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.

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

Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2016-10-06T18:58:26Z No. of bitstreams: 1 2016 - Daniela Pinto Gomes.pdf: 2118994 bytes, checksum: df26092e9f2c17eb3ae1a9ff3e3aad9a (MD5) / Made available in DSpace on 2016-10-06T18:58:26Z (GMT). No. of bitstreams: 1 2016 - Daniela Pinto Gomes.pdf: 2118994 bytes, checksum: df26092e9f2c17eb3ae1a9ff3e3aad9a (MD5) Previous issue date: 2016-02-26 / CAPES / FAPERJ / This work entitled "Organic farming tomatoes in potponics and greenhouse with irrigation management by controller automatic" aimed to provide information to serve as a recommendation for a new tomato cultivation system potted and greenhouse with respect to inputs water and fertilizer, to ensure a sustainable production, mainly based on the use of residu?rios materials as a source of nutrients and automatic irrigation low cost. The specific objectives of this study were to assess: (a) the effect of different doses of wood ash and castor bean cake in the growth and productivity of tomato culture; and (b) the effect of different irrigation levels on growth and productivity of the tomato crop. Three experiments were conducted from 2013 to 2015 using a randomized block. In 2013, we evaluated the effect of five crecentes doses of wood ash and castor bean cake (160 g of ash and 90 g of castor bean cake per plant, 195 g of ash and 120 g of castor bean cake per plant, 230 g of ash and 140 g of castor bean cake per plant; 265 g of ash and 160 g of castor bean cake per plant, and 300 g of ash and 180 g of castor bean cake per plant) productivity of tomato cultivation. In 2014, in a factorial scheme (3 x 2), evaluated the effect of three wood ash levels (40, 80 and 120 g per plant) and two castor bean cake doses (140 and 280 g per plant) growth and productivity of the tomato crop. In 2015 we evaluated the effect of four irrigation levels (135, 165, 191 and 213 mm) on the growth and productivity of the tomato crop. The results in 2013 showed that the ash doses tested and castor bean cake did not influence the marketable productivity of tomato (1.24 to 1.59 kg per plant (24.8 to 31.8 t ha-1)). In 2014 most of the growth variables were positively influenced by the increase in wood ash and castor bean cake doses. Most marketable yield was obtained with the dose of 280 g of castor bean cake (1.99 kg per plant (39.8 t ha-1)), not significantly influenced by the ash doses. In 2015 most of the growth variables and marketable productivity were negatively with decreasing water depth. The level of 213 mm was responsible for greater marketable productivity (4.04 kg per plant (80.8 t ha-1)). / O presente trabalho intitulado ?Cultivo org?nico de tomate em vasoponia e ambiente protegido com manejo da irriga??o por acionador autom?tico? teve por objetivo proporcionar informa??es que sirvam de recomenda??o para um novo sistema de cultivo de tomate em vaso e em ambiente protegido, com rela??o aos insumos ?gua e fertilizante, visando uma produ??o sustent?vel, baseada principalmente na utiliza??o de materiais residu?rios como fonte de nutrientes e irriga??o automatizada de baixo custo. Os objetivos espec?ficos deste trabalho foram avaliar: (a) o efeito de diferentes doses de cinza e torta de mamona no crescimento e na produtividade da cultura do tomate; e (b) o efeito de diferentes l?minas de irriga??o no crescimento e na produtividade da cultura do tomate. Foram realizados tr?s experimentos no per?odo de 2013 a 2015 utilizando o delineamento experimental em blocos casualizados. Em 2013, avaliou-se o efeito de cinco doses crescentes de cinza e torta de mamona (160 g de cinza e 90 g de torta de mamona por planta; 195 g de cinza e 120 g de torta de mamona por planta; 230 g de cinza e 140 g de torta de mamona por planta; 265 g de cinza e 160 g de torta de mamona por planta; e 300 g de cinza e 180 g de torta de mamona por planta) na produtividade da cultura do tomate. Em 2014, em esquema fatorial (3 x 2), avaliou-se o efeito de tr?s doses de cinza (40, 80 e 120 g por planta) e duas doses de torta de mamona (140 e 280 g por planta) no crescimento e na produtividade da cultura do tomate. Em 2015 avaliou-se o efeito de quatro l?minas de irriga??o (135, 165, 191 e 213 mm) no crescimento e na produtividade da cultura do tomate. Os resultados obtidos em 2013 mostraram que as doses de cinza e torta de mamona n?o influenciaram significativamente a produtividade comercial do tomateiro (1,24 a 1,59 kg por planta (24,8 a 31,8 t ha-1)). Em 2014 a maioria das vari?veis de crescimento avaliadas foram influenciadas positivamente pelo aumento das doses de cinza e torta de mamona. A maior produtividade comercial foi obtida com a dose de 280 g de torta de mamona (1,99 kg por planta (39,8 t ha-1)), n?o sendo influenciada significativamente pelas doses de cinza. Em 2015 a maioria das vari?veis de crescimento e a produtividade comercial foram influenciadas negativamente com a diminui??o da l?mina de irriga??o. A l?mina de 213 mm foi a respons?vel pela maior produtividade comercial (4,04 kg por planta (80,8 t ha-1)).

