BIODIVERSITY,,CLIMATE,CHANGE,AND,LIVELIHOODS:,A,STUDY,ON,ECONMOIC,AND,
ECOLOGICAL,SUSTAINABILITY,AMONG,COFFEE,PRODUCERS,IN,THE,HIGHLANDS,OF,
NICARAGUA,,
By,
Aniseh,Sjona,Bro,,
,

	
  
	
  

	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
A	
  DISSERTATION	
  
Submitted	
  to	
  
Michigan	
  State	
  University	
  
in	
  partial	
  fulfillment	
  of	
  the	
  requirements	
  
for	
  the	
  degree	
  of	
  
	
  
Community	
  Sustainability	
  –	
  Doctor	
  of	
  Philosophy	
  
	
  
2016	
  
	
  

	
  
	
  

	
  

ABSTRACT	
  
BIODIVERSITY,	
  CLIMATE	
  CHANGE	
  AND	
  LIVELIHOODS:	
  A	
  STUDY	
  ON	
  ECONMOIC	
  AND	
  
ECOLOGICAL	
  SUSTAINABILITY	
  AMONG	
  COFFEE	
  PRODUCERS	
  IN	
  THE	
  HIGHLANDS	
  OF	
  
NICARAGUA	
  	
  
By	
  
Aniseh	
  Sjona	
  Bro	
  
Efforts	
  to	
  slow	
  down	
  and	
  eventually	
  reverse	
  the	
  trend	
  of	
  climate	
  change	
  will	
  take	
  time,	
  and	
  
in	
  some	
  cases,	
  its	
  negative	
  impacts	
  will	
  be	
  felt	
  long	
  before	
  long-­‐‑term	
  solutions	
  to	
  this	
  
problem	
  can	
  bear	
  fruit.	
  	
  Adaptation	
  and	
  mitigation	
  strategies	
  constitute	
  the	
  front	
  line	
  of	
  
attack	
  for	
  rural	
  households	
  in	
  low-­‐‑income	
  countries	
  that	
  rely	
  on	
  agricultural	
  production	
  
and	
  natural	
  resource	
  use	
  as	
  their	
  main	
  sources	
  of	
  income	
  and	
  growth,	
  and	
  whose	
  
livelihoods	
  are	
  threatened	
  by	
  climate	
  change.	
  	
  	
  
Coffee	
  in	
  Nicaragua	
  is	
  the	
  main	
  source	
  of	
  income	
  for	
  thousands	
  of	
  smallholder	
  producers,	
  
and	
  is	
  the	
  country’s	
  primary	
  agricultural	
  export.	
  	
  Given	
  the	
  vulnerability	
  of	
  coffee	
  to	
  the	
  
impacts	
  of	
  climate	
  change	
  there	
  is	
  a	
  growing	
  consensus	
  among	
  development	
  practitioners	
  
and	
  policy	
  makers	
  that	
  adaptation	
  strategies	
  are	
  necessary	
  and	
  in	
  some	
  cases	
  urgent	
  for	
  
those	
  producers	
  who	
  depend	
  on	
  coffee	
  production	
  as	
  their	
  main	
  source	
  of	
  income.	
  
In	
  this	
  dissertation,	
  comprised	
  of	
  three	
  empirical	
  papers,	
  I	
  study	
  the	
  coffee	
  sector	
  in	
  the	
  
Matagalpa	
  region	
  of	
  Nicaragua	
  and	
  explore	
  potential	
  pathways	
  for	
  climate	
  change	
  
adaptation	
  among	
  its	
  coffee	
  producers	
  by	
  studying	
  their	
  options	
  for	
  building	
  adaptive	
  
capacity	
  and	
  the	
  necessary	
  conditions	
  to	
  help	
  them	
  adopt	
  technologies	
  and	
  practices	
  that	
  
promote	
  successful	
  adaptation.	
  
The	
  focus	
  of	
  the	
  first	
  paper	
  is	
  on	
  the	
  characteristics	
  of	
  coffee	
  producers	
  in	
  northern	
  
Nicaragua	
  and	
  their	
  capacities	
  for	
  climate	
  change	
  adaptation	
  and	
  vulnerabilities	
  its	
  shocks,	
  

including	
  an	
  exploration	
  of	
  their	
  attitudes	
  towards	
  risk	
  through	
  the	
  use	
  of	
  experimental	
  
risk	
  games.	
  An	
  important	
  finding	
  from	
  this	
  study	
  is	
  that	
  household	
  food	
  insecurity	
  is	
  a	
  key	
  
determinant	
  of	
  risk	
  aversion,	
  and	
  that	
  income	
  is	
  relevant	
  insofar	
  as	
  it	
  results	
  in	
  greater	
  
food	
  security.	
  
In	
  the	
  second	
  paper,	
  I	
  use	
  choice	
  experiments	
  to	
  elicit	
  farmers’	
  preferences	
  for	
  shade	
  
incorporation	
  into	
  coffee	
  farms.	
  	
  Shade	
  is	
  an	
  important	
  farm	
  management	
  practice	
  in	
  coffee	
  
production	
  because	
  it	
  helps	
  to	
  protect	
  soils,	
  promote	
  biodiversity,	
  and	
  helps	
  to	
  mitigate	
  the	
  
impacts	
  of	
  higher	
  temperatures	
  induced	
  by	
  climate	
  change.	
  I	
  find	
  that	
  for	
  a	
  small	
  premium	
  
farmers	
  are	
  willing	
  to	
  incorporate	
  additional	
  shade	
  into	
  their	
  farms.	
  An	
  unexpected	
  finding	
  
from	
  this	
  study	
  is	
  that	
  farmers	
  are	
  not	
  willing	
  to	
  give	
  up	
  any	
  coffee	
  income	
  to	
  have	
  access	
  
to	
  pesticides	
  for	
  their	
  farms,	
  a	
  likely	
  reflection	
  of	
  the	
  recent	
  leaf	
  rust	
  outbreak	
  in	
  the	
  
country	
  and	
  the	
  poor	
  institutional	
  response	
  to	
  the	
  outbreak.	
  
Finally,	
  I	
  analyze	
  the	
  degree	
  to	
  which	
  cooperatives	
  can	
  help	
  farmers	
  adopt	
  a	
  set	
  of	
  ten	
  
production	
  practices	
  that	
  can	
  help	
  farmers	
  build	
  adaptive	
  capacity	
  to	
  climate	
  change.	
  
Results	
  show	
  that	
  coffee	
  farmers	
  who	
  belong	
  to	
  cooperatives	
  have	
  already	
  adopted	
  these	
  
practices	
  at	
  higher	
  rates	
  than	
  non-­‐‑members,	
  and	
  econometric	
  analyses	
  confirm	
  this	
  result.	
  
A	
  factor	
  analysis	
  is	
  also	
  conducted	
  to	
  determine	
  the	
  underlying	
  structural	
  differences	
  
among	
  the	
  ten	
  practices,	
  and	
  from	
  this	
  analysis	
  three	
  factors	
  emerged	
  and	
  are	
  modeled.	
  	
  
Cooperative	
  membership	
  emerges	
  as	
  a	
  significant	
  determinant	
  of	
  adoption	
  of	
  practices	
  that	
  
promote	
  water	
  conservation.	
  	
  
	
  

	
  

To	
  my	
  mom	
  and	
  dad.	
  Mis	
  dos	
  pilares.	
  	
  
To	
  Jason.	
  My	
  love.	
  

	
  
	
  

iv	
  

ACKNOWLEDGEMENTS	
  
	
  

	
  
	
  
	
  
This	
  work	
  would	
  have	
  not	
  been	
  possible	
  without	
  the	
  support,	
  both	
  academic	
  and	
  personal,	
  
that	
  my	
  advisor	
  and	
  mentor,	
  Dr.	
  Daniel	
  Clay,	
  has	
  given	
  me	
  during	
  these	
  past	
  years.	
  Dan	
  has	
  
taught	
  me	
  the	
  value	
  that	
  good	
  mentorship	
  can	
  bring	
  to	
  a	
  relationship,	
  from	
  helping	
  me	
  with	
  
my	
  professional	
  and	
  academic	
  development	
  to	
  giving	
  me	
  instructions	
  on	
  how	
  to	
  build	
  a	
  
bench	
  for	
  my	
  dining	
  room;	
  from	
  giving	
  me	
  the	
  opportunity	
  to	
  be	
  closely	
  involved	
  in	
  his	
  
projects,	
  while	
  encouraging	
  me	
  to	
  pursue	
  my	
  personal	
  research	
  interests.	
  	
  Dan,	
  working	
  
with	
  you	
  and	
  learning	
  from	
  you	
  has	
  truly	
  been	
  the	
  highlight	
  of	
  my	
  tenure	
  as	
  a	
  PhD	
  student,	
  
and	
  I	
  hope	
  to	
  someday	
  be	
  as	
  good	
  a	
  mentor	
  to	
  others,	
  as	
  you	
  have	
  been	
  to	
  me.	
  
	
  
I	
  would	
  also	
  like	
  to	
  thank	
  my	
  advisory	
  committee:	
  Dr.	
  Maria	
  Claudia	
  Lopez,	
  thank	
  you	
  for	
  
helping	
  me	
  stay	
  focused	
  on	
  the	
  long	
  term	
  goals,	
  and	
  for	
  allowing	
  me	
  to	
  go	
  to	
  you	
  whenever	
  
I	
  felt	
  too	
  overwhelmed	
  with	
  the	
  task	
  ahead.	
  Dr.	
  Robert	
  Richardson,	
  thank	
  you	
  for	
  pushing	
  
me	
  to	
  think	
  more	
  critically	
  about	
  my	
  work	
  and	
  for	
  all	
  of	
  your	
  encouragement	
  and	
  advise	
  
through	
  the	
  years.	
  Dr.	
  David	
  Ortega,	
  thank	
  you	
  for	
  the	
  countless	
  hours	
  that	
  you	
  dedicated	
  
to	
  helping	
  me	
  with	
  the	
  research;	
  the	
  time	
  that	
  you	
  dedicated	
  to	
  teaching	
  me	
  new	
  methods	
  
has	
  been	
  invaluable.	
  
	
  
The	
  friendships	
  forged	
  at	
  Michigan	
  State	
  University	
  have	
  made	
  this	
  journey	
  an	
  adventure	
  
that	
  I	
  would	
  gladly	
  take	
  again,	
  being	
  away	
  from	
  home	
  often	
  means	
  looking	
  outside	
  our	
  
blood	
  relations	
  to	
  get	
  the	
  companionship	
  and	
  support	
  that	
  we	
  all	
  need	
  when	
  we	
  are	
  far,	
  
and	
  I	
  trust	
  that	
  the	
  friendships	
  that	
  I	
  have	
  made	
  here	
  will	
  last	
  through	
  the	
  years.	
  
	
  

v	
  

My	
  mom,	
  dad,	
  brother,	
  sister,	
  and	
  nephews	
  accompanied	
  me	
  and	
  kept	
  me	
  motivated	
  in	
  the	
  
distance,	
  and	
  I	
  thought	
  of	
  them	
  every	
  day.	
  
	
  	
  
Finally,	
  I	
  have	
  to	
  thank	
  my	
  husband,	
  Jason	
  Snyder,	
  without	
  whom	
  I	
  do	
  not	
  know	
  how	
  all	
  of	
  
this	
  would	
  have	
  been	
  possible.	
  	
  Thank	
  you	
  for	
  encouraging	
  me	
  to	
  push	
  myself	
  and	
  to	
  
pursue	
  my	
  dreams,	
  for	
  the	
  many	
  hours	
  that	
  you	
  spent	
  proofreading	
  my	
  papers,	
  for	
  hugging	
  
me	
  when	
  it	
  all	
  felt	
  like	
  too	
  much,	
  for	
  never	
  doubting	
  in	
  me,	
  and	
  for	
  never	
  losing	
  your	
  sense	
  
of	
  humor	
  that	
  helped	
  me	
  navigate	
  this	
  PhD	
  labyrinth.	
  I	
  love	
  you.	
  Every	
  day.	
  	
  	
  	
  	
  
	
  
I	
  would	
  also	
  like	
  to	
  acknowledge	
  the	
  financial	
  support	
  of	
  the	
  United	
  States	
  Agency	
  for	
  
International	
  Development	
  through	
  the	
  Borlaug	
  Fellows	
  in	
  Global	
  Food	
  Security	
  at	
  Purdue	
  
University,	
  the	
  support	
  of	
  the	
  Graduate	
  School	
  at	
  Michigan	
  State	
  University	
  through	
  its	
  
Dissertation	
  Completion	
  Fellowship,	
  and	
  the	
  financial	
  support	
  of	
  the	
  Department	
  of	
  
Community	
  Sustainability	
  at	
  Michigan	
  State	
  University.	
  

	
  

	
  

	
  
	
  
	
  
	
  
	
  
	
  

vi	
  

TABLE	
  OF	
  CONTENTS	
  

LIST	
  OF	
  TABLES	
  ...............................................................................................................	
  ix	
  
LIST	
  OF	
  FIGURES	
  ...............................................................................................................	
  x	
  
KEY	
  TO	
  ABBREVIATIONS	
  .............................................................................................	
  xi	
  
Chapter	
  1:	
  Introduction	
  .................................................................................................	
  1	
  
REFERENCES	
  ..........................................................................................................................................	
  8	
  

	
  
Chapter	
  2:	
  Climate	
  Change	
  Adaptation,	
  Food	
  Security	
  and	
  Attitudes	
  
Toward	
  Risk	
  among	
  Smallholder	
  Coffee	
  Farmers	
  in	
  Nicaragua	
  ................	
  10	
  
Introduction	
  ..........................................................................................................................................	
  10	
  
Background	
  ...........................................................................................................................................	
  14	
  
Coffee	
  in	
  Nicaragua	
  ...........................................................................................................................	
  14	
  
Explaining	
  Farmer	
  Behavior	
  under	
  Conditions	
  of	
  Scarcity	
  ..............................................	
  16	
  
Description	
  of	
  the	
  Study	
  Site	
  and	
  Sample	
  ................................................................................	
  18	
  
Description	
  of	
  the	
  Framed	
  Field	
  Experiments	
  .......................................................................	
  22	
  
Experiment	
  Mechanics	
  in	
  the	
  Field	
  .............................................................................................	
  24	
  
Results	
  and	
  Discussion	
  .....................................................................................................................	
  26	
  
Descriptive	
  Analyses	
  ..........................................................................................................................	
  27	
  
Experiment	
  Results	
  ............................................................................................................................	
  32	
  
Conclusion	
  ..............................................................................................................................................	
  37	
  
REFERENCES	
  ........................................................................................................................................	
  41	
  

Chapter	
  3:	
  	
  Adaptive	
  Capacity	
  to	
  Climate	
  Change:	
  Coffee	
  Farmer	
  
Preferences	
  for	
  Crop	
  Diversification	
  in	
  Nicaragua	
  ...........................................	
  47	
  
Introduction	
  ..........................................................................................................................................	
  47	
  
Background	
  ...........................................................................................................................................	
  49	
  
Coffee	
  Leaf	
  Rust	
  in	
  Nicaragua	
  .......................................................................................................	
  52	
  
Theoretical	
  Framework	
  ...................................................................................................................	
  54	
  
Data	
  and	
  Choice	
  Experiment	
  Design	
  ..........................................................................................	
  56	
  
Data	
  and	
  Sample	
  Characteristics	
  ................................................................................................	
  56	
  
Choice	
  Experiment	
  ..............................................................................................................................	
  59	
  
Results	
  and	
  Discussion	
  .....................................................................................................................	
  66	
  
Conclusions	
  ...........................................................................................................................................	
  72	
  
REFERENCES	
  ........................................................................................................................................	
  77	
  

Chapter	
  4:	
  Determinants	
  of	
  Adoption	
  of	
  Sustainable	
  Production	
  
Practices	
  among	
  Smallholder	
  Coffee	
  Producers	
  in	
  Nicaragua	
  ....................	
  81	
  
	
  

vii	
  

Introduction	
  ..........................................................................................................................................	
  81	
  
Coffee	
  in	
  Nicaragua	
  ............................................................................................................................	
  87	
  
Methodology	
  and	
  Data	
  ......................................................................................................................	
  88	
  
Results	
  and	
  Discussion	
  .....................................................................................................................	
  95	
  
Conclusion	
  ...........................................................................................................................................	
  104	
  

	
  	
  	
  	
  REFERENCES	
  .............................................................................................................	
  107	
  
Chapter	
  5:	
  Conclusions	
  ..............................................................................................	
  111	
  
	
  

	
  

	
  

	
  

viii	
  

LIST	
  OF	
  TABLES	
  
	
  
Table	
  1.	
  Characteristics	
  of	
  Game	
  Participants	
  and	
  Non-­‐‑Participants	
  .............................................	
  19	
  
Table	
  2.	
  Sample	
  Characteristics	
  .......................................................................................................................	
  21	
  
Table	
  3.	
  Risk	
  Experiment	
  Payoffs	
  and	
  Risk	
  Coefficient	
  .........................................................................	
  23	
  
Table	
  4.	
  Gender	
  Differences	
  ..............................................................................................................................	
  32	
  
Table	
  5.	
  Distribution	
  of	
  Risk	
  Choices	
  ............................................................................................................	
  33	
  
Table	
  6.	
  Results	
  from	
  Ordered	
  Probit	
  Model	
  for	
  Risk	
  Aversion	
  ........................................................	
  34	
  
Table	
  7.	
  Sample	
  Characteristics	
  .......................................................................................................................	
  59	
  
Table	
  8.	
  Coffee	
  Production	
  Attributes	
  Used	
  in	
  Choice	
  Experiments	
  ...............................................	
  62	
  
Table	
  9.	
  Parameter	
  Estimates	
  from	
  a	
  Random	
  Parameter	
  Logit	
  Model	
  ........................................	
  67	
  
Table	
  10.	
  Cholesky	
  and	
  Correlation	
  Matrix	
  for	
  RPL	
  Model	
  .................................................................	
  68	
  
Table	
  11.	
  Willingness	
  to	
  Change	
  Estimates	
  ................................................................................................	
  69	
  
Table	
  12.	
  Sample	
  Characteristics	
  ....................................................................................................................	
  89	
  
Table	
  13.	
  List	
  of	
  Coffee	
  Farming	
  Practices	
  ..................................................................................................	
  90	
  
Table	
  14.	
  Comparison	
  of	
  Adopted	
  Practices	
  by	
  Cooperative	
  Membership	
  ..................................	
  91	
  
Table	
  15.	
  Ordered	
  Probit	
  Model	
  Results	
  .....................................................................................................	
  96	
  
Table	
  16.	
  Characterization	
  of	
  Production	
  Practices	
  ...............................................................................	
  98	
  
Table	
  17.	
  Ordered	
  Probit	
  Model	
  Results	
  for	
  Disaggregated	
  Practices	
  ........................................	
  100	
  
Table	
  18.	
  Marginal	
  Effects	
  of	
  Significant	
  Variables	
  on	
  Adoption	
  Intensity	
  ...............................	
  101	
  
	
  

	
  
	
  

	
  

ix	
  

LIST	
  OF	
  FIGURES	
  
	
  
Figure	
  1.	
  Research	
  Conceptual	
  Framework	
  ..................................................................................................	
  4	
  
Figure	
  2.	
  Map	
  of	
  Study	
  Area	
  ..............................................................................................................................	
  20	
  
Figure	
  3.	
  Risk	
  Experiment	
  ..................................................................................................................................	
  26	
  
Figure	
  4.	
  Perceptions	
  About	
  Climate	
  Change	
  ............................................................................................	
  28	
  
Figure	
  5.	
  Responses	
  to	
  Climate	
  Shocks	
  ........................................................................................................	
  29	
  
Figure	
  6.	
  Member	
  Satisfaction	
  with	
  Cooperative	
  .....................................................................................	
  30	
  
Figure	
  7.	
  Percentage	
  of	
  Household	
  Vulnerable	
  to	
  Climate	
  Change	
  Shocks	
  ..................................	
  31	
  
Figure	
  8.	
  Game	
  Choices	
  by	
  Households	
  with	
  Food	
  Insecurity	
  ...........................................................	
  34	
  
Figure	
  9.	
  Map	
  of	
  Study	
  Area	
  ..............................................................................................................................	
  57	
  
Figure	
  10.	
  Example	
  of	
  Choice	
  Set	
  ....................................................................................................................	
  65	
  
	
  
	
  

	
  

	
  

x	
  

KEY	
  TO	
  ABBREVIATIONS	
  
	
  
DCE	
   Discreet	
  Choice	
  Experiments	
  
CIAT	
   International	
  Center	
  for	
  Tropical	
  Agriculture	
  
RPL	
  

Random	
  Parameter	
  Logit	
  

WTC	
   Willingness	
  to	
  Change	
  
WTP	
   Willingness	
  to	
  Pay	
  
IPCC	
   Intergovernmental	
  Panel	
  on	
  Climate	
  Change	
  
SLM	
   Sustainable	
  Land	
  Management	
  
	
  

	
  

xi	
  

Chapter	
  1:	
  Introduction	
  
“Let	
  us	
  not,	
  however,	
  flatter	
  ourselves	
  overmuch	
  on	
  account	
  of	
  our	
  human	
  conquest	
  over	
  
nature.	
  For	
  each	
  such	
  conquest	
  takes	
  its	
  revenge	
  on	
  us.	
  Each	
  of	
  them,	
  it	
  is	
  true,	
  has	
  in	
  the	
  first	
  
place	
  the	
  consequences	
  on	
  which	
  we	
  counted,	
  but	
  in	
  the	
  second	
  and	
  third	
  places	
  it	
  has	
  quite	
  
different,	
  unforeseen	
  effects	
  which	
  only	
  too	
  often	
  cancel	
  out	
  the	
  first.	
  The	
  people	
  who,	
  in	
  
Mesopotamia,	
  Greece,	
  Asia	
  Minor,	
  and	
  elsewhere,	
  destroyed	
  the	
  forests	
  to	
  obtain	
  cultivable	
  
land,	
  never	
  dreamed	
  that	
  they	
  were	
  laying	
  the	
  basis	
  for	
  the	
  present	
  devastated	
  condition	
  of	
  
these	
  countries,	
  by	
  removing	
  along	
  with	
  the	
  forests	
  the	
  collecting	
  centres	
  and	
  reservoirs	
  of	
  
moisture.	
  When,	
  on	
  the	
  southern	
  slopes	
  of	
  the	
  mountains,	
  the	
  Italians	
  of	
  the	
  Alps	
  used	
  up	
  the	
  
pine	
  forests	
  so	
  carefully	
  cherished	
  on	
  the	
  northern	
  slopes,	
  they	
  had	
  no	
  inkling	
  that	
  by	
  doing	
  so	
  
they	
  were	
  cutting	
  at	
  the	
  roots	
  of	
  the	
  dairy	
  industry	
  in	
  their	
  region;	
  they	
  had	
  still	
  less	
  inkling	
  
that	
  they	
  were	
  thereby	
  depriving	
  their	
  mountain	
  springs	
  of	
  water	
  for	
  the	
  greater	
  part	
  of	
  the	
  
year,	
  with	
  the	
  effect	
  that	
  these	
  would	
  be	
  able	
  to	
  pour	
  still	
  more	
  furious	
  flood	
  torrents	
  on	
  the	
  
plains	
  during	
  the	
  rainy	
  seasons.	
  Those	
  who	
  spread	
  the	
  potato	
  in	
  Europe	
  were	
  not	
  aware	
  that	
  
they	
  were	
  at	
  the	
  same	
  time	
  spreading	
  the	
  disease	
  of	
  scrofula.	
  Thus	
  at	
  every	
  step	
  we	
  are	
  
reminded	
  that	
  we	
  by	
  no	
  means	
  rule	
  over	
  nature	
  like	
  a	
  conqueror	
  over	
  a	
  foreign	
  people,	
  like	
  
someone	
  standing	
  outside	
  nature—but	
  that	
  we,	
  with	
  flesh,	
  blood,	
  and	
  brain,	
  belong	
  to	
  nature,	
  
and	
  exist	
  in	
  its	
  midst,	
  and	
  that	
  all	
  our	
  mastery	
  of	
  it	
  consists	
  in	
  the	
  fact	
  that	
  we	
  have	
  the	
  
advantage	
  over	
  all	
  other	
  beings	
  of	
  being	
  able	
  to	
  know	
  and	
  correctly	
  apply	
  its	
  laws.”	
  
	
  
	
  
Frederick	
  Engels,	
  1883	
  
	
  

	
  

1	
  

Human	
  influence	
  on	
  the	
  climate	
  system,	
  driven	
  by	
  economic	
  and	
  population	
  growth,	
  is	
  
clear,	
  and	
  has	
  caused	
  global	
  greenhouse	
  gas	
  emissions	
  to	
  reach	
  the	
  highest	
  levels	
  in	
  
recorded	
  history.	
  	
  As	
  a	
  result,	
  these	
  changes	
  have	
  had	
  a	
  significant	
  impact	
  on	
  human	
  and	
  
natural	
  systems.	
  	
  Water	
  resources	
  have	
  been	
  affected	
  by	
  changes	
  in	
  precipitation	
  and	
  to	
  
melting	
  snow	
  and	
  ice.	
  	
  Some	
  studies	
  have	
  found	
  a	
  shift	
  in	
  crop	
  suitability1	
  in	
  many	
  regions	
  
of	
  the	
  world	
  (Laderach	
  et	
  al.,	
  2011;	
  Fischer	
  et	
  al.,	
  2002).	
  At	
  this	
  rate,	
  the	
  planet	
  will	
  see	
  
long-­‐‑lasting	
  changes	
  in	
  all	
  components	
  of	
  the	
  climate	
  system,	
  and	
  the	
  likelihood	
  of	
  severe	
  
and	
  irreversible	
  impacts	
  on	
  people	
  and	
  ecosystems	
  will	
  increase.	
  	
  The	
  latest	
  IPCC	
  report	
  
paints	
  a	
  dark	
  future	
  for	
  our	
  planet.	
  The	
  authors	
  state	
  that	
  “it	
  is	
  virtually	
  certain	
  that	
  there	
  
will	
  be	
  more	
  frequent	
  hot	
  and	
  fewer	
  cold	
  temperature	
  extremes	
  over	
  most	
  land	
  areas	
  on	
  
daily	
  and	
  seasonal	
  timescales,	
  as	
  global	
  mean	
  surface	
  temperature	
  increases.	
  It	
  is	
  very	
  
likely	
  that	
  heat	
  waves	
  will	
  occur	
  with	
  a	
  higher	
  frequency	
  and	
  longer	
  duration.	
  Occasional	
  
cold	
  winter	
  extremes	
  will	
  continue	
  to	
  occur”	
  (IPCC,	
  2014).	
  
	
  
Climate	
  change	
  impacts	
  will	
  be	
  felt	
  worldwide,	
  yet	
  the	
  scale	
  and	
  intensity	
  of	
  these	
  impacts	
  
will	
  differ	
  by	
  region.	
  	
  Urban	
  areas	
  will	
  experience	
  a	
  loss	
  of	
  assets,	
  increased	
  air	
  pollution,	
  
and	
  water	
  scarcity,	
  while	
  rural	
  areas	
  will	
  be	
  at	
  greater	
  risk	
  of	
  food	
  insecurity,	
  changes	
  in	
  
agricultural	
  incomes,	
  and	
  shifts	
  in	
  production	
  areas	
  for	
  food	
  and	
  non-­‐‑food	
  crops.	
  
Displacement	
  of	
  people	
  will	
  occur	
  in	
  both	
  sectors,	
  rural	
  and	
  urban,	
  with	
  poor	
  households	
  
being	
  affected	
  disproportionately.	
  	
  
	
  

1	
  Crop	
  suitability	
  refers	
  to	
  the	
  agro-­‐‑ecological	
  suitability	
  of	
  a	
  region	
  for	
  the	
  production	
  of	
  

particular	
  crops	
  or	
  types	
  of	
  crops.	
  
	
  

2	
  

The	
  IPCC	
  (2014)	
  states	
  that	
  for	
  the	
  world	
  population	
  to	
  adapt	
  to	
  climate	
  change,	
  effective	
  
decision-­‐‑making	
  and	
  policy	
  implementation	
  are	
  necessary	
  and	
  that	
  they	
  should	
  be	
  
informed	
  by	
  a	
  range	
  of	
  analytical	
  approaches	
  that	
  evaluate	
  the	
  risks	
  and	
  benefits	
  of	
  
interventions,	
  while	
  considering	
  the	
  significance	
  of	
  institutions,	
  equity,	
  economic	
  
implications,	
  and	
  the	
  diverse	
  perceptions	
  and	
  response	
  to	
  risk	
  and	
  uncertainty.	
  
	
  
Nicaragua	
  is	
  one	
  of	
  the	
  poorest	
  countries	
  in	
  Latin	
  America	
  with	
  more	
  than	
  42%	
  of	
  the	
  
population	
  living	
  under	
  the	
  poverty	
  line	
  and	
  most	
  in	
  rural	
  areas	
  (World	
  Bank,	
  2016).	
  	
  
Coffee	
  in	
  Nicaragua	
  is	
  by	
  far	
  the	
  most	
  important	
  crop	
  in	
  the	
  economy,	
  and	
  is	
  its	
  highest	
  
source	
  of	
  agricultural	
  export	
  revenues.	
  There	
  are	
  more	
  than	
  48,000	
  coffee	
  producers	
  in	
  
Nicaragua,	
  producing	
  mostly	
  Arabica	
  coffee,	
  and	
  the	
  majority	
  of	
  them	
  farm	
  on	
  plots	
  of	
  less	
  
than	
  3.5	
  hectares	
  (Valkila	
  and	
  Nygreen,	
  2010).	
  	
  
	
  
Climate	
  change	
  is	
  expected	
  to	
  affect	
  a	
  large	
  proportion	
  of	
  coffee	
  growing	
  areas	
  in	
  
Nicaragua.	
  Overall,	
  the	
  climate	
  will	
  be	
  marked	
  by	
  greater	
  seasonality	
  in	
  terms	
  of	
  the	
  
variability	
  in	
  temperatures	
  and	
  precipitation.	
  	
  Areas	
  of	
  Nicaragua,	
  including	
  Matagalpa-­‐‑-­‐‑	
  
where	
  this	
  research	
  takes	
  place-­‐‑-­‐‑	
  will	
  see	
  a	
  loss	
  in	
  agro-­‐‑ecological	
  suitability	
  for	
  coffee	
  of	
  
up	
  to	
  60%	
  due	
  to	
  climate	
  change	
  (Laderach	
  et	
  al.,	
  2011).	
  
	
  
Efforts	
  to	
  slow	
  down	
  and	
  eventually	
  reverse	
  the	
  trend	
  of	
  climate	
  change	
  will	
  take	
  time,	
  and	
  
in	
  some	
  cases,	
  the	
  negative	
  impacts	
  of	
  climate	
  change	
  will	
  be	
  felt	
  long	
  before	
  long-­‐‑term	
  
solutions	
  to	
  this	
  problem	
  can	
  take	
  hold.	
  	
  Adaptation	
  and	
  mitigation	
  strategies	
  for	
  rural	
  
households	
  constitute	
  the	
  front	
  line	
  of	
  attack	
  for	
  rural	
  households	
  in	
  developing	
  countries	
  

	
  

3	
  

that	
  rely	
  on	
  agricultural	
  production	
  and	
  natural	
  resources	
  use	
  as	
  their	
  main	
  source	
  of	
  
income	
  and	
  growth,	
  and	
  whose	
  livelihoods	
  are	
  threatened	
  by	
  climate	
  change.	
  Among	
  these	
  
strategies,	
  the	
  adoption	
  of	
  sustainable	
  and	
  “climate	
  smart”	
  production	
  practices	
  has	
  been	
  
identified	
  as	
  critical	
  for	
  smallholder	
  producers,	
  but	
  it	
  is	
  uncertain	
  how	
  best	
  they	
  should	
  be	
  
promoted	
  (Laderach	
  et	
  al.,	
  2013).	
  	
  	
  	
  
	
  
The	
  research	
  presented	
  in	
  the	
  following	
  chapters	
  explores	
  the	
  implications	
  of	
  climate	
  
change	
  on	
  the	
  livelihoods	
  of	
  coffee	
  producers	
  in	
  Nicaragua.	
  It	
  examines	
  the	
  pathways	
  to	
  
climate	
  change	
  resilience	
  for	
  coffee	
  producers	
  in	
  the	
  region	
  along	
  three	
  main	
  dimensions:	
  
livelihoods,	
  biodiversity	
  conservation,	
  and	
  climate	
  change	
  adaptation	
  (Figure	
  1).	
  	
  
Figure	
  1.	
  Research	
  Conceptual	
  Framework	
  

Livelihoods

Climate	
  
Change

Biodiversity

	
  
	
  
Throughout	
  this	
  dissertation	
  I	
  make	
  use	
  of	
  the	
  terms	
  resilience	
  and	
  adaptive	
  capacity	
  on	
  
numerous	
  occasions.	
  For	
  purposes	
  of	
  this	
  research,	
  I	
  define	
  adaptive	
  capacity	
  as	
  the	
  

	
  

4	
  

capacity	
  of	
  coffee	
  producers	
  in	
  Matagalpa	
  to	
  adopt	
  technologies	
  and	
  practices	
  that	
  help	
  
them	
  become	
  more	
  resilient	
  to	
  climate	
  change,	
  while	
  resiliency	
  is	
  defined	
  as	
  the	
  capacity	
  of	
  
coffee	
  farmers	
  to	
  recover	
  from	
  climate	
  change	
  shocks	
  and	
  minimize	
  the	
  losses.	
  
	
  
This	
  dissertation	
  is	
  divided	
  into	
  three	
  main	
  empirical	
  chapters,	
  each	
  one	
  meant	
  to	
  stand	
  on	
  
its	
  own	
  as	
  a	
  publishable	
  manuscript.	
  	
  It	
  is	
  for	
  this	
  reason	
  that	
  sections	
  presenting	
  a	
  
description	
  of	
  the	
  data	
  and	
  the	
  study	
  appear	
  partially	
  repetitive	
  across	
  the	
  three	
  empirical	
  
chapters.	
  
	
  
Chapter	
  2,	
  Climate	
  Change	
  Adaptation,	
  Food	
  Security	
  and	
  Attitudes	
  Toward	
  Risk	
  among	
  
Smallholder	
  Coffee	
  Farmers	
  in	
  Nicaragua,	
  paints	
  a	
  broad	
  picture	
  of	
  the	
  coffee	
  sector	
  in	
  the	
  
region,	
  and	
  it	
  explores	
  the	
  incentives	
  and	
  the	
  capacities	
  of	
  coffee	
  producers	
  in	
  Nicaragua	
  to	
  
adopt	
  technologies	
  that	
  will	
  help	
  them	
  be	
  resilient	
  to	
  climate	
  change.	
  Adaptation	
  to	
  climate	
  
change	
  is	
  essential	
  for	
  poor	
  rural	
  households	
  that	
  choose	
  to	
  make	
  a	
  living	
  from	
  coffee	
  
production,	
  and	
  the	
  strategies	
  that	
  they	
  can	
  adopt	
  are	
  multiple.	
  I	
  explore	
  these	
  questions	
  
through	
  the	
  use	
  of	
  descriptive	
  analyses	
  and	
  experimental	
  economic	
  methods.	
  I	
  find	
  that	
  
producers	
  in	
  the	
  region	
  have	
  already	
  experienced	
  environmental	
  shocks	
  and	
  have	
  had	
  to	
  
respond	
  to	
  some	
  of	
  these	
  shocks	
  through	
  various	
  coping	
  mechanisms,	
  some	
  of	
  which	
  leave	
  
them	
  even	
  more	
  vulnerable	
  to	
  future	
  shocks.	
  	
  I	
  also	
  explore	
  coffee	
  farmer	
  attitudes	
  towards	
  
risk	
  by	
  analyzing	
  data	
  from	
  lottery	
  games	
  with	
  real	
  pay-­‐‑offs	
  that	
  were	
  implemented	
  in	
  the	
  
field.	
  This	
  study	
  helps	
  us	
  understand	
  the	
  options	
  for	
  building	
  adaptive	
  capacity	
  and	
  the	
  
vulnerabilities	
  to	
  climate	
  change	
  experienced	
  by	
  coffee	
  farmers;	
  an	
  important	
  first	
  step	
  in	
  

	
  

5	
  

exploring	
  the	
  best	
  way	
  to	
  help	
  the	
  sector	
  become	
  more	
  resilient	
  to	
  the	
  impacts	
  of	
  climate	
  
change.	
  
	
  
Chapter	
  3,	
  Adaptive	
  Capacity	
  to	
  Climate	
  Change:	
  Coffee	
  Farmers’	
  Preferences	
  for	
  Crop	
  
Diversification	
  in	
  Nicaragua,	
  is	
  focused	
  on	
  the	
  conditions	
  under	
  which	
  coffee	
  producers	
  
diversify	
  their	
  coffee	
  production	
  with	
  additional	
  shade	
  crops	
  to	
  help	
  protect	
  the	
  
biodiversity	
  of	
  the	
  regions	
  where	
  coffee	
  is	
  produced.	
  Shade	
  incorporation	
  into	
  coffee	
  fields	
  
is	
  important	
  because	
  it	
  promotes	
  biodiversity	
  conservation,	
  it	
  helps	
  with	
  climate	
  change	
  
adaptation	
  by	
  lowering	
  the	
  temperatures	
  of	
  fields	
  and	
  by	
  protecting	
  the	
  soils,	
  and	
  it	
  has	
  the	
  
potential	
  to	
  generate	
  income	
  and	
  food	
  for	
  consumption.	
  I	
  employ	
  choice	
  experiments	
  to	
  
elicit	
  farmers’	
  willingness	
  to	
  change	
  their	
  production	
  practices	
  to	
  include	
  shade	
  in	
  their	
  
coffee	
  farms.	
  In	
  a	
  choice	
  experiment,	
  respondents	
  are	
  asked	
  to	
  choose	
  between	
  bundles	
  
containing	
  a	
  series	
  of	
  different	
  attributes	
  (of	
  varying	
  levels)	
  from	
  hypothetical	
  choice	
  
scenarios.	
  By	
  controlling	
  the	
  variation	
  in	
  the	
  levels	
  of	
  the	
  attributes,	
  I	
  am	
  able	
  to	
  analyze	
  
the	
  choices	
  made	
  by	
  the	
  respondents	
  and	
  to	
  estimate	
  marginal	
  values	
  for	
  the	
  attributes	
  
presented	
  in	
  the	
  choice	
  sets.	
  Results	
  from	
  this	
  paper	
  highlight	
  how	
  some	
  of	
  the	
  institutional	
  
responses	
  to	
  the	
  leaf	
  rust	
  epidemic	
  in	
  Nicaragua	
  have	
  affected	
  the	
  preferences	
  of	
  farmers	
  
and	
  I	
  discuss	
  its	
  impacts	
  on	
  the	
  level	
  of	
  trust	
  that	
  farmers	
  have	
  in	
  the	
  ability	
  of	
  
organizations	
  to	
  provide	
  help	
  under	
  stressful	
  conditions.	
  
	
  
Chapter	
  4,	
  Determinants	
  of	
  Adoptions	
  of	
  Sustainable	
  Production	
  Practices	
  among	
  
Smallholder	
  Coffee	
  Producers	
  in	
  Nicaragua,	
  models	
  the	
  determinants	
  of	
  adoption	
  of	
  ten	
  
different	
  production	
  practices	
  that	
  can	
  help	
  producers	
  become	
  more	
  resilient	
  to	
  climate	
  

	
  

6	
  

change.	
  	
  I	
  explore	
  the	
  extent	
  to	
  which	
  farmer	
  cooperatives	
  affect	
  the	
  adoption	
  of	
  improved	
  
production	
  practices	
  and	
  I	
  create	
  an	
  indicator	
  for	
  these	
  ten	
  practices	
  and	
  explore	
  the	
  
degree	
  to	
  which	
  membership	
  affects	
  adoption.	
  Not	
  all	
  technologies	
  are	
  equal,	
  some	
  may	
  be	
  
more	
  important	
  than	
  others,	
  so	
  a	
  factor	
  analysis	
  was	
  conducted	
  to	
  determine	
  the	
  
underlying	
  structural	
  differences	
  among	
  the	
  ten	
  practices,	
  and	
  from	
  this	
  analysis	
  three	
  
factors	
  emerged	
  and	
  were	
  modeled,	
  to	
  measure	
  the	
  degree	
  to	
  which	
  membership	
  affects	
  
adoption	
  of	
  each	
  set	
  of	
  practices.	
  Results	
  from	
  this	
  study	
  help	
  inform	
  more	
  efficient	
  and	
  
effective	
  pathways	
  to	
  help	
  farmers	
  to	
  adopt	
  practices	
  that	
  aid	
  them	
  in	
  building	
  adaptive	
  
capacity	
  to	
  climate	
  change.	
  
	
  
The	
  goal	
  of	
  this	
  dissertation	
  is	
  to	
  generate	
  findings	
  that	
  are	
  valuable	
  for	
  policy	
  makers,	
  
donors	
  as	
  well	
  as	
  development	
  and	
  extension	
  practitioners	
  in	
  the	
  coffee	
  sector	
  as	
  they	
  
endeavor	
  to	
  forge	
  future	
  courses	
  of	
  action	
  and	
  guide	
  policy	
  toward	
  more	
  effective	
  
solutions.	
  
	
  
	
  	
  
	
  
	
  
	
  	
  
	
  
	
  
	
  

	
  

	
  

7	
  

REFERENCES	
  

	
  
	
  

8	
  

REFERENCES	
  
	
  

	
  
	
  
	
  
Engels,	
  Friedrich.	
  Engel,	
  F.	
  Dialectics	
  of	
  Nature.	
  (1883).	
  	
  
	
  
Fischer,	
  Gßnther,	
  Mahendra	
  Shah,	
  and	
  Harrij	
  Van	
  Velthuizen.	
  "Climate	
  change	
  and	
  
agricultural	
  vulnerability."	
  (2002).	
  
	
  
Intergovernmental	
  Panel	
  on	
  Climate	
  Change.	
  Climate	
  change	
  2014:	
  mitigation	
  of	
  climate	
  
change.	
  Vol.	
  3.	
  Cambridge	
  University	
  Press,	
  2015.	
  
	
  
Laderäch,	
  Peter,	
  et	
  al.	
  "Predicted	
  impact	
  of	
  climate	
  change	
  on	
  coffee	
  supply	
  chains."	
  The	
  
Economic,	
  Social	
  and	
  Political	
  Elements	
  of	
  Climate	
  Change.	
  Springer	
  Berlin	
  
Heidelberg,	
  2011.	
  703-­‐‑723.	
  
	
  
Laderäch,	
  Peter,	
  Carlos	
  Zelaya,	
  Oriana	
  Ovalle,	
  Samuel	
  Garcia,	
  Anton	
  Eitzinger,	
  and	
  Maria	
  
Baca.	
  "Escenarios	
  del	
  Impacto	
  del	
  Clima	
  Futuro	
  en	
  Areas	
  de	
  Cultivo	
  de	
  Cafe	
  en	
  
Nicaragua."	
  CIAT	
  Blog.	
  13	
  Mar.	
  2013.	
  Web.	
  http://dapa.ciat.cgiar.org/wp-­‐‑
content/uploads/2012/03/Informe-­‐‑Nicaragua-­‐‑final.pdf	
  
	
  
Valkila,	
  Joni,	
  and	
  Anja	
  Nygren.	
  "Impacts	
  of	
  Fair	
  Trade	
  certification	
  on	
  coffee	
  farmers,	
  
cooperatives,	
  and	
  laborers	
  in	
  Nicaragua."	
  Agriculture	
  and	
  Human	
  Values	
  27.3	
  
(2010):	
  321-­‐‑333.	
  
	
  
World	
  Bank.	
  (2016)	
  Retrieved	
  from	
  http://data.worldbank.org/data-­‐‑catalog/gdp-­‐‑ppp-­‐‑
based-­‐‑table	
  
	
  
	
  
	
  

	
  

9	
  

Chapter	
  2:	
  Climate	
  Change	
  Adaptation,	
  Food	
  Security	
  and	
  Attitudes	
  Toward	
  Risk	
  among	
  
Smallholder	
  Coffee	
  Farmers	
  in	
  Nicaragua	
  
	
  

Introduction	
  
The	
  earth’s	
  climate	
  is	
  changing	
  rapidly.	
  Climate	
  scientists	
  forecast	
  higher	
  temperatures	
  and	
  
significant	
  changes	
  in	
  precipitation	
  patterns,	
  that	
  in	
  turn	
  will	
  alter	
  crop	
  suitability	
  and	
  land	
  
use	
  in	
  many	
  agricultural	
  regions	
  of	
  the	
  world	
  (IPCC,	
  2014).	
  Farmers	
  will	
  see	
  changes	
  in	
  
their	
  agricultural	
  productivity,	
  their	
  farm	
  income,	
  and	
  their	
  food	
  security	
  (Laderach	
  et	
  al.,	
  
2011).	
  	
