Géraldine Abrami

Chirawat Vejpas

François Bousquet

Guy Trebuil

Participatory modelling and simulation of the rice seed system in northeast Thailand

Keywords: Seed system, rainfed lowland rice, participatory modelling, agent-based model, Northeast Thailand système semencier, riz de bas fond, modélisation participative, modèle multi-agent, Nord-Est Thaïlande

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INTRODUCTION

The northeastern region of Thailand has the largest area planted to rice in the kingdom and produces more than one third of the total national output. But 85% of the 5.2 million ha of rice in this region are cultivated under rainfed conditions, with a single crop per year. The low physical productivity (average paddy yield is 1.8 t ha -1 ) of the paddies is mainly due to the combined effects of low water-holding and infertile coarse-textured soils, erratic rainfall distribution (1200 mm/year mainly from May to October) and low input use [START_REF] Jintrawet | A decision support system for rapid assessment of lowland rice-based cropping alternatives in Thailand[END_REF][START_REF] Somrith | Proceedings of Breeding strategies for rainfed lowland rice in drought-prone environments[END_REF]. But for more than 7 millions farm workers living in this most populated region of the kingdom (21 millions out of a national total of 65 millions, regional population density of 126 inhabitants/km 2 ), small-scale RLR farming has been a way of living for centuries with an average size of land holding of 3.2 ha (OAE, 2007). Rice is the staple food crop of both dominating Lao Issan (who eat glutinous rice, mainly RD6 variety) and Khmer (who eat non glutinous rice, mainly KDML105 variety) people who live mainly in the lower sub-region where paddies cover some 80% of the area. Rice is grown on undulating land made of adjacent mini catchments composed of well-drained upper, medium and flood-prone lower paddies that are usually planted to different RLR varieties with different duration cycles. RLR is also a major economic crop because of the local production of renowned strains of aromatic rices that find their way to international markets. Paddy sales could represent some 20% of the households total cash income. Under very constraining soil-water conditions, the grain quality of these aromatic jasmine cultivars is the main comparative advantage allowing to secure higher farm gate price for paddy that partly compensate the average low yields and single crop per year. Seed quality is important to maximize this comparative advantage.

In this farming context, RLR varietal and seed management is complex and involves decisions on varietal choice, desired degree of agrobiodiversity, and the procurement of good quality seed. In recent years, facing the inadequate supply of good quality seed of dominant RLR varieties, the Thai rice seed system (RSS) has been undergoing profound changes, especially the entry of different types of private seed producers (from large private companies like the CP Group to village level Community Seed Centers), and the subsequent increased adoption of a few recommended varieties (particularly KDML105, RD6 and the shorter duration non glutinous RD15) replacing hundreds of traditional cultivars exchanged among farmers. A growing number of diverse stakeholders interact in the RSS without knowing each other or coordinating their operations, and each of them having only a very incomplete understanding of how the whole system operates.

The main objective of this study was to test the use of participatory modelling to build a shared representation of rice varietal and seed management at the provincial scale with the main concerned stakeholders (different types of rice growers, extension agencies, cooperatives, public and private seed suppliers, and community seed production centres) in the major RLR producing province of Ubon Ratchathani in lower northeast Thailand. Based of this better understanding of the local RSS, the second objective was to implement this shared representation of the RSS into an agent-based model (ABM) used as a simulator to explore different possible future scenarios identified by the main stakeholders and to assess the ability of the RSS to meet farmers' demand and to adapt to its unpredictability under those different circumstances [START_REF] Vejpas | Participatory modeling for managing rainfed lowland rice variety and seed systems in lower northeast Thailand: methodology and preliminary findings[END_REF]. In this paper, following a short presentation of the participatory modelling methodology and tools used in this case study, the emphasis is on the description of the ABM co-designed with the local actors and its use to simulate scenarios. The paper ends with a discussion of the results and a presentation of the future perspectives.

MATERIAL AND METHODS

The so-called Companion Modelling (ComMod, www.commod.org ) approach was used to facilitate the dialogue among the RSS stakeholders and the exchange of knowledge and points of view about the seed system because it is a proven methodology to set-up and manage inclusive, open, and highly interactive collaborative modelling processes (Bousquet & Trebuil, 2005). ComMod processes are iterative but evolving and rely on different kinds of models (conceptual, role-playing games, agent-based ones) to stimulate the interactions among stakeholders. These models are co-designed, validated, played or simulated with stakeholders and evolve according to their changing needs following the acquisition of new knowledge or change in the context.

The main phases of the implemented ComMod process were as follows:

-A preliminary sample description survey at the farm level was carried at the provincial level, among 258 farmers in 25 districts, to gather baseline data on the spatial, quantitative, and qualitative distribution of farmers' needs of rice seed in every district of Ubon Ratchathani province. In particular, it provided a picture of the heterogeneity of farmer' situations regarding access to seed suppliers, the districts located in the northern part of the province having a more diverse choice compared to the more remote southern ones. Representatives of key organizations involved in the RLR see system were also interviewed to provide information of the activities of different kinds of seed suppliers and to sensitize them to participate in the ComMod process.

-Short workshops with selected seed producers: To understand the rice seed system structure and functioning at the provincial scale, a series of meetings were organized with representatives of the main seed producing organizations. For most of them, it was the first opportunity to meet and discuss the RSS issue. During these meetings, a conceptual model of the seed system was built based on the knowledge provided by the participants. This knowledge base was displayed on the screen as a set of Unified Modelling Language (UML) class and activity diagrams to be gradually enriched and validated by them. After several sessions, an agreement on the structure and functioning of the provincial RSS was reached [START_REF] Vejpas | Participatory modeling for managing rainfed lowland rice variety and seed systems in lower northeast Thailand: methodology and preliminary findings[END_REF].

-In the next phase, this conceptual model was translated into two complementary roleplaying games:

(i) the fist one was used with farmers at different locations (according to a gradient of remoteness of their villages to seed suppliers) to validate the research team understanding of the generation of the demand for seeds at the village level. This gaming and simulation tool allowed to better understand the farmers' decision-making processes dealing with the selection and allocation of a combination of RLR varieties in their paddies, their seed management practices, and choice of seed suppliers.

(ii) the second gaming tool simulated the production and provision of seeds in a virtual province by various suppliers operating at the village, sub-district, district or whole province levels. These participatory modelling and simulation sessions were used to elucidate seed supplier practices, decision-making processes and interactions. This activity was instrumental in the validation of the conceptual model representing the relationships (or lack of) among the RSS stakeholders and the flow of different types of (foundation, stock and certified) seeds among them [START_REF] Vejpas | Participatory modeling for managing rainfed lowland rice variety and seed systems in lower northeast Thailand: methodology and preliminary findings[END_REF].

-Finally an autonomous Agent-Based Model (ABM) was built.

Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. This ABM implemented under the CORMAS platform simulates the seed requirements from farmers and the allocation of seed from public and private suppliers to meet the demand for certified seeds of the two most popular glutinous (RD6) and non glutinous (KDML 105) rice varieties and is described extensively in the following section. It implements the conceptual models developed in the previous phases and runs simulations on a yearly time step and at the village, district, and provincial levels.

The ABM was instanciated on the specific case of the virtual province used during the second gaming workshops and presented to key stakeholders during a series of meetings and demos in their offices. The features and operating rules of the simulator were explained and discussed with representatives from the main seed suppliers to improve and gradually validate them. More particularly, the decision making rules concerning seeds quantity planning and allocation priorities were

The potential use of this simulator to help manage the current reform of the RSS (favouring a growing role for the private seed suppliers, especially the community seed centres) was also discussed. These potential users proposed possible future scenarios of interest to be simulated and assessed for such a purpose.

RESULTS

1 The Conceptual models

The representation of the seed consumption (at the farm and village levels) and seed supply (from the village community to the provincial level -and even the national level regarding the provision of foundation seeds) systems are described elsewhere [START_REF] Vejpas | Participatory modeling for managing rainfed lowland rice variety and seed systems in lower northeast Thailand: methodology and preliminary findings[END_REF], but key findings from the conceptual modelling and role-playing game workshops are summarized in figures 1 and 2 showing the decision model for the choice of seed sources and suppliers at the farm level and the seed supply system at the provincial scale respectively. This conceptual model was used as a base for designing the initial version of the Agent-Based model. Whereas focus of the first phase of conceptual modelling and role-playing game was on the provincial seeds system as well as on the farmers scale, in this phase the focus was brought exclusively on the provincial seeds system. As a consequence, contract farmers and detailed farmers behaviors were not considered in this phase which focused on provincial seeds system flows and decision making. . A prototype was then use to hold workshops with the stakeholders where they discussed, refined and corrected the hypotheses displayed through the demonstration of the prototype. The result can be seen on figure 3. The diagram on the left is equivalent to the UML diagram of figure 2. The diagram on the right is the conceptual model such as it is implemented in the ABM : priorities and specification in seeds flows are specified, as well as the status of the stakeholders.

