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

Outline

• Background

• Model overview & methodology

• Malawi case study & data

• Results

• Conclusions & future model development

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

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]

PMP methodology 1

1. Max 𝜋 = 𝑦𝑝 ′𝑥 − 𝑐′𝑥

s.t. 𝐴𝑥 ≤ 𝑏

obj. function (LP model)

resource constr.

𝑥 ≤ 𝑥𝑜𝑏𝑠 calibration constr.

𝑥 ≥ 0

2. Use the shadow prices of the calibrating constraint (𝜆𝐿𝑃) to estimate the implicit cost parameters that calibrate the model to the survey data: 𝑄𝑗𝑗 = (𝜆𝐿𝑃𝑗+𝑐𝑗) 𝑥𝑜𝑏𝑠𝑗

3. Max 𝜋 = 𝑦𝑝 ′𝑥 − 𝑥′𝑄 𝑥/2

s.t. 𝐴𝑥 ≤ 𝑏

𝑥 ≥ 0

obj. function (QP model)

resource constr.

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

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

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

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

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)

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?

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

Mai

ze a

rea

/ to

t cr

op

are

a

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

MSD

mai

ze a

rea/

tot

mai

ze

are

a

Resource availability (as % of optimal solution)

• Capital constraint has strong effect on crop choice with a small change on the proportion that is MSD

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

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]

Thank you!

If interested in FAO-EPIC CSA evidence-base:

www.fao.org/climatechange/epic