Integrated Assessment of Smallholder Farming’s Vulnerability to
drought in the Brazilian Semi-Arid: a case study in Ceará
Diego Pereira Lindosoa,b,*, Juliana Rocha Dalbonia, Nathan Debortolia, Izabel Ibiapina
Parentea, Flávio Eiróa, Marcel Bursztyna, Saulo Rodrigues Filhoa
a
Center for Sustainable Development, University of Brasilia
b
CAPES Foundation, Ministry of Education of Brazil, Brasília – DF 70040-020, Brazil
*Corresponding author at: Campus Universitário Darcy Ribeiro - Gleba A, Bloco C - Av. L3
Norte, Asa Norte - Brasília-DF, CEP: 70.904-970, Brazil
E-mail address: diegoplindoso@gmail.com
Supplementary Material
Online Resource 1 – Relevant aspects of socioeconomic context of
Brazilian Northeast and Sertão de Quixeramobim microregion
Brazilian Northeast Region stands out because of its socioeconomic inequalities.
It has a history of political manipulation and productive negligence (Broad et al,
2007). Until today, the lowest layers of society are often trapped into a poverty
sustaining cycle, a relationship of dependency with the so-called colonels 1 when
it comes to their basic needs. In 1991, 40% of Brazilian population was
considered poor2 while in the Brazilian Northeast, in the State of Ceará, and in the
study area (Sertão de Quixeramobim), poverty rates reached 67%, 68% and 82%,
respectively (IPEAdata, 2011). Even though such imbalances have decreased
between 1991 and 2000, poverty rates were still alarming in 2000 (Figure S1).
The income per capita followed the same pattern (Figure S2).
Figure S1. Evolution of poverty rates (%) between 1991 and 2000 in: Brazil, Northeast, State of
Ceará and Sertão de Quixeramobim microregion. (Source: IPEA data, 2011)
1
A clientelistic political system that comes from the beginning of the colonization
process in Brazil during XVI century
2
A poor person was defined as someone whose household income per capita is less than
R$ 75.50 (equivalent to ½ minimum wage in August, 2000).
1
Income per capita (R$)
350
300
250
200
1991
150
2000
100
50
0
Brazil
Northeast
Ceará
Sertão de
Quixeramobim
Figure S2. Evolution of income per capita (R$) between 1991 and 2000 in: Brazil, Northeast,
State of Ceará, Sertão de Quixeramobim microregion. (Source: IPEA data, 2011)
Similar information for the last few years is not available in IPEA’s database.
However, the latest Brazilian Demographic Census 2010 provides some
interesting information. According to the census, the percentage of the population
aged ten years and older earning less than half a minimum wage monthly has
increased in the last 10 years in the entire country, but more intensely in the
studied territory (Figure S3). This may suggest a setback, nevertheless, this trend
is partly explained by the substantial increase in the minimum wage during this
period, which rose from R$ 150.00 in 2000 to R$ 510.00 3 in 2010. The extreme
poverty rates in rural Northeast Brazil are also striking. While in Brazil 25.5% of
the rural population was considered extremely poor in 2010, in the Northeast
Brazil, the rates were 35.4% for the rural population (Brasil, 2011). This situation
was the same as in the Brazilian North Region (35.7%), but much higher than the
ones featured by other Brazilian Regions (10.2%, 6.8% and 11.7% in Southeast,
South and in Center-West, respectively).
People earning monthly income
lower than ½ minimum wage (%)
20
15
10
2000
2010
5
0
Brazil
Northeast
Ceará
Sertão de
Quixeramobim
Figure S3. Percentage of people ten years of age and older earning a monthly income lower than
½ minimum wage in 2000 and 2010 (source: IBGE SIDRA, table 1384 and table 2903).
