INDEX OF TABLES

 2. Four genetic population structures, composition and location of eleven varieties evaluated during 2011/12 season in Brittany. 13 Table 3. F probabilities from the analysis of a crossed and nested factor design of 17 traits among 3 wheat varieties in a multi-trial on-farm evaluation during 2010/2011. 17 Table 4. The phenotypic diversity within each variety (Hs), the total phenotypic diversity across the three varieties (H T ), the average phenotypic diversity among varieties (D ST ), the proportion of phenotypic diversity among varieties considering the total phenotypic diversity (G ST ), the average phenotypic diversity within the three varieties (Ĥs), and the average phenotypic diversity across all traits of each variety (Ĥ P ) for 15 traits among 3 wheat varieties at multi-location on-farm trials during 2010/11. 17 Table 5. The phenotypic diversity within each variety (Hs), the total phenotypic diversity across the three varieties (H T ), the average phenotypic diversity among varieties (D ST ), the proportion of phenotypic diversity among varieties considering the total phenotypic diversity (G ST ), the average phenotypic diversity within the three varieties (Ĥs), and the average phenotypic diversity across all traits of each variety (Ĥ P ) for 26 traits among 3 wheat varieties at location DH during 2010/11. 18 Table 6. Descriptor list for indicators of phenotypic diversity of wheat populations under organic conditions and the appropriate time for assessment. 20 Table 7. Descriptor list for indicators of phenotypic diversity of wheat populations under organic conditions for the experiment as carried out during 2011/12 season. 21 Table 9. Individual Discriminant Analysis of 15 traits in 11 populations with different genetic structure during 2011/12. 22 Table 8. The total phenotypic diversity within each population (Hs) and the average phenotypic diversity across all traits (Ĥ P ) for 15 traits in 11 populations with different genetic structure during 2011/12. 22 Table 10. The total phenotypic diversity within each population (Hs) and the average phenotypic diversity across all traits (Ĥ P ) for 8 quantitative traits in 11 populations with different genetic structure during 2011/12. 23 Table 11. The total phenotypic diversity within each population (Hs) and the average phenotypic diversity across all traits (Ĥ P ) for 7 qualitative traits in 11 populations with different genetic structure during 2011/12.

INTRODUCTION

The purpose of this report is to explain what I did and learned during my internship period at INRA Centre de Rennes, SAD-Paysage unit. The report is also a requisite for the final assessment of the course MSc. Internship Plant Breeding (PBR-70424) from Wageningen University, MSc. Plant Sciences Program. The report includes an explanation of INRA Centre de Rennes and SAD-Paysage unit, a background about the SOLIBAM program and the PaysBlé project, a description of the work done during the internship, the scientific research, and a self-reflexion paper about the general experience and the learning outcomes. Finally, some conclusions were drawn. The duration of this internship is of sixteen weeks from May 7 th to August 24 th . The learning outcomes previously established for this internship are: (1) to apply knowledge and skills acquired during the courses, (2) to enhance the ability to work within a multidisciplinary team of a public and scientific research institute, (3) to improve the professional skills, (4) to expand the professional network, (5) to gain practical experience in organic and participatory plant breeding on wheat, and (6) to deepen academic (report on result) and reflective (self-reflection paper) writing skills.

PROFILE: INRA CENTRE DE RENNES & SAD-PAYSAGE UNIT

General Organisation

The Institut National de la Recherche Agronomique (INRA) is a French public research institute dedicated to scientific studies of mission-oriented agronomic research surrounding the problems of agriculture. It was founded in 1946, and since 1984 is a public scientific and technological establishment under the joint authority of the Ministry of High Education & Research and the Ministries of Agriculture. The general objectives of INRA are (www.international.inra.fr):

 To produce and disseminate scientific knowledge.  To develop innovations and know-how for the benefit of society.  Through its expertise, to inform decision-making by public and private sector players.  To develop scientific and technical culture and participate in the debate between science and society.  Training in research and through research.

The institute presents two levels of organisation, by research divisions and by regional centres. The 14 researches divisions cover diversified topics: nutrition, microbiology food safety, food processes, plant biology and animal physiology, plant and animal genetics/breeding, plant and animal heath, applied mathematics, agronomy, environment, forest, ecology, social-economical sciences. In the 19 regional centres, several divisions are represented (www.international.inra.fr/the_institute/organisation/research_resources).

The research centre of Rennes (INRA Centre de Rennes) is the office of the institute for the regions of Brittany and Lower-Normandy. INRA Rennes includes 1000 people in its 23 research and experimentation units. Its general objective is to promote sustainable agriculture. The fundamental and final researches in INRA Centre de Rennes are divided into four themes, which common interest is the sustainability of food chains: (1) quality of dairy products and food innovations, (2) overall assessment of animal industries, (3) water quality and aquatic ecosystems, and (4) plant resistance to biological pests and environmental. It works with the partnership of an agronomy higher education institution (Agrocampus), diverse development actors (associations, groups of scientific interest, and stakeholder of the dairy industry), the University of Rennes, and life sciences and environment research institutes, among others. Additionally, INRA Centre de Rennes maintains numerous collaborations with the international community both from developed and developing countries. INRA participation in the European research is strengthened through its involvement in the Framework Programme for Research and Development (www.rennes.inra.fr).

In Rennes, the research unit SAD-Paysage (Sciences pour l'Action et le Développement-Paysage), in charge of accompanying and analysing the impacts of the public politics, investigates on the interactions between farming, landscape and biodiversity, at the level of agricultural and rural areas (www.rennes.inra.fr/sad). During 2005, SAD-Paysage unit added in some extend the activities involving participatory research and cultivated biodiversity in charge of Véronique Chable. Her research topic was previously developed in the Genetic and Plant Breeding division (Chable, personal communication 2012).

Activities

In order to accomplish its objectives, the SAD-Paysage unit in INRA Centre de Rennes researches on three aspects, which are essential parts of the agriculture-landscape-biodiversity interaction (www.rennes.inra.fr/sad): a. Land management of agricultural exploitations: in order to understand the logic behind the land management of the farmers, three specific points are considered:  Explain the reasoning of rotations in the farms,  Analyze the spatial organization of crop and grassland management practices,  Understanding the diversity of agricultural practices for the plot development.

b. Biodiversity and ecological functions: in order to reveal the effect of agricultural systems on the cultivated and spontaneous biodiversity, and on the associated ecosystem services; two complementary approaches are considered:  The effects of spatial and temporal organization of agricultural systems on people and animal communities, and related services (Spontaneous biodiversity and biological services of conservation and regulation),  The mechanisms of adaptation of plant populations based on the farming systems and environments in which they are grown (Cultivated biodiversity and service of agricultural production).

c. Modelling interactions among agriculture, biodiversity and landscape: in order to implement a collective and interdisciplinary model to represent, assess and simulate the interactions among farmer's decisions, forms of landscape organization, and ecological functions; two approaches are considered:  Elaborate methods for indicators of these interactions and implement them to produce new tools for action,  Continue the effort on computer modelling of the agricultural landscape and associated ecological functions.

Discussion

As described before, the SAD-Paysage unit conducts research on the role of agriculture in the management of landscape resources and biodiversity, and the dynamics of associated ecosystem services, at scales of local farming. These issues are assessed according to two levels that match with the areas of research: the level of the farm (for understanding farmer's practices) and the landscape level (for consideration of ecological processes involved in the dynamics of biodiversity). Obviously these two relevant levels feedback each other. For that reason the unit is structured into a single team that includes landscape ecologists, agronomists, animal and plant scientists, geneticists and modeling engineers who work interdisciplinary within the unit and between the research networks of collaboration. The main motivation is to gain knowledge that can be translated into actions. For the work, it implies the design of tools for action. Within the unit, it is aimed to join together biotechnical approaches to analyze the interactions between the agricultural practices and the resources in landscape and biodiversity. This is particularly relevant in the western French agriculture and landscape, where the prominence of crops, pastures and livestock on farms is very varied in quantity, role, conduct and spatial-temporal organization.

Additionally, the research inside the unit is done as a mix of analytical and systemic approaches. For the analytical part of the research, facts or information already available need to be used to make a critical evaluation; however, for the systemic part of the approach, the research is based on mutual learning methods and knowledge between biotechnological science and ecology in order to develop common representations of the interactions between agriculture, biodiversity and landscape. Nonetheless, the activities dedicated to the development of participatory researches for cultivated biodiversity and the development of organic/low input agriculture, in charge of Véronique Chable, are not totally included in the research programme of the SAD Paysage unit. Nowadays, a project is emerging to build a new organisation which will involve INRA and local/organic partners in order to give a better readability of the programmes for organic/low input agricultures. The Technical Institute for Organic Agriculture (Institute Technique pour l'Agriculture Biologique = ITAB) is also concerned by this new organisation, as it is involved in most of the projects of participatory researches for organic agriculture (Chable, personal communication 2012).

