Farmers' Perceptions of Climate Change Affect Their Adoption of Sustainable Agricultural Technologies in the Brazilian Amazon and Atlantic Forest Biomes

Abstract

Farmers' perceptions about climate change may help to explain the farming systems that they adopt and the effectiveness of their production practices in mitigating the negative impacts of the agricultural sector on the environment. This study analyzed the perceptions of 273 farmers participating in the Sustainable Rural Project - a large-scale climate change mitigation project in Brazil - that promoted the adoption of sustainable agricultural technologies in the Brazilian Amazon and Atlantic Forest biomes. Using a principal component analysis, we developed a Climate Change Perception Indicator (CCPI): an index to categorize farmers according to their perceptions about the impacts of climate change on agriculture. Our results indicate that farmers' motivations to adopt sustainable agricultural practices were strongly driven by economic factors. We also found evidence to suggest that political agendas can influence farmers' environmental perceptions. Moreover, older farmers with a higher level of education and more experience tended to demonstrate a stronger concern about climate change. However, the level of adoption of sustainable agricultural technologies was generally low, and a lack of technical knowledge and financial support may hinder the widespread adoption of these practices. Thus, an approach that includes consideration of farmers' perceptions about the impacts of climate change on their business may improve outcomes from the Sustainable Rural Project and other projects that aim to enhance the adoption of sustainable agriculture technologies.

Full text

Vol.:(0123456789)
Climatic Change (2024) 177:8
https://doi.org/10.1007/s10584-023-03657-3
1 3
Farmers’ perceptions ofclimate change affect their adoption
ofsustainable agricultural technologies intheBrazilian
Amazon andAtlantic Forest biomes
TarikTanure1 · RafaelFariadeAbreuCampos2· JúlioCésardosReis3·
RaynaBenzeev4· PeterNewton4· RenatodeAragãoRibeiroRodrigues5·
AnaMariaHermetoCamilodeOliveira6
Received: 1 September 2022 / Accepted: 5 December 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract
Farmers’ perceptions about climate change may help to explain the farming systems that
they adopt and the effectiveness of their production practices in mitigating the negative
impacts of the agricultural sector on the environment. This study analyzed the perceptions
of 273 farmers participating in the Sustainable Rural Project—a large-scale climate change
mitigation project in Brazil—that promoted the adoption of sustainable agricultural tech-
nologies in the Brazilian Amazon and Atlantic Forest biomes. Using a principal compo-
nent analysis, we developed a Climate Change Perception Indicator (CCPI): an index to
categorize farmers according to their perceptions about the impacts of climate change on
agriculture. Our results indicate that farmers’ motivations to adopt sustainable agricultural
practices were strongly driven by economic factors. We also found evidence to suggest that
political agendas can influence farmers’ environmental perceptions. Moreover, older farm-
ers with a higher level of education and more experience tended to demonstrate a stronger
concern about climate change. However, the level of adoption of sustainable agricultural
technologies was generally low, and a lack of technical knowledge and financial support
may hinder widespread adoption of these practices. Thus, an approach that includes con-
sideration of farmers’ perceptions about the impacts of climate change on their business
may improve outcomes from the Sustainable Rural Project and other projects that aim to
enhance the adoption of sustainable agriculture technologies.
Keywords Projeto Rural Sustentável· Perceptions· Climate change· Policy· Technology
Adoption
JEL Q01· Q16· Q54
Extended author information available on the last page of the article

Climatic Change (2024) 177:8
1 3
8 Page 2 of 24
1 Introduction
Many global challenges are inextricably linked to the interconnection between economic
activities and environmental resource use. One of these important challenges is the ques-
tion of how to produce enough food to meet global demand while mitigating environmental
impacts and/or without reducing the potential for sustained food production in the long
term (Godfray etal. 2010; Foley etal. 2011; Steffen etal. 2015). This is a particularly chal-
lenging issue given that agricultural production may need to double to meet the demands
of the increasing global population, expected to reach 9.8 billion people by 2050 (United
Nations 2017). Agriculture is responsible for the largest area of land use of any activity
on the planet, accounting for around 38% of the earth’s surface, using more water than
any other sector, and being the second-largest contributor to climate change (approximately
24% of total global greenhouse gas (GHG) emissions) (Foley etal. 2011; Davis etal. 2012;
IPCC 2013; Tubiello etal. 2015). Effectively addressing this challenge will require changes
in agricultural production strategies, which could have important economic ramifications
for agricultural producers.
The effects of climate change on food security and well-being for rural populations may
be severe (Gwimbi 2009; Alexandratos and Bruinsma 2012; IPCC 2013; Lindoso et al.
2014). However, the impacts of climate change on agriculture are globally heterogeneous
and therefore require a range of solutions related to differences in productivity as well as the
spatial distribution of agricultural production (Lobell et al. 2008; Carter 2013; Strassburg
etal. 2014; Assad etal. 2019; Rattis etal. 2021). Initiatives are already being implemented
that promote the adoption of agricultural activities both to mitigate the effects of climate
change and/or to adapt to climate change, including by maintaining agricultural productivity
over time (Mertz etal. 2009; Jellason etal. 2019; Talanow etal. 2021). In addition, multi-
lateral agents such as the Food and Agriculture Organization (FAO) of the United Nations,
the United Nations Environment Programme (UNEP), and governmental institutions such as
the UK’s Department for Environment, Food and Rural Affairs (DEFRA) have a number of
past and ongoing initiatives to promote better agricultural policies and practices, especially
in low- and middle-income countries (DEFRA 2008; UNEP 2011; FAO 2015).
Various agencies have invested in research, improvement, and dissemination of on-
farm practices in Brazil that enhance economic gains from agriculture and simultaneously
contribute to the reduction of negative social and environmental impacts of farming prac-
tices (Brasil 2010, 2012a). Following international commitments made at the 2009 United
Nations Framework Convention on Climate Change (COP-15) in Copenhagen to reduce
GHG emissions, in 2012 Brazil implemented the Sectoral Climate Change Mitigation and
Adaptation Plan for the Consolidation of a Low Carbon Economy in Agriculture (ABC
Plan). This plan focused on supporting the adoption of sustainable agricultural practices
through technical assistance and subsidized funding for farmers as a strategy to promote
the mitigation of, and adaptation to, climate change (Brasil 2012a).
Despite being the primary Brazilian program aimed at mitigating GHG emissions
from the agricultural sector, the total annual value of loans made by the ABC Plan has
decreased over time, due to both a decrease in the ABC Plan’s budget and a reduction in
the number of farmers applying, particularly since 2015. In 2021, the total amount spent
on loans reached R$2.6 billion, which is approximately 51% of the total credit available
from the ABC Plan (Banco Central do Brasil - Bacen 2022). Furthermore, the ABC Plan
has been criticized for concentrating its resources in regions with relatively stronger econo-
mies, such as the Southeast and Midwest regions of Brazil (Gianetti and Ferreira Filho

Climatic Change (2024) 177:8
1 3
Page 3 of 24 8
2021). Other challenges of the ABC plan have been limited knowledge about the ABC Plan
objectives among rural producers, lack of technical knowledge among producers about the
low-carbon agricultural technologies (i.e., agricultural practices that increase productivity
and also reduce GHG emissions and/or increase carbon sequestration) supported by the
program, lack of technical support from government agents, and bureaucratic barriers to
accessing credit (Stabile etal. 2012; Newton etal. 2016; Carrer etal. 2020).
To support the aims of the ABC Plan to encourage widespread adoption of sustainable
agriculture practices in the Amazon and the Atlantic Forest biomes, the Projeto Rural Sus-
tentável (Sustainable Rural Project, from hereon) was implemented in 2015 as a technical
cooperation between the Brazilian Ministry of Agriculture, Livestock and Supply (MAPA),
and the Inter-American Development Bank (BID) and financed by DEFRA through the
International Climate Fund (Rural Sustainable Project 2022). The Sustainable Rural Pro-
ject has three main objectives: (i) to provide financial subsidies to small- and medium-sized
farmers for the adoption of sustainable agricultural technologies; (ii) to provide technical
training for farmers and for rural assistants to disseminate information about the sustain-
able agricultural practices supported by the project; and (iii) to conduct monitoring and
evaluation of technical cooperation activities (Rural Sustainable Project 2022).
This study focused on the third objective, aiming to evaluate farmers’ perceptions on
climate change in the Amazon and Atlantic Forest biomes in relation to the adoption of
sustainable agricultural technologies. Understanding farmers’ perceptions about the use
of sustainable agricultural practices is fundamental to assess the project’s effectiveness, to
inform new stages of the project, and/or to improve the design of future public policies
(Litre etal. 2014; Lindoso et al. 2014). Perceptions on climate change risks and impacts
can help explain farmers’ decisions about the production practices that they adopt since
this can reflect farmers’ attitudes, motivations, and values (Gwimbi 2009; Arbuckle etal.
2013; Hyland etal. 2016; Robert etal. 2017; Latawiec etal. 2017). Moreover, increasing
farmers’ awareness about the potentially negative impacts of climate change on their busi-
nesses may result in a greater willingness to adopt sustainable agricultural practices and to
participate in programs that contribute to GHG emissions reduction targets (Adger etal.
2007; Howden etal. 2007; DEFRA 2008; Mertz etal. 2009; Arbuckle etal. 2017; Talanow
etal. 2021).
In this paper, we assessed perceptions of climate change for 273 farmers that partici-
pated in the Sustainable Rural Project to mitigate climate change through the adoption of
sustainable agricultural technologies. We aimed to identify the most commonly used agri-
cultural technologies and to assess whether the use of certain technologies corresponded to
farmers’ socioeconomic characteristics. Our primary research question was: What factors
describe farmers’ perceptions of climate change in the Amazon and Atlantic Forest biomes
in relation to the adoption of sustainable agricultural technologies?
2 Methods
2.1 Case study
The Rural Sustainable Project is a large-scale collaborative project that aims to improve
farmers’ land and forest management in the Amazon and the Atlantic Forest biomes
with the broader goals of sustainable rural development, poverty reduction, biodiver-
sity conservation, and climate protection. The Rural Sustainable Project promotes and

