Master of Science / Department of Biological and Agricultural Engineering / Kyle Douglas-Mankin / Opportunities for alternative biofuel feedstocks are widespread for a number of reasons: increased environmental and economic concerns over corn production and processing, limitations in the use of corn-based ethanol to 57 billion L (15 billion gal) by the Energy Independence and Security Act (US Congress, 2007), and target requirements of 136 billion L (36 billion gal) of renewable fuel production by 2022. The objective of this study was to select the most promising among currently available crop models that have the potential to model sweet sorghum biomass production in the central US, specifically Kansas, Oklahoma, and Texas, and to develop and test sweet sorghum crop parameters for this model. Five crop models were selected (CropSyst, CERE-Sorghum, APSIM, ALMANAC, and SORKAM), and the models were compared based on ease of use, model support, and availability of inputs and outputs from sweet sorghum biomass data and literature. After reviewing the five models, ALMANAC was selected as the best suited for the development and testing of sweet sorghum crop parameters. The results of the model comparison show that more data are needed about sweet sorghum physiological development stages and specific growth/development factors before the other models reviewed in this study can be readily used for sweet sorghum crop modeling. This study used a unique method to calibrate the sweet sorghum crop parameter development site. Ten years of crop performance data (Corn and Grain Sorghum) for Kansas Counties (Riley and Ellis) were used to select an optimum soil water (SW) estimation method (Saxton and Rawls, Ritchie et al., and a method that added 0.01 m m [superscript]-1 to the minimum SW value given in the SSURGO soil database) and evapotranspiration (ET) method (Penman-Montieth, Priestley-Taylor, and Hargraeves and Samani) combination for use in the sweet sorghum parameter development. ALMANAC general parameters for corn and grain sorghum were used for the calibration/selection of the SW/ET combination. Variations in the harvest indexes were used to simulate variations in geo-climate region grain yield. A step through comparison method was utilized to select the appropriate SW/ET combination. Once the SW/ET combination was selected the combination was used to develop the sweet sorghum crop parameters. Two main conclusions can be drawn from the sweet sorghum crop parameter development study. First, the combination of Saxton and Rawls (2006) and Priestley-Taylor (1972) (SR-PT) methods has the potential for wide applicability in the US Central Plains for simulating grain yields using ALMANAC. Secondly, from the development of the sweet sorghum crop model parameters, ALMANAC modeled biomass yields with reasonable accuracy; differences from observed biomass values ranged from 0.89 to 1.76 Mg ha [superscript]-1 (2.8 to 9.8%) in Kansas (Riley County), Oklahoma (Texas County), and Texas (Hale County). Future research for sweet sorghum physiology, Radiation Use Efficiency/Vapor Pressure Deficit relationships, and weather data integration would be useful in improving sweet sorghum biomass modeling.

Com o objetivo de caracterizar a variação temporal da massa de matéria seca de plantas de milho, foram conduzidos experimentos de campo na área experimental do Departamento de Produção Vegetal da Universidade de São Paulo em Piracicaba, SP (latitude: 22o42\'30\'\' Sul, longitude: 47o38\'00\'\' Oeste, e altitude: 546 metros), na safra 2003/2004 (semeadura: 20 de setembro de 2003). Foram utilizados três genótipos de milho, BRS-1001, BRS-1010 e BRS-3003, sob condições de sequeiro e irrigado, com duas repetições em delineamento inteiramente casualizado. Em função dos resultados obtidos, pode-se concluir que: (i) sob condição de deficiência hídrica, a planta de milho aloca mais carboidrato para produção de raiz e de colmo durante a fase vegetativa; (ii) sob condição de suficiência hídrica, a planta de milho aloca mais carboidrato para produção de folha durante o ciclo; e (iii) em geral, há maior produção absoluta de massa de matéria seca de grãos (maior produtividade) e total sob condição de suficiência hídrica. / With the purpose of caractherizing the temporal variation of the corn plants dry matter, field experiments were carried out at experimental area of the Crop Science Department of the University of São Paulo in Piracicaba, SP (latitude: 22o42\'30\'\' South, longitude: 47o38\'00\'\' West, and altitude: 546 meters), in the 2003/2004 season (sowing date: 20 September 2003). Three corn genotypes were used, BRS-1001, BRS-1010 and BRS-3003, under non irrigated and irrigated production systems, with two replications using completly randomized statistical design. According to the results, there were the following conclusions: (i) under water stress condition, the corn plant alocates more carbohydrate to produce root system and stem during the vegetative phase; (ii) under no water stress, the corn plant alocates more carbohydrate to produce leaf during the whole cycle; and (iii) in general, there is higher production (absolute values) of grain (higher productivity) and total dry mass under no water stress condition.

