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A garantia da segurança alimentar global é um dos grandes desafios da humanidade para as próximas décadas. O aumento populacional do planeta, até 2050, em cerca de 2 bilhões de pessoas em 2050, a tendência de ascensão da classe média e as projeções de mudanças climáticas têm sido consideradas como um dos grandes desafios futuros para as políticas internacionais de seguridade alimentar. As projeções de alteração climática levam em consideração o aumento da concentração de gases de efeito estufa, sendo o CO2 o principal deles. O setor agrícola pode ser o mais afetado pelas mudanças no clima. O Brasil é o maior exportador e o segundo maior produtor de soja (Glycine max L.) do mundo. Essa cultura representa mais de 60% de toda a proteína alimentar de origem vegetal produzida no mundo. Considerando essa problemática, o presente estudo teve como objetivo geral simular o crescimento da cultura da soja em pontos estrategicamente selecionados no Brasil, com base em séries históricas observadas e em cenários climáticos futuros. Para isso, o modelo DSSAT/CROPGRO-SOYBEAN foi calibrado para diferentes grupos de maturação relativa (6.0, 7.0, 8.0 e 9.0), de modo a abranger pelo menos 80% de toda a produção nacional. Especificamente para o grupo 6.0, foi necessário a instalação de um experimento de campo, em Piracicaba-SP nas safras 2015/16 e 2016/17. Para a projeção dos cenários climáticos futuros adotou-se a metodologia do projeto internacional Agricultural Model Intercomparison and Improvement Project (AgMIP). Esses cenários foram baseados nas projeções de concentrações futuras de CO2 atmosférico (RCP 4.5 e RCP 8.5). Considerando as duas possibilidades de concentração futura de CO2, selecionou-se três modelos climáticos globais (GCM) para cada zona homogênea. As zonas homogêneas foram agrupadas considerando a soma térmica, aridez e sazonalidade de temperatura. As produtividades futuras de soja foram simuladas para o período 2040-2069 (representando 2050). Notou-se que as mudanças climáticas podem contribuir para o aumento da produtividade de soja no Brasil para a maioria das zonas homogêneas nos cenários simulados, mas com aumento do risco climático da cultura em algumas regiões. As simulações e zonas homogêneas que apresentaram perdas de produtividade estavam estritamente relacionadas com o défict hídrico. / Ensuring global food security is one of humanity\'s greatest challenges for the coming decades. The rising population of the planet by about 2 billion people, the rising trend of the middle class and the projections of climate change have been considered as one of the great future challenges for international food security policies. The projections of climate change take into account the increase in the concentration of greenhouse gases, with CO2 being the main one. The agricultural sector may be most affected by changes in climate. Brazil is the largest exporter and the second largest producer of soybeans (Glycine max) in the world. This crop represents more than 60% of all plant protein produced in the world. Considering this problem, the present study had as general objective to simulate soybean crop growth in strategically selected points in Brazil, based on observed historical series and future climatic scenarios. For this, the DSSAT / CROPGRO-SOYBEAN model was calibrated for different maturation groups (6.0, 7.0, 8.0 and 9.0), to cover at least 80% of all national production. Specifically for group 6.0, it was necessary to install a field experiment in Piracicaba-SP in the 2015/16 and 2016/17 seasons. For the projection of the future climate scenarios the methodology of the international Agricultural Model Intercomparison and Improvement Project (AgMIP) was adopted. These scenarios were based on projections of future concentrations of atmospheric CO2 (RCP 4.5 and RCP 8.5). Considering the two possibilities of future CO2 concentration, three global climate models (GCM) were selected for each homogeneous zone. The homogeneous zones were grouped considering the thermal sum, aridity and seasonality of temperature. Future soybean yields were simulated for the period 2040-2069 (representing 2050). It was noted that climate change may contribute to increase soybean productivity in Brazil for most of the homogeneous zones in the simulated scenarios, but with increasing climatic risk of the crop in some regions. The simulations and homogeneous zones that presented productivity losses were strictly related to the water deficit.