  Poor,	
  rural	
  households	
  in	
  developing	
  countries	
  that	
  depend	
  directly	
  on	
  natural	
  
resources	
  for	
  income	
  generation	
  and	
  their	
  own	
  food	
  consumption	
  will	
  be	
  burdened	
  
disproportionately	
  by	
  the	
  adverse	
  impacts	
  of	
  climate	
  change;	
  because	
  their	
  livelihoods	
  are	
  
so	
  closely	
  tied	
  to	
  the	
  local	
  agroecology	
  they	
  will	
  be	
  among	
  the	
  most	
  vulnerable	
  to	
  sudden	
  
shocks	
  like	
  droughts,	
  floods,	
  famine,	
  fires,	
  epidemics,	
  and	
  potentially	
  violent	
  conflict	
  (Ellis,	
  
2000).	
  For	
  farmers,	
  especially	
  vulnerable	
  smallholder	
  farmers,	
  the	
  adoption	
  of	
  new	
  
practices	
  and	
  technologies	
  that	
  help	
  them	
  become	
  more	
  resilient	
  to	
  these	
  changes	
  will	
  be	
  
one	
  of	
  the	
  most	
  important	
  paths	
  for	
  protecting	
  their	
  livelihoods.	
  	
  Their	
  willingness	
  to	
  adopt	
  
these	
  improved	
  practices	
  and	
  technologies,	
  their	
  level	
  of	
  risk	
  tolerance,	
  and	
  the	
  
institutional	
  response	
  mechanisms	
  will	
  go	
  a	
  long	
  way	
  in	
  determining	
  their	
  success	
  in	
  
adapting	
  to	
  these	
  changes.	
  	
  	
  
	
  
Policy	
  makers,	
  non-­‐‑governmental	
  organizations	
  (NGOs),	
  and	
  other	
  organizations,	
  but	
  
especially	
  vulnerable	
  smallholder	
  farmers,	
  will	
  need	
  to	
  understand	
  how	
  their	
  livelihoods	
  
will	
  be	
  impacted	
  by	
  climate	
  change	
  and	
  must	
  take	
  the	
  necessary	
  actions	
  towards	
  increased	
  
resiliency.	
  Stakeholders	
  in	
  the	
  agricultural	
  sector	
  will	
  need	
  to	
  provide	
  vulnerable	
  farmers	
  
	
  

10	
  

with	
  the	
  support	
  needed	
  to	
  transition	
  toward	
  more	
  resilient	
  livelihoods.	
  	
  Among	
  those	
  
most	
  vulnerable	
  to	
  the	
  impacts	
  of	
  climate	
  change	
  are	
  women	
  and	
  the	
  elderly,	
  so	
  adaptation	
  
strategies	
  must	
  incorporate	
  equitable	
  coping	
  mechanisms	
  that	
  will	
  enhance	
  resiliency	
  even	
  
among	
  the	
  most	
  disadvantaged	
  groups	
  (Tompkins	
  and	
  Adger,	
  2004).	
  	
  For	
  the	
  purposes	
  of	
  
this	
  research	
  I	
  draw	
  on	
  the	
  IPCC	
  (2001)	
  definition	
  of	
  vulnerability,	
  which	
  hinges	
  on	
  the	
  
sensitivity	
  of	
  agriculture	
  to	
  changes	
  in	
  climate,	
  the	
  adaptive	
  capacity	
  of	
  the	
  ecosystem,	
  and	
  
the	
  degree	
  of	
  exposure	
  to	
  climate	
  hazards.	
  
	
  
Smallholder	
  farmers’	
  attitudes	
  and	
  incentives	
  towards	
  new	
  technology	
  adoption	
  or	
  
alternative	
  production	
  practices	
  have	
  long	
  been	
  documented	
  in	
  the	
  literature	
  (Duflo	
  et	
  al.,	
  
2009;	
  Laderach	
  et	
  al.,	
  2011).	
  	
  Schultz’s	
  (1964)	
  “poor	
  but	
  efficient”	
  hypothesis	
  that	
  small	
  
farmers	
  in	
  traditional	
  agricultural	
  settings	
  respond	
  positively	
  to	
  price	
  incentives	
  by	
  
efficiently	
  allocating	
  their	
  resources	
  has	
  been	
  an	
  enduring	
  theme	
  in	
  agricultural	
  
development	
  economics	
  for	
  many	
  decades.	
  	
  But	
  beyond	
  price	
  incentives,	
  successful	
  
adaptation	
  will	
  also	
  depend	
  on:	
  (a)	
  farmer	
  attitudes	
  and	
  preferences	
  and	
  (b)	
  their	
  binding	
  
constraints	
  to	
  investment.	
  
	
  
Climate	
  change	
  will	
  intensify	
  already	
  existing	
  vulnerabilities,	
  and	
  although	
  farmers	
  in	
  
developing	
  economies	
  have	
  shown	
  that	
  they	
  can	
  respond	
  to	
  short-­‐‑term	
  changes	
  in	
  
environmental	
  conditions,	
  they	
  may	
  not	
  have	
  the	
  ability	
  to	
  cope	
  with	
  events	
  of	
  a	
  
transnational	
  nature	
  without	
  support	
  (Challinor	
  et	
  al.,	
  2007).	
  	
  	
  For	
  this	
  reason,	
  the	
  
development	
  of	
  institutions,	
  both	
  formal	
  and	
  informal,	
  play	
  an	
  instrumental	
  role	
  in	
  
influencing	
  the	
  livelihoods	
  and	
  the	
  resiliency	
  of	
  rural	
  households.	
  These	
  institutions	
  can	
  

	
  

11	
  

help	
  to	
  determine	
  whether	
  climate	
  change	
  adaptation	
  responses	
  are	
  organized	
  collectively	
  
or	
  individually,	
  the	
  emergence	
  of	
  leadership	
  in	
  different	
  contexts,	
  and	
  the	
  mediation	
  of	
  
external	
  interventions	
  into	
  a	
  local	
  context	
  (Agrawal,	
  2010).	
  	
  	
  
	
  
Even	
  when	
  farmers	
  are	
  willing	
  improve	
  their	
  adaptive	
  capacity	
  by	
  adopting	
  (potentially	
  
risky)	
  new	
  technologies	
  and	
  practices,	
  they	
  may	
  face	
  binding	
  constraints	
  that	
  will	
  make	
  it	
  
hard	
  or	
  impossible	
  to	
  do	
  so,	
  such	
  as	
  high	
  transaction	
  costs,	
  poor	
  physical	
  infrastructure,	
  
lack	
  of	
  access	
  to	
  inputs	
  and	
  seeds,	
  and	
  low	
  levels	
  of	
  institutional	
  support	
  and	
  capacity	
  
(Hazell	
  et	
  al.,	
  2010).	
  	
  	
  For	
  example,	
  although	
  crop	
  diversification	
  can	
  help	
  to	
  mitigate	
  the	
  
impacts	
  of	
  climate	
  change,	
  Bradshaw	
  et	
  al.	
  (2004)	
  find	
  that	
  farmers	
  increasingly	
  specialize	
  
their	
  production	
  systems	
  when	
  faced	
  with	
  economic	
  factors	
  such	
  as	
  high	
  start-­‐‑up	
  costs	
  and	
  
economies	
  of	
  scale.	
  
	
  
Risk	
  aversion	
  and	
  barriers	
  to	
  investment	
  can	
  both	
  be	
  lowered	
  by	
  improving	
  farmer	
  access	
  
to	
  information	
  and	
  knowledge	
  (related	
  to	
  production,	
  marketing,	
  etc.).	
  	
  Often	
  this	
  
knowledge	
  already	
  exists	
  within	
  the	
  farming	
  communities	
  in	
  the	
  form	
  of	
  local	
  knowledge	
  
about	
  seasonal	
  patterns	
  that	
  determine	
  how	
  and	
  when	
  to	
  plant	
  and	
  apply	
  inputs;	
  but	
  some	
  
of	
  this	
  knowledge	
  will	
  have	
  to	
  come	
  from	
  outside	
  the	
  local	
  communities,	
  such	
  as	
  through	
  
trainings	
  on	
  climate	
  smart	
  practices	
  or	
  through	
  other	
  extension	
  services	
  (Challinor	
  et	
  al.,	
  
2007).	
  	
  	
  	
  
	
  
Moreover,	
  institutional	
  capacity	
  is	
  needed	
  to	
  produce	
  long	
  term	
  strategic	
  interventions	
  that	
  
facilitate	
  networking,	
  information	
  sharing,	
  and	
  the	
  creation	
  of	
  safety	
  nets.	
  The	
  formation	
  of	
  

	
  

12	
  

agricultural	
  cooperatives,	
  for	
  example,	
  has	
  been	
  successful	
  in	
  helping	
  smallholder	
  farmers	
  
to	
  overcome	
  barriers	
  associated	
  with	
  access	
  to	
  inputs,	
  financial	
  services	
  and	
  market	
  
participation,	
  through	
  the	
  dissemination	
  of	
  inputs,	
  loans,	
  and	
  training	
  opportunities	
  
(Abebaw	
  and	
  Haile,	
  2013).	
  Community-­‐‑based	
  natural	
  resource	
  management	
  strategies	
  can	
  
also	
  enhance	
  the	
  adaptive	
  capacity	
  of	
  farmers	
  by	
  creating	
  social	
  networks	
  that	
  are	
  essential	
  
for	
  coping	
  with	
  extreme	
  events	
  and	
  by	
  retaining	
  the	
  resilience	
  of	
  ecological	
  systems	
  
(Tompkins	
  and	
  Adger,	
  2004).	
  	
  
	
  
Among	
  crops	
  that	
  will	
  see	
  a	
  shift	
  in	
  suitability,	
  coffee	
  has	
  received	
  much	
  attention,	
  given	
  its	
  
importance	
  in	
  the	
  global	
  market	
  and	
  the	
  large	
  number	
  of	
  smallholder	
  producers	
  worldwide	
  
that	
  depend	
  on	
  it	
  as	
  a	
  main	
  source	
  of	
  income.	
  	
  Coffee	
  has	
  long	
  been	
  known	
  as	
  a	
  commodity	
  
product	
  with	
  a	
  large	
  footprint	
  in	
  poor	
  countries	
  in	
  the	
  tropics,	
  and	
  as	
  a	
  leading	
  source	
  of	
  
economic	
  growth	
  for	
  many	
  of	
  them.	
  	
  At	
  a	
  global	
  scale,	
  it	
  is	
  considered	
  one	
  of	
  the	
  most	
  
traded	
  commodities	
  (Ponte,	
  2002).	
  As	
  the	
  climate	
  changes,	
  coffee	
  regions	
  will	
  be	
  
characterized	
  by	
  seasons	
  marked	
  with	
  higher	
  temperatures,	
  erratic	
  and	
  severe	
  rainy	
  
seasons,	
  and	
  longer	
  periods	
  of	
  drought.	
  	
  All	
  of	
  these	
  changes	
  will	
  impact	
  coffee	
  production	
  
and	
  the	
  farmers	
  that	
  depend	
  on	
  it,	
  as	
  the	
  coffee	
  tree	
  is	
  vulnerable	
  to	
  droughts,	
  excessive	
  
rain,	
  and	
  temperature	
  extremes	
  (Conde	
  et	
  al.,	
  2013).	
  
	
  
This	
  study	
  presents	
  results	
  of	
  an	
  analysis	
  of	
  the	
  vulnerabilities	
  of	
  smallholder	
  coffee	
  
producers	
  in	
  Nicaragua	
  to	
  climate	
  change;	
  and	
  I	
  study	
  their	
  capacities	
  to	
  build	
  adaptive	
  
strategies	
  in	
  response	
  to	
  these	
  changes.	
  In	
  this	
  study	
  I	
  use	
  descriptive	
  analyses	
  and	
  
experimental	
  economic	
  methods	
  –	
  risk	
  games	
  -­‐‑	
  to	
  evaluate	
  the	
  preferences,	
  attitudes,	
  and	
  

	
  

13	
  

capacities	
  of	
  coffee	
  producers	
  in	
  Nicaragua	
  to	
  build	
  adaptive	
  capacity	
  to	
  climate	
  change.	
  I	
  
show	
  that	
  food	
  insecure	
  households	
  are	
  more	
  risk	
  averse	
  than	
  those	
  that	
  are	
  not	
  food	
  
insecure	
  and	
  that	
  much	
  improvement	
  is	
  needed	
  in	
  the	
  sector	
  in	
  terms	
  of	
  equity	
  and	
  
institutional	
  development.	
  
	
  
The	
  remainder	
  of	
  this	
  chapter	
  is	
  organized	
  as	
  follows:	
  in	
  Section	
  2	
  I	
  discuss	
  the	
  coffee	
  
sector	
  in	
  Nicaragua	
  and	
  how	
  it	
  is	
  expected	
  to	
  change	
  as	
  a	
  result	
  of	
  climate	
  change,	
  and	
  I	
  
provide	
  a	
  brief	
  literature	
  review	
  of	
  farmers’	
  behaviors	
  under	
  conditions	
  of	
  scarcity.	
  In	
  
Section	
  3	
  I	
  describe	
  the	
  study	
  site	
  and	
  data	
  collection	
  methods.	
  Section	
  4	
  focuses	
  on	
  the	
  
economic	
  games	
  that	
  are	
  used	
  to	
  assess	
  attitudes	
  towards	
  risk.	
  Section	
  5	
  presents	
  the	
  
results	
  and	
  a	
  discussion	
  about	
  the	
  findings.	
  I	
  conclude	
  with	
  a	
  review	
  of	
  policy	
  implications	
  
and	
  recommendations	
  for	
  future	
  research.	
  
	
  

Background	
  
Coffee	
  in	
  Nicaragua	
  
Nicaragua	
  is	
  one	
  of	
  the	
  poorest	
  countries	
  in	
  Latin	
  America	
  with	
  more	
  than	
  42%	
  of	
  the	
  
population	
  living	
  under	
  the	
  poverty	
  line	
  and	
  most	
  in	
  rural	
  areas	
  (World	
  Bank,	
  2016).	
  	
  
Coffee	
  in	
  Nicaragua	
  is	
  by	
  far	
  the	
  most	
  important	
  crop	
  in	
  the	
  economy,	
  and	
  is	
  the	
  highest	
  
source	
  of	
  agricultural	
  export	
  revenues	
  in	
  the	
  country.	
  There	
  are	
  more	
  than	
  48,000	
  coffee	
  
producers	
  in	
  Nicaragua,	
  producing	
  mostly	
  Arabica	
  coffee,	
  the	
  majority	
  of	
  them	
  farm	
  on	
  
plots	
  of	
  less	
  than	
  3.5	
  hectares	
  (Valkila	
  and	
  Nygreen,	
  2010).	
  The	
  economic	
  dependence	
  of	
  
smallholder	
  farmers	
  on	
  coffee	
  cannot	
  be	
  overstated.	
  	
  
	
  

	
  

14	
  

Climate	
  change	
  is	
  expected	
  to	
  affect	
  a	
  large	
  proportion	
  of	
  coffee	
  growing	
  areas	
  in	
  
Nicaragua,	
  which	
  will	
  be	
  marked	
  by	
  greater	
  seasonal	
  variability	
  in	
  temperatures	
  and	
  
precipitation.	
  	
  Areas	
  of	
  Nicaragua,	
  including	
  Matagalpa,	
  will	
  see	
  up	
  to	
  60%	
  decrease	
  of	
  area	
  
suitable	
  for	
  coffee	
  production	
  (Laderach	
  et	
  al.,	
  2011).	
  
	
  
For	
  vulnerable	
  households	
  suffering	
  from	
  food	
  insecurity	
  and	
  at	
  the	
  mercy	
  of	
  market	
  and	
  
climatic	
  fluctuations,	
  finding	
  a	
  pathway	
  to	
  resilience	
  and	
  adaptability	
  is	
  urgent	
  and	
  the	
  only	
  
way	
  forward.	
  	
  Earlier	
  research	
  by	
  Laderach	
  et	
  al.	
  (2011)	
  has	
  identified	
  the	
  potential	
  
pathways	
  for	
  these	
  farmers	
  to	
  improve	
  their	
  income	
  potential;	
  they	
  include:	
  (a)	
  the	
  
adoption	
  of	
  coffee	
  production	
  practices	
  that	
  will	
  improve	
  their	
  adaptive	
  capacity	
  to	
  climate	
  
change,	
  or	
  (b)	
  moving	
  from	
  coffee	
  production	
  altogether	
  to	
  a	
  different	
  high	
  value	
  crop,	
  such	
  
as	
  cocoa,	
  which	
  can	
  maintain	
  or	
  increase	
  their	
  current	
  income,	
  or	
  by	
  (c)	
  dropping	
  out	
  of	
  
agricultural	
  production	
  and	
  finding	
  non-­‐‑farm	
  employment	
  (perhaps	
  still	
  related	
  to	
  
agriculture).	
  	
  
	
  
The	
  search	
  for	
  adaptation	
  strategies	
  within	
  agricultural	
  systems	
  has	
  mostly	
  focused	
  on	
  
technical	
  and	
  productivity	
  interventions	
  –	
  such	
  as	
  the	
  development	
  of	
  forecasting	
  systems,	
  
and	
  changes	
  in	
  the	
  location	
  of	
  production	
  (Perfecto	
  and	
  Vandermeer,	
  2015).	
  	
  Less	
  common,	
  
however,	
  is	
  the	
  recognition	
  that	
  farm	
  management	
  practices	
  can	
  significantly	
  contribute	
  to	
  
improved	
  adaptation	
  by	
  producers	
  through,	
  for	
  example,	
  the	
  adoption	
  of	
  integrated	
  pest	
  
management	
  and	
  through	
  the	
  incorporation	
  of	
  shade	
  into	
  coffee	
  farms.	
  Evidence	
  has	
  
shown	
  that	
  agro-­‐‑ecological	
  management	
  practices	
  can	
  significantly	
  improve	
  resiliency	
  to	
  
climate	
  change	
  (Perfecto	
  and	
  Vandermeer,	
  2015;	
  Philpott	
  and	
  Dietsch,	
  2003).	
  

	
  

15	
  

For	
  example,	
  in	
  a	
  study	
  of	
  880	
  paired	
  experimental	
  plots	
  in	
  Nicaragua,	
  Holt-­‐‑Gimenez	
  
(2002)	
  found	
  that	
  after	
  Hurricane	
  Mitch	
  hit	
  the	
  country	
  in	
  1998,	
  plots	
  that	
  had	
  been	
  
following	
  Sustainable	
  Land	
  Management	
  (SLM)	
  practices	
  were	
  able	
  to	
  recover	
  more	
  
quickly	
  than	
  plots	
  conventionally	
  managed.	
  	
  SLM	
  includes	
  a	
  variety	
  of	
  soil	
  conservation,	
  
agro-­‐‑ecological	
  and	
  agroforestry	
  practices	
  that	
  generally	
  avoid	
  external	
  inputs.	
  	
  The	
  study	
  
finds	
  that	
  farms	
  following	
  SLM	
  practices	
  had	
  more	
  topsoil,	
  higher	
  field	
  moisture	
  measures,	
  
more	
  vegetation	
  within	
  the	
  system	
  and	
  lower	
  economic	
  losses	
  than	
  the	
  conventional	
  plots.	
  
	
  
Explaining	
  Farmer	
  Behavior	
  under	
  Conditions	
  of	
  Scarcity	
  	
  
Farmers	
  may	
  have	
  attitudes	
  and	
  preferences	
  that	
  prevent	
  them	
  from	
  taking	
  steps	
  that	
  will	
  
ensure	
  their	
  long	
  run	
  viability.	
  Among	
  these,	
  we	
  know	
  it	
  is	
  known	
  that	
  their	
  attitudes	
  
toward	
  taking	
  risks	
  are	
  paramount.	
  Poor	
  households	
  are	
  living	
  at	
  the	
  margin	
  and	
  are	
  often	
  
highly	
  risk	
  averse	
  –	
  and	
  for	
  good	
  reason.	
  They	
  can	
  be	
  one	
  exogenous	
  shock	
  (e.g.	
  climate	
  
shock	
  or	
  market	
  fluctuation)	
  away	
  from	
  losing	
  most	
  or	
  all	
  of	
  their	
  assets	
  (Tanaka	
  et	
  al.,	
  
2010).	
  With	
  each	
  sequential	
  shock,	
  compounded	
  upon	
  previous	
  shocks	
  and	
  vulnerabilities,	
  
these	
  household	
  are	
  at	
  risk	
  of	
  spiraling	
  downward	
  and	
  falling	
  into	
  a	
  poverty	
  trap	
  from	
  
which	
  they	
  cannot	
  easily	
  emerge	
  (Carter	
  and	
  Barrett,	
  2006).	
  	
  	
  
	
  
The	
  question	
  of	
  uncertainty	
  and	
  risk	
  in	
  the	
  adoption	
  of	
  new	
  agricultural	
  technologies	
  has	
  
been	
  explored	
  extensively	
  in	
  the	
  literature.	
  Risk	
  aversion	
  –	
  argued	
  to	
  be	
  a	
  direct	
  result	
  of	
  
socio-­‐‑economic	
  conditions	
  (Yesuf	
  and	
  Bluffstone,	
  2009)	
  -­‐‑	
  has	
  often	
  been	
  considered	
  a	
  
major	
  factor	
  in	
  reducing	
  the	
  rate	
  of	
  adoption	
  (Wossen	
  et	
  al.,	
  2015;	
  Duflo	
  et	
  al.,	
  2009;	
  Marra	
  
et	
  al.,	
  2003;	
  Feder	
  et	
  al.,	
  1985).	
  In	
  a	
  study	
  by	
  Ayenew	
  et	
  al.	
  (2015)	
  the	
  authors	
  find	
  that	
  risk	
  
behavior	
  is	
  significantly	
  and	
  positively	
  associated	
  to	
  on-­‐‑farm	
  diversification	
  in	
  Ethiopia,	
  in	
  
	
  

16	
  

other	
  words,	
  that	
  farmers	
  who	
  are	
  willing	
  to	
  take	
  risks	
  are	
  more	
  likely	
  to	
  also	
  incorporate	
  
additional	
  crops	
  into	
  their	
  farms.	
  	
  In	
  Peru,	
  Engle-­‐‑Warnick	
  et	
  al.	
  (2007)	
  find	
  that	
  risk	
  averse	
  
farmers	
  are	
  less	
  likely	
  to	
  adopt	
  new,	
  higher	
  yielding,	
  potato	
  varieties.	
  In	
  a	
  large	
  study	
  
across	
  multiple	
  countries	
  in	
  Latin	
  America,	
  Cardenas	
  and	
  Carpenter	
  (2013)	
  find	
  that	
  
women	
  are	
  more	
  risk	
  averse	
  than	
  men	
  and	
  the	
  older	
  participants	
  are	
  more	
  willing	
  to	
  take	
  
risks	
  than	
  younger	
  participants.	
  Understanding	
  attitudes	
  towards	
  risk,	
  therefore,	
  can	
  
provide	
  important	
  insights	
  into	
  why	
  and	
  when	
  farmers	
  may	
  choose	
  to	
  adopt	
  new	
  
technologies	
  and	
  production	
  practices.	
  
	
  
Much	
  of	
  the	
  literature	
  about	
  risk	
  aversion	
  has	
  been	
  motivated	
  by	
  the	
  proposition	
  that	
  
poverty	
  can	
  be	
  explained	
  by	
  risk	
  aversion,	
  or	
  that	
  people	
  remain	
  poor	
  due	
  to	
  preferences	
  
and	
  attitudes	
  that	
  are	
  incompatible	
  with	
  growth	
  (Thaler,	
  1997),	
  or	
  that	
  people	
  are	
  too	
  risk	
  
averse	
  to	
  take	
  the	
  opportunities	
  and	
  chances	
  needed	
  to	
  increase	
  their	
  resources	
  and	
  
improve	
  their	
  wellbeing	
  (Cardenas	
  and	
  Carpenter,	
  2008).	
  Yet	
  in	
  an	
  extensive	
  review	
  of	
  the	
  
experimental	
  literature,	
  Cardenas	
  and	
  Carpenter	
  (2013)	
  find	
  that	
  the	
  literature	
  does	
  not	
  
support	
  this	
  proposition,	
  in	
  fact,	
  they	
  find	
  very	
  little	
  evidence	
  that	
  poor	
  people	
  in	
  
developing	
  countries	
  are	
  more	
  risk	
  averse	
  than	
  others.	
  	
  
	
  
On	
  the	
  one	
  hand,	
  some	
  studies	
  have	
  found	
  that	
  there	
  is	
  a	
  relationship	
  between	
  income	
  and	
  
risk	
  aversion,	
  mainly,	
  that	
  lower	
  income	
  households	
  are	
  more	
  risk	
  averse	
  than	
  higher	
  
income	
  households	
  	
  (Tanaka,	
  2010;	
  Hartog	
  et	
  al.,	
  2002;	
  Donkers	
  et	
  al.,	
  2001;	
  Moscardi	
  and	
  
De	
  Janvry,	
  1977).	
  	
  On	
  the	
  other	
  hand,	
  another	
  set	
  of	
  similar	
  studies	
  have	
  been	
  unable	
  to	
  

	
  

17	
  

show	
  this	
  relationship,	
  and	
  do	
  not	
  find	
  that	
  poor	
  households	
  are	
  more	
  risk	
  averse	
  than	
  
other	
  households	
  (Bosch-­‐‑Domènech	
  and	
  Silvestre,	
  2006;	
  Henrich	
  and	
  McElreath,	
  2002)	
  
	
  
A	
  related	
  and	
  important	
  area	
  of	
  research,	
  therefore,	
  also	
  studies	
  how	
  scarcity	
  affects	
  
behavior	
  and	
  attitudes.	
  In	
  a	
  study	
  of	
  behavior	
  under	
  scarcity	
  Haushofer	
  and	
  Fehr	
  (2014)	
  
state	
  that	
  material	
  scarcity	
  detrimentally	
  changes	
  people’s	
  allocation	
  of	
  their	
  attention,	
  
affecting	
  their	
  behavior	
  and	
  decision-­‐‑making.	
  Agarwal	
  (2000)	
  finds	
  that	
  households	
  facing	
  
the	
  most	
  financial	
  constraints	
  would	
  steal	
  wood	
  from	
  a	
  protected	
  forest,	
  and	
  risked	
  getting	
  
caught	
  and	
  getting	
  a	
  fine,	
  in	
  order	
  to	
  provide	
  cooking	
  fuel	
  for	
  their	
  homes,	
  exhibiting	
  riskier	
  
behavior.	
  While	
  Levy	
  et	
  al.	
  (2013)	
  find	
  that	
  a	
  person	
  who	
  on	
  average	
  tends	
  to	
  be	
  risk	
  
tolerant	
  (willing	
  to	
  take	
  risks)	
  when	
  he/she	
  is	
  not	
  deprived	
  of	
  food,	
  will	
  shift	
  towards	
  high	
  
risk	
  aversion,	
  when	
  they	
  experience	
  hunger	
  and	
  deprivation.	
  	
  	
  
	
  
This	
  study	
  contributes	
  to	
  this	
  body	
  of	
  research	
  by	
  examining	
  the	
  risk	
  perceptions	
  of	
  coffee	
  
farmers	
  in	
  Nicaragua	
  who	
  are	
  suffering	
  from	
  severe	
  food	
  insecurity.	
  	
  I	
  use	
  experimental	
  
games	
  to	
  measure	
  risk	
  aversion,	
  and	
  use	
  the	
  results	
  of	
  these	
  experiments,	
  with	
  a	
  series	
  of	
  
descriptive	
  analyses	
  to	
  analyze	
  the	
  capacities	
  and	
  incentives	
  of	
  coffee	
  producers	
  to	
  seek	
  
adaptive	
  strategies	
  to	
  address	
  climate	
  change.	
  	
  
	
  

Description	
  of	
  the	
  Study	
  Site	
  and	
  Sample	
  
This	
  study	
  was	
  conducted	
  in	
  the	
  department	
  of	
  Matagalpa	
  in	
  northern	
  Nicaragua	
  between	
  
June	
  and	
  July	
  2015.	
  The	
  department	
  of	
  Matagalpa	
  is	
  divided	
  into	
  13	
  municipalities	
  that	
  
contain	
  one	
  or	
  more	
  communities,	
  the	
  smallest	
  administrative	
  unit.	
  	
  A	
  sample	
  of	
  236	
  

	
  

18	
  

households	
  was	
  selected	
  using	
  a	
  two	
  stage	
  stratified	
  random	
  selection	
  strategy.	
  	
  First,	
  
communities	
  in	
  Matagalpa	
  were	
  stratified	
  by	
  level	
  of	
  vulnerability	
  to	
  climate	
  change.	
  	
  
Vulnerability	
  was	
  determined	
  by	
  the	
  average	
  elevation	
  in	
  which	
  the	
  community	
  was	
  
located.	
  Higher	
  elevation	
  (above	
  1000	
  meters	
  above	
  sea	
  level)	
  had	
  a	
  lower	
  vulnerability	
  
index	
  than	
  those	
  at	
  lower	
  elevations,	
  as	
  households	
  in	
  higher	
  elevations	
  will	
  be	
  less	
  
affected	
  by	
  increased	
  temperatures.	
  In	
  this	
  first	
  stage,	
  a	
  random	
  sample	
  of	
  communities	
  
was	
  selected	
  based	
  on	
  their	
  vulnerability	
  index.	
  	
  In	
  the	
  second	
  stage,	
  households	
  in	
  each	
  of	
  
the	
  selected	
  communities	
  were	
  drawn	
  from	
  a	
  census	
  listing	
  of	
  coffee	
  producers	
  in	
  the	
  
region.	
  	
  The	
  households	
  surveyed	
  in	
  this	
  study	
  form	
  part	
  of	
  an	
  ongoing	
  project	
  on	
  climate	
  
change	
  and	
  food	
  security	
  conducted	
  by	
  the	
  International	
  Center	
  for	
  Tropical	
  Agriculture	
  
(CIAT).	
  From	
  the	
  sample	
  of	
  236	
  households,	
  88	
  households	
  were	
  randomly	
  selected	
  to	
  
participate	
  in	
  the	
  risk	
  experiment.2	
  	
  Table	
  1	
  compares	
  the	
  two	
  groups	
  (participants	
  and	
  
non-­‐‑participants)	
  across	
  a	
  set	
  of	
  key	
  demographic	
  and	
  farm	
  characteristics.	
  The	
  data	
  show	
  
that	
  there	
  are	
  no	
  statistically	
  significant	
  difference	
  between	
  the	
  two	
  groups,	
  confirming	
  
that	
  the	
  subsample	
  introduces	
  no	
  measurable	
  bias	
  to	
  the	
  risk	
  experiment	
  analysis.	
  	
  	
  
	
  
Table	
  1.	
  Characteristics	
  of	
  Game	
  Participants	
  and	
  Non-­‐‑Participants	
  
	
  Variable	
  
HH	
  Size	
  
Education	
  
	
  
Table	
  1(Cont’d)	
  
	
  
Age	
  
Total	
  Income	
  

Non-­‐‑Participant	
  
5.34	
  
3.49	
  

Participant	
  
5.59	
  
4.22	
  

p-­‐‑value	
  
0.39	
  
0.14	
  

45.41	
  
157,879	
  

48.11	
  
238,415	
  

0.19	
  
0.19	
  

2	
  Due	
  to	
  budget	
  constrains	
  it	
  was	
  not	
  possible	
  to	
  conduct	
  the	
  experiments	
  with	
  236	
  

households.	
  	
  
	
  

19	
  

Table	
  1	
  (cont’d)	
  
Area	
  	
  
Coffee	
  Experience	
  
Male	
  

8.86	
  
16.12	
  
66.2%	
  

10.95	
  
16.93	
  
64.7%	
  

0.19	
  
0.63	
  
0.82	
  

	
  
After	
  eliminating	
  households	
  for	
  which	
  data	
  were	
  missing	
  or	
  incomplete,	
  the	
  data	
  set	
  for	
  
this	
  analysis	
  was	
  reduced	
  to	
  221	
  households	
  for	
  the	
  surveys	
  overall,	
  and	
  82	
  for	
  the	
  risk	
  
experiments.	
  Nine	
  out	
  of	
  13	
  municipalities	
  are	
  represented	
  in	
  the	
  data;	
  municipalities	
  not	
  
sampled	
  were	
  in	
  regions	
  of	
  Matagalpa	
  where	
  coffee	
  is	
  not	
  grown.	
  A	
  map	
  of	
  the	
  study	
  area	
  is	
  
presented	
  in	
  Figure	
  2.	
  
Figure	
  2.	
  Map	
  of	
  Study	
  Area	
  

Matagalpa
Lake Nicaragua

	
  
Producer	
  information	
  was	
  collected	
  on,	
  among	
  other	
  things,	
  demographic	
  and	
  socio-­‐‑
economic	
  characteristics,	
  agricultural	
  production,	
  and	
  experiences	
  with	
  economic	
  and	
  

	
  

20	
  

climatic	
  shocks.	
  	
  Table	
  1	
  summarizes	
  some	
  of	
  the	
  characteristics	
  of	
  the	
  producers	
  in	
  the	
  
sample.	
  	
  The	
  average	
  age	
  of	
  the	
  respondents	
  is	
  46	
  years	
  with	
  an	
  average	
  of	
  3.8	
  years	
  of	
  
formal	
  education	
  completed.	
  	
  The	
  mean	
  area	
  under	
  production	
  is	
  4.85	
  hectares	
  and	
  the	
  
mean	
  annual	
  coffee	
  production	
  is	
  9.7	
  quintales	
  (312.8	
  kg)	
  of	
  wet	
  parchment3	
  per	
  hectare.	
  	
  
Forty-­‐‑six	
  percent	
  of	
  the	
  sample	
  are	
  members	
  of	
  a	
  coffee	
  cooperative	
  and	
  65%	
  are	
  male-­‐‑
headed	
  households.	
  	
  
	
  
Table	
  2.	
  Sample	
  Characteristics	
  
Variable	
  

Means	
  
(%	
  where	
  
noted)	
  

	
  

Male	
  
65.4%	
  
	
  
Age	
  	
  
46.4	
  
(15.28)	
  
Household	
  Size	
  
5.4	
  
(2.12)	
  
Years	
  of	
  Education	
  
3.8	
  
(3.48)	
  
Years	
  in	
  Coffee	
  
16.5	
  
(12.48)	
  
Total	
  Coffee	
  Income	
  (USD)	
  per	
  ha	
  
821.8	
  
(875.11)	
  
Total	
  income	
  (USD)	
  
5,648.3	
  
(7,377.11)	
  
Total	
  area	
  under	
  coffee	
  production	
  (ha)	
  
4.8	
  
(5.38)	
  
1
Total	
  Coffee	
  Production	
  (quintales )	
  per	
  ha	
  	
  
9.7	
  
(120.59)	
  
Cooperative	
  membership	
  
45.5%	
  
	
  
11	
  Quintal=	
  46kg;	
  Standard	
  deviations	
  are	
  presented	
  in	
  parentheses	
  
	
  
In	
  addition	
  to	
  these	
  primary	
  data,	
  I	
  use	
  data	
  for	
  two	
  variables	
  provided	
  by	
  the	
  International	
  
Center	
  for	
  Tropical	
  Agriculture	
  (CIAT):	
  (1)	
  an	
  indicator	
  of	
  household	
  vulnerability	
  to	
  

Wet	
  parchment	
  is	
  a	
  state	
  of	
  the	
  coffee	
  in	
  its	
  transformation	
  from	
  cherry	
  to	
  bean.	
  	
  After	
  
harvesting,	
  the	
  freshly	
  harvested	
  cherries	
  are	
  passed	
  through	
  a	
  pulping	
  machine	
  to	
  
separate	
  the	
  skin	
  and	
  pulp	
  from	
  the	
  bean.	
  	
  After	
  depulping,	
  the	
  bean	
  is	
  transported	
  to
water	
  filled	
  tanks	
  for	
  fermentation	
  where	
  they	
  remain	
  from	
  12	
  to	
  48	
  hours.	
  When	
  
fermentation	
  is	
  complete,	
  the	
  beans	
  are	
  rinsed	
  and	
  are	
  ready	
  for	
  drying.	
  Coffee	
  at	
  this	
  stage	
  
of	
  the	
  wet	
  milling	
  process	
  is	
  known	
  as	
  wet	
  parchment.
	
  
3	
  

	
  

21	
  

climate	
  change	
  which	
  takes	
  into	
  consideration	
  predictions	
  of	
  temperature	
  changes	
  in	
  2020	
  
and	
  2050	
  in	
  combination	
  with	
  the	
  elevation	
  at	
  which	
  the	
  household	
  is	
  located.;	
  (2)	
  an	
  
indicator	
  of	
  household	
  food	
  insecurity	
  that	
  is	
  based	
  on	
  whether	
  households	
  have	
  had	
  to	
  
compromise	
  the	
  quality	
  and	
  quantity	
  of	
  the	
  food	
  consumed	
  by	
  the	
  adults	
  and	
  the	
  children	
  
in	
  the	
  household.	
  	
  	
  
	
  

Description	
  of	
  the	
  Framed	
  Field	
  Experiments	
  
Economic	
  experiments	
  were	
  used	
  to	
  measure	
  attitudes	
  towards	
  risk	
  by	
  observing	
  the	
  
behavior	
  of	
  farmers	
  in	
  a	
  set	
  of	
  one-­‐‑period	
  lottery	
  games	
  with	
  real	
  pay-­‐‑offs.	
  The	
  
experiments	
  were	
  designed	
  with	
  gains-­‐‑only	
  payoffs;	
  farmers	
  playing	
  these	
  experiments	
  are	
  
very	
  poor	
  and	
  should	
  not	
  be	
  to	
  put	
  in	
  a	
  situation	
  in	
  which	
  the	
  worst	
  possible	
  loss	
  exceeds	
  
their	
  current	
  cash	
  holdings.	
  	
  	
  
	
  
Following	
  Binswager’s	
  (1980)	
  design,	
  I	
  conducted	
  one	
  lottery	
  choice	
  experiment	
  intended	
  
to	
  assess	
  participants’	
  attitudes	
  towards	
  risk.	
  In	
  the	
  game,	
  the	
  participant	
  was	
  shown	
  a	
  
lottery	
  choice	
  on	
  a	
  laminated	
  card	
  with	
  six	
  different	
  possible	
  binary	
  payoffs	
  and	
  asked	
  to	
  
pick	
  one	
  to	
  play.	
  To	
  avoid	
  problems	
  that	
  might	
  arise	
  if	
  participants	
  had	
  a	
  hard	
  time	
  
understanding	
  probabilistic	
  outcomes,	
  a	
  simple	
  50-­‐‑50	
  chance	
  scenario	
  was	
  presented	
  to	
  
the	
  producer.	
  
	
  
The	
  risk	
  experiment	
  was	
  framed	
  as	
  a	
  situation	
  in	
  which	
  the	
  farmer	
  makes	
  a	
  decision	
  about	
  
his/her	
  coffee	
  production	
  given	
  uncertain	
  future	
  climate.	
  Interviewers	
  told	
  farmers	
  that	
  
due	
  to	
  uncertain	
  weather,	
  the	
  yield	
  from	
  the	
  upcoming	
  coffee	
  season	
  would	
  be	
  affected	
  in	
  

	
  

22	
  

such	
  a	
  way	
  that	
  the	
  likelihood	
  of	
  crop	
  failure	
  and	
  crop	
  success	
  were	
  the	
  same	
  (equal	
  
probability).	
  Farmers	
  had	
  the	
  choice	
  to	
  follow	
  one	
  of	
  six	
  paths	
  of	
  action,	
  given	
  this	
  
uncertainty.	
  
	
  
Table	
  2	
  describes	
  the	
  parameters	
  of	
  the	
  experiment	
  and	
  method	
  employed.	
  Holt	
  and	
  Laurie	
  
(2002)	
  suggest	
  that	
  a	
  good	
  starting	
  point	
  to	
  determine	
  the	
  payoff	
  levels	
  is	
  to	
  use	
  the	
  daily	
  
pay	
  rate	
  of	
  a	
  farmer	
  in	
  the	
  region	
  where	
  the	
  study	
  will	
  be	
  conducted.	
  	
  At	
  the	
  time	
  of	
  the	
  
study,	
  a	
  farmer	
  in	
  the	
  regions	
  earned	
  an	
  average	
  of	
  C$100	
  a	
  day	
  (100	
  Nicaraguan	
  Córdobas,	
  
or	
  approximately	
  3.77USD).	
  	
  Once	
  the	
  starting	
  point	
  was	
  determined,	
  the	
  remaining	
  payoff	
  
options	
  were	
  determined	
  following	
  recommendations	
  from	
  Yesuf	
  and	
  Bluffstone	
  (2009).	
  
	
  
Table	
  3.	
  Risk	
  Experiment	
  Payoffs	
  and	
  Risk	
  Coefficient	
  
Choice	
  

1	
  
2	
  
3	
  
4	
  
5	
  

Payoffs	
  
Bad	
  
Harvest	
  
(p=0.5)	
  
100	
  
80	
  
60	
  
40	
  
20	
  

Good	
  Harvest	
  
(p=0.5)	
  
100	
  
150	
  
190	
  
240	
  
300	
  

Expected	
  
payoff	
  

Risk	
  Aversion	
  
Class	
  

Coefficient	
  of	
  
Relative	
  Risk	
  
Aversion	
  

100	
  
115	
  
125	
  
140	
  
160	
  

Extreme	
  
r	
  >	
  2.96	
  
Severe	
  
2.96	
  ≥	
  r	
  >	
  0.78	
  
Intermediate	
   0.78	
  ≥	
  r	
  >	
  0.62	
  
Moderate	
  
0.62	
  ≥	
  r	
  >	
  0.49	
  
Slight	
  
0.49	
  ≥	
  r	
  >	
  0.23	
  
Neutral	
  to	
  
6	
  
0	
  
350	
  
175	
  
0.23	
  ≥	
  r	
  
Preferring	
  
*1USD	
  =	
  26.5	
  Córdobas	
  in	
  July	
  2015	
  	
  when	
  the	
  experiments	
  were	
  conducted	
  
	
  
The	
  constant	
  relative	
  risk	
  aversion	
  utility	
  function,	
  𝑈 𝑥 =

$ (&'()
*+,

,	
  is	
  used	
  to	
  measure	
  the	
  

risk	
  attitudes	
  at	
  which	
  people	
  should	
  be	
  indifferent	
  between	
  any	
  two	
  neighboring	
  lotteries.	
  	
  
For	
  example,	
  the	
  relative	
  risk	
  aversion	
  r	
  that	
  would	
  make	
  one	
  indifferent	
  between	
  the	
  first	
  

	
  

23	
  

and	
  second	
  lotteries	
  (or	
  in	
  other	
  words,	
  the	
  utility	
  of	
  any	
  lottery	
  does	
  not	
  exceed	
  the	
  utility	
  
of	
  getting	
  the	
  average	
  monetary	
  payoff	
  of	
  the	
  lottery	
  with	
  certainty)	
  is	
  calculated	
  as	
  
follows:	
  
	
  
	
  
	
  

𝑈 100 = 𝑈 80 + 𝑈(150)	
  

	
  

100(*+,) 1 80 *+,
1 150 *+,
= ∗
+ 	
   ∗
= 0, 𝑟 = 2.96	
  
1−𝑟
2 1−𝑟
2
1−𝑟

	
  
(1)	
  

	
  
	
  
	
  Experiment	
  Mechanics	
  in	
  the	
  Field	
  
The	
  experiments	
  were	
  conducted	
  at	
  the	
  farmer’s	
  house,	
  preceding	
  the	
  survey	
  and	
  they	
  took	
  
close	
  to	
  thirty	
  minutes	
  to	
  complete.	
  The	
  information	
  from	
  the	
  surveys	
  and	
  experiments	
  
were	
  registered	
  in	
  tablets	
  by	
  the	
  enumerator.	
  	
  
	
  
I	
  started	
  the	
  experiment	
  by	
  describing	
  the	
  task	
  as	
  a	
  situation	
  in	
  which	
  the	
  farmer	
  had	
  to	
  
make	
  a	
  decision	
  regarding	
  his	
  coffee	
  production	
  that	
  would	
  involve	
  some	
  risk	
  of	
  crop	
  
success	
  or	
  crop	
  failure.	
  All	
  possible	
  outcomes	
  were	
  described	
  before	
  the	
  farmer	
  had	
  to	
  
make	
  a	
  decision.	
  	
  Farmers	
  were	
  shown	
  a	
  laminated	
  card	
  (Figure	
  3)	
  containing	
  the	
  risk	
  
lottery	
  that	
  had	
  6	
  possible	
  alternatives.	
  Farmers	
  had	
  to	
  choose	
  one	
  of	
  the	
  6	
  different	
  
alternatives,	
  which	
  in	
  turn	
  had	
  two	
  potential	
  outcomes	
  (depending	
  on	
  the	
  crop	
  success	
  or	
  
crop	
  failure).	
  The	
  alternatives	
  were:	
  with	
  alternative	
  (1)	
  the	
  farmers	
  simply	
  received	
  
C$100,	
  in	
  other	
  words,	
  the	
  payoff	
  was	
  the	
  same	
  regardless	
  of	
  the	
  outcome	
  of	
  the	
  game;	
  

	
  

24	
  

with	
  alternative	
  (2)	
  the	
  farmer	
  could	
  receive	
  either	
  C$80	
  or	
  C$150,	
  in	
  other	
  words,	
  by	
  not	
  
choosing	
  (1)	
  the	
  individual	
  stood	
  to	
  lose	
  C$20	
  but	
  could	
  also	
  gain	
  C$50.	
  The	
  payoffs	
  for	
  
alternatives	
  3,	
  4,	
  and	
  5,	
  were	
  60/190,	
  40/250,	
  and	
  20/300,	
  respectively.	
  Finally,	
  by	
  
choosing	
  (6)	
  an	
  individual	
  could	
  either	
  receive	
  no	
  money	
  at	
  all	
  or	
  get	
  C$350.	
  	
