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Nitrogen-limited light use efficiency in wheat crop simulators: comparing three model approaches

Published online by Cambridge University Press:  08 December 2015

A. M. RATJEN  and
H. KAGE
A. M. RATJEN*
Affiliation:
Institute for Crop Science and Plant Breeding, University Kiel, 24098 Kiel, Germany
H. KAGE
Affiliation:
Institute for Crop Science and Plant Breeding, University Kiel, 24098 Kiel, Germany
*
*To whom all correspondence should be addressed. Email: ratjen@pflanzenbau.uni-kiel.de
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Summary

Three different explanatory indicators for reduced light use efficiency (LUE) under limited nitrogen (N) supply were evaluated. The indicators can be used to adapt dry matter production of crop simulators to N-limited growth conditions. The first indicator, nitrogen factor (NFAC), originates from the CERES-Wheat model and calculates the critical N concentration of the shoot as a function of phenological development. The second indicator, N nutrition index (NNI), calculates a critical N concentration as a function of shoot dry matter. The third indicator, specific leaf nitrogen (SLN) index (SLNI), has been newly developed. It compares the actual SLN with the maximum SLN (SLNmax). The latter is calculated as a function of the green area index (GAI). The comparison was based on growth curves and fitted to empirical data, and was carried out independently from a dynamic crop model. The data set included four growing seasons (2004–2006, 2012) in Northern Germany and seven modern bread wheat cultivars with varying N fertilization levels (0–320 kg N/ha). The influence of N shortage on LUE was evaluated from the beginning of stem elongation until flowering. With the exception of 2005, the highest productivity was observed for the highest N level. A moderate N shortage primarily reduced GAI and therefore light interception, while LUE remained stable under moderate N shortage. The relative LUE (rLUE) of a specific day was defined as the ratio of actual to maximal LUE. None of the indicators was proportional to rLUE, but the relationships were described well by quadratic plateau curves. The correlation between simulated and measured rLUE was significant for all explanatory indicators, but different in terms of mean absolute error and coefficient of determination (R2). The performance of SLNI and NNI was similar, but the goodness of prediction was much lower for NFAC. Compared with NNI and NFAC, SLNI corresponded to leaf N and was therefore sensitive to N translocation from leaves to growing grains during the reproductive stage. For this reason, SLNI may have the potential to improve simulation of dry matter production in wheat crop simulators.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 154 , Issue 6 , August 2016 , pp. 1090 - 1101
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Archontoulis, S. V., Vos, J., Yin, X., Bastiaans, L., Danalatos, N. G. & Struik, P. C. (2011). Temporal dynamics of light and nitrogen vertical distributions in canopies of sunflower, kenaf and cynara. Field Crops Research 122, 186–198.Google Scholar
Bertheloot, J., Andrieu, B., Fournier, C. & Martre, P. (2008). A process-based model to simulate nitrogen distribution in wheat (Triticum aestivum) during grain-filling. Functional Plant Biology 35, 781–796.Google Scholar
BGR (2005). Bodenkundliche Kartieranleitung (Manual of Soil Mapping), 5th edn, Hannover, Germany: Schweizerbart'sche Verlagsbuchhandlung.Google Scholar
Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., 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, J. M., Meynard, J. M. & Delecolle, 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.CrossRefGoogle Scholar
Dreccer, M. F., van Oijen, M., Schapendonk, A. H. C. M., Pot, C. S. & Rabbinge, R. (2000). Dynamics of vertical leaf nitrogen distribution in a vegetative wheat canopy. Impact on canopy photosynthesis. Annals of Botany 86, 821–831.Google Scholar
Fischer, R. A. (1983). Wheat. In Potential Productivity of Field Crops under Different Environments, pp. 129–154. Los Banos, The Phillipines: IRRI.Google Scholar
Gastal, F. & Belanger, G. (1993). The effects of nitrogen-fertilization and the growing-season on photosynthesis of field-grown tall fescue (Festuca arundinacea schreb) canopies. Annals of Botany 72, 401–408.Google Scholar
Gastal, F. & Lemaire, G. (2002). N uptake and distribution in crops: an agronomical and ecophysiological perspective. Journal of Experimental Botany 53, 789–799.Google Scholar
