Soil fertility and the yield response to the
System of Rice Intensi?cation
Marie-Soleil Turmel1,2, Benjamin L. Turner1, and Joann K. Whalen2*
1Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancon, Republic of Panama.
2Department of Natural Resource Sciences, Macdonald Campus of McGill University, 21 111 Lakeshore Road,
Ste-Anne-de-Bellevue, Quebec H9X 3V9, Canada.
*Corresponding author: joann.whalen@mcgill.ca
Accepted 28 December 2010; First published online 16 February 2011 Preliminary Report
Abstract
The System of Rice Intensi?cation (SRI) is a low-input rice (Oryza sativa L.) production system that differs from
conventional systems in several ways: seedlings are transplanted earlier and are more widely spaced, organic fertilizer is
often used in addition to mineral fertilizer, and soils are irrigated intermittently rather than ?ooded for long periods. The
yield bene?ts of SRI compared to conventional systems can be substantial, and yet are regionally variable and have been the
subject of considerable debate, due partly to a lack of mechanistic understanding. Here we show that soil properties may in
part explain the variability in yield response to SRI. A meta-analysis of data from 72 ?eld studies where SRI was compared
with conventional systems indicates that yields increased signi?cantly (P < 0.0001) when SRI was implemented on highly
weathered infertile soils rich in iron and aluminum oxides (Acrisols and Ferralsols), but there was no difference in yield
between SRI and conventional systems in more fertile favorable soils for rice production (Gleysols, Luvisols and
Fluvisols). The yield difference between SRI and conventional rice production therefore appears to be related in part to soil
properties linked to weathering. This should help resolve the debate about the value of SRI and allow research to be targeted
toward understanding the biological and chemical processes in soils under SRI management.
Key words: System of Rice Intensi?cation, highly weathered soils, marginal soils, low-input rice production
Introduction
Rice (Oryza sativa L.) is the staple food for more than
half of the world?s population, and yet by 2030, global rice
production must double to meet demand1, placing greater
stress on already threatened land and water resources. The
rising costs of fertilizers produced using fossil fuel,
agricultural inputs and transportation also contribute to
the increasing food insecurity in rice-dependent areas. To
meet future rice demand while preserving environmental
resources, new low-input solutions that can lead to stable,
locally produced rice supplies are necessary2.
The System of Rice Intensi?cation (SRI) has been used
as a method to increase yield and reduce water and mineral
fertilizer consumption3,4. Developed in Madagascar in the
1980s, SRI was adopted in parts of Asia during the early
1990s, and more recently in Africa and Latin America. The
SRI method relies on early transplanting, wide row spacing,
organic fertilizer use and intermittent wetting and drying of
the soil rather than the prolonged ?ooding practiced in
conventional rice paddy systems5. Most SRI studies so far
have involved small ?eld trials comparing SRI methods
with conventional methods of rice cultivation. Yield
differences between the SRI and conventional system are
highly variable and the potential of SRI has therefore been
debated at length in the peer-reviewed literature6?13.
Proponents claim that SRI increases the physiological yield
potential of rice, which can increase yield by 50?100%.
The ?SRI controversy? stemmed from reports of SRI yields
in Madagascar as high as 20 t ha - 1. These yields were
considered to be erroneous based on theoretical models of
the photosynthetic capabilities of rice and led to major
criticism of SRI in general from the rice research
community9,10,13. Critics argue that yield increases reported
with SRI were related to a decline in iron toxicity to rice
plants and restricted to the highly ferralitic soils
of Madagascar11. A previous meta-analysis of 40 studies
comparing SRI with the conventional system concluded
that SRI had no potential to increase yields outside of
Madagascar11. However, these authors reported yield
differences ranging from a 22% yield increase to a 61%
yield decrease with SRI in regions outside Madagascar;
they did not provide an explanation for the wide variability
in yield response to SRI11.
Renewable Agriculture and Food Systems: 26(3); 185?192 doi:10.1017/S174217051100007X
# Cambridge University Press 2011
Table 1. Soil types, soil fertility group and rice yields from ?eld experiments where SRI was evaluated against the conventional method.
The yield difference was calculated as SRI Yield ? Conventional Yield and the ln RR was ln(SRI Yield/Conventional Yield).
