Toward more efficient model development for farming systems research – An integrative review

Reinmuth, Evelyn; Dabbert, Stephan

doi:10.1016/j.compag.2017.04.007

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…This issue is only amplified by the fact that personnel who use APSIM often do not possess enough IT knowledge and training, especially when it is required to operate additional third-party software. Similar findings regarding lack of IT expertise we pointed out by (Reinmuth and Dabbert, 2017) for instance. Some of these issues will be hopefully handled in the APSIM Next Generation as outlined by (Holzworth et.…”

Section: Resultssupporting

confidence: 90%

Data Pre-processing for Agricultural Simulations

Jarolímek

Pavlík

Kholova

et al. 2019

AOL

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The process of agricultural simulation using APSIM requires input meteorological data to be prepared in a specific format and the simulation setting file to be ready before the simulation processing starts. Because of possible time savings when conducting large number of simulations at once, it is preferable to create all the input and settings files for all the simulations beforehand and process the simulations in batches as large as possible. This article specifically deals with the data acquisition, transformation and preparation process. It also outlines initial testing and computing time estimations and discusses scheduling, parallel processing and other possible simulation optimization methods..

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Section: Resultssupporting

confidence: 90%

Data Pre-processing for Agricultural Simulations

Jarolímek

Pavlík

Kholova

et al. 2019

AOL

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“…Economic considerations are not incorporated in the field allocation process. The ARS score is the missing link to be used at the time of planning [ 61 ] for climate change impact analysis.…”

Section: Discussionmentioning

confidence: 99%

Modeling perceptions of climatic risk in crop production

et al. 2017

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In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of “still-good yield” (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.

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