rotat, a tool for systematically generating crop rotations

Dogliotti, Santiago; Rossing, W.A.H.; Ittersum, M.K. van

doi:10.1016/s1161-0301(02)00047-3

Cited by 158 publications

(77 citation statements)

References 16 publications

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“…The development of the set of activities was either performed "by hand" (Berentsen and Giesen 1995) possibly based on consultative participation of stakeholders through farm surveys (Castelan-Ortega et al 2003) or with computer models (Cittadini et al 2006). For instance, Dogliotti et al (2003) developed a crop rotation generator that combines crops from a predefined list to produce all possible rotations, given a number of agronomic filters related, for example, to undesirable crop successions. Quantification of inputs and outputs for each activity used consultative participation of experts (Cittadini et al 2006;Dogliotti et al 2004) and computer models.…”

Section: Design Processmentioning

confidence: 99%

Farming system design to feed the changing world. A review

Martin

Martin‐Clouaire

Duru

2012

Agron. Sustain. Dev.

115

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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 Processmentioning

confidence: 99%

Farming system design to feed the changing world. A review

Martin

Martin‐Clouaire

Duru

2012

Agron. Sustain. Dev.

115

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“…The frequency constraints were introduced to take into account the minimum period of time before repeating the cultivation of the same crop on a specific plot. Without it, the model outcomes will produce negative effects on the physical, chemical and biological soil quality (Dogliotti et al 2003). Cereals, tubers and oil crops cannot be cultivated on the same plot in two consecutive years.…”

Section: Constraintsmentioning

confidence: 99%

An ex-ante impact assessment of the Common Agricultural Policy reform in the North-Western Romania

Jitea¹,

Dumitraş²,

Simu³

2015

AGR ECON-CZECH

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Th e paper aims to assess the impact of the Common Agricultural Policy reform (post 2014) in the environmental vulnerable areas from Romania. It proposes a bio-economic dynamic farm model calibrated with the positive mathematical programming (PMP). Th e standard PMP approach was extended to simulate the mixed farming systems through the introduction of a livestock production activity in the fi rst calibration step. It allows incorporating activities not available in the base year, but that can be performed by the farmers after the future policy reforms. Th e model provides supply-responses for both the crop and livestock farms according to the agricultural and fi nancial policy shifts. It maximizes the farm net fi nancial fl ows subject to the resource, livestock, fi nancial and agricultural policy constraints. Th e initialisation data were obtained from a face-to-face stratifi ed survey applied on the mixed-sheep farms that have used grassland areas under the Agri-Environment Schemes (AES) in the North-Western Romania (Transylvania). Th e results show that the most vulnerable group to the policy changes is represented by the small-size farms, to which the AES are very important economic drivers. Th us, the diversifi cation and the off -farm employments possibilities should be integrated into the future rural development plans as a premise for their survival.

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“…Such an approach provides the farmers with a user-friendly alternative to the estimative crop plan decision making relentlessly used. Note that including the crop succession requirements into a mathematical programming model is a nontrivial task that is permanently in the focus of the operations researchers (Seppelt 2000;Dogliotti et al 2003;Klein Haneveld and Stegeman 2005;Bachinger and Zander 2007;Detlefsen and Jensen 2007;Castellazzi et al 2008;Jatoe et al 2008;Benjamin et al 2009;Parsons et al 2009;dos Santos et al 2011). Moreover, the combination of the crop succession restrictions and the risk consideration in a single optimization model is very rare (Myers et al 2008) and leads to a complex optimization problem.…”

mentioning

confidence: 99%

Crop plan optimization under risk on a farm level in the Czech Republic

Janová¹

2014

AGR ECON-CZECH

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Th e precise agricultural operations research models claiming complex solutions and specialized software tools may appear too complicated to truly support the decision making process on the farm level. Th e specifi c balance between the simplifi cations made and the correspondence to the real problem must be found when building decision support for a local farmer. In this paper, a decision support model for agricultural production planning in the Czech Republic is developed covering both the randomness of parameters entering the problem and the complex crop succession requirements. Crucially, the model can be approached using software tools commonly available to farmers. Th e structure of the simplifi ed model is described in detail, its validity proved by the recently suggested simulation procedures and the general applicability of the model structuring for a local agriculture decision support is discussed.

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rotat, a tool for systematically generating crop rotations

Cited by 158 publications

References 16 publications

Farming system design to feed the changing world. A review

Farming system design to feed the changing world. A review

An ex-ante impact assessment of the Common Agricultural Policy reform in the North-Western Romania

Crop plan optimization under risk on a farm level in the Czech Republic

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