Made available in DSpace on 2017-07-21T14:19:23Z (GMT). No. of bitstreams: 1 Albino Szesz Junior.pdf: 2551855 bytes, checksum: ad2fa2da3cef74797c4e91a3da0e453b (MD5) Previous issue date: 2015-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The aim of this work is to present the SEMEARE, a methodology for defining periods of sowing of crops, with four stages, including planning, modeling, experimentation and decision making. At the planning stage the goals and strategies for defining the sowing periods are defined and analyzed the agricultural context of the study area. In the modeling stage, the simulation models are set to be used, sowing dates and other necessary data. After system modeling and simulations performed are obtained yield values, germination time, number of days to harvest, among others, for each specific date. At the trial stage analysis of the information and you can set the best times for sowing culture defined and then make decision making. For validation, the methodology we used the MarkSimGCM a climate data simulator, to generate daily weather data, and the DSSAT, Version 4.5, using CERES-Maize model to simulate growth of corn. The simulation was applied to a hypothetical maize cultivar with average maturity characteristics in a real location (S25 ° 09'18.70 "W 50 ° 05'15.65"),with planting carried out under rainfed conditions, sowing depth 5 cm spacing planting 20 cm between seeds and 50 cm between plant rows and plant population: 10 plants / m2. Thus the DSSAT addresses the data of corn, while the MarkSimGCM simulates weather data for use in DSSAT in creating a weather station with daily data of maximum and minimum temperature, precipitation and solar radiation between 2011 and 2014. We simulated in 29 different sowing dates in Vintage 2011-2012, 2012- 2013, 2013-2014. From this income was generated graphs, maturation cycle,information used in decision making. This research resulted in the development of a generic methodology, which enables the use of different simulation models of crops and climate data, interacting with different systems of decision support as agricultural. / O objetivo desta dissertação é apresentar a SEMEARE uma metodologia para definição de períodos de semeadura de culturas agrícolas, com quatro etapas, incluindo planejamento, modelagem, experimentação e tomada de decisão. Na etapa de planejamento são definidos os objetivos e estratégias para definição dos períodos de semeadura, bem como analisado o contexto agrícola da área de estudo. Na etapa de modelagem são definidos os modelos de simulação a serem utilizados, datas de semeadura e demais dados necessários. Após a modelagem do sistema e executadas as simulações são obtidos os valores de rendimento, tempo de germinação, número de dias para colheita, entre outros, para cada data definida. Na etapa de experimentação são analisadas as informações sendo possível definir os melhores períodos de semeadura para a cultura definida e, então, realizar a tomada de decisão. Para validação, a metodologia utilizou-se do MarkSimGCM, um simulador de dados climáticos, para geração de dados climáticos diários, e o DSSAT, Versão 4.5, através do Modelo CERES-Maize, para simulação de crescimento da cultura do milho. A simulação foi aplicada em um cultivar hipotético do milho com características de maturidade média, em uma localização real (S25°09'18.70" W50°05'15.65"), com plantio realizado em condições de sequeiro, profundidade de semeadura 5 cm, espaçamento de plantio 20 cm entre sementes e 50 cm entre linhas de plantio e população de plantas: 10 plantas/m2. Dessa forma o DSSAT aborda os dados da cultura do milho, enquanto o MarkSimGCM simula dados climáticos para serem utilizados no DSSAT na criação de uma estação meteorológica com dados diários de temperatura máxima e mínima, precipitação e radiação solar entre 2011 e 2014. Foram realizadas simulações em 29 diferentes datas de semeadura, nas Safras 2011- 2012, 2012-2013, 2013-2014. A partir disso gerou-se gráficos de rendimento, ciclo de maturação, informações utilizadas na tomada de decisão. Esta pesquisa resultou no desenvolvimento de uma metodologia genérica, a qual possibilita a utilização de diferentes modelos de simulação de culturas agrícolas e de dados climáticos, interagindo com diferentes sistemas de apoio a decisão no âmbito agrícola.

A garantia da segurança alimentar global é um dos grandes desafios da humanidade para as próximas décadas. O aumento populacional do planeta, até 2050, em cerca de 2 bilhões de pessoas em 2050, a tendência de ascensão da classe média e as projeções de mudanças climáticas têm sido consideradas como um dos grandes desafios futuros para as políticas internacionais de seguridade alimentar. As projeções de alteração climática levam em consideração o aumento da concentração de gases de efeito estufa, sendo o CO2 o principal deles. O setor agrícola pode ser o mais afetado pelas mudanças no clima. O Brasil é o maior exportador e o segundo maior produtor de soja (Glycine max L.) do mundo. Essa cultura representa mais de 60% de toda a proteína alimentar de origem vegetal produzida no mundo. Considerando essa problemática, o presente estudo teve como objetivo geral simular o crescimento da cultura da soja em pontos estrategicamente selecionados no Brasil, com base em séries históricas observadas e em cenários climáticos futuros. Para isso, o modelo DSSAT/CROPGRO-SOYBEAN foi calibrado para diferentes grupos de maturação relativa (6.0, 7.0, 8.0 e 9.0), de modo a abranger pelo menos 80% de toda a produção nacional. Especificamente para o grupo 6.0, foi necessário a instalação de um experimento de campo, em Piracicaba-SP nas safras 2015/16 e 2016/17. Para a projeção dos cenários climáticos futuros adotou-se a metodologia do projeto internacional Agricultural Model Intercomparison and Improvement Project (AgMIP). Esses cenários foram baseados nas projeções de concentrações futuras de CO2 atmosférico (RCP 4.5 e RCP 8.5). Considerando as duas possibilidades de concentração futura de CO2, selecionou-se três modelos climáticos globais (GCM) para cada zona homogênea. As zonas homogêneas foram agrupadas considerando a soma térmica, aridez e sazonalidade de temperatura. As produtividades futuras de soja foram simuladas para o período 2040-2069 (representando 2050). Notou-se que as mudanças climáticas podem contribuir para o aumento da produtividade de soja no Brasil para a maioria das zonas homogêneas nos cenários simulados, mas com aumento do risco climático da cultura em algumas regiões. As simulações e zonas homogêneas que apresentaram perdas de produtividade estavam estritamente relacionadas com o défict hídrico. / Ensuring global food security is one of humanity\'s greatest challenges for the coming decades. The rising population of the planet by about 2 billion people, the rising trend of the middle class and the projections of climate change have been considered as one of the great future challenges for international food security policies. The projections of climate change take into account the increase in the concentration of greenhouse gases, with CO2 being the main one. The agricultural sector may be most affected by changes in climate. Brazil is the largest exporter and the second largest producer of soybeans (Glycine max) in the world. This crop represents more than 60% of all plant protein produced in the world. Considering this problem, the present study had as general objective to simulate soybean crop growth in strategically selected points in Brazil, based on observed historical series and future climatic scenarios. For this, the DSSAT / CROPGRO-SOYBEAN model was calibrated for different maturation groups (6.0, 7.0, 8.0 and 9.0), to cover at least 80% of all national production. Specifically for group 6.0, it was necessary to install a field experiment in Piracicaba-SP in the 2015/16 and 2016/17 seasons. For the projection of the future climate scenarios the methodology of the international Agricultural Model Intercomparison and Improvement Project (AgMIP) was adopted. These scenarios were based on projections of future concentrations of atmospheric CO2 (RCP 4.5 and RCP 8.5). Considering the two possibilities of future CO2 concentration, three global climate models (GCM) were selected for each homogeneous zone. The homogeneous zones were grouped considering the thermal sum, aridity and seasonality of temperature. Future soybean yields were simulated for the period 2040-2069 (representing 2050). It was noted that climate change may contribute to increase soybean productivity in Brazil for most of the homogeneous zones in the simulated scenarios, but with increasing climatic risk of the crop in some regions. The simulations and homogeneous zones that presented productivity losses were strictly related to the water deficit.

