Title page
Title: A Green Revolution for rural Rwanda: Reconciling production growth with
small-scale risk management
Authors:
1. An Ansoms, Institute of Development Policy and Management, University of Antwerp
2. Ann Verdoodt, Laboratory of Soil Science, Department of Geology and Soil Science,
Ghent University
3. Eric Van Ranst, Laboratory of Soil Science, Department of Geology and Soil Science,
Ghent University
Contact details of corresponding author:
An Ansoms
Address: Lange St. Annastraat 7, 2000 Antwerp, Belgium
Tel: 0032/3265.5698
Email: [email protected]
Bio of lead author:
An Ansoms works as an assistant at the Institute for Development Policy and Management
(IOB) of the University of Antwerp (Belgium) where she focuses on poverty and inequality
in the Great Lakes Region of Africa. She has published articles on Rwanda in African
Affairs, the Journal of Modern African Studies, and other journals. She recently finished
her PhD in Economics at Antwerp with a dissertation entitled “Faces of rural poverty in
contemporary Rwanda: Linking livelihood profiles and institutional processes.”
Acknowledgements:
Acknowledgements: The authors greatly acknowledge the methodological input of Cynthia
Donovan, Renato Flores, Peter Goos, and Stefan Kesenne.
1
A Green Revolution for rural Rwanda:
Is there a future for small-scale farming and pro-poor rural growth?
“What goes on in Rwandan fields is not just a battle against the natural elements; it is
a cultural struggle as well. The former will not be won if the latter is lost.”
(Pottier & Nkundabashaka 1992: 164)
Abstract
The Rwandan government has recently adopted new agricultural and land policies that strive
to increase productivity in the agricultural sector through land consolidation and
concentration, and through crop intensification. These policies have been identified in line
with Rwandan policy makers’ ‘Green Revolution’ ambition to transform the agrarian sector
into a professionalized motor for economic growth and pro-poor rural development. The first
part of this paper, however, identifies an inverse relationship between farm size and
productivity; and proves that risk-averse production techniques such as farm fragmentation
and multicropping are not necessarily counterproductive to maximizing farmers’ production
output. A second part considers how peasants themselves value the (potential) impact of land
consolidation and government-led crop intensification upon their livelihoods. In its
conclusion, the paper reflects upon the crucial dilemmas for policy makers in the
materialization of the Green Revolution in Rwanda specifically, and in Sub-Saharan Africa
more broadly.
Key words: Green revolution, agriculture, small-scale farming, economic growth,
productivity, poverty reduction, equity, WDR 2008, Rwanda, Sub-Saharan Africa
2
1. Introduction: a green revolution for rural Rwanda?
Agricultural systems in Africa are severely challenged by various drivers of change such as
increasing population growth, an increasing demand for food and livestock products, and
climate change (IAASTD, 2009; Jones and Thronton, 2009). At the same time, both scientific
scholars and policy makers have rediscovered the agricultural sector - after decades of
neglect - as an important motor for pro-poor growth. The 2008 World Development Report
focuses on agriculture’s role in poverty reduction and overall development. It pleads for a
Green Revolution in Sub-Saharan Africa (World Bank, 2007). The report specifies that this
Green Revolution will have to be essentially different from the one that took place in Asia
during the 1960s and 1970s, as it should be adapted to the agricultural ecosystems and the
institutions in the African continent.
The term “Green Revolution” was first used by William Gaud (USAID administrator in the
late 1960s) to point to the improved yields in Asian, Middle and South American countries
through the use of new technologies in the agricultural sector: “Developments in the field of
agriculture contain the makings of a new revolution. It is not a violent Red Revolution like
that of the Soviets, nor is it a White Revolution like that of the Shah of Iran. I call it the
Green Revolution” (Speech to the Society for International Development in 1968).
Indeed, global food production increased with 58% between 1970 and 1990 (World Bank,
2010). On the other hand, Patel et alii (2009) point to the fact that increased food production
was not merely the result of technological progress; another major explanatory factor was the
extension of cultivated land. And the question as to whether the Green Revolution during the
1960s and 1970s has contributed to poverty reduction, is even more heavily debated. Already
3
in 1979, Griffin (1979: xi) noted that “the new technology has not revolutionized production
but it has often helped to worsen the distribution of income.” He argues that a lot of the
technical changes in the green revolutions of Asian, Middle and South American countries
were biased in favor of larger farmers, with perverse consequences for the well-being of the
majority of small-scale peasants.
The 2008 World Development Report seems to draw lessons from past experiences by
stressing upon the importance of smallholders in a green revolution for Sub-Saharan Africa.
The report points to the importance of smallholder farming as a form of agricultural
organization, and to the dependence of large parts of the rural population upon this type of
farming. The report warns policy makers to not ‘tilt the playing field’ against smallholders,
but instead enhance their competitiveness to facilitate their participation in agricultural
growth (World Bank, 2007). But at the same time, the report only sees a future for those
small-scale peasants who are capable of converting into capitalist entrepreneurs, able to
integrate themselves into the market logic (Veltmeyer, 2009; Akram-Lodhi, 2008).
Veltmeyer (and others in a special issue of the Journal of Peasants Studies on the WDR 2008)
criticises the way in which the ideological blinders of the report result in a failure to consider
policy options that provide alternative ‘pathways out of poverty’.
Indeed, the question as to which kind of policy instruments are suitable for supporting
smallholder agricultural production modes is still open. On the one hand, there are those
actors pleading for maximal agricultural growth through technological improvements,
increasing commercialization of agricultural products, and through private sector
involvement (see e.g. the rationale of the Bill and Melinda Gates Foundation, criticized in
Patel et alii, 2009). Others (f.e. Borras, 2003) point out that agricultural reforms occur in a
4
space where various social actors have unequal bargaining and negotiation positions due to
the asymmetry of social class power. Policies should therefore focus upon facilitating
smallholders’ access to markets, technology, risk-coping mechanisms (World Bank 2005)
and the distribution of crucial assets, particularly land (see e.g. the alternative to market-led
land reform promulgated by La Vía Campesina, Desmarais, 2002). The question as to where
policy makers put there focus, has obvious consequences upon the policies themselves: do
they opt for maximal growth of agricultural production and productivity, or do they focus on
a maximal pro-poor effect by improving the bargaining power of the poor in the production
process?
This debate is extremely relevant in Rwanda. Rwanda has long been a densely populated
country, confronted much earlier with severe land-scarcity than the rest of the African
continent. Since the early sixties, the expansion of arable land could not keep pace with
Rwanda’s impressive population growth (Figure 1A). As a result of this gap, per capita arable
land availability declined during the seventies and eighties (Figure 1B). However, despite
declining land availability, food production per head remained considerably high (with
fluctuations) until the mid 1980s in comparison to Sub-Saharan Africa (Figure 1C). The
inventiveness of the small-scale farming model seemed to avoid or at least postpone a
Malthusian trap.
Figure 1A
Trends in population and land
Figure 1B
Arable land per head
Figure 1C
Food production per head
5
250
0,16
200
Hect.
0,14
150
0,12
100
140
130
120
110
100
90
80
2005
2000
1995
1990
1985
1980
1975
1970
1965
2005
2000
1995
1990
1985
1980
1975
1970
1965
Population
Arable land
0,10
Food production per head
Index (1965=100)
0,18
2005
2000
1995
1990
1985
1980
1975
1970
1965
Index (1965 = 100)
300
Rw anda
SSA
Source: World Bank, 2010.
By the second half of the 1980s, a severe economic crisis combined with political tumult had
a negative impact upon overall food production. The effect of the war and genocide in the
early 1990s led to a total economic collapse. Despite an impressive post-war recovery of the
agricultural sector in the second half of the nineties, food production per head did not recover
up to the 1980s’ standards. Agricultural growth was very volatile in the early 2000s (World
Bank, 2010). In 2008, primary growth was impressive at 15% (IMF, 2009) after very limited
(or even negative) growth rates until 2007. A rather problematic finding is that the pro-poor
degree of agricultural growth (measured by the growth elasticity of poverty) during the
implementation period of the first PRSP (2001-2005) has been extremely low in comparison
to the industry and service sectors (Ansoms, 2008).
