ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
Contents lists available at ScienceDirect
Global Food Security
journal homepage: www.elsevier.com/locate/gfs
How much of the world's food do smallholders produce?
Vincent Ricciardi
a,b,
, Navin Ramankutty
a,b
, Zia Mehrabi
a,b
, Larissa Jarvis
a,b
,
Brenton Chookolingo
a,b
a
The Institute for Resources, Environment, and Sustainability, University of British Columbia, Canada
b
School of Public Policy and Global Aairs, University of British Columbia, Canada
ABSTRACT
The widely reported claim that smallholders produce 7080% of the worlds food has been a linchpin of agri-
cultural 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 pro-
duction 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 2831% of total crop production and 3034% 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.
1. Introduction
It has been widely reported that smallholder farmers (dened gen-
erally as being less than 2 ha) produce 7080% of the world's food ETC,
2009;Maass Wolfenson, 2013;FAO, 2014), are central to conserving
crop diversity (Altieri, 2008; Badstue et al., 2005; Conway, 2011),
produce more food crops than larger farms (Horrigan et al., 2002;
Naylor et al., 2005), and yet are largely food insecure (IFAD and UNEP,
2013). These arguments have been a linchpin in recent agricultural
development policy. For example, in 2014, the International Year of
the Family Farm, the United Nations (UN) and other food security
agencies reiterated these arguments to garner increased support for
family farmers, who are predominantly smallholders (FAO, 2014). The
COP21 agreement (the 2015 UN Conference of Parties on Climate
Change) includes mitigation and adaptation commitments pertaining to
agriculture from 179 countries that include the need to bolster small-
holder adaptive capacity to climate change. Goal 2 of the UN Sustain-
able Development Goals (SDGs) aims to end hunger and achieve food
security through sustainable agriculture; a key target (SDG 2.3) is by
2030, [to] double the agricultural productivity and incomes of small-
scale food producers, in particular women, indigenous peoples, family
farmers, pastoralists, and shers(UN, 2015). Yet, despite progress in
steering development policy towards smallholder farmers, there is scant
empirical data on smallholder farms, and their role in the food system.
Key to enacting and monitoring progress on these international
agreements and policies is a global baseline on the contribution of
smallholders to global food production and security. However, the data
underlying three widely reported claims on smallholder crop produc-
tion remain non-transparent or contradictory. First, the source of var-
ious UN reports citing smallholder production is a communiqué from
the ETC group (ETC, 2009), which suggests that peasantsgrow at least
70% of the world's food; yet, the derivation of the estimate is obscure in
this report. Second, the claim that smaller farms produce more food
directly consumed by people, with larger industrialized farms produ-
cing more non-food crops, such as biofuels and animal feed (Horrigan
et al., 2002; Naylor et al., 2005), has been brought into question by the
observation that smaller farms have larger amounts of post-harvest loss
due to lack of market and cold storage access (Hodges et al., 2011;
Tefera, 2012). Thirdly, while some authors argue that economies of
scale are needed for farms to produce a diversity of crops (Rahman and
Kazal, 2015), others suggest that larger farms face labor constraints that
hamper mixed-cropping systems (Van den Berg et al., 2007), so it is
unknown if smaller farms produce a greater diversity of crop species
than larger farms. In sum, our current understanding how much food
smallholders produce, what kinds of food they produce, where their
food is destined in the food system, and how much nutrition it contains,
are all key knowledge gaps in global agricultural research.
The need to ll these knowledge gaps has been recently recognized
by scientists (Graeub et al. (2016);Herrero et al. (2017);Lowder et al.
(2016);Samberg et al., 2016 (referred to as Graeub, Lowder, Herrero,
https://doi.org/10.1016/j.gfs.2018.05.002
Received 8 January 2018; Received in revised form 8 May 2018; Accepted 9 May 2018
Corresponding author at: The Institute for Resources, Environment, and Sustainability, University of British Columbia, Canada.
E-mail address: vinnyricciardi@gmail.com (V. Ricciardi).
Global Food Security 17 (2018) 64–72
2211-9124/ © 2018 Elsevier B.V. All rights reserved.
T
and Samberg respectively hereafter). In 2016, a pair of studies eval-
uated the contribution of smallholders and family farms to global crop
and food production. Lowder was the rst to report on global farm size
trends from 1960 to 2010 derived from 167 countries in the World
Census of Agriculture (WCA). They found that small-farms (dened as
being < 2 ha) constituted only 12% of the global available farmland,
but represented 84% of all farms. Their study did not report on crop
production, but their results implied that smallholders do not produce
70% global crops; it is unlikely they could produce this much food on
12% of available farmland, even if we assumed that small farms had
higher yields and produced more food crops than larger farms. The
second of these studies (Graeub) quantied the number and extent of
family farms in the world and their production contributions. By using
national family farm denitions, dening family farms based on farm
size, or a combination thereof to represent regionally appropriate fa-
mily farm denitions they estimated that ~98% of all farms globally
are family farms, collectively managing 53% of all cropland, and
meeting an estimated 36114% of domestic caloric requirements for
dierent countries. While Graeub's study highlighted the contribution
of family farms, they also challenge the idea that all family farms are
small farms. For example, farms in Brazil may be family owned but are
large in size (while ~ 85% of farms in Brazil are family owned and
cover ~ 25% of agricultural land, only 21% of farms are less than 2 ha
in size and cover only 0.25% of the agricultural area). Together these
two studies, quantied the global number of smallholders or family
farmers, their cropping area, and detailed the dierences between
smallholders and family farms.
Two additional studies were recently published that tried to better
estimate the proportion of food coming from smallholder farmers glob-
ally. Samberg estimated the contributions of smallholders in an analysis
of 41 crops and 83 countries in smallholder dominant regions (Latin
America, sub-Saharan Africa, and South and East Asia) that represent
35% of global cropland. They estimated that smallholders (which they
dened as all administrative units with a mean agricultural area<5
ha) produced 52.5% of food calories in their cross-regional sample.
While, this study was a valuable step in mapping the geographic dis-
tribution of smallholders, using mean agricultural area within an ad-
ministrative unit as an index of smallholder production is problematic
because farm size distributions are highly skewed (e.g. Lowder).
Following this, Herrero presented an analysis which modeled crop and
livestock production, micro-nutrition production, and agricultural land-
scape diversity. Crop and animal data were related to farm size classes by
combining crowd-sourced data on eld sizes (Fritz et al., 2015) with
national farm size distributions (Lowder) as a proxy for per pixel pro-
duction by farm size. They reported that farms < 50 ha produce 56% of
commodities and nutrients in their sample of 41 crops, 7 livestock, 14
aquaculture and sh products, across 161 countries. They also estimated
that ~18% of food calories globally come from farms < 2 ha, and
highlighted the valuable micronutrient contribution of smallholders,
with farms < 20 ha producing ~ 70% of the world's vitamin A. While
both Samberg and Herrero provided clear steps forward in understanding
the role of smallholders in the food system, and in particular Herrero
covering both animal and crop products, they did not use direct mea-
surements of crop production and/or area by farm size, compute di-
versity calculations based on these direct calculations of production and/
or area, or report on the broader role of smallholders in the food system
(e.g. how much of their food is wasted and destined to non-food crops).
To ll these gaps, we compiled the rst open source dataset to es-
timate crop production by farm size derived from actual farmer surveys
containing crop-specic measurements of production or area that are
cross-tabulated against each farm size class. Our dataset includes 154
crop types and covers 55 countries, which represents 51.1% of global
agricultural area. We compare these direct estimates to those from the
previous modeling studies (e.g., Herrero et al 2017; Samberg et al.,
2016).In addition, we provide global estimates of the type of produc-
tion (i.e., food, feed, processing, seed, waste, and other) across farm
sizes and within each farm size class, to understand if more production
from small farms is wasted from storage and transportation, and if this
cancels the larger losses to biofuels and animal feed grown on large
farms. Finally, we evaluate how the type of crops grown, crop species
diversity, and macro-nutrient production varies by farm size. Our study
is the rst to directly evaluate the relationship between farm size, crop
types, and crop diversity across a large range of farm sizes and geo-
graphic regions, and to assess how this diversity inuences the amount
of macro-nutrients available from crops. Together, these results provide
the most comprehensive empirically grounded estimates of crop pro-
duction by farm size currently available.
