The current state of the art in water quality profiling is reviewed to lay a foundation in addressing concerns over poor data in developing countries which has not been adequately covered by previous models. A particular focus is made on Ogbese River, southwest Nigeria as a case study. A process-based model with data-filling capability is projected which transforms processes into an event as a reasonably easy way for assessing and predicting river-water quality in the event of constraints in data collection. The structure of the study involves: (i) hydrologic modelling, (ii) hydraulic load modelling and (iii) instream water quality modelling. The hydrologic modelling assesses and makes use of satellite based rainfall estimates subject to processing and reliability tests. A modification to the conceptual relationship of rainfall distribution frequency which makes the model output sensitive to the season was derived. The hydraulic load modelling integrates diffuse sources of pollutant as spatial data in combination with the catchment runoff. A distance decay weighing factor was introduced into the export coefficient to better determine the effective load delivered into the stream. The utility of the model, implemented on WASP platform, was demonstrated by showing how it can be used for scenario testing. Different modelling concepts were evaluated in view of their ability to produce predictions under changing circumstances using the predictions as guide to management. This study promotes a knowledge base in water quality processes by evaluation of the processes which lead to the end product rather than using data monitoring. The study structures understanding of the phenomena that characterises river water quality and tailors it towards regulatory applications and catchment planning. It, also, provides a sustainable strategy to predict the river water quality, evaluate the risks, and take proactive action in setting up an early warning system, for data-poor regions.

Carbon (C) pools and fluxes in northern hemisphere forest ecosystems are attracting increasing attention concerning predicted climate change. This thesis studied C fluxes, particularly soil C dynamics, in spruce forest ecosystems in relation to interactions between physical/biological processes using a process-based ecosystem model (CoupModel) with data for Swedish conditions. The model successfully described general patterns of C and N dynamics in managed spruce forest ecosystems with both tree and field layers. Using regional soil and plant data, the change in current soil C pools was -3 g C m-2 yr-1 in northern Sweden and +24 g C m-2 yr-1 in southern Sweden. Simulated climate change scenarios resulted in increased inflows of 16-38 g C m-2 yr-1 to forest ecosystems throughout Sweden, with the highest increase in the south and the lowest in the north. Along a north-south transect, this increased C sequestration mainly related to increased tree growth, as there were only minor decreases in soil C pools. Measurements at one northern site during 2001-2002 indicated large soil C losses (-96 g C m-2 yr-1), which the model successfully described. However, the discrepancy between these large losses and substantially smaller losses obtained in regional simulations was not explained. A simulation based on Bayesian calibration successfully reproduced measured C, water and energy fluxes, with estimated uncertainties for major components of the simulated C budget. Site-specific measurements indicated a large contribution from field layer fine roots to total litter production, particularly in northern Sweden. Mean annual tree litter production was 66% higher at the most southerly site (240 g C m-2 yr-1 compared with 145 g C m-2 yr-1 in the north), but when field and bottom layers were included the difference decreased to 16% (total litter production 276 g C m-2 yr-1 and 239 g C m-2 yr-1 respectively). Regional simulations showed that decomposition rate for the stable soil C fraction was three times higher in northern regions compared with southern, providing a possible explanation why soil C pools in southern Sweden are roughly twice as large as those in the north. / QC 20100922

The biogeochemical processes responsible for production and emission of ammonia from stored liquid dairy manure are governed by environmental factors (e.g. manure temperature, moisture) and manure characteristics (e.g. total ammoniacal nitrogen concentration, pH). These environmental factors and manure characteristics vary spatially as a result of spatially heterogeneous physical, chemical, and biological properties of manure. Existing process-based models used for estimating ammonia emission consider stored manure as a homogeneous system and do not consider these spatial variations leading to inaccurate estimations. In this study, a one-dimensional compartmental biogeochemical model was developed to (i) estimate spatial variation of temperature and substrate concentration (ii) estimate spatial variations and rates of biogeochemical processes, and (iii) estimate production and emission of ammonia from stored scraped liquid dairy manure. A one-dimension compartmentalized modeling approach was used whereby manure storage is partitioned into several sections in vertical domain assuming that the conditions are spatially uniform within the horizontal domain. Spatial variation of temperature and substrate concentration were estimated using established principles of heat and mass transfer. Pertinent biogeochemical processes were assigned to each compartment to estimate the production and emission of ammonia. Model performance was conducted using experimental data obtained from National Air Emissions Monitoring Study conducted by the United States Environmental Protection Agency. A sensitivity analysis was performed and air temperature, manure pH, wind speed, and manure total