In-field automatable tools for the determination of plant - - PowerPoint PPT Presentation

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In-field automatable tools for the determination of plant - - PowerPoint PPT Presentation

Mar 26, 2024 26 likes •349 views

In-field automatable tools for the determination of plant physiological responses and fruit quality parameters in September Bright nectarines subjected to deficit irrigation strategies Alessio Scalisi Department of Agricultural, Food and

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In-field automatable tools for the determination of plant physiological responses and fruit quality parameters in ‘September Bright’ nectarines subjected to deficit irrigation strategies

Alessio Scalisi

Department of Agricultural, Food and Forest Sciences University of Palermo, Italy

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SLIDE 2

e.g. Water saving in orchard management

Estimate correctly when and how much water to provide with irrigation based on plant water status, rather than on soil water content/status.

Are there any methods for continuous determination of tree water status?

Background

Orchard management

  • Input

+Output >Efficiency >Sustainability (ECOs)

Economic Ecological

Precision horticulture

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SLIDE 3

HYPOTHESES

  • Continuous fruit growth rates and leaf turgor pressure dynamics change under different

irrigation treatments in nectarines.

  • Deficit irrigation applied at different fruit growth stages differently affect tree physiology

OBJECTIVES

  • Find out the most sensitive continuous indicator of water deficit.
  • Test portable, non-destructive devices for in-field determination of leaf and fruit composition

RESEARCH QUESTIONS

  • Are fruit growth and leaf turgor pressure related to each other?
  • Can we associate fruit growth and leaf turgor pressure to midday stem water potential

(Ψstem)?

  • Are near-infrared (NIR) and fluorescence spectroscopy suitable for in-field non-destructive

determination of fruit and leaf composition (e.g. sugars, dry matter and flavonoids)?

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SLIDE 4

Materials & methods

  • 144 measurement trees Planting density: 2200

trees/ha

  • 4-years-old trees
  • ‘September Bright’ nectarines grafted on ‘Elberta’

rootstock

  • 6 randomized blocks
  • Open Tatura system
  • Drip irrigation
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SLIDE 5

Randomized block design

  • 6 blocks
  • 12 irrigation treatments

▪ 2 Canopy orientation treatments

144 measurement trees

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SLIDE 6

IRRIGATION TREATMENTS

  • 12 treatments

CANOPY ORIENTATION TREATMENTS

  • 2 treatments (72 West & 72 East trees)

East West

Full factorial design (12x2)

Fruit growth stages I II IIIa IIIb Irrigation treatments 0% of ETc 0% of ETc 0% of ETc 0% of ETc 20% of ETc 20% of ETc 20% of ETc 20% of ETc 40% of ETc 40% of ETc 40% of ETc n.a. Full irrigation: 100% of ETc

Multiple measurements were taken over time:

  • Throughout the day (daily curves)
  • At weekly intervals
  • At growth stage intervals

Fruit diameter

STAGE I STAGE II STAGE IIIa STAGE IIIb

shuck fall cell division pit hardening cell expansion fruit physiological maturity and harvest

sugar accumulation chlorophyll degradation

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SLIDE 7

7

Equipment used for field measurements

  • Fruit gauges for continuous measurements of fruit growth
  • Leaf patch clamp pressure (LPCP) probes for continuous measurements of leaf turgor pressure
  • Calibit (digital calliper) for fruit diameter measurements
  • DeltaT AP4 dynamic porometer for leaf stomatal conductance (gs)
  • Light trolley and ceptometer for canopy light interception
  • LICOR 6400 for photosynthesis and leaf fluorescence
  • SPAD meter (SPAD index) for determination of chlorophyll content
  • Pressure chamber for determination of stem water potential
  • DA-meter (IAD index) for determination of chlorophyll degradation
  • Felix F-750 NIR portable device for determination of SSC and DM
  • Multiplex Force-A fluorometer for determination of flavonoids
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SLIDE 8

Results (p

(presented in in red)

