Carbon and nitrogen cycling in oil palm plantations: keys to productivity and

sustainability

Paul  Nelson,    James  Cook  University  paul.nelson@jcu.edu.au    

 Neil  Huth,  Michael  Webb,  Tony  Webster  (CSIRO)  Murom  Banabas  (PNGOPRA),  Iain  Goodrick  (JCU)  

José  Álvaro  Cristancho  (Mejisulfatos),  Rafael  Dominguez  (Palmeras  al  Carolina),    

Many microbes

NO3-

N2O

NO

½

Energy  (ATP)  

Most organisms

6O2

6H2O

Light  

Carbon dioxide in atmosphere

Photo-synthesizers 6CO2

C6H12O6

6O2

6H2O

Glucose & other organic compounds

C cycle

Proteins

HO2CH2NH2

N2

Nitrogen in atmosphere

Some microbes

NH3

NH4+

½ N cycle

Carbon and nitrogen cycles

Important links with water cycle and other 15 essential elements

Soil organic matter (50% C)

•  Energy  source  •  Reservoir  of  nutrients  

Biological  func1ons  

•  Structural  stability  •  Water  retenQon  properQes  •  Dark  colour  

Physical  func1ons   Chemical  func1ons  •  CaQon  exchange  capacity  •  pH  buffering  capacity  •  Adsorbs  contaminants  

Soil C and N in the tropics

Ziegler et al. 2012. Global Change Biology

Soil N is ~ 1/12 soil C

Soil C and N under oil palm

Khasanah  et  al.  2015;  Goodrick  et  al.  2015;  Wakelin  et  al.  (in  review)  

Years after planting

0 5 10 15 20 25C

hang

e in

SO

C s

tock

(kg

m-2

)-6

-4

-2

0

2

4

6

Ex-­‐forest,  Indonesia      Ex-­‐grassland,  PNG  

Microbial  abundance  (DNA)  and  community  structure  linked  to  amount  and  nature  of  soil  organic  C  

Soil C and N under oil palm

Nelson et al. 2014, 2015

Are current sampling methods adequate for monitoring?

Soil carbon content (0-10 cm) within ‘tree unit’

min.   max.  Total  C  (%)   1.1   3.9  Bulk  dens.  (g/cm3)   0.85   1.23  

Tree-scale variability is large How to scale several points into stocks/ha?

A better soil sampling design

Nelson et al. 2015.

Linear transects can account for tree-scale variability Make composite samples from many sub-samples along a transect Or use points for proximal sensing

Soil sampling and analysis

•   Sampling  and  analysis  is  important,  but:  1.   Measurements  are  difficult  and  expensive  to  

do  in  a  way  that  detects  trends  

2.   Results  come  too  late-­‐  need  10  years  or  so  

3.   Doesn’t  allow  us  to  predict  trends  in  new  situaQons  

•   Is  there  a  beder  way  to  predict  trends  using  informaQon  we  already  have?  

•  PredicQon  requires  understanding  of  processes  

Predicting changes in soil C

Organic matter input to soil

0 5 10 15 20 Time since planting (years)

OM

inpu

t

Max. possible,

including sequest’n

in palm (no limiting

factors)

Cover crop inputs

Pruned fronds, root death

+ = Mill by-products

+ Felled palms

+

Field management recording

Standard recording

Crop system model Carbon cycling

Crop physiology & phenology

Weather recording

Soil properties

We also wanted to estimate N losses, which are impossible to measure routinely

Integrating knowledge for prediction

Need  a  mechanisQc  model  that:    •   Simulates  plant  growth  and  yield  •   and  stocks  and  flows  of  water,  C  and  N,  •   in  relaQon  to  soil  and  weather  

Links modules for crop growth, biophysical processes and management Was no crop growth module for oil palm So we built one and tested it

www.apsim.info

Use of APSIM

Now 41 crops, >50 scientific papers/year, >900 citations/year

Holzworth et al. 2014

-60

-50

-40

-30

-20

-10

0

10

20

30

40Harvest (depending on plantation age)

Leaf spear

Frond initiation

Flower abortion stage

Frond expansion

Sex determination stage

Bunch growth

Age0 5 10 15

Pla

stoc

hron

0

5

10

C Stress0 1

FFF

0.0

0.1

0.2C Stress

0 1

FAF

0.0

0.1

0.2Age

0 5 10 15

BS

max

05

1015

Temperature10 20 30 40 50

RD

R

0.0

0.5

1.0

Age0 5 10 15

FAm

ax

05

1015

Leaf

Ran

k

Huth et al. 2014

APSIM oil palm module

Runoff

Water Balance

Irrig

atio

n

Inte

rcep

tion

Evap

orat

ion

Tran

spira

tion

(o

il pa

lm)

(cov

er c

rop)

D

rain

age

Uptake

Runoff

Rai

nfal

l

Runoff

Carbon Balance N

PP

(oil

palm

)

Cov

er c

rop

litte

r

Prun

ed fr

onds

Bunches

Litter decomposition Soil organic matter decomposition

Root growth and death (c

over

cro

p)

Runoff

Nitrogen Balance

Fixa

tion

Bunches

Fert

ilise

r

Leac

hing

Den

itrifi

catio

n

Cov

er c

rop

litte

r

Prun

ed fr

onds

Litter decomposition

Soil organic matter decomposition Root growth and death

0 250 500

Kilometres

HargyVolcanic Ash4350 mm

SagaraiAlluvial Clay2400 mm

SangaraSandy Clay Loam2400 mm

6o S

Month0 2 4 6 8 10 12

0

5

10

15

20

25

Hargy Sangara Sagarai

0

200

400

600

800

0

24

26

28

30

Solar Radiation (MJ)

Rainfall (mm)

Mean Temperature (oC)

