IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v360y2017icp230-243.html
   My bibliography  Save this article

Evaluation of the DNDC model for simulating soil temperature, moisture and respiration from monoculture and rotational corn, soybean and winter wheat in Canada

Author

Listed:
  • Li, Zhuoting
  • Yang, J.Y.
  • Drury, C.F.
  • Yang, X.M.
  • Reynolds, W.D.
  • Li, Xiaogang
  • Hu, Chunsheng

Abstract

Process-based modelling studies can help to interpret experimental results and gain more insights into CO2 fluxes in agro-ecosystems. The objective of this study was to evaluate the ability of the DeNitrification-DeComposition (DNDC) model to predict field-measured soil temperature, moisture, and respiration (daily and seasonal) in monoculture versus rotational cropping systems. Field measurements (2003–2005) from monoculture cropping (corn, Zea mays L.; soybean, Glycine max L.; and winter wheat, Triticum aestivum L.), as well as from 2-yr (corn-soybean) and 3-yr (corn-soybean-winter wheat) crop rotations in southwestern Ontario, Canada were used for model calibration and evaluation. Model simulated soil (0–10cm) temperature (nRMSE=8–9%, d=0.97 and EF=0.87–0.90) and moisture (nRMSE=15–22%, d≥0.7 and EF≥0) agreed well with the growing season field measurements. Predicted daily soil CO2 fluxes were accurate for corn (under both monoculture and rotation) and winter wheat (under monoculture) (nRMSE=56–70%, d>0.7, slope=0.60–0.88, R2=0.36–0.48). The overestimation in daily soil CO2 fluxes for winter wheat phase under the 3-yr rotation could be addressed by decreasing the root: shoot ratio. The simulated temporal offset in the soil CO2 fluxes for soybean could be improved by adjusting crop parameters (thermal degree days for crop maturity and root: shoot ratio). The simulated cumulative seasonal soil CO2 fluxes were not statistically different from the measured CO2 fluxes for corn, soybean and winter wheat as indicated by the paired-t test (p=0.17–1.00). The impacts of monoculture and rotation cropping on seasonal root autotrophic respiration (RA), soil heterotrophic respiration (RH) and RH/RS ratios for corn, soybean and winter wheat were attributed to the different biomass production and residue C input management systems. The RH contributed more to total soil respiration for corn (during the growing season) under monoculture (RH/RS=42%) than when it was grown in rotation (26–29%), while ratios of RH/RS were similar among monocultural and rotational soybean (41–49%) and winter wheat (31–40%). The current litter C level and consequently annual RH were driven by the litter C input carried over from previous crop. When carbon in the harvested grain was taken into account in the carbon budget, corn would act as a strong net carbon sink (3year average of −975, −2675 and −3151kgCha−1yr−1 under monoculture, 2-yr and 3-yr rotations, respectively), while soybean and winter wheat would act as weak net carbon source under both monoculture and rotation. The DNDC model was found to be able to simulate soil temperature, moisture, and soil respiration for corn and winter wheat under both monoculture and rotation in humid southwestern Ontario, Canada but crop parameter adjustments are required to simulate soybean.