Nonetheless, the Rwandan government is rather clear in its ambition to achieve a Green
Revolution-type agrarian reform. The Rwandan government aims for GDP growth to be led
by a major increase in overall output and productivity in the agricultural sector, while
devoting little attention to the distribution of such growth. Policy makers thereby implicitly
assume that a growth strategy based on agriculture is ‘designed’ to be pro-poor (GoR,
2002:31).
6
The ambition is materialized in the various rural policy documents. The Rwandan
government has recently adopted new agricultural and land policies that strive to increase
overall output and productivity through land consolidation and concentration, and through the
promotion of crop intensification by means of regional specialization and monocropping.
The land law was adopted in 20051 and aims to enhance the security of tenure and reduce
conflicts by registering land holdings. It subscribes to the overall goal of increasing
agricultural productivity and land efficiency, while attempting to avoid environmental
degradation. One of the underlying assumptions of the law is that fragmented land use has a
counterproductive impact upon these objectives. The land policy states, “… the Rwandan
family farm unit is no longer viable. … The re-organization of the available space and
technological innovations are necessary in order to ensure food security for a steadily and
rapidly increasing population” (GoR, 2004A: 16). To achieve these objectives, article 20 of
the land law prohibits the division of land parcels of one hectare or less (no ceiling on
landholdings was included in the final approved law). Land consolidation2 is presented as one
of the main objectives of the land law (GoR, 2005). It is hoped that this consolidation will
increase land concentration and provide economies of scale in two ways: first, the
consolidation movement should lead to more concentrated farms (instead of a farm scattered
over many land plots); and second, the consolidation movement should increase land
concentration among a smaller number of modern and more efficient farmers who
concentrate on market-oriented agricultural production (see interviews with policy makers in
Ansoms, 2009).
1
Its full name is the Organic Law determining the use and management of land in Rwanda (N°08/2005 of
14/07/2005, GoR 2005).
2
Land consolidation is defined as, “a procedure of putting together small plots of land in order to manage the
land and use it in an efficient uniform manner so that the land may give more productivity” (Organic Law N°
08/2005 of 14/07/2005, article 2).
7
The Rwandan Agricultural Policy, and the subsequent Strategic Plans for Agricultural
Transformation (2005-2008, and 2009-2012) (see GoR, 2004A; GoR, 2009) aim to transform
the primary sector into a growth engine through a focus upon agricultural modernization,
intensification, professionalization and private sector involvement. The strategies focus upon
the development of commodity chains with export potential, or upon crops with great
importance for internal markets where the policy makers see a major role for the private
sector. Crop intensification - through regional specialization and the promotion of
monocropping - is seen as an important trigger for the commercialization of agricultural
production, and the modernization of the sector as a whole.
The first experiments with the forceful implementation of these policy objectives have not
been without controversy. In 2006, officials urged peasants in the Eastern Province to plant
their crops ‘in row’ and adopt monocropping. In the autumn of 2006, local administrators in
certain districts pulled out crops when peasants had not followed the guidelines (i.e. they had
planted beans in between banana trees, Reyntjens, 2007, 2). In September 2006, the Mayor in
charge of Muhanga (in the Southern Province) urged the population to replace their banana
trees with other cash crops, flowers or pineapple. After a BBC broadcast, the
recommendations were suspended (Cros, 2006). In early January 2007, the Governor of the
Eastern Province, Mr. Mutsindashyaka, placed a ban on sweet potatoes. His decision was
later revoked by the Minister of State for Agriculture (New Times, 2007). But despite more
recent claims by both the Rwandan government and supporting international donors that such
enforcement is no longer carried out, Twizeyimana (2009) cites peasants in the Eastern
province in May 2009, who explain local malnutrition as the result of a draught in
combination with the obligation imposed upon them to only produce maize.
8
The underlying assumptions of the Rwandan rural policies seem to be that risk-averse
production techniques - such as farm fragmentation, multicropping and local crop
diversification - are counterproductive to achieve maximal productivity. The first empirical
part of this paper, however, identifies an inverse relationship between farm size and
productivity; and proves that risk-averse production techniques such as farm fragmentation
and multicropping are not necessarily counterproductive to maximizing farmers’ production
output. We use socio-economic quantitative data gathered by the Food Security Research
Project (FSRP) combined with data from the Household Living Conditions Survey (EICV)3
coordinated by the Rwandan government. The data used in this paper refer to the year 2001
and are representative for rural Rwanda. Conditional upon the dependent variable considered
(see later), the sample includes a total of 1312 / 1357 households from 125 cellules4
distributed over the different provinces of Rwanda5.
In a second empirical part, the paper considers how peasants themselves value the (potential)
impact of land consolidation and regional crop specialization upon their livelihoods. These
data are based upon qualitative field research conducted by the author from May until July
3
The Food Security Research Project is a joint initiative of Michigan State University, the Rwandan Ministry of
Agriculture and USAID. The FSRP sample is a sub-sample of the nationally representative Household Living
Conditions Survey (EICV) that was undertaken by the Rwandan government (2001). In general, the FSRP
sample retained the same households as included in the EICV sample, and replaced those non-available with
households from the EICV’s reserve list. The FSRP panel survey focused on land use and agricultural
production for 6 seasons between 2000 and 2002. Compared to the EICV data regarding land and livestock
ownership, the FSRP data is more reliable for variables; given the effort put into correct data measurement and
follow-up. However, the data on the monetary value of agricultural production per household is only available
in the EICV dataset. We combine the EICV dataset with the 2001 FSRP dataset in order to have compatible
data.
4
Rwandan households are typically scattered over the hills. Before the administrative reforms of 2005, the
cellule was the administrative division that corresponded with one or a few hills.
5
The EICV dataset was gathered data between July 2000 and June 2001. The FSRP data was gathered during 3
years (between 2000 and 2002), each time for both season A (September – January) and season B (March –
August). This means that the FRSP 2001 data cover both season A (September 2000 – January 2001) and season
B (March 2001 – August 2001), a period that is more or less compatible with the collection period of the EICV
dataset.
9
2007 in six imidugudu6 in the Southern province of rural Rwanda7. In each umudugudu, we
interviewed between 11 and 15 focus groups. Segmentation between focus groups was
mainly based on locally defined socio-economic categories (for more information on
methodology, see Ansoms, 2010).
2. Risk-management and land productivity: quantitative evidence
Concentrating on the Rwandan case, Byiringiro and Reardon (1996) use pre-conflict data to
conclude that smaller farmers have higher average and marginal land productivity than larger
farmers. We test whether their findings still hold for the post-conflict period. In addition, we
consider the impact of various risk-averse production techniques such as farm fragmentation,
crop diversification, and multicropping, on peasants’ productivity.
Productivity can be measured in different ways: results in terms of labor productivity8 are
essentially different from efficiency in terms of land productivity. In this chapter, we focus
on land productivity, given that this is the scarce factor for Rwandan peasant households.
Still, there are different ways to measure land productivity. Productivity per unit of land may
be expressed in yield (ton per hectare); however, this measure makes it difficult to compare
productivity for different crops / combinations of crops on the total farm surface. Productivity
6
Rwandan households are typically scattered over the hills. The umudugudu (plural: imidugudu) is the
administrative division that corresponds with one or a few hills. The boundaries of the umudugudu after the
administrative reform often concur with the boundaries of what was called the cellule before the administrative
reform (2006), at least in the rural setting.
7
Five out of six of the imidugudu were drawn from the settings included in the FSRP and EICV quantitative
surveys. The selection of the five imidugudu was directed by the aim to select settings with diverging
characteristics both in terms of “average wealth” (very poor to quite well-off), as in terms of location (very
remote to very central). The sixth umudugudu is located very near to Kigali, which gives it special features in
comparison to the other settings. It was added to the sample based upon its location.
