Genet Resour Crop Evol (2013) 60:145–163 DOI 10.1007/s10722-012-9822-x
RESEARCH ARTICLE
Phenotypic diversity and evolution of farmer varieties of bread wheat on organic farms in Europe J. C. Dawson • E. Serpolay • S. Giuliano • N. Schermann N. Galic • J.-F. Berthellot • V. Chesneau • H. Ferte´ • F. Mercier • A. Osman • S. Pino • I. Goldringer
•
Received: 11 September 2011 / Accepted: 27 February 2012 / Published online: 8 April 2012 Ó Springer Science+Business Media Dordrecht 2012
Abstract The contribution of farmers to the creation and maintenance of genetic diversity is beginning to receive more recognition in developed countries. Although the use of landraces and historic varieties has largely disappeared in countries with industrialized agricultural systems and formal seed markets, certain varieties with particular cultural significance have been J. C. Dawson N. Galic I. Goldringer UMR de Ge´ne´tique Vegetale, Ferme du Moulon, 91190 Gif-sur-Yvette, France J. C. Dawson (&) Department of Plant Breeding and Genetics, Cornell University, 422 Bradfield Hall, Ithaca, NY 14853, USA e-mail:
[email protected] E. Serpolay N. Schermann INRA SAD Paysage, 65 rue de St. Brieuc, 35042 Rennes, France S. Giuliano Ecole d’Inge´nieurs de Purpan, 75 voie du Toec, 31076 Toulouse, France J.-F. Berthellot V. Chesneau H. Ferte´ F. Mercier Re´seau Semences Paysannes Cazalens, 81600 Brens, France A. Osman Louis Bolk Institute, 3972 LA Driebergen, The Netherlands S. Pino Istituto di Genetica e Sperimentazione Agraria, Lonigo, Veneto, Italy
continuously cultivated by farmers and other varieties have been preserved ex situ in genebanks. Recently, associations of organic farmers have become involved in the conservation and use of landraces and historic varieties (called farmer varieties in this article) because these varieties possess agronomic and quality traits that they have not found in modern varieties. In this study, eight farmer varieties of bread wheat from Europe selected by participating farmers were evaluated in onfarm trials during 3 years of cultivation. Each variety was grown on each farm, and phenotypic changes in each variety were measured the third year in a replicated split-plot trial on each farm comparing the version of each variety the farmer had multiplied to a sample of each variety from the region of origin. All varieties, including the two modern pureline varieties used as checks, showed statistically significant phenotypic changes over 3 years of multiplication. However, the magnitude of these changes was moderate and did not call into question varietal identity or distinctness. In addition, some traits of putative agronomic and adaptive importance, such as grain weight per spike and thousand kernel weight, did not respond positively to natural selection (environmental conditions and management practices) which suggests the necessity of farmer selection to maintain and improve varietal performance. Keywords Crop adaptation Genetic diversity Landraces On-farm conservation Participatory research
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Abbreviations EU European Union FSO Farm seed opportunities DUS Distinctness, uniformity and stability VCU Value for cultivation and use RSP Re´seau Semences Paysannes UPOV Union International pour la protection des obtentions ve´ge´tales, International union for the protection of new varieties of plants NIRS Near-infrared spectrometry ANOVA Analysis of variance PH Plant height SL Spike length LLSD Last-leaf-to-spike-distance SpTot Total number of spikelets per spike SW Spike weight GW/spike Grain weight per spike KN/spike Kernel number per spike TKW Thousand kernel weight
Introduction In developed countries, the use of landraces has largely disappeared except in small pockets where environmental conditions are marginal or where landraces hold particular cultural significance (Newton et al. 2010). Many of the previously widespread landraces now exist as a few accessions in ex-situ collections, which is cause for concern because selfpollinating species conserved in gene banks may lose their initial genetic diversity during cycles of regeneration due to drift in small effective population sizes and phenotypic homogenization by curators (Parzies et al. 2000; Cross and Wallace 1994; Horneburg and Becker 2008). In addition, these varieties have not had the opportunity to evolve with changing environmental conditions and agricultural practices. Little is known about the evolution and adaptability of these heterogeneous crops (populations, mix of varieties) over the short and long term when they are taken out of the genebank and re-cultivated in their region of origin or in other regions (Mercer and Perales 2010). However, where they exist, historic varieties and landraces may have agronomic and quality traits of interest to organic farmers as they were developed
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before synthetic inputs were available and often selected by farmers for their quality in traditional foods (Murphy et al. 2007; Newton et al. 2010; Dawson et al. 2011). Due to the absence of varieties specifically developed for their systems, some organic farmers in Europe have started to select and create their own varieties by seeking out historic varieties and landraces grown before the widespread use of chemical inputs and then conducting mass selection within landraces or mixing varieties and letting natural selection work under their particular environmental and agricultural practices (Chable and Berthellot 2006; Newton et al. 2010). They obtained the seed of these landraces from ex situ gene banks or from farmers who had continued to cultivate landrace and historic populations even after the transition to modern varieties in the twentieth century. Because these varieties are not commercially available and because the exchange of non-registered seeds is prohibited under EU seed legislation (European Council 1966), farmers must produce their own seed for planting each year. This is a key component of traditional farming systems and important to the development of local adaptation but is no longer common practice in most European countries, and farmers must learn techniques of seed saving and selection. To facilitate the dissemination of information and exchanges of knowledge and skills, several farmers’ seed networks were created in the late 1990s, including the Red de Semillas in Spain, Rete Semi Rurali in Italy and Re´seau Semences Paysannes (RSP) in France (Thomas et al. 2011; Chable 2009). In addition to seeking varieties with certain agronomic and quality traits, the farmers involved in these networks are concerned about the loss of biodiversity in cultivated species. While ex situ collections in gene banks are important resources, they do not allow crop populations to continuously evolve with changing environmental conditions. When landraces and historic varieties are conserved on-farm, evolutionary forces act on existing genetic diversity and farmer management and selection maintains agronomic and quality characteristics of importance (Almekinders and Elings 2001; Elias et al. 2001; Louette and Smale 2000; Berthaud et al. 2001; Smith et al. 2001). The value of on-farm conservation of genetic resources is becoming more apparent as genetic studies reveal both the complementary nature of ex situ and in situ
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conservation and the extent of genetic diversity maintained in crop populations cultivated on diverse farms (Demeulenaere et al. 2008). The study presented in this paper followed eight farmer varieties of bread wheat over 2 years of onfarm evolution in contrasting environments, to measure the degree of evolution possible in a short time frame and the effects of on-farm management on varietal phenotypic characteristics and diversity. The work was done in the context of the new European legislation on conservation varieties, which provides a legal status for landraces and historic varieties but sets strict limits on the geographic area in which they can be used (their ‘‘region of origin’’) and also requires phenotypic traits to be defined and registered in the special catalog for conservation varieties (European Commission 2008). The project was funded by the European Commission in order to inform legislation on conservation varieties adapted to organic and lowinput systems. This project evaluated phenotypic changes in historic wheat varieties, landraces and variety mixtures (all referred to as farmer varieties in this article) currently grown by organic farmers over
Fig. 1 a Locations of the eight farmers involved in the FSO wheat project (2007–2009). b Experimental protocol for field trials 2006–2009
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3 years in very diverse environments (on farms located in Italy, France and the Netherlands). The range of environments was chosen to represent as much as was feasible the breadth of potential conditions in which conservation varieties may be used if permitted to be cultivated outside their region of origin. The experiment presented in this paper was conducted in the third year of the project to compare the varieties that had been multiplied on each farm with the same varieties multiplied in their region of origin. A complementary experiment was also carried out to compare all the varieties multiplied on each farm in a common environment to provide information on varietal distinctness, uniformity and stability relative to the proposed registration requirements for the catalog of conservation varieties. That experiment is reported in Dawson et al. (2012).
