WOFOST: a simulation model of crop production

Diepen, C.A. van; Wolf, J.; Keulen, H. van; Rappoldt, C.

doi:10.1111/j.1475-2743.1989.tb00755.x

Cited by 592 publications

(269 citation statements)

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“…ent weather data, soil characteristics and crop species. The principles underlying this model have been discussed in detail by van Keulen and Wolf ( 1986) and the implementation and structure have been described by van Diepen et al (1988van Diepen et al ( , 1989. Its application for quantitative land evaluation and for regional analysis of the physical potential of crop production has been described by and van Diepen et al ( 1990) and its use for analysis of the effects of climate change on crop production has been discussed by and Wolf and van Diepen (1991 ).…”

Section: Methodology L\t1odel Descriptionmentioning

confidence: 99%

Effects of climate change on wheat production potential in the European Community

Wolf

1993

European Journal of Agronomy

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Section: Methodology L\t1odel Descriptionmentioning

confidence: 99%

Effects of climate change on wheat production potential in the European Community

Wolf

1993

European Journal of Agronomy

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“…Process-based crop models, such as CERES (Jones and Kiniry 1986), EPIC (Thomson et al, 2002), and WOFOST (Vandiepen et al, 1989) offer the option to estimate crop water use from simplified climate input, irrigation design, and initial soil water condition. The ability to simulate wheat yield by CERES-Wheat has been evaluated in a wide range of environments across the world under different management conditions (Kovács et al 1995, Timsina et al 1998, Pathak et al, 2006.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

Montazar

Mohseni²

2011

JAS

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The present study evaluated the applicability of the CropSyst model under variable climatic, irrigation, and fertilizer-nitrogen regimes. The objective was to analyze wheat productivity responses to water and N-application for optimizing water productivity in an arid irrigated environment. Evaluation analysis showed that the model provided very satisfactory estimates for the emergence, flowering and physiological maturity dates. The performance of the model was reasonable as demonstrated by the close correspondence between simulated grain yield, biomass accumulation, seasonal ET, and irrigation water productivity (WP I ) with measured data. The normalized root mean square error ranged between 5 and 10% for most of the parameters. Overall, the Willmott index of agreement between simulated and observed values of grain yield, biomass and seasonal ET were 0.99, 0.98 and 0.97, respectively. The validated model was employed to assess interactive effects of irrigation and fertilizer N on grain yield and water productivity indices. Scenario analyses indicated that WP I and WP ET (ET water productivity) ranged from 0.16 to 2.07 kg m -3 , and from 0.07 to 1.49 kg m -3 , respectively. For predicting the best N and water application practices for maximization of water productivity, the best option found by the model was application of water and nitrogenous fertilizer in 70% and 90% of the required values, respectively, for WP I , and equal to the required values (100%) for WP ET . The simulations demonstrated that the current wheat productivity of 5.0 Mg ha -1 obtained by the local farmers can be achieved at 140 kg ha -1 fertilizer N and 30% deficit irrigation regime with a WP I of 1.73 kg m -3 . The CropSyst model can be applied to derive best management options in terms of N and irrigation application of wheat under arid conditions.

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“…This kind of spatial relationship cannot be captured with a classical one dimensional plant growth and nutrient turnover model, including a percolation model. Examples for this approach are PnET (Aber et al, 1997), EPIC (Williams et al, 1984), CENTURY (Parton et al, 1983) and WOFOST (Diepen et al, 2007). On the other hand, two dimensional Richards equation based models, such as CATFLOW (Maurer, 1997;Zehe et al, 2001) or HYDRUS 2D (Simunek et al, 1999) do neither include submodels to calculate nutritional uptake by plants, nor models for decomposition of the crop residuals after harvest.…”

Section: Discussionmentioning

confidence: 99%

Using Python as a coupling platform for integrated catchment models

Kraft¹,

Multsch²,

Vaché

et al. 2010

Adv. Geosci.

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Abstract. Interdisciplinary sharing of knowledge is a key for understanding matter fluxes in landscapes. However, models of transport and reactive fluxes from different disciplines need to work seamlessly together, to capture the tight feedback loops between different compartments and process domains of a landscape. Techniques to facilitate the integration of model codes for integrated catchment modelling exist, but are still scarcely used. In this paper, we are testing a scripting language, Python as a model coupling platform, and demonstrates effects of feedback loops on a virtual agriculturally used hillslope.

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WOFOST: a simulation model of crop production

Cited by 592 publications

References 1 publication

Effects of climate change on wheat production potential in the European Community

Effects of climate change on wheat production potential in the European Community

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

Using Python as a coupling platform for integrated catchment models

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