Looking beyond the average agricultural impacts in defining adaptation needs in Europe.

The effects of climate change on agriculture are often characterised by changes in the average productivity of crops; however, these indicators provide limited information regarding the risks associated with fluctuations in productivity resulting from future changes in climate variability that may also affect agriculture. In this context, this study evaluates the combined effects of the risks associated with anomalies reflected by changes in the mean crop yield and the variability of productivity in European agroclimatic regions under future climate change scenarios. The objective of this study is to evaluate adaptation needs and to identify regional effects that should be addressed with greater urgency in the light of the risks and opportunities that are identified. The results show differential effects on regional agriculture and highlight the importance of considering both regional average impacts and the variability in crop productivity in setting priorities for the adaptation and maintenance of rural incomes and agricultural insurance programmes

Generalización de variables medioambientales mediante interpolación GIS

La investigación de esta tesis se centra en el estudio de técnicas geoestadísticas y su contribución a una mayor caracterización del binomio factores climáticos-rendimiento de un cultivo agrícola. El inexorable vínculo entre la variabilidad climática y la producción agrícola cobra especial relevancia en estudios sobre el cambio climático o en la modelización de cultivos para dar respuesta a escenarios futuros de producción mundial. Es información especialmente valiosa en sistemas operacionales de monitoreo y predicción de rendimientos de cultivos Los cuales son actualmente uno de los pilares operacionales en los que se sustenta la agricultura y seguridad alimentaria mundial; ya que su objetivo final es el de proporcionar información imparcial y fiable para la regularización de mercados. Es en este contexto, donde se quiso dar un enfoque alternativo a estudios, que con distintos planteamientos, analizan la relación inter-anual clima vs producción. Así, se sustituyó la dimensión tiempo por la espacio, re-orientando el análisis estadístico de correlación interanual entre rendimiento y factores climáticos, por el estudio de la correlación inter-regional entre ambas variables. Se utilizó para ello una técnica estadística relativamente nueva y no muy aplicada en investigaciones similares, llamada regresión ponderada geográficamente (GWR, siglas en inglés de “Geographically weighted regression”). Se obtuvieron superficies continuas de las variables climáticas acumuladas en determinados periodos fenológicos, que fueron seleccionados por ser factores clave en el desarrollo vegetativo de un cultivo. Por ello, la primera parte de la tesis, consistió en un análisis exploratorio sobre comparación de Métodos de Interpolación Espacial (MIE). Partiendo de la hipótesis de que existe la variabilidad espacial de la relación entre factores climáticos y rendimiento, el objetivo principal de esta tesis, fue el de establecer en qué medida los MIE y otros métodos geoestadísticos de regresión local, pueden ayudar por un lado, a alcanzar un mayor entendimiento del binomio clima-rendimiento del trigo blando (Triticum aestivum L.) al incorporar en dicha relación el componente espacial; y por otro, a caracterizar la variación de los principales factores climáticos limitantes en el crecimiento del trigo blando, acumulados éstos en cuatro periodos fenológicos. Para lleva a cabo esto, una gran carga operacional en la investigación de la tesis consistió en homogeneizar y hacer los datos fenológicos, climáticos y estadísticas agrícolas comparables tanto a escala espacial como a escala temporal. Para España y los Bálticos se recolectaron y calcularon datos diarios de precipitación, temperatura máxima y mínima, evapotranspiración y radiación solar en las estaciones meteorológicas disponibles. Se dispuso de una serie temporal que coincidía con los mismos años recolectados en las estadísticas agrícolas, es decir, 14 años contados desde 2000 a 2013 (hasta 2011 en los Bálticos). Se superpuso la malla de información fenológica de cuadrícula 25 km con la ubicación de las estaciones meteorológicas con el fin de conocer los valores fenológicos en cada una de las estaciones disponibles. Hecho esto, para cada año de la serie temporal disponible se calcularon los valores climáticos diarios acumulados en cada uno de los cuatro periodos fenológicos seleccionados P1 (ciclo completo), P2 (emergencia-madurez), P3 (floración) y P4 (floraciónmadurez). Se calculó la superficie interpolada por el conjunto de métodos seleccionados en la comparación: técnicas deterministas convencionales, kriging ordinario y cokriging ordinario ponderado por la altitud. Seleccionados los métodos más eficaces, se calculó a nivel de provincias las variables climatológicas interpoladas. Y se realizaron las regresiones locales GWR para cuantificar, explorar y modelar las relaciones espaciales entre el rendimiento del trigo y las variables climáticas acumuladas en los cuatro