2008
DOI: 10.1016/j.agsy.2007.10.006
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Abstract: Crop rotations are allocations by growers of crop types to specific fields through time. This paper aims at presenting i) a systematic and rigorous mathematical representation of crops rotations; and ii) a concise mathematical framework to model crop rotations, which is useable on landscape scale modelling of agronomical practices. Rotations can be defined as temporal arrangements of crops and can be classified systematically according to their internal variability and cyclical pattern. Crop sequences in a rot… Show more

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Cited by 136 publications
(78 citation statements)
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“…Thus, two different sequences of events would most likely lead to performing two different plans. Some activities may be canceled in one case but not in the other depending on whether they are optional or subject to a context-dependent choice (Bralts et al 1993;Castellazzi et al 2008Castellazzi et al , 2010Dury et al 2010).…”
Section: Flexible Plan With Optional Paths and Interchangeable Activimentioning
confidence: 99%
See 1 more Smart Citation
“…Thus, two different sequences of events would most likely lead to performing two different plans. Some activities may be canceled in one case but not in the other depending on whether they are optional or subject to a context-dependent choice (Bralts et al 1993;Castellazzi et al 2008Castellazzi et al , 2010Dury et al 2010).…”
Section: Flexible Plan With Optional Paths and Interchangeable Activimentioning
confidence: 99%
“…Crops were identified to enable farmers to adapt to certain conditions. Multiple mathematical approaches were used to model flexible crop rotations: Detlefsen and Jensen (2007) used a network flow, Castellazzi et al (2008) regarded a rotation as a Markov chain represented by a stochastic matrix, and Dury (2011) used a weighted constraint satisfaction problem formalism to combine both spatial and temporal aspects of crop allocation.…”
Section: Adaptation For the Agricultural Season And The Farmmentioning
confidence: 99%
“…According to the parameter values of this model, the landscape mosaic can be more or less heterogeneous. Other processes could be used, based on the statistical study of spatial and temporal patterns of real field mosaics [9,10]. Furthermore, stochastic neighbourhood rules can be learnt from a real dataset, as proposed in [35], where Hidden Markov Models are applied on land-use temporal and spatial data.…”
Section: Simulating the Land-use Mosaic Of Agricultural Landscapesmentioning
confidence: 99%
“…The mosaic of fields together with their land-use can be seen as a noisy picture generated by these different processes. Recent studies Castellazzi et al, 2008) the order of the Markov process. In the space domain, the theory of the random Markov fields is an elegant mathematical way for accounting neighbouring dependencies (Geman & Geman, 1984;Julian, 1986).…”
Section: Mining Agricultural Landscapesmentioning
confidence: 99%