ipni southeast asia program - grepalma...godfray hcj et al. science 327, 812 –818 (2010) yield...

IPNI Southeast Asia Program

Plantation Intelligence®

Management Processes for Planters

Thomas Oberthür on behalf of IPNI Southeast Asia’s Oil Palm Program

and its plantation partners, for the 2nd C//PAL Palm Congress, 22-24

August 2016 Santo Domingo del Cerro, La Antigua Guatemala


1. PI Development Context

2. PI Conceptual Approach

3. PI and Harvest Efficiency

4. PI and Water Stress

5. PI and Nutrient Efficiency

6. PI Final Notes

1 Context

Sustainable Return on Investment

2nd C//PAL Palm Congress

22-24 August 2016Santo Domingo del Cerro

Sustainable

IntensificationProducing More from the same Land

while Reducing Environmental Impacts.

Garnett T. Science 341, 33 – 34 (2013)

Closing

Yield GapsReducing the difference between

Realized and Achievable Best Yields

Godfray HCJ et al. Science 327, 812 – 818 (2010)

Yield Gaps in Mature Palm



50

60

70

80

90

100R

ela

tive

bu

nch

yie

ld (

%)

Inadequate

Agronomy

Potential of

progeny

Inadequate

crop recovery

Water Stress

Nutrient

Deficiency

Managing

Continuous

Improvement

Benchmarking

Simulation

Modeling

Prospecting

Plantation

Intelligence

Agricultural Systems 131: 1-10 (2014)

The Planter, 91: 81-96 (2015)

Prospecting with PALMSIM

Simulated, water limited potential

yield (FFB t/ha), Kalimantan

< 25

< 30

< 35

< 40

> 40

Agricultural Systems 131: 1-10 (2014)

The Planter, 91: 81-96 (2015)


0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30

PY PYW

Fre

sh

fru

it b

un

ch

we

igh

t (t

/ha

/yr)

Years after planting

Yield Gap Estimation, Sumatra EstateForthcoming:

Agricultural Systems


0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30

PY PYW

Fre

sh

fru

it b

un

ch

we

igh

t (t

/ha

/yr)

Years after planting

Forthcoming:


Yield Gap Estimation, Ghana Estate

2 Approach

Concept of Business Intelligence

Hans Peter Luhn, IBM Journal, 1958

A Business Intelligence System

(business) intelligence is “the

ability to apprehend the interrelationships

of presented facts in such a way as to

guide action towards a desired goal.”



Data Richness



Note that PI uses

Commercial Data

System is

Monitored

in Extraordinary Detail


Review and

evaluate

Benchmarked

performance

Organize

and visualize

existing

performance

data

Decide on

options for

management

intervention /

change

Generate

performance

indicators

and

metrics



Exogenous

Exogenous


1 2 3 4 5 6 7 8

Engage

senior

management

Acquire data Clean and

assemble

data

Organize

data

First analysis

Follow-up

analysis Discuss with

field

managers

Review and

repeat




Plantation

Management

Data Analyses

Agronomy &

Research

PI couples insights in the area

shared by 3 disciplines



Analytical Protocols

Y(F)



★ MG★ E Yield Trends

Yield Age Profiling Water Stress

Soil Impact

Naïve Gross

Margins

Harvest

Efficiency

Fertilizer Response

Analyses

Analytical Protocols

Naïve Gross

MarginsYield Age Profiling

Yield Trends

Fertilizer Response

Analyses

Water Stress

Soil Impact



Harvest

Efficiency

3 Harvest Efficiency




50

60

70

80

90

100R

ela

tive

bu

nch

yie

ld (

%)

