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Higher yields and more biodiversity on smaller farms


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Small farms constitute most of the world’s farms and are a central focus of sustainable agricultural development. However, the relationship between farm size and production, profitability, biodiversity and greenhouse gas emissions remains contested. Here, we synthesize current knowledge through an evidence review and meta-analysis and show that smaller farms, on average, have higher yields and harbour greater crop and non-crop biodiversity at the farm and landscape scales than do larger farms. We find little conclusive evidence for differences in resource-use efficiency, greenhouse gas emission intensity and profits. Our findings highlight the importance of farm size in mediating some environmental and social outcomes relevant to sustainable development. We identify a series of research priorities to inform land- and market-based policies that affect smallholders globally.
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1School of Public Policy and Global Affairs, University of British Columbia, Vancouver, British Columbia, Canada. 2Institute for Resources, Environment and
Sustainability, University of British Columbia, Vancouver, British Columbia, Canada. 3Center for Sustainable Food Systems, University of British Columbia,
Vancouver, British Columbia, Canada. e-mail:
Farm size has become a key variable of interest in discussions
surrounding food security, development, and the environ-
ment1. Most of the world’s farms are small—of the 570 million
farms in the world, 84% are <2 ha in size2. Smallholders are facing
growing pressure on their livelihoods from low prices in global mar-
kets and climate change-induced production losses3. Accordingly,
smallholders have been the target of global development policies
such as the Sustainable Development Goal (SDG) target 2.3, which
seeks to support smallholders by increasing their productivity,
incomes, and access to land. Many countries’ Intended Nationally
Determined Contributions (INDCs) of the UN Conference of
Parties on Climate Change (COP21) also aim to bolster smallhold-
ers’ adaptive capacity.
Numerous scholars argue that smaller farms perform better than
larger farms in terms of production, environmental, and socioeco-
nomic outcomes4. On the basis of these arguments, scholars, policy
makers, and social movements argue in favour of land reforms to
redistribute farmland5,6. Although 84% of the world’s farms are
<2 ha in size, they only constitute 12% of farmland—increasing the
proportion of farmland in smaller farms will arguably increase its
benefits. At the same time, consumers have increased their willing-
ness to pay for products with labels associated with smaller farms7,8.
Thus, there is a growing call for support for small farms. While this
support is important, the performance of small farms in terms of
productivity, resource efficiency, biodiversity, and greenhouse gas
(GHG) emissions has itself remained highly contested914.
Here, we synthesize the relationship between farm size and six
socioeconomic and environmental outcomes, leveraging the past
50 yr of empirical evidence that directly assessed crop production,
environmental performance, and economic outcomes as they relate
to farm size. Our systematic assessment of the multidimensional
outcomes related to farm size builds on past reviews that focused
on single outcomes (for example, yield, economic performance,
or biodiversity metrics for specific species)1518, non-systematic
reviews15,16, studies based on indirect measurements of farm size
and the outcome variables of interest1921, and studies with specific
regional foci15,17. We present evidence from 118 studies (318 obser-
vations) from 51 countries on the relationship between relative farm
size (along a continuum) and: (1) yields as value of crop output per
area (value ha–1) or total crop production per area (kg ha–1), (2) crop
diversity at species and varietal levels, (3) non-crop biodiversity at
field and landscape levels, (4) resource-use efficiency as measured
in terms of technical efficiency22, (5) GHG emissions per unit out-
put, and (6) profit per unit area.
Our analysis finds that smaller farms have higher yields and har-
bour greater crop diversity and higher levels of non-crop biodiver-
sity at the field and landscape scales than larger farms (Table 1). We
find no conclusive evidence for a relationship between farm size and
resource-use efficiency, GHG emissions, or profit. In the remainder
of this article, we will address each of these key findings in turn and
discuss their implications for policy initiatives and consumer sup-
port for small farms globally.
Smaller farms have higher yields. Our synthesis shows that, in
the literature, when primary studies assess yield across farm sizes,
79% (95% confidence interval, CI = 58–100%) of them report that
smaller farms have higher yields (in either weight ha–1 and value ha–1
terms) (Fig. 1). We also find that yields typically decrease by 5% for
each hectare increase in farm size (5% mean effect; 95% CI = 9
to 1%; Fig. 2a and Fig. 3), within the range of studied observa-
tions (mean = 7.5 ha; s.d. = 22.7 ha). While the distribution of effects
includes deviant cases (Fig. 3 and Supplementary Fig. 1), these new
findings show that, on average, the available evidence supports the
idea—which originated in the 1920s and has been studied exten-
sively since the 1960s—that smaller farms are higher yielding than
larger farms14,23,24. Moreover, we find that controlling for labour
removed the effect—our model not controlling for labour has
stronger effect size (although not statistically different from zero at
the 95% confidence level) than the one that does. This result is in
line with Sen’s 1964 prediction24 and subsequent literature25, that
Higher yields and more biodiversity on
smaller farms
Vincent Ricciardi 1,2 ✉ , Zia Mehrabi 1,2,3, Hannah Wittman2,3, Dana James 2,3 and
Navin Ramankutty 1,2,3
Small farms constitute most of the world’s farms and are a central focus of sustainable agricultural development. However, the
relationship between farm size and production, profitability, biodiversity and greenhouse gas emissions remains contested.
Here, we synthesize current knowledge through an evidence review and meta-analysis and show that smaller farms, on average,
have higher yields and harbour greater crop and non-crop biodiversity at the farm and landscape scales than do larger farms.