  Each	
  choice	
  is	
  
associated	
  with	
  a	
  classification	
  of	
  a	
  risk	
  class,	
  from	
  risk	
  averse	
  in	
  alternative	
  (1)	
  to	
  risk	
  
neutral-­‐‑to-­‐‑preferring	
  in	
  alternative	
  (6)	
  as	
  shown	
  in	
  Table	
  2.	
  The	
  payoffs	
  for	
  each	
  lottery	
  
choice	
  were	
  chosen	
  so	
  that	
  the	
  expected	
  payoff	
  and	
  the	
  variance	
  of	
  each	
  lottery	
  increases	
  in	
  
clockwise	
  order.	
  
	
  
In	
  addition,	
  the	
  enumerator	
  had	
  a	
  bag	
  with	
  equal	
  number	
  of	
  white	
  and	
  orange	
  balls,	
  and	
  
once	
  the	
  farmer	
  decided	
  on	
  the	
  lottery	
  that	
  he/she	
  wanted	
  to	
  play,	
  he/she	
  randomly	
  drew	
  
a	
  ball	
  from	
  the	
  bag	
  to	
  determine	
  the	
  payoff	
  for	
  the	
  activity.	
  If	
  the	
  farmer	
  withdrew	
  an	
  
orange	
  ball	
  they	
  received	
  the	
  low	
  payoff,	
  if	
  he/she	
  withdrew	
  a	
  white	
  ball	
  they	
  received	
  the	
  
high	
  payoff.	
  	
  
	
  

	
  

25	
  

Figure	
  3.	
  Risk	
  Experiment	
  
100

100

100

100

100
0
0

100
80

350

150

80

350

150

0

350

80

150

20

300

60

190

20

300
20

300

40

240

40

240
40

60

190
60

190

240

	
  
	
  
	
  
	
  
Payoffs	
  from	
  the	
  experiment	
  were	
  paid	
  in	
  cash.	
  In	
  average	
  I	
  paid	
  farmers	
  150.25	
  
Nicaraguan	
  Córdobas	
  (or	
  5.77USD),	
  an	
  amount	
  consisting	
  of	
  the	
  average	
  wages	
  for	
  1.5	
  
days.	
  
	
  

Results	
  and	
  Discussion	
  
In	
  addition	
  to	
  results	
  from	
  the	
  experiments,	
  in	
  this	
  section	
  I	
  present	
  a	
  series	
  of	
  descriptive	
  
analyses	
  that	
  help	
  us	
  understand	
  Nicaraguan	
  coffee	
  producers’	
  perceptions	
  about	
  climate	
  
change	
  and	
  to	
  shed	
  some	
  light	
  on	
  the	
  capacities	
  that	
  they	
  have	
  to	
  mitigate	
  its	
  impacts.	
  	
  
	
  

	
  

26	
  

Descriptive	
  Analyses	
  
I	
  begin	
  this	
  section	
  by	
  examining	
  how	
  all	
  farmers	
  in	
  the	
  sample	
  have	
  perceived	
  changes	
  in	
  
the	
  Matagalpa	
  climate	
  over	
  the	
  past	
  ten	
  years.	
  Overall,	
  most	
  farmers	
  have	
  perceived	
  major	
  
changes	
  in	
  regional	
  climate	
  patterns;	
  over	
  90%	
  of	
  households	
  stated	
  that	
  they	
  have	
  seen	
  
changes	
  in	
  overall	
  climate,	
  and	
  in	
  temperatures	
  specifically,	
  over	
  the	
  past	
  ten	
  years.	
  A	
  
majority	
  of	
  famers	
  have	
  also	
  perceived	
  temporal	
  changes	
  in	
  the	
  rainy	
  season	
  (74%)	
  as	
  well	
  
as	
  changes	
  in	
  the	
  frequency	
  of	
  rainfall	
  (58%).	
  	
  Furthermore,	
  65%	
  of	
  households	
  believe	
  
that	
  the	
  frequency	
  of	
  extreme	
  events	
  has	
  changed	
  over	
  the	
  past	
  10	
  years	
  (Figure	
  4).	
  The	
  
direction	
  of	
  these	
  changes	
  are	
  estimated	
  based	
  on	
  field	
  observation	
  during	
  field	
  visits	
  and	
  
data	
  collection,	
  many	
  farmers	
  talked	
  about	
  recent	
  droughts	
  that	
  had	
  destroyed	
  their	
  maize	
  
and	
  bean	
  plantations	
  (changes	
  in	
  the	
  frequency	
  of	
  rainfall	
  -­‐‑	
  fewer),	
  and	
  of	
  early	
  rains	
  that	
  
caused	
  their	
  coffee	
  trees	
  to	
  flower	
  early	
  (temporal	
  changes	
  in	
  the	
  rainy	
  season).	
  Farmers,	
  
due	
  to	
  the	
  nature	
  of	
  their	
  work,	
  have	
  their	
  finger	
  on	
  the	
  pulse	
  of	
  the	
  weather	
  and	
  the	
  land	
  
they	
  work,	
  and	
  although	
  they	
  may	
  not	
  be	
  aided	
  by	
  computerized	
  tools	
  and	
  models	
  to	
  
measure	
  or	
  estimate	
  climatic	
  events,	
  their	
  experience	
  has	
  taught	
  them	
  to	
  recognize	
  
patterns	
  and	
  changes	
  that	
  affect	
  the	
  production	
  of	
  their	
  crops.	
  As	
  seen	
  with	
  the	
  households	
  
from	
  this	
  study,	
  the	
  vast	
  majority	
  of	
  them	
  have	
  concluded	
  that	
  there	
  have	
  been	
  changes	
  in	
  
the	
  climate,	
  both	
  in	
  frequency	
  and	
  in	
  the	
  timing	
  of	
  those	
  events.	
  
	
  

	
  

27	
  

Figure	
  4.	
  Perceptions	
  About	
  Climate	
  Change	
  

Percentage	
  of	
  HH

100%

96%

94%
74%

80%

65%

60%

58%

40%
20%
0%
Climate	
  has	
   Temperatures	
   Rainy	
  Season	
   Frequency	
  of	
   Frequency	
  of	
  
Changed have	
  Changed has	
  Changed
Extreme	
  
Rain	
  has	
  
Events	
  have	
  
Changed
Changed

Perceived	
  Changes

	
  
	
  
Despite	
  the	
  high	
  proportion	
  of	
  farmers	
  reporting	
  changes	
  in	
  weather	
  patterns,	
  a	
  smaller	
  
proportion	
  of	
  them	
  reported	
  experiencing	
  losses	
  due	
  to	
  these	
  changes	
  over	
  the	
  past	
  5	
  years	
  
(Figure	
  5).	
  Of	
  the	
  surveyed	
  households,	
  75%,	
  43%	
  and	
  17%	
  reported	
  experiencing	
  pests,	
  
droughts,	
  and	
  floods	
  (respectively)	
  in	
  their	
  coffee	
  fields.	
  	
  The	
  most	
  common	
  overall	
  
response	
  to	
  these	
  shocks	
  is	
  for	
  producers	
  to	
  increase	
  the	
  number	
  of	
  household	
  labor	
  hours	
  
and	
  to	
  spend	
  their	
  savings	
  to	
  cope	
  with	
  losses.	
  	
  In	
  addition,	
  40%	
  of	
  farmers	
  who	
  
experienced	
  pests	
  switched	
  to	
  a	
  different	
  crop	
  or	
  to	
  a	
  new	
  coffee	
  variety	
  and	
  29%	
  of	
  them	
  
changed	
  their	
  production	
  practices	
  to	
  respond	
  to	
  the	
  pest	
  (e.g.,	
  applying	
  more	
  pesticides,	
  
pruning,	
  or	
  stumping	
  coffee	
  trees).	
  Fifteen	
  percent	
  of	
  households	
  experiencing	
  droughts	
  
had	
  to	
  decrease	
  their	
  food	
  consumption.	
  It	
  is	
  likely	
  that	
  these	
  are	
  households	
  that	
  grow	
  
subsistence	
  crops,	
  such	
  as	
  beans	
  and	
  maize	
  in	
  addition	
  to	
  coffee.	
  Overall,	
  farmers	
  respond	
  
to	
  pests	
  at	
  a	
  higher	
  rate	
  than	
  to	
  droughts	
  and	
  floods,	
  most	
  likely	
  due	
  to	
  the	
  institutional	
  
response	
  and	
  support	
  in	
  the	
  area	
  to	
  the	
  recent	
  leaf	
  rust	
  epidemic.	
  	
  	
  	
  

	
  

28	
  

Figure	
  5.	
  Responses	
  to	
  Climate	
  Shocks	
  

Percent	
  of	
  HHs	
  Responding	
  to	
  Shocks

50%

40%

30%

20%

10%

0%
Worked	
  
More	
  Hours

Spent	
  
Savings

Changed	
  
Crops

Pests	
  (75%)

Changed	
  
Practices

Drought	
  (43%)

Fell	
  into	
  
Debt

Searched	
   Consumed	
  
for	
  Work Less	
  Food

Flood	
  (17%)

	
  
	
  
Cooperatives,	
  through	
  the	
  support	
  that	
  they	
  can	
  provide	
  in	
  the	
  provision	
  of	
  inputs,	
  
trainings,	
  and	
  other	
  extension	
  services,	
  can	
  play	
  an	
  important	
  role	
  in	
  helping	
  farmers	
  to	
  
transition	
  towards	
  more	
  resilient	
  livelihoods.	
  The	
  sample	
  in	
  this	
  study	
  is	
  evenly	
  split	
  
between	
  cooperative	
  members	
  and	
  non-­‐‑members,	
  with	
  45.5%	
  of	
  farmers	
  belonging	
  to	
  a	
  
coffee	
  cooperative.	
  	
  Of	
  these	
  cooperative	
  members,	
  however,	
  60%	
  have	
  expressed	
  being	
  
dissatisfied	
  with	
  their	
  cooperatives,	
  and	
  a	
  paltry	
  6.4%	
  of	
  them	
  stated	
  that	
  they	
  were	
  very	
  
satisfied	
  with	
  their	
  cooperatives	
  (Figure	
  6).	
  Issues	
  of	
  trust,	
  transparency,	
  lack	
  of	
  support,	
  
and	
  corruption	
  have	
  all	
  come	
  up	
  in	
  the	
  literature	
  on	
  cooperatives	
  in	
  Nicaragua	
  (Bacon,	
  
2010),	
  and	
  these	
  results	
  confirm	
  that	
  to	
  some	
  extent	
  these	
  are	
  lingering	
  issues	
  for	
  coffee	
  
farmers	
  in	
  the	
  country.	
  	
  
	
  

	
  

29	
  

Figure	
  6.	
  Member	
  Satisfaction	
  with	
  Cooperative	
  
70

Percentage	
  of	
  Cooperative	
  Member

60
60
50
40
30
20
20
10

13.64
6.36

0
Very	
  Satisfied

Somewhat	
  Satisfied

Not	
  Satisfied

Very	
  Unsatisfied

Satisfaction	
  with	
  Cooperative

	
  
	
  
Moving	
  beyond	
  farmer	
  perceptions	
  and	
  preferences,	
  I	
  focus	
  on	
  an	
  indicator	
  of	
  climate	
  
change	
  vulnerability	
  (developed	
  by	
  CIAT)	
  that	
  allows	
  us	
  to	
  examine	
  the	
  degree	
  to	
  which	
  
respondents	
  in	
  this	
  sample	
  are	
  currently	
  living	
  in	
  areas	
  that	
  are	
  at	
  risk	
  of	
  suffering	
  losses	
  
due	
  to	
  climate	
  change	
  impacts.	
  I	
  find	
  that	
  by	
  2020,	
  16%	
  of	
  households	
  located	
  in	
  Matagalpa	
  
will	
  not	
  see	
  any	
  significant	
  impacts	
  due	
  to	
  climate	
  change,	
  while	
  47%	
  of	
  them	
  will	
  see	
  
medium	
  impact,	
  and	
  37%	
  will	
  see	
  high	
  impacts	
  induced	
  by	
  climate	
  change.	
  	
  The	
  situation	
  
becomes	
  even	
  more	
  dire	
  in	
  2050,	
  by	
  which	
  time	
  everyone	
  in	
  this	
  sample	
  will	
  be	
  located	
  in	
  
regions	
  estimated	
  to	
  be	
  impacted	
  by	
  climate	
  change,	
  with	
  the	
  majority	
  of	
  them	
  (62%)	
  
experiencing	
  high	
  impacts	
  (Figure	
  7).	
  	
  	
  
	
  

	
  

30	
  

Figure	
  7.	
  Percentage	
  of	
  Household	
  Vulnerable	
  to	
  Climate	
  Change	
  Shocks	
  

Vulnerability	
  to	
  
Climate	
  Change	
  in	
  
2020

37%

16%

Vulnerability	
  to	
  
Climate	
  Change	
  in	
  
2050

No	
  Impact

38%

Medium	
  
Impact	
  

47%

62%

Medium	
  
Impact	
  
High	
  Impact

High	
  Impact

In	
  addition	
  to	
  vulnerability	
  to	
  climate	
  change,	
  data	
  from	
  this	
  study	
  indicates	
  that	
  88.6%	
  of	
  
households	
  in	
  the	
  sample	
  are	
  severely	
  food	
  insecure,	
  and	
  have	
  had	
  to	
  lower	
  the	
  amount	
  of	
  
food	
  consumed	
  and	
  also	
  compromise	
  the	
  quality	
  of	
  the	
  food	
  they	
  consume	
  at	
  home.	
  
	
  
Men	
  and	
  women	
  respond	
  to	
  shocks	
  differently,	
  and	
  their	
  vulnerability	
  to	
  these	
  shocks	
  is	
  
also	
  different.	
  	
  Women,	
  in	
  addition	
  to	
  restrictions	
  that	
  they	
  face	
  due	
  to	
  cultural	
  norms	
  (such	
  
as	
  not	
  being	
  able	
  to	
  own	
  land,	
  or	
  responsibilities	
  as	
  homemakers	
  and	
  primary	
  caregivers	
  of	
  
children	
  and	
  the	
  elderly),	
  often	
  have	
  lower	
  access	
  to	
  extension	
  services	
  and	
  fertile	
  land	
  
(Ruben	
  and	
  Zuniga,	
  2011;	
  Bacon,	
  2010).	
  	
  When	
  I	
  standardized	
  yield	
  and	
  income	
  in	
  the	
  
sample	
  by	
  the	
  amount	
  of	
  land	
  operated	
  by	
  the	
  household,	
  I	
  find	
  that	
  women	
  heads	
  of	
  
households	
  hold	
  significantly	
  less	
  land	
  (1.66ha	
  vs.	
  2.77ha),	
  which	
  produces	
  less	
  coffee	
  
(10.02	
  quintales	
  vs.	
  13.03	
  quintales),	
  in	
  turn	
  generating	
  less	
  income	
  (568.9USD	
  vs.	
  
952.3USD).	
  Additionally,	
  women	
  are	
  significantly	
  more	
  food	
  insecure	
  than	
  men.	
  Of	
  the	
  
survey	
  respondents,	
  96%	
  of	
  female	
  headed	
  households	
  suffered	
  from	
  food	
  insecurity	
  
versus	
  85%	
  of	
  male	
  headed	
  households	
  (Table	
  3).	
  	
  	
  
	
  

31	
  

	
  
Table	
  4.	
  Gender	
  Differences	
  
	
  	
  

Male	
  

Average	
  Area	
  Under	
  Coffee	
  Production	
  (ha)	
  

2.77	
  

1.66	
  

0.002	
  

Average	
  Yield	
  per	
  Hectare	
  of	
  Coffee	
  (kg)	
  

599.38	
  

460.92	
  

0.086	
  

Average	
  Income	
  Per	
  Hectare	
  of	
  Coffee	
  

952.30	
  

568.90	
  

0.001	
  

85%	
  

96%	
  

0.019	
  

Percentage	
  of	
  Farmers	
  Suffering	
  from	
  Food	
  Insecurity	
  

Female	
   p-­‐‑value	
  

	
  
Experiment	
  Results	
  
I	
  use	
  an	
  ordered	
  probit	
  model	
  to	
  examine	
  the	
  determinants	
  of	
  farmer	
  risk	
  preference.	
  The	
  
econometric	
  specification	
  for	
  this	
  model	
  is	
  presented	
  below:	
  
	
  
	
  
𝑅𝑖𝑠𝑘? = 	
   𝛽* 𝐻𝐻𝑆𝑖𝑧𝑒? + 𝛽E 𝑆𝑒𝑥? + 𝛽F 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛? + 𝛽O 𝐴𝑔𝑒? + 𝛽R Income? + 	
   𝛽Y 𝐼𝑛𝑐𝑜𝑚𝑒?E
+ 	
   𝛽\ 𝐴𝑟𝑒𝑎? + 𝛽] 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒? + 𝛽_ 𝐹𝑜𝑜𝑑	
  𝐼𝑛𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑦1?

(2)	
  

+ 𝛽*b 𝐹𝑜𝑜𝑑	
  𝐼𝑛𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑦2? + 𝜀? 	
  
	
  
	
  
𝑅𝑖𝑠𝑘? 	
  represents	
  individual	
  i’s	
  choice	
  from	
  1	
  to	
  6	
  in	
  the	
  risk	
  activity,	
  and	
  where	
  a	
  higher	
  
value	
  represents	
  higher	
  riskiness.	
  	
  𝛽? 	
  represents	
  the	
  estimated	
  coefficient	
  for	
  each	
  
regressor	
  and	
  𝜀? 	
  is	
  stochastic	
  component	
  of	
  this	
  model.	
  	
  The	
  model	
  includes	
  household	
  
demographics,	
  farm	
  characteristics,	
  and	
  a	
  dummy	
  variable	
  indicating	
  level	
  of	
  food	
  
insecurity	
  experienced	
  by	
  households.	
  
I	
  begin	
  the	
  analysis	
  of	
  risk	
  preferences	
  by	
  looking	
  at	
  the	
  distribution	
  of	
  risk	
  choices	
  among	
  
the	
  different	
  variables	
  in	
  the	
  model	
  (Table	
  4).	
  	
  On	
  average,	
  smaller	
  households,	
  older	
  

	
  

32	
  

people,	
  and	
  farmers	
  with	
  less	
  land	
  under	
  coffee	
  production	
  are	
  more	
  likely	
  to	
  choose	
  the	
  
less	
  risky	
  option,	
  while	
  more	
  educated	
  households	
  and	
  men	
  tend	
  to	
  choose	
  the	
  riskier	
  
options.	
  There	
  is	
  no	
  clear	
  trend	
  of	
  risk	
  choices	
  for	
  different	
  income	
  levels,	
  the	
  average	
  
income	
  for	
  the	
  less	
  risky	
  choice	
  is	
  the	
  highest	
  in	
  the	
  group,	
  yet	
  it	
  decreases	
  after	
  the	
  first	
  
option	
  and	
  then	
  goes	
  back	
  up	
  for	
  the	
  riskier	
  choice.	
  
	
  
Table	
  5.	
  Distribution	
  of	
  Risk	
  Choices	
  
	
  
	
  
HH	
  Size	
  
Male	
  
Education	
  
Age	
  
Total	
  Income	
  (USD)	
  
Total	
  Area	
  (ha)	
  
Coffee	
  Experience	
  

Low	
  Risk←-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑-­‐‑→High	
  Risk	
  
Choice	
  	
   Choice	
   Choice	
   Choice	
   Choice	
   	
  	
  Choice	
  	
  
1	
  
2	
  
3	
  
4	
  
5	
  
6	
  
6.2	
  
5.1	
  
6.2	
  
5.4	
  
4.4	
  
5.0	
  
57%	
  
75%	
  
68%	
  
56%	
  
63%	
  
80%	
  
3.3	
  
4.2	
  
3.6	
  
4.5	
  
6.4	
  
5.0	
  
51.8	
  
43.9	
  
47.5	
  
47.9	
  
47.4	
  
47.6	
  
18,807	
   5,721	
   3,162	
   6,896	
   6,925	
   10,371	
  
11.4	
  
4.6	
  
4.6	
  
9.1	
  
6.4	
  
7.6	
  
13.5	
  
14.0	
  
20.3	
  
17.2	
  
18.6	
  
19.3	
  

	
  
	
  
When	
  the	
  lotteries	
  are	
  numbered	
  in	
  increasing	
  riskiness	
  from	
  one	
  to	
  six,	
  clockwise,	
  the	
  
average	
  choice	
  in	
  the	
  risk	
  game	
  is	
  3.1	
  which	
  puts	
  the	
  average	
  close	
  to	
  the	
  60|90	
  gamble.	
  	
  
	
  
A	
  quick	
  look	
  at	
  the	
  distribution	
  of	
  choices	
  and	
  food	
  insecurity	
  helps	
  us	
  understand	
  how	
  
these	
  households	
  made	
  their	
  choice	
  selections	
  (Figure	
  8).	
  Households	
  with	
  no	
  food	
  
insecurity	
  tend	
  to	
  make	
  riskier	
  choices,	
  while	
  households	
  suffering	
  from	
  severe	
  food	
  
insecurity	
  consistently	
  select	
  the	
  less	
  risky	
  options.	
  Not	
  a	
  single	
  household	
  in	
  the	
  sample	
  
that	
  belonged	
  to	
  the	
  highest	
  food	
  insecurity	
  category	
  chose	
  option	
  6	
  in	
  the	
  game.	
  Table	
  5	
  
presents	
  the	
  results	
  from	
  the	
  econometric	
  model.	
  
	
  

33	
  

	
  
Figure	
  8.	
  Game	
  Choices	
  by	
  Households	
  with	
  Food	
  Insecurity	
  

Percentage	
  of	
  Houeholds

40%

30%

20%

10%

0%
Choice	
  1
No	
  food	
  insecurity	
  (n=16)

Choice	
  2

Choice	
  3

Choice	
  4

Moderate	
  Food	
  Insecurity	
  (n=46)

Choice	
  5

Choice	
  6

Severe	
  Food	
  Insecurity	
  (n=26)

	
  
	
  
Table	
  6.	
  Results	
  from	
  Ordered	
  Probit	
  Model	
  for	
  Risk	
  Aversion	
  
	
  	
  

Coefficient	
  

HH	
  Size	
  
Male	
  
Education	
  
Age	
  
Income	
  
Income2	
  
Total	
  Area	
  
Coffee	
  Experience	
  
Moderate	
  Food	
  Insecurity	
  
Severe	
  Food	
  Insecurity	
  
Log	
  Likelihood	
  
Chi-­‐‑square	
  
n	
  

-­‐‑0.258	
  
-­‐‑0.069	
  
0.027	
  
-­‐‑0.017	
  
0.005	
  
-­‐‑4.28e-­‐‑6	
  
-­‐‑0.027	
  
0.036	
  
-­‐‑2.436	
  
-­‐‑2.237	
  
-­‐‑127.56	
  
28.50	
  
82	
  

	
  	
  
***	
  
	
  
	
  
	
  
*	
  
**	
  
	
  
*	
  
***	
  
***	
  
	
  	
  
	
  

Std.	
  Error	
  
0.106	
  
0.444	
  
0.064	
  
0.017	
  
0.003	
  
1.92e-­‐‑6	
  
0.020	
  
0.022	
  
0.765	
  
0.838	
  

	
   	
  
	
  	
  
	
  

Note:	
  ***,	
  **,	
  and	
  *	
  represent	
  significance	
  at	
  the	
  0.01,	
  0.05,	
  and	
  0.1	
  confidence	
  	
  
level	
  respectively	
  	
  

	
  

	
  

34	
  

From	
  the	
  probit	
  model	
  a	
  number	
  of	
  important	
  findings	
  can	
  be	
  seen:	
  	
  Men,	
  higher	
  educated	
  
respondents,	
  and	
  younger	
  respondents	
  tend	
  to	
  have	
  a	
  higher	
  likelihood	
  of	
  selecting	
  the	
  
riskier	
  options.	
  	
  Households	
  with	
  more	
  land,	
  however,	
  have	
  a	
  higher	
  likelihood	
  of	
  choosing	
  
the	
  less	
  risky	
  option,	
  although	
  the	
  differences	
  are	
  not	
  statistically	
  significant.	
  
	
  
Larger	
  households	
  with	
  more	
  members	
  are	
  significantly	
  more	
  likely	
  to	
  “play	
  it	
  safe”	
  in	
  their	
  
choices.	
  With	
  each	
  additional	
  household	
  member,	
  the	
  likelihood	
  of	
  selecting	
  the	
  riskier	
  
C$0|C$350	
  choice	
  decreases	
  by	
  14.7%.	
  	
  Larger	
  households	
  face	
  constraints	
  that	
  might	
  
explain	
  this	
  choice,	
  for	
  example,	
  larger	
  households	
  may	
  have	
  a	
  higher	
  proportion	
  of	
  
dependents	
  (children	
  and	
  elderly)	
  and	
  are	
  not,	
  therefore,	
  willing	
  to	
  take	
  the	
  risks	
  that	
  they	
  
would	
  take	
  if	
  they	
  did	
  not	
  have	
  any	
  dependents.	
  Any	
  risks	
  that	
  they	
  take	
  could	
  affect	
  a	
  
higher	
  proportion	
  of	
  vulnerable	
  household	
  members,	
  as	
  explained	
  in	
  findings	
  from	
  a	
  study	
  
of	
  risk	
  attitudes	
  and	
  preferences	
  of	
  agricultural	
  households	
  in	
  Ethiopia	
  (Yesuf	
  and	
  
Bluffstone,	
  2009),	
  where	
  large	
  households	
  with	
  a	
  higher	
  proportion	
  of	
  elderly	
  and	
  young	
  
children	
  showed	
  higher	
  risk	
  aversion	
  than	
  other	
  households.	
  
	
  
As	
  household	
  income	
  increases,	
  so	
  does	
  the	
  likelihood	
  of	
  the	
  selection	
  of	
  riskier	
  options	
  in	
  
the	
  game.	
  	
  The	
  significance	
  of	
  the	
  square	
  term	
  in	
  the	
  model	
  points	
  toward	
  a	
  curvilinear	
  
relationship	
  between	
  risk	
  and	
  income.	
  In	
  other	
  words,	
  although	
  there	
  is	
  a	
  higher	
  likelihood	
  
of	
  selecting	
  a	
  riskier	
  option	
  for	
  higher	
  income	
  households,	
  this	
  positive	
  trend	
  occurs	
  at	
  a	
  
decreasing	
  rate.	
  
	
  

	
  

35	
  

Finally,	
  severely	
  food	
  insecure	
  and	
  moderately	
  food	
  insecure	
  households	
  are	
  158%	
  
and127%	
  less	
  likely	
  to	
  select	
  a	
  risky	
  choice,	
  respectively,	
  than	
  households	
  that	
  do	
  not	
  
suffer	
  from	
  food	
  insecurity.	
  This	
  result	
  can	
  be	
  interpreted	
  in	
  two	
  ways:	
  on	
  the	
  one	
  hand	
  
these	
  results	
  are	
  encouraging	
  because	
  it	
  means	
  that	
  households	
  that	
  are	
  already	
  vulnerable	
  
are	
  less	
  likely	
  to	
  risk	
  exacerbating	
  their	
  vulnerabilities	
  by	
  engaging	
  in	
  risky	
  behavior.	
  On	
  
the	
  other	
  hand,	
  this	
  risk	
  aversion	
  could	
  mean	
  that	
  vulnerable	
  households	
  will	
  be	
  less	
  likely	
  
to	
  engage	
  in	
  activities	
  that	
  they	
  may	
  deem	
  risky	
  but	
  that	
  could	
  potentially	
  have	
  great	
  
benefits	
  to	
  their	
  wellbeing,	
  for	
  example,	
  the	
  adoption	
  of	
  new	
  technologies	
  or	
  practices	
  that	
  
could	
  help	
  them	
  become	
  more	
  resilient	
  to	
  climate	
  and	
  market	
  shocks.	
  
	
  
Given	
  that	
  there	
  is	
  still	
  no	
  consensus	
  about	
  the	
  relationship	
  between	
  risk	
  aversion	
  and	
  
poverty	
  (Cardenas	
  and	
  Carpenter,	
  2008),	
  the	
  results	
  from	
  this	
  study	
  can	
  contribute	
  to	
  this	
  
body	
  of	
  research	
  in	
  the	
  literature.	
  In	
  agreement	
  with	
  several	
  previous	
  studies	
  (Tanaka,	
  
2010;	
  Hartog	
  et	
  al.,	
  2002;	
  Donkers	
  et	
  al.,	
  2001;	
  Moscardi	
  and	
  De	
  Janvry,	
  1977),	
  the	
  present	
  
research	
  shows	
  that	
  poor	
  households	
  are	
  more	
  risk	
  averse	
  than	
  non-­‐‑poor.	
  	
  An	
  important	
  
contribution	
  from	
  this	
  study	
  is	
  that	
  even	
  when	
  income	
  is	
  controlled	
  for,	
  the	
  degree	
  of	
  
household	
  food	
  security	
  emerges	
  as	
  a	
  significant	
  determinant	
  of	
  risk	
  aversion,	
  in	
  other	
  
words,	
  the	
  household’s	
  capacity	
  to	
  provide	
  nutritious	
  food	
  without	
  uncertainty	
  about	
  food	
  
access	
  and	
  availability	
  in	
  the	
  future	
  helps	
  to	
  determine	
  whether	
  the	
  household	
  may	
  be	
  
more	
  or	
  less	
  risk	
  averse.	
  	
  	
  
	
  
Not	
  many	
  studies	
  that	
  measure	
  the	
  effect	
  of	
  food	
  insecurity	
  on	
  risk	
  attitudes	
  have	
  been	
  
found,	
  but	
  these	
  results	
  are	
  consistent	
  with	
  similar	
  studies	
  that	
  have	
  accounted	
  for	
  food	
  

	
  

36	
  

insecurity	
  and	
  hunger	
  and	
  their	
  relationship	
  with	
  risk	
  (Levy	
  et	
  al.,	
  2013;	
  Onyemauwa	
  et	
  al.,	
  
2013)	
  
	
  

Conclusion	
  
How	
  will	
  coffee	
  farmers	
  in	
  Nicaragua	
  face	
  the	
  growing	
  threat	
  of	
  climate	
  change?	
  Will	
  they	
  
be	
  prepared?	
  Will	
  they	
  know	
  what	
  to	
  expect?	
  	
  In	
  this	
  paper	
  I	
  examined	
  the	
  perceptions,	
  
capacities,	
  and	
  attitudes	
  (including	
  risk	
  aversion)	
  of	
  coffee	
  producers	
  towards	
  climate	
  
change	
  and	
  the	
  mitigation	
  of	
  its	
  impacts.	
  	
  	
  
	
  
The	
  situation	
  in	
  which	
  coffee	
  producers	
  in	
  Nicaragua	
  find	
  themselves	
  is	
  dire.	
  	
  There	
  is	
  
already	
  a	
  high	
  level	
  of	
  food	
  insecurity;	
  compounding	
  this,	
  they	
  live	
  in	
  a	
  region	
  of	
  the	
  
country	
  that	
  is	
  experiencing	
  climate	
  change	
  and	
  will	
  see	
  medium	
  to	
  severe	
  impacts	
  from	
  
weather	
  and	
  climatic	
  events	
  in	
  the	
  coming	
  decades.	
  	
  The	
  suitability	
  of	
  their	
  coffee	
  farms	
  to	
  
the	
  changing	
  environment	
  will	
  continue	
  to	
  decline,	
  threatening	
  their	
  income	
  potential	
  and	
  
food	
  security	
  even	
  further.	
  	
  
	
  
Farmers	
  are	
  already	
  experiencing	
  droughts,	
  floods,	
  and	
  pests	
  and	
  some	
  have	
  had	
  to	
  
respond	
  by	
  increasing	
  the	
  number	
  of	
  work	
  hours	
  that	
  they	
  dedicate	
  to	
  their	
  fields.	
  	
  This	
  
additional	
  physical	
  labor	
  (mostly)	
  can	
  result	
  in	
  potential	
  health	
  loss	
  due	
  to	
  accidents,	
  
longer	
  exposure	
  time	
  to	
  chemical	
  inputs,	
  and	
  lack	
  of	
  proper	
  nutrition	
  to	
  support	
  their	
  
increased	
  physical	
  exertion.	
  Adding	
  to	
  this	
  vulnerability,	
  some	
  of	
  these	
  farmers	
  have	
  also	
  
responded	
  by	
  decreasing	
  their	
  food	
  consumption	
  as	
  a	
  coping	
  mechanism,	
  a	
  response	
  that	
  
further	
  jeopardizes	
  their	
  health	
  and	
  productive	
  capacity.	
  	
  

	
  

37	
  

	
  
This	
  danger	
  is	
  especially	
  acute	
  for	
  women.	
  	
  I	
  find	
  that	
  women	
  are	
  significantly	
  more	
  food	
  
insecure	
  than	
  men,	
  they	
  own	
  less	
  land,	
  produce	
  less,	
  and	
  have	
  lower	
  income	
  than	
  their	
  
male	
  counterparts.	
  	
  Policies	
  designed	
  to	
  improve	
  the	
  adaptive	
  capacity	
  of	
  farmers	
  in	
  the	
  
region	
  must	
  account	
  for	
  these	
  differences	
  by	
  introducing	
  interventions	
  that	
  directly	
  
address	
  the	
  barriers	
  (cultural	
  and	
  economic)	
  that	
  women	
  face	
  (e.g.,	
  by	
  developing	
  
interventions	
  that	
  depend	
  on	
  the	
  participation	
  of	
  women).	
  	
  Further	
  research	
  is	
  needed	
  to	
  
examine	
  specifically	
  how	
  gender	
  differences	
  affect	
  adaptive	
  capacity,	
  and	
  to	
  guide	
  the	
  
development	
  of	
  strategies	
  that	
  will	
  be	
  responsive	
  to	
  these	
  differences.	
  
	
  
My	
  literature	
  review	
  suggests	
  that	
  institutions	
  play	
  an	
  important	
  role	
  in	
  providing	
  services	
  
to	
  smallholder	
  producers	
  through	
  access	
  to	
  training	
  opportunities	
  and	
  financial	
  services.	
  
Yet,	
  in	
  this	
  region,	
  farmers	
  have	
  expressed	
  high	
  rates	
  of	
  dissatisfaction	
  with	
  their	
  
cooperatives	
  and	
  the	
  services	
  they	
  provide.	
  There	
  is	
  need	
  for	
  more	
  research	
  -­‐‑	
  and	
  in	
  
particular	
  participatory	
  research	
  –	
  that	
  systematically	
  takes	
  farmers	
  reported	
  experiences	
  
and	
  attitudes	
  into	
  account	
  in	
  promoting	
  equitable	
  and	
  efficient	
  adaptive	
  strategies.	
  
	
  
Despite	
  studies	
  that	
  have	
  found	
  that	
  poorer	
  agrarian	
  households	
  tend	
  to	
  be	
  more	
  risk	
  
averse	
  than	
  higher	
  income	
  households	
  (Tanaka,	
  2010;	
  Hartog	
  et	
  al.,	
  2002;	
  Donkers	
  et	
  al.,	
  
2001;	
  Moscardi	
  and	
  De	
  Janvry,	
  1977),	
  many	
  other	
  studies	
  have	
  found	
  the	
  opposite	
  (Bosch-­‐‑
Domènech	
  and	
  Silvestre,	
  2006;	
  Henrich	
  and	
  McElreath,	
  2002).	
  	
  The	
  literature	
  on	
  this	
  issue	
  
is	
  inconclusive.	
  Furthermore,	
  in	
  a	
  review	
  of	
  the	
  literature,	
  I	
  find	
  little	
  research	
  that	
  explores	
  
risk	
  aversion	
  among	
  households	
  that	
  are	
  already	
  highly	
  vulnerable,	
  especially	
  households	
  

	
  

38	
  

who	
  are	
  suffering	
  from	
  food	
  insecurity.	
  These	
  results	
  provide	
  an	
  important	
  contribution	
  to	
  
this	
  debate.	
  While	
  holding	
  income	
  level	
  constant,	
  I	
  find	
  that	
  households	
  that	
  are	
  severely	
  and	
  
moderately	
  food	
  insecure	
  are	
  significantly	
  less	
  likely	
  to	
  make	
  riskier	
  choices	
  than	
  are	
  those	
  	
  
not	
  suffering	
  from	
  food	
  insecurity.	
  
	
  
The	
  implications	
  of	
  these	
  results	
  should	
  not	
  be	
  taken	
  lightly,	
  climate	
  shocks	
  can	
  destroy	
  
crops,	
  livestock,	
  and	
  other	
  household	
  assets;	
  for	
  households	
  in	
  chronic	
  poverty,	
  
conventional	
  risk	
  management	
  strategies	
  simply	
  may	
  not	
  be	
  enough	
  (Barrett	
  et	
  al.,	
  2007).	
  	
  
The	
  challenge	
  lies	
  in	
  the	
  development	
  and	
  provision	
  of	
  services,	
  institutions	
  and	
  
interventions	
  that	
  enable	
  the	
  accumulation	
  of	
  productive	
  assets	
  and	
  the	
  adoption	
  of	
  
improved	
  agricultural	
  production	
  technologies	
  that	
  will	
  be	
  instrumental	
  to	
  building	
  the	
  
capacity	
  of	
  households	
  in	
  their	
  struggle	
  to	
  adapt	
  to	
  climate	
  change.	
  
Interventions	
  that	
  intend	
  to	
  reduce	
  vulnerability	
  to	
  shocks	
  should	
  consider	
  how	
  
households	
  that	
  are	
  severely	
  food	
  insecure,	
  perhaps	
  already	
  trapped	
  in	
  poverty,	
  will	
  
respond	
  to	
  possible	
  adaptation	
  pathways	
  and	
  the	
  inherent	
  risks	
  associated	
  with	
  them.	
  	
  An	
  
important	
  recommendation	
  for	
  programs	
  focused	
  on	
  helping	
  the	
  most	
  vulnerable	
  
populations	
  to	
  adopt	
  technologies	
  and	
  practices	
  that	
  can	
  help	
  with	
  climate	
  change	
  emerges	
  
from	
  this	
  study.	
  These	
  programs	
  must	
  first	
  address	
  issues	
  of	
  food	
  insecurity	
  among	
  poor	
  
households;	
  by	
  doing	
  so,	
  there	
  is	
  a	
  non-­‐‑negative	
  likelihood	
  that	
  the	
  targeted	
  farmers	
  will	
  be	
  
more	
  open	
  to	
  taking	
  the	
  risks	
  associated	
  with	
  the	
  adoption	
  of	
  new	
  practices	
  and	
  
technologies.	
  	
  
	
  

	
  

39	
  

While	
  the	
  results	
  reported	
  from	
  this	
  analysis	
  help	
  us	
  to	
  assess	
  how	
  risk	
  and	
  food	
  security	
  
combine	
  to	
  affect	
  potential	
  farmer	
  decisions	
  concerning	
  the	
  adoption	
  of	
  climate	
  change	
  
friendly	
  practices,	
  I	
  must	
  acknowledge	
  that	
  the	
  research	
  is	
  not	
  without	
  limitations.	
  First,	
  
the	
  number	
  of	
  participants	
  in	
  the	
  experimental	
  games	
  is	
  small,	
  information	
  from	
  only	
  82	
  
farmers	
  is	
  used	
  in	
  the	
  econometric	
  analysis.	
  Future	
  research	
  on	
  a	
  larger	
  sample	
  of	
  
participants	
  will	
  help	
  to	
  validate	
  the	
  results	
  of	
  this	
  study.	
  	
  Second,	
  food	
  security	
  data	
  on	
  the	
  
study	
  households	
  was	
  collected	
  a	
  year	
  earlier	
  than	
  other	
  data	
  presented	
  in	
  this	
  analysis.	
  
This	
  lag	
  loses	
  any	
  changes	
  in	
  the	
  food-­‐‑security	
  related	
  conditions	
  surrounding	
  these	
  
households.	
  	
  In	
  other	
  words,	
  households	
  identified	
  as	
  food	
  insecure	
  in	
  this	
  study,	
  may	
  not	
  
have	
  been	
  food	
  insecure	
  at	
  the	
  time	
  of	
  the	
  risk	
  activity,	
  thus	
  potentially	
  diluting	
  the	
  
strength	
  of	
  the	
  coefficients	
  reported	
  and	
  the	
  relationships	
  they	
  represent.	
  Finally,	
  although	
  
these	
  experiments	
  attempt	
  to	
  emulate	
  real	
  life	
  behavior	
  and	
  measure	
  how	
  farmers	
  respond	
  
to	
  risks,	
  farmers	
  may	
  not	
  feel	
  the	
  same	
  way	
  about	
  taking	
  a	
  relatively	
  small	
  risk	
  presented	
  
to	
  them	
  by	
  the	
  enumerators	
  of	
  the	
  study	
  as	
  they	
  would	
  about	
  a	
  decision	
  that	
  could	
  lock	
  
them	
  into	
  poverty	
  in	
  real	
  life,	
  or	
  help	
  them	
  rise	
  from	
  poverty.	
  	
  To	
  further	
  explore	
  the	
  
relationships	
  revealed	
  in	
  this	
  study	
  between	
  risk,	
  food	
  security	
  and	
  climate	
  change	
  
adaptation	
  I	
  see	
  the	
  need	
  for	
  a	
  more	
  qualitative	
  approach,	
  one	
  that	
  will	
  provide	
  deeper	
  
insight	
  into	
  how	
  coffee	
  producers	
  evaluate	
  the	
  potential	
  risks	
  and	
  rewards	
  of	
  adopting	
  
alternative	
  practices	
  and	
  how,	
  in	
  turn,	
  cooperatives	
  and	
  other	
  institutions	
  can	
  work	
  more	
  
effectively	
  to	
  support	
  them	
  in	
  their	
  decisions.	
  	
  
	
  

	
  

40	
  

	
  

	
  

REFERENCES

	
  

41	
  

REFERENCES	
  
	
  
	
  
	
  
	
  
Abebaw,	
  Degnet,	
  and	
  Mekbib	
  G.	
  Haile.	
  "The	
  impact	
  of	
  cooperatives	
  on	
  agricultural	
  
technology	
  adoption:	
  Empirical	
  evidence	
  from	
  Ethiopia."	
  Food	
  policy	
  38	
  (2013):	
  82-­‐‑
91.	
  
	
  
Agrawal,	
  Arun.	
  "Local	
  institutions	
  and	
  adaptation	
  to	
  climate	
  change."	
  Social	
  dimensions	
  of	
  
climate	
  change:	
  Equity	
  and	
  vulnerability	
  in	
  a	
  warming	
  world(2010):	
  173-­‐‑197.	
  
	
  
Agrawal,	
  Arun.	
  "Common	
  resources	
  and	
  institutional	
  sustainability."	
  The	
  drama	
  of	
  the	
  
commons	
  (2002):	
  41-­‐‑85.	
  
	
  
Agarwal,	
  Bina.	
  "Conceptualising	
  environmental	
  collective	
  action:	
  why	
  gender	
  
matters."	
  Cambridge	
  journal	
  of	
  economics	
  24.3	
  (2000):	
  283-­‐‑310.	
  
	
  
Araral,	
  Eduardo.	
  "What	
  explains	
  collective	
  action	
  in	
  the	
  commons?	
  Theory	
  and	
  evidence	
  
from	
  the	
  Philippines."	
  World	
  development	
  37.3	
  (2009):	
  687-­‐‑697.	
  
	
  
Ayenew,	
  Habtamu	
  Yesigat,	
  Johannes	
  Sauer,	
  and	
  Getachew	
  Abate-­‐‑Kassa.	
  "On	
  Smallholder	
  
Farmers'	
  Exposure	
  to	
  Risk	
  and	
  Adaptation	
  Mechanisms:	
  Panel	
  Data	
  Evidence	
  from	
  
Ethiopia."	
  89th	
  Annual	
  Conference,	
  April	
  13-­‐‑15,	
  2015,	
  Warwick	
  University,	
  Coventry,	
  
UK.	
  No.	
  204223.	
  Agricultural	
  Economics	
  Society,	
  2015.	
  
	
  
Bacon,	
  Christopher	
  M.	
  "A	
  spot	
  of	
  coffee	
  in	
  crisis	
  Nicaraguan	
  smallholder	
  cooperatives,	
  fair	
  
trade	
  networks,	
  and	
  gendered	
  empowerment."	
  Latin	
  American	
  Perspectives	
  37.2	
  
(2010):	
  50-­‐‑71.	
  
	
  
Bardhan,	
  Pranab.	
  "Analytics	
  of	
  the	
  institutions	
  of	
  informal	
  cooperation	
  in	
  rural	
  
development."	
  World	
  Development	
  21.4	
  (1993):	
  633-­‐‑639.	
  
	
  
Barrett,	
  Christopher	
  B.,	
  et	
  al.	
  "Poverty	
  traps	
  and	
  climate	
  risk:	
  limitations	
  and	
  opportunities	
  
of	
  index-­‐‑based	
  risk	
  financing."	
  (2007).	
  
