Com os objetivos de (i) elaborar uma adaptação do método da zona agroecológica, proposto por De Wit, para estimar a produtividade potencial e deplecionada da cultura de milho, utilizando procedimento estocástico, no Rio Grande do Sul; e de (ii) testar procedimentos estocásticos (distribuição normal truncada, triangular assimétrica e triangular simétrica) para simular dados de temperatura e de insolação para estimar produtividade potencial e deplecionada da cultura de milho, foi desenvolvida uma metodologia computacional. A quantidade de energia solar disponível às plantas (em função da latitude, declinação solar e nebulosidade), bem como a capacidade de sua conversão em fotossintetizado, contabilizado em termos de carboidrato, possibilita prever produtividade potencial de grãos de milho. Sem limitação de água no solo, o CO2 assimilado é convertido em massa de carboidrato em função do índice de área foliar (IAF), temperatura, radiação solar absorvida. fotoperíodo e duração do ciclo. Considerando os fatores de correção quanto à respiração e ao IAF, pode-se transformar a massa de carboidrato total final em massa de matéria seca referente a cada órgão (folha, raiz, colmo e órgão reprodutivo), considerando-se as partições de fotoassimilados e a composição da matéria seca. A produtividade potencial (PP) foi calculada com base na fitomassa seca total, no índice de colheita e no teor de água nos grãos. O balanço hídrico cíclico (Thornthwaite & Mather, 1955) foi utilizado para estimar o armazenamento de água no solo no decêndio que antecede a semeadura de milho. O balanço hídrico seqüencial, com variação do coeficiente de cultivo (Kc), foi utilizado para estimar a evapotranspiração da cultura (ETc) e a real (ETr). A produtividade deplecionada foi estimada a partir dos valores de PP, ETr, ETc e do coeficiente de resposta da cultura (Ky). Os dados climáticos de 16 municípios no Rio Grande do Sul, a capacidade de água dispoível (50 mm) e procedimentos estocásticos (distribuição normal truncada, triangular assimétrica e triangular simétrica para simular dados de temperatura e de insolação), foram usados para estimar PP e deplecionada para cada localidade, em diferentes épocas de semeadura. Com base nos resultados obtidos, conclui-se que: (i) a adaptação do método da zona agroecológica possibilita definir a ordem de grandeza das PP e deplecionada da cultura de milho, e produziu resultados coerentes com valores citados na literatura, identificando a melhor época de semeadura, e (ii) o procedimento estocástico pode ser utilizado nos seguintes casos: quando se dispõe de uma série histórica de temperatura, utiliza-se a distribuição normal truncada; quando não se dispõe de uma série histórica utiliza-se a distribuição triangular assimétrica, preferencialmente, ou a distribuição triangular simétrica. / With de purpose of (i) adapting of the agroecological zone method, proposed by De Wit, to estimate potential and depleted corn crop productivity, using stochastic procedure, in the Rio Grande do Sul State, Brazil; and (ii) testing stochastic procedures (normal, and symmetric and non-symmetric distributions) to simulate air temperature and insolation data to estimate potential and depleted corn crop productivity, was developed a computational methodology. The amount of available solar energy to the plants (as function of the latitude, solar declination and cloudiness), as well as the capacity of energy conversion in photosyntates, computed in terms of carbohydrate, makes it possible to forecast corn productivity. Without soil water shortage the assimilated CO2 can be converted in mass of carbohydrate as a function of leaf area index (LAI), air temperature, absorbed solar radiation, photoperiod and cycle duration. Considering the correction factors related to respiration and LAI, this value can be converted into mass of carbohydrate, per hectare, produced during the cycle. To transform carbohydrate mass in dry biomass referring to each organ (leaf, root, stem and reproductive organs), the assimilates partition and dry matter composition. The potential productivity (PP) was computed using the total dry mass, harvest index and the grain water content. The water balance (Thornthwaite & Mather, 1955) was used to estimate the amount of soil water in the period before corn crop sowing date. The sequential water balance, with temporal variation of crop coefficient (Kc), was used to estimate crop (ETc) and actual (ETr) evapotranspirations. The depleted productivity was estimated using PP, ETr, ETc and the yield response factor (Ky) values. The climatic data of 16 counties in the Rio Grande do Sul State, the soil water holding capacity (50 mm) and stochastic procedures (normal and triangular distributions to simulate air temperature and insolation) were used to estimate PP and depleted corn productivity for each local in different sowing dates. The following conclusions can be reported: (i); the adaptation of the agroecological zone method allowed to calculate the PP and depleted productivity with coherent productivity values, as well as it identifies the best sowing date; and (ii) the stochastic procedure can be used in the following cases: the normal distribution approach can be utilized when air temperature historic series is available; and the triangular distribution (non-symmetric triangular distribution is preferable in relation to symmetric distribution) approach can be used when there is no historic series.