ammoniacal nitrogen concentration were identified as the most sensitive model inputs. The model was used to estimate ammonia emission from a liquid dairy manure storage of a dairy farm located in Rockingham and Franklin counties in Virginia. Ammonia emission was estimated under different management and weather scenarios: two different manure storage periods from November to April and May to October using historical weather data of the two counties. Results suggest greater ammonia emissions and manure nitrogen loss for the manure storage period in warm season from May to October compared to the storage period in cold season from November to April. / Ph. D. / Dairy manure is a byproduct of dairy farming that can be used as a fertilizer to provide essential plant nutrients such as nitrogen, phosphorus, and potassium. However, manure can only be applied to crop lands in a certain time of the year during growing seasons. Further, discharge of dairy manure into natural environment is prevented by the environmental regulations. Therefore, manure storage structures are used to store liquid dairy manure until time permits for land application or use for other purposes. During the storage, liquid dairy manure goes through biological, chemical, and physical processes and release manure gases that are linked to deteriorate human and animal health and contribute to environmental pollution. Ammonia is one of the manure gases released to atmosphere from stored liquid dairy manure. Furthermore, release of ammonia from stored manure reduce nitrogen content and reduce fertilizer value of stored manure. Implementing control measures to mitigate ammonia emission is necessary to prevent ammonia emission and reduce nitrogen loss from stored manure. Deciding and applying of appropriate control measures require knowledge of the rate at which ammonia emission occurs and when ammonia emission occurs. Use of process-based models is one of the less expensive and reliable method for estimating ammonia emission from stored liquid dairy manure. Process-based model is a mathematical model that simulates processes related to ammonia production and emission from stored manure. Even though, there are several process-based models available for estimating ammonia emission from stored liquid dairy manure, these models do not fully represent the actual processes and conditions relevant to production and emission of ammonia. For instance, spatial variation of temperature and total ammoniacal nitrogen concentration within stored manure is not considered in existing process-based models. Therefore, in this study a new compartmental process-based model was developed for estimating these spatial variations and production and emission of ammonia from stored liquid dairy manure. The model uses weather data and manure management information as inputs for estimating ammonia emission and nitrogen loss. The performance evaluation of the compartmental process-based model revealed that air temperature, manure pH, wind speed, manure total ammoniacal nitrogen concentration are important model inputs for estimating ammonia emission from stored liquid dairy manure. The model was used to estimate ammonia emission from a dairy farm located in Rockingham and Franklin counties in Virginia. Results suggest greater ammonia emissions and manure nitrogen loss for the manure storage period in warm season from May to October compared to the storage period in cold season from November to April.

This thesis presents the first modeling study on long-term carbon dynamics for the University of Guelph Elora Agricultural Research Station and the Environment Canada Borden Forest Research Station at the daily and half-hourly time-step. The daily version of the CENTURY (DayCENT) model and the Carbon- and Nitrogen-coupled Canadian Land Surface Scheme (CN-CLASS) model were validated for quantifying the effects of agricultural management and component respiration on the carbon budget. DayCENT indicated that conventional tillage (CT) enhanced the annual heterotrophic respiration relative to no-till (NT) by 38.4, 93.7 and 64.2 g C m-2 yr-1 for corn, soybean and winter wheat, respectively. The seasonal variation of total soil organic carbon (SOC) pool was greater in CT than NT due to tillage effects on carbon transfer from the active surface SOC pool to the active soil SOC pool at a rate of 50-100 g C m-2 yr-1. NT accounted for a 10.7 g C m-2 yr-1 increase in the slow SOC pool (20-year turnover time) at a site in Elora, Ontario, Canada. I found that the plant phenology algorithms used in CN-CLASS were not constructed and validated for crop growth, resulting in a high degree of uncertainty in the simulations. Therefore, I designed and tested a new agricultural module for CN-CLASS. The regression analysis indicated that the new crop module improved the net ecosystem productivity (NEP) simulation for a cornfield, with the coefficient of determination (r2) of annual NEP increasing from 0.51 in the original CN-CLASS to 0.78 in the modified version of the model. I verified CN-CLASS to simulate the dynamics of component respiration for tracing the contributions from litterfall, SOC and root respiration in a deciduous mixedwood forest in Borden, Ontario, Canada. The model estimated that the annual ecosystem CO2 respiration was 1366 g C m-2 yr-1, contributed by heterotrophic respiration (57%), maintenance respiration (37%) and growth respiration (6%). The annual accumulated soil respiration was estimated at 782 g C m-2 yr-1, which was dominated by CO2 emissions from soil organic matter (60%). The base respiration rates required further verification based on field measurements. Based on the verified modeling approach in this thesis, the modeling core of DayCENT can be constructed as an integral platform for Agriculture and Agri-Food Canada National Carbon and Greenhouse Gas Accounting and Verification System. The crop phenological module in CN-CLASS allows us to conduct further agricultural studies concerning global carbon budget and environmental change. The validated respiration algorithms in CN-CLASS would be helpful in developing global biological CO2 transport model for tracing emission sources. / Natural Science and Engineering Research Council of Canada