	
  
Barrett,	
  Christopher	
  B.,	
  and	
  John	
  G.	
  McPeak.	
  "Poverty	
  traps	
  and	
  safety	
  nets."	
  Poverty,	
  
inequality	
  and	
  development.	
  Springer	
  US,	
  2006.	
  131-­‐‑154.	
  
	
  
Barrett,	
  Christopher	
  B.,	
  Barry	
  J.	
  Barnett,	
  Michael	
  R.	
  Carter,	
  Sommarat	
  Chantarat,	
  James	
  W.	
  
Hansen,	
  Andrew	
  G.	
  Mude,	
  Daniel	
  Osgood,	
  Jerry	
  R.	
  Skees,	
  Calum	
  G.	
  Turvey,	
  and	
  M.	
  
Neil	
  Ward.	
  "Poverty	
  traps	
  and	
  climate	
  risk:	
  limitations	
  and	
  opportunities	
  of	
  index-­‐‑
based	
  risk	
  financing."	
  (2007).	
  
	
  

	
  

42	
  

Blanco,	
  Esther,	
  Maria	
  Claudia	
  Lopez,	
  and	
  Sergio	
  Villamayor-­‐‑Tomas.	
  "Exogenous	
  
degradation	
  in	
  the	
  commons:	
  Field	
  experimental	
  evidence."Ecological	
  
Economics	
  120	
  (2015):	
  430-­‐‑439.	
  
	
  
Bosch-­‐‑Domènech,	
  Antoni,	
  and	
  Joaquim	
  Silvestre.	
  "Do	
  the	
  wealthy	
  risk	
  more	
  money?	
  An	
  
experimental	
  comparison."	
  Institutions,	
  Equilibria	
  and	
  Efficiency.	
  Springer	
  Berlin	
  
Heidelberg,	
  2006.	
  95-­‐‑116.	
  
	
  
Bradshaw,	
  Ben,	
  Holly	
  Dolan,	
  and	
  Barry	
  Smit.	
  "Farm-­‐‑level	
  adaptation	
  to	
  climatic	
  variability	
  
and	
  change:	
  crop	
  diversification	
  in	
  the	
  Canadian	
  prairies."Climatic	
  Change	
  67.1	
  
(2004):	
  119-­‐‑141.	
  
	
  
Cardenas,	
  Juan	
  Camilo,	
  and	
  Jeffrey	
  Carpenter.	
  "Risk	
  attitudes	
  and	
  economic	
  well-­‐‑being	
  in	
  
Latin	
  America."	
  Journal	
  of	
  Development	
  Economics	
  103	
  (2013):	
  52-­‐‑61.	
  
	
  
Cardenas,	
  Juan	
  Camilo,	
  and	
  Jeffrey	
  Carpenter.	
  "Behavioural	
  development	
  economics:	
  
Lessons	
  from	
  field	
  labs	
  in	
  the	
  developing	
  world."	
  The	
  Journal	
  of	
  Development	
  
Studies	
  44.3	
  (2008):	
  311-­‐‑338.	
  
	
  
Carter,	
  Michael	
  R.,	
  and	
  Christopher	
  B.	
  Barrett.	
  "The	
  economics	
  of	
  poverty	
  traps	
  and	
  
persistent	
  poverty:	
  An	
  asset-­‐‑based	
  approach."	
  The	
  Journal	
  of	
  Development	
  
Studies	
  42.2	
  (2006):	
  178-­‐‑199.	
  
	
  
Challinor,	
  Andrew,	
  et	
  al.	
  "Assessing	
  the	
  vulnerability	
  of	
  food	
  crop	
  systems	
  in	
  Africa	
  to	
  
climate	
  change."	
  Climatic	
  change	
  83.3	
  (2007):	
  381-­‐‑399.	
  
	
  
Conde,	
  Cecilia,	
  Marta	
  Vinocur,	
  Carlos	
  Gay,	
  Roberto	
  Seiler,	
  and	
  Francisco	
  Estrada.	
  "Climatic	
  
threat	
  spaces	
  in	
  Mexico	
  and	
  Argentina."	
  Climate	
  Change	
  and	
  Vulnerability	
  and	
  
Adaptation:	
  Two	
  Volume	
  Set	
  (2013):	
  279.	
  
	
  
Ellis,	
  Frank.	
  Rural	
  livelihoods	
  and	
  diversity	
  in	
  developing	
  countries.	
  Oxford	
  university	
  press,	
  
2000.	
  
	
  
Engle-­‐‑Warnick,	
  Jim,	
  Javier	
  Escobal,	
  and	
  Sonia	
  Laszlo.	
  "Ambiguity	
  aversion	
  as	
  a	
  predictor	
  of	
  
technology	
  choice:	
  Experimental	
  evidence	
  from	
  Peru."CIRANO-­‐‑Scientific	
  Publications	
  
2007s-­‐‑01	
  (2007).	
  
	
  
Donkers,	
  Bas,	
  Bertrand	
  Melenberg,	
  and	
  Arthur	
  Van	
  Soest.	
  "Estimating	
  risk	
  attitudes	
  using	
  
lotteries:	
  A	
  large	
  sample	
  approach."	
  Journal	
  of	
  Risk	
  and	
  Uncertainty	
  22.2	
  (2001):	
  
165-­‐‑195.	
  
	
  
Duflo,	
  Esther,	
  Michael	
  Kremer,	
  and	
  Jonathan	
  Robinson.	
  Nudging	
  farmers	
  to	
  use	
  fertilizer:	
  
Theory	
  and	
  experimental	
  evidence	
  from	
  Kenya.	
  No.	
  w15131.	
  
	
  
FAO,	
  ComitÊ	
  Científico	
  de	
  la	
  ELCSA.	
  "Escala	
  Latinoamericana	
  y	
  Caribeùa	
  de	
  Seguridad	
  
Alimentaria	
  (ELCSA):	
  Manual	
  de	
  uso	
  y	
  aplicaciones."	
  Roma:	
  FAO(2012).	
  
	
  

43	
  

Feder,	
  Gershon,	
  Richard	
  E.	
  Just,	
  and	
  David	
  Zilberman.	
  "Adoption	
  of	
  agricultural	
  innovations	
  
in	
  developing	
  countries:	
  A	
  survey."	
  Economic	
  development	
  and	
  cultural	
  
change	
  (1985):	
  255-­‐‑298.	
  
	
  
Hartog,	
  Joop,	
  Ada	
  Ferrer-­‐‑i-­‐‑Carbonell,	
  and	
  Nicole	
  Jonker.	
  "Linking	
  measured	
  risk	
  aversion	
  to	
  
individual	
  characteristics."	
  Kyklos	
  55.1	
  (2002):	
  3-­‐‑26.	
  
	
  
Haushofer,	
  Johannes,	
  and	
  Ernst	
  Fehr.	
  "On	
  the	
  psychology	
  of	
  poverty."	
  Science	
  344.6186	
  
(2014):	
  862-­‐‑867.	
  
	
  
Hazell,	
  Peter,	
  Colin	
  Poulton,	
  Steve	
  Wiggins,	
  and	
  Andrew	
  Dorward.	
  "The	
  future	
  of	
  small	
  farms:	
  
Trajectories	
  and	
  policy	
  priorities."	
  World	
  Development	
  38,	
  no.	
  10	
  (2010):	
  1349-­‐‑1361.	
  
	
  
Henrich,	
  Joseph,	
  and	
  Richard	
  McElreath.	
  "Are	
  Peasants	
  Risk-­‐‑Averse	
  Decision	
  Makers?	
  
1."	
  Current	
  Anthropology	
  43.1	
  (2002):	
  172-­‐‑181.	
  
	
  
Holt-­‐‑Giménez,	
  Eric.	
  "Measuring	
  farmers’	
  agroecological	
  resistance	
  after	
  Hurricane	
  Mitch	
  in	
  
Nicaragua:	
   a	
   case	
   study	
   in	
   participatory,	
   sustainable	
   land	
   management	
   impact	
  
monitoring."	
  Agriculture,	
  Ecosystems	
  &	
  Environment	
  93.1	
  (2002):	
  87-­‐‑105.	
  
	
  
Holt,	
  Charles	
  A.,	
  and	
  Susan	
  K.	
  Laury.	
  "Risk	
  aversion	
  and	
  incentive	
  effects."American	
  
economic	
  review	
  92,	
  no.	
  5	
  (2002):	
  1644-­‐‑1655.	
  
	
  
Intergovernmental	
  Panel	
  on	
  Climate	
  Change.	
  Climate	
  change	
  2014:	
  mitigation	
  of	
  climate	
  
change.	
  Vol.	
  3.	
  Cambridge	
  University	
  Press,	
  2015.	
  
	
  
Intergovernmental	
  Panel	
  On	
  Climate	
  Change.	
  "Climate	
  change	
  2007:	
  impacts,	
  adaptation	
  and	
  
vulnerability."	
  Genebra,	
  Suíça	
  (2001).	
  
	
  
Laderäch,	
  Peter,	
  et	
  al.	
  "Predicted	
  impact	
  of	
  climate	
  change	
  on	
  coffee	
  supply	
  chains."	
  The	
  
Economic,	
  Social	
  and	
  Political	
  Elements	
  of	
  Climate	
  Change.	
  Springer	
  Berlin	
  
Heidelberg,	
  2011.	
  703-­‐‑723.	
  
	
  
Levy,	
  Dino	
  J.,	
  Amalie	
  C.	
  Thavikulwat,	
  and	
  Paul	
  W.	
  Glimcher.	
  "State	
  dependent	
  valuation:	
  the	
  
effect	
  of	
  deprivation	
  on	
  risk	
  preferences."	
  PloS	
  one8.1	
  (2013):	
  e53978.	
  
	
  
Marra,	
  Michele,	
  David	
  J.	
  Pannell,	
  and	
  Amir	
  Abadi	
  Ghadim.	
  "The	
  economics	
  of	
  risk,	
  
uncertainty	
  and	
  learning	
  in	
  the	
  adoption	
  of	
  new	
  agricultural	
  technologies:	
  where	
  
are	
  we	
  on	
  the	
  learning	
  curve?."	
  Agricultural	
  systems	
  75,	
  no.	
  2	
  (2003):	
  215-­‐‑234.	
  
	
  
Moscardi,	
  Edgardo,	
  and	
  Alain	
  De	
  Janvry.	
  "Attitudes	
  toward	
  risk	
  among	
  peasants:	
  an	
  
econometric	
  approach."	
  American	
  Journal	
  of	
  Agricultural	
  Economics	
  59.4	
  (1977):	
  
710-­‐‑716.	
  
	
  
Onyemauwa,	
  C.	
  S.,	
  et	
  al.	
  "Risk	
  aversion	
  among	
  farmers	
  of	
  the	
  national	
  program	
  for	
  food	
  
security	
  in	
  Imo	
  State	
  Southeast	
  Nigeria."	
  Risk	
  4.10	
  (2013).	
  
	
  

44	
  

Ostrom,	
  E.	
  "Ostrom,	
  E.	
  1990.	
  Governing	
  the	
  Commons:	
  The	
  Evolution	
  of	
  Institutions	
  for	
  
Collective	
  Action."	
  (1990).	
  
	
  
Perfecto,	
   Ivette,	
   and	
   John	
   Vandermeer.	
   Coffee	
   Agroecology:	
   A	
   new	
   approach	
   to	
  
understanding	
   agricultural	
   biodiversity,	
   ecosystem	
   services	
   and	
   sustainable	
  
development.	
  Routledge,	
  2015.	
  
	
  
Philpott,	
  Stacy	
  M.,	
  and	
  Thomas	
  Dietsch.	
  "Coffee	
  and	
  conservation:	
  a	
  global	
  context	
  and	
  the	
  
value	
  of	
  farmer	
  involvement."	
  Conservation	
  Biology	
  17.6	
  (2003):	
  1844-­‐‑1846.	
  
	
  
Ponte,	
  Stephano.	
  “The	
  ‘Latte	
  Revolution'?	
  Regulation,	
  Markets	
  and	
  Consumption	
  in	
  the	
  
Global	
  Coffee	
  Chain”.	
  World	
  Development,	
  30(7),	
  pp.1099-­‐‑1122.	
  
	
  
Prediger,	
  Sebastian,	
  BjÜrn	
  Vollan,	
  and	
  Benedikt	
  Herrmann.	
  Resource	
  scarcity,	
  spite	
  and	
  
cooperation.	
  Department	
  of	
  Economics	
  (Inst.	
  fßr	
  Wirtschaftstheorie	
  und	
  
Wirtschaftsgeschichte),	
  2013.	
  
	
  
Ruben,	
  Ruerd,	
  and	
  Guillermo	
  Zuniga.	
  "How	
  standards	
  compete:	
  comparative	
  impact	
  of	
  coffee	
  
certification	
   schemes	
   in	
   Northern	
   Nicaragua."	
   Supply	
   Chain	
   Management:	
   An	
  
International	
  Journal	
  16.2	
  (2011):	
  98-­‐‑109.	
  
	
  
Santos,	
   Paulo,	
   and	
   Christopher	
   B.	
   Barrett.	
   "Informal	
   insurance	
   in	
   the	
   presence	
   of	
   poverty	
  
traps:	
  Evidence	
  from	
  southern	
  Ethiopia."	
  Available	
  at	
  SSRN	
  998541	
  (2006).	
  
	
  
Schultz,	
  Theodore	
  William.	
  "Transforming	
  traditional	
  agriculture."	
  	
  (1964).	
  
	
  
Tanaka,	
   Tomomi,	
   Colin	
   F.	
   Camerer,	
   and	
   Quang	
   Nguyen.	
   "Risk	
   and	
   time	
   preferences:	
   linking	
  
experimental	
   and	
   household	
   survey	
   data	
   from	
   Vietnam."	
   Behavioral	
   Economics	
   of	
  
Preferences,	
  Choices,	
  and	
  Happiness.	
  Springer	
  Japan,	
  2016.	
  3-­‐‑25.	
  
	
  
	
  Thaler,	
  Richard	
  H.	
  "Irving	
  Fisher:	
  modern	
  behavioral	
  economist."	
  The	
  American	
  economic	
  
review	
  87.2	
  (1997):	
  439-­‐‑441.	
  
	
  
Tompkins,	
   Emma	
   L.,	
   and	
   W	
   Neil	
   Adger.	
   "Does	
   adaptive	
   management	
   of	
   natural	
   resources	
  
enhance	
  resilience	
  to	
  climate	
  change?."	
  Ecology	
  and	
  society	
  9.2	
  (2004):	
  10.	
  
	
  
Uphoff,	
  Norman,	
  M.	
  Wickramasinghe,	
  and	
  C.	
  Wijayaratna.	
  ""	
  Optimum"	
  participation	
  in	
  
irrigation	
  management:	
  issues	
  and	
  evidence	
  from	
  Sri	
  Lanka."Human	
  
Organization	
  49.1	
  (1990):	
  26-­‐‑40.	
  
	
  
Valkila,	
  Joni,	
  and	
  Anja	
  Nygren.	
  "Impacts	
  of	
  Fair	
  Trade	
  certification	
  on	
  coffee	
  farmers,	
  
cooperatives,	
  and	
  laborers	
  in	
  Nicaragua."	
  Agriculture	
  and	
  Human	
  Values	
  27.3	
  
(2010):	
  321-­‐‑333.	
  
	
  
World	
  Bank.	
  (2016)	
  Retrieved	
  from	
  http://data.worldbank.org/data-­‐‑catalog/gdp-­‐‑ppp-­‐‑
based-­‐‑table	
  
	
  

45	
  

Wossen,	
  Tesfamicheal,	
  Thomas	
  Berger,	
  and	
  Salvatore	
  Di	
  Falco.	
  "Social	
  capital,	
  risk	
  
preference	
  and	
  adoption	
  of	
  improved	
  farm	
  land	
  management	
  practices	
  in	
  
Ethiopia."	
  Agricultural	
  Economics	
  46.1	
  (2015):	
  81-­‐‑97.	
  
	
  
Yesuf,	
  Mahmud,	
  and	
  Randall	
  A.	
  Bluffstone.	
  "Poverty,	
  risk	
  aversion,	
  and	
  path	
  dependence	
  in	
  
low-­‐‑income	
  countries:	
  Experimental	
  evidence	
  from	
  Ethiopia."	
  American	
  Journal	
  of	
  
Agricultural	
  Economics	
  91.4	
  (2009):	
  1022-­‐‑1037.	
  
	
  
	
  
	
  
	
  
	
  

	
  

46	
  

Chapter	
  3:	
  	
  Adaptive	
  Capacity	
  to	
  Climate	
  Change:	
  Coffee	
  Farmer	
  Preferences	
  for	
  Crop	
  
Diversification	
  in	
  Nicaragua	
  
	
  

Introduction	
  
Coffee	
  is	
  grown	
  in	
  some	
  of	
  the	
  most	
  important	
  biodiversity	
  hotspots	
  in	
  the	
  world	
  (Perfecto	
  
and	
  Vandermeer,	
  2015)—environments	
  also	
  known	
  for	
  their	
  susceptibility	
  to	
  the	
  negative	
  
impacts	
  of	
  climate	
  change.	
  	
  Higher	
  temperatures,	
  droughts,	
  and	
  extreme	
  and	
  erratic	
  
rainfalls	
  will	
  all	
  affect	
  the	
  suitability	
  of	
  these	
  and	
  other	
  coffee	
  producing	
  regions,	
  and	
  
intensification	
  of	
  farming	
  systems	
  compounds	
  this	
  effect	
  by	
  diminishing	
  biodiversity	
  which	
  
could	
  otherwise	
  help	
  to	
  mitigate	
  some	
  of	
  these	
  impacts.	
  	
  Moreover,	
  coffee	
  is	
  produced	
  by	
  
millions	
  of	
  farmers	
  in	
  the	
  tropics,	
  the	
  majority	
  of	
  which	
  are	
  smallholder	
  producers	
  who	
  
depend	
  on	
  this	
  crop	
  as	
  a	
  main	
  source	
  of	
  income.	
  	
  	
  These	
  three	
  aspects	
  of	
  coffee	
  production	
  
(biodiversity,	
  climate	
  change,	
  and	
  livelihoods)	
  are	
  intrinsically	
  connected,	
  and	
  the	
  study	
  of	
  
any	
  one	
  must	
  be	
  carried	
  out	
  with	
  an	
  appreciation	
  for	
  its	
  dynamic	
  relationships	
  with	
  the	
  
others.	
  Shade-­‐‑grown	
  coffee	
  production	
  management	
  systems	
  help	
  to	
  promote	
  these	
  three	
  
aspects	
  of	
  the	
  human-­‐‑environmental	
  relationship	
  in	
  important	
  ways:	
  by	
  preserving	
  and	
  
promoting	
  biodiversity	
  richness,	
  by	
  helping	
  coffee	
  producers	
  to	
  build	
  adaptive	
  capacity	
  to	
  
the	
  negative	
  impacts	
  of	
  climate	
  change,	
  and	
  by	
  providing	
  alternative	
  sources	
  of	
  food	
  or	
  
income.	
  Understanding	
  the	
  conditions	
  under	
  which	
  producers	
  are	
  willing	
  to	
  adopt	
  shade	
  
grown	
  coffee	
  production	
  is	
  of	
  paramount	
  importance	
  given	
  the	
  overwhelming	
  evidence	
  
that	
  our	
  climate	
  is	
  changing	
  and	
  that	
  coffee	
  producers	
  are	
  among	
  those	
  most	
  vulnerable	
  to	
  
its	
  effects.	
  	
  	
  
	
  

	
  

47	
  

Farmers,	
  households,	
  and	
  communities	
  that	
  are	
  most	
  prone	
  to	
  the	
  negative	
  impacts	
  of	
  
climate	
  change	
  must	
  take	
  steps	
  that	
  will	
  help	
  them	
  to	
  mitigate	
  these	
  shocks.	
  	
  Yet	
  adoption	
  
of	
  mitigation	
  strategies	
  is	
  not	
  always	
  simple;	
  farmers	
  often	
  are	
  forced	
  to	
  make	
  difficult	
  
economic	
  trade-­‐‑offs	
  under	
  risky	
  conditions	
  and	
  in	
  an	
  uncertain	
  climate.	
  	
  Implicit	
  in	
  this	
  
problem	
  is	
  the	
  fact	
  that	
  the	
  impacts	
  of	
  climate	
  change	
  are	
  compounded	
  upon	
  already	
  
existing	
  vulnerabilities,	
  so	
  not	
  only	
  is	
  their	
  capacity	
  to	
  adapt	
  lower,	
  but	
  the	
  barriers	
  that	
  
farmers	
  face	
  can	
  be	
  even	
  more	
  pronounced.	
  	
  For	
  example,	
  high	
  rainfalls	
  due	
  to	
  El	
  Niùo	
  in	
  
1998,	
  followed	
  by	
  two	
  years	
  of	
  erratic	
  rainfall,	
  forced	
  farmers	
  in	
  Tanzania	
  to	
  give	
  up	
  maize	
  
production	
  and	
  instead	
  sell	
  their	
  labor	
  to	
  more	
  productive	
  areas.	
  	
  Although	
  in	
  the	
  short	
  
term	
  this	
  was	
  a	
  good	
  coping	
  mechanism	
  for	
  households,	
  their	
  dependence	
  on	
  labor	
  as	
  their	
  
sole	
  endowment	
  increased	
  their	
  long	
  term	
  vulnerability,	
  since	
  the	
  resulting	
  disease	
  and	
  
malnutrition	
  reduce	
  their	
  capacity	
  for	
  manual	
  labor	
  (ADB,	
  2003).	
  	
  
	
  
Farmers	
  intensify	
  when	
  the	
  value	
  generated	
  by	
  the	
  land	
  is	
  higher	
  from	
  a	
  crop	
  grown	
  in	
  it	
  
than	
  from	
  the	
  forest	
  that	
  would	
  otherwise	
  occupy	
  it	
  (Tittonell	
  and	
  Giller,	
  2013).	
  	
  Unless	
  
farmers	
  value	
  the	
  ecosystem	
  services	
  (such	
  as	
  biodiversity	
  conservation)	
  that	
  shaded	
  
coffee	
  farms	
  provide	
  over	
  the	
  value	
  of	
  their	
  crop,	
  they	
  will	
  be	
  unwilling	
  to	
  adopt	
  practices	
  
that	
  help	
  promote	
  biodiversity	
  conservation.	
  Rural	
  poverty	
  is	
  intertwined	
  with	
  biodiversity	
  
conservation,	
  and	
  different	
  conservation	
  programs	
  and	
  policies	
  must	
  deal	
  with	
  the	
  threat	
  
of	
  poverty	
  and	
  of	
  economic	
  tradeoffs	
  required	
  to	
  mitigate	
  that	
  threat.	
  	
  At	
  no	
  time	
  has	
  this	
  
issue	
  been	
  more	
  glaring	
  than	
  it	
  is	
  today,	
  a	
  time	
  when	
  farmers	
  who	
  depend	
  on	
  coffee	
  as	
  their	
  
main	
  source	
  of	
  income	
  are	
  experiencing	
  increased	
  vulnerability	
  to	
  the	
  impacts	
  of	
  climate	
  

	
  

48	
  

change,	
  and	
  when	
  the	
  planet	
  is	
  seeing	
  its	
  highest	
  rates	
  of	
  biodiversity	
  loss	
  in	
  modern	
  
history.	
  
	
  
In	
  this	
  research	
  I	
  use	
  discrete	
  choice	
  experiments	
  (DCEs)	
  to	
  study	
  the	
  preferences	
  and	
  
behaviors	
  among	
  producers	
  regarding	
  the	
  adoption	
  of	
  shade	
  into	
  coffee	
  fields.	
  DCEs	
  allow	
  
the	
  ex-­‐‑ante	
  analysis	
  of	
  the	
  drivers	
  of	
  adoption,	
  which	
  in	
  turn	
  help	
  to	
  inform	
  programs	
  and	
  
other	
  interventions	
  designed	
  to	
  build	
  farmer	
  adaptive	
  capacity	
  in	
  the	
  face	
  of	
  growing	
  
climate	
  threats.	
  	
  I	
  use	
  choice	
  experiments	
  in	
  this	
  study	
  to	
  examine	
  the	
  conditions	
  under	
  
which	
  farmers	
  will	
  be	
  willing	
  to	
  diversify	
  their	
  coffee	
  farms	
  with	
  shade	
  crops.	
  	
  Given	
  the	
  
vulnerability	
  of	
  coffee	
  producers	
  to	
  pests,	
  changes	
  in	
  market	
  prices	
  and	
  climate	
  shocks,	
  
understanding	
  their	
  incentives	
  to	
  adopt	
  practices	
  that	
  will	
  help	
  them	
  build	
  better	
  adaptive	
  
capacity	
  to	
  these	
  shocks	
  is	
  of	
  paramount	
  importance.	
  This	
  study	
  examines	
  the	
  tradeoffs	
  
that	
  Nicaraguan	
  coffee	
  producers	
  face	
  as	
  they	
  consider	
  alternative	
  production	
  strategies	
  
that	
  will	
  help	
  them	
  build	
  that	
  adaptive	
  capacity.	
  
	
  

Background	
  
The	
  coffee	
  tree,	
  especially	
  the	
  Arabica	
  variety,	
  grows	
  at	
  elevations	
  ranging	
  from	
  1300	
  to	
  
1500	
  meters	
  above	
  the	
  sea	
  level	
  and	
  needs	
  ample	
  and	
  consistent	
  rainfall	
  within	
  a	
  narrow	
  
temperature	
  range.	
  	
  Coffee	
  is	
  prized	
  by	
  ecologists	
  because	
  it	
  grows	
  well	
  under	
  a	
  canopy	
  of	
  
shade	
  trees,	
  allowing	
  for	
  the	
  development	
  of	
  rich	
  biodiverse	
  ecosystems.	
  	
  The	
  value	
  of	
  
shaded	
  coffee	
  lies	
  in	
  its	
  capacity	
  as	
  a	
  refuge	
  for	
  biodiversity;	
  the	
  push	
  towards	
  
intensification	
  of	
  coffee	
  production,	
  however,	
  has	
  had	
  dramatic	
  impacts	
  on	
  the	
  biodiversity	
  
composition	
  of	
  these	
  traditional	
  coffee	
  farms	
  (Perfecto	
  et	
  al.,	
  2007).	
  

	
  

49	
  

In	
  the	
  1970s	
  and	
  1980s	
  Latin	
  America	
  saw	
  a	
  rapid	
  shift	
  from	
  polycultures	
  to	
  monocultures	
  
in	
  the	
  coffee	
  sector	
  in	
  response	
  to	
  higher	
  demand	
  for	
  coffee	
  and	
  to	
  trade	
  policies	
  
encouraged	
  by	
  the	
  Global	
  North	
  (Perfecto	
  and	
  Vandermeer,	
  2015).	
  	
  In	
  an	
  effort	
  to	
  intensify	
  
coffee	
  production,	
  farmers	
  radically	
  reduced	
  the	
  number	
  of	
  shade	
  trees	
  in	
  their	
  farms,	
  
planting	
  higher	
  densities	
  of	
  new	
  coffee	
  varieties	
  and	
  intensifying	
  the	
  use	
  of	
  chemical	
  inputs.	
  	
  
An	
  immediate	
  effect	
  of	
  these	
  practices	
  was	
  seen	
  in	
  the	
  precipitous	
  decline	
  of	
  migratory	
  bird	
  
populations	
  in	
  North	
  America	
  and	
  a	
  decline	
  in	
  the	
  richness	
  of	
  bird	
  diversity	
  in	
  Latin	
  
America	
  (Borrero,	
  1986).	
  	
  Despite	
  the	
  push	
  towards	
  intensification,	
  studies	
  have	
  found	
  a	
  
positive	
  relationship	
  between	
  planned	
  biodiversity	
  in	
  farms	
  (such	
  as	
  farms	
  with	
  greater	
  
density	
  of	
  shade	
  trees)	
  and	
  their	
  richness	
  of	
  flora	
  and	
  fauna	
  (e.g.,	
  vertebrates,	
  
invertebrates,	
  plants	
  and	
  fungi)	
  (Hernandez	
  et	
  al.,	
  2013;	
  Murrieta	
  et	
  al.,	
  2013;	
  Saldaùa	
  et	
  
al.,	
  2013).	
  Armbrecht	
  et	
  al.	
  (2004)	
  find	
  that	
  while	
  it	
  is	
  generally	
  beneficial	
  to	
  incorporate	
  
shade	
  trees	
  into	
  coffee	
  plantations,	
  it	
  is	
  even	
  better	
  when	
  there	
  is	
  a	
  diversity	
  of	
  shade	
  trees	
  
planted	
  rather	
  than	
  just	
  one	
  variety.	
  	
  	
  When	
  diverse	
  trees	
  drop	
  leaves	
  and	
  twigs	
  onto	
  the	
  
ground	
  they	
  find	
  that	
  there	
  is	
  a	
  significant	
  impact	
  on	
  consequent	
  biodiversity	
  of	
  the	
  flora	
  
and	
  fauna	
  in	
  the	
  fields.	
  	
  	
  The	
  importance	
  of	
  coffee	
  production	
  in	
  enhancing	
  biodiversity	
  
conservation	
  is	
  clear,	
  but	
  how	
  coffee	
  is	
  grown	
  also	
  matters.	
  	
  Studies	
  have	
  found	
  that	
  shade-­‐‑
grown	
  coffee	
  farms	
  in	
  Mexico	
  contain	
  almost	
  as	
  much	
  biodiversity	
  as	
  native	
  forests,	
  while	
  
sun	
  coffee	
  monocultures	
  in	
  Brazil	
  are	
  reported	
  to	
  be	
  “biodiversity	
  deserts”	
  (Perfecto	
  et	
  al.,	
  
2009).	
  As	
  such,	
  the	
  authors	
  find	
  that	
  in	
  monoculture	
  systems	
  the	
  most	
  important	
  physical	
  
factor	
  contributing	
  to	
  the	
  loss	
  of	
  species	
  diversity	
  is	
  the	
  direct	
  effect	
  of	
  sunlight.	
  
	
  

	
  

50	
  

The	
  incorporation	
  of	
  shade	
  into	
  coffee	
  farms	
  plays	
  another	
  important	
  role	
  besides	
  
biodiversity	
  conservation.	
  	
  Shade	
  systems	
  also	
  help	
  store	
  carbon	
  from	
  the	
  atmosphere,	
  and	
  
protect	
  the	
  watershed	
  by	
  reducing	
  run-­‐‑off	
  and	
  soil	
  loss	
  (Perfecto	
  et	
  al.,	
  2007;	
  Valkila,	
  
2009).	
  	
  Moreover,	
  planting	
  trees	
  on	
  the	
  farm	
  can	
  contribute	
  to	
  household	
  livelihoods	
  by	
  
generating	
  products	
  for	
  human	
  consumption	
  (food	
  security)	
  and	
  sales	
  (income	
  generation)	
  
(Mendez	
  et	
  al.,	
  2010).	
  A	
  third	
  advantage	
  to	
  shade-­‐‑grown	
  coffee	
  is	
  that	
  it	
  is	
  known	
  to	
  be	
  of	
  
higher	
  quality	
  (in	
  the	
  cup)	
  and	
  thus	
  draws	
  higher	
  prices	
  from	
  coffee	
  buyers,	
  particularly	
  
those	
  at	
  the	
  higher	
  end	
  of	
  the	
  specialty	
  coffee	
  market.	
  	
  
	
  
In	
  addition	
  to	
  the	
  ecosystem	
  services	
  that	
  traditional	
  coffee	
  farms	
  provide,	
  farmers	
  have	
  
yet	
  another	
  incentive	
  to	
  incorporate	
  shade	
  into	
  their	
  farms.	
  	
  Climate	
  change	
  scientists	
  
predict	
  that	
  tropical	
  regions	
  where	
  coffee	
  grows	
  will	
  be	
  increasingly	
  impacted	
  by	
  the	
  
changing	
  climate,	
  and	
  as	
  a	
  result	
  their	
  suitability	
  for	
  coffee	
  production	
  will	
  decline	
  rapidly.	
  	
  	
  
	
  
The	
  adoption	
  of	
  climate	
  change	
  adaptation	
  strategies	
  is	
  necessary,	
  indeed,	
  urgent,	
  in	
  some	
  
coffee	
   growing	
   areas.	
   	
   Regions	
   that	
   have	
   already	
   experienced	
   periods	
   of	
   seasonal	
   droughts	
  
will	
  see	
  a	
  rise	
  in	
  the	
  frequency	
  of	
  these	
  droughts.	
  Similarly,	
  some	
  areas	
  will	
  experience	
  severe	
  
flooding	
   due	
   to	
   increased	
   and	
   more	
   intensive	
   rainfall.	
   	
   In	
   addition,	
   changes	
   in	
   global	
  
temperatures	
   will	
   result	
   in	
   areas	
   that	
   will	
   no	
   longer	
   be	
   suitable	
   for	
   agricultural	
   production	
  
at	
  all	
  (Fischer	
  et	
  al.,	
  2002).	
  	
  No	
  other	
  population	
  is	
  more	
  vulnerable	
  to	
  these	
  changes	
  than	
  
poor	
  agrarian	
  households	
  that	
  depend	
  on	
  agricultural	
  production	
  for	
  their	
  livelihoods.	
  	
  As	
  
the	
  intensity	
  and	
  frequency	
  of	
  these	
  events	
  increase,	
  the	
  affected	
  households	
  will	
  experience	
  
a	
  loss	
  of	
  household	
  assets	
  and	
  crops,	
  declining	
  access	
  to	
  water,	
  and	
  challenges	
  to	
  health	
  and	
  

	
  

51	
  

nutrition.	
   Moreover,	
   they	
   will	
   be	
   left	
   with	
   less	
   time	
   to	
   recover	
   from	
   the	
   previous	
   shocks,	
  
resulting	
  in	
  severe,	
  potentially	
  chronic,	
  food	
  insecurity	
  (Laderach	
  et	
  al.,	
  2013;	
  Vermeulen	
  et	
  
al.,	
  2012;	
  Fischer	
  et	
  al.,	
  2002).	
  	
  
	
  
In	
  this	
  paper	
  I	
  use	
  choice	
  experiments	
  to	
  study	
  the	
  conditions	
  under	
  which	
  coffee	
  farmers	
  
would	
  be	
  willing	
  to	
  diversify	
  their	
  coffee	
  farms	
  with	
  additional	
  shade	
  crop.	
  	
  This	
  method	
  
has	
  been	
  used	
  widely	
  in	
  the	
  environmental	
  and	
  development	
  economics	
  literature.	
  Birol	
  et	
  
al.	
  (2009)	
  used	
  choice	
  experiments	
  to	
  estimate	
  how	
  Mexican	
  maize	
  growers	
  valuate	
  three	
  
components	
  of	
  traditional	
  maize	
  production	
  practices	
  (milpa):	
  crop	
  species	
  richness,	
  maize	
  
variety	
  richness,	
  and	
  maize	
  landraces.	
  	
  They	
  find	
  that	
  while	
  conservationists	
  derive	
  the	
  
highest	
  value	
  from	
  traditional	
  milpa	
  production	
  and	
  the	
  highest	
  economic	
  loss	
  from	
  GM	
  
maize	
  adoption,	
  marginalized	
  maize	
  producers	
  receive	
  little	
  economic	
  value	
  from	
  maize	
  
and	
  crop	
  diversification,	
  and	
  experience	
  the	
  smallest	
  negative	
  impact	
  from	
  the	
  adoption	
  of	
  
GM	
  maize.	
  	
  Similarly,	
  Ortega	
  et	
  al.	
  (2016)	
  use	
  choice	
  experiments	
  to	
  examine	
  farmers’	
  
preferences	
  for	
  groundnut,	
  soybean,	
  and	
  pigeon	
  pea	
  crop	
  diversification	
  in	
  maize	
  fields	
  in	
  
Malawi.	
  They	
  find	
  that	
  farmers	
  have	
  significant	
  labor	
  constraints	
  that	
  limit	
  their	
  uptake	
  of	
  
new	
  crops	
  to	
  diversify	
  their	
  maize	
  with,	
  and	
  that	
  the	
  uptake	
  of	
  legume	
  and	
  maize	
  intercrop	
  
systems	
  would	
  increase	
  if	
  practitioners	
  focus	
  on	
  legumes	
  that	
  have	
  better	
  marketability.	
  
	
  
Coffee	
  Leaf	
  Rust	
  in	
  Nicaragua	
  
The	
  coffee	
  leaf	
  rust	
  problem	
  in	
  Nicaragua	
  and	
  the	
  rest	
  of	
  Mesoamerica	
  has	
  been	
  
devastating	
  and	
  merits	
  special	
  attention.	
  Coffee	
  in	
  Nicaragua	
  is	
  grown	
  mostly	
  by	
  small	
  scale	
  

	
  

52	
  

coffee	
  farmers	
  within	
  the	
  central	
  mountains	
  in	
  Jinotega	
  and	
  Matagalpa,	
  which	
  are	
  known	
  
for	
  their	
  rich	
  volcanic	
  soils	
  and	
  humid	
  tropical	
  climate.	
  	
  Farmers	
  in	
  this	
  region	
  depend	
  on	
  
coffee	
  production	
  as	
  their	
  main	
  source	
  of	
  income.	
  In	
  recent	
  years,	
  however,	
  coffee	
  in	
  
Nicaragua	
  has	
  been	
  devastated	
  by	
  the	
  coffee	
  leaf	
  rust	
  fungus,	
  and	
  farmers	
  have	
  suffered	
  
large	
  yield	
  losses.	
  
	
  
The	
  leaf	
  rust	
  is	
  caused	
  by	
  a	
  fungus	
  (Hemileia	
  vastatrix)	
  and	
  although	
  it	
  originates	
  from	
  Sri	
  
Lanka,	
  in	
  Latin	
  America	
  it	
  was	
  first	
  reported	
  in	
  1970.	
  The	
  disease	
  attacks	
  Arabica	
  coffee	
  
more	
  severely	
  than	
  other	
  varieties	
  and	
  it	
  causes	
  leaves	
  to	
  fall	
  off	
  and,	
  when	
  acute,	
  can	
  cause	
  
branches	
  to	
  die,	
  resulting	
  in	
  heavy	
  crop	
  losses.	
  The	
  most	
  significant	
  outbreak	
  of	
  the	
  disease	
  
in	
  Latin	
  America	
  occurred	
  during	
  the	
  closing	
  months	
  of	
  2012,	
  with	
  other	
  outbreaks	
  in	
  the	
  
late	
  1980s,	
  mid	
  1990s,	
  and	
  early	
  2000s.	
  	
  Some	
  studies	
  reported	
  as	
  much	
  as	
  50%	
  reduction	
  
of	
  yields	
  over	
  a	
  region	
  that	
  extends	
  from	
  southern	
  Mexico	
  to	
  Colombia,	
  during	
  this	
  latest	
  
outbreak	
  (Cressey,	
  2013).	
  Researchers	
  surmise	
  that	
  a	
  lack	
  of	
  proper	
  economic	
  incentives	
  to	
  
invest	
  in	
  their	
  farms	
  (e.g.,	
  better	
  credit,	
  higher	
  cherry	
  prices,	
  and	
  lower	
  input	
  prices)	
  as	
  
well	
  as	
  meteorological	
  factors	
  (such	
  as	
  earlier	
  rainy	
  seasons	
  and	
  rainy	
  seasons	
  
interspersed	
  with	
  bright	
  periods)	
  have	
  led	
  to	
  the	
  string	
  of	
  coffee	
  rust	
  epidemics	
  in	
  Latin	
  
America	
  (Avelino	
  et	
  al.,	
  2015).	
  	
  This	
  disease	
  has	
  impacted	
  the	
  coffee	
  sector	
  in	
  Nicaragua	
  
beyond	
  simple	
  crop	
  losses;	
  it	
  has	
  resulted	
  in	
  wholesale	
  changes	
  in	
  farmers’	
  perceptions	
  and	
  
behaviors,	
  and	
  much	
  is	
  still	
  unknown	
  about	
  how	
  these	
  changes	
  will	
  affect	
  the	
  future	
  of	
  
coffee	
  production	
  in	
  the	
  region.	
  	
  
	
  

	
  

53	
  

Theoretical	
  Framework	
  
The	
  empirical	
  framework	
  of	
  this	
  study	
  is	
  based	
  on	
  experimental	
  choice	
  modeling	
  methods	
  
to	
  analyze	
  farmers’	
  preferences	
  for	
  different	
  coffee	
  production	
  strategies.	
  In	
  a	
  choice	
  
experiment,	
  respondents	
  are	
  asked	
  to	
  choose	
  between	
  option	
  bundles	
  containing	
  a	
  series	
  
of	
  different	
  attributes	
  (of	
  varying	
  levels)	
  from	
  hypothetical	
  choice	
  scenarios.	
  By	
  controlling	
  
the	
  variation	
  in	
  the	
  levels	
  of	
  the	
  attributes,	
  researchers	
  areI	
  am	
  able	
  to	
  analyze	
  the	
  choices	
  
made	
  by	
  the	
  respondents	
  and	
  to	
  estimate	
  marginal	
  values	
  for	
  the	
  attributes	
  presented	
  in	
  
the	
  choice	
  sets.	
  
	
  
The	
  theoretical	
  foundation	
  of	
  choice	
  experiments	
  is	
  based	
  on	
  random	
  utility	
  theory,	
  and	
  
relies	
  on	
  the	
  assumptions	
  of	
  economic	
  rationality	
  and	
  Lancastrian	
  utility	
  maximization	
  
(Lancaster,	
  1966).	
  	
  In	
  the	
  context	
  of	
  the	
  present	
  study,	
  a	
  coffee	
  farming	
  system	
  is	
  described	
  
as	
  a	
  collection	
  of	
  its	
  physical	
  and	
  managerial	
  characteristics,	
  including	
  the	
  inputs	
  applied	
  
and	
  the	
  crop	
  diversity	
  within	
  the	
  farm.	
  By	
  stating	
  a	
  preference	
  for	
  a	
  specified	
  farming	
  
system,	
  faremrs	
  are	
  assumed	
  to	
  have	
  chosen	
  the	
  alternative	
  that	
  will	
  yield	
  the	
  highest	
  
utility	
  or	
  value	
  to	
  them.	
  	
  Random	
  utility	
  can	
  be	
  characterized	
  by	
  the	
  following	
  function:	
  
	
  
𝑢?de = 𝛽?f 𝑥?de + 𝜀?de 	
  

(1)	
  

	
  
	
  
Where	
  𝑢?de 	
  is	
  the	
  utility	
  derived	
  by	
  farmer	
  i	
  choosing	
  alternative	
  j	
  in	
  choice	
  task	
  s,	
  𝑥?de 	
  is	
  a	
  
vector	
  of	
  observable	
  attributes,	
  𝛽? 	
  is	
  a	
  vector	
  of	
  estimated	
  parameters	
  and	
  𝜀?de 	
  is	
  the	
  
random	
  error	
  component	
  of	
  the	
  model.	
  The	
  error	
  terms	
  are	
  assumed	
  to	
  be	
  independent	
  

	
  

54	
  

and	
  identically	
  distributed	
  with	
  a	
  Gumbel	
  distribution,	
  which	
  captures	
  variations	
  in	
  
preferences	
  and	
  errors	
  in	
  individuals’	
  perceptions.	
  	
  
	
  
Since	
  I	
  cannot	
  directly	
  observe	
  the	
  vector	
  of	
  utilities	
  for	
  each	
  individual,	
  I	
  observe	
  the	
  
sequence	
  of	
  choices	
  that	
  the	
  individual	
  makes,	
  and	
  estimate	
  the	
  conditional	
  probability	
  of	
  
this	
  observed	
  sequence	
  as	
  follows:	
  
	
  

	
  
𝑃 𝑦? 𝑥?*e , 𝑥?Ee , … , 𝑥?de , 𝜑 =

exp 𝛽?f 𝑥?lmn e
𝑓 𝛽 𝜑 𝑑𝛽	
  
f
o exp 𝛽? 𝑥?oe

	
  
(2)	
  

	
  
	
  
Equation	
  2	
  represents	
  a	
  random	
  parameter	
  logit	
  model	
  (RPL).	
  In	
  it	
  I	
  assume	
  that	
  
producers’	
  preferences	
  for	
  different	
  farming	
  management	
  systems	
  are	
  heterogeneous,	
  or	
  in	
  
other	
  words,	
  that	
  not	
  every	
  farmer	
  has	
  the	
  same	
  preference.	
  This	
  model	
  is	
  used	
  to	
  allow	
  a	
  
random	
  preference	
  variation,	
  it	
  relaxes	
  the	
  limitation	
  of	
  a	
  traditional	
  logit	
  model	
  by	
  
allowing	
  these	
  random	
  preferences	
  to	
  come	
  from	
  a	
  sample	
  with	
  a	
  specified	
  distribution	
  
(McFadden	
  and	
  Train,	
  2000).	
  	
  Allowing	
  for	
  free	
  correlation	
  of	
  the	
  random	
  parameters	
  in	
  
the	
  RPL	
  model	
  also	
  allows	
  us	
  to	
  study	
  the	
  preference	
  relationship	
  between	
  attributes.	
  For	
  
the	
  analysis	
  in	
  this	
  study,	
  I	
  let	
  the	
  coefficients	
  corresponding	
  to	
  each	
  attribute	
  to	
  take	
  a	
  
normal	
  distribution	
  to	
  allow	
  for	
  both	
  positive	
  and	
  negative	
  preferences	
  for	
  each	
  of	
  the	
  
attributes.	
  