SARAIVA, Kleiton Rocha. Manejo da irrigação no cultivo da melancia, sob diferentes coberturas e déficits hídricos, utilizando o modelo ISAREG. 2014. 165 f. : Tese (doutorado) - Universidade Federal do Ceará, Centro de Ciências Agrárias, Departamento de Engenharia Agrícola, Programa de Pós-Graduação em Engenharia Agrícola, Fortaleza-CE, 2014. / Submitted by demia Maia (demiamlm@gmail.com) on 2016-08-08T15:03:20Z No. of bitstreams: 1 2014_tese_krsaraiva.pdf: 3484862 bytes, checksum: 1bdb44d33f3f5c2dfe4843e4a7f7f90e (MD5) / Approved for entry into archive by demia Maia (demiamlm@gmail.com) on 2016-08-08T15:04:04Z (GMT) No. of bitstreams: 1 2014_tese_krsaraiva.pdf: 3484862 bytes, checksum: 1bdb44d33f3f5c2dfe4843e4a7f7f90e (MD5) / Made available in DSpace on 2016-08-08T15:04:04Z (GMT). No. of bitstreams: 1 2014_tese_krsaraiva.pdf: 3484862 bytes, checksum: 1bdb44d33f3f5c2dfe4843e4a7f7f90e (MD5) Previous issue date: 2014 / A growing shortage of water due to population growth and economic development is increasing the challenges for agriculture, is which wasteful in its use, and thus necessitates effective new solutions for the management of water resources in irrigated areas, mainly located in the semiarid region, where water is limited. Moreover, in the semiarid Northeast of Brazil, predominantly irrigation is still empirical, ie, it is not performed in the appropriated irrigation management, causing wastage of scarce water resources in the region. One alternative to improve this scenario is the use of computer software used in irrigation management. The ISAREG has been used in many countries, being able to assist in the simulation of irrigation. Moreover, with adjustments in the input data it should be possible to quantify water irrigation depths that may allow maintenance of soil moisture at different percentages of storage (the available water capacity, CAD), saving water resources and increasing efficiency irrigation. Another way to reduce the use of water in agriculture is through the use of soil cover, which is a simple technology and the whose benefits on production and crop yields are compelling, especially in situations of low water availability. Added to this, it is the fact that the interaction of these technologies can extend these effects. Therefore, this research aimed to diffuse a rational management of irrigation, in the cultivation of watermelon in semiarid region, from different proposition of the "software" ISAREG and the use of different coverage on the soil to increase efficiency in water use, and reduction in resource use of water. To this end, six (6) research actions were performed in UEPE Unit (Teaching, Research and Extension) IFCE the campus Limon North-Ce, the District Irrigation Jaguaribe-Apodi DIJA. In Research Action I was setting up a field experiment with watermelon crop, aiming at determining the variables used in the simulation of ISAREG software. In Action II a field research was carried out with the DIJA irrigators, regarding irrigation management adopted by them. During the Research Action III simulations using the ISAREG, and the preparation of proposals for irrigation were performed. The Action IV consisted of the analysis of efficiency in the simulation of the ISAREG model process. In the Action V field experiments, with the propositions generated by ISAREG model, and the modal management adopted by the DIJA irrigators versus differentiated coverage conditions in soil were performed. These were conducted under design in randomized complete block, split-plot design with 4 replications. Treatments included a combination of 04 irrigation propositions (three propositions of ISAREG: M1 = 100% maintenance of CAD, M2 and M3 80% 60% and M4, modal water depth irrigators) that constituted the plots and 04 sub-plots, with 03 soil cover