DESCRIPTION OF THE WORK

3.1. Background

History of Participatory Selection for Organic Agriculture in Brittany

In 2000, INRA created the Intern Committee for Organic Agriculture (Comité Interne pour l'AB = CIAB). One year later, CIAB stimulated the first programs for organic agriculture. As a result, the Mix Research Unit of Plant Breeding and Plant Biotechnologies (Unité Mixte de Recherche de Amélioration des Plantes et Biotechnologies Végétales = UMR APBV) of Rheu established and coordinated the first collaborative project between Inter Bio Bretagne (IBB) -an inter professional association for the development of the organic farming sector in Brittany-and INRA: the PAIS project. PAIS aimed to initiate an evaluation of genetic resources of cabbage and cauliflower for the needs of Breton organic farmers, and a reflection about the organization of the selection of suitable varieties. At the end of this project in 2003, the lack of varieties adapted to organic farming was evident. As a consequence, the professionals questioned INRA for many other crops throughout France. Under this context, a new perspective of plant breeding for organic and low input farming was opened: Participatory Selection, a form of research where all stakeholders are involved at the same time. This so-called "peasant selection" opened two additional areas of research: (a) the proposition of an appropriate regulatory space for cultivated population varieties, and (b) the study of plant populations as a lever of change in the agro system and the food system as a whole. a. Identify traits specific for adaptation to low-input/organic conditions over a wide range of agroclimatic conditions in Europe (WP1). b. Develop efficient phenotyping, genotyping and molecular tools to monitor heritable variation during selection. Molecular analysis of functional polymorphisms will increase accuracy in breeding methodologies and improve monitoring of genetic diversity and adaptation along generations. It will also increase the understanding of adaptive phenomena (WP2). c. Develop the use of within-crop diversity to stabilize yield and quality in the face of current and increasing variation in organic and low-input agriculture (WP3). d. Design, develop and test innovative arable and vegetable cropping systems based on integration of a high level of diversification in crop management with the use of genetically diverse populations or varieties (WP4). e. Compare the effectiveness of different breeding strategies under conventional, low input and certified organic farming to set up optimal strategies for the production of varieties suitable for organic and low input farming taking into account the traits which are avoided in conventional breeding (WP5). f. Develop methodologies for farmers' participatory research that exploit SOLIBAM's advances in low-input and organic farming (WP6). g. Quantify the effects and interactions of breeding and management innovations on crop nutritional, organoleptic and end-use quality (WP7). h. Evaluate socio-economic and environmental impacts of SOLIBAM breeding and management innovations in order to identify farm business, consumer preference, food supply and legislation related issues that are likely to influence their adoption (WP8). i. Disseminate knowledge to key stakeholders (farmers and extension services, seed companies, policy-makers, national offices for registration and seed control, food industry and consumers) and create a participatory framework that will allow exchanges between researchers and key stakeholders (WP9). j. Coordination and project management (WP10).

The SOLIBAM Program focuses in cereals (durum wheat, soft wheat, barley, and maize), legume (beans, cowpea, and faba bean) and vegetables (tomato, broccoli, and cabbage). Those crops are tested in diverse agro ecological conditions in Europe and Africa, in order to supply models for different climates and breeding approaches (www.solibam.eu). Figure 1 shows SOLIBAM Program with nine WP and the place of the specific project PaysBlé for wheat.

PaysBlé Project

The whole title of the project is "PaysBlé: Development of a regional network to experiment, maintain and promote the cultivated diversity of local Breton wheat in organic farming". It is carried by INRA Centre de Rennes and Triptolème, in partnership with the University of Rennes (represented by the EcoBio laboratory), Kaol Kozh, Inter Bio Brittany (IBB) and the Regional Federation of Agrobiologists of Brittany (FRAB). The project is included in the SOLIBAM Program, and is financed by the region of Brittany within the framework of the "Actions for the Social appropriation of Sciences" (ASOSC) projects. It has a scheduled of 3.5 years (2009-2012) duration and is organized in three stages (18 months, 12 months and 12 months), with a subsidy of € 25 000 each one. The objectives are: (1) define agronomic criteria and qualitative considerations relevant for characterizing the behavior of wheat populations (with different levels of intra-varietal diversity) under crop management associated with the production of peasant wheat, and (2) develop specific cultivation methods that are accessible to the farmer practices in order to optimize the selection process and meet the needs for artisanal bakery. PaysBlé is completely integrated in the dynamics of the European SOLIBAM Program and constitutes a network of experiments that focus mainly on the exploitation of diversity in participatory plant breeding and agricultural management. It is based on the results achieved during various research programs and, in particular, those obtained within the framework of the Farm Seed Opportunities European project (2007)(2008)(2009). The actions to be done in the scope of this project in order to find and revive Breton wheat landraces are (Chable, 2010): a. Find Breton landraces: by inventorying and exploring the genetic resources of patrimonial varieties cultivated by the peasants, and creating/selecting new varieties from the patrimony of other regions. b. Participatory approach: experimentation among farmers for selection of wheat varieties and cultural practices; or evaluation/selection of wheat under different crop managements. This implies to develop a definite experimental methodology in a collective way by associating the disciplines of plant breeding and agronomy. c. Linking knowledge: harmonizing the methods of academic research with traditional knowledge and specific know-how of the innovative organic farmers and organic agriculture experts. This implies to work together in seminars and exchange information, followed by a joint experiment designed and a participatory conduction. d. Communication and dissemination/or diffusion: by promoting landraces particularly adapted to the organic farming, to inform the public about these quality products. The goal is an outward journey from the agriculture to the culture and vice versa.

The intention of this project is to research, put back in culture, and rebuilt a future for the Breton peasant wheat. Additionally, it consolidates and disseminates the actions of the farmers and all those added value products (flour, bread and straw) by bringing their experiences together to those one of the scientists (Chable, 2012).

Work Description

Most of the work done during the internship in INRA Rennes is part of the SOLIBAM Program WP3 (exploitation of diversity in breeding) and WP4 (exploitation of diversity in crop management). The results are also integrated in WP1 which has an umbrella activity about trait evaluation and methodologies to describe diversity. The work was basically divided into tasks: (1) the report writing, which was done in parallel to the rest of activities during the duration of the internship; (2) the scientific research, which has been done step by step including literature research, data analysis of the PaysBlé first results, protocol elaboration from the lessons learn after the first two steps, field data collection, and finally testing the new protocol;

(3) the collaboration in field data collection with other interns (wheat characterization, soil sampling, Coccinellidae, Carabidae and Aphids monitoring) in order to be familiar with the wheat morphology, development and data collection, understanding the global scope of the SAD-Paysage unit and the crop phenotypic diversity; additionally (4) the attendance of one field day at the Pont de l'Arche farm (Pays de la Loire region) under participatory plant breeding of wheat landraces, farmer varieties, and mixtures in order to recognize the wheat biodiversity potential and the farmer selection criteria.

EVALUATION OF STRATEGIES FOR POPULATION DIVERSITY UNDER THE FRAMEWORK OF ON-FARM WHEAT BREEDING AND CROP MANAGEMENT: METHODOLOGICAL ASPECTS

Introduction

At the end of century 20 th an increasing number of European farmers became interested in low-input farming systems, due mainly to a rising environmental concern. Organic agriculture has gained significance since the 1990s; however, the fact that organic producers employ modern, conventional varieties does not mean that they are the best to optimise organic or low input systems. Consequently, nowadays there is a growing demand for varieties specifically adapted to the ecological and ethical principles of organic farming (Lammerts van Bueren et al., 2003;Lammerts van Bueren & Verhoog, 2006). The relevance of genetic diversity for organic and low-input farming generates a need to identify sources of variation and strategies to introduce that diversity in plant breeding for organic systems (Serpolay, 2011). Additionally, the participatory selection of peasant varieties for organic and low-input farming requires the intervention of all the stakeholders at the same time (Chable, 2012). Participatory Plant Breeding (PPB) is designed to incorporate the perspective of the farmers and it is seen by several scientists as a way to overcome the limitations of conventional breeding by offering farmers the possibility to choose which varieties suit better their needs (Ceccarelli & Grando, 2009). Consequently, as part of the toolbox in PPB under organic or low-input conditions, a measurement of population diversity within and between varieties is necessary for comparisons and tracing of on-farm trials (Chable, personal communication 2012). During this scientific research at INRA Centre de Rennes, work was done on the ending part of the 3 ½ year (2009)(2010)(2011)(2012) project PaysBlé, as part of the SOLIBAM program. The main objective was to present a simple diversity index, data collection, and data analysis methodology in order to assist the monitoring of phenotypic diversity in on-farm trials under low-input and/or organic conditions.

Problem Description

The preservation of diversity is important for all (conventional, high input, low input and organic) farming systems due to the changing climate conditions, which alter both the types and levels of biotic and abiotic stress that cultivated plants face (Serpolay et al., 2011). Farmers using organic practices need varieties well-adapted to low-input conditions. Additionally, the maintenance of the diversity within varieties is essential to preserve the potential of evolving specific adaptation to these conditions. More heterogeneous varieties might buffer the crop responses to unpredictable environmental conditions (Ceccarelli et al., 2001;Serpolay et al., 2011). Therefore, for the SOLIBAM program, assessing changes in the status of plant population diversity levels, compare several populations, and link them to environmental, agricultural management, and ecosystem diversities are key aspect; especially important because they represent the material used to deal with issues such as climate change or development of more sustainable agriculture. However, the measuring of population diversity in on-farm trials is a bottleneck since the data collection for morphological and agronomical traits is tedious and time consuming (Chable, personal communication, 2012).

As a response to this necessity, the objective of this research is to provide a straightforward and easyassessed index in order to assist the monitoring of diversity on-farm trials for low-input and organic farmers, researchers and breeders. At the same time, develop a methodology for data collection and analysis on-farm trials considering the characteristic of interest for artisanal bakery and wheat farmers.

