Vol.1, No.2, 24-34 (2011)
Open Journal of Ecology
Copyright © 2011 SciRes. OPEN A CCESS
Influence of early dry season fires on primary
production in western Serengeti grasslands, Tanzania
Shombe Ntaraluka Hassan
Department of Wildlife Management, Sokoine University of Agriculture, Morogoro, Tanzania;
Corresponding Author: hassanshombe@yahoo.co.uk
Received 25 May 2011; revised 24 June 2011; accepted 2 July 2011.
Short-term, i.e. 4-9 weeks aboveground net
primary production (ANPP) temporal patterns
during the first post-fire year in western Seren-
geti National Park, and potential differences in
the factors limiting ANPP between burnt and
non burnt grasslands were examined and es-
tablished. Fire stimulated growth at early post-
fire stages, even during the dry season, July-
October and led to larger increments in green
phytomass compared to the non burnt grass-
land at the onset of short rains, October-De-
cember. Further, ANPP in burnt plots correlated
well with the ratio leaf/total standing phytomass
suggesting that the accumulation of standing
dead material can be a limiting factor to ANPP in
burnt grass-lands. However, ANPP in burnt plot-
s was unrelated to rainfall contrary to earlier
arguments, but reached peak earlier and de-
clined early in the rain season, perhaps due to
the interactive effects of fire and grazing in the
area. In non burnt plots, the temporal change in
ANPP w as more related to rainfall availability, at
least until mid-growing season. Also, the phy-
tomass structure differed between burnt and
non burnt grasslands, and together with litter
did not recover to non burnt levels within the
first post-fire year. The study has demonstrated
that the desire of the fire management program
in Serengeti National Park, which is to supply
green forage to both migratory and resident
populations during dry season is being fulfilled.
Keywords: Aboveground Net Primary Production;
Energy Limitation; Fire-Grazing Interaction;
Savanna; Western Serengeti
Fire can alter fundamental biogeochemical processes
and functions in ecosystems, affecting nutrient and
carbon budgets and fluxes [1-4]. The effect on primary
production is crucial since biomass and net primary
production are essential to ecosystem performance and
function [5], and primary production determines the
energy available for other trophic levels [6,7]. Frequent
fires are inherent in some ecosystems such as tropical
savannas [8]. Thus, in such systems the understanding
of its effects, in interaction with other ecological de-
terminants on primary production is critical to guiding
management practices that can maintain ecosystem’s
sustainability [9].
There is evidence that fire affects primary productiv-
ity, but with apparently contradictory results. The vari-
ety in responses appears to depend on the biomes in
question, the characteristics of the fire regime [10] and
the spatial scales and temporal scopes at which the
studies have been conducted [3,11,12]. Important to
these differences are the factors that limit primary pro-
duction in each case and the time lags in the responses
to the controlling biophysical processes [1].
Nitrogen and soil water availability are important
determinants of grass growth in East African savannas
[13] and fire can change the amounts of these resources
available for the vegetation [4]. Through the effect on
soil mineralization rates and the volatilization of N
from combusted plant material, fire can reduce the av-
ailability of N in frequently burnt grasslands compared
to long-term non burnt grasslands [11,14,15]. However,
despite the observed reduced N availability, frequently
burnt grasslands, can sustain significantly higher pro-
ductivity than non burnt grasslands [11,15] likely, as a
consequence of fire releasing energy limitations to pho-
tosynthesis and soil temperature through the removal of
phytomass [11,15]. Fire affects the structure of the
sward [16] by removing old leaves, dead material and
litter [17] and through post-fire re-growth [18,19].
Further, although the mechanisms are poorly under-
stood, the removal of dead matter by fire appears to
stimulate re-growth in savanna [20], particularly in
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34 25
grassland patches [21]. Contrarily, fires can reduce
above-ground net primary production (ANPP) by con-
trolling the amount of total biomass and photosynthetic
area, which are typically low immediately after the fire
[22]. During this phase, primary production can in-
crease steadily before levelling off at a full-developed
sward [23].
Rainfall is a critical factor controlling biomass and
primary production in savannas [5,7,24,25,26]. Above-
ground net primary production is strongly correlated
with mean annual precipitation in Serengeti grasslands
[27] and in other African grasslands, and phytomass
production follows within-year (monthly) variation in
rainfall [28]. Further, the rate of post-fire recovery of
the vegetation has been observed to correlate with the
rainfall [4,29]. However, fire through its effect on the
vegetation and litter cover [17a], can reduce the amount
of water availability in the soil by increasing runoff and
reducing infiltration [30] which can lead to compara-
tively lower net primary production in burnt grasslands
Despite a relatively large number of studies about the
effects of fire on semi-arid grasslands and savannas, the
current understanding of the processes determining
fire-mediated ANPP is insufficient to establish the key
controlling factors in each case. Most evidence includ-
ing [10] and [20] refer to long term differences among
fire regimes in terms of frequency of burning. Fewer
studies such as [4] and [22] have focused on the de-
velopment during early (first year) stages of the sward
recovery, when important differences in the amount of
green biomass and in the degree of sward shading are
expected to be determinants of production. This knowl-
edge is critical to understanding the factors that limit
carbon fixation in frequently burnt systems.