Com o objetivo de caracterizar a variação temporal da massa de matéria seca de plantas de milho, foram conduzidos experimentos de campo na área experimental do Departamento de Produção Vegetal da Universidade de São Paulo em Piracicaba, SP (latitude: 22o42\'30\'\' Sul, longitude: 47o38\'00\'\' Oeste, e altitude: 546 metros), na safra 2003/2004 (semeadura: 20 de setembro de 2003). Foram utilizados três genótipos de milho, BRS-1001, BRS-1010 e BRS-3003, sob condições de sequeiro e irrigado, com duas repetições em delineamento inteiramente casualizado. Em função dos resultados obtidos, pode-se concluir que: (i) sob condição de deficiência hídrica, a planta de milho aloca mais carboidrato para produção de raiz e de colmo durante a fase vegetativa; (ii) sob condição de suficiência hídrica, a planta de milho aloca mais carboidrato para produção de folha durante o ciclo; e (iii) em geral, há maior produção absoluta de massa de matéria seca de grãos (maior produtividade) e total sob condição de suficiência hídrica. / With the purpose of caractherizing the temporal variation of the corn plants dry matter, field experiments were carried out at experimental area of the Crop Science Department of the University of São Paulo in Piracicaba, SP (latitude: 22o42\'30\'\' South, longitude: 47o38\'00\'\' West, and altitude: 546 meters), in the 2003/2004 season (sowing date: 20 September 2003). Three corn genotypes were used, BRS-1001, BRS-1010 and BRS-3003, under non irrigated and irrigated production systems, with two replications using completly randomized statistical design. According to the results, there were the following conclusions: (i) under water stress condition, the corn plant alocates more carbohydrate to produce root system and stem during the vegetative phase; (ii) under no water stress, the corn plant alocates more carbohydrate to produce leaf during the whole cycle; and (iii) in general, there is higher production (absolute values) of grain (higher productivity) and total dry mass under no water stress condition.

<div> <p>Calibration of crop models is an expensive and time intensive procedure, which is essential to accurately predict the possible crop yields given changing climate conditions. One solution is the utilization of unmanned aircraft systems (UAS) deployed with Red Green Blue Composite (RGB), and multispectral sensors, which has the potential to measure and collect in field biomass and yield in a cost and time effective manner. The objective of this project was to develop a relationship between remotely sensed data and crop indices, similar to biomass, to improve the ability to parametrize crop models for local conditions, which in turn could potentially improve the quantification of the effect of hydrological extremes on predicted yield. An experiment consisting of 750 plots (350 varieties) was planted in 2018, and a subset of 18 plots (9 varieties) were planted in 2019. The in-situ above ground biomass along with multispectral and RGB imagery was collected for both experiments throughout the growing season. The imagery was processed through a custom software pipeline to produce spectrally corrected imagery of individual plots. A model was fit between spectral data and sampled biomass resulting in an R-square of 0.68 and RMSE of 160 g when the model was used to estimate biomass for multiple flight dates flights. The VIC-CropSyst model, a coupled hydrological and agricultural system model, was used to simulate crop biomass and yield for multiple years at the experiment location. Soybean growth was parametrized for the location using CropSyst’s Crop Calibrator tool. Biomass values generated from UAS imagery, along with the in-situ collected biomass values were used separately to parametrize soybean simulations in CropSyst resulting in very