Freshwater zooplankton communities are highly sensitive to environmental change and are critical indicators of water quality. Zooplankton are central organisms in freshwater food webs, composed of diverse taxa playing different functional roles in freshwater food webs as food sources for upper trophic level predators (e.g., fish and invertebrates) and as grazers of phytoplankton. Therefore, changes in zooplankton community density, biomass, composition, and migration behavior over time have direct implications for trophic level interactions and water quality. Climate change has altered freshwater ecosystem functioning through several mechanisms, including warming surface waters, declining dissolved oxygen concentrations, and changes in the timing and magnitude of phytoplankton blooms, each of which has implications for zooplankton communities. To better understand and predict zooplankton community responses to variable environmental conditions due to climate change, I used field, laboratory, modeling, and forecasting approaches. First, I assessed zooplankton community structure and migration across five 24-hour field sampling campaigns that spanned three years in a eutrophic, temperate reservoir. Specifically, I intensively sampled zooplankton dynamics across different sampling days, hours within a day, and reservoir sites and found that zooplankton community structure and migration was most variable among sampling days, suggesting that routine water quality monitoring programs aiming to characterize zooplankton should prioritize sampling efforts over several days to capture the greatest variability. Second, I used field data and multivariate analyses to assess patterns and drivers of zooplankton taxon density over six summers in the same reservoir. My findings suggested that zooplankton communities in years with warmer surface waters, lower precipitation, deeper Secchi depths, higher Schmidt stability, and lower epilimnetic nutrient concentrations favored rotifer dominance and lower cyclopoid densities. Third, I used a process-based ecosystem model to examine how warming air temperatures affect zooplankton biomass and community composition over an eight-year time series in the reservoir. I showed that warming temperatures promote greater rotifer biomass and lower crustacean biomass, which has implications for water quality. Finally, I forecasted reservoir water temperature from 1-35 days into the future using different observation frequencies to identify the lowest temporal frequency of data assimilation required to generate accurate forecasts. I found that weekly observations could be used to generate accurate water temperature forecasts up to a week in advance. This work highlighted that accurate forecasts may not necessarily require the most high-frequency observations, and that observation frequency is likely dependent on the variable and time horizon of interest. Generating accurate water temperature forecasts is particularly relevant for future development of zooplankton forecasts that need accurate water temperature forecasts as model driver data. Overall, my dissertation explores the dynamic relationship between freshwater zooplankton communities and water quality, highlighting the high variability in zooplankton structure, migration behavior, and environmental drivers over time. I demonstrate how zooplankton responses to climate change vary by taxon and emphasize their role in shaping freshwater food webs and ecosystem functioning, underscoring the important role of zooplankton communities in mediating water quality. / Doctor of Philosophy / Lakes and reservoirs are important ecosystems that provide drinking water, food, and recreation. However, climate change and other human activities are altering the functioning of these ecosystems and the benefits they provide. One well-documented change is that lake and reservoir surface water temperatures are warming during the summer in many regions. These rising temperatures can impact both living organisms and physical and chemical processes in freshwater environments. Predicting how organisms will respond to warming temperatures is challenging because their dynamics depend on both interactions with other organisms and changing environmental conditions. My Ph.D. research focused on freshwater zooplankton because they play a key role in lake and reservoir ecosystems—serving as both food for fish and helping to control phytoplankton by grazing. When zooplankton are abundant, they can reduce phytoplankton blooms, resulting in better water quality. The goal of my Ph.D. dissertation was to understand how zooplankton communities respond to climate change and how these responses in turn impact water quality. I used both field surveys and computer models to study freshwater zooplankton communities. Field surveys were used to identify how zooplankton communities vary over time and space in a reservoir over three years, while models were used to explore how climate change might affect zooplankton in the future. My findings suggest that as temperatures rise, smaller zooplankton that are less effective at controlling phytoplankton are likely to become more common. This shift could lead to more frequent and intense phytoplankton blooms, worsening water quality. Because zooplankton play a critical role in maintaining healthy freshwater ecosystems, understanding how they respond to climate change can help us predict and manage future changes to lake and reservoir water quality.