Materials and methods The experiment involved eight organic farmers from Italy, France and the Netherlands (Fig. 1a). These
a
b
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Table 1 Description of the ten varieties studied during the FSO project Name (Abbr)
Type of variety
Farm of origin
Description and source of original variety
Aubusson (AU)
Modern pure line
None
Commercial modern variety (Nickerson) selected for conventional agriculture. Resistant to powdery mildew and brown rust
Renan (RN)
Modern pure line
None
Commercial modern variety (INRA), the reference for organic agriculture. The most common variety used by organic farmers in France
Ble´s de Redon (RD)
Landrace mixture
VC
Mixture composed by a Breton farmer from 7 spikes, each one originated from a sample from the French National Gene Bank of a landrace from the Redon Region in Britanny
Haute Loire 1433 (HL)
Landrace population
FM
Landrace from a mountain region in Central France, but contributed by a farmer in Maine-et-Loire (Northwestern France) who obtained it from the French National Gene Bank in 2004
Me´lange de Touselles (TO)
Landrace mixture
HF
Mixture composed by a farmer in Southern France from four different «Touselles» landraces from the French National Genebank: three T. aestivum and one T. turgidum L. Touselles varieties have been cultivated in Southern France since the middle ages for baking quality, which is what the name Touselles signifies—a bread wheat
Rouge de Bordeaux (RB)
Historic variety population
JFB
Well known historic variety from the Bordeaux region in South West France, originally selected from the variety Noe´ (from Odessa in the Ukraine) around 1880. Sample obtained from a farming community in the Bordeaux region, never conserved ex situ
Piave (PI)
Landrace population
GC
Landrace from Northern Italy (region of Veneto, North of Venice), kindly provided by Silvio Pino from IGSA (Istituto di Genetica e Sperimentazione Agronomica di Vicenza, Italy), from the regional gene bank
Solina d’Abruzzo (SO)
Landrace population
TDS
Landrace from a mountain region in the centre of Italy (Abruzzo). Known for baking quality, in continuous cultivation and never conserved ex situ
Zonnehoeve (ZH)
Modern mixture
PVI
Mixture of two modern pureline commercial German varieties, Rektor and Bussard released in 1980 and 1990, provided by the farmer who cultivated it as a mixture in a Polder (reclaimed land) of the centre of the Netherlands for more than 10 years
farmers were already experienced seed-savers and most of them were members of seed-saving networks. Each participating farmer chose a variety they were currently using because of particular agronomic or quality traits that they felt would also be of interest to other organic farmers. These varieties had been grown on their farms for at least 3 years, and several had been grown for 10 or more years on the same farm. Varieties The varieties chosen represent a range of different farmer varieties and regions (Table 1). There are three true landraces—‘Haute Loire’ (‘HL’), ‘Piave’ (‘PI’) and ‘Solina d’Abruzzo’ (‘SO’), all with different histories of conservation. ‘SO’ has been cultivated continuously in its region of origin in Italy without exsitu conservation. ‘HL’ and ‘PI’ were conserved in gene bank collections and were recently (within the
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past 5 years) requested by the farmer from the ex-situ collections. ‘PI’ was then cultivated in its initial region of origin in Italy, while ‘HL’ was cultivated slightly outside its region of origin in France, at a lower elevation. ‘Redon’ (‘RD’) and ‘Touselles’ (‘TO’) are also landraces from ex-situ collections, but were reconstituted by the farmers as mixtures of several distinct accessions with different phenotypic types conserved under the same general name in the genebank. ‘Redon’ is the name given to around three hundred accessions from the Redon region of NW France, generally with the village of collection added to the name Redon to identify the accession. The ‘RD’ used in this experiment was constructed from seven spikes, each from a different accession. ‘Touselle’ is the name given to many accessions from SW France, usually with a descriptor such as ‘‘awnless’’ in addition to the name ‘Touselle’, as this was historically the regional name for any population of high
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quality bread wheat. The ‘TO’ in this experiment was formed as a mixture of seed samples from four different ‘TO’ accessions, and is particularly diverse because it has three T. aestivum components and one T. durum component. Both ‘RD’ and ‘TO’ have been cultivated about 10 years on-farm. ‘Rouge de Bordeaux’ (‘RB’) is an historic French variety from the late 1800’s. The particular population of ‘RB’ used in this study came from a farming community near the Bordeaux region and had never been conserved ex-situ. The last farmer variety, ‘Zonnehoeve’ (‘ZH’) is a mixture of two modern pureline varieties, cultivated, harvested and re-planted as a mixture on an organic farm in the Netherlands for over 10 years. One farmer variety originally included in the experiment, ‘Zeeuwse Witte’, from farm PVZ, was not able to be analyzed as there was a mixture at harvest for the origin version in 2008. Two modern varieties were used as checks, as they are theoretically pure lines, supposed to have very low evolutionary potential: Aubusson (‘AU’), especially developed for high-input conventional agriculture and Renan (‘RN’), a French variety developed with multiple disease resistances which was found to be suitable for low-input and organic systems and is currently the
most widely cultivated variety under organic conditions in France. Experimental design Samples of each variety were collected on the farm of origin in 2006 and seed of each variety was sent to each farmer for sowing in 2006 (Fig. 1b). Farmers multiplied each variety on a 10 m2 plot with sufficient space between plots to prevent mixing seed at harvest. Each farmer harvested and replanted a sample of each variety on their own farm in 2007 and 2008 without conscious selection during the growing season or at harvest. In 2008, in addition to the farmers’ versions of each variety, a sample of each variety from the production fields of the farm of origin was sent to each farmer to be sown next to the version they had multiplied from 2006, to measure differences between the original variety and the versions that had been multiplied on each farm. The versions from the farm of origin are referred to as the ‘‘origin’’ version and the farmers’ 3rd multiplication generation as the ‘‘3G’’ version. Management of the experiment was the responsibility of the farmers, following their normal
Table 2 Environmental and management conditions at each field site Farm
Seeding
Pre-crop
Management
Soil type
Location
FM
15/12/2008
Mixed pasture
Plowed, harrow, mechanical weeding twice
