Der verstärkte Wassermangel und die ökologische Belastung durch Düngemittel und Pestizide erfordert eine kontrollierte und präzise Applikation von Wasser und Nährlösung. Die messtechnische Ermittlung des Wasserhaushalts im SPAC-System erfolgt durch Messungen an den einzelnen Komponenten Boden, Pflanze und Atmosphäre. Eine stärkere Aussagekraft über den tatsächlichen Wasserstatus der Pflanze bietet die sensorische Informationsgewinnung an der Pflanze selbst (Phytomonitoring). Ein neuentwickelter Phytomonitor (EPM 2005 bzw. 2006), der auf dem Prinzip einer Gaswechselmessung an einzelnen Pflanzenblättern basiert, wurde zur Messung der momentanen Transpirationsintensität von Pflanzenbeständen eingesetzt. Die Präzision und Repräsentativität der Methode wurde in unterschiedlichen Jahreszeiten unter mitteleuropäischen (Berlin, Deutschland) und südeuropäischen Gewächshausbedingungen (Thessaloniki, Griechenland) getestet. Als Vergleichsbewässerungsverfahren wurden die Saugspannungsregelungsmethode und die Bewässerung nach Zeitsteuerung herangezogen. Gleichzeitig wurden Tensiometer zur Überwachung des Saugspannungsverlaufs im Substrat eingesetzt. Im Vergleich zu weiteren Phytomonitoringsystemen ist dadurch eine bessere Übertragbarkeit der Messdaten auf größeren Pflanzenbeständen gegeben. Die Methode ist dabei nicht sortenspezifisch. Durch ein hohes Bestimmtheitsmaß der Regressionsfunktion der gemessenen Transpirationsmassenstromdichte bzw. Nettophotosyntheseleistung an zwei Standorten der gleichen Konditionen wurde die Homogenität des pflanzlichen Stoffaustausches in Gewächshausbeständen bewiesen. Die Übertragbarkeit der Messdaten auf den gesamten Bestand wurde durch die Bildung der Wasserbilanz über längere Zeitperioden geprüft. Hohe Korrelationen wurden zwischen den gemessenen und den berechneten Transpirationssummen erzielt. Die Bewässerungssteuerung nach den gemessenen Transpirationssummen hat die vorbestimmte Überschussmenge, sogar unter hohen Strahlungs- und Luftfeuchtigkeitsbedingungen, erzielen können. Der durch Tensiometer registrierte Saugspannungsverlauf eines transpirationsgesteuerten Tomatenbestands war vergleichbar zu dem eines tensiometergeregelten Bestandes. Die zwei Bewässerungsmethoden ergaben keine signifikante Unterschiede in der Fruchtanzahl, Blattanzahl oder Pflanzengröße. Das Wasser wurde leicht effizienter in dem tensiometergeregelten Bestand eingesetzt, ohne dass sich die Erträge signifikant unterschieden. Minimalste Drainagemengen ( / The increased shortage of water, were as the ecological pollution through fertilisers and pesticides, requires a controlled and precise amount of water inset. The determination of the water balance in the SPAC-System by technological measurements is reached by measuring on the individual components substrate, plant and atmosphere. The direct measuring on the plant by the Phytomonitoring-Technology is meaningful for the determination of the factual water status of the plant. A new developed Phytomonitoring (EPM 2005 or 2006), which is based on gas exchange measurements of individual plant leaves by cuvettes, was used for measuring the momentary transpirations intensity of plant cultures. The precision and representatively of the method was tested on different seasons under north European (Berlin, Germany) and south European (Thessaloniki, Greece) greenhouse conditions. The tensiometer control method as well as a time-scheduled irrigation system were used for comparison purposes. Tensiometers were used for monitoring the suction course in the substrate. In comparison to other Phytomonitoring-systems gives this one a better transmissibility of the measurement on big canopies. A high coefficient of determination of the regression line between the measured transpirations intensity (and photosynthesis intensity) of two sites with the same conditions, was found. This improves the homogeneity of the mass exchange in greenhouse canopies. Water balances were builded over long periods to prove the transmissivity of the measurements over the whole canopy. The measured transpiration sums were high correlated to the calculated ones (through the water balance equation). The drain target of 20-30% (usual for the practical experience) of the irrigation scheduling method, based on the measured transpiration sum, could be reached even under high radiation and low humidity conditions. The substrate of a tomato stand irrigated after the transpirationsummethod (switch threshold: 3 l transpiration sum, irrigation amount: 4 l) showed a suction-course comparable to the one of a tensiometercontroled stand (switch threshold: 50 hPa, irrigation amount: 3 l). There were no significant differences in the fruit number, leaf number or planthight between the two canopies. The water was efficiently consummated in the tensiometercontroled stand, were as the yields were significantly not different. Minimal drain water amounts (