Climatic Change (2024) 177:8
1 3
8 Page 4 of 24
supports the adoption of one or more of a set of sustainable low-carbon agricultural
technologies from the ABC Plan, namely, Integrated Crop-Livestock-Forestry Systems
(ICLFS), Agroforestry Systems (AS); Planted Forests (PF), Native Forest Manage-
ment (NFM), Recovery of Degraded Areas using Pastures (RDA-P), and Recovery of
Degraded Areas using Forests (RDA-F) (Rural Sustainable Project 2022).
Integrated Crop-Livestock-Forestry Systems (ICLF) are agricultural production
strategies that combine farming, cattle, and timber production within the same area, in
rotation, combination, or succession. The objective of ICLFS is to change land use and
productivity by integrating system components and creating synergies between these
components to achieve higher levels of product quality, better environmental outcomes,
and price competitiveness (Rural Sustainable Project 2022).
Agroforestry Systems (AS) are production strategies that integrate crops and forests
through the combination of tree species and agricultural (annual or perennial) crops.
These systems are a feasible production mechanism to recuperate and restore degraded
areas, particularly for small farmers. The integration of trees with crops allows the soil
to be economically productive all year round and leads to a diversification of products
(Matos etal. 2022; Castle etal. 2021; Sagastuy and Krause 2019).
Planted Forests (PF) of high-value fast-growing species (e.g., pine, eucalyptus) cul-
tivated for commercial purposes can serve a production benefit and can also promote
environmental conservation. Planting commercial forests reduces pressure on native for-
ests, supplies raw materials for industrial and non-industrial use, and contributes to the
supply of ecosystem services. This technology is not restricted to exotic species but can
also include a wide variety of native tree species. PF systems are pure forest plantations
and do not integrate agriculture or livestock (Rural Sustainable Project 2022).
Native Forest Management (NFM) is the effective exploitation of forests to obtain
economical, societal, and environmental benefits by following strict sustainability prin-
ciples and mechanisms. This approach commercializes multiple tree species for timber,
a range of non-timber forest products, and other forest-based goods and services (Ros-
Tonen, etal. 2008; Condé etal. 2022).
Recovery of Degraded Areas using Pastures (RDA-P) and Recovery of Degraded
Areas using Forests (RDA-F) are techniques that seek to restore degraded pasture and
forests areas. RDA-P consists of the recovery of a degraded pasture, carried out by rees-
tablishing forage production while maintaining the same pasture species, or by introduc-
ing a new species, replacing the degraded one. Pasture reform can also be carried out to
designate corrections or repairs after pasture establishment. Recovery or renewal can be
carried out directly through mechanical, chemical, and agronomic practices or carried
out indirectly through the intermediate use of annual crops or pastures (SENAR 2018).
Regarding RDA-F, forest restoration is carried out with techniques that vary from those
that do not require any direct intervention, such as the simple isolation of the degraded
area, to those that present a high degree of interventionism, such as removal of degra-
dation factors, selective elimination of competing species, enrichment with seedlings
or seeds, and planting economic species, among others (Moraes, et al 2006; SENAR
2018). RDA-F enables restoration and can also help rural producers take steps towards
meeting their legal obligations under Brazil’s Forest Code including regarding the resto-
ration of Areas of Permanent Protection (APPs) and Legal Reserves (LRs). RDA-P and
RDA-F technologies can contribute to socio-economic development and environmental
protection by generating higher yields and reducing deforestation.
The project’s activities were focused on the Amazon and the Atlantic Forest biomes in
Brazil, which are regions with distinct edaphoclimatic, social, and economic characteristics.

Climatic Change (2024) 177:8
1 3
Page 5 of 24 8
These biomes are globally relevant due to the biodiversity that they conserve and the vital
ecosystem services they provide (Ribeiro etal. 2009; Joly etal. 2014; Caviglia-Harris etal.
2016; Strand etal. 2018; Urzedo et al. 2020), and because they are fast-growing agricul-
tural frontiers (Lira etal. 2012; Alves-Pinto etal. 2017; Kastens etal. 2017; Picoli etal.
2018; Simoes etal. 2020) that have experienced or are experiencing extensive deforestation
(Scarano and Ceotto 2015; Rezende etal. 2018; Carvalho etal. 2019; da Cruz etal. 2021;
Silva Junior etal. 2021; Kruid etal. 2021).
The Sustainable Rural Project aims to reduce the negative economic, social, and envi-
ronmental impacts of traditional agriculture practices in both regions, by promoting and
supporting the adoption of sustainable low-carbon agricultural technologies. The Sus-
tainable Rural Project works in both the Amazon and Atlantic Forest biomes. The typi-
cal land use strategies in both regions are broadly similar in terms of crop and livestock
systems. The typical crop farm may be defined by an intensive and specialized production
system with two crop seasons per year: soybean (Glycine max) (October–February) and
corn (Zea mays) (February–June/July). A typical farm possesses a high level of technol-
ogy in all stages of production and invests highly in infrastructure and inputs. In contrast,
a typical livestock farm may be characterized by traditional cattle ranches with a low level
of technology, low productivity, and a large area. Cattle ranchers typically do not invest
in sophisticated infrastructure: only basic equipment, such as corrals, troughs, and fences.
Also, cattle ranchers typically do not invest in pasture management. As a consequence, in
the dry season, they can experience difficulties providing adequate nutrition to their herd.
The most common cattle breed is Zebu cattle (Bos taurus indicus) and the most common
pasture grasses is Urochloa brizantha cv. Marandu.
The Sustainable Rural Project included 352 participants from demonstration units.
Demonstration units are model farms that received technical assistance and financial sup-
port to showcase the use of the focal technologies by hosting dedicated “field days” during
which they invite neighboring farmers to view these technologies in practice, to learn from
the demonstration unit’s experiences, and to promote technology transfer to other farmers.
Demonstration units were selected via an open call for proposals to participate. The call
established prerequisites to select farmers to participate in the project as a demonstration
unit, including the requirement for farmers to already be using at least one of the tech-
nologies supported by the Sustainable Rural Project. Participation in the program was vol-
untary. There was considerable heterogeneity in farmer profiles and farm characteristics,
including different levels of education and income, a range of ages, and variation in the
agricultural techniques used. There was also variance in the numbers of farms per region,
with a higher number of farms responding to the call in the Atlantic Forest biome than the
Amazon.
2.2 Data
To assess farmers’ perceptions of climate change, we developed and implemented a struc-
tured survey (Supplementary Information). The survey was organized to collect data on
five sets of variables: (i) socioeconomic; (ii) production and connection with the market;
(iii) practices and technologies used; (iv) environmental perceptions; and (v) institutional
and political perceptions. We distributed the survey to, and received responses from, farm-
ers from all 352 demonstrative units that voluntarily signed up for the program. However,
79 responses were incomplete and we thus disregarded these. The final sample therefore

Climatic Change (2024) 177:8
1 3
8 Page 6 of 24
included 273 demonstration units, representing 78% of the demonstration unit farmers sup-
ported by Sustainable Rural Project (Table1).
2.3 Data analysis
2.3.1 Climate Change Perception Indicator (CCPI)
We created a new indicator, the Climate Change Perception Indicator (CCPI), using princi-
pal component analysis (PCA) methodology (Hair etal. 2009). To build the CCPI, we used
the survey responses from the environmental perception set of variables (Table 2; supple-
mentary material). Categorical answers were converted to a 5-point Likert scale (strongly
disagree = 1 and strongly agree = 5) (Barnes and Toma 2012; Arbuckle etal. 2013).
We selected six variables to assess farmer perceptions, drawing on established themes
from the literature on farmers’ perceptions on climate change, and considering both the
Amazon and Atlantic Forest contexts. We included the variables rain and temperature
since they strongly influence daily farming activities and it is relatively easy for farmers to
express reliable perceptions about them. Furthermore, they have been considered in sev-
eral other studies about farmers’ perceptions on climate change (Gwimbi 2009; Mertz etal.
2009; Lindoso etal. 2014; Jellason et al. 2019; Foguesatto et al. 2019). We included the
variable related to climate change impacts on economic performance since it is a signifi-
cant predictor of investments in sustainable agricultural practices (Arbuckle etal. 2013).
Also, when farmers do not recognize climate change as a serious threat to their businesses,
they have tended to demonstrate less propensity to adopt sustainable agricultural practices
(Howden etal. 2007; Morgan etal. 2015; Arbuckle etal. 2017). We selected the variable
farmer willingness to change personal habits to adapt to the negative impacts of climate
change because it may illustrate a higher sense of social awareness, indicating that climate
change may not only impact farmers’ businesses, but that it can also threaten their neigh-
borhoods and society at large (Howden etal. 2007; Morgan etal. 2015). We selected the
variable farmers’ willingness to change production practices even without financial sup-
port because dealing with the negative impacts of climate change may reveal a higher level
of awareness about the contribution of traditional farming to the ongoing degradation of
Table 1 Rural producers with demonstration units, interviewed by state and biome
% of DUs in the sample refers to percentage of farmers surveyed considering the total number of farmers
supported by the Sustainable Rural Project for each state
State Biome Nº of demonstra-
tion units
Nº of complete sur-
vey responses
% of DUs
in the final
sample
Bahia Atlantic Forest 70 52 76%
Minas Gerais Atlantic Forest 60 49 85%
Paraná Atlantic Forest 35 27 80%
Rio Grande do Sul Atlantic Forest 54 54 100%
Mato Grosso Amazon 30 20 67%
Pará Amazon 41 36 95%
Rondônia Amazon 62 35 56%
Tot a l 352 273 78%