Although Serengeti National Park has a long history
of more quantitative ecological research in Africa,
comparatively little work has been directed to under-
standing the effects of fire in this system. So far, burn-
ing practices in the area are conducted without proper
understanding about the influence of early dry season
burns on ANPP in grasslands. Equally, the relationship
between the post-fire sward development and ANPP is
unknown. The combined effects of fire, other distur-
bances such as grazing and rainfall on grassland ANPP
are also largely unknown for the Serengeti and for
other semi-arid systems with large wild herbivore
In this study, the influence of early dry-season burn-
ing on grassland productivity in western Serengeti Na-
tional Park was assessed by establishing short-term (4 -
9 weeks) temporal patterns of ANPP during the first
post-fire year. The aim was to test hypotheses about
water availability and photosynthetic limitations on
ANPP between burnt and non burnt grasslands, and to
establish whether there is a correspondence between
ANPP and rainfall, and between ANPP and sward
structure attributes i.e. quantity and proportions of live
leaf, flowers/fruits and standing dead material in the
two strata It was hypothesised that: 1) The small
amount of photosynthetic biomass is a constraint to
ANPP during the early stages of post-fire sward recov-
ery. 2) ANPP would increase in burnt grasslands along
with sward development and would reach levels higher
than in non burnt grasslands. 3) Also, in the burnt
grassland ANPP would increase with rainfall whereas
shading would set a limit for productivity in the non
burnt grassland. 4) Amount of phytomass and litter in
burnt grasslands will not reach steady-state levels
within one year of post-fire sward recovery.
2.1. Study Site
The study was conducted in the Western Corridor of
Serengeti National Park (SNP). Serengeti is situated
between 1˚ and 3˚30' S, and 34˚ and 36˚ E [31]. The SNP
(14763 km2) is the main part of the 25000 km2 large
Serengeti ecosystem which extends to the Masaai Mara
in Kenya [32], and is characterised by annual move-
ments of migratory wildebeests (Connochaetes taurinus
Thomas), zebras (Equus burche lli Matschie), Thomson’s
gazelles (Gazella thomsoni Günther) and elands (Tauro-
tragus oryx Lydekker) [7,31]. Generally, the migrants
spend the wet season, December-May in the South East
Plains and the dry season, August-October in northern
Serengeti and Masaai Mara area in southern Kenya. The
Western Corridor is primarily used by migrating herds
while moving between dry and wet season grazing
grounds. Wildebeest, Burchell’s zebra, Thomson’s ga-
zelle, African buffalo (Syncerus caffer Sparrman) and
topi (Damaliscus korrigum Matschie) are the key graz-
ing species [27]. Annual rainfall ranges between ca 600
mm in the Southeast Plains and ca 1100 mm in the north
[33], and averages 700 mm. The rainfall distribution is
bimodal, with a period of short rains from November to
December and the main rain season from March to May
2.2. Sampling Procedure
Phytomass dynamics and ANPP were assessed in the
period from 5th July 2003 to 21st July 2004 by repeated
harvesting of samples taken at intervals of 2 to 9 weeks
(Tab le 1). Study sites (n = 6) were in the main area of
the wildebeest migratory route. Each site consisted of
Copyright © 2011 SciRes. OPEN A CCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34
Ta b l e 1 . periods for phytomass change assessments from July
2003 to July 2004, with shortenings and mean time interval in
days between consecutive samplings on burnt and non burnt
plots in six sites in the Western Corridor, Serengeti National
Park. Average rainfall for whole months in the sampling period
calculated on monthly records at the stations Nyaruswiga,
Mareo and Musabi in Serengeti National Park.
Burnt Non
burnt Rainfall
Period Shortenings of sampling
periods days mm/month
Jul-Sep T1-TO 45 54 35
Sep-Oct T2-T1 33 33 48
Oct-Dec T3-T2 66 67 58
Dec-Feb T4-T3 37 37 100
Feb-Mar T5-T4 32 31 98
Mar-May T6-T5 61 61 81
May-Jun T7-T6 37 38 51
Jun-Jul T8-T7 19 11 21
one burnt and one non burnt grassland patch, with each
grassland patch measuring at least 10 ha in size. The
grassland patches were either contiguous or opposite
each other to ensure that they were similar in the general
aspect of the landscape. One plot (50 m × 50 m) was
established in each burnt and non burnt grassland patch
at each of the 6 sites, making twelve main plots in total.
Therefore, the plots were in medium-high Themeda
grasslands with Themeda triandra, Pennisetum mezianu-
m and Digitaria macroblephara [34] as dominant grass
species. The distance between the study sites ranged
between 1 and 40 km, and the distance between the plots
and the closest road ranged between 0.45 and 0.75 km.
The burnt patches were burnt during the annual early
dry-season burning operations in May-July 2003 per-
formed by the Serengeti Ecological Monitoring Program
(SEMP) unit.
Movable cages were used to temporarily exclude large
herbivores from the quadrats between samplings occa-
sions. The cages were conical in shape with 1 m2 (1 m ×
1 m) base on the ground and 2 m tall. On the first sam-
pling time (T0), in each of the twelve plots, phytomass
and litter samples were collected in 6 randomly distrib-
uted quadrats of 0.625 m2 each [35] in total 72 samples
(6 samples × 12 plots). Phytomass were hand-clipped to
ground level. At the same time six cages were erected
over other randomly selected quadrats. From each of the
twelve plots, at each sampling time from T1 onwards
(time T1 - T8), six “fenced” and six “open” phytomass
samples, in total 12 samples were collected (in total 144
samples). After clipping the cages were moved to new
randomly selected quadrats.
Phytomass samples were hand-sorted into five com-
partments: live leaf, live stem (referring to grass repro-
ductive culms without the leaves), flower/fruit, standing
dead (dead material attached to living plants and dead
material that remained attached to the ground) and litter.
Sorted materials were air-dried for two weeks in paper
bags and later oven-dried at 70˚C [36] for 48 hrs and
then weighed using a digital scale (Soehnle ultra, [Leif-
heit AG. D-56377 Nassau, Germany] with maximum 200
g, d = 0.1 g precision). A total of 1152 samples were
collected. Seventy-two samples were lost due to two
wildfires which burnt four plots, the first one in May and
the second one in June. Monthly rainfall data from the
stations Nyaruswiga, Mareo and Musabi with the Ser-
engeti National Park Ecological Monitoring Department
(Table 1) were averaged for the months on which ANPP
was calculated (Table 5). Each station consists of one
rain gauge and the distance between the sites and the
rain gauge varied between 0.5 - 1.2 km.