The object of study – spring barley (Hordeum vulgare L.) and winter wheat (Triticum aestivum L.). Research tasks: 1. To investigate the effect of growing intensity, applying fertilisers according to normative for target yield, on spring barley and winter wheat leaf index, biomass and grain yield formation. 2. To quantitatively assess accumulation of nitrogen and carbon in the biomass during vegetation. 3. To explore the effect of water and nitrogen induced stresses on productivity of photosynthesis and to evaluate feasibility of DSSAT v4.0.2.0 model for the diagnosis. 4. To estimate likely effect of climate change on winter wheat and spring barely yield. / Darbo uždaviniai: 1. Ištirti skirtingo auginimo intensyvumo, tręšiant pagal normatyvus planuojamam derliui, poveikį vasarinių miežių ir žieminių kviečių lapų indekso, biomasės ir grūdų derliaus formavimuisi. 2. Nustatyti kiekybinius azoto ir anglies kaupimosi biomasėje pokyčius vegetacijos metu. 3. Ištirti vandens ir azoto trūkumo sukeltų stresų pasireiškimą migliniuose javuose ir įvertinti galimybes juos diagnozuoti modeliu DSSAT v4.0.2.0. 4. Įvertinti tikėtiną klimato kaitos poveikį žieminių kviečių ir vasarinių miežių derlingumui. Tyrimų objektas – vasarinis miežis (Hordeum vulgare L.), žieminis kvietys (Triticum aestivum L.).

Réalisées aux échelles internationales et nationales, les études de vulnérabilité aux changements et à la variabilité climatiques sont peu pertinentes dans un processus de prise de décisions à des échelles géographiques plus petites qui représentent les lieux d’implantation des stratégies de réponses envisagées. Les études de vulnérabilité aux changements et à la variabilité climatiques à des échelles géographiques relativement petites dans le secteur agricole sont généralement rares, voire inexistantes au Canada, notamment au Québec. Dans le souci de combler ce vide et de favoriser un processus décisionnel plus éclairé à l’échelle de la ferme, cette étude cherchait principalement à dresser un portrait de l’évolution de la vulnérabilité des fermes productrices de maïs-grain des régions de Montérégie-Ouest et du Lac-St-Jean-Est aux changements et à la variabilité climatiques dans un contexte de multiples sources de pression. Une méthodologie générale constituée d'une évaluation de la vulnérabilité globale à partir d’une combinaison de profils de vulnérabilité aux conditions climatiques et socio-économiques a été adoptée. Pour la période de référence (1985-2005), les profils de vulnérabilité ont été dressés à l’aide d’analyses des coefficients de variation des séries temporelles de rendements et de superficies en maïs-grain. Au moyen de méthodes ethnographiques associées à une technique d’analyse multicritère, le Processus d’analyse hiérarchique (PAH), des scénarios d’indicateurs de capacité adaptative du secteur agricole susmentionné ont été développés pour la période de référence. Ceux-ci ont ensuite servi de point de départ dans l’élaboration des indicateurs de capacité de réponses des producteurs agricoles pour la période future 2010-2039. Pour celle-ci, les deux profils de vulnérabilité sont issus d’une simplification du cadre théorique de « Intergovernmental Panel on Climate Change » (IPCC) relatif aux principales composantes du concept de vulnérabilité. Pour la dimension « sensibilité » du secteur des fermes productrices de maïs-grain des deux régions agricoles aux conditions climatiques, une série de données de rendements a été simulée pour la période future. Ces simulations ont été réalisées à l’aide d’un couplage de cinq scénarios climatiques et du modèle de culture CERES-Maize de « Decision Support System for Agrotechnology Transfer » (DSSAT), version 4.0.2.0. En ce qui concerne l’évaluation de la « capacité adaptative » au cours de la période future, la construction des scénarios d’indicateurs de cette composante a été effectuée selon l’influence potentielle des grandes orientations économiques et environnementales considérées dans l’élaboration des lignes directrices des deux familles d’émissions de gaz à effet de serre (GES) A2 et A1B. L’application de la démarche méthodologique préalablement mentionnée a conduit aux principaux résultats suivants. Au cours de la période de référence, la région agricole du Lac-St-Jean-Est semblait être plus vulnérable aux conditions climatiques que celle de Montérégie-Ouest. En effet, le coefficient de variation des rendements du maïs-grain pour la région du Lac-St-Jean-Est était évalué à 0,35; tandis que celui pour la région de Montérégie-Ouest n’était que de 0,23. Toutefois, par rapport aux conditions socio-économiques, la région de Montérégie-Ouest affichait une vulnérabilité plus élevée que celle du Lac-St-Jean-Est. Les valeurs des coefficients de variation pour les superficies en maïs-grain au cours de la période de référence pour la Montérégie-Ouest et le Lac-St-Jean-Est étaient de 0,66 et 0,48, respectivement. Au cours de la période future 2010-2039, la région du Lac-St-Jean-Est serait, dans l’ensemble, toujours plus vulnérable aux conditions climatiques que celle de Montérégie-Ouest. Les valeurs moyennes des coefficients de variation pour les rendements agricoles anticipés fluctuent entre 0,21 et 0,25 pour la région de Montérégie-Ouest et entre 0,31 et 0,50 pour la région du Lac-St-Jean-Est. Néanmoins, en matière de vulnérabilité future aux conditions socio-économiques, la position relative des deux régions serait fonction du scénario de capacité adaptative considéré. Avec les orientations économiques et environnementales considérées dans l’élaboration des lignes directrices de la famille d’émission de GES A2, les indicateurs de capacité adaptative du secteur à l’étude seraient respectivement de 0,13 et 0,08 pour la Montérégie-Ouest et le Lac-St-Jean-Est. D’autre part, en considérant les lignes directrices de la famille d’émission de GES A1B, la région agricole du Lac-St-Jean-Est aurait une capacité adaptative légèrement supérieure (0,07) à celle de la Montérégie-Ouest (0,06). De façon générale, au cours de la période future, la région du Lac-St-Jean-Est devrait posséder une vulnérabilité globale plus élevée que la région de Montérégie-Ouest. Cette situation s’expliquerait principalement par une plus