Effects of Regulated Deficit Irrigation (RDI) on Fruit Yield,

Quality and Physiology of Smith’s Early Navel Orange Trees

Presenter: Emily Wieber

Committee Members:

Dr. Jochen Schenk, Dr. Joel Abraham, & Dr. Darren Sandquist

UCR Advisors:

Dr. Peggy Mauk & Dr. Mary Lu Arpaia

Outline 2

• Introduction

→ Hypotheses

→ Citrus reproductive cycle

• Methods

→ Layout of field equipment and

irrigation

→ Fruit quality (internal and external )

and yield measurements

→ WUE, Plant Physiology, SI

• Results

→ Effects of RDI on fruit quality

(Table 2-3)

→ Effects of fruit position on fruit

quality (Table 4-5)

→A summary of the effects of RDI

(Table 6)

→ Fruit size distribution & correlation

between yield and Canopy (Fig. 2)

→ Correlation between yield, WUE

and Irrigation (Fig. 3)

→A summary of the irrigation, plant-

based and weather-based parameters

(Table 7)

→An overview of plant-based and

weather-based parameters (Fig. 4)

During phase IIA (Fig.5)

During phase IIB (Fig. 6)

During phase III (Fig. 7)

→ Signal Intensity (Fig. 8), signal to

noise ratio (Table 8)

→ Correlation among plant-based

parameters (Fig. 9)

→ Correlation between plant-based

parameters to weather-based

parameters (Fig. 10)

• Discussion

• Conclusion

• Citation

Hypotheses & Citrus Reproductive Cycle3

Phase I Phase IIA Phase IIB Phase III

Flowering Early Fruit Growth Late Fruit Growth Fruit Ripening

Dec16 - May15 May16 - Jul15 Jul16 - Oct15 Oct16 - Dec15

Treatment

Control 100% ETc 100% ETc 100% ETc 100% ETc

RDI1 100% ETc 25% ETc 100% ETc 100% ETc

RDI2 100% ETc 100% ETc 100% ETc 75% ETc

RDI3 100% ETc 25% ETc 100% ETc 75% ETc

I. Navel orange trees can withstand a moderate irrigation reduction (25%

ETc) during phase IIA and (75% ETc) during phase III without

compromising fruit yield and quality

II. Sap flow (SF) is more sensitive indicator of the onset of plant water stress

than MDS

Table1: Citrus Reproductive Cycle & RDI Application (Goldhamer & Salinas 2000)

Dendrometer Sap Flow Sensor

Methods: Layout of Field Equipment and Irrigation4

Minolta Inc. (2007)

Methods: Quality and Yield Measurements5

f

Fig. 1 Three-dimensional color solid: lightness (ranging from

light, lower value to dark, higher value), chroma (color intensity,

ranging from dull, lower value to dark, higher value), and hue

(angle value, ranging from green, lower value to red, higher value)

• Fruit Quality: Internal and External

→ % juice wt. to fruit wt.,

→ Soluble solids concentration (SSC)

→ Titratable acid (TA)

→ BrimA = SSC – 4TA (sweetness)

→ Ratio of SSC to TA (maturity)

→ Rind thickness & texture

→ Color (lightness, chroma, & hue)

• Yield

→ Avg. fruit weight per tree (kg tree-1)

→Avg. number of fruit per tree

→ Percentage of fruit per tree based on

three size categories (L - XL, M, XS - S)

Inte

rnal

Exte

rnal

Methods: Measured WUE, Plant Physiology & SI6

f

• Water Use Efficiency:

Agricultural (kg m-3) vs. financial ($ fruit $ water-1)

WUEagr =Yield

Irrigation+Rainvs. WUEf =

Profits

Investment

• Plant Physiological Responses

→ Ψstem, SF, & MDS

→ Use to calculate signal intensity (aka water stress indicator)

• Signal Intensity (SI)

for (Ψstem & MDS) =RDI1+RDI3

Control

Signal Intensity

for (SF) = Control

RDI1+RDI3

Results: Effects of RDI on Fruit Quality7

f

Table 2: Effects of RDI on the percentage of juice weight over fruit weight, soluble solids

concentration (SSC), titratable acid percentage (TA), BrimA index, and the ratio of SSC to TA

Table 3: Effects of RDI on the fruit’s rind thickness, texture, lightness, chroma, and hue

low high

light dark

low high

dull bright/vivid

low high

green red

low high

smooth rough

Results: Effects of the Fruit Position on Fruit Quality8

f

Table 4: Effects of the fruit position (north vs. south) on the percentage of juice

weight to fruit weight, soluble solids concentration (SSC), titratable acid

percentage (TA), BrimA index, and the ratio of SSC to TA

Table 5: Effects of the fruit position on rind thickness, texture, color lightness,

chroma, and hue

low high

light dark

low high

green red

low high

smooth rough

low high

dull bright/vivid

Table 6: Summary of the Effects of RDI on Yield, Cost, Profits, WUEagr & WUEf

9

f

Results: Table 6 Effects of RDI on Yield, Cost, Profits, and WUE &

Fig. 2 A) Fruit Weight Distribution & B) Correlation between yield and canopy

Fig.2: A) Distribution of Fruit Size and B) Correlation between Yield and Tree Canopy

