Assessing the solar irradiation potential for solar photovoltaic applications in buildings at low latitudes ¿ Making the case for Brazil

In Brazil, a low-latitude country characterized by its high availability and uniformity of solar radiation, the use of PV solar energy integrated in buildings is still incipient. However, at the moment there are several initiatives which give some hints that lead to think that there will be a change shortly. In countries where this technology is already a daily reality, such as Germany, Japan or Spain, the recommendations and basic criteria to avoid losses due to orientation and tilt are widespread. Extrapolating those measures used in high latitudes to all regions, without a previous deeper analysis, is standard practice. They do not always correspond to reality, what frequently leads to false assumptions and may become an obstacle in a country which is taking the first step in this area. In this paper, the solar potential yield for different surfaces in Brazilian cities (located at latitudes between 0° and 30°S) are analyzed with the aim of providing the necessary tools to evaluate the suitability of the buildings’ envelopes for photovoltaic use

Dynamical influences of El Niño on maize yield in Spain

Climate variability and changes in the frequency of extremes events have a direct impact on crop damages and yield. In a former work of Capa et al. (2013) the crop yield variability has been studied using different reanalyses datasets with the aim of extending the time series of potential yield. The reliability of these time series have been checked using observational data. The influence of the sea surface temperature on the crop yield variability has been studied, finding a relation with El Niño phenomenon. The highest correlation between El Niño and yield was during 1960-1980. This study aims to analyse the dynamical mechanism of El Niño impacts on maize yield in Spain during 1960-1980 by comparison with atmospheric circulation patterns.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

Refined sandwich model for the vibration of beams with embedded shear piezoelectric actuators and sensors

This work extends a previously presented refined sandwich beam finite element (FE) model to vibration analysis, including dynamic piezoelectric actuation and sensing. The mechanical model is a refinement of the classical sandwich theory (CST), for which the core is modelled with a third-order shear deformation theory (TSDT). The FE model is developed considering, through the beam length, electrically: constant voltage for piezoelectric layers and quadratic third-order variable of the electric potential in the core, while meclianically: linear axial displacement, quadratic bending rotation of the core and cubic transverse displacement of the sandwich beam. Despite the refinement of mechanical and electric behaviours of the piezoelectric core, the model leads to the same number of degrees of freedom as the previous CST one due to a two-step static condensation of the internal dof (bending rotation and core electric potential third-order variable). The results obtained with the proposed FE model are compared to available numerical, analytical and experimental ones. Results confirm that the TSDT and the induced cubic electric potential yield an extra stiffness to the sandwich beam. (C) 2007 Elsevier Ltd. All rights reserved.

Managing the nutrition of plants and people

One definition of food security is having sufficient, safe, and nutritious food to meet dietary needs. This paper highlights the role of plant mineral nutrition in food production, delivering of essential mineral elements to the human diet, and preventing harmful mineral elements entering the food chain. To maximise crop production, the gap between actual and potential yield must be addressed. This gap is 15–95% of potential yield, depending on the crop and agricultural system. Current research in plant mineral nutrition aims to develop appropriate agronomy and improved genotypes, for both infertile and productive soils, that allow inorganic and organic fertilisers to be utilised more efficiently. Mineral malnutrition affects two-thirds of the world's population. It can be addressed by the application of fertilisers, soil amelioration, and the development of genotypes that accumulate greater concentrations of mineral elements lacking in human diets in their edible tissues. Excessive concentrations of harmful mineral elements also compromise crop production and human health. To reduce the entry of these elements into the food chain, strict quality requirements for fertilisers might be enforced, agronomic strategies employed to reduce their phytoavailability, and crop genotypes developed that do not accumulate high concentrations of these elements in edible tissues.