2. Methods
2.1. Data compilation
We compiled a global convenience sample of datasets that directly
measured crop production and/or area by farm size for 55 countries at
either the national, or subnational level (for a total of 3410 national or
subnational units; see Fig. 1). These datasets were either agricultural
census data or nationally (or sub-nationally) representative sample
surveys, aggregated by administrative unit (n= 34 countries) or
available at the micro-level (e.g., anonymized individual household
level records) (n= 21 countries; of which 18 were household surveys
and 3 were censuses that captured both family and non-family farms).
The median year of the data was from 2013, with the oldest datasets
from 2001 and the newest from 2015. The database has 154 crops
which we matched with commodity names outlined in the Food and
Agricultural Organization's (FAO) statistical database (2017) [FAO-
STAT hereafter]. Where farm size and production were not cross-ta-
bulated in the survey instrument (i.e. for 33 countries), we calculated
production by farm size by rst extracting either harvest area, culti-
vated area, crop area, or planted area to calculate farm size, and then
converted area to production using FAOSTAT's national yield data. We
tested the validity of this method, and found it to slightly underestimate
production (full details of bias tests, inclusion criteria, variable de-
scriptions, summary statistics, and per country statistics are given in the
accompanying Data in Brief article). When farm size data was not
available for a country, but we had micro-level data, we used the sum of
farm plot areas for a given household as a proxy for farm size. Internal
validation of the use of micro-data to ll in data gaps was not possible
with our data, because we did not have both micro-data and farm size
metrics for any of our countries, but we think the impact of using ag-
gregate plot area is likely to be negligible for our results, as this was
only used on 4.8% of administrative units in our dataset. Finally, all
crop production data was tallied per country and validated against
available national level reports, and to the FAOSTAT crop production
database, both of which are computed from aggregated crop area es-
timates. In total, our dataset captures 51.1% of global crop production
and 52.9% of global cropland area. We harmonized the datasets to
match the WCA farm size categories: 01 ha, 12 ha, 25 ha, 510 ha,
1020 ha, 2050 ha, 50100 ha, 100200 ha, 200500 ha,
5001000 ha, and above 1000 ha. While we recognize that per country
denitions of smallholders may not fall within these farm size bins, the
majority of the datasets included reported these farm size breaks. We
report our estimates by each WCA farm size class and cumulatively to
allow exible denitions of smallholders that are consistent with past
attempts to quantify the relationship between farm size and crop pro-
duction. Future researchers may use the accompanying, open-access
dataset to redene smallholders based on country specicdenitions.
Where European data included a > 100 ha category, we included this
in the 100200 ha range, making our classication less precise in >
100 ha groupings, in comparison to < 100 ha. Future researchers may
wish to aggregate all largefarms into a > 100 ha bin for their specic
needs, but here we present the results maintaining the disaggregation
for surveys that reported it.
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
65
2.2. Crop allocation
Following data compilation, we converted all tonnes of production
to their kilocalorie (kcal/capita/day) equivalents using FAOSTAT con-
version values per crop per country per year. We then applied the
percent of feed, food, processing, seed, waste, or otherbased on
FAOSTAT's food balance sheets per crop per country per year. For ex-
ample, in many countries maize can be used for human consumption,
animal feed, a processed biofuel commodity, and seed, while some
maize may be lost due to storage and transportation. FAOSTAT contains
national totals for each of these types of crop allocation categories. We
used these totals to calculate percentages per crop per country per year
to allocate a certain portion of each crop's production towards food,
feed, and the other crop allocation categories. While this approach does
not account for the actual distribution of crop allocation by farm size, it
is the most detailed information available and represents a proxy in-
dicator based on what type and quantities of crops each farm size
produces.
While certain FAOSTAT categories were straightforward to interpret
and contained detailed denitions (e.g., feedtowards livestock and
poultry and seedset aside for sowing or planting), the processing ca-
tegory was ambiguous and required us to make assumptions. We fol-
lowed Cassidy et al. (2013) and assumed that the reported oil crop
processing category already separated out oil crop production for
human consumption from that for industrial use, as well as protein
dense cakes for animal feed. The waste category encompassed any loss
of a given commodity during storage and transportation; losses incurred
before and during harvest were excluded, as were losses due to
household consumption. The othercategory encompassed any uses not
already accounted for.
After allocating all crop production to type of production (e.g., feed,
food, other, etc.) in kcal/capita/day we evaluated how the global
quantity of each varied across farm size classes. We also provide cu-
mulative distributions of our estimates to encompass a sliding scale of
denitions of small-farms (e.g., farms under 2 ha, farms under 50 ha,
etc.), as may be required by dierent researchers and regional policy
makers who might dene smallusing dierent thresholds. In addition
to comparing how the type of production varies across farm sizes, we
also analyzed how the types of production are distributed within each
farm size class.
To obtain global estimates for the proportions presented in this
manuscript, we computed 95% condence intervals using the ac-
celerated bias-corrected percentile limits bootstrap method (BCa), with
1000 iterations. BCa is useful extension of the basic percentile
bootstrap, that decreases coverage error by accounting for bias in
sample parameters (i.e., when the sample parameter computed from
the 55 countries does not equal that of the average bootstrapped
parameter, which in our case is our best estimate of global trends), and
allowing the standard deviation of the bootstrap parameter to vary with
the sample parameter (Manly, 2006). We chose to bootstrap all of the
parameters of interest at the level of the country (n = 55), not the
administrative unit (n = 3410), in an attempt to account for de-
pendencies amongst administrative units in the same country and
sampling campaign. Accuracy in uncertainty estimation for the global
trends could be improved in future by adding to the number of coun-
tries in the dataset. While the BCa does not make any assumptions
about the distribution of underlying random variable we use the natural
log transform of production in our analysis for data visualization.
2.3. Crop species diversity and crop types
To estimate the relationship between crop diversity and farm size,
we counted the proportion of unique number of species each farm size
category produced within each administrative unit, and estimated the
95% CI's for each category using BCa. We note that dierent survey
instruments have dierent crops included, and that farmer responses
may not include the full diversity of crops that farmers actually pro-
duce. Thus, our estimates represent our current state of knowledge
given empirical data, and are likely to be conservative. We present BCa
estimates of crop diversity for each farm size using the administrative
unit level to compare crop diversity distributions across farms within
similar biogeographical landscapes (e.g., climate, soil, etc.). We also
present BCa estimates of crop diversity while controlling for cumulative
farm area, to give an indication of how diversity scales across the world
in each farm size class. To do this we plotted cumulative numbers of
unique species against cumulative area of administrative units for each
farm size class, and estimated uncertainty for these curves by resam-
pling the distribution of administrative units for each size class at
random, a 1000 times (taking the 2.5th and 97.5th percentiles as lower
and upper bounds, respectively).
To examine the variation in crop groups by farm size, we aggregated
our crop species data into major commodity groups according to
FAOSTAT denitions of cereals, fruit, oil crops, pulses, roots and tubers,
tree nuts, vegetables, and other, and we estimated 95% CI's using BCa.
Relying on the FAOSTAT classication has its limitations. For example,
soy was classied as an oil crop, but it is also a pulse; therefore, this
classication should be used as a guideline (see accompanying Data in
Brief for crop grouping details). In order to examine whether dierent
Fig. 1. Spatial coverage and resolution of our data on crop production by farm size. Countries shaded purple had directly measured data on crop production or
harvested area.
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
66
farm sizes grew a dierent portfolio of crop groups, we used Sorensen's
similarity index (Chao et al., 2006):
=+
C
SS
C
C2
ij
ij
ij
+
S
S
i
j
where C
i,j
is the number of species two farm size classes have in
common, Sis the total number of species found in the given farm size
class, and iand jare the two farm size classes being compared; a score
of 1.0 would represent perfect overlap in the crop groups grown be-
tween the two farm size classes.
2.4. Macro-nutrient production
We converted production of each crop in our dataset to its macro-
nutrient (i.e., carbohydrate, protein, or fats in grams/capita) equivalent
using FAOSTAT food balance sheets, and conversion factors per crop
per country for the year matching the farm size data survey year. Any
temporal data gaps in FAOSTAT were linearly interpolated per crop and
country. As with production, we analyzed how macro-nutrient pro-
duction varied both across farm-size classes and within farm-size classes
and computed 95% CI's using BCa at the country level to estimate
global gures.