2.3. Data Analyses
Differences in phytomass between open (Ti) and
fenced (Ti+1) samples were tested with univariate
ANOVAs independently for each phytomass component
and sampling occasion, T (T = 0 to 8). Since the length
of the interval between two consecutive samplings var-
ied among samplings, ‘Sampling interval’, in days was
included in the model as a covariate (Table 1). The
model included burnt and non burnt as main treatments
plots (Fire), ‘Phytomass change’ (fenced, Ti+1 vs. open,
Ti), ‘plot’, the interaction term ‘Phytomass change *Fire’
and ‘Sampling interval’. ‘Fire’ and ‘Phytomass change’
were fixed factors, and ‘plot’ random. The analyses were
conducted using the General Linear Model-Univariate
ANOVA routine in SPSS v. 15 for windows [37]. Sig-
nificant positive differences in total above-ground phy-
tomass (including litter) between fenced samples, Ti+1
and open samples, Ti indicated phytomass gain (produc-
tion). A significant interaction effect of ‘Phytomass
change*Fire’ indicated differences in production be-
tween burnt and non burnt plots.
Daily ANPP in each fire treatment was calculated as
the phytomass increment, i.e. the positive difference in
total phytomass (live, standing dead and litter) between
consecutive samplings divided by the number of days
between samplings. Phytomass increments were based
on plot averages, i.e. on 6 open and 6 fenced samples
respectively. The structural attributes of the sward, i.e.
the amount of leaf, stem, flower-fruits, standing dead
material and litter, and the ratios of phytomass com-
partments were computed for the eight sampling periods
(T1 - T8) on the ‘fenced’ samples. Pearson correlations
(tow-tailed significance test) were calculated between
the daily ANPP and the average sward attributes per
treatment using the correlations routine in SPSS v. 15.0.
Phytomass ratios were calculated on each sample and
arcsine transformed for the ANOVAs and Pearson corre-
lations. Data which showed skewed distribution were
Copyright © 2011 SciRes. OPEN A CCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34
Copyright © 2011 SciRes.
square-root transformed to improve normality and vari-
ance homocedasticity [37].
3.1. Fire, Sward Structure and Phytomass
Total above-ground phytomass (including litter) was
at all sampling times higher in the non burnt plots than
in the burnt plots with averages of ca 301.5 gm–2 and ca
151.3 gm–2, respectively (Table 2). The differences were
significant in six of the eight periods. Total live phy-
tomass was also generally larger in non burnt plots, dif-
fering significantly at four occasions. Phytomass of leaf,
stem and flower/fruit were significantly higher in non
burnt plots at 3, 5 and 2 sampling times, respectively.
Only in June was the phytomass of flower/fruits higher
in the burnt plots (Table 2). Mean total live biomass was
123.2 gm–2 and 87.0 gm–2 for non burnt and burnt plots,
Fire had an effect on the temporal distribution of live
phytomass. The peaks for live leaf and total live phy-
tomass differed between treatments. It was highest in
burnt plots for live leaf during December and for total
live phytomass in non burnt plots during February (Ta-
ble 2 and Figure 1). Also the first peak in live stem
phytomass, related to the reproductive phase in grasses,
was earlier in burnt plots, December than in non burnt
plots, February (Ta b le 2). In contrast, the phytomass of
flowers/fruits followed similar temporal patterns in burnt
and non burnt plots with peaks in December, May and
Fire had also an effect on the amount of plant debris.
There was more standing dead phytomass and litter in
non burnt plots than in burnt plots at all times (Ta b l e 2
and Figure 1). In both treatments, standing dead phy-
tomass increased steadily during the early stages of the
growth season with first peak in December (Table 2),
and a significant net accumulation in February-March
(Figure 1) after the short rain. Non burnt plots had a
second peak at the end of the long rain-season, in July.
Further, fire changed the relative phytomass composition
of the sward (Table 3). Burnt plots had significantly
higher ratios of live leaf/total standing phytomass in
October, December and February; significantly lower
ratios of live stem/total standing phytomass at early
post-fire stages (September and October), and higher
ratios of total live/total standing phytomass (December
and February). Non burnt plots had generally higher
standing dead/total above ground phytomass ratios.
Fire also changed the relative distribution of live phy-
tomass, between vegetative and reproductive structures
(Tab le 4). Burnt plots had significantly higher ratios of
live leaf/total live phytomass (October and February),
and generally lower ratios of live stems plus flower-fruits
phytomass/total live phytomass, were significantly lower
at five sampling times. There were no differences be-
tween treatments in the ratios of flower and fruit phy-
tomass/total live phytomass.
3.2. Variation in Productivity
Total live phytomass changed significantly between-
sampling periods, i.e. at the end of the dry season (Sep
tember-October), during the short rains (December-
February) and during the long rains, March-June
Table 2. Mean values (raw scores-gm–2) of total aboveground mass (including litter), total standing phytomass and phytomass com-
partments: live leaf, live stem, flower and fruit, total live, standing dead, and litter in fenced samples in burnt and non burnt plots in
Western Corridor grasslands, Serengeti National Park from September 2003 to July 2004.