Le transport de contaminants, l’altération des roches ainsi que les grands cycles biogéochimiques sont contrôlés par les temps de séjour de l’eau. Ces temps de séjour représentent le temps de transit durant lequel l’eau « voyage » dans un bassin versant. Contraindre ces temps de transit est donc un enjeu essentiel pour quantifier l’impact de l’homme sur la qualité de l’eau en rivières et dans les aquifères et pour évaluer la résilience des écosystèmes aquatiques continentaux. Cependant, les rivières comme les nappes phréatiques sont constituées d’un mélange d’eau de différents âges (une distribution des temps de transit ou des temps de résidence) qui varie avec le temps, en fonction des aléas météorologiques et climatiques, rendant difficile leur caractérisation ainsi que leur prédiction. Dans cette thèse, nous inférons ces temps de résidence à l’aide de traceurs géochimiques et de modèles guidés par les données. Nous montrons comment cette connaissance permet de quantifier l’altération des roches cristallines. Nous développons ensuite un cadre original de modélisation à base physique, capable de représenter la variabilité saisonnière et interannuelle des débits et des temps de transit mesurés en rivière. Nous montrons comment le processus de battements de nappes et son interaction avec les couches perméables du sol mène à la génération d’un ruissellement qui explique les fluctuations saisonnières de qualité de l’eau en rivières, traduites par des mesures de silice dissoute. Enfin, nous esquissons un cadre général de représentation de la réactivité à l’échelle du versant capable de rendre compte des processus biogéochimiques. En effet, représenter la dégradation des éléments réactifs (oxygène, nitrates, carbone) permettra d’évaluer les mesures de réduction d’intrants agricoles, de prédire l’évolution long terme de ces solutés en rivières, et donc leur potentiel d’eutrophisation ainsi que d’évaluer des mesures pour réconcilier agriculture et environnement. Cette réactivité apparaît comme le dernier maillon manquant pour comprendre, mesurer et prédire, les impacts anthropiques sur la zone critique. / Groundwater travel time controls contaminant transport, weathering processes and biogeochemical cycles. Groundwater travel time is a fundamental descriptor characterizing the transit time of water inside the catchment, from precipitation events to the streams. Quantifying these transit times is pivotal to predict the impact of anthropogenic pressure and assess freshwater ecosystems resilience. However, streamwater and groundwater are a mixture of water of different ages (the transit time and the residence time distribution), which vary according to climatic forcings. This makes difficult its characterization and prediction. Here we infer residence times with geochemical tracers and data-driven models. We show how this can be constrained by silicate weathering at the catchment scale. We then develop a novel process-based framework, which can model discharge and transit time seasonal and interannual variabilities. We identify water table fluctuations, its interaction with permeable soil layers and the resulting subsurface stormflow generation as a key process for seasonal water quality variations described by dissolved silica measurements. Finally, we draw a reactivity framework to represent biogeochemical processes. Indeed, evaluating reactive solute degradation is needed to assess the efficiency of reducing fertilizer loads, to predict the long term evolution of in stream solute concentrations and the eutrophication potential of freshwater bodies. Modeling the reactivity at the catchment scale is the missing link to understand, quantify and predict the effect of anthropogenic pressure on the critical zone.

