Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models
7-8 May 2015, Bioversity, Rome
DAHBSIM, a Dynamic
Agricultural Household BioEconomic Simulation Model
Theoretical and methodological issues regarding bio-economic model
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
1
DAHBSIM TEAM
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Coordination: Guillermo Flichman (a)
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Model code development: María Blanco (b)*; Sophie Drogué **(c)
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Agronomic modeling: Hatem Belhouchette (a), Roza Chenoune (a), Wajid
Nasim (a)
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Livestock module: Adam Komarek (d), James Hawkins (d)
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Household Typology: Roza Chenoune and Loubna El Ansari (a)
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DAHBSIM is part of IFPRI BioSight Project, coordinated by Siwa Msangi
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(a) Centre International de Hautes Etudes Agronomiques Méditerranéennes
Institut Agronomique Méditerranéen de Montpellier
(b) Universidad Politécnica de Madrid-Escuela Técnica Superior de Ingenieros Agrónomos
(c) Institut National de la Recherche Agronomique, UMR MOISA.
(d) International Food Policy Research Institute
* in 2014;
**after January 2015
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
2
DAHBSIM TEAM
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The DAHBSIM team participated in the development of
several bio-economic models.
The most relevant are:
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Cebalat Model: A recursive stochastic supply model (1)
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FSSIM-MP: static, generic, positive, supply model (2)
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FSSIM-DEV: static, generic, positive household model (3)
DAHBSIM has combined characteristics of these previous
models and new features
(1) Blanco M.Belhouchette H. Flichman G. (2012)
(2) Louhichi K.,Belhouchette H.Blanco M.,Flichman G. et al ( 2010)
(3) Louhichi K. Belhouchette H., Blanco M., Flichman G. et al (2013)
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
3
Principal characteristics of DAHBSIM
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The production “side” of the model :
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Based on the representation of activities: production
processes
The demand “side” of the model :
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Based on a demand function.
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The model applies the hypothesis of non-separability of
production and consumption decisions as well as allocation of
available household labor: this is reflected in the objective
function.

Dynamics are based on a re-initialization of soil conditions after
each iteration, allowing to evaluate the sustainability of the
system in term of natural resources
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
4
Activities and Products (2)
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The diagram shows the causal relationships implied in this type of
model.
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“Products” (wheat, straw, NO3 emissions…) are outputs of the
activities.
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One activity (or production process) has several outputs – joint
production
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One product can be produced by several activities
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Considers positive and negative jointness associated to the
production process
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It permits assessing in an integrated manner policies linked as
well to products as to production processes
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
5
Activities and Products (3)
The above diagram represents an input-output linear
vector concerning one single production activity.
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
6
Joint products in DAHBSIM
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Two types of joint products can be considered:
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Those that can be source of externalities (positive or negative)
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Those that are consequence of the production process and will affect
production on time
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In the first type we consider all type of emissions (nitrate and pesticides
pollution, GHG emissions as well as impacts on biodiversity, on nutrition, etc.

In the second case we consider impacts of production processes in period t
that change the conditions for the production in period t+1
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The change in state variables implied in the second case will influence the
production (including all joint products) in the following periods.
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HOW CAN WE BETTER CAPTURE THESE ISSUES ?
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Developing feed-backs between the economic and the agronomic processes
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
7
Dynamics in DAHBSIM
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Dahbsim has intertemporal and recursive dynamics.
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Intertemporal because equations are indexed over years and the decision
of households are optimized given a discounted utility.
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Recursive because the year-1’s results obtained at the end of the first
simulation feed the next simulation and so on. Yields obtained at the end
of the first (intertemporal) simulation are multiplied by the biophysical
stress coefficient which increase or decrease the yields in the next
(intertemporal) simulation depending on the effects the precedent crop
produces on the state of the soil (water an N content) .

