Robustness and Strategies of Adaptation among Farmer
Varieties of African Rice (Oryza glaberrima) and Asian
Rice (Oryza sativa) across West Africa
Alfred Mokuwa1., Edwin Nuijten1., Florent Okry1,2., Béla Teeken1., Harro Maat1, Paul Richards1,
Paul C. Struik3*
1 Technology and Agrarian Development group, Wageningen University, Wageningen, The Netherlands, 2 Africa Rice Center, Cotonou, Bénin, 3 Centre for Crop Systems
Analysis, Wageningen University, Wageningen, The Netherlands
Abstract
This study offers evidence of the robustness of farmer rice varieties (Oryza glaberrima and O. sativa) in West Africa. Our
experiments in five West African countries showed that farmer varieties were tolerant of sub-optimal conditions, but
employed a range of strategies to cope with stress. Varieties belonging to the species Oryza glaberrima – solely the product
of farmer agency – were the most successful in adapting to a range of adverse conditions. Some of the farmer selections
from within the indica and japonica subspecies of O. sativa also performed well in a range of conditions, but other farmer
selections from within these two subspecies were mainly limited to more specific niches. The results contradict the rather
common belief that farmer varieties are only of local value. Farmer varieties should be considered by breeding programmes
and used (alongside improved varieties) in dissemination projects for rural food security.
Citation: Mokuwa A, Nuijten E, Okry F, Teeken B, Maat H, et al. (2013) Robustness and Strategies of Adaptation among Farmer Varieties of African Rice (Oryza
glaberrima) and Asian Rice (Oryza sativa) across West Africa. PLoS ONE 8(3): e34801. doi:10.1371/journal.pone.0034801
Editor: John P. Hart, New York State Museum, United States of America
Received October 6, 2011; Accepted March 8, 2012; Published March 1, 2013
Copyright: ß 2013 Mokuwa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding for this study was provided by NWO-WOTRO (Science for Global Development, part of the Netherlands Organisation for Scientific Research),
CSG (Centre for Society and Genomics), NUFFIC (Netherlands Organisation for International Cooperation in Higher Education) and AfricaRice Center. The funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: paul.struik@wur.nl
. These authors contributed equally to this work.
Today, farmers in the region mainly grow the two types of Asian
rice. Nevertheless in certain areas African rice remains an
important crop type [2–6]. These areas all seem to have a shared
history of rice cultivation taking place against a background of
special difficulty, such as war, population displacement or harsh
ecological conditions [7]. This suggests the species may be selected
for its greater tolerance to sub-optimal conditions when compared
to Asian rice. The logic of the present study, therefore, is to
compare African and Asian rice, in farmer conditions, in order to
understand the extent to which plasticity and adaptability are
factors in farmer varietal choice. The overall aim of the study is to
secure a better knowledge base for possible complementary
strategies of variety promotion. These complementary strategies
would give due consideration both to varieties developed through
scientific research and varieties produced by farmer selection. The
objective is to assess the case for protecting farmer varieties as an
important aspect of local food security, in an environment in
which development agencies seek more generally to expand the
range of high-yielding cultivars to meet urban rice demand across
the region. Our study reports on differences in response to varying
environments of a large sample of farmer varieties across five West
African countries in the high-rainfall coastal zone.
The study tests the hypothesis that African rice may be more
robust than Asian rice in West African farmer conditions. Here
Introduction
It is often supposed that crops should only be grown where
conditions are favourable. This is not an option for farmers
cultivating food crops with limited resources. They have to grow
what they need with the conditions they have been given. In short,
they have to cope with sub-optimality. For these farmers,
adaptability of varieties under sub-optimal conditions is an
essential requirement [1,2]. Hypothetically, we should expect to
find this adaptability among farmer varieties since these are to a
large extent the product of farmer selection. This would mean that
farmer varieties are the result of interplay between local ecological
and social factors.
In large parts of West Africa small-scale farmers rely upon the
cultivation of upland rice under low input conditions in a great
diversity of micro-environments. The first rice farming in West
Africa was based exclusively on African rice (O. glaberrima Steud.).
The cultivation of African rice is entirely a result of farmer agency
as African rice has never been disseminated by extension
programmes. Asian rice (Oryza sativa) is a more recent introduction,
perhaps during the period of the Atlantic Slave trade (beginning c.
1550), or earlier via trans-Saharan trade routes. Asian rice has two
main subspecies: Oryza sativa var. japonica (short-grained, mainly
grown as upland rice) and O. sativa var. indica (long-grained, mainly
a lowland type).
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How Robust Are Rice Varieties in West Africa?
environments. In the context of this paper, robustness is taken to
be the ability of a variety or a group of varieties to yield well across
distinct environments.
robustness is seen as the ability of a variety or group of varieties to
perform well in a diversity of cultivation conditions.
The following research questions are posed:
1. Are farmer varieties of O. glaberrima better suited to sub-optimal
agro-ecological conditions than varieties of the two subspecies
of O. sativa?
2. Do farmer varieties of O. glaberrima adapt better to different
environmental conditions than varieties of the two subspecies
of O. sativa?
3. What are the physiological processes and social and ecoregional patterns underlying the adaptation of farmer varieties
across environments?
Adaptability
The ability of a variety or a group of varieties to be robust.
Adaptability implies significant Genotype (G) 6 Environment (E)
interactions.
Plasticity: the physiological process through which varieties
adjust their phenotypes in response to different environmental
conditions [13]. A plastic response of this nature does not require
changes in gene frequencies (i.e. evolution). Such phenotypic shifts
can allow varieties to achieve adaptability [9].
In achieving robustness, varieties can respond to environmental
conditions by showing phenotypic plasticity in a range of traits
[8,9]. Different varieties or groups of varieties achieve robustness
by combining variability and stability of different traits, thus
constituting different physiological strategies. Hence, this study
investigates whether different botanical groups of rice, or certain
groups of varieties within those botanical groups, have developed
different physiological strategies to achieve adaptation.
The hypothesis that African rice might be more robust than
Asian rice in West African conditions would make sense of a
number of observations already reported. Richards [7] has offered
some general evidence that African rice is an important food
reserve for communities facing special difficulty (e.g. when
displaced by war). Dingkuhn et al. [10] and Johnson et al. [11]
showed evidence that O. glaberrima has a vegetative vigour superior
to that of O. sativa, thus is better able to suppress weeds. Sumi and
Katayama [12] provided evidence that African rice has a yield
potential similar to Asian counterparts.
For a proper understanding of the paper we offer the following
definitions of concepts and notions.
Sub-optimal farming
characterised by no or limited mineral fertilisation, no or
natural pest and disease control, rain fed moisture conditions,
rarely mono cropping, and below an optimal or standard level of
output.
Tolerance
The ability of a variety to survive adverse conditions with only a
small reduction in performance.
Results
In the following sections ten relevant variables are investigated
for each botanical group (glaberrima, indica or japonica) and
molecular cluster (see section on Materials and Methods). These
ten variables were used to analyse the vegetative growth and yield
components (see section on Materials and methods): maximum
canopy cover (Vmax; %), accumulated canopy cover (A; %.day),
plant height (cm), number of tillers per plant (# tillers), days to
50% flowering (50% flowering), number of panicles per plant (#
panicles), panicle length (cm), panicle weight (g), 200 grain weight
(g) and grain yield (kg/ha). The variables are dealt with one by one
and cross references are made among them to unravel strategies of
adaptation. Graphs are used to compare performance of each
Robustness
The persistence of a system’s characteristic behaviour under
sub-optimal conditions, implying stable performance across
Table 1. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of 24 genotypes (all botanical groups and molecular clusters together).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
f
a
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.000***
0.758
0.026*
0.092
0.881
0.029*
-
0.000***
0.435
0.027*
0.014*
0.444
0.001***
-
0.000***
0.922
0.002**
0.612
0.000***
0.000***
0.264
0.000***
0.533
0.006**
0.043*
0.000***
0.000***
0.986
0.000***
0.011*
0.000***
0.008**
0.000***
0.003**
0.000***
0.000***
0.334
0.112
0.005**
0.000***
0.000***
0.947
Panicle length
a
0.000***
0.890
0.003**
0.023*
0.000***
0.000***
0.017*
Panicle weight
e
0.000***
0.140
0.502
0.236
0.157
0.194
0.012*
0.000***
0.318
0.006**
0.069
0.018*
0.031*
0.850
0.000***
0.070
0.042*
0.583
0.873
0.020*
0.000***
# Panicles
200 grain weight
Yield
c
b
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t001
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How Robust Are Rice Varieties in West Africa?
Table 2. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the Glaberrima botanical group.
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Panicle length
a
Panicle weight
e
200 grain weight
Yield
c
b
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.190
0.373
0.083
0.464
0.319
0.000***
-
0.260
0.217
0.055
0.268
0.132
0.000***
-
0.000***
0.797
0.009**
0.471
0.001***
0.000***
0.469
0.097
0.246
0.003**
0.268
0.000***
0.014*
0.612
0.000***
0.007**
0.001***
0.069
0.014*
0.024*
0.000***
0.314
0.267
0.117
0.025*
0.000***
0.000***
0.998
0.000***
0.810
0.001***
0.024*
0.004**
0.009**
0.024*
0.051
0.255
0.081
0.359
0.088
0.279
0.563
0.000***
0.457
0.003**
0.584
0.019*
0.103
0.940
0.000***
0.458
0.254
0.619
0.981
0.002**
0.000***
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t002
variable across environments. ANOVAs provided important
information on adaptability, as they provided estimates of G6E
interactions (Tables 1–10).
Table 11 shows the average performance of the studied
genotypes (grouped into botanical groups and molecular clusters)
for the ten variables used. Table 12 shows the yield and yield
components averaged for the five countries, whereas Table 13
shows the estimates of the wide sense heritability for the ten
variables listed in Tables 1–11.
Maximum canopy cover (Vmax) and accumulated canopy
cover (A)
Vmax and A correlated positively (r = 0.984**) at 0.01 level. The
same trend was observed for all botanical groups and molecular
clusters in all environments (Tables 14–22; Figure 1). Accumulated
canopy cover (A) can therefore represent Vmax and vice versa. In
all cases the surface under the canopy curves (A) can be conceived
as a triangle with the cycle length (Te) as base and Vmax as height.
Variations in cycle length (Te), inflexion point (Tm1) and the time
Vmax was reached (T1) appear to confirm that A is linearly related
to Vmax.
Table 3. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the cluster of Glaberrima from Lower Guinea Coast (Glab_LowerCoast).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.137
0.737
0.176
0.330
0.877
0.172
-
0.740
0.464
0.082
0.129
0.609
0.053
-
0.567
0.566
0.218
0.685
0.665
0.641
0.042*
0.852
0.061
0.002**
0.638
0.026*
0.347
0.935
0.014*
0.001***
0.004**
0.086
0.061
0.534
0.022*
0.840
0.243
0.086
0.145
0.091
0.008**
0.963
Panicle length
a
0.582
0.164
0.178
0.144
0.791
0.441
0.393
Panicle weight
e
0.274
0.081
0.370
0.641
0.330
0.926
0.517
0.056
0.421
0.119
0.654
0.325
0.258
0.218
0.099
0.316
-
0.570
0.899
0.604
0.017*
200 grain
weight b
Yield
c
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t003
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How Robust Are Rice Varieties in West Africa?