similar parameter sets that were distinct from the default parameter values. The parametrized crop files along with the default files were used separately to run the VIC-CropSyst model and results were evaluated by comparing simulated and observed values of yield and biomass values. Both parametrized crop files (using in-situ samples and UAS imagery) produced approximately identical results with a max difference of 0.03 T/Ha for any one year, compared to a base value of 3.6 T/Ha, over a 12-year period in which the simulation was ran. The parametrized runs produced yield estimates that were closer to in-situ measured yield, as compared to unparametrized runs, for both bulk varieties and the run experiments, with the exception of 2011, which was a flooding year. The parametrized simulations consistently produced simulated yield results that were higher than the measured bulk variety yields, whereas the default parameters produced consistently lower yields. Biomass was only assessed for 2019, and the results indicate that the biomass after parametrization is lower than the default, which is attributed to the radiation use efficiency parameter being lower in the parametrized files, 2.5 g/MJ versus 2.25 g/MJ. The improved accuracy of predicting yield is evidence that the UAS based methodology is a suitable substitute for the more labor intensive in-situ sampling of biomass for soybean studies under similar environmental conditions.</p> </div><p> <br></p>

By 2050, food consumption and agricultural water use will increase as a result of a global population that is projected to reach 9 billion people. To address this food and water security challenge, there has been increased attention towards the concept of sustainable agriculture, which has a broad aim of securing food and water resources while preserving the environment for future generations. An element of this is the use of precision agriculture, which is designed to provide the right inputs, at the right time and in the right place. In order to optimize nutrient application, water intakes, and the profitability of agricultural areas, it is necessary to improve our understating and predictability of agricultural systems at high spatio-temporal scales. The underlying goal of the research presented herein is to advance the monitoring of croplands and crop yield through high-resolution satellite data. In addressing this, we explore the utility of daily CubeSat imagery to produce the highest spatial resolution (3 m) estimates of leaf area index and crop water use ever retrieved from space, providing an enhanced capacity to provide new insights into precision agriculture. The novel insights on crop health and conditions derived from CubeSat data are combined with the predictive ability of crop models, with the aim of improving crop yield predictions. To explore the latter, a sensitivity analysis-linked Bayesian inference framework was developed, offering a tool for calibrating crop models while simultaneously quantifying the uncertainty in input parameters. The effect of integrating higher spatio-temporal resolution data in crop models was tested by developing an approach that assimilates CubeSat imagery into a crop model for early season yield prediction at the within-field scale. In addition to satellite data, the utility of even higher spatial resolution products from unmanned aerial vehicles was also examined in the last section of the thesis, where future research avenues are outlined. Here, an assessment of crop height is presented, which is linked to field biomass through the use of structure from motion techniques. These results offer further insights into small-scale field variabilities from an on-demand basis, and represent the cutting-edge of precision agricultural advances.

No description available.