Research Questions

At the first stage of the internship, the next questions were formulated: 4.3.1. What is a practical and straightforward approach to assess "between populations" and "within a population" diversity? (Literature review + Data analysis) 4.3.2. Which traits are really necessary to take into account for quantifying the population diversity? (Literature review)

At a second stage of the internship, the next questions were derived: 4.3.3. How to monitor the diversity in on-farm trials under participatory approach? (Protocol) 4.3.4. Which methodology, qualitative or quantitative, is better for a fast and accurate screening of population diversity on-farm? (Data analysis)

Materials & Methods

4.4.1. First Literature Review: different scientific articles about measurement of population diversity, inside and outside the agricultural sciences field, were consulted during the first stage of this internship in order to find, if it already exists, an adequate index and/or approach able to account for between and within population diversity (research question 1). The articles varied from social sciences to statistical studies, and were as old as 1948 through to recent studies in 2012. 4.4.2. Data Analysis 2010/11: after selecting an adequate diversity index/approach, data taken during 2010/11 season from the PaysBlé project was analyzed in order to confirm the efficiency of the selected option. During the 2010/11 season the PaysBlé project sowed 3 varieties with a different genetic structure:

 Renan: modern variety (single pure line = SPL, inbreeding population)  Sixt-sur-Aff 15746: farmer variety (single landrace = SL, considered as a stable population)  Mélange: mixture of 11 farmer varieties (mix of landraces = ML, considered as a dynamic population).

The experiment consisted of 2 plots per variety (2 plot x 3 varieties = 6 plots per trial) installed at 6 organic wheat production farms. The denominations of the six trials were: DH, FM, GS, JPC, PT. The plots were managed under the same conditions as the production field of the farmer. Each experimental plot has a surface of 100 m². The trials (Figure 2) were located in the western part of France, (regions of Bretagne and Pays de la Loire). Trial FM was split in two trials (FM-A and FM-B) before maturity to account for the two previous crops present on the plots of this essay. The aim of the project was to determinate which genetic structure of wheat varieties does fit better the needs of the baker-farmers under organic farming, practicing a traditional sourdough bread making. Particular attention was paid to the agronomic performance of the three varieties in the heterogeneous environments of organic and/or peasant farming. For this experiment 26 traits in total were evaluated divided in 3 categories: quantitative continuous ( 11), quantitative discrete ( 9) and nominal (6). All these traits (Table 1) were determinate according to the SOLIBAM's Guidelines for the assessment of common parameters WP4 (Bàrberi, 2010), as they represent descriptors in the case of wheat. For a detailed description of the PaysBlé project during 2010/11 season and its context of execution refer Corfdir (2011). The statistical methodologies employed were the analysis of variance (ANOVA) as a nested and crossed factor design (Kuehl, 2000) in order to test the GxE interaction of each trait, and the Tukey mean's Test to identify differences among means of traits and varieties. 4.4.4. Protocol: considering the lesson learnt after two literature reviews and a practical example, a general protocol was elaborated to be taken from the trials in order to assess population diversity on-farm under organic farming conditions (research question 3). 4.4.5. Data Analysis 2011/12: in order to determinate whether qualitative or quantitative traits are better for a fast and accurate screening of the diversity on-farm (research question 4), the population diversity was assessed in the project "Effet de la variété de blé et de la haie sur la biodiversité (coleoptera: carabidae) et la régulation biologique en agriculture biologique". This project is part of the SOLIBAM program and was run during 2011/12 season. Eleven populations with four different genetic structures were evaluated in seven organic farms at eleven fields presenting borders with and without trees. The aim is to identify the effect of the interaction between the wheat genetic structure and the border type on the pest regulation (Laurent, personal communication 2012). The genetic structure, composition and location of the eleven varieties are resumed in Table 2 and Figure 3:

Results

4.5.1. Diversity Index (Literature Review): Nei's gene diversity method (Nei, 1973) was selected and adapted in order to assess "between populations" and "within a population" phenotypic diversity in a practical and straightforward approach. Below, the approach is described. Data analysis from 2010/11 season (section 4.5.2.) can be taken as a practical example:

4.5.1.1. Principle: for this method diversity is defined as the probability that randomly paired members of a population will be different on a specified trait (Lieberson, 1969). It is designed for data showing discrete variation, since the phenotypic diversity of a trait is calculated from the number and frequencies of distinguishable classes (Gregorius, 1978). In summary, the Nei's variation index measures a probability (Hennink & Zeven, 1991). The following formula needs to be applied (Nei, 1973):

H = 1 -∑ x 2 i
Where: H is the probability of non-identity, and x i is the frequency of the i th class in each variety or across all varieties. 4.5.1.2. Parameters: applying the previous formula per each variety and across all varieties, the next parameters can be calculated for each trait separately:

 H T : total phenotypic diversity across varieties (total diversity of the collection).  Hs: phenotypic diversity within each variety.  Ĥs: average phenotypic diversity within varieties (mean diversity within variety).  D ST : average phenotypic diversity between varieties, including the comparisons of varieties with themselves (mean diversity between varieties). D ST = H T -Ĥs.  G ST : proportion of phenotypic diversity between varieties, relative to the total phenotypic diversity. G ST = (H T -Ĥs)/H T = D ST /H T .  Ĥ P : average phenotypic diversity across all traits of each variety (mean diversity considering more than one trait).

4.5.1.3. Requirements: some conditions need to be met before performing the analysis:

 Quantitative data needs to be transformed into ordinal data (discrete ranks) by dividing the range of the trait into equal parts (Bechere et al., 1996).  Frequency of each rank needs to be calculated per variety and across varieties.  Comparison between pairs of Ĥs (across varieties) and/or Ĥ P (across traits) needs to be carried out with a statistical mean test.  Comparison of traits with different number of classes needs to be corrected with the next formula (Terzopoulos et al., 2008):

Ĥs (modified) = Ĥs (original) + Z/(Y*X)
Where X is the original number of classes, Y is the modified number of classes, and Z is the difference between Y and X (considering Y>X).

4.5.1.4. Data management: according to the trait type and the nature of the research, the managing of the data should be determinate. In short, three kinds of scenarios can be distinguish:  Single-location trial: whether different traits have been measured for populations at a single environment and during the same season. Thus the analysis of diversity can be done for all the descriptors considered during the characterization.  Multi-location trials: whether different traits have been measured for populations at different environments and during the same season. Thus the phenotypic diversity analysis has to be done on traits without or with little genotype * environment (GxE) interaction overall locations. Traits showing significant GxE interaction should be treated separately per location.  Multi-year trials: whether different traits have been measured for populations at a single environment and during different years. Thus the phenotypic diversity analysis has to be done on traits without or with little genotype * year (GxY) interaction overall seasons. Traits showing significant GxY interaction should be treated separately per season.

For multi-location and multi-year trials, factorial ANOVA for continuous and discrete quantitative traits can be carried out with different approaches. A first option is using the two-way ANOVA in order to observe the main effects of the genotype (diversity between populations) and the GxE or GxY interaction.

However, the differences within each separate population (diversity within populations) cannot be observed (Terzopoulos, 2008). To solve this problem, a second option is the use of the crossed and nested design, which shows also the variation within each population (Kuehl, 2000). It has to be noticed that a residual analysis should be carried out per trait in order to corroborate the normality and homoscedasticity (constant variance) of the error, and the linearity of the systematic part of the model. 4.5.2. Data Analysis 2010/11: the above described method was applied to the data collected for characterization during 2010/11 season of the PaysBlé project.

4.5.2.1. ANOVAs: 17 of the 20 quantitative traits were analysed for between and within diversity comparisons under ANOVAs having the 3 varieties crossed with the trials, with samples of varieties nested within each variety, and 2 plots for each sample in each trial (Table 3). The other 3 traits, namely soil coverage during full tillering stage, soil coverage during 2 nodes stage and soil coverage during maturity stage, were not analysed in this way since not samples were taken during their assessment, but notes were made at the plot scale after general and subjective observations. The GxE interaction (Trial x Variety row) was not significant for n° plants at the end of winter, biomass at 2 nodes stage, and n° tillers, n° spikes, straw biomass and total biomass at maturity stage. Therefore, it was possible to conclude about the significance of the differences between and within varieties in these traits across all the environments. For those 6 traits, varieties differed significantly (p<0.05) from each other (Variety row), meaning that there is statistical evidence for diversity between varieties. Significant (p<0.05) diversity within variety occurred only for biomass at 2 nodes stage (Trial x Sample[Variety] row). The other eleven traits showed significant (p<0.05) GxE interaction, thus the analysis need to be done separately per environment.

4.4.2.2. Diversity analysis across locations: the multi-location analysis was performed on the 6 qualitative traits without significant GxE interaction, the 3 soil coverage at different wheat stages, and the 6 nominal traits (Table 4). The 9 quantitative traits were transformed in discrete ranks by forming five classes in order to be comparable with the 6 nominal traits. Trait spike awn presence only got two classes and was afterwards modified for comparison. Most of the phenotypic diversity (Hs) within variety Renan is observed in traits such as: number of tillers and total biomass at maturity (0.72) and the least in the spike colour (0.05); within variety Sixt Hs is highest for soil coverage at maturity (0.77) and lowest for the spike colour (0.09); variety Mélange, however, showed highest Hs for soil coverage, straw biomass, and lodging at maturity (0.73) and lowest for habit at full tillering stage (0.47). The average phenotypic diversity within the three varieties (Ĥs) ranged from 0.27 (spike colour) to 0.74 (total biomass at maturity) with mean 0.61. Tukey's mean comparison test indicated that traits with the lowest significant Ĥs are spike awn presence and spike colour (≤0.37), and traits with the high Ĥs are considered from values equal or larger than 0.48. The total phenotypic diversity across the three varieties (H T ) ranged from 0.38 (spike awn presence) to 0.79 (soil coverage at maturity) with a mean value of 0.67. Phenotypic diversity among varieties (D ST ) and its proportion (G ST ) are higest for the trait spike colour and lowest for the traits number of tillers and spikes at maturity, and spike sanity. Tukey comparison for the average phenotypic diversity across all traits (Ĥ P ) confirmed that exists more diversity within a the mix of 11 farmer varieties (Mélange=0.67), than within a pure line (Renan=0.55). Ĥ P of the farmer variety Sixt (0.61) is placed in between both Renan and Mélange. 4.5.2.3. Diversity analysis of each single location: taking location DH as an example, the diversity analysis was performed on all quantitative (20) and qualitative (6) traits (Table 5). Quantitative traits were transformed in discrete ranks by forming five classes in order to be comparable with the 6 nominal traits. Trait spike awn presence only got two classes and was afterwards modified for comparison. Phenotypic diversity (Hs) within variety Renan is highest in trait number of grains per spike (0.76) and great uniformity is observed in traits soil coverage at full tillering stage, lodging at maturity, and spike colour (0.00). Within variety Sixt Hs is the highest for plants at the end of winter, number of tillers at maturity and number of fertile spikelets per spike (0.72) and the lowest for the traits habit at full tillering, soil coverage at 2 nodes stage, sanity status and lodging at maturity, and spike colour (0.00). Variety Mélange showed higher Hs for the trait biomass at 2 nodes stage (0.75) and great homogenity for the trait lodging at maturity (0.00).