periodos fenológicos. Al comparar la eficiencia de los MIE no destaca una técnica por encima del resto como la que proporcione el menor error en su predicción. Ahora bien, considerando los tres indicadores de calidad de los MIE estudiados se han identificado los métodos más efectivos. En el caso de la precipitación, es la técnica geoestadística cokriging la más idónea en la mayoría de los casos. De manera unánime, la interpolación determinista en función radial (spline regularizado) fue la técnica que mejor describía la superficie de precipitación acumulada en los cuatro periodos fenológicos. Los resultados son más heterogéneos para la evapotranspiración y radiación. Los métodos idóneos para estas se reparten entre el Inverse Distance Weighting (IDW), IDW ponderado por la altitud y el Ordinary Kriging (OK). También, se identificó que para la mayoría de los casos en que el error del Ordinary CoKriging (COK) era mayor que el del OK su eficacia es comparable a la del OK en términos de error y el requerimiento computacional de este último es mucho menor. Se pudo confirmar que existe la variabilidad espacial inter-regional entre factores climáticos y el rendimiento del trigo blando tanto en España como en los Bálticos. La herramienta estadística GWR fue capaz de reproducir esta variabilidad con un rendimiento lo suficientemente significativo como para considerarla una herramienta válida en futuros estudios. No obstante, se identificaron ciertas limitaciones en la misma respecto a la información que devuelve el programa a nivel local y que no permite desgranar todo el detalle sobre la ejecución del mismo. Los indicadores y periodos fenológicos que mejor pudieron reproducir la variabilidad espacial del rendimiento en España y Bálticos, arrojaron aún, una mayor credibilidad a los resultados obtenidos y a la eficacia del GWR, ya que estaban en línea con el conocimiento agronómico sobre el cultivo del trigo blando en sistemas agrícolas mediterráneos y norteuropeos. Así, en España, el indicador más robusto fue el balance climático hídrico Climatic Water Balance) acumulado éste, durante el periodo de crecimiento (entre la emergencia y madurez). Aunque se identificó la etapa clave de la floración como el periodo en el que las variables climáticas acumuladas proporcionaban un mayor poder explicativo del modelo GWR. Sin embargo, en los Bálticos, países donde el principal factor limitante en su agricultura es el bajo número de días de crecimiento efectivo, el indicador más efectivo fue la radiación acumulada a lo largo de todo el ciclo de crecimiento (entre la emergencia y madurez). Para el trigo en regadío no existe ninguna combinación que pueda explicar más allá del 30% de la variación del rendimiento en España. Poder demostrar que existe un comportamiento heterogéneo en la relación inter-regional entre el rendimiento y principales variables climáticas, podría contribuir a uno de los mayores desafíos a los que se enfrentan, a día de hoy, los sistemas operacionales de monitoreo y predicción de rendimientos de cultivos, y éste es el de poder reducir la escala espacial de predicción, de un nivel nacional a otro regional. ABSTRACT This thesis explores geostatistical techniques and their contribution to a better characterization of the relationship between climate factors and agricultural crop yields. The crucial link between climate variability and crop production plays a key role in climate change research as well as in crops modelling towards the future global production scenarios. This information is particularly important for monitoring and forecasting operational crop systems. These geostatistical techniques are currently one of the most fundamental operational systems on which global agriculture and food security rely on; with the final aim of providing neutral and reliable information for food market controls, thus avoiding financial speculation of nourishments of primary necessity. Within this context the present thesis aims to provide an alternative approach to the existing body of research examining the relationship between inter-annual climate and production. Therefore, the temporal dimension was replaced for the spatial dimension, re-orienting the statistical analysis of the inter-annual relationship between crops yields and climate factors to an inter-regional correlation between these two variables. Geographically weighted regression, which is a relatively new statistical technique and which has rarely been used in previous research on this topic was used in the current study. Continuous surface values of the climate accumulated variables in specific phenological periods were obtained. These specific periods were selected because they are key factors in the development of vegetative crop. Therefore, the first part of this thesis presents an exploratory analysis regarding the comparability of spatial interpolation methods (SIM) among diverse SIMs and alternative geostatistical methodologies. Given the premise that spatial variability of the relationship between climate factors and crop production exists, the primary aim of this thesis was to examine the extent to which the SIM and other geostatistical methods of