We find little conclusive evidence for differences in resource-use efficiency, greenhouse gas emission intensity and profits. Our
findings highlight the importance of farm size in mediating some environmental and social outcomes relevant to sustainable
development. We identify a series of research priorities to inform land- and market-based policies that affect smallholders
AnAlysis NaTure SuSTaiNabiliTy
suggests that labour markets are an important reason for the inverse
farm size–productivity relationship (Fig. 2b).
Smaller farms have greater crop diversity. While many field studies
have explored in situ crop diversity on small farms2628, few directly
measured the relationship between farm size and crop diversity.
In our review, four studies show higher crop diversity on smaller
farms, while three found the opposite; much too small a sample for
statistical inference. But we previously conducted an in-depth quan-
titative analysis on the relationship between crop diversity and farm
size across 55 countries and 154 crops using a newly harmonized
dataset of nationality representative farmer surveys and agricul-
tural censuses29. We found that, except for an unexplained dip in
the 2–5 ha size range, there is a strong inverse relationship globally
between farm size and the number of crop species found across the
landscape—with higher species diversity within smaller farms than
larger farms when controlling for area (Supplementary Fig. 2). Crop
diversity on small farms is selected by farmers for a range of reasons
such as improved nutrition30, market diversification31 and mitiga-
tion of drought risk32.
Smaller farms harbour greater non-crop biodiversity. There
are three key pathways by which smaller farms could be benefi-
cial for non-crop biodiversity covered in the literature. The first is
through ecological management practices, such as limited insecti-
cide use and use of organic management practices. The second is
through increased field edges (increased margin-to-field area ratio);
increased field edges can lead to larger available breeding habi-
tats for arthropods33,34, provide refuge for arthropods and smaller
species to colonize after escaping recently disturbed fields35,36,
increase the number of pollinators and beneficial predators within
fields4,34 and act as conservation corridors for arthropods and small
mammals37,38. The third is through landscape composition, with
small-farm-dominated landscapes harbouring diverse land cover
types such as forests and wetlands, fields of different crops or fields
in different phenological stages of production39,40. In the studies we
reviewed, there is evidence for all of these effects. When combined,
77% of studies (95% CI = 61–99%) reported that smaller farms and
fields have greater biodiversity at the farm and landscape levels
compared to larger farms and fields (Fig. 1).
The three remaining variables we tested—GHG emissions,
resource-use efficiency, and profits—did not show conclusive
relationships for the effect size magnitude and sign (Figs. 2, 4,
and 5), even though the majority of studies concluded that larger
farms had greater resource efficiency than smaller farms (Fig. 1).
For example, while the evidence we reviewed shows that GHG
Table 1 | Main results and mechanisms
Variable Result Mechanisms favouring small farms Mechanisms favouring large farms
Yield Smaller farms have
higher yields Reliance on family labour (for example, Fig. 2). Mechanization enables higher yields with less
labour but is only cost-effective on larger fields57.
(non-crop) Smaller farms have
higher biodiversity Smaller fields have more edges that provide
Independently managed smaller fields and farms
may create a more heterogeneous landscape59.
The link between field and farm size is relatively
understudied; large farms with small fields may also
benefit biodiversity but this was untested in the
reviewed literature.
Crop diversity Smaller farms have
higher crop diversity Subsistence farmers plant a greater diversity of
traditional crops to meet nutritional needs30.
Small farms are incentivized to cultivate landraces
when there are niche markets for traditional crops31.
Varietal diversity requires a minimum amount of
space to prevent genetic erosion for wind-pollinated
Diversified crops can reduce long-term risk at the
expense of short-term profit, which may require
financial buffers62,63.
efficiency Inconclusive evidence In contexts where off-farm labour opportunities
were greater, there was less available on-farm
family labour and, in turn, greater technical
Increased access to information from extension
and advisory services was associated with
greater technical efficiency, which is often only
cost-effective on larger fields6467.
GHG emissions Inconclusive evidence Smaller farms may use less input-intensive
production methods but this was untested in the
reviewed literature.
Agricultural mechanization can enable higher yields
with less input use, and mechanization is often only
cost-effective on larger fields57.
Profit Inconclusive evidence Specialty markets for traditional foods offer higher
Smallholder credit access can increase access to
inputs and markets68.
Better market access for larger farms69,70.
Recovering fixed costs requires a minimum scale69,71.
Better access to land-based subsidies72.
(n = 69)
(n = 87)
(n = 20)
Resource efficiency
(n = 34)
Probability of finding (%)
Small farms
are better
No trend No trendLarge farms
are better
Small farms
are better
Large farms
are better
a b
Fig. 1 | The probability of studies finding relationships between farm size
and each outcome variable. Results are as per the vote count findings
(for example, small farms have more biodiversity when compared to larger
farms, compared to no trend emerging between farm size and profitability).
ad, Results are shown for the following outcome variables: yield (a),
biodiversity (b), resource efficiency (c), and profitability (d). The average
and 95% CIs are given (see Supplementary Table 3 for underlying data).
Note, GHG emission studies were typically on individual farms so we could
not conduct vote counts on this variable.
NaTure SuSTaiNabiliTy
emissions per unit output tend to be higher on smaller farms, sug-
gesting that smaller farms might be less efficient per unit output,
the confidence intervals around this effect crossed zero (4% mean
effect; 95% CI = 10–2%). We found no clear difference between
small or large farms in technical efficiency (our proxy for resource
efficiency per unit output with 0% mean effect; 95% CI = 1–2%),
even after controlling for a variety of moderating factors, such as
access to credit, extension services or cooperative membership
(Fig. 2c). See the Supplementary Information for further discus-
sion of these results. Similarly, while profitability per unit area also
declined with increasing farm size, statistical confidence in the
effect was also low (2% mean effect; 95% CI = 10–5%) (Fig. 2a).