grande vulnérabilité de la région du Lac-St-Jean-Est aux conditions climatiques. Les résultats de cette étude doivent être appréciés dans le contexte des postulats considérés, de la méthodologie suivie et des spécificités des deux régions agricoles examinées. Essentiellement, avec l’adoption d’une démarche méthodologique simple, cette étude a révélé les caractéristiques « dynamique et relative » du concept de vulnérabilité, l’importance de l’échelle géographique et de la prise en compte d’autres sources de pression et surtout de la considération d’une approche contraire à celle du « agriculteur réfractaire aux changements » dans les travaux d’évaluation de ce concept dans le secteur agricole. Finalement, elle a aussi présenté plusieurs pistes de recherche susceptibles de contribuer à une meilleure évaluation de la vulnérabilité des agriculteurs aux changements climatiques dans un contexte de multiples sources de pression. / The undertaking of vulnerability studies in relation to climatic change and vulnerability at the international and national levels renders them less relevant to a decision-making process at smaller spatial scales where specific response strategies are implemented. Vulnerability studies to climatic change and variability at relatively small geographic scales within the agriculture sector are rare in general, and even nonexistent in Canada, including Quebec. In order to fill in this gap and to contribute to a better decision-making process at the farm level, this study aimed at presenting a description and analysis of the evolution of grain corn growers’ vulnerability to climatic change and variability and other stressors within the Montérégie-Ouest and Lac-St-Jean-Est regions. A general methodology consisting of an assessment of farmers’ overall vulnerability by combining vulnerability profiles to climate and socio-economic conditions has been considered. For the reference period (1985-2005), vulnerability profiles were constructed by analyzing the variation coefficients of grain corn yields and crop area data. By means of ethnographic methods associated with a multicriteria analysis technique, the Analytic Hierarchy Process (AHP), adaptive capacity indices of the agriculture sector have been elaborated upon for the reference period. These indices have then been used as a starting point in the construction of scenario indices of future adaptive capacity of farmers for the future period 2010-2039. For this future period (2010-2039), vulnerability profiles for both regions have been created using a simplified version of the Intergovernmental Panel on Climate Change (IPCC) conceptual framework on the components of vulnerability. For the « sensitivity » component of grain corn growers to climate conditions within the selected agricultural regions, a set of grain corn yields has been simulated using five climate scenarios coupled with CERES-Maize, one of the crop models embedded in the Decision Support System for Agrotechnology Transfer (DSSAT 4.0.2.0 version) software. In regards to the evaluation of the « adaptive capacity » for the future period (2010-2039), the elaboration of indices for this component has been undertaken by considering the potential influence of the main economic and environmental drivers used in the development of the storylines for two greenhouse gas (GHG) emission scenarios families, namely the A2 and A1B families. The application of the methodological approach mentioned above produced the following key results. For the reference period, the Lac-St-Jean-Est region appeared to be more vulnerable to climate conditions than Montérégie-Ouest region. The coefficient of variation for grain corn yields within the Lac-St-Jean-Est region was evaluated to be 0,35, while the value for the Montérégie-Ouest region was only 0,23. However, with respect to the socio-economic conditions, the Montérégie-Ouest region showed greater vulnerability than the Lac-St-Jean-Est region. The values of the coefficient of variation for the areas under grain corn during the reference period (1985-2005) within Montérégie-Ouest and Lac-St-Jean-Est were 0,66 and 0,48 respectively. For the future period (2010-2039), the Lac-St-Jean-Est region, once again, would seem to be more vulnerable to climate conditions than the Montérégie-Ouest region. The average values of the coefficient of variation for the simulated grain corn yields fluctuate between 0,21 and 0,25 for the Montérégie-Ouest region and between 0,31 and 0,50 for Lac-St-St-Jean-Est region. However, from a socio-economic perspective, the relative vulnerability status of both regions would seem to vary according to the scenario of adaptive capacity considered. With the economic and environmental drivers considered in the storylines of the A2 GHG emissions scenario family, the adaptive capacity indices for the sector under study would be 0,13 and 0,08 for Montérégie-Ouest and Lac-St-Jean-Est, respectively. On the other hand, by considering the economic and environmental drivers considered for the A1B GHG emissions scenario family, the Lac-St-Jean-Est agricultural region would appear to have an adaptive capacity slightly higher (0,07) than that of the Montérégie-Ouest region (0,06). In general, for the future period, the Lac-St-Jean-Est region would appear to have greater overall vulnerability than the Montérégie-Ouest. This situation can be explained mainly by a greater vulnerability of Lac-St-Jean-Est region to climate conditions. The results of this study have to be interpreted within the context of the assumptions considered, the methodology used, and the characteristics of the two regions under study. In general, using a simple methodological approach, this study revealed the « dynamic and relative » characteristics of the vulnerability concept, the importance of spatial scale and consideration of multiple stressors and the integration of an approach different to the commonly used« dumb-farmer » approach for the evaluation of this concept of vulnerability within the agriculture sector. Finally, this study has also identified some new research pathways likely to contribute to a better evaluation of farmers’ vulnerability to climate change in the context of multiple stressors.