local regression (which are integrated tools of the GIS software) are useful in relating crop production and climate variables. The usefulness of these methods was examined in two ways; on one hand the way this information could help to achieve higher production of the white wheat binomial (Triticum aestivum L.) by incorporating the spatial component in the examination of the above-mentioned relationship. On the other hand, the way it helps with the characterization of the key limiting climate factors of soft wheat growth which were analysed in four phenological periods. To achieve this aim, an important operational workload of this thesis consisted in the homogenization and obtention of comparable phenological and climate data, as well as agricultural statistics, which made heavy operational demands. For Spain and the Baltic countries, data on precipitation, maximum and minimum temperature, evapotranspiration and solar radiation from the available meteorological stations were gathered and calculated. A temporal serial approach was taken. These temporal series aligned with the years that agriculture statistics had previously gathered, these being 14 years from 2000 to 2013 (until 2011 for the Baltic countries). This temporal series was mapped with a phenological 25 km grid that had the location of the meteorological stations with the objective of obtaining the phenological values in each of the available stations. Following this procedure, the daily accumulated climate values for each of the four selected phenological periods were calculated; namely P1 (complete cycle), P2 (emergency-maturity), P3 (flowering) and P4 (flowering- maturity). The interpolated surface was then calculated using the set of selected methodologies for the comparison: deterministic conventional techniques, ordinary kriging and ordinary cokriging weighted by height. Once the most effective methods had been selected, the level of the interpolated climate variables was calculated. Local GWR regressions were calculated to quantify, examine and model the spatial relationships between soft wheat production and the accumulated variables in each of the four selected phenological periods. Results from the comparison among the SIMs revealed that no particular technique seems more favourable in terms of accuracy of prediction. However, when the three quality indicators of the compared SIMs are considered, some methodologies appeared to be more efficient than others. Regarding precipitation results, cokriging was the most accurate geostatistical technique for the majority of the cases. Deterministic interpolation in its radial function (controlled spline) was the most accurate technique for describing the accumulated precipitation surface in all phenological periods. However, results are more heterogeneous for the evapotranspiration and radiation methodologies. The most appropriate technique for these forecasts are the Inverse Distance Weighting (IDW), weighted IDW by height and the Ordinary Kriging (OK). Furthermore, it was found that for the majority of the cases where the Ordinary CoKriging (COK) error was larger than that of the OK, its efficacy was comparable to that of the OK in terms of error while the computational demands of the latter was much lower. The existing spatial inter-regional variability between climate factors and soft wheat production was confirmed for both Spain and the Baltic countries. The GWR statistic tool reproduced this variability with an outcome significative enough as to be considered a valid tool for future studies. Nevertheless, this tool also had some limitations with regards to the information delivered by the programme because it did not allow for a detailed break-down of its procedure. The indicators and phenological periods that best reproduced the spatial variability of yields in Spain and the Baltic countries made the results and the efficiency of the GWR statistical tool even more reliable, despite the fact that these were already aligned with the agricultural knowledge about soft wheat crop under mediterranean and northeuropean agricultural systems. Thus, for Spain, the most robust indicator was the Climatic Water Balance outcome accumulated throughout the growing period (between emergency and maturity). Although the flowering period was the phase that best explained the accumulated climate variables in the GWR model. For the Baltic countries where the main limiting agricultural factor is the number of days of effective growth, the most effective indicator was the accumulated radiation throughout the entire growing cycle (between emergency and maturity). For the irrigated soft wheat there was no combination capable of explaining above the 30% of variation of the production in Spain. The fact that the pattern of the inter-regional relationship between the crop production and key climate variables is heterogeneous within a country could contribute to one is one of the greatest challenges that the monitoring and forecasting operational systems for crop production face nowadays. The present findings suggest that the solution may lay in downscaling the spatial target scale from a national to a regional level.