The Agent-Based Model

The way these multi-level models were merged into a single ABM integrating all the relevant scales of seed consumption, production and distribution is shown.

Overview

This ABM represents the Rainfall Lowland Rice (RLR) seeds system of North-East Thailand at the provincial scale. Only "active" stakeholders are represented (the fact that the decisions are taken by Bangkok institutions is not represented). Agents are RLR seeds system stakeholders that act from the Province level to the Farm level and that might have preferential relations between each others. These agents exchange and/or multiply seeds stocks from 2 varieties: a glutinous (RD6) and a non glutinous one (KDML105).

These agents hold roles in the seeds supply chain. They might be seed producers, middlemen or rice growers, respectively to each variety. These roles are connected through provider and customer links ranked with priorities. These links and their priorities depend on which stakeholder they are held by. One time step represents one year and reproduces the phases of one time step of the RPG : planning of needed quantities and orders, production or multiplication of seeds in stock from last year, treatment of orders and direct requests, seeds provision to farmers. From one year to the other, seeds production is adjusted on previous years and farmers demand is adjusted on paddy market prices. Seeds prices are not considered in the model as it was not considered as a determinant parameter for seeds supplier choice. The number of multiplication and origin of seeds is traced but only used marginally. The model can be described abstractly as follows: A resource is exchanged among a supply chain from producers to consumers. Consumption and production of the resource can happen only once a year. 1 producer only is able to produce a high quality resource in small quantities. Other producers multiply the resource they manage to get from the supply chain. Each time the resource is multiplied, its quality decreases. All stakeholders in the supply chain have preferences among their providers and customers. Each time step happens as following : 1. Planning: customers plan their demand and stakeholders establish their needs 2. Resource production 3. Resource supply : the resource is distributed accross the supply chain up to the customers 4. Assesment : customers assess supply and may change supplier 2.2.2. Entities and scales

Biophysical Entities

Seeds entity represents seeds stocks that circulate between the stakeholders. A seeds stock is described by :

a quantity, given in tons a variety. 2 varieties are available in the model : KDML (aromatic) and RD6 (glutinous). It is the 2 main RLR varieties that are recommended and produced by RRC. -a quality : FS (Foundation -produced from breeder seeds in RRC), SS (Stockproduced from FS), CS (Certified -produced from SS), or NC (non certified, produced from CS) -a source : the list of all its multipliers an age : the number of years since its last multiplication

Space

The space represents an archetypal province with its administrative areas represented by aggregates. All aggregate spatial entities are AdministrativeArea. Each Cell belongs to a Tambon, that belong to an Amphoe, that belong to a Province. Amphoe are also characterized by their accessibility. In normal amphoes, farmers and farmers communities have access to the provincial public seeds producers (RRC and SC). In remote amphoes, a part of seeds renewal is done through informal exchanges between farmers.

Socio-economics

The international rice market is roughly represented by a Market agent that fixes paddy prices. It is characterized by the up and down limit of paddy prices. Farmer individuals are represented by Farmer agents. In each tambon, their individual needs generate a global seeds demand. A Farmer updates his needs each year considering the proportion of rice varieties on its plots and whether he renews its seeds or not. This demand is attributed to a supplier. This supplier is attributed by initial conditions but it may change according to the farmer experience. Stakeholder is the generic entity representing the seed system stakeholders. These stakeholders are :

Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. - -TambonFarmers : virtual entity aggregating demand and seeds distribution for the farmers of one Tambon. TambonFarmers defines n, the number of farmers in the tambon, as well as providersDistr which is the repartition of providers share within one tambon. Can get seeds from any stakeholder but RRC. In remote amphoes, no acces to SC.

Supply Chain Roles

5 different roles in the seeds supply chain have been identified. These roles define the behaviors of the stakeholders in the seeds systems within the various supply chain processes. (needs planning, seeds production or multiplication, seeds supply, seeds use). These roles are :

-FSProducer : produces FS according to estimate needs and limiting capacity; supply FS to customers. Held by RRC -Multipliers : purchase FS or SS from providers, multiplies into SS or CS, supply production to customers. Held by SC, CSC, producing AC -MiddleMen : purchase seeds from providers, supply it to customers. Held by ST, BA, non producing AC and DAO. -RiceGrowers: purchase seeds from providers and use it for paddy. Held by TambonFarmers Supply Chain Roles are characterized by :

a variety they deal with. A stakeholder should have as many roles as varieties he is dealing with providers and customers ranked with priorities. Links are set up according to stakeholders and priorities are set up according to rules discussed with stakeholders and displayed in the parameters table in the appendix.

Time Scales

The time step is 1 year for all entities. The year is separated in different phases (planning, production, distribution, estimation) where the entities may or may not act depending on their roles.

As stakeholders planning have to adjust on sales from an arbitrary initial value, a simulation is initialised first with a "blank" 20 years run so that the system gets to an equilibrium through stakeholders adjustments.

Spatial Scales

The smaller spatial unit is the tambon (sub-district) and the grid represents a province. There is an intermediary administrative area : the Amphoe. Every stakeholder distributes seeds within an administrative area : CSC acts at the tambon level. At this level, TambonFarmers aggregates demand and provision for the tambon Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. farmers. Farmer individuals are only used to set up the demand within a tambon and get seeds from a specific supplier, they are not linked to specific cells. AC, ST and DAO act at the Amphoe level. RRC, SC and BA act at the Province level

Processes and Scheduling

Biophysical Processes

The only biophysical processes in the model are held by FSProducer and Multiplier agents and concern seeds production and multiplication. They consist in applying a stochastic hazard on expected seeds production or multiplication values. Resulting seeds are put in their distributionStock.

Socio-economical processes

Paddy price evolution (Market) has been asserted as unpredictable: it consists in a series of randomly drawn values between the lower and upper limits identified with the stakeholders.

Farmers' seeds demand is calculated each year individually for each farmer. It depends on previous years seeds provision in that farmers demand for seeds when they get too old according to its own criterion (a fixed frequency) and renew their demand every year until it is satisfied. However the demand may be satisfied by informal exchanges. In consequence, a farmer's seeds demand results from his rice growing area, its seeds age, its seeds replacement frequency, its propensity to get seeds from other farmers, and its varieties distribution. Depending on market prices, farmers may raise their proportion of KDML and renew more seeds.

Farmers' supplier choice. Farmers may decide to get a new supplier when their current supplier does not succeed in delivering seeds, or delivers seeds which have been multiplied too much or coming from outside the system1 . There is a fixed set of suppliers that can be accessed by all the farmers within a tambon. We have chosen to implement the simplest possible set of hypotheses to represent the choice of a new supplier. This set of hypotheses is realistic enough and allows to test the dynamicity of the model but it would need to be refined to represent realistic dynamics. These hypotheses are : (1) seeds price has no impact on supplier choice (2) farmers assess their supplier on their reliability in answering a demand with adequate seeds (which have not been multiplied too much or coming from outside the system) (3) farmers assessment of a supplier is cumulative all along his life and a good assessment increases the chance to be chosen again (4) even with a bad assessment, it is always possible that a farmer choose a supplier again.

Seeds supply planning is performed by all supply chain actors and consists in making previsions on customers demand every beginning of year: the planned quantity is the objective quantity to get its distribution stock. There is 2 way of planning in the model : collecting information about customers needs and adjusting the prevision on this information (order based planning) or adjusting the prevision on previous years actual sales (adjusted planning). RRC and DAO have registered customers and practise order based planning. All the other one do not collect orders and adjust to previous sales. The depth of memory used can be adjusted.

Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. Seeds purchase is performed by all supply chain actors except FSProducer. It consists in sending a direct request to a provider in order to get the planned quantity of seeds.