O evento climático conhecido como El Niño Oscilação Sul (ENOS) é formado pelos episódios de El Niño e La Niña e é classificado como um fenômeno de grande escala que ocorre no Oceano Pacífico Equatorial. Em razão do grande efeito do fenômeno ENOS na variabilidade climática e, consequentemente, na produção agrícola, se faz necessário o conhecimento adequado das consequências dos eventos de El Niño e La Niña nos regimes térmicos e hídricos de diferentes regiões brasileiras e de seus impactos na produção de alimentos, sobretudo na cultura da cana-de-açúcar. Para tanto, dados meteorológicos foram analisados a fim de se verificar algum efeito causado pelos eventos do ENOS na temperatura do ar, na radiação solar e precipitação pluvial. Em seguida, utilizou-se o modelo DSSAT CSMCANEGRO parametrizado para as condições brasileiras para simular a produtividade da cana-planta de 12 meses em quatro localidades de diferentes regiões do Brasil (Jataí, GO; João Pessoa, PB; Londrina, PR; e Piracicaba, SP), empregando-se séries históricas de dados meteorológicos, de 1979 a 2010, para três tipos de solos com diferentes características físico-hídricas (capacidade de água disponível), e para dois tipos de simulação da produtividade da cana pelo modelo DSSAT CSM-CANEGRO, o tipo Seasonal e o tipo Sequence. Foi possível notar nos resultados que para a temperatura do ar houve uma maior frequência de anos com essa variável acima da mediana nas localidades situadas na região central e nordeste no país durante os eventos de El Niño, ao passo que na região sul, representada por Londrina, tal frequência foi indefinida. Para os anos de La Niña, não houve, em geral, tendência clara de variação em nenhuma das localidades. Já nos anos neutros as maiores frequências foram de temperaturas abaixo da mediana nas localidades das regiões central e sul, enquanto em João Pessoa, PB, não houve tendência bem definida. Para a radiação solar, em geral, não se detectaram tendências expressivas, apesar de valores levemente acima da mediana em anos de La Niña, em todas as regiões. Finalmente, para as chuvas houve tendências um pouco mais expressivas, sendo que nas localidades da região central do país (Jataí e Piracicaba) as precipitações acima da mediana foram mais frequentes nos anos de El Niño e La Niña, ficando abaixo da mediana nos anos neutros. Nas demais localidades analisadas, as chuvas tenderam a ficar abaixo ou igual à mediana durante todas as fases do ENOS. Quanto à produtividade, algumas tendências também puderam ser observadas. Em Jataí, GO, não houve alterações da produtividade média maiores do que ± 1 t ha-1. Em João Pessoa, PB, a tendência de menores produtividades durante os anos de El Niño e de La Niña e de maiores produtividades em anos neutros. Situação oposta foi observa em Piracicaba, SP, e Londrina, PR, onde as produtividades tenderam a serem maiores do que a média histórica nos eventos tanto de El Niño como de La Niña, ao passo que nos anos de neutralidade do ENOS as produtividades tenderam a ser menores do que a média. / The climatic event known as El Niño Southern Oscillation (ENSO) is formed by episodes of El Niño and La Niña and is classified as a large-scale phenomenon that occurs in the Equatorial Pacific Ocean. Given the large effect of ENSO on climate variability and hence in agricultural production, proper knowledge of the consequences of El Niño and La Niña on the thermal and water regimes of different Brazilian regions and their impact on food production is needed, especially for sugarcane crop. To this end, climate variables were analyzed in order to verify any effect caused by the ENSO events on air temperature, solar radiation and rainfall. The DSSAT CSM-CANEGRO model, parameterized for the Brazilian conditions, was used to simulate sugarcane yield (plant cane of 12 months) in four sites of different Brazilian regions (Jataí, GO; João Pessoa, PB; Londrina, PR; and Piracicaba, SP), using historical series of meteorological data from 1979 to 2010, for three types of soils, with different physical-hydric properties (Soil water holding capacity), and two types of simulations in DSSAT CSM-CANEGRO model, with the Seasonal and Sequence procedures It was possible to notice in the results that the air temperature was a greater frequency of years with this variable above the median in localities situated in the central and northeastern region of the country during the El Niño events, while in the south, represented by Londrina, this frequency was undefined. For La Niña years, there was generally clear trend of variation in any of the locations. Already in neutral years, the highest frequency was below the median temperatures in the localities in central and southern regions, while in João Pessoa, PB, and no well-defined trend. Solar radiation, in general, no significant trends were detected, although values slightly above the median in La Niña years in all regions. Finally, to the rains there was a little more expressive tendency, and the locations of the central region (Jataí and Piracicaba) precipitation above the median were more frequent in years of El Niño and La Niña, below the median in neutral years. In other areas analyzed, rainfall tended to be below or equal to the median during all phases of ENSO. With regard to productivity, some trends were also observed. In Jataí, GO, no changes greater than ± 1 t ha-1 was observed. João Pessoa, PB, there was a trend of lower yields during El Niño and La Niña years and higher yields during neutral years. Opposite situation was observed in Piracicaba, SP, and Londrina, PR, where the yields tended to be higher than the historical average in both El Niño and La Niña events, while during neutral years the yield tended to be smaller than average.