Geochemistry, isotopic ratios and aldehyde concentrations of frost flowers, snow and ice near Barrow, Alaska

Frost flowers, intricate featherlike crystals that grow on refreezing sea ice leads, have been implicated in lower atmospheric chemical reactions. Few studies have presented chemical composition information for frost flowers over time and many of the chemical species commonly associated with Polar tropospheric reactions have never been reported for frost flowers. We undertook this study on the sea ice north of Barrow, Alaska to quantify the major ion, stable oxygen and hydrogen isotope, alkalinity, light absorbance by soluble species, organochlorine, and aldehyde composition of seawater, brine, and frost flowers. For many of these chemical species we present the first measurements from brine or frost flowers. Results show that major ion and alkalinity concentrations, stable isotope values, and major chromophore (NO3- and H2O2) concentrations are controlled by fractionation from seawater and brine. The presence of these chemical species in present and future sea ice scenarios is somewhat predictable. However, aldehydes, organochlorine compounds, light absorbing species, and mercury (part 2 of this research and Sherman et al. (2012, doi:10.1029/2011JD016186)) are deposited to frost flowers through less predictable processes that probably involve the atmosphere as a source. The present and future concentrations of these constituents in frost flowers may not be easily incorporated into future sea ice or lower atmospheric chemistry scenarios. Thinning of Arctic sea ice will likely present more open sea ice leads where young ice, brine, and frost flowers form. How these changing ice conditions will affect the interactions between ice, brine, frost flowers and the lower atmosphere is unknown.

Do upwelled waters from the California Undercurrent contribute to high pCO2 within the fjord system of Nootka Sound, BC?

Senior thesis written for Oceanography 445

Influência das condições ambientais no verdor da vegetação da caatinga frente às mudanças climáticas

The Caatinga biome, a semi-arid climate ecosystem found in northeast Brazil, presents low rainfall regime and strong seasonality. It has the most alarming climate change projections within the country, with air temperature rising and rainfall reduction with stronger trends than the global average predictions. Climate change can present detrimental results in this biome, reducing vegetation cover and changing its distribution, as well as altering all ecosystem functioning and finally influencing species diversity. In this context, the purpose of this study is to model the environmental conditions (rainfall and temperature) that influence the Caatinga biome productivity and to predict the consequences of environmental conditions in the vegetation dynamics under future climate change scenarios. Enhanced Vegetation Index (EVI) was used to estimate vegetation greenness (presence and density) in the area. Considering the strong spatial and temporal autocorrelation as well as the heterogeneity of the data, various GLS models were developed and compared to obtain the best model that would reflect rainfall and temperature influence on vegetation greenness. Applying new climate change scenarios in the model, environmental determinants modification, rainfall and temperature, negatively influenced vegetation greenness in the Caatinga biome. This model was used to create potential vegetation maps for current and future of Caatinga cover considering 20% decrease in precipitation and 1 °C increase in temperature until 2040, 35% decrease in precipitation and 2.5 °C increase in temperature in the period 2041-2070 and 50% decrease in precipitation and 4.5 °C increase in temperature in the period 2071-2100. The results suggest that the ecosystem functioning will be affected on the future scenario of climate change with a decrease of 5.9% of the vegetation greenness until 2040, 14.2% until 2070 and 24.3% by the end of the century. The Caatinga vegetation in lower altitude areas (most of the biome) will be more affected by climatic changes.

Seawater carbonate chemistry and microbioerosion of coral skeletons

Biological mediation of carbonate dissolution represents a fundamental component of the destructive forces acting on coral reef ecosystems. Whereas ocean acidification can increase dissolution of carbonate substrates, the combined impact of ocean acidification and warming on the microbioerosion of coral skeletons remains unknown. Here, we exposed skeletons of the reef-building corals, Porites cylindrica and Isopora cuneata, to present-day (Control: 400 µatm - 24 °C) and future pCO2-temperature scenarios projected for the end of the century (Medium: +230 µatm - +2 °C; High: +610 µatm - +4 °C). Skeletons were also subjected to permanent darkness with initial sodium hypochlorite incubation, and natural light without sodium hypochlorite incubation to isolate the environmental effect of acidic seawater (i.e., Omega aragonite <1) from the biological effect of photosynthetic microborers. Our results indicated that skeletal dissolution is predominantly driven by photosynthetic microborers, as samples held in the dark did not decalcify. In contrast, dissolution of skeletons exposed to light increased under elevated pCO2-temperature scenarios, with P. cylindrica experiencing higher dissolution rates per month (89%) than I. cuneata (46%) in the high treatment relative to control. The effects of future pCO2-temperature scenarios on the structure of endolithic communities were only identified in P. cylindrica and were mostly associated with a higher abundance of the green algae Ostreobium spp. Enhanced skeletal dissolution was also associated with increased endolithic biomass and respiration under elevated pCO2-temperature scenarios. Our results suggest that future projections of ocean acidification and warming will lead to increased rates of microbioerosion. However, the magnitude of bioerosion responses may depend on the structural properties of coral skeletons, with a range of implications for reef carbonate losses under warmer and more acidic oceans.