8
Regarding labor productivity, we find that small-scale farmers are less productive. The Rwandan countryside
is characterized by high underemployment; certainly in small-scale farms. For these households, the marginal
increase in output when adding additional labor is extremely limited due to the lack of land.
10
of land may also be expressed in monetary (in Rwf – Rwandan francs) or in caloric value.9
We use both measures in our regression models.
We want to measure the impact of several variables on productivity. The farm sizeproductivity relationship is typically represented by the model (see Bhalla and Roy, 1988;
Carter, 1984; Deolalikar, 1981):
ln Y = β 0 + β1 ln L + e
where Y is the output per hectare and L represents the farm size in hectares10.
INSERT TABLE 1
Bhalla and Roy (1988) have adapted the original model by adding several coefficients
accounting for soil quality (i.e. land type, land color and land depth). Indeed, farm size may
be correlated with soil quality11. To account for this factor in the analysis, we include a soil
quality index, developed at the Laboratory of Soil Science, Ghent University, for the 125
selected cellules included in the EICV-FSRP datasets. The index is based upon the soil
profile database and soil map of Rwanda at a scale of 1:50.000 (Imerzoukene and Van Ranst
9
The correlation between the two seems logical but it is not obvious. Peasants may, for example, choose to
produce cash crops such as coffee or tea. The cultivation and sale of these crops may have a considerable impact
upon the monetary value of overall production, but the output’s caloric value is very low. On the other hand,
food crops such as sweet potatoes may have a low market value but can be an important component of the food
diet because of their high caloric value. In this paper, calculations for added value of production per hectare are
based on the combined EICV-FSRP dataset (2001). Calculations for caloric value of production per hectare are
based on FSRP (2001) and FAO (2007) datasets.
10
The log transformation highly improves the variation in productivity that is explained by the model (R²). It
allows one to interpret the coefficient as an elasticity, representing the percentage change in the dependent
variable when the independent variable increases by one percent. A significant negative β1 coefficient would
indicate a negative elasticity between farm size and productivity, which would provide support for the inverse
relationship.
11
Ellis (1990) mentions that large farms may have less fertile land than small farms and provides two possible
explanations. First, the more fertile regions with a higher soil quality tend to have a higher population density
and more fragmentation. Another thesis is that small-scale peasants are obliged to fully exploit the productive
potential of all their land, while larger farmers only concentrate on their best land which brings down their
‘average’ productivity (taking into account their entire property). Therefore, the observed inverse relationship
may result from a correlation between farm size and soil quality. Bhalla and Roy (1988) found for the Indian
case that indeed, part of the inverse relationship can be accounted for by the soil quality factor.
11
2001) 12. It was calculated by multiplying the scores (values between 0 and 100) attributed to
five soil characteristics: soil texture (A), soil depth (B), topsoil sum of basic cations (C), pH
(D), and organic carbon content (E). As such, it evaluates the physical and chemical soil
fertility and gives an expression of the soil quality for crop production13. In our analysis, we
consider the weighted average index for the dominant soil series at the cellule level. A formal
definition of the soil quality index is given by14:
Index =
A
B
C
D
E
×
×
×
×
.
100 100 100 100 100
In their analysis on the inverse relationship, Bhalla and Roy (1988) further include a
coefficient for land fragmentation. They identify this variable as a potentially important
additional aspect of soil quality and, in any case, a variable that reveals important
information. In an environment where peasants are often driven into distress sales of land, the
worse land plots are assumed to get sold first. Therefore, a greater level of fragmentation is
likely to be correlated with lower soil quality and with lower productivity rates. Blarel et alii
(1992), however, point to the fact that in pre-war Rwanda, land fragmentation was
advantageous for farmers’ risk management and productivity. De Lame (2005) presents farm
fragmentation as a typical characteristic of the Rwandan farming system: “Some fields,
12
The cellules, taking part in the socio-economic study, were indicated on hardcopy maps of the sectors within
each Rwandan province. This map of studied sectors was digitized and overlaid with the soil map sheets at a
scale of 1:50.000 giving information on the soil series present within each of the studied cellules. The physical
and chemical properties of each of these soil series were extracted from the related soil profile database.
13
The weighted average soil quality index has certain limitations. First, the soil profile database is based on
information gathered over the eighties and early nineties while combined with the EICV - FSRP data of 2001.
Over this period, it is likely that further land degradation has taken place in different degrees for different areas.
This factor can not be accounted for in this analysis. Second, as the exact location of the household fields is not
known, the soil quality needed to be reported at an aggregate (cellule) level. The variability in soil quality
reported within each of the cellules may however be quite high. For example: the soil quality of fields located in
the valleys can be higher than the soil quality of fields on steep slopes. Both elements may possibly bias the
analysis.
14
When calculating a soil quality index, one may opt for additive versus multiplicative methods. In this case we
opted for a multiplicative method as, in comparison with additive methods, it does a better job in revealing
important limitations in only one or a few aspects of soil quality that may – despite good scores for all the other
factors – have an important impact on overall productivity.
12
almost always including the banana grove, surround the house. Others are scattered and
pieced out following divisions aimed at making sure that each heir possesses each type of
field on the land inherited from the father or on purchased or given land.” (De Lame, 2005:
128) We include farm fragmentation as a separate coefficient, measured by the number of
plots over which the farm is divided15.
Another variable that may be important with regards to farmers’ risk management is the
number of crops cultivated on the farm (sum of number of crops in season A – September
2000 to February 2001 - and season B – March 2001 to August 2001). Agricultural policies
often encourage crop specialization to realize economies of scale and to orient the
agricultural sector more towards the cash market. However, the rationale for concentrating on
one market crop may be irrelevant for subsistence farmers and/or for farmers with limited
bargaining power in the local markets. Moreover, diversification of crop types may be an
effective method for subsistence farmers to spread and thus reduce risk (i.e. weather risks,
crop disease, etc.) when land is very scarce.
In the same way, farmers may choose to cultivate different crops on the same piece of land.
This technique (i.e. ‘multicropping’) is frequently used in Rwanda. We incorporate this
factor into the regression analysis by adding a variable that accounts for the share of the farm
used for multicropping (taking into account land use in seasons A and B).
In addition, we add a variable that accounts for household size - the number of adult
‘equivalents’ present in the household. Indeed, households with a lot of household members
15
The Pearson correlation between land fragmentation and land quality is significant and positive, but very
small (0.037).
13
may be obliged to intensify their agricultural activities on their available land, which could
result in higher productivity.
When plotting these variables into a regression analysis, we are however confronted with
different types of problems. First, there is the problem of combining data from different
levels. In an Ordinary Least Squares regression, all variables are treated as if they were
household-level characteristics. This is however not the case for the contextual variable
accounting for soil quality (measured at the cellule level).
In addition to this, lower-level independent variables measured at the household level – in
their relation to the dependent variable - may be influenced by contextual factors (soil quality
but also others) that are specific to the cellule/province/agricultural region in which the
households are nested. Applying OLS to nested data results in deflated standard errors. This
entails the risk of erroneously rejecting the null hypothesis (Type I error of finding statistical
significance, when in fact there is none). Random coefficient or multilevel regression
analyses - with REML16 estimators as substitutes for OLS estimators - are then the
appropriate tools with which to analyze these data.
Random coefficient regression allows addressing the joint problems of dependent
observations and (within-group) correlated residuals due to nesting of observations. This
technique permits the intercepts and slopes of coefficients of the lower level explanatory
variables to vary across groups (data grouped in cellules/provinces/agricultural regions). All
random regression coefficients have a fixed component, this is the summary average of a
population intercept and the slopes (that vary from one cellule to another). In most empirical
16
REML stands for REstricted Maximum Likelihood. In contrast to the Maximum Likelihood procedure, this
REML procedure takes into account the number of parameters to estimate the model, which is important in the
case of smaller samples.
