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Statistical Methods and Models for Analyzing Sugarcane

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Statistical Methods and Models for Analyzing Sugarcane Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2009 Statistical methods and models for analyzing sugarcane (Saccharum species hybrids) plant breeding data Marvellous Mabeza Zhou Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Recommended Citation Zhou, Marvellous Mabeza, "Statistical methods and models for analyzing sugarcane (Saccharum species hybrids) plant breeding data" (2009). LSU Doctoral Dissertations. 1177. https://digitalcommons.lsu.edu/gradschool_dissertations/1177 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. STATISTICAL METHODS AND MODELS FOR ANALYZING SUGARCANE (SACCHARUM SPECIES HYBRIDS) PLANT BREEDING DATA A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy In The School of Plant, Environmental and Soil Sciences by Marvellous Mabeza Zhou B.Sc. Agriculture Honors (Crop Science), University of Zimbabwe, 1989 M.Sc. Agriculture, University of Natal, South Africa, 2003 Masters of Applied Statistics, Louisiana State University, 2008 August, 2009 DEDICATION This Dissertation is dedicated to my son, Tapiwanashe. ii ACKNOWLEDGEMENTS I wish to express my gratitude and appreciation to my major professor, Dr Collins Kimbeng, my mentor whose advice, guidance, willingness to help and the freedom he gave me to learn was a great inspiration. I would like to acknowledge the help from my graduate committee Dr Kenneth Gravois, Dr Kevin McCarter and Dr Gerald Myers who spared time from their busy schedules to help me. The comments from the Graduate School representatives Dr Michael Saska (general examination) and Dr Roberto Barbosa (final examination) are acknowledged. I am grateful to Dr Bill White, USDA-ARS, for allowing me to use his data for my dissertation, for his advice, comments and help with chapter 6 and Dr Tom Tew for allowing me to collect data from his trial and help with data collection for chapters 2, 3 and 4. Help with data collection from staff at the USDA-ARS, Houma and LSU AgCenter, St. Gabriel is greatly appreciated. Special thank you goes to Dr Freddie Martin (Director, School of Plant, Environment and Soil sciences) who guaranteed the assistantship that supported my studies and linked me with Dr Kimbeng. Profound gratitude goes to Mr. Karl Nuss (Head: SASRI Plant Breeding), who encouraged me to pursue a PhD and helped establish contact with Dr Martin. The support Dr Muntubani Nzima (ZSAES Director, who encouraged me to pursue a PhD in the USA) and the ZSAES board of directors (granted study leave and financed travel and settling costs) are acknowledged. The American Sugar Cane League is thanked for providing the fellowship that supported my stipend. I would like to acknowledge the help from my fellow graduate assistants in the sugarcane lab (Sreedhar Alwala and Suman Andru) for their help in getting me settled and teaching me the molecular lab techniques. Special thanks go to Samuel Ordonez Jr. for being a great friend and for just listening during those stressful times. Thanks also go Nkosinathi Dhlamini, for always iii dragging me to the gym and jogging sessions, and being my most wonderful roommate and being always positive and encouraging. I would like to thank my internet friends for their encouragement and making me realize it was a matter of time. I would like to thank my colleagues and staff at ZSAES and the Zimbabwe sugar industry for their goodwill and encouragement. I would like to thank my sister and my bother in law (Mr. and Mrs. Dube) for taking in my son, Tapiwanashe as their own during the course of my studies. It is their unconditional love for my son that I will cherish forever. I would like to acknowledge the support of my mother (Mrs. Tendai Zhou) for always having me in her prayers and always encouraging me to aim for the best. Special tribute goes to my late father, whose passion for education was a great inspiration, and wherever he is, he must be proud of this achievement. I would like to thank my brothers and sisters, my brother in laws and sister in laws, and my nieces and nephews for their support. Special thanks go to my son, Tapiwanashe for his unconditional love and never, complaining at least directly to me for my absence. His maturity and always asking how my studies were going was my greatest inspiration, I could never afford to fail. It is to Tapiwanashe that I dedicate this work and I hope this work inspires Tapiwanashe to realize that the sky should always be the only limit. Finally and most importantly, I would like to acknowledge and thank the Lord God Almighty, without whom I would not have been able to embark on or even complete these studies. If there be any glory arising from this work, let it be directed to thee Him, the Creator and Sustainer of all things. iv TABLE OF CONTENTS DEDICATION……………………………………………………………………………. ii ACKNOWLEDGEMENTS………………………………………………………………. iii LIST OF TABLES………………………………………………………………………... ix LIST OF FIGURES………………………………………………………………………. xiv ABSTRACT……………………………………………………………………................. xvi CHAPTER 1: GENERAL INTRODUCTION…………………………………................ 