=Paper=
{{Paper
|id=Vol-3282/icaiw_aiesd_12
|storemode=property
|title=The Effects of Dry Spells on Crop Diversification in the Brazilian Northeast Region
|pdfUrl=https://ceur-ws.org/Vol-3282/icaiw_aiesd_12.pdf
|volume=Vol-3282
|authors=Elena Piedra-Bonilla,Laís Oliveira,Denis Da Cunha,Marcelo Braga
|dblpUrl=https://dblp.org/rec/conf/icai2/Piedra-BonillaO22
}}
==The Effects of Dry Spells on Crop Diversification in the Brazilian Northeast Region==
https://ceur-ws.org/Vol-3282/icaiw_aiesd_12.pdf
The Effects of Dry Spells on Crop Diversification in
the Brazilian Northeast Region
Elena Piedra-Bonilla1,* , Laís Oliveira2 , Dênis Antônio Da Cunha2 and Marcelo Braga2
1
Universidad Ecotec, Samborondón, Ecuador
2
Universidade Federal de Viçosa, Viçosa, Brazil
Abstract
The Northeast region of Brazil is characterized by low annual averages of rainfall, so it has been impacted
by many droughts. Crop diversification is an adaptation practice to climate change that helps reduce
farmers’ vulnerability. This study aims to evaluate the effect of dry spells on crop diversification in the
Brazilian Northeast region, using fixed effects panel models in 1322 municipalities between 1995, 2006,
and 2017. The results show that Northeastern municipalities have adopted crop diversification as an
adaptation strategy for Consecutive Dry Days. Furthermore, legal status and farm size have negative
effects on crop diversification. Therefore, technical assistance promoting crop diversification should
focus on small farms [15].
Keywords
Drys Spells, Consecutive Dry Days, Drought, Crop Diversification, Northeast Brazilian
1. Introduction
In some regions, drought is expected to increase under future global warming. For example,
precipitation is expected to decrease in Southwestern South America, tropical Central America,
and the Amazon basin [1]. The lack of rainfall is worrying because it can adversely affect
economic outcomes, whether agricultural productivity, economic growth, or conflict [2].
Agricultural production depends especially on resources of water. So, a deficit in water
availability can cause stress on plants and animals, which can lead to agricultural and economic
losses [3, 4]. Severe drought conditions can cause premature plant death, while discontinuous
drought conditions affect plant growth and development [5]. Thus, there is a need to look for
strategies to overcome the reduction of precipitations.
Crop diversification is an adaptive strategy to reduce climate risks [6, 7]. This practice
has different forms as intercropping, agroforestry, or integration of crop-livestock systems.
Diversification results in benefits in the use of nutrients from the soil, water, light, and pest
prevention [8], as well as in managing water deficits for crops [9]. Crop diversification is known
ICAIW 2022: Workshops at the 5th International Conference on Applied Informatics 2022, October 27–29, 2022, Arequipa,
Peru
*
Corresponding author
$ epiedrab@ecotec.edu.ec (E. Piedra-Bonilla); lais.rosa@ufv.br (L. Oliveira); denis.cunha@ufv.br (D. A. Da Cunha);
mjbraga@ufv.br (M. Braga)
0000-0003-0387-9260 (E. Piedra-Bonilla); 0000-0003-4222-4953 (L. Oliveira); 0000-0003-4838-3795 (D. A. Da
Cunha); 0000-0002-8161-405X (M. Braga)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Proceedings
http://ceur-ws.org
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Elena Piedra-Bonilla et al. CEUR Workshop Proceedings 195–205
as a Climate-Smart technology, that helps to mitigate emissions of greenhouse gases (GHG)
and manage climate risk resilience [10]. Additionally, diversification is considered an important
adaptation strategy in agriculture, according to the last Intergovernmental Panel on Climate
Change Assessment Report [1].