Fig. 2. A-F) Distribution of total global crop production (in kcal equivalents) across farm size groups dierent uses (e.g., food, feed, other, etc.). Grey shows
bootstrapped 95% condence intervals and red indicates the average. G) Allocation of use of production within each farm size class. H) Cumulative percent of global
food production by farm size group with 95% condence intervals. See Table S1 for underlying data. (For interpretation of the references to color in this gure
legend, the reader is referred to the web version of this article).
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
67
3. Results
3.1. Crop allocation
The smallest two farm size classes (01 ha and 12 ha) are the
greatest contributors to global food production compared to all other
classes. Farms less than 2 ha produce 2831% of total crop production
and 3034% of the global food supply (by calories; Fig. 2A-H) as ex-
trapolated from the 55 countries in our dataset. Their contribution is
slightly higher than their areal coverage of 24% of gross harvested area,
suggesting small farmers have greater cropping intensity or higher
yields than larger farms.
We found smallholders (farms < 2 ha) also allocate the largest
percentage (5559%) of their crop production to food compared to all
other farm size classes (Fig. 2G). Generally, larger farms devote more of
their production towards feed and processing. Farms between 200 and
500 ha have the largest allocation of their production to feed (1629%)
compared to farms < 2 ha who allocate 1216% to feed. Farms >
1000 ha allocated 1232% of their production to processing.
Farms < 2 ha contribute the most 28.1% (2630%) to total food
waste (on-farm and post-harvest loss) (Fig. 2E); however, this is mainly
driven by this farm size group's large contribution to the total crop
production. In our dataset, only 4% (2.36.1%) of smallholder pro-
duction is wasted, compared to farms > 1000 ha that have the greatest
amount of within farm size class waste at 7.5% (0.018.5%). However,
the large uncertainty indicates both that there is substantial variation
within large farms, and low condence in the trend between farm size
and waste holds at the global level. All farm sizes have fairly consistent
allocations towards seed (means ranged from 2% to 5% with over-
lapping 95% CI's), while there is a trend that smaller farms allocate
more to the othercategory.
3.2. Crop species diversity and crop types
We found that species richness declined with increasing farm size
(Fig. 3A). Diversity also scaled dierently with area within dierent
farm size classes, with greater turnover in unique species in small farms
than in land allocated to larger farms (Fig. 3B).
Between farm size dissimilarity in species shows that larger farms,
while harboring less diversity, and lower turnover in crop diversity
across space, show greater specialization in certain crop groups than
other farm sizes. Farms < 5 ha grow similar crops as each other
(Sorensen's coecient of 0.94), and farms > 100 ha have a perfect
overlap in crops grown (Sorensen's coecient of 1.0; Fig. 4). But farms
greater than 20 ha grow a dierent array of crops compared to farms
smaller than 20 ha (Sorensen's coecient of 0.40.67) and farms
greater than 100 ha have the lowest overlap with other farm size
classes.
The crop portfolio of each farm size class shows that smaller farms
(< 2 ha) produce a greater share of the world's fruits, pulses, and roots
and tubers, while medium sized farms produce more vegetables and
nuts, and large farms produce more oil crops and 'other' (Fig. 5). While
all farm sizes contribute a large proportion to cereals, smaller farms
devote a greater percentage of their overall production to cereals
compared to other farm size classes.
3.3. Macro-nutrient production
The trends in macro-nutrient (carbohydrates, proteins, and fats)
production across farm sizes follows that of the food production. Yet, of
their own production, smaller farms produce a slightly higher percen-
tage of carbohydrates (~ 0.08% more than the largest farm size class)
while larger farms grow a slightly higher percentage of proteins (~
0.05% more than the smallest farm size class). But these dierences are
minute, and considering the uncertainty estimates, there are no sig-
nicant dierences in the percentage of macro-nutrients produced
within each farm size class (Fig. 6).
4. Discussion
4.1. Comparison to previous studies
Our dataset is the rst global sample of direct crop-specic mea-
surements of production or area by farm size. We found that farms <
2 ha produce 2831% of total crop production and 3034% of the food
supply on 24% of gross agricultural land when using our directly
measured farm size dataset. While our dataset covers 55 countries, with
distinct datagaps in smallholder dominant Southeast and East Asia, our
ndings are in line with Samberg and Herrero's global estimates. This
suggests that these three studies, using dierent methodologies, agree
Fig. 3. A) Distribution of total species richness across farm size classes. Grey represents bootstrapped 95% condence intervals; red is the bootstrapped average. The
area of each administrative unit polygon weighted the data. See Table S2 for underlying data. B) Cumulative area to cumulative species richness curves. 1000
iterations generated the cumulative distributions between species richness and farm size. The starting point for cumulative distributions were randomly chosen each
iteration. The lighter the colors, the larger the farm size classes. (For interpretation of the references to color in this gure legend, the reader is referred to the web
version of this article).
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
68
that the previous estimate of smallholders producing 7080% of global
food production needs to be revised.
While our results are similar to the previous two modeling studies
that estimated global smallholder production, there are several key
dierences. Our results oer more rened estimates using direct mea-
surements of production by farm size instead of relying on modeling,
includes a larger range of crop species than previously assessed, and our
accompanying open-access dataset allows individual countries to have
a reliable SDG baseline for how much of their food production is grown
by smallholders (according to their own regional denitions of farm
size). Samberg reported that farms < 5 ha produced 55% of global food
calories, which is slightly larger than our equivalent estimate of
4448% (Table 1, Samberg A.). To arrive at this estimate, they divided
the total calories produced in each farm size category in their 83-
country sample by total global calories produced by all countries. Their
estimate could be considered a global estimate if one assumes that their
sample of smallholder dominant regions account for most of the world's
small farms (and their purposeful sample might suggest that inter-
pretation). An alternative interpretation (which is similar to ours) is
that the 83 countries in their sample are globally representative; in that
case one would divide the calories produced by each farm size class by
the total calories produced in those 83 countries. By this estimate,
farms < 5 ha produced 76% of global food calories (Table 1, Samberg
B.). The dierences between Samberg and our dataset may be due to
the countries and crops sampled and that Samberg relying on modeled
results instead of direct measurements. Samberg used 41 crop species
(while we included 154), and they use mean agricultural area instead of
farm size distributions to understand crop production in smallholder
dominant areas rather than crop production by farm size. Both Samberg
and our study relied on household sample surveys to varying degrees
(Samberg relied primarily on household surveys, while our dataset re-
lied on them for 22.5% of total crop production). Household sample
surveys systematically do not sample non-family farms and hence may
be presumed to over-represent smaller farms when compared to agri-
cultural censuses that survey all farm types. However, in our accom-
panying Data in Brief article we show that using household surveys to
estimate national production is not signicantly dierent than using
FAOSTAT's national production estimates.
Our estimates were also close to Herrero's global estimates. < 50
ha. We found that farms < 2 ha produce 30-34% of the worlds food
and < 50 ha produce 6266% of the world's food, which is near
Herrero's estimate of 18% and 56%, respectively (as in Herrero et al.
(2017) Table 3). Our two studies capture dierent aspects of the global
food system. Herrero incorporated livestock and sheries, which are
important source of nutrients and income for smallholders, while we
only focus on crop production; our focus was due to data constraints
and denitional mismatches between using farm size versus herd size,
shing area, or common pasture land. There are also crop species dif-
ferences between the datasets, where Herrero used 41 crop species
while we used 154. One key analytical dierence is that Herrero's
modeled results used eld size as a proxy for farm size instead of actual
reported farm size; we used eld size as a proxy for farm size for only
4.8% of our data and direct measurements of production by farm size
instead of modeled estimates. We found that using eld size as a farm
size proxy measure may slightly over estimate small-farmsproduction
since it does not account for non-eld elements of a farm (see the ac-
companying Data in Brief article). Additionally, Herrero disaggregated
production to the pixel level based on eld size, while Samberg dis-
aggregated pixel-level production based on mean agricultural areas.
Essentially, both methods assume a constant yield for each farm size
class since they cannot directly link crop production with farm size.