3
1.00 US$ = R$ 1.87 (December 30, 2011)
2
The impacts of extreme climatic events are felt with increasing intensity and
generate serious economic losses. Along with economic difficulties and landtenure conflicts, catastrophic climate events lead to the displacement of
smallholder farmers to other rural areas or to cities (Dillon et al, 2011; Ibnouf,
2011). In Brazil, this process has been intensified during the past decades due to
the migration of youngsters seeking better education and employment
opportunities in urban areas (Krol & Bronstert, 2007). As a result of this
phenomenon, a growing tendency of the ageing of the rural population and an
unplanned growth of cities has been evidenced. In regions where agriculture is the
main economic activity, the aging of population may represent an increase of
vulnerability since the elderly population has less physical ability to undertake
changes or interventions in agricultural systems in response to impacts.
The territory analyzed by the research has followed the same national tendency in
terms of urban growth and rural exodus (Figure S4). At a municipality level, the
urbanization and the aging process were also observed, even though some of the
case study’s municipalities are still essentially rural. This is the case of Ibaretama
and Choró, in which around 70% and 65% of the population still live in the rural
zone, respectively (IBGE, 2010). Furthermore, the local population is becoming
clearly older (Figure S4 and Table S1). In Sertão de Quixeramobim microregion,
the elderly population has substantially increased between 2000 and 2010,
reaching rates over 25% of the population (Table S1).
Population living in rural
zone(%)
70
60
50
40
Brazil
30
Northeast
20
Ceará
Sertão de Quixeramobim
10
0
1991
2000
2010
Year
Figure S4. Trends in rural population in: Brazil, Brazilian Northeast, State of Ceará and Sertão de
Quixeramobim microregion (source: IBGE SIDRA, tables: 200, 1552, 1378).
Table S1. Percentage of the population aged 60 years or older
2000 (%)
2010 (%)
8.56
23.01
Brazil
8.42
22.23
Northeast
8.87
23.5
Ceará
10.55
27.08
Sertão de Quixeramobim
Source: IBGE Sidra table 1522 and table 137
3
Online Resources 2 – Comparison with other indexes in literature
Other indexes have been proposed in literature to assess smallholder farming’
vulnerability to climate. They differ from the present index in some aspects:
database used, scale of analysis and number of indicators (Table S2).
Table S2. Comparison of different assessment systems of agriculture vulnerability in some
developing countries regarding: data base, scale of assessment and number of indicators
Scale of
No of
Authors
Country
Data base
assessment indicators
Present paper
Brazil
Official Secondary data Municipality
11
O’Brien et al, 2004
India
Official Secondary data
District
10
Swain and Swain,
Blocks of
India
Official Secondary data
19
2011
District
Field surveys
Hahn et al, 2009
Mozambique
District
31
Official Secondary data
Gbetibouo and
South Africa
Official Secondary data
Province
19
Ringler, 2009
Field Survey
Etwire et al, 2013
Ghana
Region
31
Official Secondary data
Differences in the way indicators are organized into vulnerability framework were
also identified (Table S3)
Table S3. Comparison of different assessment systems of agriculture vulnerability in some
developing countries according to vulnerability framework
Authors
Sub-indices
Present paper
Sensitivity, adaptive capacity, exposure
O’Brien et al 2004
Sensitivity, adaptive capacity, exposure
Swain and Swain,
Drought adaptability, physical exposure and
2011
drought risk
Hahn et al, 2009
Sensitivity, adaptive capacity, exposure
Gbetibouo and
Potential impact, adaptive capacity
Ringler, 2009
Etwire et al, 2013
Sensitivity adaptive capacity, exposure
The differences are due to particularities of each country to which the indexes
have been developed, as well as preferences of researches and stakeholders
involved in assessment elaboration. This suggests that a universal assessment
framework may be very hard to be achieved. The first barrier is the quality and
particularities of official database used to feed the assessment systems. On the one
hand, socioeconomic indicators usually have some equivalence. On the other
hand, biophysical data and agricultural census present great variability of quality,
periodicity, geographic resolution and methodology of aggregation. Field surveys
were used in some of the assessment systems (Table S2), which allow gathering
high detailed information about many aspects of vulnerability. However, such
approach is expensive and demands a great field effort, which allows such
methodology to a small set of farmers.