Réalisées aux échelles internationales et nationales, les études de vulnérabilité aux changements et à la variabilité climatiques sont peu pertinentes dans un processus de prise de décisions à des échelles géographiques plus petites qui représentent les lieux d’implantation des stratégies de réponses envisagées. Les études de vulnérabilité aux changements et à la variabilité climatiques à des échelles géographiques relativement petites dans le secteur agricole sont généralement rares, voire inexistantes au Canada, notamment au Québec. Dans le souci de combler ce vide et de favoriser un processus décisionnel plus éclairé à l’échelle de la ferme, cette étude cherchait principalement à dresser un portrait de l’évolution de la vulnérabilité des fermes productrices de maïs-grain des régions de Montérégie-Ouest et du Lac-St-Jean-Est aux changements et à la variabilité climatiques dans un contexte de multiples sources de pression. Une méthodologie générale constituée d'une évaluation de la vulnérabilité globale à partir d’une combinaison de profils de vulnérabilité aux conditions climatiques et socio-économiques a été adoptée. Pour la période de référence (1985-2005), les profils de vulnérabilité ont été dressés à l’aide d’analyses des coefficients de variation des séries temporelles de rendements et de superficies en maïs-grain. Au moyen de méthodes ethnographiques associées à une technique d’analyse multicritère, le Processus d’analyse hiérarchique (PAH), des scénarios d’indicateurs de capacité adaptative du secteur agricole susmentionné ont été développés pour la période de référence. Ceux-ci ont ensuite servi de point de départ dans l’élaboration des indicateurs de capacité de réponses des producteurs agricoles pour la période future 2010-2039. Pour celle-ci, les deux profils de vulnérabilité sont issus d’une simplification du cadre théorique de « Intergovernmental Panel on Climate Change » (IPCC) relatif aux principales composantes du concept de vulnérabilité. Pour la dimension « sensibilité » du secteur des fermes productrices de maïs-grain des deux régions agricoles aux conditions climatiques, une série de données de rendements a été simulée pour la période future. Ces simulations ont été réalisées à l’aide d’un couplage de cinq scénarios climatiques et du modèle de culture CERES-Maize de « Decision Support System for Agrotechnology Transfer » (DSSAT), version 4.0.2.0. En ce qui concerne l’évaluation de la « capacité adaptative » au cours de la période future, la construction des scénarios d’indicateurs de cette composante a été effectuée selon l’influence potentielle des grandes orientations économiques et environnementales considérées dans l’élaboration des lignes directrices des deux familles d’émissions de gaz à effet de serre (GES) A2 et A1B. L’application de la démarche méthodologique préalablement mentionnée a conduit aux principaux résultats suivants. Au cours de la période de référence, la région agricole du Lac-St-Jean-Est semblait être plus vulnérable aux conditions climatiques que celle de Montérégie-Ouest. En effet, le coefficient de variation des rendements du maïs-grain pour la région du Lac-St-Jean-Est était évalué à 0,35; tandis que celui pour la région de Montérégie-Ouest n’était que de 0,23. Toutefois, par rapport aux conditions socio-économiques, la région de Montérégie-Ouest affichait une vulnérabilité plus élevée que celle du Lac-St-Jean-Est. Les valeurs des coefficients de variation pour les superficies en maïs-grain au cours de la période de référence pour la Montérégie-Ouest et le Lac-St-Jean-Est étaient de 0,66 et 0,48, respectivement. Au cours de la période future 2010-2039, la région du Lac-St-Jean-Est serait, dans l’ensemble, toujours plus vulnérable aux conditions climatiques que celle de Montérégie-Ouest. Les valeurs moyennes des coefficients de variation pour les rendements agricoles anticipés fluctuent entre 0,21 et 0,25 pour la région de Montérégie-Ouest et entre 0,31 et 0,50 pour la région du Lac-St-Jean-Est. Néanmoins, en matière de vulnérabilité future aux conditions socio-économiques, la position relative des deux régions serait fonction du scénario de capacité adaptative considéré. Avec les orientations économiques et environnementales considérées dans l’élaboration des lignes directrices de la famille d’émission de GES A2, les indicateurs de capacité adaptative du secteur à l’étude seraient respectivement de 0,13 et 0,08 pour la Montérégie-Ouest et le Lac-St-Jean-Est. D’autre part, en considérant les lignes directrices de la famille d’émission de GES A1B, la région agricole du Lac-St-Jean-Est aurait une capacité adaptative légèrement supérieure (0,07) à celle de la Montérégie-Ouest (0,06). De façon générale, au cours de la période future, la région du Lac-St-Jean-Est devrait posséder une vulnérabilité globale plus élevée que la région de Montérégie-Ouest. Cette situation s’expliquerait principalement par une plus grande vulnérabilité de la région du Lac-St-Jean-Est aux conditions climatiques. Les résultats de cette étude doivent être appréciés dans le contexte des postulats considérés, de la méthodologie suivie et des spécificités des deux régions agricoles examinées. Essentiellement, avec l’adoption d’une démarche méthodologique simple, cette étude a révélé les caractéristiques « dynamique et relative » du concept de vulnérabilité, l’importance de l’échelle géographique et de la prise en compte d’autres sources de pression et surtout de la considération d’une approche contraire à celle du « agriculteur réfractaire aux changements » dans les travaux