Table 4. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the cluster of Glaberrima from Upper Guinea Coast (Glab_UpperCoast).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Panicle length
a
Panicle weight
e
200 grain weight
Yield
c
b
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.589
0.276
0.076
0.973
0.178
0.001***
-
0.545
0.170
0.055
0.667
0.184
0.002**
-
0.003**
0.702
0.027*
0.209
0.000***
0.000***
0.956
0.664
0.397
0.031*
0.270
0.008**
0.056
0.145
0.000***
0.017*
0.005**
0.455
0.290
0.091
0.000***
0.372
0.294
0.144
0.025*
0.000***
0.000***
0.982
0.018*
0.919
0.010**
0.003**
0.000***
0.000***
0.439
0.309
0.300
0.242
0.322
0.128
0.221
0.454
0.202
0.581
0.001***
0.464
0.013*
0.329
0.980
0.000***
0.519
0.412
0.344
0.902
0.001***
0.039*
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t004
result in a yield increase as Glab_UpperCoast yielded more than
Glab_LowerCoast (Table 11).
Of the indica group, it was only in the Ind_Gc cluster that
significant sowing 6location interactions were found for Vmax and
A. The indica group showed a significant location effect for A. No
significant effects were found for the Ind_Gh cluster. This
indicates that the Ind_Gh maintained better Vmax and A than
the Ind_Gc but often failed to yield (Figures 2 and 3).
The japonica group showed significant sowing 6 location
interactions, suggesting that (for the two japonica clusters) Vmax
and A varied across environments. At cluster level significant
sowing 6 location interactions were found for Jap_SL for Vmax
None of the botanical groups or molecular clusters showed
G6E interactions for Vmax or A (Tables 2–10). This means that
within all botanical groups and molecular clusters the varieties
responded comparably for Vmax and A across environments.
However, for all three botanical groups significant sowing 6
location interactions were found, in particular for glaberrima and
japonica. Sowing 6 location interactions were highly significant for
the glaberrima botanical group and Glab_UpperCoast but not
significant for the Glab_LowerCoast cluster. Glab_LowerCoast
therefore maintained better Vmax and A across environments,
since its genotypes reacted in a similar way to different
environments. However, the better developed canopy did not
Table 5. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the Indica botanical group.
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
f
a
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.017*
0.931
0.060
0.160
0.746
0.171
-
0.031*
0.588
0.038*
0.177
0.508
0.055
-
0.089
0.591
0.000***
0.720
0.000***
0.010**
0.057
0.553
0.998
0.001***
0.022*
0.001***
0.006**
0.979
0.027*
0.005**
0.000***
0.233
0.003**
0.432
0.120
0.829
0.358
0.654
0.149
0.100
0.002**
0.315
Panicle length
a
0.162
0.474
0.002**
0.595
0.063
0.377
0.047*
Panicle weight
e
0.174
0.029*
0.230
0.377
0.271
0.732
0.457
0.001***
0.053
-
0.339
0.794
0.866
0.365
0.001***
0.002**
0.358
0.630
0.441
0.916
0.000***
# Panicles
200 grain
weight b
Yield
c
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t005
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How Robust Are Rice Varieties in West Africa?
Table 6. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the cluster of Indica from Ghana (Ind_Gh).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
f
a
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.057
0.362
estimate
0.229
0.943
0.756
-
0.099
0.762
0.439
0.253
0.891
0.370
-
0.385
0.480
0.001 ***
0.798
0.022*
0.124
0.012*
0.361
0.580
0.005 **
0.078
0.055
0.201
0.702
0.026*
0.026*
0.011*
0.245
0.172
0.539
0.019*
0.448
0.548
0.864
0.222
0.038*
0.644
0.440
Panicle length
a
0.158
0.872
0.081
0.475
0.170
0.287
0.139
Panicle weight
e
-
0.119
-
-
-
-
-
# Panicles
200 grain weight
Yield
c
b
-
-
-
-
-
-
-
0.016*
0.062
0.061
0.385
0.192
0.342
0.000 ***
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t006
interaction for yield (Tables 1–10). This suggests that the studied
rice varieties generally responded differently in yield across
environments and sowing dates. The yield variability studied at
cluster level also revealed significant G6E interactions (Tables 3,
4, 6, 9 and 10) with the exception of the indica cluster from Guinea
(Ind_Gc) (Table 7). The yield therefore varied in a similar manner
across environments for genotypes of Ind_Gc.
The glaberrima botanical group showed the highest yields across
all environments (Table 11 and Figure 3). ‘‘Zero’’ yields (complete
crop failure) occurred only with indica and japonica. At cluster level,
glaberrima from Upper Guinea Coast (Glab_UpperCoast) showed
the highest yield. Glaberrima from the Lower Guinea Coast
only, while for the Jap_GbGh cluster the location effects were
significant for both Vmax and A. This suggests that Jap_SL
maintained A across environments better than Jap_GbGh.
However Jap_SL showed considerable yield variation (Figure 3),
suggesting that the relative stability observed for A did not
contribute to yield stability.
Generally, the highest A was observed in Ghana followed by
Togo and Guinea (Figure 2).
Yield
The analyses of variance performed for all genotypes and at
botanical group level usually showed a highly significant three-way
Table 7. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield from the cluster of Indica from Guinea (Ind_Gc).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.103
0.657
0.025*
0.242
0.074
0.033*
-
0.052
0.439
0.017*
0.122
0.100
0.035*
-
0.962
0.957
0.000***
0.829
0.025*
0.008**
0.964
0.634
0.440
0.018*
0.384
0.006**
0.031*
0.973
0.286
0.003**
0.029*
0.551
0.118
0.823
0.391
0.500
0.189
0.114
0.774
0.038*
0.242
0.876
Panicle length
a
0.781
0.369
0.021*
0.416
0.180
0.397
0.368
Panicle weight
e
0.412
0.032*
0.377
0.336
0.358
0.761
0.540
0.272
0.481
0.350
0.535
0.573
0.494
0.302
0.598
0.097
0.090
0.112
0.454
0.022*
0.501
200 grain
weight b
Yield
c
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t007
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5
March 2013 | Volume 8 | Issue 3 | e34801
How Robust Are Rice Varieties in West Africa?
Table 8. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the Japonica botanical group.
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Panicle length
a
Panicle weight
e
200 grain weight
Yield
b
c
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing 6
Location
0.047**
0.178
0.047**
0.703
0.468
0.011**
-
0.176
0.318
0.065
0.818
0.285
0.002***
-
0.021*
0.562
0.000***
0.846
0.000***
0.001***
0.404
0.000***
0.755
0.033*
0.965
0.008**
0.000***
0.963
0.001***
0.431
0.005**
0.108
0.007**
0.000***
0.012*
0.010**
0.803
0.653
0.946
0.282
0.020*
0.121
0.000***
0.860
0.038*
0.043*
0.000***
0.000***
0.784
0.182
0.158
0.405
0.813
0.608
0.368
0.022*
0.000***
0.197
0.085
0.178
0.936
0.216
0.660
0.001***
0.006**
estimate
0.644
0.987
0.884
0.000***
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t008
between yield and A was similarly low for Glab_LowerCoast and
Glab_UpperCoast (r = 0.451 and r = 0.476**, respectively). This
shows that glaberrima can yield well even when relatively low
accumulated canopy cover is produced.
For the indica and japonica clusters clear differences in the
relationship between grain yield and A were found. A significant
relationship between yield and A was found for Ind_Gc
(r = 0.857**) but not for Ind_Gh (r = 0.137). Also a significant
Pearson correlation coefficient was found for Jap_GbGh
(r = 0.848**) but not for Jap_ SL (r = 0.497). These findings
suggest that Ind_Gc and Jap_GbGh increased their yields by
producing a correspondingly dense canopy. The absence of
significant correlation values for Ind_Gh and Jap_SL was caused
(Glab_LowerCoast) had the same yield range as japonica from
Guinea Bissau and Ghana (Jap_GbGh) and Ind_Gc. Ind_Gh and
Jap_SL showed the lowest average yield.
A comparison of the botanical groups on the yield across
environments (Figure 3) shows that, within the same environment,
glaberrima yielded more than indica and japonica. In Ghana where
the average plot yield was generally high, some indica varieties
showed ‘‘zero’’ yield. Zero yields occurred for japonica only in
Guinea Bissau and Togo. These are the two countries where the
overall yield was generally lowest.
Figures 4a–c show the graphical representations of the
relationships between yield and A for each botanical group. At
cluster level different relationships were observed. The relation
Table 9. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the cluster of Japonica from Guinea Bissau and Ghana (Jap_GbGh).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Panicle length
a
Panicle weight
e
200 grain weight
Yield
c
b
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6Sowing 6
Location
0.331
0.116
0.030*
0.637
0.472
0.142
-
0.355
0.205
0.028*
0.725
0.347
0.069
-
0.080
0.607
0.000***
0.693
0.004**
0.045*
0.229
0.000 ***
0.764
0.035*
0.891
0.714
0.005**
0.661
0.857
0.574
0.007**
0.851
0.006**
0.000***
0.408
0.027*
0.805
0.466
0.860
0.995
0.106
0.036*
0.005 **
0.808
0.028*
0.014*
0.001***
0.000***
0.835
0.074
0.188
0.576
0.495
0.547
0.352
0.091
0.000 ***
0.571
0.129
0.339
0.917
0.278
0.705
0.856
0.329
0.089
0.442
0.605
0.016*
0.039*
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t009
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How Robust Are Rice Varieties in West Africa?
Table 10. Main effects of and interactions between genotype, sowing date and trial location regarding crop characteristics,
including maximum canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days
to 50% flowering (50% Flowering), number of panicles per plant (# Panicles), panicle length, panicle weight, 200 grain weight and
yield of the cluster of Japonica from Sierra Leone (Jap_SL).
Variable
Vmax
A
d
d
Plant height
# Tillers
f
f
50% Flowering
# Panicles
f
a
Panicle length
a
Panicle weight
e
200 grain weight
Yield
c
b
Genotype
Sowing
Location
Genotype 6
Sowing
Genotype 6
Location
Sowing 6
Location
Genotype 6 Sowing
6 Location
0.433
0.293
0.097
0.526
0.461
0.133
-
0.550
0.473
0.128
0.578
0.306
0.044*
-
0.072
0.568
0.003**
0.736
0.005**
0.005**
0.845
0.062
0.747
0.049*
0.775
0.072
0.023*
0.949
0.067
0.305
0.002**
0.044*
0.069
0.037*
0.052
0.199
0.812
0.218
0.880
0.125
0.088
0.816
0.032*
0.988
0.229
0.251
0.006**
0.020*
0.637
0.977
0.634
-
0.917
0.673
0.728
0.082
0.328
1.000
-
0.735
0.948
0.925
0.067
0.114
0.082
0.619
0.516
0.943
0.422
0.000***
Values in the table are p values (three-way ANOVA). *: Significant at 0.05 level. **: Significant at 0.01 level. ***: Significant at 0.001 level. a: ANOVA performed for Guinea
Bissau, Guinea and Sierra Leone. b: ANOVA performed for Guinea Bissau, Guinea, Ghana and Togo. c: ANOVA performed for Guinea Bissau, Ghana, Sierra Leone and
Togo. d: ANOVA performed for Ghana, Guinea and Togo. e: ANOVA performed for Ghana and Togo. f: ANOVA performed for all five countries. -: not assessed.
doi:10.1371/journal.pone.0034801.t010
high Vmax and A for Ind_Gc, imply that Ind_Gc had a better
vegetative growth compared to Ind_Gh. Cluster Ind_Gc also
displayed the same average plant height as Glab_UpperCoast.