The average phenotypic diversity within the three varieties (Ĥs) ranged from 0.00 (lodging at maturity) to 0.73 (spike total length) with mean 0.52. Tukey's mean comparison test indicated that traits with the lowest significant Ĥs are spike colour and lodging at maturity (≤0.23), and traits with the high Ĥs are considered from values equal or larger than 0.33. The total phenotypic diversity across the three varieties (H T ) ranged from 0.36 (spike awn presence) to 0.78 (biomass at 2 nodes stage) with a mean value of 0.65. Phenotypic diversity among varieties (D ST ) and its proportion (G ST ) are higest for the trait lodging at maturity and lowest for the trait spike awn presence. Tukey comparison for the average phenotypic diversity across all traits (Ĥ P ) showed higher diversity in variety Mélange (0.67) compared with Renan (0.48) and Sixt (0.47).

Table 5. The phenotypic diversity within each variety (Hs), the total phenotypic diversity across the three varieties (HT), the average phenotypic diversity among varieties (DST), the proportion of phenotypic diversity among varieties considering the total phenotypic diversity (GST), the average phenotypic diversity within the three varieties (Ĥs), and the average phenotypic diversity across all traits of each variety (ĤP) for 26 traits among 3 wheat varieties at location DH during 2010/11.

*Traits and Varieties not connected by the same letter were significant different from one another at p<0,05  Characterization descriptors: traits that do not change with different environments (they show none or low GxE interactions). The characters assessed are visible to the naked eye, allow for quick and easy discrimination between populations, and are normally controlled by major genes. However, they may also include a limited number of additional traits considered to be desirable by a consensus of users of a particular crop or farming system.  Evaluation descriptors: characters such as yield, agronomic and other economically important traits, and reaction to biotic and abiotic stresses. Their expression is subject to GxE interactions and is usually multigenic, involving minor genes. To score them, it is necessary to establish proper experimental designs and statistical analyses of the trials.

According to the USDA Genetic Resources Information Network (GRIN), the Nordic Gene Bank (NGB), the International Board for Plant Genetic Resources (IBPGR), and the Council for Mutual Economic Aid (COMECON); the minimum set of highly discriminating wheat characteristics that should be recorded for each population for the European Wheat Database (EWDB) are: awnedness, pericarp (grain) colour, glume colour, glume pubescence, spike density, seasonality, principal utilization, plant height, lodging, 1000grain weight (TGW), susceptibility to different biotic stress (specially fungi), protein content, yield level, and cold susceptibility (Gass et al., 1997). Under organic conditions, other wheat traits of interest not included in the previous descriptor list are: growth habit, last leaf spike distance (LLSD), soil coverage, early vigour, plant top habit, and tillering (Chable & Serpolay, personal communication 2012). Since susceptibility to biotic and abiotic stress is extremely influenced by the environment, causing high GxE interaction especially under organic conditions, they were not accounted for the final list of traits included in the protocol. Additionally, considering the scenario of on-farm trials under participatory approach, four other traits were discarded due to the lack of applicability: seasonality, principal utilization, protein content and cold susceptibility. Finally, to determine the yield level three yield components were included: number of spikelets per spike, number of seeds per spikelet, and number of seeds per spike.

4.5.4. Protocol: in order to monitor the phenotypic diversity of wheat populations in on-farm trials under participatory approach and low-input/organic conditions, the minimum set of traits that should be evaluated by the farmer or the researcher are listed in Table 6: Obviously, depending on the nature of the research and the goal of the investigation, different traits can be included or excluded from this list. Here, the characters which are particularly useful indicators of diversity in collections and the ones relevant under organic farming are mentioned, considering the conditions for wheat breeding on-farm and the crop management.

Qualitative traits (Characterization descriptors): since they are self-explanatory and independent labels determining a class, therefore they are not computable by arithmetic calculations and are expressed in discontinuous states (Biodiversity International, 2007).

For this protocol all qualitative traits were assigned with five classes. The numbers of observation are:

 Lodging: sub-plot general observations after dividing the plot in 3 equivalent parts (sub-plots or samples)  Early vigour, growth habit, tillering, and plant top habit: at least 15 observations (plants) per repetition (plot), avoiding plants in the border.  Awnedness, glume colour, glume pubescence, spike density and seed colour: at least 15 observations (spikes) per repetition (plot), avoiding spikes of plants in the border.

The 10 qualitative descriptors are explained in detail in Annex 9.1.

Quantitative traits (Evaluation descriptors): when variation of a characteristic within the population is prevalent, it is essential to describe how the samples are to be selected and how many samples are needed (Biodiversity International, 2007). In a single location trial, data collected should have at least 2 repetitions (plots), with a minimum of 3 samples per repetition in order to have a consistent data set. In multi-location analysis at least 2 locations should be included and multi-year trials require a minimum of 2 seasons. 4.5.5. Data Analysis 2011/12: in this experiment ("Effet de la variété de blé et de la haie sur la biodiversité (coleoptera: carabidae) et la régulation biologique en agriculture biologique"), due to the stage of the wheat (maturity), the first 4 traits of the descriptor list (Table 6) were omitted. Additionally, due to the interest of the researcher two other traits were included: spike length and spike base height. Therefore, 15 traits were assessed for phenotypic population diversity (Table 7), 8 quantitative and 7 qualitative. Qualitative traits were assigned five classes; the quantitative traits were transformed in discrete ranks by forming five classes in order to be comparable with the qualitative traits. Due to the nature of the experiment, only within population diversity was able to be assessed in each field. Since different genotypes were tested in different environments, each environment was treated individually and each variety generated a within population diversity measurement considering the descriptor list of Table 7. The diversity analysis of the eleven fields (Table 8) showed the total phenotypic diversity (Hs) within each population for each trait, and across all traits (Ĥ P ). Single Pure Lines (SPL): for population SPL1, Hs was observed the most in trait LLSD (0.79) and the least for the traits awnedness, glune colour, glume pubescence, and seed colour (0.00). Within population SPL2, Hs was higest for spike length (0.76) and lowest for the traits lodging, plant top habit, awnedness, glume colour, and glume pubescence (0.00). SPL3 showed the most Hs in trait plant height and the lowest in traits lodging, awnedness, glune colour, and glume pubescence (0.00). Ĥ P for those single pure lines were 0.41, 0.42 and 0.39 for SPL1, SPL2 and SPL3 respectively.

Mix of Pure Lines (MPL): traits LLSD (0.78), spikeltes/spike (0.78), and spike length (0.76) were the higest for Hs in populations ML1, ML2 and ML3 respectively. For all those three populations, the trait lodging presented the minimum Hs (0.00). Ĥ P were 0.53, 0.55 and 0.50 for MPL1, MPL2 and MPL3 respectively.

Single Landraces (SL): SL1 was only based on 13 traits because plant height and spike base height were not able to be measured on the field. Trait seeds/spike presented the highest Hs for both populations SL1 (0.77) and SL2 (0.76). SL1 showed minimum Hs for traits plant top habit and seeds/spikelet (0.50). Seed colour had the lowest Hs (0.06) for population SL2. Ĥ P for those single landraces were 0.63 and 0.59 for SL1 and SL2 respectively.

Mix of Landraces (ML): for population ML1, Hs was the most in trait plant height (0.78), and the least in trait seed colour (0.44). Within population ML2, spike base height showed the highest Hs (0.78), and seeds/spikelet the lowest Hs (0.50). ML3 presented highest Hs in seeds/spike (0.78) and the lowest Hs in the trait awnedness (0.44). Ĥ P were 0.64, 0.63 and 0.64 for ML1, ML2 and ML3 respectively.

Discriminant analysis per environment (Table 9) indicated that the order of the traits contributing to the sample discrimination, and therefore to the diversity within populations was different in each field. However, the eight quantitative traits were the first in the three SPL fields. Seven out of the eight quantitative traits were the first in the three MPL fields. At least three quantitative traits were the first in both SL fields. Finally, ML fields presented four quantitative traits on the top of the list. This evidenced that quantitative traits were far from qualitative traits about phenotypic diversity discrimination between occurring groups. In other words, quantitative characters vary the most within the populations.

In order to compare qualitative and quantitative methodologies for diversity assessment, two separated analysis were performed with only quantitative (Table 10) and qualitative (Table 11) traits. Quantitative data only showed Ĥ P between 0.65 and 0.72 among the eleven populations. On the other hand, qualitative data showed Ĥ P between 0.05 and 0.11 for the SPL fields, 0.34 to 0.37 for MPL fields, 0.47 and 0.61 for SL fields, and 0.56 to 0.60 for ML fields.

