MATERIALS and METHODS
Literature search strategy and data
collection
To identify candidate studies, we screened a dataset included in a
mapping review carried out by Ratto et al . (2022) that described
the existing literature on biocontrol interventions for insect pests of
crops in SSA. Ratto et al. (2022) systematically searched Web of
Science All Databases and Scopus, using a combination of search terms
relating to a wide range of biocontrol techniques and insect pests
(e.g., “biocontrol”, intercrop*”, “armyworm”), agricultural
settings (e.g., “agri*”, “farm*”) and the target geographical
location (e.g., “sub-Saharan Africa”, “Southern Africa”)(Table S1).
Grey literature was captured by conducting additional searches on Google
and Google Scholar and by searching websites of relevant institutions.
This mapping review covered a period between 2005 and April 2021 and was
summarised narratively, with no quantitative analysis performed.
We integrated this initial dataset (149 articles) (Ratto et al. 2022)
with a follow up search of relevant papers published between April 2021
and December 2021. Overall, we found 146 eligible articles. Only
articles published after 2005 were included to reflect modern biocontrol
practices and to determine biocontrol effectiveness within a short
timeframe. We focused on the sub-Saharan region, which has a large
population of smallholder farmers who depend on local food production,
and who suffer substantial incidences of insect pest outbreaks and crop
damage that threatens their food security.
We included in the definition of biocontrol interventions any practice
that utilises natural enemies of pests, or chemical products derived
from nature, for the control of pest populations. These include the
augmentation, introduction, or inoculation of natural enemies (i.e.,
predators, parasitoids and entomopathogens, such as bacteria, viruses
and fungi), and conservation biocontrol (Table 1). Conservation
biocontrol was defined as the manipulation of habitat to enhance natural
enemy abundance and diversity (Amoabeng et al. 2020) and included
push-pull, intercropping and field margins. Botanical pesticides,
defined as substances derived from natural materials (e.g. plant
extracts), were also included.
To ensure biologically meaningful comparisons, we applied further
inclusion criteria to all articles in Ratto et al. (2022). Only
articles that quantitatively measured biocontrol performance on the
outcome measures were included in the analysis. Only studies with
replicated treatments at one or more sites were included. We screened
studies wherein pest abundance (PA), crop damage (CD), crop yield (Y) or
natural enemy abundance (NEA) (hereafter “outcome measures”) were
compared between crops following the implementation of a biocontrol
intervention and untreated crops. We also extracted, where available,
data on the outcome measures in crops treated with synthetic pesticides.
Measures of crop damage included dead hearts (i.e., drying of the
central shoot), damage to stems (e.g., stem tunnelling), pods, leaves,
fruits, shoots that were specific to the target pests. Crop yield was
reported as either kg/ha or tonne/ha, which was standardised to the
latter for analysis.
We categorised the sites that had been exposed to a biocontrol
intervention as “treatment”, with those that were left untreated as
“negative control (-)” and those treated with synthetic pesticides as
“positive control (+)”. The mean, standard deviation (SD) and sample
size of outcome measures were recorded for both the treatment and
controls. When data were presented only in figures, we extracted data
using ImageJ software (Schneider et al. 2012). We contacted the
lead authors of the studies that had incomplete data and abandoned these
studies if we could not obtain the missing statistics.
For articles that presented multiple years of data sampling at the same
site, we used the most recent data to control for non-independence of
temporal data (Gurevitch & Hedges 1993). When the study was conducted
in two or more spatially independent sites, we recorded them as
independent observations. When a study presented outcome measures for
several successive weeks, we averaged the means and recorded it as a
single effect size. When different concentrations or different types of
biocontrol agent were applied (e.g., entomopathogens, botanical
pesticides), we used the highest concentration and recorded each
biocontrol type as an independent observation. The screening resulted in
a total of 99 articles and 512 studies included in the analysis
(Supplementary information S2).
Statistical analysis
In our meta-analysis, the log of the response ratio (lnRR )
represents the influence of biocontrol interventions on the outcome
measures and expresses the proportional difference between the treatment
and the control groups (Hedges et al. 1999):
lnRR = ln (x1 ) –
ln(x2 )
where x1 is the mean of the outcome measure when
biocontrol is applied (treatment) and x2 is the
mean of the outcome measures under the untreated condition (control -)
or after synthetic pesticide application (control +). The use of the
natural logarithm linearizes the metric, treating changes in nominator
and denominator equally, and produces a normalised sampling distribution
(Hedges et al. 1999).
All outcome measures were analysed separately (pest abundance, crop
damage, crop yield, natural enemy abundance). Fitted random effects
models were used to calculate the overall means and 95% confidence
intervals for each outcome measure to determine if biocontrol
interventions significantly affected the outcome measures when compared
to control areas (both untreated and pesticide treated). Random effect
models do not assume that any variation in the effect size is due only
to sampling error, and, instead, allow for a real random component of
variation in effect size between studies (e.g., regional differences in
study location). An effect of biocontrol intervention was considered
significant if the 95% biased-corrected bootstrap confidence intervals
(C.I.) of the effect size did not overlap zero (Koricheva et al.2013).
Meta-regression was used to explore sources of heterogeneity across each
dataset. Our analysis focussed on the following ecological,
environmental, and experimental parameters: (1) biocontrol technique;
(2) crop type; (3) target pest taxon; (4) farming system. However, we
could not use landscape complexity as a moderator as we found too few
studies that investigated landscape context. To elucidate the
variability of biocontrol efficacy across biocontrol techniques, we
grouped studies according to whether they applied botanical pesticides,
intercropping, field margins (border planting including legumes, sorghum
or wild grasses), push-pull or augmentation/introduction methods. To
determine if the effectiveness of biocontrol was dependent on crop type,
we classified the study focus crops into cereal, fibre, fruits,
vegetables, and pulses. We did not include stimulants (e.g., coffee,
cocoa) and nuts due to small sample sizes. To establish whether
biocontrol effectiveness varied across different pest insect taxa, we
classified studies according to taxon of the targeted pest (Coleoptera,
Hemiptera, Lepidoptera and Blattodea). Lastly, we classified studies
into two field types: small farm (real smallholder farming conditions)
and research farm (experimental field within a research centre), to
identify any difference between these systems. Large commercial
horticulture farms were not included in the meta-analysis as we
primarily focussed on smallholder farmers and their food security. The
above parameters were tested one by one as a sole moderator (i.e., fixed
effects) for each outcome measure. To account for multiple comparisons
from the same article, each model included “Study” nested within
“Article” as random effects. The mean log response ratios and upper
and lower bounds of 95% confidence intervals around the mean were
back-transformed with the formula (elnR-1) *100 and
expressed as percent change relative to the controls to facilitate
interpretation.
Publication bias
We assessed publication bias in three ways. We first visually assessed
funnel plots for strong asymmetries (Fig S1). Visual inspection of the
funnel plots revealed symmetrical distribution of effect size around the
meta-analytical mean of all outcome measures apart from pest abundance.
We then ran Egger’s regression test (Egger et al. 1997; Nakagawa
& Santos 2012) and the trim-and-fill test (Duval & Tweedie 2000).
Egger’s test indicated that publication bias was significant for the
pest abundance (z= -2.1065, p=0.0352), crop damage (z= -2.3886,
p=0.0169), and NEA datasets (z=-2.4708, p=0.0135), but not significant
for the crop yield dataset (CY: z= 0.0362, p=0.9711). This was
inconsistent with the trim-and-fill tests showing no missing studies for
all datasets. All statistical analysis was performed using the
“metaphor” package in R (version 4.1.2) (Viechtbauer 2010).