Japonica clusters did not show significant differences for plant
height (Table 11) nor for the relationship between plant height and
A: r = 0.635** and r = 0.640** for Jap_GbGh and Jap_SL,
respectively.
by a number of crop failures that could be related to them being
narrowly adapted to Sierra Leone only (Figures 4b and 4c).
A minimum A is indispensable for yield formation, as shown by
the various associations between A and yield observed for the
various clusters. But from our observation only the glaberrima
clusters were able to yield well with low canopy development.
Plant height
Number of panicles
Significant G6E interactions for plant height were observed for
all botanical groups and their respective clusters. This implies that
across environments genotypes within botanical groups and
clusters responded differently in plant height, suggesting the
existence of varied strategies of adaptation for the different
botanical groups and clusters. This finding confirms that plant
height is in general sensitive to environmental conditions.
A decreasing trend was observed for plant height from countries
with higher yield to countries with lower yield (Figure 5). The O.
glaberrima group showed significantly greater average plant height
than the indica and japonica groups (Table 11). At cluster level, we
found that Glab_UpperCoast had taller plants than Glab_LowerCoast and that Ind_Gc had taller plants than Ind_Gh. The japonica
clusters did not show significant differences for plant height
(Table 11).
The relation between plant height and A is more strongly
positive for Glab_UpperCoast (r = 0.826**, Figure 6a) than for
Glab_LowerCoast. This difference is, however, absent when
considering the relation between plant height and yield
(Figure 6b), confirming that when more canopy was produced
Glab_LowerCoast no longer invested in its height but rather in the
number of its tillers, which was significantly higher for Glab_LowerCoast than for Glab_UpperCoast (Table 11, Figure 7). This
suggests two distinct strategies adopted by the Glab_LowerCoast
cluster and the Glab_UpperCoast cluster to arrive at similar A,
and Vmax: the second cluster produces taller plants and fewer
tillers and the first cluster produces shorter plants but more tillers.
Within indica, the cluster Ind_Gc had the tallest plants and
showed a highly significant relationship between plant height and
A (r = 0.784**). These observations, together with observations of
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The glaberrima and indica groups showed significant G6E
interactions for number of panicles, while the japonica group did
not (Tables 2, 5 and 8). At cluster level Glab_UpperCoast,
Ind_Gh, Ind_Gc and Jap_GbGh showed significant G6E
interactions (Tables 4, 6, 7 and 9). There was no such interaction
for genotypes of the clusters Glab_LowerCoast and Jap_SL
(Tables 3 and 10).
The glaberrima group showed the highest average number of
panicles. Cluster Ind_Gc showed a significantly higher average
number of panicles than Ind_Gh and performed similar to the
glaberrima group (Table 11). Within the japonica group, the highest
number of panicles was observed with Jap_SL cluster in Sierra
Leone, the origin of the cluster. For all botanical groups and
variety clusters, the number of panicles was relatively low in Sierra
Leone and Guinea Bissau and highest in Guinea (Figure 8). An
opposite trend was observed only with Jap_SL. This cluster
showed more panicles in Sierra Leone. This underlines our
observation that Jap_SL is specifically adapted to conditions in
Sierra Leone.
The japonica group showed the lowest numbers of panicles
throughout the whole range of A and yield values (Figures 6c and
6d) and across locations (Figure 8). The number of panicles in
relation to A and yield hardly overlapped for glaberrima and japonica
(Figures 6c and 7d) and differed significantly (Table 11). The
glaberrima group showed a decreasing trend in panicle number as
yield values increased (r = 20.453**). For the japonica and indica
groups no such decreasing trend was observed. For the indica
group, the relation between panicle number and yield seemed to
be intermediate between the tendencies for the glaberrima and
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March 2013 | Volume 8 | Issue 3 | e34801
551 a
Number of tillers
The three botanical groups showed significant G6E interactions
for the number of tillers produced per plant. This means that, in
general, genotypes composing the three botanical groups followed
different strategies in tiller production across environments
(Figure 9). At cluster level, G6E interactions were also found for
the two glaberrima clusters and for the Ind_Gc cluster, but were
absent for the Ind_Gh cluster and the two clusters of japonica. This
implies that within the japonica clusters and the Ind_Gh cluster
genotypes all vary in tiller production in a similar way across
environments.
Indica as well as glaberrima showed intensive tillering (Table 11).
An increase in tiller number was observed from more favourable
(Sierra Leone and Ghana) to less favourable environments
(Guinea, Togo and Guinea Bissau) for the indica cluster
(Figure 9). One of the underlying mechanisms facilitating the
increase of tillers under less favourable conditions is that generally
(for all botanical groups and clusters) under less favourable
conditions (Guinea and Togo) the time to flowering is longer than
under more favourable conditions (Sierra Leone and Ghana)
(Figure 10). It seems particularly the case that the indica group uses
this time to produce tillers while the japonica and glaberrima groups
responded in various other ways.
Figures 7b and 7d indicate that for the indica group there is a
positive relationship between canopy cover and tillering in Guinea
and Togo, while tillering remains constant at high A in Ghana
(Figures 7b). However the positive relation in Guinea and Togo
does not match with the relation between number of tillers and
yield at low A because tillering remained high even when the crop
failed to yield (Figure 7e).
Japonica showed a positive relationship between number of tillers
and A (r = +0.604**, Figure 7c), but not for number of tillers and
yield (Figure 7f). The two japonica clusters showed a similar positive
relation between A and number of tillers. The Jap_GbGh cluster
clearly produced more tillers than the Jap_SL cluster (Table 11).
This higher number of tillers contributed to a higher panicle
number (although not significantly higher) which in turn might be
linked to the significantly higher yield observed for Jap_GbGh.
Ind_Gh
Means in a column followed by the same letter are not significantly different from each other at 0.05% (based on Tukey tests for the botanical groups and clusters separately).
*See materials and methods section for coding of the clusters.
doi:10.1371/journal.pone.0034801.t011
3.7 a
1.5 a
22.4 a
110.7 d
7.4 d
40.0 bc
Ind_Gc
2826 cd
91.8 a
110.0 d
44.2 bcd
Jap_SL
2984 cd
104.2 de
7.7 d
4.8 b
691 a
1064 b
4.5 bc
1.7 ab
21.6 a
1095 bc
31.1 a
Jap_GbGh
2085 a
98.7 cd
107.8 d
6.2 cd
3.9 a
2.9 c
22.0 a
1265 bcd
3.3 a
2.2 a
4.6 bc
4.9 c
1.8 ab
2.9 c
22.7 ab
21.9 a
101.9 c
7.2 d
98.4 bc
4.4 b
97.0 abc
36.8 ab
Glab_LowerCoast
2320 ab
50.2 d
Glab_UpperCoast
3214 d
92.7ab
7.5 d
3.1 a
967 B
1376 cd
4.1 ab
4.3 A
3.1 B
2.1 b
23.9 b
22.5 A
2.8 A
6.2 cd
101.8 B
96.7 b
4.0 A
6.5 c
97.2 A
104.2 de
35.0 A
44.5 bcd
Japonica
2269 A
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2794 bcd
757 A
4.1 A
1.9 A
22.1 A
108.9 C
2889 B
41.7 B
Indica
97.8 A
7.6 C
5.5 B
Yield (kg/ha)
1349 C
4.3 A
2.0 A
23.4 B
97.1 A
2908 B
46.1 C
Glaberrima
101.1 B
6.8 B
6.4 C
200 grain
weight (g)
Panicle
weight (g)
Panicle
length (cm)
50% Flowering (d)
A (%d)
Vmax (%)
Plant
height (cm)
# Tillers
# Panicles
japonica groups (Figure 6d), thus confirming its group distinctiveness (Table 11).
Botanical groups
and Clusters*
Table 11. Average performance of several clusters of rice (including three botanical groups and six related molecular clusters) for main crop characteristics, including maximum
canopy development (Vmax), accumulated canopy (A), plant height, number of tillers per plant (# Tillers), days to 50% flowering (50% Flowering), number of panicles per plant (#
Panicles), panicle length, panicle weight, 200 grain weight and yield in five West African countries.
How Robust Are Rice Varieties in West Africa?
Time to 50% flowering
We observed that at low yield levels the time to 50% flowering
was consistently longer for all genotypes than at higher yield levels
(Figure 10). This suggests that under less favourable conditions
genotypes generally delayed their flowering.
Panicle weight
Significant G6E interactions were found only for japonica.
Sowing effects were observed for the japonica group (as part of the
three way interaction between sowing, location and genotype), for
the indica botanical group, and for the Ind_Gc cluster. Of the
clusters only Ind_Gc showed variations in panicle weight by
sowing dates. The panicle weight and yield correlated highly and
positively for Ind_Gc (r = 0.755*) and Jap_SL (r = 0.824**). For
other clusters no significant relations were observed between
panicle weight and yield. These observations suggest that the
japonica and indica groups were more sensitive to sowing date (less
robust) than the glaberrima group and its clusters.
Panicle weight for glaberrima and indica was significantly lower
than for japonica (Table 11). When yield and A increased, panicle
weight also increased, for the indica group (0.549*). For the japonica
group there was no relation between panicle weight and A.
8
March 2013 | Volume 8 | Issue 3 | e34801
How Robust Are Rice Varieties in West Africa?
Table 12. Yield and yield components for different botanical groups and countries: Average yield (kg/ha) in descending order
from left to right, number of panicles per plant, number of tillers per plant and ratio between the number of panicles and the
number of tillers across countries. The values for Guinea are put in the uttermost right column as the yield was not assessed.