O feijoeiro-comum (Phaseolus vulgaris L.) é uma planta leguminosa que se destaca por ser uma das principais culturas agrícolas do mundo, com forte expressão cultural e econômica na agricultura brasileira. Com base na relevância do feijão no contexto mundial, é importante considerar as projeções futuras de produção dessa leguminosa dentro do debate internacional de mudanças climáticas e previsão do aumento populacional global, de 2 bilhões de habitantes, até o ano de 2050. Levando em consideração que o setor agrícola poderá ser um dos mais afetados com os efeitos das mudanças climáticas previstas para o decorrer do século XXI, o presente trabalho teve como objetivo simular a produção brasileira de feijão-comum para locais que representem, ao mínimo, 80% da quantidade produzida deste grão. Para isso, o modelo DSSAT/CROPGRO-Drybean foi calibrado para dois grupos comerciais representativos (\"Carioca\" e \"Preto\"). Esta tarefa foi caracterizada pelo ajuste do modelo através da análise comparativa com dados observados, coletados a partir de experimentos conduzidos em Piracicaba-SP e Santo Antônio de Goiás-GO. As projeções climáticas foram obtidas por meio da metodologia recomendada pelo projeto internacional AgMiP (Agricultural Model Intercomparison and Improvement Project), do qual foram considerados os dados climatológicos estimados por 20 modelos de circulação global (GCM), para os cenários futuros de concentração atmosférica de CO2 RCP 4.5 e RCP 8.5, em 24 zonas homogêneas que representam as características edafoclimáticas dos principais locais de produção no Brasil. Por fim, combinando-se as informações ambientais com o modelo de culturas, foi possível simular para o período de 2040 a 2070, a produtividade futura para cultura do feijão-comum. Observou-se que as mudanças climáticas acarretarão em aumento da produção nacional para a maioria das regiões produtoras, entretanto haverá aumento do risco de se produzir abaixo da média esperada em regiões de grande importância para a produção nacional, como os cultivos de 1ª safra de feijão \"Preto\" no Sul do Brasil, e para cultivos de 3ª safra (grupo comercial \"Carioca\") presentes no Centro-Oeste. / Common beans (Phaseolus vulgaris L.) are leguminous plants which represents one of the main world crops, with strong cultural and economical expression in Brazil\'s agriculture. Based on the common beans world relevance, it\'s important to consider the future projections for this crop production inside the international subject of climate change and the global population increase, predicted to 2 billion inhabitants, until 2050. Being aware that the agricultural sector would be severely affected by the effects of the predicted climate changes in 21st century, the main goal of this work was simulate the Brazilian production of common beans for locals which represents at least 80% of the grain produced quantity. For that, the model DSSAT/CROPGRO-Drybean was calibrated for two representative commercial groups (\"Carioca\" and \"Black\"). This task was caracterized by the adjustment of the model through the comparatively analyse of observed data, collected on field experiments performed in Piracicaba/SP and Santo Antônio de Goiás/GO. The climate projections were obtained through a methodology proposed by the international AgMiP project (Agricultural Model Intercomparison and Improvement Project), which were considered the climatological data estimated by 20 global circulation models (GCM), for scenarios of representative atmospheric CO2 concentration pathways RCP 4.5 and RCP 8.5, in 24 homogenic zones representing the edafoclimatic profiles of main producers in Brazil. Lastly, combining all environmental data with the crop model, was possible to simulate for the period of 2040 to 2070, the future productivity for nacional common bean crops. Was observed that climate changes would accomplish a increase in the mean production for most of the regions, by the other hand, it will also increase the risk of productions above the expected average on key regions, as such the 1st crops of \"Black\" beans in the south, and 3rd crops (commercial group \"Carioca) at the Middle-West.

A Previsão de Safras tem se constituído em importante ferramenta para o estabelecimento de políticas agrícolas públicas e privadas. Em geral, a Previsão de Safras consiste na previsão do clima e na estimativa do rendimento das partes de interesse econômico de uma cultura. A previsão do clima pode ser realizada pela análise de séries históricas dos parâmetros climáticos e dos efeitos de fenômenos conhecidos, a exemplo do El Niño Oscilação Sul (ENSO), o qual pode ser medido pelo Índice de Oscilação Sul (IOS). Também pode ser realizada pela integração numérica das equações diferenciais que regem os movimentos da atmosfera no planeta Terra, também conhecida como previsão numérica. A estimativa do rendimento das culturas também pode ser realizada pela análise estatística de séries históricas ou pela integração numérica de equações diferenciais que regem a fisiologia e o desenvolvimento das plantas, ambos conhecidos como modelo de culturas. O principal objetivo deste trabalho foi propor uma metodologia para a Previsão de Safras no Brasil, tendo como ponto de partida e protótipo o estudo do rendimento de grãos de milho na região Centro-Sul do país. Para tanto, séries históricas com 60 anos de precipitação pluvial em 24 locais da região Centro-Sul do Brasil foram comparadas aos Índices de Oscilação Sul medidos no mesmo período, inferindo-se