  • Multiple Vs single winter buds – Influence of previous year irrigation treatments
  • Multiple Vs single spring fruitlets – Influence of previous year irrigation treatments
  • Fruit diameter
  • Effective area of shade (estimate of tree vigour and light interception)
  • Stomatal conductance (gs)
  • Trunk cross-sectional area (TCSA)
  • Leaf photosynthetic activity (Pn)
  • Efficiency of PSII (ΦPSII)
  • Leaf relative water content (RWC)
  • Stem water potential (Ψstem)
  • Leaf water potential (Ψleaf)
  • SPAD index
  • Yield, fruit weight, crop load and flesh firmness
  • IAD index
  • Anthocyanin and flavonol indices in fruit and leaves
  • SSC and DM
  • Starch and sugars in wood
  • Leaf turgor pressure dynamics
  • Fruit growth dynamics
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SLIDE 9

01/11/17 01/12/17 01/01/18 01/02/18 01/03/18

Temperature (°C)

10 20 30 40

RH (%)

20 40 60 80 100

Solar radiation (MJ m-2)

20 40 60 80 100

Tmean RHmean Solar radiation

20 40 60 80 100 Tmean (°C) RHmean (%) Solar radiation (MJ m-2)

Stage I Stage II Stage IIIa Stage IIIb

Weather and irrigation

Rainfall (mm) Full irrigation (100% of ET

c, mm)

Rainfall + full irrigation (mm) Stage I 64 73 137 Stage II 141 78 219 Stage IIIa 35 81 116 Stage IIIb 3 83 86 Total 243 315 559

01/11/17 01/12/17 01/01/18 01/02/18 01/03/18

ET0 (mm)

2 4 6 8 10 12 14

Rainfall (mm)

20 40 60 80

VPD (kPa)

1 2 3 4 5

ET0 Rainfall VPD

Stage I Stage II Stage IIIa Stage IIIb

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SLIDE 10

Fruit diameter (irrigation treatments aggregated by stage)

Beginning I I<->II II<->IIIa IIIa<->IIIb Harvest

Fruit diameter (mm)

10 20 30 40 50 60

Control DI_I DI_II DI_IIIa DI_IIIb

HSD

10

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SLIDE 11

Fruit diameter (in-stage weekly trends)

STAGE I

16/10/2017 23/10/2017 30/10/2017 6/11/2017 13/11/2017

Fruit diameter (mm)

5 10 15 20 25 30 35

DI_0 DI_20 DI_40 Control

HSD

STAGE II

12/11/2017 22/11/2017 2/12/2017 12/12/2017 22/12/2017 1/1/2018 28 30 32 34 36 38 40 HSD STAGE IIIa 1/1/2018 8/1/2018 15/1/2018 22/1/2018 29/1/2018 34 36 38 40 42 44 46 48 HSD

STAGE IIIb

5/2/2018 10/2/2018 15/2/2018 20/2/2018 25/2/2018 2/3/2018 40 45 50 55 60 65 HSD

11

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SLIDE 12

2017/18 season

01/10/17 01/11/17 01/12/17 01/01/18 01/02/18 01/03/18

Effective area of shade (EAS)

0.45 0.50 0.55 0.60 0.65 0.70 0.75 Control DI_I DI_II DI_IIIa DI_IIIb

HSD

summer pruning

Tree vigour and light interception (Effective area of shade, EAS)

12

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SLIDE 13

Diurnal stomatal conductance (gs)

Stage II

Time of day

7 8 9 10 11 12 13 14 15 16 17 18 19

gs (mmol m-2 s-1)

50 100 150 200 250 300 350 Control DI_0 DI_20 DI_40 7 8 9 10 11 12 13 14 15 16 17 18 19

PPFD ( mol m-2 s-1)

500 1000 1500 2000 2500

PPFD West PPFD East gs West gs East

13

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SLIDE 14

Diurnal stomatal conductance (gs)

Stage IIIa

Time of day

7 8 9 10 11 12 13 14 15 16 17 18 19

gs (mmol m-2 s-1)