Our evidence review and meta-analysis of the current evidence base
for these six outcomes associated with farm size finds strong sup-
port for the inverse farm size–productivity and farm size–diversity
relationship (empirical findings that both have strong theoretical
support24,4143). While a couple of emerging studies44,45, with limited
coverage, suggest that the inverse size–productivity relationship
might simply be a result of measurement errors and while we were
unable to rule out this possibility, our synthesis did find the inverse
farm size–yield effect was removed when controlling for labour,
suggesting that smaller farms may be more productive due to the
availability of family labour24. Similarly, we recognize that socio-
political context is important and small farms may not always be
more biodiverse46 but most of the evidence base across a broad geo-
graphic range of agricultural systems is in support of this positive
An important caveat to interpreting the past literature for each of
these outcomes are regional biases and variation. Regional biases are
most evident in the biodiversity literature that has predominantly
focused on higher-income countries, mainly in North America and
Europe (Supplementary Fig. 4). Different regions may contain spe-
cies that prefer environments that larger farms foster, either through
their larger fields or different farm management techniques (Table
1). While we could not test for these factors explicitly due to our
sample size, a dominant theme in the literature for other indica-
tors (yield, profitability, and resource efficiency) was that a farms
political, socioeconomic, and geographic context (a farmer’s access
to training, credit, machinery, insurance, inputs, markets, and/or
subsidies) may explain the farm size to outcome relationships. For
instance, the relationships between farm size, resource efficiency,
and profit were the most spatially heterogeneous across all variables
examined. For certain smallholder-dominant countries (for exam-
ple, India and Ethiopia) we found that smaller farms were more
profitable, whereas larger farms were more profitable in countries
dominated by large farms, higher incomes, and better rural infra-
structure (for example, the United States). This may suggest that
smallholders have better access to markets, inputs, and technolo-
gies in a smallholder-dominant system that may affect their profit-
ability and resource efficiency (see Supplementary Information for
expanded discussion).
Systematic evidence syntheses, such as meta-analyses, are an
iterative process47. For some of the outcomes (such as GHG emis-
sions, profits, and resource-use efficiency) we were unable to
identify consistent or confident outcomes from the existing liter-
ature. This may be due to the small number of included studies,
limited cross-country analyses, or that these relationships are too
context-specific to be generalized. Future work should continue
to assess these outcomes and build on this study to include other
important outcomes, such as mental and physical health of workers
and farmers, employment opportunities, pesticide or fertilizer use
efficiency, and other key ecosystem services, such as pollination, in
Yields (n = 33)
Profitability (n = 15)
Resource efficiency (n = 18)
Resource efficiency (n = 18)
Resource efficiency
GHG emissions (n = 100)
–10 –5 0 5 10 –10 0 10
Percentage change Percentage change
Yields (n = 33)
Management not controlled (n = 16)
Extension not controlled (n = 7)
Extension controlled (n = 11)
Cooperative not controlled (n = 14)
Cooperative controlled (n = 4)
Credit not controlled (n = 8)
Credit controlled (n = 10)
Management controlled (n = 17)
Labour not controlled (n = 17)
Labour controlled (n = 16)
Institutions not controlled (n = 26)
Institutions controlled (n = 7)
a b
–10 0 10
Fig. 2 | The pooled effect sizes for each outcome variable that show the percent change per 1ha increase in farm size. a, Pooled effect size per variable
as derived from the random effect meta-regressions. The vertical black line indicates the 1:1 response ratio where, for a 1-ha change in farm size, there is
no change in the outcome variable. A response ratio <0 suggests that smaller farms have a higher effect (for example, smaller farms have higher yields)
and, if it is >0, then larger farms have a higher effect. The number of observations (n) and 95% CIs are given per variable. b, For yield, sensitivity analyses
fit separate models to explore heterogeneity in the effect for studies that controlled for common explanations for the inverse farm size–yield relationship:
institutional characteristics, farm management and family labour. c, For resource-use efficiency, separate models were fit to test if the effect was
moderated by common development interventions to improve smallholder resource-use efficiency: extension access, farmer cooperatives/groups, and
credit access. Profit and GHG emissions had no additional models. Note, biodiversity studies typically did not include regression coefficients, so we could
not conduct a random effects meta-regression. (See Supplementary Table 4 for underlying data).
AnAlysis NaTure SuSTaiNabiliTy
addition to examining literature in languages other than English.
New primary work is also needed to further explore the functional
form of the effects we present here, and to explore how different
socioecological and political conditions and measurement methods
may mediate positive or negative outcomes across farm size classes.
This could further inform policies on land reform (such as redistri-
bution or consolidation) that address market failures so that such
policies can maximize the multidimensional benefits of farming
systems to society48.
To support sustainable transitions in farming practices across
a range of farm sizes, more evidence-based synthesis is needed at
broad regional scales. Until recently, the role of farm size in the
global food system has been largely assessed by independent case
studies. As international commitments (for example, SDGs and
COP21 INDCs) begin to evolve into actionable funding plans and
as countries continue to decide on land use policies that directly
affect the size of farms, it is critical to identify how farm size affects
different social, economic and environmental outcomes.
Our study lends evidence to boost support for policies target-
ing smallholders. Most of the worlds farms (84%) are operated
by smallholders2 and smallholders in lower-income countries are
also among the poorest people on the planet49. Our study shows
that smallholders are both productive and stewards of biodiversity.