Location Soil type (FAO)
Soil
fertility
group
SRI yield
t ha - 1
Conventional
yield
Yield
difference lnRR
Madagascar (Anjomakely)25 Ferralic Cambisol Low 10.4 3.0 7.4 1.24
Madagascar (Anjomakely)25 Ferralic Cambisol Low 6.4 2.0 4.4 1.14
Myanmar (Kachin)36 Orthic Acrisol Low 6.4 2.1 4.3 1.11
Gambia (Sapu)37 Gleyic Luvisol Moderate 5.3 1.8 3.5 1.07
Madagascar (Morondova)25 Ferralic Cambisol Low 6.0 2.1 3.9 1.04
Madagascar (Morondova)25 Ferralic Cambisol Low 6.8 2.8 4.0 0.88
Nigeria (Sabongida)38 Eutric Regosol Moderate 5.8 2.9 2.9 0.69
Indonesia (Central Sulawesi)39 Orthic Acrisol Low 7.1 3.8 3.3 0.62
India (Uttarakahan)40 Dystric Cambisol Low 5.2 2.8 2.4 0.62
Indonesia (South Sulawesi)39 Orthic Luvisol Low 7.8 4.3 3.5 0.61
India (Hamchal Pradesh)40 Dystric Cambisol Low 5.3 2.9 2.4 0.60
Indonesia (Nusa Tengarra)39 Orthic Acrisol Low 6.6 3.6 3.0 0.60
Iran (Mazandaran)41 Calcic Cambisols Moderate 8.8 5.6 3.2 0.45
Indonesia (Sukamandi)42 Orthic Acrisol Low 7.5 4.9 2.6 0.43
Indonesia (West Sumatra)42 Dystric Fluvisol Low 5.3 3.5 1.8 0.41
India (Balrampur)15 Dystric Cambisol Low 6.3 4.2 2.1 0.40
Indonesia (Sukamandi)42 Orthic Acrisol Low 6.9 4.7 2.2 0.38
India (Orissa)18 Orthic Acrisol Low 6.4 4.5 1.9 0.35
Indonesia (Sukamandi)42 Orthic Acrisol Low 7.7 5.5 2.2 0.33
China (Hangzhou)19 Eutric Gleysol High 7.1 5.1 2.0 0.33
Indonesia (West Nusa Tenggara)42 Vertic Luvisol Moderate 5.9 4.3 1.6 0.32
Indonesia (Sukamandi)42 Orthic Acrisol Low 7.7 5.7 2.0 0.30
Indonesia (Sukamandi)42 Orthic Acrisol Low 8.3 6.4 1.9 0.26
Bangladesh (Burichang)43 Eutric Gleysol High 7.0 5.4 1.6 0.26
Indonesia (Sukamandi)42 Orthic Acrisol Low 8.4 6.5 1.9 0.25
Indonesia (Sukamandi)42 Orthic Acrisol Low 8.4 6.5 1.9 0.25
Madagascar (Beforona)3 Xanthic Ferralsol Low 6.3 4.9 1.3 0.24
Indonesia (Bali)42 Ochric Andosol Moderate 7.3 5.7 1.6 0.24
Indonesia (West Nusa Tenggara)42 Vertic Luvisol Moderate 7.1 5.7 1.4 0.22
Indonesia (West Sumatra)42 Dystric Fluvisol Low 4.7 3.8 0.9 0.21
Indonesia (South Sulawesi)42 Vertic Luvisol Moderate 8.0 6.5 1.5 0.21
India (Coimbatore)16 Pellic Vertisol Moderate 7.0 5.7 1.3 0.20
Laos (Pakcheng)44 Orthic Acrisol Low 5.6 4.6 1.0 0.20
Indonesia (East Java)42 Vitric Andosol Moderate 8.9 7.4 1.6 0.19
Indonesia (North Sumatra)42 Dystric Fluvisol Low 6.1 5.0 1.1 0.19
Bangladesh (Comilla)22 Eutric Gleysol High 5.3 4.4 0.9 0.19
India (Pondicherry)24 Dystric Regosol Low 6.4 5.4 1.0 0.18
China (Yantze River)23 Eutric Gleysol High 12.2 10.2 2.0 0.17
Indonesia (Central Java)42 Mollic Andosol Moderate 7.0 5.9 1.1 0.16
Laos (Vientiane)44 Gleyic Acrisol Low 7.5 6.5 1.0 0.15
Indonesia (West Nusa Tenggara)42 Eutric Fluvisol High 7.4 6.5 0.9 0.13
Bangladesh (Debidwar)43 Eutric Gleysol High 7.0 6.2 0.9 0.13
Thailand (Chiang Mai)27 Orthic Acrisol Low 2.3 2.1 0.3 0.11
Indonesia (South Sulawesi)42 Orthic Luvisol Moderate 6.5 5.8 0.7 0.11
India (Jhalda)15 Ferric Luvisol Low 4.2 3.8 0.4 0.11
Sri Lanka (Hinguraggoda)26 Chromic Luvisol Moderate 7.6 6.9 0.7 0.10
Laos (Phonengam)44 Gleyic Acrisol High 3.6 3.3 0.3 0.09
China (Jiangsu)45 Calcaric Gleysol High 9.9 9.1 0.8 0.08
Iraq (Najaf)46 Calcaric Fluvisol High 5.5 5.1 0.3 0.07
Indonesia (Central Java)42 Pellic Vertisol Moderate 8.0 7.6 0.4 0.05