Arable crop rotations under abiotic stress: a dynamic economic model

Break crops and multi-crop rotations are common in arable farm management, and the soil quality inherited from a previous crop is one of the parameters that determine the gross margin that is achieved with a given crop from a given parcel of land. In previous work we developed a dynamic economic model to calculate the potential yield and gross margin of a set of crops grown in a selection of typical rotation scenarios, and we reported use of the model to calculate coexistence costs for GM maize grown in a crop rotation. The model predicts economic effects of pest and weed pressures in monthly time steps. Validation of the model in respect of specific traits is proceeding as data from trials with novel crop varieties is published. Alongside this aspect of the validation process, we are able to incorporate data representing the economic impact of abiotic stresses on conventional crops, and then use the model to predict the cumulative gross margin achievable from a sequence of conventional crops grown at varying levels of abiotic stress. We report new progress with this aspect of model validation. In this paper, we report the further development of the model to take account of abiotic stress arising from drought, flood, heat or frost; such stresses being introduced in addition to variable pest and weed pressure. The main purpose is to assess the economic incentive for arable farmers to adopt novel crop varieties having multiple ‘stacked’ traits introduced by means of various biotechnological tools available to crop breeders.

Produção e heterose de híbridos experimentais de pepino do tipo japonês

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Diagnóstico da colheita e beneficiamento de sementes de algodão na região sul de Goiás

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

O plantio associado entre banana (Musa acuminata Cultivar Cavendish "Grande Naine") e feijão-de-porco (Canavalia ensiformis (L.) D. C.): uma avaliação agronômica dos efeitos da competição por água, nitrogênio e energia luminosa

Nas regiões de clima tropical, o monocultivo da banana vem causando conseqüências ambientais desastrosas e, muitas vezes, impedindo uma exploração continuada de uma mesma área. A redução do rendimento é devido principalmente as limitações físico-químicas do solo e a rápida degradação do sistema radicular, agravada pela ação de parasitas do solo (nematóides, fungos, etc.). Em virtude destas limitações, várias iniciativas vem sendo buscadas para a minimização das perdas agronômicas e ambientais, destacando-se o melhoramento e a modificação genética, e a associação deste cultivo com espécies leguminosas. Porém uma das grandes dificuldades de avaliarmos os novos sistemas de cultivo alternativos concentra-se na falta de referenciais agronômicos relacionados principalmente com o funcionamento de sistemas de cultivos associados, especialmente relacionados aos fatores e condições que interferem diretamente na definição do rendimento da espécie principal. O presente estudo testou , em campo experimental, o uso de plantas de serviço associada a bananeira e seus efeitos na produção de biomassa durante seu o ciclo vegetativo. Isto porque é durante esta fase que a bananeira constrói sua capacidade de reservas de fotoassimilados e, consequentemente, define o potencial de produção e enchimento dos frutos. Além do monocultivo, definiu-se mais duas parcelas associadas com o feijão-de- porco: 1) o plantio simultâneo das duas espécies e; 2) o plantio de feijão-de-porco e, após 2 meses, a introdução da banana. Além de acompanhamento semanal das parcelas, realizou-se, bimensalmente, coletas destrutivas de dados sobre produção de matéria seca, superfície foliar e análise nutricional das plantas. Após a análise agronômica da fase vegetativa, aplicou-se a modelização dos sistemas de cultivo estudados e comparou-se os possíveis cenários sobre o rendimento final da bananeira, além de outros indicadores sobre os fatores de crescimento das plantas. Após o acompanhamento dos 7 primeiros meses do ciclo vegetativo, concluiu-se que a data de estabelecimento da associação foi determinante para o sucesso do cultivo associado. Podemos destacar que a associação entre a bananeira e o feijão-de-porco não causou limitações na produção de biomassa (4,2 ton/ha), quando comparada com o monocultivo (4,5 ton/ha). A redução do número de capinas também foi um indicador animador deste sistema de cultivo alternativo. Por outro lado, quando a bananeira foi plantada 60 dias após a leguminosa, a mesma representou uma séria limitação na produção de biomassa (2,7 ton/ha). Esta limitação deveu-se ao estado de forte competição devido a agressividade com que o feijão-de-porco recobria toda a parcela e alcançando uma altura (74 cm) superior que a muda de banana (29 cm). Em relação a primeira parte da metodologia aplicada - o diagnóstico agronômico -, a mesma foi eficiente para a avaliação do ciclo vegetativo da associação estudada, ficando a necessidade da continuidade do acompanhamento do ciclo reprodutivo, para a confirmação dos resultados em termos de formação e produção de frutos. Na fase de modelização, chegou-se a uma leitura dos resultados próxima dos resultados obtidos no campo. Em termos de rendimento em frutos, o monocultivo com adubação (400 kg/ha de nitrogênio) e irrigação (133 mm) teve um aumento na ordem de 50% no rendimento final (28 ton/ha) Quando comparada com a parcela nas condições reais do experimento (19,6 ton/ha). Já o rendimento em frutos da associação, apresentou o mesmo resultado com e sem adubação e irrigação (16 ton/ha). No tocante a contrução dos cenários, confirmou-se novamente algumas das vantagens da associação, principalmente na redução da adubação nitrogenada aplicada nos sistemas convencionais de cultivo. Finalmente, podemos imaginar a construção de várias formas de testar e otimizar o uso destes sistemas associados (cenários). Porém, confirma-se que a construção de novos referenciais agronômicos sobre sistemas de cultivo mais complexos (os cultivos associados) torna-se ainda muito necessário para a realização de avaliações mais precisas sobre estas alternativas. E, com estes novos referenciais técnicos, podemos imaginar, a médio e longo prazo, alguns dos benefícios das leguminosas sobre as propriedades físico-químicas do solo cultivado (cobertura viva, adubo verde, redução de adventícias, etc) e sobre a manutenção do rendimento dos cultivos (adubação verde).