Hydro-morphic sandy
47°250 N
Sorghum
Disc cultivator and harrow before seeding, hand weeding
Silt–clay, calcareous
43°470 N
Minimum tillage, ripper and disc before seeding
Clay
45°370 N
Harrow
Poor clay, calcareous
44°160 N
Rich clay
52°180 N
Small plot seeder 300 seeds/m HF
26/11/2008 Hand seeding, broadcast
GC
300 seeds/m 13/01/2009 200 seeds/m
Maize
PVI PVZ VC
Beans
Harrow
2
Small plot seeder 400 seeds/m
Alfalfa
2
Small plot seeder 400 seeds/m
Peas
Harrow
Rich, heavy clay
2
53°240 N 6°650 E
Hand seeding Not able to be cultivated in 2008–2009
0°220 E 5°220 E
No weed control
300 seeds/m2 TDS
11°260 E
2
End of Feb 2009 300 seeds/m
4°200 E
2
Small plot seeder JFB
00°390 W
2
NA
NA
Deep sandy drained soil
47°000 N
NA
42°010 N
01°080 W 13°540 E
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management practices for wheat. Table 2 and Fig. 2 present a summary of environmental conditions and management practices on each farm. On each farm, all varieties received identical management during both the multiplication phase and the experimental trial. Seeding density was the same for all varieties, based on the measured thousand kernel weight of each seed lot and each farmer’s normal seeding rate. Seed samples were treated with Tillecur before being sent to farmers to prevent infestation with Tilletia caries. Each farmer planted the experiment on their farm using a split-plot experimental design with two
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replications. This meant that each farmer had 40 plots in the 2008–2009 season (10 varieties 9 2 versions 9 2 replications). Varieties formed the principle experimental unit, and the two versions (origin and 3G) formed the nested unit. This permitted a more precise comparison between the two versions of the same variety. Measurements Measurements were taken just prior to maturity by the same three people for all locations; in Italy and
Fig. 2 Meterological data for trial sites total precipitation (bars) and average temperature (lines) by month during the growing season 2008–2009 (Note higher precipitation scale for GC location due to a unusually wet season)
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S. France in early July, SW and NW France in midJuly, and the Netherlands in late July. Plots were scored for lodging, overall plant health and weed pressure, on a scale of 1–5 (with 1 being good and 5 being poor). Twenty plants were also measured for traits chosen based on UPOV variety descriptors (UPOV 1996) and characteristics of importance to farmers for each rep of each version of each variety on all farms. Plants were chosen at random within each plot, avoiding the exterior rows to minimize border effects. Heads infected with Tilletia caries were also avoided. Qualitative observations and quantitative measurements were made on the spike of the principal tiller for each plant: Awns: on a scale of 0 (awnless) to 2 (fully awned) Color: on a scale of 0 (white) to 2 (dark red) Plant height (PH mm)—(without awns) at the top of the highest spikelet, even if sterile; Bottom of the spike (SB mm)—base of the lowest spikelet, even if sterile; Last leaf insertion (LI mm), the height of the insertion of the stem into the flag-leaf. Spike length (SL mm) was calculated as PH–SB Last-leaf-spike-distance (LLSD) was calculated as SB–LI After field measurements were made on each plant, the spike from each plant was cut and bagged with the other spikes from that plot. At the technical facilities at INRA Ferme du Moulon, measurements were taken on all spikes collected: Length of the spike (SL): measured to the nearest mm, used in calculations only Number of spikelets (SpTot): including those that may be sterile or missing Missing spikelets (SpMi): spikelets that had fallen off in the field or the bag Sterile spikelets (SpSt): spikelets at the base or summit of the spike that had zero kernels Number of kernels per spike (KN): counted after threshing each spike individually Grain weight per spike (GW): measured for each spike to the nearest 0.01 g. If the end of the spike was broken, the existing spike length and SpTot were used for calculations of spike density. If only the last spikelet was missing, 5 mm was added to the spike length measurement.
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The following calculations were made: Kernels per spikelet (KN/Spikelet) was calculated as KN/(SpTot – SpSt – SpMi) Kernels per spike (corrected) (KN/Spike) was calculated as KN ? (KN/Spikelet) * SpMi Grain weight per spike (corrected) (GW/Spike) was calculated as (KN/Spike) * GW/KN Spike density (density, spikelets/cm) = SpTot/SL Proportion sterile (sterility) = (sterile spikelets)/ SpTot Thousand kernel weight (TKW) = 1,000 * GW/ KN On four farms, the grain weight per plot (GW/plot, g) was measured and converted to kg/ha. Grain protein was measured with NIRS (Near-Infra Red Spectroscopy) using a Foss NIRSystem 6500, using a 6 g grain sample from each plot. Statistical analysis Analysis of variance (ANOVA), using SAS proc GLM (SAS Institute, Cary, NC), was performed on each dependent variable to determine the effects of environment, variety and version within variety. The ANOVA model was: Yijkm = u ? farmj ? varietyk ? rep(farm) i(j) ? version(variety)m(k) ? (farm * variety)jk ? farm * version(variety)jm(k) ? errorijkm. LS means were calculated for each variety across all farms, and for each version within variety on each farm. Tukey’s multiple comparison procedure was used to control the overall error rate for comparisons of all varieties using the option ‘‘lines’’, and for comparisons of the 3G and origin version of each variety on each farm using the option ‘‘slice’’. Because of the large effect of environment, in order to present results that could be visually compared across farms, data were standardized to a mean of zero and variance of 1 by farm for graphics. Means and standard deviations used for the standardization are given in the results section. This standardized data was used for a multivariate analysis across all quantitative descriptive traits: PH_mm, LLSD, SL_mm, SpTot, density, sterlity, KN_spikelet, KN_spike, GW_Spike and TKW. Principal component analysis was performed using the R package FactoMine.R. Vegetative and grain trait indexes were also constructed and used to analyse changes in
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varieties using SAS proc mixed. The vegetative index included PH_mm, SL_mm and LLSD and the grain trait index included GW_Spike, KN_Spike and TKW. This was done using the standardized data (so trait values would be comparable) and equal weights for all three traits in each index. The ANOVA model was Yijkm = u ? varietyk ? rep ? version(variety)m(k) ? errorijkm. Tukey’s multiple comparison procedure was used to control the overall error rate comparisons of the 3G and origin version of each variety on each farm using the option ‘‘slice’’.
