kernel weight science and simulation wheatquality04/s... · kernel weight science and simulation...

Kernel Weight

Science and Simulation

Tony HuntUniversity of Guelph

Canada

Greg McMasterUSDA-ARS

USA

Why consider kernel weight, kernel weight uniformity and kernel shape?

….. Because they are important quality traits …. And they do have a value in the market-placeCurrently, payment generally based on measurement of a ‘surrogate’ aspect, specific weight (test weight, bushel weight). But, now have equipment to measure rapidly kernel size and its variability .… and this equipment already being used by some breeders, and being considered for use at mills and possibly also, elevators.

Why are they important quality traits?

….. Because millers can obtain more flour per unit weight of grain from:

Large, ‘round’, kernelsUniform kernels lotsWell-filled kernels

(Hence kernel weight not the sole criteria)

….. Because maltsters can obtain moreenzyme from small kernels

….. Because maltsters and brewers canobtain more extract from large kernels

Kernel size determination

1. Large genetic component

Cereal cultivars 20 - 50 mg Easy recurrent selection

(eg.Wiersma et al., 2001)

2. But, big environmental effects also

Pre-anthesis On flower sizesPost-anthesis On kernel development

and growth

Pre-anthesis effects mediated through:

Size of main shootTiller number per plantSpikelet number per shootFloret number per spikelet

Known for many years but recently receiving renewed attention (eg.Calderini et al.)

Effects of flower size apparent throughout kernel growth … as shown in many studies … for example, that of Hanft and Wych:

Time for Development vs. Temperature

Growth of top, middle, and bottom kernels Data from Hanft and Wych

0510152025303540

0 10 20 30 40 50Days after anthesis

Wei

ght

mg

Top Middle Bottom

The overall effects of the pre-anthesis environment on floret sizes …. and henceon potential kernel sizes in a crop …. havenot yet been associated with any simple variable

….. However, it may be possible to start with kernel number per plant:


Kernel weight vs kernel number per plant Data from files in Dssat package

05101520253035404550

0 50 100 150Kernel number per plant

Ker

nel w

eigh

t m

g

USA;Ks England Canada

Post-anthesis effects determine both:

The size of the ‘sac’ (Not implying a fixed ‘bin’ but the maximum size of a ‘baloon’)

The filling of the ‘sac’

But within the framework set by flower size.

Effects could differ depending on the phase of grain development …. and thus there have been a number of attempts to develop staging schemes (eg. Rogers and Quatrano; Noda et al).

Staging of grain development.

Many schemes based around dry weight curve … but others (eg.Geslin and Jonard, Jonard) place emphasis on water content:

Sac formation …. water content increasesWater plateauPhase of dessication … water content

decreases:


Pattern of Kernel Growth After Rogers and Quatrano and others

0102030405060708090100

0 10 20 30 40 50 60Days after anthesis

Wei

ght

mg

Fresh Dry Water

Post-anthesis environmental effects.

Most studies of environmental effects deal with the duration and rate of dry matter accumulation during the water plateau (or the linear phase of dry matter accumulation) ….

but, environment can affect the level of thewater plateau:


Patterns of Kernel GrowthIrrigated and Unirrigated (After Ballot)

0

10

20

30

40

50

60

0 10 20 30 40 50 60Days after anthesis

Wei

ght

mg

DryWt Ir DryWt U WaterWt Ir WaterWt U


All studies show a big effect of temperature on the duration of the linear (or overall) phase

… but, where the development rate approaches zero (or if it does?) is unclear:


Kernel development rate vs temperature

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30Temperature

Dev

elop

men

t rat

e / M

axim

um

Spiertz74 Spiertz77 Sofield PoortenWarrington Linear (Spiertz74) Linear (Sofield)


Most studies show a big effect of temperature up to about 23-25C on the rate of dry matter accumulation during the linear (or overall) phase:


Kernel growth rate vs temperature

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30Temperature

GR

OW

TH R

ATE

/ M

AXI

MU

M

Spiertz Sofield Poorten Linear (Spiertz) Linear (Sofield)


As expected from the effects on duration and rate, many studies show a big effect of temperature on kernel weight, as shown in theclassic data of Wiegand and Cuellar:


Kernel weights of winter and spring wheats After Wiegand and Cuellar

0

10

20

30

40

50

60

0 5 10 15 20 25 30Temperature

Wei

ght

mg

Springs Winters Linear (Springs) Linear (Winters)


… but, the situation with kernel weight may not be as ‘clean’ as at first sight ….