Botanical groups and clusters*
Glaberrima
Ghana
Japonica
1660
1510
1164
1034
-
5.0
-
5.9
8.0
Tillers
6.6
5.0
7.9
1.00
Ghana
Togo
Guinea Bissau
Guinea
1248
1132
329
317
-
Panicles
4.5
-
-
4.9
7.2
Tillers
4.7
6.3
9.3
8.2
8.3
Ratio
0.96
0.60
0.88
Guinea
Ghana
Sierra Leone
Guinea Bissau
Togo
Yield
1513
1061
759
504
-
Panicles
-
2.9
2.6
-
3.0
Tillers
4.9
2.9
5.1
4.0
0.98
0.52
Sierra Leone
Togo
Guinea Bissau
Guinea
Yield
1664
1568
1160
1100
-
-
5.1
-
5.5
7.8
Tillers
6.5
5.1
7.5
1.01
6.4
6.9
0.86
1.13
Ghana
Sierra Leone
Togo
Guinea Bissau
Guinea
Yield
1651
1356
1174
872
-
Panicles
-
4.7
-
7.0
8.6
Tillers
6.7
4.7
9.0
8.1
8.2
0.87
1.06
Guinea
1.00
Ghana
Sierra Leone
Guinea Bissau
Togo
Yield
1699
1476
553
529
-
Panicles
-
4.4
5.4
-
8.8
Tillers
6.4
4.6
7.8
9.4
0.96
0.69
8.7
1.02
Sierra Leone
Ghana
Togo
Guinea Bissau
Guinea
Yield
1096
742
196
153
-
Panicles
4.6
-
-
4.5
5.7
Tillers
4.9
6.3
9.2
8.5
7.9
Ratio
0.95
0.53
0.72
Sierra Leone
Guinea Bissau
Togo
Guinea
Ghana
Yield
1741
1123
869
662
-
Panicles
-
2.9
2.9
-
3.6
Tillers
5.1
3.0
5.5
4.4
0.98
0.52
Ratio
Jap_SL
3.5
0.86
Panicles
Ratio
Jap_GbGh
7.2
1.11
Sierra Leone
Ratio
Ind_Gh
6.9
0.86
Yield
Ratio
Ind_Gc
Guinea
-
Ghana
Glab_LowerCoast
Guinea Bissau
Panicles
Ratio
Glab_UpperCoast
Togo
Yield
Ratio
Indica
Sierra Leone
4.1
0.88
Ghana
Sierra Leone
Guinea Bissau
Togo
Guinea
Yield
1127
958
525
242
-
Panicles
-
2.7
2.1
-
2.0
Tillers
4.4
2.8
4.0
3.3
0.98
0.51
Ratio
2.4
0.81
-: not measured. *See materials and methods section for coding of the clusters.
doi:10.1371/journal.pone.0034801.t012
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How Robust Are Rice Varieties in West Africa?
Table 13. Wide sense heritability estimates (for all genotypes together and per botanical group).
Vmax
A
Plant
height
#
Tillers
50%
Flowering
#
Panicles
Panicle
length
Panicle
weight
200 grain
weight
Yield per
ha
All genotypes
60
45
60
79
86
77
67
75
49
76
Glaberrima
35
12
68
17
86
1
61
48
65
43
Indica
50
55
61
0
64
5
30
56
80
90
Japonica
76
63
45
62
59
56
69
48
32
59
doi:10.1371/journal.pone.0034801.t013
Significant genotype effects were observed for the japonica group
and the Jap_GbGh cluster. No significant genotype effect was
observed for the varieties of the Jap_SL cluster, suggesting little
variation for 200 grain weight in the Jap_SL cluster and large
genotypic variation in the Jap-GbGh cluster. The indica group also
showed a significant genotype effect. Not enough data were
available for an ANOVA of the Ind_Gh group.
The botanical groups showed little variation for 200 grain
weight, but the average 200 grain weight varied significantly
among the clusters of each botanical group. Within the glaberrima
group the Glab_UpperCoast average was lower than that of the
Glab_LowerCoast cluster. The average 200 grain weight for the
Jap_GbGh cluster was higher than that of the Jap_SL cluster and
the Ind_Gc cluster average was higher than that of the Ind_Gh
cluster.
Japonica showed a fairly strong positive correlation between A
and 200 grain weight: r = 0.70**, against r = 0.596** and
r = 0.581** for the glaberrima and indica groups, respectively. At
low values of A, the Ind_Gh cluster and japonica group tended to
produce more empty or poorly developed grains, as represented in
Figure 12. This is consistent with our findings under the section on
number of tillers that extra tillers were produced at lower levels of
A and yield contained more empty grains. The trends observed
between A and 200 grain weight were also observed between 200
grain weight and yield, but only with the indica and japonica groups.
A clear divide was observed for the 200 grain values for
Glab_UpperCoast and Glab_LowerCoast (Figures 6g, 6h).
Figures 6g and 6 h show that when canopy cover decreased the
200 grain weight for the Glab_UpperCoast cluster decreased more
than the 200 grain weight for the Glab_LowerCoast cluster.
Therefore, it can be concluded that the Glab_LowerCoast cluster
was less susceptible to variation in environment. The 200 grain
weight for clusters within indica and japonica decreased in a similar
way when A and yield decreased. These clusters were similarly
sensitive to the environment. In general, all glaberrima clusters (and
also Ind_Gc) maintained their grain weight across environments
even at low yield (Figure 12). This is contrary to the Ind_Gh and
two japonica clusters, for which the empty grains increased at lower
yield levels. This underscores the claim we make for the robustness
of farmer varieties of glaberrima and Ind_Gc, and the consequent
ability of these types consistently to produce good grains
throughout a range of difficult environments.
However, an increasing trend in panicle weight was observed
when yield increased (0.601**) (Figures 6e and 7f). Such trends
were not observed for the glaberrima group, suggesting that panicle
weight of glaberrima was more stable. No significant differences or
trends were found, for clusters within the glaberrima, japonica and
indica groups, for panicle weight, with the exception of Jap_SL,
which showed a positive relation with A (r = 0.674*). Panicle
weight for cluster Jap_GbGh showed no relation with A.
Panicle length
Significant G6E interactions were found for all botanical
groups. The Glab_UpperCoast, Jap_GbGh and Jap_SL clusters
all showed significant G6E interactions. There was a tendency
towards short panicle production in Ghana and Sierra Leone, the
countries where the yields were generally high (Figure 11). The
cluster Glab_UpperCoast produced significantly longer panicles
than all other clusters except for Jap_GbGh. The fact that the
Glab_UpperCoast cluster had a panicle weight similar to that of
Glab_LowerCoast implies that Glab_UpperCoast produced more
grains of smaller size per panicle than Glab_LowerCoast. The
cluster Glab_UpperCoast also showed a rather slight negative
correlation between panicle length and yield (r = 20.332**), A
(r = 20.335*) and a somewhat stronger negative correlation with
the 200 grain weight (r = 20.427**). This means that for the
Glab_UpperCoast, cluster production of short panicles corresponded with high A, yield and grain weight. This implies that
under stress conditions (i.e. low yield and low A) Glab_UpperCoast invested more in panicle length (Figure 11). The negative
relation between yield and panicle length was also observed,
somewhat more strongly, for Glab_LowerCoast (r = 20.708**),
Ind_Gc (r = 20.850**), Ind_Gh (r = 20.664**) and Jap_GbGh
(r = 20.450**). Jap_SL did not show any relation between yield
and panicle length.
200 grain weight
Significant G6E interactions were found for 200 grain weight
for the glaberrima group and the Glab_UpperCoast cluster,
suggesting that the genotypes composing the Glab_UpperCoast
cluster responded differently across environments for 200 grain
weight. This might be a factor in observed robustness in yield for
this cluster. The absence of G6E interactions within the other
botanical groups suggests that the 200 grain weight is genetically
determined. The high estimate of wide sense heritability
(H2 = 80%; Table 13) confirms this general trend for indica.
However, the relatively low wide sense heritability estimate for
japonica (H2 = 32%; Table 13) as compared to other botanical
groups indicates that environmental conditions might have some
considerable impact on the 200 grain weight of japonica. However,
it is only with the glaberrima group, and not for japonica or indica,
that a significant location effect was found.
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Discussion
Figure 3 showed that the two clusters of the glaberrima group
maintained a minimum yield of 660 kg/ha in all environments.
We observed that in trials in two countries where yields were
relatively high (Ghana and Sierra Leone) the indica sourced from
Guinea maintained a yield level close to that of glaberrima. But in
the Guinea Bissau and Togo trials, the likelihood of crop failure
10
March 2013 | Volume 8 | Issue 3 | e34801
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Table 14. Pearson correlations between yield components and days to 50% flowering.
Cluster
Plant
height (cm)
Panicle
length (cm)
Number
of tillers
Number of
panicles
Panicle
weight (g)
200 grain
weight (g)
Plot yield
(kg/ha)
Canopy
cover A (%)
All
20.390**
0.073
20.018
0.045
0.101
20.581**
20.298**
20.661**
0.080
20.538**
Glab
20.194
Ind
20.693
Jap
20.593
Glab_UpperCoast
*
0.211
**
*
0.111
0.115
**
0.413
0.138
0.272
20.113
20.432
*
20.335
0.189
0.193
Ind_Gc
20.751**
0.119
Ind_Gh
20.649**
0.073
Jap_GbGh
20.699
Jap_SL
20.548
0.058
**
0.355
**
0.043
Glab_LowerCoast
**
0.304
**
0.464
**
20.306
20.029
0.385
**
20.237
0.641
**
20.515
20.839
20.716
20.705
**
**
**
**
**
20.511
0.266
0.497*
0.589*
20.416
20.878**
20.403
0.370
0.262
20.221
20.862**
20.273
0.459
20.619
**
*
20.449
20.054
20.611
20.685
20.702
**
**
**
*
0.245
20.714
20.855**
20.316
0.099
20.274
0.289
*
20.428
20.559
20.880**
20.482**
*
20.668**
20.854**
20.873**
**
20.342
20.896**
20.877**
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t014
11
Table 15. Pearson correlations between yield components and plant height (cm).
Cluster
Days to 50%
flowering
Panicle
length (cm)
Number of tillers
Number of
panicles
Panicle
weight (g)
200 grain
weight (g)
Plot yield
(kg/ha)
Canopy
cover A (%)
20.390**
0.225**
20.206**
20.168*
0.179
0.301**
0.346**
0.596**
20.194*
0.337**
20.384**
20.530**
20.067
0.051
0.168
0.671**
Ind
20.693**
0.274
20.495**
20.113
0.580*
0.631**
Jap
**
*
0.348
**
0.420
0.438
**
0.181
0.826**
Glab_UpperCoast
Glab_LowerCoast
Ind_Gc
20.593
0.290
20.113
0.093
**
0.152
20.335
20.751
0.034
**
**
0.123
20.583
*
20.408
**
**
**
20.677
20.673
**
**
*
20.098
20.788
0.670
**
**
0.555**
0.621**
0.020
0.796**
0.615
*
0.228
0.784**
*
0.359
Ind_Gh
20.649
0.143
0.674
0.682
0.393
0.485
Jap_GbGh
20.699**
20.139
0.061
20.134
0.229
0.359*
0.482**
0.635**
Jap_SL
20.548**
0.323
0.300
0.254
0.727*
0.368
0.452*
0.640**
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t015
0.450
20.191
20.550
0.442
20.017
0.392*
20.520
How Robust Are Rice Varieties in West Africa?
March 2013 | Volume 8 | Issue 3 | e34801
All
Glab
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Table 16. Pearson correlations between yield components and panicle length (cm).