Climatic Change (2024) 177:8
1 3
Page 7 of 24 8
environmental resources (DEFRA 2008; Tubiello etal. 2015). Finally, we select the vari-
able voting consideration since land use in the Amazon and the Atlantic Forest biomes are
strongly driven by the political context (Lira et al. 2012). Hence, it is relevant to evaluate
if farmers consider candidates’ environmental proposals, specifically for land use, defor-
estation policy, and environmental legislation, for choosing their political representatives.
Moreover, previous studies suggested that farmers’ perceptions of environmental issues are
heavily influenced by political agendas (Ricart etal. 2018; Hyland etal. 2016).
We calculated the CCPI using the statistical software STATA 13. First, we performed
the Bartlett’s test to verify the adequacy of principal component analysis. Then, we per-
formed a PCA to identify the linear combinations from the original set of variables that
retained the original variability within a reduced number of new variables (the PCs). The
CCPI was built based on the characteristic vectors
ei
from the PC (Hair etal. 2009) (Eq.1).
In the determination of the principal component (PC), the characteristic roots or “eigen-
value” and the proportion of variance explained by each component were evaluated. The
eigenvalue of a factor represents the amount of the total variance explained by that factor.
By the Kaiser-s criterion, only factors with an eigenvalue of 1.0 or more were retained for
further investigation (Ferreira Filho etal. 2013; Pallant 2011).We used Cronbach’s alpha
to evaluate the internal consistency and reliability of the factor loadings (Barnes and Toma
2012; Jellason etal. 2019).
The results from the CCPI were normalized to the interval [0, 1], where values close to
zero indicate the lowest level of awareness of climate change and values close to one indi-
cated highest level of awareness of climate change. We used Jenks natural breaks optimi-
zation to categorize farmers into groups. This process involves a unidimensional k-means
(1)
CCPI
=∑
n
i
ei×v
i
Table 2 Variables selected for elaboration of the Climate Change Perception Indicator (CCPI)
* Variables: 1, voting consideration; 2, rain; 3, temperature; 4, personal habits; 5, production practices; 6,
economic performance
Attitudinal statements Characteristic
vectors
ei
*
1. When choosing someone to vote for, do you consider the candidate’s environmental
proposals?
0.998
2. Do you think there are changes in the rainy season of your region in the last 10 years? 0.003
3. Do you think there have been changes in the temperature of your region in the last 10
years?
0.004
4. Would you be willing to change some of your personal habits (not related to production)
to contribute to the fight against climate change?
0.005
5. Would you be willing to change some of your production techniques, even without
receiving financial support to do so, to contribute to the fight against climate change?
0.004
6. Do you think the climate is changing to such an extent that it will harm your agricultural
and/or livestock production?
0.055
Cronbach’s alpha 0.437
Bartlett test 143.38
Eigenvalue 1.92
% explained variance 69.0

Climatic Change (2024) 177:8
1 3
8 Page 8 of 24
clustering method to organize the dataset into multiple categories that display the small-
est intra-group variance and the largest inter-group variance. The number of categories (k)
was determined from an iterative process based on the CCPI distribution, and it was vali-
dated using the goodness of variance fit (GVF) method. GVF also ranged from the interval
[0,1], where the highest values indicated a better fit to the data distribution (Hair et al.
2009). Finally, we labelled each category to highlight key distinctions between the different
groups.
3 Results
3.1 PCA appropriateness
The results of Bartlett’s tests (143.38) and significance levels (p < 0.05) confirmed that the
data were appropriate for the PCA (Hair etal. 2009; Hyland etal. 2016). As the first PC
showed 69% of primary data variation and an eigenvalue of 1.92 (Table2), it met the Kai-
ser criterion with eigenvalue ≥ 1 (Ferreira Filho etal. 2013; Hair etal. 2009; Pallant 2011),
and its characteristics vectors
ei
were used to build the CCPI (Eq.1). The Cronbach’s alpha
statistics value (0.437) can be classified as moderate and acceptable to attest the internal
consistency and reliability of the factor loads (Landis and Koch 1977). The relatively low
value of this statistic may be explained by two main factors: (i) the small number of vari-
ables used to build the CCPI, and (ii) the large heterogeneity in farmers’ answers regarding
their perceptions on climate change impacts since the survey was carried out in two diverse
biomes of Brazil.
3.2 The Climate Change Perception Indicator categories
The Jenks natural breaks optimization indicated three categories (k = 3) (with GVF = 0.9),
indicating that the three groups were simultaneously internally homogeneous and hetero-
geneous from each other in terms of the CCPI results. In addition, according to the charac-
teristic vectors
ei
from the first PC (Table2), voting consideration was the key factor defin-
ing the CCPI categories. Hence, the three groups were categorized as follows: [0, 0.29],
low CCPI, indicating low climate change perception; [0.3, 0.79], medium CCPI, indicat-
ing intermediate climate change perception; and [0.80, 1], high CCPI, indicating high cli-
mate change perception. The climate change perception levels take into account farmers’
answers to the six questions from environmental perception set. For instance, the maximum
degree of perception about climate change, with a value of 1, was given to farmers who
answered that they are very concerned about climate change, noticed changes in precipita-
tion and temperature levels in the previous 10years, are willing to change personal habits
and productive practices contributing to cope with climate change, and always take into
account a candidate’s environmental proposals when choosing their voting preferences.
The CCPI assigned higher values to farmers that were most concerned about climate
change. The categorization from the CCPI provides useful information to better understand
the issues influencing farmers’ decisions about the adoption of sustainable agricultural
practices (Barnes and Toma 2012; Hyland etal. 2016; Foguesatto etal. 2019). Since the
CCPI is a dimensionless index, it enabled us to comparatively assess farmers’ preferences
despite differences in socioeconomic factors and biomes.

Climatic Change (2024) 177:8
1 3
Page 9 of 24 8
3.3 The Climate Change Perception Indicator results
The distinctive issue to define farmers’ climate change perceptions was voting consider-
ation (Fig. 1). For the other features considered, the farmers displayed similar patterns.
However, we highlight some particularities. For example, farmers from the high CCPI
group showed the lowest economic performance value, but also had the highest percep-
tion of changes in rain and production practices. On the other hand, the low CCPI group
showed the highest value for perceptions of temperature change, but the lowest values for
personal habits change. Finally, the medium CCPI group showed intermediate values for
almost all variables, as it displayed the highest value for economic performance and the
lowest value for production practices.
3.4 Farmers’ profiles
Our sample indicated a higher male participation (85% of farmers) compared to female
participation (Table 3). Also, the CCPI results indicated that older farmers tended to
have higher perception of climate change than young farmers. A large number of farm-
ers (around 42%) did not complete elementary school, while the medium CCPI group
displayed the largest number of farmers with higher education level. Regarding farming
experience, 60% of surveyed farmers were between 6 and 20years of age using sustainable
agricultural practices and, while the low CCPI group showed the highest percentage on the
11–20 years category (38%), the higher CCPI group concentrated 19% of farmers using
sustainable agricultural practices for more than 20years.
The farmers surveyed showed considerable on-farm income dependence, where 57% of
farmers depended exclusively on on-farm revenue. The low CCPI group displayed a large
number of famers totally dependent on-farm income (57%), while the high CCPI group
showed the highest number of famers with no dependence on on-farm income (4%). More-
over, farmers who were members of unions and representative groups tended to have a
higher perception of climate change than those who did not participate in such groups, and
the medium CCPI group showed the highest value number of farmers supported by techni-
cal assistance in the last year (92%), while the low CCPI showed 88%—a relatively low
number compared to the average of all samples surveyed.
Fig. 1 Farmers’ perception diagram. The figure displays the mean values for each variable from Table 2
regarding the farmer’s category defined for this study. For each variable, the axis higher value is 5. Low
CCPI: voting consideration, 1.55; economic performance, 4.61; rain, 4.76; temperature, 4.71; personal hab-
its, 3.98; and production practices, 3.92. Medium CCPI: voting consideration, 3.38; economic performance,
4.64; rain, 4.72; temperature, 4.70; personal habits, 4.01; and production practices: 3.79. High CCPI: voting
consideration, 5.00; economic performance, 4.47; rain, 4.78; temperature, 4.67; personal habits, 4.01; and
production practices, 4.00