Rewarding smaller farms for their conservation benefits may be
one policy pathway towards supporting smallholders. Biodiversity
could be promoted on larger farms by promoting more ecologically
friendly management practices and increasing biodiversity refuges
such as buffer strips and increased natural perimeters.
These findings come at a time where donor countries need to
invest an estimated US$14 billion annually to achieve the goal of
SDG 2.3 to double the incomes and productivity of smallholders49.
Our review adds to the motivation for these investments. We found
that, despite smallholders’ increased yields and role in provision of
ecosystem services, there is not enough evidence for equivalent gains
in smallholders’ profits. Thus, development support for smallhold-
ers is imperative from multiple viewpoints: the data not only show
that investing in smallholders could lead to humanitarian benefits
but also to increases in food production and benefits to biodiversity.
Haji, 2007 − Ethiopia (many) (n = 150)
Majumder, 2016 − Bangladesh (Rice) (n = 944)
Majumder, 2016 − Bangladesh (rice) (n = 944)
Binici, 2006 − Turkey (cotton) (n = 54)
Abedullah, 2007 − Pakistan (rice) (n = 200)
Wang, 2010 − China (wheat) (n = 432)
Tolga, 2009 − Turkey (rice) (n = 70)
Bojnec, 2013 − Slovenia (many) (n = 1,784)
Kilic, 2009 − Turkey (hazelnut) (n = 78)
Bakhshoodeh, 2001 − Iran (wheat) (n = 164)
Hussien, 2011 − Ethiopia (many) (n = 252)
Omonona, 2010 − Nigeria (cowpea) (n = 120)
Alene, 2003 − Ethiopia (maize) (n = 60)
Majumder, 2016 − Bangladesh (rice) (n = 944)
Kulekci, 2010 − Turkey (sunflower) (n = 117)
Majumder, 2016 − Bangladesh (rice) (n = 944)
Majumder, 2016 − Bangladesh (rice) (n = 944)
Abdallah, 2016 − India (cotton) (n = 569)
Pooled effect 0 (−0.01 to 0.02)
−20 0 20
Percentage change per 1 ha
increase in farm size
Fig. 4 | Forest plot for resource efficiency, where observations are in
standardized form and 95% CI are given. The size of each point estimate
relates to the inverse standard error. The pooled effect and 95% CI are
given in the lower plot. The country, crop name, and sample size (n) for
each observation are given on the y axis. ‘National’ sample sizes indicate
that the author used tabulated national statistics and did not include the
sample size. Please see the source data in the Supplementary Information
for complete list of references shown in the figure.
Pooled effect −0.05 (−0.09 to −0.01)
−75 −50 −25 0 25
Percentage change per 1 ha
increase in farm size
Headey, 2014 − Ethiopia (Teff) (n = 1,240)
Headey, 2014 − Ethiopia (Maize) (n = 1,240)
Li, 2013 − China (Many) (n = 2,155)
Heltberg, 1998 − Pakistan (Many) (n = 930)
Carletto, 2013 − Uganda (Maize) (n = 107)
Stifel, 2008 − Madagascar (Rice) (n = 163)
Benjamin, 1995 − Indonesia (Many) (n = 4,605)
Rada, 2015 − China (Wheat) (n = 6,964)
Rada, 2015 − China (Maize) (n = 12,478)
Rada, 2015 − China (Rice) (n = 8,589)
Benjamin, 1995 − Indonesia (Rice) (n = 4,605)
Assuncao, 2007 − India (Many) (n = 3,973)
Newell, 1997 − India (Many) (n = 400)
Ghose, 1979 − India (Many) (n = 100)
Carter, 1984 − India (Many) (n = 406)
Kagin, 2016 − Mexico (Many) (n = 1,361)
Ghose, 1979 − India (Many) (n = 148)
Khan, 1977 − Pakistan (Rice) (n = National)
Ghose, 1979 − India (Many) (n = 85)
Henderson, 2015 − Nicaragua (Many) (n = 5,743)
Chen, 2011 − China (Many) (n = 386)
Barrett, 2010 − Madagascar (Rice) (n = 286)
Ghose, 1979 − India (Many) (n = 149)
Khan, 1977 − Pakistan (Rice) (n = National)
Garrett, 2013 − Brazil (Soy) (n = 2,155)
Khan, 1977 − Pakistan (Cotton) (n = National)
Ghose, 1979 − India (Many) (n = 100)
Khan, 1977 − Pakistan (Wheat) (n = National)
Khan, 1977 − Pakistan (Cotton) (n = National)
Ghose, 1979 − India (Many) (n = 150)
Khan, 1977 − Pakistan (Wheat) (n = National)
Ghose, 1979 − India (Many) (n = 94)
Dorward, 1999 − Malawi (Many) (n = National)
Fig. 3 | Forest plot for yields, where observations are in standardized
form and 95% CI are given. The size of each point estimate relates to the
inverse standard error. The pooled effect and 95% CI are given in the lower
plot. The country, crop name, and sample size (n) for each observation are
given on the y axis. ‘National’ sample sizes indicate that the author used
tabulated national statistics and did not include the sample size. Please
see the source data in the Supplementary Information for complete list of
references shown in the figure.
NaTure SuSTaiNabiliTy
Such a triple reward confirms that support for smallholders globally
is an essential pathway for sustainable development.
