International Crop Modelling Symposium
15-17 March 2016, Berlin
MODELING WHEAT GRAIN YIELD AND PROTEIN CONTENT WITH PYG
MODEL.
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Berger, A.G. – Vazquez, D.
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Rainfed Crops, INIA La Estanzuela, Ruta 50km 11 Colonia UY, [email protected]
Introduction
Wheat grain yield has increased constantly over time due to management and genetic
improvement accompanied in some cases with high risk of reductions in grain protein
content (GPC) and baking quality. This reduction in GPC may result from the imbalance
between carbon assimilation and, nitrogen assimilation and remobilization during
grain growth (Pask et al., 2012). The balance between these components is determines
by the conditions and length of vegetative growth that determine biomass and
nitrogen accumulation, and the conditions during grain fill that determine grain
protein content and grain biomass accumulation (Martre et al., 2003). These
components are difficult to manipulate experimentally and modeling can contribute to
better understand and quantify the relevance of each component.
Materials and Methods
The relationship among all the components of the carbon and nitrogen balance in the
crop were analyzed and simulated with the model pyG. This model is a derivative of
the model GECROS (Yin and vanLaar, 2005), which uses an hourly time step, simulates
photosynthesis with a biochemical type model, and assumes an optimal allocation of
nitrogen and carbon between crop components, among some of the main
characteristics. The model was re-written in python, and several modifications relating
nitrogen demand, grain number determination, and nitrogen balance were tested in
this work. The results were contrasted with field data from experiments conducted
during 2012, 2013, and 2014 with 11, 15 and 7 cultivars and three contrasting
treatments, low nitrogen availability (lowN-lowN), high nitrogen availability (highNhighN), and low nitrogen availability until anthesis and high nitrogen availability
thereafter (lowN-highN).
Results and Discussion
The experimental setup allowed us to observe all the range of low-high grain yield and
low-high grain protein content. The model was able to capture the variability in yield
and grain protein content as well as the dynamics of growth and nitrogen
accumulation (Figure 1). It contributed also to determine the relevance of pre and post
anthesis nitrogen uptake and the relevance of nitrogen remobilization in grain nitrogen
balance and protein formation. Allowing the capacity to have deficiency driven soil
nitrogen absorption in the post anthesis period significantly improved the fit to data.
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International Crop Modelling Symposium
15-17 March 2016, Berlin
The proportion of nitrogen assimilated before anthesis was greater than after anthesis,
however in the lowN-highN treatment and some cultivars of highN-highN, the amount
of nitrogen assimilated in post anthesis was significant. The capacity to assimilate
nitrogen in post anthesis was significantly related to leaf senescence in the field and in
simulations, and contributed (as indicated by simulations) to a larger capacity to
assimilate carbon during grain fill.
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lowN-lowN
highN-highN
CheckHighN
CheckLowN
Optimum
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30
Nitrogen in aerial biomass (g N m-2)
Nitrogen in aerial biomass (gN m-2)
40
a
25
20
15
10
5
30
25
20
lowN-lowN
highN-highN
CheckHighN
CheckLowN
highN-highN_obs
lowN-lowN_obs
b
15
10
5
0
0
0
500
1000
1500
Aerial biomass (g m-2)
2000
2500
6-May-13
25-Jun-13
14-Aug-13
3-Oct-13
22-Nov-13
11-Jan-14
Figure 1. Acummulation of biomass and nitrogen for four contrasting simulated treatments and observed
data (a). Evolution over the growing season of the same treatments and nitrogen in grain (b).
Conclusions
The model was able to simulate well the plant nitrogen balance, including the
trajectory of leaf senescence and the response of leaf senescence to post anthesis
nitrogen availability.
Post anthesis nitrogen absorption from the soil significantly improved the fit to
observed data, in particular for lowN-HighN and highN-highN scenarios (high proteinlow yield and high protein – high yield respectively).
Acknowledgements
Founding provided by INIA projects CS-13 and CS-09. We also acknowledge the field support of M.X. Morales
and R. Calistro.
References
Martre, P., J.R. Porter, P.D. Jamieson et al. (2003). Plant Physiology, 133: 1959–1967.
Pask, A.J.D., R. Sylvester-Bradley, P.D. Jamieson et al. (2012). Field Crop Research 126:104–118.
Yin, X. and H.H. vanLaar (2005). Wageningen Academic Publishers, The Netherlands, 155pp.
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