Climatic Change (2024) 177:8
1 3
8 Page 10 of 24
Table 3 Farmer’s profile for each CCPI category from the surveyed demonstration units
Farmer’s Profile CCPI
High
(n = 74)
Medium
(n = 114)
Low
(n = 85)
Total
Attribute Category
Gender Female 10.81% 20.18% 10.59% 14.65%
Male 89.19% 79.82% 89.41% 85.35%
Age < 25years 1.35% 2.63% 1.18% 1.83%
26–35years 4.05% 9.65% 3.53% 6.23%
36–45years 13.51% 20.18% 28.24% 20.88%
46–55years 35.14% 36.84% 27.06% 33.33%
56–65years 31.08% 21.05% 24.71% 24.91%
> 65years 14.86% 9.65% 15.29% 12.82%
Schooling No response 0.00% 0.88% 0.00% 0.37%
Illiterate 0.00% 1.75% 2.35% 1.47%
Elementary school incomplete 45.95% 36.84% 47.06% 42.49%
Elementary school 14.86% 9.65% 17.65% 13.55%
High school incomplete 10.81% 7.89% 5.88% 8.06%
High school 14.86% 27.19% 15.29% 20.15%
Bachelor’s degree 13.51% 15.79% 11.76% 13.92%
Farmer experience using sustainable agricultural practices No response 0.00% 3.51% 2.35% 2.20%
0–5years 21.62% 23.68% 17.65% 21.25%
6–10years 31.08% 28.95% 28.24% 29.30%
11–20years 28.38% 25.44% 37.65% 30.04%
> 20years 18.92% 18.42% 14.12% 17.22%

Climatic Change (2024) 177:8
1 3
Page 11 of 24 8
Table 3 (continued)
Farmer’s Profile CCPI
High
(n = 74)
Medium
(n = 114)
Low
(n = 85)
Total
On-farm income dependence No response 20.27% 18.42% 10.59% 16.48%
0% 4.05% 0.88% 3.53% 2.56%
10–40% 2.70% 13.16% 9.41% 9.16%
41–79% 9.46% 6.14% 9.41% 8.06%
80–99% 8.11% 5.26% 9.41% 7.33%
100% 55.41% 56.14% 57.65% 56.41%
Participation in agricultural associations No response 1.35% 0.88% 0.00% 0.73%
No 35.14% 36.84% 41.18% 37.73%
Yes 63.51% 62.28% 58.82% 61.54%
Farmers supported by technical assistance last year No response 1.35% 0.00% 0.00% 0.37%
No 8.11% 7.89% 11.76% 9.16%
Yes 90.54% 92.11% 88.24% 90.48%

Climatic Change (2024) 177:8
1 3
8 Page 12 of 24
3.5 Farmers’ general environmental perceptions
Our findings indicated that more than 90% of farmers in the demonstration units reported
were concerned or very concerned about climate change, with 76% saying that the climate
is changing and that this change will certainly have impacts on rural production. Many
farmers (62%) identified deforestation as the biggest environmental problem in the country.
Among the major impacts on rural production, farmers pointed to droughts (77%), dam-
ages through pests and diseases (73%), and droughts during the rainy season (75%). Farm-
ers tended to link the effects of climate change to the lack of rain and drought rather than to
floods and storms. Furthermore, 94% of farmers stated that they had perceived changes in
the rainy season during the previous 10years, and 77% of them noticed a decrease in rain-
fall. When asked about changes in temperature, also over last 10years, 93% said they had
noticed temperature changes, and 98% indicated an increase in temperature. Despite this
perception, 90% of farmers recognized that they had superficial and/or incomplete knowl-
edge about climate change (Table4).
Regarding precautionary practices and adaptation to the negative effects of climate
change, around 85% of farmers claimed that they had already implemented changes in
their rural activities, and indicated adoption of agroforestry systems (24%), conservation
of native forest and/or headwater (22%), and crop rotation and diversification (15%) as the
three most widely implemented changes. Moreover, only 15% said that they had not imple-
mented any changes in their activities, indicating the main reason for not doing so as the
lack of knowledge about how to adapt their activities to new productive conditions and the
financial difficulties of implementing adaptive strategies. Moreover, considering farmers’
perceptions of the relationship between their productive decisions and climate change, 58%
said they believe that their production practices can lead to climate change, while 42% said
they do not. Also, 96% of farmers would be willing to change their personal habits (not
related to production) to deal with climate change, and 94% would be willing to change
production techniques, even without any financial support.
Finally, the farmers indicated that access to technical assistance, training, and courses
were crucial issues to the widespread adoption of sustainable agriculture practices, high-
lighting the relevance of initiatives such as the Sustainable Rural Project. A substantial
proportion of farmers (78%) heard about the Sustainable Rural Project from the project’s
technical assistance and rural extension agents. In addition, 21% indicated that the main
benefit from the Sustainable Rural Project was the technical assistance provided. On the
other hand, 44% reported that the main benefit from participating was gaining recognition
for their decision to adopt sustainable agricultural practices.
3.6 CCPI andsustainable agricultural technologies adoption
All eight sustainable agricultural technologies supported by the ABC Plan were used by
farmers in the demonstration units (Table5). The recovery of degraded areas with pas-
ture, the Integrated Crop-Livestock-Forest Systems, and the Agroforestry Systems were the
most widely used technologies across all groups. The high CCPI group had the highest
values for recovery of degraded areas with forest and for native forest management. The
ABC Plan technologies that were not directly supported by the Rural Sustainability Project
had the lowest values for all groups. This is particularly relevant for No-Till Farming Sys-
tem technology since it represents a basic and structural technology for sustainable systems

Climatic Change (2024) 177:8
1 3
Page 13 of 24 8
Table 4 Farmer’s general environmental perceptions
Farmer’s general perception on climate change CCPI
High
(n = 74)
Medium
(n = 114)
Low
(n = 85)
Total
Variables Category
Concerned for climate change No concerned 4.05% 0.00% 2.35% 1.83%
Little concerned 5.41% 6.14% 7.06% 6.23%
Concerned 40.54% 44.74% 41.18% 42.49%
Really concerned 50.00% 49.12% 49.41% 49.45%
Climate change will damage
my agricultural performance
No response 0.00% 0.88% 1.18% 0.73%
I do not know 4.05% 0.00% 0.00% 1.10%
Maybe not 8.11% 4.39% 4.71% 5.49%
No 1.35% 0.00% 1.18% 0.73%
Maybe yes 14.86% 18.42% 14.12% 16.12%
Yes 71.62% 76.32% 78.82% 75.82%
Biggest environmental problems Air pollution 8.11% 13.16% 5.88% 9.52%
Water pollution 14.86% 14.04% 20.00% 16.12%
Climate change 6.76% 8.77% 4.71% 6.96%
Deforestation 63.51% 59.65% 63.53% 61.90%
Others 6.76% 4.39% 5.88% 5.49%
Major climate change impacts on agricultural production Storms 45.95% 49.12% 54.12% 49.82%
Floods 16.22% 18.42% 12.94% 16.12%
Droughts 77.03% 71.93% 83.53% 76.92%
Droughts over rain season 78.38% 73.68% 72.94% 74.73%
Desertification 27.03% 17.54% 17.65% 20.15%
Pests and diseases 71.62% 70.18% 78.82% 73.26%

Climatic Change (2024) 177:8
1 3
8 Page 14 of 24
Table 4 (continued)
Farmer’s general perception on climate change CCPI
High
(n = 74)
Medium
(n = 114)
Low
(n = 85)
Total
Knowledge about climate change Unknown 2.70% 1.75% 2.35% 2.20%
Superficial knowledge 40.54% 40.35% 58.82% 46.15%
Incomplete knowledge 50.00% 48.25% 32.94% 43.96%
Comprehensive knowledge 6.76% 9.65% 5.88% 7.69%
Changes in raining season over last 10years No 5.41% 7.02% 5.88% 6.23%
Yes 94.59% 92.98% 94.12% 93.77%
Changes in temperature over last 10years No 9.46% 5.26% 8.24% 7.33%
Yes 90.54% 94.74% 91.76% 92.67%
Knowledge about sustainable agricultural practices Unknown 0.00% 8.77% 9.41% 6.59%
Superficial knowledge 24.32% 29.82% 47.06% 33.70%
Incomplete knowledge 60.81% 47.37% 36.47% 47.62%
Comprehensive knowledge 14.86% 14.04% 7.06% 12.09%
Willing to change personal behavior to reduce climate change No 4.05% 2.63% 4.71% 3.66%
Yes 95.95% 97.37% 95.29% 96.34%
Willing to change productive practices to adapt or mitigate climate change No 4.05% 7.89% 5.88% 6.23%
Yes 95.95% 92.11% 94.12% 93.77%
Your productive decisions lead to climate change No response 0.00% 0.88% 1.18% 0.73%
No 40.54% 48.25% 31.76% 41.03%
Yes 59.46% 50.88% 67.06% 58.24%

Climatic Change (2024) 177:8
1 3
Page 15 of 24 8
Table 4 (continued)
Farmer’s general perception on climate change CCPI
High
(n = 74)
Medium
(n = 114)
Low
(n = 85)
Total
Need to adopt sustainable agriculture practices No response 4.05% 0.00% 1.18% 1.47%
Additional financial support 17.57% 19.30% 28.24% 21.61%
Technical assistance 25.68% 23.68% 56.47% 34.43%
Training and courses about new practices 27.03% 17.54% 31.76% 24.54%
Rural credit or funding 25.68% 14.04% 16.47% 17.95%
How the producer found out
about the Sustainable Rural Project
Other farmers 6.76% 4.39% 4.71% 5.13%
Field days—Rural Sustainable Project 6.76% 2.63% 4.71% 4.40%
Media—newspapers, social media, radio… 5.41% 0.88% 2.35% 2.56%
Technical assistant—Rural Sustainable Project 68.92% 82.46% 78.82% 77.66%
Others 12.16% 9.65% 9.41% 10.26%
What is the main benefit considered by the producer to participate in the
Sustainable Rural Project
Technical assistance 18.92% 23.68% 20.00% 21.25%
Financial benefit 22.97% 16.67% 20.00% 19.41%
Training and courses 12.16% 7.02% 16.47% 11.36%
Others 5.41% 3.51% 4.71% 4.40%
Acknowledgement in adopt sustainable agricul-
tural practices
40.54% 49.12% 38.82% 43.59%