d’évaluation de ce concept dans le secteur agricole. Finalement, elle a aussi présenté plusieurs pistes de recherche susceptibles de contribuer à une meilleure évaluation de la vulnérabilité des agriculteurs aux changements climatiques dans un contexte de multiples sources de pression. / The undertaking of vulnerability studies in relation to climatic change and vulnerability at the international and national levels renders them less relevant to a decision-making process at smaller spatial scales where specific response strategies are implemented. Vulnerability studies to climatic change and variability at relatively small geographic scales within the agriculture sector are rare in general, and even nonexistent in Canada, including Quebec. In order to fill in this gap and to contribute to a better decision-making process at the farm level, this study aimed at presenting a description and analysis of the evolution of grain corn growers’ vulnerability to climatic change and variability and other stressors within the Montérégie-Ouest and Lac-St-Jean-Est regions. A general methodology consisting of an assessment of farmers’ overall vulnerability by combining vulnerability profiles to climate and socio-economic conditions has been considered. For the reference period (1985-2005), vulnerability profiles were constructed by analyzing the variation coefficients of grain corn yields and crop area data. By means of ethnographic methods associated with a multicriteria analysis technique, the Analytic Hierarchy Process (AHP), adaptive capacity indices of the agriculture sector have been elaborated upon for the reference period. These indices have then been used as a starting point in the construction of scenario indices of future adaptive capacity of farmers for the future period 2010-2039. For this future period (2010-2039), vulnerability profiles for both regions have been created using a simplified version of the Intergovernmental Panel on Climate Change (IPCC) conceptual framework on the components of vulnerability. For the « sensitivity » component of grain corn growers to climate conditions within the selected agricultural regions, a set of grain corn yields has been simulated using five climate scenarios coupled with CERES-Maize, one of the crop models embedded in the Decision Support System for Agrotechnology Transfer (DSSAT 4.0.2.0 version) software. In regards to the evaluation of the « adaptive capacity » for the future period (2010-2039), the elaboration of indices for this component has been undertaken by considering the potential influence of the main economic and environmental drivers used in the development of the storylines for two greenhouse gas (GHG) emission scenarios families, namely the A2 and A1B families. The application of the methodological approach mentioned above produced the following key results. For the reference period, the Lac-St-Jean-Est region appeared to be more vulnerable to climate conditions than Montérégie-Ouest region. The coefficient of variation for grain corn yields within the Lac-St-Jean-Est region was evaluated to be 0,35, while the value for the Montérégie-Ouest region was only 0,23. However, with respect to the socio-economic conditions, the Montérégie-Ouest region showed greater vulnerability than the Lac-St-Jean-Est region. The values of the coefficient of variation for the areas under grain corn during the reference period (1985-2005) within Montérégie-Ouest and Lac-St-Jean-Est were 0,66 and 0,48 respectively. For the future period (2010-2039), the Lac-St-Jean-Est region, once again, would seem to be more vulnerable to climate conditions than the Montérégie-Ouest region. The average values of the coefficient of variation for the simulated grain corn yields fluctuate between 0,21 and 0,25 for the Montérégie-Ouest region and between 0,31 and 0,50 for Lac-St-St-Jean-Est region. However, from a socio-economic perspective, the relative vulnerability status of both regions would seem to vary according to the scenario of adaptive capacity considered. With the economic and environmental drivers considered in the storylines of the A2 GHG emissions scenario family, the adaptive capacity indices for the sector under study would be 0,13 and 0,08 for Montérégie-Ouest and Lac-St-Jean-Est, respectively. On the other hand, by considering the economic and environmental drivers considered for the A1B GHG emissions scenario family, the Lac-St-Jean-Est agricultural region would appear to have an adaptive capacity slightly higher (0,07) than that of the Montérégie-Ouest region (0,06). In general, for the future period, the Lac-St-Jean-Est region would appear to have greater overall vulnerability than the Montérégie-Ouest. This situation can be explained mainly by a greater vulnerability of Lac-St-Jean-Est region to climate conditions. The results of this study have to be interpreted within the context of the assumptions considered, the methodology used, and the characteristics of the two regions under study. In general, using a simple methodological approach, this study revealed the « dynamic and relative » characteristics of the vulnerability concept, the importance of spatial scale and consideration of multiple stressors and the integration of an approach different to the commonly used« dumb-farmer » approach for the evaluation of this concept of vulnerability within the agriculture sector. Finally, this study has also identified some new research pathways likely to contribute to a better evaluation of farmers’ vulnerability to climate change in the context of multiple stressors.

