Ano de publicação: 2022

Este trabalho teve como objetivo realizar um estudo de caso para chuvas intensas e seus impactos nas ocorrências de alagamentos na cidade de Campinas, São Paulo, no ano de 2019. Dados de chuva acumulada diária foram obtidos de 8 pluviômetros distribuídos na cidade e dados de alagamento foram obtidos junto à Defesa Civil municipal, referentes aos chamados atendidos. Na análise espacial, foi constatada uma relativa concordância com a ocorrência ou não de chuva entre os pluviômetros. No entanto, ficou evidente a variabilidade espacial da quantidade de chuva, o que demonstra a ocorrência de chuva localmente forte. A análise dos dados de alagamento permitiu determinar áreas mais propensas a esse tipo de evento. Aspectos como volume acumulado de chuva e configuração do relevo certamente proporcionam condições ideais para alagamentos. No entanto, a área mais afetada por alagamentos é relativamente mais elevada, o que sugere, por ser parte da região central e amplamente urbanizada, que a urbanização também contribui significativamente para a ocorrência de alagamentos. 

Chuva intensa, Alagamentos, Variabilidade espacial da chuva, Urbanização.

https://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/8667944

Ano de publicação: 2019

o presente artigo apresenta a reconstrução histórica dos eventos e documentos que caracterizaram a institucionalização do ambiente em Moçambique, entre 1980 e 2014. Inicialmente, apresenta a metodologia que orientou o artigo e debates teóricos, estabelecendo diálogo entre a História Ambiental da América Latina, abordagens pós-coloniais e Sociologia do Conhecimento. Na sequência, apresenta os eventos que delimitaram a dimensão local dos riscos ambientais, e documentos que marcaram a visão global destes.

História Ambiental. Pós-Colonialidade. Risco Ambiental.

https://periodicos.unb.br/index.php/emtempos/article/view/27089

Ano de publicação: 2019

Acquiring meaningful data to be employed in building training sets for
classification models is a costly task, both in terms of difficult to
find suitable samples as well as their quantity. In this sense, Active
Learning (AL) improves the training set building by providing an
efficient way to select only essential data to be attached to the
training set, consequently reducing its size and even enhancing model's
accuracy, when compared to random sample selection. In this paper, we
proposed a framework for time series classification in order to monitor
sugarcane area in São Paulo, Brazil. The AL approach consisted of
selecting seasonal time series information from less than 1 percent of
each class' pixels to build the training set and evaluate this selection
by an expert user supported by distance measurements, repeating this
process until both distance measurement thresholds were satisfied. In
most years, the classification results presented about 90 percent of
correlation with official estimates based on both traditional and
satellite image analysis methods. This framework can then help Land Use
Change (LUC) monitoring as it produced similar results compared to other
methods that demands more human and financial resources to be adopted.

Environment, pixel classification, remote sensing, time series analysis 

https://ieeexplore.ieee.org/document/8701458

Ano de publicação: 2019

Global terrestrial models currently predict that the Amazon rainforest
will continue to act as a carbon sink in the future, primarily owing to
the rising atmospheric carbon dioxide (CO₂) concentration.
Soil phosphorus impoverishment in parts of the Amazon basin largely
controls its functioning, but the role of phosphorus availability has
not been considered in global model ensembles—for example, during the
Fifth Climate Model Intercomparison Project. Here we simulate the
planned free-air CO₂ enrichment experiment AmazonFACE with an
ensemble of 14 terrestrial ecosystem models. We show that phosphorus
availability reduces the projected CO₂-induced biomass carbon growth by about 50% to 79 ± 63 g C m⁻² yr⁻¹
over 15 years compared to estimates from carbon and carbon–nitrogen
models. Our results suggest that the resilience of the region to climate
change may be much less than previously assumed. Variation in the
biomass carbon response among the phosphorus-enabled models is
considerable, ranging from 5 to 140 g C m⁻² yr⁻¹,
owing to the contrasting plant phosphorus use and acquisition strategies
considered among the models. The Amazon forest response thus depends on
the interactions and relative contributions of the phosphorus
acquisition and use strategies across individuals, and to what extent
these processes can be upregulated under elevated CO₂.

Carbon cycle, Climate and Earth system modelling, Climate-change ecology, Element cycles, Tropical ecology.

https://www.nature.com/articles/s41561-019-0404-9#Ack1

Ano de publicação: 2019

All over the world, there is a pressing need to better understand how
climate change has been incorporated into governmental agendas, and
evaluate the status of adaptation planning and interventions at the
local level. In this paper, we seek to contribute towards bridging this
gap by identifying local practices connected to climate adaptation in
six large Brazilian cities, and presenting a framework, based on the
existing literature, for assessing constraints to adaptation across the
municipal level. Although local governments are not the only actors who
can take the lead through their actions, the employed framework
considers that effective adaptation planning in urban areas is highly
dependent on municipal efforts. Our findings indicate that six aspects
have the highest levels of impact on adaptation in the Brazilian cities
studied: administrative practices, political will, level of commitment,
mismatch between the scale of urban issues and the extent of local
government authority, pressures from private sectors, and inspection.
Although these barriers are not specific only to climate issues and can
be identified in other environmental arenas, when combined, they cause
and worsen constraints to advancing urban adaptation at the local level.
Specifically concerning the local dynamics of urban planning, the
combination of pressures from private sectors and insufficient
inspection negatively affects the ability of these cities to consolidate
adaptation interventions. Our results are helpful in the context of
large cities, particularly in Global South, where, as in Brazil,
competitive urbanism and specific interest groups confront municipal
efforts, and make achieving adaptation more difficult.

Climate change, Adaptation, Local governments, Cities, Brazil 

https://link.springer.com/article/10.1007/s10113-019-01570-z

Ano de publicação: 2019

In this research, we combine data analyses with hotspots method to
identify the spatio-temporal trend of São Paulo’s coffee cultivation
area. Our hypothesis is that coffee cultivation area has been changing
significantly in the study area since 1990. Therefore, the main goal of
this research was to map the spatial pattern of coffee land use change. 
For coffee land use diagnostics, official data of cultivated area,
hotspot analyses and growth rate were used. The results demonstrated
that coffee cultivation area decreased and concentrated in smaller
areas, which are traditionally recognized as “coffee quality regions”.
The producer size analyses evidenced that, not only the localization,
but also the producer profile changes as well. Smallholders increased
but medium and large producers decreased significantly in the studied
period. The coffee abandonment analyses demonstrated that, over the
study period, 51.46% of the coffee area cultivated in the study region
was abandoned.

Land cover changes, hotspots, coffee abandonment         

http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/1540

Ano de publicação: 2019

Payments for Ecosystem Services (PES) schemes have increasingly expanded to consider ecosystem services (ESS). In Brazil, the Forest Code permits PES but does not specify the scheme operationalization. The way ESS should be quantified and valued has not yet been implemented country-wide, nor has the funding source for PES. Through interviews with farmers in Rio Claro-SP, Brazil, and in Cairngorms National Park in the highlands and lowlands of Scotland, UK, we compared farmers’ perspectives concerning ESS and PES, focusing on the PES implementation in sugarcane landscape in São Paulo state. While Scottish farmers perceived more cultural services, Brazilian farmers focused on regulating services, which we attribute to socio-political and landscape differences. Despite these differences, farmers in both areas preferred opportunity cost approach for ESS valuation because this method captures efforts to maintain ESS. Thereby, the opportunity cost should be considered for valuation in PES schemes, but conversely, budgetary constraints make it impossible to satisfy farmers with PES in regions of high productivity in the southeast of Brazil. Lessons learned concerning the PES subsidies in Scotland indicates the importance of co-designing schemes with stakeholders, minimizing trade-offs between the environment. Therefore, the participants as ESS providers, beneficiaries and intermediaries in the public policies arena was recognized for co-optimize the trade-offs between costs and effectiveness in PES.