Sugarcane crop is the main source of sugar and the second largest source of biofuel in the world. Since the 1980s, Brazil has been the largest sugarcane producing nation, producing half of the global amount. Ethanol and biomass from sugarcane account for more than 15% of the country´s energy source. Nevertheless, commercial Brazilian sugarcane yield has plateaued at 75 t ha-1, and to meet the increasing demand for sugar and ethanol, the crop has strongly expanded towards central-western regions, where irrigation is mandatory to offset water stress risks. To support decision making and scientific guidance towards where and how the crop should expand and/or to increase yields, a heuristic view of the crop system is needed, which can mathematically be translated into a crop model. In turn, the effects of crop management, land use change, climate variability and agro-economic change factors on crop production and associated quantities can and have been assessed by using crop process-based models (PBM). In contrast to other crops, however, sugarcane has only two PBMs available for end users (DSSAT-CANEGRO and APSIM-Sugar), and further modifications of these models are required to better assess and support sustainable sugarcane production in Brazil. Therefore, this study aimed to develop, calibrate and evaluate different crop modelling approaches for Brazilian sugarcane farming systems, water management strategies, climate change impacts and canopy structures to support improved decisions for private and public stakeholders in the sugarcane sector, provide scientific guidance and establish a Brazilian platform of crop simulations. A new version of the sugarcane process-based model (SAMUCA) was developed to operate at phytomer level, focusing on soil mulch effects on crop growth and development, tillering process under competition for light and sucrose accumulation based on source-sink relations. The model was embedded into a modular platform dedicated to simulating the soil-plant-atmosphere and the management of the sugarcane farm system. The previous version of SAMUCA was also re-structured and coupled to the SWAP (Soil, Water, Atmosphere and Plant) agrohydrological model platform, focusing on soil water relations to crop growth. Moreover, a Functional-Structural Plant Model (FSPM) for sugarcane was developed by integrating the main crop components at the organ level (phytomer), based on a relative source-sink approach and a robust light model embedded into a three-dimensional modelling platform (GroIMP). All approaches were evaluated, and the performance under experimental conditions for different Brazilian conditions was determined. The performance of the new version of SAMUCA in a long-term experiment and under different Brazilian conditions was satisfactory, with agreement indices close to those of other widely used sugarcane crop models (CANEGRO and APSIM-Sugar). In addition, the modulated crop simulation platform can be used to host more crop models and integrate new features of Brazilian farming systems. The coupling of the SWAP-SAMUCA model was accomplished, and although non-expressive improvements in model performance regarding crop yield were noticed (with an overall 6% lower RMSE), the ability of SWAP-SAMUCA to simulate soil water content was higher than that of the original \"tipping bucket\" approach (32% lower RMSE). The Functional-Structural Plant Model for sugarcane was able to satisfactorily simulate canopy development, tillering and sucrose accumulation at the organ level and its integration at the whole-plant level. Besides its ability to simulate competition for light, helping to understand intra-specific competition among tillers, the sugarcane FSPM framework can be used to support sucrose accumulation and translocation mechanism studies as well as intercropping studies for sugarcane, which has already successfully been done for other crops. / A cultura da cana-de-açúcar é a principal fonte de açúcar e a segunda maior fonte de biocombustíveis do mundo. O Brasil é o maior produtor mundial desde a década de 80 e atualmente representa metade da produção mundial, enquanto que ao mesmo tempo o etanol e a biomassa correspondem a mais de 15% da fonte de energia do país. Contudo, a produtividade comercial da cana-de-açúcar brasileira atingiu um limiar de cerca de 75 t ha-1 e para atender à crescente demanda de açúcar e etanol, a cultura expandiu-se fortemente para a região centro-oeste, onde a irrigação é obrigatória para manter os níveis de produção e diminuir riscos de quebra de safra. Para dar suporte a tomada de decisão e avanço científico sobre onde e como a cultura deve se expandir e/ou aumentar a produtividade, é necessária uma visão heurística do sistema agrícola brasileiro que pode ser traduzida matematicamente para um modelo de cultura. Desta forma, os efeitos do manejo e tipo de solo, variabilidade climática e fatores econômicos na produtividade de culturas agrícolas podem ser avaliados quantitativamente por meio de modelos de culturas baseados em processos (MBP). No entanto, em contraste a outras culturas, a cana-de-açúcar possui apenas dois MBPs disponíveis para usuários finais (DSSAT-CANEGRO e APSIM-Sugar) que requerem calibração e parametrização para melhor representar o sistema agrícola de cana-de-açúcar do Brasil. Portanto, este estudo teve como objetivo desenvolver, calibrar