O aumento das concentrações dos gases-estufa, principalmente o dióxido de carbono, e as mudanças climáticas se tornaram assuntos científico, econômico e político importantes nos últimos anos. O Mecanismo de Desenvolvimento Limpo, do Protocolo de Kyoto concede créditos de carbono comercializáveis para projetos que promovam o seqüestro de carbono nos países em desenvolvimento. Portanto, avaliar a capacidade de absorção de CO2 pela vegetação terrestre é um aspecto importante, o que justifica o interesse em desenvolver modelos de fluxo e troca desse gás em diferentes escalas. O desenvolvimento desses modelos é dificultado pela não-linearidade dos processos ecofisiológicos. Este trabalho apresenta um método de modelagem de fotossíntese no nível da folha, como um primeiro passo para um método de quantificação do potencial de seqüestro de carbono. A técnica utilizada foi a de redes neurais artificiais, uma vez que ela permite ajustar relações não lineares entre as variáveis de entrada e de saída. O trabalho foi divido em duas partes: fitomonitoração e modelagem. A fitomonitoração foi realizada em jatobá (Hymenaea courbaril), durante um ano. Medindo-se variáveis fisiológicas: taxa de fotossíntese, taxa de transpiração, condutância estomática, temperatura da folha, e fluorescência, e variáveis ambientais: concentração de CO2, radiação fotossintética ativa, umidade relativa e temperatura do ar. Uma quantidade de dados inédita para esse tipo de experimento e para essa espécie vegetal foi obtida. A análise dos resultados da fitomonitoração mostra características importantes sobre o comportamento das variáveis fisiológicas em plântulas de jatobá e das variáveis ambientais de seu entorno, casa de vegetação, nas quatro estações do ano. Os dados coletados foram utilizados para a modelagem da rede neural. Os treinamentos foram realizados com diferentes combinações de variáveis de entrada para observar qual era o conjunto de variáveis às quais a rede respondia melhor. A análise dos resultados dos treinamentos mostrou que com a técnica de redes neurais é possível atingir uma aproximação da função fotossíntese com 92% de acertos para entradas com dados filtrados. / The increases in greenhouse gas concentrations, mainly carbon dioxide, and the climatic changes have become important scientific, economic, and political subjects in the past years. The Kyoto Protocol establishes the Clean Development Mechanism, which grants carbon credits for projects that promote the sequestration of carbon in developing countries. Therefore, it is important to evaluate the CO2 absorption capacity by terrestrial plants, and this requires the development of gas flow and gas exchange models in different scales. That development is usually complicated, because the ecophysiological processes are non-linear. This work presents a method to model photosynthesis at the leaf level, as a first step toward quantifying the potential of carbon sequestration. The technique used was artificial neural networks (ANNs), as it allows the adjustment of non-linear relationships between input and output variables. The work was divided in two parts: phytomonitoring and modeling. The phytomonitoring was accomplished in jatoba (Hymenaea courbaril) during one year. The following physiologic variables were measured: photosynthesis rate, transpiration rate, stomatal conductance, leaf temperature, and fluorescence; and environmental variables: CO2 concentration, photosynthetic activity radiation, relative humidity, and air temperature. An unprecedented amount of data for that type of experiment and for that plant species was obtained. The analysis of these data showed important characteristics about the relationship of the physiologic variables in Hymenaea courbaril and the environmental variables, in the four seasons. The data collected were used for the modeling and fine-tuning of the neural network. The network was trained with different combinations of input variables to observe to which group of variables the neural network responded better. The analysis of the training results showed that with the ANN technique it is possible to achieve a very good approximation of the photosynthesis function, with 92% success rate for entries consisting of filtered data.