A meta-analysis was conducted using the PRISMA guidelines50 (see Supplementary
Fig. 3 for inclusions/omissions and Supplementary Table 1 for Boolean search
terms). Below, we outline our data collection and synthesis methods.
Data. We searched the Web of Science and Scopus databases for studies in
English published before December 2017. We used four inclusion criteria:
(1) peer-reviewed; (2) directly measured farm size and the outcome variable(s) of
interest; (3) reported error estimates/significance tests in determining effect size;
and (4) compared farms with similar management systems (for example, compared
small and large cereal farms, not small vegetable farms to large cereal farms) or
controlled for the cropping systems’ differences (for example, converted different
crops to their value amount and/or controlled for the different types of crop species
planted). The inclusion of studies that compared farms with similar management
systems enabled us to examine if policies should target certain farm sizes to
grow particular crops. Future research may want to omit this inclusion criteria to
examine policy questions relevant to which types of crops should a country grow
given their farm size distribution.
Studies were coded at the observational level to analyse multiple crops, years
and locations per study; studies had multiple observations if they separately
reported different crops, years and/or locations per outcome variable. The main
conclusions were categorically coded as vote counts, where an increase in farm
size was associated with a decrease, increase or null relationship to the variable of
interest (we found no nonlinear results in the literature). For yield, resource-use
efficiency and profit, we extracted several additional variables to calculate pooled
effect sizes of regression model coefficients. To augment the sparse crop diversity
and GHG emission literature on farm size, we used results from Ricciardi et al.29 and
Clark & Tilmans (2017)51 dataset, respectively. We leveraged the Clark & Tilman
meta-analysis database containing 742 agricultural life-cycle analysis observations
from 152 unique studies51; we coded observations that reported average farm size to
construct a dataset containing crop species, GHG emissions per unit output (in CO2
equivalents), average farm size and sample size for 100 observations (11 studies)
that met our inclusion criteria. As part of our systematic assessment we extracted
information from the broader literature on causal mechanisms behind the main
trends, as well as factors that caused deviations from the main trends (Table 1).
Our search yielded 1,474 studies. In total, we identified 118 studies (318
observations) that met our inclusion criteria. From these, we included seven solely
in the life-cycle analysis and coded 111 studies (218 observations) as vote counts,
of which we extracted regression coefficients from 40 studies (66 observations)
(Supplementary Table 2 shows summary statistics).
Synthesis of results. We ran three types of meta-regressions to synthesize the vote
count findings, extracted regression slopes and the GHG emission estimates. Due
to differing data availability across variables (for example, biodiversity studies did
not typically report regression coefficients), not all variables were analysed in each
meta-regression. First, we used cumulative link multilevel models (CLMM) to
synthesize the ordinal vote count findings for yield, resource-use efficiency, profit
and biodiversity52,53. We used CLMMs to examine the probability of the ordinal
outcome variable (observation finding negative, null or positive relationships
with farm size). For all CLMM and subsequent models (detailed in the following
paragraphs), we set the study as a random effect. Hierarchical models are
commonplace in meta-analyses and applied in our study because of the a priori
expectation that observations within studies and across similar crop types would
be correlated in the response, with random effects allowing us to account for
non-independence. In addition, as used in meta-analyses, random effects estimate
a variance component in addition to the sampling variance that fixed effects
models assume; this extra variance component has enabled meta-analyses
using random effects to be applied more generally and allows data to be
interpreted as a random population of outcomes instead of a single ‘true effect’,
as is a common interpretation of fixed effects meta-analyses47. For yields and
non-crop biodiversity, we also set crop type as random effects. For non-crop
biodiversity, we also set non-crop species type as a random effect. We tested if the
additional random effects used for yields and non-crop biodiversity changed the
results compared to using only studies as random effects and found no differences
in our conclusions.
Second, we used hierarchical meta-regressions of the standardized regression
slopes and standard errors54,55 to calculate pooled effects for yield, resource-use
efficiency and profit. Since certain variables contained multiple currencies,
efficiency units or measurement metrics, we relied on the Rodríguez-Barranco
et al. technique to convert farm size regression coefficients and standard errors
into standardized regression coefficients56. Our standardized coefficients represent
a relative change in the outcome variable per 1-ha change in farm size (we note
that these coefficients are limited by the range of the underlying farm sizes in
each study and should not be extrapolated). We used a linear model to synthesize
results because the literature predominantly provided linear coefficients. We used
the same random effects variable set up as in the CLMM models. Sensitivity tests
were conducted through cumulative meta-regressions for continuous variables
(for example, year of study and average farm size study observed) and subsetted
meta-regression for categorical variables (for example, type of diversity metric
used, if yield was defined by weight ha–1 or value ha–1, if resource efficiency was
derived from data envelopment analysis or stochastic frontier and so on). All
sensitivity tests found no differences in results. Forest plots are given in Figs. 35.
An inclusion of bias analysis was conducted through funnel plots that compare the
observed outcomes to standard errors. There were no clear biases for yields and
resource efficiency but a slight positive bias for profit (Supplementary Fig. 5).
This meta-regression framework also enabled us to further test if the variation
in findings between different studies could be attributed to the inclusion/omission
of variables that authors used when estimating the relationship between farm size
and the variable of interest, through sensitivity analyses using moderators. For
yield, we assessed the importance of moderators such as the types of production
methods, institutional characteristics (credit markets and access, extension access
and involvement in farmer cooperatives) and types of labour (general labour
market imperfections, family labour and household size). Our logic was that, if the
relationship is moderated by these factors (for example, if the main relationship
became null), it would indicate that there is a systematic variable omission bias in
the literature that, once corrected for, could explain the inverse farm size to yield
relationship. For resource-use efficiency, we conducted similar sensitivity analyses,
by including moderators that described development interventions (credit access,
extension access or farmer group membership). Our key hypothesis was that
having similar access to credit, extension or inputs and markets (through farmer
groups) may enable small farms to be equally or more efficient than large farms.