14
applications, their estimates differ little from the OLS estimates. However, the standard errors
for random coefficient regressions are typically larger than the deflated values reported in the
OLS regression, which reduces the risk of committing type I errors (i.e. finding false
significances). The random components measure the extent to which the random intercept
and slopes vary across cellules. The model also allows the estimation of the covariances
between intercepts and slopes. These determine whether the random components vary
together or not17. The random coefficient analysis may be transformed into a multilevel
analysis by including contextual variables from a higher level (in this case soil quality) to see
whether they account for the variability in the random intercept of lower level variables, and
by including cross-level interaction terms as additional explanatory variables to see whether
they explain variability in the random slopes (Bickel, 2007).
For the purpose of this chapter, a random coefficient regression model with two levels
(households and cellules) seems most appropriate; given that the inclusion of a third level
(either agricultural zone or administrative province) would lead to a problematic reduction of
the effective sample size (there are only 12 agricultural zones and 11 provinces in which
lower-level data are nested). In addition, contextual factors related to the cellule level are
more relevant than those at a more aggregated level for our type of agriculture-related
analysis.
When defining a random coefficient or multilevel model, the first question to answer is
whether there are coefficients that should be permitted to vary across higher-level groups. To
17
With the “variance components” default option of SPSS for the covariance structure, the variances of the
random coefficients are allowed to vary, but the model specifies that they do not vary together. As a result, the
estimates of covariance parameters will not include any covariances. When choosing the “unstructured” option
instead, no constraints upon relationships among random components are imposed: random intercepts and
slopes may vary together. The option, however, requires more parameters to estimate, which decreases the
degrees of freedom (Bickel, 2007).
15
formulate an answer, we calculate the unconditional intra-class correlation coefficient18 (ICC,
with no explanatory variables in the equation). For productivity in terms of monetary value,
the ICC amounts to 0,245 whereas it equals 0,365 for productivity in terms of caloric value.
This implies that, respectively, 24,5% and 36,5% of the variability in the productivity
variable occurs between cellules, while 75,5% and 63,5% occurs within cellules. This nestedengendered intra-class correlation seems to be sufficiently large to justify random coefficients
in the regression analysis.
The second question is which independent variables should be assigned fixed slopes and
which have to be treated as random coefficients. Bickel (2007) points to the importance of
substantive theoretical knowledge when making this decision. He highlights that the inclusion
of too many random coefficients may make the model too complex and difficult to interpret.
Table 2, however, indicates that there are significant differences across different agricultural
zones for the variables accounting for farm size, farm fragmentation, crop diversification and
multicropping. Therefore, we opt for specifying the effect of all four variables as random.
INSERT TABLE 2
The final question to answer is which contextual variables may account for the variation in
the random intercept and slope of these four variables. Differences in soil fertility are one
possible explanation, but there may be others unaccounted for in our model.
18
The intra-class correlation coefficient is calculated by dividing between-group variability by the sum of
between-group variabilities and within group variabilities. ‘Unconditional’ means that there are no explanatory
variables in the equation when calculating this coefficient.
16
For the purpose of this analysis, all variables have been grand-mean centered. This reduces
the risk of problematic correlations between random components19; and it facilitates the
interpretation of the intercept as the estimated value of the dependent variable when all
independent variables are equal to their means (Bickel, 2007).
The estimated level-one model is given by:
Level-one model
ln YIJ = β 0 J + β1J ln L + β 2 J F + β 3 J C + β 4 J M + β 5 A +β 6 ln L * A + eIJ
where YIJ is productivity of household I in cellule J, β0J is the intercept for cellule J with a
fixed and random component. β1J,, β2J, β3J, β4J are the random slopes of the explanatory
variables accounting for farm size (L – area of cultivated land), farm fragmentation (F –
number of plots over which the land is fragmented), crop diversification (C – number of
crops cultivated on the farm in season A and season B), and multicropping (M – share of the
farm used for multicropping) - with a fixed and a random component. β5, and β6 are the fixed
slopes of explanatory variables accounting for family size (A – adult equivalents per
household), and the interaction term lnL*A, which accounts for the possible variation in the
relationship between farm size and productivity dependent upon household size.
The level-two models for the intercept and the slopes of the random coefficients L, F, C and
M are:
Level-two model
19
“Covariances among random slopes and between random slopes and random intercepts have consequences
that are comparable to multicollinearity. When relationships among these various factors are strong, they
interfere with efficient estimation of random regression coefficients. Grand-mean centring of all independent
variables is a useful corrective” (Bickel, 2007: 137).
17
β0 J
β1J
β2J
β3 J
β4J
= γ 00 + γ 01S + u0 J
= γ 10 + γ 11S + u1J
= γ 20 + γ 21S + u2 J
= γ 30 + γ 31S + u3 J
= γ 40 + γ 41S + u4 J
where the random intercept (β0J) and random slopes (β1J,, β2J, β3J, β4J) are expressed as
functions of the contextual level-two variable S (soil quality at the cellule level).
The complete multilevel model can be specified as follows:
Full model
ln YIJ = γ 00 + γ 10 ln L + γ 20 F + γ 30C + γ 40 M + β 5 A + β 6 ln L * A
+ γ 01S + γ 11 ln L * S + γ 21F * S + γ 31C * S + γ 41M * S
(Model 1 and 3)
+ (u0 J + u1J ln L + u2 J F + u3 J C + u4 J M +eIJ )
The full model combines the level-one and level-two models. γ00 is the common intercept
across cellules; and γ01 is the effect of the cellule-level variable S on cellule-specific
intercepts. γ10, γ20, γ30, γ40, are the common slopes of household-level variables L, F, C and M
across cellules; γ11, γ21, γ31, and γ41 are the effects of the group-level variable S on the cellulespecific slope of L, F, C and M. β5, and β6 have been defined above.
Considering the estimated multilevel model (see Table 3, model 1 and 3), we find that the
coefficients of the contextual variable and cross-level interaction term are insignificant.
Indeed, the conditional intra-class correlation20 for the model with the contextual variable
included (23,3% for productivity in monetary value, 35,6% for productivity in caloric value)
is not much smaller than the unconditional intra-class coefficient calculated above. This
20
The conditional intra-class correlation coefficient is calculated in the same way as the unconditional
coefficient, except for the fact that the contextual variable and cross-level interactions are included as
explanatory variables. If the conditional intra-class correlation coefficient is considerably smaller than the
unconditional coefficient, then the contextual factor explains a considerable part of the nesting-engendered
intra-class correlation.
18
suggests that the inclusion of soil fertility as a contextual variable adds little to the
explanatory power of the overall model.
INSERT TABLE 3
Therefore, as an alternative to this complex multilevel model, we might as well consider the
simpler random coefficient model. Such a model still allows coefficients to vary across
groups (cellules), but does not try to explain this variability using contextual variables and
cross-level interaction terms. The simplified model is:
Level-one model
ln YIJ = β 0 J + β1J ' ln L + β 2 J F + β 3 J C + β 4 J M + β 5 A +β 6 ln L * A + eIJ
Level-two model
β0 J
β1J
β2J
β3 J
β4J
= γ 00 + u0 J
= γ 10 + u1J
= γ 20 + u2 J
= γ 30 + u3 J
= γ 40 + u4 J
Random coefficient model
ln Y = γ 00 + γ 10 ln L + γ 20 F + γ 30C + γ 40 M + β 5 ln A
+ (u0 J + u1J ln L + u2 J F + u3 J C + u4 J M +eIJ )
(Model 2 and 4)
19
A comparison of the predictive value of the multilevel versus random coefficient models
brings forward several elements in favor of the latter21. Comparing the variance – covariance
parameters of both estimation methods (tables 4.1 to 4.4), indicates that household-level
variances and covariances – both in terms of magnitude and in terms of their significance are barely influenced by the inclusion of soil fertility as a contextual variable. The betweencellule variability is, therefore, likely to be caused by other contextual factors for which no
data are available.
