Simulation of soybean nitrogen nutrition for a silty clay soil in southern France

Bouniols, A.; Cabelguenne, M.; Jones, Charles; Chalamet, A.; Charpenteau, J.L.; Marty, Jean-Robert

doi:10.1016/0378-4290(91)90054-y

Cited by 28 publications

(17 citation statements)

References 18 publications

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“…Leguminous N 2 fixation was simulated for soybean and alfalfa; all other N processes were simulated for all three cropping systems. Fixation is simulated in EPIC by accounting for the effects of early nodule development, nodule senescence late in the growth cycle, soil water in the top 30 cm, and soil mineral N in the root zone (Williams, 1995; Bouniols et al, 1991).…”

Section: Simulation Methodologymentioning

confidence: 99%

EPIC Tile Flow and Nitrate Loss Predictions for Three Minnesota Cropping Systems

et al. 2001

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Subsurface tile drains are a key source of nitrate N (NO3-N) losses to streams in parts of the north central USA. In this study, the Erosion Productivity Impact Calculator (EPIC) model was evaluated by comparing measured vs. predicted tile flow, tile NO3-N loss, soil profile residual NO3-N, crop N uptake, and yield, using 4 yr of data collected at a site near Lamberton, MN, for three crop rotations: continuous corn (Zea mays L.) or CC, corn-soybean [Glycine max (L.) Merr.] or CS, and continuous alfalfa (Medicago sativa L.) or CA. Initially, EPIC was run using standard Soil Conservation Service (SCS) runoff curve numbers (CN2) for CC and CS; monthly variations were accurately tracked for tile flow (r2 = 0.86 and 0.90) and NO3-N loss (r2 = 0.69 and 0.52). However, average annual CC and CS tile flows were underpredicted by -32 and -34%, and corresponding annual NO3-N losses were underpredicted by -11 and -52%. Predicted average annual tile flows and NO3-N losses generally improved following calibration of the CN2; tile flow underpredictions were -9 and - 12%, whereas NO3-N losses were 0.6 and -54%. Adjusting a N parameter further improved predicted CS NO3-N losses. Predicted monthly tile flows and NO3-N losses for the CA simulation compared poorly with observed values (r2 values of 0.27 and 0.19); the annual drainage volumes and N losses were of similar magnitude to those measured. Overall, EPIC replicated the relative impacts of the three cropping systems on N fate.

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Section: Simulation Methodologymentioning

confidence: 99%

EPIC Tile Flow and Nitrate Loss Predictions for Three Minnesota Cropping Systems

et al. 2001

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“…Inorganic nitrogen transport within the soil system is simulated using a daily time step approach followed by Corwin et al (1991). SWB-Sci model simulates nitrogen transformation processes (net mineralization, nitrification, and denitrification) using first order kinetics and ammonium sorption is simulated using the approach presented by Stöckle and Campbell (1989), while symbiotic N fixation is based on Bouniols et al (1991). Crop nitrogen uptake is modelled using a modified version of the Godwin and Jones (1991) approach, where crop nitrogen uptake is determined as the minimum of crop nitrogen demand and potential nitrogen uptake (Stöckle et al, 1994).…”

Section: Model Descriptionmentioning

confidence: 99%

Use of the SWB-Sci model for nitrogen management in sludge-amended land

Tesfamariam

Annandale

Steyn

et al. 2015

Agricultural Water Management

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“…Further applications and development focused on a modified version of EPIC referred to as EPICPhase, which included real-time corn irrigation simulations (Cabelguenne et al 1997), expanded water extraction and stress simulation capabilities for corn, sunflower, soybean, sorghum, and wheat (Cabelguenne and Debaeke 1998), and a yield validation study of the same five crops (Cabelguenne, Debaeke, and Bouniols 1999). ( (Table 1) and modified functions for sunflowers (Kiniry et al 1992b;Texier, Blanchet, and Bouniols 1992) and an improved nitrogen fixation routine for legume crops (Table 1) developed by Bouniols et al (1991). It is notable that second-generation usage of EPICphase has occurred in Spain, where the model has been further modified, validated, and applied for irrigation scenarios (Cavero et al 2000;Santos et al 2000;Cavero et al 2001).…”

