1,173 research outputs found

    The Functioning of Ecosystems

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    The ecosystems present a great diversity worldwide and use various functionalities according to ecologic regions. In this new context of variability and climatic changes, these ecosystems undergo notable modifications amplified by domestic uses of which it was subjected to. Indeed the ecosystems render diverse services to humanity from their composition and structure but the tolerable levels are unknown. The preservation of these ecosystemic services needs a clear understanding of their complexity. The role of the research is not only to characterise the ecosystems but also to clearly define the tolerable usage levels. Their characterisation proves to be important not only for the local populations that use it but also for the conservation of biodiversity. Hence, the measurement, management and protection of ecosystems need innovative and diverse methods. For all these reasons, the aim of this book is to bring out a general view on the biogeochemical cycles, the ecological imprints, the mathematical models and theories applicable to many situations

    Modeling regional supply responses using farm-level economic data and a biophysical model: a case study on Brazilian land-use change

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    Estimating farmers’ supply responses to changes in framework conditions is important to inform decision-makers on the expected impacts on production volume as well as the resulting land-use shifts. Existing agricultural supply response models generally require either larger databases with farm-level data for microregional analysis or are implemented with a coarse resolution (e.g., country level) due to the lack of data. While such approaches are suitable for regions with abundancy of data or for global-scale analysis, there is a need for an alternative for micro-level analysis in countries with low data availability. In addition, it is important to include the spatial component in the regional supply response analysis, allowing not only the quantification of the overall change in output but also the likely spatial land-use change. Against this background, this dissertation aims to answer the research question whether a combination of a biophysical model with farm-level economic data can be used to estimate farm-level profitability of individual crops and respective cropping systems and thereby simulate farmers’ supply responses in countries with limited data availability. To answer this question, a new modeling approach called Profitability Assessment Model (PAM) is developed, tested and validated. This new modeling approach follows the principles of minimum data, focusing on delivering timely and quantitative analyses with satisfactory accuracy to inform decision-makers. That is an important feature since the overall goal of the concept is to limit the data required by the model to a minimum, allowing quick implementation while accepting moderate accuracy. The PAM is a spatially explicit model with simulation units’ size of spatial resolution grid varying between 5 and 30 arcmin (10x10 to 50x50 km in area), following that used by the Global Biosphere Management Model (GLOBIOM). PAM estimates the profitability of each farming alternative at the simulation unit level and allocates the land to maximize farmers’ return to land. The PAM model is developed and calibrated for the Brazilian agricultural sector. Using Brazil as the case study is interesting due to its overall importance in the global production of agricultural commodities as well as the environmental impact of land-use changes. For this case study, four production system are represented in the PAM model: (a) double cropping of soybeans and maize, (b) soybeans with a cover crop, (c) sugarcane monoculture and (d) beef production. While the profitability of the arable crops is endogenously estimated, beef is considered as an opt-out option, which is modeled based on exogenous return-to-land information. Since soybean, maize and sugarcane production accounts for 84% of the total seeded area in Brazil, the current version of the PAM model represents the most important cropping alternatives to farmers in Brazil, but not all. An important methodological contribution of the dissertation is the development of routines for the extrapolation of each production cost component from the known typical farms’ data to all regions in the country. These routines are based on local expertise as well as existing information on yield levels, prevailing production systems and farming conditions. Each cost component is analyzed individually and, based on theoretical discussions, specific cost functions are proposed following the expected behavior of each cost item – e.g., linear relationship with yields or fixed per ha. That should improve the accuracy of the model in estimating production costs (and finally profitability) while also allowing the model to be adapted to simulate changes in framework conditions that may affect only selected cost items (e.g., a significant increase in fuel prices). In addition, the PAM model improves on existing models because it accounts for specific cost components such as the transport of sugarcane from farm to mill, which is required due to the perishability of the crop. Besides the important impact of inbound transport cost on the overall profitability of sugarcane production, the endogenous simulation of this cost item allows the model to spatially differentiate among regions depending on the current availability of mills. A major constraint for regional profitability analysis is the lack of information regarding farm input and output prices. To overcome this problem, the PAM model provides an interesting alternative by endogenously estimating prices via the transport module. By considering the different transportation costs of each crop and basing the distance estimation on the actual availability of roads, the model allows a straightforward conversion of reference prices to farm-gate prices. The ability to endogenously simulate transport cost is a useful feature for the simulation of scenarios based on price shocks. Apart from the development of the modeling approach, this dissertation focuses on the quantitative model validation as a key step to identify strengths and limitations of the concept. Projected yields are validated against regional statistics and production cost estimates are benchmarked against the two available datasets, with a suitable number of primary typical-farm data. Furthermore, the resulting land-use maps are evaluated against two simplified validation maps representing current land use. In the business-as-usual scenario, the PAM model estimates a national weighted average of returns to land of 248 USD/ha for double cropping and 188 USD/ha for sugarcane. This relationship, however, is different in the states of Sao Paulo and Minas Gerais, where, on average, sugarcane has a higher return to land than double cropping. Benchmarking PAM’s production cost estimates with observed local data shows a satisfactory model accuracy with a relative mean absolute error (rMAE) lower than 14%. The lowest error found in the production cost estimation is in sugarcane (rMAE of 8.7%) and the highest in second-crop maize (rMAE of 14%). The validation of the business-as-usual land-use map shows that the PAM model is able to satisfactorily reproduce the current land use in Brazil. The visual and quantitative validation results show a strong correlation between the available land-use maps, with PAM allocating the same crop as observed in 86% of total arable land. To test the ability of the PAM model to predict land-use and output changes due to changing framework conditions, a scenario analysis is carried out: What will happen in case yields of key crops change significantly as a consequence of climate change? Due to the strong reduction in the returns to land for grains (i.e., maize and soybeans) in the tropical region more than 24% of the current arable land is simulated to move from grains to sugarcane production. These results, however, vary significantly in the different regions, where the most affected states are Goiás, Paraná and Mato Grosso, jointly accounting for more than 55% of the total land-use change. This dissertation contributes to the overall development of regional farmers’ supply response models for countries with limited data availability, showing that it is feasible to combine a biophysical model and farm-level economic data as the basis for the profitability estimation in a high spatial resolution. The ability to estimate individual cost components separately gives the model the required flexibility for the simulation of market- and policy-related questions, providing timely and accurate information for decision-makers. The bottom-up approach based on local expertise is an important strength of the PAM model, avoiding unrealistic parametrization and ensuring that the majority of local features of production systems are included in the estimation. Finally, considering the overall goal of using minimum data, the model accuracy indicates a strong potential of the model to answer research questions, with additional parametrization and integration expected to further improve its performance.2021-11-1