Climatic Change (2024) 177:8
1 3
8 Page 16 of 24
adoption. Finally, the findings indicated that the high CCPI group was more receptive to
sustainable agricultural technologies usage since this group showed the highest values for
five to eight ABC Plan technologies.
4 Discussion
4.1 The CCPI andeconomic motivations foradopting sustainable agricultural
practices
Research on farmers’ perceptions of climate change has demonstrated that if farmers do
not believe that climate change is occurring and/or do not perceive it to be a threat to their
livelihoods and/or businesses, then they are less likely to act to adapt to or mitigate climate
change (Howden etal. 2007; Arbuckle etal. 2013; Morgan etal. 2015; Hyland etal. 2016).
The CCPI can be a useful instrument to assess farmers’ motivations to adopt sustainable
agricultural technologies and their effectiveness as a way to encourage technology transfer.
Our findings suggest that although farmers from demonstration units already had rel-
atively high awareness of climate change impacts, their motivations to adopt sustainable
agricultural practices were strongly driven by economic factors. Specifically, they chose to
adopt (a) recovery of degraded pastures and (b) integrated systems, which, of the technolo-
gies promoted by the ABC Plan, were the two technologies most likely to directly increase
farm revenue and/or profitability (Table 4). Restoring degraded pastures is a key oppor-
tunity for increasing the economic and environmental performance of livestock in Brazil
(Strassburg etal. 2014; Oliveira etal. 2017; Gil et al. 2018). Integrated systems, particu-
larly Integrated Crop-Livestock Systems, have demonstrated higher and faster economic
returns than conventional crop-only or livestock-only systems (dos Reis etal. 2020). All of
the other technologies encompass some degree of reforestation and/or forest management,
which are practices that require longer periods to generate an economic return and that
involve higher market uncertainty. Farmers from demonstration units in our study could be
characterized as “profit-driven adopters” (Morgan etal. 2015) or behaving in an “anthropo-
centric” manner (Foguesatto etal. 2019), since their choice of pro-environmental behaviors
(i.e., choosing to adopt recovery of degraded pastures and crop-livestock systems, rather
Table 5 Sustainable agricultural technologies adoption regarding CCPI categorization
*Technologies supported by Rural Sustainable Project
Technologies CCPI
High Medium Low
*Recovery of Degraded Areas with Pasture 64.9% 49.1% 48.2%
*Recovery of Degraded Areas with Forests 44.6% 24.6% 21.2%
*Integrated Crop-Livestock-Forest and/or Agroforestry
Systems
55.4% 55.3% 63.5%
*Planted Forests 28.4% 30.7% 30.6%
*Native Forest Management 20.3% 21.1% 16.5%
No-Till Farming System 33.8% 22.8% 23.5%
Biological Nitrogen Fixation 21.6% 11.4% 12.9%
Animal Waste Treatment 14.9% 14.0% 08.2%

Climatic Change (2024) 177:8
1 3
Page 17 of 24 8
than other ABC Plan technologies) seems to have been motivated at least in part by eco-
nomic returns and a faster and higher potential return of their investments.
4.2 CCPI, voting consideration, andland use change
The CCPI results were heavily determined by the voting consideration variable (Fig.1).
Six variables were used in the construction of the CCPI. The variable with the highest
eigenvalue (1.92) was obtained from the question “When choosing someone to vote for, do
you consider the candidate’s environmental proposals?”, which represents 69% of the vari-
ance of the other variables. Therefore, producers classified with high CCPI, notably, with
greater concern about the candidates’ environmental proposals, also have greater knowl-
edge about climate change and sustainable production techniques, in relation to producers
classified with low CCPI. This highlights that although most of the farmers in our sam-
ple indicated that they had superficial or incomplete knowledge about climate change, it is
higher for producers with high CCPI, who understand the influence of the political context
on their businesses. This aligns with the idea that political agendas can have a profound
influence on farmers’ environmental perceptions (Lira etal. 2012; Ricart etal. 2018).
Land use change in Brazil, particularly in the Amazon and Atlantic Forest biomes, can
be strongly influenced by federal government decisions (Becker2005; Malhi etal. 2008;
Lira etal. 2012; Alves-Pinto etal. 2017). If institutions related to land use management,
such as the Brazilian Institute for Environment and Renewable Resources (IBAMA),
become weaker that can lead to increases in deforestation rates (Rodrigues-Filho et al.
2015; INPE 2021). After 2015, there was a pronounced intensification of the weakening
of the environmental agenda in Brazil (Silva Junior etal. 2021; Barbosa etal. 2021). Some
examples illustrate this observation. First, IBAMA suffered a substantial reduction in its
number of Environmental Inspection Agents, which limited its monitoring and surveillance
capacity which, in turn, helps explain the increasing deforestation rates (ASCEMA 2020).
Second, several bills were passed that may legalize illegally grabbed public lands (Barlow
etal. 2020). Third, there have been proposed controversial potential changes to the New
Brazilian Forest Code (Brasil 2012b), including the removal of protections for environmen-
tally fragile areas, the concession of amnesty for fines incurred for violating the preced-
ing legislation, and allowing farming or the maintenance of infrastructure to continue in
areas protected by law, without full recovery of native vegetation (Brancalion etal. 2016;
ASCEMA 2020). To sum up, this weakened environmental policy context indicates that
the environmental agenda has been subordinate to the prioritization of economic activities
and the consolidation of the agribusiness sector’s interests, which considers environmental
legislation as an obstacle to agriculture (Soares-Filho etal. 2014; Arvor etal. 2018). Given
that the agricultural sector has been decisive for the Brazilian economy (Barros 2016;
Freitas 2016; MAPA 2017), this context signals a strong incentive to adopt strictly profit-
driven agricultural practices.
Farmers must recognize and act in accordance to Brazilian environmental legislation,
which may counteract some of the solely profit-driven goals (Hyland et al. 2016). Private
commercial agreements focused on environmental issues such as the soybean moratorium
and the G4 cattle agreement (Gibbs etal. 2015, 2016) as well as changes in consumption
perspectives towards sustainable food supply chains (de Haen and Réquillart 2014) have
motivated producers to adopt environmental-friendly practices in order to access the global
food market.

Climatic Change (2024) 177:8
1 3
8 Page 18 of 24
In aggregate, these various dynamics have led to a propensity to change their practices
in response to different pressures. On the one hand, there are pressures to shift towards
sustainable agricultural systems, driven by consideration of environmental-political agenda
and the focus on global markets. On the other hand, economic prosperity is a key motiva-
tor. We see the combination of these factors among the farmers in our study, with higher
rates of adoption of those technologies from within the ABC Plan that are both more sus-
tainable and likely to be more profitable (Gianetti and Ferreira Filho 2021).
4.3 CCPI andfarmer’s profile
Previous studies have found that older farmers tended to demonstrate higher perceptions
about climate change impacts, but tended to demonstrate lower propensity for adopting
sustainable agricultural practices (Evans etal. 2011; Hyland etal. 2016; Foguesatto etal.
2019). We found that older farmers indeed tended to be associated with a higher level of
climate change perception (Morgan etal. 2015). We also found that this awareness about
climate change that comes with age may encourage farmers to adopt sustainable agricul-
tural practices. Moreover, the high CCPI group included a large percentage (18%) of farm-
ers that had been using at least one of the production techniques supported by the Sustain-
able Rural Project for more than 20years, and 47% of farmers from this group had used at
least one of them for 11years or more.
Previous empirical research has illustrated the association between higher education
levels (and/or access to information) and pro-environmental behavior (Barnes and Toma
2012; Morgan etal. 2015; Manda etal. 2016; Hyland etal. 2016; Foguesatto etal. 2019).
Our results build upon these studies since farmers from the high CCPI group had higher
education levels compared to the other two groups. Moreover, we found that there were
some illiterate farmers in both the low and medium CCPI groups. There were an also larger
number of farmers from high CCPI group that reported knowing about the ABC Plan and
the technologies encouraged by the Sustainable Rural Project compared to farmers from
the low CCPI group.
Finally, our results indicated that farmers from the high CCPI group were less depend-
ent on on-farm income. This result contradicts Foguesatto etal. (2019), which categorized
farmers with less dependence from on-farm income as presenting both the lowest level
of pro-environmental and economically-motivated behavior. Our findings also suggest that
farmers from the high CCPI group are likely to be effective in their role as demonstration
units. Although they are not heavily dependent on on-farm income, they displayed a higher
propensity to implement sustainable agricultural practices, which is an important criterion
to be a focal point to promote technology transfer.
4.4 Limitations andnextsteps
Our findings correspond to other literature that suggests that categorizing farmer groups is
useful to improve broader understanding on the issues influencing the adoption of sustain-
able agricultural practices (Arbuckle etal. 2013; Morgan etal. 2015; Hyland etal. 2016).
However, our study also has limitations. First, our focus on farmers from demonstration
units limited generalizations since this group was not selected randomly. Farmers who vol-
unteer to act as demonstration units may have greater propensity to adopt new technolo-
gies than other farmers. Second, the approach provided is a snapshot in time. Longitudi-
nal research tracking farmers’ potential shifts in beliefs, motivations, and attitudes would