Payment for ecosystem services, 

Public policies, 

Sugarcane production, 

Participatory methods, 

Cairngorms National Park

https://www.sciencedirect.com/science/article/pii/S2212041618301748?via%3Dihub#kg005

Ano de publicação: 2019

The precise estimation of reference evapotranspiration (ET₀)
is crucial for the planning and management of water resources and
agricultural production. In this study, the applicability of the
Hargreaves Samani (HS), artificial neural network (ANN), multiple linear
regression (MLR) and extreme learning machine (ELM) models were
evaluated to estimate ET₀ based on temperature data from the
Verde Grande River basin, southeastern Brazil. These models were
evaluated in two scenarios: local and pooled. In the local scenario,
training, calibration and validation of the models were performed
separately at each station. In the pooled scenario, meteorological data
from all stations were grouped for training and calibration and then
separately tested at each station. The ET₀ values estimated
by the Penman-Monteith model (FAO-56 PM) were considered the target
data. All the developed models were evaluated by cluster analysis and
the following performance indices: relative root mean square error
(RRMSE), Pearson correlation coefficient (r) and Nash-Sutcliffe
coefficient (NS). In both scenarios evaluated, local and pooled, the
results revealed the superiority of the artificial intelligence methods
(ANN and ELM) and the MLR model compared to the original and adjusted HS
models. In the local scenario, the ANN (with r of 0.751, NS of 0.687
and RRMSE of 0.112), ELM (with r of 0.747, NS of 0.672 and RRMSE of
0.116) and MLR (with r of 0.743, NS of 0.665 and RRMSE of 0.068) models
presented the best performance, in addition to being grouped in the same
cluster. Similar to the observations from the local scenario, the ANN
(with r of 0.718, NS of 0.555 and RRMSE of 0.165), ELM (with r of 0.724,
NS of 0.601 and RRMSE of 0.151) and MLR (with r of 0.731, NS of 0.550
and RRMSE of 0.091) models presented the best performance in the pooled
scenario and were grouped in the same cluster. The locally trained
models presented higher precision than the models generated with pooled
data; however, the models generated in the pooled scenario could be used
to estimate ET₀ in cases of unavailability of local
meteorological data. Although the MLR, ANN and ELM models, based on
temperature data, are appropriate alternatives to accurately estimate ET₀
in the Verde Grande River basin, southeastern Brazil, the MLR model
presents the advantage of the use of explicit algebraic equations,
facilitating its application.

Soft computing, Artificial neural networks, Multiple linear regression, Extreme learning machines, Artificial intelligence, Meteorological data

https://www.sciencedirect.com/science/article/pii/S0168169919306787?via%3Dihub#bi005

Ano de publicação: 2019

O objetivo deste trabalho foi simular a distribuição geográfica do período de incubação da ferrugem do cafeeiro Coffea arabica, com uso de dados de dois modelos climáticos regionais, o Eta-HadGEM2-ES e o Eta-MIROC5. O cenário de alta emissão de gases de efeito estufa (RCP 8,5 W m-2) foi utilizado para os estados de Minas Gerais e São Paulo, para os cenários climáticos atual e futuro. O comportamento de seis diferentes equações de regressão do período de incubação (PI), disponíveis na literatura, também foi analisado em função dos dados dos modelos climáticos regionais. Os resultados indicam possibilidade de aumento de área afetada na região estudada, com PI inferior a 19 dias, de 0,5% para Eta-MIROC5 a 14,2% para Eta-HadGEM2-ES. A severidade da ferrugem do cafeeiro em cenários futuros deverá aumentar nos meses mais quentes e úmidos do ano, estendendo-se para os meses mais secos e frios. O potencial de infecção da ferrugem é estimado de forma diferente pelas equações estudadas. Em cenários de temperaturas mais elevadas, a equação de Kushalappa & Martins indica um potencial muito alto de severidade.

Coffea arabica, Hemileia vastatrix, Arabica coffee, plant disease severity, regional climate modelling, spatial analyses.

https://www.scielo.br/j/pab/a/NVvRWdRH7Kh5Z33g7YgKqsB/?lang=en

Ano de publicação: 2019

Brazilian cities host 86% of the country’s population and have been more intensely hit by rising temperatures than the average of cities across the world over the last century. Nevertheless, assessments of the vulnerability of Brazilian urban dwellers to urban heat islands (UHI) are scarce. In this study, we take advantage of the availability of high-resolution data to calculate the heat stress vulnerability and risk indexes (HSVI and HSRI, respectively) for people inhabiting six Brazilian metropolitan areas—Manaus, Natal, Vitória, São Paulo, Curitiba, and Porto Alegre. The indexes are calculated by mathematically relating indicators of exposure (distribution of >65-year-old elderly people), sensitivity/adaptive capacity (human development index, HDI), and hazard (surface temperature). The resulting HSVI maps reflect the socioeconomic (HDI) differences found among the studied cities, with the most vulnerable people located in the poorest neighborhoods in Manaus (0.720) and Natal (0.733), distributed among lower- and mid-class zones in São Paulo (0.794) and Vitória (0.772), or invariably located in the wealthy zones of Curitiba (0.783) and Porto Alegre (0.762). Two distinct patterns are identified for the HSRI: in São Paulo, Vitória, Curitiba, and Porto Alegre, high and very high risks are found in the wealthy zones of the cities, whereas in Natal and Manaus, high and very high risks are encountered in the poorly developed city zones, a result that was strongly driven by the UHI pattern. Better communication of heat stress risk and the improvement of city greenness should be the focus of near-term adaptation strategies for the mapped vulnerable population.

Cities, Adaptation, Vulnerability mapping, Brazil, Urban heat island, Climate change. 

https://link.springer.com/article/10.1007/s10584-019-02459-w

Ano de publicação: 2019

Coffee is one of the most consumed beverages in the world, and its international market has been growing for many years. Unfortunately, the Brazilian coffee production is threatened by high temperatures projected by climate change models. We evaluated three schemes of low levels of shade, which avoid the loss of production, as a strategy to adapt coffee to possible climate change. Additionally, as field measurements are expensive and often difficult to implement, we used numerical simulation to complement the evaluation. The microclimate simulator software Envi-met is a computer program often used to simulate urban environments, and we tested it on agriculture design. We verified that the shaded schemes assessed in the field decreased the air temperature in 0.6 °C in the studied period and reduced other possible climate stressors such as wind speed, radiation and raised air humidity in the dry period. Envi-met described the studied meteorological variable cycle very well, showing that combining numerical modelling and field research may be an important tool for planning the adaptation of the coffee sector to possible climate change, allowing growers choose a proper technique for their regions and environmental conditions. Finally, we highlighted the importance of planning the shade scheme on coffee areas in an interdisciplinary approach, including local climate evaluation to achieve a balance between temperature attenuation and production.

