IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i11p500-d434640.html
   My bibliography  Save this article

Does the Organ-Based N Dilution Curve Improve the Predictions of N Status in Winter Wheat?

Author

Listed:
  • Ke Zhang

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China)

  • Xue Wang

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
    Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

  • Xiaoling Wang

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
    Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

  • Syed Tahir Ata-Ul-Karim

    (Institute for Sustainable Agro-ecosystem Services, The University of Tokyo. Department of Global Agricultural Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8654, Japan)

  • Yongchao Tian

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
    Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

  • Yan Zhu

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
    Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

  • Weixing Cao

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
    Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

  • Xiaojun Liu

    (National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
    Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

Accurately summarizing Nitrogen (N) content as a prelude to optimal N fertilizer application is complicated during the vegetative growth period of all the crop species studied. The critical nitrogen (N) concentration (Nc) dilution curve is a stable diagnostic indicator, which performs plant critical N concentration trends as crop grows. This study developed efficient technologies for different organ-based (plant dry matters (PDM), leaf DM (LDM), stem DM (SDM), and leaf area index (LAI)) estimation of Nc curves to enrich the practical applications of precision N management strategies. Four winter wheat cultivars were planted with 10 different N treatments in Jiangsu province of eastern China. Results showed the SDM-based curve had a better performance than the PDM-based curve in N nutrition index (NNI) estimation, accumulated N deficit (AND) calculation, and N requirement (NR) determination. The regression coefficients ‘a’ and ‘b’ varied among the four critical N dilution models: Nc = 3.61 × LDM –0.19 , R 2 = 0.77; Nc = 2.50 × SDM –0.44 , R 2 = 0.89; Nc = 4.16 × PDM –0.41 , R 2 = 0.87; and Nc = 3.82 × LAI –0.36 , R 2 = 0.81. In later growth periods, the SDM-based curve was found to be a feasible indicator for calculating NNI, AND, and NR, relative to curves based on the other indicators. Meanwhile, the lower LAI-based curve coefficient variation values stated that leaf-related indicators were also a good choice for developing the N curve with high efficiency as compared to other biomass-based approaches. The SDM-based curve was the more reliable predictor of relative yield because of its low relative root mean square error in most of the growth stages. The curves developed in this study will provide diverse choices of indicators for establishing an integrated procedure of diagnosing wheat N status, and improving the accuracy and efficiency of wheat N fertilizer management.

Suggested Citation

  • Ke Zhang & Xue Wang & Xiaoling Wang & Syed Tahir Ata-Ul-Karim & Yongchao Tian & Yan Zhu & Weixing Cao & Xiaojun Liu, 2020. "Does the Organ-Based N Dilution Curve Improve the Predictions of N Status in Winter Wheat?," Agriculture, MDPI, vol. 10(11), pages 1-19, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:11:p:500-:d:434640
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/11/500/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/11/500/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bouman, B.A.M. & van Laar, H.H., 2006. "Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions," Agricultural Systems, Elsevier, vol. 87(3), pages 249-273, March.
    2. Lundström, Christina & Lindblom, Jessica, 2018. "Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT," Agricultural Systems, Elsevier, vol. 159(C), pages 9-20.
    Full references (including those not matched with items on IDEAS)