Cluster
Days to 50%
flowering
Plant
height (cm)
Number
of tillers
Number
of panicles
Panicle
weight (g)
200 grain
weight (g)
Plot yield
(kg/ha)
Canopy
cover A (%)
All
0.073
0.225**
0.182**
0.120
0.102
20.187*
20.293**
20.256**
**
20.355**
Glab
0.211
*
0.337
**
0.107
0.023
0.731
**
20.542
**
20.338
Ind
0.115
0.274
0.484**
0.124
20.128
0.240
20.767**
20.132
Jap
0.138
0.034
0.192
20.085
0.065
20.159
20.338**
20.317*
**
20.335*
Glab_UpperCoast
0.272
*
0.290
**
0.220
0.130
0.728
**
20.427
**
20.332
**
Glab_LowerCoast
0.189
0.152
0.338
0.099
0.525
20.319
20.708
Ind_Gc
0.119
0.123
0.463*
20.145
20.488
20.328
20.850**
20.227
Ind_Gh
0.073
0.450*
0.600**
0.485*
0.868
0.511
20.664**
0.040
Jap_GbGh
0.058
20.139
0.335*
0.091
0.087
20.136
20.450**
20.319
Jap_SL
0.289
0.323
20.142
20.353
0.465
20.379
20.313
20.479
20.362
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t016
12
Table 17. Pearson correlations between yield components and number of tillers.
Cluster
Days to 50%
flowering
Plant
height (cm)
Panicle
length (cm)
Number of
panicles
Panicle
weight (g)
All
20.018
20.206**
0.182**
0.800**
Glab
0.111
20.384**
0.107
0.815**
Ind
0.413
Jap
20.432**
0.093
Glab_UpperCoast
0.043
20.191
Glab_LowerCoast
0.193
20.495
20.550
*
**
**
**
0.484
**
Canopy
cover A (%)
20.562**
0.147
20.125
0.165*
0.025
0.145
20.328**
**
20.130
20.361
0.089
20.573
0.192
0.518**
20.018
0.564**
0.239
0.604**
0.220
0.768**
0.232
20.137
20.272*
20.087
0.338
0.857
**
0.296
20.389
20.446*
20.512*
0.895
**
*
20.314
Ind_Gc
0.497
20.527
20.488
20.616
Ind_Gh
0.370
20.520**
0.600**
0.525*
20.110
0.211
20.594**
20.170
Jap_GbGh
20.274
0.061
0.335*
0.301
20.357
0.394*
0.042
0.608**
Jap_SL
20.619**
0.300
20.142
0.420
0.446
0.705**
0.236
0.784**
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t017
20.583
0.463
*
0.677
**
Plot yield
(kg/ha)
20.532
How Robust Are Rice Varieties in West Africa?
March 2013 | Volume 8 | Issue 3 | e34801
**
200 grain
weight (g)
20.034
was high overall. This might be due to the relatively short rainy
season in Guinea Bissau and to the acidity of the soil in Togo. In
contrast, varieties in the Ind_Gh cluster yielded only in Sierra
Leone and to a lesser extent in Ghana, with a high frequency of
zero yield. In Ghana and Sierra Leone Jap_GbGh showed a yield
level similar to that of the glaberrima clusters. In Guinea Bissau and
Togo, Jap_GbGh had a low yield but still reached at least 320 kg/
ha.
In contrast, Jap_SL only showed a good yield level (without zero
yield) in Sierra Leone. In Guinea Bissau the yield for Jap_SL
dropped to 200 kg/ha and the frequency of crop failure increased
in Togo and Ghana. Jap_SL thus seemed to be specifically well
adapted to the ecology of Sierra Leone. Like Jap_SL, Ind_Gh
produced only in Sierra Leone. This might be attributed to the
characteristics of the varieties (Viono tall and Zomojo). These
varieties from Ghana are mostly cultivated in the lowlands but
have proven to suit certain specific upland niches in Ghana for
which the conditions were apparently not met in the Ghana trial
but were approached best in Sierra Leone. Okry et al. [14] also
reported on such transfer of varieties across agro-ecologies. They
provided a case where farmers were trying CK 21, a typical
lowland variety in the upland in the region of Guinea known as
Guinea Maritime. Given that farmers have decided, for their own
reasons, to shift this variety from the recommended domain, it
could be counted as an instance of G6E6S (society) interaction.
These findings on the yield show that clusters differed in yield
performance across environments. Glab_UpperCoast, Glab_LowerCoast, Jap_GbGh and Ind_Gc were best able to maintain their
yield across environments. Farmers often look for varieties that
assure minimum yield in environments with variable and stressful
conditions. These varieties seemingly satisfy such objectives of
farmers.
Observations of average performance at cluster level revealed
that canopy development and yield scenarios differed between and
within botanical groups. Glab_UpperCoast and Glab_LowerCoast showed the highest values for Vmax, A and yield. The two
clusters of indica, Ind_Gh and Ind_Gc, showed similar values for
Vmax and A, although the latter significantly outperformed the
former in yield. Moreover, Ind_Gc had a canopy development
(Vmax and A) and yield similar to Glab_LowerCoast and
Jap_GbGh. Whereas Jap_GbGh and Jap_SL did not significantly
differ in Vmax or A, Jap_GbGh had a significantly higher yield
than Jap_SL. Additionally, Jap_GbGh – although displaying low
values of Vmax and A – showed an average yield similar to that of
glaberrima and Ind_Gc. The clusters Jap_SL and Ind_Gh
developed a smaller canopy and also had the lowest yield. From
these findings we infer that lower A can be associated with higher
yield, and high canopy growth can be associated with lower yields.
These associations are strongest for Ind_Gh (lower yield with
higher A) and Jap_GbGh (higher yield with lower A).
Looking at the overall averages in Table 11 the ratio number of
panicles over number of tillers was highest for glaberrima (0.94),
followed by indica (0.72) and japonica (0.70), suggesting that the
tillers of glaberrima produced more panicles. Particularly under less
favourable conditions (e.g. Guinea Bissau) a difference was
observed between botanical groups in the ratio of the number of
panicles and tillers (Table 12). Of the botanical groups, only the
clusters of the indica group varied, with tillers of Ind_Gc producing
more panicles than those of Ind_Gh (0.80 and 0.65 respectively).
However, looking at the averages per country for each botanical
group and molecular cluster we observed that the increase in
tillering for the indica group resulted in increased panicle
production: the ratio of number of panicles over number of tillers
remained stable or even increased at lower yield (Table 12). The
0.321
a
: not estimated.
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t018
0.254
20.449
Jap_SL
20.353
0.420
.a
0.717**
0.076
0.314
20.022
0.038
20.116
0.707
.a
0.301
20.134
0.459
Jap_GbGh
0.091
.a
0.525*
*
0.143
0.262
Ind_Gh
0.485*
20.521
0.478
20.677
20.002
.a
20.673*
0.589*
0.895**
20.228
Ind_Gc
-0.145
20.824**
20.281
20.335
20.009
0.474**
0.207
0.159
.a
.
0.099
0.768
0.130
20.677**
20.408
0.385
0.099
Glab_UpperCoast
Glab_LowerCoast
0.857**
a
.a
**
0.518**
20.085
**
20.017
20.029
Jap
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**
20.280
0.137
20.201
0.638*
.a
0.677**
0.124
0.355
Ind
20.113
0.122
0.150
20.453**
0.304
Glab
20.530
0.282*
0.083
0.023
0.815
.
a
.a
**
0.800**
0.120
**
20.168*
*
0.045
All
Panicle
length (cm)
Plant
height (cm)
Days to 50%
flowering
Cluster
Table 18. Pearson correlations between yield components and number of panicles.
Number of
tillers
Panicle
weight (g)
200 grain
weight (g)
Plot yield
(kg/ha)
Canopy
cover A (%)
How Robust Are Rice Varieties in West Africa?
13
March 2013 | Volume 8 | Issue 3 | e34801
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Table 19. Pearson correlations between yield components and panicle weight (g).
Cluster
Days to 50%
flowering
Plant
height (cm)
Panicle
length (cm)
Number
of tillers
Number of
panicles
200 grain
weight (g)
Plot yield
(kg/ha)
Canopy
cover A (%)
All
0.101
0.179
0.102
20.562**
.a
0.231*
0.228*
20.225*
0.109
20.417**
Glab
0.464
**
0.731
20.067
*
**
a
0.025
.
a
20.625
**
**
0.701
**
0.503
0.251
Ind
20.306
0.580
20.128
20.361
.
0.716
Jap
20.237
0.442*
0.065
20.018
.a
0.379*
0.563**
Glab_UpperCoast
0.641**
20.098
0.728**
0.232
.a
20.553**
0.243
20.268
Glab_LowerCoast
0.245
20.788**
0.525
0.296
.a
20.299
20.347
20.551
Ind_Gc
20.416
0.670
20.488
20.527
.a
0.778*
0.755*
0.623
a
0.574
Ind_Gh
20.221
0.674
0.868
20.110
.
0.617
0.702
Jap_GbGh
20.054
0.229
0.087
20.357
.a
0.563**
0.382
20.046
Jap_SL
20.611
0.727*
0.465
0.446
.a
0.320
0.824**
0.674*
a
: not estimated.
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t019
14
Table 20. Pearson correlations between yield components and 200 grain weight (g).
Days to 50%
flowering
Plant
height (cm)
Panicle
length (cm)
Number
of tillers
Number
of panicles
Panicle weight (g)
Plot yield
(kg.ha21)
Canopy
cover A (%)
All
20.581**
0.301**
20.187*
0.147
0.282*
0.231*
0.369**
0.568**
0.218
0.596**
Glab
20.515
Ind
Jap
Glab_UpperCoast
Glab_LowerCoast
Ind_Gc
Ind_Gh
Jap_GbGh
Jap_SL
**
0.051
20.542
20.839**
0.631**
20.716**
0.348**
**
**
20.705
20.714
20.878
20.862
20.685
20.702
**
**
**
**
**
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t020
0.438
0.359
0.615
0.682
*
0.359
*
0.368
**
0.145
0.083
0.240
0.089
0.638*
0.716**
0.809**
0.581**
20.159
0.564**
0.207
0.379*
0.621**
0.692**
0.223
0.725**
20.427
20.319
*
**
20.328
0.511
20.136
20.379
**
20.137
20.389
20.488
0.211
20.335
0.159
20.002
0.707
0.394
*
0.705
**
20.116
0.321
20.625
20.553
20.299
0.778
*
0.617
0.563
0.320
**
**
0.766
**
0.499*
0.902
**
0.834**
0.861
*
0.612*
0.600
**
0.708**
0.599
*
0.628*
How Robust Are Rice Varieties in West Africa?
March 2013 | Volume 8 | Issue 3 | e34801
Cluster
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Table 21. Pearson correlations between yield components and plot yield (kg/ha).
Cluster
Days to 50%
flowering
Plant
height (cm)
Panicle
length (cm)
Number
of tillers
Number
of panicles
Panicle
weight (g)
200 grain
weight (g)
Canopy
cover A (%)
All
20.298**
0.346**
20.293**
20.125
0.150
0.228*
0.369**
0.478**
0.109
0.218
0.450**
**
**
**
Glab
0.080
0.168
20.338
Ind
20.316
0.392*
20.767**
20.573**
20.201
0.701**
0.809**
0.483*
Jap
20.511**
0.420**
20.338**
0.239
0.474**
0.563**
0.621**
0.706**
0.243
0.223
0.476**
Glab_UpperCoast
0.266
*
Glab_LowerCoast
20.428
Ind_Gc
20.403
0.181
*
20.332
**
0.020
20.708
20.850**
**
Ind_Gh
20.273
0.393
20.664
Jap_GbGh
20.559**
0.482**
20.450**
Jap_SL
0.452
20.342
20.272
**
0.228
*
20.328
20.446
20.453
*
20.281
*
20.824
-0.616*
20.594
20.677
**
0.042
0.236
20.313
**
**
20.347
0.766
0.755*
0.902**
20.022
0.702
0.861
0.382
0.600**
0.717
0.824
**
0.599
0.857**
*
0.038
**
0.451
0.137
0.848**
*
0.497
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t021
15
Table 22. Pearson correlations between yield components and canopy cover A (%).