Agriculture, 

Environmental science.

https://www.sciencedirect.com/science/article/pii/S2405844018347698?via%3Dihub

Ano de publicação: 2019

Tropical vegetation biomass represents a key component of the carbon stored in global forest ecosystems. Estimates of aboveground biomass commonly rely on measurements of tree size (diameter and height) and then indirectly relate, via allometric relationships and wood density, to biomass sampled from a relatively small number of harvested and weighed trees. Recently, however, novel in situ remote sensing techniques have been proposed, which may provide nondestructive alternative approaches to derive biomass estimates. Nonetheless, we still lack knowledge of the measurement uncertainties, as both the calibration and validation of estimates using different techniques and instruments requires consistent assessment of the underlying errors. To that end, we investigate different approaches estimating the tropical aboveground biomass in situ. We quantify the total and systematic errors among measurements obtained from terrestrial light detection and ranging (LiDAR), hypsometer-based trigonometry, and traditional forest inventory. We show that laser-based estimates of aboveground biomass are in good agreement (<10% measurement uncertainty) with traditional measurements. However, relative uncertainties vary among the allometric equations based on the vegetation parameters used for parameterization. We report the error metrics for measurements of tree diameter and tree height and discuss the consequences for estimated biomass. Despite methodological differences detected in this study, we conclude that laser-based electronic devices could complement conventional measurement techniques, thereby potentially improving estimates of tropical vegetation biomass.

carbon storage; central-eastern Amazonia; forest structure; terra-firme forest; terrestrial laser scanning; light detection and ranging (LiDAR);

https://www.mdpi.com/2072-4292/11/5/510#

Ano de publicação: 2018

The operational challenges of integrated ecosystem service (ES) appraisals are determined by study purpose, system complexity and uncertainty, decision-makers’ requirements for reliability and accuracy of methods, and approaches to stakeholder–science interaction in different decision contexts. To explore these factors we defined an information gap hypothesis, based on a theory of cumulative uncertainty in ES appraisals. When decision context requirements for accuracy and reliability increase, and the expected uncertainty of the ES appraisal methods also increases, the likelihood of methods being used is expected to drop, creating a potential information gap in governance. In order to test this information gap hypothesis, we evaluate 26 case studies and 80 ecosystem services appraisals in a large integrated EU research project. We find some support for a decreasing likelihood of ES appraisal methods coinciding with increasing accuracy and reliability requirements of the decision-support context, and with increasing uncertainty. We do not find that information costs are the explanation for this information gap, but rather that the research project interacted mostly with stakeholders outside the most decision-relevant contexts. The paper discusses how alternative definitions of integrated valuation can lead to different interpretations of decision-support information, and different governance approaches to dealing with uncertainty.

Integrated valuation, 

Ecosystem service appraisal, 

Ecosystem service governance, 

Information costs, 

Uncertainty, 

Valuation, 

Eccosystem services cascade

https://www.sciencedirect.com/science/article/pii/S2212041617300220?via%3Dihub

Ano de publicação: 2018

This work presents sensitivity tests of a model for calculation of rocket effluent dispersion, with respect to the source size. The model employs the Generalized Integral Laplace Transform Technique (GILTT) to solve, analytically, the advection – diffusion equation. By employing different virtual sources, the point source was changed into volume sources with previously defined crosswind radius (0, 10, 25 and 50 m), and the impact of such modification was assessed in terms of the vertical distribution of atmospheric contaminants and the concentration fields close to the surface. The tests were conducted for cases of stable and unstable planetary boundary layer. 

Dispersão de poluentes, Foguetes, [en] GILTT,  [en] Pollution dispersion,  [en] Volume source,

 

https://lume.ufrgs.br/handle/10183/187679

Ano de publicação: 2018

The scientific notion that the Amazon forest could be deeply impacted by climate change through large-scale replacement of the rainforest by a drier forest, a savannah or even nonanalogous degraded vegetation will soon be 20 years old (White et al., 1999; Cox et al., 2000). The mere prospect of undermining a significant fraction of the world's largest tropical rainforest due to global climate change – even if deforestation is completely stopped – should be alarming for the nine Amazonian countries, and the world as a whole, given the bundled ecosystem services at stake. This possibility, however, has not caused widespread concern among governments and societies because the lingering scientific uncertainties prevent any well-informed decisions from being made. The most pressing of these uncertainties regarding the resilience of the Amazon forest to ongoing climatic changes and rising atmospheric CO2 concentrations are as follows:

• the impacts of the future rainfall regime – either drier, wetter or simply more seasonal – on the forest's structure and functioning
• the existence and extent of nutrient – notably phosphorus (P) – limitation on forest productivity
• the existence, magnitude and duration of a supposed CO2 fertilization effect.

Amazon forest, AmazonFACE (Free-Air CO2 Enrichment experiment in the Amazon forest), biomass loss, climate change, drought effects, field experimental data, phosphorus, vegetation models.

https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.15342

Ano de publicação: 2018

O objetivo deste trabalho é avaliar a utilidade dos dados de precipitação do produto orbital TRMM3B42, do satélite TRMM, na elaboração do zoneamento agrícola da soja, cana-de-açúcar e café arábica, para o estado de São Paulo. O período de análise foi 1998-2012. Dados de precipitação pluviométrica de 195 estações de superfície do Departamento de Águas e Energia Elétrica do estado de São Paulo (DAEE-SP) foram considerados como referência para o trabalho. A série de dados pluviométricos remotos foi gerada substituindo-se os dados de cada um dos 195 postos do DAEESP pelos dados da estação virtual mais próxima. As temperaturas médias mensal e anual, a evapotranspiração potencial decendial média e a probabilidade de geadas foram estimadas com base em modelos de regressão, utilizando as coordenadas geográficas (latitude, longitude e altitude) dos 195 postos pluviométricos do DAEESP. A deficiência hídrica anual e o índice de satisfação das necessidades de água (ISNA) foram calculados com base em modelos de simulação do balanço hídrico. Concluiu-se que os dados do produto orbital TRMM3B42 têm potencial para melhorar a quantidade e a distribuição espacial dos dados pluviométricos necessários para definição do zoneamento agrícola de culturas perenes e semiperenes, como a canade-açúcar e o café arábica. No caso de culturas anuais, como a soja, são necessárias adaptações no método de zoneamento devido ao impacto provocado pela tendência de superestimativa da precipitação pelo sensoriamento remoto.

zoneamento agrícola, sensoriamento remoto, geoprocessamento.

https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1103170/comparacao-do-risco-climatico-da-soja-cana-de-acucar-e-cafe-arabica-para-o-estado-de-sao-paulo-calculado-com-dados-terrestres-e-orbitai

Ano de publicação: 2018

The Ecosystem Services (ES) concept highlights the varied contributions the environment provides to humans and there are a wide range of methods/tools available to assess ES. However, in real-world decision contexts a single tool is rarely sufficient and methods must be combined to meet practitioner needs. Here, results from the OpenNESS project are presented to illustrate the methods selected to meet the needs of 24 real-world case studies and better understand why and how methods are combined to meet practical needs. Results showed that within the cases methods were combined to: i) address a range of ES; ii) assess both supply and demand of ES; iii) assess a range of value types; iv) reach different stakeholder groups v) cover weaknesses in other methods used and vi) to meet specific decision context needs. Methods were linked in a variety of ways: i) as input–output chains of methods; ii) through learning; iii) through method development and iv) through comparison/triangulation of results. The paper synthesises these case study-based experiences to provide insight to others working in practical contexts as to where, and in what contexts, different methods can be combined and how this can add value to case study analyses.

David M. Lapola, Ecosystem Services 

https://www.sciencedirect.com/science/article/pii/S2212041617300190?via%3Dihub

Ano de publicação: 2018

Large uncertainties still dominate the hypothesis of an abrupt large-scale shift of the Amazon forest caused by climate change [Amazonian forest dieback (AFD)] even though observational evidence shows the forest and regional climate changing. Here, we assess whether mitigation or adaptation action should be taken now, later, or not at all in light of such uncertainties. No action/later action would result in major social impacts that may influence migration to large Amazonian cities through a causal chain of climate change and forest degradation leading to lower river-water levels that affect transportation, food security, and health. Net-present value socioeconomic damage over a 30-year period after AFD is estimated between US dollar (USD) $957 billion (×109) and $3,589 billion (compared with Gross Brazilian Amazon Product of USD $150 billion per year), arising primarily from changes in the provision of ecosystem services. Costs of acting now would be one to two orders of magnitude lower than economic damages. However, while AFD mitigation alternatives—e.g., curbing deforestation—are attainable (USD $64 billion), their efficacy in achieving a forest resilience that prevents AFD is uncertain. Concurrently, a proposed set of 20 adaptation measures is also attainable (USD $122 billion) and could bring benefits even if AFD never occurs. An interdisciplinary research agenda to fill lingering knowledge gaps and constrain the risk of AFD should focus on developing sound experimental and modeling evidence regarding its likelihood, integrated with socioeconomic assessments to anticipate its impacts and evaluate the feasibility and efficacy of mitigation/adaptation options. 