Model testing Tested  in    3  long-­‐term  ferQliser  trials  in  Papua  New  Guinea  

0 250 500

Kilometres

HargyVolcanic Ash4350 mm

SagaraiAlluvial Clay2400 mm

SangaraSandy Clay Loam2400 mm

6o S

Month0 2 4 6 8 10 12

0

5

10

15

20

25

Hargy Sangara Sagarai

0

200

400

600

800

0

24

26

28

30

Solar Radiation (MJ)

Rainfall (mm)

Mean Temperature (oC)

Huth et al. 2014

Huth et al. 2014

Huth et al. 2014

Now we have APSIM Oil Palm …

•   Might  be  possible  to  predict:    1.   Trends  in  soil  condiQon,  including  soil  organic  C,  

acidificaQon  2.   Greenhouse  gas  emissions  from  the  field    3.   Effects  of  management  on  water  quality  4.   Response  of  yield  and  N  use  efficiency  to  changes  in  

management  5.   PotenQal  producQvity  in  new  situaQons  of  climate,  soil  

type,  irrigaQon,  previous  land  use….  6.   Consequences  for  sustainability  of  removing  biomass  

•   Forecast  FFB  yield  over  the  next  year  •   Educate:  interacQons  between  crop  growth,  environment  and  management  over  the  crop  cycle  

To run APSIM Oil Palm… Requirements: - Soil and weather data (rainfall, radiation) - Trained operator

Soil type and yield- simulation

Simulated yield at Palmeras La Carolina on 4 soil types, assuming 5 t/ha previous biomass, 1 kg N/palm fertiliser (mature)

N fert. and yield- simulation

Simulated yield at Palmeras La Carolina on Carolina soil type, at 3 N rates (0-2 kg N/palm/a once mature), assuming 5 t/ha previous biomass

Soil C stocks- simulation

Simulated soil carbon at Palmeras La Carolina on 4 soil types, assuming 5 t/ha previous biomass, 1 kg N/palm fertiliser (mature) and Barbascal climate.

N gas exchanges- simulation

Simulated N fluxes at Palmeras La Carolina on Carolina soil type, at 3 N rates (0-2 kg N/palm/a), assuming 5 t/ha previous biomass, 1 kg N/palm fertiliser (mature)

N leaching loss- simulation

Simulated N fluxes at Palmeras La Carolina on Carolina soil type, at 3 N rates (0-2 kg N/palm/a), assuming 5 t/ha previous biomass, 1 kg N/palm fertiliser (mature)

4,0  

4,5  

5,0  

5,5  

6,0  

6,5  

7,0  

AMC   AMM  NITR  

DAP   SOA   UREA  

Soil  pH  

N  ferQliser  type  

0  g  N/palm/year  

420  g  N/palm/year  

840  g  N/palm/year  

1680  g  N/palm/year  

PNG, Planted 1996, treatments imposed 2001, sampled 2011

N and soil acidification

Conclusions 1.   Knowledge  of  C,  N  and  water  cycling  has  been  

integrated  in  a  well-­‐tested  framework  

2.   Oil  palm  growth  and  yield  were  predicted  well  

3.   Model  might  be  used  to  predict  how  management,  soil  and  climate  affect  yield  and  environmental  processes  over  the  whole  oil  palm  cycle,  if  soil  and  climate  data  are  available  

4.   It  needs  to  be  tested  with  data  from  long-­‐term  trials  or  monitoring  programs  from  a  wider  range  of  environments  

References

Goodrick  et  al.  2014.  Soil  carbon  balance  following  conversion  of  grassland  to  oil  palm.  Global  Change  Biology  Bioenergy  7,  263-­‐272.  

Holzworth  et  al.  2014.  APSIM  -­‐  EvoluQon  towards  a  new  generaQon  of  agricultural  systems  simulaQon.  Environmental  Modelling  &  SoLware,  62,  327-­‐350.  

Huth  et  al.  2014.  Development  of  an  oil  palm  cropping  systems  model:  Lessons  learned  and  future  direcQons.  Environmental  Modelling  &SoLware  62,  411-­‐419.  

Khasanah  et  al.  2015.  Carbon  neutral?  No  change  in  mineral  soil  carbon  stock  under  oil  palm  plantaQons  derived  from  forest  or  non-­‐forest  in  Indonesia.  Agriculture,  Ecosystems  and  Environment  211:  195–206.  

Nelson  et  al.  2014.  Methods  to  account  for  tree-­‐scale  variaQon  in  soil-­‐  and  plant-­‐related  parameters  in  oil  palm  plantaQons.  Plant  and  Soil  374,  459-­‐471.    Plus  Erratum  Plant  and  Soil  378,  415.  

Nelson  et  al.  2015.  Soil  sampling  in  oil  palm  plantaQons:  a  pracQcal  design  that  accounts  for  lateral  variability  at  the  tree  scale.  Plant  and  Soil  394,  421-­‐429.  

Ziegler  et  al.  2012.  Carbon  outcomes  of  major  land-­‐cover  transiQons  in  SE  Asia:  Great  uncertainQes  and  REDD+  policy  implicaQons.  Global  Change  Biology  18(10):  3087-­‐3099  

   

Improving productivity and sustainability

•   There are many limitations to productivity •   How can we apply our knowledge to new

situations or questions? eg. 1.  New climate, soil type, management practice? 2.  What parameters to select for in breeding? 3.  Greenhouse gas balance, soil condition..?

•   We can use experience, but: –  Limited to known situations

•   We can monitor and do experiments, but: –  Expensive, time consuming

Carbon and nitrogen cycling - field

•   Fell forest/palms

•   Plant palms

•   Sow legumes

•   Apply fertiliser

•   Prune & harvest

•   Extract oil

•   Manage mill by-products

•   Poison, replant