Com os objetivos de (i) elaborar uma adaptação do método da zona agroecológica, proposto por De Wit, para estimar a produtividade potencial e deplecionada da cultura de milho, utilizando procedimento estocástico, no Rio Grande do Sul; e de (ii) testar procedimentos estocásticos (distribuição normal truncada, triangular assimétrica e triangular simétrica) para simular dados de temperatura e de insolação para estimar produtividade potencial e deplecionada da cultura de milho, foi desenvolvida uma metodologia computacional. A quantidade de energia solar disponível às plantas (em função da latitude, declinação solar e nebulosidade), bem como a capacidade de sua conversão em fotossintetizado, contabilizado em termos de carboidrato, possibilita prever produtividade potencial de grãos de milho. Sem limitação de água no solo, o CO2 assimilado é convertido em massa de carboidrato em função do índice de área foliar (IAF), temperatura, radiação solar absorvida. fotoperíodo e duração do ciclo. Considerando os fatores de correção quanto à respiração e ao IAF, pode-se transformar a massa de carboidrato total final em massa de matéria seca referente a cada órgão (folha, raiz, colmo e órgão reprodutivo), considerando-se as partições de fotoassimilados e a composição da matéria seca. A produtividade potencial (PP) foi calculada com base na fitomassa seca total, no índice de colheita e no teor de água nos grãos. O balanço hídrico cíclico (Thornthwaite & Mather, 1955) foi utilizado para estimar o armazenamento de água no solo no decêndio que antecede a semeadura de milho. O balanço hídrico seqüencial, com variação do coeficiente de cultivo (Kc), foi utilizado para estimar a evapotranspiração da cultura (ETc) e a real (ETr). A produtividade deplecionada foi estimada a partir dos valores de PP, ETr, ETc e do coeficiente de resposta da cultura (Ky). Os dados climáticos de 16 municípios no Rio Grande do Sul, a capacidade de água dispoível (50 mm) e procedimentos estocásticos (distribuição normal truncada, triangular assimétrica e triangular simétrica para simular dados de temperatura e de insolação), foram usados para estimar PP e deplecionada para cada localidade, em diferentes épocas de semeadura. Com base nos resultados obtidos, conclui-se que: (i) a adaptação do método da zona agroecológica possibilita definir a ordem de grandeza das PP e deplecionada da cultura de milho, e produziu resultados coerentes com valores citados na literatura, identificando a melhor época de semeadura, e (ii) o procedimento estocástico pode ser utilizado nos seguintes casos: quando se dispõe de uma série histórica de temperatura, utiliza-se a distribuição normal truncada; quando não se dispõe de uma série histórica utiliza-se a distribuição triangular assimétrica, preferencialmente, ou a distribuição triangular simétrica. / With de purpose of (i) adapting of the agroecological zone method, proposed by De Wit, to estimate potential and depleted corn crop productivity, using stochastic procedure, in the Rio Grande do Sul State, Brazil; and (ii) testing stochastic procedures (normal, and symmetric and non-symmetric distributions) to simulate air temperature and insolation data to estimate potential and depleted corn crop productivity, was developed a computational methodology. The amount of available solar energy to the plants (as function of the latitude, solar declination and cloudiness), as well as the capacity of energy conversion in photosyntates, computed in terms of carbohydrate, makes it possible to forecast corn productivity. Without soil water shortage the assimilated CO2 can be converted in mass of carbohydrate as a function of leaf area index (LAI), air temperature, absorbed solar radiation, photoperiod and cycle duration. Considering the correction factors related to respiration and LAI, this value can be converted into mass of carbohydrate, per hectare, produced during the cycle. To transform carbohydrate mass in dry biomass referring to each organ (leaf, root, stem and reproductive organs), the assimilates partition and dry matter composition. The potential productivity (PP) was computed using the total dry mass, harvest index and the grain water content. The water balance (Thornthwaite & Mather, 1955) was used to estimate the amount of soil water in the period before corn crop sowing date. The sequential water balance, with temporal variation of crop coefficient (Kc), was used to estimate crop (ETc) and actual (ETr) evapotranspirations. The depleted productivity was estimated using PP, ETr, ETc and the yield response factor (Ky) values. The climatic data of 16 counties in the Rio Grande do Sul State, the soil water holding capacity (50 mm) and stochastic procedures (normal and triangular distributions to simulate air temperature and insolation) were used to estimate PP and depleted corn productivity for each local in different sowing dates. The following conclusions can be reported: (i); the adaptation of the agroecological zone method allowed to calculate the PP and depleted productivity with coherent productivity values, as well as it identifies the best sowing date; and (ii) the stochastic procedure can be used in the following cases: the normal distribution approach can be utilized when air temperature historic series is available; and the triangular distribution (non-symmetric triangular distribution is preferable in relation to symmetric distribution) approach can be used when there is no historic series.

There is a growing societal concern about excessive water and fertilizer use in agricultural systems. High water productivity while maintaining high crop yields can be achieved with appropriate irrigation scheduling. Moreover, freshwater pollution through nitrogen (N) leaching due to the widespread use of N fertilizers demands for an efficient N fertilization management. However, sustainable crop management requires good knowledge of soil water and N dynamics as well as of crop water and N demand. Crop growth models, which describe physical and physiological processes of crop growth as well as water and matter transport, are considered as powerful tools to assist in the optimization of irrigation and fertilization management. It is of a general nature that the reliability of simulation based predictions depends on the quality and quantity of the data used for model calibration and validation which can be obtained e.g. in field experiments. A lack of data or low data quality for model calibration and validation may cause low performance and large uncertainties in simulation results. The large number of model parameters to be calibrated requires appropriate calibration methods and a sequential calibration strategy. Moreover, a simulation based planning of the field design saves costs and expenditure while supporting maximal outcomes of experiments. An adjustment of crop growth modeling and experiments is required for model improvement and development to reliably predict crop growth and to generalize predicted results. In this research study, a new approach for simulation based optimal experimental design was developed aiming to integrate simulation models, experiments, and optimization methods in one framework for optimal and sustainable irrigation and N fertilization management. The approach is composed of three steps: 1. The preprocessing consists of the calibration and validation of the crop growth model based on existing experimental data, the generation of long time-series of climate data, and the determination of the optimal irrigation control. 2. The implementation comprises the determination and experimental application of the simulation based optimized deficit irrigation and N fertilization schedules and an appropriate experimental data collection. 