que o fenômeno ENSO apresenta efeito marcante, e distinto, apenas em locais mais ao Sul e a Nordeste da região Centro-Sul. Concluiu-se pela impossibilidade de utilização do IOS para a previsão de parâmetros climáticos diários, o que também é prejudicado pela carência de séries históricas dos parâmetros climáticos com 60 ou mais anos no Brasil. Ainda quanto à previsão do clima, as previsões de radiação solar, precipitação pluvial, temperaturas máxima e mínima e umidade relativa do ar, geradas pelo modelo Eta a cada seis horas entre os dias 16/7/1997 e 15/6/2002, foram comparadas às respectivas medidas diárias desses parâmetros climáticos, concluindo-se pela possibilidade da aplicação das previsões geradas pelo modelo Eta na Previsão de Safras, à exceção dos locais mais ao Sul e mais a Nordeste da região Centro-Sul do Brasil. Acerca da estimativa do rendimento de grãos de milho, foi proposto um modelo de cultura baseado na integração das equações que regem a fisiologia e o desenvolvimento das plantas. Comparando-se os rendimentos de grãos de milho estimados nos 24 locais durante as safras 1997/98 a 2001/02, conclui-se pela possibilidade da estimativa do rendimento de grãos de milho na região Centro-Sul pelo modelo proposto. Porém, as discrepâncias entre os rendimentos estimados e os respectivos rendimentos medidos nos locais mais ao Sul e nos locais com textura de solo arenosa apontam a necessidade de correção da estimativa da dinâmica de água realizada pelo modelo de cultura proposto. Como conclusão geral, verificou-se que a metodologia proposta para a Previsão de Safras tem virtudes que devem ser exploradas no sentido de sua implementação no Brasil. Porém, essa implementação depende substancialmente da gestão dos trabalhos, de modo a propiciar as condições necessárias. Cabe destacar que o país tem realizado notáveis avanços nesse setor, caso da implementação da rede meteorológica nacional e do conhecimento gerado pelo Centro de Estudos e Previsões do Clima e pela Empresa Brasileira de Pesquisa Agropecuária, entre outras instituições. Ainda assim, essa área do conhecimento, fundamental para um país agrícola como o Brasil, carece de estudos. / Crop forecast has become an important tool for the private and public agricultural policies to be established. Generally, crop forecast is composed by climatic forecast and the yield estimative of growth of economically interesting parts of crops. The climatic forecast can be performed through the analyses of historical series of the climatic features and of the known phenomena, such as the El Niño Southern Oscillation (ENSO), which can be measured by the Southern Oscillation Index (IOS). It can also be done through a numerical integration of differential equations that rule the atmospheric movements of the Earth, a.k.a. numerical forecast. The estimate of crop yields can also be done through the statistical analysis of historical series or through the integration of differential equations that rule the plant physiology and development, both known as crop models. The main objective of this study was to indicate a methodology for Crop Forecast in Brazil, having as a starting point and prototype the study of corn grain yield in the Center-South region of Brazil. Thus, historical series of 60 years of precipitation in 24 sites of the studied region were compared to the IOS measured in the same period, inferring that the phenomenon ENSO has a remarkable effect, distinctly in the most southern and northeast portions of the studied region. One concluded due to the impossibility of using the IOS for daily climatic forecast, which is threatened by the lack of historical series of climatic features with 60 or more years in Brazil. Regarding the climatic forecast, the forecasts of solar radiation maximum and minimum temperatures and air moisture generated by the model Eta on every 6 hours between July 16, 1997 and June 15, 2002 were compared to the respective daily measurements of these climatic parameters. This provided subsidies for the conclusion that the data generated by the model Eta could be used in the Crop Forecast, except for the most southern and northeast regions in the Center-South region of Brazil. For the estimate of corn grain yield, a model based in the integration of equations that rule the plant physiology and development was proposed. Comparing corn grain yields estimated in 24 sites from the agricultural year 1997/98 to 2001/02, one concluded the possibility of estimating the corn grain yield for the studied region by the proposed model. Although the differences between the estimated and the measured yields in the most southern sites and in those with sandy soils indicate the demand for correction of the estimative of water dynamics performed by the proposed model. As a general conclusion, the methodology proposed for crop forecasting brings positive points which should be explored in the sense of its implementation in Brazil. On the other hand, this implementation depends substantially on the work management, propitiating the necessary conditions. One should highlight that the country has developed notably in this sector, such as the cases of the implementation of the national meteorological net and of the knowledge broadcasted by the Center of Climatic Studies and Forecasting and by the The Brazilian Agricultural Research Corporation (EMBRAPA), among other institutions. Even though, this area of knowledge - vital to an agricultural country as Brazil - demands more research.