There is a widely observed inverse relationship between yield and farm
size (IR), where smaller farms have higher yields (Bevis and Barrett,
2016; Henderson, 2015; Sen, 1962). For 66.7% of our dataset we also
needed to use constant yields since direct data on production by farm
size was not always available (we did have harvest area per crop by
farm size, and minimally used data on planted area, cropped area,
plotted area). Our dataset allowed us to test for the bias introduced by
constant yield methods, and provides the relationship which re-
searchers may use to correct for it. In the accompanying Data in Brief
article, we found a small eect size that using constant yields slightly
underestimates small-farmsproduction. Hence, our numbers, Herrero's
and Samberg's may all slightly underestimate smallholderscrop pro-
duction owing to this assumption.
Fig. 4. Heat-map of Sorensen's coecient between each farm size class pair. Purple indicates a greater similarity of crops grown between pairs of farm size classes,
while brown indicates greater dissimilarity between crops grown.
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
69
4.2. Crop allocation
Our new ndings on crop allocation across dierent farm sizes has
important implications for food access and availability, as well as
farmer livelihoods, since food, feed, processing, and seed market prices
may dier from one another. We found nearly 60% of smallholder
production is allocated to food. A smaller percentage is allocated to-
wards feed (1216%), which was surprising since smallholders often
engage in mixed crop-animal farming systems (Smith et al., 2012); this
nding may be explained by the fact that smallholders likely rely more
on rearing animals that graze on pasture compared to largeholders.
Our results counter common thinking about smallholderspost-
harvest loss, where improving cold storage and road infrastructure is a
common development intervention to improve smallholder income by
reducing wastage. Our dataset suggests that only a small percentage of
smallholdersproduction is wasted. However, one reason for the low
amount of smallholder waste in our results may be due to food allocated
to the othercategory. From our data, 1923% of smallholder pro-
duction went towards otheruses. This may be indicative of the need
for smaller, farms to make use of all grown material in integrated
farming systems (e.g., using rice stocks as a cover crop to promote soil
health). Smallholderslarge allocation towards othermay be in-
dicative that waste reduction practices are common since smallholders
are often resource poor and would achieve higher relative benet
Fig. 5. A-H) Distribution of global production by crop type across farm size classes. Grey shows bootstrapped 95% condence intervals and red is the average. I) Crop
type portfolio within each farm size class. See Table S3 for underlying data.
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
70
compared to largeholders to nd a use for wasted crops.
While, interventions aimed to reduce smallholder post-harvest loss
are still needed in many locales, there is also a need for agricultural
researchers to identify why larger farms are wasting crops, because this
group showed the greatest proportion of waste in any category (al-
though this was country dependent, as shown by the wide bootstrapped
condence intervals). An estimated 1/4 of global food production from
croplands is wasted from farm to market (Kummu et al., 2012). The
waste data we used takes into consideration the quantities lost in the
transformation of crop to processed goods (FAOSTAT, 2017). Hence,
one possible reason for the increased wastage of larger farmsis that
large farms on a whole engage in more crop production allocated for
processing. Since FAOSTAT's denition of waste also encompassed
waste incurred from poor distribution and storage, it was surprising
that smaller farms did not have larger proportions of their crop wasted
when the majority of smallholders are in countries with hot and humid
climates and poorer storage infrastructure (Cohn et al., 2017). Future
studies should disaggregate the types of crop production waste each
farm size contributes to and local dependencies for these relationships.
4.3. Crop species diversity, crop types, and macro-nutrient production
Our data suggests a negative relationship between farm size and
crop species richness. This adds signicantly to the evidence on the
literature's mixed nding on this relationship (Assunção and Braido,
2007; Rahman and Kazal, 2015; Van den Berg et al., 2007), as our study
contains a wider range of farm sizes and more crop species than ever
compared in previous studies. Due to the heterogeneity in data sources
used to construct our dataset, there were not always a wide list of crop
species included in each national survey, which may indicate a larger
portion of primary crops were documented compared to local species.
This limitation indicates that our ndings are conservative and suggest
that smaller farms, which are associated with producing many non-
primary crops (Fifanou et al., 2011; Keleman et al., 2013), may have
even higher degrees of crop diversity than we found.
There are several food access, nutrition, and climate resilience im-
plications of higher crop diversity in smallholder systems. Since
smallholders may be tied to subsistence-surplus production models and
constrained to highly localized rural markets, their food access is often
more reliant on their local communitiescrop production compared to
large farms (Baiphethi and Jacobs, 2009). The dierences in types of
crops produced by dierent farm sizes, and macro-nutrient contents
that follow overall food production trends, supports the dierences in
micro-nutrient and macronutrient production across farm sizes, as
found in Herrero. However, there are discrepancies. While smallholders
produce a large amount of the world's protein rich pulses, we did not
nd that they produced a greater relative percentage of proteins than
larger farms (i.e., all farm sizes allocated a similar percentage of their
production to proteins). This suggests potential benets from the pro-
motion of mixed animal-crop systems for smallholders to access protein
of which they are often decient (Smith et al., 2012).
Our results suggest a nuanced view of the benets of landscapes
harboring dierent farm sizes, beyond the basic relationship between
farm size and crop species richness. More diversied farming land-
scapes may need to include smaller farms because they collectively
grow a higher diversity of crops than large farms, but also include
larger farms because of their unique crop composition. Each farm size
produces a greater quantity of certain types of crops than other farm
sizes: smaller farms produce more fruits, pulses, and roots and tubers,
Fig. 6. A) Percentage of macro-nutrient production across farm size classes with 95% condence intervals. B) Percentage macro-nutrient production within each
farm size class with 95% condence intervals. See Table S4 for underlying data.
Table 1
Comparison between global estimates for the percentage of food smallholders
produce. For Samberg, A. uses estimates compared to global total food pro-
duction, while B. compares estimates to total food production within their 83
sampled countries.
< 2 ha < 5 ha < 50 ha Methodological Distinctions
Our study 3034% 4448% 6266% Direct measurements
154 Crops
55 Countries
Herrero 18% 56% Modeled estimates
41 Crops; 7 Livestock; 14 Aquatic
Species
Near global coverage
Samberg A.
a
37% 55% Modeled estimates
41 Crops
Samberg B. 52% 76% 92% 83 Countries
Mean Agricultural Area as farm
size proxy
a
Note that we do not provide < 50 ha estimates for Samberg A because we
cannot support the assumption that there are no farms < 50 ha outside of the
83 countries sampled by Samberg.
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
71
while medium sized farms produce more treenuts and vegetables, and
larger farms produce more oil crops. Promoting a diversity of farm sizes
may encourage a greater diversity of crop types at the landscape level
that can better provide more balanced diets and non-food needs, while
potentially mitigating climate risks to the food system as a whole.
5. Conclusion
This study attempted to provide a global baseline for international
policy measures aimed to support smallholder agriculture. These in-
clude a need for improved monitoring of SDG Goal 2.3, which aims to
double food production of smallholders and increase nutrient avail-
ability; yet, Goal 2.3's monitoring framework does not use crop pro-
duction by farm size as a national indicator (Sustainable Development
Solutions Network (SDSN), 2015). Our ndings suggest that previous
estimates of the percentage of food produced by smallholders were
either overinated by public-sector opinions (ETC, 2009;Maass
Wolfenson, 2013;FAO, 2014) or still needed directly measured data to
assess quality (Herrero et al., 2017; Samberg et al., 2016), and that a
nutrient diverse farming landscape would include a diversity of farm
sizes, since each farm size produces a unique crop portfolio.
Critically, while our dataset is the rst to use directly measured crop
specic data on production or area by farm size, we were only able to
nd 55 countries with the necessary data to do this analysis. To monitor
SDG Goal 2.3, there needs to be increased eort to build on datasets like
ours through leveraging stakeholder networks. Ongoing eorts to use
and add to our dataset will enable continuous food system monitoring
over time with more geographic precision. We urge researchers and
food system advocates towards data-driven policy monitoring to accu-
rately assess the scale and progress of policy interventions.
Declarations of interest
None.
Role of funding source
This research was funded through the University of British
Columbia 4 Year Doctoral Fellowship & Social Sciences and Humanities
Research Council (SSHRC) Insight Grant (#435-2016-0154). These
funding sources had no role in data collection or analysis.
Appendix A. Supplementary material
Supplementary data associated with this article can be found in the
online version at http://dx.doi.org/10.1016/j.gfs.2018.05.002.
References
Altieri, M.A., 2008. Small Farms as a Planetary Ecological Asset: Five Key Reasons Why
we Should Support the Revitalisation of Small Farms in the Global South. Third
World Network, Penang, Malaysia.