For Muliplier and MiddleMen agents, direct requests are sent to providers one after each other, following their priority ranking for the whole needed quantity of seeds, until this quantity has been purchased or the providers list is over. The offer of a provider is always accepted, and its answer is needed before sending the next direct request. Multiplier agents put received seeds in their productionStock, MiddleMen agents put received seeds in their distributionStock.

When it has completed its purchase, SC checks its productionStock. If plannedQuantity is not reached it transfers the needed quantity from its distributionStock to its productionStock.

For RiceGrower, the quantity of seeds to purchase is determined by the aggregated Farmers' seeds demand. If a supplier does not provide enough seed, farmers randomly drawn among its suppliers do not get seeds.

Seeds distribution is performed by all supply chain actors except RiceGrower. At the end of a "distribution round", the received direct requests are treated. If there is enough seeds in the distributionStock to satisfy all the direct requests, all the direct requests are answered with the requested seeds quantity. If not, the seeds are distributed using weights according to buyers priorities so that higher priority buyers get more seeds but that nobody gets nothing. If different qualities of seeds are available, best quality are given first.

Description of scheduling

One time step is scheduled as followed: 1.

Step initialisation : surplus stocks are destroyed : distribution stocks are set to 0 2. Planning : Paddy price evolves with market; farmers set their demand; stakeholders plan their objectives recursively (the top producer asks their plans to all its customers who ask their plans to all their customers and so on until end customers. When a provider has received the answers from all its customers, it operates its planning (either order-based or adjusted) and answers all its providers and so on). The way these sessions are populated and sequences has been quite a delicate parameter of the model : it may greatly influence the simulation results and can only be a very rough approximation of the real seeds market sequencement. We've considered different possibilities but after a few sensitivity analysis, we've decided to Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. use a slight enhancement of the sequencement represented during the game, which had already been validated by the stakeholder. The only difference with the game is that traders are second-order providers (they can provide seeds to other providers) and that some CSC may be provided by DAO, as we learnt during ABM sessions. 5. Assesment : farmers assess their suppliers and possibly choose a new one for the following year.

2.3

The prototype and the workshops 2.3.1. Workshops progress and ABM interface elements for interacting with the stakeholders

The workshops were designed so that the ABM demonstration could be used as a restitution support of our understanding of the system. The model structure and dynamics was explained step by step through the different viewpoints provided by the CORMAS interface. Special emphasis was put on data and hypothesis concerning the agents that represents the stakeholders attending the workshop. The stakeholders could react and complete or correct the model all along the demonstration. Then a debriefing was held in order to define what are the stakeholders expecting from the model. Providers and customers lists and priority levels can be modified at any time of the simulation through a dedicated interface. The procedure used during the demonstration was the following: the spatial elements of the interface are presented and explained; the different agents are shown within the territory they supply: are all the stakeholders represented? Is their spatial allocation right? ; for each agent, the supplying and buying links are shown: are all the links correct? Is there some links missing? Are the priorities right?; the simulation is run for a year step by step, showing the information and seeds exchange between the agents; simulation results figured as histograms are shown for a few years (figures 4 and 5). The model structure description was very efficient in definitively validating the stakeholders roles and relations. The histograms were very efficient in getting back on the hypothesis we made on agents decision-making about seeds distribution.

Workshops main results

The workshops were very efficient in getting information gaps filled. In particular, the following elements of the model were extracted and validated from the workshops : validation and completion of stakeholder links and priorities, definition of the institutional status, impact of tambons remoteness, refinement of the seeds allocation function, hypotheses on farmers demand evolution with paddy market price, definitive choice of varieties, AC networks. 5 potential scenarios that could be explored with the ABM simulations were also defined :

-baseline scenario, in the current state of the model -institutional reform : merging of RRC and SC. This scenario is translated in the model by RRC providing seeds only to SC -decentralisation : number of CSC increases with institutional reform. This scenario is translated in the model by creating 1 CSC in each tambon.

-modernisation : all farmers get mechanized and renew their seeds every year -decentralisation + modernisation

Simulation results

The 5 scenarios were simulated with an identical random paddy price series and with and without farmers being able to change supplier. The model is always more efficient in delivering seeds to farmers without generating too much surplus when farmers are not able to change suppliers. The main reason is certainly that as one hypothesis being that farmers tend to prefer suppliers who deliver only province grew certified seeds, there are less farmers getting their seeds from AC which is giving a 100% success in the cases where suppliers are not fixed. There is no subsequent difference in the different scenarios, except the ones with modernisation where the demands are answered more efficiently. This is due to the fact that even if the demands are increasing, they are more stable, which makes the seed system stakeholders able to predict is correctly and answer it (figure 6). It can be surprising looking at the customers ratio evolution of the different suppliers that multipliers production and delivery to farmers is not rising when the demand is rising. This is due to the fact that the system has still not reach its full capacity (many farmers can work with SS obtained from DAO who obtained it easily from SC as its only concurrent which is AC is getting most of its provisions from its network as a pervert consequence of farmers adaptation) and that as multipliers production is driven by demand and demand is driven by suppliers reliability, AC and CSC never manage to increase significantly their customers numbers and indeed increase the number of seeds in the system by making new multiplications because their supply is not reliable enough (production hazards + completion by outside seeds) compared to DAO which is getting SS directly from DAO with a very high priority (figure 7).

The conclusion is that the hypotheses in the model allow representing faithfully a stable situation but are not good enough to represent a self adapting system.

DISCUSSION

Knowledge integration from various sources and at different scales

The multi level ABM implemented in this process makes use of indigenous as well as expert and scientific knowledge about the local RSS. It also represents interactions between the formal seed production and distribution system operated by public and private agencies, and the village and farm levels more informal practices regarding the use of rice seeds.

While the RPG was very useful to communicate the contents of a conceptual model, to enrich and validate it with its end users, the ABM allows to run simulations at the provincial scale with out being limited by either the number of participating players (which is always limited to less than twenty in a RPG session), or the cost (time and cash) of a simulation.

Potentially, there is no restriction on the number of stakeholders that can be simulated with the ABM. However we chose to use in the ABM the same simplified setting as in the RPG where only 4 sub-districts are represented (there are around 300 in a province) for several reasons : (1) it made it possible to design an interface very similar to the representations used during the gaming workshops that would hence look familiar to the stakeholders and would allowed to keep on deepening the discussions started during these workshops (2) it kept the simulation simple and allowed to easily check what is happening at the level of specific sub-districts (3) it creates a gap between real world data and simulation that prevent users to focus on results and figures instead of focusing on processes and hypotheses. This last reason was particularly important as we did not want our partners to confuse our simulator with a decision-support tool.

Used in the workshops, the model did reach its goal of extracting and confronting stakeholders information. It was particularly efficient in giving direct feedback on the hypotheses validated by the stakeholder in the form of histograms.

Limits and potential improvements of the ABM

From a more methodological point of view, modelling such a seed system leads to the representation of a very particular type of supply chain and so we could not find examples of simulations on which we could rely on in the operational systems literature :

-The production of certified seeds is a lengthy process while seed distribution occurs over a very short period of the year.

-Seed quality issues have to be taken into account during seed multiplication as seed quality is more important than seed price in the selection of a supplier by local farmers.

-Each stakeholder in the RSS thinks and acts at a very different scale.

The potential use of the simulator was as a tool for getting feedback on implications and potential unpredictable consequences of management options. The prototype that was developed in the time frame of the project only exhibits potential contributions of such a simulator. Developing this prototype into an operational tool would necessity a new round of interaction with stakeholders in order to identify focal aspects that should be deepened and collect further data and hypotheses. For instance the main limits are :

-the suppliers volume planning is only driven by the demand, may it be with perfect information (registered customers) or crude estimation on previous years. The consequence is that when farmers increase the demand on more reliable suppliers, the less reliable ones enter a positive feedback loop where they keep on reducing their volume planning as they keep on loosing customers. This is particularly true for seeds multipliers which volume planning is shifted by 1 year (time for producing the seeds). This results in a strong dependency to initial conditions (number of initial customers and initial score of a supplier) which does not look very realistic. It means that more information of main constraints and drivers of suppliers volume planning are needed.

-seeds producers cannot be as flexible as middlemen as they have constraints on the seeds production. At which rate are they able to increase or decrease their production capacity? Are there some upper and lower limits ? An important constraint on seeds producers dynamic that is not represented would be for instance the way contract farmers are contracted.

