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Application of physiological traits and remote

sensing in crop improvement

Dr John Foulkes, Erik Murchie, Pedro Carvalho

Canopy Sensors Workshop

NCARE Amman, Jordan

25-26 February 2013

SWIM - Sustainable Water

Integrated Management

Demonstration Project

The science and art of obtaining information

about an object, area, or phenomenon

through the analysis of data acquired by a

device that is not in contact with object, area,

or phenomenon under investigation.

Remote Sensing

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Overview

Background

Traits/sensors for enhancing water productivity

Traits/sensors for enhancing nitrogen productivity

Conclusions

A Growing World Population Requires an

Increased Global Food Supply

Food production will have to increase by 50% by 2025

and double in 30 years to help solve the current food

crisis.(Royal Society, Reaping the Benefits Report 2009)

At least 30 to 50% of crop yield can be attributed to

commercial fertilizer inputs.” Stewart et al. (2005)

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1970 1980 1990 2000 2010 2020

Gra

in y

ield

t h

a-1

(85%

DM

)

Titolo asse

UK Breeders plots

UK On farm yield

0

1

2

3

4

5

6

1970 1980 1990 2000 2010 2020

Wo

rld

wid

e A

ve

rag

e Y

ield

t h

a-1

Maize

Wheat

Rice

World Cereal Production Trends

Recent trends in UK wheat yields

• Global grain production decreasing

per capita

• Yields gains must be achieved

without increased inputs

• On-farm yields plateauing in some

countries

FAOSTAT 2010

6 Braun et al. 2010 Proceedings 8th International Wheat Conference, St Petersburg, Russia

Global Food Security Challenges

From 1.6 to 2.4% for wheat

From 0.9% to 1.5% for rice

From 1.0% to 2.3% for wheat

On approximately the same land area, with less water, nutrients, fossil

fuel, labour

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Challenge: Increasing the Efficiency of

Key Inputs

75% of all water used for agriculture

– Increasing and competing demands for water

>>>> especially from urban users and industry

– Climate change to increase H2O demand in

many places

Nitrogen-use efficiency = global 33%

– N fertiliser >>>> nitrate leaching and N2O

emissions: China, India and Pakistan biggest

users

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Increasing Crop Water Productivity

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Climate change & water supply:

runoff ≈ year 2050

The Wet gets wetter! - The Dry gets drier!

The Wet gets wetter! - The Dry gets drier!

runoff ≈ year 2050

Climate change & water supply:

By 2025, two-thirds of the

world population could be

under “stress conditions” (500-

1000 m3 per year per capita),

and 1800 million people are

expected to be living in

countries or regions with

“absolute water scarcity” (<500 m3 per year per capita)

The good news is

that 1% of water

productivity gain in

agriculture means

10% increase of

availability for other

uses

Litres per day per person

Drinking 2-4

Domestic 40-400

Food 1000-5000 (and more)

Water supply and Productivity

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Climate change and its impacts - a global perspective Document (PDF) http://www.meto.gov.uk/research/hadleycentre/pubs/posters/index.html

Future predictions for global cereals production: Percentage

change in yields (wheat, maize and rice) 2050

Strategies to respond to

water scarcity

1. Augment the “supply”

3. Increase water use

“efficiency” & water

“productivity”

2. Preserve/conserve the “quality”

Application of

remote sensing

techniques

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Increase water use “efficiency”

More crop per drop

More crops per drops

Increase water uses & “productivity”

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• Low heritability

• Large Genotype x Environment

• Low genetic variance, small

potential gains

• Complex, polygenic tolerance

mechanisms -large GxG

How to make progress?

Breeding for Drought Environments:

the Challenges

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Drought Phenotyping

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Rapid screening in breeding programs

Grain yield:

Root length density

Ears per plant

Leaf senescence (NDVI, SPAD)

Water-use efficiency (12/13C)

Canopy temperature

Current research programs in collaboration

with CGIAR centres: CIMMYT, Mexico

IRRI, Phillipines

SPAD Chlorophyll meter

NDVI

Spectroradiometer

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Reynolds & Tuberosa 2008

Trait-based breeding: to combine complementary traits

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Kirkegaard J A , Hunt J R J. Exp. Bot. 2010;61:4129-4143

Improving water productivity: Genetics and

Management

Increasing Crop Nitrogen Productivity

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IFA Statistics 2010

• Half the synthetic N fertilizer

ever used has been utilized since

1985 (Howarth, 2005).

