Kurniatun Hairiah*, Meine van Noordwijk**, Subekti Rahayu**, Sandy E. Williams** and Ni’matul Khasanah**

* Brawijaya University, Malang, Indonesia

**World Agroforestry Centre (ICRAF) SE Asia, Bogor, Indonesia

Material and Methods Measurement of root mortality in the field using minirhizotrons was carried out in long-term experiments in Lampung and Jambi (Indonesia).

Images from roots growing along an observation surface were obtained with a telephoto lens with a camera at the soil surface.

Inflatable minirhizotrons were inserted into soil close to annual crops (rice, maize, groundnuts, velvet beans (Mucuna pruriens var. utilis) and Imperata cylindrica) and trees i.e. Peltophorum dasyrrachis and Gliricidia sepium, to an effective depth of 60 cm. Similar observations were made in young rubber in Jambi (Indonesia).

Roots were observed monthly and a total of 18 sets of photographs (representing depths of 0-10, 10-20, 20-30, 30-40 and 40-60 cm), from the period December 1995 through March 1996.

The images were assessed as a time series and all changes (+ or -) in root intersections with a standard grid of lines were recorded. From these data the cumulative pattern of root growth was derived and root disappearance was expressed as fraction of cumulative root growth, using a new algorithm that is less sensitive to the temporal pattern of root growth than the one formerly used.

Conclusions

• The shortest median fine root lifespans and highest daily turnover rates (1–daily survival probability) were found to occur in the leguminous cover crop Mucuna pruriens, followed by groundnut and maize.

• The median longevities were between 18 and 25 days, whilst all three annuals had a turnover rate of about 5% day-1.

• Three species of trees studied i.e. Peltophorum dasyrrachis, Gliricidia sepium and Flemingia congesta had median fine root longevities of about 100 days and turnover rates of about 1% day-1.

• The longevity and turnover rate of the perennial grass Imperata cylindrica fell between those of the annual crops and trees. The rubber data for Jambi showed a daily turnover of around 0.5% and a median lifespan of 290 days.

Table 2. Root dynamics as observed using minirhizotrons in a rubber (Hevea brasiliensis) agroforestry experiment in Rantaupandan, Jambi, Indonesia, during 1997 and 1998, with weeding intensity as the main experimental factor and the distance from the tree as the sampling position. Data from 4 minirhizotrons per sampling position and weeding intensity combination, with daily survival rate and median lifespan evaluated for individual replicates.

Results

Table 1. Root dynamics as observed using minirhizotrons at the Biological Management of Soil Fertility (BMSF) site in N. Lampung, Sumatra, Indonesia, in cropping system trials during the period 1996–1999. Data from 6 minirhizotrons per species, with daily survival rate and median lifespan (age expected to be reached by 50% of each cohort of roots) evaluated for individual replicates.

Plant species Remarks Dailysurvival

probability2

Medianlifespan (days)

Dailyturnover(%)

Gliricidia sepium Regularly pruned in alleycropping

0.9904a 116.8a 0.96

Peltophorumdasyrrachis

Regularly pruned in alleycropping

0.9877a 121.1a 1.23

Flemingia congesta Regularly pruned in alleycropping

0.9916a 96.1a,b 0.84

Zea mays(maize)1

In alleys or as mono-crop 0.9435c 25.2c 5.65

Arachis hypogaea(groundnut)

In alleys or as mono-crop 0.9547b,c 20.4c 4.53

Mucuna pruriensvar. utilis

As cover crop in rotationalsystem

0.9534b,c 17.7c 4.66

Oryza sativa(upland rice)

In alleys or as mono-crop 0.9614b,c 33.8c 3.86

Imperata cylindrica As weed on fallow land 0.9844a,b 55.2b,c 1.56

Grand mean 0.9679 59.8Probability P=0.005 P<0.001

Standard error ofdifference between

means

0.0188 30.1

Experimental factor/sampling position Daily survivalprobability

Median lifespan(days)

Daily turnover (%)

‘High’ weeding intensity 0.9960 241 0.40

‘Low’ weeding intensity 0.9955 344 0.45

Within tree row

(0.25 m from tree)

