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Improving the estimation and partitioning of plant nitrogen in the RiceGrow model

Published online by Cambridge University Press:  28 November 2018

L. Tang ,
R. J. Chang ,
B. Basso ,
T. Li ,
F. X. Zhen ,
L. L. Liu ,
W. X. Cao  and
Y. Zhu
L. Tang
Affiliation:
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
R. J. Chang
Affiliation:
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
B. Basso
Affiliation:
Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University, USA
T. Li
Affiliation:
International Rice Research Institute, Los Baños, Philippines
F. X. Zhen
Affiliation:
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
L. L. Liu
Affiliation:
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
W. X. Cao
Affiliation:
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
Y. Zhu*
Affiliation:
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
*
Author for correspondence: Y. Zhu, E-mail: yanzhu@njau.edu.cn
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Abstract

Plant nitrogen (N) links with many physiological progresses of crop growth and yield formation. Accurate simulation is key to predict crop growth and yield correctly. The aim of the current study was to improve the estimation of N uptake and translocation processes in the whole rice plant as well as within plant organs in the RiceGrow model by using plant and organ maximum, critical and minimum N dilution curves. The maximum and critical N (Nc) demand (obtained from the maximum and critical curves) of shoot and root and Nc demand of organs (leaf, stem and panicle) are calculated by N concentration and biomass. Nitrogen distribution among organs is computed differently pre- and post-anthesis. Pre-anthesis distribution is determined by maximum N demand with no priority among organs. In post-anthesis distribution, panicle demands are met first and then the remaining N is allocated to other organs without priority. The amount of plant N uptake depends on plant N demand and N supplied by the soil. Calibration and validation of the established model were performed on field experiments conducted in China and the Philippines with varied N rates and N split applications; results showed that this improved model can simulate the processes of N uptake and translocation well.

Keywords

Critical nitrogen dilution curvenitrogenriceRiceGrowsimulation
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 8 , October 2018 , pp. 959 - 970
Copyright
Copyright © Cambridge University Press 2018 