Live phytomass Dead phytomass
Sampling time Treatment
mass Leaf Stem
live Standing Litter
Sep Burnt 71.7** 32.0**13.8*- 45.8**23.7** 2.2**
Non burnt 201.7 38.2 62.9 - 101.1 87.2 13.4
Oct Burnt 73.1** 35.2**4.8**- 40.0**25.4** 7.7**
Non burnt 210.1 63.4 39.8 - 103.2 86.1 20.8
Dec Burnt 227.0 115.2 40.2 2.6 158.0 62.0** 7.0**
Non burnt 290.7 100.0 46.8 4.7 151.5 115.3 23.9
Feb Burnt 145.0** 74.3*22.0**0.7**97.0**40.2** 7.8**
Non burnt 373.6 110.3 64.2 3.9 178.4 163.5 31.7
Mar Burnt 167.5** 55.2 27.4*0.3*82.9*78.1** 6.5**
Non burnt 340.9 61.3 53.2 1.3 115.8 200.6 24.5
May Burnt 183.4** 77.9 39.9*5.1 122.9 50.9** 9.6**
Non burnt 363.3 89.7 57.7 5.4 152.8 171.8 38.7
Jun Burnt 222.3 73.5 44.0 0.4*117.9 89.4* 15.0**
Non burnt 323.9 63.2 65.2 0.0 128.4 168.3 27.2
Jul Burnt 120.2** 10.7 17.8 3.3 31.8 78.2** 10.2*
Non burnt 308.0 18.8 30.8 4.6 54.2 219.2 34.6
* Difference between burnt and non burnt plots in plant mass statistically significant at P < 0.05; ** P 0.001.
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34
Ta ble 3. Mean ratios (raw scores) of phytomass compartments in fenced samples on burnt and non burnt plots in six sites in the
Western Corridor, Serengeti National Park, from September 2003 to July 2004. Live leaf, live stem and total live is shown in relation
to total standing phytomass (live + attached dead plant material), and standing dead material and litter in relation to total
above-ground mass.
time Treatment Live leaf/total stand-
ing phytomass
Live stem/total stand-
ing phytomass
Total live/total
standing phytomass
Standing dead/total
above-ground mass
total above-ground
Sep Burnt 0.460 0.199* 0.659 0.331 0.031
burnt 0.203 0.334 0.537 0.432 0.066
Oct Burnt 0.538* 0.073* 0.612 0.347* 0.105
burnt 0.335 0.210 0.545 0.410 0.099
Dec Burnt 0.524* 0.183 0.718* 0.273** 0.031
burnt 0.375 0.175 0.568 0.397 0.082
Feb Burnt 0.542** 0.160 0.707* 0.277** 0.054*
burnt 0.323 0.188 0.522 0.438 0.085
Mar Burnt 0.343 0.170 0.515 0.466** 0.039
burnt 0.194 0.168 0.366 0.588 0.072
May Burnt 0.448 0.230 0.707 0.278** 0.052*
burnt 0.276 0.178 0.471 0.473 0.107
Jun Burnt 0.355 0.212 0.569* 0.402** 0.067*
burnt 0.213 0.220 0.433 0.520 0.084
Jul Burnt 0.097 0.162 0.289 0.651 0.085
burnt 0.069 0.113 0.198 0.712 0.112
* Difference between burnt and non burnt plots in biomass ratio statistically significant at P < 0.05; **P 0.001.
Table 4. Mean ratios (raw scores) of live phytomass compartments in fenced samples on burnt and non burnt plots in six sites in the
Western Corridor, Serengeti National Park, from September 2003 to July 2004.
Sampling time Treatment Live leaf/ total live
Live stem/ total live
Flower-fruit/total live
Live stem
+ flower-fruit/total live phytomass
Sep Burnt 0.699 0.301** - 0.301*
Non burnt 0.378 0.622 - 0.622
Oct Burnt 0.880** 0.120** - 0.120**
Non burnt 0.614 0.386 - 0.386
Dec Burnt 0.729 0.254 0.016 0.271*
Non burnt 0.660 0.309 0.031 0.340
Feb Burnt 0.766** 0.227* 0.007 0.234*
Non burnt 0.618 0.360 0.022 0.382
Mar Burnt 0.666 0.331 0.004 0.334
Non burnt 0.529 0.459 0.011 0.471
May Burnt 0.634 0.325* 0.041 0.366*
Non burnt 0.587 0.378 0.035 0.413
Jun Burnt 0.623 0.373 0.003 0.377
Non burnt 0.492 0.508 0.000 0.508
Jul Burnt 0.336 0.560 0.104 0.664
Non burnt 0.347 0.568 0.085 0.653
*Difference between burnt and non burnt plots in biomass ratio statistically significant at P < 0.05; ** at P 0.001
(Ta b le 5 and Figure 1). In four periods, the production
of total live phytomass differed between the fire treat-
ments with significant interaction fire × phytomass
change (Table 5).
Burnt plots had significantly lower amounts of stand-
ing dead and less variability of the litter compartment
compared to non burnt plots (Figure 1). The mass of
litter changed significantly from September to February
and in May-July, demonstrating a significant effect of
phytomass change and/or of its interactions with fire
(Table 5), with net accumulation in non burnt plots in
September-October and March-May, and a decrease in
October-December and in Jun-Jul. In burnt plots net
accumulation occurred in May-June (Figure 1).
Total above-ground phytomass changed with signifi-
cant main effect of phytomass change and/or of its in-
teractions with fire in all periods except in Septem-
ber-October and February-March (Table 5). A signifi
Copyright © 2011 SciRes. OPEN ACCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34 29
Table 5. ANOVA model factors, F statistics and P values for total live phytomass, standing dead, litter and total above-ground mass.
‘Phytomass change’: samples at Ti vs. Ti+1, ‘Fire’: samples on burnt vs. non burnt plots and ‘Sites’: samples at 6 sites. Phytomass
difference: Difference (standardised per day) between mean Ti vs. Ti+1 on burnt and non burnt grasslands. ANPP: mean daily above-
ground net primary production (gm–2·day–1) on burnt and non burnt grasslands from July 2003 to July 2004, in six sites in the West-
ern Corridor, Serengeti National Park.