Heltberg, 1998 − Pakistan (many) (n = 930)
Gaurav, 2015 − India (Many) (n = 51,770)
Rada, 2015 − China (maize) (n = 33,369)
Rada, 2015 − China (rice) (n = 20,584)
Deininger, 2003 − Nicaragua (many) (n = 1,352)
Rada, 2015 − China (wheat) (n = 17,794)
Myyra, 2002 − Finland (cereals) (n = 962)
Lamb, 2003 − India (many) (n = 1,060)
Li, 2013 − China (many) (n = 2,155)
Sewando, 2014 − Tanzania (cassava) (n = 98)
Rahman, 2016 − Nigeria (yam) (n = 400)
Rahman, 2016 − Nigeria (cassava) (n = 400)
Savastano, 2009 − Kyrgyzstan (many) (n = 114)
Dauda, 2009 − Nigeria (many) (n = 250)
Rahman, 2016 − Nigeria (rice) (n = 400)
Pooled effect −0.02 (−0.1 to 0.05)
040 80 120
Percentage change per 1 ha
increase in farm size
Fig. 5 | Forest plot for profitability, where observations are in standardized
form and 95% CI are given. The size of each point estimate relates to the
inverse standard error. The pooled effect and 95% CI are given in the lower
plot. The country, crop name, and sample size (n) for each observation
are given on the y axis. Please see the source data in the Supplementary
Information for complete list of references shown in the figure.
AnAlysis NaTure SuSTaiNabiliTy
Third, for the GHG emission observations, we used robust linear mixed-effects
models where we set the study and crop type as random effects. To estimate GHG
emissions per unit output, we used the log average farm size of a study as a fixed
effect. The key difference in the GHG emission model is that the data are at the
aggregated farm level, as opposed to extracted regression coefficients for the yield,
resource-use efficiency and profit models. (Formulas and further detail on each
meta-regression used are available in the Supplementary information.)
Data availability
The data that support the findings of this study are available in the Supplementary
Information. Source data are provided with this paper.
Code availability
The computer code that support the findings of this study is available in the
Supplementary Information.
Received: 14 July 2020; Accepted: 18 February 2021;
Published: xx xx xxxx
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We acknowledge funding from the University of British Columbia 4-Year Doctoral
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no. 435-2016-0154.
Author contributions
V.R., N.R. and H.W. conceived the idea and designed the data collection process. V.R.
collected and coded the data. V.R., Z.M. and N.R. designed the analysis. V.R. and Z.M.
conducted the analysis. V.R., Z.M., N.R., H.W. and D.J. contributed interpretations of
results. All authors wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at
Correspondence and requests for materials should be addressed to V.R.
Peer review information Nature Sustainability thanks Michael Clark and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
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© The Author(s), under exclusive licence to Springer Nature Limited 2021
... Agriculture investment has been proved as an effective strategy for eradicating poverty, inequality and hunger [1], and shows great potential in biodiversity maintenance and rural environmental protection [2,3]. Based on this knowledge, agriculture has grown into a fundamental and pillar industry in many countries, especially in some developing countries where the sector shares a large part of the population [4]. ...
... Based on this knowledge, agriculture has grown into a fundamental and pillar industry in many countries, especially in some developing countries where the sector shares a large part of the population [4]. Consequently, there is a continuous debate regarding what type or scale of agriculture should be promoted for better achieving the above mentioned functions [3,5]. For some scholars, smallholder farming or family farms are recommended as optimal types with the fact that they contribute a large share of the world's food production and that most food consumed in Africa and Asia is produced by local smallholders [6]. ...
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... 231). It is a uniquely holistic approach to food systems that includes a triple focus on: (1) specific food and fibre production practices designed to enhance agroecosystem health over the long term; (2) research and innovation systems that incorporate both western scientific methods and traditional, Indigenous, and local knowledges; and (3) collective action to shift power dynamics and push for systemic food system change in policy and institutional spheres. As a result, agroecology is today widely considered to have the potential to bring about the transformative change necessary to address the issues facing our current food system [7][8][9]. ...
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... First, it is well-established that smaller farms have higher yields when assessed by both weight and value per hectare, although this is disputed by Lowder et al., who work with the assumption that yields per area of land are not impacted by farm size [30]. Smaller farms also have higher crop and non-crop biodiversity [62]. We also know that, across operations small and large, farmers already produce enough food to feed more than 10 billion people a healthy diet [63,64]. ...
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... Hence, other systems of production that increase landscape heterogeneity-such as crop rotation, intercropping and agroforestry-have positive effects on biodiversity as we have shown here, while also generating good production results and socioenvironmental benefits (Fung et al., 2019;Ricciardi et al., 2021;Shah et al., 2021). ...
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... Smallholder farms of this type thus generally achieve higher yields per unit of land-and higher yields per unit of non-human energy input, but less per unit of human labour-than industrial farming. They also provide better outcomes in terms of food security, environmental sustainability, employment, and community cohesion and development (FAO-IFAD 2019; Committee on World Food Security 2019, 2020; IAASTD 2009; Herren et al. 2020;Ricciardi et al. 2021). ...