Assunção, J.J., Braido, L.H.B., 2007. Testing household-specic explanations for the in-
verse productivity relationship. Am. J. Agric. Econ. 89, 980990. http://dx.doi.org/
10.1111/j.1467-8276.2007.01032.x.
Badstue, L.B., Bellon, M.R., Berthaud, J., Ramírez, A., 2005. Collective Action for the
Conservation of On-farm Genetic Diversity in A Center of Crop Diversity: an
Assessment of the Role of Traditional Farmers' Networks.
Baiphethi, M.N., Jacobs, P.T., 2009. The contribution of subsistence farming to food se-
curity in South Africa. Agrekon 48, 459482. http://dx.doi.org/10.1080/03031853.
2009.9523836.
Bevis, L.E.M., Barrett, C.B., 2016. Close to the Edge: Do Behavioral Explanations Account
for the Inverse Productivity Relationship? Cornell University working paper, Mimeo
(accessed May. 10, 2018). http://barrett.dyson.cornell.edu/les/papers/Close%20to
%20the%20Edge%20July%202017%20Bevis%20&%20Barrett.pdf.
Cassidy, E.S., West, P.C., Gerber, J.S., Foley, J.A., 2013. Redening agricultural yields:
from tonnes to people nourished per hectare. Environ. Res. Lett. 8, 34015. http://dx.
doi.org/10.1088/1748-9326/8/3/034015.
Chao, A., Chazdon, R.L., Colwell, R.K., Shen, T.J., 2006. Abundance-based similarity
indices and their estimation when there are unseen species in samples. Biometrics 62,
361371. http://dx.doi.org/10.1111/j.1541-0420.2005.00489.x.
Cohn, A.S., Newton, P., Gil, J.D., Kuhl, L., Samberg, L., Ricciardi, V., Manly, J.R.,
Northrop, S., 2017. Smallholder agriculture and climate change. Ann. Rev. Envir.
Res. 42, 347375.
Conway, G., 2011. On Being a Smallholder. Conference new Dir. Smallhold. Agric. IFAD
Rome 117.
ETC -group, 2009. Who Will Feed Us? Questions for the Food and Climate Crises.
FAO, 2014. The State of Food and Agriculture 2014: Innovation in Family Farming Food
and Agriculture Organization of the United Nations.
FAOSTAT, 2017. Food and Agriculture Organization of the United Nations FAOSTAT
database. http://www.fao.org/faostat/en/. Last (Accessed 18 January 2018).
Fifanou, V.G., Ousmane, C., Gauthier, B., Brice, S., 2011. Traditional agroforestry systems
and biodiversity conservation in Benin (West Africa). Agrofor. Syst. 82, 113. http://
dx.doi.org/10.1007/s10457-011-9377-4.
Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht,
F., Schill, C., Perger, C., Havlik, P., 2015. Mapping global cropland and eld size.
Global change biology 21 (5), 19801992.
Graeub, B.E., Chappell, M.J., Wittman, H., Ledermann, S., Kerr, R.B., Gemmill-Herren, B.,
2016. The state of family farms in the world. World Dev. 87, 115. http://dx.doi.org/
10.1016/j.worlddev.2015.05.012.
Henderson, H., 2015. Considering technical and allocative eciency in the inverse farm
size-productivity relationship. J. Agric. Econ. 66, 442469. http://dx.doi.org/10.
1111/1477-9552.12086.
Herrero, M., Thornton, P., Power, B., Bogard, J., Remans, R., Fritz, S., Gerber, J., Nelson,
G., See, L., Waha, K., Watson, R., West, P., Samberg, L., van de Steeg, J., Stephenson,
E., van Wijk, M., Havlík, P., 2017. Farming and the geography of nutrient production
for human use: a transdisciplinary analysis. Lancet Planet. Health 1 (1), e33e42.
Hodges, R.J., Buzby, J.C., Bennett, B., 2011. Postharvest losses and waste in developed
and less developed countries: opportunities to improve resource use. J. Agric. Sci.
149, 3745. http://dx.doi.org/10.1017/S0021859610000936.
Horrigan, L., Lawrence, R.S., Walker, P., 2002. How sustainable agriculture can address
the environmental and human health harms of industrial agriculture. Environ. Health
Perspect. 110, 445456. http://dx.doi.org/10.1289/ehp.02110445.
IFAD, UNEP, 2013. Smallholders, food security and the environment. Rome Int. Fund.
Agric. Dev. 54.
Keleman, A., Hellin, J., Flores, D., 2013. Diverse varieties and diverse markets: scale-
related maize protability crossoverin the central Mexican highlands. Hum. Ecol.
41, 683705. http://dx.doi.org/10.1007/s10745-013-9566-z.
Kummu, M., de Moel, H., Porkka, M., Siebert, S., Varis, O., Ward, P.J., 2012. Lost food,
wasted resources: global food supply chain losses and their impacts on freshwater,
cropland, and fertiliser use. Sci. Total Environ. 438, 477489. http://dx.doi.org/10.
1016/j.scitotenv.2012.08.092.
Lowder, S.K., Skoet, J., Raney, T., 2016. The number, size, and distribution of farms,
smallholder farms, and family farms worldwide. World Dev. 87, 1629.
Maass Wolfenson, K.D., 2013. Coping with the food and agriculture challenge: small-
holders' agenda. In: Proceedings of the 2012 United Nations Conference on
Sustainable Development. 47.
Manly, B.F.J., 2006. Randomization Bootstrap and Monte Carlo Methods in Biology, 3rd
ed. Chapman and Hall/CRC.
Naylor, R., Steinfeld, H., Falcon, W., Galloway, J., Smil, V., Bradford, E., Alder, J.,
Mooney, H., 2005. Losing the links between. Science 80, 16211622. http://dx.doi.
org/10.1126/science.1117856.
Rahman, S., Kazal, M.M.H., 2015. Determinants of crop diversity in the regions of
Bangladesh (19902008). Singap. J. Trop. Geogr. 36, 8397. http://dx.doi.org/10.
1111/sjtg.12086.
Samberg, L.H., Gerber, J.S., Ramankutty, N., Herrero, M., West, P.C., 2016. Subnational
distribution of average farm size and smallholder contributions to global food pro-
duction. Environ. Res. Lett. 11, 124010. http://dx.doi.org/10.1088/1748-9326/11/
12/124010.
Sen, A.K., 1962. An aspect of Indian agriculture. Econ. Wkly. 14, 243246.
Smith, J., Sones, K., Grace, D., MacMillan, S., Tarawali, S., Herrero, M., 2012. Beyond
milk, meat, and eggs: role of livestock in food and nutrition security. Anim. Front. 3,
613. http://dx.doi.org/10.2527/af.2013-0002.
Sustainable Development Solutions Network (SDSN), 2015. Indicators and a Monitoring
Framework for Sustainable Development Goals - Launching a Data Revolution for the
SDGs.
Tefera, T., 2012. Post-harvest losses in African maize in the face of increasing food
shortage. Food Secur. 4, 267277. http://dx.doi.org/10.1007/s12571-012-0182-3.
UN, 2015. Sustainable Development Goals (SDGs) [WWW Document]. URL https://
sustainabledevelopment.un.org/topics/sustainabledevelopmentgoals#(Accessed 1
January 2017).
Van den Berg, M.M., Hengsdijk, H., Wolf, J., Van Ittersum, M.K., Guanghuo, W., Roetter,
R.P., 2007. The impact of increasing farm size and mechanization on rural income
and rice production in Zhejiang province, China. Agric. Syst. 94, 841850. http://dx.
doi.org/10.1016/j.agsy.2006.11.010.
V. Ricciardi et al. Global Food Security 17 (2018) 64–72
72
... Global crop production has more than doubled over the last 40 years and the world now produces enough food to feed six billion people, although the distribution of this food is uneven. Numerous efforts are created to estimate the share of the world's food created by smallholders (FAO, 2014;HERRERO et al., 2017;RICCIARDI et al., 2018) and by family farms (FAO, 2014;GRAEUB et al., 2016). In some instances, the estimates made by Food and Agriculture Organization of the United Nations (2014) that family farms turn out quite 80% of food within the world have been erroneously taken like smallholders produce more than 80% of the world's food (RICCIARDI et al., 2018). ...