50 100 150 200 250 300 350 Control DI_0 DI_20 DI_40 7 8 9 10 11 12 13 14 15 16 17 18 19

PPFD ( mol m-2 s-1)

500 1000 1500 2000 2500

PPFD West PPFD East gs West gs East

14

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SLIDE 15

Efficiency of PSII (ФPSII)

18

Stage I Stage IIIa Stage IIIb

PSII

0.05 0.10 0.15 0.20 0.25 Control DI_40 DI_20 DI_0 ns a b ab ab A B B

ANOVA and mean separation by Tukey’s multiple comparison test Different letter represent significant differences for p<0.05

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SLIDE 16

20

Stage II

Time of day

04 07 10 13 16 19

stem (MPa)

  • 3.5
  • 3.0
  • 2.5
  • 2.0
  • 1.5
  • 1.0
  • 0.5

Control DI_0 DI_20 DI_40 Pre-dawn

Stage IIIa

04 07 10 13 16 19 Pre-dawn

Daily stem water potential (Ψstem) curve

Error bars represent standard errors. N of replicates for treatment = 6

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SLIDE 17

22

I<>II II<>IIIa IIIa<>IIIb Post-harvest

SPAD index

40 41 42 43 44 45

East West

HSD 39 40 41 42 43 44 45 46

Control DI_I DI_II DI_IIIa DI_IIIb

HSD

I<>II II<>IIIa IIIa<>IIIb Post-harvest 40 41 42 43 44 45

SPAD index (i.e. leaf chlorophyll content)

Error bars represent standard errors.

ANOVA:

  • Irrigation → p<0.001
  • Growth stage → p<0.001
  • Canopy Orientation → p<0.001
  • Irr Treat * Growth Stage → ns
  • Canopy orientation * growth

stage→ p<0.05

Chlorophyll likely to be converted into fruit secondary metabolites (i.e. anthocyanins, flavonols, etc)

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SLIDE 18

Fluorometer Multiplex 3 Force A on leaves → flavonol index

ANOVA:

  • Irrigation Treatment → p<0.001
  • Growth stage → p<0.001
  • Irr Treat * Growth Stage → p<0.01
  • Canopy orientation → p<0.001
  • Error bars represent standard errors.

Fruit growth stage

I<->II II<->IIIa IIIa<->IIIb Harvest

Flavonol index

6 8 10 12 14 Control DI-I DI-II DI-IIIa DI-IIIb

HSD Canopy Orientation

West East

Flavonol index

8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4

HSD

23

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SLIDE 19

Fluorometer Multiplex 3 Force A on leaves → anthocyanin index

ANOVA:

  • Irrigation Treatment → p<0.001
  • Growth stage → p<0.001
  • Irr Treat * Growth Stage → p<0.001
  • Canopy orientation → p<0.001
  • Error bars represent standard errors.

Fruit growth stage

I<->II II<->IIIa IIIa<->IIIb Harvest

Anthocyanin index

1 2 3 4 5 6 7 Control DI-I DI-II DI-IIIa DI-IIIb

HSD Canopy Orientation

West East

Anthocyanin index

3.6 3.8 4.0 4.2

HSD

24

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SLIDE 20

Multiplex 3 Force A fluorometer on fr fruit → Fla lavonol index

ANOVA:

  • Merged Irrigation Treatment (by fruit growth stage) → p<0.01
  • Date → p<0.001
  • Irrigation Treatment * Date→ p<0.05

05-Feb 12-Feb 19-Feb 26-Feb 04-Mar

Flavonol index

4 6 8 10 12 14 Control DI_I DI_II DI_IIIa DI_IIIb

HSD

26

Harvest

Canopy Orientation

W E

Flavonols index

5 6 7 8 9

HSD

  • Canopy orientation → p<0.001
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SLIDE 21

Fluorometer Multiplex 3 Force A on fr fruit it → fluorescence excitation ratio anthocyanin in relative index (FERARI)

ANOVA:

