5th International Symposium for Farming Systems Design
What’s new with bioeconomic models for the analysis and design of agricultural systems?
Applying a dynamic approach developing an inter‐disciplinary collaboration. Past experience and present challenges
Guillermo Flichman
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Sustainable Intensification
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Increasing agricultural production is a clear objective in nowadays context in some regions of the world.
For attaining this objective, it is necessary to intensify agriculture, defining intensification as introducing production processes able to increase the output per unit of land.
In the particular context of certain regions, it appears that intensification may lead to deterioration of natural resources.
This is the reason making necessary to think about sustainable intensification, taking into account the impact of production processes on future conditions of natural resources.
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Sustainable Intensification
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In general terms, there is not a necessary relationship between population growth, agricultural intensification and natural resources degradation. The classical work of Boserup
(1965) demonstrated that the Malthusian vision could be wrong if agricultural intensification takes place.
“Even rapid and prolonged population increase in tribal regions seems more likely to be a blessing than a curse, if the political problems connected with land tenure and the technical problems connected with prevention of soil erosion can be solved”.
Boserup’s analysis was in contradiction with the Malthusian vision, and it was partially correct
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Sustainable Intensification
A Russian economist and agronomist, A.V.Chayanov (1925) showed also the existence of a positive relationship in Russian rural communities between the size of the family and its wealth, developing the first theory that to some extent leads many years later to the development of household models.
It is still also possible to find in our days land tenure systems in which the allocation of land to households is related with the size of the households, being in some cases the cause of a positive relationship between wealth and number of children.
But this relationship does not work after a certain limit of exploitation of natural resources under specific ecological conditions
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Sustainable Intensification
Question: what type of model is adapted to capture the issues related with a sustainable intensification?
A model that takes into account in an integrated framework the socio-economic dimensions as well as the natural resources ones.
Bio-economic models applied to agricultural systems have these characteristics, but an additional problem appears, that is the problem of dealing with TIME.
Bio-economic models can integrate the socio-economic dimensions with the natural recourse's ones in different manners
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
5

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Sustainable Intensification
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Using the results of a biophysical process model (BPM) as parameters of an economic model
This procedure may be done at least in two ways: a) creating a database out of a big number of simulations done with the BPM and using these data in a static economic model (FSSIM is an example of this)
Developing a meta-model out of the BPM simulations and introducing it a dynamic economic model (recursive, recursivedynamic or intertemporal)
Including a BPM in the same code of the Economic model
There are examples of these procedures in the recent (and not so recent) literature
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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DAHBSIM
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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)
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 (d)
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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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015

Principal characteristics of DAHBSIM
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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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Principal characteristics of DAHBSIM
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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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Activities and Products (3)
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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.
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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Joint products in DAHBSIM
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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.
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HOW CAN WE BETTER CAPTURE THESE ISSUES ?
Developing feed-backs between the economic and the agronomic processes
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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 a 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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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 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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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 utility: value of sales plus self consumption and revenue obtained from off-farm activities minus costs.
– The biophysical module re-initializes 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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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– 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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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• 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:
– Further development of the Household Module
– Investment and credit
– Perennial crops
– Calibration (partially developed already)
– Risk
The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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– 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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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 activities as input output vectors
Definition of constraints
Costs, prices of inputs and outputs of activities and consumption goods
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
Activities’vectors are redefined out of biophysical simulations
Intertemporal Optimization from T2…T11
And so on up to T10…T19
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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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
Soil water
Rainfall Irrigation
Water limited crop evapotranspiration
Potential crop evapotranspirationdependent yield
Drainage
Actual to potential evapotranspiration
Evapotranspiration limited yield
N residue
Nitrogen limited yield
Actual yield (minimum of the two calculations)
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
N fertilization
Organic fertilization
Soil N
N Leaching
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Socio-economic
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Labor use, discriminated by gender
Income, from hh production, from off-farm activities
Environmental
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Emissions of GHG, NO3, water use
Soil fertility
Nutrition conditions
• Calories, and nutrients consumption. Information is available concerning nutrients amounts per unit of food products
Biodiversity
• Possible only if changes 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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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Total fertilization remains almost constant, yields change also in relation with different weather. Yields, org. matter and manure fertilization numbers correspond to one type of soil, one type of household, one production activity.
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Yields, org. matter and manure fertilization numbers correspond to one type of soil, one type of household, one production activity.
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Linkages with food consumption of the Household
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As the consumption function is not yet fully developed in
DAHBSIM, we present here the results of a former research
(Chenoune, R., 2014). The model is a static one, allowing to represent the relationships between production and consumption decisions.
We applied the Rotterdam consumption function, that we will also be used in DAHBSIM
This model takes the assumption of non-separability between consumption and production decisions in the household: this means that there is a simultaneous decision, considering the existence of high transaction costs and high level of selfconsumption.
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Linkages with food consumption of the Household
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This model was applied in one region of Sierra Leone. Results for three scenarios are shown below, following envisaged policies for ameliorate the situation of agricultural households. Results are compared to Baseline scenario and to nutrition target of FAO and nutrition indicators for neighbor countries:
Subsidy for rice seeds (Sseeds)
Subsidies for allowing increases of the rice surface on lowlands
(Srice)
Subsidies for palm-oil production in highlands (Sop)
RESULTS ALLOW TO OBSERVE THAT INCREASE IN RICE
CONSUMPTION MAY NOT LEAD TO AN AMELIORATION IN CALORIES
INTAKES (result of the apparently most efficient scenario)
In terms of calories, the three scenarios give non significant differences.
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Figure 1: Variation of rice consumption (a) and total calorie (b) for the 4 farm types, Guinea
(average national values), Liberia (average national values) and the targets set by the Sierra
Leone Government for the National Agricultural Strategy (2010-2030) and the FAO organization.
(a)
(b)
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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Conclusion
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A model designed for analyzing the issues concerning sustainable intensification should be:
Generic: can be be applied in different case studies
Modular: providing the possibility of using part of the modules and of including new modules for specific case studies
Dynamic: able to simulate feedbacks between socio-economic and natural processes
Represent households’ behavior, in many cases assuming inseparability between production and consumption decisions
Conceptually, should allow consistency between the agronomic and the economic components (activities as common cells of the production side of the model)
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The 5th International Symposium for
Farming Systems Design - Montpellier, September 2015
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