3. The postprocessing includes the evaluation of the experimental results namely observed yield, water productivity (WP), nitrogen use efficiency (NUE), and economic aspects, as well as a model evaluation. Five main tools were applied within the new approach: an algorithm for inverse model parametrization, a crop growth model for simulating crop growth, water balance and N balance, an optimization algorithm for deficit irrigation and N fertilization scheduling, and a stochastic weather generator. Furthermore, a water flow model was used to determine the optimal irrigation control functions and for simulation based estimation of the optimal field design. The approach was implemented within three case studies presented in this work. The new approach combines crop growth modeling and experiments with stochastic optimization. It contributes to a successful application of crop growth modeling based on an appropriate experimental data collection. The presented model calibration and validation procedure using the collected data facilitates reliable predictions. The stochastic optimization framework for deficit irrigation and N fertilization scheduling proved to be a powerful tool to enhance yield, WP, NUE and profit. / In der heutigen Gesellschaft gibt es zunehmend Bedenken gegenüber übermäßigem Wasser- und Düngereinsatz in der Landwirtschaft. Eine hohe Wasserproduktivität kann jedoch durch geeignete Bewässerungspläne mit hohen landwirtschaftlichen Erträgen in Einklang gebracht werden. Die mit der weitverbreiteten Stickstoffdüngung einhergehende Gewässerbelastung aufgrund von Stickstoffauswaschung erfordert zudem ein effizientes Stickstoffmanagement. Eine entsprechende ressourceneffiziente Landbewirtschaftung bedarf präzise Kenntnisse der Bodenwasser- und Stickstoffdynamiken sowie des Pflanzenwasser- und Stickstoffbedarfs. Als leistungsfähige Werkzeuge zur Unterstützung bei der Optimierung von Bewässerungs-und Düngungsplänen werden Pflanzenwachstumsmodelle eingesetzt, welche die physischen und physiologischen Prozesse des Pflanzenwachstums sowie die physikalischen Prozesse des Wasser- und Stofftransports abbilden. Hierbei hängt die Zuverlässigkeit dieser simulationsbasierten Vorhersagen von der Qualität und Quantität der bei der Modellkalibrierung und -validierung verwendeten Daten ab, welche beispielsweise in Feldversuchen erfasst werden. Fehlende Daten oder Daten mangelhafter Qualität bei der Modellkalibrierung und -validierung führen zu unzuverlässigen Simulationsergebnissen und großen Unsicherheiten bei der Vorhersage. Die große Anzahl an zu kalibrierenden Parametern erfordert zudem geeignete Kalibrierungsmethoden sowie eine sequenzielle Kalibrierungsstrategie. Darüber hinaus kann eine simulationsbasierte Planung des Versuchsdesigns Kosten und Aufwand reduzieren und zu weiteren experimentellen Erkenntnissen führen. Die Abstimmung von Pflanzenwachstumsmodellen und Versuchen ist zudem für die Modellentwicklung und -verbesserung sowie für eine Verallgemeinerung von Simulationsergebnissen unabdingbar. Im Rahmen dieser Arbeit wurde ein neuer Ansatz für ein simulationsbasiertes optimales Versuchsdesign entwickelt. Ziel war es, Simulationsmodelle, Versuche und Optimierungsmethoden in einem Ansatz für optimales und nachhaltiges Bewässerungs- und Düngungsmanagement zu integrieren. Der Ansatz besteht aus drei Schritten: 1. Die Vorbereitungsphase beinhaltet die auf existierenden Versuchsdaten basierende Kalibrierung und Validierung des Pflanzenwachstumsmodells, die Generierung von Klimazeitreihen und die Bestimmung der optimalen Bewässerungssteuerung. 2. Die Durchführungsphase setzt sich aus der Erstellung und experimentellen Anwendung der simulationsbasierten optimierten Defizitbewässerungs- und Stickstoffdüngungspläne und der Erfassung der relevanten Versuchsdaten zusammen. 3. Die Auswertungsphase schließt eine Evaluierung der Versuchsergebnisse anhand ermittelter Erträge, Wasserproduktivitäten (WP), Stickstoffnutzungseffizienzen (NUE) und ökonomischer Aspekte, sowie eine Modellevaluierung ein. In dem neuen Ansatz kamen im Wesentlichen folgende fünf Werkzeuge zur Anwendung: Ein Algorithmus zur inversen Modellparametrisierung, ein Pflanzenwachstumsmodell, welches das Pflanzenwachstum sowie die Wasser- und Stickstoffbilanzen abbildet, ein evolutionärer Optimierungsalgorithmus für die Generierung von defizitären Bewässerungs- und Stickstoffplänen und ein stochastischer Wettergenerator. Zudem diente ein Bodenwasserströmungsmodell der Ermittlung der optimalen Bewässerungssteuerung und der simulationsbasierten Optimierung des Versuchsdesigns. Der in dieser Arbeit vorgestellte Ansatz wurde in drei Fallbeispielen angewandt. Der neue Ansatz kombiniert Pflanzenwachstumsmodellierung und Experimente mit stochastischer Optimierung. Er leistet einen Beitrag zu einer erfolgreichen Pflanzenwachstumsmodellierung basierend auf der Erfassung relevanter Versuchsdaten. Die vorgestellte Modellkalibrierung und -validierung unter Verwendung der erfassten Versuchsdaten trug wesentlich zu zuverlässigen Simulationsergebnissen bei. Darüber hinaus stellt die hier vorgestellte stochastische Optimierung von defizitären Bewässerungs- und Stickstoffplänen ein leistungsfähiges Werkzeug dar, um Erträge, WP, NUE und den Profit zu erhöhen.

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Submitted by Roberta Lorenzon (roberta.lorenzon@fgv.br) on 2011-06-01T14:19:41Z No. of bitstreams: 2 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia(suzinei.garcia@fgv.br) on 2011-06-01T14:54:51Z (GMT) No. of bitstreams: 2 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia(suzinei.garcia@fgv.br) on 2011-06-01T14:55:48Z (GMT) No. of bitstreams: 2 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) / Made available in DSpace on 2011-06-01T15:02:41Z (GMT). No. of bitstreams: 2 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) 65080100022.pdf: 2118563 bytes, checksum: e772406b416ab303d53d7648ac10d40b (MD5) Previous issue date: 2010-11-12 / Brazil is the second major soybean [Glycine max (L.) Merr.] producer and the seventh one on soybean oil. Brazilian production reached 61 million tons at 2008 and the forecast to 2020 is 105 million tons. Biodiesel consumption at 2008 was one million tons and the demand for this biofuel will reach 3,1 million tons at 2020. To amount this demand, the planting area on centerwest region of Brazil will increase, but also efforts on productivity must be required. Looking for a better knowledge on the climate variables temperature and global radiation over soybean development, yield and oil productivity was purposed a stochastic model with truncated normal distribution for maximum, minimum and average temperature data. Included in this model, a triangular asymmetric distribution to determine the probable oil productivity. Eight sowing dates were stipulated on Piracicaba/SP where the climate data was given from ESALQ/USP agrometeorologic station. The conclusions were: (i) there were decreases on soybean cycle duration with the average temperature increase; (ii) the soybean cycle decrease restricted soybean yield and oil productivity; (iii) the global radiation thirty days after antesis reflected on photo assimilates partition and soybean yield and oil productivity; (iv) stochastic models can be used for soybean yield and oil productivity forecast. / O Brasil é o segundo produtor mundial de soja [Glycine max (L.) Merr.] e o sétimo de óleo vegetal. A produção brasileira desta oleaginosa alcançou 61 milhões de toneladas na safra 2007/08 e projeta-se, para 2020, produção de 105 milhões de toneladas. O consumo de biodiesel em 2008 representou um milhão de toneladas e a demanda por este biocombustível deverá atingir 3,1 milhões de toneladas em 2020. Para atender esta demanda haverá ampliação da área plantada principalmente na região Centro-Oeste, mas também exigirá esforços no aumento de produtividade. Visando melhor conhecimento das inferências das variáveis climáticas temperatura e radiação global sobre o desenvolvimento da soja e sua produtividade de grãos e óleo, foi proposto um modelo estocástico com distribuição normal truncada para os dados de temperatura máxima, mínima e média. Também foi incluído neste modelo distribuição triangular assimétrica para determinação da produtividade de óleo mais provável. Foram estipuladas oito datas de semeadura para a localidade de Piracicaba/SP onde está localizada a estação meteorológica da ESALQ/USP, fornecedora dos dados climáticos utilizados neste estudo. Conclui-se que: (i) ao longo das datas de semeadura houve redução do ciclo com o aumento da temperatura média; (ii) a redução do ciclo da cultura de soja interferiu nas produtividades de grãos e de óleo; (iii) a radiação global média nos trinta dias após a antese refletiram-se na partição de fotoassimilados e na produtividade de grãos e óleo; (iv) os modelos estocásticos podem ser utilizados para a previsão das produtividades de soja e óleo.