China (Jiangsu)45 Eutric Gleysol High 9.3 9.1 0.3 0.03
China (Nanjing)45 Eutric Gleysol High 11.7 11.5 0.3 0.02
Indonesia (West Java)42 Dystric Nitosol Low 5.5 5.4 0.1 0.02
China (Guangdong)10 Eutric Gleysol High 7.2 7.2 - 0.1 - 0.01
China (Nanjing)45 Eutric Gleysol High 7.8 8.3 - 0.5 - 0.06
186 M.-S. Turmel et al.
Some researchers suggest that SRI has the potential
to increase yield in marginal soils with low nutrient
availability and low potential for rice production, but has
little potential to increase yields in more favorable soils
where rice is already grown near the yield potential12,13.
This seems likely, because soil biological contributions to
soil fertility and improved microbial turnover of organic
phosphorus under aerobic soil conditions may be more
important in highly weathered low-fertility soils where
phosphorus is often the limiting nutrient to crop pro-
duction4,14.
The objective of this study was to determine (1) whether
SRI has a positive effect on rice yields in regions other than
Madagascar and (2) if soil fertility can explain some of the
regional variability in yield response to SRI management.
Materials and Methods
We collected yield data on SRI experiments and trials from
peer-reviewed and non-peer-reviewed publications, reports
and conference proceedings (n = 72, Table 1). Only data
that ful?lled the following criteria were retained for
analysis: (1) the SRI treatment used intermittent ?ooding
and drying and an early planting date (rice seedlings were
less than 15 days old at transplanting), whereas the
conventional treatment had continuously ?ooded soils and
a later planting date (seedlings were more than 20 days old
at transplanting); (2) the SRI and conventional treatments
were grown in the same season and the same location; and
(3) each treatment was replicated at least thrice during the
study. In total, 81 SRI?Conventional system comparisons
were collected; 72 were included and 9 were not included
because they did not meet the criteria outlined above. Of
the 40 data points used in the previous analysis by
McDonald et al. (2006), 29 were included and 11 were
not included because they did not meet the previously
stated criteria (n = 5) or they were cited as personal com-
munication. Treatment means (Table 1) were the average
yield of each rice variety when replicated plots existed at
a single site. In studies testing several varieties, values in
Table 1 represent the average yield of all varieties grown at
a site or the average yield of varieties grown on multiple
farms in the same farming area. We chose to include studies
that had not been peer-reviewed since there were only six
peer-reviewed studies available at the time10,15?19. So far,
SRI has been mainly a grass-roots agricultural movement
and there have been only a limited number of studies at
major research institutions; most of the existing reports on
SRI therefore come from local non-governmental and
agricultural organizations. Given the stringent criteria for
the inclusion of data in this analysis, we feel that the data
set is strengthened by including non-peer-reviewed sources,
which allows us to examine the effects of SRI on rice yield
over a broader range of geographic regions and soil types.