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References

Ata-Ul-Karim, ST, Yao, X, Liu, XJ, Cao, WX and Zhu, Y (2013) Development of critical nitrogen dilution curve of Japonica rice in Yangtze River Reaches. Field Crops Research 149, 149–158.Google Scholar
Ata-Ul-Karim, ST, Yao, X, Liu, X, Cao, W and Zhu, Y (2014) Determination of critical nitrogen dilution curve based on stem dry matter in rice. PLoS One 9, e104540. https://doi.org/10.1371/journal.pone.0104540.Google Scholar
Bélanger, G, Walsh, JR, Richards, JE, Milburn, PH and Ziadi, N (2001) Critical nitrogen curve and nitrogen nutrition index for potato in eastern Canada. American Journal of Potato Research 78, 355–364.Google Scholar
Bouman, BAM and Van Laar, HH (2006) Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems 87, 249–273.Google Scholar
Brisson, N, Mary, B, Ripoche, D, Jeuffroy, MH, Ruget, F, Nicoullaud, B, Gate, P, Devienne-Barret, F, Antonioletti, R, Durr, C, Richard, G, Beaudoin, N, Recous, S, Tayot, X, Plenet, D, Cellier, P, Machet, JM, Meynard, JM and Delécolle, R (1998) STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18, 311–346.Google Scholar
Colnenne, C, Meynard, JM, Reau, R, Justes, E and Merrien, A (1998) Determination of a critical nitrogen dilution curve for winter oilseed rape. Annals of Botany 81, 311–317.Google Scholar
Confalonieri, R, Debellini, C, Pirondini, M, Possenti, P, Bergamini, L, Barlassina, G, Bartoli, A, Agostoni, EG, Appiani, M, Babazadeh, L, Bedin, E, Bignotti, A, Bouca, M, Bulgari, R, Cantore, A, Degradi, D, Facchinetti, D, Fiacchino, D, Frialdi, M, Galuppini, L, Gorrini, C, Gritti, A, Gritti, P, Lonati, S, Martinazzi, D, Messa, C, Minardi, A, Nascimbene, L, Oldani, D, Pasqualini, E, Perazzolo, F, Pirovano, L, Pozzi, L, Rocchetti, G, Rossi, S, Rota, L, Rubaga, N, Russo, G, Sala, J, Seregni, S, Sessa, F, Silvestri, S, Simoncelli, P, Soresi, D, Stemberger, C, Tagliabue, P, Tettamanti, K, Vinci, M, Vittadini, G, Zanimacchia, M, Zenato, O, Zetta, A, Bregaglio, S, Chiodini, ME, Perego, A and Acutis, M (2011) A new approach for determining rice critical nitrogen concentration. Journal of Agricultural Science, Cambridge 149, 633–638.Google Scholar
Confalonieri, R, Bregaglio, S, Adam, M, Ruget, F, Li, T, Hasegawa, T, Yin, X, Zhu, Y, Boote, K, Buis, S, Fumoto, T, Gaydon, D, Lafarge, T, Marcaida, M, Nakagawa, H, Ruane, AC, Singh, B, Singh, U, Tang, L, Tao, F, Fugice, J, Yoshida, H, Zhang, Z, Wilson, LT, Baker, J, Yang, Y, Masutomi, Y, Wallach, D, Acutis, M and Bouman, B (2016 a) A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation. Environmental Modelling & Software 85, 332–341.Google Scholar
Confalonieri, R, Orlando, F, Paleari, L, Stella, T, Gilardelli, C, Movedi, E, Pagani, V, Cappelli, G, Vertemara, A, Alberti, L, Alberti, P, Atanassiu, S, Bonaiti, M, Cappelletti, G, Ceruti, M, Confalonieri, A, Corgatelli, G, Corti, P, Dell'Oro, M, Ghidoni, A, Lamarta, A, Maghini, A, Mambretti, M, Manchia, A, Massoni, G, Mutti, P, Pariani, S, Pasini, D, Pesenti, A, Pizzamiglio, G, Ravasio, A, Rea, A, Santorsola, D, Serafini, G, Slavazza, M and Acutis, M (2016 b) Uncertainty in crop model predictions: what is the role of users? Environmental Modelling & Software 81, 165–173.Google Scholar
Debaeke, P, Oosterom, EJV, Justes, E, Champolivier, L, Merrien, A, Aguirrezabal, LAN, González-Dugo, V, Massignam, AM and Montemurro, F (2012) A species-specific critical nitrogen dilution curve for sunflower (Helianthus annuus L.). Field Crops Research 136, 76–84.Google Scholar
Fumoto, T, Kobayashi, K, Li, C, Yagi, K and Hasegawa, T (2008) Revising a process-based biogeochemistry model (DNDC) to simulate methane emission from rice paddy fields under various residue management and fertilizer regimes. Global Change Biology 14, 382–402.Google Scholar
Gayler, S, Wang, E, Priesack, E, Schaaf, T and Maidl, FX (2002) Modeling biomass growth, N-uptake and phenological development of potato crop. Geoderma 105, 367–383.Google Scholar
Ghosh, M, Mandal, BK, Mandal, BB, Lodh, SB and Dash, AK (2004) The effect of planting date and nitrogen management on yield and quality of aromatic rice (Oryza sativa). Journal of Agricultural Science, Cambridge 142, 183–191.Google Scholar