A disponibilidade de recursos, tanto de ordem financeira quanto de mão-de-obra, é escassa. Sendo assim, deve-se incentivar o planejamento regional que minimize a utilização de recursos. A previsão de safra por intermédio de técnicas de modelagem deve ser realizada anteriormente com base nas características regionais, indicando assim as diretrizes básicas da pesquisa, bem como o planejamento regional. Dessa forma, os objetivos deste trabalho são: (i) caracterizar as variáveis do clima por intermédio de diferentes distribuições de probabilidade; (ii) verificar a homogeneidade espacial e temporal para as variáveis do clima; (iii) utilizar a distribuição normal bivariada para simular parâmetros utilizados na estimação de produtividade da cultura de soja; e (iv) propor um modelo para estimar a ordem de magnitude da produtividade potencial (dependente da interação genótipo, temperatura, radiação fotossinteticamente ativa e fotoperíodo) e da produtividade deplecionada (dependente da podutividade potencial, da chuva e do armazenamento de água no solo) de grãos de soja, baseados nos valores diários de temperatura, insolação e chuva, para o estado de São Paulo. As variáveis utilizadas neste estudo foram: temperatura média, insolação, radiação solar fotossinteticamente ativa e precipitação pluvial, em escala diária, obtidas em 27 estações localizadas no Estado de São Paulo e seis estações localizadas em Estados vizinhos. Primeiramente, verificou-se a aderência das variáveis a cinco distribuições de probabilidade (normal, log-normal, exponencial, gama e weibull), por intermédio do teste de Kolmogorov-Smirnov. Verificou-se a homogeneidade espacial e temporal dos dados por intermédio da análise de agrupamento pelo método de Ward e estimou-se o tamanho de amostra (número de anos) para as variáveis. A geração de números aleatórios foi realizada por intermédio do método Monte Carlo. A simulação dos dados de radiação fotossinteticamente ativa e temperatura foram realizadas por intermédio de três casos (i) distribuição triangular assimétrica (ii) distribuição normal truncada a 1,96 desvio padrão da média e (iii) distribuição normal bivariada. Os dados simulados foram avaliados por intermédio do teste de homogeneidade de variância de Bartlett e do teste F, teste t, índice de concordância de Willmott, coeficiente angular da reta, o índice de desempenho de Camargo (C) e aderência à distribuição normal (univariada). O modelo utilizado para calcular a produtividade potencial da cultura de soja foi desenvolvido com base no modelo de De Wit, incluindo contribuições de Van Heenst, Driessen, Konijn, de Vries, dentre outros. O cálculo da produtividade deplecionada foi dependente da evapotranspiração potencial, da cultura e real e coeficiente de sensibilidade a deficiência hídrica. Os dados de precipitação pluvial foram amostrados por intermédio da distribuição normal. Sendo assim, a produção diária de carboidrato foi deplecionada em função do estresse hídrico e número de horas diárias de insolação. A interpolação dos dados, de modo a englobar todo o Estado de São Paulo, foi realizada por intermédio do método da Krigagem. Foi verificado que a maior parte das variáveis segue a distribuição normal de probabilidade. Além disso, as variáveis apresentam variabilidade espacial e temporal e o número de anos necessários (tamanho de amostra) para cada uma delas é bastante variável. A simulação utilizando a distribuição normal bivariada é a mais apropriada por representar melhor as variáveis do clima. E o modelo de estimação das produtividades potencial e deplecionada para a cultura de soja produz resultados coerentes com outros resultados obtidos na literatura. / The availability of resources, as much of financial order and human labor, is scarse. Therefore, it must stimulates the regional planning that minimizes the use of resources. Then, the forecast of harvests through modelling techniques must previously on the basis of be carried through the regional characteristics, thus indicating the routes of the research, as well as the regional planning. Then, the aims of this work are: (i) to characterize the climatic variables through different probability distributions; (ii) to verify the spatial and temporal homogeneity of the climatic variables; (iii) to verify the bivaried normal distribution to simulate parameters used to estimate soybean crop productivity; (iv) to propose a model of estimating the magnitud order of soybean crop potential productivity (it depends on the genotype, air temperature, photosynthetic active radiation; and photoperiod) and the depleted soybean crop productivity (it pedends on the potential productivity, rainfall and soil watter availability) based on daily values of temperature, insolation and rain, for the State of São Paulo. The variable used in this study had been the minimum, maximum and average air temperature, insolation, solar radiation, fotosynthetic active radiation and pluvial precipitation, in daily scale, gotten in 27 stations located in the State of São Paulo and six stations located in neighboring States. First, it was verified tack of seven variables in five probability distributions (normal, log-normal, exponential, gamma and weibull), through of Kolmogorov-Smirnov. The spatial and temporal verified through the analysis of grouping by Ward method and estimating the sample size (number of years) for the variable. The generation of random numbers was carried through the Monte Carlo Method. The simulation of the data of photosyntetic active radiation and temperature had been carried through three cases: (i) nonsymetric triangular distribution (ii) normal distribution truncated at 1.96 shunting line standard of the average and (iii) bivaried normal distribution. The simulated data had been evaluated through the test of homogeneity of variance of Bartlett and the F test, t test, agreement index of Willmott, angular coefficient of the straight line, the index of performance index of Camargo (C) and tack the normal distribution (univarieted). The proposed model to simulate the potential productivity of soybean crop was based on the de Wit concepts, including Van Heenst, Driessen, Konijn, Vries, and others researchers. The computation of the depleted productivity was dependent of the potential, crop and real evapotranspirations and the sensitivity hydric deficiency coefficient. The insolation and pluvial precipitation data had been showed through the normal distribution. Being thus, the daily production of carbohydrate was depleted as function of hydric stress and insolation. The interpolation of the data, in order to consider the whole State of Sao Paulo, was carried through the Kriging method. The results were gotten that most of the variable can follow the normal distribution. Moreover, the variable presents spatial and temporal variability and the number of necessary years (sample size) for each one of them is sufficiently changeable. The simulation using the bivaried normal distribution is most appropriate for better representation of climate variable. The model of estimating potential and depleted soybean crop productivities produces coherent values with the literature results.