• Recent rapid increase in N

fertilizer costs and legislative

moves to reduce N inputs

World fertilizer N Consumption Trends

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10

20

30

40

50

60

70

802

002

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03

20

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M T

on

ne

s N

Fe

rtil

izer

AfricaN AmericaS AmericaEropeOceaniaAsia

http://www.whrc.org/policy/global_nitrogen.htm

N loss consequences

Decreased crop production and profitability: Inefficient land

use, reduced performance of other crop inputs, reduced water

use efficiency

Water resource contamination: eutrophication: lakes, rivers;

groundwater contamination; coastal water contamination -

urea and harmful algal blooms (neurotoxin poisoning)

Water resource: Ammonia and particulates, nitrous oxide and

NOx (global warming, acid rain))

GHGS

Carbon Dioxide (CO2): fossil fuels (oil, natural gas, and coal), and

also as a result of other chemical reactions (e.g., manufacture of

cement).

Nitrous Oxide (N2O): agricultural and industrial activities, as well as

during combustion of fossil fuels and solid waste.

GWP = Global Warming Potential

N2O x 296 = CO2 equivalent

Background: Nitrogen losses

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Greenseeker (measures NDVI) delay N application

until you have a accurate estimation of a crop’s

potential; optimize N application. Ramp Calibration

Strip Technology

Opportunities to improve NUE simply by applying

the necessary nutrients in the correct amounts at

the correct time

Improving N-Use Efficiency: Agronomy

Raun et al. 2008 Agronomy Journal 100: 1088-93.

NDVI, what is it?

It is Normalized Difference Vegetation Index.

Used to measure green area and biomass

Degree of greenness = chlorophyll concentration

NDVI values vary with absorption of red light by plant chlorophyll and the reflection of infrared radiation by water-filled leaf cells. It is correlated with Intercepted Photo-synthetically Active Radiation (IPAR).

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NDVI, what is it? It is a function of Incident and reflected light

RNDVI = NIR – Red ,

NIR + Red

NIR 750-1300 nm

Red 600-700 nm

Where 0< NDVI< 1

Ramp Calibration Strip Applicator ~

apply 15 different N rates (urea ammonium

nitrate).

Ramp Calibration Strip applied preplant in

winter wheat; rates ranged from 0-192 kg N ha–1

in 12 kg increments.

• Automated gradients used for determining midseason N rates based on plant response.

• Approach assumes midseason biomass estimated using NDVI sensor is

directly related to grain yield

• Delaying applied N until midseason can result in near-maximum yields.

Example of Use of NDVI

(Greenseeker) to predict

Fertilizer N input

Raun et al. 2008 Agronomy Journal 100: 1088-93.

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WatNitMED Management Improvements

of WUE and NUE of Mediterranean Wheat and

Barley (2006-2010)

ESCOLA TÈCNICA SUPERIOR D’ENGINYERIA AGRÀRIA (ETSEA) www.icrea.es

J. Foulkes, M. Karrou, F. Karam, C. Thabet, H.J.

Spiertz, R. Dahan, J. Foulkes, S. Nogues, P.

Peltonen-Sainio, R. Albrizio, J.Y. Ayad, H.J. Mellouli

WatNitMED - Management Improvements of WUE and NUE of Mediterranean Wheat and Barley

Tunisia was selected for the ‘pilot study’ of the alternative(s) proposed

Two areas in Tunisia were selected. The first has a semiarid climate

(Siliana) and the second is sub humid one (Béja).

Fifteen farmers from each region committed to set up the demonstration

trials . Each grow the cereal (wheat and/or barley) as “normal” and with

the N management we suggested as an alternative

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Scheme for N recommendation in 20 fields in which expt carried out.

Cossani et al. Experimental Agriculture 2011

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N-fertilization rates applied by farmers and those derived

from WatNitMED

Béja (high-yielding) region

Siliana (low-yielding) region.

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Grain yield as a function of rainfall in season (Solid line

= upper WUE threshold (Sadras and Angus, 2006)).

Unfertilized Siliana

Unfertilized Béja

○ Farmer fertilization Béja

WATNITMED Siliana

Δ WATNITMED Béja

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Using the NDVI sensor to monitor crop growth in Minimum

tillage experiments: Example from Mexican Highlands

Verhulst, N., B., Govaerts, K.D. Sayre, P. De Corte, J.

Crossa, J. Deckers. 2010. Field Crop Res.,

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Using the NDVI sensor to monitor crop growth in Minimum

tillage experiments: Example from Mexican Highlands

Verhulst, N., B., Govaerts, K.D. Sayre, P. De Corte, J. Crossa, J.

Deckers. 2010. Field Crop Res.,

Fig. 1. Correlation between standardized NDVI (day−1) and biomass

measurements (t ha−1) until milking stage in the 2008 crop cycle for maize (a)

and wheat (b).

34 Verhulst, N., B., Govaerts, K.D. Sayre, P. De Corte, J. Crossa, J.

Deckers. 2010. Field Crop Res.

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Genetic Improvement of N productivity

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N-Use Efficiency (NUE) is the grain yield per unit of N

available in the soil.