Discussion

The present research was done in two parts. The first part tries to find a population diversity index.

The second part tries to look for the best methodology in order to assess population diversity on-farm conditions, under a participatory plant breeding approach. Literature review and application of the theoretical knowledge in real data from two different experiments were the tools for answering the four research questions that were formulated on the way. The discussion below is focussed on the methodological aspects of the population diversity assessment, since that was the aim of the research.

4.6.1. What is a practical and straightforward approach to assess "between populations" and "within a population" diversity?

After an exhaustive literature review the concept of Nei's gene diversity method (Nei, 1973) was studied and adapted for assessing phenotypic diversity in a practical and straightforward approach. However, different other indices were consulted and studied. Among them, the Shannon-Weaver index and the Simpson index. The Shannon-Weaver index quantifies the uncertainty in predicting the species identity of an individual taken at random from the dataset of interest (Shannon, 1948). It has been a popular diversity index in ecology, and it is mostly used to measure the heterogeneity of variety distribution in the landscape. The Simpson index measures the degree of concentration when entities are classified into types. The measure equals the probability that two individuals taken at random from the dataset of interest represent the same type (Simpson, 1949). This index has been used to assess the distribution of dominant varieties in the landscape. Nevertheless, both Shannon and Simpson indices focus on spatial diversity and lack the measurement of the diversity between varieties (Hennink & Zeven, 1991;Goffaux et al, 2011). On the other hand, Nei's method has the advantages of measuring diversity within populations (Kosman, 2003), and measuring the degree of differentiation between populations (Nei, 1973;Hennink & Zeven, 1991). This index was initially formulated to analyze the genetic diversity (heterozigocity) of a subdivided population (Nei, 1973). However, on-farm trials should be assessed employing phenotypic diversity, since the farmers and researchers need to count with a useful kind of diversity that can be calculated under a participatory in situ approach. Consequently, the Nei's gene diversity concept was applied to population phenotypic diversity and tested with real data from two different organic wheat projects during 2010/11 and 2011/12 seasons. Data analysis was satisfactory and proved the utility and usefulness of the index. Both projects presented different research situations. The first example (2010/11) worked comparing phenotypic diversity within and between three populations in a single environment and across different environments. The second example (2011/12) gave within population diversity measurements to eleven separated populations in different organic environments. Therefore, the fact that Nei's diversity index assess at the same time between and within population diversity -a relevant feature for participatory breeding and crop management-makes it adequate to the present study. The examples analysed offered scenarios to work in populations with different genetic structure sowed at the same season in a single and/or different organic environments. It is recommended to test the performance of the Nei's index for monitoring diversity in populations through different seasons, or a multi-year situation. When the diversity index had been selected, the data set to work with is essential. From the two projects the index was tested on, it was observed that the descriptor list varies according to three aspects:

A. Nature of the research: the specific goals of the investigation and the interest of the researcher are important considerations in order to select the right traits for the descriptor list. During this study, three situations were found:

(a) The study is based in a single location with several varieties. Normally wheat farmers want to know the diversity of different populations in their farms on terms of morphological, agronomical and/or bread quality traits. In this situation the descriptor list includes all those traits of interest to the farmer. After the analysis, farmers can decide which traits reflex better the diversity according to the populations in comparison. Table 5 is an example of this situation. (b) The study is established across a finite number of locations with several varieties. Usually the research of breeders implies multi-trials plots. In this situation the descriptor list includes qualitative traits (characterization descriptors), and quantitative traits (evaluation descriptors) without GxE interaction, previously identified by the ANOVA. After the analysis, breeders can decide which traits reflex better the diversity according to the populations in comparison. Table 4 is an example of this situation.

(c) The study is designed with several populations, with a single population per location.

Frequently ecologists tried to determinate the effect of population diversity as a factor in the regulation of biological controllers and pest. In this situation the descriptor list includes only traits that show a high discrimination power among the populations of interest. This feature is linked to the genetic structure of the population in study. Tables 8 and11 show descriptors with average high discrimination power in eleven populations with four levels of genetic diversity: SPL, MPL, SL, and ML.

However those situations are not the only ones and different investigations can adapt or implement new characters in the descriptor list. For instance, traits related to the presence of certain plagues or diseases, e.g. leaf shape or self-shadow of the plant. Another common situation is the research with several varieties through different seasons. In this case characterization descriptors and evaluation descriptors without GxY interaction, previously identified by the ANOVA, are useful for the descriptor list.

B. Feasibility of trait measurement: one of the main reasons for this research is that trait data collection is tedious and time consuming (Chable, personal communication 2012). On-farm methodology moreover has to consider the fact that farmers needs to assess those traits without special instruments, chemicals or technologies. The descriptor list elaborated for data collection during 2011/12 season (Table 7) intentionally mixed qualitative and quantitative descriptors in order to experience the difficulty of each trait evaluation. Plant height, spike base height, spike length, LLSD, lodging, plant top habit, spike density, and awnedness were easily measured in the fields. At first labelling and identification was required to take those spikes to the laboratory. Here, spikelets/spike, seeds/spikelet, and glume colour were rapidly assessed. However four of them, namely glume pubescence, seed colour, seeds/spike, and TGW demanded major time and effort. Glume pubescence required a magnifying glass of good augment and a lot of patience in order to obtain an adequate characterization. The other three traits were only accessible after drying and threshing the spikes, which demanded a second labelling and identification of the spikes. Data for quality of bread is of extreme interest for the bakery farmer and was originally considered as part of the descriptor list; however their collection on-farm for quality flour characters is difficult and not practical without bakery experience. Moreover, most of the quantitative quality characters demand equipment and instruments from specialised laboratories (Serpolay, personal communication 2012).

C. The genetic structure of the population: for comparison a reference is always needed. All the examples presented in this study used SPL as references of low population diversity (sections 4.4.2 and 4.4.5). If the traits selected are able to show on average this difference, the index works adequately. However, if comparison is intended among populations with the same genetic structure, e.g. SPL vs SPL or MPL vs MPL, probably different traits would be necessary for the descriptor list in order to detect differences between and within varieties.

The genetic structure of the populations in evaluation also determinate the type of descriptor, qualitative or quantitative, to be used for characterization (section 4.6.4).

For all these reasons, it is not possible to indicate a static list of traits for quantify the population diversity. The descriptor list is a dynamic concept and varies according to and depending on the goal of the research, the environment selected, the feasibility of the trait measurement, and the genotypes involved. Equilibrium between evaluation and characterization descriptors is recommended. In general, in order to select traits for quantify the population diversity; the researcher should decide to work with the easier and faster measurable traits without missing the specific goals of the investigation.

4.6.3. How to monitor the diversity in on-farm trials under participatory approach?

As mentioned previously, phenotypic diversity implies observable attributes at the morphological level that can be assessed on-farm and analyzed without needing sophisticated equipments, chemicals or molecular tools. Consequently not all traits are able to be included into a protocol for these conditions. Additionally, the descriptor list is a dynamic concept (section 4.6.2). Thus, define a general protocol is actually a difficult task since it can vary according to the specific goal of the researcher and the specific genetic material and conditions of each investigation. The protocol presented in this study was established considering useful wheat indicators of diversity in collections, and characters relevant for organic agriculture. Below, generalities in order to design a protocol are discussed from the experiences in this study.

A descriptor should consist of: (1) a descriptive and specific denomination, (2) clearly definable states of expression (classes), for instance: green, awnless, erect, lax; and (3) a method explaining as detailed as possible how the descriptor should be measured or scored, the adequate plant growth state for assessment, and the number of samples for observation. References are very useful to harmonize descriptors, e.g. draws, imagines, colour charts, units to be employed: cm or mm; especially when observations are made by different observers. Examples of descriptors are included in Annex 9.1. Qualitative traits should include all possible classes present in the populations (nominal scale) in order to describe the whole range of characteristics. Since within and between populations diversities are relevant, the classes for qualitative descriptors need to be merged into a common scale used for scoring through all populations under study. In this study scales of five classes were used for convenience. However, the scale classes depend on the different expression of the character evaluated. Quantitative continuous traits use exact measurements (continuous scale) that can be easily transformed into discrete classes for comparison (Biodiversity International, 2007). At the beginning of this research, it was thought that scoring quantitative descriptors directly into discrete scales (taking a countable number of values rather than measuring using a metric scale) would save time during the data collection. Nevertheless, the practical experiences taught that continuous scales have the great benefit of the statistical analysis. Moreover, transformation from continues to discrete scale is simple, but the other way around is not possible. Scoring quantitative traits directly in discrete scales is only recommended if the sole objective is the visualization of diversity present, without regarding precision. Furthermore, the discrete classes are only determinate after counting and knowing what is "low/short" or "high/long" in the populations of interest, which is sort of difficult beforehand data collection (Biodiversity International, 2007). The experimental design included per location a minimum of two repetitions, three samples per repetition, and five plants per sample. In total 30 plants where evaluated in the majority of the traits. Traits with general observations, e.g. lodging and soil coverage, or traits where five plants together were needed for measurement, e.g. TGW, only counted with six samples. It is always recommended the highest number of samples possible, although under on-farm conditions and considering the time and workforce limitations for data collection 30 samples looks like a reasonable sample size. The protocol listed 17 traits in the descriptor list. For 2011/12 season only 15 traits were used due to the maturity stage of the fields. It should also consider that any of the projects taken as examples in this study were originally designed for measuring population diversity. Consequently this methodology developed had to be adapted to the experimental designs of the projects. Keeping in mind that Nei's index result from an average across different descriptor traits, the type of descriptors (section 4.6.4) selected is more relevant than the number of them. As an example, Table 5 showed no differences in average values between Renan (0.48) and Sixt (0.47) probably because 20 quantitative and only 6 qualitative descriptors were included in the analysis. Since not all the traits indicated in the original protocol were tested, it is recommended future research including those missing traits, namely soil coverage, early vigour, growth habit, and tillering.