Suggested Citation

  • Li, Zhuoting & Yang, J.Y. & Drury, C.F. & Yang, X.M. & Reynolds, W.D. & Li, Xiaogang & Hu, Chunsheng, 2017. "Evaluation of the DNDC model for simulating soil temperature, moisture and respiration from monoculture and rotational corn, soybean and winter wheat in Canada," Ecological Modelling, Elsevier, vol. 360(C), pages 230-243.
    • Handle: RePEc:eee:ecomod:v:360:y:2017:i:c:p:230-243
    DOI: 10.1016/j.ecolmodel.2017.07.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380016308456
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2017.07.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Zhuo Ting & Yang, J.Y. & Drury, C.F. & Hoogenboom, G., 2015. "Evaluation of the DSSAT-CSM for simulating yield and soil organic C and N of a long-term maize and wheat rotation experiment in the Loess Plateau of Northwestern China," Agricultural Systems, Elsevier, vol. 135(C), pages 90-104.
    2. Li, Yong & White, Robert & Chen, Deli & Zhang, Jiabao & Li, Baoguo & Zhang, Yuming & Huang, Yuanfang & Edis, Robert, 2007. "A spatially referenced water and nitrogen management model (WNMM) for (irrigated) intensive cropping systems in the North China Plain," Ecological Modelling, Elsevier, vol. 203(3), pages 395-423.
    3. Yang, J.M. & Yang, J.Y. & Liu, S. & Hoogenboom, G., 2014. "An evaluation of the statistical methods for testing the performance of crop models with observed data," Agricultural Systems, Elsevier, vol. 127(C), pages 81-89.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kamkar, Behnam & Feyzbakhsh, Mohammad Taghi & Mokhtarpour, Hassan & Barbir, Jelena & Grahić, Jasmin & Tabor, Sylwester & Azadi, Hossein, 2023. "Effect of heat stress during anthesis on the Summer Maize grain formation: Using integrated modelling and multi-criteria GIS-based method," Ecological Modelling, Elsevier, vol. 481(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anar, Mohammad J. & Lin, Zhulu & Hoogenboom, Gerrit & Shelia, Vakhtang & Batchelor, William D. & Teboh, Jasper M. & Ostlie, Michael & Schatz, Blaine G. & Khan, Mohamed, 2019. "Modeling growth, development and yield of Sugarbeet using DSSAT," Agricultural Systems, Elsevier, vol. 169(C), pages 58-70.
    2. Liang, Hao & Lv, Haofeng & Batchelor, William D. & Lian, Xiaojuan & Wang, Zhengxiang & Lin, Shan & Hu, Kelin, 2020. "Simulating nitrate and DON leaching to optimize water and N management practices for greenhouse vegetable production systems," Agricultural Water Management, Elsevier, vol. 241(C).
    3. Liang, Hao & Qi, Zhiming & Hu, Kelin & Li, Baoguo & Prasher, Shiv O., 2018. "Modelling subsurface drainage and nitrogen losses from artificially drained cropland using coupled DRAINMOD and WHCNS models," Agricultural Water Management, Elsevier, vol. 195(C), pages 201-210.
    4. Chen, Shichao & Parsons, David & Du, Taisheng & Kumar, Uttam & Wang, Sufen, 2021. "Simulation of yield and water balance using WHCNS and APSIM combined with geostatistics across a heterogeneous field," Agricultural Water Management, Elsevier, vol. 258(C).
    5. Huanyuan Wang & Baoguo Li & Liang Jin & Kelin Hu, 2020. "Exploring a Sustainable Cropping System in the North China Plain Using a Modelling Approach," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    6. Liang, Hao & Chen, Qing & Liang, Bin & Hu, Kelin, 2020. "Modeling the effects of long-term reduced N application on soil N losses and yield in a greenhouse tomato production system," Agricultural Systems, Elsevier, vol. 185(C).
    7. Nasca, J.A. & Feldkamp, C.R. & Arroquy, J.I. & Colombatto, D., 2015. "Efficiency and stability in subtropical beef cattle grazing systems in the northwest of Argentina," Agricultural Systems, Elsevier, vol. 133(C), pages 85-96.
