Nitrogen Dynamics in Soi-Plant Systems

Godwin, D. C.; Jones, Charles

doi:10.2134/agronmonogr31.c13

Cited by 57 publications

(15 citation statements)

References 30 publications

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“…Plant growth was simulated with the crop canopy models CERES (Ritchie, 1991; Godwin and Jones, 1991; Jones and Kiniry, 1986; Ritchie et al, 1987; Ritchie and Godwin, 1989) and SPASS (Wang, 1997; Wang and Engel, 1998, 2000; Gayler et al, 2002). These models simulate crop phenology, root and leaf development, photosynthesis, nitrogen demand and uptake, biomass growth, potential and actual transpiration, and senescence.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Simulated Transpiration from Maize Plants on Lysimeters

Heinlein

Biernath

Klein

et al. 2017

Vadose Zone Journal

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In central Europe expected climate change will lead to strongly changing regional water availability and will affect future crop production systems and yields. To adapt these production systems and estimate the irrigation necessity for yield optimization-today and in the future-crop water demand as a function of its environment and development stage must be understood. Crop models are often applied to simulate water demands, but the accuracy of the simulations and the underlying mechanisms remain unclear. We therefore grew maize (Zea mays L.) in field lysimeters in 2013 and tested the ability of six model configurations (two crop models CERES (Crop Environment Resource Synthesis) and SPASS (Soil-Plant-Atmosphere System Simulation) combined with three evapotranspiration models) to simulate measured sap flow and components of the water balance. Sap flow measurements (i.e., heat ratio method [HRM]) determined transpiration. All models simulated the measured diurnal cycles of sap flow rates. Higher simulated leaf area indices by the CERES model runs caused an overestimation of transpiration in the beginning of the measurement period. The models overestimated daily actual evapotranspiration when water input was high due to an overestimation of actual evaporation and transpiration resulting from high water contents at the top soil layers. All models simulated the occurrence of measured percolation peaks, but only partly captured their intensities. Soil water contents in the 50-and 80-cm depths and the daily water content change of the whole lysimeter were well simulated by the models. Deviations between models and measurements might have been caused by the so-called pot effect and by drought stress influencing the root distribution in the lysimeter.Abbreviations: ASCE, American Society of Civil Engineers; CERES, Crop Environment Resource Synthesis; DOY, Day of Year; ET, evapotranspiration; HRM, heat ratio method; IA, index of agreement; LAI, leaf area index; NSE, Nash-Sutcliffe model efficiency coefficient; SPASS, Soil-Plant-Atmosphere System Simulation; TDR, time-domain reflectometry.Climate change will have an impact on future growth and yields of agricultural plants. Rosenzweig and Parry (1994) suggested that doubling the atmospheric CO 2 concentration will hardly affect global crop production, but will strongly change regional crop yields. In middle and high latitudes, heat or water stress does not occur, and the effect of increased atmospheric CO 2 concentration on crop growth and yields may outweigh the effect of shorter crop development stages due to higher temperatures. In contrast, at low latitudes the negative effects of shorter growing periods, as well as of heat and water stress on crop yields, can dominate over beneficial direct impacts of higher atmospheric CO 2 concentrations. As a consequence, disparities between more developed countries in temperate climate zones and less developed countries in the subtropical and tropical zones will increase.In central Europe, expected major aspects of climate change...

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Section: Methodsmentioning

confidence: 99%

Evaluation of Simulated Transpiration from Maize Plants on Lysimeters

Heinlein

Biernath

Klein

et al. 2017

Vadose Zone Journal

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“…The CSM-GROPGRO-Canola model comes with two soil organic matter (SOM) modules in DSSAT. The default is the original SOM module based on the CERES model (Godwin and Jones, 1991;Godwin and Singh, 1998) and is denoted as G-SOM in this study. The other one was developed by Gijsman et al (2002) based on the CENTURY model (Parton et al, 1994) and is denoted as P-SOM hereafter.…”

Section: Crop Modelmentioning

confidence: 99%

Evaluation of the CSM‐CROPGRO‐Canola Model for Simulating Canola Growth and Yield at West Nipissing in Eastern Canada

et al. 2016

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With increasing demands for renewable energy and dietary vegetable oils, the production of canola has become widespread in recent years. Modeling canola growth and yield is a helpful approach to predict canola responses to various environments, especially under climate change. However, few studies have been performed for predicting growth and yield of canola in Canada. In this study, we evaluated the CSM‐CROPGRO‐Canola model in Decision Support System for Agrotechnology Transfer v4.6 for simulating spring canola at West Nipissing in Eastern Canada. The model was evaluated using plant and soil data collected from field experiments over three growing seasons (2012–2014). The model could predict the observed crop development and successfully mimic the characteristics of canola regarding light absorption and utilization using combinations of leaves and pods. The accumulations of aboveground biomass were satisfactorily simulated in the life cycle under different nitrogen (N) fertilizer application rates, with a normalized RMSE of 19%. The seed yields were successfully predicted with different N application rates except for an underestimation under zero N application. The underestimation of yield under low N rates was possibly related to the deficiency in the simulated N mineralization that could also be associated with inaccurate input soil data. A better simulation of seed yields under low N application was achieved when the soil organic matter module based on the CENTURY model was used in DSSAT v4.6. The calibrated model simulated soil moisture and inorganic N contents satisfactorily, showing a good performance of the CSM‐CROPGRO‐Canola model for the study region.

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“…Nitrogen (N) is widely recognized, together with water, as the most crucial resources to promote the crop yield increase (Sinclair and deWit , Godwin and Jones , Soltani and Sinclair ). However, an intensive use of mineral fertilizer to increase the N availability of crops during the green revolution has resulted in concern for environmental issues, such as groundwater pollution, eutrophication of aquatic ecosystems and increase in greenhouse gas emissions (Tilman et al , IPCC ).…”

Section: Introductionmentioning

confidence: 99%

Is nitrogen accumulation in grain legumes responsive to growth or ontogeny?

Marrou

Ricaurte

Ghanem

et al. 2017

Physiologia Plantarum

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Nitrogen (N) accumulation in legumes is one of the main determinants of crop yield. Although N accumulation from symbiotic nitrogen fixation or N absorption from the soil has been widely investigated, there is no clear consensus on timing of the beginning of N accumulation and the termination of N accumulation and the physiological events that may be associated with these two events. The analyses conducted in this study aimed at identifying the determinant of N accumulation in two grain legume species. Nitrogen accumulation dynamics and mass accumulation and development stages were recorded in the field for several genotypes of common bean (Phaseolus vulgaris) and faba bean (Vicia faba) under different growing conditions. This study showed that during the vegetative stages, N accumulation rate was correlated with mass accumulation rate. However, the maximum accumulation of N did not correspond to the time of the maximum mass accumulation. In fact, for both species, N accumulation was found to persist in seed growth. This challenges a common hypothesis that seed growth causes a decrease in N accumulation because of a shift of the photosynthate supply to support the seed growth. Even more surprising was the shift of the active accumulation of N in faba bean to late in the growing season as compared with common bean. N accumulation by faba bean only was initiated at high rates very late in vegetative growth and persisted at high rates well into seed fill.

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Nitrogen Dynamics in Soi-Plant Systems

Cited by 57 publications

References 30 publications

Evaluation of Simulated Transpiration from Maize Plants on Lysimeters

Evaluation of Simulated Transpiration from Maize Plants on Lysimeters

Evaluation of the CSM‐CROPGRO‐Canola Model for Simulating Canola Growth and Yield at West Nipissing in Eastern Canada

Is nitrogen accumulation in grain legumes responsive to growth or ontogeny?

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