1 1.1 Early Generation Selection…………………………………………………................ 2 1.1.1 Family Evaluation…………………………………………………………... 3 1.1.2 Seedling Selection…………………………………………………………... 4 1.2 Multivariate Repeated Measures Analysis of Data from Advanced Variety Trials…... 6 1.3 Cross Resistance Between the Sugarcane Borer and the Mexican Rice Borer……….. 7 1.4 Objectives of the Study……………………………………………………………….. 8 1.5 References…………………………………………………………………………….. 9 CHAPTER 2: EVALUATING SUGARCANE FAMILIES FOR YIELD POTENTIAL AND REPEATABILITY USING RANDOM COEFFICIENT MODELS. 14 2.1 Introduction…………………………………………………………………………… 14 2.2 Materials and Methods………………………………………………………………... 18 2.2.1 Experimental Materials and Data Collection……………………………….. 18 2.2.1.1 Families…………………………………………………………… 18 2.2.1.2 Stage I Trial (Seedlings)………………………………………….. 19 2.2.1.3 Stage II Trial (Clones)…………………………………………….. 20 2.2.2 Statistical Considerations and Data Analysis Using Random Coefficient Models………................................................................................................ 21 2.2.3 Data Analysis Using Simple Linear Regression, ANCOVA and ANOVA… 24 2.3 Results………………………………………………………………………………… 26 2.3.1 Population Parameters………………………………………………………. 26 2.3.2 Family Evaluation Using ANCOVA……………………………………….. 27 2.3.3 Interrelationships Among the Family Parameters…………………………... 29 2.3.4 Covariance Parameter Estimates Derived From the Random Coefficient Models Analysis……………………………………………………………. 31 2.3.5 Family Evaluation Using Random Coefficient Models….………................. 31 2.3.6 Random Coefficient Models Analysis of Four Classified Family Groups..... 34 2.3.7 Family Group Parameters…………………………………………………... 36 2.3.8 Distribution Patterns Within the Four Classified Family Groups…………... 37 2.3.9 Comparison of Families Selected Using RCM, Family Means and ANCOVA…………………………………………………………………... 39 2.4 Discussions…………………..………………………………………………………... 40 2.5 Conclusions…………………………………………………………………………… 43 2.6 References…………………………………………………………………………….. 44 v CHAPTER 3: ARTIFICIAL NEURAL NETWORK MODELS: A DECISION SUPPORT TOOL FOR ENHANCING SEEDLING SELECTION IN SUGARCANE BREEDING…………………............................................ 48 3.1 Introduction…………………………………………………………………………… 48 3.2 Materials and Methods………………………………………………………………... 51 3.2.1 Experimental Materials and Data Collection……………………………….. 51 3.2.2 Estimation of Seedling Cane Yield From Yield Components….................... 52 3.2.3 Data Analysis Using Artificial Neural Network Models…………................ 53 3.3 Results………………………………………………………………………………… 54 3.3.1 Coefficients of the Prediction Models……………………………................ 54 3.3.2 Model Fit Statistics…………………………………………………………. 54 3.3.3 Probabilities and Seedling Selection………………………………………... 57 3.3.4 Discriminating Ability of the Artificial Neural Network Models Versus Visual Selection.............................................................................................. 58 3.3.5 Selection Efficiency of the Artificial Neural Network Models Versus Visual Selection…………………………………………………………….. 62 3.3.6 Seedling Cane Yield Increased With Increasing Selection Probabilities........ 66 3.3.7 Artificial Neural Network Models Versus Visual Selection at Identical Selection Rates…………………………………………............................... 66 3.4 Discussions…………………………………………………………………................. 68 3.5 Conclusions…………………………………………………………………………… 72 3.6 References…………………………………………………………………………….. 73 CHAPTER 4: LOGISTIC REGRESSION MODELS: A DECISION SUPPORT STATISTICAL TOOL FOR ENHANCING SEEDLING SELECTION IN SUGARCANE BREEDING…………………………………………... 76 4.1 Introduction…………………………………………………………………………… 76 4.1.1 Statistical Considerations in Logistic Regression Models………………….. 77 4.2 Materials and Methods………………………………………………………………... 80 4.2.1 Experimental Materials and Data Collection……………………………….. 80 4.2.2 Estimation of Seedling Cane Yield From Yield Components….................... 81 4.2.3 Data Analysis……………………………………………………………….
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