NUE is the multiple of two components:

NUE can be improved by both uptake and utilisation

of N

NUE = N Uptake E * N Utilisation E

N uptake / N supply Grain yield / N uptake

Understanding N-Use Efficiency

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Figure 1. Strategies to improve N economy in wheat

MAXIMIZE PHOTOSYNTHETIC CAPACITY PER UNIT N: ● Leaf and stem N storage ● Vertical distribution of canopy N ● RuBisCo catalytic properties ● C4 metabolism

MAXIMIZE N CAPTURE: ● Distribute roots deeper ● Decrease specific root weight ● Optimize root to shoot ratio ● N transporter systems

OPTIMIZE N REMOBILIZATION AND GRAIN PROTEIN: ● Optimize N remobilization efficiency and stay-green ● Optimize grain N% ● Optimize gliadin to glutenin ratio

OPTIMIZE NITRATE ASIMILATION: ● Gutamine synthetase activity ● Organic acid metabolism

Foulkes et al. 2009 FCR

Trait-based breeding: to combine complementary traits: N use efficiency

Sites Nottingham Norwich Mons Clermont

N+ N- N+ N- N+ N- N+ N-

N applied (kg /ha) 210 0 180 20 240 50 240 40

2 years x 4 sites x 2 N x 16 varieties x 3 reps

Blue = parent UK DH population, Purple -=

parent of INRA DH population; Green = mutant

population

1. Alchemy 9. Quebon

2. Arche 10. Recital

3. Beaver 11. Renan

4. CF9107 12. Rialto

5. CF99102 13. Robigus

6. Consort 14. Savannah

7. Paragon 15. Soissons

8. Perfector 16. Toisondor

Norwich

Nottingham

Mons

Clermont-Ferrand

Harvest years 2007 and 2008

EU Wheat NUE Collaborative Project

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Genetic variation in ability to maintain yield under lowe

N in experiments in UK and France

Mean 2006-7 and 2007-8 at 4 sites: LSD (5%) N

x genotype = 0.28 t ha-1

Equation with 5 parameters = a monomolecular + a logistic functionGénard et al. (1999 Journal of horticultural science & biotechnology 74 : 772-776)Plot of Fitted Model

STA

Leaf

_1

_sc

ore

0 200 400 600 800 1000 1200

0

2

4

6

8

10

p0

p1p2

p4

p5

Starting End

Maximum

rate

Initial rate

= date at which

score is 9.5

= date at which the

second derivative is nil

Starting End

Maximum

rate

Initial rate

= date at which

score is 9.5

= date at which the

second derivative is nil

Starting End

Maximum

rate

Initial rate

= date at which

score is 9.5

= date at which the

second derivative is nil

Senescence scoring per leaf

Flag leaf senescence score

Fitting the senescence data

)))0110/()5(4*4exp(1/(0110())1/*2exp((1(*10 pppSTAppppSTApppscore

Thermal time post GS61 oCd

Target trait: Stay green

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Mean 2006-7 and 2007-8

Gaju et al. 2011Field Crops Res

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300 400 500 600 700 800

N U

t E

ff (k

g D

M k

g-1

N)

Start of senescence oCd (HN)

CF INRA

EM INRA

JIC

SB

High N

y = 0.085x + 13.1 R² = 0.32 *

y = 0.047x + 25.5 R² = 0.46 **

y = 0.064x + 32.2 R² = 0.54 **

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100 300 500 700 900

N U

t E

ff (

Kg

DM

kg

-1 N

)

Start of senescence oCd (LN)

CF INRA

EM INRA

JIC

SB

Low N

• Onset of senescence determines

NUtE and grain yield under low N

amongst 16 wheat cvs at 4 sites in

UK and France

Field-based Phenomics

Established technologies

- Colour images • Plant area, volume, mass • Senescence, relative chlorophyll content

- NR imaging • Tissue water content • Soil water content

- Far IR imaging • Canopy / leaf temp. / water use

-Fluorescence imaging • Physiological state of photosynthetic machinery

- Hyperspectral imaging • CHO and protein

Future technologies -X ray CT images of roots in soil - Nottingham - High resolution NMR-based imaging of roots in soil - Teraherz imaging of water content

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Acknowledgements

Collaborators:

Nottingham group: Erik Murchie, Pedro De Carvalho (PDRA) , Reshmi Gaju

(PDRA), Alistair Pask (PhD), Jayalath DeSilva (Technician)

Roger Sylvester-Bradley

John Snape

Pierre Martre, Jacques LeGouis

Yahya Shakhatreh

NCARE

Matthew

Reynolds

ACLIMAS is SWIM-DP funded by the European

Commission and coordinated by CIHEAM –

Mediterranean Agronomic Institute of Bari