INSERT TABLES 4.1 TO 4.4
Let us have a closer look at the results of the random coefficient model which fits our data
best (see Table 3, models 2 and 4). First of all, farm fragmentation and the frequency of
multicropping have a small but significant positive impact upon productivity. An additional
plot adding to the number of plots over which the farm is distributed, results in a 0,1%
increase in productivity (for both measures). The effect of a percentage increase in soil
covered with multicropping is marginal: it raises productivity with 0,007 or 0,004%
(dependent upon the productivity measure used). The relationship between productivity and
crop diversification on the other hand, is not significant. These data suggest that farm
fragmentation and multicropping are not necessarily counterproductive to maximizing
farmers’ productive output.
The most important finding of this random coefficient model, however, is that the strong
inverse relationship between farm size and productivity holds. Whereas coefficients for all
21
Indeed, the summary measure R1² - indicating the percentage with which the model reduces errors in
predicting productivity when compared with the null unconditional model - is not much better for the multilevel
regression (Table 3, models 1 and 3) than for the random coefficient model (Table 3, models 2 and 4). Also,
when comparing the information criteria for both estimation methods, we find that the multilevel model does not
provide a substantially better fit in comparison to the random coefficient model.
20
other variables are small, the effect of farm size upon productivity is not only significant and
negative, but also quite considerable: if farm size doubles, then productivity per hectare
decreases with 53%.
This inverse relationship between farm size and productivity has also been identified in other
settings. The issue was identified early in the twentieth century (in the work of Chayanov,
1926 on Russian farms), but took up speed with the work of Sen (1962) on India. The
influential research of Berry and Cline (1979) and Cornia (1985) also pointed to a strong
inverse relationship22. Heltberg (1998) and Kimhi (2006) found an inverse relationship after
controlling for diverse plot characteristics (e.g. soil quality). However, even if an inverse
relationship is identified, one should be careful to not automatically interpret this as a mere
reflection of small-scale farmers’ higher efficiency.
In fact, small-scale peasants may be obliged to overexploit the land at their disposal. AkramLodhi mentions that the greater productivity of small-scale farmers may be a ‘survival
mechanism of the poor’ rather than a ‘mechanism of potentially poverty-eliminating
accumulation’ (Akram-Lodhi, 2007: 560). Examples of these survival mechanisms have been
elaborated by other authors. Binswanger and Rosenzweig (1986), for example, point to the
possibility that imperfections on the labor market may prevent labor-selling households from
allocating their labor force in the most optimal way, resulting in overemployment on the own
farm that leads to an inverse relationship. Barrett (1996) adds that food price risks may incite
small-scale peasants to deliberately opt for employing their labor force in an excessive way,
“beyond even their shadow valuation of labor” (Barrett, 1996). Assunção and Ghatak (2003)
22
Dyer (2004), however, found significant flaws in the approach of Berry and Cline, and pointed to the
importance of disaggregating data. In their recent work, Johnston and Le Roux (2007) gave a short overview of
disaggregated studies and found a diverse pattern of results, “… with some finding a clear inverse relationship,
others a positive relationship and still others describing a convex or concave relationship.” (Johnston and Le
Roux, 2007: 357).
21
point to the possibility that the inverse relationship might be the result of self-selection
among the peasants, where efficient small-scale peasants have higher opportunity costs to
engage in wage labor.
Another issue is whether the inverse relationship will hold with the introduction of new
technologies. A study focussing on the Indian case indicates that, “the inverse relationship
between yields and farm size, although valid for a traditional agriculture, cannot be assumed
to exist in an agriculture experiencing technical change” – certainly when the transformation
is of the Green Revolution type (Deolalikar, 1981: 275). Based on data for the Thar Desert in
India, Ram et alii (1999) find that the inverse relationship weakened with the increased
availability of size-neutral biotechnology and differences in management input. Bhalla and
Roy (1988), on the other hand, do confirm a weakened inverse relationship comparing Indian
data for 1970 and 1976. But they reject the hypothesis that this was the result of technological
change induced by the green revolution23. In any case, the World Development Report 2008
specifies that “economies of scale in the ‘new agriculture’ often are the key for obtaining
inputs, technology, and information and in getting products to the market. As agriculture
becomes more technology driven and access to consumers is mediated by agroprocessors and
supermarkets, economies of scale will pose major challenges for the future competitiveness
of smallholders” (World Bank, 2007: 91-92).
This is what happened in several settings during the first Green Revolution phase (1960s and
1970s) in Asia, Middle and South America. Griffin (1979: 53) points to the responsibility of
policy makers in this process: “Perhaps the most important reason for the bias [in favor of
large-scale farming] of the ‘green revolution’ is the bias of government policy. For many
23
Another study by Carter (1984), finds that even with post-Green Revolution data for India a strong inverse
relationship continues to exist. The author concludes that, “these results suggest that small-scale agriculture
warrants attention as a base for agriculture development in a land scarce economy” (Carter, 1984: 144).
22
years research, extension and investment programs in agriculture have been devoted to
raising output (preferably exportable output); their primary concern has not been to increase
the welfare of the rural population and improve the distribution of income and wealth. […]
Large farmers have been granted generous incentives to mechanize, while small farmers have
been denied the credit necessary to improve their operations.”
Regardless of whether the inverse relationship is the result of small-scale farmers’ higher
efficiency or whether it is caused by other factors, small-scale farming is a reality for a large
majority of peasants in Sub-Saharan Africa. Policy instruments should therefore enhance
smallholders’ competitiveness in order to ensure the pro-poor effect of agricultural growth.
But these can only be developed in line with the rationale of small-scale peasants. It is
therefore crucial to consider the ‘views from below’ in order to determine peasants’
opportunities and challenges, and to identify the institutional barriers that constrain
smallholders from taking up a key role in pro-poor agricultural growth.
3. Views from below: a qualitative insight into peasants’ rationale
Whereas the empirical evidence in the previous section was based upon quantitative data, this
section draws upon more than seventy qualitative focus group interviews with peasants from
various socio-economic layers in six different settings (see table 5). We consider peasants’
opinions on land consolidation and concentration; and on monocropping and regional crop
specialization.
With regards to the issue of land consolidation and concentration, we asked peasants’
opinions on the idea to cultivate land together on consolidated plots which would facilitate all
23
sorts of economies of scale effects (improved crop types, improved access to fertilizer,
potential use of technologies on larger surfaces, …). Peasants in all different settings and
from all socio-economic layers raised considerable concerns. The objecting arguments can be
subdivided into three categories.
First, peasants referred to the importance of ownership rights on their landholdings. In fact,
peasants have some experience with government-imposed land consolidation in the
swamplands. However, swampland has a different status in comparison to land in the hills. It
was officially qualified as state property under both the Habyarimana and the Kagame
administration; and even when peasants claim implicit ownership rights on swamplands, they
are more ready to accept state involvement on those plots. As a result, respondents in various
settings referred to land consolidation as ‘possible’ (although often very much resented) in
the swamps, but considered it as totally impossible in the hills. In those hills, land ownership
is perceived as an individual and fundamental right. ‘People have their own ways of doing
things’, and value their individuality in making agricultural decisions. A participant of a
better-off focus group in umudugudu D put it very clear: ‘You can not touch upon the land of
another. If you do that, he will cut you into pieces’. A fellow participant added that pleading
for plot consolidation would be considered as an act of aggression towards others.