Days to 50%
flowering
Plant
height (cm)
Panicle
length (cm)
Number
of tillers
Number
of panicles
All
20.661**
0.596**
20.256**
0.165*
Glab
20.538**
0.671**
20.355**
20.130
**
0.555
**
0.621
**
0.826
**
0.796
**
Ind
Jap
Glab_UpperCoast
20.855
20.880
20.482
**
**
**
Glab_LowerCoast
20.668
Ind_Gc
20.854**
**
0.784**
20.132
20.317
20.335
*
0.604
**
20.512
20.227
20.532
Plot yield
(kg/ha)
0.122
20.225*
0.568**
0.478**
20.280
20.417**
0.596**
0.450**
20.009
20.087
20.362
200 grain
weight (g)
0.137
20.314
*
Panicle
weight (g)
20.228
*
0.503
0.251
20.268
0.581
**
0.483*
0.692
**
0.706**
0.725
**
0.476**
*
0.451
-0.521
20.551
0.499
0.478
0.623
0.834**
*
0.857**
Ind_Gh
20.873
0.485
0.040
20.170
0.314
0.574
0.612
Jap_GbGh
20.896**
0.635**
20.319
0.608**
0.076
20.046
0.708**
0.848**
Jap_SL
20.877**
0.640**
20.479
0.784**
20.034
0.674*
0.628*
0.497
*: Significant at 0.05 level. **: Significant at 0.01 level.
doi:10.1371/journal.pone.0034801.t022
0.137
How Robust Are Rice Varieties in West Africa?
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Cluster
How Robust Are Rice Varieties in West Africa?
Figure 1. Relation between the accumulated canopy cover over the whole growing cycle (A; y-axis, in %.days) and the maximum
canopy cover (Vmax; x-axis, in %). Data refer to all combinations of location 6 genotype 6 sowing time, whereas different symbols refer to
different botanical groups (glaberrima, indica and japonica).
doi:10.1371/journal.pone.0034801.g001
combination of the high number of tillers and panicles for Ind_Gh
together with low yield suggests that its panicles have a large
percentage of non-formed (i.e. empty) grains.
In general the number of tillers correlated (r = 0.800**) with the
number of panicles per plant which in turn correlated with A. The
fact that the relationship between the number of tillers and A was
not clear for all botanical groups might imply that other variables
such as the size of the tillers, leaf width, leaf length and leaf blade
angle, which were not measured in these experiments, might
account for the overall poor relationships we observed between A
and the number of tillers per plant. Vigour-related variables are
known to vary between rice species, O. glaberrima being often more
vigorous than O. sativa [10–12].
The longest average period until 50% flowering was observed
with the indica group. The glaberrima group showed the shortest
period until 50% flowering, suggesting that this group had a
shorter vegetative cycle. The result agrees with farmers’ assertions
that glaberrima (e.g. farmer varieties Malaa and Jangjango) are often
earlier than other traditional sativa varieties and thus are used to
beat the pre-harvest hunger gap [15].
Comparing the negative relationship between time to 50%
flowering and A it can be said that this relation is most clear for
japonica and indica (r = 20.880** and r = 20.855** respectively).
The same relation was observed at cluster level for these two
botanical groups. The glaberrima group and its clusters showed
lower correlations between 50% flowering and A (r = 20.538** for
Figure 2. Box plots for accumulated canopy cover (A; %.days) of 26 varieties in three experimental sites: Ghana (1); Togo (4) and
Guinea (5). See materials and methods section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g002
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Figure 3. Box plots for grain yield (in kg/ha) of 26 varieties in four experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea Bissau;
and 4: Togo; in 5: Guinea yield was not measured. See materials and methods section for coding of the botanical groups and
molecular clusters.
doi:10.1371/journal.pone.0034801.g003
shortest time to 50% flowering, a useful property for farmers
affected by a pre-harvest hunger gap [15].
Overall, accumulated canopy, maximum canopy cover and
yield were similar for Glab_LowerCoast and Glab_UpperCoast
clusters. But the two clusters differed in their strategy of canopy
building: Glab_LowerCoast invested more in tiller production
while Glab_UpperCoast produced taller plants. When A decreased, Glab_LowerCoast was better able to maintain its grain
weight than Glab_UpperCoast and therefore appears to be more
stable in grain weight. Under stress conditions (i.e. low yield and
low A) Glab_UpperCoast invested more in panicle length. Also
glaberrima from the Lower Coast showed higher values for 200
grain weight and the decrease of the 200 grain weight at lower
yield levels was also less. However, the panicle weight for
Glab_LowerCoast was less than that of the cluster Glab_UpperCoast. This also applies to panicle length and plant height. The
Glab_LowerCoast varieties thus tended to invest more in grain
weight, whereas Glab_UpperCoast varieties produced more grains
per panicle. These two distinct strategies led to similar yields for
these two clusters.
In sum, among the studied genotypes, those of O. glaberrima
developed different strategies of adaptation, but interestingly, these
strategies led to similar performance throughout the range of
environments tested, demonstrating the robustness of this group of
rices when compared to other botanical groups. These strategies
relate to the area of collection of the varieties and also coincide
with molecular groupings [16].
The glaberrima showed more G6E interactions than indica and
japonica. This is worthy of note, since it is sometimes assumed that O.
the botanical group). This might imply that the environmental
conditions determining accumulated canopy cover (A) affected
50% flowering of the glaberrima and its clusters less than that of the
other varieties. This suggests that glaberrima is more stable in terms
of time to 50% flowering. An advantage of such stability would be
that even under high stress conditions farmers do not run the risk
that the crop will delay its flowering beyond the scope of the rainy
season. This is more likely the case for the varieties from Upper
Guinea Coast. Varieties from Lower Guinea Coast usually
experience a short dry period 2 to 4 weeks after planting. In such
conditions it is important for the rice crop not to flower too early.
The stability in flowering time for the glaberrima group takes care of
that.
When summarizing the relation between the yield and yield
determining variables, our study has shown that a large number of
farmer varieties are able to adapt to large variations in
environment. Our findings on tillering, yield, A, flowering and
number of panicles suggest the existence of three different
physiological strategies of adaptability for each of the botanical
groups, which we now attempt to summarise.
Glaberrima
Across environments O. glaberrima consistently showed the
highest values for maximum canopy, plant height, number of
panicles and yield. Also remarkable was the absence of crop failure
for the glaberrima group; this helps explain why it makes a more
reliable and secure choice for sub-optimal farming or situations of
special difficulty. In addition, the glaberrima group showed the
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How Robust Are Rice Varieties in West Africa?
vigorous, as the increase in number of tillers did not lead to an
increase in A. These tillers were, however, productive because an
increase in tillering led to an increase in panicle production. The
fact that an increase in panicle production did not lead to an
increase in yield is a product of the crop failure observed for many
plots in the less favourable environments, and the many panicles
with unfilled grains.
The cluster Ind_Gc showed the highest plant height. This
observation together with observations of high Vmax and A for
Ind_Gc implies that Ind_Gc is more vigorous compared to
Ind_Gh. This vigour resulted in higher yields for Ind_Gc. The
Ind_Gc cluster also displayed the same average plant height as the
Glab_UpperCoast cluster.
This shows that the Ind_Gc cluster, like glaberrima, is able to
maintain its yield. At lower yield levels, however, it follows a
different physiological strategy of adaptation than glaberrima, as it
produced the largest number of tillers. But compared to glaberrima,
these tillers contributed less to A and contributed also less to yield
maintenance, as there were high numbers of unfilled grains.
In sum, the indica from Guinea resembled the glaberrima group in
several ways. Like glaberrima it was able to maintain its number of
tillers and also increased its number of panicles at low yield levels.
Like glaberrima, it showed significant G6E interactions that helped
to stabilise A and Vmax.
Japonica
Low canopy cover and limited tiller and panicle production
seem typical for the japonica group. At a high level of A, japonica
consistently produced more tillers. This relation seemed linear, as
was the relation between yield and accumulated canopy, thus
suggesting that an increase in tillering contributes to canopy
formation and yield. In addition, japonica slightly increased its
panicle number while tillering, A and Vmax were not maintained at
low yield levels. Instead of investing in high tiller number japonica
invested more in panicle weight: when compared with glaberrima
and indica panicle weight was approximately 50% to 100% higher.
The Jap_GbGh cluster maintained a yield across environments
similar to that of the glaberrima group and indica cluster from
Guinea, although it failed to maintain A at lower yield level. In
contrast, varieties in the Jap_SL cluster only yielded well in Sierra
Leone. This might suggest that these japonica varieties were highly
adapted to a specific niche. In Sierra Leone, however, varieties in
the japonica group are often found bridging an ecological gradient
from lowland to upland [15].
Figure 4. The relation between yield (in kg/ha; y-axis) and
accumulated canopy cover (A in %.days; x-axis) for three
botanical groups. Different symbols refer to different molecular
clusters. Values presented are averages of 5 replications. Correlation
coefficients are: a (varieties belonging to glaberrima): r = 0.476 (P,0.01);
b (varieties belonging to indica): r = 0.483 (P,0.05); c (varieties
belonging to japonica): r = 0.706 (P,0.01).
doi:10.1371/journal.pone.0034801.g004
Observed behaviour of the studied genotypes in relation
to the area of collection
Glab_LowerCoast. Farmers in the Togo Hills (Togo mountain ranges) in Ghana and Togo traditionally used these varieties
mainly on stony hills and slopes with poor soil because political
conflict and war drove them into mountainous areas, since life on
the plains was too dangerous. Reliability of yield was very
important in these conditions and rice was probably once the main
carbohydrate crop. The data for this cluster indeed show that they
are highly reliable in relation to yield. Nowadays these varieties are
cultivated on the Ghanaian slopes of the Togo Hills only for
ceremonial reasons, because lowland farming has been added to
the local farming repertoire since the 1960s, and other crops like
cassava and maize are now more important than previously [17].
Occasionally African rice is used on the Ghanaian slopes and in
the lowlands of the Togo Hills when farmers are very late with
sowing rice. African rice is used because of its short cycle. Farmers
in the Togo Hills (Danyi Plateau) grow only African rice, which is
an important secondary crop. They said they have tried other
glaberrima is genetically less diverse than indica and japonica. Molecular
analysis conducted by Nuijten et al. [16] showed that glaberrima and
japonica were roughly similar in terms of genetic diversity:
(He = 0.034; n = 66) and (He = 0.045; n = 87), respectively).
Indica
In less favourable environments varieties of the indica group
produced more tillers than in the more favourable environments.