    8. Marrou, Hélène & Ghanem, Michel Edmond & Amri, Moez & Maalouf, Fouad & Ben Sadoun, Sarah & Kibbou, Fatimaezzhara & Sinclair, Thomas R., 2021. "Restrictive irrigation improves yield and reduces risk for faba bean across the Middle East and North Africa: A modeling study," Agricultural Systems, Elsevier, vol. 189(C).
    9. Kamini Yadav & Hatim M. E. Geli, 2021. "Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period," Land, MDPI, vol. 10(12), pages 1-27, December.
    10. Zhang, Hongyuan & Batchelor, William D. & Hu, Kelin & Liang, Hao & Han, Hui & Li, Ji, 2022. "Simulation of N2O emissions from greenhouse vegetable production under different management systems in North China," Ecological Modelling, Elsevier, vol. 470(C).
    11. Wang, Xiangping & Huang, Guanhua & Yang, Jingsong & Huang, Quanzhong & Liu, Haijun & Yu, Lipeng, 2015. "An assessment of irrigation practices: Sprinkler irrigation of winter wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 159(C), pages 197-208.
    12. Wulder, Michael A. & White, Joanne C. & Coops, Nicholas C. & Nelson, Trisalyn & Boots, Barry, 2007. "Using local spatial autocorrelation to compare outputs from a forest growth model," Ecological Modelling, Elsevier, vol. 209(2), pages 264-276.
    13. Katzin, David & van Henten, Eldert J. & van Mourik, Simon, 2022. "Process-based greenhouse climate models: Genealogy, current status, and future directions," Agricultural Systems, Elsevier, vol. 198(C).
    14. Yane Freitas Silva & Rafael Vasconcelos Valadares & Henrique Boriolo Dias & Santiago Vianna Cuadra & Eleanor E. Campbell & Rubens A. C. Lamparelli & Edemar Moro & Rafael Battisti & Marcelo R. Alves & , 2022. "Intense Pasture Management in Brazil in an Integrated Crop-Livestock System Simulated by the DayCent Model," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    15. Liang, Hao & Xu, Junzeng & Hou, Huijing & Qi, Zhiming & Yang, Shihong & Li, Yawei & Hu, Kelin, 2022. "Modeling CH4 and N2O emissions for continuous and noncontinuous flooding rice systems," Agricultural Systems, Elsevier, vol. 203(C).
    16. Correndo, Adrian A. & Hefley, Trevor J. & Holzworth, Dean P. & Ciampitti, Ignacio A., 2021. "Revisiting linear regression to test agreement in continuous predicted-observed datasets," Agricultural Systems, Elsevier, vol. 192(C).
    17. Wang, Xiangping & Liu, Guangming & Yang, Jingsong & Huang, Guanhua & Yao, Rongjiang, 2017. "Evaluating the effects of irrigation water salinity on water movement, crop yield and water use efficiency by means of a coupled hydrologic/crop growth model," Agricultural Water Management, Elsevier, vol. 185(C), pages 13-26.
    18. Nayebloie, Fatemeh & Kouchakzadeh, Mahdi & Ebrahimi, Kumars & Homaee, Mahdi & Abbasi, Fariborz, 2022. "Improving fertigation efficiency by numerical modelling in a lettuce subsurface drip irrigation farm," Agricultural Water Management, Elsevier, vol. 270(C).
    19. Zhang, Wei & Liu, Chunyan & Zheng, Xunhua & Zhou, Zaixing & Cui, Feng & Zhu, Bo & Haas, Edwin & Klatt, Steffen & Butterbach-Bahl, Klaus & Kiese, Ralf, 2015. "Comparison of the DNDC, LandscapeDNDC and IAP-N-GAS models for simulating nitrous oxide and nitric oxide emissions from the winter wheat–summer maize rotation system," Agricultural Systems, Elsevier, vol. 140(C), pages 1-10.
    20. Henrique Figueiredo Moura da Silva, Evandro & Boote, Kenneth J. & Hoogenboom, Gerrit & Gonçalves, Alexandre Ortega & Junior, Aderson Soares Andrade & Marin, Fabio Ricardo, 2021. "Performance of the CSM-CROPGRO-soybean in simulating soybean growth and development and the soil water balance for a tropical environment," Agricultural Water Management, Elsevier, vol. 252(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:360:y:2017:i:c:p:230-243. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.