Climatic Change (2024) 177:8
1 3
Page 19 of 24 8
improve our understanding of their changing perceptions about climate change, offering
relevant information for policy makers to develop public policy tools allowing interven-
tions tailored towards the needs of specific groups. Third, we did not consider farmers’
previous agricultural systems, including what they converted or adapted from, and how
large a change in practices was needed. For future research, these considerations should be
included to improve the assessment of farmers’ perceptions about climate change impacts
on their business. Furthermore, given that one of the main objectives of the Rural Sus-
tainable Project is the adoption of sustainable agricultural practices, it could be impor-
tant to assess the project’s effectiveness in supporting new farmers who adopted these
technologies.
5 Conclusion
The Sustainable Rural Project is a large-scale initiative aiming to enhance widespread
adoption of sustainable agricultural technologies in the Amazon and Atlantic Forest
biomes of Brazil by providing technical and financial assistance for farmers. In this study,
we assessed farmers’ perceptions of climate change, and propose a categorization based
on farmers’ environmental perceptions. We found that voting consideration and the envi-
ronmental policy context were important predictors of farmers’ levels of climate change
perception. Furthermore, farmers demonstrated strong economically oriented preferences
in their decisions of which sustainable agricultural technologies to adopt.
Our findings indicated that most of the farmers participating in the Sustainable Rural
Project perceived some impacts of climate change even though many lacked the specific
technical knowledge needed to pursue climate change adaptation. Also, they showed strong
economic motivations to adopt sustainable agricultural practices. These results highlight
the importance of programs that emphasize “win–win” technologies (i.e., programs that
simultaneously promote benefits for the environment and for farmers’ economic gains),
such as some of the technologies promoted by the Sustainable Rural Project.
Finally, by understanding the motivations, socioeconomic characteristics, and farm-
ers’ perceptions of climate change, our approach can provide recommendations to improve
environmental policy effectiveness in the Amazon and Atlantic Forest biomes. Our results
demonstrate the key factors that may increase the adoption rate of sustainable agricultural
practices. In this sense, our findings are particularly relevant for the next phases of Rural
Sustainable Project and other public policies and programs that aim to improve the adop-
tion of agricultural practices that either mitigate or adapt to climate change.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s10584- 023- 03657-3.
Author contributions TMdPT: Conceptualization, methodology, investigation, writing—original draft.
RFdAC: Conceptualization, methodology, investigation, writing—original draft. JCdR: Conceptualiza-
tion, investigation, writing—original draft. RB: Methodology, writing (original draft), formal analysis. PN:
Writing, original draft; writing, review and editing; formal analysis. RdARR: Writing—review and editing.
AMHCdO: Conceptualization, methodology, writing—review and editing.
Funding This research was developed within the framework of the Sustainable Rural Project – Amazon
and Atlantic Forest, funded by Technical Cooperation approved by the Inter-American Development Bank
(IDB), with resources from the UK Government’s International Climate Finance, with the Ministry of Agri-
culture, Livestock and Supply (MAPA) as institutional beneficiary. The Brazilian Institute for Development
and Sustainability (IABS) is responsible for the execution and administration of the project and the ILPF

Climatic Change (2024) 177:8
1 3
8 Page 20 of 24
Network Association, through the Brazilian Agricultural Research Corporation (Embrapa) is responsible for
the scientific coordination and technical support.
Data availability The dataset generated during the current study is available in the supplementary material.
Declarations
Competing interests The authors declare no competing interests.
References
Adger WN, Agrawala S, Mirza MMQ, Conde C, O’Brien K, Pulhin J, Pulwarty R, Smit B, Takahashi K
(2007) Assessment of adaptation practices, options, constraints and capacity. In: Parry M, Canziani
OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Climate Change 2007 – Impacts, Adaptation and
Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovern-
mental Panel on Climate Change. Cambridge University Press, New York, NY, pp 717–743
Alexandratos N, Bruinsma J (2012) World Agriculture towards 2030/2050: the 2012 revision. Italy, Rome
Alves-Pinto HN, Latawiec AE, Strassburg BBN etal (2017) Reconciling rural development and ecological
restoration: strategies and policy recommendations for the Brazilian Atlantic Forest. Land Use Policy
60:419–426. https:// doi. org/ 10. 1016/j. landu sepol. 2016. 08. 004
Arbuckle JG, Morton LW, Hobbs J (2013) Farmer beliefs and concerns about climate change and attitudes
toward adaptation and mitigation: evidence from Iowa. Clim Change 118:551–563. https:// doi. org/ 10.
1007/ s10584- 013- 0700-0
Arbuckle JG, Tyndall JC, Morton LW, Hobbs J (2017) Climate change typologies and audience segmentation
among Corn Belt farmers. J Soil Water Conserv 72:205–214. https:// doi. org/ 10. 2489/ jswc. 72.3. 205
Arvor D, Daugeard M, Tritsch I etal (2018) Combining socioeconomic development with environmental
governance in the Brazilian Amazon: the Mato Grosso agricultural frontier at a tipping point. Environ
Dev Sustain 20:1–22. https:// doi. org/ 10. 1007/ s10668- 016- 9889-1
ASCEMA (2020) Cronologia de um desastre anunciado: ações do governo Bolsonaro para desmontar as
políticas de meio ambiente no Brasil. Associação Nacional dos Servidores de Meio Ambiente. Bra-
silia - DF, Brasil. available at: https:// www. biodi versi dadla. org/ Docum entos/ Crono logia- de- um- desas
tre- anunçado- acoes- do- Gover no- Bolso naro- para- desmo ntar- as- polit icas- de- Meio- Ambie nte- no- Brasil
Assad ED, Ribeiro RRR, Nakai AM (2019) Assessments and how an increase in temperature may have an
impact on agriculture in Brazil and mapping of the current and future situation. Climate change risks
in Brazil. Springer International Publishing, Cham, pp 31–65
Banco Central do Brasil - Bacen (2022) ABC Plan - Rural Credit Report. Credit Granted Statistics. https://
www. bcb. gov. br/ estab ilida defin ancei ra/ repor tmicr rural/? path= conte udo% 2FMDCR% 2FRep or ts%
2Fqvc Modal idade Produ to. rdl. Accessed 7 Mar 2022
Barbosa LG, Alves MAS, Grelle CEV (2021) Actions against sustainability: dismantling of the environmen-
tal policies in Brazil. Land Use Policy 104:105384. https:// doi. org/ 10. 1016/j. landu sepol. 2021. 105384
Barlow J, Berenguer E, Carmenta R, França F (2020) Clarifying Amazonia’s burning crisis. Glob Chang
Biol 26:319–321. https:// doi. org/ 10. 1111/ gcb. 14872
Barnes AP, Toma L (2012) A typology of dairy farmer perceptions towards climate change. Clim Change
112:507–522. https:// doi. org/ 10. 1007/ s10584- 011- 0226-2
Barros GSADC (2016) Medindo o crescimento do agronegócio: bonança externa e preços relativos. In:
Filho JERV, Gasques JG (eds) Agricultura, transformação produtiva e sustentabilidade. Instituto de
Pesquisa Econômica Aplicada - IPEA, Brasília - DF, Brazil
Becker BK (2005) Geopolítica da amazônia. Estudos Avançados 19:71–86. https:// doi. org/ 10. 1590/ S0103-
40142 00500 01000 05
Brancalion PHS, Garcia LC, Loyola R etal (2016) A critical analysis of the native vegetation protection
law of Brazil (2012): updates and ongoing initiatives. Nat Conserv 14:1–15. https:// doi. org/ 10. 1016/j.
ncon. 2016. 03. 003
Brasil (2010) Presidência da República. Casa Civil. Subchefia para Assuntos Jurídicos. Decreto No 7.390 de
09 de Dezembro de 2010 - Política Nacional sobre Mudança do Clima. Available at: https:// www. plana
lto. gov. br/ ccivil_ 03/_ ato20 07- 2010/ 2010/ decre to/ d7390. htm
Brasil (2012a) Ministério da Agricultura, Pecuária e Abastecimento. Plano setorial de mitigação e de adap-
tação às mudanças climáticas para a consolidação de uma economia de baixa emissão de carbono