Results Overall variety performance and intra-varietal variability The effects of farm, variety, version within variety, and the interactions between farm and variety and farm and version were significant at the p \ 0.05 level for all traits measured on individual plants (Table 3). For plot level data, farm and variety and the interaction between farm and variety were always significant, and version within variety was significant at the p \ 0.1 level for lodging score. The interaction between farm
and version within variety was never significant for plot-level data. These results show significant main effects of environment (farm) and genotype (variety). There were also significant differences between the origin and 3G versions. Individual variety performance and phenotypic variability within the varieties in the first year of onfarm cultivation was discussed in Serpolay et al. (2011). This article focuses on changes observed within farmer varieties cultivated on-farm over time, and the objective was not to produce recommendations on particular varieties. Environmental means and standard deviations are presented in Table 4, and LS means by variety across environments and groupings of varieties not significantly different from each other are presented in Table 5. Varieties were not greatly differentiated for any plot level data, probably reflecting greater environmental influence on agronomic traits. ‘ZH’, ‘RN’ and ‘AU’ had greater grain yield than ‘PI’, which could be due to grain loss from bird damage and shattering in the extremely early variety ‘PI’. Protein groupings also overlapped considerably, with the range of protein levels being fairly narrow, from 11.16 % for ‘AU’ to 13.89 % for ‘RB’. In general the farmer varieties had higher protein than the modern varieties
Table 3 Overall significance values for ANOVA F-tests, for each measured trait Plant-level data
PH
SL
LLSD
Color
Awns
SpTot
Density
Sterility
KN per spikelet
KN per spike
GW per spike
TKW
Block (farm)
***
***
***
**
ns
***
***
***
***
***
***
***
Farm Variety
*** ***
*** ***
*** ***
*** ***
* ***
*** ***
*** ***
*** ***
*** ***
*** ***
*** ***
*** ***
Version(variety)
***
***
***
***
***
***
***
**
***
***
***
***
Farm * variety
***
***
***
***
***
***
***
***
***
***
***
***
Farm * version(variety)
***
***
***
***
***
***
**
***
***
***
***
***
Protein
Grain yield
Lodging
Plant health
Weed pressure
Block (farm)
***
**
***
?
ns
Farm
***
***
***
***
***
Variety
***
***
***
***
***
Version(variety)
ns
ns
?
ns
ns
Farm * variety
***
***
***
***
***
Farm * version(variety)
ns
ns
ns
ns
ns
Plant-level data was measured on 20 plants/plot 9 2 reps and plot-level data was measured on the entire plot ns not significant *** p \ 0.001; ** P \ 0.1; * p \ 0.5; ? p \ 0.1
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Table 4 Means and standard deviations for each trait measured on each farm Farm
PH (mm)
SL (mm)
LLSD (mm)
SpTot
Density
Sterility
GW/Spike
KN/Spike
TKW
1,001
100
187
20.10
2.08
0.0902
2.00
44.48
45.60
251
18
88
3.40
0.30
0.0692
0.74
16.51
7.62
Mean
719
79
133
17.52
2.29
0.1637
1.18
28.76
41.58
Var
170
17
74
3.15
0.31
0.0914
0.54
13.20
7.91
1,020
79
225
18.36
2.40
0.2039
1.25
29.20
42.72
263
15
102
3.31
0.41
0.0905
0.55
11.96
6.53
Mean
958
108
150
21.05
2.00
0.0666
1.82
45.12
40.42
Var
276
17
113
2.58
0.30
0.0581
0.83
16.09
12.11
1,118
101
223
20.61
2.09
0.1102
1.99
42.87
46.76
279
16
98
2.86
0.32
0.0673
0.67
13.54
7.86
1,006
78
253
16.12
2.10
0.1679
1.30
29.18
45.43
295
12
127
2.39
0.29
0.0748
0.41
9.50
7.15
1,169
91
233
19.41
2.20
0.1643
1.49
35.77
41.62
237
16
90
3.09
0.30
0.0758
0.58
12.05
8.40
FM Mean Var GC
HF Mean Var JFB
PVI Mean Var PVZ Mean Var VC Mean Var
Values plotted in Fig. 4 were standardized using the values listed in this table
and ‘ZH’, although ‘PI’ and ‘TO’ were not significantly different from ‘ZH’ or ‘RN’ and ‘RD’ was not significantly different from ‘ZH’ on the lower side or ‘RB’ on the higher side. ‘AU’ was the only variety that fell below 12 % protein. Lodging was not a significant problem on most farms with only ‘HL’ and ‘SO’ having average scores higher than 3, primarily due to lodging in the highly fertile soils of the Netherlands. A variety with a score of 3 can still be harvested with a combine, and heads are not trapped under vegetation or on the ground, while scores of 4 and 5 lead to difficulties with mechanical harvest and possibly problems with grain quality depending on when during the season lodging occurred. Disease scores were also low on-farm, with ‘AU’ the only variety with a score above 3. Weed pressure scores were fairly low, with weeds more prevalent in plots of ‘AU’, ‘PI’ and ‘RN’. For individual plant measurements, farmer varieties had high PH, other than ‘PI’ and ‘ZH’, which had moderate height. ‘RN’ and ‘AU’ were the shortest, and all varieties were significantly different from all other varieties. ‘RB’ and ‘ZH’ had the longest spikes,