Climate-Driven Range Shifts Within Benthic Habitats Across a Marine Biogeographic Transition Zone.

Anthropogenic climate change is causing unprecedented rapid responses in marine communities, with species across many different taxonomic groups showing faster shifts in biogeographic ranges than in any other ecosystem. Spatial and temporal trends for many marine species are difficult to quantify, however, due to the lack of long-term datasets across complete geographical distributions and the occurrence of small-scale variability from both natural and anthropogenic drivers. Understanding these changes requires a multidisciplinary approach to bring together patterns identified within long-term datasets and the processes driving those patterns using biologically relevant mechanistic information to accurately attribute cause and effect. This must include likely future biological responses, and detection of the underlying mechanisms in order to scale up from the organismal level to determine how communities and ecosystems are likely to respond across a range of future climate change scenarios. Using this multidisciplinary approach will improve the use of robust science to inform the development of fit-for-purpose policy to effectively manage marine environments in this rapidly changing world.

Climate-Driven Range Shifts Within Benthic Habitats Across a Marine Biogeographic Transition Zone.

Anthropogenic climate change is causing unprecedented rapid responses in marine communities, with species across many different taxonomic groups showing faster shifts in biogeographic ranges than in any other ecosystem. Spatial and temporal trends for many marine species are difficult to quantify, however, due to the lack of long-term datasets across complete geographical distributions and the occurrence of small-scale variability from both natural and anthropogenic drivers. Understanding these changes requires a multidisciplinary approach to bring together patterns identified within long-term datasets and the processes driving those patterns using biologically relevant mechanistic information to accurately attribute cause and effect. This must include likely future biological responses, and detection of the underlying mechanisms in order to scale up from the organismal level to determine how communities and ecosystems are likely to respond across a range of future climate change scenarios. Using this multidisciplinary approach will improve the use of robust science to inform the development of fit-for-purpose policy to effectively manage marine environments in this rapidly changing world.

Climate change and impacts on family farming in the North and Northeast of Brazil.

The starting point for this study was the consideration of future climate change scenarios and their uncertainties. The paper presents the global projections from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and compares them with regional scenarios (downscaling) developed by the Brazilian National Institute for Space Research (Instituto Nacional de Pesquisas Espaciais - INPE), with a focus on two main IPCC scenarios (RCP4.5 and RCP8.5) and two main global models (MIROC and Hadley Centre) for the periods 2011-2040 and 2041-2070. It aims to identify the main trends in terms of changes in temperature and precipitation for the North and Northeast regions of Brazil (more specifically, in the Amazon, semi-arid and cerrado biomes).

Understanding, defining and assessing food systems’ social and economic sustainability indicators: implementation of mixed methodologies for policy insights

Sustainability encompasses the presence of three dimensions that must coexist simultaneously, namely the environmental, social, and economic ones. The economic and social dimensions are gaining the spotlight in recent years, especially within food systems. To assess social and economic impacts, indicators and tools play a fundamental role in contributing to the achievements of sustainability targets, although few of them have deepen the focus on social and economic impacts. Moreover, in a framework of citizen science and bottom-up approach for improving food systems, citizen play a key role in defying their priorities in terms of social and economic interventions. This research expands the knowledge of social and economic sustainability indicators within the food systems for robust policy insights and interventions. This work accomplishes the following objectives: 1) to define social and economic indicators within the supply chain with a stakeholder perspective, 2) to test social and economic sustainability indicators for future food systems engaging young generations. The first objective was accomplished through the development of a systematic literature review of 34 social sustainability tools, based on five food supply chain stages, namely production, processing, wholesale, retail, and consumer considering farmers, workers, consumers, and society as stakeholders. The second objective was achieved by defining and testing new food systems social and economic sustainability indicators through youth engagement for informed and robust policy insights, to provide policymakers suggestions that would incorporate young generations ones. Future food systems scenarios were evaluated by youth through focus groups, whose results were analyzed through NVivo and then through a survey with a wider platform. Conclusion addressed the main areas of policy interventions in terms of social and economic aspects of sustainable food systems youth pointed out as in need of interventions, spanning from food labelling reporting sustainable origins to better access to online food services.