-farmers suppliers choice possibilities should not be homogeneous in a tambon. In reality they are driven strongly by accessibility to suppliers. It would be necessary to represent heterogeneities in terms of access to suppliers and information of the farmers.

-the simple situation should be upscaled to a situation with a number of stakeholders that would reflect the size orders of a province.

CONCLUSIONS AND PERSPECTIVES

The model presented in this paper was built in partnership with local researchers, representatives of local public and private seed producing organizations, as well as different types of rice growers in Ubon Ratchatani province. To our knowledge, there is no existing reference in the literature describing similar process and outputs.

The co-construction of the conceptual model and its use in role-playing games helped the stakeholders to understand the operation of the computer ABM used to simulate the RSS at a later stage. The acquisition of a more comprehensive perception of the provincial RSS triggered the identification of several relevant scenarios to be explored in order to anticipate the possible local consequences of the current reform of the RSS at the national level.

The potential and limits of the co-construction and dissemination of such a common model of the RSS among local actors was an important step to improve communication, build Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. mutual understanding, facilitate information exchange, and promote coordinated action among them is discussed. Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. Figure (5 cooperatives) (25 offices)

(1 center in Ubon Ratchathani) (1 for 3 provinces) (258 in 3 provinces) (7 for 3 provinces)

(1)

(1) Figure 6: ratio of satisfied demands for KDML105 in baseline scenario, decentralisation scenario and modernisation scenario : in the baseline and decentralisation scenatios, the ratio of satisfied demands oscillates between 75% and 100 % when it is hardly not getting Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. below 90% in the modernisation scenario. Simulations where farmers do not change supplier are in black and those where they change are in white. Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G.

Seeds production function

It

Farmers' seeds demand

The actual seed's demand submodel is described on the following activity diagram. There is an implicit hypothesis that if farmers decide to cultivate more surface of one variety because of market prices, they have to get additional seeds outside.

Seeds Supply planning

This function calculates QPlanned, the quantity a supply chain entity plans to provide during the year and aims at having in its distribution stock.

For orders based planning, with ordersReceived being the total of seeds orders received before planning and margin a security margin parameter : QPlanned = (1+margin)*ordersReceived For adjusted planning, the entity computes the mean of requests received it can remember (memory parameter) and adds a security margin. Seeds producers divide by their multiplication rate. With requestsReceived being the total of seeds requests received during the previous year, margin the security margin parameter, memory the length of memory :

QPlanned(t)= (1 + margin) (

Seeds Purchase

Multiplier and MiddleMen : providers priorities are fixed through the initialisation. 1 st priority providers are asked first, and if several providers have same priority, one is randomly drawn. The provider is asked for the amount of seeds still needed to get to the planned quantity, is removed from the list, and its offer is automatically accepted. Requests are sent only to providers that still have seeds. Special cases : SC and AC always fulfil their needs : AC always get needed quantity of seeds from their network SC complete its needed quantity of seeds with its own production

Seeds Distribution

If within a round, there is not enough seeds for all requested, a distribution function is built (and there will be no more seeds for next rounds!).

) ( Re ) 1 ( 1 , 1 max for i>1
It is possible that not all seeds are distributed if high priorities buyers ask for low quantities of seeds. In this case, iterations are done on remaining demands until all seeds are distributed.

Suppliers adaptation

We use gross hypotheses and make a simple disaggregation of suppliers choice so that we can make this choice dynamic without complexifying the model. These hypotheses are :

all farmers in a tambon have an equal information and access to the suppliers in this tambon the accessibility to suppliers within a tambon is fixed during the time of the simulation each farmer has 1 supplier which is attributed initially from the statistics used currently in the aggregate demand each farmer has a unique supplier for RD6 and KDML during 1 year the seeds price has no impact on the supplier choice we do not consider loyalty or trust to suppliers : a farmer changes supplier when his demand is not satisfied or when he gets off-type seeds

Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. - a supplier scores +1 when he satisfy the farmer demand a supplier scores -1 when he does not satisfy the farmer demand when its demand is not satisfied, the farmer draws a new supplier in a collection where each supplier is represented his current scores times if > 1, once if not o random (3) When farmer do not change supplier when they get off-type seeds but score them negatively, the results are stable but somehow locked for AC customers. As a consequence, it seems that the behaviour providing the more stability while providing some dynamics possibility is when farmers change suppliers when they get off-type seeds but score them positively (which means I try to find a new supplier because this one is not providing top quality but it is still ok because it has a reliable supply).

APPENDIX 2. DETAILS FROM THE MODEL -DIAGRAMS AND PARAMETERS

The class diagram shows how main classes are structured in the model. These last 2 diagrams show stakeholders and roles hierarchies.

MsgSeeds

Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G.

Units

The only quantity in the model is the quantity of resource : ton

Parameters table : Supply chain configuration parameters

These parameters are those who set the structure of the supply chain that was used and discussed during games and ABM sessions. Participatory modelling and simulation of the rice seed system in Northeast Thailand Abrami G., Vejpas, C., Bousquet, F., Trebuil, G. (

Name

  3. Seeds production and multiplication. In multiplication, productionStocks from previous year are used 4. Seeds distribution and purchase : distribution and purchase functions of the agents are scheduled within a distribution protocol. This protocol consists of 4 sessions where some agents act as customers (purchase function) and other as provider (distribution function). Each session consists of several rounds. During each round : a. Customers sends their request (first part of the purchase function) b. providers treat the requests (distribution function) c. Customers receive the seeds according to the answers from the providers (last part of the distribution function) New rounds are launched until all customers have not received the seeds they wanted or have not browsed their whole providers list. Then the session ends. The 3 sessions are populated as follow: d. RRC answers orders : customers = (producing AC, SC); providers = (RRC) -(producing AC and SC fill their multiplication stocks for next year e. everybody but SC and RRC request seeds : customers = (AC, ST, DAO, CSC, BA); providers = (RRC, SC, AC, ST, BA) f. DAO answers orders : customers = (TambonFarmers, CSC); providers = (DAO) g. TambonFarmers receive seeds from everybody except DAO : customers = (TambonFarmers); providers = (AC, ST, CSC, BA, SC, RRC)

Figure 1 .

 1 Figure 1. Decision-making model for the choice of seed sources and suppliers at the farm level in Ubon Ratchathani, lower northeast Thailand.

Figure 3 :

 3 Figure 3 : seeds flow among RLR suppliers such as identified in the initial conceptual model and such as elicitated during the interactive workshops and implemented in the ABM

Figure 7 :

 7 Figure7: ratio of customers from the different suppliers of tambon 1 in modernisation scenatio : the ratio of AC is getting up to 60 % and the ratio of CSC is getting down to 5 %

  RRC : Provincial Rice Research Center. Provides FS within the province to SC and seeds producing AC. Also provides seeds to CSC and ST from non remote amphoes. SC and seeds producing AC are registered customers for RRC. -SC : Seeds Center. Public SS multiplier (province level). Provides SS to DAO, ST, BA and grown up CSC. -BA : Bank of Agriculture. Private SS trader (province level). -AC : Agricultural Cooperative. Farmers organisation SS multiplier and trader (amphoe level). Not all AC produce seeds. The seeds producing AC provide seeds to seeds non-producing AC. All AC provide seeds to ST and grown up CSC. AC can always satisfy farmers demand through networking. -ST : Seed Traders. Private seeds traders (amphoe level). Provides seeds to BA -DAO : District Agricultural Office. Agricultural extension, used to be official public seeds distributor (amphoe level). Provides seeds to young CSC. Young CSC and farmers are registered customers for DAO. -CSC : Community Seeds Center. Seeds production farmers community structures (tambon level). CSC are also characterized by their age. After 4 years, CSC status is changing.

  2. The RLR seed supply system in Ubon Ratchathani, lower northeast Thailand. NB: Seed flow among institutions deals mainly with KDML105, RD6, and RD15 recommended cultivars. Seed of other cultivars circulate through farmer-to-farmer exchanges only. Numbers below each agent indicate the number of locations or persons of each agent in Ubon Ratchathani province, except for Seed Center (SC) and CP seed company.