followed by ‘RD’ and ‘TO’, then ‘RN’, then ‘AU’, ‘SO’, ‘PI’ and ‘HL’. LLSD had the same ranking as PH, except for ‘RD’, which ranked lower for LLSD than PH. ‘ZH’ had the highest number of spikelets, followed by ‘TO’, ‘AU’ and ‘RB’, then ‘PI’ and ‘RD’, then ‘RN’, ‘HL’ and ‘SO’. For GW/Spike, KN/Spike, ‘PI’ had the highest values, with almost identical variety rankings for both traits. ‘RN’ and ‘RD’ had the highest TKW, and ‘ZH’ and ‘AU’ the lowest. Spike sterility varied from 0.07 for ‘PI’ to 0.2 for ‘HL’. ‘SO’, ‘RD’ and ‘TO’ also had relatively high sterility (0.15–0.19), with ‘RB’, ‘RN’, ‘ZH’ and ‘AU’ having moderate sterility (0.11–0.13). ‘ZH’ had the highest spike density, followed by ‘AU’, ‘PI’, ‘TO’, ‘HL’, a group of ‘RD’, ‘RN’, ‘RB’, then ‘SO’ with the least dense spikes. Differences among versions within each variety Results from the tests of differences among versions are presented in Table 6 and discussed by variety, in order of the fewest to greatest number of significant
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1.58
1.53
1.48
1.20
1.06
GW/plot
2,897.90
2,839.18
2,791.96
2,439.23
2,368.91 2,354.16
2,125.44
1,977.44
1,386.36
TO
RB
RD
HL
SO
Variety
ZH
RN
AU
RB
TO RD
HL
SO
PI
B
AB
AB
AB AB
AB
A
A
A
Group
F
E
D
CD
C
B
B
B
A
Group
I
H
G
F
E
D
C
A B
Group
AU
RN
ZH
TO PI
RD
SO
HL
RB
Variety
SO
HL
RD
RB
TO
RN
ZH
AU
PI
Variety
HL
PI
SO
AU
RN
TO
RD
RB ZH
Variety
11.16
12.28
12.43
13.01 12.65
13.14
13.34
13.66
13.89
Protein
23.16
27.62
30.97
34.93
35.60
36.63
45.01
46.11
47.50
KN/Spike
81
83
83
86
91
95
95
100 98
SL (mm)
F
E
DE
BCDE BCDE
ABCD
ABC
AB
A
Group
G
F
E
D
CD
C
B
AB
A
Group
F
EF
E
D
C
B
B
A A
AU
ZH
HL
RB
PI
TO
SO
RD
RN
Variety
AU
RN
ZH
PI
RD
SO
RB
TO HL
HL
AU
ZH
RN
RB PI
RD
TO
SO
37.58
38.42
42.87
43.30
43.36
43.99
45.74
47.37
47.78
TKW
3.56
0.13
0.30
0.36
2.29 1.34
2.31
2.88
3.27
54
101
156
230
244
251
257
288 258
E
E
E
C D
C
BC
AB
A
Group
E
E
D
CD
CD
C
B
A
A
Group
LLSD (mm)
Lodging
Variety
Variety
Group
Variety means with the same letter grouping are not significantly different at the p \ 0.05 level
1.74
AU
626
AU
1.76
738
RN
RN
870
PI
1.77
905
ZH
2.07
1,078
SO
ZH
1,162
HL
PI
1,192
RB
GW/Spike
1,269 1,232
TO RD
Variety
PH (mm)
Variety
Table 5 LS means for each variety across farms
RD
PI
TO
ZH RB
RN
SO
HL
AU
Variety
SO
RB
RN
RD
HL
TO
PI
AU
ZH
Variety
G
F
E
D
C
BC
B
A B
Group
1.89
2.03
2.04
2.15 2.15
2.37
2.57
2.65
3.07
Disease
1.95
2.03
2.04
2.07
2.12
2.23
2.33
2.40
2.45
Density
SO
HL
RN
RD
PI
RB
AU
ZH TO
Variety
C
BC
BC
BC BC
BC
AB
AB
A
Group
G
F
F
F
E
D
C
B
A
Group
15.76
16.65
18.51
19.16
19.25
19.75
19.90
23.98 20.08
SpTot
HL
RB
TO
ZH RD
SO
RN
PI
AU
Variety
PI
AU
ZH
RN
RB
TO
RD
SO
HL
Variety
F
E
D
C
C
B
B
A B
Group
0.44
0.48
0.50
0.94 0.62
1.15
1.36
2.05
2.05
Weeds
0.07
0.11
0.12
0.12
0.13
0.15
0.15
0.19
0.20
Sterility
C
C
C
CB C
CB
AB
A
A
Group
F
E
D
D
D
C
C
B
A
Group
154 Genet Resour Crop Evol (2013) 60:145–163
JFB
PVZ
VC
FM
GC
HF
PVI
PVZ VC
FM
GC
HF
JFB
PVI
VC
FM
GC
HF
JFB
PVI
VC
FM
GC HF
JFB
PVI
AU
AU
HL
HL
HL
HL
HL HL
PI
PI
PI
PI
PI
PI
RB
RB
RB
RB
RB
RB
RD
RD RD
RD
RD
HF
AU
PVI
GC
AU
AU
FM
AU
AU
Farm
Variety
ns
?
ns ns
ns
ns
ns
ND
ns
?
–
ns
ns
ns
ns
?
ns
? ns
ns
–
ns
ND
ns
ns
ns
ns
ns
ns
ns
ns
?
? ns
?
ns
?
ND
ns
?
ns
ns
–
ns
ns
ns
ns
ns ns
ns
ns
ns
ND
–
ns
ns
ns
ns
ns
ns
ns
?
ns –
ns
?
ns
ND
ns
–
ns
ns
–
ns
ns
ns
ns
ns ns
–
–
ns
ND
ns
ns
ns
ns
ns
ns
ns
ns
–
ns –
–
ns
ns
ND
ns
ns
ns
ns
ns
ns
ns
?
ns
ns ns
ns
ns
ns
ND
ns
ns
ns
ns
ns
ns
ns
Awns
ns
?
ns ?
ns
ns
?
ND
ns
?
ns
ns
ns
ns
ns
?
?
ns ns
?
ns
ns
ND
ns
ns
ns
ns
ns
ns
ns
Color
ns
?
? ND
ns
–
ns
ND
ND
ns
ns
–
ns
ns
ND
ns
ns
ns ns
ns
ns
ns
ND
–
ns
ns
ns
ns
?
ns
SpTot
ns
–
ns ND
–
ns
ns
ND
ND
ns
ns
ns
ns
ns
ND
?
–
ns ns
ns
ns
ns
ND
–
ns
ns
ns
–
ns
ns
KN/ Spikelet
ns
ns
– ND
ns
ns
–
ND
ND
ns
ns
ns
ns
ns
ND
ns
?
ns ns
ns
ns
ns
ND
?
ns
ns
ns
ns
ns
ns
Density
ns
ns
– ns
?
ns
ns
ND
ns
ns
ns
ns
ns
ns
ns
ns
?
ns ns
ns
ns
ns
ND
?
ns
ns
ns
ns
–
ns
Sterility
ns
–
? –
ns
ns
ns
ND
–
ns
ns
ns
ns
ns
–
?
–
ns ns
ns
ns
ns
ND
–
ns
ns
ns
–
ns
ns
KN/ Spike
ns
ns
ns –
ns
ns
ns
ND
ns
?
ns
–
ns
ns
–
?
–
ns ns
ns
ns
ns
ND
–
ns
ns
?
–
ns
ns
GW/ Spike
ns
?
– ns
ns
ns
ns
ND
ns
?
–
–
ns
ns
ns
ns
ns
ns ns
ns
ns
ns
ND
ns
ns
ns
?
ns
ns
ns
TKW
ns
ns
ns ns
ns
ns
ns
ns
ns
?
ns
ns
ns
ns
ns
ns
ns
ns ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ND
ns ns
ns
ND
ns
ND
ns
ns
ns
ND
?
ND
ns
ns
ns
ns ND
ns
ns
ns
ND
ND
ns
ns
ND
ns
ns
ns
Grain yield
Protein
LLSD (mm)
PH (mm)
SL (mm)
Plot-level traits
Traits measured on individual plants
Table 6 Tests for significant differences between 3G and origin versions of each variety on each farm and direction of change
ns
ns
ns ns
ns
ns
ns
ND
ns
ns
ns
ns
ns
ns
–
ns
ns
ns ns
ns
ns
ns
ND
ns
ns
ns
ns
ns
ns
ns
Lodging
ns
ns
– ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
?
ns
ns
ns ns
ns
ns
–
ND
ns
ns
ns
ns
ns
ns
ns
Plant health
ns
ns
ns ND
ns
ns
ns
ns
ND
ns
ns
ns
–
ns
ND
ns
ns
ND ns
ns
ND
ns
ns
ns
ns
ns
ns
ns
ns
ns
Weed pressure
Genet Resour Crop Evol (2013) 60:145–163 155
123
123
PVZ
VC
FM
GC
HF
JFB
PVI
PVZ
VC
FM
GC HF
JFB
PVI
PVZ
VC
FM
GC
HF
PVI
PVZ
VC
FM
GC
HF
PVI
VC
RD
RD
RN
RN
RN
RN
RN
RN
RN
SO
SO SO
SO
SO
SO
SO
TO
TO
TO
TO
TO
TO
ZH
ZH
ZH
ZH
ZH
?
ns
ns
ns
ns
ns
ns
ns
–
ns
ns
ns
ns
ns
–
ns ns
–
–
ns
ns
ns
ns
–
ns
ns
?