... Numerous efforts are created to estimate the share of the world's food created by smallholders (FAO, 2014;HERRERO et al., 2017;RICCIARDI et al., 2018) and by family farms (FAO, 2014;GRAEUB et al., 2016). In some instances, the estimates made by Food and Agriculture Organization of the United Nations (2014) that family farms turn out quite 80% of food within the world have been erroneously taken like smallholders produce more than 80% of the world's food (RICCIARDI et al., 2018). Agriculture in Ethiopia is the foundation of the country's economy, accounting for half of gross domestic product (GDP), 83.9% of exports, and 80% of total employment (MAC DONALD and SIMON, 2010). ...
Article
Full-text available
Az éghajlatváltozás negatív hatásai intenzíven sújtják az afrikai mezőgazdasági ágazatot. Az olyan országok számára, mint Etiópia, amelyek megélhetése elsősorban a csapadékra támaszkodó önellátó mezőgazdaságon alapul, nagyon fontos, hogy alkalmazkodjanak a változó helyzethez. Ezért az éghajlatváltozás negatív hatásainak mérsékléséhez elengedhetetlen a kontextus-specifikus alkalmazkodási stratégiák kidolgozása. A tanulmány célja az volt, hogy azonosítsa azokat a mezőgazdasági gyakorlatokat, amelyek maximalizálják a termelést és kímélik a természeti erőforrásokat. A vizsgálati körzet, a falvak és a reprezentatív válaszadók kiválasztása során háromlépcsős mintavételi eljárást követtek. Ennek megfelelően egy közigazgatási egységet (kebele) és 60 háztartásfőt választottunk ki célzott mintavétellel, illetve szisztematikus véletlenszerű mintavétellel. A talajadatokon és a másodlagos adatokon kívül szerkezetinterjú ütemtervet dolgoztunk ki, előzetesen teszteltük és felhasználtuk a kvantitatív adatok gyűjtésére. A talajelemzés eredménye azt mutatta, hogy az ökológiai mezőgazdaság hozzájárul a talaj kémiai tulajdonságainak javításához, vagyis a vegyes gazdálkodási gyakorlat a talaj pH-értéke, nitrogénje, szervesanyag-tartalma, szerves szén- és elérhető foszfortartalma szignifikánsan eltér a monokultúrás gazdálkodási rendszertől. The negative impact of climate change has been striking the agricultural sector in Africa. For countries like Ethiopia, whose livelihood occupation of the nation is mainly based on subsistence agriculture that highly rely on rainfall, making an adjustment to adapt to the changing situation is very crucial. Therefore, designing contextual specific adaptation strategies are essential to moderate the negative effect of climate change. The aim of the study was to identify the agricultural practices and conserve natural resources. Three stages sampling procedure was followed in selecting the study district, villages and representative respondents. Accordingly, one kebele and 60 household heads were selected using purposive sampling and systematic random sampling, respectively. In addition to the soil data and secondary data, structure interview schedule was developed, pre-tested and used for collecting quantitative data. The soil analysis result showed that organic agriculture contributed for the improvement of soil chemical property that is the mixed cropping farming practice was significantly different in soil pH, nitrogen, organic matter, organic carbon and available phosphorous than in mono cropping farming system. The descriptive result showed that all the farmers (100%) of the sample respondent participated in land preparation and 98.3% of them participated in manure application and also 98.3% of the sample respondents in participated weeding practices. 98.3% of the sample respondents were participated in harvesting activities. 93.3% of the sample respondents were participated in the fertilizer application activities. 68.3% of the sample respondents were participated in irrigation activities. 13.3% sample respondents were participated in seed treatment activates. To adopt organic agriculture practices whereby financial institutions and other agricultural funders should extend credit or support to agricultural activities with farming practices and technologies that promote organic agriculture. This study has shown that organic agriculture can contribute to better soil health and therefore better crop yields thereby strengthening the adaptation efforts of smallholder farmers. It is therefore recommended that smallholder farmers in Sofi district practice organic agriculture farming practices for adaptation efforts.
... Проблемі розвитку сільських територій і фермерських господарств України присвячені праці багатьох учених економістів, серед яких: П. Саблук [2], В. Юрчишин [3], К. Якуба [4], М. Малік [5], Т. Козир [6], В. Борщевський [7], В. Попович [8], Т. Гоголь [9], М. Тимошенко [10], О. Маслак [11] та інші. У зарубіжній науковій літературі дослідження фермерських господарств і бізнес-моделей їх функціонування висвітлені в працях: D. Byerlee [12], S. K. Lowder [13], V. Ricciardi [14], S. Jefferies [15], C. Nicholson et al. [16], J. Tyrychtr [17], Graeub B. E. et al. [18], H. Chesbrough, [19], W. Jansen [20], D. Teece [21], G. Kolackova [22], O. Sivertsson [23], J. Poláková [24], N. Galluzzo [25], H. Van der Meulen [26], J. Savickiene [27] та інших. Проте недостатньо дослідженими, на наш погляд, залишаються питання щодо економічних параметрів розвитку фермерських господарств, зокрема сімейних, для сприяння реалізації економічного потенціалу розвитку сільських територій України. ...
... Загалом близько 75 % сільськогосподарських земель світу використовують сімейні ферми, які виробляють значну частку світових продуктів харчування та сільськогосподарської сировини [13]. Зокрема, на малі ферми площею до 2 га припадає близько третини світового виробництва продуктів харчування з високим рівнем різноманітності [14]. Успішні фермерські господарства мають дві важливі складові частини: вони прибуткові і, можливо, більш важливо, відповідають потребам людей, які ними володіють і керують. ...
Article
Мета. Метою статті є дослідження економічних аспектів та обґрунтування бізнес-моделі розвитку сільських територій України на основі фермерських господарств, зокрема сімейного типу, для зростання виробництва сільськогосподарської продукції. Методологія / методика / підхід. У процесі проведеного дослідження використано загальнонаукові методи, зокрема монографічний при комплексному та всебічному вивченні основних економічних проблем розвитку сільських територій України; аналіз і синтез – для визначення основних чинників і складників розвитку сільських територій на основі фермерських господарств, у тому числі сімейного типу; статистичний – для оцінки потенційного ринку продовольчих товарів з розрахунку на 1000 жителів сільських територій; розрахунково-конструктивний метод використано під час визначення основних економічних параметрів розвитку сімейних ферм та інших пов’язаних промислів з урахуванням критеріїв розширеного відтворення, самоокупності й економічного інтересу. Результати. У статті проведено оцінку потенційної місткості продовольчого ринку сільських населених пунктів з розрахунку на 1000 жителів, відповідно до якої запропоновано виробничу бізнес-модель розвитку сільських територій. Ця економічна модель ґрунтується на створенні спеціалізованих товарних ферм сімейного типу та пов’язаних з ними промислів для забезпечення продовольчих потреб місцевого населення за основними видами сільськогосподарської продукції. Визначено економічні показники та критерії розвитку успішних ферм, зокрема граничні масштаби виробництва, ціни продажу продукції за основними напрямами агробізнесу. Обґрунтовано суму економічного ефекту з розрахунку на 1 ферму чи промисел, який сприятиме розширеному відтворенню місцевого агробізнесу. Оригінальність / наукова новизна. Уперше запропоновано бізнес-модель розвитку сільських територій України на основі малих підприємницьких формувань з виробництва агропродовольчої продукції. У статті представлено результати дослідження, які економічно обґрунтовують розвиток спеціалізованих сімейних ферм за визначеними напрямами агробізнесу з орієнтацією на задоволення продовольчих потреб споживачів внутрішніх сегментів ринку, що сприятиме зростанню зайнятості та соціально-економічному піднесенню сільських територій України. Практична цінність / значущість. Результати проведеного дослідження можуть використовуватися під час формування загальнонаціональних і регіональних програм підтримки розвитку малих форм агробізнесу, зокрема сімейного фермерства, з метою реалізації економічного потенціалу агропродовольчого комплексу України.
... Furthermore, parametric techniques are recommended, since small-scale production in developing nations is characterized by a wide range of variability [91]. Ricciardi et al. [92] were able to determine the average levels of formal education, the number of people living in each farming household, the number of years they had been raising chickens, their average age, and their ability to obtain credit by utilizing the stochastic frontier analysis technique. ...