	
  

	
  

55	
  

Individual	
  coefficients	
  estimated	
  in	
  the	
  random	
  parameter	
  logit	
  model	
  have	
  limited	
  
economic	
  interpretation	
  due	
  to	
  the	
  non-­‐‑cardinal	
  nature	
  of	
  utility.	
  	
  However,	
  attribute	
  
trade-­‐‑offs	
  can	
  be	
  calculated	
  using	
  a	
  relative	
  combination	
  of	
  selected	
  coefficients	
  from	
  this	
  
model	
  to	
  provide	
  meaningful	
  insights	
  into	
  producer	
  behavior.	
  	
  I	
  follow	
  Nahuelhual	
  et	
  al.	
  
(2004)	
  and	
  Rigby	
  and	
  Burton	
  (2005)	
  to	
  estimate	
  how	
  willing	
  are	
  producers	
  to	
  change	
  their	
  
production	
  practices.	
  
	
  
𝑊𝑇𝐶 =

𝑀𝑈
	
  
𝑀𝑈𝐼

(3)	
  

	
  
where	
  MU	
  is	
  the	
  marginal	
  utility	
  of	
  the	
  various	
  production	
  attributes	
  and	
  MUI	
  is	
  the	
  
marginal	
  utility	
  of	
  profit,	
  which	
  is	
  proxied	
  with	
  the	
  premium/discount	
  coefficient.	
  The	
  term	
  
willingness	
  to	
  change	
  (WTC)	
  captures	
  both	
  the	
  willingness	
  to	
  pay	
  (WTP)	
  and	
  the	
  
willingness	
  to	
  accept	
  (WTA)	
  terms.	
  	
  A	
  negative	
  WTC	
  reflects	
  a	
  premium	
  that	
  producers	
  
would	
  have	
  to	
  receive	
  to	
  change	
  their	
  behavior,	
  and	
  a	
  positive	
  WTC	
  reflects	
  a	
  discount	
  that	
  
they	
  are	
  willing	
  to	
  accept	
  when	
  providing	
  a	
  given	
  attribute.	
  
	
  

Data	
  and	
  Choice	
  Experiment	
  Design	
  
Data	
  and	
  Sample	
  Characteristics	
  
Data	
  for	
  this	
  research	
  were	
  collected	
  in	
  the	
  department	
  of	
  Matagalpa	
  in	
  northern	
  Nicaragua	
  
between	
  June	
  and	
  July	
  2015	
  using	
  a	
  two	
  stage	
  stratified	
  random	
  sample	
  of	
  236	
  coffee	
  
producing	
  households.	
  	
  First,	
  communities	
  in	
  Matagalpa	
  were	
  stratified	
  by	
  level	
  of	
  
vulnerability	
  to	
  climate	
  change.	
  	
  Vulnerability	
  was	
  determined	
  by	
  the	
  average	
  elevation	
  in	
  
which	
  the	
  community	
  was	
  located.	
  Higher	
  elevation	
  (above	
  1000	
  meters	
  above	
  sea	
  level)	
  

	
  

56	
  

had	
  a	
  lower	
  vulnerability	
  index	
  than	
  those	
  at	
  lower	
  elevations,	
  as	
  coffee-­‐‑growing	
  
households	
  in	
  higher	
  elevations	
  will	
  be	
  less	
  affected	
  by	
  increased	
  temperatures.	
  In	
  this	
  first	
  
stage,	
  a	
  random	
  sample	
  of	
  communities	
  was	
  selected	
  based	
  on	
  their	
  vulnerability	
  index	
  
score.	
  	
  In	
  the	
  second	
  stage,	
  households	
  in	
  each	
  of	
  the	
  selected	
  communities	
  were	
  drawn	
  
from	
  a	
  census	
  of	
  coffee	
  producers	
  in	
  the	
  region.	
  	
  The	
  households	
  surveyed	
  in	
  this	
  study	
  
form	
  part	
  of	
  an	
  ongoing	
  project	
  on	
  climate	
  change	
  and	
  food	
  security	
  conducted	
  by	
  the	
  
International	
  Center	
  for	
  Tropical	
  Agriculture	
  (CIAT).	
  After	
  eliminating	
  households	
  for	
  
which	
  data	
  were	
  missing	
  or	
  incomplete,	
  the	
  data	
  set	
  for	
  this	
  analysis	
  was	
  reduced	
  to	
  221	
  
households	
  in	
  the	
  department	
  of	
  Matagalpa,	
  with	
  9	
  out	
  13	
  municipalities	
  being	
  
represented;	
  municipalities	
  not	
  sampled	
  were	
  in	
  regions	
  of	
  Matagalpa	
  where	
  coffee	
  is	
  not	
  
grown.	
  	
  A	
  map	
  of	
  the	
  study	
  area	
  is	
  presented	
  in	
  Figure	
  9	
  
Figure	
  9.	
  Map	
  of	
  Study	
  Area	
  

Matagalpa
Lake Nicaragua

	
  
	
  

57	
  

Household	
  surveys	
  enable	
  us	
  to	
  collect	
  information	
  on	
  demographic	
  and	
  socio-­‐‑economic	
  
characteristics,	
  agricultural	
  production,	
  and	
  experiences	
  with	
  economic	
  and	
  climatic	
  
shocks.	
  	
  These	
  additional	
  sources	
  of	
  information	
  help	
  us	
  to	
  understand	
  the	
  preference	
  
heterogeneity	
  of	
  survey	
  respondents,	
  and	
  to	
  examine	
  the	
  determinants	
  of	
  farmer	
  
preferences	
  and	
  behavior.	
  Table	
  6	
  summarizes	
  some	
  of	
  the	
  characteristics	
  of	
  the	
  producers	
  
in	
  the	
  sample.	
  	
  The	
  average	
  age	
  of	
  the	
  respondents	
  is	
  46	
  years	
  with	
  an	
  average	
  of	
  3.8	
  years	
  
of	
  formal	
  education	
  completed.	
  	
  The	
  mean	
  area	
  under	
  production	
  is	
  4.8	
  hectares	
  and	
  the	
  
mean	
  annual	
  coffee	
  production	
  is	
  9.7	
  quintales	
  of	
  wet	
  parchment4	
  (312.8kg)	
  per	
  hectare.	
  	
  
Forty-­‐‑six	
  percent	
  of	
  the	
  sample	
  are	
  members	
  of	
  a	
  coffee	
  cooperative	
  and	
  65%	
  are	
  male-­‐‑
headed	
  households.	
  	
  Among	
  study	
  households,	
  30.9%	
  and	
  32.2%	
  of	
  have	
  access	
  to	
  
subsidized	
  pesticides	
  and	
  fertilizers	
  respectively,	
  while	
  only	
  38.5%	
  of	
  farmers	
  report	
  
having	
  had	
  access	
  to	
  on-­‐‑farm	
  extension	
  services	
  in	
  the	
  year	
  prior	
  to	
  data	
  collection.	
  The	
  
majority	
  of	
  farmers	
  grow	
  only	
  one	
  variety	
  of	
  coffee	
  (Catimor)	
  (59.8%)	
  and	
  45.7%	
  of	
  
farmers	
  intercropped	
  their	
  coffee	
  with	
  two	
  additional	
  shade	
  crops	
  (banana	
  and	
  citrus	
  
trees).	
  
	
  

Wet parchment is a state of the coffee in its transformation from cherry to bean. After
harvesting the freshly harvested cherries are passed through a pulping machine to separate the
skin and pulp from the bean. After depulping, the bean is transported to water-filled tanks for
fermentation where they remain from 12 to 48 hours. When fermentation is complete, the beans
are rinsed and are ready for drying. Coffee at this stage of the wet milling process is known as
wet parchment.
	
  
4

	
  

58	
  

Table	
  7.	
  Sample	
  Characteristics	
  
Variable	
  

Means	
  
(%	
  where	
  
noted)	
  

	
  

Male	
  
65.4%	
  
	
  
Age	
  	
  
46.4	
  
(15.28)	
  
Household	
  Size	
  
5.4	
  
(2.12)	
  
Years	
  of	
  Education	
  
3.8	
  
(3.48)	
  
Years	
  in	
  Coffee	
  
16.5	
  
(12.48)	
  
Total	
  Coffee	
  Income	
  (USD)	
  per	
  ha	
  
821.8	
  
(875.11)	
  
Total	
  income	
  (USD)	
  
5,648.3	
  
(7,377.11)	
  
Total	
  area	
  under	
  coffee	
  production	
  
4.8	
  
(5.38)	
  
(ha)	
  
Total	
  Coffee	
  Production	
  (quintales1)	
  
(120.59)	
  
9.7	
  
per	
  ha	
  	
  
Cooperative	
  membership	
  
45.5%	
  
	
  
Access	
  to	
  Pesticides	
  
30.9%	
   	
  
Access	
  to	
  Fertilizers	
  
32.2%	
   	
  
Extension	
  Services	
  
38.5%	
   	
  
Farms	
  with	
  1	
  variety	
  of	
  coffee	
  
59.8%	
   	
  
Farms	
  with	
  2	
  varieties	
  of	
  coffee	
  
32.7%	
   	
  
Farms	
  with	
  1	
  additional	
  shade	
  crop	
  
18.2%	
   	
  
Farms	
  with	
  2	
  additional	
  shade	
  crop	
  
45.7%	
   	
  
1	
  1	
  Quintal=	
  46kg;	
  Standard	
  deviations	
  are	
  presented	
  in	
  parentheses	
  
	
  
Choice	
  Experiment	
  
The	
  choice	
  experiment	
  in	
  this	
  study	
  was	
  designed	
  to	
  compare	
  the	
  producers’	
  management	
  
of	
  their	
  current	
  coffee	
  field	
  to	
  other	
  hypothetical	
  coffee	
  fields.	
  To	
  make	
  this	
  comparison,	
  
information	
  about	
  their	
  farm	
  characteristics	
  were	
  collected	
  during	
  the	
  implementation	
  of	
  
the	
  survey.	
  
	
  
To	
  identify	
  relevant	
  coffee	
  production	
  attributes,	
  interviews	
  with	
  key	
  informants	
  and	
  
coffee	
  producers	
  were	
  carried	
  out	
  in	
  March	
  and	
  April	
  2015.	
  	
  Six	
  attributes	
  were	
  selected	
  to	
  
for	
  inclusion	
  in	
  the	
  choice	
  experiment:	
  input	
  provision,	
  access	
  to	
  extension	
  services,	
  labor	
  

	
  

59	
  

requirements,	
  coffee	
  diversification,	
  crop	
  diversification,	
  and	
  income	
  generated	
  from	
  the	
  
farm.	
  These	
  attributes	
  are	
  reviewed	
  below.	
  	
  
	
  
Input	
  Provision.	
  Unless	
  coffee	
  production	
  is	
  profitable	
  for	
  farmers,	
  they	
  will	
  not	
  invest	
  in	
  
their	
  farms.	
  Input	
  constraints,	
  such	
  as	
  lack	
  of	
  access	
  or	
  high	
  costs,	
  induce	
  farmers	
  to	
  adjust	
  
their	
  preferences	
  for	
  characteristics	
  associated	
  with	
  their	
  production	
  (Wale	
  et	
  al.,	
  2005).	
  	
  
From	
  preliminary	
  interviews	
  with	
  farmers,	
  I	
  learned	
  that	
  the	
  cost	
  of	
  commercial	
  inputs	
  
(notably	
  fertilizers	
  and	
  pesticides)	
  is	
  a	
  high	
  barrier	
  to	
  their	
  adoption,	
  and	
  that	
  most	
  rely	
  on	
  
the	
  distribution	
  of	
  these	
  from	
  organizations	
  in	
  their	
  region.	
  	
  Many	
  studies	
  assert	
  that	
  unless	
  
inputs	
  are	
  subsidized,	
  farmers	
  with	
  income	
  constraints	
  will	
  not	
  use	
  them	
  (Duflo	
  et	
  al.,	
  
2011;	
  Dugger,	
  2007).	
  A	
  recent	
  study	
  of	
  smallholder	
  coffee	
  producers	
  in	
  Rwanda	
  highlights	
  
this	
  issue,	
  where	
  71%	
  and	
  45%	
  of	
  households	
  surveyed	
  cited	
  low	
  and	
  unstable	
  cherry	
  
prices,	
  respectively,	
  as	
  their	
  main	
  barrier	
  to	
  investment	
  in	
  their	
  coffee	
  (Clay	
  et	
  al.,	
  2016).	
  	
  In	
  
the	
  same	
  study,	
  the	
  majority	
  of	
  farmers	
  who	
  did	
  not	
  apply	
  any	
  inputs	
  stated	
  that	
  a	
  lack	
  of	
  
access	
  to	
  free	
  or	
  subsidized	
  inputs	
  was	
  their	
  main	
  reason	
  for	
  non-­‐‑use.	
  	
  It	
  is	
  clear	
  that	
  access	
  
to	
  inputs	
  plays	
  an	
  important	
  role	
  in	
  farmers’	
  on-­‐‑farm	
  investment	
  decisions;	
  in	
  this	
  study	
  I	
  
test	
  how	
  highly	
  farmers	
  value	
  this	
  access.	
  Four	
  levels	
  of	
  input	
  distribution	
  are	
  specified	
  in	
  
the	
  choice	
  experiment:	
  No	
  access	
  to	
  subsidized	
  inputs,	
  access	
  to	
  subsidized	
  pesticides	
  only,	
  
access	
  to	
  subsidized	
  fertilizers	
  only,	
  and	
  access	
  to	
  subsidized	
  pesticides	
  and	
  fertilizer.	
  
	
  
Access	
  to	
  Extension	
  Services.	
  	
  Extension	
  services	
  have	
  the	
  potential	
  to	
  influence	
  farmers’	
  
decisions	
  to	
  change	
  their	
  production	
  practices	
  in	
  response	
  to	
  climate	
  change	
  (Maddison,	
  
2007).	
  Indeed,	
  a	
  lack	
  of	
  training	
  and	
  information	
  was	
  an	
  issue	
  frequently	
  raised	
  by	
  coffee	
  

	
  

60	
  

producers	
  during	
  the	
  piloting	
  period	
  of	
  the	
  study.	
  Farmers	
  expressed	
  a	
  deep	
  dissatisfaction	
  
with	
  the	
  low	
  level	
  of	
  on-­‐‑farm	
  support	
  provided	
  by	
  government	
  agencies	
  and	
  by	
  their	
  own	
  
cooperatives,	
  especially	
  during	
  and	
  after	
  the	
  devastating	
  leaf	
  rust	
  outbreak.	
  	
  These	
  services	
  
were	
  included	
  as	
  a	
  binary	
  variable	
  that	
  captured	
  whether	
  the	
  field	
  received	
  on-­‐‑farm	
  
extension	
  services.	
  
	
  
Labor	
  Requirements.	
  Similarly,	
  practices	
  that	
  require	
  high	
  levels	
  of	
  labor	
  investments	
  need	
  
to	
  be	
  considered	
  in	
  this	
  study.	
  	
  Maintaining	
  proper	
  shade	
  in	
  farms,	
  mulching,	
  and	
  pruning	
  
all	
  are	
  labor	
  labor-­‐‑intensive	
  practices,	
  and	
  while	
  better-­‐‑endowed	
  coffee	
  producers	
  may	
  be	
  
able	
  to	
  overcome	
  some	
  of	
  these	
  labor	
  requirements	
  by	
  hiring	
  outside	
  labor,	
  smallholder	
  
producers	
  mostly	
  rely	
  on	
  household	
  labor	
  for	
  these	
  tasks.	
  Two	
  levels	
  of	
  labor	
  requirement	
  
are	
  used:	
  high	
  and	
  low,	
  which	
  correspond	
  to	
  a	
  50%	
  increase/decrease	
  of	
  their	
  current	
  
person-­‐‑day	
  requirements.	
  	
  
	
  
Coffee	
  Diversification.	
  	
  The	
  importance	
  of	
  coffee	
  diversification	
  derives	
  from	
  two	
  main	
  
factors.	
  On	
  the	
  one	
  hand,	
  farmers	
  can	
  adopt	
  varieties	
  that	
  are	
  resistant	
  to	
  droughts	
  and	
  
higher	
  temperatures	
  to	
  cope	
  with	
  the	
  impacts	
  of	
  climate	
  change,	
  and	
  on	
  the	
  other	
  hand,	
  
new	
  varieties	
  of	
  coffee	
  are	
  being	
  developed	
  that	
  keep	
  the	
  quality	
  of	
  Arabica	
  varieties	
  but	
  
take	
  the	
  physical	
  attributes	
  of	
  lower	
  quality	
  coffee	
  varieties.	
  Two	
  levels	
  were	
  included	
  in	
  
this	
  attribute,	
  corresponding	
  to	
  the	
  establishment	
  of	
  one	
  or	
  two	
  coffee	
  varieties	
  in	
  the	
  field.	
  
	
  
Crop	
  Diversification.	
  	
  The	
  incorporation	
  of	
  shade	
  into	
  coffee	
  fields	
  cannot	
  be	
  overstated.	
  As	
  
reviewed	
  earlier	
  in	
  this	
  article,	
  shade	
  helps	
  to	
  protect	
  biodiversity	
  and	
  soils,	
  lowers	
  farm	
  

	
  

61	
  

temperatures,	
  and	
  provides	
  alternative	
  food	
  sources.	
  Shade	
  crops	
  can	
  also	
  expand	
  the	
  
income	
  potential	
  of	
  the	
  farm	
  and	
  help	
  to	
  retain	
  water	
  in	
  the	
  soils.	
  Four	
  levels	
  were	
  included	
  
in	
  this	
  attribute	
  corresponding	
  to	
  a	
  field	
  containing	
  coffee	
  alone,	
  coffee	
  plus	
  one	
  additional	
  
shade	
  crops,	
  coffee	
  plus	
  two	
  additional	
  shade	
  crops,	
  and	
  coffee	
  plus	
  three	
  additional	
  shade	
  
crops.	
  The	
  additional	
  crops	
  that	
  were	
  used	
  as	
  examples	
  in	
  the	
  choice	
  experiment	
  were	
  
identified	
  from	
  the	
  climate	
  change	
  literature	
  that	
  looks	
  at	
  successful	
  coffee	
  crop	
  pairings,	
  
and	
  they	
  are	
  banana,	
  citrus,	
  and	
  cacao.	
  
	
  
Income.	
  	
  Finally,	
  an	
  additional	
  parameter	
  capturing	
  the	
  percentage	
  change	
  in	
  income	
  
generated	
  from	
  coffee	
  fields	
  was	
  included	
  to	
  help	
  estimate	
  farmers’	
  willingness	
  to	
  change.	
  	
  
Four	
  levels	
  were	
  included	
  and	
  correspond	
  to	
  a	
  25%	
  and	
  50%	
  increase	
  or	
  decrease	
  in	
  
income	
  generated	
  from	
  their	
  coffee	
  fields.	
  A	
  percentage	
  specification	
  was	
  used	
  since	
  it	
  is	
  
difficult	
  to	
  estimate	
  the	
  exact	
  income	
  generated	
  from	
  a	
  field	
  due	
  to	
  differences	
  in	
  cropping	
  
intensities,	
  farm	
  sizes,	
  and	
  productivity	
  levels.	
  
	
  
Detailed	
  information	
  on	
  the	
  selected	
  attributes	
  and	
  their	
  levels	
  is	
  presented	
  in	
  Table	
  8.	
  
	
  
Table	
  8.	
  Coffee	
  Production	
  Attributes	
  Used	
  in	
  Choice	
  Experiments	
  
Attribute	
  
Input	
  Provision	
  
	
  
Extension	
  
Services	
  
	
  
	
  
	
  

Levels	
  
None,	
  Pesticides	
  only,	
  Fertilizer	
  
only,	
  Pesticide	
  and	
  fertilizers	
  
together	
  
	
  
Yes,	
  No	
  
	
  

62	
  

Definition	
  
Producer	
  access	
  to	
  subsidized	
  
inputs.	
  
	
  
	
  
Producer	
  access	
  to	
  on	
  farm	
  
extension	
  services.	
  
	
  
	
  

Table	
  8	
  (Cont’d)	
  
	
  
	
  
Labor	
  
High,	
  Low	
  
Requirements	
  
Coffee	
  
Diversification	
  

1	
  variety,	
  2	
  varieties	
  

Crop	
  
Diversification	
  

1	
  (sole	
  coffee),	
  2	
  (coffee	
  and	
  
banana,	
  coffee	
  and	
  citrus,	
  coffee	
  
and	
  cacao),	
  3	
  (coffee	
  and	
  
banana/citrus,	
  coffee	
  and	
  
banana/cacao,	
  coffee	
  and	
  
citrus/cacao),	
  4	
  (coffee	
  and	
  
banana/citrus/cacao)	
  
	
  
	
  
-­‐‑50%,	
  -­‐‑25%,	
  +25%,	
  +50%	
  

	
  
	
  
Income	
  

Labor	
  requirement	
  defined	
  as	
  a	
  
50%	
  increase	
  in	
  labor	
  (high)	
  or	
  a	
  
50%	
  decrease	
  in	
  labor	
  (low).	
  
	
  
The	
  number	
  of	
  established	
  coffee	
  
varieties.	
  
	
  
Total	
  number	
  of	
  crops	
  established	
  
with	
  the	
  coffee.	
  
	
  

	
  
	
  
Percentage	
  change	
  of	
  expected	
  
coffee	
  income	
  relative	
  to	
  the	
  
farmer’s	
  coffee	
  income	
  for	
  the	
  
previous	
  year.	
  

	
  
	
  
Given	
  the	
  above	
  attribute	
  selection,	
  the	
  econometric	
  specification	
  of	
  the	
  choice	
  experiment	
  
takes	
  the	
  following	
  functional	
  form:	
  
	
  
𝑢?de = 𝛽?* 𝐼𝑛𝑝𝑢𝑡𝑠?de + 𝛽?E 𝐸𝑥𝑡𝑒𝑛𝑠𝑖𝑜𝑛?de + 𝛽?F 𝐿𝑎𝑏𝑜𝑟?de + 𝛽?O 𝐶𝑜𝑓𝑓𝑒𝑒𝐷?de 	
  

(4)	
  

+𝛽?R 𝐶𝑟𝑜𝑝𝐷?de + 𝛽?* 𝐼𝑛𝑐𝑜𝑚𝑒?de + 𝜀?de 	
  
	
  
Where	
  𝑢?de 	
  is	
  the	
  utility	
  derived	
  from	
  mapping	
  the	
  coffee	
  farming	
  system	
  into	
  utility	
  space,	
  
𝐼𝑛𝑝𝑢𝑡?de 	
  is	
  the	
  level	
  of	
  input	
  subsidy,	
  𝐸𝑥𝑡𝑒𝑛𝑠𝑖𝑜𝑛?de 	
  is	
  a	
  binary	
  extension	
  service	
  provision,	
  
𝐿𝑎𝑏𝑜𝑟?de 	
  is	
  a	
  binary	
  for	
  labor	
  requirement,	
  𝐶𝑜𝑓𝑓𝑒𝑒𝐷?de 	
  is	
  a	
  binary	
  variable	
  for	
  coffee	
  

	
  

63	
  

diversification,	
  𝐶𝑟𝑜𝑝𝐷?de 	
  is	
  the	
  level	
  of	
  crop	
  diversification,	
  and	
  𝐼𝑛𝑐𝑜𝑚𝑒?de 	
  is	
  the	
  level	
  of	
  
income	
  change.	
  	
  The	
  indices	
  i,	
  j,	
  and	
  s,	
  represent	
  the	
  individual,	
  the	
  choice	
  alternative,	
  and	
  
the	
  choice	
  set,	
  respectively;	
  and	
  𝛽 	
  is	
  the	
  coefficient	
  associated	
  with	
  each	
  attribute	
  
preference.	
  
	
  
Following	
  the	
  selection	
  of	
  the	
  attributes,	
  a	
  pretest	
  was	
  conducted	
  in	
  early	
  June	
  2015	
  to	
  test	
  
the	
  comprehension	
  and	
  suitability	
  of	
  choice	
  experiment	
  parameters.	
  	
  
A	
  D-­‐‑optimal	
  design	
  (one	
  which	
  optimizes	
  the	
  model	
  fit	
  while	
  minimizing	
  the	
  covariance	
  of	
  
the	
  parameter	
  estimates)	
  with	
  null	
  priors	
  was	
  used	
  for	
  the	
  choice	
  experiment.5	
  The	
  design	
  
resulted	
  in	
  three	
  choice	
  tasks	
  which	
  were	
  blocked	
  into	
  six	
  groups	
  to	
  help	
  alleviate	
  response	
  
fatigue.	
  Each	
  coffee	
  producer	
  was	
  presented	
  with	
  five	
  different	
  choice	
  tasks	
  consisting	
  of	
  
two	
  alternative	
  coffee	
  farming	
  operations	
  containing	
  the	
  study	
  attributes.	
  A	
  third	
  
alternative	
  included	
  in	
  the	
  design	
  allowed	
  the	
  respondents	
  to	
  opt-­‐‑out	
  of	
  the	
  hypothetical	
  
scenarios	
  and	
  choose	
  to	
  continue	
  producing	
  coffee	
  under	
  their	
  current	
  management	
  
practices,	
  defined	
  as	
  the	
  “status-­‐‑quo	
  option.”	
  Data	
  for	
  this	
  alternative	
  were	
  collected	
  as	
  part	
  
of	
  the	
  household	
  questionnaire.	
  To	
  avoid	
  issues	
  of	
  comprehension	
  and	
  to	
  accommodate	
  
different	
  levels	
  of	
  farmer	
  literacy,	
  the	
  choice	
  sets	
  were	
  illustrated	
  and	
  presented	
  to	
  
producers	
  on	
  laminated	
  cards.	
  	
  
	
  

5	
  Null	
  priors	
  were	
  used	
  in	
  the	
  design	
  due	
  to	
  a	
  lack	
  of	
  information	
  on	
  farmer	
  valuations	
  of	
  

the	
  attributes	
  selected,	
  as	
  well	
  as	
  time	
  and	
  logistical	
  constraints	
  associated	
  with	
  conducting	
  
a	
  representative	
  pilot	
  study.	
  
	
  

64	
  

After	
  gathering	
  information	
  from	
  the	
  farmers	
  about	
  their	
  current	
  production	
  practices	
  and	
  
farm	
  characteristics	
  (status	
  quo),	
  enumerators	
  introduced	
  the	
  choice	
  experiment	
  to	
  the	
  
respondents	
  and	
  asked	
  them	
  to	
  answer	
  the	
  following	
  question:	
  “Considering	
  the	
  current	
  
amount	
  of	
  land	
  that	
  you	
  dedicate	
  to	
  coffee	
  production,	
  would	
  you	
  be	
  willing	
  to	
  change	
  that	
  
land	
  to	
  one	
  that	
  takes	
  the	
  following	
  characteristics?”	
  at	
  this	
  point,	
  farmers	
  were	
  presented	
  
the	
  laminated	
  card	
  depicting	
  the	
  choice	
  sets	
  and	
  each	
  option	
  was	
  explained.	
  An	
  example	
  of	
  
a	
  choice	
  set	
  is	
  presented	
  in	
  Figure	
  10.	
  	
  	
  
	
  
	
  

Figure	
  10.	
  Example	
  of	
  Choice	
  Set	
  

	
  

	
  
	
  
	
  

	
  

65	
  

Results	
  and	
  Discussion	
  
Maximum	
  likelihood	
  estimates	
  for	
  a	
  random	
  parameter	
  logit	
  model	
  are	
  presented	
  in	
  Table	
  
9.	
  	
  The	
  significant	
  standard	
  deviation	
  coefficients	
  in	
  Table	
  8	
  in	
  the	
  RPL	
  indicate	
  that	
  coffee	
  
farmers	
  have	
  heterogeneous	
  preferences	
  with	
  respect	
  to	
  the	
  production	
  practices,	
  and	
  do	
  
not	
  derive	
  the	
  same	
  level	
  utility	
  from	
  the	
  same	
  attributes.	
  	
  
	
  
The	
  following	
  conclusions	
  are	
  presented	
  in	
  terms	
  of	
  the	
  utility	
  that	
  these	
  choices	
  generate	
  
for	
  the	
  respondents.	
  	
  Utility	
  is	
  here	
  defined	
  as	
  the	
  value	
  or	
  the	
  satisfaction	
  that	
  a	
  producer	
  
gains	
  from	
  the	
  attributes	
  in	
  the	
  choice	
  experiment.	
  I	
  find	
  that	
  pesticide	
  provision	
  has	
  a	
  
negative	
  effect	
  on	
  utility	
  for	
  farmers	
  in	
  Matagalpa,	
  but	
  that	
  when	
  it	
  is	
  provided	
  together	
  
with	
  fertilizer	
  the	
  effect	
  on	
  utility	
  is	
  positive	
  and	
  significant,	
  as	
  is	
  the	
  provision	
  of	
  fertilizer	
  
alone.	
  I	
  also	
  find	
  that	
  extension	
  services	
  have	
  a	
  positive	
  and	
  significant	
  utility	
  for	
  farmers	
  
and	
  that	
  labor	
  requirements	
  have	
  a	
  negative	
  and	
  significant	
  effect	
  on	
  utility.	
  	
  I	
  did	
  not	
  find	
  a	
  
significant	
  effect	
  on	
  utility	
  from	
  the	
  diversification	
  of	
  their	
  fields	
  with	
  alternative	
  coffee	
  
varieties	
  or	
  crops.	
  However,	
  as	
  seen	
  in	
  the	
  distribution	
  of	
  the	
  standard	
  deviations,	
  the	
  
preferences	
  on	
  crop	
  and	
  coffee	
  diversification	
  are	
  heterogeneous	
  in	
  the	
  sample,	
  confirming	
  
the	
  hypothesis	
  that	
  preferences	
  are	
  not	
  homogenous	
  across	
  coffee	
  producers	
  in	
  the	
  region.	
  	
  
I	
  also	
  find	
  evidence	
  of	
  preference	
  heterogeneity	
  (significant	
  standard	
  error	
  coefficients)	
  
regarding	
  input	
  subsidies,	
  extension	
  services,	
  and	
  labor	
  requirements.	
  	
  
	
  

	
  

66	
  

Table	
  9.	
  Parameter	
  Estimates	
  from	
  a	
  Random	
  Parameter	
  Logit	
  Model	
  
	
  
Income	
  
Pesticide	
  
Fertilizer	
  
Pesticide/Fertilizer	
  
Extension	
  
Labor	
  
Coffee	
  Div.	
  
Crop	
  Div.	
  

Coefficient	
  
Random	
  parameter	
  means	
  
0.031	
  
-­‐‑0.632	
  
0.300	
  
0.800	
  
0.422	
  
-­‐‑0.002	
  
0.169	
  
-­‐‑0.114	
  

Std.	
  Error	
  
0.003***	
  
0.146***	
  
0.126***	
  
0.130***	
  
0.187***	
  
0.001**	
  
0.158	
  
0.067**	
  

	
  
Random	
  parameter	
  standard	
  deviations	
  
Income	
  
0.021	
  
0.003***	
  
Pesticide	
  
0.383	
  
0.219**	
  
Fertilizer	
  
0.078	
  
0.250	
  
Pesticide/Fertilizer	
  
0.036	
  
0.257	
  
Extension	
  
1.465	
  
0.296***	
  
Labor	
  
0.003	
  
0.003	
  
Coffee	
  Div.	
  
1.028	
  
0.257***	
  
Crop	
  Div.	
  
0.513	
  
0.084***	
  
N	
  
1,100	
  
	
  
Log-­‐‑Likelihood	
  
-­‐‑891.9	
  
	
  
Adjusted	
  Pesudo	
  R-­‐‑Squared	
  
0.262	
  
	
  
AIC	
  
1,872	
  
	
  
Standard	
  errors	
  are	
  provided	
  for	
  each	
  coefficient:	
  *,	
  **,	
  ***	
  denotes	
  statistical	
  significance	
  at	
  the	
  0.10,	
  0.05,	
  and	
  
0.01	
  levels,	
  respectively.	
  	
  Income	
  represents	
  the	
  profit	
  variable,	
  Pesticide,	
  Fertilizer,	
  and	
  Pesticide/Fertilizer	
  are	
  
binary	
  variables	
  indicating	
  access	
  to	
  subsidized	
  pesticides,	
  fertilizers,	
  and	
  pesticides	
  and	
  fertilizers,	
  respectively,	
  
Extension	
  is	
  a	
  binary	
  variable	
  indicating	
  access	
  to	
  on	
  farm	
  extension	
  services,	
  Labor	
  is	
  a	
  binary	
  variable	
  that	
  
indicates	
  high	
  or	
  low	
  labor	
  requirements,	
  Coffee	
  Div.	
  is	
  a	
  binary	
  variable	
  that	
  indicates	
  the	
  presence	
  of	
  1	
  or	
  2	
  
coffee	
  varieties,	
  and	
  Crop	
  Div.	
  indicates	
  level	
  of	
  crop	
  diversity	
  richness.	
  	
  

	
  
Allowing	
  for	
  free	
  correlation	
  of	
  the	
  random	
  parameters	
  in	
  the	
  RPL	
  model	
  allows	
  us	
  to	
  
interpret	
  their	
  correlations.	
  	
  	
  The	
  correlation	
  matrix	
  presented	
  in	
  Table	
  9	
  shows	
  a	
  
significant	
  correlation	
  between	
  income	
  and	
  coffee	
  diversification	
  (0.45),	
  income	
  and	
  
pesticide	
  subsidies	
  (0.59),	
  and	
  income	
  and	
  labor	
  (0.84),	
  implying	
  that	
  farmers	
  who	
  value	
  
coffee	
  diversification,	
  pesticide	
  provision,	
  and	
  higher	
  labor	
  investments	
  are	
  also	
  motivated	
  
by	
  higher	
  returns	
  from	
  their	
  farms.	
  Similarly,	
  I	
  find	
  that	
  access	
  to	
  fertilizer	
  and	
  access	
  to	
  

	
  

67	
  

fertilizers	
  and	
  pesticides	
  together	
  are	
  negatively	
  correlated	
  with	
  income	
  (-­‐‑0.54	
  and	
  -­‐‑0.57	
  
respectively).	
  	
  There	
  was	
  no	
  significant	
  correlation	
  between	
  income	
  and	
  extension	
  services,	
  
and	
  income	
  and	
  crop	
  diversification.	
  	
  Although	
  correlation	
  does	
  not	
  ensure	
  causality,	
  I	
  can	
  
attempt	
  to	
  explain	
  some	
  of	
  these	
  relationships.	
  	
  For	
  example,	
  it	
  is	
  likely	
  that	
  lower	
  farm	
  
income	
  as	
  a	
  result	
  of	
  low	
  yields	
  leads	
  farmers	
  to	
  place	
  greater	
  value	
  on	
  the	
  provision	
  of	
  
yield-­‐‑boosting	
  fertilizers.	
  Similarly,	
  farmers	
  with	
  higher	
  incomes	
  appear	
  to	
  be	
  more	
  likely	
  
to	
  value	
  the	
  work	
  from	
  paid	
  labor.	
  
	
  
Table	
  10.	
  Cholesky	
  and	
  Correlation	
  Matrix	
  for	
  RPL	
  Model	
  
Cholesky	
  Matrix	
  
	
  
Income	
  (1)	
  
Pesticide	
  (2)	
  
Fertilizer	
  (3)	
  
Pesticide/Fertilizer	
  (4)	
  
Extension	
  (5)	
  
Labor	
  (6)	
  
Coffee	
  Div.	
  (7)	
  
Crop	
  Div.	
  (8)	
  

(1)	
  
0.287	
  

	
  
Correlation	
  Matrix	
  
	
  
Income	
  (1)	
  
Pesticide	
  (2)	
  
Fertilizer	
  (3)	
  
Pesticide/Fertilizer	
  (4)	
  
Extension	
  (5)	
  
Labor	
  (6)	
  
Coffee	
  Div.	
  (7)	
  
Crop	
  Div.	
  (8)	
  
	
  

	
  

(2)	
  

(3)	
  
	
  

(4)	
  

	
  

(5)	
  

(6)	
  

(7)	
  

(8)	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

0.022	
  

1.018	
  

	
  

-­‐‑0.011	
  

-­‐‑0.849	
  

0.301	
  

	
  

-­‐‑0.017	
  

-­‐‑0.959	
  

0.330	
  

0.302	
  

	
  

-­‐‑0.006	
  

-­‐‑1.145	
  

0.873	
  

-­‐‑0.176	
  

0.177	
  

	
  

0.000	
  

0.007	
  

-­‐‑0.004	
  

-­‐‑0.005	
  

0.001	
  

0.001	
  

	
  

0.017	
  

1.230	
  

-­‐‑0.596	
  

-­‐‑0.779	
  

-­‐‑0.885	
  

0.004	
  

0.310	
  

	
  

-­‐‑0.002	
  

0.193	
  

-­‐‑0.101	
  

-­‐‑0.106	
  

-­‐‑0.126	
  

0.000	
  

-­‐‑0.199	
  

0.253	
  

	
  

	
  

(1)	
  

	
  

(2)	
  

	
  

	
  

(3)	
  

	
  

(4)	
  

	
  

(5)	
  

	
  

	
  

(6)	
  

(7)	
  

(8)	
  

1	
  

	
  

0.599	
  

1	
  

	
  

-­‐‑0.541	
  

-­‐‑0.912	
  

1	
  

	
  

-­‐‑0.571	
  

-­‐‑0.749	
  

0.447	
  

1	
  

	
  

-­‐‑0.012	
  

-­‐‑0.548	
  

0.725	
  

-­‐‑0.106	
  

1	
  

	
  

0.837	
  

0.560	
  

-­‐‑0.576	
  

-­‐‑0.569	
  

0.083	
  

1	
  

	
  

0.427	
  

0.681	
  

-­‐‑0.572	
  

-­‐‑0.544	
  

-­‐‑0.379	
  

0.287	
  

1	
  

	
  

-­‐‑0.084	
  

0.242	
  

-­‐‑0.221	
  

-­‐‑0.168	
  

-­‐‑0.122	
  

-­‐‑0.049	
  

-­‐‑0.223	
  

1	
  

	
  

68	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  
	
  

The	
  derived	
  WTC	
  estimates,	
  presented	
  in	
  Table	
  10,	
  put	
  these	
  results	
  in	
  a	
  context	
  that	
  is	
  
easier	
  to	
  interpret.	
  	
  A	
  negative	
  WTC	
  coefficient	
  represents	
  the	
  income	
  premium	
  that	
  
farmers	
  would	
  need	
  to	
  receive	
  to	
  make	
  a	
  change,	
  while	
  a	
  positive	
  WTC	
  coefficient	
  
represents	
  how	
  much	
  income	
  a	
  farmer	
  would	
  be	
  willing	
  to	
  give	
  up	
  to	
  receive	
  a	
  good	
  or	
  
service.	
  	
  Non-­‐‑significant	
  coefficients	
  represents	
  changes	
  that	
  farmers	
  are	
  willing	
  to	
  make	
  
without	
  needing	
  any	
  incentives.	
  
	
  
Table	
  11.	
  Willingness	
  to	
  Change	
  Estimates	
  
Attribute	
  

Mean	
  

Confidence	
  Interval	
  

Pesticide	
  
Fertilizer	
  
Pesticide/Fertilizer	
  
Extension	
  
Labor	
  
Coffee	
  Div.	
  
Crop	
  Div.	
  

-­‐‑27.93	
  
13.33	
  
35.66	
  
21.87	
  
-­‐‑0.10	
  
4.25	
  
-­‐‑5.19	
  

[-­‐‑35.43,	
  0.75]	
  
[-­‐‑2.64,	
  17.35]	
  
[19.31,	
  35.88]	
  
[-­‐‑18.79,	
  34.59]	
  
[-­‐‑0.14,	
  -­‐‑0.08]	
  
[-­‐‑9.82,	
  58.78]	
  
[-­‐‑8.86,	
  3.81]	
  

Pesticide,	
  Fertilizer,	
  and	
  Pesticide/Fertilizer	
  are	
  binary	
  variables	
  indicating	
  access	
  to	
  subsidized	
  pesticides,	
  
fertilizers,	
  and	
  pesticides	
  and	
  fertilizers,	
  respectively,	
  Extension	
  is	
  a	
  binary	
  variable	
  indicating	
  access	
  to	
  on	
  
farm	
  extension	
  services,	
  Labor	
  is	
  a	
  binary	
  variable	
  that	
  indicates	
  high	
  or	
  low	
  labor	
  requirements,	
  Coffee	
  Div.	
  is	
  
a	
  binary	
  variable	
  that	
  indicates	
  the	
  presence	
  of	
  1	
  or	
  2	
  coffee	
  varieties,	
  and	
  Crop	
  Div.	
  indicates	
  level	
  of	
  crop	
  
diversity	
  richness.	
  

	
  
Our	
  model	
  results	
  reveal	
  that	
  coffee	
  producers	
  from	
  Matagalpa	
  require	
  a	
  premium	
  of	
  
42.65USD	
  per	
  hectare	
  of	
  coffee	
  (5.19%	
  of	
  their	
  annual	
  coffee	
  income)	
  to	
  introduce	
  an	
  
additional	
  shade	
  crop	
  into	
  their	
  coffee	
  fields,	
  but	
  they	
  are	
  willing	
  to	
  accept	
  a	
  34.9USD	
  
discount	
  (per	
  hectare	
  of	
  coffee)	
  to	
  introduce	
  an	
  additional	
  coffee	
  variety	
  in	
  their	
  fields.	
  
These	
  results	
  suggest	
  that	
  while	
  farmers	
  are	
  willing	
  to	
  give	
  up	
  part	
  of	
  their	
  income	
  to	
  adopt	
  
new	
  coffee	
  varieties	
  in	
  their	
  fields,	
  they	
  would	
  require	
  a	
  premium	
  before	
  introducing	
  an	
  
additional	
  shade	
  crop	
  into	
  their	
  fields.	
  
	
  
	
  

69	
  

These	
  results	
  are	
  consistent	
  with	
  observations	
  made	
  by	
  the	
  researchers	
  in	
  the	
  field,	
  where	
  
they	
  note	
  farmers	
  actively	
  seeking	
  out	
  newer	
  coffee	
  varieties	
  resistant	
  to	
  the	
  leaf	
  rust	
  pest,	
  
which	
  suffered	
  a	
  major	
  outbreak	
  in	
  the	
  region	
  2012	
  and	
  farmers	
  reported	
  yield	
  losses	
  of	
  up	
  
to	
  60	
  percent.	
  	
  Leaf	
  rust	
  attacks	
  mostly	
  Arabica	
  varieties	
  and	
  farmers	
  have	
  been	
  
systematically	
  uprooting	
  their	
  Arabica	
  coffee	
  and	
  replacing	
  them	
  with	
  varieties	
  of	
  coffee	
  
that	
  are	
  resistant	
  to	
  the	
  leaf	
  rust.	
  These	
  big	
  losses,	
  compounded	
  with	
  recent	
  droughts	
  that	
  
have	
  impacted	
  food	
  crops	
  (bean	
  and	
  maize),	
  have	
  meant	
  that	
  farmers	
  have	
  been	
  looking	
  for	
  
coping	
  mechanisms	
  that	
  help	
  to	
  mitigate	
  some	
  of	
  these	
  shocks	
  (income	
  loss	
  from	
  coffee	
  
failures,	
  and	
  food	
  loss	
  from	
  droughts).	
  	
  Included	
  in	
  this	
  coping	
  strategy	
  is	
  the	
  establishment	
  
of	
  varieties	
  of	
  coffee	
  resistant	
  to	
  the	
  leaf	
  rust,	
  the	
  Catimor	
  coffee	
  tree	
  is	
  the	
  most	
  commonly	
  
cited	
  hybrid	
  variety	
  that	
  farmers	
  are	
  planting.	
  	
  
	
  
Why	
  do	
  farmers	
  require	
  a	
  premium	
  to	
  establish	
  new	
  shade	
  trees?	
  It	
  is	
  commonly	
  assumed	
  
that	
  shaded	
  plantations	
  are	
  less	
  profitable,	
  but	
  this	
  is	
  an	
  assumption	
  that	
  is	
  often	
  based	
  on	
  
incomplete	
  cost-­‐‑benefit	
  data.	
  	
  For	
  the	
  most	
  part,	
  the	
  productivity	
  of	
  coffee	
  is	
  used	
  as	
  an	
  
indicator	
  for	
  profitability,	
  which	
  is	
  assumed	
  to	
  be	
  lower	
  for	
  shaded	
  coffee	
  fields.	
  	
  These	
  
calculations,	
  however,	
  do	
  not	
  account	
  for	
  the	
  different	
  costs	
  of	
  production,	
  the	
  quality	
  
differential,	
  nor	
  for	
  the	
  direct	
  and	
  indirect	
  benefits	
  that	
  shaded	
  trees	
  provide.	
  (Jezeer	
  and	
  
Verweij,	
  2015).	
  Although	
  many	
  farmers	
  in	
  the	
  sample	
  express	
  that	
  they	
  liked	
  having	
  
additional	
  crop	
  trees	
  in	
  their	
  coffee	
  fields,	
  they	
  mostly	
  spoke	
  of	
  only	
  needing	
  a	
  handful	
  of	
  
these	
  trees,	
  and	
  do	
  not	
  wish	
  to	
  have	
  a	
  coffee	
  system	
  with	
  managed	
  shade,	
  which	
  would	
  
optimize	
  their	
  field	
  (economically	
  and/or	
  environmentally).	
  	