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns ns
ns
–
ns
ns
?
ns
–
?
ns
?
ns
ns
ns
ns
ns
ns
–
ns
ns
ns
ns
ns
ns
ns
–
ns ns
–
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
?
ns
ns
ns
ns
ns
ns ns
ns
ns
ns
ns
ns
ns
ns
ns
–
–
Awns
ns
ns
ns
ns
ns
?
ns
ns
ns
–
ns
ns
–
ns
ns
? ?
ns
ns
ns
ns
ns
ns
ns
ns
?
?
Color
SpTot
KN/ Spikelet
Density
Sterility
KN/ Spike
GW/ Spike
TKW
ns
ns
ns
ns
ns
ns
–
ns
–
ns
ns
ns
ns
?
ns
ns ?
?
ns
ns
ns
ns
ND
–
ns
ns
ns
ns
ns
ND
ns
?
ns
ns
ns
ns
ns
ns
?
ns
?
–
ns ns
ns
ns
ns
ns
?
ND
–
ns
–
–
ns
ns
ND
ns
ns
ns
–
ns
–
ns
ns
ns
ns
ns
ns
ns ?
ns
ns
ns
ns
–
ND
ns
ns
ns
–
–
ns
ND
?
ns
ns
?
ns
ns
–
ns
ns
ns
ns
ns
ns –
–
ns
–
ns
ns
ns
?
ns
?
?
ns
ns
–
–
?
ns
–
ns
ns
?
ns
ns
ns
?
ns
ns ns
?
ns
ns
ns
?
ns
–
ns
–
ns
ns
–
–
ns
ns
ns
–
ns
ns
?
ns
ns
ns
?
–
ns ns
ns
ns
ns
ns
ns
ns
–
ns
–
ns
ns
ns
ns
ns
–
?
ns
ns
ns
ns
?
ns
–
ns
–
ns –
ns
ns
ns
ns
ns
ns
ns
ns
ns
?
ns
ns
ns
ns
ns
ns
ND
ns
ns
ns
ns
–
ns
ns
ns
ns ns
ns
–
ns
ns
ns
ns
ns
ns
ns
ns
ND
ns
ns
ns
ns
ND
ns
ns
ns
ns
ns
ND
ns
ns
ND
ns ns
ns
ND
ns
ns
ND
ns
ns
ns
ND
ns
Grain yield
Protein
LLSD (mm)
PH (mm)
SL (mm)
Plot-level traits
Traits measured on individual plants
Overall significance level of p \ 0.05 using Tukey’s multiple comparison procedure
Farm
Variety
Table 6 continued
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
–
ns
ns
ns
ns ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
Lodging
ns
ns
–
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns ns
–
ns
ns
ns
ns
–
ns
ns
ns
ns
Plant health
ns
ns
ND
ns
ns
ns
ND
ns
ND
ns
ns
ns
ND
ns
ns
ns ND
ns
ns
ND
ns
?
ND
ns
ns
ns
ND
Weed pressure
156 Genet Resour Crop Evol (2013) 60:145–163
Genet Resour Crop Evol (2013) 60:145–163
157
a
b
Fig. 3 Standardized means for traits PH (a), SpTot (b), GW/spike (c) and TKW (d) of the 3G (symbol o) and origin (symbol ?) versions of each variety by farm. Values are standardized by farm using means and variances presented in Table 3
changes. Performance of the two versions of each variety on each farm are shown in Fig. 3 for a sample of the traits measured. LS Means were standardized by farm for the graphic, to remove the environmental effects and allow for an easier comparison of varieties and versions across farms. In addition to the potential for evolution seen in the number of significant differences between the two versions on-farm, it is important to consider the magnitude and direction of change. In general, the
magnitude of change was modest, as would be expected for only 2 years of multiplication. A few versions of a few varieties showed large changes, such as ‘AU’ for KN/Spike at farm VC, ‘RB’ for TKW at farm FM and GC, ‘RD’ for GW/Spike at farm HF, ‘RD’ for TKW at farm PVZ, ‘RN’ for KN/Spike at farm JFB, ‘SO’ for PH, LLSD at farm JFB, ‘SO’ for GW/Spike at farm JFB and PVI, ‘SO’ for TKW at farms HF, JFB and PVZ, ‘TO’ for GW/Spike at farm GC and PVZ, in opposite directions, and ‘TO’ for
123
158
Genet Resour Crop Evol (2013) 60:145–163 c
d
Fig. 3 continued
KN/Spike at farm PVZ. In many cases, for a given trait and variety, the direction of change was not consistent from one farm to another. Across all farms, ‘HL’ evolved the least from the version of the region of origin to the 3G version. ‘ZH’ showed the next fewest changes. Following these two varieties were the two modern varieties, ‘AU’ and ‘RN’, then ‘PI’ and ‘SO’. ‘RB’, ‘RD’ and ‘TO’ showed the most frequent evolution in the on-farm trials. The vegetative plant traits PH, SL, LLSD, awns and color were the least variable in the modern varieties.
123
Spike traits (SpTot, Density, Sterility, and KN/spikelet, KN/spike, GW/spike and TKW) were least variable in ‘HL’, and ‘RB’, intermediate for ‘AU’, ‘PI’, ‘RN’, ‘TO’ and ‘ZH’, and most variable in ‘RD’ and ‘SO’. ‘AU’, ‘HL’, ‘RB’, ‘RD’, and ‘TO’ all showed one or fewer changes over all plot variables (yield/plot, grain protein, lodging, plant health and weed pressure). ‘RB’, ‘RD’, ‘RN’, ‘SO’, and ‘ZH’ each showed lower plant health in one trial (two in GC, two in HF and one in FM). ‘SO’ and ‘RN’ both had one decrease in grain protein, ‘RB’ showed one increase.