However, in the literature, rainfall shocks present ambiguous results on crop diversification
and even without statistically significant effects in some cases. These results depend on the
geographic conditions of the regions. For example, rainfall shortage did not affect the diversifi-
cation index in Ethiopia [7]. In South Africa, decreasing rainfall shows a negative effect on the
diversification of crops [11]. Instead, in South America, the adoption of crop-livestock systems
becomes more attractive with a slight increase in rainfall (<130 mm per month), then decreases.
But it does not show effects with decreasing rainfall [12].
On the other hand, this topic research has focused on African countries, leaving a gap in
other regions, especially in South America. Furthermore, most studies have measured rainfall
shocks using drought events or precipitation data monthly or seasonally. There is a research
gap using climate extreme indices recommended by the WMO Expert Team on Sector-Specific
Climate Indices (ET-SCI) for the agriculture sector. Regarding decreasing rainfall, the maximum
number of consecutive dry days index is used to measure the length of a dry spell, using daily
precipitation [13].
In this context, this study evaluates the effect of dry spells on crop diversification in the
Brazilian Northeast region, which is characterized by low annual averages of precipitation. We
analyzed fixed-effects panel models in 1322 municipalities, using the last three Agricultural
Census of Brazil (1995, 2006, 2017). The results indicate that an increase in the maximum
dry spell raises the diversification in the municipalities, showing that this practice is used as
an adaptation practice to reduce climate risks. Therefore, this study’s findings have policy
implications for accessing effective strategies to overcome water scarcity problems in agriculture.
The rest of the article is organized as follows. Section 2 briefly describes the Northeast region
context. Section 3 describes the methodology and data. Section 4 presents the results and
discussion. Section 5 presents the conclusions.
2. Area of study
The Brazilian Northeast region is limited to the north and east by the Atlantic Ocean and
northwest and west to the Amazon Basin (Figure 1). This region has nine states: Bahia, Sergipe,
Alagoas, Piauí, Ceará, Maranhão, Pernambuco, Rio Grande do Norte, and Paraíba. The area
covers about 18% of the Brazilian territory and it has around 57.7 million habitants [14]. Accord-
ing to the last Agricultural Census, around 46% of Brazilian farms (2,3 million units) stand in
this region [15]. Most of these agricultural units are considered family farming (79.2%), which
engages the labor of more than 4.7 million people [16].
Nevertheless, this region has mostly a semiarid climate, which shows low and irregular
precipitation, and high temperatures, so drought has been reported frequently [17]. The
droughts have affected agricultural production, which impacts upon region’s economy. For
example, the drought caused by the severe El Niño phenomenon (2015-2016) caused high
mortality of cocoa trees (15%) and decreased cocoa yields by 89% in Bahia [18]. In the same
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Figure 1: Northeast region of Brazil.
way, the severe drought of 2012-2013 in Ceará led to a reduction of the planted area by 43%,
resulting in average losses of 75% in crops, and caused losses in livestock, with the mortality
rate of the cattle herd passing from 0.33% in 2010 to 3.05% in 2013 [19].
Additionally, future climate scenarios for the region project that temperature would increase
between, approximately, 1°C (optimistic scenario) and 4°C (pessimistic scenario) [17]. Regarding
precipitation, it is expected rainfall reductions and longer periods of dry spells, would let land
degradation [17]. These scenarios show that the region’s farmers are vulnerable, therefore it is
imperative to analyze adaptation measures to reduce climate risks.
3. Methods
3.1. Econometrical model
The allocation of crops depends on several factors, such as climate variables [20]. Nevertheless,
the relationship between dry spells and crop diversification can be considered a natural experi-
ment because extreme weather cannot be predicted exactly [2]. Regarding the regional level, it
is possible to know the effect of climate variation without having problems of selection bias
[21, 22]. Therefore, we analyze the effects of dry spells on crop diversification in the Brazilian
Northeast region at the municipal1 level:
𝑆 = 𝑓 (𝐶, 𝑋) (1)
1
The term municipality means the Minimum Comparable Area (MCA), which is an aggregation of municipalities in
broader geographic areas that ensures consistent comparisons over time. The municipalities of the Demographic
Censuses were made compatible from 1980 to 2010, following the methodology proposed by [23].