  
	
  

	
  

70	
  

I	
  find	
  that	
  farmers	
  are	
  willing	
  to	
  accept	
  a	
  179.7USD	
  discount	
  per	
  hectare	
  of	
  coffee	
  in	
  order	
  
to	
  have	
  access	
  to	
  on	
  farm	
  extension	
  services.	
  	
  This	
  brings	
  to	
  light	
  how	
  important	
  these	
  
services	
  are	
  for	
  smallholder	
  farmers.	
  	
  Extension	
  services	
  can	
  play	
  a	
  pivotal	
  role	
  in	
  helping	
  
farmers	
  build	
  adaptive	
  capacity	
  to	
  climate	
  change	
  by	
  holding	
  trainings,	
  providing	
  on-­‐‑farm	
  
recommendations,	
  and	
  by	
  sharing	
  information	
  about	
  markets	
  (Agrawal	
  and	
  Perrin,	
  2009).	
  	
  
Additionally,	
  many	
  farmers	
  rely	
  on	
  these	
  services	
  for	
  recommendations	
  on	
  farm	
  
management	
  and	
  to	
  learn	
  about	
  climatic	
  and	
  market	
  forecasts.	
  	
  Farmers	
  in	
  the	
  sample	
  
require	
  a	
  premium	
  of	
  8.21USD	
  per	
  person	
  per	
  day	
  before	
  they	
  will	
  double	
  their	
  labor	
  
dedicated	
  to	
  coffee.	
  Smallholder	
  producers	
  often	
  rely	
  on	
  their	
  household	
  labor	
  to	
  manage	
  
their	
  farms;	
  in	
  Nicaragua,	
  hired	
  labor	
  for	
  coffee	
  production	
  is	
  very	
  common	
  (Valkila	
  and	
  
Nygreen,	
  2010).	
  Unless	
  coffee	
  farms	
  yield	
  higher	
  returns	
  households	
  may	
  choose	
  to	
  sell	
  
their	
  labor	
  instead	
  of	
  investing	
  it	
  in	
  their	
  own	
  farms.	
  
	
  
Finally,	
  regarding	
  the	
  provision	
  of	
  inputs,	
  I	
  find	
  that	
  farmers	
  are	
  willing	
  to	
  accept	
  110USD	
  
and	
  293USD	
  discounts	
  in	
  exchange	
  for	
  subsidized	
  fertilizers	
  and	
  fertilizers	
  and	
  pesticides	
  
together	
  respectively.	
  Yet,	
  they	
  would	
  require	
  a	
  premium	
  of	
  229USD	
  to	
  accept	
  pesticides	
  
provided	
  through	
  a	
  subsidy.	
  The	
  premium	
  associated	
  with	
  subsidized	
  pesticides	
  can	
  be	
  
explained	
  by	
  the	
  failure	
  of	
  organizations	
  to	
  adequately	
  respond	
  to	
  the	
  leaf	
  rust	
  epidemic.	
  	
  A	
  
large	
  segment	
  of	
  farmers	
  in	
  this	
  study	
  expressed	
  their	
  discontent	
  with	
  the	
  effectiveness	
  of	
  
the	
  pesticides	
  provided	
  by	
  private	
  and	
  public	
  extension	
  officers;	
  they	
  experienced	
  even	
  
greater	
  coffee	
  losses	
  when	
  they	
  applied	
  pesticides	
  that	
  were	
  ineffective	
  at	
  treating	
  leaf	
  rust,	
  
because	
  their	
  coffee	
  trees	
  became	
  even	
  weaker	
  and	
  were	
  still	
  vulnerable	
  to	
  the	
  rust.,	
  in	
  
some	
  cases	
  with	
  complete	
  crop	
  failures.	
  Following	
  a	
  season	
  of	
  devastating	
  losses,	
  many	
  

	
  

71	
  

farmers	
  in	
  the	
  sample	
  pulled	
  out	
  their	
  coffee	
  trees	
  (Arabica	
  variety)	
  and	
  replaced	
  them	
  
with	
  hybrid	
  varieties	
  that	
  they	
  believed	
  would	
  be	
  more	
  resistant	
  to	
  leaf	
  rust.	
  	
  This	
  premium	
  
for	
  pesticides	
  suggests	
  that	
  farmers	
  are	
  seeking	
  compensation	
  for	
  yield	
  losses	
  in	
  previous	
  
years	
  induced	
  by	
  the	
  failures	
  of	
  proper	
  pesticides	
  to	
  prevent	
  the	
  treatment	
  and	
  the	
  spread	
  
of	
  the	
  leaf	
  rust.	
  
	
  
Although	
  farmers	
  indicate	
  they	
  would	
  need	
  a	
  premium	
  to	
  accept	
  pesticides,	
  this	
  effect	
  is	
  
erased	
  when	
  pesticides	
  are	
  offered	
  together	
  with	
  fertilizers.	
  	
  Fertilizers	
  play	
  an	
  important	
  
role	
  in	
  the	
  production	
  cycle	
  of	
  the	
  coffee	
  tree	
  as	
  they,	
  together	
  with	
  other	
  practices	
  (such	
  as	
  
pruning),	
  help	
  with	
  the	
  healthy	
  development	
  of	
  the	
  fruit	
  (Van	
  der	
  Vossen,	
  2005).	
  	
  
	
  

Conclusions	
  
The	
  earth’s	
  climate	
  is	
  changing	
  at	
  an	
  alarming	
  rate,	
  and	
  these	
  changes	
  will	
  not	
  only	
  result	
  in	
  
biodiversity	
  loss	
  but	
  will	
  also	
  have	
  dire	
  consequences	
  for	
  the	
  livelihoods	
  of	
  people	
  around	
  
the	
  world	
  (Cardinale	
  et	
  al.,	
  2012).	
  	
  In	
  particular,	
  these	
  changes	
  have	
  a	
  direct	
  impact	
  on	
  the	
  
livelihoods	
  of	
  rural	
  coffee	
  producing	
  households,	
  many	
  of	
  which	
  will	
  surely	
  witness	
  a	
  
future	
  decline	
  in	
  the	
  suitability	
  of	
  their	
  agroecology	
  for	
  coffee	
  production,	
  and	
  will	
  
experience	
  additional	
  shocks	
  to	
  their	
  food	
  security	
  and	
  wellbeing	
  as	
  a	
  result	
  of	
  these	
  
climatic	
  events.	
  	
  Expediency	
  is	
  needed	
  in	
  responding	
  to	
  these	
  impacts	
  by	
  protecting	
  and	
  
improving	
  the	
  conditions	
  under	
  which	
  biodiversity	
  can	
  flourish.	
  
	
  
How	
  farmers	
  in	
  Nicaragua	
  will	
  adapt	
  to	
  climate	
  driven	
  changes	
  in	
  the	
  country’s	
  suitability	
  
for	
  coffee	
  production,	
  is	
  a	
  top	
  priority	
  for	
  policy-­‐‑makers.	
  	
  This	
  study	
  provides	
  an	
  innovative	
  

	
  

72	
  

approach	
  to	
  studying	
  the	
  incentives	
  of	
  coffee	
  farmers	
  to	
  adopt	
  practices	
  that	
  will	
  help	
  them	
  
to	
  build	
  adaptive	
  capacity	
  in	
  response	
  to	
  these	
  changes.	
  Discrete	
  choice	
  experiments	
  are	
  
used	
  to	
  examine	
  farmers’	
  preferences	
  for	
  crop	
  diversification	
  in	
  coffee	
  farming	
  systems,	
  
and	
  to	
  estimate	
  these	
  preferences	
  using	
  a	
  random	
  parameter	
  model	
  that	
  captures	
  the	
  
heterogeneity	
  of	
  farmer	
  preferences	
  at	
  the	
  individual	
  level.	
  	
  
	
  
Unless	
  farmers	
  value	
  the	
  services	
  that	
  shade	
  grown	
  coffee	
  provides	
  they	
  will	
  be	
  unwilling	
  
to	
  incorporate	
  it	
  into	
  their	
  farms.	
  I	
  find	
  that	
  farmers	
  in	
  Nicaragua	
  would	
  need	
  a	
  premium	
  to	
  
add	
  additional	
  shade	
  to	
  their	
  farms,	
  so	
  an	
  important	
  question	
  to	
  explore	
  in	
  future	
  research	
  
is	
  how	
  and	
  for	
  what	
  reasons	
  farmers	
  value	
  the	
  services	
  that	
  shade	
  provides?	
  Do	
  they	
  value	
  
it	
  for	
  the	
  ecosystem	
  services	
  that	
  they	
  provide	
  or	
  do	
  they	
  value	
  its	
  potential	
  for	
  alternative	
  
sources	
  of	
  food	
  and	
  income?	
  	
  In	
  this	
  research,	
  we	
  explore	
  the	
  benefits	
  of	
  shade	
  in	
  light	
  of	
  its	
  
potential	
  to	
  help	
  farmers	
  become	
  more	
  resilient	
  to	
  climate	
  change,	
  yet,	
  we	
  know	
  that	
  there	
  
are	
  other	
  benefits	
  that	
  shade	
  trees	
  can	
  provide,	
  giving	
  farmers	
  other	
  reasons	
  for	
  choosing	
  
to	
  use	
  shade,	
  and	
  understanding	
  the	
  benefits	
  that	
  shade	
  provides	
  to	
  the	
  farmers	
  an	
  
important	
  question	
  to	
  explore.	
  
	
  
The	
  devastating	
  impacts	
  of	
  leaf	
  rust	
  in	
  Nicaragua	
  and	
  the	
  rest	
  of	
  Central	
  America	
  have	
  led	
  
farmers	
  to	
  respond	
  in	
  unexpected	
  ways.	
  Farmers	
  are	
  not	
  willing	
  to	
  give	
  up	
  income	
  to	
  have	
  
access	
  to	
  subsidized	
  pesticides	
  from	
  the	
  government	
  or	
  other	
  stakeholders	
  in	
  the	
  coffee	
  
value	
  chain.	
  To	
  the	
  contrary,	
  they	
  require	
  a	
  price	
  premium	
  before	
  they	
  will	
  accept	
  such	
  
subsidies.	
  	
  This	
  speaks	
  to	
  potential	
  issues	
  of	
  trust	
  between	
  farmers	
  and	
  organizations	
  
supporting	
  farmers	
  in	
  Nicaragua.	
  	
  Coffee	
  producers	
  expressed	
  frustration	
  and	
  mistrust	
  of	
  

	
  

73	
  

organizations	
  that	
  were	
  not	
  able	
  to	
  properly	
  support	
  them	
  when	
  the	
  rust	
  outbreak	
  
occurred.	
  Climate	
  change	
  science	
  is	
  a	
  somewhat	
  new	
  field	
  of	
  study,	
  and	
  adaptation	
  to	
  
climate	
  change	
  an	
  emerging	
  field	
  of	
  research.	
  	
  There	
  is	
  no	
  doubt	
  that	
  the	
  path	
  towards	
  
adaptive	
  capacity	
  will	
  be	
  marked	
  with	
  false	
  starts,	
  and	
  as	
  new	
  interventions	
  are	
  introduced	
  
organizations	
  must	
  take	
  into	
  consideration	
  the	
  possibility	
  of	
  failure	
  and	
  be	
  transparent	
  
with	
  farmers	
  about	
  the	
  inherent	
  risks	
  of	
  such	
  failure.	
  	
  	
  
	
  
An	
  important	
  area	
  for	
  future	
  research	
  lies	
  in	
  the	
  question	
  of	
  how	
  relationships	
  are	
  built	
  and	
  
where	
  markets	
  and	
  organizations	
  are	
  failing	
  farmers	
  when	
  they	
  experience	
  natural	
  shocks,	
  
such	
  as	
  pest	
  and	
  disease	
  outbreaks	
  or	
  extended	
  periods	
  of	
  drought.	
  	
  This	
  research	
  will	
  be	
  
particularly	
  germane	
  given	
  the	
  likelihood	
  that	
  such	
  shocks	
  will	
  occur	
  at	
  higher	
  rates	
  and	
  
with	
  potentially	
  higher	
  intensities	
  as	
  the	
  grip	
  of	
  climate	
  changes	
  tightens.	
  	
  To	
  prepare	
  for	
  
these	
  events,	
  all	
  stakeholders	
  in	
  the	
  coffee	
  sector,	
  and	
  beyond,	
  need	
  to	
  understand	
  how	
  and	
  
when	
  farmers	
  react	
  to	
  these	
  shocks	
  and	
  how	
  to	
  build	
  effective	
  pathways	
  for	
  their	
  solution.	
  	
  
	
  
This	
  study	
  also	
  highlights	
  the	
  need	
  for	
  support	
  and	
  collaboration	
  among	
  coffee	
  sector	
  
stakeholders	
  and	
  other	
  groups	
  that	
  wish	
  to	
  promote	
  biodiversity	
  conservation	
  and	
  
environmental	
  sustainability.	
  Stakeholders	
  in	
  both	
  groups	
  must	
  heed	
  coffee	
  producers’	
  call	
  
and	
  understand	
  that	
  unless	
  farmers	
  have	
  the	
  proper	
  incentives	
  to	
  invest	
  in	
  production	
  
practices	
  that	
  will	
  conserve	
  the	
  ecological	
  integrity	
  of	
  coffee	
  fields	
  (through	
  shade	
  and	
  
other	
  practices),	
  they	
  will	
  make	
  decisions	
  based	
  entirely	
  on	
  the	
  financial	
  utility	
  of	
  their	
  
coffee	
  plantations,	
  which	
  often	
  means	
  intensifying	
  their	
  production	
  by	
  increasing	
  the	
  use	
  of	
  

	
  

74	
  

chemical	
  inputs	
  and	
  monocultural	
  production,	
  or	
  in	
  the	
  worst	
  case	
  scenario,	
  abandon	
  their	
  
coffee	
  fields	
  altogether.	
  
	
  
Study	
  findings	
  also	
  demonstrate	
  that	
  there	
  is	
  significant	
  heterogeneity	
  of	
  preferences	
  
amongst	
  farmers,	
  and	
  organizations	
  that	
  wish	
  to	
  help	
  farmers	
  build	
  adaptive	
  capacity	
  to	
  
climate	
  change,	
  while	
  also	
  promoting	
  biodiversity	
  conservation,	
  must	
  take	
  this	
  into	
  
consideration	
  when	
  designing	
  interventions	
  that	
  promote	
  crop	
  diversification,	
  and	
  
measures	
  that	
  help	
  farmers	
  build	
  adaptive	
  capacity	
  to	
  climate	
  change.	
  	
  	
  
	
  
Although	
  I	
  employ	
  in	
  this	
  research	
  a	
  quantitative	
  method	
  for	
  studying	
  producers’	
  
preferences	
  for	
  crop	
  diversification,	
  I	
  recognize	
  that	
  a	
  qualitative	
  approach	
  to	
  
understanding	
  the	
  implicit	
  tradeoffs	
  involved	
  in	
  the	
  decisions	
  farmers	
  make,	
  as	
  well	
  as	
  the	
  
barriers	
  and	
  contexts	
  in	
  which	
  they	
  make	
  those	
  decisions,	
  is	
  needed	
  to	
  confirm	
  and	
  
validate	
  the	
  initial	
  interpretations	
  of	
  the	
  data.	
  	
  Additionally,	
  providing	
  tools	
  to	
  enhance	
  
farmers’	
  understanding	
  of	
  the	
  ecological	
  complexity	
  of	
  shaded	
  coffee	
  fields	
  and	
  the	
  diverse	
  
ecosystem	
  services	
  that	
  they	
  provide	
  may	
  prove	
  to	
  be	
  useful	
  for	
  researchers	
  and	
  
practitioners	
  committed	
  to	
  incentivizing	
  coffee	
  farmers	
  to	
  adopt	
  practices	
  that	
  will	
  
promote	
  greater	
  sustainability	
  in	
  the	
  sector.	
  	
  
	
  
The	
  livelihoods	
  of	
  hundreds	
  of	
  thousands	
  of	
  coffee	
  producers	
  around	
  the	
  world	
  are	
  at	
  risk	
  
due	
  to	
  the	
  threat	
  climate	
  change	
  poses	
  to	
  the	
  suitability	
  of	
  coffee	
  producing	
  regions	
  to	
  
continue	
  to	
  grow	
  coffee.	
  At	
  the	
  same	
  time,	
  biodiversity	
  richness	
  is	
  decreasing	
  in	
  coffee	
  
regions	
  that	
  are	
  transitioning	
  towards	
  more	
  intensive	
  production.	
  While	
  this	
  study	
  

	
  

75	
  

provides	
  new	
  insights	
  into	
  how	
  coffee	
  producers	
  value	
  key	
  attributes	
  related	
  to	
  coffee	
  
production	
  in	
  Nicaragua,	
  this	
  approach	
  and	
  the	
  insights	
  from	
  this	
  study	
  can	
  be	
  easily	
  
adapted	
  for	
  use	
  in	
  other	
  coffee	
  growing	
  regions	
  of	
  the	
  world	
  and	
  even	
  to	
  other	
  crops,	
  such	
  
as	
  cacao,	
  which	
  grown	
  under	
  similar	
  agro-­‐‑ecological	
  conditions.	
  
	
  
	
  
	
  
	
  
	
  

	
  

	
  

76	
  

	
  

	
  

REFERENCES	
  

	
  

77	
  

REFERENCES	
  
	
  
	
  
	
  
	
  
Agrawal,	
  Arun,	
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  Perrin.	
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  adaptation,	
  local	
  institutions	
  and	
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livelihoods."	
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367.	
  
	
  
Armbrecht,	
  Inge,	
  Ivette	
  Perfecto,	
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  John	
  Vandermeer.	
  "Enigmatic	
  biodiversity	
  
correlations:	
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  Science	
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284-­‐‑286.	
  
	
  
Avelino,	
  Jacques,	
  et	
  al.	
  "The	
  coffee	
  rust	
  crises	
  in	
  Colombia	
  and	
  Central	
  America	
  (2008–
2013):	
  impacts,	
  plausible	
  causes	
  and	
  proposed	
  solutions."Food	
  Security	
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303-­‐‑321.	
  
	
  
Baca,	
  María,	
  Peter	
  Läderach,	
  Jeremy	
  Haggar,	
  GÜtz	
  Schroth,	
  and	
  Oriana	
  Ovalle.	
  "An	
  
integrated	
  framework	
  for	
  assessing	
  vulnerability	
  to	
  climate	
  change	
  and	
  developing	
  
adaptation	
  strategies	
  for	
  coffee	
  growing	
  families	
  in	
  Mesoamerica."	
  PloS	
  one	
  9,	
  no.	
  2	
  
(2014):	
  e88463.	
  
	
  
Bennett,	
  Jeff,	
  and	
  Ekin	
  Birol,	
  eds.	
  Choice	
  experiments	
  in	
  developing	
  countries:	
  
Implementation,	
  challenges	
  and	
  policy	
  implications.	
  Cheltenham:	
  Edward	
  Elgar,	
  
2010.	
  
	
  
Birol,	
  Ekin,	
  Eric	
  Rayn	
  Villalba,	
  and	
  Melinda	
  Smale.	
  "Farmer	
  preferences	
  for	
  milpa	
  diversity	
  
and	
  genetically	
  modified	
  maize	
  in	
  Mexico:	
  a	
  latent	
  class	
  approach."	
  Environment	
  and	
  
Development	
  Economics	
  14.04	
  (2009):	
  521-­‐‑540.	
  
	
  
Borrero	
  H,	
  JosÊ	
  Ignacio.	
  "La	
  substitución	
  de	
  cafetales	
  de	
  sombrío	
  por	
  caturrales	
  y	
  su	
  efecto	
  
negativo	
  sobre	
  la	
  fauna	
  de	
  vertebrados."	
  Caldasia(1986):	
  725-­‐‑732.	
  
	
  
Cardinale,	
  Bradley	
  J.,	
  et	
  al.	
  "Biodiversity	
  loss	
  and	
  its	
  impact	
  on	
  humanity."Nature	
  486.7401	
  
(2012):	
  59-­‐‑67.	
  
	
  
Cressey,	
  Daniel.	
  "Coffee	
  rust	
  regains	
  foothold."	
  Nature	
  493.7434	
  (2013):	
  587.	
  
	
  
Duflo,	
  Esther,	
  Michael	
  Kremer,	
  and	
  Jonathan	
  Robinson.	
  "Nudging	
  farmers	
  to	
  use	
  fertilizer:	
  
Theory	
  and	
  experimental	
  evidence	
  from	
  Kenya."	
  The	
  American	
  Economic	
  
Review	
  101.6	
  (2011):	
  2350-­‐‑2390.	
  
	
  
Dugger,	
  Celia	
  W.	
  "Ending	
  famine,	
  simply	
  by	
  ignoring	
  the	
  experts."	
  New	
  York	
  Times	
  2.12	
  
(2007):	
  07.	
  

	
  

78	
  

Feder,	
  Gershon,	
  Richard	
  E.	
  Just,	
  and	
  David	
  Zilberman.	
  "Adoption	
  of	
  agricultural	
  innovations	
  
in	
  developing	
  countries:	
  A	
  survey."	
  Economic	
  development	
  and	
  cultural	
  
change	
  (1985):	
  255-­‐‑298.	
  
	
  
Hernandez,	
  Sonia	
  M.,	
  et	
  al.	
  "Coffee	
  agroforests	
  remain	
  beneficial	
  for	
  Neotropical	
  bird	
  
community	
  conservation	
  across	
  seasons."	
  PloS	
  one	
  8.9	
  (2013):	
  e65101.	
  
	
  
Jezeer,	
  Rosalien,	
  and	
  Pita	
  Verweij.	
  "SHADE-­‐‑GROWN	
  COFFEE."	
  (2015).	
  
	
  
Laderach,	
  Peter,	
  Jeremy	
  Haggar,	
  Charlotte	
  Lau,	
  Anton	
  Eitzinger,	
  Oriana	
  Ovalle,	
  María	
  Baca,	
  
Andy	
  Jarvis,	
  and	
  Mark	
  Lundry.	
  "Mesoamerican	
  coffee:	
  building	
  a	
  climate	
  change	
  
adaptation	
  strategy."	
  (2013).	
  
	
  
Laderach,	
  Peter,	
  Mark	
  Lundy,	
  Andy	
  Jarvis,	
  Julian	
  Ramirez,	
  Emiliano	
  Perez	
  Portilla,	
  Kathleen	
  
Schepp,	
  and	
  Anton	
  Eitzinger.	
  "Predicted	
  impact	
  of	
  climate	
  change	
  on	
  coffee	
  supply	
  
chains."	
  In	
  The	
  Economic,	
  Social	
  and	
  Political	
  Elements	
  of	
  Climate	
  Change,	
  pp.	
  703-­‐‑
723.	
  Springer	
  Berlin	
  Heidelberg,	
  2011.	
  
	
  
Lancaster,	
  Kelvin	
  J.	
  "A	
  new	
  approach	
  to	
  consumer	
  theory."	
  The	
  journal	
  of	
  political	
  
economy	
  (1966):	
  132-­‐‑157.	
  
	
  
Maddison,	
  David	
  J.	
  "The	
  perception	
  of	
  and	
  adaptation	
  to	
  climate	
  change	
  in	
  Africa."	
  World	
  
Bank	
  Policy	
  Research	
  Working	
  Paper	
  4308	
  (2007).	
  
	
  
McFadden,	
  Daniel,	
  and	
  Kenneth	
  Train.	
  "Mixed	
  MNL	
  models	
  for	
  discrete	
  response."	
  Journal	
  
of	
  applied	
  Econometrics	
  15,	
  no.	
  5	
  (2000):	
  447-­‐‑470.	
  
	
  
Mendez,	
   V.	
   Ernesto,	
   et	
   al.	
   "Agrobiodiversity	
   and	
   Shade	
   Coffee	
   Smallholder	
   Livelihoods:	
   A	
  
Review	
  and	
  Synthesis	
  of	
  Ten	
  Years	
  of	
  Research	
  in	
  Central	
  America*."	
  The	
  Professional	
  
Geographer	
  62.3	
  (2010):	
  357-­‐‑376.	
  
	
  
Murrieta-­‐‑Galindo,	
  Rene,	
  et	
  al.	
  "Coffee	
  agrosystems:	
  an	
  important	
  refuge	
  for	
  amphibians	
  in	
  
central	
  Veracruz,	
  Mexico."	
  Agroforestry	
  systems	
  87.4	
  (2013):	
  767-­‐‑779.	
  
	
  
Nahuelhual,	
   Laura,	
   Maria	
   L.	
   Loureiro,	
   and	
   John	
   Loomis.	
   "Using	
   random	
   parameters	
   to	
  
account	
   for	
   heterogeneous	
   preferences	
   in	
   contingent	
   valuation	
   of	
   public	
   open	
  
space."	
  Journal	
  of	
  Agricultural	
  and	
  Resource	
  Economics	
  (2004):	
  537-­‐‑552.	
  
	
  
Ortega	
  D.L.,	
  Waldman	
  K.B.,	
  Richardson	
  R.B.,	
  Clay	
  D.C.,	
  Snapp	
  S.S.,	
  Sustainable	
  Intensification	
  
and	
  Farmer	
  Preferences	
  for	
  Crop	
  System	
  Attributes:	
  Evidence	
  from	
  Malawi’s	
  Central	
  
and	
  Southern	
  Regions”.	
  World	
  Development	
  (2016)	
  
	
  
Ovalle-­‐‑Rivera,	
  Oriana,	
  Peter	
  Läderach,	
  Christian	
  Bunn,	
  Michael	
  Obersteiner,	
  and	
  GÜtz	
  
Schroth.	
  "Projected	
  Shifts	
  in	
  Coffea	
  arabica	
  Suitability	
  among	
  Major	
  Global	
  
Producing	
  Regions	
  Due	
  to	
  Climate	
  Change."	
  (2015):	
  e0124155.	
  
	
  
	
  

79	
  

Perfecto,	
  Ivette,	
  and	
  John	
  Vandermeer.	
  Coffee	
  Agroecology:	
  A	
  new	
  approach	
  to	
  
understanding	
  agricultural	
  biodiversity,	
  ecosystem	
  services	
  and	
  sustainable	
  
development.	
  Routledge,	
  2015.	
  
	
  
Perfecto,	
  Ivette,	
  Armbrecht,	
  I.,	
  Philpott,	
  S.	
  M.,	
  Soto-­‐‑Pinto,	
  L.,	
  &	
  Dietsch,	
  T.	
  V.	
  "Shaded	
  coffee	
  
and	
  the	
  stability	
  of	
  rainforest	
  margins	
  in	
  northern	
  Latin	
  America."	
  Stability	
  of	
  
Tropical	
  Rainforest	
  Margins.	
  Springer	
  Berlin	
  Heidelberg,	
  2007.	
  225-­‐‑261.	
  
	
  
Perfecto,	
  Ivette,	
  John	
  Vandermeer,	
  and	
  Angus	
  Wright.	
  Nature's	
  matrix:	
  linking	
  agriculture,	
  
conservation	
  and	
  food	
  sovereignty.	
  Earthscan,	
  2009.	
  
	
  
Ponte,	
  Stefano.	
  "The	
  latte	
  revolution'?	
  Regulation,	
  markets	
  and	
  consumption	
  in	
  the	
  global	
  
coffee	
  chain."	
  World	
  development	
  30,	
  no.	
  7	
  (2002):	
  1099-­‐‑1122.	
  
	
  
Rigby,	
  Dan,	
  and	
  Michael	
  Burton.	
  "Preference	
  heterogeneity	
  and	
  GM	
  food	
  in	
  the	
  
UK."	
  European	
  Review	
  of	
  Agricultural	
  Economics	
  32,	
  no.	
  2	
  (2005):	
  269-­‐‑288.	
  
	
  
Saldaña-­‐‑Vázquez,	
  Romeo	
  A.,	
  et	
  al.	
  "Population	
  composition	
  and	
  ectoparasite	
  prevalence	
  on	
  
bats	
  (Sturnira	
  ludovici;	
  Phyllostomidae)	
  in	
  forest	
  fragments	
  and	
  coffee	
  plantations	
  
of	
  central	
  Veracruz,	
  Mexico."	
  Biotropica	
  45.3	
  (2013):	
  351-­‐‑356.	
  
	
  
Tittonell,	
  Pablo,	
  and	
  Ken	
  E.	
  Giller.	
  "When	
  yield	
  gaps	
  are	
  poverty	
  traps:	
  the	
  paradigm	
  of	
  
ecological	
  intensification	
  in	
  African	
  smallholder	
  agriculture."	
  Field	
  Crops	
  
Research	
  143	
  (2013):	
  76-­‐‑90.	
  
	
  
Valkila,	
  Joni,	
  and	
  Anja	
  Nygren.	
  "Impacts	
  of	
  Fair	
  Trade	
  certification	
  on	
  coffee	
  farmers,	
  
cooperatives,	
  and	
  laborers	
  in	
  Nicaragua."	
  Agriculture	
  and	
  Human	
  Values	
  27.3	
  
(2010):	
  321-­‐‑333.	
  
	
  
Valkila,	
  Joni.	
  "Fair	
  Trade	
  organic	
  coffee	
  production	
  in	
  Nicaragua—Sustainable	
  development	
  
or	
  a	
  poverty	
  trap?."	
  Ecological	
  Economics	
  68.12	
  (2009):	
  3018-­‐‑3025.	
  
	
  
van	
   Rikxoort,	
   Henk,	
   GÜtz	
   Schroth,	
   Peter	
   Läderach,	
   and	
   Beatriz	
   Rodríguez-­‐‑Sánchez.	
   "Carbon	
  
footprints	
  and	
  carbon	
  stocks	
  reveal	
  climate-­‐‑friendly	
  coffee	
  production."	
  Agronomy	
  for	
  
Sustainable	
  Development	
  (2014):	
  1-­‐‑11.	
  
	
  
Van	
  der	
  Vossen,	
  H.	
  A.	
  M.	
  "A	
  critical	
  analysis	
  of	
  the	
  agronomic	
  and	
  economic	
  sustainability	
  of	
  
organic	
  coffee	
  production."	
  Experimental	
  agriculture	
  41.04	
  (2005):	
  449-­‐‑473.	
  
	
  
Vermeulen,	
   Sonja	
   J.,	
   et	
   al.	
   "Addressing	
   uncertainty	
   in	
   adaptation	
   planning	
   for	
  
agriculture."	
  Proceedings	
  of	
  the	
  National	
  Academy	
  of	
  Sciences	
  110.21	
  (2013):	
  8357-­‐‑
8362.	
  
	
  
Wale,	
  Edilegnaw,	
  et	
  al.	
  "Economic	
  analysis	
  of	
  farmers'	
  preferences	
  for	
  coffee	
  variety	
  
attributes:	
  lessons	
  for	
  on-­‐‑farm	
  conservation	
  and	
  variety	
  adoption	
  in	
  
Ethiopia."	
  Quarterly	
  Journal	
  of	
  International	
  Agriculture	
  44.2	
  (2005):	
  121-­‐‑140.	
  
	
  

80	
  

Chapter	
  4:	
  Determinants	
  of	
  Adoption	
  of	
  Sustainable	
  Production	
  Practices	
  among	
  
Smallholder	
  Coffee	
  Producers	
  in	
  Nicaragua	
  
	
  

Introduction	
  
Efforts	
  to	
  slow	
  down	
  and	
  eventually	
  reverse	
  the	
  trend	
  of	
  climate	
  change	
  will	
  take	
  time,	
  and	
  
in	
  some	
  cases,	
  the	
  negative	
  impacts	
  of	
  climate	
  change	
  will	
  be	
  felt	
  long	
  before	
  long-­‐‑term	
  
solutions	
  to	
  this	
  problem	
  can	
  bear	
  fruit.	
  	
  Adaptation	
  and	
  mitigation	
  strategies	
  constitute	
  
the	
  front	
  line	
  of	
  attack	
  for	
  rural	
  households	
  in	
  developing	
  countries	
  that	
  rely	
  on	
  
agricultural	
  production	
  and	
  natural	
  resource	
  use	
  as	
  their	
  main	
  source	
  of	
  income	
  and	
  
growth,	
  and	
  whose	
  livelihoods	
  are	
  threatened	
  by	
  climate	
  change.	
  Amongst	
  these	
  strategies,	
  
the	
  adoption	
  of	
  sustainable	
  and	
  climate	
  smart	
  production	
  practices	
  have	
  been	
  identified	
  as	
  
critical	
  for	
  smallholder	
  producers,	
  but	
  it	
  is	
  uncertain	
  just	
  how	
  they	
  should	
  be	
  promoted	
  
(Laderach	
  et	
  al.,	
  2013).	
  Agricultural	
  organizations	
  and	
  cooperatives	
  have	
  a	
  long	
  history	
  in	
  
promoting	
  the	
  adoption	
  of	
  new	
  technologies	
  and	
  practices	
  among	
  smallholder	
  producers	
  
and	
  could	
  potentially	
  play	
  a	
  sizable	
  role	
  in	
  helping	
  farmers	
  to	
  build	
  better	
  adaptive	
  
capacity,	
  yet	
  further	
  research	
  is	
  needed	
  to	
  understand	
  how	
  and	
  the	
  degree	
  to	
  which	
  they	
  
contribute	
  to	
  specific	
  adaptation	
  and	
  mitigation	
  strategies	
  for	
  climate	
  change.	
  
	
  
The	
  impacts	
  of	
  climate	
  change	
  will	
  become	
  more	
  severe	
  with	
  time;	
  regions	
  of	
  the	
  world	
  
that	
  have	
  already	
  experienced	
  periods	
  of	
  seasonal	
  droughts	
  will	
  see	
  a	
  rise	
  in	
  the	
  frequency	
  
of	
  these	
  droughts.	
  Similarly,	
  some	
  areas	
  will	
  experience	
  severe	
  flooding	
  due	
  to	
  increased	
  
rainfall	
  occurrence.	
  	
  And	
  changes	
  in	
  global	
  temperatures	
  will	
  also	
  result	
  in	
  certain	
  areas	
  
becoming	
  no	
  longer	
  suitable	
  for	
  agricultural	
  production	
  (Fischer	
  et	
  al.,	
  2002).	
  	
  Perhaps	
  no	
  
population	
  is	
  more	
  vulnerable	
  to	
  the	
  consequences	
  of	
  these	
  changes	
  than	
  poor	
  agrarian	
  
	
  

81	
  

households	
  that	
  depend	
  on	
  crop	
  production	
  to	
  make	
  a	
  living.	
  	
  As	
  the	
  intensity	
  and	
  
frequency	
  of	
  these	
  events	
  increase,	
  households	
  can	
  experience	
  loss	
  of	
  assets	
  (such	
  as	
  
household	
  assets,	
  crops,	
  access	
  to	
  water,	
  and	
  loss	
  of	
  health)	
  and	
  are	
  left	
  with	
  less	
  time	
  to	
  
recover	
  from	
  the	
  previous	
  shocks,	
  resulting	
  in	
  severe	
  food	
  insecurity	
  (Laderach	
  et	
  al.,	
  2013;	
  
Vermeulen	
  et	
  al.,	
  2012;	
  Fischer	
  et	
  al.,	
  2002).	
  	
  Given	
  the	
  vulnerability	
  of	
  smallholder	
  farmers	
  
to	
  climate	
  change,	
  there	
  is	
  growing	
  consensus	
  that	
  the	
  research	
  and	
  development	
  on	
  
adaptation	
  strategies	
  will	
  become	
  increasingly	
  needed	
  (van	
  Rikxoort	
  et	
  al.,	
  2014),	
  and	
  that	
  
farmer	
  adoption	
  of	
  sustainable,	
  mitigating	
  technologies	
  and	
  practices	
  constitute	
  one	
  of	
  the	
  
critical	
  pieces	
  to	
  a	
  longer-­‐‑term	
  solution	
  (Nelson	
  et	
  al.,	
  2009).	
  Included	
  in	
  their	
  mitigation	
  
strategies	
  is	
  how	
  they	
  perceive	
  and	
  use	
  collective	
  organization	
  and	
  action	
  as	
  a	
  vehicle	
  for	
  
their	
  response.	
  	
  
	
  
Smallholder	
  farmers	
  are	
  also	
  facing	
  sizable	
  challenges	
  and	
  barriers	
  to	
  entering	
  profitable	
  
markets.	
  Amongst	
  these	
  barriers	
  are	
  costs	
  associated	
  with	
  poor	
  physical	
  infrastructure,	
  
such	
  as	
  a	
  lack	
  of	
  roads	
  or	
  transportation	
  networks,	
  a	
  lack	
  of	
  market	
  and	
  pricing	
  
information,	
  poor	
  access	
  to	
  inputs,	
  and	
  little	
  access	
  to	
  technical	
  support	
  and	
  training	
  
(Barrett,	
  2008).	
  Cooperatives	
  can	
  help	
  smallholders	
  to	
  overcome	
  some	
  of	
  these	
  barriers	
  
and	
  are	
  widely	
  recognized	
  in	
  agricultural	
  markets	
  as	
  an	
  effective	
  mechanism	
  that	
  brings	
  
together	
  smallholder	
  producers	
  who	
  wish	
  to	
  work	
  together	
  to	
  overcome	
  some	
  of	
  the	
  costs	
  
associated	
  with	
  market	
  participation.	
  In	
  addition,	
  cooperatives	
  have	
  better	
  bargaining	
  
power	
  and	
  can	
  extract	
  more	
  favorable	
  terms	
  of	
  trade	
  from	
  downstream	
  buyers	
  (Barrett,	
  
2008).	
  	
  Cooperatives	
  help	
  farmers	
  not	
  only	
  by	
  increasing	
  productivity	
  but	
  they	
  can	
  also	
  add	
  

	
  

82	
  

value	
  to	
  agricultural	
  products	
  through	
  processing.	
  	
  This	
  is	
  particularly	
  true	
  in	
  the	
  coffee	
  
industry	
  where	
  coffee	
  processing	
  facilities	
  are	
  cooperatively	
  owned	
  and	
  operated.	
  	
  
	
  
Investments	
  in	
  and	
  adoption	
  of	
  improved	
  technologies	
  and	
  production	
  practices	
  by	
  
smallholder	
  producers	
  require	
  significant	
  support	
  and	
  investment	
  from	
  the	
  public	
  and	
  
private	
  sectors	
  (Barrett,	
  2008).	
  	
  At	
  no	
  time	
  is	
  this	
  support	
  more	
  critical	
  than	
  when	
  
smallholder	
  farmers	
  experience	
  low	
  yields,	
  high	
  production	
  costs,	
  relatively	
  high	
  labor	
  
requirements	
  for	
  production,	
  and	
  face	
  unstable	
  prices	
  for	
  their	
  agricultural	
  products	
  
(Donovan	
  and	
  Poole,	
  2014),	
  all	
  of	
  which	
  are	
  likely	
  to	
  be	
  exacerbated	
  by	
  climate	
  change.	
  In	
  
many	
  instances,	
  the	
  formation	
  of	
  agricultural	
  cooperatives	
  have	
  been	
  successful	
  in	
  helping	
  
smallholders	
  farmers	
  to	
  overcome	
  many	
  of	
  these	
  barriers	
  through	
  the	
  dissemination	
  of	
  
inputs,	
  loans,	
  and	
  training	
  opportunities	
  (Abebaw	
  and	
  Haile,	
  2013),	
  and	
  can	
  play	
  an	
  
important	
  role	
  in	
  helping	
  farmers	
  to	
  transition	
  towards	
  production	
  practices	
  that	
  will	
  help	
  
them	
  build	
  adaptive	
  capacity	
  against	
  the	
  impacts	
  of	
  climate	
  change.	
  
	
  
In	
  a	
  study	
  of	
  agricultural	
  cooperatives	
  in	
  Ethiopia,	
  Abebaw	
  and	
  Haile	
  (2013)	
  find	
  that	
  
cooperatives	
  membership	
  has	
  a	
  strong	
  and	
  positive	
  effect	
  on	
  the	
  adoption	
  of	
  fertilizers	
  and	
  
that	
  their	
  members	
  have	
  better	
  access	
  to	
  extension	
  services.	
  	
  Cooperatives	
  can	
  also	
  play	
  an	
  
important	
  role	
  in	
  providing	
  financial	
  incentives	
  to	
  adopt	
  new	
  technologies;	
  in	
  a	
  study	
  by	
  
(Mounir	
  et	
  al.,	
  2016)	
  the	
  authors	
  find	
  that	
  incentives	
  in	
  the	
  form	
  of	
  payments	
  for	
  
agricultural-­‐‑environmental	
  services	
  can	
  increase	
  the	
  adoption	
  of	
  improved	
  technologies.	
  
Climate	
  change	
  and	
  increasing	
  environmental	
  pressures	
  are	
  pushing	
  demand	
  for	
  

	
  

83	
  

alternative	
  management	
  approaches	
  (Virapongse	
  et	
  al.,	
  2016).	
  Wollni	
  and	
  Zeller	
  (2007)	
  
find	
  that	
  cooperative	
  membership	
  has	
  a	
  positive	
  impact	
  in	
  income	
  and	
  the	
  adoption	
  of	
  
specialty	
  coffee	
  varieties	
  amongst	
  coffee	
  producers	
  in	
  Costa	
  Rica.	
  	
  Wollni	
  et	
  al.	
  (2010)	
  also	
  
find	
  that	
  smallholder	
  farmers	
  in	
  Honduras	
  who	
  participated	
  in	
  cooperatives	
  were	
  more	
  
likely	
  to	
  have	
  adopted	
  a	
  higher	
  number	
  of	
  soil	
  conservation	
  practices	
  than	
  farmers	
  who	
  did	
  
not	
  belong	
  to	
  cooperatives.	
  They	
  conclude	
  that	
  in	
  addition	
  to	
  all	
  the	
  technical	
  support	
  that	
  
cooperatives	
  offer	
  their	
  members,	
  they	
  are	
  also	
  important	
  in	
  increasing	
  the	
  odds	
  of	
  
adoption	
  of	
  sustainable	
  soil	
  management	
  practices.	
  	
  Other	
  studies	
  have	
  also	
  found	
  that	
  
cooperative	
  membership	
  is	
  a	
  significant	
  determinant	
  of	
  farmer	
  adoption	
  of	
  technologies	
  
and	
  improved	
  production	
  practices	
  (Verhofstadt	
  and	
  Maertens,	
  2014a;	
  Fischer	
  and	
  Qaim,	
  
2012).	
  	
  Another	
  set	
  of	
  studies	
  have	
  found	
  that	
  farmers	
  who	
  belong	
  to	
  farmers’	
  associations	
  
or	
  cooperatives	
  are	
  more	
  likely	
  to	
  have	
  higher	
  incomes	
  and	
  receive	
  higher	
  prices	
  for	
  their	
  
products	
  than	
  those	
  who	
  do	
  not	
  belong	
  (Verhofstadt	
  and	
  Maertens,	
  2014b;	
  Jena	
  et	
  al.,	
  
2012).	
  
	
  
Producers	
  in	
  regions	
  where	
  Arabica	
  coffee	
  is	
  produced	
  are	
  particularly	
  susceptible	
  to	
  a	
  
changing	
  climate	
  due	
  to	
  the	
  narrow	
  band	
  of	
  elevation	
  in	
  which	
  Arabica	
  coffees	
  can	
  be	
  
grown	
  and	
  the	
  fact	
  that	
  it	
  requires	
  3-­‐‑5	
  years	
  to	
  mature	
  and	
  that	
  significant	
  investment	
  is	
  
required	
  to	
  plant	
  and	
  maintain	
  coffee.	
  Moreover,	
  coffee	
  has	
  long	
  been	
  known	
  as	
  a	
  
commodity	
  product	
  with	
  a	
  large	
  “footprint”	
  in	
  poor,	
  often	
  mountainous	
  countries	
  in	
  the	
  
tropics,	
  and	
  as	
  a	
  leading	
  source	
  of	
  economic	
  growth	
  for	
  many	
  of	
  them.	
  On	
  a	
  global	
  scale	
  it	
  is	
  
recognized	
  as	
  one	
  of	
  the	
  most	
  traded	
  agricultural	
  commodities	
  (Ponte,	
  2002).	
  
	
  
	
  

84	
  

In	
  recent	
  decades,	
  the	
  productive	
  potential	
  of	
  the	
  coffee	
  growing	
  regions	
  has	
  become	
  
increasingly	
  compromised	
  by	
  the	
  impacts	
  of	
  climate	
  change	
  and	
  is	
  further	
  exacerbated	
  by	
  
the	
  intensification	
  of	
  agricultural	
  practices,	
  a	
  predictable	
  consequence	
  of	
  a	
  growing	
  global	
  
demand	
  for	
  coffee	
  and	
  increasingly	
  competitive	
  markets,	
  particularly	
  for	
  specialty	
  coffees	
  
(Donovan	
  and	
  Poole,	
  2014).	
  Intensification	
  often	
  involves	
  unsustainable	
  practices	
  –	
  such	
  as	
  
shifting	
  coffee	
  plantations	
  from	
  polycultural	
  to	
  monocultural	
  production	
  (van	
  Rikxoort	
  et	
  
al.,	
  2014)	
  and	
  the	
  overuse	
  of	
  toxic	
  chemical	
  inputs	
  that	
  can	
  have	
  dire	
  consequences	
  for	
  the	
  
agro-­‐‑ecological	
  composition	
  of	
  the	
  tropical	
  soils	
  (Perfecto	
  et	
  al.,	
  1996	
  and	
  2007).	
  In	
  
contrast,	
  there	
  are	
  a	
  number	
  of	
  sustainable	
  practices	
  that	
  can	
  help	
  farmers	
  become	
  more	
  
resilient	
  to	
  a	
  changing	
  climate.	
  	