1. BIOLOGICAL SCIENCES, ENVIRONMENTAL SCIENCES, SOCIAL SCIENCES, SUSTAINABILITY SCIENCE 

https://www.pnas.org/doi/full/10.1073/pnas.1721770115

Ano de publicação: 2018

Ecosystem service (ES) spatial modelling is a key component of the integrated assessments designed to support policies and management practices aiming at environmental sustainability. ESTIMAP (“Ecosystem Service Mapping Tool”) is a collection of spatially explicit models, originally developed to support policies at a European scale. We based our analysis on 10 case studies, and 3 ES models. Each case study applied at least one model at a local scale. We analyzed the applications with respect to: the adaptation process; the “precision differential” which we define as the variation generated in the model between the degree of spatial variation within the spatial distribution of ES and what the model captures; the stakeholders’ opinions on the usefulness of models. We propose a protocol for adapting ESTIMAP to the local conditions. We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs. We then present the opinion of stakeholders; that in general considered the approach useful for stimulating discussion and supporting communication. Major constraints identified were the lack of spatial data with sufficient level of detail, and the level of expertise needed to set up and compute the models.

David M. Lapola

https://www.sciencedirect.com/science/article/pii/S2212041617302358?via%3Dihub

Ano de publicação: 2018

The Brazilian sugar-energy sector (SES) is facing an increasing challenge due to the worldwide expansion of biofuel consumption as a strategy to reduce greenhouse gas emissions, making yield improvement and land and water availability key factors in addressing this situation. Consequently, our main aim here is to identify SES vulnerability under climate change conditions, based on the methodology used by the Agricultural Zoning of Climatic Risks (ZARC) program. We assessed changes of the sugarcane ZARC in light of the current and near-future climatic conditions given by eight general circulation models (GCM) of the 5th IPCC report and under the representative concentration pathway (RCP) 8.5. We identified a conversion of the current climatic risk levels in the Brazilian central-southern macro-region for sugarcane in future climate change scenarios, but the spatial distribution of these changes is heterogeneous. The current expansion areas located mainly in southern Goiás and northwest of São Paulo are marked by an increase in areas of low water availability in the future, while the traditional production areas, east of São Paulo, do not present this same vulnerability. Sugarcane cultivation in the south of Goiás is already occurring based on a complementary irrigation (of about 50 mm per month from April to September) to reach a yield similar to traditional areas located in São Paulo state. The development of drought-resistant cultivars based on genetic engineering and the efficiency of the irrigation systems used on a large spatial scale and also in the long term are two key points of concern in the Brazilian context of greenhouse gas emission mitigation. The challenges for the traditional production regions are related to the production system’s ability to regulate the capacity and idleness of sugarcane mills aiming to avoid current and future competition by same raw material.

Sugar-energy, Brazilian central-southern macro-region, Climatic Change

https://link.springer.com/article/10.1007/s10584-018-2249-4

Ano de publicação: 2017

Brazil is home to the largest tracts of tropical vegetation in the world, harbouring high levels of biodiversity and carbon. Several biomass maps have been produced for Brazil, using different approaches and methods, and for different purposes. These maps have been used to estimate historic, recent, and future carbon emissions from land use change (LUC). It can be difficult to determine which map to use for what purpose. The implications of using an unsuitable map can be significant, since the maps have large differences, both in terms of total carbon storage and its spatial distribution. This paper presents comparisons of Brazil's new ‘official’ carbon map; that is, the map used in the third national communication to the UNFCCC in 2016, with the former official map, and four carbon maps from the scientific literature. General strengths and weaknesses of the different maps are identified, including their suitability for different types of studies. No carbon map was found suitable for studies concerned with existing land use/cover (LULC) and LUC outside of existing forests, partly because they do not represent the current LULC sufficiently well, and partly because they generally overestimate carbon values for agricultural land. A new map of aboveground carbon is presented, which was created based on data from existing maps and an up‐to‐date LULC map. This new map reflects current LULC, has high accuracy and resolution (50 m), and a national coverage. It can be a useful alternative for scientific studies and policy initiatives concerned with existing LULC and LUC outside of existing forests, especially at local scales when high resolution is necessary, and/or outside the Amazon biome. We identify five ongoing climate policy initiatives in Brazil that can benefit from using this map.

Brazil; carbon map; GIS; aboveground biomass; land use policy

https://onlinelibrary.wiley.com/doi/abs/10.1002/geo2.45

Ano de publicação: 2017

The intensification of drought incidence is one of the most important threats of the 21st century with significant effects on food security. Accordingly, there is a need to improve the understanding of the regional impacts of climate change on this hazard. This study assessed long-term trends in probability-based drought indices (Standardized Precipitation Index and Standardized Evapotranspiration Index) in the State of São Paulo, Brazil. Owing to the multi-scalar nature of both indices, the analyses were performed at 1 to 12-month time scales. The indices were calculated by means of a relativist approach that allowed us to compare drought conditions from different periods. The years 1961-1990 were used as the referential period. To the authors’ best knowledge, this is the first time that such relativist approach is used in historical trend analysis. The results suggest that the evapotranspiration rates have intensified the regional drought conditions. The time scale used to calculate the indices significantly affected the outcomes of drought trend assessments. The reason behind this feature is that the significant changes in the monthly regional patterns are limited to a specific period of the year. More specifically, virtually all significant changes have been observed during the first trimester of the rainy season (October, November and December). Considering that this period corresponds to critical plant growth stages (flowering/regrowth/sprouting) of several major crops (e.g. Sugarcane and Citrus), we may conclude that these significant changes have increased the risk of crop yield reductions due to agricultural drought.

intensification of drought; drought Index; agricultural drought; crop yields

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052018000100201&lng=en&tlng=en

Ano de publicação: 2017

Enhancing the capability of both standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for quantifying wet and dry events under distinct climate conditions is of paramount importance. The different recommendations of recent studies regarding the best distribution to calculate the SPEI and the lack of studies addressing the effect of different parameters estimation methods on the SPI motivated us to apply and adapt distinct testing methodologies to select candidate models for calculating these standardized drought indices (SDI). The study is based on two data sets. The first represents a tropical–subtropical region of Brazil. The second comprises the same weather stations that were used for developing the original version of the SPEI. The study also emphasized the performance of the models within the range of typical SDI values [−2.0 : 2.0]. Along with goodness‐of‐fit tests, we calculated the mean absolute errors between the indices values estimated from the candidate distributions, and their corresponding theoretical values derived from the standard normal distribution. The two‐parameter gamma and the generalized extreme value distributions are, respectively, recommended for general use in SPI and SPEI algorithms (1–12‐month timescales). The unbiased probability weighted moments are recommended to estimate the distributions parameters. The study also described a trade‐off between choosing the best model for the central part and for the tails of the distributions. This trade‐off suggests that the methodologies used to select models for the SDI algorithms may have to decide which part of the distribution (central or tails) should be emphasized. The behaviour of the errors among different wet/dry categories showed that both indices were only capable of representing drought and floods in a similar probabilistic way within the range [−2.0 : 2.0]. This feature supports our decision to emphasize model performances within such range.

drought; standardized precipitation index; standardized precipitation evapotranspiration index; trade-off; param-eter estimation

https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.5381

Ano de publicação: 2016

The impacts of elevated atmospheric CO2 (eCO2) and alterations in nutrient availability on the carbon (C) storage capacity and resilience of the Amazon forest remain highly uncertain. Carbon dynamics are controlled by multiple eco-physiological processes responding to environmental change, but we lack solid experimental evidence, hampering theory development and thus representation in ecosystem models. Here, we present two ecosystem-scale manipulation experiments, to be carried out in the Amazon, that examine tropical ecosystem responses to eCO2 and alterations in nutrient availability and thus will elucidate the representation of crucial ecological processes by ecosystem models. We highlight current gaps in our understanding of tropical ecosystem responses to projected global changes in light of the eco-physiological assumptions considered by current ecosystem models. We conclude that a more detailed process-based representation of the spatial (e.g., soil type; plant functional type) and temporal (seasonal and inter-annual) variability of tropical forests is needed to enhance model predictions of ecosystem responses to projected global environmental change.