e avaliar diferentes abordagens de modelagem de culturas voltadas a produção de cana-de-açúcar no Brasil, para servir como ferramenta de tomada de decisão para o setor público e privado, auxilio no manejo da água e avaliação dos impactos nas mudanças climáticas. Portanto, uma nova versão do modelo baseado em processo de cana-de-açúcar (SAMUCA) foi desenvolvida para operar a nível de fitômeros, incluindo os efeitos no crescimento e desenvolvimento da cana com base na cobertura da palha no solo, competição por luz no processo de perfilhamento e acúmulo de sacarose com base nas relações fonte-dreno. O modelo foi incorporado em uma plataforma modular dedicada a simular o sistema solo-planta-atmosfera e manejo do sistema agrícola. Além disso, a versão anterior do SAMUCA também foi reestruturada e acoplada à plataforma agro-hidrológica SWAP (\"Soil, Water, Atmosphere and Plant\") com objetivo de aprimorar as simulações de balanço hídrico no solo e efeito no crescimento da cana-de-açúcar. Por fim, um Modelo Funcional-Estrutural de Plantas (MFEP) para a cana-de-açúcar foi desenvolvido integrando os principais componentes da cultura a nível de órgãos (fitômeros) com base em uma abordagem de fonte-dreno e um modelo robusto de radiação que foram introduzidos em uma plataforma de modelagem tridimensional (GroIMP). As três abordagens foram avaliadas e seu desempenho foi determinado com base em condições experimentais para diferentes regiões brasileiras. O desempenho da nova versão do modelo SAMUCA em experimento de longo prazo e em diferentes condições brasileiras foi satisfatório e os índices de concordância foram próximos de outros modelos de cana-de-açúcar amplamente utilizados (CANEGRO e APSIM-Sugar). Além disso, a plataforma de simulação de culturas modulada pode ser usada para hospedar mais modelos de culturas e integrar novas características do sistema de cultivo brasileiro. O acoplamento do modelo SWAP-SAMUCA foi realizado e apesar não apresentar melhorias expressivas no desempenho do modelo em simular os componentes da cultura (com erro médio quadrático [RMSE] 6% menor), a habilidade do modelo SWAP-SAMUCA em simular o teor de água no solo mostrou-se consideravelmente superior em comparação ao modelo original (RMSE 32% menor). O MFEP para cana-de-açúcar foi capaz de simular o desenvolvimento do dossel, o processo de perfilhamento e o acúmulo de sacarose ao nível de órgãos e planta de forma satisfatória. Além de sua capacidade em simular com precisão a interceptação da radiação por cada estrutura do dossel, podendo auxiliar na compreensão do processo de competição intraespecífica entre perfilhos, a estrutura do MFEP da cana-de-açúcar também pode ser usada no apoio à pesquisa focando os mecanismos de acúmulo de sacarose e translocação de açúcares bem como em estudos de consórcio em cana-de-açúcar, como têm sido realizado com sucesso para outras culturas nos últimos anos.

Forests are a valuable resource for humans providing a range of products and services such as construction timber, paper and fuel wood, recreation, as well as living quarters for indigenous populations and habitats for many animal and bird species. Most recent international political agreements such as the Kyoto Protocol emphasise the role of forests as a major sink for atmospheric carbon dioxide mitigation. However, forest areas are rapidly decreasing world wide. Thus, it is vital that efficient strategies and tools are developed to encourage sustainable ecosystem management. These tools must be based on known ecological principles (such as tree physiological and soil nutrient cycle processes), capable of supplying fast and accurate temporal and spatial predictions of the effects of management on both timber production and carbon sequestration. This thesis had two main objectives. The first was to investigate the environmental factors affecting growth and carbon sequestration of Scots pine (Pinus sylvestris L.) across Scotland, by developing a knowledge base through a statistical analysis of old and novel field datasets. Furthermore, the process-based ecosystem model 3-PGN was developed, by coupling the existing models 3-PG and ICBM. 3-PGN calibrated using a Bayesian approach based on Monte Carlo Markov Chain simulations and it was validated for plantation stands. Sensitivity and uncertainty analyses provided an understanding of the internal feedbacks of the model. Further simulations gave a detailed eco-physiological interpretation of the environmental factors affecting Scots pine growth and it provided an assessment of carbon sequestration under the scenario of sustainable, normal production and its effects from the environment. Finally, the study investigated the spatial and temporal patterns of timber production and carbon sequestration by using the spatial version of the model and applying advanced spatial analyses techniques. The second objective was to help close the gap between environmental research and forest management, by setting a strategic framework for a process-based tool for sustainable ecosystem management. The thesis demonstrated the procedures for a site classification scheme based on modelling results and a yield table validation procedure, which can provide a way forward in supporting policies for forest management and ensuring their continued existence in the face of the present and future challenges.