Most studies did not provide information on soil
properties, and so the soil type at each site was determined
by entering the latitude and longitude into the ISRIC-WISE
global data set of derived soil properties on a 0.5 by 0.5
degree grid using ARC-GIS software. Soils were then
grouped on the basis of their fertility and potential for rice
production20,21. Low-fertility soils were highly weathered
soils rich in iron and aluminum oxides, namely Acrisols,
Table 1 (Continued)
Location Soil type (FAO)
Soil
fertility
group
SRI yield
t ha - 1
Conventional
yield
Yield
difference lnRR
Nepal (Bhairahawa)47 Dystric Regosol Low 5.4 5.7 - 0.3 - 0.06
China (Jiangyin)45 Eutric Gleysol High 8.4 8.9 - 0.5 - 0.06
Bangladesh (Comilla)17 Eutric Gleysol High 7.1 7.6 - 0.5 - 0.07
China (Nanjing)45 Eutric Gleysol High 9.8 10.6 - 0.7 - 0.07
Ivory Coast (M?be)48 Ferric Acrisols Low 3.7 4.0 - 0.3 - 0.08
China (Hunan)10 Eutric Gleysol High 6.7 7.4 - 0.7 - 0.10
Thailand (Chiang Mai)27 Orthic Acrisol Low 4.4 4.8 - 0.5 - 0.10
Bangladesh (Vangurapara)17 Eutric Gleysol High 6.0 6.8 - 0.8 - 0.12
Laos (Vientiane)44 Gleyic Acrisol Low 4.1 4.7 - 0.6 - 0.14
Bangladesh (Matiara)17 Eutric Gleysol High 5.9 7.0 - 1.1 - 0.17
Philippines (Los Banos)49 Orthic Luvisol Moderate 3.0 4.1 - 1.1 - 0.30
Laos (Savannakhet)44 Ferric Acrisol Low 3.9 5.7 - 1.8 - 0.38
Indonesia (East Java)42 Vitric Andosol Moderate 8.0 12.5 - 4.5 - 0.45
Thailand (Chiang Mai)27 Orthic Acrisol Low 2.6 4.2 - 1.6 - 0.47
Thailand (Mae Taeng)27 Orthic Acrisol Low 3.2 5.1 - 2.0 - 0.48
Thailand (San Sai)27 Eutric Fluvisol High 3.3 5.4 - 2.1 - 0.48
Philippines (Los Banos)49 Orthic Luvisol Moderate 1.4 3.1 - 1.7 - 0.77
Mean 6.5 5.5 1.1 0.20
Standard error 0.2 0.3 0.2 0.05
Soil fertility and the yield response to the System of Rice Intensi?cation 187
Ferralsols and Dystric Cambisols. The moderate-fertility
soils (Andisols, Luvisols, Regisols, Vertisols and Cambi-
sols) were moderately weathered soils with a greater
potential for agricultural production. Young alluvial soils
and soils considered ideal for rice production (Fluvisols and
Gleysols) were classi?ed as having high fertility. While
there is some uncertainty associated with using this general
system of soil identi?cation and grouping, we consider it a
necessary tradeoff in taking the ?rst step to examine the
relationship between soil fertility and yield response to SRI
at the global scale using the currently existing data. The soil
types determined by the ISRIC map were compared with
the soils information available from 22 ?eld studies to
validate the accuracy of the predicted soil types3,16,17,22?27.
The effect size of SRI management was calculated as the
natural logarithm of the response ratio (ln RR) of SRI yield
to conventional yield:
ln RR = ln X=Y? ?
where X is the yield under SRI management and Y is the
yield under conventional management28. The response ratio
is commonly used to describe the effect size in meta-
analyses testing the response of a treatment. The effect size,
expressed as the ln RR, was positive when SRI produced
greater rice yield than the conventional system and negative
when there was lower yield in SRI than the conventional
system. The lnRR is appropriate for meta-analysis because
it provides a dimensionless measure of effect sizes that can
be used to compare among studies28. The normality of the
data was con?rmed by examining Q?Q plots and using the
Shapiro?Wilk test (a < 0.5). Simple statistics (mean and
t-test) were used to describe the performance of SRI
relative to the conventional system in countries with more
than two data points. The effect of soil fertility (low,
moderate and high) on the ln RR was evaluated using the
ANOVA procedure of SPSS statistical software (SPSS
version 15.0, Chicago, IL). The model (ln RR = soil fertility
group) indicated a signi?cant (P < 0.05) effect of soil
fertility, and so a post-hoc mean separation test was
performed (least signi?cant difference (LSD), a = 0.05).
Results and Discussion
The results of 72 ?eld trials comparing SRI and conven-
tional rice production systems are summarized in Table 1.