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Food insecurity and nutrition are two of the biggest challenges facing our society. Urban agriculture can help address these challenges, though lack of awareness about opportunities for engagement and degraded soils are two barriers that could prevent people from realizing the benefits that these operations can provide. Soils in urban areas are often highly degraded due to development activities and lack the structure and microbial life needed to sustain healthy, productive plants. Many lifelong habits such as healthy eating and engagement in community gardening are best established during young adulthood. Graduate school is a particularly unique time period, as many students are living on their own for the first time with modest incomes and some have young families that are particularly vulnerable to food insecurity. Consequently, the first objective of this project was to identify which barriers, if any, Purdue graduate students face when purchasing and consuming fresh produce and participating in local urban agriculture initiatives as Purdue’s campus and much of the surrounding area are characterized as food deserts by the USDA. We also sought to determine how the COVID-19 pandemic influenced food access and motivations for healthy eating and community garden engagement. To answer these questions, we distributed a voluntary 33 question online Qualtrics® survey to all Purdue graduate students via mass email blast. Results indicate that many Purdue graduate students face individual and structural barriers to accessing fresh fruits and vegetables. International respondents, in particular, were particularly vulnerable to structural barriers. Not having access to a personal vehicle appears to be the primary predictor of who was most vulnerable, especially during the pandemic. Results also indicate that students are interested in participating in local urban agriculture initiatives, but most are unaware of their existence. Students indicated that e-mails were the best method for increasing awareness and engagement. The second objective of this study was to determine whether leaf mold compost could improve the health and productivity of degraded urban soils. In addition, we aimed to determine whether the leaf compost could better support a beneficial microbial inoculant to further enhance crop productivity, as well as the extent to which plant genotype moderates these beneficial plant-soil-microbial relationships. To answer these questions, leaf compost was obtained from a local grower and applied to experimental plots at the Purdue University Farm. Two tomato varieties, Wisconsin 55 and Corbarino, were inoculated with Trichoderma harzianum T-22 or a sterile water control, and transplanted into the field trials. 15 Survival following transplanting, vigor, disease ratings and the yield and quality of tomato fruit were quantified over the course of two growing seasons. Results indicated that several measures of soil health were significantly increased in compost-amended soils and the health and productivity of tomato plants greatly improved. The microbial inoculant dramatically reduced transplant stress, especially in Wisconsin 55. Other more subtle differences among the tomato varieties indicated that urban agriculture systems could be improved through varietal selection. These studies highlight the fact that graduate students are not immune to food insecurity and proper nutrition and they are interested in connecting with urban agriculture initiatives to address these challenges. Pairing of the two groups could prove to be a successful mutualistic symbiosis as graduate students provide the enthusiasm and manpower that urban gardens need while urban gardens offer access to low-cost fresh produce that many graduate students desire. Leaf mold compost can aid in these initiatives by providing a cost-effective approach to improve the health and productivity of urban soils and crops, while at the same time providing further benefits such as reduced accumulation of valuable carbon sources in municipal landfills. Results like these provide stark evidence that agriculture, particularly urban agriculture, can continue to improve access to nutritious foods through green initiatives and innovations.

There is a growing societal concern about excessive water and fertilizer use in agricultural systems. High water productivity while maintaining high crop yields can be achieved with appropriate irrigation scheduling. Moreover, freshwater pollution through nitrogen (N) leaching due to the widespread use of N fertilizers demands for an efficient N fertilization management. However, sustainable crop management requires good knowledge of soil water and N dynamics as well as of crop water and N demand. Crop growth models, which describe physical and physiological processes of crop growth as well as water and matter transport, are considered as powerful tools to assist in the optimization of irrigation and fertilization management. It is of a general nature that the reliability of simulation based predictions depends on the quality and quantity of the data used for model calibration and validation which can be obtained e.g. in field experiments. A lack of data or low data quality for model calibration and validation may cause low performance and large uncertainties in simulation results. The large number of model parameters to be calibrated requires appropriate calibration methods and a sequential calibration strategy. Moreover, a simulation based planning of the field design saves costs and expenditure while supporting maximal outcomes of experiments. An adjustment of crop growth modeling and experiments is required for model improvement and development to reliably predict crop growth and to generalize predicted results. In this research study, a new approach for simulation based optimal experimental design was developed aiming to integrate simulation models, experiments, and optimization methods in one framework for optimal and sustainable irrigation and N fertilization management. The approach is composed of three steps: 1. The preprocessing consists of the calibration and validation of the crop growth model based on existing experimental data, the generation of long time-series of climate data, and the determination of the optimal irrigation control. 2. The implementation comprises the determination and experimental application of the simulation based optimized deficit irrigation and N fertilization schedules and an appropriate experimental data collection. 3. The postprocessing includes the evaluation of the experimental results namely observed yield, water productivity (WP), nitrogen use efficiency (NUE), and economic aspects, as well as a model evaluation. Five main tools were applied within the new approach: an algorithm for inverse model parametrization, a crop growth model for simulating crop growth, water balance and N balance, an optimization algorithm for deficit irrigation and N fertilization scheduling, and a stochastic weather generator. Furthermore, a water flow model was used to determine the optimal irrigation control functions and for simulation based estimation of the optimal field design. The approach was implemented within three case studies presented in this work. The new approach combines crop growth modeling and experiments with stochastic optimization. It contributes to a successful application of crop growth modeling based on an appropriate experimental data collection. The presented model calibration and validation procedure using the collected data facilitates reliable predictions. The stochastic optimization framework for deficit irrigation and N fertilization scheduling proved to be a powerful tool to enhance yield, WP, NUE and profit.:Contents Nomenclature ..............................................................................................................................xii 1 Introduction..................................................................................................................................1 I Fundamentals and literature review ........................................................................................5 2 Water productivity in crop production ....................................................................................7 2.1 Water productivity .................................................................................................................7 2.2 Increase of crop yield ..........................................................................................................9 2.3 Irrigation ...............................................................................................................................10 2.3.1 Irrigation methods ...........................................................................................................10 2.3.2 Irrigation scheduling and irrigation control ................................................................11 2.3.3 The influence of the field design on profitability .......................................................12 2.4 The concept of controlled deficit irrigation ...................................................................14 3 Nitrogen use efficiency in crop production .........................................................................17 3.1 Nitrogen use efficiency ....................................................................................................18 3.2 N fertilization management .............................................................................................18 3.3 Combination of controlled deficit irrigation and deficit N fertilization ......................19 4 Crop growth modeling ............................................................................................................21 4.1 Physiological crop growth models ..................................................................................21 4.1.1 Model description of SVAT model Daisy ....................................................................22 4.1.2 Model description of crop growth model Pilote .........................................................24 4.2 Optimal experimental design for model parametrization ...........................................25 4.2.1 Experimental design ......................................................................................................25 