Amazon, carbon allocation, elevated CO2, free-air CO2 enrichment (FACE), nutrient addition, tropical forest

https://www.frontiersin.org/articles/10.3389/feart.2016.00019/full

Ano de publicação: 2016

The first generation of forest free‐air CO2 enrichment (FACE) experiments has successfully provided deeper understanding about how forests respond to an increasing CO2 concentration in the atmosphere. Located in aggrading stands in the temperate zone, they have provided a strong foundation for testing critical assumptions in terrestrial biosphere models that are being used to project future interactions between forest productivity and the atmosphere, despite the limited inference space of these experiments with regards to the range of global ecosystems. Now, a new generation of FACE experiments in mature forests in different biomes and over a wide range of climate space and biodiversity will significantly expand the inference space. These new experiments are: EucFACE in a mature Eucalyptus stand on highly weathered soil in subtropical Australia; AmazonFACE in a highly diverse, primary rainforest in Brazil; BIFoR‐FACE in a 150‐yr‐old deciduous woodland stand in central England; and SwedFACE proposed in a hemiboreal, Pinus sylvestris stand in Sweden. We now have a unique opportunity to initiate a model–data interaction as an integral part of experimental design and to address a set of cross‐site science questions on topics including responses of mature forests; interactions with temperature, water stress, and phosphorus limitation; and the influence of biodiversity.

biodiversity; climate; elevated CO2;  forest; free‐air CO2 enrichment(FACE); model–data synthesis; nitrogen (N); phosphorus (P)

https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.13593

Ano de publicação: 2016

In this paper the initial draft design of a payment for ecosystem services (PES) scheme in a municipality within the sugarcane belt of São Paulo state, Brazil (PES-RC), is compared with prevailing characteristics of successful PES cases in Latin America (PES-LA). This systematic comparison is performed by analyzing four major characteristics of PES: identity of traded ecosystem service (ES); spatial scale; type of transaction involved between ES providers and beneficiaries; and the involved actors. Information on the biophysical characteristics, institutional arrangement and financial options of PES-RC were assessed using participatory methods. We found that on the one hand there is an agreement between our case study and the prevailing successful cases of PES-LA regarding the traded ES (water) and the PES spatial scale (local). However, stakeholder opinions diverge from the success cases when it comes to the type of transaction (cash preferred in PES-RC; in-kind in successful PES-LA) and the involved actors. Our results raise the question whether stakeholder opinions or the characteristics of successful (or failure) cases should be prioritized when planning and operationalizing new PES schemes. We argue that stakeholder participation should be considered as an additional success criterion for the construction of public policies directed towards PES implementation.

Atlantic ForestBrazilian Forest CodeNature conservationPublic policiesRio Claro - SP municipalityParticipatory methods

https://www.sciencedirect.com/science/article/pii/S2212041616301176?via%3Dihub

Ano de publicação: 2016

This paper updates the SCVI (Socio-Climatic Vulnerability Index) maps developed by Torres et al. (2012) for Brazil, by using the new Coupled Model Intercomparison Project Phase 5 (CMIP5) projections and more recent 2010 social indicators data. The updated maps differ significantly from their earlier versions in two main ways. First, they show that heavily populated metropolitan areas – namely Belo Horizonte, Brasília, Salvador, Manaus, Rio de Janeiro and São Paulo – and a large swath of land across the states of São Paulo, Minas Gerais and Bahia now have the highest SCVI values, that is, their populations are the most vulnerable to climate change in the country. Second, SCVI values for Northeast Brazil are considerably lower compared to the previous index version. An analysis of the causes of such difference reveals that changes in climate projections between CMIP3 and CMIP5 are responsible for most of the change between the different SCVI values and spatial distribution, while changes in social indicators have less influence, despite recent countrywide improvements in social indicators as a result of aggressive anti-poverty programs. These results raise the hypothesis that social reform alone may not be enough to decrease people’s vulnerability to future climatic changes. Whereas the coarse spatial resolution and relatively simplistic formulation of the SCVI may limit how useful these maps are at informing decision-making at the local level, they can provide a valuable input for large-scale policies on climate change adaptation such as those of the Brazilian National Policy on Climate Change Adaptation.

Adaptive Capacity Social Indicator Climate Change Adaptation Climate Projection Social Vulnerability 

https://link.springer.com/article/10.1007%2Fs10584-016-1635-z

Ano de publicação: 2015

In this paper, soil carbon, nitrogen and phosphorus concentrations and stocks were investigated in agricultural and natural areas in 17 plot-level paired sites and in a regional survey encompassing more than 100 pasture soils In the paired sites, elemental soil concentrations and stocks were determined in native vegetation (forests and savannas), pastures and crop–livestock systems (CPSs). Nutrient stocks were calculated for the soil depth intervals 0–10, 0–30, and 0–60 cm for the paired sites and 0–10, and 0–30 cm for the pasture regional survey by sum stocks obtained in each sampling intervals (0–5, 5–10, 10–20, 20–30, 30–40, 40–60 cm). Overall, there were significant differences in soil element concentrations and ratios between different land uses, especially in the surface soil layers.

soil carbon; nitrogen; phosphorus

https://www.biogeosciences.net/12/4765/2015/bg-12-4765-2015.pdf

Ano de publicação: 2015

Leaf area index (LAI) and above ground biomass (AGB) are two parameters that are difficult to measure but very useful. In this paper we investigated the relationship between coffee biophysical properties and LAI and AGB in two coffee production systems: full sun (FS) and shaded with macadamia nuts (SH). The paper proposes an empirical relationship for calculating coffee AGB and coffee LAI which avoids destructive methods, using simple field measurements and agrometeorological data. Here, we reported that LAI is related to canopy structure but subject to strong seasonal variations, which can be identified using water requirements satisfaction index (WRSI). Coffee LAI answers to the decreased WRSI with 1 month lag (WRSI-1) and LAI values decreases more for FS systems than for SH systems during dry periods. The best empirical model to predict LAI for FS coffee production system was based on canopy height (ch) and WRSI-1 value. For SH systems, the best model used ch, WRSI-1 and the height of the first pair of branches. Coffee AGB values were measured using destructive analyses and an empirical equation was developed. Both coffee production systems stocked carbon, whereas the SH system stocked an increased carbon amount provided by the macadamia trees that contributed with 15 % of the total carbon above ground. Both systems can be considered mitigation techniques since they are able to remove atmospheric carbon and stock it in the biomass, which has been largely proposed as a compensation mechanism for greenhouse gas emissions.