Total live
phytomass Standing dead
phytomass Litter Total
F P F P F P F P Phytomass
(gm2 day1)
Daily ANPP
(gm2 day1)
(gm2 day1)
Daily ANPP
(gm2 day1)
change 2.31 0.131 0.85 0.590 2.61 0.110 3.01 0.0850.84 0.84 –0.97 0.00
Fire 50.51 0.0001 84.98 0.0001 54.090.0001113.450.0001
Fire × phytomass
change 1.23 0.270 3.39 0.068 0.01 0.924 4.29 0.040
change 4.60 0.034 0.05 0.821 6.62 0.011 1.32 0.2521.13 1.13 0.83 0.83
Fire 93.75 0.0001 92.08 0.0001 5.64 0.019113.730.0001
Fire × phytomass
change 5.05 0.026 0.85 0.357 8.99 0.003 0.92 0.339
change 0.98 0.324 1.57 0.21 16.800.00010.46 0.4972.45 2.45 0.68 0.68
Fire 5.17 0.025 34.11 0.0001 92.330.000126.080.0001
Fire × phytomass
change 7.21 0.008 0.55 0.46 12.750.001 7.19 0.008
change 4.64 0.019 3.00 0.085 10.520.0026.31 0.0130.81 0.81 3.61 3.61
Fire 18.75 0.0001 58.98 0.0001 124.690.000158.050.0001
Fire × phytomass
change 3.41 0.067 0.54 0.465 3.54 0.021 2.85 0.094
change 0.13 0.715 1.91 0.169 0.00 0.999 1.05 0.3080.74 0.74 2.6 2.60
Fire 3.98 0.048 25.41 0.0001 28.880.000119.590.0001
Fire × phytomass
change 0.12 0.729 4.92 0.028 0.07 0.795 2.17 0.144
change 4.65 0.033 1.59 0.209 0.77 0.383 3.87 0.05 0.68 0.68 0.34 0.34
Fire 7.70 0.006 30.55 0.0001 63.970.000124.120.0001
Fire × phytomass
change 8.68 0.004 0.06 0.801 0.03 0.864 2.01 0.159
change 4.53 0.035 8.24 0.005 24.120.000110.730.0011.25 1.25 0.17 0.17
Fire 0.002 0.968 13.12 0.0001 26.880.00015.17 0.025
Fire × phytomass
change 4.31 0.04 3.68 0.057 16.310.00016.65 0.011
change 0.19 0.66 14.61 0.0001 18.7 0.00018.74 0.004–1.22 0.00 2.31 2.31
Fire 1.86 0.176 0.59 0.446 1.61 0.2081.5230.220
Fire × phytomass
change 0.82 0.368 0.10 0.747 13.960.00010.0850.771
cant effect of the interaction phytomass change times
fire indicated that phytomass production was dependent
on the fire treatment. In non burnt plots, total above-gro-
und phytomass decreased in the long dry season (July-
September) and increased steadily during the growth
season showing net accumulation in December-February.
The amount of total live phytomass attained in this pe
riod in fenced samples was maintained until the end of
the rain period, May-Jun (Figure 1). In contrast, burnt
plots had net phytomass accumulation at early stages of
the post-fire period, July-December, including the dry
eson, July-September and September-October. s
Copyright © 2011 SciRes. OPEN ACCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34
Figure 1. Mean (raw scores) live, standing dead, litter and total above-ground phytomass in burnt and non burnt plots at the start,
light grey bars and the end, dark grey bars of the sampling period. Bars show 95% confidence interval.
Daily ANPP (increment of live, standing dead and lit-
ter) in burnt plots was on average 1.0 gm2 d1 (ranging
from 0.0 to 2.5 gm2 d1) and in non burnt grassland 1.2
gm2 d1 (ranging from 0.0 to 3.6 gm2 d1). Significant
biomass change × site in May-June and June-July indi-
cates that local conditions at the sites were important
determinants of production in these periods.
3.3. Relationship between Sward Structure
and Productivity
There was a significant relationship between sward
properties and ANPP, but only in the burnt treatment
(Ta ble 6 ). ANPP was positively related to leaf and total
live phytomass and to the ratio leaf/total standing phy-
tomass. ANPP was negatively correlated (P = 0.078) to
the ratio between the live stems, flower and fruits and
total live phytomass. In contrast, no significant relation-
ships were detected between ANPP and sward structure
attributes in non burnt plots.
3.4. Relationship between Precipitation and
ANPP in burnt plots was not significantly related to
rainfall (Table 6 and Figure 2a). ANPP showed high
biomass increment rates at early post-fire stages, at the
onset of short rains (October-December). After December,
ANPP declined and was generally maintained low during
the rest of the growth season, with a small increase at the
end of the rain season (May-June). In contrast, ANPP in-
creased with rainfall in non burnt plots until reaching a
peak at the short rain season and de clined abruptly in the
mid-long rain season (Figure 2b).
Copyright © 2011 SciRes. OPEN ACCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34 31
Table 6. Pearson correlation (r) and P values between sward
structural attributes in fenced samples (as in Tables 2, 3 and 4)
and daily above-ground net primary production (as in Table 5)
in burnt and non burnt grasslands for the period September
2003 to July 2004 (n = 8 pairs for estimation of the various
attributes Burnt Non burnt
r P r Pe
ANPP Leaf phytomass 0.787**0.01 0.1550.357
Live phytomass 0.696* 0.028 0.0950.411
Standing dead
phytomass –0.041 0.461 0.550.079
Leaf/Total standing
phytomass 0.626* 0.048 –0.170.344
Live/Total standing
phytomass 0.614 0.053 –0.370.183
phytomass –0.552 0.078 –0.0740.863
Leaf/Live phytomass 0.517 0.095 0.0780.427
*Pearson correlation significant at P <0.05; ** P <0.01.