4.2.2 Model parameter estimation ........................................................................................26 4.2.3 Model parameter estimation based on greenhouse data .......................................27 5 Irrigation and N fertilization scheduling ..............................................................................29 5.1 Irrigation scheduling .........................................................................................................29 5.2 N fertilization scheduling .................................................................................................30 5.3 Combination of irrigation and N fertilization scheduling ............................................30 II New approach for simulation based optimal experimental design ................................33 6 Preprocessing steps ...............................................................................................................37 6.1 Model parametrization and assessment .......................................................................37 6.1.1 Calibration of the soil parameters ...............................................................................38 6.1.2 Calibration of the plant parameters ............................................................................39 6.1.3 Model assessment .........................................................................................................41 6.1.4 Preliminary simulations for an optimal experimental layout ..................................43 6.2 Generation of long time-series of climate data ............................................................44 6.3 Determination of the optimal irrigation control functions ...........................................44 7 Stochastic optimization framework ......................................................................................47 7.1 Stochastic optimization framework .................................................................................47 7.1.1 Optimization algorithm ...................................................................................................47 7.1.2 Generation of the crop water (nitrogen) production functions ................................48 7.1.3 Application of the stochastic optimization framework ..............................................48 7.1.4 Crop growth model requirements ................................................................................49 8 Data collection during the experimentation .......................................................................51 9 Postprocessing steps .............................................................................................................55 9.1 Evaluation of the experimental results ...........................................................................55 9.1.1 Yield and total dry matter ..............................................................................................55 9.1.2 Water productivity and nitrogen use efficiency .........................................................55 9.1.3 Economic aspects ..........................................................................................................55 9.1.4 Evaluation of the model results ....................................................................................56 III Application of the new approach to three case studies ...................................................57 10 Evaluation of model transferability ....................................................................................59 10.1 Objectives and summary ................................................................................................59 10.2 Experimental site and experimental setup .................................................................61 10.3 Data collection during the experimentation ................................................................63 10.4 Calibration and validation of the model parameters .................................................63 10.4.1 Model setup and soil parametrization ......................................................................64 10.4.2 Plant parameter calibration and validation .............................................................67 10.5 Application of the stochastic optimization framework ...............................................75 10.5.1 Generation of the climate data ...................................................................................75 10.5.2 Estimation of the yield potential of wheat ................................................................75 10.5.3 Estimation of the water productivity potential of barley .........................................77 10.6 Discussion and conclusions ..........................................................................................81 11 Real-time irrigation scheduling ..........................................................................................83 11.1 Objectives and summary ................................................................................................83 11.2 Experimental site and field design ...............................................................................85 11.3 Data collection during the experiment ........................................................................86 11.4 Calibration and setup of the crop growth model Pilote .............................................87 11.5 Derivation of optimal irrigation control functions for different drip line spacings 88 11.5.1 Initial Hydrus 2D/3D simulations ...............................................................................88 11.5.2 Determination of the irrigation control functions .....................................................89 11.5.3 Verifying measurements ..............................................................................................92 11.6 Real-time deficit irrigation scheduling .........................................................................93 11.7 Evaluation of the experimental results .........................................................................96 11.7.1 Crop yields .....................................................................................................................96 11.7.2 Water productivity .........................................................................................................97 11.7.3 Prognostic simulations ................................................................................................98 11.7.4 Economic aspects ........................................................................................................99 11.8 Discussion and conclusions ........................................................................................100 12 Multicriterial optimization...................................................................................................103 12.1 Objectives and summary .............................................................................................103 12.2 Experimental site and experimental setup ...............................................................105 12.3 Data collection during the experiment ......................................................................105 12.4 Experimental layout ......................................................................................................106 12.5 Calibration and validation of the model parameters ..............................................107 12.5.1 Calibration of the soil parameters ...........................................................................107 12.5.2 Calibration and validation of the plant parameters .............................................107 12.5.3 Setup of SVAT model Daisy .....................................................................................108 12.6 Generation of the climate data ....................................................................................109 12.7 Optimized irrigation and N fertilization scheduling .................................................109 12.8 Evaluation of the experimental results .......................................................................111 12.8.1 Observed plant variables and weather data .........................................................111 12.8.2 Water productivities and nitrogen use efficiencies ...............................................111 12.8.3 Chlorophyll Meter values ..........................................................................................112 12.8.4 Recalculation of soil parameters .............................................................................113 12.9 Postprocessing simulations of yield and water and N dynamics..........................114 12.9.1 Yield predictions using Daisy 1D ............................................................................114 12.9.2 Yield predictions using Daisy 2D ............................................................................119 12.10 Discussion and conclusions .....................................................................................121 IV General discussion, conclusions and outlook ...............................................................123 13 General discussion ............................................................................................................125 14 General conclusions and outlook ....................................................................................133 Appendix ....................................................................................................................................134 A Tables and Figures ...............................................................................................................137 B Model setup and weather files ...........................................................................................145 List of Tables .............................................................................................................................153 List of Figures ............................................................................................................................153 References ................................................................................................................................159 / In der heutigen Gesellschaft