Composição físico-química e cor de clones de mandioquinha-salsa

This study aimed to characterize the physical and chemical composition of ten items of arracacha grown in the municipality of São Manuel for the 2009 harvest. In the roots of the clones BGH (4560, 5741, 5744, 5746, 5747, 6414, 6513, 6525 and 7609) and the cultivar Amarela de Senador Amaral the characteristics evaluated were: color (L *, a * and b *) and moisture, ash, crude fiber, raw grease, protein, reducing sugars, total sugars and starch. After obtaining the data, an analysis was performed for the variance of test F and comparisons between the means made by the Tukey test at 5% probability. There was no significant difference to the results of luminosity (L *) while BGH 6414 and BGH 5744 showed the highest values for chroma and * BGH 5741, BGH 6414, BGH 7609, 'Amarela de Senador Amaral' BGH 5747 presented the highest chroma values for b *. Clones BGH 7609 and BGH 6414 showed significantly higher levels of dry matter and with the potential yield of agro-industrial processes it would be best suited in the form of frying. The materials that showed significantly larger amounts of ash were BGH 6525, BGH 5747, 'Amarela de Senador Amaral ", BGH 4560, BGH 5746, BGH 6513. Regarding the contents of fatty matter BGH 6525, BGH 5741 and BGH 5744 showed the highest levels. The results of BGH 7609 showed crude fiber significantly higher than the other materials tested, it can be used in diets composed of fibers. BGH 4560 and cultivar had the highest crude protein. BGH 5741 showed the lowest reducing sugar content among the clones, but not significantly different from results found for the cultivar. All clones showed total sugar levels were higher in the cultivar, which may have more flavor. BGH 5741, BGH 5746, BGH 6525 and BGH 6513 showed significantly higher starch content than the cultivar Amarela de Senador Amaral. From these results we conclude that the clones have similar color characteristics, and are potentially a nutritionally adequate substitute for the cultivar.

Eficiência agrícola da produção de soja, milho e trigo no estado do Rio Grande do Sul entre 1980 e 2008

Eficiência agrícola (EA) é utilizada como indicador do nível de desenvolvimento agrícola regional, expressando, por meio da relação entre as produtividades real e atingível, o nível tecnológico empregado nas culturas. Com base nisso, o objetivo deste estudo foi avaliar a EA das culturas da soja, do milho e do trigo para o estado do Rio Grande do Sul, entre os anos de 1980 e 2008, identificando os principais fatores que as condicionaram. A EA foi obtida pela relação entre a produtividade atingível (PA) e a real (PR). A PR foi obtida junto ao banco de dados do Instituto Brasileiro de Geografia e Estatística (IBGE). A PA foi obtida pela estimativa da produtividade potencial (PPf), pelo método de Zona Agroecológica da FAO, deflacionada pelo déficit hídrico em cada uma das fases da cultura. Verificou-se que as EAs médias para as culturas do milho, da soja e do trigo para o RS foram iguais a 54, 61 e 43%, respectivamente. Nas localidades de Santa Rosa, São Borja e Veranópolis, a EA para a soja foi, ao contrário das demais localidades, negativa. Os principais fatores que contribuíram para o aumento da EA, na maioria das localidades, foram: mudanças no uso e fertilidade do solo; uso de mecanização agrícola; preços pagos pelas commodities; investimentos em pesquisa e desenvolvimento; adoção do zoneamento de risco climático; e melhoramento genético.