In summary, monitor diversity on-farm trials is done by designing a uniform protocol. This encompasses techniques for measurement, methods for data collection, data encoding (scales), and harmonization of concepts among observers when assessing the descriptor list. This protocol varies according to the type of traits in the descriptor list and the experimental design.

4.6.4. Which methodology, qualitative or quantitative, is better for a fast and accurate screening of population diversity on-farm?

This research question is related to the second research question (about the traits necessary for quantifying the population diversity). The type of descriptor included into the descriptor list depends on the genetic structure of the populations under study. Some traits, especially the evaluation descriptors, usually show different degrees of diversity according to the population's genetic structure of the study, and its interaction with the environment (locations, weather conditions, years). In this research eleven out of seventeen quantitative traits during the 2010/11 season showed significant (p<0.05) GxE interaction in the ANOVAs (Table 3). This indicated that they only can be informative about the population phenotypic diversity inside a single location. Similar procedures were found in Greek faba bean local populations (Terzopoulos et al, 2008) and Greek tomato landraces (Terzopoulos et al, 2009) where both studies showed genotype x year (GxY) interaction in five and three agronomic traits, respectively. Consequently, those traits were treated by year separately. For the contrary, characterization descriptors -due to their monogenic control-often show greater variation in the population with ample genetic structure, for instance SL or ML; but lower or not variation at all for a population with narrow genetic structure, for example SPL or MPL, even in different environments. In this study during 2010/11 season the traits lodging, spike awn presence and spike colour (Table 4) showed that tendency. Soil coverage in the full tillering stage and spike colour (Table 5) are also good examples of this tendency. Additionally, Table 9 is the best prove of this fact. The results of the Discriminant Analysis per location during 2011/12 season showed most of the qualitative descriptors in the bottom of the list, especially when the analysis was done for SPL and MPL fields. This demonstrates that in the experiment evaluation descriptors interacted with the environment and in average were not able to distinguish diversity among populations with different genetic structure. Table 10 shows only quantitative descriptors without discrimination power among eleven populations with four genetic structures. On the other hand, characterization descriptors were able to distinguish diversity among populations. Table 11 shows only qualitative descriptors with high discrimination power in the same populations of Table 10. Consequently, the estimation of phenotypic diversity in terms of fast and accurate screening is better based on observation of all morphological traits in this experiment. It is not necessary to spend time, effort and energy taking quantitative data since the information offered by those traits is no determinant to discriminate within those populations. Another consideration is the experimental design in terms of single location, multi-location, or multi-year trials.

During 2010/11 season, six quantitative traits without GxE interaction in the ANOVAs (Table 3) were employed for analysis across locations together with the qualitative traits. In this case the evaluation descriptors together with characterization descriptors were able to distinguish diversity among populations with different genetic structures. Similar procedures were found in the works of Terzopoulos et al. (2008) in faba bean populations and Terzopoulos et al. (2009) in tomato landraces where both studies showed no GxY interaction in seven and six quantitative traits, respectively. Consequently, those traits were analyzed across years. After this study it was corroborated that in general qualitative traits are easy to collect. However the main disadvantage is the need for standardization of concepts for the collectors, which is mainly reached by training, feedback and the use of common references (draws, pictures, colour charts) and uniform protocols. Quantitative traits, on the other hand, take more time to measure but are precise and no training is required for their evaluation. However, they need statistical design for an adequate calculation (Biodiversity International, 2007). Therefore, in order to choose the best methodology, qualitative or quantitative, for a fast and accurate screening of diversity it is necessary to know the genetic structure of the populations under study, and determinate the experimental design.

Conclusions

4.7.1. Nei's gene diversity method applied to population phenotypic diversity is a practical and simple approach to assess "between populations" and "within a population" diversity. 4.7.2. The descriptor list for quantify the population diversity is a dynamic concept. It varies according to the goal of the research, the environment selected, the feasibility of the trait measurement, and the genotypes involved. 4.7.3. Monitoring diversity in on-farm trials is achieved with a uniform protocol. This encompasses techniques for measurement, methods for data collection, data encoding, and harmonization of concepts among observers. 4.7.4. The best methodology, qualitative or quantitative, for a fast and accurate screening of diversity on-farm depends on the genetic structure of the populations under study, and the experimental design.

SELF-REFLECTION

General Experience

Before the beginning of these sixteen weeks of internship I was excited about the idea to do the last part of my Master of Science in France. I always wanted to live a period of my life here. Nevertheless, I have to admit that coming to France was challenging. Several people before my trip told me French people do not speak or do not like to speak English and I will probably have a tough time living there.

After more than three months of internship and with only two more weeks to go, I have to confess communication was one of the main difficulties outside the work environment. However I faced it with a big smile and always found interesting the way this obstacle would be got over. Additionally I took a French Audio Course, which helped me with some phrases in order to solve many daily situations. Most of the time resulted well. Something I did not expect it the fact that a lot of French people speaks Spanish, which makes the things easier. Due to my inability to express fluently in French I felt a lack of interaction with other residents in my building. Even so, most of my co-worker were able to speak Spanish and I met a bunch of Latin people who used to organize activities like barbecues, football matches, dinners, or just some chilling out time watching the UEFA European Football Championship. Therefore the social life was indeed interesting and active. Moreover, I received the visits of old friends and that made me feel really glad. Generally speaking, I adapted easily to my temporal home, mainly because in the past I already had the experience to settle in different places, so it is not the first time I pass through this process. Some shortcomings I can remember are: a really slow internet connection in my room (I missed the fast Dutch service), the lack of a bicycle (probably also a missed Dutch custom), supermarkets were sort of far from my place (and combined with the lack of a bike, it gave me sometimes hard moments), I lost my photo camera in a wheat field and my iPod in the English channel (in both cases my fault), and I missed some trips during the weekends (mainly due to the rainy, cold and cloudy weather at the beginning of the summer).

In the work, the situation was always positive and pleasant. I adapted myself rapidly after arriving to the SAD-Paysage unit. The working environment was optimal and friendly. People was always keen on contribute with my work. Good relationships were made with everyone working around me and I felt like in family. This great environment helped me to handle the assignments on time and efficiently. Cooperation and team work was part of our routine. I really like the time I spent here. Furthermore, my research topic was really challenging and interesting. I never worked assessing population diversity before and initially I had some doubts if that could be possible. After a thoughtful literature review I found myself more fascinated with the subject. This situation motivated me and made me enjoy the work and the development of the theme. Maybe, the only limitation I found was lack of technicians to help during the field and laboratory data collection. However, that forced me to do the work by my own and learnt more about the wheat plants.

Learning Outcomes

Previously to the beginning of this internship, six learning outcomes were established in order to have clear and specific intentions of this working period. Bellow, reflection is developed and a personal degree is given to each one of them.

(1) To apply knowledge and skills acquired during the courses: definitely for the scientific research, I used knowledge and several skills learnt during the courses in the MSc. Plant Sciences. I perceived that most of the knowledge was applied in the fields of Basic Principles of Plant Breeding, Organic Plant Breeding, Advance Statistics, and Reflective Process. Even a new statistical analysis (Discriminant Analysis), which I had never used before, was studied and consider useful in this study. Specific knowledge about GxE interaction, and types of traits were useful during the scientific research. For the report, reflective learning circle was also used. (3) To improve the professional skills: in this aspect I have to admit I improved some specific skills I did not exploit in the past. Considering the short period of time for this internship, I was forced to think fast, make thoughtful decisions and work one step ahead. This situation made me improve my problem identification, analysis, and resolution skills. Additionally it enhanced my decision making, creative thinking, organizing and planning abilities. Consequently, the challenging nature of the project inspired me to work self motivated, with initiative, being tolerant and flexible to different situations in a self-sufficient and proactive way. Degree: 9.0 (4) To expand the professional network: to work in INRA Centre de Rennes it was a very fruitful internship. I was able to meet respectful researchers and professionals in the field of participatory research and cultivated biodiversity. I also met young professionals who are just starting to contribute to this growing and fascinating research field. Besides I was not in the perfect situation, since I was limited for the fact I do not speak fluent French, much of the people I meet was able to communicate in English and, surprisingly, also in Spanish. Degree: 7.5 (5) To gain practical experience in organic and participatory plant breeding on wheat: for me it is not the first time I work under organic or low-input conditions. However participatory approach is a new experience that I enjoyed. On-farm trails methodology is a relatively recent concept and represents an alternative to improve local adaptation breeding, encourage diversity, and empower sustainable farmers. I knew the concept in theory last year during my courses in the University, but this is the first time I performed a practical experience about this theme. It was really pleasant. Working with wheat also was new for me. My working experience was mainly in rice, beans, cotton and maize. After this internship and the study done, I got a major knowledge about wheat production, development stages, morphology and diversity. I was also able to compare pure lines with landraces and single with mixed populations. Finally I also had the opportunity of make bread dough and enjoy the taste of artisanal farmer bakery. Degree: 9.5 (6) To deepen academic (report on result) and reflective (self-reflection paper) writing skills: after the first correction of my report I got the impression that my writing skills have improved in comparison to my MSc. thesis project for example (February 2012). This is due to my motivation for developing my writing skills, which made me read much more scientific articles since then.