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in which vectors of climate variables (𝐶) and control variables (𝑋).
In this study, we used a panel data model at the municipal level (cross-section) combined
with the years of agricultural censuses 95/96, 2006, and 2017 (time series). Panel models have
consistent estimations because allow the existence of unobserved effects potentially correlated
with the regressors [24].
The econometric model follows a theoretical reference in which crop diversification in
municipalities is affected by the dry spell and other socioeconomic, agroecological, and market
variables [25]. Thus, the full version of the equation of interest (1), which was presented earlier,
is:
𝑆𝑖𝑡 = 𝛽𝐶𝑖𝑡 + 𝛾𝑆𝐸𝑖𝑡 + 𝛿𝐴𝑖𝑡 + 𝜁𝐴𝑖𝑡 + 𝜇𝑖 + 𝜃𝑟𝑡 + 𝜀𝑖𝑡′ (2)
where 𝑆𝑖𝑡 represents the Simpson crop diversification index of the municipality 𝑖 and year 𝑡;
𝐶𝑖𝑡 represents the Consecutive Dry Days index of the municipality 𝑖; 𝑆𝐸𝑖𝑡 is the vector of the
socioeconomic characteristics of the municipality and year t; 𝐴𝑖𝑡 is the vector of the agricultural
characteristics of the municipality and the year 𝑡; 𝑀𝑖𝑡 is the vector das characteristics of the
market of the municipality 𝑖 and year 𝑡; and 𝜇𝑖 represents the fixed effects of the municipalities,
capturing fixed spatial characteristics, observed or not, removing or clashing many possible
sources of omitted variances. 𝜃𝑟𝑡 represents the fixed effects of the year of the state, neutralizing
any common state trends and ensuring that the relationships of interest are identified by
idiosyncratic local clashes. 𝜀𝑖𝑡 is the term of independent and identically distributed error (𝑖𝑖𝑑)
in the municipality and the year 𝑡, with mean 0 and variance 𝜎 [2]. The model was estimated
using Stata software (version 12.0).
The Simpson index measures how much each crop contributes to the total agricultural income
of the municipality [26]:
𝑁
∑︁
𝑆𝐼 = 1 + 𝛼𝑘2 0 ≤ 𝑆1 ≤ 1 (3)
𝑗=1
where that 𝛼𝑘 is the proportion of the production Value of each agricultural and livestock
product in the total agricultural Production Value of the municipality. This index allows
classification of diversification into four categories: very specialized (𝑆𝐼 = 0), which only
produces one product; specialized (0 < 𝑆1 ≤ 0.35), which has 80% or more of the Production
Value from just one product; diversified category (0.35 < 𝑆1 ≤ 0.65), in which the income
of the main product is less than 80 % of the Production Value; and very diversified category
(𝑆𝐼 > 0.65) in which at least three products have similar proportions in income [26].
The dry spell was measured using the Consecutive Dry Days (CDD) index, which is defined
in Table 1. This index is recommended by the World Meteorological Organization - WMO’s
Expert Team on Sector-Specific Climate Indices (ET-SCI) for the agriculture sector. The indices
were calculated using daily values of precipitation obtaining results of annual values using
standardized software (ClimPACT2) [13]. The study uses five-year moving averages of CDD
for each period of the Agricultural Census 95/96, 2006, and 2017 to consider the impact of the
medium-term climate variability on crops perennial and temporary [21]. Additionally, this
weather specification includes the five-year moving average of the annual mean temperature
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because there is a correlation between these two variables was 0.35. The controls, which include
socioeconomic, agroecological, and market variables are defined in Table 2.