In agreement with other studies in African savannas,
the present results show that early-dry season fires in Se-
rengeti affect the grassland structure by removing dead
material including litter [17] and through post-fire re-
growth [18,19]. The results further show temporal dif-
ferences in phytomass structure between burnt and non
burnt grasslands. Fire stimulated growth at early post-
fire stages, even during the dry season, July-October and
led to larger increments in green phytomass compared to
the non burnt grassland at the start of the short rain pe-
riod, October-December. These findings agree with results
from other studies in grasslands showing that fire stimu-
lates re-growth [20,21] and the standing crop of leaves
Generally, the daily ANPP values in this study (be-
tween 0 and 3.6 gm2) are comparable to those found in
other savanna communities (mean range 1 - 4 gm2, [5])
and to previous studies in Serengeti [7,27]. However, the
current results demonstrate that fire shifts the relative
importance of the factors that control above-ground net
primary production and agree with the general idea that
fire can affect fundamental processes in the ecosystem [1,
3,4]. The significant relationship between leaf phy-
tomass and ANPP in burnt plots generally supported the
hypothesis that, in western Serengeti grasslands, the
amount of photosynthetic biomass constrains primary
productivity during the first post-fire year.
However, the amount of live phytomass did not fully
explain the changes in ANPP in burnt plots. The large
increments in live phytomass at early post-fire stages
despite the small amounts of initial photosynthetic bio-
mass indicates that re-growth in this period could in part
have depended on below-ground reserves [38]. Further,
in line with other studies, the comparatively higher allo-
cation to leaf phytomass [39,40] and the lower allocation
to reproductive structures, i. e., stems and flowers /fruits,
found in the burnt plots at the early post-fire stage can be
a strategy to compensate for the lost mass [41,42] which
could additionally have contributed to the high live bio-
mass increments observed in this period.
Above-ground net primary productivity in burnt plots
had an early peak and declined after December although
this period corresponds to the main rain season. The in-
crease in live phytomass declined until May and no sig-
nificant accumulation of standing dead material and lit-
ter was found in this period. These results contradict
earlier findings showing that the rate of post-fire recovery
of the vegetation responds to rainfall [4,29]. Two reasons
may, however, explain these apparently contradictory
results. Fire can reduce the amount of water availability
in the soil by increasing runoff and reducing infiltration
[30] with negative effects on net primary production in
burnt grasslands [15,17]. Alternatively, the decline in
ANPP in burnt grassland could be a consequence of the
interplay between grazing and fire. Results from a paral-
lel study [43] showed that consumption by herbivores in
burnt plots in the period October-December led to a sig-
nificant reduction in live phytomass. In interaction with
other disturbances, fire can importantly affect plant
growth by increasing the rate and the magnitude of bio-
mass loss in the vegetation with further severe conse-
quences for the capacity of the vegetation to restore
biomass loss and to grow. Although re-growth in grasses
appears to depend only marginally on stored carbohy-
drates [44,45], repeated defoliation, can reduce the
amount of carbohydrate reserves, affecting post- distur-
bance leaf area and plant vigour [46]. Repeated defolia-
tion can also deplete the bud bank [38] and it has been
shown that meristematic limitations in grasses appear to
be of prime importance in determining re-growth after
defoliation [45]. The interactive effects of fire and other
disturbances, such as grazing, are incompletely under-
stood but earlier studies support the idea that herbivory
on burnt patches can prolong the period for recovery
from fire [47,48]. These effects are expected to be of
importance in the Serengeti and other savanna ecosys-
tems where large herbivores are a major shaping force of
ecosystem function and structure [6, 27,49].
In contrast to the pattern found in burnt plots relating
sward structure and ANPP, no correspondence was found
in non burnt plots between ANPP and the amount of live
iomass or any of the assessed sward structural attributes. b
Copyright © 2011 SciRes. OPEN ACCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34
Copyright © 2011 SciRes.
Figure 2. Above-ground daily net primary production ANPP (based on raw scores) and precipitation on (a) burnt and (b) non burnt
grasslands from July 2003 to July 2004 in six sites in the Western Serengeti Corridor, Serengeti National Park.
In non burnt plots, the temporal change in ANPP was
more related to water availability, at least until Feb ruary.
Beyond this period, the decline in the rate of live phy-
tomass increments could be attributed to two factors.
First, similarly to the effect of herbivory on burnt plots,
[43] found a significant decline in standing biomass due
to herbivory in the same grasslands in the period De-
cember-February. The reduction in the amount of pho-
tosynthetic matter could explain the low ANPP at the
peak rain season. However, the present results also show
a significant increment in the amount of standing dead
phytomass after this period (February-March) suggesting
less favourable conditions for plant growth after the
production peak in February. Possible factors could be
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34 33
shading [50] or the allocation of resources to below-
ground parts towards the end of the growth period [17].
This study has demonstrated that early-dry-season fires
in the Serengeti Western Corridor have important effects
on the grassland phytomass during the first post-fire growth
season both in terms of sward structure and ANPP. There-
fore, this study summarises five overall conclusions:
1. There was lower phytomass and slow recovery of
sward and litter on burnt than on burnt grasslands plots
2. ANPP in burnt plots did not significantly relate to
rainfall, instead, it was very much related to rainfall in non
burnt plots.
3. However, early burns caused positive relationship
between some sward properties and ANPP.
4. Also, early burns enhance daily ANPP even in dry
season, July-October.