Overall, the mean ln RR was 0.20 and the 95% con?dence
interval ranged from 0.11 to 0.29, indicating that SRI had a
positive effect on rice yields. Yield responses were grouped
by country to determine if there were regions outside of
Madagascar where SRI may have a positive effect on yield.
The lnRR ranged from -0.54 in the Philippines to 1.11 in
Myanmar (Fig. 1). The ln RR in Indonesia, India and
Madagascar were signi?cantly greater than zero, indicating
a yield bene?t from SRI compared to the conventional
system (Fig. 1). Other countries with positive ln RR values
were Iraq, Sri Lanka, Iran, Nigeria, Gambia and Myanmar
(Fig. 1). The lnRR in studies from Thailand, Laos, China
and Bangladesh did not differ signi?cantly from zero,
indicating no detectable difference in rice yield between
SRI and conventional systems in these countries (Fig. 1).
Figure 1. The ln RR of rice yield in SRI and conventional systems grouped by country. The mean ln RR and standard error are shown for
countries with more than two data points. A reference line is shown at zero (??) and the mean ln RR of the complete data set (---)
(n = 72) and its 95% con?dence intervals (......) are shown. The signi?cance of each mean from zero is shown (t-test; *, a < 0.05;
**a < 0.001; ns, not signi?cant).
188 M.-S. Turmel et al.
A previous meta-analysis of 40 SRI trials concluded that
SRI had no signi?cant positive effect on yields in countries
other than Madagascar11. The current study presents meta-
analysis of a larger data set (72 SRI trials) and demonstrates
that there are countries other than Madagascar, namely
India and Indonesia, where SRI has a signi?cant, positive
effect on rice yield. However, there is no signi?cant
negative effect of SRI on yield in most other countries
(Fig. 1). Both Thailand and the Philippines had mean
lnRRs < 0, although the means were not signi?cantly
different from 0 (P = 0.083, df = 4 and P = 0.263, df = 1
respectively; t-test) (Fig. 1). These results suggest that the
introduction of SRI could increase or maintain rice yields
over a broad range of geographic and climatic conditions.
Next we assessed if the regional variability found in the
yield response to SRI could be explained by soil fertility.
The soil type predicted by the ISRIC soil map agreed with
soil information available in 22 of the reports. Of the 72
studies, only 15 provided soil texture information and seven
provided soil pH. Soils with low pH (O5) had been
classi?ed as low-fertility Acrisols and neutral pH (P6) clay
soils had been classi?ed as Gleysols by the ISRIC soils
map. The mean rice yields in SRI were 5.8, 7.0 and
7.7 t ha - 1 in low-, moderate- and high-fertility soils,
respectively. In the conventional systems, rice yields were
on average 4.3, 6.1 and 7.4 t ha - 1 in the low-, moderate-
and high-fertility soils (Table 1). The rice yields therefore
agreed with the soil fertility groupings, because lower
yields were found in the low-fertility soils, higher yields
were found in the moderate-fertility soils and the highest
yields in the high-fertility soils.
The lnRRs were highly variable among soil types, likely
due to the wide range of climatic conditions associated with
studies included in the meta-analysis. When grouped
by soil fertility, the mean lnRR of the high-fertility soils
(n = 19) was - 0.014 with the 95% con?dence interval
ranging from -0.07 to 0.10 (Fig. 2). In the moderate-
fertility soils (n = 17), the mean ln RR was 0.18 and the
95% con?dence intervals were -0.04 and 0.40 (Fig. 2).
The mean ln RR of the low-fertility soils (n = 36) was
0.3061 and the 95% con?dence intervals were 0.16 and
0.45. ANOVA showed that there was a signi?cant effect of
Figure 2. The ln RR of rice yield in SRI and conventional systems. Values are the mean and standard error for each soil type. Soils were
assigned to fertility groups, namely high (n = 19), moderate (n = 17) and low (n = 36) fertility. A reference line is shown at zero (??)
and mean ln RR (---) and 95% con?dence intervals (.......) are shown for each soil fertility group.
Soil fertility and the yield response to the System of Rice Intensi?cation 189
soil fertility on lnRR (P = 0.026). Low- and high-fertility
soils were signi?cantly different (LSD, P = 0.007), whereas
the moderate-fertility soils were not different from the low-
(LSD, P = 0.114) and high- (LSD, P = 0.330) fertility soils.