Genet Resour Crop Evol (2013) 60:145–163
159
TO.3G TO.origin
1.0
RD.3G
RB.3G
0.5 0.0
HL.3G HL.origin
PI.origin ZH.origin
SO.origin PI.3G ZH.3G
SO.3G
-0.5
Dim 2 : 23.12 %
RB.origin RD.origin
RN.origin
-1.5
-1.0
RN.3G
AU.3G AU.origin
-2
-1
0
2
1
Dim 1 : 38.87 %
Fig. 4 Biplot of the first two dimensions of the principal component analysis of quantitative descriptive traits for the origin and 3G versions of each variety across farms
Lodging also decreased once for ‘SO’, in the same trial where protein decreased. Lodging decreased once for ‘PI’, and plant health for ‘PI’ increased in the same trial. ‘PI’ also increased once in terms of plot grain yield, in the same trial where the 3G version showed lower weed pressure. The multi-trait analysis visually presents the overall magnitude of phenotypic changes across all measured quantitative descriptive traits (Fig. 4). The measured traits that most heavily influenced the two axes of the PCA were grain related for axis 1 (GW_Spike and KN_Spike), and vegetative for axis 2 (PH_mm and LLSD). SL_mm and TKW both had moderate positive factor loadings on axis 2. From this analysis, it appears that AU, TO, HL and RN have diverged the least between the origin and 3G versions, while RB, RD, SO, PI and ZH diverged more significantly. PI and ZH had similar patterns of evolution, as did RD and RB. Grouping traits into vegetative characteristics (PH_mm, LLSD and SL_mm) and grain traits (GW_Spike, KN_Spike and TKW) permits a more
Table 7 a. Correlations among traits used in the multivariate analysis. b. Tukey’s test of pairwise significant differences between versions within varieties for vegetative (PH_mm, SL_mm and LLSD) and grain (GW_Spike, KN_Spike, TKW) trait indexes PH_mm
SL_mm
LLSD
SpTot
TKW
KN_Spikelet
KN_Spike
GW_Spike
-0.16
(a) PH_mm
1.00
0.28
0.77
-0.07
0.20
-0.30
-0.29
SL_mm
0.28
1.00
0.03
0.38
0.07
0.00
0.20
0.22
LLSD
0.77
0.03
1.00
-0.17
0.18
-0.22
-0.26
-0.14
SpTot
-0.07
0.38
-0.17
1.00
-0.14
0.12
0.58
0.47
TKW
0.20
0.07
0.18
-0.14
1.00
-0.06
-0.09
0.35
KN_Spikelet
-0.30
0.00
-0.22
0.12
-0.06
1.00
0.80
0.71
KN_Spike GW_Spike
-0.29 -0.16
0.20 0.22
-0.26 -0.14
0.58 0.47
-0.09 0.35
0.80 0.71
1.00 0.88
0.88 1.00
Variety
Vegetative p-value
Grain p-value
(b) AU
0.38
0.50
HL
0.88
0.46
PI
0.03
0.03
RB
0.01
0.14
RD
\0.0001
0.02
RN
0.64
0.56
SO
0.00
0.88
TO
0.63
0.10
ZH
0.58
0.00
123
160
summary analysis of these different types of traits (Table 7). PH_mm and LLSD are highly correlated with each other (0.77) and PH_mm is moderately correlated with SL_mm (0.28). Similarly, GW_Spike and KN_Spike are highly correlated with each other (0.88) and GW_spike is moderately correlated with TKW (0.35). For vegetative traits, a significant decrease was observed for the varieties PI and SO, and a significant increase for RB and RD. This means that RB and RD had taller plants, longer LLSD and or longer SL_mm in the 3G version than in the origin version. For grain-related traits, PI, RD and ZH all had decreases in the 3G version, meaning decreases in GW_Spike, KN_Spike and/or TKW, and no other varieties showed significant differences among versions.
Discussion Overall variety performance and intra-varietal variability Varieties were strongly differentiated from each other for the traits PH, SpTot and mid-heading and less differentiated for the other measured traits. Internal variation within varieties was also comparable for many traits. The modern varieties ‘AU’, and ‘RN’, and the mixture of modern varieties ‘ZH’ had lower internal variation for PH and LLSD but not for other traits, showing that the homogeneity of modern varieties may depend strongly on the trait evaluated. There was no significant difference in yield for varieties on-farm, other than for ‘PI’, which was significantly lower possibly due to pre-harvest losses. Protein values were generally sufficient on-farm, with only ‘AU’ falling below 12 %. The two modern varieties, ‘RN’ and ‘AU’ had comparable values to farmer varieties for GW/spike, but tended to put out relatively few tillers. The major difference between the two modern varieties is that ‘RN’ maintained high TKW while ‘AU’ did not fill grain well under stress, leading to low TKW at almost all locations. Interactions between the farm and variety were significant for all traits, and interactions between the farm and version within variety were significant for all traits measured on individual plants/spikes (Table 3). From the beginning of the experiment in 2007, farmers observed that ‘RB’ had good productivity and performance at almost all of the on-farm sites. ‘TO’ also had
123
Genet Resour Crop Evol (2013) 60:145–163
fairly good performance, possibly because of the distinct phenotypes in the mixture, which were able to compensate for environmental differences. ‘PI’ was very productive on a per-spike basis in all locations but suffered more from bird damage in northern sites due to its early maturity. ‘ZH’ was later maturing and had difficulty finishing grain fill in locations south of the Netherlands, evidenced by its lower than average TKW. ‘HL’ and ‘SO’ seemed more specifically adapted to lower fertility conditions similar to the mountain soils in their regions of origin, and had poorer performance in more fertile locations, with lodging an issue in the Netherlands and in the parallel experiment at the experimental organic farm at Le Rheu, near Rennes in France (Dawson et al. 2012). Lodging and disease pressure were also not significant problems in the on-farm trials, although these are often raised as issues when growing farmer varieties. This underlines the importance of on-farm trials, where more diverse environments can be sampled under management conditions that have been refined by farmers experienced in growing the crop of interest in their particular environments and thus more representative than experiments conducted on-station.
Differences among versions within each variety ‘HL’ and ‘ZH’ changed the least frequently, followed by ‘AU’ and ‘RN’. This may reflect lower genetic diversity and thus lower evolutionary potential, as ‘HL’ was recently obtained from the gene bank and ‘ZH’ is a mixture of two modern pure-line varieties. ‘RN’ changed the most at farms JFB and GC, two sites in more southern latitudes than where ‘RN’ was originally developed by INRA. ‘AU’ changed the most at farm VC in western France and not at all at farm FM in NW France, which is the closest site to farm VC, so these changes may be due to management practices. ‘RB’ was similar to the two modern varieties in terms of the frequency of changes, as was ‘TO’. The 3G version of ‘TO’ at farm PVZ, where changes were observed most frequently, appeared to have lost all the T. turgidum component, while the version at farm PVI, at a similar latitude, maintained this component. This probably reflects differences in management, as this component of the mixture was later to mature in northern latitudes and may have been eliminated if seeds were not mature at harvest.