Godwin, DC and Jones, CA (1991) Nitrogen dynamics in soil-plant systems. In Hanks, J and Ritchie, JT (eds), Modeling Plant and Soil Systems. Agronomy series no. 31. Madison, WI, USA: ASA, CSSA, SSSA, pp. 287–321.Google Scholar
Godwin, DC and Singh, U (1998) Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In Tsuji, GY, Hoogenboom, G, Thornton, PK (eds), Understanding Options for Agricultural Production. Systems Approaches for Sustainable Agricultural Development, Vol. 7. Dordrecht, the Netherlands: Springer, pp. 55–77.Google Scholar
Greenwood, DJ, Lemaire, G, Gosse, G, Cruz, P, Draycott, A and Neeteson, JJ (1990) Decline in percentage N of C3 and C4 crops with increasing plant mass. Annals of Botany 66, 425–436.Google Scholar
Hasegawa, T and Horie, T (1997) Modelling the effect of nitrogen on rice growth and development. In Kropff, MJ, Teng, PS, Aggarwal, PK, Bouma, J, Bouman, BAM, Jones, JW, van Laar, HH (eds), Applications of Systems Approaches at the Field Level. Systems Approaches for Sustainable Agricultural Development, Vol. 6. Dordrecht, the Netherlands: Springer, pp. 243–257.Google Scholar
Hasegawa, T, Li, T, Yin, X, Zhu, Y, Boote, K, Baker, J, Bregaglio, S, Buis, S, Confalonieri, R, Fugice, J, Fumoto, T, Gaydon, D, Kumar, SN, Lafarge, T, Marcaida Iii, M, Masutomi, Y, Nakagawa, H, Oriol, P, Ruget, F, Singh, U, Tang, L, Tao, F, Wakatsuki, H, Wallach, D, Wang, Y, Wilson, LT, Yang, L, Yang, Y, Yoshida, H, Zhang, Z and Zhu, J (2017) Causes of variation among rice models in yield response to CO2 examined with free-air CO2 enrichment and growth chamber experiments. Scientific Reports 7, 14858.Google Scholar
Jaggard, KW, Qi, A and Armstrong, MJ (2009) A meta-analysis of sugarbeet yield responses to nitrogen fertilizer measured in England since 1980. Journal of Agricultural Science, Cambridge 147, 287–301.Google Scholar
Jamieson, PD, Porter, JR and Wilson, DR (1991) A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research 27, 337–350.Google Scholar
Jones, CA, Kiniry, JR and Dyke, PT (1986) CERES-Maize: a Simulation Model of Maize Growth and Development. College Station, TX, USA: Texas A&M University Press.Google Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.Google Scholar
Ju, XT, Xing, GX, Chen, XP, Zhang, SL, Zhang, LJ, Liu, XJ, Cui, ZL, Yin, B, Christie, P, Zhu, ZL and Zhang, FS (2009) Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proceedings of the National Academy of Sciences USA 106, 3041–3046.Google Scholar
Justes, E, Mary, B, Meynard, JM, Machet, JM and Thelier-Huché, L (1994) Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany 74, 397–407.Google Scholar
Lemaire, G and Gastal, F (1997) N uptake and distribution in plant canopies. In Lemaire, G (ed), Diagnosis of the Nitrogen Status in Crops. Dordrecht, the Netherlands: Springer, pp. 3–43.Google Scholar
Li, T, Hasegawa, T, Yin, X, Zhu, Y, Boote, K, Adam, M, Bregaglio, S, Buis, S, Confalonieri, R, Fumoto, T, Gaydon, D, Marcaida, M, Nakagawa, H, Oriol, P, Ruane, AC, Ruget, F, Singh, B, Singh, U, Tang, L, Tao, F, Wilkens, P, Yoshida, H, Zhang, Z and Bouman, B (2015) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Global Change Biology 21, 1328–1341.Google Scholar
Liu, L, Wang, E, Zhu, Y and Tang, L (2012) Contrasting effects of warming and autonomous breeding on single-rice productivity in China. Agriculture, Ecosystems & Environment 149, 20–29.Google Scholar
Liu, L, Wang, E, Zhu, Y, Tang, L and Cao, W (2013) Effects of warming and autonomous breeding on the phenological development and grain yield of double-rice systems in China. Agriculture, Ecosystems & Environment 165, 28–38.Google Scholar
Naylor, REL and Stephen, NH (1993) Effects of nitrogen and the plant-growth regulator chlormequat on grain size, nitrogen content and amino acid composition of triticale. Journal of Agricultural Science, Cambridge 120, 159–169.Google Scholar
Peng, S, Buresh, RJ, Huang, J, Zhong, X, Zou, Y, Yang, J, Wang, G, Liu, Y, Hu, R, Tang, Q, Cui, K, Zhang, F and Dobermann, A (2011) Improving nitrogen fertilization in rice by site-specific N management. In Lichtfouse, E, Hamelin, M, Navarrete, M and Debaeke, P (eds), Sustainable Agriculture, Vol. 2. Dordrecht, the Netherlands: Springer, pp. 943–952.Google Scholar
