Modelling daily to seasonal carbon fluxes and annual net ecosystem carbon balance of cereal grain-cropland using DailyDayCent: A model data comparison

Nimai Senapati*, Abad Chabbi, Pete Smith

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Croplands are important not only for food and fibre, but also for their global climate change mitigation and carbon (C) sequestration potentials. Measurements and modelling of daily C fluxes and annual C balance, which are needed for optimizing such global potentials in croplands, are difficult since many measurements, and the correct simulation of different ecosystem processes are needed. In the present study, a biogeochemical ecosystem model (DailyDayCent) was applied to simulate daily to seasonal C fluxes, as well as annual net ecosystem carbon balance (NECB), in a cereal grain-cropland. The model was tested using eddy-flux data and other associated C flux measurements lasting for three years over a full cereal crop-rotation (corn-wheat-barley) from a long-term experiment (SOERE–ACBB; in France. DailyDayCent simulated seasonal crop growth, regrowth of volunteers and cumulative net primary production (NPP) at harvest successfully. Fairly consistent agreement was obtained between measured and modelled daily NPP over the full crop rotation, with model efficiency (EF) of 0.59. The model underestimated heterotrophic respiration (Rh) on daily, seasonal and annual time scales by 43–53%. Although a reasonable model fit was found for daily NEE over the entire experimental period (EF ∼ 0.47), the model overestimated cumulative annual net C uptake (NEE) by 28 times. DailyDayCent simulated net C harvest efficiently, and the leaching loss of C reasonably well. Both the modelled and measured mean annual NECB indicate that present cereal grain-cropland is a net C source and the cropland is losing C at a mean annual rate of 64.0 (modelled) to 349.4 g C m−2 yr−1 (measured), thus the model overestimated mean annual NECB (or underestimated mean annual net C loss) in the present cropland by 82%. We conclude that overestimation of cumulative NEE on seasonal and annual time scales is the most likely reason for overestimation of NECB, and underestimation of Rh was the main driver for overestimation of cumulative seasonal and annual NEE. The model would benefit from further testing, particularly against direct measurements of Rh, and subsequent calibration, parameter estimation and model development for improving its ability to simulate Rh on daily to seasonal and annul time scales, cumulative seasonal and annual NEE, and net C balance, especially in cereal grain-croplands in the study region.

Original languageEnglish
Pages (from-to)159-177
Number of pages19
JournalAgriculture, Ecosystems and Environment
Early online date5 Nov 2017
Publication statusPublished - 15 Jan 2018

Bibliographical note

The first author, Nimai Senapati (Post-doc.), took the lead in planning the present modelling study, running the model, analysing the results and writing the first draft of the manuscript. All co-authors supervised the whole study and helped in correction and preparation of the final manuscript. The lead author, Nimai Senapati (Post-doc.), was funded by the European community’s Seventh Framework programme (FP2012-2015) under grant agreement no. 262060 (ExpeER). The research leading to these results has received funding principally from the ANR (ANR-11-INBS-0001), AllEnvi, CNRS-INSU. We would like to thank the National Research Infrastructure ‘Agro-écosystèmes’, Cycles Biogéochimique et Biodiversité (SOERE-ACBB for their support in field experiment and the database. We are deeply indebted to Christophe de Berranger, Xavier Charrier and Nicolas Mascher for their substantial technical assistance. We thank the anonymous reviewers and an Associate Editor for their valuable comments and suggestions that helped us to improve the manuscript.


  • Carbon flux
  • Cereal grain-cropland
  • DailyDayCent
  • Modelling
  • Net ecosystem carbon balance


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