Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models

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dc.contributor.author

Jiang, Rong

He, Wentian

Zhou, Wei

Hou, Yunpeng

Yang, J.Y.

He, Ping

dc.date.accepted

2019-09-02

dc.date.accessioned

2024-06-07T21:15:42Z

dc.date.available

2024-06-07T21:15:42Z

dc.date.issued

2019-09-10

dc.date.submitted

2019-04-11

dc.description.abstract - en

Process-based models are valuable tools for simulating crop production, estimating agronomic efficiency and developing optimum management practices to achieve sustainable agriculture. However, a comparison of the DeNitrification-DeComposition (DNDC) and Decision Support System for Agrotechnology Transfer (DSSAT) models has not been previously used to optimize management practices for spring maize in northeast China. The objectives of this study were to evaluate the performance of the DSSAT and DNDC models in simulating maize growth and soil C & N dynamics and analyse their weaknesses and strengths based on a 7-year spring maize study in northeast China; and to explore the optimal management practices for improving maize production and nitrogen use efficiency under 20-year climate variability. Both DNDC and DSSAT exhibited “good” to “excellent” performance in simulating maize yield, above-ground biomass and plant N uptake for ecological intensification with N fertilizer (EI-N) and farmers’ practice with N fertilizer (FP-N) treatments based on percent bias (PBIAS) of −10.5–4.2%, a normalized root mean squared error (nRMSE) of 7.5–17.2%, a Nash-Sutcliffe efficiency (NSE) of 0.17–0.77 and a d index of agreement (d) of 0.81–0.94. Both models showed “fair” to “good” performance in the same simulation for EI without N fertilizer (EI-N0) and FP without N fertilizer (FP-N0) treatments, but the maize yield simulation was better for the DSSAT model. In addition, the two models provided “fair” performance for N-fertilized treatments to “poor” performance for N-unfertilized treatments in simulations of soil organic carbon (0–0.20 m) and mineral N (0–0.30 m), but the simulations were better for the DNDC model. Sensitivity analyses indicated that the optimum yield and agronomic efficiency were achieved at a planting date of late April to early May, a fertilizer N application rate of 180–210 kg N ha−1 with two timing splits in the DNDC and DSSAT model and a planting density of 7 seeds m−2 in the DSSAT model. This study suggests that comparing the management scenarios of multiple dynamic models is more beneficial to develop best management practices for improving crop production and fertilizer use efficiency.

dc.identifier.citation

Jiang, R., He, W., Zhou, W., Hou, Y., Yang, J. Y., & He, P. (2019). Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models. Computers and Electronics in Agriculture, 166, 104988. https://doi.org/10.1016/j.compag.2019.104988

dc.identifier.doi

https://doi.org/10.1016/j.compag.2019.104988

dc.identifier.issn

0168-1699

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/2578

dc.language.iso

en

dc.publisher

Elsevier B.V.

dc.rights - en

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights - fr

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights.openaccesslevel - en

Gold

dc.rights.openaccesslevel - fr

Or

dc.rights.uri - en

https://creativecommons.org/licenses/by/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by/4.0/deed.fr

dc.subject - en

Agriculture

dc.subject - fr

Agriculture

dc.subject.en - en

Agriculture

dc.subject.fr - fr

Agriculture

dc.title - en

Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models

dc.type - en

Article

dc.type - fr

Article

local.acceptedmanuscript.articlenum

104988

local.article.journaltitle

Computers and Electronics in Agriculture

local.article.journalvolume

166

local.peerreview - en

Yes

local.peerreview - fr

Oui

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