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Nitrogen Utilization Efficiency (NUtE) in Wheat: A Global Perspective
Amanda de Oliveira Silva1, Ignacio Ciampitti1, Gustavo Slafer2, Romulo Lollato1
1Kansas State University & 2Universitat de Lleida
The Second Spanish Symposium on Cereal Physiology and Breeding (SEFiMeC)March 6th, 2019
Nitrogen Utilization Efficiency (NUtE)
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NUtE = Grain yield produced per unit of N uptake at maturity (NupMAT)
Research goals
• The primary goal of this synthesis analysis was to study NUtE in wheat via;
(i) benchmarking against yield limited by NupMAT
(ii) analyzing physiological and agronomic factors contributing to the variability in NUtE.
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NupMAT = total N uptake at maturityNUtE = Nitrogen utilization efficiency
Building the database
Literature survey and data selection criteria (accessed Nov. 2017)• Keywords: “wheat nitrogen uptake anthesis” • Databases: Science Direct, Scopus, and Web of Science • Journal citation reports (JCR) 2016 criteria• 55 papers and 529 data points.
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Inclusion criteria: • field experiments with site-year information• data per unit land area• data not averaged across wheat species• no manipulation of natural conditions
Total of 54 papers and 524 data points after removing outliers
Trait deviation calculation
Data standardization• Used the mean value from each experiment to remove the influence of unknown
factors on treatment effects on NUtE. Kitonyo et al. (2017) and Slafer et al. (2014)
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Trait deviation = treatment mean – mean experiment
Total of 516 data points for parameters “deviation”• 375 data points containing information on year of genotype release
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Data distribution for grain yield, NupMAT and grain protein concentration
NupMAT deviation (kg ha-1)
-200 -100 0 100 200Gra
in y
ield
dev
iatio
n (k
g ha
-1)
-10000
-5000
0
5000
10000 y = 96 - 5647 * (1-e-0.049*x)R2 = 0.66n = 516, p < 0.0001
Actual yield
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Relationship between grain yield and NupMATdeviations
+
2000 kg ha-1
NupMAT = total N uptake at maturityTrait deviation = treatment mean – experiment mean
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Relationship between NUtE and NupMAT deviations
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What are the determinants of variability in NUtE?
• Hard: Hard Red and Hard White Wheat• Soft: Soft Red and Soft White Wheat
• Northern hemisphere“Fall” September 1st to December 21st
“Winter” from December 22nd to March 21st
• Southern hemisphere “Fall” March 1st to June 21st
“Winter” from June 22nd to September 21st
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What are the determinants of variability in NUtE?
GHI= grain harvest index (grain yield to aboveground biomass at maturity ratio on a dry weight basis)
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What are the consequences of variability in NUtE?
SNC= stover (vegetative components) N concentration at maturity.
Conclusions
• A positive and curvilinear relationship between grain yield and NupMAT.
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• Challenge to increase yield without penalizing grain protein.
• Identifying the determinants of NUtE will enable to increase yield and narrow the gap between actual and NupMAT-limited yields.
• Breeding new cultivars contributed to increases in NUtE until 1970’s; afterward, newly released cultivars did not increase average NUtE.
• Opportunities to enhance yield through improving NUtE would only be possible at greater-than-average yield and NupMAT levels.
• While, increases in NupMAT would be required to increase yields at lower levels of N uptake.
• A positive and curvilinear relationship between grain yield and NupMAT.
• Opportunities to enhance yield through improving NUtE would only be possible at greater-than-average yield and NupMAT levels.
• While, increases in NupMAT would be required to increase yields at lower levels of N uptake.
• Challenge to increase yield without penalizing grain protein.
• Breeding new cultivars contributed to increases in NUtE until 1970’s; afterward, newly released cultivars did not increase average NUtE.
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Grain yield, NupMAT and protein vs YOR
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(n=375)
Stover to grain ratio at maturity
n=516 n=330
Stover N uptake (SNY) vs. grain N uptake (GNY)
Stover biomass (BM) vs. grain yield
n=330
Stover N vs. grain N concentration
Even though the treatments imposed within several experiments produced a substantial variation in the critical variables considered, the scope of differences across experiments was broader than within them.
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Table A1. Summary descriptive statistics for absolute values of the entire dataset (n=524). Mode, mean, standard error (se), minimum and maximum, and 0.25 (25%Q) and 0.75 (75%Q) percentiles were determined for each variable tested.
*Variables with n=337.GY, grain yield per unit area with 135 g kg-1 moisture content; NupMAT, aboveground N uptake per unit area at maturity; Grain protein concentration on a dry weight basis; GNY, grain N uptake per unit area at maturity; SNC, stover N concentration at maturity; SNY, stover N uptake per unit area at maturity; NHI, nitrogen harvest index (grain N uptake to aboveground N uptake at maturity ratio); HI, harvest index (grain yield to aboveground biomass at maturity ratio on a dry weight basis); NUtE, N utilization efficiency (grain yield to aboveground N uptake per unit area at maturity ratio); STV BM, stover biomass at maturity on a dry weight basis; BM, aboveground biomass at maturity on a dry weight basis.
Variables mode mean se min 25%Q 75%Q maxGY (kg ha-1) 4086 6423 106 742 4639 7818 14880NupMAT (kg ha-1) 167 166 2.6 21 125 200 378Protein (g kg-1) 120 125 1.2 51 106 145 199GNY (kg ha-1) 104 123 2.0 14 90 150 293SNC (g kg-1)* 4 6 0.2 1 4 8 28SNY (kg ha-1) 36 43 1.0 5 27 54 164NHI (%) 80 74 0.4 30 70 80 92HI (%)* 48 43 0.4 15 39 49 59NUtE (kg kg-1) 38 40 0.5 17 33 46 112STV BM (kg ha-1)* 7966 7209 135 1386 5494 8713 18546BM (kg ha-1)* 14300 12722 217 2040 10002 15283 25340