A simulation model of grazing sheep: II. Whole farm model

Cacho, Oscar J.; Finlayson, John D.; Bywater, Anthony C.

doi:10.1016/0308-521x(95)93644-s

Cited by 37 publications

(23 citation statements)

References 11 publications

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“…These were either grasslandbased beef or dairy cattle farming systems (Andrieu et al 2007;Cacho et al 1995;Romera et al 2004) or mixed beef and dairy cattle farming systems relying on grasslands and crops (Rotz et al 2009;Vayssières et al 2009). Smallholder mixed-farming systems included more marginal crops and livestock species such as quinoa, beans and donkeys (Herve et al 2002;Pfister et al 2005).…”

Section: Design Contextmentioning

confidence: 99%

“…The function, behaviour and structure of these models were then presented in details at the expense of the wider design context and problem situation specific to a given application. A problem pointed out by most participatory and simulation-based approaches was the need to improve the understanding of farming systems, their dynamics and internal interactions in particular (Cacho et al 1995;Keating et al 2003).…”

Section: Design Contextmentioning

confidence: 99%

“…The biophysical models integrated within the farm models were either mechanistic (Keating et al 2003;Shaffer et al 2000), statistical (Herve et al 2002;Pfister et al 2005), mechanistic and statistical (Cacho et al 1995;Kerr et al 1999a, b) or mechanistic and empirical (Martin et al 2011b). The biophysical models were either static (White et al 2010) or dynamic, operating on a time step of 1 year (Kerr et al 1999a, b), 1 week (Blazy et al 2010) or 1 day (Keating et al 2003), even when the related farm model was static (Martin et al 2011a).…”

Section: Computer Model Structurementioning

confidence: 99%

“…The biophysical models were either static (White et al 2010) or dynamic, operating on a time step of 1 year (Kerr et al 1999a, b), 1 week (Blazy et al 2010) or 1 day (Keating et al 2003), even when the related farm model was static (Martin et al 2011a). The remaining dynamic farm models had a time horizon ranging from one season (Cros et al 2003) to several years (Blazy et al 2010;Cacho et al 1995). Weather was the main environmental factor influencing the behaviour of the modelled system (Andrieu et al 2007), but market conditions and policy context were considered in some cases (Blazy et al 2010;Matthews et al 2006b).…”

Section: Computer Model Structurementioning

confidence: 99%

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Farming system design to feed the changing world. A review

Martin

Martin‐Clouaire

Duru

2012

Agron. Sustain. Dev.

118

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Agricultural production is unstable as a result of complex, dynamic and interrelated factors such as climate, markets and public policy that are beyond farmers' control. Farmers must therefore develop new farming systems incorporating innovations in objectives, organization and practices adapted to changing production contexts. As a consequence, agronomists have expanded the "farming system design" field of research. A variety of quantitative and qualitative design approaches have been developed to support the analysis of current farming systems and the design and evaluation of alternatives. A comprehensive literature assessment is lacking for this emerging field of agricultural science. Here we review 41 farming system design approaches using computer models. Our main findings are the following: (1) the reviewed literature do not make reference to the theoretical approaches from the field of design science. (2) Two categories of farming system approaches can be distinguished: optimisation approaches, and participatory and simulation-based approaches. These two categories are connected to two of the main design science theories. (3) For optimisation approaches, farming system design is mainly seen as a problem-solving process. Emphasis is placed on the computational exploration of the solution space by a problem-solving algorithm. (4) For participatory and simulation-based approaches, conceptualization of the design problem is central to the farming system design process. The subsequent exploration of the solution space relies on the creativity of humans. (5) Optimisation approaches, and participatory and simulationbased approaches are oriented towards the development of exploitative rather than exploratory innovations. Exploitative innovations involve the exploitation of available knowledge while exploration innovations build on knowledge created in the course of the design process.

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Section: Design Contextmentioning

confidence: 99%

Section: Design Contextmentioning

confidence: 99%

Section: Computer Model Structurementioning

confidence: 99%

Section: Computer Model Structurementioning

confidence: 99%

See 2 more Smart Citations

Farming system design to feed the changing world. A review

Martin

Martin‐Clouaire

Duru

2012

Agron. Sustain. Dev.

118

View full text Add to dashboard Cite

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“…The extra income derived from including tactical responses can be viewed as the cost to the farmer of basing choice regarding a management strategy on analysis that neglects the tactical advantages afforded by such a strategy. This chapter describes a destocking and marketing algorithm integrated into a grazed system model (LincFarm; [4,6]) to implement tactical adjustments to climatic variability in a dryland grazed system in Canterbury Plains in New Zealand. The algorithm is implementable in any grazed system as an in an integral part of the model or a standalone subsystem.…”

Section: Background Informationmentioning

confidence: 99%

Production and Marketing Risks Management System in Grazed Systems: Destocking and Marketing Algorithm

Gicheha¹,

Edwrads²,

B³

et al. 2017

Theory and Application on Cognitive Factors and Risk Management - New Trends and Procedures

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This study was carried out to explore potential approaches to managing production and market risks associated with climatic variability in dryland grazed systems. The methodology is novel in that it considers farmers' ability to make sequential adjustments to their production activities when information on uncertain events becomes available. Traditional approaches to evaluation of farmers' response to risk assume perfect knowledge of production resources and that risk emanates from uncertainty in yield returns. Strategic approaches are mostly considered in evaluating farmer's risk attitude implying that managing the variability (risk) assumes that different production activities resource requirements are known (non-embedded risk). In real farming systems, the producers make sequential decisions and adjust the timing and methods of their activities as a season progresses and more information on uncertainty becomes available (embedded risk). This chapter describes a platform adopted in making destocking and marketing decisions by simulating the impact of implementing alternative tactical adjustments. The algorithm was successfully tested in a research that investigated the physical and economic impact of incorporating tactical responses in risk management strategies in dryland sheep production systems in New Zealand. The algorithm can be integrated into existing grazing models and can also be used as a standalone system.

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