    Spreading of Antarctic Bottom Water in the Atlantic Ocean

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    This paper describes the transport of bottom water from its source region in the Weddell Sea through the abyssal channels of the Atlantic Ocean. The research brings together the recent observations and historical data. A strong flow of Antarctic Bottom Water through the Vema Channel is analyzed. The mean speed of the flow is 30 cm/s. A temperature increase was found in the deep Vema Channel, which has been observed for 30 years already. The flow of bottom water in the northern part of the Brazil Basin splits. Part of the water flows through the Romanche and Chain fracture zones. The other part flows to the North American Basin. Part of the latter flow propagates through the Vema Fracture Zone into the Northeast Atlantic. The properties of bottom water in the Kane Gap and Discovery Gap are also analyzed

    Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using Deep Learning

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    Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

    Impact of climate change on agricultural and natural ecosystems

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    This book illustrates the main results deriving from fourteen studies, dealing with the impact of climate change on different agricultural and natural ecosystems, carried out within the Impact of Climate change On agricultural and Natural Ecosystems (ICONE) project funded by the ALFA Programme of the European Commission. During this project, a common methodology on several Global Change-related matters was developed and shared among members of scientific communities coming from Latin America and Europe. In order to facilitate this interdisciplinary approach, specific mobility programmes, addressed to post-graduate, Master and PhD students, have been organized. The research, led by the research groups, was focused on the study of the impact of climate change on various environmental features (i.e. runoff in hydrological basins, soil erosion and moisture, forest canopy, sugarcane crop, land use, drought, precipitation, etc). Integrated and shared methodologies of atmospheric physics, remote sensing, eco-physiology and modelling have been applied