Section: Yield Simulation Studies In Southern Francementioning

confidence: 99%

“…Improved and expanded crop growth submodel Williams et al (1989) Enhanced root growth functions Jones et al (1991) Improved nitrogen fixation routine for legume crops that calculates fixation as a function of soil water, soil N, and crop physiological stage Bouniols et al (1991) Incorporation of pesticide routines from GLEAMS model Improved crop growth parameters for sunflower Kiniry et al (1992a) Incorporation of CO 2 and vapor pressure effects on radiation use efficiency, leaf resistance, and transpiration of crops Stockle et al (1992a) Incorporation of functions that allow two or more crops to be grown simultaneously Kiniry et al (1992b) Improved soil temperature component Potter and Williams (1994) Improved crop growth parameters for cereal, oilseed, and forage crops grown in the North American northern Great Plains region Kiniry et al (1995) Improved and expanded weather generator component Incorporation of NRCS TR-55 peak runoff rate component Incorporation of MUSS, MUST, and MUSI water erosion routines Incorporation of nitrification-volatilization component Improved water table dynamics routine Incorporation of RUSLE water erosion equation Renard (1997) Improved snowmelt runoff and erosion component Purveen et al (1997) Improved EPIC wind erosion model (WESS) Potter et al (1998) Incorporation of Baier-Robertson PET routine Roloff et al (1998) Incorporation of Green and Ampt infiltration function Williams, Arnold, and Srinivasan (2000) Enhanced carbon cycling routine that is based on the Century model approach Izaurralde et al (2004) Incorporation of a potassium (K) cycling routine De Barros, Williams, and Gaiser (2004) a Some sources do not explicitly document the modification but are the best description of the modification available or present an application of the specific subcomponent.…”

Section: Epic Applicationsmentioning

confidence: 99%

Historical Development and Applications of the EPIC and APEX models

Gassman

Williams²,

Benson³

et al. 2004

2004, Ottawa, Canada August 1 - 4, 2004

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The development of the field-scale Erosion Productivity Impact Calculator (EPIC) model was initiated in 1981 to support assessments of soil erosion impacts on soil productivity for soil, climate, and cropping conditions representative of a broad spectrum of U.S. agricultural production regions. The first major application of EPIC was a national analysis performed in support of the 1985 Resources Conservation Act (RCA) assessment. The model has continuously evolved since that time and has been applied for a wide range of field, regional, and national studies both in the U.S. and in other countries. The range of EPIC applications has also expanded greatly over that time, including studies of (1) surface runoff and leaching estimates of nitrogen and phosphorus losses from fertilizer and manure applications, (2) leaching and runoff from simulated pesticide applications, (3) soil erosion losses from wind erosion, (4) climate change impacts on crop yield and erosion, and (5) soil carbon sequestration assessments. The EPIC acronym now stands for Erosion Policy Impact Climate, to reflect the greater diversity of problems to which the model is currently applied. The Agricultural Policy EXtender (APEX) model is essentially a multi-field version of EPIC that was developed in the late 1990s to address environmental problems associated with livestock and other agricultural production systems on a whole-farm or small watershed basis. The APEX model also continues to evolve and to be utilized for a wide variety of environmental assessments. The historical development for both models will be presented, as well as example applications on several different scales.

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Simulation of soybean nitrogen nutrition for a silty clay soil in southern France

Cited by 28 publications

References 18 publications

EPIC Tile Flow and Nitrate Loss Predictions for Three Minnesota Cropping Systems

EPIC Tile Flow and Nitrate Loss Predictions for Three Minnesota Cropping Systems

Use of the SWB-Sci model for nitrogen management in sludge-amended land

Historical Development and Applications of the EPIC and APEX models

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