... Running efficiency analyses, in accordance with Yeni [97], enables knowing the efficiency degree of input utilization and other preventative measures. Economic efficiency analysis thus provides a more comprehensive picture of a farm's competitiveness and performance [92]. ...
Article
Full-text available
An important factor in determining the success of a small-scale broiler farm is its economic viability and efficiency. During times of trouble for the industry, the idea receives more attention. The conceptual considerations of economic sustainability and efficiency are frequently quite constrained, according to the difficulties raised in this study and by other authors. There is a lack of information about South Africa's small-scale broiler production's economic viability and effectiveness. Furthermore, it is clear that small-scale broiler producers have the ability to increase their economic efficiency. By reducing the mortality rate, feed conversion rate, and production duration, both their technical and financial efficiency could be improved. Profitability in the production of broilers will be considerably increased by lowering the cost of these variable inputs, particularly feed and day-old chicks. Additionally, raising the education level, capacity utilization ratio, and broiler production would all contribute to raising the farms' efficiency levels. To ensure effective resource use and to maximize practicable profit, small-scale broiler producers who are not operating close to the profit frontier must make efforts to reduce both technical and allocation inefficiencies. Collectively, all these measures would ensure the economic sustainability of small-scale farmers in South Africa would be met. Moreover, the sustainability of small-scale broiler producers can be achieved if strategies that build local capacity and that empower them to sustain high levels of productivity are provided. In addition, the efficient use of resources will ensure that productivity is enhanced, and might increase profitability. It is therefore important to ensure that small-scale broiler producers achieve maximum profit for a given set of inputs. Approaches in assessing the farm-level profitability such as cost-benefit and gross margin analyses can be used.
... While alternate markets can act as a sink for what might otherwise be counted as food loss, larger farms are also more likely to dedicate a higher proportion of their production to the supply to feed and processing uses, compared with smallholder farms (<2 ha), where growing crops for food predominates [24]. This is a complex area involving diversion of agricultural production to non-food uses as a planned farming activity (e.g., growing of wheat varieties intended for feed production), as well as an unintended consequence of poor growing conditions or lastminute order cancellations. ...
Article
Full-text available
Global statistics on food waste were first reported by the United Nations Food and Agriculture Organization in 2011, and since that time, more attention has been given to food waste measurements at the consumer, retail and hospitality stages, whilst efforts to quantify losses during primary production have been more limited. To provide an updated view of global losses in primary production, data for the harvest and on-farm, post-harvest stages were reassessed through a systematic review of data sources and a selection of datasets for further analysis. To qualify for selection, food-loss measurements needed to be specific to primary production and to particular food commodities and production regions. The analysis covered a split between losses at the harvest and post-harvest stages linked to activity descriptions within the primary data sources. A cross-sectional sample of ten commodity/region case studies was conducted through stakeholder interviews and literature reviews to triangulate food waste estimates and to understand issues relating to food waste definitions from a farming perspective.
... In connection with changes in the management of rural areas (decentralization), the issue of further developing personal peasant households based on the unity of economic, social and environmental interests and enhancing business activities in terms of income growth, employment and social protection through the development of family farms is urgent (Malik and Shpykuliak, 2018). Ricciardi et al. (2018) presented a global sample of 55 countries, representing 51 % of global crop production and measurements of crop production, nutrient and crop diversity by farm size. Otzuka et al. (2016) called the policy of supporting small farms in Asia false, stimulating the loss of competitive advantage, and forced a number of countries to become net importers of food. ...
Article
Purpose. The purpose of the article is to analyze the contribution of agricultural enterprises and households to Ukrainian agrarian production, to determine the place of agricultural holdings in its formation and to indicate the role of the agricultural sector in Ukrainian exports. Methodology / approach. The methodological research tool is an economic analysis of the activities of business entities. The statistical data of the main indicators of the production activities of agricultural enterprises is being used as empirical information. In the process of research, the following methods of economic analysis were used: formalized – a graphical method, percentage numbers, methods of mathematical statistics; non-formalized – expert assessment, comparison, analytical tables. Results. An estimation of the dynamics of agricultural output in the basic categories of farms in 2010–2017 was made, the share of the basic categories of farms in the crop and livestock production was determined. It is noted that in the agrarian sector of Ukraine there has been a polarization in the production of agricultural products – self-employed small-scale forms of management dominate the production of labor-intensive and low-profitable products, while large industrial high-quality agricultural enterprises are highly profitable and low labor-intensive. Researches have shown that one of the leading places in the crop production is sunflower cultivation due to its high profitability. In the example of sunflower production shows the possibilities of economic and mathematical modeling as a tool for evaluating production efficiency and forecasting. The place of the agrarian sector in the structure of Ukraine’s commodity exports in 2017 has been analyzed. It was shown that an important place in the Ukrainian agro-industrial complex is occupied by agricultural holdings, which, in particular, are very significant in the formation of Ukrainian exports of cereals and oil plants. Originality / scientific novelty. Trends and the current state of the agro-industrial sector of Ukraine researches have been further developed. A comparative evaluation of the effectiveness and contribution of the basic categories of farms to the agricultural production at the present stage was made. For the first time, economic and mathematical models for the analysis of the main indicators of sunflower cultivation have been proposed. Practical value / implications. The growing role of agricultural enterprises, in particular, agricultural holdings, in the development of the agro-industrial complex is shown, the place of Ukrainian agricultural products in the world food market is defined and the importance of diversification Ukrainian exports is pointed out. The established trend lines of sunflower cultivation indicators made it possible to calculate the corresponding short-term forecasts, which can be taken into account when justifying alternatives for the future development of agricultural enterprises.
... /fsufs. . (Ricciardi et al., 2018). Here we propose an enhanced, quantitative engagement of agronomists and agricultural development experts with health and social science communities to consider the needs, desires, and behaviors of individuals alongside strategies to increase yields and reduce human labor inputs to quantify context and barriers to agricultural adoption. ...
Article
Full-text available
Agricultural scientists are pursuing sustainable intensification strategies to increase global food availability, but integration from research to impact at the local-level requires knowledge of demographic and human-environment to enhance the adaptive capacity of farmers cultivating <10 ha. Enhancing close collaboration among transdisciplinary teams and these smallholders is critical to co-elaborate policy solutions to ongoing food security crises that are likely to be attuned with local conditions. Human and socio-cultural aspects need to be considered to facilitate both adoption and dissemination of adapted management practices. Despite this well-known need to co-produce knowledge in human systems, we demonstrate the inequality of current agricultural research in smallholder farming systems with heavy focus on a few domains of the sustainable intensification agricultural framework (SIAF), ultimately reducing the overall impact of interventions due to the lack compatibility with prevailing social contexts. Here we propose to integrate agriculture and agronomic models with social and demographic modeling approaches to increase agricultural productivity and food system resilience, while addressing persistent issues in food security. Researchers should consider the scale of interventions, ensure attention is paid to equality and political processes, explore local change interactions, and improve connection of agriculture with nutrition and health outcomes, via nutrition-sensitive agricultural investments.
... Smallholders are producing about one-third of the population's food supply [58]. With the increase in population, food production should be doubled by the year 2025 in order for the market to meet this growing demand [59]. ...
Article
Full-text available
The recent COVID-19 pandemic has highlighted all the weaknesses of manufacturing systems and supply chains. In this challenging context, smallholders have faced several crises mainly related to the difficulty of finding manpower for harvesting activities and the impossibility of distributing food, due to the forced closure of many distribution channels. The main consequences were lost sales and wasted food. With the aim of increasing the responsiveness of smallholders in the face of COVID-like crises, this paper provides an overview of methodologies and approaches currently available in the literature in terms of: ICT tools, blockchain-based solutions, business models, sustainability-oriented frameworks, simulation models. The analysis of the literature provides two main outputs: (1) a list of challenges to be faced in the coming years to improve the working conditions of smallholders, (2) the definition of future research developments, which mainly concern the design of an ICT platform, which integrates multiple technological aspects.
... Herrero et al. (2017) reported that, in Africa and South and Southeast Asia, small farms with less than 2 ha produce around 30% of food and make valuable contributions to micronutrient-rich food production. Ricciardi et al. (2018) estimated that farms under 2 ha globally produce 30-34% of the food supply. Nonetheless, small farm households themselves are often unable to afford a nutritious diet. ...