Table 1
Definition of Consecutive Dry Days indices (Adapted from [13])
Index code Name Definition Unit Event type
Consecutive Maximum number of consecu- The maximum length
CDD Days
Dry Days tive days with PR<1,0 mm of dry spell
Note: PR = precipitation
3.2. Data
The data used to construct the Simpson index were extracted from 1995, 1996, 2006, and
2017 Agricultural Censuses [26]. The agricultural products include the value of cattle, swine,
and poultry heads sold and the value of gross production of Horticulture, Permanent Crops,
Temporary Crops, Forestry, and Vegetable Extraction at the municipal level.
The Terrestrial Hydrology Research Group (THRG) database was used to analyze precipitation
in the municipalities of Northeast Brazil. The construction of this database occurred through the
combination of global data based on surface observations with the reanalysis of the NCEP–NCAR
(National Center for Environmental Prediction/National Center for Atmospheric Research).
The data are available in files in the NetCDF format (Network Common Data Form) in spatial
resolutions (0.25º, 0.5º, and 1.0º) and different temporal (3 in 3 hours, daily, monthly and annual).
In this study, the daily precipitation variable (mm/day) was used with a resolution of 1.0º × 1.0º
(∼ 110𝑘𝑚 x 110𝑘𝑚). In the extraction process, the NCL language (NCAR Command Language)
was used to develop an algorithm that, from reading the precipitation data and a matrix mesh in
the same resolution that contains the Northeast municipalities, would obtain the precipitation
values for each municipality.
Data on variables representing the socioeconomic, agricultural, and market characteristics
of Northeast municipalities were also extracted from 1995/1996, 2006, and 2017 Agricultural
Censuses.
4. Results and discussion
4.1. Simpson index
Figure 2 illustrates how most of the municipalities are in diversified categories over the three
periods. However, in the first period (1995/1996), the region had more municipalities with over
0.78 Simpson index (very diversified category). In 2006, there were many municipalities below
the 0.16 Simpson index (specialized category). Similar results can be found in the literature [27],
in which the Northeast municipalities have been in the diversified category, from 1985 to 2017.
This result can be explained because, since colonial times, monocultures were not popular in
the Northeast, since, above all, their climatic conditions have been unfavorable [28].
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Figure 2: Evolution of Simpson Index in 1995/1996, 2006, and 2017 in Northeast Region of Brazil.
Table 2 shows the description of variables and summary statistics related to all northeast
municipalities of Brazil. In 1996 and 2017, the Simpson index was in a very diversified category,
while in 2006, it was in a diversified category. So, the northeast region diversified its agricultural
production.
4.2. Maximum length of dry spell
Figure 3 shows that Consecutive Dry Days has a similar pattern in 1995/1996 and 2006. But in
2017, there is an increased dispersion of high values (>96 days) of CDD. However, the average of
CDD 2006-2017 does not show the drought that affected the Northeast from 2012 to 2015, which
destroyed croplands [17]. Furthermore, we can see that the 5-year moving average of the annual
Consecutive Dry Days has the highest value in 2017 (see Table 2). The annual temperature has
similar values between the periods.
4.3. Socioeconomic, agricultural, and market variables
Table 2 shows that the average size per farm is very small, but it is even reducing over the periods.
Then, access to technical assistance has been decreasing in the last period. The highest number
of farms receiving technical assistance was in 2006. Otherwise, legal status and irrigation
adoption have been increasing between the agricultural censuses. Conversely, the number of
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Table 2
Description of variables and summary statistics Northeast region
Variable Description 1996 2006 2017
Simpson
Crop diversification index 0.70 0.64 0.67
index
5-year moving average of the an-
CDD index 47.0 40.5 48.8
nual Consecutive Dry Days
Annual tem- 5-year moving average of annual
25.9 26.2 25.1
perature temperature (∘ 𝐶)
Technical as- Number of producers receiving
71.8 155.6 130.8
sistance technical assistance
Number of producers owning the
Legal status 1130.9 1272.3 1355.9
farm
Farm size Average size per farm (ha) 53.1 41.6 39.7
Irrigation Number of farms with irrigation 86.3 106.6 173.9
Maize farms Number of farms with maize crops 934.3 874.1 726.3
Figure 3: Evolution of Consecutive Dry Days in 1995/1996, 2006, and 2017 in the Northeast Region of
Brazil.
farms producing maize crops, which is an exportable product, has been decreasing over the
periods.