To ground these rather bold statements, focus group participants raised a second group of
arguments referring to more practical objections against consolidation. With regards to
consolidation of neighboring plots, participants highlighted that adjacent plots do not
necessarily have the same fertility and productivity for a certain crop type. In addition, people
may have different capacities in terms of physical and moral force. A respondent in
umudugudu D mentioned that ‘a lazy one should stay lazy in his own place without living on
24
the expense of others’. This diverse capacity of plots (productive capacity) and of owners
(working capacity), would complicate the division of harvests on consolidated land. But also
within families, the idea of keeping inherited land consolidated by cultivating it collectively
with all descendents is considered as problematic. Respondents mentioned that power
relations within the family would play a huge role at the detriment of the weaker actors (e.g.
widows, sisters, orphans).
A third group of objecting arguments focused on the difficulty or impossibility to reconcile
the diverse needs of different socio-economic layers with regards to land management.
Whereas better-off categories can invest in crops with a longer cultivation cycle, poorer
categories are often obliged to turn to crops with shorter cultivation cycles. Moreover,
immediate food needs may oblige poorer peasants to harvest prematurely, whereas better-off
categories can wait until crops are fully grown. These diverse needs make an agreement on a
joint project nearly impossible.
Turning to the idea of the Rwandan governments’ crop intensification program, we gathered
peasants’ opinions on monocropping and regional crop specialization. Here, opinions on the
desirability of such policies were more differentiated, dependent upon the socio-economic
category and the setting in which respondents lived.
Overall, participants raised three different arguments in favor of multicropping. First, it
minimizes the risk of a crop disease or a particular climatic condition ruining the whole
harvest. Or in the words of a participant in umudugudu D, ‘multicropping is a way to be
cautious, you may loose on one side and win on the other’. Second, multicropping is
considered to be a more flexible system in the absence of manure, which is seen as a
25
prerequisite to have good harvests with monocropping. Finally, multicropping may give a
higher production when combining complementary crop types (e.g. beans and bananas in
umudugudu B; beans and maize or sorghum and cassava in umdugudu D, soya and maize in
umudugudu F).
However, some peasants had good experiences with monocropping (e.g. with cassava in
umudugudu B), and peasants in all settings did acknowledge that under the right conditions
(if improved seed/manure/fertilizers are available, if the cultivated land surface is sufficiently
large, if the appropriate training is provided) monocropping could enhance productivity. But
participants generally found that the potential benefits of monocropping in terms of
productivity do not outweigh the risk involved, and highlighted that this risk is more
prominent for poorer categories. It were indeed mostly the better-off categories that seemed
more favorable to the idea of monocropping, and only on those parts of the land not needed
for self-subsistence.
This brings us to the issue of regional crop specialization, again subject of mixed feelings in
various settings and among poorer and better-off categories. Peasants of all categories were
very reluctant to the idea of being obliged to focus on few and very particular crops. Most
often mentioned was that specializing in one (or few) crop(s) renders peasants extremely
vulnerable to the many diseases and bad climatic conditions that frequently hit the local
setting. Focus group participants also pointed out that the different crops they cultivate each
have a particular function in their livelihood cycles. And different categories of peasants with
their particular needs may prefer different types of crops. A participant used the expression
that ‘one may be neighbors, but not from the same family’ to indicate that peasants from
different categories may not agree on which crop type to specialize. Furthermore, the large
26
diversity of soil types – even up to the very local level – makes it difficult to pinpoint one or
few ‘suitable’ crops per region.
At the same time, better-off categories raised arguments in favor of voluntary regional
specialization, which would facilitate the stocking and selling of crops in collaboration with
others to bargain a good price. But at the same time, they remained attached to the idea of
self-subsistence. A person in umudugudu A raised: ‘Why would I concentrate on one crop
type to trade this while my plots can procure me of all I need’? In umudugudu B, where
better-off peasants do concentrate on cassava production, they still produce other crops that
they need for self-subsistence.
This attitude is linked to the poor functioning of the local markets. First of all, peasants raised
the problem of extreme price fluctuations, making profits unpredictable. Moreover, their
bargaining power in the commodity chain is extremely limited, which allows intermediary
brokers to speculate and make high profits at local actors’ expense. This problem is partly
due to the lack of access to transport, but also related to a lack of public space for
cooperatives that are managed from the bottom up.
Overall, we found that peasants of all types were reluctant towards the formulated
‘productivity enhancing’ governmental policies. On the one hand, they value their own local
production methods that allow them to minimize the multiple risks they are confronted with.
On the other hand, they did acknowledge that some of the proposed measures could enhance
their productivity but only under the ‘right’ conditions. Such conditions depend upon whether
the present institutional constraints – all the more pressing for poorer categories – can be
removed.
27
4. Conclusion: policy discussions
In our introduction, we have pleaded for a Green Revolution approach that provides positive
stimuli to small-scale peasants and to consider the potential and sustainability of small-scale
agricultural production. Another matter is how to achieve this objective in the Rwandan
context.
Are the promotion of monocropping and regional crop specialization the right approaches to
reach this objective? Potentially, but given the current agro-technological situation, riskminimizing techniques like crop differentiation and multicropping do not only allow
Rwandan peasants to optimize their survival chances, but they also seem to pay off in terms
of land productivity. Therefore, a blind enforcement of monocropping and regional
specialization could have a perverse impact upon agricultural growth. In any case, policy
makers should recognize the capacity of small-scale peasants to make rational choices. In
some situations, the locally-embedded traditional techniques will guarantee maximal
productivity. In other cases, new techniques may upgrade current production rates of smallscale farmers, but only under the condition that institutional constraints are removed.
Policies could for example facilitate peasants’ access to risk reduction mechanisms, e.g.
through cheap crop insurance for small-scale peasants and through social safety nets for
vulnerable groups. Such mechanisms can prevent peasants from falling into a chronic poverty
trap in case of crop failure or temporary unemployment. An easily accessible micro-credit
system with minimal or no bail requirements could allow smallholder farmers to invest more
in commercially-oriented agriculture. But at the same time, this provision would allow
28
peasants to explore the possibility to (partially) transform themselves in non agricultural
entrepreneurs. The rotating savings associations that already function at the lowest level are a
way to channel money towards the local sphere. At this point, however, credit suppliers focus
on maximum profits for minimum risk, which works in favor of large scale producers with
ambitious projects and an ability to provide a bail. This calls into question the ambition of the
Rwandan government to rely on the private sector to provide credit at the micro level.
The next step in the policy discussion is to consider the relative bargaining positions of both
small-scale and large-scale farmers in local and regional markets. Small-scale farmers face
two constraints. First, their transaction costs per unit (e.g. transport costs) to get their product
to the market will often be higher than large-scale farmers. Second, their bargaining position
in price negotiations is limited given the smaller quantities they offer. Policy makers,
therefore, could concentrate on enhancing the relative market power of peasants by
promoting initiatives that bring them together (e.g. by encouraging the development of
cooperatives). However, all depends upon the role of smallholders in the governance
structure of the cooperative. Without equilibrated power relations, cooperatives risk to
become an additional exploitative tool in the hands of powerful intermediary brokers.
Furthermore, cooperatives should be sufficiently flexible to allow for switches in crop types
and should be capable of facilitating trade of small quantities.
In the long term, however, we do acknowledge that there are limitations to small-scale
farming, certainly when farm sizes continue to decrease below a critical margin. Land
concentration will most likely be unavoidable as land scarcity will eventually lead to
crowding-out in the agricultural sector. But this should not be artificially accelerated through
government-led land consolidation and concentration movements. If one could stimulate a
29
broad-based growth within the agricultural sector at this stage, this could create a rural
middle class. Such middle class would more likely reinvest its profits into the local economy
than if growth is accumulated in the hands of fewer better-off and more urban-oriented
actors. As a result, the gains of agricultural growth could more easily trickle-down to the
remainder of rural society, e.g. through investments at the local level in small-scale
agricultural and off-farm activities that take on board pure wage laborers. At the same time,
the Rwandan government could further enhance the capacity of this last group by providing
training and technical education and by avoiding that they fall into a chronic poverty trap
through social safety nets.