Inadequate

Agronomy

Potential of

progeny

Inadequate

crop recovery

Water Stress

Nutrient

Deficiency

10

15

5

30

25

20

40

35

15 4525 355 10 20 30 40

45

50

55F

resh

Fru

it B

un

ch

Yie

ld in t

pe

r h

a

2003

2004

2005

2006

2006

2007

2008

2009

2010

2011

2012

Harvest Man Days per hectare

10

15

5

30

25

20

40

35

15 4525 355 10 20 30 40

45

50

55

Expected yield level

Fre

sh

Fru

it B

un

ch

Yie

ld in

t p

er

ha


2003

2004

2005

2006

2006

2007

2008

2009

2010

2011

2012

10

15

5

30

25

20

40

35

15 4525 355 10 20 30 40

45

50

55

Fruit is grown but

not harvested

Only with enough

labor, all fruit is

harvested

Expected yield level

Fre

sh

Fru

it B

un

ch

Yie

ld in

t p

er

ha

2003

2004

2005

2006

2006

2007

2008

2009

2010

2011

2012


Harvest Efficiency: Crop Recovery

6 Indonesian regions

6 Large plantations

4 Years of data



Implications

Inadequate crop recovery will significantly reduce the

return on investment in any agronomy management

In PI consider excluding blocks that

received Insufficient Harvest Man Days



4 Water Stress


50

60

70

80

90

100R

ela

tive

bu

nch

yie

ld (

%)

Inadequate

Potential of

progeny

Inadequate

crop recovery

Water Stress

Nutrient

Deficiency



Water Stress

Surplus water and water deficit are important non

controllable production factors (NCFs) for oil palm in

the humid tropics. They impact on crop yield in the

two years before, and the year of the harvest,

mediated by topography and soil.



Water Stress

• Neither drought nor excess water (N, normal)

• Dry period but no wet (D, dry)

• No dry period but a wet period (W, wet)

• Dry period and a wet period (E, extreme)



Homologous Events

To understand crop response under specific non-

controllable factors, we characterize each field

harvest event in terms of such NCFs, and then

group a large number of similar events into sets, or

as we call them, Homologous Events

(HEs)2nd C//PAL Palm Congress


3-Year Nomenclature of HEs

HE-0 year of harvest

HE-1 harvest preceding year

HE-2 year preceding HE-1



NNN,NND,NNW,NNE

NDN,NDD,NDW,NDE

NWN,NWD,NWW,NWE

NEN,NED,NEW,NEE

DNN,DND,DNW,DNE

DDN,DDD,DDW,DDE

DWN,DWD,DWW,DWE

DEN,DED,DEW,DEE

WNN,WND,WNW,WNE

WDN,WDD,WDW,WDE

WWN,WWD,WWW,WWE

WEN,WED,WEW,WEE

ENN,END,ENW,ENE

EDN,EDD,EDW,EDE

EWN,EWD,EWW,EWE

EEN,EED,EEW,EEE

64 (=43) combinations

Example HE Code :

W

= wet

D

= dry

= normal

N

Modifiers Wetness

threshold

Block boundaries

Soil Map & Soil properties

Monthly rainfall, mm

Digital Elevation Model (DEM)

Estimate readily available water

Compute water balance

HE grouping (N, D, W or E) for each SxTcombination for 3 consecutive years

Cross-tabulate to obtain soil and topographic (SxT) combinations

SRF

TRF

Determine dominant SxTcombinations in blocks

HE map

Compare with (600*SRF*TRF)

HE grouping (N, D, W or E) for 3 consecutive years, by block

Categorize TWI values in topographic landscape positions

Compute Topographic Wetness Index (TWI)

SxT map

Categorize soil types by sub-horizon permeability

An Example of Homologous Events

Year 1

Year 9Year 6Year 3

Year 8Year 5Year 2

Year 7Year 4



An Example of Homologous Events

HE Significance Yield change t-FFB

ha-1yr-1

Compared to

HE-2D ** -6.1

HE-2NHE-2W * -5.5

HE-2E *** -8.5

Yield for dry (HE-2D), surplus water (HE-2W) and dry with surplus water (HE-2E) two years

before the year of harvest compared with a normal year (HE-2N).

Harvest data 2009-2013, weather 2007-2013.



Forthcoming:


Implications

• Knowledge of the long term

effects of HEs coupled with past

weather and improved long term

weather forecasting will make it

possible for managers to fertilize

palms based on both the past

and predicted HEs of any

particular block.

• Plantation managers are aware

of the yield reductions in oil palm

due to dry periods; however,

little quantitative information

exists on the effects of

waterlogged soils.