conditions (coverage with rice husk with "mulching" white and "mulching" black, called C1, C2 and C3), and the sub-plot 4, the bare soil, called C0. Moisture conditions of the soil and the characteristics of development, production, productivity and post-harvest watermelon crop were analyzed. The results were subjected to analysis of variance, and when significant, were submitted to regression analysis (quantitative, water depths), the average (qualitative, coverage) and trend graphs (interaction between factors) test. The results were also certain financial indicators (TIR and VPL) and calculated the efficiency of water use. Finally, through Action Research VI was eddied a handbook of practical and informative nature, for distribution DIJA to extension agents and public users of these technologies and innovations. In conclusion, among others, can be said that: the factor of water availability (p) was 0.20 in F1, phenological phase 1; 0.19 in stage 2; 0.175 in phase 3; 0.17 to 4; F5 0.19; and 0.205 in F6; irrigators do not practice proper irrigation management, applying daily modal water depth of 6.3 mm; the irrigator applies throughout the life of the watermelon, more water than the indication of the larger water depth ISAREG (100% CAD), generating considerable loss by deep percolation; the larger moisture in the soil were observed in the experimental conditions with larger water depths applied to coverages of rice husk and "mulching" white; the lowest were found in bare soil; In general, plants irrigated M1 irrigated by the depth and covered on soils of rice hulls and "mulching" white plants demonstrated better productive and post-harvest characteristics; financial indicators showed that in all situations analyzed the investment is feasible, however, the highest returns were mainly in plants under M1 and rice husk and "mulching" white, as opposed to the M3 and bare soil; greater efficiency of water use was observed in the condition M3 with depth cover "mulching" white and the smallest in M4 with bare soil; ISAREG the model when fed properly with all the variables required for it, proved to be efficient in simulating the water balance, even under deficit irrigation in the cultivation of watermelon in DIJA. / Uma crescente escassez de água devido ao aumento populacional e ao desenvolvimento econômico está ampliando os desafios para a agricultura, perdulária no seu uso, necessitando-se que se encontrem novas soluções para a gestão dos recursos hídricos em áreas irrigadas, principalmente nas localizadas na região semiárida, onde a água é limitada. Além disso, no semiárido nordestino predominantemente ainda se irriga empiricamente, ou seja, não se realiza o correto manejo da irrigação, ocasionando o desperdício dos escassos recursos hídricos da região. Uma das alternativas para se melhorar esse cenário é a utilização de softwares computacionais usados no manejo da irrigação. O ISAREG tem sido utilizado em vários países, sendo capaz de auxiliar na simulação de lâminas de irrigação. Além disso, com adequações nos dados de entrada deve ser possível se quantificar lâminas de reposição que possam permitir a manutenção da umidade do solo em diferentes porcentagens do armazenamento (da capacidade de água disponível, CAD), economizando o recurso hídrico e aumentando a eficiência de irrigação. Outra maneira de se reduzir o uso do recurso hídrico na agricultura é através da utilização de cobertura no solo, que é uma tecnologia simples e cujos benefícios sobre a produção e a produtividade das culturas são irrefutáveis, especialmente em situações de baixa disponibilidade de água. Acrescenta-se a isto, o fato de que a interação destas tecnologias pode ampliar estes efeitos. Portanto, a pesquisa objetivou difundir um manejo de irrigação racional, no cultivo da melancia no semiárido, a