UNIVERSITÀ DEGLI STUDI DI MILANO PHD COURSE AGRICULTURE, ENVIRONMENT, AND BIOENERGY XXXI CYCLE DEPARTMENT OF AGRICULTURAL AND ENVIRONMENTAL SCIENCES – PRODUCTION, LANDSCAPE, AGROENERGY DATA AND MODEL-BASED RESOURCES TO SUPPORT ITALIAN RICE BREEDING AGR/07 GABRIELE MONGIANO SUPERVISOR: PROF. ROBERTO PILU CO-SUPERVISOR: DR. SIMONE BREGALIO CO-SUPERVISOR: DR. PATRIZIA TITONE DOCTORAL PROGRAMME COORDINATOR: PROF. DANIELE BASSI ACADEMIC YEAR 2017 – 2018 2 RINGRAZIAMENTI Ogni volta che leggo le classiche frasi del tipo “Questa tesi non sarebbe stata possibile senza il contributo di…” le etichetto velocemente come smancerie, ma ora che è il mio turno realizzo che mi sbagliavo. Viste le premesse, cercherò almeno di essere meno smielato possibile. Devo ringraziare Simone per aver reso possibile tutto questo, dall’idea di iscrivermi ad un dottorato (deve avere avuto degli insospettabili complici), per finire con l’instaurarsi non solo di una edificante collaborazione professionale ma anche di una sincera amicizia. Patrizia e Luigi, per avermi spronato e supportato in ogni fase e ambito del progetto, persino quella in cui il progetto ancora non esisteva. Roberto, per avermi sempre dato grandissima fiducia e i consigli giusti al momento giusto. I miei colleghi Simone e Davide, per le incredibili peripezie “agronomiche” e semplicemente perché senza di loro non avrei mai potuto realizzare e condurre una prova sperimentale, e pure con stile. I cari Nove, Jorghe, Ajmino e Cecio (loro sono consapevoli di chiamarsi così…) che hanno in diverse occasioni evitato la totale pazzia del sottoscritto. Infine ringrazio i miei genitori, che riescono ancora ad entusiasmarsi per ogni cosa che faccio e mi hanno permesso di diventare quello che sono. E a te lettore, grazie del tuo tempo. 3 4 MONGIANO, G., 2018. DATA AND MODEL-BASED RESOURCES TO SUPPORT ITALIAN RICE BREEDING. Ph.D. THESIS, UNIVERSITY OF MILAN, ITALY. Reference to the contents of Chapter 2 should be made by citing the original publication. 5 ABSTRACT The central challenge that humanity is facing is the need to meet the nutritional needs of a growing population. After the tremendous progress achieved during the green revolution, the yields of the primary cereal crops are now stagnating and the undergoing climatic changes represent a further threat. Among the technologies available to allow a further increase in yield, genetic improvement is the most promising. Plant breeding, though, is an expensive, time consuming and labour- intensive activity which relies on a thorough knowledge of the available germplasm for its efficient exploitation requiring the integration of the phenotypic expression with molecular data. The analysis of the interactions between genetic makeup, pedo-climatic conditions and management practices is thus essential to guide breeding programs aimed at improving the agronomic traits of the main herbaceous crops. Crop simulation modelling can be used to support such activities, via a cost- and time-efficient analysis of the performances of a wide range of phenotypes in different weather, soil and management conditions. The requirement is the minimum deviation between the phenotypic expression and its model representation, which should consider the known physiological limits and compensatory effects among traits. The lack of an extensive characterisation of available germplasm often impedes the availability of exhaustive data to support breeding programs via crop modelling. This applies to Italian rice agriculture, being characterized by a long history of cultivation with a vast varietal landscape. Crop model-based studies and services have already been developed in the area to support rice growers and local stakeholders, thus outlining a proficient case study for their implementation in breeding programs. This doctoral project aimed at analysing the morpho-physiological characteristics of the Italian rice germplasm mostly contributing to the yield increase in the 20th century, highlighting the evolutionary trends, and the associations with published molecular data. The released information enlarges previous findings and can be used to guide genetic improvement programs aimed at further improve current rice varieties. The field experimental activity produced ready-to-use quantitative data to further refine crop modelling capabilities in the area. Their integration in a crop model study allowed correlating yield component traits and model parameters, fostering the design of synthetic cultivars to facilitate and prioritize new breeding efforts. 6 TABLE OF CONTENTS Abstract .......................................................................................................................................................... 6 Table of Contents ......................................................................................................................................... 7 Chapter 1 Introduction ............................................................................................................................. 10 1.1 Characterisation of European rice cultivation ............................................................................ 11 1.2 Rice breeding in Italy ................................................................................................................... 12 1.3 Model-assisted breeding ............................................................................................................... 14 1.4 Objectives and organisation of research ...................................................................................... 16 CHAPTER 2 Evolutionary trends and phylogenetic association of key morphological traits in the Italian rice varietal landscape ......................................................................................................... 18 2.1 Abstract ......................................................................................................................................... 19 2.2 Introduction .................................................................................................................................. 20 2.3 Results ........................................................................................................................................... 23 2.3.1 Exploring evolutionary trends in Italian rice varietal landscape. ........................................ 23 2.3.2 Principal Components Analysis .............................................................................................. 28 2.3.3 Cluster analysis ....................................................................................................................... 32 2.4 Discussion ..................................................................................................................................... 36 2.5 Methods ......................................................................................................................................... 40 2.5.1 Plant material and experimental conditions. ......................................................................... 40 2.5.2 Phenotypic characterisation .................................................................................................... 41 2.5.3 Data analysis ............................................................................................................................ 42 2.6 Supplementary material .............................................................................................................. 45 2.6.1 Univariate analysis .................................................................................................................. 45 2.6.2 Principal components analysis ................................................................................................ 50 2.6.3 Clustering ................................................................................................................................. 52 CHAPTER 3 ....... Phenotypic characterisation of the main sources of variation in Italian rice cultivars. 70 3.1 Abstract ......................................................................................................................................... 71 3.1.1 Background .............................................................................................................................. 71 7 3.1.2 Findings .................................................................................................................................... 71 3.1.3 Conclusions ............................................................................................................................... 71 3.2 Background ................................................................................................................................... 72 3.3 Findings ........................................................................................................................................ 74 3.3.1 Traits variability ...................................................................................................................... 77 3.3.2 Traits relationships ................................................................................................................. 80 3.3.3 Cluster analysis ....................................................................................................................... 84 3.3.4 Discussion ................................................................................................................................