  Shade	
  grown	
  coffee	
  production	
  helps	
  to	
  protect	
  the	
  bio-­‐‑
diversity	
  of	
  the	
  tropics,	
  store	
  carbon	
  from	
  the	
  atmosphere,	
  protect	
  watersheds	
  by	
  reducing	
  
run-­‐‑off,	
  and	
  prevent	
  erosion.	
  Integrated	
  pest	
  management	
  (IPM)	
  is	
  conducive	
  to	
  
minimizing	
  toxic	
  chemical	
  use	
  (Perfecto	
  et	
  al.,	
  2007;	
  Valkila,	
  2009).	
  Finally,	
  crop	
  
diversification	
  not	
  only	
  helps	
  to	
  protect	
  the	
  ecological	
  diversity	
  of	
  the	
  land	
  but	
  also,	
  as	
  
Mendez	
  et	
  al.	
  (2010)	
  find,	
  it	
  contributes	
  to	
  household	
  livelihoods	
  by	
  generating	
  products	
  
for	
  consumption	
  (food	
  security)	
  and	
  sales	
  (income	
  generation).	
  
Although	
  there	
  is	
  evidence	
  that	
  cooperatives	
  can	
  play	
  an	
  important	
  role	
  in	
  building	
  
sustainable	
  market	
  linkages	
  between	
  smallholders	
  and	
  intermediary	
  firms,	
  in	
  reaching	
  
quality	
  standards,	
  and	
  providing	
  training	
  and	
  financial	
  services	
  to	
  smallholder	
  farmers	
  
(Donovan	
  and	
  Poole,	
  2014;	
  Barrett,	
  2008),	
  there	
  are	
  important	
  questions	
  that	
  remain	
  
unanswered.	
  In	
  particular,	
  whether	
  producers	
  associated	
  with	
  cooperatives	
  are	
  better	
  
prepared	
  to	
  cope	
  with	
  the	
  effects	
  of	
  climate	
  change	
  is	
  a	
  question	
  of	
  notable	
  importance	
  that	
  
has	
  implications	
  for	
  coffee	
  sector	
  planning	
  and	
  policies.	
  And	
  a	
  better	
  understanding	
  of	
  
	
  

85	
  

farmers’	
  perceptions	
  of	
  climate	
  change,	
  their	
  adaptation	
  strategies,	
  and	
  their	
  decision-­‐‑
making	
  processes	
  is	
  needed	
  to	
  inform	
  policies	
  aimed	
  at	
  promoting	
  successful	
  adaptation	
  
strategies	
  for	
  the	
  coffee	
  sector.	
  	
  	
  
	
  
In	
  the	
  present	
  research	
  I	
  focus	
  on	
  the	
  decision	
  making	
  process	
  of	
  farmers	
  by	
  studying	
  the	
  
different	
  practices	
  that	
  they	
  have	
  adopted	
  on	
  their	
  farms.	
  More	
  specifically,	
  this	
  research	
  
aims	
  to	
  identify	
  the	
  determinants	
  of	
  the	
  adoption	
  of	
  sustainable	
  (adaptive	
  to	
  climate	
  
change)	
  coffee	
  production	
  practices	
  by	
  producers	
  in	
  Nicaragua,	
  and	
  to	
  examine	
  to	
  what	
  
extent	
  cooperative	
  members	
  and	
  non-­‐‑members	
  differ	
  in	
  their	
  adoption	
  of	
  those	
  practices.	
  	
  
The	
  hypothesis	
  that	
  I	
  test	
  in	
  this	
  study	
  is	
  that	
  cooperative	
  membership	
  is	
  a	
  positive	
  and	
  
significant	
  determinant	
  of	
  adoption	
  of	
  sustainable	
  coffee	
  production	
  practices.	
  
	
  
This	
  study	
  contributes	
  to	
  the	
  research	
  literature	
  by	
  looking	
  at	
  the	
  impact	
  of	
  cooperatives	
  
on	
  the	
  adoption	
  of	
  improved	
  practices	
  and	
  technologies	
  specifically	
  in	
  the	
  Matagalpa	
  region	
  
of	
  Nicaragua.	
  	
  It	
  addresses	
  the	
  research	
  question:	
  “does	
  cooperative	
  membership	
  increase	
  
the	
  probability	
  of	
  adoption	
  of	
  sustainable	
  production	
  practices	
  in	
  Nicaragua?”	
  	
  This	
  
question	
  is	
  not	
  only	
  of	
  crucial	
  interest	
  to	
  policy	
  makers,	
  cooperatives,	
  and	
  environmental	
  
agencies	
  that	
  wish	
  to	
  support	
  the	
  coffee	
  sector	
  in	
  its	
  struggle	
  against	
  the	
  potentially	
  
devastating	
  impacts	
  of	
  climate	
  change,	
  but	
  also	
  in	
  achieving	
  sustainable	
  growth	
  more	
  
generally.	
  
	
  

	
  

86	
  

Coffee	
  in	
  Nicaragua	
  
In	
  Latin	
  America,	
  coffee	
  is	
  the	
  main	
  source	
  of	
  income	
  for	
  more	
  than	
  1	
  million	
  farmers.	
  
Nicaragua	
  alone	
  has	
  48,000	
  farmers,	
  80%	
  of	
  which	
  are	
  small-­‐‑scale	
  coffee	
  producers	
  
(Valkila,	
  2009).	
  Moreover,	
  as	
  the	
  largest	
  national	
  export,	
  more	
  than	
  30,000	
  smallholder	
  
farmers	
  rely	
  on	
  its	
  production	
  as	
  their	
  principal	
  livelihood	
  (Laderach	
  et	
  al.,	
  2013).	
  	
  Arabica	
  
coffee,	
  the	
  main	
  variety	
  cultivated	
  in	
  the	
  region,	
  needs	
  ample	
  and	
  stable	
  rainfall	
  and	
  a	
  
narrow	
  interval	
  of	
  average	
  temperatures	
  (19-­‐‑22°C),	
  all	
  of	
  which	
  are	
  expected	
  to	
  change	
  in	
  
coffee	
  growing	
  regions	
  as	
  their	
  climate	
  changes	
  (Vermeulen	
  et	
  al.,	
  2013).	
  	
  
	
  
Nicaragua	
  is	
  one	
  of	
  the	
  countries	
  in	
  Mesoamerica	
  that	
  will	
  be	
  the	
  hardest	
  hit	
  by	
  the	
  impacts	
  
of	
  climate	
  change,	
  and	
  all	
  eyes	
  are	
  drawn	
  to	
  the	
  Matagalpa	
  coffee-­‐‑growing	
  region	
  where	
  the	
  
challenges	
  facing	
  coffee	
  producers	
  are	
  known	
  to	
  be	
  especially	
  daunting	
  (Laderach	
  et	
  al.,	
  
2011).	
  How	
  farmers	
  there	
  will	
  respond	
  to	
  a	
  potential	
  40-­‐‑60%	
  loss	
  of	
  agro-­‐‑climatic	
  
suitability	
  driven	
  by	
  a	
  predicted	
  2.2ºC	
  temperature	
  increase	
  and	
  a	
  130mm	
  decline	
  in	
  
precipitation	
  by	
  2050	
  (Ovalle-­‐‑Rivera	
  et	
  al.,	
  2015)	
  is	
  the	
  source	
  of	
  much	
  consternation	
  and	
  
debate	
  among	
  industry,	
  policy	
  and	
  scientific	
  circles	
  (Ovalle-­‐‑Rivera	
  et	
  al.,	
  2015;	
  Baca	
  et	
  al.,	
  
2014;	
  Laderach	
  et	
  al.,	
  2013)	
  
	
  
In	
  a	
  study	
  conducted	
  by	
  Baca	
  et	
  al.	
  (2014),	
  the	
  authors	
  find	
  that	
  coffee	
  farmers	
  in	
  Nicaragua	
  
have	
  seen	
  dramatic	
  changes	
  in	
  rainfall	
  patterns	
  over	
  the	
  past	
  20	
  years,	
  noting	
  in	
  particular	
  
the	
  longer	
  and	
  hotter	
  dry	
  seasons	
  and	
  shorter	
  and	
  more	
  erratic	
  rainy	
  seasons.	
  The	
  
estimated	
  income	
  loss	
  due	
  to	
  lower	
  suitability	
  and	
  production	
  is	
  estimated	
  at	
  US$74.7	
  
millions	
  in	
  2050	
  alone	
  (Laderach	
  et	
  al.,	
  2013).	
  

	
  

87	
  

The	
  consequences	
  of	
  suitability	
  loss	
  can	
  be	
  devastating	
  to	
  the	
  livelihoods	
  of	
  smallholder	
  
producers,	
  and	
  research	
  on	
  how	
  farmers	
  can	
  adapt	
  and	
  build	
  resilience	
  to	
  the	
  impacts	
  of	
  
climate	
  change	
  is	
  urgently	
  needed.	
  	
  	
  
	
  

Methodology	
  and	
  Data	
  	
  
This	
  study	
  was	
  conducted	
  in	
  the	
  department	
  of	
  Matagalpa	
  in	
  northern	
  Nicaragua	
  between	
  
June	
  and	
  July	
  2015.	
  	
  A	
  sample	
  of	
  236	
  households	
  was	
  selected	
  using	
  a	
  two	
  stage	
  stratified	
  
random	
  selection	
  strategy.	
  	
  First,	
  communities	
  in	
  Matagalpa	
  were	
  stratified	
  by	
  level	
  of	
  
vulnerability	
  to	
  climate	
  change.	
  	
  Vulnerability	
  was	
  determined	
  by	
  the	
  average	
  elevation	
  in	
  
which	
  the	
  community	
  was	
  located.	
  Communities	
  at	
  higher	
  elevation	
  (above	
  1000	
  meters	
  
above	
  sea	
  level)	
  had	
  a	
  lower	
  vulnerability	
  index	
  scores	
  than	
  those	
  at	
  lower	
  elevations,	
  as	
  
households	
  in	
  higher	
  elevations	
  will	
  be	
  less	
  affected	
  by	
  increased	
  temperatures.	
  In	
  this	
  first	
  
stage,	
  a	
  random	
  sample	
  of	
  communities	
  was	
  selected	
  based	
  on	
  their	
  vulnerability	
  index	
  
scores.	
  	
  In	
  the	
  second	
  stage,	
  households	
  in	
  each	
  of	
  the	
  selected	
  communities	
  were	
  drawn	
  
from	
  a	
  census	
  listing	
  of	
  coffee	
  producers	
  in	
  the	
  region.	
  	
  The	
  households	
  surveyed	
  in	
  this	
  
study	
  form	
  part	
  of	
  an	
  ongoing	
  project	
  on	
  climate	
  change	
  and	
  food	
  security	
  conducted	
  by	
  
the	
  Center	
  for	
  International	
  Tropical	
  Agriculture.	
  
	
  
Structured	
  surveys	
  were	
  conducted	
  with	
  236	
  coffee	
  producing	
  households,	
  and	
  
information	
  was	
  gathered	
  on	
  main	
  household	
  characteristics,	
  field-­‐‑level	
  and	
  production	
  
statistics,	
  cooperative	
  information,	
  and	
  perceptions	
  of	
  climate	
  change	
  and	
  its	
  impacts.	
  Of	
  
these	
  236	
  households,	
  14	
  were	
  dropped	
  from	
  the	
  analysis	
  because	
  the	
  household	
  head	
  was	
  
not	
  found,	
  and	
  the	
  respondent	
  (often	
  a	
  son	
  or	
  spouse)	
  did	
  not	
  provide	
  complete	
  or	
  reliable	
  

	
  

88	
  

information.	
  	
  In	
  addition,	
  10	
  households	
  were	
  dropped	
  from	
  the	
  analysis	
  due	
  to	
  missing	
  
data.	
  	
  After	
  accounting	
  for	
  missing	
  and	
  incomplete	
  data,	
  I212	
  cases	
  served	
  as	
  the	
  basis	
  for	
  
study.	
  	
  
	
  
Table	
  12.	
  Sample	
  Characteristics	
  
Variable	
  

Mean	
  
(%	
  Where	
  
Noted)	
  
65.4	
  
46.4	
  
5.4	
  
3.8	
  
16.5	
  
821.8	
  
5,648.3	
  
4.8	
  
9.7	
  

Male	
  (%)	
  
Age	
  	
  
Household	
  Size	
  
Years	
  of	
  Education	
  
Years	
  in	
  Coffee	
  
Total	
  Coffee	
  Income	
  (USD)	
  per	
  ha	
  
Total	
  Income	
  	
  
Area	
  Under	
  Coffee	
  Production	
  (ha)	
  	
  
Total	
  Coffee	
  Production	
  (q1)	
  per	
  ha	
  	
  

Cooperative	
  Member	
  
No	
  
Yes	
  
(n=129)	
  
(n=107)	
  
60.7	
  
39.3***	
  
44.5	
  
48.8***	
  
5.2	
  
5.4***	
  
3.7	
  
3.9***	
  
14.5	
  
16.5***	
  
719.5	
  
944.1***	
  
4,484.7	
  
7,051.1***	
  
2.9	
  
4.1***	
  
6.4	
  
7.2***	
  

11	
  Quintal=46kg;	
  Note:	
  *,	
  **,	
  ***,	
  indicates	
  significance	
  at	
  10%,	
  5%,	
  and	
  1%	
  level	
  of	
  significance	
  	
  

	
  
Characteristics	
  of	
  the	
  sample	
  are	
  presented	
  in	
  Table	
  11.	
  The	
  average	
  age	
  of	
  the	
  
respondents	
  is	
  46	
  years	
  old	
  with	
  an	
  average	
  of	
  3.8	
  years	
  of	
  formal	
  education	
  completed.	
  	
  
The	
  mean	
  area	
  under	
  production	
  is	
  3.4	
  manzanas	
  (2.4	
  hectares)	
  and	
  the	
  mean	
  annual	
  
coffee	
  production	
  is	
  6.8	
  quintales	
  of	
  wet	
  parchment	
  (312.8kg).	
  	
  Forty-­‐‑six	
  percent	
  of	
  the	
  
sample	
  belongs	
  to	
  a	
  coffee	
  cooperative	
  and	
  65%	
  are	
  male-­‐‑headed	
  households.	
  I	
  see	
  that	
  
there	
  are	
  differences	
  between	
  the	
  composition	
  of	
  the	
  sample	
  with	
  respect	
  to	
  cooperative	
  
membership,	
  amongst	
  these	
  differences	
  gender	
  composition	
  emerges	
  as	
  significant,	
  with	
  a	
  
higher	
  proportion	
  of	
  female	
  members.	
  	
  In	
  addition,	
  older	
  heads	
  of	
  household	
  and	
  those	
  
with	
  more	
  years	
  of	
  coffee	
  experience	
  are	
  more	
  likely	
  to	
  belong	
  to	
  cooperatives.	
  Cooperative	
  
members	
  also	
  have	
  significantly	
  higher	
  incomes	
  and	
  more	
  land	
  under	
  coffee	
  production	
  
than	
  do	
  non-­‐‑members.	
  
	
  

89	
  

Table	
  13.	
  List	
  of	
  Coffee	
  Farming	
  Practices	
  
Practice	
  
Definition	
  
Pest	
  
Household	
  controls	
  pests	
  in	
  
Management	
   coffee	
  fields.	
  

Mulching	
  
	
  
	
  
Erosion	
  
preventing	
  
walls	
  
Water	
  
retention	
  

Household	
  uses	
  mulch	
  in	
  coffee	
  
fields.	
  	
  	
  
Household	
  has	
  built	
  erosion	
  
preventing	
  walls	
  such	
  
hedgerows	
  or	
  other	
  types	
  of	
  low	
  
walls	
  	
  
	
  
Household	
  has	
  reforested	
  
around	
  water	
  sources.	
  
	
  

Water	
  	
  
Household	
  has	
  built	
  ponds	
  to	
  
Harvesting	
  
collect	
  rainfall.	
  
	
  
Soil	
  Analysis	
   Household	
  has	
  conducted	
  soil	
  
analysis	
  from	
  their	
  coffee	
  fields	
  
	
  
Green	
  
Household	
  has	
  planted	
  nitrogen-­‐‑
Manure	
  
fixing	
  plants	
  in	
  their	
  coffee	
  
fields.	
  
	
  
Shade	
  
Household	
  has	
  planted	
  shade	
  
Management	
   trees	
  in	
  their	
  coffee	
  fields.	
  	
  	
  

Pruning	
  

Household	
  has	
  pruned	
  their	
  
coffee	
  trees.	
  

Stumping	
  

Household	
  has	
  stumped	
  their	
  
coffee	
  trees	
  

	
  	
  	
  

	
  

90	
  

Benefits	
  
Proper	
  dosage	
  of	
  pesticides	
  can	
  
help	
  prevent	
  pest	
  outbreaks,	
  such	
  
as	
  leaf	
  rust	
  and	
  coffee	
  borer	
  
beetle.	
  
	
  
Mulch	
  helps	
  with	
  water	
  retention	
  
and	
  with	
  weed	
  control.	
  
Erosion-­‐‑preventing	
  walls	
  built	
  in	
  
steep	
  hills	
  help	
  with	
  erosion	
  and	
  
mudslides.	
  	
  
Reforestation	
  around	
  water	
  
sources	
  help	
  with	
  water	
  
evaporation	
  and	
  loss.	
  
	
  
Water	
  harvesting	
  will	
  become	
  
essential	
  during	
  droughts	
  
Soil	
  analyses	
  help	
  determine	
  how	
  
to	
  properly	
  fertilize	
  soils	
  
Green	
  manure	
  is	
  an	
  organic	
  
practice	
  that	
  helps	
  soil	
  fertility	
  
Shade	
  helps	
  with	
  water	
  retention,	
  
with	
  erosion	
  prevention,	
  with	
  
temperature	
  control,	
  and	
  
provides	
  alternative	
  income	
  
opportunities	
  
	
  
Pruning	
  coffee	
  trees	
  helps	
  
improve	
  yields	
  and	
  control	
  pests	
  
	
  
Coffee	
  trees	
  need	
  to	
  be	
  stumped	
  
about	
  every	
  15	
  years,	
  when	
  
productivity	
  drops.	
  	
  

The	
  main	
  purpose	
  of	
  this	
  study	
  is	
  to	
  assess	
  whether	
  cooperative	
  membership	
  increases	
  the	
  
likelihood	
  of	
  adoption	
  of	
  sustainable	
  coffee	
  production	
  practices.	
  Specific	
  practices	
  of	
  
interests	
  were	
  guided	
  by	
  the	
  literature	
  and	
  interviews	
  with	
  key	
  informants	
  in	
  the	
  coffee	
  
sector	
  of	
  Nicaragua,	
  which	
  included	
  agronomists,	
  extension	
  officers,	
  and	
  researchers.	
  	
  Ten	
  
practices	
  emerge	
  from	
  these	
  conversations	
  and	
  literature	
  review:	
  proper	
  pest	
  
management,	
  mulching,	
  erosion	
  preventing	
  walls,	
  water	
  retention	
  techniques,	
  water	
  
harvesting,	
  use	
  of	
  soil	
  analysis,	
  green	
  manure	
  application,	
  shade	
  management,	
  pruning,	
  and	
  
stumping;	
  a	
  definition	
  of	
  each	
  of	
  these	
  practices,	
  together	
  with	
  their	
  role	
  in	
  helping	
  build	
  
adaptive	
  capacity	
  are	
  presented	
  in	
  Table	
  12.	
  
	
  
Before	
  turning	
  to	
  an	
  econometric	
  analysis	
  of	
  the	
  impact	
  of	
  cooperative	
  membership	
  on	
  
adoption	
  of	
  practices,	
  it	
  is	
  useful	
  to	
  compare	
  how	
  the	
  rate	
  of	
  adoption	
  of	
  these	
  practices	
  
differs	
  amongst	
  cooperative	
  members	
  and	
  non-­‐‑members.	
  	
  
Table	
  14.	
  Comparison	
  of	
  Adopted	
  Practices	
  by	
  Cooperative	
  Membership	
  

	
  
Practice	
  
Pruning	
  
Stumping	
  
Water	
  Harvesting	
  
Water	
  Retention	
  
Soil	
  Analysis	
  
Pest	
  Management	
  
Mulching	
  
Green	
  Manure	
  
Shade	
  Management	
  
Retention	
  Walls	
  

Cooperative	
  	
  
Member	
  
No	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
(%)	
  
86.05	
  
72.09	
  
31.78	
  
80.62	
  
13.95	
  
53.49	
  
58.14	
  
24.81	
  
87.6	
  
51.94	
  

Yes	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
   n	
  
(%)	
  
(N=236)	
  
91.59	
  
209	
  
84.11	
  
183	
  
38.32	
  
82	
  
87.85	
  
198	
  
32.71	
  
53	
  
73.83	
  
148	
  
63.55	
  
143	
  
40.19	
  
75	
  
91.59	
  
211	
  
71.03	
  
143	
  

Chi-­‐‑Square	
  
Test	
  
Statistic	
  
1.77***	
  
4.85***	
  
1.10***	
  
2.26***	
  
11.81***	
  
10.35***	
  
0.71***	
  
6.38***	
  
0.98***	
  
8.92***	
  

Note:	
  *,	
  **,	
  ***,	
  indicates	
  significance	
  at	
  10%,	
  5%,	
  and	
  1%	
  level	
  of	
  significance	
  

	
  

91	
  

Table	
  13	
  reveals	
  that	
  the	
  great	
  majority	
  of	
  the	
  sample	
  has	
  adopted	
  certain	
  practices,	
  
notably:	
  shade	
  incorporation,	
  pruning,	
  stumping	
  and	
  reforesting	
  around	
  water	
  sources	
  
(water	
  retention).	
  Yet	
  only	
  a	
  small	
  proportion	
  of	
  the	
  sample	
  has	
  adopted	
  other	
  practices	
  
such	
  as	
  soil	
  analysis	
  or	
  planting	
  nitrogen	
  fixating	
  plants	
  (green	
  manure).	
  	
  Perhaps	
  most	
  
important	
  for	
  the	
  current	
  analysis	
  is	
  the	
  finding	
  that	
  the	
  rate	
  of	
  adoption	
  of	
  cooperative	
  
members	
  is	
  consistently	
  higher	
  among	
  members	
  than	
  it	
  is	
  for	
  non-­‐‑members,	
  although	
  the	
  
differences	
  are	
  not	
  always	
  significant.	
  Significantly	
  higher	
  rates	
  of	
  adoption	
  by	
  cooperative	
  
members	
  are	
  found	
  in	
  stumping,	
  soil	
  analysis,	
  pest	
  management,	
  green	
  manure	
  application,	
  
and	
  the	
  installation	
  of	
  retention	
  walls.	
  
	
  
Turning	
  to	
  the	
  potential	
  economic	
  impact	
  of	
  cooperatives,	
  assessing	
  this	
  relationship	
  is	
  a	
  
more	
  difficult	
  task	
  due	
  to	
  potential	
  endogeneity	
  problems	
  associated	
  with	
  program	
  
placement	
  and	
  selection	
  bias.	
  Where	
  farmers	
  self-­‐‑select	
  into	
  producers’	
  cooperatives,	
  their	
  
unobserved	
  household	
  characteristics	
  may	
  systematically	
  differ	
  from	
  non-­‐‑members.	
  	
  Many	
  
studies	
  have	
  chosen	
  to	
  treat	
  cooperative	
  membership	
  as	
  exogenous,	
  and	
  a	
  few	
  have	
  used	
  
propensity	
  score	
  matching	
  (Verhofstadt	
  and	
  Maertens,	
  2014a	
  and	
  2014b;	
  Abebaw	
  and	
  
Haile,	
  2013),	
  and	
  treatment	
  effect	
  models	
  (Weber,	
  2011),	
  to	
  control	
  for	
  this	
  endogeneity.	
  	
  
The	
  approach	
  used	
  in	
  this	
  study,	
  described	
  below,	
  is	
  relatively	
  new	
  for	
  this	
  type	
  of	
  analysis	
  
and	
  it	
  offers	
  a	
  simple	
  yet	
  effective	
  way	
  to	
  control	
  for	
  the	
  potential	
  endogeneity	
  of	
  
cooperative	
  membership	
  (Wooldridge,	
  2015).	
  
	
  
I	
  employ	
  the	
  control	
  function	
  (CF)	
  approach	
  to	
  control	
  for	
  systematic	
  differences	
  between	
  
cooperative	
  members	
  and	
  non-­‐‑members.	
  This	
  approach	
  is	
  useful	
  when	
  membership	
  

	
  

92	
  

participation	
  is	
  non-­‐‑linear,	
  as	
  CF	
  estimators	
  are	
  more	
  precise	
  and	
  robust	
  than	
  two-­‐‑stage	
  
least	
  squares	
  (2SLS)	
  (Rijkers	
  et	
  al.,	
  2010)	
  estimators.	
  	
  A	
  drawback	
  from	
  the	
  CF	
  method,	
  
however,	
  is	
  that	
  it	
  forces	
  us	
  to	
  make	
  implicit	
  distributional	
  assumptions	
  that	
  are	
  difficult	
  to	
  
test	
  (Rijkers	
  et	
  al.,	
  2010).	
  	
  
	
  
I	
  use	
  the	
  control	
  function	
  method	
  to	
  alleviate	
  the	
  self-­‐‑selection	
  bias	
  of	
  cooperative	
  
membership.	
  This	
  approach	
  includes	
  extra	
  variables	
  in	
  the	
  empirical	
  specification	
  to	
  
condition	
  out	
  the	
  variation	
  in	
  the	
  unobserved	
  factor	
  that	
  is	
  not	
  independent	
  of	
  the	
  
endogenous	
  variable	
  (Petrin	
  and	
  Train,	
  2010).	
  	
  I	
  follow	
  Wooldridge’s	
  (2015)	
  approach	
  for	
  
handling	
  discreet	
  endogenous	
  explanatory	
  variables	
  using	
  the	
  CF	
  method.	
  	
  
	
  
A	
  two-­‐‑stage	
  CF	
  approach	
  requires	
  use	
  of	
  instrumental	
  variables	
  (IV)	
  to	
  test	
  for	
  
endogeneity.	
  	
  The	
  instrumental	
  variable	
  is	
  a	
  binary	
  variable	
  that	
  indicates	
  whether	
  the	
  
producer	
  was	
  a	
  cooperative	
  member	
  in	
  2013	
  (two	
  years	
  prior	
  to	
  data	
  collection).	
  	
  The	
  IV	
  
was	
  tested	
  and	
  was	
  found	
  to	
  be	
  valid;	
  in	
  other	
  words,	
  it	
  was	
  correlated	
  with	
  the	
  
endogenous	
  variable	
  (current	
  cooperative	
  membership	
  status)	
  but	
  was	
  uncorrelated	
  with	
  
𝜀,	
  the	
  error	
  term	
  in	
  the	
  explanatory	
  model.	
  	
  The	
  first	
  stage	
  involves	
  regressing	
  the	
  
instrumental	
  variable	
  on	
  the	
  suspected	
  endogenous	
  variable	
  in	
  a	
  probit	
  model	
  (eq.	
  1).	
  	
  In	
  
the	
  second	
  stage,	
  the	
  generalized	
  residuals	
  from	
  the	
  model	
  in	
  the	
  first	
  stage	
  are	
  introduced	
  
as	
  an	
  explanatory	
  variable	
  into	
  the	
  structural	
  model	
  (eq.	
  3).	
  
	
  
	
  
	
  

	
  

93	
  

Stage	
  1:	
  

𝜋?∗ = α𝑧? + 𝜈, 𝑖 = 1, … . , 𝑛	
  

(1)	
  

	
  
Stage	
  2:	
  

𝑌? = 𝛽𝑋? + 𝛽𝜋? + 	
   𝑣? + 𝜀, 𝑖 = 1, … . , 𝑛	
  

(2)	
  

	
  
	
  
In	
  the	
  first	
  stage	
  𝜋?∗ 	
  is	
  a	
  binary	
  variable	
  indicating	
  whether	
  individual	
  i	
  belongs	
  to	
  a	
  
cooperative	
  or	
  not	
  by	
  the	
  time	
  of	
  the	
  study,	
  𝑧? 	
  is	
  the	
  instrumental	
  variable	
  indicating	
  
whether	
  individual	
  i	
  was	
  a	
  cooperative	
  member	
  in	
  2013,	
  and	
  𝜈	
  is	
  the	
  error	
  term	
  which	
  will	
  
be	
  used	
  in	
  the	
  second	
  stage	
  of	
  the	
  analysis.	
  
	
  
In	
  the	
  second	
  stage	
  𝑌? 	
  represents	
  the	
  number	
  of	
  practices	
  adopted	
  by	
  the	
  household	
  
(presented	
  in	
  Table	
  2)	
  for	
  farmer	
  i,	
  ranging	
  from	
  0	
  practices	
  to	
  10,	
  𝑋? 	
  is	
  a	
  vector	
  of	
  
household	
  and	
  farm	
  characteristics,	
  𝜋?∗ 	
  is	
  a	
  dummy	
  variable	
  indicating	
  whether	
  the	
  farmer	
  
belongs	
  to	
  a	
  cooperative,	
  𝛽 	
  and	
  𝛼	
  are	
  a	
  vector	
  of	
  parameters	
  to	
  be	
  estimated	
  by	
  the	
  model,	
  
𝜈	
  ? 	
  is	
  the	
  generalized	
  error	
  term	
  estimated	
  in	
  the	
  first	
  stage,	
  and	
  𝜀 	
  is	
  the	
  error	
  random	
  term.	
  	
  
	
  
The	
  degree	
  to	
  which	
  cooperative	
  membership	
  affects	
  the	
  adoption	
  of	
  sustainable	
  coffee	
  
production	
  practices	
  is	
  studied	
  in	
  the	
  second	
  stage	
  of	
  the	
  analysis,	
  using	
  ordered	
  probit	
  
model	
  (OPM)6.	
  	
  Ordered	
  probit	
  models	
  allow	
  us	
  to	
  estimate	
  discreet	
  dependent	
  variables	
  
6	
  An	
  OPM	
  is	
  employed	
  in	
  this	
  study,	
  in	
  lieu	
  of	
  multinomial	
  or	
  poisson	
  regressions,	
  because	
  

it	
  allows	
  us	
  to	
  make	
  distinctions	
  between,	
  for	
  example,	
  farmers	
  who	
  adopt	
  only	
  one	
  
practice	
  versus	
  those	
  who	
  adopt	
  multiple	
  practices	
  in	
  combination,	
  whereas	
  the	
  alternative	
  
models	
  treat	
  the	
  number	
  of	
  practices	
  adopted	
  as	
  a	
  count	
  variable,	
  assuming	
  that	
  the	
  events	
  
have	
  the	
  same	
  probability	
  of	
  occurrence	
  (Wollni	
  et	
  al.,	
  2010).	
  	
  The	
  probability	
  of	
  adopting	
  
the	
  first	
  practice,	
  however,	
  could	
  differ	
  from	
  the	
  probability	
  of	
  adopting	
  a	
  second	
  or	
  third	
  
practice.	
  
	
  

94	
  

with	
  multiple	
  levels	
  in	
  which	
  order	
  matters	
  and	
  it	
  is	
  assumed	
  to	
  be	
  incremental	
  with	
  
unknown	
  magnitude.	
  	
  
	
  
The	
  econometric	
  specification	
  for	
  this	
  model,	
  estimated	
  in	
  the	
  second	
  stage	
  of	
  the	
  control	
  
function	
  approach	
  is	
  the	
  following:	
  
	
  

	
  

𝑦? = β* 𝐶𝑜𝑜𝑝𝑀𝑒𝑚𝑏𝑒𝑟 + βE log 𝐼𝑛𝑐𝑜𝑚𝑒 + βF 𝐴𝑟𝑒𝑎 + βO 𝐻𝐻𝑠𝑖𝑧𝑒
+ βR 𝐻𝐻𝑆𝑒𝑥 + βY 𝐻𝐻𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + β\ 𝐻𝐻𝐴𝑔𝑒 + β] 𝑅𝑎𝑑𝑖𝑜	
  
+	
  𝑣? + 	
   𝑒? 	
  

	
  
(3)	
  

	
  
Where	
  𝑦?d 	
  represents	
  the	
  number	
  of	
  production	
  practices	
  adopted	
  by	
  producer	
  i,	
  and	
  is	
  
modeled	
  with	
  household	
  characteristics	
  such	
  size,	
  age,	
  education	
  level,	
  and	
  sex	
  of	
  
household	
  head;	
  farm	
  characteristics	
  such	
  as	
  area	
  under	
  coffee	
  production;	
  and	
  other	
  
forms	
  of	
  capital,	
  such	
  as	
  income	
  and	
  ownership	
  of	
  a	
  radio.	
  Radio	
  ownership	
  is	
  included	
  in	
  
the	
  model	
  because	
  many	
  rural	
  households	
  rely	
  on	
  them	
  to	
  obtain	
  news	
  and	
  weather	
  
forecasts.	
  The	
  significance	
  of	
  cooperative	
  membership	
  is	
  explored	
  by	
  introducing	
  
CoopMember	
  in	
  the	
  model,	
  the	
  binary	
  indicator	
  that	
  established	
  whether	
  the	
  producer	
  
belongs	
  to	
  a	
  cooperative.	
  The	
  endogeneity	
  of	
  cooperative	
  membership	
  is	
  controlled	
  
through	
  𝑣? ,	
  the	
  generalized	
  residuals	
  estimated	
  in	
  the	
  first	
  stage	
  of	
  the	
  CF	
  approach.	
  	
  
	
  

Results	
  and	
  Discussion	
  
Table	
  14	
  shows	
  the	
  results	
  from	
  the	
  ordered	
  probit	
  model,	
  where	
  the	
  dependent	
  variable	
  
represents	
  the	
  degree	
  of	
  adoption	
  of	
  the	
  10	
  practices	
  described	
  above.	
  
	
  

	
  

95	
  

Table	
  15.	
  Ordered	
  Probit	
  Model	
  Results	
  
Practice	
  Intensity	
  
Ln(Income)	
  
Area	
  
HH	
  Size	
  
Cooperative	
  
Sex	
  
Education	
  
Age	
  
Radio	
  
𝑣? 	
  
N	
  
Log	
  likelihood	
  
LR	
  chi2(10)	
  	
  
Prob	
  >	
  chi2	
  

Coeff.	
  

St.	
  Error	
  

0.042	
  
0.036	
  
-­‐‑0.092	
  
0.991	
  
-­‐‑0.227	
  
0.076	
  
0.002	
  
0.264	
  
-­‐‑0.318	
  
212	
  
-­‐‑411.53	
  
54.05	
  
<0.000	
  

0.089	
  
0.007	
  
0.035	
  
0.377	
  
0.159	
  
0.022	
  
0.005	
  
0.148	
  
0.241	
  
	
  
	
  
	
  
	
  

	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
***	
  
***	
  
	
  
***	
  
	
  
*	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  
	
  
	
  
	
  

Note:	
  *,	
  **,	
  ***,	
  indicates	
  significance	
  at	
  10%,	
  5%,	
  and	
  1%	
  level	
  of	
  significance	
  

	
  
The	
  estimated	
  coefficients	
  in	
  Table	
  14	
  predict	
  the	
  changes	
  in	
  the	
  probability	
  of	
  adoption.	
  I	
  
find	
  evidence	
  suggesting	
  that	
  the	
  probability	
  of	
  adopting	
  a	
  higher	
  number	
  of	
  practices	
  
increases	
  with	
  each	
  additional	
  year	
  of	
  education,	
  and	
  for	
  households	
  that	
  own	
  a	
  radio;	
  and	
  
that	
  the	
  probability	
  of	
  adopting	
  a	
  higher	
  number	
  of	
  practices	
  decreases	
  with	
  an	
  increase	
  in	
  
the	
  number	
  of	
  household	
  members.	
  The	
  variable	
  of	
  interest	
  is	
  how	
  cooperative	
  
membership	
  affects	
  the	
  probability	
  of	
  adoption,	
  and	
  the	
  results	
  provide	
  strong	
  evidence	
  
suggesting	
  that	
  the	
  probability	
  of	
  adoption	
  is	
  higher	
  for	
  cooperative	
  members	
  than	
  for	
  non-­‐‑
cooperative	
  members.	
  
	
  
These	
  results	
  support	
  the	
  hypothesis	
  that	
  farmers	
  who	
  belong	
  to	
  cooperatives	
  will	
  be	
  
better	
  prepared	
  to	
  combat	
  the	
  impacts	
  of	
  climate	
  change,	
  due	
  to	
  their	
  adoption	
  of	
  a	
  higher	
  
number	
  of	
  practices	
  that	
  give	
  them	
  greater	
  adaptive	
  capacity.	
  	
  The	
  above	
  model,	
  however,	
  

	
  

96	
  

does	
  not	
  differentiate	
  between	
  the	
  quality	
  or	
  importance	
  of	
  practices,	
  rather,	
  it	
  looks	
  at	
  
how	
  intensively,	
  overall,	
  farmers	
  pursue	
  a	
  broad	
  regime	
  of	
  sustainable	
  practices.	
  	
  A	
  closer	
  
look	
  at	
  the	
  data	
  provides	
  further	
  insight	
  on	
  the	
  role	
  of	
  cooperatives	
  in	
  their	
  support	
  and	
  
promotion	
  of	
  practices	
  to	
  coffee	
  farmers.	
  	
  	
  
	
  
Characterization	
  of	
  Production	
  Practices.	
  	
  Production	
  practices	
  can	
  be	
  characterized	
  
differently,	
  from	
  practices	
  that	
  require	
  high	
  labor	
  investments	
  to	
  practices	
  that	
  require	
  
high	
  capital	
  investments.	
  	
  Households	
  that	
  have	
  access	
  to	
  capital	
  may	
  be	
  more	
  likely	
  to	
  
adopt	
  capital-­‐‑intensive	
  practices,	
  while	
  household	
  with	
  limited	
  capital	
  might	
  have	
  access	
  to	
  
more	
  labor	
  and	
  thus	
  may	
  be	
  more	
  likely	
  to	
  adopt	
  practices	
  that	
  require	
  a	
  greater	
  
investment	
  of	
  household	
  labor.	
  	
  Understanding	
  how	
  these	
  differences	
  play	
  out	
  will	
  give	
  us	
  
more	
  insight	
  to	
  whether	
  and	
  why	
  households	
  adopt	
  certain	
  practices	
  and	
  not	
  others.	
  	
  
	
  
To	
  capture	
  some	
  of	
  these	
  underlying	
  structural	
  differences,	
  a	
  factor	
  analysis	
  was	
  conducted	
  
using	
  the	
  10	
  sustainable	
  coffee	
  production	
  practices	
  included	
  in	
  the	
  study.	
  	
  From	
  this	
  
analysis,	
  three	
  factors	
  were	
  extracted,	
  grouping	
  the	
  practices	
  based	
  on	
  common	
  
characteristics	
  as	
  presented	
  in	
  Table	
  15.	
  	
  
	
  
	
  
	
  
	
  
	
  

	
  

97	
  

Table	
  16.	
  Characterization	
  of	
  Production	
  Practices	
  
Factor	
  1:	
  
Input	
  Application	
  	
  

Practices	
  that	
  improve	
  
productivity	
  of	
  coffee	
  through	
  
input	
  application	
  and	
  soil	
  
fertility.	
  

Factor	
  2:	
  
Field	
  Management	
  
Practices	
  

Practices	
  focused	
  on	
  better	
  field	
  
management	
  and	
  plant	
  health.	
  

Factor	
  3:	
  
Practices	
  focused	
  on	
  improved	
  
Water	
  Conservation	
   water	
  conservation	
  and	
  
Practices	
  
management	
  

1.	
  Soil	
  Analyses	
  from	
  
samples	
  
2.	
  Pesticides	
  procurement	
  
	
  	
  	
  	
  	
  and	
  application	
  	
  
3.	
  Mulch	
  procurement	
  and	
  
application	
  	
  
4.	
  Green	
  Manure	
  
procurement	
  and	
  
application	
  
1.	
  Pruning	
  
2.	
  Stumping	
  
3.	
  Shade	
  Incorporation	
  
and	
  Management	
  
(keeping	
  it	
  at	
  40%)	
  
	
  
1.	
  Reforestation	
  around	
  
water	
  sources	
  
2.	
  Water	
  Harvesting	
  
(building	
  ponds	
  for	
  
irrigation)	
  
3.	
  Building	
  retention	
  walls	
  
or	
  hedgerows	
  

	
  
The	
  first	
  factor	
  consists	
  of	
  practices	
  relevant	
  to	
  improved	
  soil	
  and	
  plant	
  fertility,	
  through	
  
the	
  application	
  of	
  inputs	
  and	
  the	
  analysis	
  of	
  soils.	
  	
  The	
  second	
  factor	
  consists	
  of	
  practices	
  
relevant	
  to	
  field	
  management	
  through	
  the	
  care	
  of	
  coffee	
  trees	
  and	
  the	
  use	
  of	
  shade	
  in	
  the	
  
field.	
  	
  The	
  final	
  group	
  consists	
  of	
  practices	
  related	
  to	
  the	
  conservation	
  and	
  management	
  of	
  
water	
  resources.	
  The	
  use	
  of	
  retention	
  walls	
  to	
  protect	
  soils	
  from	
  erosion	
  was	
  initially	
  
grouped	
  with	
  practices	
  in	
  Factor	
  1,	
  but	
  given	
  its	
  thematic	
  relevance,	
  I	
  moved	
  it	
  to	
  the	
  third	
  
group	
  after	
  ensuring	
  its	
  positive	
  and	
  high	
  correlation	
  with	
  the	
  water	
  conservation	
  practices	
  
in	
  the	
  factor	
  analysis.	
  	
  
	
  

	
  

98	
  

Using	
  this	
  categorization,	
  three	
  separate	
  ordered	
  probit	
  models	
  were	
  estimated	
  to	
  
determine	
  whether	
  there	
  are	
  differences	
  in	
  the	
  determinants	
  of	
  adoption	
  for	
  the	
  different	
  
types	
  of	
  practices.	
  	
  Most	
  notably,	
  I	
  proceed	
  to	
  test	
  whether	
  cooperative	
  membership	
  is	
  a	
  
significant	
  determinant	
  for	
  any	
  or	
  all	
  types	
  of	
  practices.	
  	
  
	
  
The	
  first	
  model	
  looks	
  at	
  the	
  determinants	
  of	
  adoption	
  of	
  input-­‐‑oriented	
  practices,	
  where	
  
the	
  dependent	
  variable	
  ranges	
  from	
  0	
  to	
  4	
  in	
  a	
  scale	
  of	
  intensity	
  of	
  adoption,	
  based	
  on	
  the	
  
number	
  of	
  practices	
  adopted	
  in	
  this	
  category.	
  	
  These	
  practices	
  are	
  often	
  subsidized	
  by	
  
cooperatives	
  or	
  by	
  public	
  and	
  private	
  agencies	
  through	
  the	
  provision	
  of	
  credit,	
  subsidized	
  
extension	
  services	
  and/or	
  inputs.	
  	
  I	
  hypothesize	
  that	
  households	
  with	
  higher	
  income	
  and	
  
higher	
  education	
  levels	
  will	
  be	
  more	
  likely	
  to	
  adopt	
  these	
  types	
  of	
  practices.	
  	
  Households	
  
with	
  higher	
  income	
  will	
  have	
  the	
  capital	
  to	
  do	
  so,	
  and	
  households	
  with	
  higher	
  education	
  are	
  
more	
  likely	
  to	
  be	
  literate	
  and	
  able	
  to	
  access	
  information	
  and	
  other	
  resources,	
  giving	
  them	
  a	
  
better	
  understanding	
  of	
  the	
  effects	
  of	
  adoption	
  on	
  productivity.	
  	
  	
  
	
  
The	
  second	
  model	
  measures	
  the	
  determinants	
  of	
  adoption	
  of	
  practices	
  involving	
  field	
  and	
  
plant	
  management,	
  the	
  dependent	
  variable	
  measures	
  the	
  intensity	
  of	
  adoption	
  of	
  these	
  
practices	
  and	
  it	
  ranges	
  from	
  0	
  to	
  3.	
  	
  These	
  are	
  practices	
  that	
  often	
  require	
  high	
  labor	
  
dedication.	
  I	
  hypothesize	
  that	
  bigger	
  households	
  and	
  households	
  with	
  higher	
  income	
  will	
  
be	
  more	
  likely	
  to	
  adopt	
  these	
  practices	
  as	
  they	
  have	
  the	
  means	
  to	
  allocate	
  household	
  labor	
  
or	
  to	
  hire	
  labor.	
  	