Reflective writing was something relatively new for me, since I just learnt it during the last University courses. Nowadays the reflection process has become part of my routine and I got used to deliberately think and analyse my everyday experiences. I even started to write a dairy with the main feelings and behaviours during this internship. This dairy helped me to refresh my memory and make this reflection paper easier to write. However, there is always space to improve my writing style. Degree: 8.0

Outlook

After this internship period I feel much more confident to work in the research field. Additionally, this experience had confirmed my vocation to work in organic and/or low-input farming. The participative research or peasant selection is an interesting option to explore in my professional career. I also enjoyed and stated a positive attitude towards this new kind of research: mixing analytical and systemic approaches. At the end of my internship, I feel that I match in this position and now I know to come here was pertinent. Consequently, for the future I would like to work in sustainable agriculture and, if possible, in the research field.

CONCLUSIONS

6.1. SAD-Paysage unit, as part of INRA Centre de Rennes, is focus in researches on the interactions among agriculture, landscape and biodiversity. However, the activities for the development of participatory researches and organic/low input agriculture are not completely included. Consequently, for the future a project is emerging to build a new organisation which involves INRA and local/organic partners. 6.2.

The research completed during this internship lays under the scope of the European program SOLIBAM, WP3 and WP4, and is linked to the Paysblé project. This program has as background the participatory peasant selection for organic farming in Brittany during the beginning of XXI century. 6.3.

Nei's diversity index is a practical and simple approach to assess population diversity.

The descriptor list for this purpose is a dynamic concept and specific protocols are needed according to the research. Qualitative or quantitative methodologies depend on the genetic structure of the populations and the experimental design of the study. 6.4. This internship resulted in: a positive general experience, a satisfactory work experience, an interesting scientific research, and the majority of the learning goals were successfully achieved.

ACKNOWLEGMENT

First and foremost, I would like to thank to my internship supervisors, Edith Lammerts van Bueren and Véronique Chable, for the valuable guidance and advice before and during this internship, respectively. Both of them inspired me greatly to work in INRA. Their willingness to motivate me contributed tremendously to my internship. I also would like to take this opportunity to thank to Estelle Serpolay, for giving me priceless advices, and helping me in working and extra-working aspects during my stay in Rennes. Besides, I would like to thank the rest of the team: Anabelle, Andoni, Bérangère, Dewi, Lucie, and Simon, for providing me with the best working environment and motivation to complete this internship period, and explaining me the functions and roles of each one inside the team. In addition, I thank to INRA Centre de Rennes and especially the SAD-Paysage unit for giving me an opportunity to participate and learn about the peasant selection and organic wheat farming.

Finally, an honourable mention goes to Magdalenita, moja kochana, for her understanding and support on me in completing this internship away from the Netherlands. Without the help of everyone mentioned above, I would have faced much more difficulties while doing this internship.

Figure 1 .

 1 Figure 1. Work packages of the SOLIBAM Program and the place of the PaysBlé Project in that context. Figure 2. Location of the 6 on-farm trials for the PaysBlé project during 2010/11 season. Figure 3. Location of the 7 farms during 2011/12 season in Brittany.

Figure 1 .

 1 Figure 1. Work packages of the SOLIBAM Program and the place of the PaysBlé Project in that context.

Figure 2 .

 2 Figure 2. Location of the 6 on-farm trials for the PaysBlé project during 2010/11 season.

Figure 3 .

 3 Figure 3. Location of the 7 farms during 2011/12 season in Brittany.

  To enhance the ability to work within a multidisciplinary team of a public and scientific research institute: the group of interns during summer 2012 was integrated by Anabelle Laurent, Andoni Gabirondo, Dewi Gléau, Lucie Le Jeanne, and Simon Rousselot. The rest of the team was formed by the workers Bérangère Storup, Estelle Serpolay, and Véronique Chable. Besides each intern got his/her own research task, cooperation among us was the common denominator in the team. In my specific case, different task were made with other interns: (a) I went to the fields to help during data collection with Dewi and Estelle, (b) I gave a hand in data collection to PhD students, (c) I made English corrections for Simon's report, and (d) I worked together with Dewi at Anabelle's fields in data collection for her study and for my own report. During my internship period I received advice from all of my team mates. Additionally, for my internship report, I received constant feedback and help from Véronique, Estelle, Anabelle, Lucie and Dewi. Degree: 8.5

  Glume colour: observed after spike collection, on the outer glume. From 15 random spikes per repetition (plot) = 15 observations x 2 plots = 30 observations per trial. Colours for the scale depend on the populations evaluated. Two examples: observed after spike collection with the help of a lupe, on the outer glume. From 15 random spikes per repetition (plot) = 15 observations x 2 plots = observed at maturity or after spike collection, a visual measurement of the density of a spike. From 15 random spikes per repetition (plot) = 15 observations x

Table 1 .

 1 Traits assessed during PaysBlé project in 2010/11 season. 12 Table

Strategies for Organic and Low Input Breeding and Management (SOLIBAM) Program SOLIBAM

  The regulatory aspect has been treated in a first European program, called Farm Seed Opportunities (FSO), where the professional partner for France was the Peasant Seeds Network (Réseau Semences Paysannes = SP). FSO run from 2007 until 2010 and involve six European countries. It also received the task to feed the studies on the adaptation of plant populations. IBB participated in conducting the experiments for two of the four crop programs, spinach and beans. At the present, the SOLIBAM Program (2010-2014) is developing specific and new breeding approaches, integrated with agronomic management practices. The aim is to improve crops adapted to organic and low-input systems, considering the diversity of European seed systems. In parallel, a regional project to approximate the "Science and Society" in Brittany, named PaysBlé, involves IBB with the support of ITAB and INRA. This project began in 2009 and is in its final year(Chable, 2012). is a European Program, coordinated by INRA, which was launched the 12th of March 2010 in Nice. This project connects 23 public and private partners from 10 European countries (France, Italy, UK, Denmark, Germany, Portugal, Spain, Hungary, Switzerland, and Austria), 2 African countries (Mali and Ethiopia) and one international research centre (ICARDA). This program is supported by the European Commission under the theme "Food, Agriculture & Fisheries, and Biotechnology" of the SEVENTH FRAMEWORK PROGRAMME forResearch and Technological Development (Grant Agreement N° 245058). Its total cost is € 7.7 million, 5.9 million funded by the European Commission. The overall objective of the SOLIBAM Program is to develop specific and new breeding approaches, integrated with agronomic management practices, for improving the performance, quality, sustainability and stability of crops adapted to organic and low-input systems, considering their diversity in Europe and taking into account small-scale farms in Africa(Chable, 2009). The base hypothesis is that diverse populations in diverse organic and low-input farming systems are resilient to stress and, consequently, are able to adapt better to environmental variation. In order to achieve this objective, SOLIBAM has been working, since 2010, in parallel with ten Work Packages (WP) (www.solibam.eu):

3.1.2.

Table 1 . Traits assessed during PaysBlé project in 2010/11 season.

 1

  define the most appropriate traits to quantify diversity in this crop. The documents focused specifically in wheat literature and guidelines for wheat descriptors from the International Board for Plant Genetic Resources (IBPGR), the Council for Mutual Economic Aid (COMECON), the Community Plant Variety Office (CPVO), and the United States Department of Agriculture Genetic Resources Information Network (USDA-GRIN).

	the aim of
					Wheat Stage
	Data Type	End of winter (Early Tillering)	Full Tillering	2 Nodes	Maturity	Spikes
				height (mm)	height until flag leaf (mm)	sterile length (cm)
				biomass (g)	height until base of the spike (mm) total length (cm)
	Continuous				height of the whole plant (mm) grain biomass (g)	grain mass (g)
					straw biomass (g)
					total biomass (g)
		plants (#)	soil coverage (%) soil coverage (%)	soil coverage (%)	sterile spikelets/spike (#)
	Discrete				tillers (#)	fertile spikelets/spike (#)
					spikes (#)	grains/spike (#)
			habit		lodging	sanity status
	Nominal				sanity status	colour
						awn presence

The software used was GenStat 13 th Edition and Microsoft Office Excel 2007. 4.4.3. Second Literature Review: in order to elaborate a protocol for population diversity evaluation in wheat (research question 2), a second literature review was performed with

Table 2 . Four genetic population structures, composition and location of eleven varieties evaluated during 2011/12 season in Brittany.

 2

	Genetic structure	Composition	Farm
	Single Pure Line (SPL)
	SPL1	Renan	A
	SPL2	Torca	B
	SPL3	Torca	E
	Mix Pure Lines (MPL)
	MPL1	Renan, Attlas, Lucullus	G
	MPL2	Renan, Attlas, Pireneo	G
	MPL3	Renan, Attlas, Camp Remy	D
	Single Landrace (SL)
	SL1	Rouge de Bordeaux	E
	SL2	Paillard	B
	Mix Landraces (ML)
	ML1	Blés de Redon 1 (330 landraces from Redon + Datel + Talisman + Concorde + Rouge de Bordeaux)	F
	ML2	Blés de Redon 2 (330 landraces from Redon + Datel + Talisman + Concorde + Rouge de Bordeaux)	F
	ML3	Blés de Redon 3 (100 landraces from Redon + Poulard + Concorde + Swedish wheat)	C

Table 3 . F probabilities from the analysis of a crossed and nested factor design of 17 traits among 3 wheat varieties in a multi-trial on-farm evaluation during 2010/2011. Table 4. The phenotypic diversity within each variety (Hs), the total phenotypic diversity across the three varieties (HT), the average phenotypic diversity among varieties (DST), the proportion of phenotypic diversity among varieties considering the total phenotypic diversity (GST), the average phenotypic diversity within the three varieties (Ĥs), and the average phenotypic diversity across all traits of each variety (ĤP) for 15 traits among 3 wheat varieties at multi-location on-farm trials during 2010/11.