The underlying mechanism seems to be that under less favourable
conditions flowering is delayed and at the same time the tillering
period is prolonged. The result is that at higher yield levels indica
produced fewer tillers. At lower yield levels indica seemed less
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Figure 5. Box plots for plant height (in cm) of 26 varieties in five experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea Bissau; 4:
Togo and 5: Guinea. See materials and methods section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g005
Ind_Gc. These varieties appeared to be stable in yield and in
that way resemble O. glaberrima and Jap_GbGh. The Ind_Gc types
are widely cultivated in the area of collection, under typical upland
conditions on poor soils. Farmers state that rices in the Ind_Gc
cluster resemble O. glaberrima in being well adapted to poor soils.
They are also drought tolerant when compared to other O. sativa
varieties (e.g. Samba, Dalifodé, Podê) and also yield well under
good conditions (as well as well enough, under poor conditions).
They dominate upland rice cultivation in their area of collection
because, as farmers state, O. glaberrima lodges at complete maturity,
as frequently mentioned as a drawback by a number of rice
researchers [7,19,20]. Farmers claim this results in low yields,
especially when they lack sufficient labour for a timely harvest.
Ind_Gh. These are varieties that performed relatively poorly
in our experiments, except in Sierra Leone. In addition to
cultivation under upland conditions (in the Ghanaian Togo Hills)
these varieties are also cultivated very successfully in the adjacent
lowlands. Since the 1960s lowland cultivation has been added to
the farming systems of the different minority groups living at the
foot of the Togo Hills. Ever since that time farmers have been
experimenting with lowland varieties in the upland area and vice
versa. The varieties in the Ind_Gh cluster are probably adapted to
very specific upland conditions in the Ghanaian Togo mountain
ranges, conditions apparently replicated in experimental conditions at the foot of the Sierra Leonean escarpment (Kamajei
Chiefdom).
Jap_GbGh. These varieties are commonly planted under
upland conditions. They are equal in yield to the two O. glaberrima
clusters and the Ind_Gc cluster. Farmers grow them for their white
pericarp, good taste and the fact that they fit the rainy season
calendar very well, being not too short, and not too long. Farmers
varieties but nothing works as well in the hills as the rices of the
Glab_LowerCoast cluster.
Glab_UpperCoast. The Upper West African Coast includes
two secondary centres of domestication and diversity for O.
glaberrima [18], so we might not expect a great deal of similarity in
the behaviour of genotypes collected from this region (on a
transect from Senegal to Sierra Leone). When comparing the
Glab_LowerCoast to Glab_UpperCoast in our experiments the
differences observed within and between clusters appear to reflect
the fact that rice farmers on the Upper Coast grow rice as their
main staple, and work a much broader range of environments (and
thus exercise a larger range of selection pressures) than the farmers
in the Togo Hills. Farmers experience quite different constraints in
their farming systems. In the semi-arid zone of the Upper Coast
(Senegal, Gambia and Guinea Bissau), a short rainy seasons (3 to
4 months) may have forced farmers to select for short duration
glaberrima types better adapted to their conditions. In these
conditions, farmers appear to have selected taller plants with
longer panicles and fewer tillers.
In the forest belt of Sierra Leone and Guinea, with a much
longer rainfall period (6 to 7 months) the environment is
favourable for longer duration crops. However, farmers still
cultivate O. glaberrima to some extent because of its adaptability to
poor, eroded soils and tolerance to drought at the beginning and
end of the rainy season. In the forest belt farmers report many
weed problems [15], particularly in areas with short fallow periods.
Selecting for tall plants could also help in suppressing weed. In
addition farmers seem to have selected glaberrima types that were
less photoperiod sensitive, facilitating the planting of shortduration types to be sown in late April and used as hunger
breaker crops.
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How Robust Are Rice Varieties in West Africa?
Figure 6. Relation between accumulated canopy cover (A; in %.days; x-axis of a, c, e, g) or grain yield (in kg/ha; b, d, f, h) and plant
height (a, b), number of panicles (c, d), panicle weight (e, f) and 200 grain weight (g, h). Different symbols refer to different botanical
groups or molecular clusters within the glaberrima botanical group. Values presented are averages of 5 replications. See materials and methods
section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g006
Elsewhere (in Ghana and Sierra Leone, for example) farmers
actually prefer varieties with red pericarp. This underlines the
importance of taking into account cultural factors in crop
development [4].
visiting the trial in Guinea Bissau were very impressed with the
growth of some varieties of this japonica cluster, and indicated they
would like to grow these varieties in the following season.
However, upon realising the pericarp colour was red these farmers
lost interest, as they have a strong preference for white seed colour.
Figure 7. The relation between accumulated canopy cover (A; in %.days; x-axis of a, b, c) or grain yield (in kg/ha; x-axis of d, e, f) and
the number of tillers per plant for each of the three botanical groups and their respective molecular clusters. Series TG, GH and GC
respectively indicate observations from Togo, Ghana and Guinea. Values presented are averages of 5 replications for each of the two sowing dates.
See materials and methods section for coding of the botanical and molecular clusters.
doi:10.1371/journal.pone.0034801.g007
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How Robust Are Rice Varieties in West Africa?
Figure 8. Box plots for number of panicles of 26 varieties in five experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea Bissau; 4:
Togo and 5: Guinea. See materials and methods section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g008
Figure 9. Box plots of number of tillers per plant of 26 varieties in five experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea
Bissau; 4: Togo and 5: Guinea. See materials and methods section for coding of the botanical and molecular clusters.
doi:10.1371/journal.pone.0034801.g009
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Figure 10. Box plots for days to 50% flowering of 26 varieties in five experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea Bissau;
4: Togo and 5: Guinea. See materials and methods section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g010
Figure 11. Box plots for panicle length of 26 varieties in five experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea Bissau; 4: Togo
and 5: Guinea. See materials and methods section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g011
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Figure 12. Box plots for average 200 grain weight of 26 varieties in five experimental sites: 1: Ghana; 2: Sierra Leone; 3: Guinea
Bissau; 4: Togo and 5: Guinea. See materials and methods section for coding of the botanical groups and molecular clusters.
doi:10.1371/journal.pone.0034801.g012
Figure 13. Geographic overview of the West African study area. Reprinted from [16] under a CC BY license, with permission from Edwin
Nuijten, copyright 2009. Original figure generated using Google Maps.
doi:10.1371/journal.pone.0034801.g013
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Table 23. List of varieties used in the study.
Code
Name of variety
Molecular cluster
Country of collection
Ecology of cultivation
O. glaberrima
333
Saali Firê
Glab_UpperCoast
Guinea
Upland
347
Safaary
Glab_UpperCoast
Guinea
Upland
334
Tombo Bokary
Glab_UpperCoast
Guinea
Upland
318
Saali Forê
Glab_UpperCoast
Guinea
Upland
420
Jangjango
Glab_UpperCoast
Guinea Bissau
Upland/transition
435
Kurekimbeli
Glab_UpperCoast
Guinea Bissau
Upland/transition
113
Kaomo black
Glab_LowerCoast
Ghana (Togo mountain ranges)
Upland
124
Xleti eve
Glab_LowerCoast
Togo (Togo mountain ranges)
Upland
135
Kpakpalipke
Glab_LowerCoast
Togo (Togo mountain ranges)
Upland
272
Saliforeh
Glab_UpperCoast
Sierra Leone
Transition/upland
249
Maalay
Glab_UpperCoast
Sierra Leone
Transition/upland
O. sativa type indica
348
Saidou Firê
Ind_Gc
Guinea
Upland
349
Saidou Gbéli
Ind_Gc
Guinea
Upland
130
Zomojo
Ind_Gh
Ghana (Togo mountain ranges)
Upland/transition/lowland
128
Viono tall
Ind_Gh
Ghana (Togo mountain ranges)
Upland/transition/lowland
163
Ataa
Ind_Gh
Ghana (Togo mountain ranges)
Upland/transition
O. sativa type japonica
407
Demba Ba
Jap_GbGh
Guinea Bissau
Upland
427
Uyeey
Jap_GbGh
Guinea Bissau
Upland
432
Usefa Udjenel
Jap_GbGh
Guinea Bissau
Upland
141
Aqua blue
Jap_GbGh
Ghana (Togo mountain ranges)
Upland/transition
274
Nduliwa
Jap_SL
Sierra Leone
Transition/upland
210
Gbengbeng
Jap_SL
Sierra Leone
Transition/upland
215
Jebbeh-komi
Jap_SL
Sierra Leone
Transition/upland
408
Buba Njie
Jap_GbGh
Guinea Bissau
Upland/transition
Transition: variety cultivated in transitional zone between lowland and upland. Ind_Gc = cluster of indica from Guinea. Ind_Gh = cluster of indica from Ghana.
Jap_GbGh = cluster of japonica from Guinea Bissau and Ghana. Jap_SL = cluster of japonica from Sierra Leone. Glab_LowerCoast = cluster of glaberrima from Lower
Guinea Coast. Glab_UpperCoast = cluster of glaberrima from Upper Guinea Coast.
doi:10.1371/journal.pone.0034801.t023
Jap_SL. These varieties seem to be very specifically adapted
to Sierra Leonean conditions. They are widely cultivated in this
area of collection. Farmers who are conversant with them typically
look for toposequences to allow flexible planting up and down
slopes, taking account of the stage of the season. They are thus
adapted to a mid-slope planting scenario, between wetland and
upland varieties. The mid-slope niche is very common in an
undulating, well-watered country such as Sierra Leone, but is less
common in the other areas in which we carried out experiments.
This may explain why this particular group only seemed to do well
in its zone of collection. It has been selected for robustness in a
niche.
This paper has presented evidence that farmer rice varieties in
coastal West Africa are, for the most part, highly robust, and welladapted to a range of sub-optimal farming conditions. A case has
been made that much of this robustness is a product of adaptation.
An implication is that many farmer varieties will maintain their
performance across a range of low-input conditions, and thus
might be very useful to farmers in neighbouring countries. More
efforts should be made to conserve, evaluate and distribute farmerselected rice planting materials in the region. Farmers themselves
should be consulted about the best way to develop relevant
modalities of dissemination, and involved directly in any such
activity.
Conclusion
Materials and Methods
It can be concluded, that the glaberrima group as a whole, and
the indica cluster from Guinea and japonica from Guinea Bissau and
Ghana, were more plastic than other rices in the study, allowing
them to be more constant in yield, A, and in number of tillers and
panicles. Seemingly, farmer selection in Guinea has created a
group of Asian rices that resemble in performance the highly
adapted African rices of the region.
Ethics statement
PLOS ONE | www.plosone.org
We confirm that no specific permits were required for the
locations where the described field trials were conducted, that
these locations were not protected in any way, and that none of
these field studies involved endangered or protected species. We
thank local authorities, NGOs, research institutions and farmers
for their support.
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Table 24. Characteristics of the experimental sites.