partir de proposições do “software” ISAREG e da utilização de coberturas no solo visando aumentar a eficiência no uso da água, com redução no uso do recurso hídrico. Para tanto, 6 (seis) ações de pesquisa foram realizadas na UEPE (Unidade de Ensino, Pesquisa e Extensão) do IFCE, campus Limoeiro do Norte-Ce, no Distrito de irrigação Jaguaribe-Apodi, DIJA. Na Ação de Pesquisa I ocorreu a constituição de experimento de campo com a cultura da melancia, visando à determinação das variáveis utilizadas na simulação do software ISAREG. Na Ação II foi realizada uma pesquisa de campo junto aos irrigantes do DIJA, quanto ao manejo da irrigação adotado pelos mesmos. Já durante a Ação de Pesquisa III foram realizadas as simulações com o uso do ISAREG, e a elaboração das proposições de irrigação. A Ação IV constou da análise da eficiência no processo de simulação do modelo ISAREG. Na V foram realizados experimentos de campo, com as proposições geradas pelo modelo ISAREG e com o manejo modal adotado pelos irrigantes do DIJA versus condições diferenciadas de cobertura no solo. Esses foram conduzidos sob delineamento em blocos completos ao acaso, com parcelas subdivididas e com 4 repetições. Os tratamentos consistiram da combinação de 04 proposições de irrigação (três proposições do ISAREG: M1 = manutenção de 100% da CAD; M2 de 80 % e M3 de 60%; e M4, lâmina modal dos irrigantes) que constituíram as parcelas, e 04 sub-parcelas, sendo 03 condições de cobertura no solo (coberturas com casca de arroz, com “mulching” branco e com “mulching” preto, denominadas C1, C2 e C3), e a sub-parcela 4, o solo sem cobertura, denominado C0. Foram analisadas as condições de umidade do solo e as características de desenvolvimento, de produção, de produtividade e de pós-colheita da cultura da melancia. Os resultados foram submetidos à análise de variância e quando significativos, à análise de regressão (quantitativo, lâminas), a teste de médias (qualitativos, coberturas) e a gráficos de tendência (interação entre os fatores). Também foram determinados indicadores financeiros (TIR e VPL) e calculada a eficiência de uso da água. Finalmente, através da Ação de Pesquisa VI foi elaborado um manual de natureza prática e informativa, para distribuição aos agentes de extensão e ao público usuário das tecnologias e inovações, atuantes no DIJA. Como conclusões, dentre outras, pode-se afirmar que: o fator de disponibilidade de água (f) foi 0,20 na F1, fase fenológica 1; 0,19 na fase 2; 0,175 na fase 3; 0,17 na 4; 0,19 na F5; e 0,205 na F6; os irrigantes não praticam o manejo correto da irrigação, sendo a lâmina modal diária de 6,3 mm; o irrigante aplica, durante todo o ciclo da melancia, mais água do que a indicação de maior lâmina do ISAREG (100% da CAD), gerando perda considerável por percolação profunda; as maiores umidades no solo foram verificadas nas condições experimentais com maiores lâminas aplicadas com coberturas de casca de arroz e “mulching” branco; as menores foram verificadas nos solos sem cobertura; em geral, as plantas irrigadas pela lâmina M1 e sobre solos cobertos por casca de arroz e “mulching” branco demonstraram melhores características produtivas e de pós-colheita; os indicadores financeiros demonstraram que em todas as situações analisadas o investimento é viável, no entanto, os maiores retornos ocorreram, principalmente nas plantas sob M1 com casca de arroz e “mulching” branco, em contraposição ao M3 e solo sem cobertura; a maior eficiência de uso da água foi verificada na condição de lâmina M3 com cobertura de “mulching” branco e a menor em M4 com solo sem cobertura; o modelo ISAREG, quando alimentado corretamente com todas as variáveis por ele requeridas, demonstrou ser eficiente na simulação do balanço hídrico, mesmo sob irrigação deficitária, no cultivo da melancia no DIJA.