XS - S: 63.5 - 74.9

M: 75 - 80

L - XL: 81 - 88

Fruit Size (mm)

Control RDI1 RDI2 RDI3

Irrigation Treatments

0

10

20

30

40

50

60

70

80

90

100

Perc

ent

of

Fru

it p

er T

ree

in T

hre

e S

ize

Cat

ego

ries

aa

bb

b

a

ab

a

a

aa

a

A) B)

Results: Fig.3 Correlation between Yield, WUEagr and WUEf to Irrigation &

Table 2: Summary of Irrigation, Plant-based and Weather-based Parameters

10

Table 7: Summary of irrigation, plant-based and weather-based parameters from phase IIA to III

R² = 0.73

20

30

40

50

1200 1400 1600

Aver

age

Yie

ld (

kg)

ETc and Rain (mm)

A) Yield and Irrigation

R² = 0.37

0.50

0.75

1.00

1200 1400 1600W

UE

agr

(kg m

-3)

ETc and Rain (mm)

B) WUEagr and Irrigation

R² = 0.43

20

30

40

1200 1400 1600

WU

Ef ($

fruit

/$w

ater

)

ETc and Rain (mm)

C) WUEf and Irrigation Fig. 3: Correlation between yield, WUEagr, and WUEf to irrigation

Results: Fig. 4 Overview of A) Irrigation &

B) Morning and Midday stem

11

0

50

100

150

200

250

ET

can

d R

ain

(m

m)

A) ETc and Rain Control

RDI1

RDI2

RDI3

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

24-May 23-Jun 23-Jul 22-Aug 21-Sep 21-Oct 20-Nov 20-Dec

st

em (

MP

a)

Date in 2014

B) Morning (7 am) and Midday (12 pm) stem

Morning

Midday

Results: Fig. 4 Overview of Plant-based and Weather-based Parameters12

0

10

20

30

40

50

60

Dai

ly A

ver

age

SF

(g/h

) C) Daily Average SF

0.00

0.05

0.10

0.15

0.20

MD

S (

mm

)

D) MDS

0

10

20

30

40

0

100

200

300

400

500

24-May 24-Jun 24-Jul 24-Aug 24-Sep 24-Oct 24-Nov 24-Dec

Aver

age

Air

Tem

per

ture

(oC

)

Sola

r R

adia

tion (

W m

-2)

Date in 2014

E) Solar Radiation, Temperature, and Precipitation

Water Stress

No Water Stress

Results: Fig. 5 During May 24th to May 31st 2014 (Phase IIA)13

f

0

50

100

150

Sap

Flo

w (

g h

-1) A) SF

0.00

0.05

0.10

0.15

0.20

MD

S (

mm

)

B) MDS

0

10

20

30

40

0

500

1000

1500

Air

Tem

per

ature

(0C

)

Sola

r R

adia

tion (

W m

-2)

Date and Time

C) Solar Radiation, Temperature, and Precipitation

Water Stress

No Water Stress

Results: Fig. 6 During Sep 29th to Oct 4th 2014 (Phase IIB)14

f

0

20

40

60

Sap

Flo

w (

g h

-1)

A) SF

0.00

0.05

0.10

0.15

0.20

MD

S (

mm

)

B) MDS

0

10

20

30

40

0

500

1000

1500

9/29/14

100

9/29/14

1300

9/30/14

100

9/30/14

1300

10/1/14

100

10/1/14

1300

10/2/14

100

10/2/14

1300

10/3/14

100

10/3/14

1300

10/4/14

100

10/4/14

1300

Air

Tem

per

ature

(oC

)

Sola

r R

adia

tion (

W m

-2)

Date and Time

C) Solar Radiation, Temperature, and Precipitation

Water Stress

No Water Stress

Results: Fig. 7 During Nov 30th to Dec 5th 2014 (Phase III)15

f

0

20

40

60

Sap

Flo

w (

g h

-1)

A) SF

0.00

0.05

0.10

0.15

0.20

MD

S (

mm

)

B) MDS

0

10

20

30

40

0

500

1000

1500

Air

Tem

per

ature

(oC

)

Sola

r R

adia

tion (

W m

-2) C) Solar Radiation, Temperature, and Precipitation

Date and Time

Water Stress

No Water Stress

Results: Signal Intensity (SI) among Three Plant-based Parameters

& Fig.9: Correlation between SF and MDS to Midday stem

16

0.0

0.5

1.0

1.5

2.0

2.5

Sig

nal

In

ten

sity

Date in 2014

Fig. 8 Signal Intensity from May 16th to Dec 12th, 2014

Table 8: Mean signal intensity, mean noise, and signal

to noise ratio from May 24th to Jun 13th, 2014

y = -6.80x - 0.71

r² = 0.16

p_value = 0.89

0

10

20

30

40

-2.5 -1.5 -0.5 0.5

Dai

ly A

ver

age

Sap

Flo

w (

g h

-1) A) SF and Midday stem

Control

RDI1

RDI2

RDI3

y = -0.03x + 0.02

r² = 0.21

p_value = 0.31

0

0.05

0.1

0.15

0.2

-2.5 -1.5 -0.5 0.5

MD

S (

mm

)