Green, C. F. (1987). Nitrogen nutrition and wheat growth in relation to absorbed solar radiation. Agricultural and Forest Meteorology 41, 207–248.Google Scholar
Hansen, S., Jensen, H. E., Nielsen, N. E. & Svendsen, H. (1991). Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY. Fertilizer Research 27, 245–259.CrossRefGoogle Scholar
Harrell, D. M., Wilhelm, W. & McMaster, G. S. (1998). Scales 2: computer program to convert among developmental stage scales for corn and small grains. Agronomy Journal 90, 235–238.Google Scholar
Heuvelink, E. (1999). Evaluation of a dynamic simulation model for tomato crop growth and development. Annals of Botany 83, 413–422.CrossRefGoogle Scholar
Johnen, T., Boettcher, U. & Kage, H. (2012). A variable thermal time of the double ridge to flag leaf emergence phase improves the predictive quality of a CERES-Wheat type phenology model. Computers and Electronics in Agriculture 89, 62–69.Google Scholar
Justes, E., Mary, B., Meynard, J. M., Machet, J. M. & Thelier-Huche, L. (1994). Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany 74, 397–407.Google Scholar
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N. G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J. P., Silburn, M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M. & Smith, C. J. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.Google Scholar
Lancashire, P. D., Bleiholder, H., van den Boom, T., Langelüddecke, P., Stauss, R., Weber, E. & Witzenberger, A. (1991). A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology 119, 561–601.Google Scholar
Large, E. C. (1954). Growth stages in cereals – illustration of the feekes scale. Plant Pathology 3, 128–129.Google Scholar
Lawless, C., Semenov, M. A. & Jamieson, P. D. (2005). A wheat canopy model linking leaf area and phenology. European Journal of Agronomy 22, 19–32.Google Scholar
Lemaire, G., Cruz, P., Gosse, G. & Chartier, M. (1985). Relationship between dynamics of nitrogen uptake and dry-matter growth for lucerne (Medicago sativa L). Agronomie 5, 685–692.Google Scholar
Mae, T., Thomas, H., Gay, A. P., Makino, A. & Hidema, J. (1993). Leaf development in lolium-temulentum – photosynthesis and photosynthetic proteins in leaves senescing under different irradiances. Plant and Cell Physiology 34, 391–399.Google Scholar
Meinke, H., Hammer, G. L., van Keulen, H. & Rabbinge, R. (1998). Improving wheat simulation capabilities in Australia from a cropping systems perspective III. The integrated wheat model (I_WHEAT). European Journal of Agronomy 8, 101–116.Google Scholar
Porter, J. R. (1993). AFRCWHEAT2: a model of the growth and development of wheat incorporating responses to water and nitrogen. European Journal of Agronomy 2, 69–82.Google Scholar
Prost, L. & Jeuffroy, M. -H. (2007). Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status. Agronomy for Sustainable Development 27, 321–330.Google Scholar
Ratjen, A. M. & Kage, H. (2013). Is mutual shading a decisive factor for differences in overall canopy specific leaf area of winter wheat crops? Field Crops Research 149, 338–346.Google Scholar
Ratjen, A. M. & Kage, H. (2015). Forecasting yield via reference- and scenario calculations. Computers and Electronics in Agriculture 114, 212–220.CrossRefGoogle Scholar
Ratjen, A. M., Böttcher, U. & Kage, H. (2012). Improved modeling of grain number in winter wheat. Field Crops Research 133, 167–175.Google Scholar
Rawson, H. M., Gardner, P. A. & Long, M. J. (1987). Sources of variation in specific leaf area in wheat grown at high temperature. Australian Journal of Plant Physiology 14, 287–298.Google Scholar
R Development Core Team (2012). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Ritchie, J. T. & Otter, S. (1985). Description and performance of CERES-wheat: a user-oriented wheat yield model. In ARS Wheat Yield Project, ARS-38 (Ed. Willis, W. O.), pp. 159–175. Springfield, VA: USDA-ARS.Google Scholar
Sinclair, T. R. & Horie, T. (1989). Leaf nitrogen, photosynthesis, and crop radiation use efficiency: a review. Crop Science 29, 90–98.Google Scholar
Van Delden, A. (2001). Yield and growth components of potato and wheat under organic nitrogen management. Agronomy Journal 93, 1370–1385.Google Scholar
Wallach, D., Makowski, D. & Jones, J. W. (2006). Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications, 1st edn. Amsterdam: Elsevier Science.Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415–421.Google Scholar
Zhao, Z., Wang, E., Wang, Z., Zang, H., Liu, Y. & Angus, J. F. (2014). A reappraisal of the critical nitrogen concentration of wheat and its implications on crop modeling. Field Crops Research 164, 65–73.Google Scholar