terms  of  frequency  and  cumula4ve  financial  losses  This  aids  in  highligh4ng  which  commodi4es  are  most  suscep4ble  to  risk-­‐induced  losses  and  therefore  might  benefit  from  closer  scru4ny  during  the  solu4ons  assessment.      In  the  risk  assessment  of  Paraiba,  Brazil,  sugar  cane  and  fruit,  especially  grapefruit,  due  to  their  large  share  in  the  total  agricultural  output  value  of  Paraiba,  are  most  suscep4ble  to  losses,  and  so,  might  be  given  priority  in  the  risk  management  strategy.    

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Another  way  is  assessing  losses  by  risk  event  type.      This  bubble  graph  provides  another  way  to  illustrate  severity  of  impact  by  illustra4ng  es4mated  losses  vs.  frequency  of  event,  and  thus,  the  priori4za4on  of  risks.      Financial   losses   at   level   of   produc4on   are   calculated   by   es4ma4ng   the   number   of  hectares  of  lost  produc4on  mul4plied  by  the  value  of  that  produc4on  (per  hectare).        Frequency   of   event   is   calculated   by   the   number   of   4mes   the   event   has   occurred  divided  by  the  total  number  of  risk  events.        In  both  cases,  assump4ons  are  derived   in  part   from  stakeholder   input  and  the  data  gathering  process.    

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Par4culalry  when  quan4ta4ve  data  is  scarce,  focus  groups  interviews  can  be  useful  to  inform   the   development   and   comparison   of   how   stakeholders   perceive   risks   from  one  region  to  the  next.  These  bar  charts  illustrate  the  risk  priori4es  for  rice  producers  in  different  regions  of  Guyana.  Flooding  was  the  highest  ranked  priority   in  Region  5  whereas  red  rice  was  the  highest  ranked  priority  in  Regions  3  and  6.      

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To  help  inform  the  risk  priori4za4on  process,  it  is  helpful  for  the  Team  to  collec4vely  iden4fy  the  leading  risks  for  each  target  commodity  to  iden4fy  similari4es  and  divergences.  This  list  can  then  be  more  easily  aggregated  at  sector  level.  

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Ranking  risks  by  region  is  yet  another  lens  that  aids  the  risk  priori4za4on  process.    

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This   chart   illustrates   how   observed   risks   can   also   be   weighted   by   percep4ons   of  stakeholder  vulnerability:  1)  expected   losses  when  a  given  risk  event  manifests  and  perceived  levels  of  exis4ng  stakeholder  capacity  to  manage  that  risk.  Risks  in  the  top  le\  quadrant  are  given  the  highest  priority.      

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This   table   illustrates   the   priori4za4on   of   risk   to   the   cocoa   supply   chain   in   Ghana  based  on  to  two  indicators:    (1)  probability  of  event,  and  (2)  severity  of  impact.    For  the  Y-­‐axis,  each  risk  is  ranked  from  remote  to  highly  probable…    

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This   table   illustrates   the   priori4za4on   of   risk   to   the   cocoa   supply   chain   in   Ghana  according  to  two  indicators:    (1)  probability  of  event,  and  (2)  severity  of  impact.    

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