5. Therefore, this study has ascertained fulfilment of
the desire of fire management
Program under the Serengeti Ecological Monitoring
Program to supply green forage to both migratory and
resident populations during the period of scanty food sup-
[1] Williams, R.J., Hutley, L.B., Cook, G.D., Russell-Smith,
J., Edwards, A. and Chen, X.Y. (2004) Assessing the
carbon sequestration potential of mesic savannas in the
Northern Territory, Australia: approaches, uncertainties
and potential impacts of fire. Functional Biology, 31,
415-422. doi:10.1071/FP03215
[2] Dezzeo, N. and Chacon, N. (2005) Carbon and nutrients
loss in aboveground biomass along a fire induced
forest-savanna gradient in the Gran Sabana, southern
Venezuela. Forest Ec olo gy and Management, 209, 343-
352. doi:10.1016/j.foreco.2005.02.008
[3] Dai, X., Boutton, T.W., Hailemichael, M., Ansley R, J.
and Jessup, K.E. (2006) Soil carbon and nitrogen storage
in response to fire in a temperate mixed-grass savanna.
Journal of Environmental Quality, 35, 1620-1628.
[4] Govander, N., Trollope, W.S.W. and Van Wilgen, B.W.
(2006) The effect of fire season, fire frequency, rainfall
and management on fire intensity in savanna vegetation
in South Africa. Journal of Applied Ecology, 43, 748-758.
[5] Bourliere, F. and Hadley, M. (1970) The ecology of
tropical savannas. Annual Review of Ecology and System-
atics, 1, 125-152.
[6] Mcnaughton, S.J. (1992) The propagation of disturbance
in savannas through food webs. Journal of Vegetation
Science, 3, 301-314. doi:10.2307/3235755
[7] Sinclair, A.R.E. (1975) The resource limitation of
tropical levels in tropical grassland ecosystem. Journal of
Animal Ecology, 44, 497-520. doi:10.2307/3608
[8] Beerling, D.J. and Osborne, C.P. (2006) The origin of the
savanna biome. Global Change Biology, 12, 2023-2031.
[9] Chapin, F.S. Iii, Torn, M.S. and Tateno, M. (1996) Princ-
iples of ecosystem sustainability. American Naturalist,
148, 1016-1037. doi:10.1086/285969
[10] Knapp, A.K., Conard, S.L. and Blair, J.M. (1998) Deter-
minants of soil CO2 flux from a sub-humid grassland: Ef-
fect of fire and fire history, Ecological Application, 8,
[11] Blair, J.M. (1997) Fire, Navailability, and plant response
in grasslands: A test of the transient maxima hypothesis.
Ecology, 78, 2359-2368.
[12] Kang, S., Kimball, J.S., Running, S.W. (2006) Simulatin-
g effects of fire disturbance and climate change on boreal
forest productivity and evapo-transpiration. Science of
the Total Environment, 362, 85-102.
[13] Georgiadis, N.J., Ruess, R.W., Mcnaughton, S.J. and
Western, D. (1989) Ecological conditions that determine
when grazing stimulates grass production, Oecologia, 81,
[14] Ojima, D.S., Schimel, D.S., Parton, W.J. and Owensby,
C.E. (1994) Long-term and short-term effects of fire on
nitrogen cycling in tallgrass prairie. Biogeochemistry, 24,
67-84. doi:10.1007/BF02390180
[15] Turner, C.L., Blair, J.M., Schartz, R.J. and Neel, J.C.
(1997) Soil N and plant responses to fire, topography,
and supplemental N in tall grass prairie. Ecology, 78,
[16] O’reagain, P.J. and Owen-Smith, R.N. (1996) Effect of
species composition and sward structure on dietary quality
in cattle and sheep grazing South African sourveld. Journal
of Agricultural Science, 127, 261-270.
[17] Snyman, H.A. (2005a) Influence of fire on litter pro-
duction and root and litter turnover in a semi arid gra-
ssland of South Africa. South African Journal of Botany,
71, 145-153, 133-144.
[18] Van De Vijver, C.A.D.M., Poot, P., Prins, H.H.T. (1999)
Causes of increased nutrient concentrations in post-fire
re-growth in an East African savanna. Plant and Soil,
214, 173-185. doi:10.1023/A:1004753406424
[19] Vesey-Fitzgerald, D. (1971) Fire and animal impact on
vegetation in Tanzania National Parks. Proceedings of
Annual Tall Timbers Fire Ecology Conference, 11, 297-
[20] Norton-Griffiths, M. (1979) The influence of grazing,
browsing and fire on the vegetation dynamics of the
Serengeti, In: Sinclair A.R. and Norton-Griffiths, M.,
Eds., Serengeti: Dynamics of an Ecosystem, University
of Chicago Press, Chicago.
[21] Briggs, J.M. and Knapp, A.K. (2001) Determinants of C3
forb growth and production in a C4 dominated grassland.
Plant Ecology, 152, 93-100.
[22] Reich, P.B., Peterson, D.W., Wedin, D.A and Wrage, K.
(2001) Fire and vegetation effects on productivity and
nitrogen cycling across a forest-grassland continuum.
Ecology, 82, 1703-1719.
[23] Gholz, H.L. (1982) Environmental limits on aboveground
ound net primary production, leaf area and biomass in
vegetation zones of the Pacific Northwest. Ecology, 63,
469-481. doi:10.2307/1938964
Copyright © 2011 SciRes. OPEN ACCESS
S. N. Hassan / Open Journal of Ecology 1 (2011) 24-34
Copyright © 2011 SciRes.
[24] Prins, H.H.T and Loth, P.E. (1988) Rainfall patterns as
background to plan phenology in northern Tanzania.
Journal of Biogeography, 15, 45-463.
[25] Nortorn-Griffiths, M., Herlocker, D. and Pennycuick, L.
