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Decadal Satellite Observations and the Myth of Malawian Farm Input Subsidy Programme

http://www.fao.org/agriculture/crops/thematic-sitemap/theme/spi/en/

Presentation by Joseph P. Messina (Michigan State University) exploring in depth the Farm Input Subsidy Program (FISP) in Malawi through a crop modelling approach. The presentation was delivered in occasion of the “Putting Perennial crops to work in practice” workshop in Bamako, Mali (1-5 September 2015).

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Decadal Satellite Observations and the Myth of Malawian Farm Input Subsidy Programme

  1. 1. 9/3/15 1 Joseph  P.  Messina  Ph.D. Associate  Dean  for  Research  and  Professor College  of  Social  Science,  Center  for  Global  Change  and  Earth   Observations,  the  Department  of  Geography,  Michigan  State   University.   jpm@msu.edu 517-­‐432-­‐3436 Decadal Satellite Observations and the Myth of Malawian Farm Input Subsidy Programme Farm Input Subsidy Program (FISP) in Malawi • This widely discussed subsidy program has been praised in the press as “brilliant” leading to “(doubled production) within one harvest season” (J. Sachs, NYT April 19, 2012) • All popular press sources referred to government statistics regarding production yields as having significantly increased and being positive in the near term for small-holder farmers. • As Predro Sanchez commented (Nature: 2015) “in spite of criticisms by donor agencies and academics, the seed and fertilizer subsidies provided food security to millions of Malawians.” • This optimistic assessment of potential for an African Green Revolution must be tempered by the fact that the Malawian production miracle is largely a myth. http://www.faceofmalawi.com/2014/10/donors-­‐demand-­‐ full-­‐details-­‐of-­‐fisp/
  2. 2. 9/3/15 2 An  Accidental  Discovery  and   how  I  started  this  process. • Perennial  grains  to  improve  resilience   to  climate  change. • Malawi,  Mali,  Ghana,  Tanzania • Pigeon  Pea  in  Malawi • Where  are  the  marginal  lands? 1. Agricultural  lands? 2. Production  trends 3. Inter-­‐ vs.  inter-­‐annual  trends Crop  Models? • DSSAT,  SALUS,  APSIM,  … • Scale • Data  uncertainty
  3. 3. 9/3/15 3 Yield? • Yield  =  f(edaphic  *  climate  *  crop  *  social) • Multiscalarprocesses • Soils  are  local  but  also  a  product  of   watershed  dynamics • Climate  is  global  but  also  a  local   physical  process • Social  is  endogenous  (labor,  capital,…)   and  exogenous  (FISP,  market  prices,   trade,  …) • Agronomy  is  local  but  also  responsive   across  scale • Food  security  scales  from  local  to   national.     Global  Change  Biology.  doi:  10.1111/gcb.12838   Uncertainty & Social vs. Biophysical Drivers • CRU trend versus ERA40 trend in rainfall. • Blue=wetter trend, • Orange = drying trend. • Different spatial scales and process-based methods that use THE SAME STATION DATA lead to drastically different outcomes based mainly on scale (i.e. where blue and orange overlap). • So, How do we fix this problem?
  4. 4. 9/3/15 4 Where  are  the  marginal   agricultural  lands? Mozambique Tanzania Zimbabwe Zambia Agricultural Land Classification Disagreement in Malawi Coverage of Agricultural Land FAO 2010 & IFPRI 2002 - 35% IFPRI 2002 Only - 26% FAO 2010 Only - 16% Non Ag 0 10050 Kilometers • What  does  this  even  mean? • Standard  LULC  products  answer   the  wrong  question  (How  much?)   and  they  try  to  minimize  overall   classification  error • I  need  to  minimize  errors  of   commission.  – a  very  reliable   sample… • Solution:  use  all  LULC  products
  5. 5. 9/3/15 5 RS  based  LAI  vs.  SALUS   Crop  Model But  where  is  the  FISP   bump??? Soils  and  climate  and  other  things  we  need  to   distinguish • Marginal  lands  – relative  terms • Soils  – need  to  extract  soil  drivers • Climate  – inter  and  intra  annual  variability • Scale  – local  heterogeneity  is  likely  a  social  process