This procedure allows a proper consideration of livestock, perennial
crops, investment as well as the possibility of feed-backs between the
optimization decisions and their impact on the natural resources
conditions, affecting as well production as all kind of joint products
(sources of externalities included)
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
8
Feed-back between agronomic and economic modules
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First option, (Barbier et al,1999) is to run a biophysical model, include the
results in a first run of recursive-dynamic optimization model, take the results
of the first period, run again the biophysical model, modify the initial
conditions of the dynamic model run it again and so on. It was not a generic
model
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Second option, (Blanco et al 2012) --- is a meta-modeling approach. Out of
simulations with a biophysical model, a simpler model is estimated and this
meta-model is included in the code of the bio-economic model allowing the
reinitialization of the initial conditions for the simulations done for t+1 periods
and so on. The meta-model is not generic
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Third option, (Holden et al 2005) the model includes a biophysical module
and the optimization is performed in an intertemporal loop. The biophysical
module is built out of information of the specific site. It is not a generic
model
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
9
Third option, the Dynamics of DAHBSIM
 DAHBSIM applies a Dynamic-recursive optimization approach:
 An inter-temporal optimization is performed over t years moving time horizon
 First year’s results are retained and recursive calculations (Summary
biophysical model) are introduced before the second optimization, for taking
into account the effects on resources of the previous year choices.
 The intertemporal optimization allows dealing with multiannual activities
(perennial crops, livestock), investment and credit,
 This procedure is repeated for all periods (recursive loop).
 Water and nitrogen contents of the soil are reinitialized before the following
inter-temporal optimization and level of outputs related with each activity
changes (as well the yields as the joint products)
________________________________________________________________________________
Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
10
Structure of the model (1)
At the actual stage of development DAHBSIM contains:
– Objective Function: The basic assumption is that
production, consumption and labor allocation choices are
made simultaneously. It maximizes present value of a
stream of full income: value of sales plus self consumption
and revenue obtained from off-farm activities minus costs.
Risk is taken into account using the mean-standard
deviation approach.
– The biophysical module re-initialize the soil conditions as a
consequence of crop pattern choice in the previous
iteration
– The crop module contains the equations describing the
cropland allocation, the labor use, the rotation constraints,
etc.
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
11
Structure of the model (2)
– The farm module contains the equations defining
the resources constraints and several balances
concerning seeds, food products, labor use, etc.
– The household module contains the equations
defining household demand and time allocation as
well as the demand function
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
12
Structure of the model (3)
• The livestock module describes the animal activities
and calculate manure supply – potential fertilizer
that can go to crop production and well as feed
demand – potentially supplied by crops and crop
residues
• The following modules will be developed in the next
months:
– Investment and credit
– Perennial crops
– Calibration
– Risk
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
13
Structure of the model (4)
• The consumption function
– The Rotterdam demand function will be applied
– We will use the elasticities of consumption goods
for Malawi estimated by Ecker and Quaim (2010)
for a large number of food and non-food products
and also for nutrients using information consistent
with our data.
– The cross-elasticities are not available, but we will
apply the procedure developed by Beguin, Bureau
and Drogué (2003) for obtaining also an
estimation of cross elasticities.
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
14
Simplified DAHBSIM scheme for cropping activities
Regional data, observed
by soil type on plots
allow defining cropping
activities
Household endowments:
available land, labor,
equipment per
Household or Household
Type
Definition of constraints
Costs, prices of inputs
and outputs of activities
and consumption goods
Definition of activities as
input output vectors
OBJECTIVE FUNCTION
Demand function
Intertemporal Optimization
from T1…T10
Soil pattern at T1 provides
information on water and N
content for a new run of the
biophysical module
Intertemporal Optimization
from T2…T11
Activities’vectors are
redefined out of biophysical
simulations
And so on up to T10…T19
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
15
Simplified scheme of the Biophysical module
THIS CALCULATION IS PERFORMED AFTER
EACH ITERATION ON THE DIFFERENT SOIL
TYPES. THE WATER AND NITROGEN CONTENT
IN THE SOIL IS A RESULT OF THE PREVIOUS
CROP IN T-1 RUN + FERTILIZATION AND
IRRIGATION IN T1
Crop Coefficient (Kc)
Weather
PM-ET0
Crop potential
evapotranspiration
Rainfall Irrigation
Soil water
Drainage
Water limited crop
evapotranspiration
Potential crop
evapotranspirationdependent yield
Actual to potential
evapotranspiration
Evapotranspiration
limited yield
N residue
N fertilization
Organic
fertilization
Nitrogen limited
yield
Actual yield (minimum of
the two calculations)
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
Soil N
N Leaching
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Data Issues
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We have access to a very complete Database (*).
But … several problems appear concerning the organization
of data for DAHBSIM
DATA structure used by DAHBSIM has two principal entries:
 Information belonging to each Household
 Information related with production processes
(activities)
 The model is applied on average HHs defined out of a
typology (cluster analysis, hierarchical classification)
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(*) Information provided by the Africa RISING M&E Team, IFPRI. C. Azzari, C. Roberts and H. Beliyou
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
17
Indicators that can be obtained from DAHBSIM
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Socio-economic
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Labor use, discriminated by gender
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Income, from hh production, from off-farm activities
Environmental
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Emissions of GHG, NO3, water use
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Soil fertility
Nutrition conditions
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Calories, and nutrients consumption
Biodiversity
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Possible only if change in land used for production allows to
build an indicator. It could be also possible to simulate
expansion of the cultivated land and impacts on biodiversity,
depending on available information
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
18
References
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Belhouchette H., Blanco M., Wery J., Flichman G. (2012). Use of a bio-economic model to assess the sustainability
of irrigated farming systems: a case study in the Cebalat region in Tunisia. Computers and Electronics in
Agriculture.
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Louhichi, K., Gomez y Paloma, S., Belhouchette,H., Allen,T., Fabre, J., Blanco,M.,Chenoune, R., Acs,S.,Flichman,G.
(2012). Modelling Agri-Food Policy Impact at Farm-Household Level in Developing Countries (FSSIM-DEV).
Application to Sierra Leone. Publisher: EC-JRC-IPTS, Editor: Kamel Louhichi & Sergio Gomez y Paloma, ISBN: 97892-79-29826-4 (European Commission, JRC Scientific and Policy Reports