	Provincial Cooperative Office (PCO)	Non-seed producing agricultural cooperatives
	(1 office in Ubon Ratchathani)	Support
	Support
	Seed-producing agricultural cooperatives (AC's)		Stock seed sold
						Stock seed
						sold
	Foundation seed	Stock seed sold
	sold			District Agricultural Office (DAO)	Stock seed distributed	Stock seed sold
	ACs' contract farmers			& support
						Community Seed Center(CSC)
					Seed
					multiplied	Certified seed
		Foundation seed		sold/exchanged
	Rice Research Center (RRC)	sold	Seed Center (SC)	StockSeed sold
					Stock seed	StockSeed
		FoundationSeed		sold	Seed traders (certified)	sold	Farmers
		sold		Stock seed sold
						produced
					Stock seed sold	/exchanged
			SC's contract farmers		Stock seed sold
	CP Seed Company			Agricultural Cooperative for Marketing (ACM)
		Stock seed sold	support
	Foundation seed sold	CP's contract farmers	Bank for Agriculture & Cooperatives (BAC)
	Foundation seed sold

  consists in producing a quantity Qdistrib of FS and storing it in FSProducer's distributionStock. QPlanned is the value decided by the planning function. The hazard is drawn from statistical field values (see initialisation tables). QDistrib is limited by the It consists in multiplying a quantity QMult of seeds into a quantity Qdistrib of the next generation seeds (FS into SS, SS into CS). Qdistrib is obtained through affecting the Multiplier's multiplicationRate with an hazard drawn from statistical field values (see initialisation tables). The original seeds where stored in the productionStock and the multiplied seeds are stored in the distributionStock.

	FSProducer productionCapacity.
	Qdistrib = min( QPlanned*(1+productionAlea), productionCapacity)
	Seeds multiplication function
	Qdistrib = QMult * multiplicationRate* (1+ productionAlea)

  The distribution function is built with the following constraints (discussed with stakeholders) : If priority 1 buyers request lest than 70 mxDemand1 % of the stock, they get 100% of their demand and the remaining seeds are distributed to the others proportionally to their priority rank Priority 1 buyers should always get at least mnAnswer1 60% of their request if there is enough seeds left for responding to mnAnswerOthers 10% of others' requests. This means that with requestsReceived(i) being the total amount of seeds requested by a priority i buyer, QDistrib being the quantity of seeds available in the provider's distributionStock, and QAnsw(i) being the total amount of seeds returned to priority i buyers, then

		QDistrib			i ( and QAnsw )	( i QAnsw	)	wi	*	requests	Re	ceived	( i	)
					i
	If	requests	Re	ceived	) 1 (	mxDemand	1	*	QDistrib	then w1=1
	If not then	w 1	max	, 1	requests	Re	ceived	) 1 (		Qdistrib mnAnswer1 hers mnAnswerOt	*	iMax i 2	requests	Re	ceived	i (	)
	and in all cases	wi			i	jMax QDistrib requests	ceived QAnsw	j
									j	2							j

  considering non adequate seeds (seeds that have been multiplied from CS or coming from outside the province through AC), there are 6 possibilities :

	o Changing and scoring -1
	o Changing and scoring 0
	o Changing and scoring 1
	o Not changing and scoring -1
	o Not changing and scoring 0
	o Not changing and scoring 1 (default)
	-choice of a new supplier : each farmer which demand is not satisfied draws a new
	supplier in a collection where each supplier is represented his final scores times if >
	1, once if not. This score can be established with 4 possibilities :
	o none (0)
	o based on reputation (1) : draw within tambon annual record of suppliers :
	a supplier scores +1 when he satisfy 1 farmer demand
	a supplier scores -1 when he does not satisfy 1 farmer demand
	o based on experience (2) : draw within own record of suppliers :

Objec t description Phase(s) / submodel where used Value / Value range

								ST, DAO	tambons)
	production	SeedProd	Alea	on	seed	2 -resource production	Amphoe b : AC2, * RRC	From RPG
	Alea	ucer		production	and		DAO P(-25%)	=	(adapted
				multiplication			Province : RRC, P(+10%) = 1/16	from real
								SC, BA P(-20%)=P(0)=	data)
	Accessibilit	Amphoe	Accessibility	of	0 -supply chain config :	a : normal 2/16	Just	for	1 Amphoe remote,
	y			Amphoe			CSC access to RRC &	b : remote P(-15%) = P(-	having	a	the other not
							farmers access to SC in non	5%)=3/16	differentiated
							remote only	P(-10%)=1/4	situation
							1a -Farmers demand :	* Others
							informal seeds exchanges in	P(-50%)	=
							remote only	P(+10%) = 1/16
	producing	AC		AC type			0a -supply chain links and	true P(-40%)=P(0)= except for	Same as in
							priority initialisation	AC b KDML 2/16	RPG
							2 -production	P(-30%) = P(-
	institutional	Stakehold	Institutional status	0a -supply chain links and	RRC, SC, DAO, 10%)=3/16	Discussed
	Status	er		of stakeholder		priority initialisation	P(-20%)=1/4	with
	mxDemand	Role		Constraints	for	3 -distribution	non grown up 70 %	stakeholders Discussed
	1			distributing seeds		60%	and experts with
	mnAnswer1			according	to		CSC : 10%	stakeholders
	mnAnswer			priority
	Others							publicOrganisatio
								n
	Annex tables
	AC, grownUpCSC Table 1 : riceGrowingAreaDistribution is the probability distribution used to draw farmers'
	rice growing area					:
				(raï)		Probability farmersOrganisati Remote Non-
							on amphoes	remote
				0-9				amphoes ST, BA : 16 15
				10-19			privateOrganisati 36	42
				20-29			n	22	17
				30-39				14	15
	Providers / customers	Role		Supply chain links 40-49 50-59	1b -Planning	* RRC supplies all SCs; producing 4 5	First RPG 9 refined 2	from and
				>= 60			ACs; non project 3	during meetings 0
										using ABM
	CSCs and STs in Table 2 : varsInitPropForRiceGrowingArea is the correspondence table between farmers' rice
	growing area and varieties intial proportion	non-remote
	tambon	Cell	riceGrowingArea (raï) 0-9 Parameters for 10-19 20-29	Origin varieties initialProportion amphoes. RD6 Other KDML+RD15 * SC supplies all Glutinous DAOs, STs and 53 18 29 0 -initialisation stakeholders archetypal Same as in 35 18 47 BAs, non-project 35 15 50	Comment (hypotheses or testing and calibration) 2 Amphoes with 2
			model configuration spatial 30-39 40-49	32 24	province with 2 CSCs and non-12 amphoe containing 2 tambons each producing ACs. 9	RPG	56 67	tambons each
	amphoe province stakeholder	Cell Cell Administr 50-59 >= 60			25 0 -initialisation stakeholders Tambon 14 * DAOs supply a1 11 11	:	60 Same as in n 79	For the province :
	s	ativeArea					CSC1 Tambon project CSCs in b2	:	RPG	RRC, SC, BA For each Amphoe :
								CSC2 their amphoe.	AC, DAO For non remote
										Amphoe : ST
								* All ACs supply	For 1 tambon in
										each Amphoe : CSC
								STs and non-
								Amphoe a : AC1,

Table 3 :

 3 seedsReplacementFrequencyDistribution is the probability distribution used to draw farmers' seeds replacement frequency

	replacementFrequency	KDML	RD6

This is quite an artificial hypothesis that was introduced on the one hand to test how farmers could use quality appreciation to modify their suppliers choice, and on the other hand to balance the AC success in always providing seeds by getting seeds fro outside the province through its network


François Affholder
email: affholder@cirad.fr

Damien Jourdain

Eric Scopel

Véronique Alary

BIOECONOMIC MODELLING: IS THERE ROOM FOR SEAMLESS INTERDISCIPLINARITY ?

Keywords: systèmes de production, systèmes de culture, multidisciplinarité farming systems, cropping systems, multidisciplinarity

A partir de trois études de cas de modélisation bioéconomique d'exploitations agricoles, l'objectif de cette communication est de tirer des leçons concernant le défi de l'interdisciplinarité entre sciences biophysiques et sociales. Le résultat des études de cas illustre le bénéfice qui peut être attendu de ces études bioéconomiques. La communication se base sur ces expériences pour discuter des difficultés provenant de l'interdisciplinarité. Certaines de ces difficultés peuvent être maîtrisées relativement simplement en prêtant attention à la construction du respect mutuel et de la confiance entre membres de l'équipe, ou plus généralement en reconnaissant que si le travail est plus difficile il faut lui allouer davantage de ressources et de temps. Mais l'interdisciplinarité peut introduire des problèmes de pouvoir plus subtils à manipuler, bien que des cadres théoriques soient disponibles pour cela. En tout état de cause, prétendre réussir des études interdisciplinaires « sans couture », simplement grâce au choix des bons outils et ressources humaines, risque fort de produire de nombreuses cicatrices, au moins dans la mémoire des participants.