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... The literature also indicates the intrinsic bond between profitability, forest preservation and land use, suggesting that smallholders can achieve better yields and safeguard biodiversity than monoculture (Ricciardi et al., 2021). In addition, increasing the income for cocoa farmers is linked to other civilisational challenges, such as climate action (Osei, 2017;Amfo and Ali, 2020) and dwindling biodiversity (Target 15.9). ...
Purpose This paper aims to assess how cocoa supply chain companies disclose sustainable development goals (SDGs) information in their sustainability reports. This assessment highlights strategic aspects of sustainable supply chain management and reveals leveraging sustainability points in the cocoa industry. Design/methodology/approach The two-step qualitative approach relies on text-mining company reports and subsequent content analysis that identifies the topics disclosed and relates them to SDG targets. Findings This study distinguishes 18 SDG targets connected to cocoa traders and 30 SDG targets to chocolate manufacturers. The following topics represent the main nexuses of connections: decent labour promotion and gender equity (social), empowering local communities and supply chain monitoring (economic) and agroforestry and climate action (environmental). Practical implications By highlighting the interconnections between the SDGs targeted by companies in the cocoa supply chain, this paper sheds light on the strategic SDGs for this industry and their relationships, which can help to improve sustainability disclosure and transparency. One interesting input for companies is the improvement of climate crisis prevention, focusing on non-renewable sources minimisation, carbon footprint and clear indicators of ecologic materiality. Social implications This study contributes to policymakers to enhance governance and accountability of global supply chains that are submitted to different regulation regimes. Originality/value To the best of the authors’ knowledge, no previous study has framed the cocoa industry from a broader SDG perspective. The interconnections identified reveal the key goals of the cocoa supply chain and point to strategic sustainability choices for companies in an important global industry.
... The value of small farms to improve food security, to provide climate change resilience, to protect rural lifestyles, and for rural economic development, is increasingly being recognized at the global and regional level (IFAD, 2013;Chancellor, 2019;Rocha et al., 2012;Cardillo and Cimio, 2022). Recent analyses also indicate that small farms result in greater yields, productivity and biodiversity, than larger farms (Ricciardi et al., 2021). ...
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This paper makes a case for the conversion of agriculture in Hawaii towards Agroecology, a scientific system of agriculture that is based on ecological concepts and principles as well as on social values that promote food sovereignty, affirmation of indigenous and cultural identity, and rural economic well-being.
The study aimed primarily to develop spatial farm models in sustainable and multifunctional development of rural areas. The secondary objective was to define the matrix of possible farm models, visualize their dislocation, and recommend multifunctional rural development. It was assumed that the types of model farms depend on the criteria that make it possible to assess the agricultural development potential and the size of agricultural production. Thus, it was assumed that agricultural production conditions influence the farm model but are not always of crucial importance. There are many possibilities for developing various production activities that are not strictly related to agricultural production conditions. The research was carried out with the GIS tool using multi‐criteria and spatial analysis techniques. The research made it possible to determine the models of farms in the rural areas in a specific region, however, the proposed model is suitable for use in other regions or countries.
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Today, agroecology is more than a science; it is a movement that advocates for a sustainable redesign of the global food system. Some of its acknowledged protagonists plead for a redesign based on the support of and for small-scale farming because small farms are considered more sustainable than large farms. The present review explores the arguments that leading agroecologists use for justifying their preference for small (frequently peasant) farms. In this review, small farms are defined as possessing a mean agricultural area of maximum two hectares, being family-owned, emphasizing outdoor production, and annually producing at least two different crops or livestock. Peasant farms are defined as subsistent small farms in developing countries. The review includes an overview of the current state of small farms and their most severe challenges. Agroecological publications of the last thirty years were scanned for arguments that sustain the hypothesis that small farms are more sustainable. It was found that there are no studies that directly compare the sustainability of farms based on their size. Instead, most studies cited to confirm the sustainability of small farms compare farms that differ in terms of both, size and farm management. Hence, it is likely that the reason for the advanced sustainability of small farms is their management, not their size. The assertion that small farms are a priori more sustainable than large ones is not supportable. Misleading use of the term "small farms" may impede the efforts of agroecology to stimulate sustainable food production.
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The widely reported claim that smallholders produce 70-80% of the world’s food has been a linchpin of agricultural development policy despite limited empirical evidence. Recent empirical attempts to reinvestigate this number have lacked raw data on how much food smallholders produce, and have relied on model assumptions with unknown biases and with limited spatial and commodity coverage. We examine variations in crop production by farm size using a newly-compiled global sample of subnational level microdata and agricultural censuses covering more countries (n=55) and crop types (n=154) than assessed to date. We estimate that farms under 2ha globally produce 28-31% of total crop production and 30-34% of food supply on 24% of gross agricultural area. Farms under 2ha devote a greater proportion of their production to food, and account for greater crop diversity, while farms over 1000ha have the greatest proportion of post-harvest loss.
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Hundreds of millions of the world’s poorest people directly depend on smallholder farming systems. These farmers now face a changing climate and associated societal responses. We use mapping and a literature review to juxtapose the climate fate of smallholder systems with that of other agricultural systems and population groups. Limited direct evidence contrasts climate impact risk in smallholder agricultural systems versus other farming systems, but proxy evidence suggests high smallholder vulnerability. Smallholders distinctively adapt to climate shocks and stressors. Their future adaptive capacity is uncertain and conditional upon the severity of climate change and socioeconomic changes from regional development. Smallholders present a greenhouse gas (GHG) mitigation paradox. They emit a small amount of CO2 per capita and are poor, making GHG regulation unwarranted. But they produce GHG intensive food and emit disproportionate quantities of black carbon through traditional biomass energy. Effectively accounting for smallholders in mitigation and adaption policies is critical and will require innovative solutions to the transaction costs that enrolling smallholders often imposes. Together, our findings show smallholder farms to be a critical fulcrum between climate change and sustainable development.