The mean lnRR of the low-fertility soils was signi?cantly
different from 0 (t-test, df = 35, P < 0.0001), whereas the
mean lnRR of the medium- and high-fertility soils were not
signi?cantly different from 0 (t-test, df = 16, P = 0.096 and
df = 18, P = 0.745, respectively). The data from peer-
reviewed sources (n = 8) followed a similar trend as the
complete data set. The average ln RR of the high-fertility
soil group (n = 4) was 0.02, whereas the average lnRR of
the low-fertility soil group (n = 3) was 0.29. These results
show that SRI has a positive effect on rice yields in low-
fertility soils, but no measurable effect on yields in
moderate- to high-fertility soils. The adoption of SRI
implies greater use of organic fertilizers than in conven-
tional systems, and so SRI could be viewed as a low-input
alternative with the potential to improve yields on low-
fertility soils while maintaining yields and conserving
resources on moderate- to high-fertility soils.
Global rice yields must increase by 50% in the next 20
years to meet the projected demand of the world?s growing
population1. Much of the increased demand will occur in
areas with low-fertility soils, such as the highly weathered
Acrisols and Ferralsols that are found in about two-thirds of
the world?s humid tropics29. By examining a larger number
of ?eld trials than considered previously11, we demonstrate
that SRI increases rice yields in regions other than
Madagascar, namely in areas of Indonesia and India, where
rice is the staple food. In most other countries, there is
generally no yield loss in SRI compared to the conventional
system. Furthermore, differences in soil fertility and
potential for rice production can explain in part the regional
variability in yield response to SRI. We found that SRI
increases rice yields on low-fertility soils, and has no effect
on yields in moderate- to high-fertility soils where yields
are already high (about 7?10 t ha - 1). This agrees with
the ideas outlined in a critical assessment of SRI by
Dobermann13. In the context of augmenting food security
while preserving natural resources, we suggest that SRI has
the greatest potential to increase rice production on
marginal soils, and thus could be an appropriate low-input
technology for resource-poor farmers. Also, in areas where
water is limited, the adoption of SRI may conserve water
without sacri?cing yields.
The mechanisms involved in the yield improvements
with SRI on low-fertility soils remain poorly understood.
Low-base cations are a key criterion in the classi?cation of
Acrisols and Ferralsols, and strongly weathered soils tend
to be extremely low in available forms of phosphorus30,31.
Here we posit four possible mechanisms whereby SRI
modi?es soil properties in a manner to increase rice yields
on low-fertility soils: (1) aerobic conditions increase
microbial activity, rates of organic fertilizer decomposition,
organic phosphorus turnover and phosphorus availability in
low-phosphorus soils14,32?34; (2) aerobic conditions favor
root growth and increase nutrient acquisition by organically
fertilized rice3,5,13; (3) wetting?drying cycles create aerobic
conditions that reduce accumulation of toxic Fe2 + and
Mn2 + (29,34); and (4) low-potential soils may have high
drainage or permeability making them unsuitable for
?ooding. Further research on the chemical and biological
mechanisms involved in yield improvements with SRI on
low-fertility soils is required in order to provide a scienti?c
basis for agronomic recommendations and predictions of
the long-term sustainability of this novel rice production
system.
Conclusion
There is signi?cant evidence that (1) SRI increases rice
yields in regions other than Madagascar; and (2) SRI
increases rice yields on low-fertility soils and has no effect
on yields in moderate- to high-fertility soils. The results of
this meta-data analysis should be considered as preliminary
and replicated ?eld trials with detailed soil fertility data are
needed to improve our con?dence in the yield differences
between SRI and conventional rice production systems. The
biological and chemical mechanisms that lead to increased
rice yields on these low-fertility soils remain to be
elucidated. Although the scienti?c debate over SRI
continues, more than a million farmers around the world
have now adopted the system35. We suggest that the
minimum data set to be provided by future ?eld studies
comparing the SRI versus conventional system should
include data on soil fertility class, texture, pH, macro-
nutrient availability, fertilizer use and standard deviation of
the mean yield value.
Acknowledgements. We thank individuals who provided
data for the meta-analysis, Rosa Orlandini and Joanna Hobbins
(Walter Hitschfeld Geographic Information Centre) for their
help with GIS, Rick Condit (STRI) for statistical advice,
and anonymous reviewers for comments on the manuscript.
Financial support for this work was from the Natural Sciences
and Engineering Research Council of Canada.
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