Carbon stock; Coffee shaded system; Empirical equations; Seasonal LAI variation 

https://link.springer.com/article/10.1007%2Fs10457-015-9799-5

Ano de publicação: 2014

The aim of this study was to model, based on the overall distribution of extreme values, the probability of occurrence of a particular level of annual maximum daily rainfall in three Brazilian regions (Midwest, Southeast and South) and study their behavior over the past 71 years. The parameters of the general distribution of extreme values were estimated by the maximum likelihood method. The Mann–Kendall test showed that there is a positive trend in the annual maximum daily rainfall data series. The non-stationarity was rejected by the augmented Dickey–Fuller test supporting the use of the density function of extreme value distribution to describe the values of the occurrence of annual maximum daily rainfall. The Kolmogorov–Smirnov/Lilliefors goodness-of-fit test showed the good fit of the studied variable to the probability distribution function. The Midwest region has a return period of more frequent annual maximum daily rainfall below 300 mm in comparison with other regions. There is a clear change in the behavior of this extreme event in the Southern region. According to the literature, in past decades annual maximum daily rainfall of 248 mm has been estimated for a return period of 100 years for the state of Santa Catarina-South region, while the results found with the current series, annual maximum daily rainfall of 250 mm was estimated for a return period of 10 years. Extreme annual maximum daily rainfalls for return periods smaller were also found in other regions.

Climate changeClimate periodRainfall variability

https://www.sciencedirect.com/science/article/pii/S2212094714000802?via%3Dihub

Ano de publicação: 2014

This case-study article presents the results from a morphology analysis of a knowledge and information network, focusing on the coordination mechanisms employed to generate a convergent arrangement. Agritempo was the first information system to offer (in 2003) free access to a broad range of agrometeorological data comprising all the Brazilian territory, representing an important technological innovation to the agricultural sector. To study this phenomenon an analytical framework of the Techno-Economic Network (TEN) and concepts from the Innovation Sociology field was employed. Results indicate that the durability of this arrangement - from 2003 to 2014 - can be explained by the effectiveness of the coordination strategies established in the network such as: trust based relationships; institutional and individual leadership actions; contracting; software applications and shared common working procedures.

techno-economics networks; innovation; agrometeorology; information and communication technology.

https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-27242014000100010&lng=en&nrm=iso&tlng=en

Ano de publicação: 2014

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top- outlier images, and the  images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Low-labor labelingSummarizationOutlier detectionQuery by exampleClusteringSatellite imagery

https://www.sciencedirect.com/science/article/pii/S0020025513008001?via%3Dihub

Ano de publicação: 2013

In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts

Time series analysis, Data mining, Remote sensing, Meteorology, Indexes, Satellites, Agriculture

http://ieeexplore.ieee.org/document/6215038/

Ano de publicação: 2013

In this paper we calculated soil carbon stocks in Brazil studying 17 paired sites where soil stocks were determined in native vegetation, pastures and crop-livestock systems (CPS), and in other regional samplings encompassing more than 100 pasture soils, from 6.58 to 31.53° S, involving three major Brazilian biomes: Cerrado, Atlantic Forest, and the Pampa. The average native vegetation soil carbon stocks at 10, 30 and 60 cm soil depth were equal to approximately 29, 64, and 92 Mg ha−1, respectively. In the paired sites, carbon losses of 7.5 Mg ha−1 and 11.6 Mg ha−1 in CPS systems were observed at 10 cm and 30 cm soil depths, respectively. In pasture soils, carbon losses were similar and equal to 7.5 Mg ha−1 and 11.0 Mg ha−1 at 10 cm and 30 cm soil depths, respectively. Differences at 60 cm soil depth were not significantly different between land uses. The average soil δ13C under native vegetation at 10 and 30 cm depth were equal to −25.4‰ and −24.0‰, increasing to −19.6‰ and −17.7‰ in CPS, and to −18.9‰, and −18.3‰ in pasture soils, respectively; indicating an increasing contribution of C4 carbon in these agrosystems. In the regional survey of pasture soils, the soil carbon stock at 30 cm was equal to approximately 51 Mg ha−1, with an average δ13C value of −19.67‰. Key controllers of soil carbon stock in pasture sites were sand content and mean annual temperature. Collectively, both could explain approximately half of the variance of soil carbon stocks. When pasture soil carbon stocks were compared with the average soil carbon stocks of native vegetation estimated for Brazilian biomes and soil types by Bernoux et al. (2002) there was a carbon gain of 6.7 Mg ha−1, which is equivalent to a carbon gain of 15% compared to the carbon soil stock of the native vegetation. The findings of this study are consistent with differences found between regional comparisons like our pasture sites and plot-level paired study sites in estimating soil carbon stocks changes due to land use changes.

carbon stocks

https://www.biogeosciences.net/10/6141/2013/

Ano de publicação: 2013

Coffee is one of the most important crops in Brazil. Monitoring the crop is necessary to understand future production and a sound understanding of coffee's biophysical properties improves such monitoring. Biophysical properties such as dry biomass can be estimated using remote sensing, including the new generation of high-resolution images (GeoEye-1, for instance). In this study we aim to investigate the relationship between vegetation indices (VI) of high-resolution images (GeoEye-1) and coffee biophysical properties, including dry biomass and carbon. The study also aims at establishing an empirical relationship between remote sensing data (vegetation indices), simple field measurements and dry biomass, allowing calculation of coffee biomass and carbon without resorting to destructive methods. Individual GeoEye-1 satellite's bands (NIR, RED and GREEN) showed significant correlation with biomass, but the best correlation occurred with vegetation index. There is a strong correlation between NDVI, RVI, GNDVI and dry biomass, allowing the estimation of coffee crops' carbon stock. RVI had correlation with plant area index (PAI). The empirical correlation was established and the forecast equation of coffee biomass was created.

Correlation, Biomass, Vegetation mapping, Carbon, Indexes, Remote sensing, Agriculture

http://ieeexplore.ieee.org/document/6520004/

Ano de publicação: 2013

The aim of this study was to model the occurrence of extreme dry spells in the Midwest, Southeast and Southern regions of Brazil and estimate the return period of the phenomenon indicating the time when the occurrence is more severe. The generalized extreme value distribution was the best fit for a series of maximum dry spell number and the parameters estimated by the maximum likelihood method. The data series adherence to the probability distribution was verified by the Kolmogorov–Smirnov test and the percentile–percentile charts. The positive trend of dry spells was verified by the Mann–Kendall test and non-stationarity rejected by Dickey–Fuller and augmented Dickey–Fuller tests. The irregular distribution of rainfall in the growing season for the Midwest region has increased the number of dry spells. The increase of rainy days in the Southeast and the South resulted in a decrease of dry spells in these regions. Regarding the return period of one year, dry spells occurred from 5 to 25 days in the Midwest region meaning a loss of productivity for Brazilian agriculture if it happened between the flowering and grain filling phases, making it, therefore the region with the largest agricultural risk. When the intensity of the dry spells was analyzed for different return periods, the Southern region was the most vulnerable.

Dry spellsGeneralized extreme value distributionNumber of rainy daysReturn period

https://www.sciencedirect.com/science/article/pii/S016980951300118X?via%3Dihub

Ano de publicação: 2013

The objective of this work was to evaluate the effect of the temperature increase forecasted by the Intergovernmental Panel on Climate Change (IPCC) on agricultural zoning of cotton production in Brazil. The Northeastern region showed the highest decrease in the low-risk area for cotton cultivation due to the projected temperature increase. This area in the Brazilian Northeast may decrease from 83 million ha in 2010 to approximately 71 million ha in 2040, which means 15% reduction in 30 years. Southeastern and Center-Western regions had small decrease in areas suitable for cotton production until 2040, while the Northern region showed no reduction in these areas. Temperature increase will not benefit cotton cultivation in Brazil because dimension of low-risk areas for economic cotton production may decrease.