    Local Production and Use of bio-ethanol for Transport in Ethiopia

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    This thesis describes bio-ethanol production and use for transport fuel in Ethiopia, focusing on current status and potential, barriers, implications and recommendations for a sustainable market. The need for such research is justified as it paves the way for bio-ethanol development in Ethiopia towards a more sustainable path as challenges are surmounted. Bio-ethanol from molasses is identified in Ethiopia to substitute gasoline in as much quantity as possible as an attempt to reduce the burden of foreign currency requirement for importation of petroleum products. Despite the availability of bio-ethanol and the potential to produce more, as well as repetitive efforts to use bio-ethanol for transport fuel, it has not materialized yet. The following research questions are addressed to investigate the problem and put forward recommendations. (1) What is the current status and future potential of bioethanol from molasses in Ethiopia? (2) What are the barriers to and implications of the production and use of bio-ethanol? (3) What lessons can be taken from Brazil to stimulate sustainable bio-ethanol development in Ethiopia? Different approaches were utilized to investigate these questions. The theoretical background on main issues of bio-ethanol that include barriers and implications associated with the development of bio-ethanol at an international level were investigated from literature. Experience of Brazil on the successful bio-ethanol development was studied. A detailed contextual assessment was investigated to determine the status and future potential of bio-ethanol production and use, the main players in the supply chain (actors, networks and institutions), the barriers and implications. The result indicates that technical and economic as well as issues related to policies and regulations are main factors for hindering the progress of the bio-ethanol use and production expansion in Ethiopia. Based on Brazil’s experience, recommendations were forwarded to overcome the barriers and minimize the negative implications to speed-up the development of a sustainable bio-ethanol market in Ethiopia

    The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts

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    To effectively meet growing food demands, the global agronomic community will require a better understanding of factors that are currently limiting crop yields and where production can be viably expanded with minimal environmental consequences. Remote sensing can inform these analyses, providing valuable spatiotemporal information about yield-limiting moisture conditions and crop response under current climate conditions. In this paper we study correlations for the period 2003-2013 between yield estimates for major crops grown in Brazil and the Evaporative Stress Index (ESI) - an indicator of agricultural drought that describes anomalies in the actual/reference evapotranspiration (ET) ratio, retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The strength and timing of peak ESI-yield correlations are compared with results using remotely sensed anomalies in water supply (rainfall from the Tropical Rainfall Mapping Mission; TRMM) and biomass accumulation (LAI from the Moderate Resolution Imaging Spectroradiometer; MODIS). Correlation patterns were generally similar between all indices, both spatially and temporally, with the strongest correlations found in the south and northeast where severe flash droughts have occurred over the past decade, and where yield variability was the highest. Peak correlations tended to occur during sensitive crop growth stages. At the state scale, the ESI provided higher yield correlations for most crops and regions in comparison with TRMM and LAI anomalies. Using finer scale yield estimates reported at the municipality level, ESI correlations with soybean yields peaked higher and earlier by 10 to 25 days in comparison to TRMM and LAI, respectively. In most states, TRMM peak correlations were marginally higher on average with municipality-level annual corn yield estimates, although these estimates do not distinguish between primary and late season harvests. A notable exception occurred in the northeastern state of Bahia, where the ESI better captured effects of rapid cycling of moisture conditions on corn yields during a series of flash drought events. The results demonstrate that for monitoring agricultural drought in Brazil, value is added by combining LAI with LST indicators within a physically based model of crop water use. Published by Elsevier Inc.Embrapa Visiting Scientist Program ; Labex US, an international scientific cooperation program - Brazilian Agricultural Research Corporation - Embrapa, ; United States Department of Agriculture (USDA
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