Agriculture is one of the most important human activities providing food and more agricultural goods for seven billion people around the world and is of special importance in sub-Saharan Africa. The majority of people depends on the agricultural sector for their livelihoods and will suffer from negative climate change impacts on agriculture until the middle and end of the 21st century, even more if weak governments, economic crises or violent conflicts endanger the countries’ food security. The impact of temperature increases and changing precipitation patterns on agricultural vegetation motivated this thesis in the first place. Analyzing the potentials of reducing negative climate change impacts by adapting crop management to changing climate is a second objective of the thesis. As a precondition for simulating climate change impacts on agricultural crops with a global crop model first the timing of sowing in the tropics was improved and validated as this is an important factor determining the length and timing of the crops´ development phases, the occurrence of water stress and final crop yield. Crop yields are projected to decline in most regions which is evident from the results of this thesis, but the uncertainties that exist in climate projections and in the efficiency of adaptation options because of political, economical or institutional obstacles have to be considered. The effect of temperature increases and changing precipitation patterns on crop yields can be analyzed separately and varies in space across the continent. Southern Africa is clearly the region most susceptible to climate change, especially to precipitation changes. The Sahel north of 13° N and parts of Eastern Africa with short growing seasons below 120 days and limited wet season precipitation of less than 500 mm are also vulnerable to precipitation changes while in most other part of East and Central Africa, in contrast, the effect of temperature increase on crops overbalances the precipitation effect and is most pronounced in a band stretching from Angola to Ethiopia in the 2060s. The results of this thesis confirm the findings from previous studies on the magnitude of climate change impact on crops in sub-Saharan Africa but beyond that helps to understand the drivers of these changes and the potential of certain management strategies for adaptation in more detail. Crop yield changes depend on the initial growing conditions, on the magnitude of climate change, and on the crop, cropping system and adaptive capacity of African farmers which is only now evident from this comprehensive study for sub-Saharan Africa. Furthermore this study improves the representation of tropical cropping systems in a global crop model and considers the major food crops cultivated in sub-Saharan Africa and climate change impacts throughout the continent. / Landwirtschaft ist eine der wichtigsten menschlichen Aktivitäten, sie stellt Nahrungsmittel und andere landwirtschaftliche Produkte für weltweit 7 Milliarden Menschen zur Verfügung und ist in den Ländern Afrikas südlich der Sahara von besonderer Bedeutung. Die Mehrheit der afrikanischen Bevölkerung bestreitet ihren Lebensunterhalt in der Landwirtschaft und wird von Klimaänderungen stark betroffen sein. Die Doktorarbeit ist durch die Frage motiviert, wie sich von Klimamodellen vorhergesagte Temperaturerhöhungen und sich verändernde Niederschlagsverteilungen auf die landwirtschaftliche Vegetation auswirken werden. Die Forschungsfragen in diesem Kontext beschäftigen sich mit regionalen Unterschieden von Klimaänderungen und ihren Auswirkungen auf die Landwirtschaft und mit möglichen Anpassungsstrategien die mit geringem technischem Aufwand genutzt werden können. In diesem Zusammenhang wird schnell deutlich, dass Daten über die komplexen landwirtschaftlichen Systeme in Afrika südlich der Sahara häufig nur selten vorhanden sind, aus fragwürdigen Quellen stammen oder von schlechter Qualität sind. Die Methoden und Modelle zur Untersuchung der Auswirkungen von Klimaänderungen auf die Landwirtschaft werden zudem ausschließlich in Europa oder Nordamerika entwickelt and häufig in den temperierten Breiten aber seltener in tropischen Gebieten