Gossypium hirsutum, evapotranspiration, global warming

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2013000100001&lng=en&tlng=en

Ano de publicação: 2012

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international marketplaces. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Sugarcane

https://www.tandfonline.com/doi/full/10.1080/01431161.2011.638334

Ano de publicação: 2011

O Brasil, o maior produtor mundial de cana-de-açúcar, é responsável por 35% da produção mundial. A produção de cana-de-açúcar se concentra nas regiões Centro-Sul e Nordeste e ocupa cerca de 8 milhões de hectares. A cana-de-açúcar, por apresentar um ciclo semi-perene, é influenciada pela variação das condições meteorológicas durante um ano inteiro. O objetivo do trabalho foi analisar os dados climáticos obtidos pelo modelo Eta (2011 a 2090) e os dados do clima atual (1991 a 2010) verificando suas implicações em relação a expansão da cana-de-açúcar no estado de São Paulo. Foram utilizados dados de precipitação e temperatura média, de 1991 a 2010, de estações meteorológicas de seis municípios de São Paulo. Para representar o cenário futuro (2011 a 2090), foram utilizados dados de precipitação e temperatura média obtidos pelo modelo Eta. A partir do balanço hídrico foi possível calcular a deficiência hídrica e o excedente hídrico para os municípios selecionados com armazenamento de água disponível no solo de 100mm. O balanço hídrico mostrou que haverá um aumento na deficiência hídrica. Com o aumento do período seco e do aumento da temperatura média poderá ocorrer uma queda na produtividade de sacarose da cana-de-açúcar.

water balance, Eta model, mean temperature

analise-comparativa-do-clima-atual-e-futuro-para-avaliar-a-expansao-da-cana-de-acucar-em-sao-paulo-02061715.pdf

http://www.cpa.unicamp.br/alcscens/docs/publicacoes/Analise%20comparativa%20do%20clima%20atual%20e%20futuro%20para%20avaliar%20a%20expansao%20da%20cana-de-acucar%20em%20Sao%20Paulo%20.pdf

Ano de publicação: 2011

Extremos de temperatura durante a fase do florescimento do café arábica causam abortamento de flores e perda na produção. Estudos de projeção climática, normalmente baseados em modelos de baixa resolução, já demonstram perdas na produção de café como conseqüência das altas temperaturas. No entanto, esses estudos têm uma menor precisão nas respostas por causa da resolução dos modelos, e novos estudos baseados em modelos de melhor resolução se tornam necessários. Este trabalho apresenta uma análise das implicações dos extremos de temperatura e precipitação na aptidão climática em municípios tradicionalmente produtores de café em Minas Gerais e São Paulo, utilizando o modelo climático Eta (40km de resolução). Primeiramente, as respostas do modelo foram comparadas com os dados climáticos atuais (dados observados). As projeções do modelo Eta foram satisfatórias porque seguem o padrão do clima já existente. Os dados do modelo demonstram um deslocamento do maior déficit hídrico do ano para o mês de setembro. O modelo projeta cenários com aumento de temperaturas, principalmente nos meses de setembro e outubro, que é o florescimento do café, impactando a produção de café arábica.

Aptidão climática, Café arábica, Eta

analise-dos-dados-de-projecao-climatica-do-modelo-eta-e-suas-implicacoes-para-a-cultura-do-cafe-arabica-02061715.pdf

Ano de publicação: 2011

O objetivo deste trabalho foi detectar municípios produtores de cana-de-açúcar no estado de São Paulo, similares, pelo método de agrupamento, analisando variáveis espectrais (NDVI), área plantada e produtividade no período de 2001 a 2009. O resultado dessa análise exploratória dos dados mostrou que a técnica é apropriada para a determinação de grupos de municípios com características semelhantes o que permite classificar as regiões automaticamente.

remote sensing, K-means biofuel

analise-temporal-de-municipios-produtores-cana-de-acucar-no-estado-de-sao-paulo-02061715.pdf

Ano de publicação: 2011

Risk analysis of climate change on plant diseases has great importance for agriculture since it allows the evaluation of management strategies to minimize future damages. This work aimed to simulate future scenarios of coffee rust (Hemileia vastatrix) epidemics by elaborating geographic distribution maps using a model that estimates the pathogen incubation period and the output from three General Circulation Models (CSIRO-Mk3.0, INM-CM3.0, and MIROC3.2.medres). The climatological normal from 1961-1990 was compared with that of the decades 2020s, 2050s and 2080s using scenarios A2 and B1 from the IPCC. Maps were prepared with a spatial resolution of 0.5 × 0.5 degrees of latitude and longitude for ten producing states in Brazil. The climate variables used were maximum and minimum monthly temperatures. The maps obtained in scenario A2 showed a tendency towards a reduction in the incubation period when future scenarios are compared with the climatological normal from 1961-1990. A reduction in the period was also observed in scenario B1, although smaller than that in scenario A2.

Coffea arabica, coffee rust, future scenarios, global warming

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-54052011000200001&lng=en&tlng=en

Ano de publicação: 2011

Agriculture appears to be one of the human activities most vulnerable to climatic changes due to its large dependence on environmental conditions. However, the diversity of Brazilian environmental conditions could be of great advantage to adapting this sector to new climatic conditions, which should be assessed as in this study on shifting Arabica coffee cultivation to the extreme south of the country. The methodology applied is the same the one used to define climatic risks in current productive regions of Brazil and their vulnerability to climatic change predicted by IPCC reports. The basic climatic parameters applied were frost probability and annual average temperature, since annual water deficit did not prove to be a restricting factor for Arabica coffee cultivation in the study area. The climatic conditions suitable for coffee production are: annual average temperature between 18°C and 22°C, annual water deficit less than 100 mm and frost probability (risk of lowest annual temperature less than 1°C) less than 25%. An area is said to have “low climatic risks” for coffee production when these three climatic conditions are met. Current climatic conditions were used and simulations of four temperature increases between 1°C and 4°C were also performed. The results indicated a substantial increase in the size of low climatic risks areas for the production of Arabica coffee in the extreme south of Brazil, mainly for mean temperature increases of 3°C in the study area in relation to present conditions. Increases of 2°C and 4°C were also favorable, but not as good as those obtained for 3°C. It should be underscored that areas with low climatic risks will be able to be found mainly in the extreme south of the study region, the border with Uruguay and North of Argentina.

Global Warming; Climatic Risk; Coffee Production; Current Climatic Condition; Arabica Coffee 

https://link.springer.com/article/10.1007%2Fs10584-011-0058-0

Ano de publicação: 2010

As condições meteorológicas são determinantes para a produção agrícola; a precipitação, em particular, pode ser citada como a mais influente por sua relação direta com o balanço hídrico. Neste sentido, modelos agrometeorológicos, os quais se baseiam nas respostas das culturas às condições meteorológicas, vêm sendo cada vez mais utilizados para a estimativa de rendimentos agrícolas. Devido às dificuldades de obtenção de dados para abastecer tais modelos, métodos de estimativa de precipitação utilizando imagens dos canais espectrais dos satélites meteorológicos têm sido empregados para esta finalidade. O presente trabalho tem por objetivo utilizar o classificador de padrões "floresta de caminhos ótimos" para correlacionar informações disponíveis no canal espectral infravermelho do satélite meteorológico GOES-12 com a refletividade obtida pelo radar do IPMET/UNESP localizado no município de Bauru, visando o desenvolvimento de um modelo para a detecção de ocorrência de precipitação. Nos experimentos foram comparados quatro algoritmos de classificação: redes neurais artificiais (ANN), k-vizinhos mais próximos (k-NN), máquinas de vetores de suporte (SVM) e floresta de caminhos ótimos (OPF). Este último obteve melhor resultado, tanto em eficiência quanto em precisão.