INTRODUCTION

As many key issues of the times concern coupled social-biophysical systems, scientists are urged to practise interdisciplinarity [START_REF] Ewel | Natural resource management: The need for interdisciplinary collaboration[END_REF]. In the domain of agricultural science, a typical case is "bioeconomical modelling", using farm decision models. These are well known tools for studying the impact of agricultural policies, technological innovation or more recently climate change, on production systems. Analyzing human decision belongs clearly to social science, but it is more and more acknowledged that the relevance of farm models strongly depends on the way they account for the biophysical processes involved in farming. Works discussing interdisciplinarity from an epistemological perspective have highlighted many difficulties explaining why it remains challenging especially at the intersection between social and biophysical science [START_REF] Klein | Evaluation of interdisciplinary and transdisciplinary research -A literature review[END_REF]. It is then not surprising that only few "bioeconomic studies" have been performed by truly multi disciplinary teams. In most cases, indeed, scientists from one domain use simulation models from the other domain as libraries containing the knowledge they need, rather than directly collaborate with scientists from the other domain. This approach, however, might not be relevant since it implies the use of concepts and models without the adequate background about their validity domain and limitations. Alternatively, some scientists pretend to develop skills in both domains, with a risk of failing to be peer-recognized in any of them. In other cases, when scientists of both domains are jointly involved in a study, issues arising from interdisciplinarity are generally not reported in papers, as if interdisciplinarity has been achieved seamlessly. Our paper first reports actual case studies in order to illustrate the benefit that can be expected from bioeconomic studies. Then it builds on these experiences in order to discuss practical issues arising from interdisciplinarity between biophysical and social science and to propose ways to reduce their potentially negative impacts on the quality of research.

METHOD

Three case studies were used in which research questions were involving both field and farm scales, and were requiring (and aiming at producing) knowledge on cropping systems, livestock systems, farming systems and household decision systems. Farm decision models were designed specifically for each study, tailored to the specific question addressed, nevertheless using the same general approach of mathematical programming models [START_REF] Hazell | Mathematical programming for economic analysis in agriculture[END_REF].

Questions addressed in the case studies

Ex-post analysis of contrasted production systems dynamics, central Brazil

The first case study (case 1) took place in the rural district of Silvânia, in the Cerrados of central Brazil, where for over a decade a R/D project observed, and contributed to an agrarian revolution. Indeed, most family farms of Silvânia, which were subsistence-oriented and maintained extensive cropping and livestock systems turned predominantly into highly intensive, specialized dairy farms within that decade. These farms adopted technologies such as: soil reclamation, animal genetic improvement, fodder production, artificial pastures. It resulted in a dramatic increase in farm revenue, net income per worker and more generally family welfare. Apart from the prerequisite of market opportunity for dairy production and the pre-existence of a collection/ conditioning chain for milk in the region, a key factor determining this revolution was the collective action of farmers: building associations, they obtained credit from the public banking system and reduced the transaction costs for commercialising their products and buying their inputs [START_REF] Bainville | Les transformations de l'agriculture familiale de la commune de Silvânia : une petite révolution agricole dans les Cerrados brésiliens[END_REF]. Many farms, however, remained out of this revolution (hereafter "undeveloped farms", as opposed to "developed farms" which experienced the revolution depicted above).

Understanding this disparity was critical, as potentially enlightening the broad question of the determinants and constraints of small scale farms development in developing countries. The hypothesis tested in this case study was that soil constraints were involved in the difference between developed and undeveloped farms, possibly increasing the risks associated with the intensive maize crop and thus with the intensive livestock system for which maize was a key production factor [START_REF] Affholder | Analyse des interactions entre risques climatiques et risques économiques dans les choix techniques des agriculteurs[END_REF].

Ex ante assessment of innovative cropping systems

Substantial initiatives are under way in the tropical world to develop and promote Direct seeding Mulch based Cropping Systems (DMC) in an attempt to improve staple crops productivity and sustainability (reducing soil erosion, improving crop nutrient and water balances and other ecological services). DMC typically introduce major transformations in the allocation of farm resources such as land, labor, cash and capital. Some DMC options have been adopted by large-scale mechanized farmers, especially in America and Australia, but seldom by resource-poor farmers in the developing world. Recent reviews suggested that many constraints at field, farm and community level are involved in this rejection of the technology by resource poor farmers [START_REF] Erenstein | Smallholder conservation farming in the tropics and sub-tropics: a guide to the development and dissemination of mulching with crop residues and cover crops[END_REF][START_REF] Giller | Conservation agriculture and smallholder farming in Africa: the heretics' view[END_REF]. In the next two case-studies, we assessed the potential economic attractiveness, and farmlevel constraints to adoption of maize-based DMC for contrasted farm households located in Central Brazil and in mountainous provinces of Vietnam. In both cases, we assumed that a necessary, but not sufficient, condition for DMC adoption is that DMC are economically sound at farm level. Symmetrically, DMC not economically sound at farm level was assumed a sufficient (but not necessary) condition for rejection. In Bac Kan province (Vietnam, case 2), most farmers have land along sloping uplands where they cultivate upland rice, maize and cassava, as well as land on valley floor or irrigable terraces were they grow irrigated rice. Off-farm activities provide an additional income, significant or tiny depending on labour market and labour force available in the farm. Livestock systems include mostly pig raising, with maize produced on the farm being one of the feeding sources, and one or two buffaloes are kept to provide draught power for ploughing paddy fields. DMC were proposed to these farms as a way to avoid soil erosion on sloping land. Seven farm types were considered, with differences regarding the biophysical environment of the farms (soils and climate), degrees of integration to market, and the following ratios: land per capita, land per worker, upland over lowland plus upland area, workers over mouth to feed. The case in central Brazil (case 3) took place in the rural district of Unai, located at 160 km south east of Brasilia. The case is very similar to Silvânia, in terms of the biophysical environment as well as regarding the dynamics of farming systems. There also, family farms have been rapidly evolving from subsistence-oriented, highly diversified farms towards more specialised, market-oriented dairy farms. The specificity of the case comes from the fact that the study focused on small farms relatively recently created and extremely resource-poor; the assentados established during the last decade in the framework of an agrarian reform. We also considered several farm types corresponding to various positions along the trajectory between subsistence oriented, just installed farms, and dairy specialized farms, positions which resulted mainly from differences in time since installation, the initial assets of the household, and the access of farms to markets, mainly for milk. DMC were proposed here as a way to reduce soil erosion on sloping land, as for the Vietnamese case, but also to reduce farmers' dependence to the market of rented farm machinery, to reduce fuel consumption, and to provide an extra fodder source via cover crops.

Models

For all these three case studies, we used Optimization Under Multiple Constraints (OUMC) approach, based on a bio-economic farm model developed using mathematical programming techniques [START_REF] Hazell | Mathematical programming for economic analysis in agriculture[END_REF]. In this approach, mathematical equations describe the farmer's objective (an utility function, namely annual farm income in our studies), the inputs and labour force required for each farm activity and each biophysical environment considered in the model and their expected production (i.e. the activities technical coefficients), and a set of constraints regarding the availability of production factors over seasons, the rules of family labour allocation, the satisfaction of food and basic needs /or preferences, the maintenance of inter-annual variations of income within certain limits corresponding to a certain risk aversion of farmers, etc …The optimizing algorithm selects the set of farming activities and their allocation across land and seasons that best fits the objective of farmers subject to those constraints and given the assumptions made on the performances of the elements of the production system. A specific model was built for each case study, differing in the sets of activities and biophysical environments considered and their technical coefficients, as dictated by the farming systems and the specific questions addressed in each case. The models developed for the two case studies in Brazil both included a relatively detailed description of the livestock system and accounted for risks resulting from inter-annual climate variability. The livestock component of the model used for the case study in Vietnam was much simpler and risk issues were ignored, but the model was accounting more precisely for off farm activities, food security constraints of the households, and the impact of fallow duration in the productivity of certain cropping systems. One important limitation of the models in the three cases was that they did not account for the dynamics of agronomic performances of cropping systems over time, as a possible result of soil degradation or improvement under aggressive or environmentally sound cropping systems, respectively. These models were first validated by comparing simulated to observed farm plans of actual farms. In cases 2 and 3, these baseline simulations were performed without incorporating DMC into the list of possible farm activities. Then several new runs of the models were performed in order to explore the impact on farm plans and income of scenarios of (i) changes in the sets of farm activities available to farmers (introduction of DMC in cases 2 and 3), (ii) changes in the economic environment of the farm (prices of inputs and outputs, of labour force, presence of subsidies supporting the adoption of DMC in cases 2 and 3, (iii) changes in farmers' risk aversion, (iv) changes in the biophysical environment of farms (increase or decrease in yields of crop or livestock systems).