0.9953 328 0.47

Between tree rows

(1.5 m from tree)

0.9962 257 0.38

Grand mean 0.9958 293

Probability NS NS

Standard error of difference

between means

0.00248 95.2

0

5

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S i n g l e p u l s e

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E x p o n e n t i a l

L i n e a r0

0 . 2

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0 . 6

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1

0 0 . 2 0 . 4 0 . 6 0 . 8 1p = w e e k l y s u r v i v a l r a t e

Est

imat

ed p

T h e m e t h o d p r e v i o u s l y u s e d f o r a n a l y z i n g m i n i r h i z o t r o nd a t a ( v a n N o o r d w i j k , 1 9 9 3 ) c a n l e a d t o o v e r e s t i m a t e o fl i f e s p a n o f r o o t s , d e p e n d i n g o n p a t t e r n o f r o o t g r o w t h

Root growh of Gliricidia at different time of observations

Belowground allocation of C (energy) may be around one-third of the C (energy) in the plant as a whole

C (energy) provided by roots is a major source of C for the food web of soil biota

As roots are lost to ‘rhizovory’ (consumption), plants needs to invest continuously in roots to maintain root length density

Through various channels, belowground plant C allocation contributes to soil organic matter (Corg)

Research questionsHow long do (fine) roots live?

IntroductionRoot turnover is important in the functioning of plants and agro-ecosystems for a number of reasons:

00.10.20.30.40.50.60.70.80.9

1

Cu

mu

lati

ve d

ecay

0 100 200 300 400Time (days)

y = 2E+06x3- 6E+06x2+ 6E+06x - 2E+06

R2= 0.9997

0

0.020.040.060.080.1

0.120.140.160.18

0.984 0.986 0.988 0.99P_estimate

Lac

k o

f fi

t

p trend with time:y = -5E-06x + 0.9885

R2= 0.0707

0.984

0.9850.986

0.987

0.9880.989

0.99

0 200 400Time (days)

Bes

t fi

t p

00.10.20.30.40.50.60.70.80.9

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0 100 200 300 400Time (days)

Cu

mu

lati

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row

th o

r D

ecay

ObsGrowthObsDecayPredDecay

0.985

0.987

0.989ObsDecay

Predicted for p =

A B

C D

00.10.20.30.40.50.60.70.80.9

1

Cu

mu

lati

ve d

ecay

0 100 200 300 400Time (days)

y = 2E+06x3- 6E+06x2+ 6E+06x - 2E+06

R2= 0.9997

0

0.020.040.060.080.1

0.120.140.160.18

0.984 0.986 0.988 0.99P_estimate

Lac

k o

f fi

t

p trend with time:y = -5E-06x + 0.9885

R2= 0.0707

0.984

0.9850.986

0.987

0.9880.989

0.99

0 200 400Time (days)

Bes

t fi

t p

00.10.20.30.40.50.60.70.80.9

1

0 100 200 300 400Time (days)

Cu

mu

lati

ve G

row

th o

r D

ecay

ObsGrowthObsDecayPredDecay

0.985

0.987

0.989ObsDecay

Predicted for p =

00.10.20.30.40.50.60.70.80.9

1

Cu

mu

lati

ve d

ecay

0 100 200 300 400Time (days)

y = 2E+06x3- 6E+06x2+ 6E+06x - 2E+06

R2= 0.9997

0

0.020.040.060.080.1

0.120.140.160.18

0.984 0.986 0.988 0.99P_estimate

Lac

k o

f fi

t

p trend with time:y = -5E-06x + 0.9885

R2R2= 0.0707

0.984

0.9850.986

0.987

0.9880.989

0.99

0 200 400Time (days)

Bes

t fi

t p

00.10.20.30.40.50.60.70.80.9

1

0 100 200 300 400Time (days)

Cu

mu

lati

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row

th o

r D

ecay

ObsGrowthObsDecayPredDecay

0.985

0.987

0.989ObsDecay

Predicted for p =

0.9850.985

0.9870.9870.987

0.9890.989ObsDecay

Predicted for p =

A B

C D

A B

C D

In a new method we find the daily survival rate (andhence lifespan) that best fits observed decay, given theobserved growth