  • Date → p<0.001
  • Irrigation Treatment → p<0.001
  • Canopy orientation → p<0.001
  • Irrigation Treatment * Date→ p<0.001

05-Feb 12-Feb 19-Feb 26-Feb 04-Mar

FERARI index

0.7 0.8 0.9 1.0 1.1 1.2 Control DI_I DI_II DI_IIIa DI_IIIb

HSD Canopy Orientation

West East

FERARI index

0.90 0.91 0.92 0.93 0.94 0.95

HSD

Despite at harvest no significant differences

  • Canopy orientation → p<0.001

27

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SLIDE 22

Felix F-750 → Dry matter and SSC

28 Harvest

Irrigation treatment

0_IIIb 20_IIIb 20_I 0_I 40_I 0_IIIa 20_IIIa 20_II Control 40_II 0_II 40_IIIa

Dry matter (%)

14 16 18 20 22 24

Harvest

Canopy orientation

West East

Dry matter (%)

17.0 17.5 18.0 18.5 19.0

HSD HSD

Harvest

Irrigation treatment

0_IIIb 20_IIIb 0_I 20_I 40_I 0_IIIa 20_II Control 20_IIIa 0_II 40_II 40_IIIa

TSS (°Brix)

12 14 16 18 20 22

HSD

SSC (°Brix)

Error bars represent standard errors.

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SLIDE 23

FRUIT Fruit gauge output is a signal in mV 1. Fruit diameter z-scores: z = (x – μ) / σ 2. Fruit relative growth rate (RGR, µm mm-1 min-1): RGR = (ln Diameter2 – ln Diameter1) / (t2 –t1) LEAVES LPCP probe output is an inverted value of leaf turgor pressure (pp) 1. pp z-scores: z = (x – μ) / σ 2. Leaf relative pressure change rate (RPCR, kPa kPa-1 min-1) RPCR = (ln pp 2 – ln pp 1) / (t2 –t1) RGR and RPCR are 2nd derivatives of fruit diameter and pp, respectively.

29

Fruit growth and leaf turgor pressure indices

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SLIDE 24

Stage I

29/10 30/10 31/10 01/11 02/11 03/11 04/11 05/11 06/11

Fruit diameter (z-scores)

  • 3
  • 2
  • 1

1 2 3 Control DI_0 DI_20 DI_40 29/10 30/10 31/10 01/11 02/11 03/11 04/11 05/11 06/11

Fruit RGR ( m mm-1 min-1)

  • 0.15
  • 0.10
  • 0.05

0.00 0.05 0.10 0.15 29/10 30/10 31/10 01/11 02/11 03/11 04/11 05/11 06/11

pp (z-scores)

  • 4
  • 2

2 4 29/10 30/10 31/10 01/11 02/11 03/11 04/11 05/11 06/11

Leaf RPCR (kPa kPa-1 min-1)

  • 0.010
  • 0.005

0.000 0.005 0.010

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SLIDE 25

09/02 10/02 11/02 12/02 13/02 14/02 15/02 16/02 17/02 18/02 19/02 20/02 21/02 22/02

Fruit diameter (z-scores)

  • 3
  • 2
  • 1

1 2 3 Control DI_0 DI_20 09/02 10/02 11/02 12/02 13/02 14/02 15/02 16/02 17/02 18/02 19/02 20/02 21/02 22/02

Fruit RGR ( m mm-1 min-1)

  • 0.10
  • 0.05

0.00 0.05 0.10 09/02 10/02 11/02 12/02 13/02 14/02 15/02 16/02 17/02 18/02 19/02 20/02 21/02 22/02

pp (z-scores)

  • 3
  • 2
  • 1

1 2 3 09/02 10/02 11/02 12/02 13/02 14/02 15/02 16/02 17/02 18/02 19/02 20/02 21/02 22/02

Leaf RPCR (kPa kPa-1 min-1)

  • 0.010
  • 0.005

0.000 0.005 0.010

Stage IIIb

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SLIDE 26

Stage IIIb Stage I Large regressions (sensor outputs)