Guinea
Guinea Bissau
Ghana
Togo
Sierra Leone
GPS coordinates
10.003 N, 12.918 W, 379 12.132 N, 15.936 W, 7.264 N, 0.470 W,
m asl
10 m asl
213 m asl
7.270 N, 0.716 W,
809 m asl
8.149 N, 11.908 W, 58 m asl
Ecology
Upland
Upland
Upland
Upland
Upland
Soil characteristics
pH
4.8
4.6
4.6
4.9
4.2
OC%
2.9
1.6
1.9
5.4
4.1
Total N (g/kg)
0.9
0.2
0.7
0.9
0.6
Mehlich-3 P (ppm)
8.1
0.6
7.8
7.0
5.5
Sand (%)
69.0
81.3
63.0
65.0
16.0
Clay (%)
13.7
12.8
8.0
19.0
7.0
Silt (%)
11.1
5.3
28.0
10.0
70.0
Soil type
Sandy loam
Loamy sand
Sandy loam
Sandy (clay) loam
Silty loam
Background of
experiment sites
One year fallow
At least 5 years
of fallow
Five years of fallow
Three years of fallow Twenty-four years of fallow
Previous crop: maize
(Zea mays)
Previous crop: maize
(Zea mays)
Previous crops (successively):
rice, groundnut (Arachis
hypogaea), cassava (Manihot
esculenta)
Presence of Imperata
cylindrica
Previous crops: rice mixed
cropping (cropped with squash,
cucumber (Cucumis spp.),
eggplant (Solanum spp.),
pepper (Capsicum spp.), sorrel
(Hibiscus spp.), legumes, Zea
mays, Manihot esculenta,
Ipomoea batatas, Arachis
hypogaea, etc.
Presence of Pennisetum
purpureum; home for natural
pests: rodents, stems borers,
etc.
Average annual rainfall (mm) 2800–4000
1500
1500
1200
2100–3000
Duration rainfall (months)
6
4 to 5
7
7
6 to 7
General observations
Stress and plant mortality
observed during crop
establishment phase
Good germination and Most plants showed
growth. The late
excellent germination
maturing varieties
and growth
suffered from drought
and rodent damage
Most plants showed Excellent germination and
some traces of acidity growth; low to moderate pest
(rodents, termites, cut worms,
damage
stem borers) incidences were
most specific to O. sativa
japonica
First sowing
28 June 2008
29 June 2008
16 July 2008
09 July 2008
12 June 2008
Second sowing
16 July 2008
13 July 2008
06 August 2008
30 July 2008
04 July 2008
Trial setup dates
doi:10.1371/journal.pone.0034801.t024
both the indica and japonica groups) were selected for further study
(Table 23). These 24 varieties reflect the popular varieties grown in
different parts of the region and therefore provide a subset of the
large set of farmer varieties identified, with good local performance
but not necessarily large robustness. All 26 varieties were included
in all five experiments described in this paper.
Results of AFLP analysis suggested several clusters within the
various botanical groups. These clusters were more or less
coinciding with the regions where the varieties were collected.
The glaberrima divided into a cluster from the Upper Guinea
Coastal region (Glab_UpperCoast) and a cluster from the Lower
Guinea Coastal region (Glab_LowerCoast) (Figure 14a). The indica
divided into indica from Ghana (Ind_Gh) and indica from Guinea
(Ind_Gc) (Figure 14b) and the japonica into japonica from Ghana
and Guinea Bissau (Jap_GbGh) and japonica from Sierra Leone
(Jap_SL) (Figure 14c). It is possible that the differences in the
japonica group reflect different histories of introduction (Portuguese
trading connections linking the Ghana and Guinea Bissau group,
Variety collection and selection
From June to December 2007 we carried out field work in seven
countries of Coastal West Africa, i.e. The Gambia, Ghana,
Guinea, Guinea Bissau, Senegal, Sierra Leone and Togo
(Figure 13). The field work aimed at (1) listing rice varieties/
accessions used by farmers, (2) observing the development/
physiology of these varieties in farmers’ fields, and (3) collecting
varieties at harvest. A total of 231 accessions were collected in
2007. After seed collection we carried out molecular analysis
(AFLP) on the collected varieties in February and March 2008.
Output of this molecular analysis was combined with the output of
an analysis of 84 accessions performed in 2002 [21]. We used
Version 2.2 of the software package ‘Structure’ to analyse genetic
population structure and to assign samples to populations and
‘SplitsTree’ to visualize phylogenetic relationships between the
samples. For further details please refer to [16]. Based on the
output of the molecular analysis, 24 commonly cultivated farmer
varieties (O. glaberrima and O. sativa, including representatives of
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How Robust Are Rice Varieties in West Africa?
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How Robust Are Rice Varieties in West Africa?
Figure 14. Phylogenetic relationships of glaberrima and its sub-clusters (a), indica and its sub-clusters (b), and japonica and its subclusters (c).
doi:10.1371/journal.pone.0034801.g014
percentage green in a photo. Based on this calibration the
percentages of canopy coverage were estimated for all photos.
and British sources supplying Sierra Leone in the late 18th/early
19th centuries [22]). We used these molecular clusters in the
analysis of robustness and adaptability.
Determination of the canopy cover development
Trials
For each plot, canopy coverage curves were made on the basis
of 6 to 12 measurements. As curves for the different replications
showed a large variation and a block effect was not found we
decided to carry out curve fitting on the average values of the five
replications.
To describe the canopy development we used a modified
version of the model developed by Khan et al. [24] for potato. The
model of Khan et al. distinguishes three development phases for
potato: the build-up phase, the phase during which the canopy
cover remains constant and the decline phase. In our case, possibly
because of stress the plants experienced, the canopy never reached
100% coverage, nor did it reach a plateau level maintained for any
period of time. This simplified the model because the time that the
maximum canopy cover was reached (t1) and the time it started to
decline (t2) coincided, resulting into a two-phase model:
Phase 1
Five trials were conducted in Guinea, Guinea
Bissau, Ghana, Togo and Sierra Leone from June 2008 to January
2009. Table 24 summarizes the characteristics of the experimental
sites. Sites were selected to be representative for upland rice
production on loamy soils. In all cases the experiments were
planted after a fallow period.
The experiments were carried out in one growing season. By
including different sowing times, we created diverse environmental
conditions within each site. The growing seasons allowed normal
performance of the crops, although the Guinea experiment
experienced some stress during crop establishment and the Guinea
Bissau experiment experienced late season drought affecting the
late-maturing varieties only.
Experimental design. In each of the five trials, the varieties
were sown in a randomized block design with two sowing dates
and five replications, resulting in 266265 = 260 plots. All 26
varieties were included in all experiments. Sowing dates were
determined by following the farmers’ practices in each region. The
time between the first and the second sowing was two to three
weeks. Each plot was 1.5 m 62.1 m and contained 70 pockets,
spaced 30 cm between rows and 15 cm within rows. Three to five
grains were sown in each pocket and pockets were thinned to one
plant within four weeks after sowing.
Measurements. Table 25 summarises the measured variables, the methodology of assessment and the trials in which they
were recorded.
The percentage of canopy coverage was determined during the
growing cycle using frames of 60 cm 675 cm (in Togo and
Ghana) and 60 cm 645 cm in Guinea that were put in the plot
and photographed from straight above. A series of about 20 photos
representing a wide range of canopy cover values was analysed
with Matlab 7 and DIP image [23], to allow calculation of the
Locations.
t1
t1 {t
t t1 {tm1
with 0ƒtƒt1
v~vmax 1z
t1 {tm1
t1
ð1Þ
Phase 2
v~vmax
te {t
te {t1
t1
t te {t1
with t1 ƒtƒte
t1
ð2Þ
where:
v = canopy cover (%).
vmax = maximum canopy cover (%).
tm1 = the inflexion point.
t1 = the time the maximum canopy cover is reached.
te = the time when the canopy has declined to 0.
tm1, t1, vmax and te were estimated using SAS.
Table 25. Measured variables and countries of measurement.
Variables
Indication on methods of measurement
Trials where variables were measured
Canopy cover
See section: Determination of the canopy cover development
Ghana, Guinea and Togo
Plant height (cm)*
Measured from the base of the plant to the tip of the panicle
of the main tiller
Ghana, Guinea, Guinea Bissau, Sierra Leone, Togo
Number of tillers*
Total number of tillers per plant
Ghana, Guinea, Guinea Bissau, Sierra Leone, Togo
Days to 50% flowering
The number of days between the sowing date and the date
50% of the plants flowered
Ghana, Guinea, Guinea Bissau, Sierra Leone, Togo
Number of panicles*
Total number of panicles per plants
Guinea, Guinea Bissau, Sierra Leone
Panicle length (cm)*
Measured from the base to the tip of the panicle of the main axis
Ghana, Guinea, Guinea Bissau, Sierra Leone, Togo
Panicle weight (g)
Weight of the grains of 14 panicles
Ghana and Togo
200 grain weight (g)
Weight of 200 filled grains. Unfilled and partially filled grains were
excluded
Ghana, Guinea, Guinea Bissau, Togo
Plot yield (kg/ha)
Weight of the three inner rows
Ghana, Guinea Bissau, Sierra Leone, Togo
*Measured on 6 plants randomly selected from the inner rows.
doi:10.1371/journal.pone.0034801.t025
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How Robust Are Rice Varieties in West Africa?
The accumulated canopy cover A, represented by the sum of
surfaces under the curves of phase 1 and 2, was estimated by using
the following formulae:
Surface under the curve for phase 1 (A1):
A1 ~vmax
2t1 (t1 {tm1 )
3t1 {2tm1
the presence of such a variation in response and indicate that the
botanical group or cluster contains varieties that respond
differently to different environments, which can be considered
an indicator of adaptability within a specific botanical group or
cluster. We used the Tukey test to compare means.
Wide sense heritability estimates. H2 = 1006Vg/(Vg+1/
rsVgs+1/rlVgl+1/rslVgls+1/rVe)
where:
H2 = wide sense heritability.
Vg = genetic variance.
Vgs = variance genetic 6 sowing interactions.
Vgl = variance genetic 6 location interactions.
Vgls = variance genetic 6 location 6 sowing interactions.
Ve = error variance.
r = number of replications (5).
s = number of sowings (2).
l = number of locations (2, 3, 5).
Descriptive statistics. Averages were calculated.
ð3Þ
Surface under the curve for phase 2 (A2):
0
1
t1
vmax (te {t1 ) @ te te {t1
te
{2t1 A
A2 ~
t1
2te {t1
ð4Þ
Estimation of the accumulated canopy cover (A):
A~A1 zA2
ð5Þ
Acknowledgments
Data analysis
The authors would like to acknowledge the contributions of all those
involved in the research, in particular the staff of the research stations and
universities in the countries involved, the farmers in the different countries
and the research assistants at the different trial sites.
G6E interactions
Author Contributions
As different botanical groups and molecular clusters were
compared, interactions between genotypes and environment were
analysed through ANOVA (analysis of variance) to assess
differences in responses to different environments within and
between botanical groups. Significant G6E interactions point to
Supervised the research: EN HM PR PCS. Conceived and designed the
experiments: AM EN FO BT HM PR PCS. Performed the experiments:
AM EN FO BT. Analyzed the data: AM EN FO BT. Contributed
reagents/materials/analysis tools: AM EN FO BT. Wrote the paper: AM
EN FO BT HM PR PCS.
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