Classificadores Supervisionados, Floresta de Caminhos Ótimos, GOES, Estimativa de Ocorrência de Precipitação

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862010000100002&lng=pt&tlng=pt

Ano de publicação: 2010

Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Paraná, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information.

Weather Forecast; Productivity Estimate; Ensemble Forecast; Forecast Period; Crop Cycle 

https://link.springer.com/article/10.1007%2Fs00704-010-0264-0

Ano de publicação: 2010

Para obter a refletância real da superfície, nas bandas 1 e 2 do sensor AVHRR, foi realizada a correção atmosférica, baseada na entrada dos parâmetros atmosféricos fornecidos pelo sensor MODIS/Terra. A utilização de dados do MODIS está diretamente relacionada à obtenção das informações, necessárias para a correção atmosférica, considerando-se a variabilidade dos parâmetros no tempo e no espaço. Utilizando-se o aplicativo SCORADIS, baseado no modelo de transferência radiativa 5S, foi proposta uma adaptação que possibilitasse a entrada das imagens correspondentes aos planos atmosféricos, através da utilização de metodologias distintas de correção atmosférica. As análises indicaram que as correções apresentaram resultados coerentes com eliminação dos efeitos de espalhamento e de absorção atmosférica. Foi avaliada a magnitude desses efeitos sobre o índice de vegetação NDVI, muito utilizado em estudos agrometeorológicos. A diferença percentual entre as imagens com e sem correção chegou a ser de aproximadamente 60 a 80% para as datas analisadas, ressaltando a importância da correção atmosférica dessas imagens.

efeitos atmosféricos, SCORADIS, MODIS

http://www.agraria.pro.br/ojs-2.4.6/index.php?journal=agraria&page=article&op=view&path%5B%5D=agraria_v5i2a530

Ano de publicação: 2009

The decision correctness in expert systems strongly depends on the accuracy of a pattern classifier, whose learning is performed from labeled training samples. Some systems, however, have to manage, store, and process a large amount of data, making also the computational efficiency of the classifier an important requirement. Examples are expert systems based on image analysis for medical diagnosis and weather forecasting. The learning time of any pattern classifier increases with the training set size, and this might be necessary to improve accuracy. However, the problem is more critical for some popular methods, such as artificial neural networks and support vector machines (SVM), than for a recently proposed approach, the optimum-path forest (OPF) classifier. In this letter, we go beyond by presenting a robust approach to reduce the training set size and still preserve good accuracy in OPF classification. We validate the method using some data sets and for rainfall occurrence estimation based on satellite image analysis. The experiments use SVM and OPF without pruning of training patterns as baselines.

Robustness, Management training, Support vector machines, Support vector machine classification, Image analysis, Expert systems, Computational efficiency, Medical expert systems, Diagnostic expert systems, Medical diagnosis

http://ieeexplore.ieee.org/document/5357415/

Ano de publicação: 2008

O conhecimento dos prováveis impactos das mudanças climáticas globais sobre a ocorrência de doenças de plantas é de grande importância para o setor agrícola, pois permite a elaboração de estratégias de controle. O presente trabalho teve por finalidade estudar os possíveis impactos das mudanças climáticas sobre a sigatoka-negra da bananeira, por meio da elaboração de mapas de distribuição da doença confeccionados a partir dos cenários disponibilizados pelo IPCC. Os mapas mostraram que haverá redução da área favorável à doença no país. Tal redução será gradativa para as décadas de 2020, 2050 e 2080 e de forma mais acentuada no cenário A2 que no B2. Apesar disso, extensas áreas ainda continuarão favoráveis à ocorrência da doença, especialmente no período de novembro a abril.

Mycosphaerella fijiensis, Musa spp., banana, plátano

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-41582007000300003&lng=pt&tlng=pt

Ano de publicação: 2008

O Brasil é o maior produtor mundial de cana-de-açúcar e conta com uma posição privilegiada para atender as necessidades mundiais de açúcar e álcool combustível. O país possui várias regiões produtoras, com safras alternadas, que podem garantir a presença do seu produto no mercado mundial, sendo o Estado de São Paulo a maior delas. Para tanto, é necessário estimar a produção da cana-de-açúcar com a maior precisão e antecipação possíveis, sendo que o sensoriamento remoto pode ser de grande utilidade nesse caso, devido à maior disponibilidade atual de dados e ao conhecimento já existente na utilização deles na área ambiental. Sendo o clima um dos principais fatores determinantes da produção agrícola, o conhecimento da sua infl uência na resposta espectral da vegetação pode ser de grande utilidade no apoio ao desenvolvimento de sistemas operacionais de monitoramento e previsão de safras da cana-de-açúcar. Sendo assim, este trabalho teve o objetivo principal de avaliar o grau de correlação existente entre a resposta espectral de plantios de cana-de-açúcar, expressa através de uma série temporal de valores do Índice de Vegetação da Diferença Normalizada (NDVI), determinada a partir de imagens do satélite AVHRR/NOAA, com dados agroclimáticos, em dez municípios localizados no Estado de São Paulo, no período de 2001 a 2007. Avaliou-se a série temporal de imagens como um todo, além de cada ano-safra, em particular, considerando a maior disponibilidade atual de dados de sensoriamento remoto orbital e a variabilidade climática existente ao longo dos seis anos-safra utilizados. Os perfi s temporais do índice NDVI foram obtidos através do processamento automático das imagens disponíveis que consistiu nas correções radiométrica e geométrica e no cálculo de imagens compostas, contendo os valores máximos mensais do NDVI. Dados agroclimáticos, ao longo do período de análise, foram descritos pelo índice ISNA (Índice de Satisfação das Necessidades de Água). Para determiná-lo, fez-se o balanço hídrico e calcularam-se as evapotranspirações real e máxima nas escalas decendial, quinzenal e mensal. As análises estatísticas realizadas apresentaram correlações signifi cativas entre os dados agroclimáticos (representado pelo ISNA) e a resposta espectral da cana-de-açúcar (representada pelos valores do NDVI), sendo que os melhores resultados foram obtidos na avaliação da série temporal como um todo. Esses resultados são de grande utilidade, por exemplo, na determinação de equações de estimativa do NDVI em relação ao ISNA, e vice-versa, a serem empregadas no apoio à estimativa da produção da cana-de-açúcar no país.

Sensoriamento remoto, Series temporais, Cana-de-açucar, Agricultura, Climatologia agricola, Processamento de imagens

http://www.sbagro.org.br/rbagro/ojs/index.php/rbagro/article/view/103

Ano de publicação: 2008

The objective of this work was to assess the potential impact of climate change on the spatial distribution of coffee nematodes (races of Meloidogyne incognita) and leaf miner (Leucoptera coffeella), using a Geographic Information System. Assessment of the impacts of climate change on pest infestations and disease epidemics in crops is needed as a basis for revising management practices to minimize crop losses as climatic conditions shift. Future scenarios focused on the decades of the 2020's, 2050's, and 2080's (scenarios A2 and B2) were obtained from five General Circulation Models available on Data Distribution Centre from Intergovernmental Panel on Climate Change. Geographic distribution maps were prepared using models to predict the number of generations of the nematodes and leaf miner. Maps obtained in scenario A2 allowed prediction of an increased infestation of the nematode and of the pest, due to greater number of generations per month, than occurred under the climatological normal from 1961–1990. The number of generations also increased in the B2 scenario, but was lower than in the A2 scenario for both organisms.

Coffea arabica, Leucoptera coffeella, Meloidogyne incognita, global warming, pest zoning.

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2008000200005&lng=en&tlng=en