(1975) The patterns of rainfall in the Serengeti ecosystem
Tanzania. East African Wildlife Journal, 13, 347-374.
[26] Sawadogo, L., Tiveau, D. and Nygård R. (2005) Influe-
nce of selective tree cutting, livestock and Pre-scribed
fire on herbaceous biomass in the savannah woodlands of
Burkina Faso West Africa. Agriculture, Ecosystems and
Environment, 105, 335-341.
[27] McNaughton, S.J. (1985) Ecology of a grazing eco-
system: The Serengeti. Ecological Monograph, 55, 259-
294. doi:10.2307/1942578
[28] Wiegand, T., Snyman, H.A., Kellner, K. and Paruelo,
J.M. (2004) Do grasslands have a memory: Modeling
phytomass production of a semiarid South African gra-
ssland. Ecosystems, 7, 243-258.
[29] Nippert, J.B., Knapp, A.K. and Briggs, J.M. (2006) Intra-
annual rainfall variability and grassland productivity: can
the past predict the future? Plant Ecology, 184, 65-74.
[30] O'connor, T.G., Haines, L.M. and Snyman, H.A. (2001)
Influence of precipitation and species composition on
phytomass of a semi-arid African grassland. Journal of
Ecology, 89, 850-860.
[31] Sinclair, A.R.E. (1995) Serengeti, past and present. In:
Sinclair A.R.E. and Arcese, P., Eds. Serengeti II: Dyn-
amics, Management, and Conservation of an Ecosystem,
University of Chicago Press, Chicago, IL., USA.
[32] Serneels, S. and Lambin, E.F. (2001) Impact of land-use
changes on the wildebeest migration in the northern part
of the Serengeti-Mara ecosystem. Journal of Biogeogra-
phy, 28, 391-407. doi:10.1046/j.1365-2699.2001.00557.x
[33] Pennycuick, L. (1975) Movement of the migratory wil-
debeest population in the Serengeti area between 1960
and 1973. East African Wildlife Journal, 13, 65-87.
[34] Clyaton, W.D., Phillips, S.M. and Renvoize, S.A. (1974)
Flora of tropical East Africa. Graminae. Crown Agents
for Overseas Governments and Administration, UK.
[35] Taylor Jr., C.A., Brooks, T.D. and Garza, N.E. (1997)
Effects of short duration and high-intensity, low freque-
ncy grazing systems on forage production and compositi-
on. Journal of Range Management, 46, 118-121.
[36] Mutanga, O., Prins, H.H.T., Van Wieren, S.E., Huizing,
H.G.J., Grant, R.F., Peel, M.J.S. and Biggs, H. (2004)
Explaining grass-nutrient patterns in a savanna rangeland
of southern Africa. Journal of Biogeography, 31,
819-829. doi:10.1111/j.1365-2699.2004.01072.x
[37] Underwood, A.J. (2002) Experiments in Ecology: their
logical design and interpretation using analysis of var-
iance, Cambridge University Press, Cambridge, UK.
[38] Briske, D. and Richards, J. (1995) Plant responses to
defoliation: A physiological, morphological and demogr-
aphic evaluation, Wildland Plants: Physiological Eco-
logy and Developmental Biology, Society for Range
Management, Denver.
[39] Bowen, B.J. and Pate, J.S. (1993) The significance of
root starch in post-fire shoot recovery of the Resprouter
Stirlingia latifolia R.Br. (Proteaceae). Annals of Botany,
72, 7-16. doi:10.1006/anbo.1993.1075
[40] Gwynne, M.D. (1966) Plant physiology and the future
Tropical pasture, Faber and Faber Limited, London.
[41] Heichel, G.H. and Turner, N.C. (1983) CO2 assimilation
of primary and re-growth foliage of red maple (Acer
rubrum L) and red oak (Quercus rubra L): response to
defoliation. Oecologia, 57, 14-19.
[42] Trumble, J.T., Kolodny-Hirsch, D.M and Ting, I.P.
(1993) Plant compensation for arthropod herbivory. An-
nual Review of Entomology, 38, 93-119.
[43] Hassan, S., Rusch, G.M., Hytteborn, H., Skarpe, C. and
Kikula, I. (2007) Effects of fire on sward structure and
grazing in western Serengeti, Tanzania. African Journal
of Ecology, 46, 174-185.
[44] Chapin, F.S. Iii, Schulze, E. and Mooney, H.A. (1990)
The ecology and economics of storage in plants. Annual
Review of Ecology and Systematics, 21, 423-447.
[45] Richards, J.H. and Caldwell, M.M. (1985) Soluble
carbohydrates, concurrent photosynthesis and efficiency
in regrowth following defoliation: A field study with
Agropyron species. Journal of Applied Ecology, 22, 907-
920. doi:10.2307/2403239
[46] Mcpherson, K. and Williams, K. (1998) The role of
carbohydrate reserves in the growth, resilience, and
persistence of cabbage palm seedlings (Sabal palmetto).
Oecologia, 117, 460-468.
[47] Letnic, M. (2004) Cattle grazing in a hummock grassland
regenerating after fire: The short-term effects of cattle
exclusion on vegetation in south-western Queensland.
Rangeland Journal, 26, 34-48. doi:10.1071/RJ04003
[48] Pratt, D. J. (1967) A note on the overgrazing of burned
grassland by wildlife. East African Wildlife Journal, 5,
[49] McNaughton, S.J. (1979) Grazing as an optimization
process: Grass-ungulate relationships in the Serengeti.
American Naturalist, 113, 691-703.
[50] Sims, P.L and Singh, J.S. (1978) The structure and
function of ten western North American grasslands IV
Compartmental transfer and energy flow within the eco-
system. Journal of Ecology, 66, 983-1009