Overall, the Rwandan case study incorporates important lessons for the Green Revolution
ambition in Sub-Saharan Africa. Instead of focusing upon maximal agricultural growth and
maximal productivity, the primary focus should lay on how poor groups can participate in
rural-oriented growth strategies. Key issues are the removal of the institutional constraints
that prevent small-scale farmers from adopting new types of agriculture and/or diversify their
income portfolio away from subsistence agriculture. Major attention should go to the
expansion of off-farm entrepreneurship opportunities which would provide peasant
households with alternative options for their labor force. Furthermore, policy makers should
be willing to enhance the bargaining position of smallholders (versus larger farmers) in food,
land and credit markets. But finally, attention should also be paid to the intra-household
distribution of assets, decision making power, and the work load in income-generating and
other household activities.
Indeed, livelihoods of rural populations will increasingly become detached from farming and
from land property in the future. But only if the rural society passes through a first stage of
30
broad-based agricultural growth in which small-scale peasants play a central role; then will
growth trickle-down to poorer groups dependent upon pure wage labor; and will it be
maximally pro-poor. Therefore, we plead for a Green Revolution with a primary focus on
optimal inclusion of smallholders instead of blindly aiming for maximal production output.
31
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36
Table 1 Summary statistics
Mean
Median
St. Dev.
Minimum
Maximum
272.804
172.836
389.131
0
9.061.270
Caloric value of production for
season 2001A and 2001B (Kcal
per year)
4.327
3.098
4.731
70
79.197
Farm size (hectares)
0,82
0,57
0,86
0,02
8,46
Monetary value of production for
season 2001A and 2001B (Rwf)
Farm fragmentation (average
number of blocs considering
season 2001A and 2001B)
Crop diversification (sum of
number of crops in season 2001A
and 2001B)
Multicropping (percentage of
cultivated land surface covered
with multicropping technique)
3,15
3,00
1,97
1,00
15,00
12,40
12,00
4,27
3,00
28,00
53,47
53,12
25,27
0,00
100,00
Adult equivalent (number of adult
equivalents in the household)
4,52
4,23
2,02
0,70
13,95
Soil quality (calculation
explained above)
0,36
0,35
0,11
0,13
0,80
386,06
357,00
206,63
26,00
1.486,00
4,18
3,00
4,53
0,00
32,00
Population density (people per
km²)
Distance to market (km)
Note: The calculation of summary statistics is based upon the sample size of 1357 households for all variables
measured at the household level. The calculation of summary statistics for cellule-level variables is based upon
the sample of 125 cellules.
Source: Calculations based upon EICV and FSRP datasets (2001)
37
Table 2: Crop choice per agricultural region
AGRICULTURAL ZONEa:
1
2
3
4
5
6
Total
Number of households
32
65
77
52
265
133
1357
Farm size
Median
0.19
0.45
0.48
0.54
0.53
0.35
Mean
0.44
0.64
0.67
0.81
0.83
0.48
St. Dev.
1.04
0.73
0.75
0.67
0.95
0.40
Farm fragmentation Median
3.00
2.00
3.00
4.50
3.00
3.50
Mean
1.97
1.94
3.81
4.58
3.05
4.00
St. Dev.
0.57
0.89
2.19
2.09
1.54
1.98
Number of crops
Median
6.00
10.00
10.00
10.00
11.00
14.00
Mean
7.27
10.51
10.53
10.38
11.36
13.90
St. Dev.
3.28
3.07
3.01
3.05
3.78
3.77
% of multicropping Median
43.34
62.05
45.65 52.48
40.81
58.70
Mean
50.22
61.02
48.36
47.40
44.47
55.93
St. Dev.
26.62
24.54
26.84
26.65
23.39
22.67
Agricultural zones: 1. Imbo, 2. Impara, 3. Kivu Lake Borders, 4. Birunga, 5. Congo-Nile Watershed Divide, 6.
Buberuka, 7. Central Plateau, 8. Granitic Ridge, 9. Mayaga, 10. Bugesera, 11. Eastern Plateaus, 12. Eastern
Savana’s.
Source: Calculations based upon EICV and FSRP datasets (2001)
Table 2 (continued): Crop choice per agricultural region
AGRICULTURAL ZONEa:
7
8
9
10
11
12
Total
Number of households
254
81
68
17
180
133
1357
Farm size
Median
0.60
0.61
0.75
0.71
0.67
1.00
0.57
Mean
0.89
0.88
1.02
0.91
0.88
1.09
0.82
St. Dev.
1.02
0.86
0.97
0.69
0.81
0.58
0.86
Farm fragmentation Median
2.00
2.50
2.50
2.50
3.00
3.00
3.00
Mean
2.72
2.60
2.83
2.89
3.44
3.12
3.15
St. Dev.
2.05
1.31
1.92
1.12
2.27
1.63
1.97
Number of crops
Median
12.00
12.00
13.00
13.00
14.00
16.00
12.00
Mean
11.79
12.44
13.22
11.48
14.18
15.48
12.40
St. Dev.
3.90
3.63
4.32
4.56
5.10
3.68
4.27
% of multicropping Median
59.00
64.21
48.75
38.35
59.18
57.40
53.12
Mean
57.01
59.18
47.30
37.39
58.54
57.37
53.47
St. Dev.
26.65
22.70
22.99
22.25
25.69
20.10
25.27
Agricultural zones: 1. Imbo, 2. Impara, 3. Kivu Lake Borders, 4. Birunga, 5. Congo-Nile Watershed Divide, 6.
Buberuka, 7. Central Plateau, 8. Granitic Ridge, 9. Mayaga, 10. Bugesera, 11. Eastern Plateaus, 12. Eastern
Savana’s.
Source: Calculations based upon EICV and FSRP datasets (2001)
38
Table 3: Farm size – productivity relationship – Various regressions
Variable
Intercept
LnL (farm size)
F (farm fragmentation)
C (crop diversification)
M (multicropping)
A (adult equivalent)
LnL*A
Productivity as independent variable
Measured in monetary value24
Random
Multilevel
coefficient
regression with 2
regression with 2
levels
levels
(1)
(2)
N1=1312
N1=1312
N2=125
N2=125
Productivity as independent variable
Measured in caloric value25
Random
Multilevel
coefficient
regression with 2
regression with 2
levels
levels
(3)
(4)
N1=1357
N1=1357
N2=125
N2=125
0.078 (0.087)
0.077 (0.087)
-0.010 (0.058)
-0.010 (0.057)
-0.528 (0.067)
***
0.133 (0.028)
***
-0.526 (0.067)
***
0.132 (0.028)
***
-0.535 (0.049)
***
0.119 (0.019)
***
-0.534 (0.049)
***
0.120 (0.019)
***
-0.025 (0.016)
-0.025 (0.016)
0.021 (0.011)
0.021 (0.011)
0.007 (0.002)
***
0.079 (0.001)
***
-0.034 (0.001)
***
0.007 (0.002)
***
0.079 (0.001)
***
-0.034 (0.001)
***
0.004 (0.001)
*
0.032 (0.001)
***
-0.014 (0.001)
***
0.004 (0.001)
*
0.032 (0.001)
***
-0.014 (0.001)
***
S (soil quality)
0.422 (0.765)
0.049 (0.507)
lnL*S
-0.424 (0.593)
-0.428 (0.435)
F*S
0.151 (0.249)
-0.300 (0.166)
C*S
-0.037 (0.145)
0.012 (0.101)
M*S
0.017 (0.018)
-0.006 (0.013)
R1²
0.229
0.220
0.297
0.284
Unstandardised coefficients, figures in parenthesis are estimated standard errors,
* significant at 0.05 level; ** significant at 0.01 level, *** significant at 0.001 level.
Source: Calculations based upon EICV and FSRP datasets (2001)
24
The sample sizes are different for both regressions. Here, the productivity variable is based on data in the
EICV survey, while the other variables at the household level are based on the FSRP sample. The sample size
represents the overlap between both samples.