Climatic Change (2024) 177:8
1 3
Page 21 of 24 8
na agricultura: plano ABC (Agricultura de Baixa Emissão de Carbono) / Ministério da Agricultura,
Pecuária e Abastecimento, Ministério do Desenvolvimento Agrário, coordenação da Casa Civil da
Presidência da República. – MAPA/ACS, Brasília, p 173
Brasil (2012b) Presidência da República. Casa Civil. Subchefia para Assuntos Jurídicos. Lei No 12.651,
de 25 de Maio de 2012 - Novo Código Florestal Brasileiro. Available at: https:// www. plana lto. gov. br/
ccivil_ 03/_ ato20 11- 2014/ 2012/ lei/ l12651. htm
Carrer MJ, Maia AG, de Mello Brandão Vinholis M, de Souza Filho HM, (2020) Assessing the effective-
ness of rural credit policy on the adoption of integrated crop-livestock systems in Brazil. Land Use
Policy 92:104468. https:// doi. org/ 10. 1016/j. landu sepol. 2020. 104468
Carter TR (2013) Multi-model yield projections. Nat Clim Chang 3:784–786. https:// doi. org/ 10. 1038/ nclim
ate19 95
Carvalho WD, Mustin K, Hilário RR etal (2019) Deforestation control in the Brazilian Amazon: a conser-
vation struggle being lost as agreements and regulations are subverted and bypassed. Perspect Ecol
Conserv 17:122–130. https:// doi. org/ 10. 1016/j. pecon. 2019. 06. 002
Castle SE, Miller DC, Ordonez PJ, Baylis K, Hughes K (2021) The impacts of agroforestry interventions on
agricultural productivity, ecosystem services, and human well‐being in low‐and middle‐income coun-
tries: a systematic review. Campbell Syst Rev 17(2):e1167
Caviglia-Harris J, Sills E, Bell A etal (2016) Busting the boom–bust pattern of development in the Brazilian
Amazon. World Dev 79:82–96. https:// doi. org/ 10. 1016/j. world dev. 2015. 10. 040
Condé TM, Tonini H, Higuchi N, Higuchi FG, Lima AJN, Barbosa RI, ... & Haas, M. A. (2022). Effects
of sustainable forest management on tree diversity, timber volumes, and carbon stocks in an ecotone
forest in the northern Brazilian Amazon.Land use policy 119: 106145. https:// doi. org/ 10. 1016/j. landu
sepol. 2022. 106145
da Cruz DC, Benayas JMR, Ferreira GC etal (2021) An overview of forest loss and restoration in the Brazilian
Amazon. New for 52:1–16. https:// doi. org/ 10. 1007/ s11056- 020- 09777-3
Davis AS, Hill JD, Chase CA etal (2012) Increasing cropping system diversity balances productivity, prof-
itability and environmental health. PLoS ONE 7(10):e47149. https:// doi. org/ 10. 1371/ journ al. pone.
00471 49
de Haen H, Réquillart V (2014) Linkages between sustainable consumption and sustainable production: some
suggestions for foresight work. Food Secur 6:87–100. https:// doi. org/ 10. 1007/ s12571- 013- 0323-3
de Oliveira SR, Barioni LG, Hall JAJ etal (2017) Sustainable intensification of Brazilian livestock production
through optimized pasture restoration. Agric Syst 153:201–211. https:// doi. org/ 10. 1016/j. agsy. 2017. 02. 001
DEFRA (2008) Understanding behaviors in a farming context: Bringing theoretical and applied evidence
together from across Defra and highlighting policy relevance and implications for future research. In:
Defra Agricultural Change and Environment Observatory Discussion Paper. Department for Environ-
ment, Food and Rural Affairs, London
dos Reis JC, Kamoi MYT, Latorraca D etal (2020) Assessing the economic viability of integrated crop−
livestock systems in Mato Grosso, Brazil. Renew Agric Food Syst 35:631–642. https:// doi. org/ 10. 1017/
S1742 17051 90002 80
Evans C, Storer C, Wardell-Johnson A (2011) Rural farming community climate change acceptance: impact
of science and government credibility. Int J Sociol Agric Food 18:217–235. https:// doi. org/ 10. 48416/
ijsaf. v18i3. 246
FAO (2015) Climate change and food security: risks and responses. Food and Agriculture Organization of
the United Nations,Rome, Italy
Ferreira Filho DB, Paranhos R, da Rocha EC, Junior JADS, Maia RG (2013) Análise de componentes prin-
cipais para construção de indicadores sociais. Revista Brasileira de Biometria 31(1):61–78
Foguesatto CR, Borges JAR, Machado JAD (2019) Farmers’ typologies regarding environmental values and
climate change: evidence from southern Brazil. J Clean Prod 232:400–407. https:// doi. org/ 10. 1016/j.
jclep ro. 2019. 05. 275
Foley JA, Ramankutty N, Brauman KA etal (2011) Solutions for a cultivated planet. Nature 478:337–
342. https:// doi. org/ 10. 1038/ natur e10452
Freitas RE (2016) A agropecuária e seus processados na balança comercial brasileira. In: Vieira Filho
JER, Gasques JG (eds) Agricultura, transformação produtiva e sustentabilidade, vol 1, 1st edn.
IPEA, Brasília, pp 281–300
Gianetti GW, Ferreira Filho JBDS (2021) O Plano e Programa ABC: uma análise da alocação dos recursos.
Revista de Economia e Sociologia Rural 59(1):e216524. https:// doi. org/ 10. 1590/ 1806- 9479. 2021. 216524
Gibbs HK, Munger J, L’Roe J etal (2016) Did ranchers and slaughterhouses respond to zero-deforestation
agreements in the Brazilian Amazon? Conserv Lett 9:32–42. https:// doi. org/ 10. 1111/ conl. 12175
Gibbs HK, Rausch L, Munger J, Schelly I, Morton DC, Noojipady P, Soares-Filho B, Barreto P, Micol L, Walker
NF (2015) Brazil’s soy moratorium. Science 347:377–378. https:// doi. org/ 10. 1126/ scien ce. aaa01 81

Climatic Change (2024) 177:8
1 3
8 Page 22 of 24
Gil JDB, Garrett RD, Rotz A etal (2018) Tradeoffs in the quest for climate smart agricultural intensi-
fication in Mato Grosso. Brazil Environ Res Lett 13:064025. https:// doi. org/ 10. 1088/ 1748- 9326/
aac4d1
Godfray HCJ, Beddington JR, Crute IR etal (2010) Food security: the challenge of feeding 9 billion people.
Science (80) 327:812–818. https:// doi. org/ 10. 1126/ scien ce. 11853 83
Gwimbi P (2009) Cotton farmers’ vulnerability to climate change in Gokwe District (Zimbabwe): impact
and influencing factors. Jàmbá J Disaster Risk Stud 2. https:// doi. org/ 10. 4102/ jamba. v2i2. 17
Hair JF, Black WC, Black B etal (2009) Multivariate data analysis, 7th editio. Prentice-Hall
Howden SM, Soussana J-F, Tubiello FN et al (2007) Adapting agriculture to climate change. Proc Natl
Acad Sci 104:19691–19696. https:// doi. org/ 10. 1073/ pnas. 07018 90104
Hyland JJ, Jones DL, Parkhill KA etal (2016) Farmers’ perceptions of climate change: identifying types.
Agric Human Values 33:323–339. https:// doi. org/ 10. 1007/ s10460- 015- 9608-9
INPE (2021) Instituto Nacional de Pesquisas Espaciais - INPE. PRODES - Programa de Monitoramento
da Floresta Amazonica Brasileira por Satelite. http:// www. obt. inpe. br/ OBT/ assun tos/ progr amas/
amazo nia/ prodes. Accessed 25 Jul 2021
IPCC (2013) Intergovernmental Panel on Climate Change - IPCC. Summary for Policymakers. In:
Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midg-
ley PM (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group
I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Cam-
bridge, CA, UK and New York, NY, US
Jellason N, Baines R, Conway J, Ogbaga C (2019) Climate change perceptions and attitudes to smallholder
adaptation in Northwestern Nigerian Drylands. Soc Sci 8:31. https:// doi. org/ 10. 3390/ socsc i8020 031
Joly CA, Metzger JP, Tabarelli M (2014) Experiences from the Brazilian Atlantic Forest: ecological
findings and conservation initiatives. New Phytol 204:459–473. https:// doi. org/ 10. 1111/ nph. 12989
Kastens JH, Brown JC, Coutinho AC etal (2017) Soy moratorium impacts on soybean and deforesta-
tion dynamics in Mato Grosso. Brazil Plos One 12:e0176168. https:// doi. org/ 10. 1371/ journ al. pone.
01761 68
Kruid S, Macedo MN, Gorelik SR, etal (2021) Beyond deforestation: carbon emissions from land grab-
bing and forest degradation in the Brazilian Amazon. Front For Glob Chang 4. https:// doi. org/ 10.
3389/ ffgc. 2021. 645282
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics
33(1):159–174
Latawiec AE, Strassburg BBN, Silva D etal (2017) Improving land management in Brazil: a perspective
from producers. Agric Ecosyst Environ 240:276–286. https:// doi. org/ 10. 1016/j. agee. 2017. 01. 043
Lindoso DP, Rocha JD, Debortoli N et al (2014) Integrated assessment of smallholder farming’s vul-
nerability to drought in the Brazilian Semi-arid: a case study in Ceará. Clim Change 127:93–105.
https:// doi. org/ 10. 1007/ s10584- 014- 1116-1
Lira PK, Tambosi LR, Ewers RM, Metzger JP (2012) Land-use and land-cover change in Atlantic Forest
landscapes. For Ecol Manage 278:80–89. https:// doi. org/ 10. 1016/j. foreco. 2012. 05. 008
Litre G, Nasuti S, Gucciardi Garcez CA etal (2014) From rainforests to drylands: comparing family
farmers’ perceptions of climate change in three Brazilian biomes. In: Leal Filho W, Alves F, Caeiro
S, Azeiteiro U (eds) International Perspectives on Climate Change. Climate Change Management.
Springer, Cham. https:// doi. org/ 10. 1007/ 978-3- 319- 04489-7_ 12
Lobell DB, Burke MB, Tebaldi C etal (2008) Prioritizing climate change adaptation needs for food
security in 2030. Science (80-) 319:607–610. https:// doi. org/ 10. 1126/ scien ce. 11523 39
Malhi Y, Roberts JT, Betts RA etal (2008) Climate change, deforestation, and the fate of the Amazon.
Science (80-) 319:169–172. https:// doi. org/ 10. 1126/ scien ce. 11469 61
Manda J, Alene AD, Gardebroek C etal (2016) Adoption and impacts of sustainable agricultural prac-
tices on maize yields and incomes: evidence from rural Zambia. J Agric Econ 67:130–153. https://
doi. org/ 10. 1111/ 1477- 9552. 12127
MAPA (2017) Ministério da Agricultura Pecuária e Abastecimento - Valor Bruto da Produção Agro-
pecuária (VBP). http:// www. agric ultura. gov. br/ assun tos/ polit ica- agric ola/ valor- bruto- da- produ cao-
agrop ecuar ia- vbp. Accessed 17 Sep 2018
Matos PS, Cherubin MR, Damian JM, Rocha FI, Pereira MG, Zonta E (2022) Short-term effects of agrofor-
estry systems on soil health in Southeastern Brazil. Agrofor Syst 96(5–6):897–908. https:// doi. org/ 10.
1007/ s10457- 022- 00749-4
Mertz O, Mbow C, Reenberg A, Diouf A (2009) Farmers’ perceptions of climate change and agricul-
tural adaptation strategies in rural Sahel. Environ Manage 43:804–816. https:// doi. org/ 10. 1007/
s00267- 008- 9197-0