  6. 6. 9/3/15 6 Factors: 1. Slope 2. Soil erosion hazard 3. Soil bulk density 4. Soil organic matter 5. Soil cation exchange capacity 6. Soil texture 7. Soil pH 8. Soil drainage 9. Soil depth Methods: Average of 1. Geometric mean 2. Rabia 3. Square root 4. Storie 5. Weighted sum Malawi  Agricultural  Land  Suitability Categories Area (ha) Percentage Highly suitable 915431 7.8 Moderately suitable 2458882 20.8 Marginal suitable 2379508 20.1 Suitable land total 5753821 48.7 Poorly suitable 1081160 9.2 Permanently unsuitable 2496676 21.1 Unsuitable land total 3577836 30.3 Land subtotal 9331657 79.0 Water 2479429 21.0 Malawi total area 11811086 100.0
  7. 7. 9/3/15 7 Optimal  Pigeon  Pea • Niche  generation • Extensive  data  – but  a  manageable   process • Organized  by  management  unit  to   facilitate  scaling  and  adoption • But, • Sensitivity  to  climate? • Variability   across  scales? • This  is  not  substantially  different  than   a  well  parameterized  crop  model
  8. 8. 9/3/15 8 Production  trends • Relative  terms • Malawi  specific  scale • Sensitive • High  inter-­‐annual  variability • Missing? • Scaled  trends • Sources  of  the  variability
  9. 9. 9/3/15 9 Rainfall  and  Productivity  Trends  on  Agricultural  Land  in  Malawi
  10. 10. 9/3/15 10 Now  what? • We  can  improve  targeting. • Biophysical  solutions   need   to  target  biophysical   problems • Social  solutions   … • Adoption  is  cultural • Next  steps? Climate  &  Changing  Seasons
  11. 11. 9/3/15 11 FISP  comments • There  are  multiple  lines  of  evidence  that  on  many  farms  soil  organic   matter  status  has  degraded  to  a  level  that  no  longer  support’s   maize   growth  or  responsiveness   to  fertilizer.   • No  clear  trends  of  improvement  (Dorward et  al.,  2010a).   • The  incremental  production  estimates  are,  however,  considerably  lower  than  those   implicit  in  the  national  crop  estimates  for  maize  production,  with  much  lower   variation.  (Dorwardet  al.,  2010a).   • Annual  changes  in  maize  prices  also  suggest  that  post-­‐subsidy  maize  supplies  have   been  lower  than  suggestedby  the  national  crop  estimates.  (Dorwardand  Chirwa,   2011) • “It  is  widely  believed  that  the  2007  Malawi  harvest  was  overestimated  by  at  least   25%. If  the  government  had  been  able  to  produce  a  more  accurate  estimate  of  crop   production,  it  might  not  have  arranged  to  export  maize,  which  in  turn  might  have   avoided  the  huge  price  surge  in  late  2007/early  2008  which  caused  great  hardship   for  maize  buying  households.”  (Jayne,  2008)
  12. 12. 9/3/15 12 The  development  orthodoxy  on  agricultural • Intensification   (all  the  time) • Better  varieties • Better  supply  chains • Better  subsidies • Scaling  matters • A  function  of  targeting • The  Climate   Change  Quandary • The  new  varieties  may  not  be  appropriate • Tools • Adoption  potential • Unintended   Consequences • Disease  – livestock,  plant,  and  human • Climate  and  Ag  feedbacks • Big  Data  solutions   will  help  solve  some  of  the   scaling,  climate,   and  targeting  challenges   faced  by  the  development  community.
  13. 13. 9/3/15 13 Funding  Provided  by: • The  Bill  &  Melinda  Gates  Foundation • USAID  – Global  Development  Lab  &  GCFSI Thank You / Questions Reference:   Decadal   Satellite   Observations   and  the   Myth  of  Malawian  Farm  Input   Subsidy   Programme.  2015.   Messina,  J.  Peter,   B.  Li,  G.  DeVisser,  M.  Snapp,   S.  Moore,  N.  Nejadhashemi,   P.   Putting   Perennial   crops  to  work  in  practice:   Pigeonpeas and   Sorghum.   Bamako,   Mali

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