Furthermore, vulnerability and adaptation are specific to local contexts and each
country has a particular set of factors. For instance, HIV and malaria prevalence is
a relevant indicator of social adaptive capacity in Africa (Gbetibouo and Ringler,
4
2009; Hahn et al, 2009). Monsoon dependency is a particular exposure indicator
of India (Swain and Swain et al, 2011; O’Brien et al, 2004).
Another difference refers to indicator classification within vulnerability
framework. Irrigation rate, for example, is considered an adaptive capacity in
some papers (O’Brien et al, 2004, Swain and Swain, 2011) and as sensitivity in
the present paper, as well as, in others (e.g. Gbetibouo and Ringler, 2009). At
same time, many indicators are shared by different assessment systems. Literacy
rate and related indicators are considered in the different assessment systems
mentioned above.
Some works also organize the indicators in major components before integrate
them in sensitivity, adaptive capacity and exposure. Etwire et al (2013) used the
major components proposed by Hanh et al (2009): water, socio-demographic
profile, livelihood strategies, social networks, health, food and natural disasters
and climate variability as major components. O’Brien et al (2004) aggregated the
indicators in factors: biophysical factors, socioeconomic factors, Technological
factors, climate sensitivity index and exposure. Gbetibouo and Ringler (2009)
classify adaptive capacity indicators in social capital, human capital, financial
capital and physical capital. The present paper did not adopt this strategy in order
to avoid implicit weighting. This occur, for instance, when one major component
has two indicators and other has four. In the composite index, this implies that
each indicator has different weights. We recognize the importance of weighting
process, but given the lack of criteria, we decided to not weight the indicators.
The literature displays a long list of assessment systems that share a related core
and goals, but many different approaches and indicators sets, adapted to each
national and subnational context. The Vulnerability Index to Drought of
smallholder farming presented in the present paper is one example and it has
many aspects in common with others assessment systems. However, it is designed
to be simple and representative of the most vulnerable smallholder farming’s
context in Brazil. In order to achieve this goal, a small set of indicators were
chosen, using a simple methodology of aggregation (normalization and simple
average) and based on official database available for free in the Internet. The
targets are stakeholders in different levels of decision making that do not have
local expertise or access to more complex data to assess vulnerability. This is
paramount in Brazilian rural zone, in which there is a lack of scientists and
expertise to develop more complex assessment systems.
Online Resources 3 – Normalization methodology
The seven municipalities, the microregion of Sertão de Quixeramobim, the State
of Ceará and Brazil compose the sample considered in this paper. In order to
stress the differences and facilitate the comparison among these ten territory units,
the indicators were normalized accordingly to the formula presented below:
𝑉𝑥−𝑉𝑚
Inorm. = = 𝑉𝑀−𝑉𝑚, in which
Inorm = normalized indicator; Vx= observed value; Vm = lower observed value; VM =
higher observed value
5
The normalization process was undertaken to each one of the indicators of
sensitivity, adaptive capacity and the exposure indicator. During the process of
normalization, the lower (Vm) and the higher (VM) values of each indicator are
identified. They are the worst and the best performances observed among the
sample, shown as the values 0 and 1, respectively. The intermediate values are
normalized using the formula above. Thus, the comparison is valid only when one
has as reference the sample’s components. Notwithstanding, if the objective is to
compare the results with other realities (municipalities/States/Countries), it is
necessary to incorporate the new values into the sample and remake the
normalization calculation.
Online resources 4 – Sensitivity, Adaptive Capacity and Exposure
Indicators
Table S4. Sensitivity Indicators
Brazil/State/
Microregion/M
unicipality
People
engaged
in
Smallho
lder
farming
(%)
Brazil
Ceará
M.R.S.Q
Banabuiu
Boa Viagem
Choró
Ibaretama
Madalena
Quixadá
Quixeramobim
7
12
24
21
34
38
24
29
15
23
Agricultural System
Sensitivity
Index
Temp.