As average global temperatures rise, cool-season C3 turfgrasses, such as the most commonly grown Kentucky bluegrass (Poa pratensis L.; KBG), struggle to tolerate extreme summer heat and increase their water consumption. Hybrid Bermudagrass (Cynodon dactylon [L.] Pers. × Cynodon transvaalensis Burtt Davy; HBG) is a warm-season C4 grass that may be increasingly suited for northern ecosystems traditionally classified as transition or cool-season climate zones. Glasshouse and field studies were conducted to compare HBG and KBG water use. The objective of the glasshouse study was to evaluate plant health and growth for two HBG cultivars (‘DT-1’ and ‘NorthBridge’) compared to a blend of KBG cultivars in all combinations of deficit, moderate, and high irrigation at optimum or short mowing height. The study was conducted in a glasshouse at Provo, UT, USA from 2020-2021. Grass was grown in pots arranged in a randomized complete block, full factorial design, with four replications of each treatment. The moderate KBG was also significantly different from both high and deficit for verdure and for the last half of NDVI. The objective of the field study was to evaluate two HBG cultivars (‘Tahoma 31’ and ‘Latitude 36’) compared to a blend of KBG cultivars for water loss and canopy health, temperature, and growth when subjected to deficit, moderate, and high irrigation. The study was arranged in a randomized complete block, full factorial design with three replications per treatment, and was conducted at Provo, UT, USA throughout the summer of 2021. In both the glasshouse and field trials, the deficit irrigated KBG consistently scored lower for NDVI and visual turf quality than all other treatments, including moderate and high KBG. This same trend was seen in the field study for percent cover. Although not observed in the glasshouse trial, it was observed in the field trial that the different irrigation levels of HBG resulted in no significant differences for any measurements but the HBG regularly scored better than KBG. The canopy temperatures of deficit irrigated KBG were also higher than all other treatments on most dates. The shoot mass, thatch mass, and total biomass of KBG were significantly less than either HBG cultivar. In the glasshouse trial it was observed that all deficit grasses were significantly lower than the other irrigation treatments and HBG had significantly deeper roots than KBG, although these results were not seen in the field trial. The data suggest that irrigation needs will be less for HBG than KBG and that HBG could provide a water-saving turfgrass alternative to KBG in semi-arid, cool-season regions with increasing water scarcity.

West Africa is subject to frequent yield losses due to erratic rainfall and degraded soils. At the same time, its population is expected to double by 2050. This situation is alarming in northern Togo, a West African dry savannah area, where rainfed maize is a staple food. Thus, it is necessary to improve agricultural productivity, e.g., by evaluating and introducing alternative irrigation management strategies, which may be implemented in this region. For this purpose, the present investigation focused on evaluating the potential of deficit and supplemental irrigation, as well as assessing the impact of climate and soil variability on maize yield under irrigated agriculture using irrigation optimisation strategies in northern Togo. The Optimal Climate Change Adaption Strategies in Irrigation (OCCASION) framework was adapted and employed to address the research objectives. It involves: (i) a weather generator for simulating long-term climate time series; (ii) the AquaCrop model, which was utilised to simulate the irrigation during the growing periods and the maize yield response to given irrigation management strategies; and (iii) a problem-specific algorithm for optimal irrigation scheduling with limited water supply. Five irrigation management strategies viz. T1: no irrigation (NI), T2: controlled deficit irrigation (CDI) and T3: full irrigation (FI) in the wet season, T4: controlled deficit irrigation (CDI) and T5: full irrigation (FI) in the dry season were assessed regarding their impact on maize yield in northern Togo. The results showed high variability in rainfall during the wet season, which led to substantial variability in the expected yield for NI. This variability was significantly lessened when optimised supplemental irrigation management strategies (CDI or FI) were applied. This also holds for the irrigation scenarios under the dry season. Finally, these findings were validated by an irrigation field experiment