Sugarcane crop is the main source of sugar and the second largest source of biofuel in the world. Since the 1980s, Brazil has been the largest sugarcane producing nation, producing half of the global amount. Ethanol and biomass from sugarcane account for more than 15% of the country´s energy source. Nevertheless, commercial Brazilian sugarcane yield has plateaued at 75 t ha-1, and to meet the increasing demand for sugar and ethanol, the crop has strongly expanded towards central-western regions, where irrigation is mandatory to offset water stress risks. To support decision making and scientific guidance towards where and how the crop should expand and/or to increase yields, a heuristic view of the crop system is needed, which can mathematically be translated into a crop model. In turn, the effects of crop management, land use change, climate variability and agro-economic change factors on crop production and associated quantities can and have been assessed by using crop process-based models (PBM). In contrast to other crops, however, sugarcane has only two PBMs available for end users (DSSAT-CANEGRO and APSIM-Sugar), and further modifications of these models are required to better assess and support sustainable sugarcane production in Brazil. Therefore, this study aimed to develop, calibrate and evaluate different crop modelling approaches for Brazilian sugarcane farming systems, water management strategies, climate change impacts and canopy structures to support improved decisions for private and public stakeholders in the sugarcane sector, provide scientific guidance and establish a Brazilian platform of crop simulations. A new version of the sugarcane process-based model (SAMUCA) was developed to operate at phytomer level, focusing on soil mulch effects on crop growth and development, tillering process under competition for light and sucrose accumulation based on source-sink relations. The model was embedded into a modular platform dedicated to simulating the soil-plant-atmosphere and the management of the sugarcane farm system. The previous version of SAMUCA was also re-structured and coupled to the SWAP (Soil, Water, Atmosphere and Plant) agrohydrological model platform, focusing on soil water relations to crop growth. Moreover, a Functional-Structural Plant Model (FSPM) for sugarcane was developed by integrating the main crop components at the organ level (phytomer), based on a relative source-sink approach and a robust light model embedded into a three-dimensional modelling platform (GroIMP). All approaches were evaluated, and the performance under experimental conditions for different Brazilian conditions was determined. The performance of the new version of SAMUCA in a long-term experiment and under different Brazilian conditions was satisfactory, with agreement indices close to those of other widely used sugarcane crop models (CANEGRO and APSIM-Sugar). In addition, the modulated crop simulation platform can be used to host more crop models and integrate new features of Brazilian farming systems. The coupling of the SWAP-SAMUCA model was accomplished, and although non-expressive improvements in model performance regarding crop yield were noticed (with an overall 6% lower RMSE), the ability of SWAP-SAMUCA to simulate soil water content was higher than that of the original \"tipping bucket\" approach (32% lower RMSE). The Functional-Structural Plant Model for sugarcane was able to satisfactorily simulate canopy development, tillering and sucrose accumulation at the organ level and its integration at the whole-plant level. Besides its ability to simulate competition for light, helping to understand intra-specific competition among tillers, the sugarcane FSPM framework can be used to support sucrose accumulation and translocation mechanism studies as well as intercropping studies for sugarcane, which has already successfully been done for other crops. / A cultura da cana-de-açúcar é a principal fonte de açúcar e a segunda maior fonte de biocombustíveis do mundo. O Brasil é o maior produtor mundial desde a década de 80 e atualmente representa metade da produção mundial, enquanto que ao mesmo tempo o etanol