    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. Dutta, S. K & Laing, Alison M. & Kumar, S. & Gathala, Mahesh K. & Singh, Ajoy K. & Gaydon, D.S. & Poulton, P., 2020. "Improved water management practices improve cropping system profitability and smallholder farmers’ incomes," Agricultural Water Management, Elsevier, vol. 242(C).
    2. José A. Martínez-Casasnovas & Alexandre Escolà & Jaume Arnó, 2018. "Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize ( Zea mays L.)," Agriculture, MDPI, vol. 8(6), pages 1-18, June.
    3. Movedi, Ermes & Valiante, Daniele & Colosio, Alessandro & Corengia, Luca & Cossa, Stefano & Confalonieri, Roberto, 2022. "A new approach for modeling crop-weed interaction targeting management support in operational contexts: A case study on the rice weeds barnyardgrass and red rice," Ecological Modelling, Elsevier, vol. 463(C).
    4. McGrath, Karen & Brown, Claire & Regan, Áine & Russell, Tomás, 2023. "Investigating narratives and trends in digital agriculture: A scoping study of social and behavioural science studies," Agricultural Systems, Elsevier, vol. 207(C).
    5. Amarasingha, R.P.R.K. & Suriyagoda, L.D.B. & Marambe, B. & Gaydon, D.S. & Galagedara, L.W. & Punyawardena, R. & Silva, G.L.L.P. & Nidumolu, U. & Howden, M., 2015. "Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka," Agricultural Water Management, Elsevier, vol. 160(C), pages 132-143.
    6. Jing, Qi & Bélanger, Gilles & Baron, Vern & Bonesmo, Helge & Virkajärvi, Perttu & Young, David, 2012. "Regrowth simulation of the perennial grass timothy," Ecological Modelling, Elsevier, vol. 232(C), pages 64-77.
    7. Hayashi, Keiichi & Llorca, Lizzida & Rustini, Sri & Setyanto, Prihasto & Zaini, Zulkifli, 2018. "Reducing vulnerability of rainfed agriculture through seasonal climate predictions: A case study on the rainfed rice production in Southeast Asia," Agricultural Systems, Elsevier, vol. 162(C), pages 66-76.
    8. Grotelüschen, Kristina & Gaydon, Donald S. & Langensiepen, Matthias & Ziegler, Susanne & Kwesiga, Julius & Senthilkumar, Kalimuthu & Whitbread, Anthony M. & Becker, Mathias, 2021. "Assessing the effects of management and hydro-edaphic conditions on rice in contrasting East African wetlands using experimental and modelling approaches," Agricultural Water Management, Elsevier, vol. 258(C).
    9. Karly Ann Burch & Dawn Nafus & Katharine Legun & Laurens Klerkx, 2023. "Intellectual property meets transdisciplinary co-design: prioritizing responsiveness in the production of new AgTech through located response-ability," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 40(2), pages 455-474, June.
    10. Boling, A.A. & Tuong, T.P. & van Keulen, H. & Bouman, B.A.M. & Suganda, H. & Spiertz, J.H.J., 2010. "Yield gap of rainfed rice in farmers' fields in Central Java, Indonesia," Agricultural Systems, Elsevier, vol. 103(5), pages 307-315, June.
    11. Sophia Xiaoxia Duan & Santoso Wibowo & Josephine Chong, 2021. "A Multicriteria Analysis Approach for Evaluating the Performance of Agriculture Decision Support Systems for Sustainable Agribusiness," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    12. Pedersen, Michael Friis & Gyldengren, Jacob Glerup & Pedersen, Søren Marcus & Diamantopoulos, Efstathios & Gislum, René & Styczen, Merete Elisabeth, 2021. "A simulation of variable rate nitrogen application in winter wheat with soil and sensor information - An economic feasibility study," Agricultural Systems, Elsevier, vol. 192(C).
    13. Confalonieri, R. & Bregaglio, S. & Acutis, M., 2010. "A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions," Ecological Modelling, Elsevier, vol. 221(6), pages 960-964.
    14. Yu, Qianan & Cui, Yuanlai, 2022. "Improvement and testing of ORYZA model water balance modules for alternate wetting and drying irrigation," Agricultural Water Management, Elsevier, vol. 271(C).
    15. Antonopoulos, Vassilis Z., 2010. "Modelling of water and nitrogen balances in the ponded water and soil profile of rice fields in Northern Greece," Agricultural Water Management, Elsevier, vol. 98(2), pages 321-330, December.
    16. Wang, Weiguang & Yu, Zhongbo & Zhang, Wei & Shao, Quanxi & Zhang, Yiwei & Luo, Yufeng & Jiao, Xiyun & Xu, Junzeng, 2014. "Responses of rice yield, irrigation water requirement and water use efficiency to climate change in China: Historical simulation and future projections," Agricultural Water Management, Elsevier, vol. 146(C), pages 249-261.
    17. Tian, Zhan & Zhong, Honglin & Sun, Laixiang & Fischer, Günther & van Velthuizen, Harrij & Liang, Zhuoran, 2014. "Improving performance of Agro-Ecological Zone (AEZ) modeling by cross-scale model coupling: An application to japonica rice production in Northeast China," Ecological Modelling, Elsevier, vol. 290(C), pages 155-164.
    18. Hochman, Zvi & Horan, Heidi & Reddy, D. Raji & Sreenivas, G. & Tallapragada, Chiranjeevi & Adusumilli, Ravindra & Gaydon, Donald S. & Laing, Alison & Kokic, Philip & Singh, Kamalesh K. & Roth, Christi, 2017. "Smallholder farmers managing climate risk in India: 2. Is it climate-smart?," Agricultural Systems, Elsevier, vol. 151(C), pages 61-72.
    19. Belder, P. & Bouman, B. A.M. & Spiertz, J.H.J., 2007. "Exploring options for water savings in lowland rice using a modelling approach," Agricultural Systems, Elsevier, vol. 92(1-3), pages 91-114, January.
    20. Borgia, Cecilia & García-Bolaños, Mariana & Li, Tao & Gómez-Macpherson, Helena & Comas, Jordi & Connor, David & Mateos, Luciano, 2013. "Benchmarking for performance assessment of small and large irrigation schemes along the Senegal Valley in Mauritania," Agricultural Water Management, Elsevier, vol. 121(C), pages 19-26.

    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:gam:jagris:v:10:y:2020:i:11:p:500-:d:434640. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.