Louhichi,K.,Kanellopoulos,A.,Janssen,S.,Flichman,G.,Blanco,M.,Hengsdijk,H.,Heckelei,T.,Berentsen,P.,Lansink,A.,
Van Ittersum,M. (2010). FSSIM, a bio-economic farm model for simulating the response of EU farming systems to
agricultural and environmental policies. Agricultural Systems, 10/2010; 103(8).
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Flichman, G., Louhichi,K., Boisson, JM, Modelling the Relationship Between Agriculture and the Environment
using Bio-Economic Models: Some Conceptual issues. In Bio-Economic Models applied to Agricultural Systems.
Springer 2011.(3-14)
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Blanco, M., Flichman, G., Belhouchette, H. Dynamic Optimisation Problems: Different Resolution Methods
Regarding Agriculture and Natural Resource Economics. . In Bio-Economic Models applied to Agricultural
Systems. Springer 2011.(29-57)
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Holden,S., Shiferaw,B., Pender,J. Policy Analysis for Sustainable Land Management and Food Security in Ethiopia.
A Bioeconomic Model with Market Imperfections. Research Report 140, IFPRI, 2005.

Beguin, J., Bureau, JC and Drogué, S. The calibration of Incomplete Demand Systems in Quantitative Analysis .
Applied Economics, 2004/5/10.
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Ecker, O.& Qaim,M Analyzing Nutritional Impacts of Policies. IFPRI Discussion Paper 01017, 2010.
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Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,
Bioversity - Rome
G. Flichman
19