5 Nutrient Efficiency


50

60

70

80

90

100R

ela

tive

bu

nch

yie

ld (

%)

Inadequate

Potential of

progeny

Inadequate

crop recovery

Water Stress

Nutrient

Deficiencies



8~10BREAK–EVEN

kg kg

point

fresh fruitbunches per kg fertilizer

Fertilizer ROI



NPKMg

NPKMg of the first ½ of the

harvest year, the full year

before harvest, and the

second ½ of the year

before that

weighted 25:50:25

kg / ha

Fertilizer ROI



Fertilizer ROI

Estat

e

1

Estat

e

2

Estat

e

3No ofBlocks

Ha

141

6,005

No ofBlocks

Ha

158

6,094

No ofBlocks

Ha

148

5,658

No ofBlocks

Ha447

17,757TOTAL 2nd C//PAL Palm Congress


Fertilizer ROI

Estat

e

1

Estat

e

2

Estat

e

3

WITHIN



WITHINWITHIN

BETWEEN

Actionable SolutionsFertilizer ROI Between Estates

Fre

sh

Fru

it B

unch

es in

tp

er

hecta

re

30

40

35

45

10

15

5

25

20

400 700500 550300 350 450 600 650

NPKMg in kg per hectare

Estate 1

Estate 2

Estate 3

23.06 t FFB / ha average yield

PI Year

1


Estate 1, -6 kg / kg

Estate 2, +12 kg / kg

Estate 3, -4 kg / kg

Fre

sh

Fru

it B

unch

es in

tp

er

hecta

re

30

40

35

45

10

15

5

25

20

400 700500 550300 350 450 600 650



PI Year

1



Fre

sh

Fru

it B

unch

es in

tp

er

hecta

re

30

40

35

45

10

15

5

25

20

400 700500 550300 350 450 600 650





PI Year

2


30

40

35

45

10

15

5

25

20

400 700500 550300 350 450 600 650


Fre

sh

Fru

it B

unch

es in

tp

er

hecta

re Estate 1, +26 kg / kg




PI Year

3

Fertilizer ROI Within Estates



Geographically

Weighted Regression

Y = F (NPKMg + HMD)


0.04

0.03

0.02

0.01

0.00

Fertilizer Impact



1.40

1.00

0.60

0.00

Harvest Man Days Impact

2.00

PI Year

1


0.03

0.02

0.00

2.00

1.20

0.80

0.40

0.00



Fertilizer Impact Harvest Man Days Impact

0.01

0.05

PI Year

2


1.20

1.00

0.60

0.40

0.00



Fertilizer Impact Harvest Man Days Impact

0.03

0.02

0.01

0.00

0.06

PI Year

3




0.03

0.02

0.01

0.00

Fertilizer Impact Year 1

0.06

0.03

0.02

0.00

Fertilizer Impact Year 3Fertilizer Impact Year 2

0.05

0.03

0.01

0.00

6 Final Notes

Managing

Continuous

Improvement

Targeting

Simulation

Modeling

Benchmarking

Plantation

Intelligence

Continuous Improvement

1. tactical management

2. strategic management


1

2

3

4

Internal solutions not ‘technology transfer’

Analysis at commercial scale using commercial data

Interpretation consistent with published knowledge

Evidence-based management through dialogue

5 Interpretation is translated at two levels:



“Simple Apps” are used for enabling of analyses, for example HE_Analyzer (seap.ipni.net)

Data Management



“Simple Apps” are used for enabling of analyses, for example PI_Mapper

Data Management



“Spotfire” is used for interactive analyses

Analytical Software





Management Processes for Planters

Enquiries: [email protected]


0.04

0.03

0.02

0.01

0.00

Fertilizer Impact



T Ratio (Significance)

2.00

1.40

1.00

0.60

0.00

1.40

1.00

0.60

0.00

Harvest Intensity Impact

2.00

PI Year

1


0.03

0.02

0.00

2.00

1.20

0.80

0.40

0.00

15.36

0.44

0.00



Fertilizer Impact Harvest Intensity Impact T Ratio (Significance)

0.01

0.05

PI Year

2


1.20

1.00

0.60

0.40

0.00

1.03

0.25

0.00



Fertilizer Impact Harvest Intensity Impact T Ratio (Significance)

0.03

0.02

0.01

0.00

0.06

PI Year

3

ipni southeast asia program - grepalma...godfray hcj et al. science 327, 812 –818 (2010) yield...

Documents