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Table 3
Effects of dry spells on crop diversification of Northeast region
Variable -1 -2
CDD index 0.000322*** 0.000344***
(0.000124) (0.000124)
Annual temperature -0.00129 -0.00124
(0.00181) (0.00183)
Technical assistance 1.92e-05
(2.14e-05)
Legal status -1.62e-05**
(6.56e-06)
Farm size -0.000346***
(6.91e-05)
Irrigation 4.83e-06
(1.52e-05)
Maize farms 1.76e-05***
(5.75e-06)
Fixed effects state/yeas YES YES
Hausman test 416,75*** 530,26***
Modified Wald test 1.3e+31*** 3.5e+31***
N 3.694 3.694
R-squared 105 91
F statistic 13,75*** 15,06***
Number of municipalities 1.322 1.322
Note: Robust standard errors are in parentheses. Significance: : *** p<0,01, ** p<0,05, * p<0,1
4.4. Econometric results
Table 3 presents the estimated results of equation (2), considering consecutive dry days. All
the models are adjusted with fixed effects for municipality and state/year. Columns (1) and
(2) analyze the model with controls and without controls, respectively. The results show that
prolonged reduction in rainfall positively and statistically significantly influences 1% of crop
diversification in the Northeast. Models (1) and (2) explain approximately 11% (with controls) and
9% (without controls), respectively, of the variation in diversification in this region. Additionally,
it is observed that there is little difference in the coefficients of climatic variables, indicating
that the relationship between CDD and Northeastern agricultural diversification was treated
randomly. Thus, Northeastern municipalities have adopted crop diversification as an adaptation
strategy for Consecutive Dry Days. Regarding the control variables, the producer’s legal status,
the size of the property, and the demand proxy for main crops were statistically significant.
Furthermore, the fact of owning the establishment increases the level of concentration of
agricultural products in the Northeastern municipalities, which is directed towards investments
in more intensive cultures. However, this result was contrary to that found in a case study
in Bahia, which analyzed the economic determinants of crop diversity at the producer level,
using the Margalef index to measure crop richness by area of the farm [27]. In addition, small-
scale farms are related to the diversification of agricultural activities in the Northeast. This
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relationship follows the results of the correlation between the percentage of establishments by
area groups and crop diversification (Simpson) in that study. It is worth mentioning that the
average size of farms in the region has been the smallest among the Brazilian regions in the
2006 and 2017 censuses [15], as well as that the region has been classified in the category of
Very Diverse throughout the three censuses. In addition, it is observed that corn production
positively influences diversification in this region. Finally, it is observed that the absolute values
of the farm size and the CDD index are similar and higher than the other coefficients, showing
the relevance of these variables in the Northeast.
5. Conclusions
The study shows that dry spells have a positive effect on crop diversification in the Brazilian
Northeast region. So, this region has adopted diversification as a strategy to manage rainfall
reduction. This has policy implications because it shows that this kind of practice should be
reinforced in technical assistance to reduce climate risks. Furthermore, it should focus on small
producers, which are mostly in this region.
6. Acknowledgements
This study was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Capes, Brazil (Financial code: 001 e Ph.D. scholarship for author EBPB). Author DAC gratefully
acknowledges the financial support of Fundação de Amparo à Pesquisa do Estado de Minas Gerais
– FAPEMIG and the Conselho Nacional de Desenvolvimento Científico e Tecnologico - CNPq,
Brazil (Grant numbers: 305807/2018-8). Author MJB thanks CNPq for the Research Productivity
Scholarship - Level 1C. Author LRO gratefully acknowledges to CNPq for the Scientific Initiation
Scholarship through the Programa Institucional de Bolsas de Iniciaçã Científica - PIBIC and for
the Master’s scholarship.
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