Model development and simulations at farm level were performed under the responsibility of agricultural economists (Vietnam, Silvania) or livestock systems specialists (Unai).

Data acquisition

Information about farmers' goals, farm structures, and the technical coefficients of most activities was obtained through farm surveys carried out under the responsibility of the agricultural economists. Field agronomists were in charge of providing technical coefficients specifically for crop activities, using trials, monitoring of networks of farmers' fields, and (in cases 1 and 2) crop simulation models built, calibrated and validated using data from these trials and field networks. In the three case studies, the field work in agronomy was carried out over three agricultural campaigns.

RESULTS

Results of the case studies

In Silvânia (Brazil, case 1), the crop model simulations of maize yields over a series of 21 years of climatic data showed that the yield gains permitted by intensification were highly dependant on the climatic conditions of each year, the soil type and the quality of crop establishment operations. The more constraining were both the soil and the way farmers have access to machinery, the smaller was the expectancy of closing the yield gap a given year by increasing N fertilization and sowing density (figure 1).

Figure 1. Probability that the relative yield gain is lower than a given value R, study case 1.

(Adapted from [START_REF] Affholder | Risques de stress hydrique sur les cultures dans les Cerrados Brésiliens. Du zonage régional à l'analyse des risques à l'échelle des exploitations familiales[END_REF] On the "SC" soil type (Stony Cambisol), there was a probability of circa 0.2 that yield is not increased when shifting from Extensive cropping system Ecs to intensive cropping system Ics. Details (not shown) of the simulation data indicated that this was due to a high frequency of strong water stresses on these soils with low water storage capacity. This probability rose up to 0.5 when considering poorly managed farm machinery, as a consequence of the fact (data not shown) that under this context, delayed sowing (relatively to last tillage operation) and inadequate seed depth jointly contributed to low stand density and consequently high weed pressure [START_REF] Affholder | Diagnosis of the productivity gap using a crop model. Methodology and case study of small-scale maize production in central Brazil[END_REF]. Contrastingly, on the much more favourable deep latosols (DL), there is a null probability that yield increase is below 30% of yield obtained under extensive system, and close to one year over two farmers can expect a doubling of their maize yields when shifting from extensive to intensive systems on these soils. The results of farm decision simulations were in agreement with the observed dynamics of production systems. Farms relying, for their maize production, on constraining soils such as the stony cambisols, would not develop toward dairy farms, in the simulations with the farm decision model, whereas the model predicted the development of farms with significant area on deep, amended latossol or similarly favourable soils. These results were thus in support of the hypothesis that the biophysical constraints and their variations across Silvânia district were sufficient conditions for certain farms to reject intensification and specialization strategies, even with unlimited access to market and credit and relatively low risk aversion [START_REF] Affholder | Risques de stress hydrique sur les cultures dans les Cerrados Brésiliens. Du zonage régional à l'analyse des risques à l'échelle des exploitations familiales[END_REF].

In the case studies 2 and 3 the simulated choice of adopting or rejecting the innovative cropping systems was variable across farm types and environments. In Vietnam (case 2), with manual cultivation on slopes, adoption was in most farm types hampered by extra requirements of the innovative cropping systems in labour and cash, as compared to conventional cropping systems. Changes in labour and cash requirements of DMC were explored, corresponding to possible adjustments in the DMC technique such as reduction in the amount of mulch and fertilizer used, and increase in the use of herbicides (part of the mulch biomass being used as a barrier against weeds, reducing the mulch used, and hence the labour force required, would have to be compensated by an increased use of herbicide). However, none of the practically feasible adjustments of DMC provoked a change in the rate of use of DMC in the simulated farm. The amount of subsidies that would have to be paid to simulated farmers for them to convert all their conventionally managed sloping land into DMC measures the "unattractiveness" of DMC and its variations across farm types with a group of farms were labour constraints predominate (figure 2A) and another (fig. 2B) were cash constraints predominate [START_REF] Affholder | Constraints to farmers' adoption of direct-seeding mulch-based cropping systems: A farm scale modeling approach applied to the mountainous slopes of Vietnam[END_REF].

Figure 2. Sensitivity of simulated adoption of DMC to subsidies, study case 2 [START_REF] Affholder | Constraints to farmers' adoption of direct-seeding mulch-based cropping systems: A farm scale modeling approach applied to the mountainous slopes of Vietnam[END_REF] In Unai (case 3), farm simulations suggested that DMC were slightly more economically attractive than conventional systems thanks to a significant reduction of cash requirements permitted by the abandon of ploughing. The farm model proved highly sensitive to small changes within the confidence interval of the fodder value of cover crops. These fodder values of the cover crops were relatively uncertain in this context of cultivation under DMC, compared to what is known about these crops when being cultivated as pure fodder. Additional measurements and analysis, currently undergoing, appeared thus necessary for completing the study in order to conclude on the interest of DMC with cover crops for the assentados of Unai.

Interdisciplinary issues

Some naïve confidence at our first steps

Most of us in the team started working together about 15 years ago at Silvânia with case 1.

At that time we had freshly acquired a relative autonomy in deciding which question to address and which collaborations to develop for addressing these questions. "Bioeconomic modelling" studies were still rare. We had previously developed skills in the emerging modelling tools of our disciplines. We were extremely confident in the power of modelling for integrating knowledge from different disciplines and scales, and absolutely naïve about the possible problems associated with interdisciplinarity. In all the case studies of this communication, the questions were brought mainly by field agronomists, aware that issues arising at farm level and above may determine farmers' strategies at field level, recognizing that specific skills were required to address questions at these levels, and requesting the contributions of scientists having such skills. The team of 4 scientists that we constituted for the first study evolved during the following 15 years, with its hard core, some abandons and some new recruits, the size ranging between 3 to 8 scientists. It remained informal throughout, i.e. it never matched any administrative unit of any kind, and resources mobilized for our projects had to be distributed across several research units.

The relationships among us were not free of some kind of frustration and tension. We assume here that this kind of roughness in the relationships had something to do with the interdisciplinary nature of the work much more than with the individual personalities.

Issues objectively turning the job more difficult.

Under this title falls all what we identified as objectively reducing our efficiency in publishing our work as compared to our activity at the core of our disciplines. We immediately faced high transaction costs between the numerous administrative entities involved in the work, our institution being mainly organised in discipline-centred units. An alternative on this latter point would have been to construct a specific administrative entity matching the contours of our team, but this would probably have cost us at least as much time as spent in managing our activity across several units. We also soon experienced the necessity to allocate time for learning the language and culture of the other disciplines. This does not only mean learning new terms. Indeed there can be conflicting understandings of shared vocabulary [START_REF] Wear | Challenges to interdisciplinary discourse[END_REF], and this is particularly treacherous because a conflict has to occur in the first place for this to be identified as an issue. Furthermore, and especially in occasions where one discipline is represented by one individual only, the lack of experience may lead some to believe that personal tempers are the cause of the conflict. It was generally not possible to have multi-voices speeches when presenting our work in seminars and lectures, not so much as a result of some kind of conservatism in the scientific community, but principally due to logistical constraints, especially in our team composed of scientists posted all over the world. As a consequence, the speaker had to master much more concepts than when he/she deals with matters from his discipline, and still exposed himself to the critique of specialists from the other disciplines, with little chance to provide relevant answers or to take the relevant benefit of the critique. An additional risk is that the speaker unintentionally draws in public some conclusions that would not be endorsed by his colleagues of the team. The more each member of the team invests in learning the concepts and tools of the other disciplines involved in the work, the better for avoiding the risks mentioned above, but there is obviously a need for a compromise between such investment and the use and maintenance of the skills acquired in the discipline of origin of each one. Failing to do so would expose scientists to the risk that most of their work is not recognized as contributing to any clearly identifiable scientific domain [START_REF] Naiman | A perspective on interdisciplinary science[END_REF]. We must admit, however, that we don't know much about the proper setting of the cursor between these two opposed risks. Regarding culture, throughout our studies there happened to be great differences between field agronomists and farm economists in their relation to time and project's life cycle. Field agronomists claimed that data collection would be valueless unless the data cover at least two agricultural campaigns, in order to capture a minimal amount of the interannual variability of crop performances. In case studies 1 and 3, model calibration demanded additional data collection campaigns in order to improve the capacity of the crop model to predict the variations of crop yields across environments and technical management. In contrast agricultural economists needed only a few months for completing their farm surveys and get enough data for "their part" of the farm models. Changes in the economic environment of the farms, especially in emerging countries such as Brazil and Vietnam, as well as changes over time in the perceptions of this environment by farmers, limit the validity of farm decision models to a few years, whereas the validity of most components of a crop models expands over a much larger period of time. For our first case study, our lack of experience on this disjunction of the production cycle in the two domains resulted in a project getting soon completely out of its Gantt chart, and this probably contributed to the very low number of articles published from this study.