25
The sample sizes are different for both regressions. Here, all variables at the household level are based on the
FSRP sample. The sample size represents the data for which all information included in the regression (seasons
A and B) is available.
39
Table 4.1: Estimates of covariance parameters for model (1)
Productivity as independent variable measured in monetary value
Parameter
Estimate
St. Error
Wald Z
Residual
0,412
0,001
423,193
Random intercept variance
0,949
0,121
7,842
Covariance between intercept and slope of L
-0,016
0,066
-0,236
Random slope variance of L
0,570
0,073
7,840
Covariance between intercept and slope of F
0,025
0,028
0,887
Covariance between slope of L and slope of F
-0,050
0,022
-2,257
Random slope variance of F
0,100
0,013
7,801
Covariance between intercept and slope of C
0,041
0,017
2,460
Covariance between slope of L and slope of C
-0,063
0,014
-4,558
Covariance between slope of F and slope of C
-0,004
0,005
-0,802
Random slope variance of C
0,034
0,004
7,845
Covariance between intercept and slope of M
-0,002
0,002
-0,908
Covariance between slope of L and slope of M
0,009
0,002
4,966
Covariance between slope of F and slope of M
0,000
0,001
0,015
Covariance between slope of C and slope of M
-0,001
0,000
-2,536
Random slope variance of M
0,001
0,000
7,838
Source: Calculations based upon EICV and FSRP datasets (2001)
Sign.
0,000
0,000
0,813
0,000
0,375
0,024
0,000
0,014
0,000
0,422
0,000
0,364
0,000
0,988
0,011
0,000
Table 4.2: Estimates of covariance parameters for model (2)
Productivity as independent variable measured in monetary value
Parameter
Estimate
St. Error
Wald Z
Residual
0,412
0,001
423,193
Random intercept variance
0,944
0,120
7,874
Covariance between intercept and slope of L
-0,018
0,066
-0,272
Random slope variance of L
0,568
0,072
7,871
Covariance between intercept and slope of F
0,025
0,028
0,917
Covariance between slope of L and slope of F
-0,050
0,022
-2,296
Random slope variance of F
0,100
0,013
7,832
Covariance between intercept and slope of C
0,040
0,016
2,455
Covariance between slope of L and slope of C
-0,062
0,014
-4,554
Covariance between slope of F and slope of C
-0,004
0,005
-0,818
Random slope variance of C
0,034
0,004
7,876
Covariance between intercept and slope of M
-0,002
0,002
-0,860
Covariance between slope of L and slope of M
0,009
0,002
4,919
Covariance between slope of F and slope of M
0,000
0,001
0,066
Covariance between slope of C and slope of M
-0,001
0,000
-2,557
Random slope variance of M
0,001
0,000
7,870
Source: Calculations based upon EICV and FSRP datasets (2001)
Sign.
0,000
0,000
0,786
0,000
0,359
0,022
0,000
0,014
0,000
0,414
0,000
0,390
0,000
0,947
0,011
0,000
Table 4.3: Estimates of covariance parameters for model (3)
Productivity as independent variable measured in monetary value
Parameter
Estimate
St. Error
Wald Z
Residual
0,191
0,000
430,762
Random intercept variance
0,417
0,053
7,841
Covariance between intercept and slope of L
-0,011
0,032
-0,355
Random slope variance of L
0,307
0,039
7,842
Covariance between intercept and slope of F
0,006
0,012
0,497
Covariance between slope of L and slope of F
-0,003
0,011
-0,310
Random slope variance of F
0,045
0,006
7,826
Covariance between intercept and slope of C
0,020
0,008
2,634
Covariance between slope of L and slope of C
-0,041
0,007
-5,582
Covariance between slope of F and slope of C
-0,005
0,002
-2,105
Random slope variance of C
0,016
0,002
7,851
Covariance between intercept and slope of M
-0,001
0,001
-0,723
Sign.
0,000
0,000
0,722
0,000
0,619
0,757
0,000
0,008
0,000
0,035
0,000
0,470
40
Covariance between slope of L and slope of M
0,004
Covariance between slope of F and slope of M
0,000
Covariance between slope of C and slope of M
0,000
Random slope variance of M
0,000
Source: Calculations based upon EICV and FSRP datasets (2001)
0,001
0,000
0,000
0,000
4,628
0,190
-2,509
7,839
0,000
0,849
0,012
0,000
Table 4.4: Estimates of covariance parameters for model (4)
Productivity as independent variable measured in monetary value
Parameter
Estimate
St. Error
Wald Z
Residual
0,191
0,000
430,762
Random intercept variance
0,414
0,053
7,873
Covariance between intercept and slope of L
-0,012
0,032
-0,364
Random slope variance of L
0,307
0,039
7,874
Covariance between intercept and slope of F
0,006
0,012
0,479
Covariance between slope of L and slope of F
-0,002
0,011
-0,150
Random slope variance of F
0,046
0,006
7,859
Covariance between intercept and slope of C
0,020
0,008
2,646
Covariance between slope of L and slope of C
-0,041
0,007
-5,594
Covariance between slope of F and slope of C
-0,005
0,002
-2,105
Random slope variance of C
0,016
0,002
7,883
Covariance between intercept and slope of M
-0,001
0,001
-0,730
Covariance between slope of L and slope of M
0,004
0,001
4,657
Covariance between slope of F and slope of M
0,000
0,000
0,255
Covariance between slope of C and slope of M
0,000
0,000
-2,521
Random slope variance of M
0,000
0,000
7,871
Source: Calculations based upon EICV and FSRP datasets (2001)
Sign.
0,000
0,000
0,716
0,000
0,632
0,881
0,000
0,008
0,000
0,035
0,000
0,465
0,000
0,799
0,012
0,000
41
Table 5: Research settings included in qualitative research
Characteristics
Location: very close to Kigali city and location of a large market that provisions Kigali with
food and meat
Average wealth and employment opportunities: among the richer imidugudu in the sample
(referring to the average peasant’s situation) but big social gap between large-scale farms (often
A
owned by outsiders) and local peasants, other employment opportunities besides agriculture but
less since some of these activities have been stopped by Rwandan authorities
Specificities: Lot of temporary immigrant workers
Location: less remote and small commercial center within its own region, fertile region, less
hilly
Average wealth and employment opportunities: best-off umudugudu in the sample (average
B
peasant’s situation), population lives from agriculture, lot of monocropping of cassava and
coffee, lot of production for the market, cassava was affected by disease but situation improves
Location: very close to Gitarama city, average fertility, less hilly
Average wealth and employment opportunities: among poorer imidugudu in the sample
(average peasant’s situation), lot of employment opportunities besides agriculture in the past
C
(brick making, bicycle taxi), but not anymore due to prohibition by Rwandan authorities
Specificities: external actor (German NGO) has set up a development project (marshland
valorization with rice cultivation)
Location: very remote and poorly accessible, overpopulated, infertile soils
Average wealth and employment opportunities: poorest umudugudu in the sample (average
peasant’s situation) , lot of agricultural labor force but limited employment opportunities in the
D
local setting, both within the agricultural and non-agricultural sector; significant group looks for
temporary work elsewhere
Specificities: lot of chronic malnutrition, distrust among local population
Location: less remote, households live dispersed over hill and no real center, quite fertile region
Average wealth and employment opportunities: poorer imidugudu in the sample (average
peasant’s situation), few peasants are auto subsistent, few other employment opportunities
besides agriculture, less agricultural commerce than before due to disease that affected cassava
E
(main crop)
Specificities: lot of people are in prison or are ex-prisoners, big distinction in well-being of
different social categories - lot of social conflicts - Ministry of Defence has a coffee washing
installation in this umudugudu
Location: remote, but small commercial center within its own region, quite fertile region with
clayish soil
Average wealth and employment opportunities: among richer imidugudu in the sample (average
F
peasant’s situation); lot of banana production to make banana bear that is sold in the local
center, lot of commerce in local ‘boutiques’
Source: Description based upon own research
42