gibt es zunehmend Bedenken gegenüber übermäßigem Wasser- und Düngereinsatz in der Landwirtschaft. Eine hohe Wasserproduktivität kann jedoch durch geeignete Bewässerungspläne mit hohen landwirtschaftlichen Erträgen in Einklang gebracht werden. Die mit der weitverbreiteten Stickstoffdüngung einhergehende Gewässerbelastung aufgrund von Stickstoffauswaschung erfordert zudem ein effizientes Stickstoffmanagement. Eine entsprechende ressourceneffiziente Landbewirtschaftung bedarf präzise Kenntnisse der Bodenwasser- und Stickstoffdynamiken sowie des Pflanzenwasser- und Stickstoffbedarfs. Als leistungsfähige Werkzeuge zur Unterstützung bei der Optimierung von Bewässerungs-und Düngungsplänen werden Pflanzenwachstumsmodelle eingesetzt, welche die physischen und physiologischen Prozesse des Pflanzenwachstums sowie die physikalischen Prozesse des Wasser- und Stofftransports abbilden. Hierbei hängt die Zuverlässigkeit dieser simulationsbasierten Vorhersagen von der Qualität und Quantität der bei der Modellkalibrierung und -validierung verwendeten Daten ab, welche beispielsweise in Feldversuchen erfasst werden. Fehlende Daten oder Daten mangelhafter Qualität bei der Modellkalibrierung und -validierung führen zu unzuverlässigen Simulationsergebnissen und großen Unsicherheiten bei der Vorhersage. Die große Anzahl an zu kalibrierenden Parametern erfordert zudem geeignete Kalibrierungsmethoden sowie eine sequenzielle Kalibrierungsstrategie. Darüber hinaus kann eine simulationsbasierte Planung des Versuchsdesigns Kosten und Aufwand reduzieren und zu weiteren experimentellen Erkenntnissen führen. Die Abstimmung von Pflanzenwachstumsmodellen und Versuchen ist zudem für die Modellentwicklung und -verbesserung sowie für eine Verallgemeinerung von Simulationsergebnissen unabdingbar. Im Rahmen dieser Arbeit wurde ein neuer Ansatz für ein simulationsbasiertes optimales Versuchsdesign entwickelt. Ziel war es, Simulationsmodelle, Versuche und Optimierungsmethoden in einem Ansatz für optimales und nachhaltiges Bewässerungs- und Düngungsmanagement zu integrieren. Der Ansatz besteht aus drei Schritten: 1. Die Vorbereitungsphase beinhaltet die auf existierenden Versuchsdaten basierende Kalibrierung und Validierung des Pflanzenwachstumsmodells, die Generierung von Klimazeitreihen und die Bestimmung der optimalen Bewässerungssteuerung. 2. Die Durchführungsphase setzt sich aus der Erstellung und experimentellen Anwendung der simulationsbasierten optimierten Defizitbewässerungs- und Stickstoffdüngungspläne und der Erfassung der relevanten Versuchsdaten zusammen. 3. Die Auswertungsphase schließt eine Evaluierung der Versuchsergebnisse anhand ermittelter Erträge, Wasserproduktivitäten (WP), Stickstoffnutzungseffizienzen (NUE) und ökonomischer Aspekte, sowie eine Modellevaluierung ein. In dem neuen Ansatz kamen im Wesentlichen folgende fünf Werkzeuge zur Anwendung: Ein Algorithmus zur inversen Modellparametrisierung, ein Pflanzenwachstumsmodell, welches das Pflanzenwachstum sowie die Wasser- und Stickstoffbilanzen abbildet, ein evolutionärer Optimierungsalgorithmus für die Generierung von defizitären Bewässerungs- und Stickstoffplänen und ein stochastischer Wettergenerator. Zudem diente ein Bodenwasserströmungsmodell der Ermittlung der optimalen Bewässerungssteuerung und der simulationsbasierten Optimierung des Versuchsdesigns. Der in dieser Arbeit vorgestellte Ansatz wurde in drei Fallbeispielen angewandt. Der neue Ansatz kombiniert Pflanzenwachstumsmodellierung und Experimente mit stochastischer Optimierung. Er leistet einen Beitrag zu einer erfolgreichen Pflanzenwachstumsmodellierung basierend auf der Erfassung relevanter Versuchsdaten. Die vorgestellte Modellkalibrierung und -validierung unter Verwendung der erfassten Versuchsdaten trug wesentlich zu zuverlässigen Simulationsergebnissen bei. Darüber hinaus stellt die hier vorgestellte stochastische Optimierung von defizitären Bewässerungs- und Stickstoffplänen ein leistungsfähiges Werkzeug dar, um Erträge, WP, NUE und den Profit zu erhöhen.:Contents Nomenclature ..............................................................................................................................xii 1 Introduction..................................................................................................................................1 I Fundamentals and literature review ........................................................................................5 2 Water productivity in crop production ....................................................................................7 2.1 Water productivity .................................................................................................................7 2.2 Increase of crop yield ..........................................................................................................9 2.3 Irrigation ...............................................................................................................................10 2.3.1 Irrigation methods ...........................................................................................................10 2.3.2 Irrigation scheduling and irrigation control ................................................................11 2.3.3 The influence of the field design on profitability .......................................................12 2.4 The concept of controlled deficit irrigation ...................................................................14 3 Nitrogen use efficiency in crop production .........................................................................17 3.1 Nitrogen use efficiency ....................................................................................................18 3.2 N fertilization management .............................................................................................18 3.3 Combination of controlled deficit irrigation and deficit N fertilization ......................19 4 Crop growth modeling ............................................................................................................21 4.1 Physiological crop growth models ..................................................................................21 4.1.1 Model description of SVAT model Daisy ....................................................................22 4.1.2 Model description of crop growth model Pilote .........................................................24 4.2 Optimal experimental design for model parametrization ...........................................25 4.2.1 Experimental design ......................................................................................................25 4.2.2 Model parameter estimation ........................................................................................26 4.2.3 Model parameter estimation based on greenhouse data .......................................27 5 Irrigation and N fertilization scheduling ..............................................................................29 5.1 Irrigation scheduling .........................................................................................................29 5.2 N fertilization scheduling .................................................................................................30 5.3 Combination of irrigation and N fertilization scheduling ............................................30 II New approach for simulation based optimal experimental design ................................33 6 Preprocessing steps ...............................................................................................................37 6.1 Model parametrization and assessment .......................................................................37 6.1.1 Calibration of the soil parameters ...............................................................................38 6.1.2 Calibration of the plant parameters ............................................................................39 6.1.3 Model assessment .........................................................................................................41 6.1.4 Preliminary simulations for an optimal experimental layout ..................................43 6.2 Generation of long time-series of climate data ............................................................44 6.3 Determination of the optimal irrigation control functions ...........................................44 7 Stochastic optimization framework ......................................................................................47 7.1 Stochastic optimization framework .................................................................................47 7.1.1 Optimization algorithm ...................................................................................................47 7.1.2 Generation of the crop water (nitrogen) production functions ................................48 7.1.3 Application of the stochastic optimization framework ..............................................48 7.1.4 Crop growth model requirements ................................................................................49 8 Data collection during the experimentation .......................................................................51 9 Postprocessing steps .............................................................................................................55 9.1 Evaluation of the experimental results ...........................................................................55 9.1.1 Yield and total dry matter ..............................................................................................55 9.1.2 Water productivity and nitrogen use efficiency .........................................................55 9.1.3 Economic aspects ..........................................................................................................55 9.1.4 Evaluation of the model results ....................................................................................56 III Application of the new approach to three case studies ...................................................57 10 Evaluation of model transferability ....................................................................................59 10.1 Objectives and summary ................................................................................................59 10.2 Experimental site and experimental setup .................................................................61 10.3 Data collection during the experimentation ................................................................63 10.4 Calibration and validation of the model parameters .................................................63 10.4.1 Model setup and soil parametrization ......................................................................64 10.4.2 Plant parameter calibration and validation .............................................................67 10.5 Application of the stochastic optimization framework ...............................................75 10.5.1 Generation of the climate data ...................................................................................75 10.5.2 Estimation of the yield potential of wheat ................................................................75 10.5.3 Estimation of the water productivity potential of barley .........................................77 10.6 Discussion and conclusions ..........................................................................................81 11 Real-time irrigation scheduling ..........................................................................................83 11.1 Objectives and summary ................................................................................................83 11.2 Experimental site and field design ...............................................................................85 11.3 Data collection during the experiment ........................................................................86 11.4 Calibration and setup of the crop growth model Pilote .............................................87 11.5 Derivation of optimal irrigation control functions for different drip line spacings 88 11.5.1 Initial Hydrus 2D/3D simulations ...............................................................................88 11.5.2 Determination of the irrigation control functions .....................................................89 11.5.3 Verifying measurements ..............................................................................................92 11.6 Real-time deficit irrigation scheduling .........................................................................93 11.7 Evaluation of the experimental results .........................................................................96 11.7.1 Crop yields .....................................................................................................................96 11.7.2 Water productivity .........................................................................................................97 11.7.3 Prognostic simulations ................................................................................................98 11.7.4 Economic aspects ........................................................................................................99 11.8 Discussion and conclusions ........................................................................................100 12 Multicriterial optimization...................................................................................................103 12.1 Objectives and summary .............................................................................................103 12.2 Experimental site and experimental setup ...............................................................105 12.3 Data collection during the experiment ......................................................................105 12.4 Experimental layout ......................................................................................................106 12.5 Calibration and validation of the model parameters ..............................................107 12.5.1 Calibration of the soil parameters ...........................................................................107 12.5.2 Calibration and validation of the plant parameters .............................................107 12.5.3 Setup of SVAT model Daisy .....................................................................................108 12.6 Generation of the climate data ....................................................................................109 12.7 Optimized irrigation and N fertilization scheduling .................................................109 12.8 Evaluation of the experimental results .......................................................................111 12.8.1 Observed plant variables and weather data .........................................................111 12.8.2 Water productivities and nitrogen use efficiencies ...............................................111 12.8.3 Chlorophyll Meter values ..........................................................................................112 12.8.4 Recalculation of soil parameters .............................................................................113 12.9 Postprocessing simulations of yield and water and N dynamics..........................114 12.9.1 Yield predictions using Daisy 1D ............................................................................114 12.9.2 Yield predictions using Daisy 2D ............................................................................119 12.10 Discussion and conclusions .....................................................................................121 IV General discussion, conclusions and outlook ...............................................................123 13 General discussion ............................................................................................................125 14 General conclusions and outlook ....................................................................................133 Appendix ....................................................................................................................................134 A Tables and Figures ...............................................................................................................137 B Model setup and weather files ...........................................................................................145 List of Tables .............................................................................................................................153 List of Figures ............................................................................................................................153 References ................................................................................................................................159

<p>As the world population continues to grow, it will be challenging to manage resources, reduce environmental pollution and maintain growing demand for food production. Controlled environment agriculture (CEA) is a novel solution to reduce freshwater use in agriculture, minimize environmental pollution from agriculture sector, and meet the growing food demand. CEA allows for the year-round cultivation in inhospitable climatic conditions. Hydroponics is a common method of growing crops in CEA, where plants grow in a solution enriched with nutrients and oxygen. The technique significantly reduces water use and fertilizer run-off during production. In the United States, lettuce is one of the most important crops grown using hydroponics.</p> <p> Hydroponic production uses several methods to grow lettuce including nutrient film technique (NFT) and constant flood table (CFT). Moreover, several cultivars of lettuce are grown in the Midwest. There is a lack of knowledge on whether optimal fertilizer concentrations change depending on the cultivar or hydroponic production system. Little information is known about the suitability of a cultivar to a specific method of hydroponic production. For year-round lettuce production in hydroponics, supplemental lighting (SL) and heating are required in the Midwestern regions of the U.S. The energy requirements for SL and heating can be too costly in winter for some growers to produce crop year-round. In addition to light quantity, spectral composition of light can impact growth. Heating the root zone to produce a micro-climate may be more efficient than heating the entire greenhouse and possibly reduce overall heating costs. However, information on spectral composition of light and the efficacy of root zone heating is unclear, at best. Certain cultivars that can tolerate cold stress can be more suitable in the U.S. Midwest during winter. Lettuce cultivar screening for yield under cooler environments is limited. </p> <p> A completely customizable hydroponic production system that can aid in conducting research related to above-mentioned issues was built as a part of my Master of Science program. Using this system, 24 popular cultivars from four lettuce groups were evaluated for productivity during summer/fall under different concentrations of fertilizer solution, and in two production methods including NFT and CFT during spring. In addition, yield of all 24 cultivars were evaluated under 10, 15.5 and 21.1 °C in a growth chamber. The eight best performing cultivars from the summer/fall trial were evaluated during the winter in a greenhouse with the addition of SL and root zone heating with minimal ambient air heating. </p> <p> Results indicated that the lowest level of electrical conductivity (EC) of the fertilizer solution used (1.3 dS·m^-1) resulted in highest yield, regardless of cultivar or method of production. Among the 24 cultivars; Red Sails (Leaf), Salvius (Romaine), Cedar (Oakleaf), and Adriana (Butterhead) had the highest yields among each group during summer. Growth chamber study indicated that Dragoon, Adriana, New Fire Red and Red Sails cultivars had higher yields than other cultivars under cooler (10 and 15.5 °C) air temperature conditions. In the winter study, lettuce cultivars did not reach harvestable size even after 40 days of growth without SL and root zone heating. Supplemental light composition significantly affected lettuce growth with higher yield under Purple (with higher proportion of red) than White LED lighting. Commercially acceptable lettuce could be produced using root zone heating. In general, plants grown under CFT yielded higher than those grown under NFT in the winter trial. Among the cultivars, Salvius, Black Seeded Simpson, Cedar, and Red Sails performed better under SL and root zone heating during winter.</p>