Crops
(%)
No
Livestock
(%)
53
67
64
71
75
58
76
91
37
64
51
69
55
31
62
33
43
52
59
51
Level of
Smallholder
farming’s
income
dependence on
vegetal and
livestock
production
(%)
75
70
53
55
53
31
54
92
50
47
Water
accessibility (%)
Number of
Rain-fed
smallholder
farms (%)
94
93
96
91
98
97
96
99
96
95
Lakes
and
Weirs
(%)
Water
Tanks
(%)
37
38
62
69
54
60
58
46
70
69
65
80
76
64
46
80
68
100
100
89
Source: IBGE, Agricultural Census 2006
Table S5. Adaptive Capacity Indicators
Number of
Household
Smallholder
Brazil/State/
heads able
farmers
Microregion/
to read
legally
Municipality
and write
owning the
(%)
land (%)
63
76
Brazil
43
56
Ceará
34
53
M.R.S.Q
Banabuiu
40
58
Boa Viagem
35
51
Choró
28
59
Ibaretama
58
61
Madalena
32
59
Households
receiving
technical
assistance from
government
cooperatives(%)
22
12
14
7
5
8
4
23
Household
s with
electric
energy
supply (%)
93
89
82
87
86
72
97
95
Household
heads involved
in associations,
unions and
cooperatives
(%)
41
43
51
73
55
59
44
49
6
Quixadá
Quixeramobim
28
36
53
52
19
71
49
22
83
42
Source: IBGE, Agricultural Census 2006
Table S6. Exposure indicator and sub-index
Aridity Index
Brazil/State/
Exposure sub-index
Microregion/
Aridity index
(1- AIn)
Aridity
Index
(AI)
Municipality
Normalized (AIn)
Brazil
Ceará
M.R.S.Q
Banabuiu
0.44
0.43
0.57
Boa Viagem
0.32
0.00
1.00
Choró
0.54
0.79
0.21
Ibaretama
0.60
1.00
0.00
Madalena
0.51
0.68
0.32
Quixadá
0.43
0.39
0.61
Quixeramobim
0.45
0.46
0.54
Source: Adapted by the authors from FUNCEME, 2011
Online resources 5 - Aridity Index
According to the United Nations definition the Aridity Index (AI) can be
described as:
AI = 100 x PR , in which
ET0
PR is the precipitation
ET0 the potential evapotranspiration
Ratios higher or equal to 1 indicate a mean precipitation equal or higher than
potential evapotranspiration. In those cases, the AI is classified as humid. Values
lower than 1 indicate precipitations lower than the evapotranspiration rate, thus
contemplating – in a decreasing aridity scale – the categories of Moist sub-humid,
Dry Sub-humid, Semiarid, Arid and Hyper-Arid zones (table S4). This
classification follows the United Nations recommendation to use the precipitation
historic mean (1975-2002) and evapotranspiration levels with a record of at least
20 years.
Table S7. Aridity Index and its categories
Aridity Index
< 0.20
0.20 < AI < 0.50
0.50 < AI < 0.65
0.65 < AI < 1.00
> 1.00
Category
Arid
Semiarid
Dry Sub-humid
Moist Sub-humid
Humid
Source: Adapted by the authors from FUNCEME, 2011
The method employed by FUNCEME in the Arid Index for the region considered
was the following. The potential evapotranspiration was estimated in each post as
7
70% of the average climatological evaporation in seven meteorological stations
provided by INMET (Instituto Nacional de Meteorologia - The Brazilian National
Institute for Meteorology). For each post the used values were the ones from the
closest meteorological stations. The INMET stations utilized were: Fortaleza,
Acaraú, Morada Nova, Barbalha, Campos Sales, Juazeiro do Norte e Tauá. Here it
is important to highlight that the data available in FUNCEME should be
considered in a decentralized way, observing punctual values since they have been
generated through the interpolation of scattered values, which have not precisely
the same amount year after year. Therefore, in the year of 1975 there were 71
stations available and in 2002 there were 305, consequently, when we look to the
available data one should account the quantity and localization employed in the
data outsets.
Alas, the interpolation method employed by FUNCEME is not entirely adequate,
due to data scarcity and deficiencies in fine scale detailing, yet, these are
government data and are the only ones officially available for the State of Ceará;
until new data are accessible, these, although biased, can be considered as an
approximated exposure indicator. Freitas (2010) has applied a series of methods to
rank water stress in Ceará but these data sets are not fully available yet.
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