Theoretical and methodological issues regarding bio-economic model DAHBSIM, a Dynamic Agricultural...

________________________________________________________________________________ Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015, Bioversity - Rome G. Flichman

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Theoretical and methodological issues regarding bio-economic modelDAHBSIM, a Dynamic Agricultural Household Bio-Economic Simulation Model

Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models7-8 May 2015, Bioversity, Rome

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DAHBSIM TEAM

Coordination: Guillermo Flichman (a) Model code development: María Blanco (b)*; Sophie Drogué **(c) Agronomic modeling: Hatem Belhouchette (a), Roza Chenoune (a), Wajid

Nasim (a) Livestock module: Adam Komarek (d), James Hawkins (d) 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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________________________________________________________________________________ Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,

Bioversity - Rome G. Flichman

(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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DAHBSIM TEAM

The DAHBSIM team participated in the development of several bio-economic models.

The most relevant are: Cebalat Model: A recursive stochastic supply model (1) FSSIM-MP: static, generic, positive, supply model (2) 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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Principal characteristics of DAHBSIM

The production “side” of the model : Based on the representation of activities: production

processes The demand “side” of the model :

Based on a demand function. 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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Activities and Products (2)

The diagram shows the causal relationships implied in this type of model.

“Products” (wheat, straw, NO3 emissions…) are outputs of the activities.

One activity (or production process) has several outputs – joint production

One product can be produced by several activities Considers positive and negative jointness associated to the

production process It permits assessing in an integrated manner policies linked as

well to products as to production processes


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Activities and Products (3)

The above diagram represents an input-output linear vector concerning one single production activity.


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Joint products in DAHBSIM

Two types of joint products can be considered: Those that can be source of externalities (positive or negative) Those that are consequence of the production process and will affect

production on time 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 The change in state variables implied in the second case will influence the

production (including all joint products) in the following periods.

HOW CAN WE BETTER CAPTURE THESE ISSUES ? Developing feed-backs between the economic and the agronomic processes


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Dynamics in DAHBSIM

Dahbsim has intertemporal and recursive dynamics. Intertemporal because equations are indexed over years and the decision

of households are optimized given a discounted utility. 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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Feed-back between agronomic and economic modules

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

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

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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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)


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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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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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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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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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Simplified DAHBSIM scheme for cropping activities

Costs, prices of inputs and outputs of activities and consumption goods

OBJECTIVE FUNCTION

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

Intertemporal Optimization from T1…T10

Definition of activities as input output vectors

Demand function

Soil pattern at T1 provides information on water and N content for a new run of the

biophysical module

Activities’vectors are redefined out of biophysical

simulationsIntertemporal Optimization

from T2…T11

And so on up to T10…T19________________________________________________________________________________ Workshop on Integrating Biodiversity and Ecosystem Services into Foresight Models 7-8 May 2015,



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Potential crop evapotranspiration-

dependent yield

PM-ET0

Weather

Crop potential evapotranspiration

Crop Coefficient (Kc)

Evapotranspiration limited yield

Actual to potential evapotranspiration

Water limited crop evapotranspiration Soil water

IrrigationRainfall

Drainage

Nitrogen limited yield

N fertilization

Organic fertilization

N residue

N Leaching

Soil N

Actual yield (minimum of the two calculations)

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

Simplified scheme of the Biophysical module


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Data Issues 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)

____________________________________________(*) Information provided by the Africa RISING M&E Team, IFPRI. C. Azzari, C. Roberts and H. Beliyou


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Indicators that can be obtained from DAHBSIM Socio-economic

Labor use, discriminated by gender Income, from hh production, from off-farm activities

Environmental Emissions of GHG, NO3, water use Soil fertility

Nutrition conditions Calories, and nutrients consumption

Biodiversity 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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References 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.

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: 978-92-79-29826-4 (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).

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)

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)

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.

Ecker, O.& Qaim,M Analyzing Nutritional Impacts of Policies. IFPRI Discussion Paper 01017, 2010.

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