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SLIDE 27

Midday Ψstem Vs CVDiam/CVpp & Midday Ψstem Vs CVRGR/CVRPCR

DIURNAL

Midday stem (Mpa)

  • 1.8
  • 1.6
  • 1.4
  • 1.2
  • 1.0
  • 0.8

NOCTURNAL

CVDiam/CVpp

0.0 0.5 1.0 1.5 2.0

  • 2.0
  • 1.8
  • 1.6
  • 1.4
  • 1.2
  • 1.0
  • 0.8

DIEL

  • 1.8
  • 1.6
  • 1.4
  • 1.2
  • 1.0
  • 0.8

STAGE I

DIURNAL NOCTURNAL

CVRGR/CVRPCR

0.0 0.1 0.2 0.3 0.4 0.5 0.6

DIEL DIURNAL

Midday stem (Mpa)

  • 4
  • 3
  • 2
  • 1

NOCTURNAL

CVDiam/CVpp

2 4 6 8

  • 4
  • 3
  • 2
  • 1

DIEL

  • 4
  • 3
  • 2
  • 1

STAGE IIIb

DIURNAL NOCTURNAL

CVRGR/CVRPCR

0.0 0.1 0.2 0.3 0.4 0.5 0.6

DIEL

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SLIDE 28

Stage I and IIIb merged

Midday

stem= -1.7013 - 2.3587*CVRGR/CVRPCR

Nocturnal CVRGR/CVRPCR

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Midday stem (Mpa)

  • 5
  • 4
  • 3
  • 2
  • 1

R2 = 0.51 p < 0.001

Several regressions tested:

  • Diel
  • Midday Ψstem Vs CVDiam/CVpp
  • Midday Ψstem Vs CVRGR/CVRPCR
  • Diurnal
  • Midday Ψstem Vs CVDiam/CVpp
  • Midday Ψstem Vs CVRGR/CVRPCR
  • Nocturnal
  • Midday Ψstem Vs CVDiam/CVpp
  • Midday Ψstem Vs CVRGR/CVRPCR
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SLIDE 29
  • 3.5
  • 3.0
  • 2.5
  • 2.0
  • 1.5
  • 1.0
  • 0.5

0.0

stem =-2.491-0.150*Nocturnal CVAGR/CVpp R2=0.76 P<0.01 NB

27-Sep

Nocturnal CVAGR/CVpp

  • 10
  • 8
  • 6
  • 4
  • 2

2 4 6 8

stem (MPa)

  • 4.0
  • 3.5
  • 3.0
  • 2.5
  • 2.0
  • 1.5
  • 1.0
  • 0.5

0.0

stem =-2.353-0.183*Nocturnal CVAGR/CVpp R2=0.83 P<0.01 SAF10

t-test show no significant differences between the slope of the two regressions, implying a genotype-independent relationship

What’s going on in olive?

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SLIDE 30

36

Conclusions

  • A combined index of fruit growth and leaf turgor pressure is a more

powerful tool for continuous Ψstem estimation, compared to considering them individually.

  • An integrated approach for continuous monitoring of water status in plant
  • rgans (e.g. fruit, leaves, trunk, etc.) can be used for remote, automated

irrigation management when trees reach well defined Ψstem thresholds (e.g. via I/O Arduino board).

  • Fruit quality parameters may be determined by non-destructive field

measurements by NIR spectroscopy and fluorescence spectroscopy. These technologies might potentially be automated and installed on trees for regular and real-time determination of desired parameters.

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SLIDE 31

37

Acknowledgements

  • Dario and Mark for supervising me
  • Ian, Lexie, Des, Wendy, Colin, Subhash, Christine, Janine,

Maddy, Iris for scientific and field support

  • James and Andrew for our fun time (and work) in the orchard
  • Athulya for being a great hard working student
  • Angela, Cathy, Melly, Andy, Bruce, Megan and Aimee for

support and coffee breaks

  • All the other people working at the Tatura Research centre

& my family in Italy…

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