O evento climático conhecido como El Niño Oscilação Sul (ENOS) é formado pelos episódios de El Niño e La Niña e é classificado como um fenômeno de grande escala que ocorre no Oceano Pacífico Equatorial. Em razão do grande efeito do fenômeno ENOS na variabilidade climática e, consequentemente, na produção agrícola, se faz necessário o conhecimento adequado das consequências dos eventos de El Niño e La Niña nos regimes térmicos e hídricos de diferentes regiões brasileiras e de seus impactos na produção de alimentos, sobretudo na cultura da cana-de-açúcar. Para tanto, dados meteorológicos foram analisados a fim de se verificar algum efeito causado pelos eventos do ENOS na temperatura do ar, na radiação solar e precipitação pluvial. Em seguida, utilizou-se o modelo DSSAT CSMCANEGRO parametrizado para as condições brasileiras para simular a produtividade da cana-planta de 12 meses em quatro localidades de diferentes regiões do Brasil (Jataí, GO; João Pessoa, PB; Londrina, PR; e Piracicaba, SP), empregando-se séries históricas de dados meteorológicos, de 1979 a 2010, para três tipos de solos com diferentes características físico-hídricas (capacidade de água disponível), e para dois tipos de simulação da produtividade da cana pelo modelo DSSAT CSM-CANEGRO, o tipo Seasonal e o tipo Sequence. Foi possível notar nos resultados que para a temperatura do ar houve uma maior frequência de anos com essa variável acima da mediana nas localidades situadas na região central e nordeste no país durante os eventos de El Niño, ao passo que na região sul, representada por Londrina, tal frequência foi indefinida. Para os anos de La Niña, não houve, em geral, tendência clara de variação em nenhuma das localidades. Já nos anos neutros as maiores frequências foram de temperaturas abaixo da mediana nas localidades das regiões central e sul, enquanto em João Pessoa, PB, não houve tendência bem definida. Para a radiação solar, em geral, não se detectaram tendências expressivas, apesar de valores levemente acima da mediana em anos de La Niña, em todas as regiões. Finalmente, para as chuvas houve tendências um pouco mais expressivas, sendo que nas localidades da região central do país (Jataí e Piracicaba) as precipitações acima da mediana foram mais frequentes nos anos de El Niño e La Niña, ficando abaixo da mediana nos anos neutros. Nas demais localidades analisadas, as chuvas tenderam a ficar abaixo ou igual à mediana durante todas as fases do ENOS. Quanto à produtividade, algumas tendências também puderam ser observadas. Em Jataí, GO, não houve alterações da produtividade média maiores do que ± 1 t ha-1. Em João Pessoa, PB, a tendência de menores produtividades durante os anos de El Niño e de La Niña e de maiores produtividades em anos neutros. Situação oposta foi observa em Piracicaba, SP, e Londrina, PR, onde as produtividades tenderam a serem maiores do que a média histórica nos eventos tanto de El Niño como de La Niña, ao passo que nos anos de neutralidade do ENOS as produtividades tenderam a ser menores do que a média. / The climatic event known as El Niño Southern Oscillation (ENSO) is formed by episodes of El Niño and La Niña and is classified as a large-scale phenomenon that occurs in the Equatorial Pacific Ocean. Given the large effect of ENSO on climate variability and hence in agricultural production, proper knowledge of the consequences of El Niño and La Niña on the thermal and water regimes of different Brazilian regions and their impact on food production is needed, especially for sugarcane crop. To this end, climate variables were analyzed in order to verify any effect caused by the ENSO events on air temperature, solar radiation and rainfall. The DSSAT CSM-CANEGRO model, parameterized for the Brazilian conditions, was used to simulate sugarcane yield (plant cane of 12 months) in four sites of different Brazilian regions (Jataí, GO; João Pessoa, PB; Londrina, PR; and Piracicaba, SP), using historical series of meteorological data from 1979 to 2010, for three types of soils, with different physical-hydric properties (Soil water holding capacity), and two types of simulations in DSSAT CSM-CANEGRO model, with the Seasonal and Sequence procedures It was possible to notice in the results that the air temperature was a greater frequency of years with this variable above the median in localities situated in the central and northeastern region of the country during the El Niño events, while in the south, represented by Londrina, this frequency was undefined. For La Niña years, there was generally clear trend of variation in any of the locations. Already in neutral years, the highest frequency was below the median temperatures in the localities in central and southern regions, while in João Pessoa, PB, and no well-defined trend. Solar radiation, in general, no significant trends were detected, although values slightly above the median in La Niña years in all regions. Finally, to the rains there was a little more expressive tendency, and the locations of the central region (Jataí and Piracicaba) precipitation above the median were more frequent in years of El Niño and La Niña, below the median in neutral years. In other areas analyzed, rainfall tended to be below or equal to the median during all phases of ENSO. With regard to productivity, some trends were also observed. In Jataí, GO, no changes greater than ± 1 t ha-1 was observed. João Pessoa, PB, there was a trend of lower yields during El Niño and La Niña years and higher yields during neutral years. Opposite situation was observed in Piracicaba, SP, and Londrina, PR, where the yields tended to be higher than the historical average in both El Niño and La Niña events, while during neutral years the yield tended to be smaller than average.

The study was aimed to provide information on local biomass development during crop growth using ground based optical sensors and to incorporate the local crop status to a crop growth simulation model to improve understanding on inherent variability of crop field. The experiment was conducted in a farmer’s field located near Princeton in Caldwell County, Western Kentucky. Data collection on soil, crop and weather variables was carried out in the farm from 2006 December to 2008 October. During this period corn (Zea mays L.) and winter wheat (Triticum sp) were grown in the field. A 450 m long representative transect across the field consisting of 45 locations each separated by 10 m was selected for the study. Soil water content was measured in a biweekly interval during crop growth from these locations. Measurements on crop growth parameters such as plant height, tiller count, biomass and grain yield were able to show spatial variability in crop biomass and grain yield production. Crop reflectance measured at important crop growth stages. Soil water sensing capacitance probe was site specifically calibrated for each soil depth in each location. Various vegetation indices were calculated as proxy variables of crop growth. Inherent soil properties such as soil texture and elevation were found playing a major role in influencing spatial variability in crop yield mainly by affecting soil water storage. Temporal persistence of spatial patterns in soil water storage was not observed. Optimum spatial correlation structure was observed between crop growth parameters and optical sensor measurements collected early in the season and aggregated at 2*2 m2 sampling area. NDVI, soil texture, soil water storage and different crop growth parameters were helpful in explaining the spatial processes that influence grain yield and biomass using state space analysis. DSSAT was fairly sensitive to reflect site specific inputs on soil variability in crop production.