Genet Resour Crop Evol (2013) 60:145–163
Both ‘PI’ and ‘SO’ showed more significant changes than ‘RB’, ‘TO’ and the modern varieties, and the magnitude of change was comparatively high in ‘SO’ for several traits. The pattern of changes for these two varieties was different, however, with ‘PI’ showing high productivity outside of its region of origin from the first year of the experiment, and ‘SO’ showing very low productivity. ‘PI’ surprisingly showed a significant difference among versions for several traits on its farm of origin, and a few decreases in productivity on other farms although the decreases were from a relatively higher initial productivity level. ‘SO’ showed decreases in PH and increases in some productivity traits, although at almost half the sites there was a significant decrease in TKW in the 3G version. Despite significant evolution of some traits, in the field it still appeared mal-adapted at all but two sites in Southern France (HF) and Northern Italy (GC). Unfortunately, the trial in the region of origin, the Abruzzo region of Italy, was lost in 2008–2009. ‘RD’ changed significantly for many traits, and the significant changes in both awns and color probably indicate that certain components of the mixture were lost and others increased in frequency at certain farms. From the multivariate analysis, similar conclusions can be drawn, although there are a few exceptions. HL, TO, RN and AU show very little divergence among the two versions, according with the small number of significant changes in individual quantitative traits. RB shows greater divergence, because the magnitude of changes for vegetative traits were greater than for other varieties. Similarly, ZH shows greater divergence in the multivariate analysis because of significant decreases in grain traits. PI, SO and RD are more divergent than the other varieties in the multivariate analysis which is in agreement with their higher frequency of significant changes in the individual trait analysis. PI and RD showed significant changes for both the vegetative and grain index trait comparison. While the modern varieties had among the lowest levels of divergence, the varieties HL and TO also showed very little directional change among versions. The three varieties that showed the most and least divergence were all landraces, with the two landrace mixtures, TO and RD, landing on opposite ends of the spectrum. This illustrates the difficulties in categorizing farmers varieties, and shows that some can be phenotypically quite stable while others may change over a few years of cultivation in a new environment.
161
For traits where measures were taken on individual plants, the direction of change in mean values was often variable across environments for a given trait. This may be due to different selection pressures, but for traits related to agricultural productivity and quality this may mean that natural selection is not sufficient to improve or maintain desired characteristics. There were tendencies for improvement in the 3G for almost all the plot-level data, although there were very few significant differences. The increase in terms of plot grain yield for ‘PI’ may be tied to the lower weed pressure in plots of the 3G version, potentially due to better competitive ability, or potentially due to a chance difference in the weed seed bank although initial weed density was expected to be fairly uniform in neighboring plots. Likewise, the decrease in lodging and increase in plant health for ‘PI’ in a different trial may also potentially be correlated as plants that have severely lodged are more susceptible to fungal diseases. Evolutionary and adaptive capacity of farmer and modern varieties Varieties evolved differently depending on the environment, as the 3G version did not show consistently higher or lower mean values than the origin version for any given trait. This dependence on the particular variety and selection location is consistent with the results of (Horneburg and Becker 2008) who found varying responses to natural selection over lentil landraces and locations. Differences among versions within each variety were less often significant for agronomic traits evaluated at the plot level, and there were significant effects of variety but few pairwise differences among varieties. Under these conditions most farmer varieties were not significantly different than modern varieties for productivity, and had as high or higher protein levels. Non-significant differences for grain yield and protein among very diverse modern and farmer varieties within the range of variation encountered under real farm conditions makes it more difficult to detect more subtle differences among versions for these traits. It appears that 2 years of on-farm evolution is sufficient to significantly change most of the descriptive measures we evaluated at an individual plant level, but not in a predictable manner. Studies of landrace evolution over a few generations are rare, as most studies are on patterns of existing diversity that
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has had many generations to develop. Eagles and Lothrop (1994) found environmental clines in landraces due to natural selection but this was over many years of evolution, and much more information is needed on geographic and annual patterns of ongoing natural selection (Mercer and Perales 2010). Tin et al. (2001) found no difference in yield for landraces of rice populations conserved in situ, collected in 1986, 1991, 1997 and compared in the same year. However, populations showed some changes for height, flowering time, grain shape, grain color and grain quality, although this was not always in the desired direction, in agreement with our results. Vom Brocke et al. (2003) found that farmers perceive phenotypic changes in pearl millet landraces if grown outside the village of origin for 2–3 years. In their experiment, the original landraces diverged from introduced versions of the same name in every case. Pearl millet is an outcrossing species, so the changes observed could be due to cross-pollination with existing landraces rather than individual population responses to changes in environmental conditions. In composite cross populations of barley developed by Suneson (1956), Danquah and Barrett (2002) observed a decrease in yield after populations encountered a new environment before a gradual increase, and then observed a period of no change in yield before it began to increase in composite crosses of barley. Soliman and Allard (1991) found there was a steady increase in grain yield over many years in barley composite cross populations (16 or 25 depending on the population) but do not state at what point this became significant.
Conclusion This study showed that under conditions encountered on working organic farms, farmer varieties often show similar agronomic performance to modern varieties. Farmers also prefer these varieties because of quality traits important to breadmaking, which were not specifically analyzed in this project. While other studies have shown some significant local adaptation or increases in adaptation through natural selection (Danquah and Barrett 2002; Ghaouti et al. 2008; Horneburg and Becker 2008; Soliman and Allard 1991), our study showed that many of the varieties evaluated did not show either local adaptation or increased adaptation to new environmental conditions
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when measured in agronomic terms. This may be due to a lack of genetic diversity in varieties that have gone through gene bank conservation, or it may be that these varieties need a longer time to respond to natural selection. It may also be that natural selection does not immediately favor improved agronomic performance under certain environmental conditions. For example, under relatively favorable conditions, plants that produce many small grains (thus lower TKW and possibly lower GW/spike and per plant) may increase in frequency because they do not need larger grain reserves to survive. Similarly, intraplant competition may favor plants that are taller and produce more seed in a heterogeneous stand but are not necessarily more productive once all the plants are tall (Dawson and Goldringer 2012). The varieties studied here did not show significant specific adaptation or globally positive responses to natural selection when cultivated on-farm over a short time period. We did, however, find that these varieties may change rapidly when cultivated in different environmental conditions. Further investigation is needed on the mechanisms that govern the evolution and adaptation of heterogeneous varieties. In addition, the farmers’ role in creating and managing this diversity should not be overlooked. Selection by the farmer, whether involved in a participatory plant breeding program or not, may be needed to direct variety evolution for certain traits of agronomic importance. Acknowledgments This work was funded by a Specific Targeted Research Project of the European Commission 6th Framework Program Priority 8.1 SSP: Opportunities for farm seed conservation, breeding and production Proposal/Contract no.: SSP-CT-2006-044345. JD was supported by an INRA postdoctoral fellowship. We would especially like to thank the Italian and Dutch farmers who have participated in the project: Giandomenico Cortiana, Tonino Del Santis, Piet van IJzendoorn and Piet van Zanten. Thanks also to the technical staff at the INRA station of le Rheu and le Moulon and to collaborators Veronique Chable, Edith Lammerts van Bueren, and Mathieu Thomas.
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