Rule-based management for simulation in agricultural decision support systems

Shaffer, M. J.; Brodahl, M. K.

doi:10.1016/s0168-1699(98)00031-3

Cited by 46 publications

(17 citation statements)

References 16 publications

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“…Following the same idea, Kemp and Michalk (2007) point out that "farmers can manage more successfully over a range than continually chasing optimum or maximum values." In practice, one can easily identify an ideal time window in which to execute an activity that is preferable or desirable based on production objectives instead of setting a specific execution date in advance (Shaffer and Brodahl 1998;Aubry et al 1998;Taillandier et al 2012). Timing flexibility helps in managing uncontrollable factors.…”

Section: Relaxed Constraints On Executing Activitiesmentioning

confidence: 99%

Processes of adaptation in farm decision-making models. A review

Robert¹,

Thomas

Bergez³

2016

Agron. Sustain. Dev.

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Agricultural production systems should evolve fast to cope with risks induced by climate change. Farmers should adapt their management strategies to stay competitive and satisfy the societal demand for sustainable food systems. It is therefore important to understand the decision-making processes used by farmers for adaptation. Processes of adaptation are in particular addressed by bio-economic and bio-decision models. Here, we review bio-economic and bio-decision models, in which strategic and tactical decisions are included in dynamic adaptive and expectation-based processes, in 40 literature articles. The major points are: adaptability, flexibility, and dynamic processes are common ways to characterize farmers' decisionmaking. Adaptation is either a reactive or a proactive process depending on farmer flexibility and expectation capabilities. Various modeling methods are used to model decision stages in time and space, and some methods can be combined to represent a sequential decision-making process.

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Section: Relaxed Constraints On Executing Activitiesmentioning

confidence: 99%

Processes of adaptation in farm decision-making models. A review

Robert¹,

Thomas

Bergez³

2016

Agron. Sustain. Dev.

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“…The interview method has not been very formalised by agronomists. Shaffer and Brodahl (1998) propose to organise the interview by identifying (i) each general farming operation and assigned resources and conventions, (ii) the constraints and how the farmer deals with them, (iii) when the operation takes place and, finally (iv) the bail-out rule set the farmer uses. Such a methodology may be helpful to understand the farmer's general crop management approach and to represent it through an action model (Sebillotte and Soler, 1988).…”

Section: Significance Of the Interview And Methodological Questionsmentioning

confidence: 99%

“…Such rules, once clearly formalised, could be a first step towards a decisional model (Shaffer and Brodahl, 1998) that could be used to optimise the set of farmers' rules-of-thumb, as proposed by Bergez et al (2002) for constrained irrigation devices. Such a decisional model should not include rules that may be incompatible with agronomic knowledge.…”

Section: Importance Of Constraints On Irrigation Practicesmentioning

confidence: 99%

Survey-based analysis of irrigation and N fertilisation practices in apple orchards

Nesme

Bellon²,

Lescourret

et al. 2006

Agron. Sustain. Dev.

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. Survey-based analysis of irrigation and N fertilisation practices in apple orchards. Agronomy for Sustainable Development, Springer Verlag/EDP Sciences/ INRA, 2006, 26 (3), pp.215-225. 215 Agron. Sustain. Dev. 26 (2006 Abstract -Farmers are increasingly required to justify their practices and to change their technical management approach due to the development of integrated production, new regulations and private standards. The feasibility of these changes can be assessed by analysing the practices of farmers. Here, we analysed the practices of farmers in apple orchards from an agronomic viewpoint, focusing on irrigation and N fertilisation. Our study is based on interviewing farmers about their practices and factors that influence their decisions. Interviews were supported by model-generated irrigation and fertilisation schedules related to the considered plots. Fertilisation data recorded over a two-year period were compared to data from interviews. Our results show that (1) irrigation practices were highly dependent on pest control practices; (2) chemical spraying against the codling moth strongly influenced irrigation frequency; (3) irrigation practices were dependent on irrigation devices; and (4) fertilisation practices were highly dependent on the conceptions that farmers had about the role of N fertilisers. These conceptions were associated with timing, fertiliser formula and modulation rules. Based on data recorded by farmers over a two-year period, fertilisation practices were highly variable. Likewise, these data also differed from data collected during interviews. Overall, our findings underline the importance of considering interactions between cultural practices and farmers conceptions in order to understand farmers practices. It also stressed the need to consider data recorded by the farmers themselves only as a partial source of information and to design better interviewing procedures with farmers.apple orchard / farmers' practices / farmers' conceptions / interviews / data recorded by farmers

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“…Single rules may represent operational activities, groups of rules represent tactical adjustments, and the rule set as a whole expresses the farm management strategy (Pietersma et al 1998;Romera 2004). Such rule-based representations of farm management have shown promise as a component of farm system modelling (aubry et al 1998;Shaffer & Brodahl 1998;Sherlock & Neil 1999;Romera et al 2004). This is not surprising since, due to the complexity of decision making in the real world, human action is often determined by conscious and subconscious habits or rules, rather than by rigorous analysis (Hodgson 1997).…”

Section: Establishing a Common Epistemologymentioning

confidence: 99%

Better simulation modelling to support farming systems innovation: Review and synthesis

Woodward

Romera

Beskow

et al. 2008

New Zealand Journal of Agricultural Research

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Simulation modelling is a methodology that appears highly suitable for use in farming systems innovation. However, if the aim is to improve farming systems by supporting farmer behaviour change, model-based approaches seem to have delivered surprisingly little observable benefit to date. This paper identifies the problems underlying this apparent lack of impact, and proposes better approaches to improve on-farm benefits from farming systems modelling. A key principle that has been neglected in farm simulation modelling is intimate involvement of clients (defined as "the individuals or groups whose approval is needed for change to be implemented") throughout the innovation process. We argue that client participation is essential in the problem definition, model design and testing and policy design and evaluation phases of model-based research projects. The role of a simulation model within the innovation process, then, is to be a jointlycreated "virtual world" wherein experiments may be conducted to facilitate learning about the relevant system. We argue that whole farm simulation models that use decision rules to specify alternative farm management strategies are the best available form of virtual world models of farming systems. Besides appropriate client input, high quality models require excellent software development practices and strenuous attention to building user confidence. The latter should include analysing the model to assess its stability and sensitivity properties, before using it to simulate experiments that compare several management alternatives under a range of environmental and local conditions. This approach allows estimation of the variations in farm system performance that are likely to result from interactions between initial farm state, farm management policy and future weather and markets. On the other hand, using simulation models to discover "optimal" farm systems would usually be inappropriate due to the complexity arising from multiple-stakeholder views, multiple-criteria, and the dynamic nature of farming systems problems. An improved systems modelling methodology is proposed that should be better able to provide benefits into farming practice.

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Rule-based management for simulation in agricultural decision support systems

Cited by 46 publications

References 16 publications

Processes of adaptation in farm decision-making models. A review

Processes of adaptation in farm decision-making models. A review

Survey-based analysis of irrigation and N fertilisation practices in apple orchards

Better simulation modelling to support farming systems innovation: Review and synthesis

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