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Global agricultural feeds over 7 billion people, but is also a leading cause of environmental degradation. Understanding how alternative agricultural production systems, agricultural input efficiency, and food choice drive environmental degradation is necessary for reducing agriculture's environmental impacts. A meta-analysis of life cycle assessments that includes 742 agricultural systems and over 90 unique foods produced primarily in high-input systems shows that, per unit of food, organic systems require more land, cause more eutrophication, use less energy, but emit similar greenhouse gas emissions (GHGs) as conventional systems; that grass-fed beef requires more land and emits similar GHG emissions as grain-feed beef; and that low-input aquaculture and non-trawling fisheries have much lower GHG emissions than trawling fisheries. In addition, our analyses show that increasing agricultural input efficiency (the amount of food produced per input of fertilizer or feed) would have environmental benefits for both crop and livestock systems. Further, for all environmental indicators and nutritional units examined, plant-based foods have the lowest environmental impacts; eggs, dairy, pork, poultry, non-trawling fisheries, and non-recirculating aquaculture have intermediate impacts; and ruminant meat has impacts ~100 times those of plant-based foods. Our analyses show that dietary shifts towards low-impact foods and increases in agricultural input use efficiency would offer larger environmental benefits than would switches from conventional agricultural systems to alternatives such as organic agriculture or grass-fed beef.
We show that non-classical measurement errors (NCME) on both sides of a regression can bias the parameter estimate of interest in either direction. Furthermore, if these NCME are correlated, correcting for either one alone can aggravate bias relative to ignoring mismeasurement in both variables, a ‘second best’ result with implications for a broad class of economic phenomena of policy interest. We then use a unique Ethiopian dataset of matched farmer self-reported and precise ground-based measures for both plot size and agricultural output to re-investigate the long-debated relationship between plot size and crop productivity. Both self-reported variables contain substantial NCME that are negatively correlated with the true variable values, and positively correlated with one another, consistent with prior studies. Eliminating both sources of NCME eliminates the estimated inverse size-productivity relationship. But correcting neither variable generates a parameter estimate not statistically significantly different from that generated using two improved measures, while correcting for just one source of NCME significantly aggravates the bias in the parameter estimate. Numerical simulations demonstrate that over a relatively large parameter space, expensive collection of objective measures of only one variable or correcting only one variable's NCME may be inadvisable when NCME are large and correlated. This has practical implications for survey design as well as for estimation using existing survey data.
Ecological intensification aims to increase crop productivity by enhancing biodiversity and associated ecosystem services, while minimizing the use of synthetic inputs and cropland expansion. Policies to promote ecological intensification have emerged in different countries, but they are still scarce and vary widely across regions. Here, we propose ten policy targets that governments can follow for ecological intensification.
The farm size and productivity debate has been limited by the focus on land or labor productivity, generally showing respective productivity advantages to smaller or larger sized farms. Our purpose is to provide new perspectives on the debate by bringing together evidence from a set of novel case studies in both rich and poor countries. Common to them are the adoption of total factor productivity (TFP) as the comparative performance measure, and the reliance on panels of farm micro data. The present article presents a synthesis of findings from five case studies in (i) Malawi, Tanzania, and Uganda; (ii) Bangladesh; (iii) Brazil; (iv) Australia; and (v) the United States. The preponderance of evidence from these studies suggests that there is no single economically optimal agrarian structure; rather, it appears to evolve with the stage of economic development. Certain farm sizes face relative productivity advantages, such as small farms in Africa. But with economic and market growth, that smallholder advantage will likely attenuate, moving toward constant and eventually increasing returns to size. Yet, importantly, small farms may be quite dynamic, and need not be a drag on agricultural growth until perhaps well into the development process.
Meta-analysis is the quantitative, scientific synthesis of research results. Since the term and modern approaches to research synthesis were first introduced in the 1970s, meta-analysis has had a revolutionary effect in many scientific fields, helping to establish evidence-based practice and to resolve seemingly contradictory research outcomes. At the same time, its implementation has engendered criticism and controversy, in some cases general and others specific to particular disciplines. Here we take the opportunity provided by the recent fortieth anniversary of meta-analysis to reflect on the accomplishments, limitations, recent advances and directions for future developments in the field of research synthesis.
The persistence of agricultural protectionism throughout the world is intriguing given the widely recognized benefits of free trade. The political economy literature over the last decades has considered groups’ interest, politicians’ preferences, and their interactions within domestic politics as the primary forces driving agricultural protection. Yet, a growing body of studies suggests that it would be judicious to weigh in consumers’ or taxpayers’ perspectives in deciphering the nature of agricultural protection. This study examines US citizens’ preferences about government intervention in agriculture and trade. Results show that they are in strong support of agricultural protection and their perceptions of national food security, family farms, environmental sustainability, and multifunctionality of agriculture play a significant role in shaping their support/opposition toward government intervention. The conventional political economy literature theorizes that consumers or taxpayers would oppose public policies that increase their tax burden; however, in the case of the farm sector, they have little incentive to voice their objections given the costs of farm programs are spread across a large number of consumers and taxpayers. US citizens’ support for government involvement in agriculture as reported in this and other prior studies does not lend support for such political economy explanation. This article is protected by copyright. All rights reserved