There is some indication that the response depends on which kernels are considered:


Kernel size vs temperatureAFRC, Ceres3.5, and Swheat models

00.20.40.60.81

1.21.41.6

0 10 20 30 40Temperature

Ker

nel s

ize

(Rel

ativ

e)

AFRC Ceres3.5 Swheat Field data

Kernel weight vs temperatureGrowth chamber data

0

0.2

0.4

0.6

0.8

1

1.2


Ker

nel w

eigh

t (R

elat

ive)

Spiertz Poorten


Thus, the temperature response of grain grain growth rate may be an ‘emergent’ aspect … with the real drivers being development and assimilate supply!!!

Such a concept may make it easier to understand why under some circumstances we can find kernels that appear to have ‘burst’ !

Simulation

Pre-anthesis conditions and kernel weight (via carpel size)

Not accounted for in most models.But:Spikegro calculates kernel weight for each floret. Assigns different sink strengths depending on day of anthesis.Cropsim calculates potential kernel weight depending on kernel number per plant:


Calculated vs measured kernel weightsPotential adjusted for kernels/plant

05101520253035404550

0 10 20 30 40 50 60Measured kernel weight mg

Cal

cula

ted

kern

el w

eigh

t m

g

USA;Ks England Canada

Simulation

Post-anthesis conditions and final weight

Temperature effects on development and growth accounted for in most models.

But, responses used differ markedly amongmodels:


Kernel development rate vs temperatureAFRC, Ceres3.5, and Swheat models

0

0.5

1

1.5

2

2.5

3


Ker

nel d

evel

opm

ent r

ate

(Rel

ativ

e)

AFRC Ceres3.5 Swheat


Kernel growth rate vs temperatureAFRC, Ceres3.5, and Swheat models

00.20.40.60.81

1.21.41.61.82


Ker

nel g

row

th ra

te (R

elat

ive)


Simulation


The temperature responses of development and growth embedded in the models considered would produce (in the absence of assimilate limitations) some ‘strange’ kernel weights responses:


Kernel size vs temperatureAFRC, Ceres3.5, and Swheat models

00.20.40.60.81

1.21.41.6


Ker

nel s

ize

(Rel

ativ

e)

AFRC Ceres3.5 Swheat Field data

Kernel weight vs temperatureAFRC, Ceres3.5, and Swheat models

00.20.40.60.81

1.21.41.6


Ker

nel w

eigh

t (R

elat

ive)


Simulation


In operation, the models considered do not produce such ‘strange’ kernel weight patterns …. so emphasizing the controlling importance of photosynthesis and reserve build-up and utilization.But, the impact of environment on photosynthesis, on the pattern of senescence, and on reserve accumulation and use differ markedly among models.

Conclusions

Basic knowledgeGood fund of (classical) information. But not yet clear on the extent to which the water content plateau is really an indicator of ‘sac’ size (kernel volume increases throughout the plateau)

SimulationModels generally do not account for pre-anthesis effects, and differ markedly in post-anthesis temperature responses. Of these, the grain growth responses could well be artifacts that simply help account for assimilate limitations to distal kernels.

Concluding Thought

Simulation models should be able to account for as much of the observed variability as regression models - and be able to provide some information onuniformity.

The challenge for simulation modellers is thus to account for 80% of the reported variation in kernel weight, this being the level achieved anumber of years ago by a regression model for small(but not large!) kernel weight cultivars in France(Masse)

And Yes ….

Because kernel weight and its uniformity are important ‘milling’ quality traits …. it will be worthwhile to take up this challenge and attemptto develop models that simulate ‘packaging’ as well as production.

kernel weight science and simulation wheatquality04/s... · kernel weight science and simulation...

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