angewendet. Vor allem werden globale, dynamische Vegetationsmodelle in Kombination mit Klimamodellen eingesetzt um Änderungen in der landwirtschaftlichen Produktion auf Grund von Klimaänderungen in der zweiten Hälfte des 21.Jahrhunderts abzuschätzen. Die Ergebnisse der Arbeit zeigen einen mittleren Ertragsrückgang für die wichtigsten landwirtschaftlichen Pflanzen um 6% bis 24% bis 2090 je nach Region, Klimamodell und Anpassungsstrategie. Dieses Ergebnis macht deutlich, dass Landwirte die negativen Folgen von Klimaänderungen abschwächen können, wenn sie die Wahl der Feldfrucht, die Wahl des Anbausystems und den Aussaattermin an geänderte Klimabedingungen anpassen. Die Arbeit stellt methodische Ansätze zur Berechung des Aussaattermins in temperierten und tropischen Gebieten (Kapitel 2) sowie zur Simulation von Mehrfachanbausystemen in den Tropen vor (Kapitel 3). Dabei werden wichtige Parameter für das globale, dynamische Vegetationsmodell LPJmL überprüft und neu berechnet. Es zeigt sich, dass das südliche Afrika und die Sahelregion die am stärksten betroffenen Regionen sind, vor allem aufgrund von Niederschlagsänderungen, weniger aufgrund von Temperaturerhöhungen. In den meisten anderen Teilen, vor allem Zentral- und Ostafrikas bedingen Temperaturerhöhungen Rückgänge der Erträge (Kapitel 4). Diese Arbeit leistet einen wichtigen und umfassenden Beitrag zum Verständnis der Auswirkung von Klimaänderung auf die landwirtschaftliche Vegetation und damit zu einem großen Teil auf die Lebensgrundlage von afrikanischen Landwirten.

A Previsão de Safras tem se constituído em importante ferramenta para o estabelecimento de políticas agrícolas públicas e privadas. Em geral, a Previsão de Safras consiste na previsão do clima e na estimativa do rendimento das partes de interesse econômico de uma cultura. A previsão do clima pode ser realizada pela análise de séries históricas dos parâmetros climáticos e dos efeitos de fenômenos conhecidos, a exemplo do El Niño Oscilação Sul (ENSO), o qual pode ser medido pelo Índice de Oscilação Sul (IOS). Também pode ser realizada pela integração numérica das equações diferenciais que regem os movimentos da atmosfera no planeta Terra, também conhecida como previsão numérica. A estimativa do rendimento das culturas também pode ser realizada pela análise estatística de séries históricas ou pela integração numérica de equações diferenciais que regem a fisiologia e o desenvolvimento das plantas, ambos conhecidos como modelo de culturas. O principal objetivo deste trabalho foi propor uma metodologia para a Previsão de Safras no Brasil, tendo como ponto de partida e protótipo o estudo do rendimento de grãos de milho na região Centro-Sul do país. Para tanto, séries históricas com 60 anos de precipitação pluvial em 24 locais da região Centro-Sul do Brasil foram comparadas aos Índices de Oscilação Sul medidos no mesmo período, inferindo-se que o fenômeno ENSO apresenta efeito marcante, e distinto, apenas em locais mais ao Sul e a Nordeste da região Centro-Sul. Concluiu-se pela impossibilidade de utilização do IOS para a previsão de parâmetros climáticos diários, o que também é prejudicado pela carência de séries históricas dos parâmetros climáticos com 60 ou mais anos no Brasil. Ainda quanto à previsão do clima, as previsões de radiação solar, precipitação pluvial, temperaturas máxima e mínima e umidade relativa do ar, geradas pelo modelo Eta a cada seis horas entre os dias 16/7/1997 e 15/6/2002, foram comparadas às respectivas medidas diárias desses parâmetros climáticos, concluindo-se pela possibilidade da aplicação das previsões geradas pelo modelo Eta na Previsão de Safras, à exceção dos locais mais ao Sul e mais a Nordeste da região Centro-Sul do Brasil. Acerca da estimativa do rendimento de grãos de milho, foi proposto um modelo de cultura baseado na integração das equações que regem a fisiologia e o desenvolvimento das plantas. Comparando-se os rendimentos de grãos de milho estimados nos 24 locais durante as safras 1997/98 a 2001/02, conclui-se pela possibilidade da estimativa do rendimento de grãos de milho na região Centro-Sul pelo modelo proposto. Porém, as discrepâncias entre os rendimentos estimados e os respectivos