 3

	Stage	End of winter	2 Nodes				Maturity						Spike
	Source of variation	N° Plants	Height (mm)	Biomass (g)	N° Tillers	N° Spikes	Height until spike base (mm)	Height until flag leaf (mm)	Height whole plant (mm)	Total biomass (g)	Straw biomass (g)	Grain biomass (g)	N° Sterile spikelets/ spike	N° Fertile spikelets/ spike	N° Grains/ spike	Sterile length (cm)	Total length (cm)	Grain mass (g)
	Trial	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001
	Variety	0.016	<.001	0.002	0.004	0.005	<.001	<.001	<.001	0.003	<.001	<.001	<.001	<.001	0.129	<.001	0.001	<.001
	Trial x Variety	0.324	<.001	0.114	0.135	0.103	<.001	<.001	<.001	0.373	0.189	<.001	<.001	<.001	0.020	<.001	<.001	<.001
	Sample[Variety]	0.194	0.226	0.124	0.596	0.482	0.800	0.970	0.730	0.873	0.908	0.838	0.465	0.147	0.037	0.435	0.042	0.235
	Trial x Sample[Variety]	0.299	0.403	0.027	0.286	0.129	0.940	0.862	0.950	0.901	0.934	0.865	<.001	<.001	<.001	<.001	<.001	<.001
		Trait		Hs Renan	Hs Sixt	Hs Mélange	H T	D ST		G ST	Ĥs	Ĥs Tukey's mean test*
	Total biomass M			0.72		0.76	0.72		0.76	0.02		0.03	0.74	a
	Soil Coverage M			0.69		0.77	0.73		0.79	0.06		0.08	0.73	a
	N° Tillers M			0.72		0.70	0.72		0.72	0.01		0.01	0.71	ab
	Biomass 2N			0.69		0.72	0.72		0.75	0.04		0.05	0.71	ab
	Straw biomass M		0.61		0.76	0.73		0.74	0.04		0.06	0.70	ab
	Sanity M				0.69		0.69	0.70		0.71	0.01		0.02	0.70	ab
	N° Spikes M			0.71		0.66	0.69		0.69	0.01		0.01	0.69	ab
	N° Plants at the end of winter	0.66		0.64	0.69		0.68	0.02		0.03	0.66	ab
	Spike Sanity			0.61		0.64	0.63		0.63	0.01		0.01	0.63	abc
	Soil Coverage FT		0.53		0.66	0.63		0.68	0.08		0.11	0.60	abc
	Soil Coverage 2N		0.47		0.63	0.63		0.64	0.07		0.11	0.57	abc
	Lodging M			0.24		0.69	0.73		0.72	0.16		0.23	0.56	abc
	Habit FT				0.56		0.41	0.47		0.51	0.03		0.05	0.48	abc
	Spike Awn Presence		0.30		0.30	0.51		0.38	0.01		0.02	0.37	bc
	Spike Colour			0.05		0.09	0.68		0.60	0.33		0.54	0.27	c
	Mean				0.55		0.61	0.67		0.67	0.06		0.09	0.61
	Ĥ P Tuk ey's mean test*		a		ab	b

*Traits and Varieties not connected by the same letter were significant different from one another at p<0,05

  Traits to be considered for the diversity assessment (Literature Review): according to Biodiversity International (2007), a descriptor is an attribute, characteristic or measurable trait that is observed in a population. A descriptor list is a set of individual descriptors used for the description of germplasm of a particular crop or species. Different traits can be classified as descriptors, but two kinds are distinguished(Biodiversity International, 2007):

	4.5.3.
									Ĥs Tukey's
	Trait	Hs Renan	Hs Sixt	Hs Mélange	HT	DST	GST	Ĥs	mean test*
	Spike Total Length	0.75	0.70	0.74	0.73	0.01	0.01	0.73	a
	N° Tillers M	0.72	0.72	0.72	0.77	0.05	0.06	0.72	ab
	N° Grains/Spike	0.76	0.67	0.72	0.73	0.02	0.02	0.72	ab
	N° Fertile Spikelets/Spike	0.72	0.72	0.71	0.72	0.01	0.01	0.72	ab
	N° Spikes M	0.72	0.67	0.72	0.77	0.06	0.08	0.70	ab
	N° Plant at the end of winter	0.72	0.72	0.67	0.75	0.04	0.06	0.70	ab
	Grain Mass	0.73	0.70	0.67	0.71	0.01	0.01	0.70	ab
	Biomass 2N	0.63	0.63	0.75	0.78	0.11	0.14	0.67	ab
	N° Sterile Spikelets/Spike	0.64	0.67	0.69	0.73	0.07	0.10	0.66	ab
	Height 2N	0.60	0.62	0.65	0.68	0.06	0.08	0.62	ab
	Grain Biomass M	0.61	0.50	0.67	0.73	0.14	0.19	0.59	ab
	Straw Biomass M	0.28	0.67	0.72	0.64	0.08	0.13	0.56	ab
	Height until flag leaf M	0.35	0.62	0.58	0.75	0.23	0.31	0.52	ab
	Soil Coverage M	0.50	0.50	0.50	0.72	0.22	0.31	0.50	ab
	Height whole plant M	0.22	0.59	0.66	0.74	0.25	0.34	0.49	abc
	Height until base of spike M	0.22	0.59	0.64	0.73	0.25	0.34	0.48	abc
	Total Biomass M	0.44	0.50	0.50	0.67	0.19	0.28	0.48	abc
	Spike Sterile Length	0.54	0.27	0.63	0.63	0.16	0.25	0.48	abc
	Spike Sanity	0.49	0.31	0.54	0.47	0.02	0.05	0.45	abc
	Spike Awn Presence	0.30	0.30	0.48	0.36	0.00	0.01	0.36	abc
	Sanity M	0.50	0.00	0.50	0.48	0.15	0.31	0.33	abc
	Soil Coverage FT	0.00	0.50	0.50	0.72	0.39	0.54	0.33	abc
	Soil Coverage 2N	0.50	0.00	0.50	0.50	0.17	0.33	0.33	abc
	Habit FT	0.50	0.00	0.50	0.44	0.11	0.25	0.33	abc
	Spike Colour	0.00	0.00	0.68	0.60	0.38	0.62	0.23	bc
	Lodging M	0.00	0.00	0.00	0.44	0.44	1.00	0.00	c
	Mean	0.48	0.47	0.60	0.65	0.14	0.22	0.52
	Ĥ P Tuk ey's mean test*	a	a	b

Table 6 . Descriptor list for indicators of phenotypic diversity of wheat populations under organic conditions and the appropriate time for assessment.

 6

Table 7 . Descriptor list for indicators of phenotypic diversity of wheat populations under organic conditions for the experiment as carried out during 2011/12 season.

 7

Table 8 . The total phenotypic diversity within each population (Hs) and the average phenotypic diversity across all traits (ĤP) for 15 traits in 11 populations with different genetic structure during 2011/12.

 8

	Trait	Wheat Stage
	lodging	maturity
	plant height (cm)	maturity
	last leaf spike distance = LLSD (cm) maturity
	spike length (cm)	maturity
	spike base height (cm)	maturity
	plant top habit	maturity
	awnedness	spike
	glume colour	spike
	glume pubescence	spike
	spike density	spike
	number of spikelets per spike	spike
	number of seeds per spikelet	seed
	number of seeds per spike	seed
	seed colour	seed
	1000-grain weight = TGW (g)	seed

Table 10 . The total phenotypic diversity within each population (Hs) and the average phenotypic diversity across all traits (ĤP) for 8 quantitative traits in 11 populations with different genetic structure during 2011/12.

 10

	Trait	Hs SPL1 Hs SPL2 Hs SPL3 Hs MPL1 Hs MPL2 Hs MPL3 Hs SL1	Hs SL2	Hs ML1	Hs ML2	Hs ML3
	Plant height	0.75	0.72	0.78	0.68	0.78	0.73		0.75	0.78	0.73	0.72
	Spike base height	0.73	0.72	0.76	0.70	0.76	0.72		0.73	0.77	0.78	0.70
	Spike length	0.57	0.76	0.76	0.73	0.70	0.76	0.58	0.71	0.75	0.53	0.76
	LLSD	0.79	0.72	0.74	0.78	0.74	0.63	0.75	0.70	0.68	0.75	0.75
	Spikelets/spike	0.68	0.74	0.75	0.73	0.78	0.69	0.74	0.76	0.74	0.63	0.76
	Seeds/spikelet	0.58	0.46	0.36	0.32	0.53	0.37	0.50	0.46	0.53	0.50	0.52
	Seeds/spike	0.73	0.75	0.72	0.71	0.77	0.68	0.77	0.76	0.66	0.73	0.78
	TGW	0.61	0.67	0.67	0.72	0.72	0.61	0.61	0.72	0.67	0.61	0.72
	Ĥ P	0.68	0.69	0.69	0.67	0.72	0.65	0.66	0.70	0.70	0.66	0.72

Table 11 . The total phenotypic diversity within each population (Hs) and the average phenotypic diversity across all traits (ĤP) for 7 qualitative traits in 11 populations with different genetic structure during 2011/12.

 11

*SPL = Single Pure Line, MPL = Mix of Pure Lines, SL = Single Landrace, ML = Mix of Landraces 1000-Grain Weight (TGW): weight in grams of thousand kernels. In case not enough seeds are available, the sum of 5 spikes is weighted and interpolated to 1000. From 3 samples per repetition (plot) = 3 samples x 2 repetitions = 6 samples per trial.