Midday stem (MPa)

B) MDS and Midday stem

Fig.9: Correlation between SF and MDS to

Midday stem

Results: Fig. 10: Correlation between Plant-based Parameters

to Weather-Based Parameters

17

f

y = -0.0062x - 0.64

r² = 0.36

p_value = 0.23

-3

-2

-1

0

0 50 100 150 200 250

Mid

day

st

em(M

Pa)

A) Midday stem and ETo

y = 1.88x - 0.05

r² = 0.44

p_value = 0.99

0

5

10

15

20

25

0 2 4 6 8 10

Dai

ly A

ver

age

SF

(g h

-1) B) SF and ETo

y = 0.01x + 0.05

r² = 0.21

p_value = 0.00

0.00

0.05

0.10

0.15

0.20

0 2 4 6 8 10

MD

S (

mm

)

ETo (mm day-1)

C) MDS and ETo

y = -0.73x - 0.67

r² = 0.12

p_value = 0.55

-3

-2

-1

0

0 0.5 1 1.5 2 2.5

Mid

day

st

em(M

Pa) D) Midday stem and VPD

y = 2.22x + 6.64

r² = 0.03

p_value = 0.00

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5

Dai

ly A

ver

age

SF

(g h

-1) E) SF and VPD

y = 0.03x + 0.05

r² = 0.11

p_value = 0.00

0.00

0.05

0.10

0.15

0.20

0 0.5 1 1.5 2 2.5

MD

S (

mm

)

VPD (kPa)

F) MDS and VPD

Discussion 18

f

I. Effects of RDI on Fruit Yield and Quality• Severe RDI (25% ETc) imposed during the early fruit growth would reduce

fruit size at harvest (Goldhamer & Salinas 2000)

• RDI had similar gross yields, fruit load, and packable cartons (e.g. Fancy and

Choice fruit) as the control (Goldhamer & Salinas 2000)

• RDI resulted in equal or higher fruit quality (e.g. SSC and TA values) when

compared to the control (Kallsen et al. 2011), which was due to the fruit’s

osmotic adjustments(Yakushiki et al. 1996)

II. Determining Non-Destructive Plant-Based Measures• The trends in SF and MDS data were highly variable and opposite to what

were expected, which made them difficult to differentiate the effects of RDI

• In order to show distinguishable patterns in SF and MDS across treatments, it

might have required at least ten sensors or more to off-set the variations, which

could be unrealistic for growers to use the equipment for irrigation scheduling

• SF heaters easily got overheated, which contributed to the variation in SF

patterns across treatments

Conclusion19

f

I. The 1st hypothesis was supported• RDI had equal or higher fruit quality when compared to the control (Table 2 ),

reduced fruit size (Fig. 2A), and did not negatively impact fruit yield (Table 6)

• All RDI treatments saved water, with RDI3 saving the most water (21%), RDI2

saved the least (2%), RDI1 saved 19% (Table 6)

• Considering the loss in profits (RDI3 lost 18%) or increase in profits (RDI2

increased 18% in profits) versus the benefits of water savings, RDI2 and RDI3

were effective irrigation strategies

II. The 2nd hypothesis was partially supported • During phase IIA, there was no significant difference in SF rate between RDI1

and RDI3 and the control. During III, SF rates of RDI2 and RDI3were

significantly higher than that of the control (Table 7)

• During IIA, RDI1 had a significant higher MDS value than the control; during

phase IIB, all RDI had significant higher MDS values than the control (Table 7)

• SF appeared to have higher signal to noise ratio (Table 8), which suggested that

SF was a better water stress indicator than MDS.

Citation20

f

1) Goldhamer, D.A. & Salinas, M. (2000) Evaluation of regulated deficit irrigation

on mature orange trees grown under high evaporative demand. In: Proceedings of

the International Society of Citriculture IX Congress, Orlando, FL, 227-231.

2) Kallsen, C. E., Sanden, B., & Arpaia, M. L. (2011) Early navel orange fruit yield,

quality, and maturity in response to late-season water stress. HortScience, 46,

1163-1169.

3) Minolta, K. (2007). Precise color communication. Kónica Minolta Sensing. Inc.

Japan.

4) Yakushiji, H., Nonami, H., Fukuyama, T., Ono, S., Takagi, N., Hashimoto, Y.

(1996) Sugar accumulation enhanced by osmoregulation in Satsuma mandarin

fruit. Journal of the American Society for Horticultural Science, 121, 466-472.