Assessing scenarios on the future of work

In this paper will be discussed different types of scenarios and the aims for using scenarios. Normaly they are being used by organisations due to the need to anticipate processes, to support policy-making and to understand the complexities of relations. Such organisations can be private companies, R&D organisations and networks of organisations, or even by some public administration institutions. Some cases will be discussed as the methods for ongoing scenario-building process (Shell Internacional). Scenarios should anticipate possible relations among social actors as in the Triple Helix Model, and is possible to develop strategic intelligence in the innovation process that would enable the construction of scenarios. Such processes can be assessed. The focus will be made in relation to the steps chosen for the WORKS scenarios. In this case is there a model of work changes that can be used for foresight? Differences according to sectors were found, as well on other dimensions. Problems of assessment are analysed with specific application to the scenario construction methods.

The International Multilateral System: Imperatives of Change, Potential Scenarios and Suggestions for the Future

Thesis presented to satisfy the necessary requirements for obtaining a PhD degree in International Relation with specialization in Globalization and the Environment,

Un modelo contable para la evaluación económico-financiera en escenarios futuros de la empresa agropecuaria. An accounting model for the economic and financial evaluation of future scenarios in farm enterprises.

El problema: En todo momento y sobre todo cuando estamos en presencia de escenarios económicos turbulentos resulta imprescindible utilizar herramientas que permitan realizar análisis de sensitividad sobre las distintas situaciones que podrían plantearse. La elaboración de modelos matemáticos deterministas desde las aplicaciones realizadas por Richard Mattessich han constituido un instrumento idóneo para el caso de empresas comerciales o industriales. Los modelos informáticos utilizados para las empresas agropecuarias han abordado fundamentalmente la temática relacionada con la producción, no así las otras variables económicas y financieras. Por lo tanto, entendemos que se hace necesario trabajar con modelos agropecuarios que comprendan todas las variables económicas y financieras, de manera de observar otro tipo de cuestiones, tales como: el modo de financiarse, los costos financieros, necesidades de capital de trabajo. Hipótesis: Es posible, a través de la utilización de la información contable en sentido prospectivo, interpretar adecuadamente los escenarios futuros de las organizaciones agropecuarias, cuantificando los impactos que generan tanto las estrategias y políticas aplicables, como las distorsiones del contexto. Objetivo general: determinar la incidencia de las decisiones internas y las que provengan del funcionamiento del sistema económico, a través de la información contable prospectiva. Objetivos específicos: a. Describir los impactos que se producen en la estructura patrimonial, financiera y en los resultados, como consecuencia de los cambios en las estrategias y políticas de la empresa agropecuaria, así como los efectos macroeconómicos en la estructura de la empresa que pudieran estar conmoviendo la gestión económico-financiera. b. Identificar mecanismos y proponer criterios para la elaboración de modelos que permitan visualizar los impactos en los escenarios futuros y las adecuaciones necesarias en la estructura que permitan soportar las modificaciones. Metodología: será un estudio a nivel teórico, donde una vez identificadas las variables y planteados los modelos, se propondrán distintas situaciones y se testearán las respuestas. Resultados esperados: lograr un avance en la evaluación económico-financiera prospectiva de empresas agropecuarias y constituir un avance para futuras investigaciones. Importancia del proyecto: La producción agropecuaria es vital tanto para el desarrollo económico de Argentina, como en particular para la provincia de Córdoba. Elaborar herramientas que eficientizen la administración de este tipo de empresas, redundará en beneficio colectivo. Pertinencia: El producto verificable será la construcción de un modelo distinto a los actuales, tanto en su desarrollo, objetivo al que está destinado y sencillez de su aplicación, posibilitando la inserción del productor en el proceso de planificación, reduciendo el riesgo en la toma de decisiones. Esperando generar un avance sobre los modelos preexistente.

Climate change and future scenarios for palisade grass production in the state of São Paulo, Brazil

The objective of this work was to analyze future scenarios for palisade grass yield subjected to climate change for the state of São Paulo, Brazil. An empirical crop model was used to estimate yields, according to growing degree-days adjusted by one drought attenuation factor. Climate data from 1963 to 2009 of 23 meteorological stations were used for current climate conditions. Downscaled outputs of two general circulation models were used to project future climate for the 2013-2040 and 2043-2070 periods, considering two contrasting scenarios of temperature and atmospheric CO2 concentration increase (high and low). Annual dry matter yield should be from 14 to 42% higher than the current one, depending on the evaluated scenario. Yield variation between seasons (seasonality) and years is expected to increase. The increase of dry matter accumulation will be higher in the rainy season than in the dry season, and this result is more evident for soils with low-water storage capacity. The results varied significantly between regions (<10% to >60%). Despite their higher climate potential, warmer regions will probably have a lower increase in future forage production.