O aumento das concentrações dos gases-estufa, principalmente o dióxido de carbono, e as mudanças climáticas se tornaram assuntos científico, econômico e político importantes nos últimos anos. O Mecanismo de Desenvolvimento Limpo, do Protocolo de Kyoto concede créditos de carbono comercializáveis para projetos que promovam o seqüestro de carbono nos países em desenvolvimento. Portanto, avaliar a capacidade de absorção de CO2 pela vegetação terrestre é um aspecto importante, o que justifica o interesse em desenvolver modelos de fluxo e troca desse gás em diferentes escalas. O desenvolvimento desses modelos é dificultado pela não-linearidade dos processos ecofisiológicos. Este trabalho apresenta um método de modelagem de fotossíntese no nível da folha, como um primeiro passo para um método de quantificação do potencial de seqüestro de carbono. A técnica utilizada foi a de redes neurais artificiais, uma vez que ela permite ajustar relações não lineares entre as variáveis de entrada e de saída. O trabalho foi divido em duas partes: fitomonitoração e modelagem. A fitomonitoração foi realizada em jatobá (Hymenaea courbaril), durante um ano. Medindo-se variáveis fisiológicas: taxa de fotossíntese, taxa de transpiração, condutância estomática, temperatura da folha, e fluorescência, e variáveis ambientais: concentração de CO2, radiação fotossintética ativa, umidade relativa e temperatura do ar. Uma quantidade de dados inédita para esse tipo de experimento e para essa espécie vegetal foi obtida. A análise dos resultados da fitomonitoração mostra características importantes sobre o comportamento das variáveis fisiológicas em plântulas de jatobá e das variáveis ambientais de seu entorno, casa de vegetação, nas quatro estações do ano. Os dados coletados foram utilizados para a modelagem da rede neural. Os treinamentos foram realizados com diferentes combinações de variáveis de entrada para observar qual era o conjunto de variáveis às quais a rede respondia melhor. A análise dos resultados dos treinamentos mostrou que com a técnica de redes neurais é possível atingir uma aproximação da função fotossíntese com 92% de acertos para entradas com dados filtrados. / The increases in greenhouse gas concentrations, mainly carbon dioxide, and the climatic changes have become important scientific, economic, and political subjects in the past years. The Kyoto Protocol establishes the Clean Development Mechanism, which grants carbon credits for projects that promote the sequestration of carbon in developing countries. Therefore, it is important to evaluate the CO2 absorption capacity by terrestrial plants, and this requires the development of gas flow and gas exchange models in different scales. That development is usually complicated, because the ecophysiological processes are non-linear. This work presents a method to model photosynthesis at the leaf level, as a first step toward quantifying the potential of carbon sequestration. The technique used was artificial neural networks (ANNs), as it allows the adjustment of non-linear relationships between input and output variables. The work was divided in two parts: phytomonitoring and modeling. The phytomonitoring was accomplished in jatoba (Hymenaea courbaril) during one year. The following physiologic variables were measured: photosynthesis rate, transpiration rate, stomatal conductance, leaf temperature, and fluorescence; and environmental variables: CO2 concentration, photosynthetic activity radiation, relative humidity, and air temperature. An unprecedented amount of data for that type of experiment and for that plant species was obtained. The analysis of these data showed important characteristics about the relationship of the physiologic variables in Hymenaea courbaril and the environmental variables, in the four seasons. The data collected were used for the modeling and fine-tuning of the neural network. The network was trained with different combinations of input variables to observe to which group of variables the neural network responded better. The analysis of the training results showed that with the ANN technique it is possible to achieve a very good approximation of the photosynthesis function, with 92% success rate for entries consisting of filtered data.