e a biomassa correspondem a mais de 15% da fonte de energia do país. Contudo, a produtividade comercial da cana-de-açúcar brasileira atingiu um limiar de cerca de 75 t ha-1 e para atender à crescente demanda de açúcar e etanol, a cultura expandiu-se fortemente para a região centro-oeste, onde a irrigação é obrigatória para manter os níveis de produção e diminuir riscos de quebra de safra. Para dar suporte a tomada de decisão e avanço científico sobre onde e como a cultura deve se expandir e/ou aumentar a produtividade, é necessária uma visão heurística do sistema agrícola brasileiro que pode ser traduzida matematicamente para um modelo de cultura. Desta forma, os efeitos do manejo e tipo de solo, variabilidade climática e fatores econômicos na produtividade de culturas agrícolas podem ser avaliados quantitativamente por meio de modelos de culturas baseados em processos (MBP). No entanto, em contraste a outras culturas, a cana-de-açúcar possui apenas dois MBPs disponíveis para usuários finais (DSSAT-CANEGRO e APSIM-Sugar) que requerem calibração e parametrização para melhor representar o sistema agrícola de cana-de-açúcar do Brasil. Portanto, este estudo teve como objetivo desenvolver, calibrar e avaliar diferentes abordagens de modelagem de culturas voltadas a produção de cana-de-açúcar no Brasil, para servir como ferramenta de tomada de decisão para o setor público e privado, auxilio no manejo da água e avaliação dos impactos nas mudanças climáticas. Portanto, uma nova versão do modelo baseado em processo de cana-de-açúcar (SAMUCA) foi desenvolvida para operar a nível de fitômeros, incluindo os efeitos no crescimento e desenvolvimento da cana com base na cobertura da palha no solo, competição por luz no processo de perfilhamento e acúmulo de sacarose com base nas relações fonte-dreno. O modelo foi incorporado em uma plataforma modular dedicada a simular o sistema solo-planta-atmosfera e manejo do sistema agrícola. Além disso, a versão anterior do SAMUCA também foi reestruturada e acoplada à plataforma agro-hidrológica SWAP (\"Soil, Water, Atmosphere and Plant\") com objetivo de aprimorar as simulações de balanço hídrico no solo e efeito no crescimento da cana-de-açúcar. Por fim, um Modelo Funcional-Estrutural de Plantas (MFEP) para a cana-de-açúcar foi desenvolvido integrando os principais componentes da cultura a nível de órgãos (fitômeros) com base em uma abordagem de fonte-dreno e um modelo robusto de radiação que foram introduzidos em uma plataforma de modelagem tridimensional (GroIMP). As três abordagens foram avaliadas e seu desempenho foi determinado com base em condições experimentais para diferentes regiões brasileiras. O desempenho da nova versão do modelo SAMUCA em experimento de longo prazo e em diferentes condições brasileiras foi satisfatório e os índices de concordância foram próximos de outros modelos de cana-de-açúcar amplamente utilizados (CANEGRO e APSIM-Sugar). Além disso, a plataforma de simulação de culturas modulada pode ser usada para hospedar mais modelos de culturas e integrar novas características do sistema de cultivo brasileiro. O acoplamento do modelo SWAP-SAMUCA foi realizado e apesar não apresentar melhorias expressivas no desempenho do modelo em simular os componentes da cultura (com erro médio quadrático [RMSE] 6% menor), a habilidade do modelo SWAP-SAMUCA em simular o teor de água no solo mostrou-se consideravelmente superior em comparação ao modelo original (RMSE 32% menor). O MFEP para cana-de-açúcar foi capaz de simular o desenvolvimento do dossel, o processo de perfilhamento e o acúmulo de sacarose ao nível de órgãos e planta de forma satisfatória. Além de sua capacidade em simular com precisão a interceptação da radiação por cada estrutura do dossel, podendo auxiliar na compreensão do processo de competição intraespecífica entre perfilhos, a estrutura do MFEP da cana-de-açúcar também pode ser usada no apoio à pesquisa focando os mecanismos de acúmulo de sacarose e translocação de açúcares bem como em estudos de consórcio em cana-de-açúcar, como têm sido realizado com sucesso para outras culturas nos últimos anos.

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