The development of the farm model, including the several submodels, followed the usual modeling procedure as, e.g. described in Bossel (1992, p. 41f) and Joergensen (1994, p. 20f). This procedure comprises the subsequent steps of

- definition of the problem,
- making the conceptual model,
- formulating the mathematical equations,
- verification and long-term stability,
- sensitivity analysis,
- calibration,
- validation.

After the problem is defined in terms of time, space, subsystems and complexity, its main features such as state variables, internal connections, forcing functions and processes are selected and determined. Ideally, the data requirement to develop a model should be determined after the conceptual model (in the form of a diagram, for instance) has been formulated (Joergensen, 1994). In this case the development of the conceptual model and the imbedded submodels are biased in advance by the data availability. So each model can be considered as a compromise between the scope of the model and data availability.

The next step, the mathematical formulation, also resulted in compromises, mainly between the intended features of the (sub)model, data availability, desired complexity and the amount of time given for the development of this thesis.

Verification and long-term stability were carried out for all submodels to answer the following questions: 1. Does the model react as expected ? 2. Is the model stable in the long term ? (Joergensen, 1994). The logic of the internal structure was checked by implementing the model's equations and comparing the outcomes to the expected results under a range of conditions.

Afterwards follows a sensitivity analysis which gives an impression of the most sensitive and crucial parameters of each model. This will tell us which parameters have to be determined with higher accuracy or have to be handled with care. In practice, the sensitivity analysis is carried out by changing parameters, forcing functions or submodels and observing the corresponding change of selected state variables. Thus, the sensitivity, S, of a parameter, P, is defined as

S = [dx/x ] / [ dP/P ],

where x is the selected state variable.

The relative change of the parameters is based on our knowledge of the uncertainty of the parameter. It can be appropriate to vary the parameter at more than one level because the response of the reference state variable might not be linear (Joergensen, 1994).

When possible or sensible a calibration of certain parameters was conducted in order to fit the model to Philippine conditions. Validation was difficult to carry through in most cases, due to the lack of appropriate data. This task will surely be up to future possible users of the model.

This typical procedure of developing a mathematical ecological model is, of course, always adapted to the specific problem and type of model and not always every step can be taken as recommended. Moreover, 'the sequence of verification, sensitivity analysis and calibration must consequently not be considered a rigid step-by-step procedure, but rather as an iterative operation, which must be repeated a few times' (Joergensen, 1994 p. 48). This also applies to the first three steps.

The definition of the problem was already given in the introduction. In the following the conceptual model is developed and the resulting steps are the development and formulation of the submodels. For each submodel the described modeling procedure is applied as far as necessary or possible.

- the importance of certain stocks and their significance concerning the whole farm,
- potential recycling flows which were not quantified in the RESTORE tables,
- at what time of the season rice cultivation methods are carried through, like e.g. land preparation, rice transplanting, harvest, etc.,
- daily rates of certain flows, e.g. manure application to pond,
- average growth rates of livestock
- management strategies in general throughout a season which found no entry in the RESTORE data tables.

Provided with this information the conceptual model of an average Philippine rice farm can be established. Major stocks and flows were identified and at least roughly quantified.

Tab. 1: As a first step towards formulating the conceptual model, the considered farm is defined in space. Also the main submodels are identified.

Compartments (ha) | Enterprises (ha/no.) |
---|---|

Homestead 0.375 ha | buffalos 3
poulty 15 pigs 2 (fruit)trees 19 |

ricefield 1.5 ha | rice variety IR 74 |

rice-fish 0.1 ha | rice variety IR 74
Tilapia |

fishpond | Tilapia |

total size: 2 ha |

Compartments (ha) | Enterprises (ha/no.) |
---|---|

Homestead 0.475 ha | buffalos 2
poulty 25 pigs 2 vegetable 0.125 ha (fruit)trees 27 |

ricefield 1.5 ha | rice variety IR 74 |

fallow land 0.025 ha | |

rice.fish 0.1 ha | rice variety IR 74
Tilapia |

fishpond | Tilapia |

total size: 2 ha |

inflows | enterprise | outflows |
---|---|---|

rice residues/ leaves
grass from
ricefields bunds | buffalo | manure |

golden snail
often no special inputs padddy rice broken rice/rice hulls | poultry | manure
poultry |

golden snail
rice bran
broken rice | pigs | manure
pig |

buffalo manure
pond water | (fruit)trees | leaves
fruits wood |

fertilizer
buffalo manure
pondmud | rice field | rice bran
rice
bundweeds |

chicken manure
pig manure leaves (ipil, spinach> buffalo manure | rice-fish | rice
fish |

rice bran
pig manure
leaves (ipil, spinach) poultry manure td align=center> fishhpond | fish
pondmud | |

buffalo manure
chicken manure leaves fertilizer rice straw pond water | vegetables
(DS) | vegetable |

fallow | grass | |

Tab.2: Flows and stocks in italics are quantified
and modelled in this first version of FARMSIM. |

Sometimes a part of the rice field is used for rice-cum-fish culture. The fishpond with Nile Tilapia as a main culture crop is assumed to present a farm compartment, too. Although not yet established on all farms it is taken into account for testing its impact on overall farm performance. In DS a large portion of the rice field cannot be cultured due to the lack of water. This also often affects the use of a fishpond.

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