conducted at an agricultural research institute in northern Togo. Under a moderate level of deficit irrigation during the vegetative and reproductive growth stages, the above-ground biomass and the maize grain yield were reduced. However, a moderate level of deficit irrigation during the vegetative growth stage could result in similar values of water productivity to that of fully irrigated treatment. It was found that, based on the values of the statistical indicators, AquaCrop has accurately simulated the maize grain yield for all the irrigation strategies evaluated. The results of this study revealed that climate variability might engender a higher variability in the maize yields of northern Togo than soil variability does. Large- and smallscale water harvesting, access to groundwater, and irrigation infrastructures would be required for implementing the irrigation management strategies assessed in this study.:Declaration iii Declaration of Conformity v Dedication vii Acknowledgements ix Abstract xi Table of Contents xv List of Figures xvii List of Tables xix List of Acronyms and Abbreviations xxi 1. Introduction 1 1.1 Background and Problem Statement 1 1.1.1 Global Fresh and Agricultural Water Use 1 1.1.2 Erratic Rainfall, Rising Temperatures, and Soil fertility depletion in West Africa 2 1.1.3 Transboundary Water Issues in West Africa 3 1.1.4 Agriculture and Water Use in Togo 3 1.2 Objectives of the Study 4 2. State of the Art 6 2.1 Relevant Agroecosystems, Farming Systems and Irrigation Management in West Africa 6 2.2 Key Performance Indicators: Water productivity and Food Security 8 2.3 Common Approaches Used to Evaluate Crop Water Productivity 9 2.4 Key production Factors: Climate, Soil and Management 9 2.5 Crop Yield Modelling 12 2.6 Integrated Modelling 13 3. Novel Framework for Optimising Irrigation Systems in West Africa 15 3.1 Model-based Sensitivity Analysis of Climate and Management Impact on Crop Water Productivity, Water Demand and Food Security 15 3.2 Experimental Validation of the Farm Model and Management Strategies, Soil Data Analysis and Modelling 17 3.3 Joint Stochastic Analysis of the Impact of Climate and Soil Variability on Crop Water Productivity and Food Security 19 4. Overview of Publications 21 4.1 Potential of Deficit and Supplemental Irrigation under Climate Variability in Northern Togo, West Africa 21 4.2 Impact of Irrigation Strategies on Maize (Zea mays L.) Production in the Savannah Region of Northern Togo (West Africa) 22 4.3 Impact of climate and soil variability on maize (Zea mays L.) yield under full and deficit irrigation in the savannah region of northern Togo, West Africa 23 5. Conclusion and Outlook 26 References 28 A. Selected Publications of the Author 37 A.1 Potential of Deficit and Supplemental Irrigation under Climate Variability in Northern Togo, West Africa 39 A.2 Impact of Irrigation Strategies on Maize (Zea mays L.) Production in the Savannah Region of Northern Togo (West Africa) 61 A.3 Impact of Climate and Soil Variability on Maize (Zea mays L.) Yield under Full and Deficit Irrigation in the Savannah Region of Northern Togo, West Africa 81 B. Histograms of distributions of the expected maize yield in northern Togo (scenarios in the third paper) 121

MENVSC / Department of Geography and Geo-Information Sciences / Agricultural abandonment is a challenge in areas of South Africa more especially in rural areas. The study employs both qualitative and quantitative methods of in sampling and collection and analysis. It adopted a case cross-sectional study design. This design is a case study in nature, hence this study compared the active and none active farmers in identifying the causes and effects of the phenomenon studied. Comparatively, the study sought to find out if there are factors influencing farm abandonment and its consequences in Tshiombo village. The study was based on field surveys allowing for observation and with respect to agricultural abandonment. In addition to observation open ended questions together with questionnaires were administered to extension officers. The data collected from open ended questions and questionnaires was recorded and subjected to descriptive analysis. Several factors have been identified influencing farm abandonment. The challenges ranges from needs prioritization, infrastructure and capacity. It was recommended that government should reinstate its support to small scale farmers prioritize the maintenance of irrigation canals. The prioritisation of agriculture will attract many into agricultural practice with the aim of reducing poverty and starvation in rural communities. / NRF