From his own experience in contributing to and managing interdisciplinary studies, Naiman (1999) described many difficulties similar to those we mentioned above, and suggested a set of guidelines for minimizing their impact on the quality of science (and the relationships between scientists, obviously). These guidelines converge mostly towards management principles favouring mutual respect in the team, including at the stage of choosing members of the team willing to jointly assume leadership and responsibility. This suggests that a team constituted as a decision from the hierarchical management is not necessarily the best formula for interdisciplinarity. One other particular consequence for institutions willing to develop interdisciplinary studies is that the way individual and/or collective performances are assessed should at least not discourage efforts in that direction. The incautious use of the raw number of peer reviewed papers per individual and year will not completely extinguish interdisciplinary studies only because there always are a number of scientists who care less for their consideration by others than for what excites their curiosity. But if institutions count on this, then what are those indicators made for ? Incidentally, but this deserves to be mentioned given the tendency of research funding agencies promoting big projects, small teams are probably more able than big ones to cope with the specific issues of interdisciplinarity. There might be a threshold, however, below which debates between disciplines are likely to turn into conflicts among persons.

Where the fruits of interdisciplinarity are potentially poisonous

The results of the first study in Brazil were rather disappointing for the agricultural economists of the team, who were expecting social and economical constraint to predominate over biophysical constraints in farmers' strategies. In the two other studies, field agronomists were the more surprised by the results especially those obtained in Vietnam, since they were expecting the innovative cropping systems to be economically attractive to the well informed, rationale farmers that were idealized in the farm models. The way interdisciplinarity brings surprise is where most of its added value resides. But at the same time it generates a series of questions regarding the quality of the work of each member of the team that are potentially threatening their willing to further take part of the adventure. Another illustration of that potential threat is provided by the fact that we were not able, in the case studies on DMC adoption, to account for the evolution over time of the performances of cropping systems. This limitation of our models was indeed a frustration for everyone, since it prevented us to determine whether the low attractiveness on the short term of some DMC options would be counterbalanced by better performances, relatively to conventional cropping systems, on the longer term. But furthermore, this limitation in the power of the biophysical models was a surprise for the agricultural economists of our team. Actually, this could surprise many biophysical scientists as well, since articles are not uncommon in which models are used to predict the behaviour of a particular biophysical system over the next 50 years or beyond. But arguing that we are not confident enough in our model to use it for long term predictions is probably much easier in front of a circle of biophysical scientists who are not likely to blame you for any excess of honesty on that point, than within a circle of agricultural economists, where some might suspect your concerns for rigour to hide lacks in your modelling skills.

The disjunction in the production cycles of the two domains was also a medal with two faces.

On one side it brought some comfort to every one, since it allowed us to alternate the more risky phases of truly collaborative interdisciplinary tasks, with periods during which we were dealing with activities closer to the core of our respective disciplines, with more opportunities for each individual to build his scientific credit vis-à-vis his peers. But on the other side it strengthened the centrifugal forces that repulse scientists from the interface between disciplines, forces against which at least some have to struggle for the project to deliver its expected outputs. Perhaps as a consequence of this, many of us, whatever their discipline, felt at a moment or another, an asymmetry or a kind of hierarchical relationship between disciplines. For instance, in the two studies in Brazil, field agronomists felt the pressure of farm economists looking forward the moment where at last, after a series of experiments / modelling / validation loops at field scale, their colleague would provide them with sound data to use in the model at farm scale. The question of "how sound" the data should be echoed with "how soon" they would be available.

In our kind of simulation of farmers' decisions, the question of "how sound" is the question of the sensitivity of the simulated decision to errors in the input variables of the farm model. In the studies in Brazil (cases 1 and 3), the farm model proved to be highly sensitive to some of its input variables that were actually output variables of the crop model, with an initially wide confidence interval. Reducing this confidence interval justified a series of modelling loops at field level that had not been anticipated. An alternative would have been to publish that the biophysical model was not precise enough for the analysis at farm level to provide robust conclusions. This might have satisfied the biophysical scientists as justifying more efforts in their disciplines, but would probably have frustrated the agricultural economists who had already paid their tribute to interdisciplinarity and would not harvest its fruits. In other words, the expectations and constraints associated to linking biophysical to social sciences in our projects pushed the field agronomists into a kind of rush ahead, under the pressure of agricultural economists. The latter were considering that the change in scale between field and farm levels imposed to greatly simplify the information relative to field level. They considered the pressure put on field agronomist as an effective way to ensure that the latter will accept these simplifications. The symmetrical situation may occur as well, i.e. agricultural economists endlessly refining their model whereas field agronomist plea for mercy, although it is less likely due to the relatively short validity period of models in social sciences as mentioned above. MacMynowski (2007) proposed a process of "differentiation, clarification, and synthesis" in order to better deal with the relationship between power and knowledge that is at play in interdisciplinarity. This framework would greatly facilitate dealing with the kind of issues reported in the present subsection. One practical consequence of this approach would be that today, for studies such as those reported here, we would probably start from sharing conceptual (or very simply quantified) models and raw data at both field and farm scales, and jointly identify what aspect of the models should be refined (and in fact what we have done in case 2 in Vietnam was already close to that), instead of starting with preconceived ideas about what each of us had to do first. Note, however, that it is unsure whether we would have accepted such a raw merging of models, data and expert knowledge 15 years ago, because at that time we did not trust and respect each other enough to show right at the first meeting how few things we knew about the systems we pretended to study together.

CONCLUSION

Our 15 years experience of bioeconomic modelling suggests us some lessons about the issues relative to interdisciplinarity between biophysical and social sciences.

Naively ignoring the need for some teamwork devoted at building mutual respect and trust, and for patience in learning the culture and language of the other disciplines involved would probably the most effective way to prevent a multidisciplinary team from reaching its goal.

Pretending to achieve interdisciplinary studies seamlessly, just thanks to the use of the right tools and human resources would most probably result in numerous scars at least in the memories of the participants ! But it is not so difficult for scientists to admit that interdisciplinarity, especially when cultures are as different as between social and biophysical sciences, will strongly exacerbate many of the small difficulties with which they routinely cope when interacting with others within the scope of their disciplines. Once this is acknowledged, guiding principles expected to reduce the impact of these issues are relatively easy to draw. Organizational patterns and management techniques can be adapted mostly on the basis that if it is more difficult, then more means and more time should be devoted the job [START_REF] Marzano | Working to make interdisciplinarity work: Investing in communication and interpersonal relationships[END_REF].

At the intersection of disciplines there are a lot of unclear or even unknown processes, the understanding of which being critical for interdisciplinarity to honour its promises. This requires innovative conceptual approaches that are more than the addition of the different conceptual framework of each discipline. The sooner the group identifies and address these processes the better. But two prerequisites are critical: trust and mutual respect have to be firmly acquired, and it must be recognized that there is some unavoidable share of subjectivity associated with the shift from purely biophysical systems to systems where human decision takes place. Otherwise, this unavoidable subjectivity may pave the way to the suspicion that a competition for some kind of power is at stake in the debate rather than the search for a better understanding of something.

Figure 3 .

 3 Figure 3. Annual deviation of net profit with the introduction of the DMC system with fodder cover (comparison with the baseline scenario in %), study case 3.

ISDA 2010, Montpellier 28-30 Juin 2010