Climatic Change (2024) 177:8
1 3
Page 23 of 24 8
Moraes LFD, Assumpcao JM, Pereira TS, Luchiari C (2013) Manual técnico para a restauração de áreas
degradadas no Estado do Rio de Janeiro. Jardim Botânico do Rio de Janeiro, Rio de Janeiro, p 84
Morgan MI, Hine DW, Bhullar N, Loi NM (2015) Landholder adoption of low emission agricultural prac-
tices: A profiling approach. J Environ Psychol 41:35–44. https:// doi. org/ 10. 1016/j. jenvp. 2014. 11. 004
Newton P, Gomez AEA, Jung S, etal (2016) Overcoming barriers to low carbon agriculture and forest
restoration in Brazil: the rural Sustentável project. World Dev Perspect 4. https:// doi. org/ 10. 1016/j.
wdp. 2016. 11. 011
Pallant J (2011) Survival manual. In: A step by step guide to data analysis using SPSS, 4th edn. Allen &
Unwin, Berkshire
Picoli MCA, Camara G, Sanches I etal (2018) Big earth observation time series analysis for monitoring
Brazilian agriculture. ISPRS J Photogramm Remote Sens 145:328–339. https:// doi. org/ 10. 1016/j.
isprs jprs. 2018. 08. 007
Rattis L, Brando PM, Macedo MN etal (2021) Climatic limit for agriculture in Brazil. Nat Clim Chang
11:1098–1104. https:// doi. org/ 10. 1038/ s41558- 021- 01214-3
Rezende CL, Scarano FR, Assad ED etal (2018) From hotspot to hopespot: an opportunity for the Brazil-
ian Atlantic Forest. Perspect Ecol Conserv 16:208–214. https:// doi. org/ 10. 1016/j. pecon. 2018. 10. 002
Ribeiro MC, Metzger JP, Martensen AC et al (2009) The Brazilian Atlantic forest: how much is left, and how
is the remaining forest distributed? Implications for conservation. Biol Conserv 142:1141–1153. https://
doi. org/ 10. 1016/j. biocon. 2009. 02. 021
Ricart S, Olcina J, Rico AM (2018) Evaluating public attitudes and farmers’ beliefs towards climate change
adaptation: awareness, perception, and populism at European level. Land 8(1):4
Robert M, Thomas A, Sekhar M etal (2017) Farm typology in the Berambadi Watershed (India): farming
systems are determined by farm size and access to groundwater. Water 9:51. https:// doi. org/ 10. 3390/
w9010 051
Rodrigues-Filho S, Verburg R, Bursztyn M etal (2015) Election-driven weakening of deforestation control in
the Brazilian Amazon. Land Use Policy 43:111–118. https:// doi. org/ 10. 1016/j. landu sepol. 2014. 11. 002
Ros-Tonen MA, Van Andel T, Morsello C, Otsuki K, Rosendo S, Scholz I (2008) Forest-related partnerships
in Brazilian Amazonia: there is more to sustainable forest management than reduced impact logging.
For Ecol Manage 256(7):1482–1497. https:// doi. org/ 10. 1016/j. foreco. 2008. 02. 044
Rural Sustainable Project (2022) Rural Sustainable Project. http:// mata- atlan tica- amazo nia. rural suste ntavel.
org/. Accessed 7 Mar 2022
Sagastuy M, Krause T (2019) Agroforestry as a biodiversity conservation tool in the atlantic forest? Motiva-
tions and limitations for small-scale farmers to implement agroforestry systems in north-eastern Brazil.
Sustainability 11(24):6932. https:// doi. org/ 10. 3390/ su112 46932
Scarano FR, Ceotto P (2015) Brazilian Atlantic forest: impact, vulnerability, and adaptation to climate
change. Biodivers Conserv 24:2319–2331. https:// doi. org/ 10. 1007/ s10531- 015- 0972-y
SENAR (2018) Serviço Nacional de Aprendizagem Rural - SENAR. Recuperação de áreas degradadas. Pro-
jeto Rural SUstentável. Termo de cooperação técnica e execução no 001/2017. Available at : https://
edito ra. iabs. org. br/ site/ index. php/ portf olio- items/ recup eracao- de- areas- degra dadas- rad/
Silva Junior CHL, Pessôa ACM, Carvalho NS etal (2021) The Brazilian Amazon deforestation rate in 2020
is the greatest of the decade. Nat Ecol Evol 5:144–145. https:// doi. org/ 10. 1038/ s41559- 020- 01368-x
Simoes R, Picoli MCA, Camara G etal (2020) Land use and cover maps for Mato Grosso State in Brazil
from 2001 to 2017. Sci Data 7:34. https:// doi. org/ 10. 1038/ s41597- 020- 0371-4
Soares-Filho B, Rajão R, Macedo M etal (2014) Cracking Brazil’s Forest Code. Science (80-) 344:363–
364. https:// doi. org/ 10. 1126/ scien ce. 12466 63
Stabile MCC, Azevedo A, Nepstad D (2012) Brazil’s “Low Carbon Agriculture Program”: barriers to
implementation. Amazon Environmental Research Institute (IPAM), Belém
Steffen W, Richardson K, Rockström J etal (2015) Planetary boundaries: guiding human development on a
changing planet. Science (80-) 347:1259855–1259855. https:// doi. org/ 10. 1126/ scien ce. 12598 55
Strand J, Soares-Filho B, Costa MH etal (2018) Spatially explicit valuation of the Brazilian Amazon Forest’s Eco-
system Services. Nat Sustain 1:657–664. https:// doi. org/ 10. 1038/ s41893- 018- 0175-0
Strassburg BBN, Latawiec AE, Barioni LG etal (2014) When enough should be enough: Improving the use
of current agricultural lands could meet production demands and spare natural habitats in Brazil. Glob
Environ Chang 28:84–97. https:// doi. org/ 10. 1016/j. gloen vcha. 2014. 06. 001
Talanow K, Topp EN, Loos J, Martín-López B (2021) Farmers’ perceptions of climate change and adapta-
tion strategies in South Africa’s Western Cape. J Rural Stud 81:203–219. https:// doi. org/ 10. 1016/j. jrurs
tud. 2020. 10. 026
Tubiello FN, Salvatore M, Ferrara AF etal (2015) The contribution of agriculture, forestry and other land
use activities to global warming, 1990–2012. Glob Chang Biol 21:2655–2660. https:// doi. org/ 10. 1111/
gcb. 12865

Climatic Change (2024) 177:8
1 3
8 Page 24 of 24
UNEP (2011) Towards a green economy: pathways to sustainable development and poverty eradication.
United Nations Environment Programme, Geneva
United Nations (2017) World population prospects 2017. United Nations Department of Economic and
Social Affairs,New York
Urzedo DI, Neilson J, Fisher R, Junqueira RGP (2020) A global production network for ecosystem ser-
vices: the emergent governance of landscape restoration in the Brazilian Amazon. Glob Environ Chang
61:102059. https:// doi. org/ 10. 1016/j. gloen vcha. 2020. 102059
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under
a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted
manuscript version of this article is solely governed by the terms of such publishing agreement and applicable
law.
Authors and Aliations
TarikTanure1 · RafaelFariadeAbreuCampos2· JúlioCésardosReis3·
RaynaBenzeev4· PeterNewton4· RenatodeAragãoRibeiroRodrigues5·
AnaMariaHermetoCamilodeOliveira6
* Tarik Tanure
tariktanure@ufu.br
Rafael Faria de Abreu Campos
rfacampos@ufv.br
Júlio César dos Reis
julio.reis@embrapa.br
Rayna Benzeev
rayna.benzeev@colorado.edu
Peter Newton
peter.newton@colorado.edu
Renato de Aragão Ribeiro Rodrigues
renatorodrigues.clima@gmail.com
Ana Maria Hermeto Camilo de Oliveira
ahermeto@cedeplar.ufmg.br
1 Institute ofEconomics andInternational Relations - IERI, Federal University ofUberlândia - UFU,
Uberlândia, Brazil
2 Economics Department – DEE, Federal University ofViçosa – UFV, Viçosa, Brazil
3 Embrapa Cerrados, BR-020, Km 18, S/N - Planaltina, Brasília, DF73310-970, Brazil
4 Environment andSociety Program, Institute ofBehavioral Science, University ofColorado
Boulder, 4001 Discovery Drive, Boulder, CO80303-0397, USA
5 Geochemistry Department, Federal Fluminense University, Outeiro São João Baptista s/n – Centro,
Niterói, RJ24.020-141, Brazil
6 Economics Department, Federal University ofMinas Gerais, Av. Pres. Antônio Carlos, 6627 -
Pampulha, BeloHorizonte, MG31270-901, Brazil