rendimentos medidos nos locais mais ao Sul e nos locais com textura de solo arenosa apontam a necessidade de correção da estimativa da dinâmica de água realizada pelo modelo de cultura proposto. Como conclusão geral, verificou-se que a metodologia proposta para a Previsão de Safras tem virtudes que devem ser exploradas no sentido de sua implementação no Brasil. Porém, essa implementação depende substancialmente da gestão dos trabalhos, de modo a propiciar as condições necessárias. Cabe destacar que o país tem realizado notáveis avanços nesse setor, caso da implementação da rede meteorológica nacional e do conhecimento gerado pelo Centro de Estudos e Previsões do Clima e pela Empresa Brasileira de Pesquisa Agropecuária, entre outras instituições. Ainda assim, essa área do conhecimento, fundamental para um país agrícola como o Brasil, carece de estudos. / Crop forecast has become an important tool for the private and public agricultural policies to be established. Generally, crop forecast is composed by climatic forecast and the yield estimative of growth of economically interesting parts of crops. The climatic forecast can be performed through the analyses of historical series of the climatic features and of the known phenomena, such as the El Niño Southern Oscillation (ENSO), which can be measured by the Southern Oscillation Index (IOS). It can also be done through a numerical integration of differential equations that rule the atmospheric movements of the Earth, a.k.a. numerical forecast. The estimate of crop yields can also be done through the statistical analysis of historical series or through the integration of differential equations that rule the plant physiology and development, both known as crop models. The main objective of this study was to indicate a methodology for Crop Forecast in Brazil, having as a starting point and prototype the study of corn grain yield in the Center-South region of Brazil. Thus, historical series of 60 years of precipitation in 24 sites of the studied region were compared to the IOS measured in the same period, inferring that the phenomenon ENSO has a remarkable effect, distinctly in the most southern and northeast portions of the studied region. One concluded due to the impossibility of using the IOS for daily climatic forecast, which is threatened by the lack of historical series of climatic features with 60 or more years in Brazil. Regarding the climatic forecast, the forecasts of solar radiation maximum and minimum temperatures and air moisture generated by the model Eta on every 6 hours between July 16, 1997 and June 15, 2002 were compared to the respective daily measurements of these climatic parameters. This provided subsidies for the conclusion that the data generated by the model Eta could be used in the Crop Forecast, except for the most southern and northeast regions in the Center-South region of Brazil. For the estimate of corn grain yield, a model based in the integration of equations that rule the plant physiology and development was proposed. Comparing corn grain yields estimated in 24 sites from the agricultural year 1997/98 to 2001/02, one concluded the possibility of estimating the corn grain yield for the studied region by the proposed model. Although the differences between the estimated and the measured yields in the most southern sites and in those with sandy soils indicate the demand for correction of the estimative of water dynamics performed by the proposed model. As a general conclusion, the methodology proposed for crop forecasting brings positive points which should be explored in the sense of its implementation in Brazil. On the other hand, this implementation depends substantially on the work management, propitiating the necessary conditions. One should highlight that the country has developed notably in this sector, such as the cases of the implementation of the national meteorological net and of the knowledge broadcasted by the Center of Climatic Studies and Forecasting and by the The Brazilian Agricultural Research Corporation (EMBRAPA), among other institutions. Even though, this area of knowledge - vital to an agricultural country as Brazil - demands more research.