Senanayake, N, Naylor, REL and DeDatta, SK (1996) Effect of nitrogen fertilization on rice spikelet differentiation and survival. Journal of Agricultural Science, Cambridge 127, 303–309.Google Scholar
Sheehy, JE, Dionora, MJA, Mitchell, PL, Peng, S, Cassman, KG, Lemaire, G and Williams, RL (1998) Critical nitrogen concentrations: implications for high-yielding rice (Oryza sativa L.) cultivars in the tropics. Field Crops Research 59, 31–41.Google Scholar
Singh, U, Ritchie, JT and Godwin, DC (1993) A Users Guide to CERES-Rice V2. 10, Simulation Manual. Muscle Shoals, AL, USA: International Fertilizer Development Center.Google Scholar
Tang, L, Zhu, Y, Hannaway, D, Meng, Y, Liu, L, Chen, L and Cao, W (2009) Ricegrow: a rice growth and productivity model. NJAS – Wageningen Journal of Life Sciences 57, 83–92.Google Scholar
Tei, F, Benincasa, P and Guiducci, M (2002) Critical nitrogen concentration in processing tomato. European Journal of Agronomy 18, 45–55.Google Scholar
Van Oosterom, EJ, Carberry, PS and Muchow, RC (2001) Critical and minimum N contents for development and growth of grain sorghum. Field Crops Research 70, 55–73.Google Scholar
Yao, X, Ata-Ul-Karim, ST, Zhu, Y, Tian, Y, Liu, X and Cao, W (2014 a) Development of critical nitrogen dilution curve in rice based on leaf dry matter. European Journal of Agronomy 55, 20–28.Google Scholar
Yao, X, Zhao, B, Tian, YC, Liu, XJ, Ni, J, Cao, WX and Zhu, Y (2014 b) Using leaf dry matter to quantify the critical nitrogen dilution curve for winter wheat cultivated in eastern China. Field Crops Research 159, 33–42.Google Scholar
Ye, HB, Liu, XJ, Zhu, Y and Cao, WX (2007) Study on growth model-based decision support system for rice-wheat rotation production. Journal of Triticeae Crops 2, 020.Google Scholar
Yin, X and Van Laar, HH (2005) Crop Systems Dynamics: An Ecophysiological Simulation Model for Genotype-by-Environment Interactions. Wageningen, the Netherlands: Wageningen Academic Pub.Google Scholar
Yin, X, Kersebaum, KC, Kollas, C, Manevski, K, Baby, S, Beaudoin, N, Öztürk, I, Gaiser, T, Wu, L, Hoffmann, M, Charfeddine, M, Conradt, T, Constantin, J, Ewert, F, De Cortazar-Atauri, IG, Giglio, L, Hlavinka, P, Hoffmann, H, Launay, M, Louarn, G, Manderscheid, R, Mary, B, Mirschel, W, Nendel, C, Pacholski, A, Palosuo, T, Ripoche-Wachter, DP, Rötter, RP, Ruget, F, Sharif, B, Trnka, M, Ventrella, D, Weigel, HJ and Olesen, JE (2017) Performance of process-based models for simulation of grain N in crop rotations across Europe. Agricultural Systems 154, 63–77.Google Scholar
Yue, S, Meng, Q, Zhao, R, Li, F, Chen, X, Zhang, F and Cui, Z (2012) Critical nitrogen dilution curve for optimizing nitrogen management of winter wheat production in the North China Plain. Agronomy Journal 104, 523–529.Google Scholar
Zadoks, JC, Chang, TT and Konzak, CF (1974) A decimal code for the growth stages of cereals. Weed Research 14, 415–421.Google Scholar
Zhang, W, Wu, L, Wu, X, Ding, Y, Li, G, Li, J, Weng, F, Liu, Z, Tang, S, Ding, C and Wang, S (2016) Lodging resistance of japonica rice (Oryza sativa). Rice 9, 31. https://doi.org/10.1186/s12284-016-0103-8.Google Scholar
Zhao, B, Yao, X, Tian, Y, Liu, X, Ata-Ul-Karim, ST, Ni, J, Cao, W and Zhu, Y (2014 a) New critical nitrogen curve based on leaf area index for winter wheat. Agronomy Journal 106, 379–389.Google Scholar
Zhao, Z, Wang, E, Wang, Z, Zang, H, Liu, Y and Angus, JF (2014 b) A reappraisal of the critical nitrogen concentration of wheat and its implications on crop modeling. Field Crops Research 164, 65–73.Google Scholar
Ziadi, N, Brassard, M, Bélanger, G, Cambouris, AN, Tremblay, N, Nolin, MC, Claessens, A and Parent, LE (2008) Critical nitrogen curve and nitrogen nutrition index for corn in eastern Canada. Agronomy Journal 100, 271–276.Google Scholar
Ziadi, N, Bélanger, G, Claessens, A, Lefebvre, L, Cambouris, AN, Tremblay, N, Nolin, MC and Parent, LÉ (2010) Determination of a critical nitrogen dilution curve for spring wheat. Agronomy Journal 102, 241–250.Google Scholar