Chapter
Full-text available
The number of people living in rural areas of low and middle-income countries is projected to increase in the coming decades. It is in the rural areas of these countries where a large majority of the world’s extreme poor reside. The livelihoods of two to three billion rural people depend on small farms. These small farms are responsible for the production and supply of a large portion of the calories feeding low- and middle-income countries. Small farms are also preservers of crops and associated biodiversity and with the right incentives can contribute to land stewardship. Small farms are diverse, and, hence, so are their associated challenges. We categorize small farms as commercial farms, small farms in transition and subsistence-oriented farms and highlight evidence-based innovations for the sustainable transformation of each type of small farm. Broadly, small farms face high transaction costs, lack collective action, and experience coordination failure in production and marketing. Lack of market access is also a major challenge. Investments in infrastructure, including those that support access to digital technologies, can improve farmers’ access to markets and incentives as well as foster growth in the midstream segments of the value chain that provide inputs, storage, processing, and logistics to small farms. Rural Non-Farm Employment (RNFE) is increasingly the main source of income for most small farmers and provides them with a risk diversification strategy and cash, both to purchase food and for farm investments to raise productivity, expand commercial activities, and produce higher-value products. Public investments and policies that facilitate growth of the agrifood system must pay more attention to creating enabling environments for the development of RNFE and strengthening the synergy between agriculture and RNFE in rural areas.
Article
Small-scale commercial integrated farming is regarded as an effective tool for creating the policy of developing farming, regionally integrated farming, applying advanced technologies, and preventing excess immigration of rural people in developing agriculture. This research aims to analyze articles dealing with integrated farming or the diversity of rural livelihood in Iran and the world by focusing on small-scale integrated farming systems and using the meta-analysis method. Meta-analysis for small-scale integrated farming systems shows this plan's strong and weak points. Most studied research affirms that the "Process of industrialization of the market and adopting technology" factor with an upper average effect size has significant positive effects on small-scale integrated commercial agriculture.
Article
Full-text available
The burning of agricultural residue from previous season’s rice crop, primarily in the states of Punjab and Haryana, is a key contributor to poor air quality during the winter across North India. Air quality can deteriorate to catastrophic levels during the Agricultural Waste Burning (AWB) season in October–November, when fine particulate matter (PM2.5) concentrations can exceed WHO daily maxima over a sustained period by an order of magnitude or more, over a large swathe of the Indo-Gangetic plain. Over the past decade, attempts by Indian governments to change farmer behavior by incentivizing the use of novel technologies for managing rice residue without burning it have been met with little success. This paper uses farmer and expert interviews, as well as secondary data, to examine the barriers to adoption of these technologies in the state of Punjab. We analyze how operational factors (such as farm size, timing, technology availability, and choice) affect a farmers’ decision to choose (or not) a rice residue management practice. We develop a financial model for analyzing the costs of residue management technologies that are consistent with the decision-making process of both small and large farmers. We find that more sustainable residue management practices can be cost effective relative to residue burn, especially when existing subsidies are applied. However, difficulties in accessing technological alternatives to AWB and subsidies for their use and a lack of trust in the government’s ability to deliver the full benefits of subsidies, all contribute the low adoption of technological alternatives to AWB.
Article
Full-text available
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.
Article
Full-text available
Background Information about the global structure of agriculture and nutrient production and its diversity is essential to improve present understanding of national food production patterns, agricultural livelihoods, and food chains, and their linkages to land use and their associated ecosystems services. Here we provide a plausible breakdown of global agricultural and nutrient production by farm size, and also study the associations between farm size, agricultural diversity, and nutrient production. This analysis is crucial to design interventions that might be appropriately targeted to promote healthy diets and ecosystems in the face of population growth, urbanisation, and climate change.
Article
Full-text available
Smallholder farming is the most prevalent form of agriculture in the world, supports many of the planet's most vulnerable populations, and coexists with some of its most diverse and threatened landscapes. However, there is little information about the location of small farms, making it difficult both to estimate their numbers and to implement effective agricultural, development, and land use policies. Here, we present a map of mean agricultural area, classified by the amount of land per farming household, at subnational resolutions across three key global regions using a novel integration of household microdata and agricultural landscape data. This approach provides a subnational estimate of the number, average size, and contribution of farms across much of the developing world. By our estimates, 918 subnational units in 83 countries in Latin America, sub-Saharan Africa, and South and East Asia average less than five hectares of agricultural land per farming household. These smallholder-dominated systems are home to more than 380 million farming households, make up roughly 30% of the agricultural land and produce more than 70% of the food calories produced in these regions, and are responsible for more than half of the food calories produced globally, as well as more than half of global production of several major food crops. Smallholder systems in these three regions direct a greater percentage of calories produced toward direct human consumption, with 70% of calories produced in these units consumed as food, compared to 55% globally. Our approach provides the ability to disaggregate farming populations from non-farming populations, providing a more accurate picture of farming households on the landscape than has previously been available. These data meet a critical need, as improved understanding of the prevalence and distribution of smallholder farming is essential for effective policy development for food security, poverty reduction, and conservation agendas.
Article
Full-text available
Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million farms worldwide, most of which are small and family-operated. It shows that small farms (less than 2 ha) operate about 12% and family farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information.
Article
Full-text available
2014 was the United Nations' International Year of Family Farming, yet the importance of family farming for global food security is still surprisingly poorly documented. In a review of agricultural census data, we find that globally family farms constitute over 98% of all farms, and work on 53% of agricultural land. Across distinct contexts, family farming plays a critical role for global food production. We present two examples of policy approaches toward family farmers-Brazil and Malawi-to provide insight into some of the complexities and challenges behind the global numbers. © 2015 Food and Agriculture Organization of the United Nations.
Article
Full-text available
• Livestock contribute to food supply by converting low-value materials, inedible or unpalatable for people, into milk, meat, and eggs; livestock also decrease food supply by competing with people for food, especially grains fed to pigs and poultry. Currently, livestock supply 13% of energy to the world's diet but consume one-half the world's production of grains to do so. • However, livestock directly contribute to nutrition security. Milk, meat, and eggs, the "animal-source foods," though expensive sources of energy, are one of the best sources of high quality protein and micronutrients that are essential for normal development and good health. But poor people tend to sell rather than consume the animal-source foods that they produce. • The contribution of livestock to food, distinguished from nutrition security among the poor, is mostly indirect: sales of animals or produce, demand for which is rapidly growing, can provide cash for the purchase of staple foods, and provision of manure, draft power, and income for purchase of farm inputs can boost sustainable crop production in mixed crop-livestock systems. • Livestock have the potential to be transformative: by enhancing food and nutrition security, and providing income to pay for education and other needs, livestock can enable poor children to develop into healthy, well-educated, productive adults. The challenge is how to manage complex trade-offs to enable livestock's positive impacts to be realized while minimizing and mitigating negative ones, including threats to the health of people and the environment.
Article
Full-text available
A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website. © 2015 John Wiley & Sons Ltd.
Article
Full-text available
The paper measures the level of crop diversity and identifies factors influencing diversification using a panel data of 17 regions of Bangladesh covering a 19 year period (1990–2008). Results revealed the trends that agricultural areas allocated to high-yielding variety rice, spices and vegetables has increased, while areas cultivating traditional rice, minor cereals, oilseeds, pulses, jute and sugarcane has declined at variable rates across regions with significant differences. The level of crop diversity is also significantly different across regions and has decreased in 2008 from its 1990 level in most regions except Faridpur, Khulna and Sylhet. Among the determinants, an increase in the relative prices of vegetables and urea fertilizer, extension expenditure, labour stock per farm, average farm size, irrigation and a reduction in livestock per farm significantly increase crop diversity. Price policies to improve vegetable prices and investment in irrigation infrastructure and extension services are suggested to promote crop diversity in Bangladesh.
Article
In the leading explanations for the oft-observed inverse relationship (IR) between farm size and productivity in developing country agriculture, labour market imperfections have commonly occupied a central role. However, an emerging literature suggests that disparities in technical or allocative efficiency may be driving productivity differentials. Using nationally-representative panel data from Nicaragua, we develop and employ a four-stage empirical framework to simultaneously test the competing explanations for the IR. While efficiency differences exert a significant impact on all productivity indicators, their explanatory power is insufficient to rule out labour market imperfections as the driving force behind the relationship.