Vigor de sementes e desempenho agronômico de plantas de algodão

O vigor das sementes pode influenciar a emergência, o desempenho e produtividade das plantas, dependendo de cada espécie e de fatores ambientais. Dessa forma, um dos principais desafios das pesquisas com sementes é averiguar a influência do potencial fisiológico das sementes sobre o ciclo das plantas. O objetivo deste trabalho foi avaliar o efeito do vigor de sementes sobre as características morfofisiológicas de plantas de algodão, bem como, sobre o rendimento de caroço e de fibra. Três lotes de sementes da cultivar FMT 701 (safra 2005/2006) foram selecionados com base em resultados de testes realizados em laboratório. O efeito do vigor foi avaliado em campo, nas safras 2006/2007 e 2007/2008, no município de Jaciara, Mato Grosso. Determinou-se a porcentagem de plântulas emergidas, índice de velocidade de emergência, altura das plantas, número de ramos reprodutivos, número de nós, número de posições frutíferas, número de capulhos, rendimento de fibras e caroço. O desempenho inicial e reprodutivo de plantas de algodão em campo depende, além de outros fatores, do nível de vigor das sementes. Plantas com maior vigor apresentam maior rendimento de fibras e de caroço.

Contextualizing local-scale point sample data using global-scale spatial datasets: Lessons learnt from the analysis of large-scale land acquisitions

This paper examines how the geospatial accuracy of samples and sample size influence conclusions from geospatial analyses. It does so using the example of a study investigating the global phenomenon of large-scale land acquisitions and the socio-ecological characteristics of the areas they target. First, we analysed land deal datasets of varying geospatial accuracy and varying sizes and compared the results in terms of land cover, population density, and two indicators for agricultural potential: yield gap and availability of uncultivated land that is suitable for rainfed agriculture. We found that an increase in geospatial accuracy led to a substantial and greater change in conclusions about the land cover types targeted than an increase in sample size, suggesting that using a sample of higher geospatial accuracy does more to improve results than using a larger sample. The same finding emerged for population density, yield gap, and the availability of uncultivated land suitable for rainfed agriculture. Furthermore, the statistical median proved to be more consistent than the mean when comparing the descriptive statistics for datasets of different geospatial accuracy. Second, we analysed effects of geospatial accuracy on estimations regarding the potential for advancing agricultural development in target contexts. Our results show that the target contexts of the majority of land deals in our sample whose geolocation is known with a high level of accuracy contain smaller amounts of suitable, but uncultivated land than regional- and national-scale averages suggest. Consequently, the more target contexts vary within a country, the more detailed the spatial scale of analysis has to be in order to draw meaningful conclusions about the phenomena under investigation. We therefore advise against using national-scale statistics to approximate or characterize phenomena that have a local-scale impact, particularly if key indicators vary widely within a country.

Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina

Solar radiation is the most important source of renewable energy in the planet; it's important to solar engineers, designers and architects, and it's also fundamental for efficiently determining irrigation water needs and potential yield of crops, among others. Complete and accurate solar radiation data at a specific region are indispensable. For locations where measured values are not available, several models have been developed to estimate solar radiation. The objective of this paper was to calibrate, validate and compare five representative models to predict global solar radiation, adjusting the empirical coefficients to increase the local applicability and to develop a linear model. All models were based on easily available meteorological variables, without sunshine hours as input, and were used to estimate the daily solar radiation at Cañada de Luque (Córdoba, Argentina). As validation, measured and estimated solar radiation data were analyzed using several statistic coefficients. The results showed that all the analyzed models were robust and accurate (R2 and RMSE values between 0.87 to 0.89 and 2.05 to 2.14, respectively), so global radiation can be estimated properly with easily available meteorological variables when only temperature data are available. Hargreaves-Samani, Allen and Bristow-Campbell models could be used with typical values to estimate solar radiation while Samani and Almorox models should be applied with calibrated coefficients. Although a new linear model presented the smallest R2 value (R2 = 0.87), it could be considered useful for its easy application. The daily global solar radiation values produced for these models can be used to estimate missing daily values, when only temperature data are available, and in hydrologic or agricultural applications.

Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.