  
Finally,	
  in	
  the	
  third	
  model	
  I	
  measure	
  the	
  determinants	
  of	
  adoption	
  of	
  water	
  conservation	
  
practices,	
  which	
  range	
  in	
  intensity	
  from	
  0	
  to	
  3.	
  	
  These	
  practices	
  are	
  especially	
  relevant,	
  

	
  

99	
  

given	
  their	
  potential	
  for	
  climate	
  change	
  mitigation	
  strategies.	
  	
  I	
  hypothesized	
  that	
  
households	
  with	
  higher	
  income	
  and	
  higher	
  education	
  will	
  be	
  more	
  likely	
  to	
  have	
  adopted	
  
these	
  practices.	
  	
  The	
  overarching	
  hypothesis	
  is	
  that	
  cooperative	
  membership	
  is	
  positive	
  
and	
  significant	
  in	
  all	
  three	
  models,	
  particularly	
  in	
  the	
  third	
  one,	
  because	
  it	
  consists	
  of	
  
practices	
  that	
  are	
  not	
  yet	
  commonly	
  used	
  in	
  the	
  coffee	
  sector,	
  and	
  are	
  in	
  the	
  early	
  stages	
  of	
  
adoption.	
  	
  Results	
  from	
  these	
  analyses	
  are	
  presented	
  in	
  Table	
  16.	
  
	
  
Table	
  17.	
  Ordered	
  Probit	
  Model	
  Results	
  for	
  Disaggregated	
  Practices	
  
Variable	
  
Ln(income)	
  
Area	
  
HH	
  Size	
  
Cooperative	
  
Sex	
  
Education	
  
Age	
  
Radio	
  
𝑣? 	
  
N	
  
Log	
  likelihood	
  
LR	
  chi2(9)	
  	
  
Prob	
  >	
  chi2	
  
Pseudo	
  R2	
  

(1)	
  Input	
  
Practices	
  

(2)	
  Field	
  
Practices	
  

0.044***	
  
0.019***	
  
-­‐‑0.081***	
  
0.469***	
  
-­‐‑0.121***	
  
0.048***	
  
-­‐‑0.006***	
  
0.226***	
  
0.029***	
  
212***	
  
-­‐‑305.1***	
  
31.90***	
  
0.0002***	
  
0.049***	
  

-­‐‑0.090***	
  
0.074***	
  
-­‐‑0.109***	
  
0.644***	
  
-­‐‑0.241***	
  
0.086***	
  
0.005***	
  
0.367***	
  
-­‐‑0.264***	
  
212***	
  
-­‐‑168.4***	
  
32.97***	
  
0.0001***	
  
0.089***	
  

(3)	
  Water	
  
Practices	
  
0.079***	
  
0.026***	
  
0.004***	
  
1.183***	
  
-­‐‑0.227***	
  
0.062***	
  
0.004***	
  
0.096***	
  
-­‐‑0.615***	
  
212***	
  
-­‐‑247.5***	
  
31.55***	
  
<0.0002***	
  
0.059***	
  

Note:	
  *,**,***,	
  indicates	
  significance	
  at	
  10%,	
  5%,	
  and	
  1%	
  level	
  of	
  significance	
  

	
  
Results	
  from	
  these	
  models	
  provide	
  further	
  insight	
  into	
  the	
  role	
  of	
  cooperative	
  membership	
  
in	
  the	
  adoption	
  of	
  production	
  practices.	
  I	
  find	
  that	
  cooperative	
  membership	
  has	
  a	
  
significant	
  effect	
  on	
  adoption	
  only	
  in	
  the	
  third	
  model.	
  	
  But	
  before	
  discussing	
  each	
  model	
  in	
  

	
  

100	
  

greater	
  depth,	
  we	
  compute	
  the	
  marginal	
  effects	
  of	
  the	
  significant	
  variables	
  to	
  make	
  
interpretation	
  of	
  the	
  coefficients	
  easier,	
  these	
  marginal	
  effects	
  are	
  presented	
  in	
  Table	
  17.	
  
	
  
Table	
  18.	
  Marginal	
  Effects	
  of	
  Significant	
  Variables	
  on	
  Adoption	
  Intensity	
  
Intensity	
   (1)	
  Input	
  Practices	
  
of	
  
HH	
  
Practice	
  
Educ	
  
Size	
  

(3)	
  Water	
  
Practices	
  

(2)	
  Field	
  Practices	
  
Area	
  

HH	
  
Size	
  

Educ	
  

Radio	
  

Coop	
  

Educ	
  

0	
  

*0.014	
  

-­‐‑0.008	
  

-­‐‑0.002	
  

*0.004	
  

-­‐‑0.003	
  

-­‐‑0.012	
  

-­‐‑0.148	
  

-­‐‑0.001	
  

1	
  

*0.017	
  

-­‐‑0.011	
  

-­‐‑0.009	
  

*0.013	
  

-­‐‑0.01	
  

-­‐‑0.043	
  

-­‐‑0.249	
  

-­‐‑0.015	
  

2	
  

-­‐‑0.005	
  

*0.003	
  

-­‐‑0.014	
  

*0.020	
  

-­‐‑0.016	
  

-­‐‑0.068	
  

*0.063	
  

*0.005	
  

3	
  

-­‐‑0.015	
  

*0.008	
  

*0.025	
  

-­‐‑0.037	
  

*0.029	
   *0.123	
  

*0.334	
  

*0.017	
  

4	
  

-­‐‑0.012	
  

*0.066	
  

	
  

	
  

	
  

	
  

	
  

	
  

	
  
Model	
  1	
  looks	
  at	
  the	
  determinants	
  of	
  adoption	
  of	
  practices	
  that	
  help	
  improve	
  health	
  and	
  
soil	
  nutrition	
  of	
  coffee	
  fields	
  through	
  the	
  application	
  of	
  inputs.	
  I	
  find	
  that	
  producers	
  with	
  
higher	
  education	
  levels	
  are	
  more	
  likely	
  to	
  adopt	
  and	
  that	
  household	
  size	
  is	
  a	
  significant	
  and	
  
negative	
  determinant	
  of	
  adoption.	
  This	
  runs	
  counter	
  to	
  our	
  hypothesis	
  that	
  larger	
  
households	
  are	
  able	
  to	
  allocate	
  more	
  household	
  labor	
  to	
  their	
  farms,	
  and	
  hence	
  are	
  more	
  
likely	
  to	
  adopt	
  these	
  types	
  of	
  practices.	
  Further	
  studies	
  should	
  look	
  at	
  household	
  
dependency	
  ratios,	
  or	
  the	
  number	
  of	
  active	
  adults,	
  as	
  these	
  results	
  could	
  represent	
  
households	
  with	
  a	
  low	
  proportion	
  of	
  active	
  members	
  who	
  are	
  able	
  to	
  support	
  farm	
  efforts.	
  	
  
I	
  find	
  that	
  each	
  additional	
  year	
  of	
  education	
  improves	
  the	
  odds	
  of	
  adoption	
  of	
  all	
  four	
  
practices	
  in	
  this	
  first	
  model	
  by	
  6.6	
  percentage	
  points,	
  and	
  that	
  for	
  each	
  additional	
  
household	
  member	
  the	
  odds	
  of	
  adoption	
  of	
  all	
  four	
  practices	
  decrease	
  by	
  1.2	
  percentage	
  
points.	
  	
  	
  
	
  

	
  

101	
  

Model	
  2	
  looks	
  at	
  the	
  determinants	
  of	
  adoption	
  of	
  field	
  management	
  practices,	
  these	
  include	
  
the	
  use	
  and	
  management	
  of	
  shade,	
  and	
  pruning	
  and	
  stumping	
  of	
  coffee	
  trees.	
  	
  In	
  this	
  model	
  
I	
  see	
  three	
  variables	
  that	
  emerge	
  as	
  positive	
  and	
  significant:	
  area	
  under	
  coffee	
  production,	
  
education	
  and	
  ownership	
  of	
  a	
  radio.	
  	
  Household	
  size	
  surprisingly,	
  as	
  in	
  the	
  first	
  model,	
  is	
  a	
  
negative	
  and	
  significant	
  determinant	
  of	
  adoption.	
  	
  The	
  practices	
  included	
  in	
  this	
  model	
  are	
  
practices	
  that	
  are	
  generally	
  considered	
  to	
  require	
  higher	
  labor	
  demands,	
  and	
  I	
  expected	
  
that	
  larger	
  households	
  would	
  have	
  more	
  available	
  labor	
  to	
  allocate	
  to	
  these	
  types	
  of	
  
practices,	
  yet	
  the	
  results	
  suggest	
  something	
  different.	
  	
  Households	
  that	
  do	
  not	
  own	
  a	
  radio	
  
are	
  1.2%	
  more	
  likely	
  to	
  have	
  not	
  adopted	
  any	
  of	
  the	
  practices	
  in	
  this	
  category.	
  Each	
  
additional	
  year	
  of	
  education	
  increases	
  the	
  odds	
  of	
  adopting	
  all	
  three	
  practices	
  by	
  2.9%,	
  and	
  
each	
  additional	
  manzana	
  of	
  land	
  increases	
  the	
  same	
  odds	
  by	
  2.5	
  percentage	
  points.	
  
	
  
In	
  the	
  third	
  and	
  final	
  model	
  I	
  measure	
  the	
  determinants	
  of	
  adoption	
  of	
  practices	
  that	
  
improve	
  water	
  retention	
  and	
  conservation.	
  	
  This	
  set	
  of	
  practices	
  is	
  particularly	
  relevant	
  to	
  
agricultural	
  households	
  facing	
  increasing	
  rates	
  of	
  droughts	
  and	
  extreme	
  rainfall.	
  	
  In	
  2010,	
  
for	
  example,	
  Nicaragua	
  experienced	
  intense	
  and	
  sustained	
  rainfall	
  in	
  which	
  entire	
  crops	
  
were	
  wiped	
  out,	
  this	
  was	
  followed	
  by	
  a	
  drought	
  in	
  2012	
  which	
  saw	
  historically	
  low	
  rainfall,	
  
also	
  resulting	
  in	
  massive	
  crop	
  losses	
  (Gourdji	
  et	
  al.,	
  2014).	
  	
  The	
  incidence	
  of	
  extreme	
  
weather	
  events	
  is	
  projected	
  to	
  increase	
  with	
  climate	
  change,	
  making	
  it	
  critical	
  to	
  plan	
  for	
  
agricultural	
  adaptation.	
  	
  Cooperative	
  membership	
  and	
  education	
  are	
  the	
  two	
  variables	
  that	
  
emerge	
  as	
  significant	
  and	
  positive	
  in	
  this	
  model.	
  	
  I	
  find	
  that	
  non-­‐‑cooperative	
  members	
  are	
  
14.8%	
  more	
  likely	
  than	
  members	
  to	
  have	
  no	
  practices	
  adopted	
  in	
  this	
  category,	
  and	
  24.9%	
  
more	
  likely	
  to	
  have	
  only	
  adopted	
  one	
  practice,	
  in	
  comparison	
  with	
  cooperative	
  members.	
  

	
  

102	
  

By	
  contrast,	
  I	
  find	
  that	
  members	
  are	
  6.3%	
  more	
  likely	
  to	
  have	
  adopted	
  two	
  out	
  of	
  the	
  three	
  
practices	
  and	
  33.4%	
  more	
  likely	
  to	
  have	
  adopted	
  all	
  three	
  practices	
  in	
  this	
  category	
  than	
  
are	
  non-­‐‑members.	
  	
  These	
  results	
  are	
  encouraging,	
  as	
  water	
  conservation	
  and	
  harvesting	
  
can	
  supply	
  supplemental	
  irrigation	
  during	
  droughts,	
  and	
  perhaps	
  even	
  more	
  important,	
  
sufficient	
  soil	
  moisture	
  helps	
  crops	
  cope	
  with	
  higher	
  temperatures	
  through	
  transpirational	
  
cooling	
  (Lobell	
  et	
  al.,	
  2011).	
  	
  	
  
	
  
Cooperative	
  membership	
  does	
  not	
  emerge	
  as	
  significant	
  in	
  models	
  1	
  and	
  2	
  and	
  this	
  is	
  a	
  
surprising	
  finding	
  given	
  the	
  overwhelming	
  literature	
  that	
  finds	
  that	
  cooperative	
  
membership	
  to	
  be	
  a	
  significant	
  determinant	
  of	
  adoption	
  of	
  new	
  technologies	
  and	
  
agricultural	
  practices.	
  	
  Three	
  possible	
  explanations	
  are	
  considered	
  to	
  help	
  account	
  for	
  
these	
  differences.	
  	
  First,	
  some	
  studies	
  that	
  measure	
  cooperative	
  membership	
  as	
  a	
  
determinant	
  of	
  adoption,	
  do	
  not	
  account	
  for	
  the	
  potential	
  endogeneity	
  of	
  membership,	
  
possibly	
  misattributing	
  significance	
  to	
  membership	
  when	
  none	
  actually	
  exists.	
  	
  Second,	
  
many	
  studies	
  that	
  look	
  at	
  the	
  determinants	
  of	
  adoption	
  of	
  technologies	
  and	
  practices	
  often	
  
stop	
  after	
  modeling	
  practices	
  in	
  an	
  aggregate	
  manner.	
  This	
  study	
  is	
  different	
  in	
  that	
  a	
  factor	
  
analysis	
  is	
  used	
  to	
  identify	
  and	
  extract	
  the	
  underlying	
  structural	
  characteristics	
  that	
  unify	
  
groups	
  of	
  practices	
  and	
  give	
  us	
  insights	
  into	
  the	
  determinants	
  and	
  patters	
  of	
  adoption	
  of	
  
practices.	
  	
  And	
  finally,	
  although	
  cooperative	
  membership	
  is	
  not	
  statistically	
  significant	
  in	
  
these	
  two	
  models,	
  it	
  is	
  important	
  to	
  note	
  that	
  the	
  direction	
  of	
  the	
  relationships	
  are	
  positive	
  
and	
  thus	
  consistent	
  with	
  the	
  literature.	
  We	
  expect	
  that	
  a	
  study	
  replication	
  with	
  a	
  larger	
  
sample	
  could	
  find	
  that	
  cooperative	
  membership	
  does	
  in	
  fact	
  emerge	
  as	
  a	
  significant	
  
determinant	
  of	
  adoption	
  across	
  all	
  groups	
  of	
  practices.	
  	
  It	
  is	
  important	
  to	
  mention	
  that	
  this	
  

	
  

103	
  

lack	
  of	
  significance	
  could	
  be	
  attributed	
  to	
  the	
  already	
  high	
  adoption	
  rate	
  of	
  these	
  practices.	
  
As	
  shown	
  at	
  the	
  outset,	
  a	
  large	
  proportion	
  of	
  farmers	
  in	
  Matagalpa	
  have	
  already	
  adopted	
  
the	
  majority	
  of	
  these	
  practices,	
  making	
  it	
  more	
  difficult	
  to	
  discern	
  the	
  effects	
  of	
  cooperative	
  
membership.	
  	
  
	
  
The	
  generalized	
  residuals	
  variable	
  used	
  to	
  control	
  for	
  the	
  endogeneity	
  of	
  cooperative	
  
membership	
  is	
  significant	
  only	
  in	
  the	
  third	
  model.	
  This	
  implies	
  that	
  there	
  are	
  unidentified	
  
characteristics	
  that	
  can	
  explain	
  the	
  adoption	
  of	
  practices	
  that	
  promote	
  water	
  conservation,	
  
variables	
  that	
  are	
  not	
  modeled	
  in	
  this	
  analysis.	
  
	
  

Conclusion	
  
This	
  study	
  analyzes	
  the	
  determinants	
  of	
  adoption	
  of	
  sustainable	
  production	
  practices	
  by	
  
coffee	
  farmers	
  in	
  Nicaragua	
  and	
  it	
  looks	
  at	
  the	
  degree	
  to	
  which	
  coffee	
  cooperatives	
  play	
  a	
  
significant	
  role	
  in	
  increasing	
  the	
  capacity	
  of	
  farmers	
  to	
  adopt	
  these	
  practices.	
  Past	
  research	
  
has	
  shown	
  that	
  farmers	
  who	
  belong	
  to	
  farmers’	
  associations	
  or	
  cooperatives	
  are	
  financially	
  
better	
  off	
  than	
  those	
  who	
  do	
  not	
  (Jena	
  et	
  al.,	
  2012;	
  Wollni	
  and	
  Zeller,	
  2007),	
  and	
  that	
  
cooperative	
  membership	
  is	
  a	
  significant	
  determinant	
  of	
  adoption	
  of	
  technologies	
  and	
  
production	
  practices	
  (Verhofstadt	
  and	
  Maertens,	
  2014a;	
  Wollni	
  et	
  al.,	
  2010).	
  	
  	
  	
  I	
  find	
  that	
  
cooperative	
  membership	
  is	
  a	
  positive	
  and	
  significant	
  determinant	
  of	
  adoption	
  of	
  
sustainable	
  production	
  practices	
  overall,	
  and	
  more	
  significantly,	
  that	
  farmers	
  who	
  belong	
  
to	
  cooperatives	
  have	
  higher	
  odds	
  of	
  adopting	
  practices	
  that	
  help	
  with	
  water	
  conservation.	
  
	
  

	
  

104	
  

Amongst	
  all	
  the	
  practices	
  studied	
  in	
  this	
  research,	
  the	
  water	
  conservation	
  practices	
  are	
  
perhaps	
  the	
  most	
  relevant	
  to	
  climate	
  change	
  adaptation.	
  	
  Reforestation	
  around	
  water	
  
sources	
  is	
  one	
  of	
  these	
  practices,	
  this	
  practice	
  helps	
  to	
  protect	
  water	
  sources,	
  such	
  as	
  
streams,	
  ponds,	
  and	
  wells;	
  it	
  also	
  helps	
  to	
  prevent	
  erosion	
  and	
  water	
  runoff,	
  and	
  it	
  helps	
  to	
  
protect	
  the	
  biodiversity	
  of	
  the	
  area.	
  A	
  second	
  practice	
  is	
  the	
  use	
  of	
  retention	
  walls,	
  these	
  
walls	
  play	
  an	
  important	
  role	
  in	
  preventing	
  soil	
  erosion.	
  	
  With	
  seasons	
  marked	
  with	
  longer	
  
periods	
  of	
  drought	
  followed	
  by	
  short,	
  erratic,	
  and	
  severe	
  rainy	
  seasons,	
  fields	
  located	
  on	
  
steep	
  slopes	
  are	
  particularly	
  susceptible	
  to	
  potential	
  mudslides	
  and	
  erosion	
  and	
  these	
  walls	
  
can	
  help	
  diminish	
  some	
  of	
  these	
  risks.	
  	
  Finally,	
  water	
  harvesting	
  is	
  a	
  technology	
  that	
  helps	
  
farmers	
  to	
  collect	
  rainwater	
  and	
  ground	
  water	
  for	
  irrigation	
  of	
  fields,	
  a	
  practice	
  that	
  will	
  
become	
  increasingly	
  important	
  with	
  drought	
  becoming	
  a	
  more	
  common	
  occurrence	
  in	
  the	
  
region.	
  
	
  
Future	
  research	
  should	
  develop	
  an	
  institutional	
  framework	
  to	
  analyze	
  the	
  emergence	
  of	
  
cooperatives	
  in	
  Nicaragua	
  and	
  study	
  how	
  they	
  have	
  shaped	
  farmers’	
  perceptions	
  of	
  
collective	
  action	
  and	
  their	
  likelihood	
  of	
  joining	
  one.	
  	
  Further	
  research,	
  which	
  relies	
  on	
  both	
  
qualitative	
  and	
  quantitative	
  measures,	
  could	
  help	
  us	
  evolve	
  the	
  understanding	
  of	
  how	
  
cooperatives	
  operate	
  and	
  how	
  they	
  work	
  with	
  their	
  members.	
  
	
  
	
  
The	
  policy	
  implications	
  of	
  this	
  study	
  are	
  relevant	
  and	
  applicable	
  to	
  many	
  coffee-­‐‑producing	
  
countries	
  around	
  the	
  world.	
  	
  In	
  order	
  to	
  meet	
  the	
  growing	
  global	
  demand	
  for	
  coffee	
  and	
  to	
  
prevent	
  the	
  negative	
  economic	
  and	
  environmental	
  impacts	
  of	
  climate	
  change	
  on	
  coffee	
  

	
  

105	
  

producers,	
  cooperatives	
  and	
  other	
  agricultural	
  organizations	
  can	
  be	
  instrumental	
  in	
  
developing	
  strategies	
  that	
  reach	
  producers	
  and	
  helping	
  them	
  to	
  build	
  adaptive	
  capacity	
  in	
  
response	
  to	
  these	
  changes.	
  	
  Climate	
  change	
  will	
  affect	
  the	
  suitability	
  of	
  coffee	
  growing	
  
regions	
  around	
  the	
  world;	
  indeed	
  the	
  pressures	
  from	
  climate	
  change	
  may	
  force	
  some	
  
producers	
  to	
  move	
  out	
  of	
  coffee	
  production	
  entirely,	
  but	
  those	
  who	
  remain	
  will	
  need	
  
climate	
  adaptation	
  support	
  and	
  cooperatives	
  are	
  strategically	
  well	
  placed	
  to	
  help	
  provide	
  
such	
  support.	
  	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  

	
  

	
  

106	
  

	
  
	
  

	
  

REFERENCES	
  

	
  

107	
  

REFERENCES	
  
	
  
	
  
	
  
	
  
Abebaw,	
  Degnet,	
  and	
  Mekbib	
  G.	
  Haile.	
  "The	
  impact	
  of	
  cooperatives	
  on	
  agricultural	
  
technology	
  adoption:	
  Empirical	
  evidence	
  from	
  Ethiopia."	
  Food	
  policy	
  38	
  (2013):	
  82-­‐‑
91.	
  
	
  
Baca,	
  María,	
  Peter	
  Läderach,	
  Jeremy	
  Haggar,	
  GÜtz	
  Schroth,	
  and	
  Oriana	
  Ovalle.	
  "An	
  
integrated	
  framework	
  for	
  assessing	
  vulnerability	
  to	
  climate	
  change	
  and	
  developing	
  
adaptation	
  strategies	
  for	
  coffee	
  growing	
  families	
  in	
  Mesoamerica."	
  PloS	
  one	
  9,	
  no.	
  2	
  
(2014):	
  e88463.	
  
	
  
Barrett,	
   Christopher	
   B.	
   "Smallholder	
   market	
   participation:	
   concepts	
   and	
   evidence	
   from	
  
eastern	
  and	
  southern	
  Africa."	
  Food	
  Policy	
  33,	
  no.	
  4	
  (2008):	
  299-­‐‑317.	
  
	
  
Donovan,	
  Jason,	
  and	
  Nigel	
  Poole.	
  "Changing	
  asset	
  endowments	
  and	
  smallholder	
  
participation	
  in	
  higher	
  value	
  markets:	
  Evidence	
  from	
  certified	
  coffee	
  producers	
  in	
  
Nicaragua."	
  Food	
  Policy	
  44	
  (2014):	
  1-­‐‑13.	
  
	
  
Fischer,	
  Elisabeth,	
  and	
  Matin	
  Qaim.	
  "Linking	
  smallholders	
  to	
  markets:	
  determinants	
  and	
  
impacts	
  of	
  farmer	
  collective	
  action	
  in	
  Kenya."	
  World	
  Development	
  40.6	
  (2012):	
  
1255-­‐‑1268.	
  
	
  
Greene,	
  William	
  H.,	
  and	
  David	
  A.	
  Hensher.	
  Modeling	
  ordered	
  choices:	
  A	
  primer.	
  Cambridge	
  
University	
  Press,	
  2010.	
  
	
  
Gourdji,	
  Sharon,	
  et	
  al.	
  "Sustainable	
  Development	
  Opportunities	
  at	
  the	
  Climate,	
  Land,	
  
Energy,	
  and	
  Water	
  Nexus	
  in	
  Nicaragua."	
  Center	
  for	
  Latin	
  American	
  Studies,	
  Working	
  
Paper	
  33	
  (2014).	
  
	
  
Jena,	
  Pradyot	
  Ranjan,	
  et	
  al.	
  "The	
  impact	
  of	
  coffee	
  certification	
  on	
  small-­‐‑scale	
  producers’	
  
livelihoods:	
  a	
  case	
  study	
  from	
  the	
  Jimma	
  Zone,	
  Ethiopia."Agricultural	
  
economics	
  43.4	
  (2012):	
  429-­‐‑440.	
  
	
  
Laderäch,	
  Peter,	
  Carlos	
  Zelaya,	
  Oriana	
  Ovalle,	
  Samuel	
  Garcia,	
  Anton	
  Eitzinger,	
  and	
  Maria	
  
Baca.	
  "Escenarios	
  del	
  Impacto	
  del	
  Clima	
  Futuro	
  en	
  Areas	
  de	
  Cultivo	
  de	
  Cafe	
  en	
  
Nicaragua."	
  CIAT	
  Blog.	
  13	
  Mar.	
  2013.	
  Web.	
  http://dapa.ciat.cgiar.org/wp-­‐‑
content/uploads/2012/03/Informe-­‐‑Nicaragua-­‐‑final.pdf	
  
	
  
Laderäch,	
  Peter,	
  et	
  al.	
  "Predicted	
  impact	
  of	
  climate	
  change	
  on	
  coffee	
  supply	
  chains."	
  The	
  
Economic,	
  Social	
  and	
  Political	
  Elements	
  of	
  Climate	
  Change.	
  Springer	
  Berlin	
  
Heidelberg,	
  2011.	
  703-­‐‑723.	
  
Lobell,	
  David	
  B.,	
  et	
  al.	
  "Nonlinear	
  heat	
  effects	
  on	
  African	
  maize	
  as	
  evidenced	
  by	
  historical	
  
yield	
  trials."	
  Nature	
  Climate	
  Change	
  1.1	
  (2011):	
  42-­‐‑45.	
  
	
  

108	
  

	
  
Mendez,	
  V.	
  Ernesto,	
  et	
  al.	
  "Agrobiodiversity	
  and	
  Shade	
  Coffee	
  Smallholder	
  Livelihoods:	
  A	
  
Review	
  and	
  Synthesis	
  of	
  Ten	
  Years	
  of	
  Research	
  in	
  Central	
  America*."	
  The	
  
Professional	
  Geographer	
  62.3	
  (2010):	
  357-­‐‑376.	
  
	
  
Louhaichi,	
  Mounir,	
  et	
  al.	
  "Financial	
  incentives:	
  Possible	
  options	
  for	
  sustainable	
  rangeland	
  
management?."	
  Journal	
  of	
  Environmental	
  Management	
  180	
  (2016):	
  493-­‐‑503.	
  
	
  
Ovalle-­‐‑Rivera,	
  Oriana,	
  Peter	
  Läderach,	
  Christian	
  Bunn,	
  Michael	
  Obersteiner,	
  and	
  GÜtz	
  
Schroth.	
  "Projected	
  Shifts	
  in	
  Coffea	
  arabica	
  Suitability	
  among	
  Major	
  Global	
  
Producing	
  Regions	
  Due	
  to	
  Climate	
  Change."	
  (2015):	
  e0124155.	
  
	
  
Perfecto,	
  Ivette,	
  et	
  al.	
  "Shade	
  Coffee:	
  A	
  Disappearing	
  Refuge	
  for	
  
Biodiversity."	
  BioScience	
  46.8	
  (1996).	
  
	
  
Perfecto,	
  Ivette,	
  et	
  al.	
  "Shaded	
  coffee	
  and	
  the	
  stability	
  of	
  rainforest	
  margins	
  in	
  northern	
  
Latin	
  America."	
  Stability	
  of	
  Tropical	
  Rainforest	
  Margins.	
  Springer	
  Berlin	
  Heidelberg,	
  
2007.	
  225-­‐‑261.	
  
	
  
Petrin,	
  Amil,	
  and	
  Kenneth	
  Train.	
  "A	
  control	
  function	
  approach	
  to	
  endogeneity	
  in	
  consumer	
  
choice	
  models."	
  Journal	
  of	
  Marketing	
  Research	
  47.1	
  (2010):	
  3-­‐‑13.	
  
	
  
Ponte,	
  Stefano.	
  "Thelatte	
  revolution'?	
  Regulation,	
  markets	
  and	
  consumption	
  in	
  the	
  global	
  
coffee	
  chain."	
  World	
  development	
  30,	
  no.	
  7	
  (2002):	
  1099-­‐‑1122.	
  
	
  
Rijkers,	
  Bob,	
  Caterina	
  Ruggeri	
  Laderchi,	
  and	
  Francis	
  Teal.	
  "Who	
  benefits	
  from	
  promoting	
  
small	
  and	
  medium	
  scale	
  enterprises?	
  Some	
  empirical	
  evidence	
  from	
  Ethiopia."	
  Some	
  
Empirical	
  Evidence	
  from	
  Ethiopia	
  (May	
  1,	
  2008).	
  World	
  Bank	
  Policy	
  Research	
  
Working	
  Paper	
  Series,	
  Vol	
  (2008).	
  
	
  
Valkila,	
  Joni.	
  "Fair	
  Trade	
  organic	
  coffee	
  production	
  in	
  Nicaragua—Sustainable	
  development	
  
or	
  a	
  poverty	
  trap?."	
  Ecological	
  Economics	
  68.12	
  (2009):	
  3018-­‐‑3025.	
  
	
  
van	
  Rikxoort,	
  Henk,	
  GÜtz	
  Schroth,	
  Peter	
  Läderach,	
  and	
  Beatriz	
  Rodríguez-­‐‑Sánchez.	
  "Carbon	
  
footprints	
  and	
  carbon	
  stocks	
  reveal	
  climate-­‐‑friendly	
  coffee	
  production."	
  Agronomy	
  
for	
  Sustainable	
  Development	
  (2014):	
  1-­‐‑11.	
  
	
  
Verhofstadt,	
  Ellen,	
  and	
  Miet	
  Maertens.	
  "Smallholder	
  cooperatives	
  and	
  agricultural	
  
performance	
  in	
  Rwanda:	
  do	
  organizational	
  differences	
  matter?."Agricultural	
  
Economics	
  45.S1	
  (2014a):	
  39-­‐‑52.	
  
	
  
Verhofstadt,	
  Ellen,	
  and	
  Miet	
  Maertens.	
  "Can	
  Agricultural	
  Cooperatives	
  Reduce	
  Poverty?	
  
Heterogeneous	
  Impact	
  of	
  Cooperative	
  Membership	
  on	
  Farmers'	
  Welfare	
  in	
  
Rwanda."	
  Applied	
  Economic	
  Perspectives	
  and	
  Policy(2014b):	
  ppu021.	
  

	
  

109	
  

Vermeulen,	
  Sonja	
  J.,	
  et	
  al.	
  "Addressing	
  uncertainty	
  in	
  adaptation	
  planning	
  for	
  
agriculture."	
  Proceedings	
  of	
  the	
  National	
  Academy	
  of	
  Sciences	
  110.21	
  (2013):	
  8357-­‐‑
8362.	
  
	
  
Virapongse,	
  Arika,	
  et	
  al.	
  "A	
  social-­‐‑ecological	
  systems	
  approach	
  for	
  environmental	
  
management."	
  Journal	
  of	
  environmental	
  management	
  178	
  (2016):	
  83-­‐‑91.	
  
	
  
Weber,	
  Jeremy	
  G.	
  "How	
  much	
  more	
  do	
  growers	
  receive	
  for	
  Fair	
  Trade-­‐‑organic	
  
coffee?."	
  Food	
  Policy	
  36.5	
  (2011):	
  678-­‐‑685.	
  
	
  
Wollni,	
  Meike,	
  David	
  R.	
  Lee,	
  and	
  Janice	
  E.	
  Thies.	
  "Conservation	
  agriculture,	
  organic	
  
marketing,	
  and	
  collective	
  action	
  in	
  the	
  Honduran	
  hillsides."	
  Agricultural	
  
Economics	
  41.3-­‐‑4	
  (2010):	
  373-­‐‑384.	
  
	
  
Wollni,	
  Meike,	
  and	
  Manfred	
  Zeller.	
  "Do	
  farmers	
  benefit	
  from	
  participating	
  in	
  specialty	
  
markets	
  and	
  cooperatives?	
  The	
  case	
  of	
  coffee	
  marketing	
  in	
  Costa	
  
Rica1."	
  Agricultural	
  Economics	
  37.2-­‐‑3	
  (2007):	
  243-­‐‑248.	
  
	
  
Wooldridge,	
  Jeffrey	
  M.	
  "Control	
  function	
  methods	
  in	
  applied	
  econometrics."	
  Journal	
  of	
  
Human	
  Resources	
  50.2	
  (2015):	
  420-­‐‑445.	
  
	
  
	
  
	
  
	
  
	
  

	
  

110	
  

Chapter	
  5:	
  Conclusions	
  
	
  
“Saving	
  our	
  planet,	
  lifting	
  people	
  out	
  of	
  poverty,	
  advancing	
  economic	
  growth...	
  these	
  are	
  one	
  
and	
  the	
  same	
  fight.	
  We	
  must	
  connect	
  the	
  dots	
  between	
  climate	
  change,	
  water	
  scarcity,	
  energy	
  
shortages,	
  global	
  health,	
  food	
  security	
  and	
  women's	
  empowerment.	
  Solutions	
  to	
  one	
  problem	
  
must	
  be	
  solutions	
  for	
  all.”	
  	
  
	
  
Ban	
  Ki-­‐‑Moon	
  
	
  
	
  
From	
  the	
  chapters	
  in	
  this	
  dissertation,	
  a	
  picture	
  begins	
  to	
  emerge,	
  describing	
  a	
  sector	
  in	
  
great	
  peril,	
  yet	
  with	
  high	
  potential.	
  	
  Coffee	
  in	
  Nicaragua	
  is	
  the	
  main	
  source	
  of	
  income	
  for	
  
thousands	
  of	
  smallholder	
  producers,	
  and	
  is	
  the	
  country’s	
  most	
  important	
  agricultural	
  
export.	
  	
  Given	
  the	
  vulnerability	
  of	
  coffee	
  to	
  the	
  impacts	
  of	
  climate	
  change	
  there	
  is	
  a	
  
consensus	
  amongst	
  scientists,	
  development	
  practitioners	
  and	
  policy	
  makers	
  that	
  
adaptation	
  strategies	
  are	
  imperative	
  and	
  in	
  some	
  cases	
  urgently	
  needed	
  to	
  ensure	
  a	
  
sustainable	
  future	
  for	
  the	
  coffee	
  value	
  chain,	
  and	
  especially	
  for	
  the	
  producers	
  who	
  depend	
  
on	
  coffee	
  production	
  as	
  their	
  main	
  source	
  of	
  income.	
  
	
  
The	
  dissertation	
  starts	
  with	
  a	
  broad	
  description	
  of	
  the	
  characteristics	
  of	
  coffee	
  farmers	
  in	
  
Matagalpa,	
  exploring	
  their	
  capacities	
  and	
  incentives	
  for	
  climate	
  change	
  adaptation,	
  
including	
  an	
  exploration	
  of	
  their	
  attitudes	
  towards	
  risk.	
  It	
  continues	
  with	
  an	
  analysis	
  of	
  the	
  
conditions	
  under	
  which	
  farmers	
  choose	
  to	
  incorporate	
  shade	
  into	
  their	
  coffee	
  fields;	
  a	
  

	
  

111	
  

practice	
  that	
  helps	
  to	
  promote	
  biodiversity	
  conservation	
  and	
  known	
  to	
  mitigate	
  some	
  of	
  
the	
  impacts	
  of	
  climate	
  change.	
  	
  Finally,	
  I	
  conclude	
  by	
  analyzing	
  how	
  and	
  the	
  degree	
  to	
  
which	
  cooperatives	
  support	
  farmers	
  in	
  the	
  adoption	
  of	
  improved	
  production	
  practices	
  that	
  
enable	
  them	
  build	
  adaptive	
  capacity	
  to	
  climate	
  change.	
  
	
  
How	
  will	
  these	
  coffee	
  producers	
  cope	
  with	
  higher	
  temperatures,	
  droughts,	
  and	
  erratic	
  and	
  
extreme	
  rainfall?	
  There	
  is	
  no	
  doubt	
  that	
  the	
  institutional	
  capacity	
  of	
  organizations	
  within	
  
the	
  sector	
  will	
  play	
  an	
  important	
  role.	
  	
  The	
  research	
  shows	
  that	
  farmer	
  cooperatives	
  can	
  
and	
  do	
  provide	
  many	
  services	
  to	
  farmers,	
  from	
  input	
  provision	
  to	
  trainings	
  and	
  extension	
  
services.	
  	
  And	
  I	
  have	
  learned	
  that	
  farmers	
  who	
  belong	
  to	
  cooperatives	
  tend	
  to	
  adopt	
  
practices	
  that	
  help	
  them	
  build	
  adaptive	
  capacity	
  to	
  climate	
  change,	
  in	
  particular,	
  through	
  
the	
  adoption	
  of	
  water	
  conservation	
  practices.	
  	
  Yet	
  I	
  have	
  also	
  learned	
  that	
  about	
  74%	
  of	
  
farmers	
  who	
  belong	
  to	
  cooperatives	
  are	
  not	
  satisfied	
  with	
  the	
  services	
  that	
  their	
  
cooperatives	
  provide	
  and	
  that	
  when	
  the	
  2012	
  leaf	
  rust	
  outbreak	
  began	
  damaging	
  coffee	
  
trees	
  and	
  reducing	
  yields,	
  the	
  response	
  from	
  cooperatives	
  and	
  other	
  organizations	
  within	
  
the	
  sector,	
  was	
  seen	
  as	
  woefully	
  insufficient.	
  	
  Farmers	
  reported	
  that	
  pesticides	
  were	
  
provided	
  that	
  did	
  not	
  work	
  to	
  eliminate	
  the	
  leaf	
  rust,	
  and	
  that	
  without	
  an	
  effective	
  solution,	
  
the	
  leaf	
  rust	
  spread	
  and	
  resulted	
  in	
  a	
  catastrophic	
  yield	
  reduction	
  and	
  plant	
  loss.	
  	
  Nearly	
  
75%	
  of	
  farmers	
  in	
  the	
  sample	
  reported	
  losses	
  due	
  to	
  plant	
  diseases,	
  many	
  of	
  them	
  
uprooting	
  their	
  coffee	
  trees	
  and	
  establishing	
  varieties	
  reported	
  to	
  be	
  more	
  resistant	
  to	
  leaf	
  
rust,	
  yet	
  of	
  lower	
  coffee	
  quality.	
  
	
  

	
  

112	
  

It	
  is	
  for	
  this	
  reason,	
  then,	
  that	
  building	
  better	
  institutional	
  capacity	
  within	
  the	
  organizations	
  
that	
  serve	
  farmers	
  must	
  be	
  prioritized.	
  	
  I	
  know,	
  from	
  this	
  work	
  and	
  from	
  a	
  review	
  of	
  the	
  
literature,	
  that	
  organizations	
  can	
  help	
  farmers	
  in	
  measurable	
  ways,	
  such	
  as	
  through	
  the	
  
provision	
  of	
  inputs	
  and	
  trainings,	
  as	
  well	
  as	
  more	
  indirectly	
  through	
  the	
  creation	
  of	
  social	
  
capital	
  and	
  safety	
  nets.	
  	
  I	
  have	
  also	
  observed	
  that	
  in	
  Nicaragua	
  issues	
  of	
  mismanagement	
  
and	
  corruption	
  have	
  tarnished	
  the	
  reputation	
  of	
  many	
  of	
  these	
  organizations;	
  compounded	
  
upon	
  this	
  are	
  problems	
  arising	
  from	
  the	
  leaf	
  rust	
  epidemic.	
  	
  An	
  important	
  task	
  ahead,	
  then,	
  
lies	
  in	
  strengthening	
  the	
  capacity	
  of	
  cooperatives	
  and	
  other	
  organizations	
  to	
  enable	
  
farmers	
  to	
  cope	
  with	
  natural	
  shocks	
  as	
  they	
  become	
  more	
  challenging	
  and	
  more	
  frequent.	
  	
  
An	
  approach	
  that	
  incorporates	
  scientific	
  knowledge	
  on	
  effective	
  strategies	
  that	
  coffee	
  
farmers	
  can	
  adopt,	
  together	
  with	
  institutional	
  capacity	
  building	
  for	
  the	
  organizations	
  
already	
  in	
  place	
  to	
  support	
  improved	
  management	
  of	
  their	
  resources	
  must	
  be	
  implemented	
  
together.	
  
	
  
This	
  work	
  also	
  highlights	
  the	
  importance	
  of	
  developing	
  interventions	
  that	
  account	
  for	
  the	
  
preferences	
  and	
  needs	
  of	
  marginalized	
  populations.	
  	
  In	
  Nicaragua,	
  and	
  around	
  the	
  globe,	
  
women	
  are	
  disadvantaged	
  in	
  their	
  access	
  to	
  goods	
  and	
  services.	
  	
  The	
  women	
  in	
  the	
  sample	
  
not	
  only	
  have	
  less	
  land	
  to	
  grow	
  coffee,	
  but	
  the	
  land	
  that	
  they	
  own	
  is	
  less	
  productive	
  and	
  
yields	
  less	
  income.	
  	
  Additionally,	
  these	
  women	
  are	
  significantly	
  more	
  food	
  insecure	
  than	
  
their	
  male	
  counterparts.	
  Food	
  insecure	
  households,	
  defined	
  as	
  households	
  that	
  lack	
  
sufficient	
  and	
  nutritious	
  food,	
  are	
  also	
  less	
  prone	
  to	
  making	
  riskier	
  choices,	
  I	
  find.	
  While	
  
such	
  choices	
  may	
  be	
  beneficial	
  for	
  households	
  that	
  cannot	
  afford	
  to	
  compound	
  their	
  
already	
  existing	
  vulnerabilities	
  with	
  added	
  risk,	
  for	
  other	
  households	
  it	
  may	
  mean	
  missing	
  

	
  

113	
  

out	
  on	
  potential	
  opportunities	
  brought	
  by	
  the	
  adoption	
  of	
  improved	
  technologies	
  and	
  
production	
  practices,	
  opportunities	
  that	
  hold	
  potential	
  for	
  building	
  adaptive	
  capacity	
  in	
  the	
  
face	
  of	
  a	
  changing	
  climate.	
  
	
  
I	
  believe	
  that	
  a	
  multidimensional	
  and	
  customizable	
  approach	
  is	
  needed	
  in	
  support	
  of	
  
farmer	
  organizations	
  in	
  a	
  position	
  to	
  promote	
  farm-­‐‑level	
  adaptive	
  capacity	
  to	
  climate	
  
change.	
  	
  What	
  works	
  for	
  some	
  may	
  be	
  ineffective	
  for	
  others,	
  and	
  special	
  attention	
  must	
  be	
  
given	
  to	
  issues	
  of	
  equity	
  and	
  access.	
  
	
  
Despite	
  the	
  vulnerability	
  of	
  coffee	
  growing	
  regions	
  to	
  declining	
  crop	
  suitability	
  due	
  to	
  
climate	
  change,	
  there	
  is	
  also	
  great	
  potential	
  in	
  the	
  response	
  of	
  the	
  sector	
  to	
  this	
  threat.	
  	
  
During	
  the	
  years	
  in	
  which	
  the	
  demand	
  for	
  coffee	
  was	
  growing,	
  many	
  policies	
  promoted	
  the	
  
intensification	
  of	
  coffee	
  production,	
  endangering	
  the	
  biodiversity	
  richness	
  that	
  traditional	
  
coffee	
  farms	
  often	
  enjoyed.	
  	
  The	
  results	
  from	
  this	
  research	
  suggest	
  that	
  if	
  the	
  price	
  of	
  coffee	
  
goes	
  up,	
  increasing	
  coffee	
  farmer	
  incomes,	
  farmers	
  will	
  be	
  more	
  willing	
  to	
  introduce	
  shade	
  
trees	
  into	
  their	
  farms.	
  	
  This	
  is	
  an	
  important	
  innovation	
  that	
  helps	
  at	
  all	
  levels	
  of	
  the	
  value	
  
chain	
  and	
  beyond.	
  	
  It	
  improves	
  the	
  suitability	
  of	
  the	
  farms	
  for	
  coffee	
  production,	
  which	
  in	
  
turn	
  protects	
  the	
  livelihoods	
  of	
  coffee	
  farmers	
  (through	
  income	
  generation	
  and	
  alternative	
  
food	
  sources).	
  	
  Additionally,	
  planting	
  shade	
  trees	
  helps	
  to	
  conserve	
  biodiversity,	
  protect	
  the	
  
soils,	
  and	
  serves	
  as	
  a	
  refuge	
  for	
  migratory	
  species.	
  With	
  an	
  eye	
  to	
  the	
  longer-­‐‑term	
  viability	
  
of	
  the	
  country’s	
  coffee	
  sector,	
  there	
  is	
  good	
  reason	
  to	
  believe	
  that	
  even	
  small	
  coffee	
  price	
  
incentives	
  for	
  shade	
  grown	
  coffee	
  will	
  result	
  in	
  positive	
  human	
  and	
  natural	
  synergies	
  and	
  a	
  
more	
  sustainable	
  future	
  for	
  coffee	
  in	
  Nicaragua’s	
  Matagalpa	
  region.	
  

	
  

114