www.fao.org/climatechange/epic This presentation was prepared to as background to the Scientific conference on Climate-Smart Agriculture held in Montpellier, France, on 16-18 March 2015.

1. Using whole-farm models
for policy analysis of
Climate Smart Agriculture
A. Paolantonio1, G. Branca12, R. Cavatassi1, A.
Arslan1, L. Lipper1, O. Cacho3
(1FAO, 2Tuscia University, 3University of New England)
Montpellier
March 16-18, 2015

2. Outline
• Background
• Model overview & methodology
• Malawi case study & data
• Results
• Conclusions & future model
development

3. Background
• The FAO-EPIC program aims at building evidence-based
agricultural development strategies, policies and
investment frameworks to achieve the objectives of CSA
in Malawi, Zambia and Viet Nam
• Why? To create a strong link between research, policy,
and investments
• How? By providing solid and scientific evidence
combining qualitative with quantitative analysis using
primary and secondary data at HH and community level +
climate and agro-ecological data + institutional data

4. A model for CSA policy analysis
• Econometric models based on HH data are essential tools
for policy analysis (but ex-post only)
• Mathematical programming (MP) models of farm HHs
allow ex-ante analyses to be conducted as well
• The key is to calibrate MP optimization models to be
consistent with the evidence base (and thus believable) 
Positive Mathematical Programming (PMP) [Howitt, 1995]
• PMP was developed for a policy analysis that utilizes all the
available information, no matter how scarce [especially
suitable in agricultural economics]

5. PMP methodology 1
1. Max 𝜋 = 𝑦𝑝 ′
𝑥 − 𝑐′
𝑥
s.t. 𝐴𝑥 ≤ 𝑏
obj. function (LP model)
resource constr.
𝑥 ≤ 𝑥 𝑜𝑏𝑠 calibration constr.
𝑥 ≥ 0
2. Use the shadow prices of the calibrating constraint (𝜆 𝐿𝑃) to
estimate the implicit cost parameters that calibrate the
model to the survey data: 𝑄𝑗𝑗 = (𝜆 𝐿𝑃𝑗+𝑐𝑗) 𝑥 𝑜𝑏𝑠𝑗
3. Max 𝜋 = 𝑦𝑝 ′ 𝑥 − 𝑥′ 𝑄 𝑥/2
s.t. 𝐴𝑥 ≤ 𝑏
𝑥 ≥ 0
obj. function (QP model)
resource constr.

6. PMP methodology 2
• Sensitivity analysis implies parametric change in:
- output prices; or
- technological coefficients (technical relationships
between inputs and outputs); or
- resource availability (constraints)
that will produce a response on the model’s new solution
• Basically, it determines which resource constraint has the
most potential impact given the optimal solution
• It helps identifying relevant areas of policy intervention
based on the observed situation

7. The MP matrix model
Technical
coefficients
Activities Constraints
Crops Livestock
Off-
farm
labor… …
Max/Min C1 … Cn L1 … Ln X1
Land ac11 … ac1n al11 … al1n …  b1
Labour ac21 … ac2n al21 … al2n …  b2
Capital … … … … … … …  b3
Fertilizer … … … … … … …  b4
Water … … … … … … …  b5
… acm1 … acmn alm1 … almn …  b6
Obj. function

8. PMP applied to the case of Malawi
• We develop a whole-farm model using PMP with ad
hoc collected plot level data on CSA in MW
• So the model:
- is based on economic theory (optimizing
behaviour)
- …but has the beauty of utilizing objective data,
and therefore
- a great potential to provide policy insights through
simulations based on observed outcomes

9. Malawi case study & data
• CSA survey carried out in 2013 by FAO-EPIC in
collaboration with country FAO office
• HH sample and CSA practices selection on the basis
of agriculture screening and field visits
• Final statistical sample made of 524 HHs cultivating
1,433 fields over 11 Extension Planning Areas (EPA)
located in 4 districts (Mzimba, Kasungu, Balaka,
Ntcheu) across 4 AEZ
• Reference cropping season is 2012-13
• Main evidence found suggests:
- Low diffusion of SLM for all crops: 84% tillage
systems (conventional), only 16% MSD systems
[mainly maize = 61% tillage vs 39% MSD]
- No significant difference by AEZ and district/EPA
- High heterogeneity of SLM technology packages

10. Results from the Base Case 1/2
0 1,000 2,000 3,000
Tobacco tillage
S-beans tillage
G-nuts tillage
Maize MSD
Maize tillage
Yield (kg/ha)
0 200 400 600
Tobacco tillage
S-beans tillage
G-nuts tillage
Maize MSD
Maize tillage
Capital required ($/ha)
0 50 100 150 200 250
Tobacco tillage
S-beans tillage
G-nuts tillage
Maize MSD
Maize tillage
Labour required (pd/ha)
0 100 200 300 400 500
Tobacco tillage
S-beans tillage
G-nuts tillage
Maize MSD
Maize tillage
Fertilizer required (kg/ha)

11. Results from the Base Case 2/2
0 50 100 150 200 250 300
Tobacco tillage
S-beans tillage
G-nuts tillage
Maize MSD
Maize tillage
Area planted (ha)
How can we increase the
adoption of this system?

12. Sensitivity Analysis
• Labour constraint has
almost no effect on crop
choice but it significantly
matters in the decision to
adopt MSD vs tillage
0.40
0.45
0.50
0.55
0.60
0.65
0.70
50 60 70 80 90 100
Maizearea/totcroparea
Resource availability (as % of optimal solution)
Labour
Capital
0.00
0.05
0.10
0.15
0.20
0.25
50 60 70 80 90 100
MSDmaizearea/totmaize
area
Resource availability (as % of optimal solution)
• Capital constraint has
strong effect on crop
choice with a small
change on the
proportion that is MSD

13. Conclusions
• PMP models have great potential in providing evidence-
based insights for CSA policy recommendations
• Maize under MSD systems show higher yields, but also
higher capital and labour requirements compared to
tillage systems in Malawi
• Mainly labour constraints the adoption of MSD systems
in Malawi, whereas the effects of changes in the
availability of capital are limited
• Interventions should be primarily targeted to address the
labour constraint

14. Future model development
• More simulations on different model parameters
• Exploit full sample information: calibrate the
model for individual HHs (but need a correct
statistical approach)
• Multi-period modelling
• Extend the analysis to Zambia for cross-country
comparison
• Add livestock component [Zambia]

15. Thank you!
If interested in FAO-EPIC CSA evidence-base:
www.fao.org/climatechange/epic