Introduction
The threats faced by crop landraces grown in traditional farming by large-scale adoption of improved cultivars have led to the creation of conservation programmes to preserve crop resources for future generations. One type of crop genetic resource conservation is ex situ (static) conservation, i.e. maintenance of genetic resources in genebanks under subzero temperatures (Plucknett et al., Reference Plucknett, Smith, Williams and Anishetty1987), and another type is in situ (dynamic) conservation, i.e. maintenance of genetic resources on farm under traditional production conditions (Brush, Reference Brush1991, Reference Brush and Brush2000; Maxted et al., Reference Maxted, Ford-Lloyd, Hawkes, Maxted, Ford-Lloyd and Hawkes1997). Ex situ conservation is well suited to capture and store alleles and genotypes and is also well suited to users who need ready access. It also acts as a back-up for existing diversity that might otherwise be lost in human-managed agricultural landscapes (Cohen et al., Reference Cohen, Williams, Plucknett and Shands1991). However, ex situ conservation is not suited to conserve the other components of the agroecosystem that generate crop genetic resources (Brush, Reference Brush and Brush2000). In situ conservation is specifically intended to maintain these components in living, viable agroecosystems. Considering that in situ conservation is ecologically dynamic, it would fail if we stop change or preserve agroecosystem in a particular state (Brown, Reference Brown and Brush2000). Sources of change that can be expected and must be tolerated include the introduction of new crops and crop varieties; the exchange of varieties between farmers and localities; the use of inputs to improve the productivity of land and labour, such as fertilizers and pesticides; and commercialization (Brush, Reference Brush and Brush2000).
There is now widespread recognition globally within the plant genetic resource community that attention should be paid to in situ conservation of cultivated populations on farms. Certain case studies of crop diversity indicate that substantial diversity still persists on farms in regions of crop origin and diversity, despite the advent of modem cultivars of crops to these regions (Brush, Reference Brush1995; Kumar, Bisht et al., Reference Kumar, Pandey, Bisht, Bhat and Mehta2010; Pandey et al., Reference Pandey, Bisht, Bhat and Mehta2011, Reference Pandey, Bisht and Bhat2012). There is now widespread recognition of the need to plan for in situ conservation to continue and indeed to improve its capacity to maintain genetic diversity as an adjunct to conservation of ex situ collections (Brown, Reference Brown and Brush2000). However, limited information is available on the scientific basis and optimal procedures for on-farm conservation of crop landraces. As a basis for guiding the supporting research in population biology for on-farm conservation, the present study was carried out on the genetic structure of traditional rice landraces from parts of Odisha state of India, under both static and dynamic management.
Two sets of attributes can be considered as important indicators of the genetic composition of rice landraces. The first set is marker diversity or the extent of differences between individual copies of genes at DNA level and is informative as to the ancestry or breeding history of the populations (Brown, Reference Brown and Brush2000). This set comprises indicators of the recency of bottlenecks in population size, the prevalence of outcrossing, the ease with which genes are recombined, and the level of gene flow between populations. The second set is variation in adaptation. This set comprises indicators of the degree to which populations are adapted to their environment and of their potential for continued performance or being donors of characteristics in plant breeding. Both biotic and abiotic aspects of the environment are involved.
Materials and methods
Five named rice landraces obtained from parts of Odisha (Orissa) state of India, representing both static (ex situ in genebanks) and dynamic (in situ on-farm) conservation, were included in the present study (Table 1). The genebank-conserved populations were collected during 1986 and 1987, whereas the on-farm managed accessions were collected during 2008, as these landraces are still continuously cultivated under a traditional farming system. As none of these named landraces maintained on farm could be collected from the same household, utmost care was taken to explore and collect landrace populations from the close-by areas from where populations were collected earlier and conserved ex situ in the National Genebank at the National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India.
Table 1 Rice landraces studied for population genetic structure under static and dynamic management
Molecular characterization
Sample preparation
Thirty individual plants were taken for each landrace population. The seeds were selected randomly from the sample and planted in the field nursery for collecting leaf samples. Young actively growing leaves of 30-d-old plants were collected and used for DNA extraction. Leaves were harvested and immediately stored at − 80°C until total genomic DNA was extracted using a modified cetyltrimethylammonium bromide method (Saghai-Maroof et al., Reference Saghai-Maroof, Soliman, Jorgensen and Allard1984). DNA quantification was carried out using the Nanodrop™ 1000 Spectrophotometer (Thermo Scientific). These estimates were confirmed by staining DNA with ethidium bromide after electrophoresis in 0.8% agarose gel at 100 V for 1 h in Tris-acetate-EDTA (TAE) buffer (0.04 M Tris acetate and 0.001 M EDTA, pH 8.0) using known DNA concentration standards.
Sequence-tagged microsatellite site genotyping
A group of 14 highly polymorphic and diverse primer pairs representing 11 rice chromosomes were chosen for the sequence-tagged microsatellite site (STMS) analysis. Polymerase chain reactions (PCRs) were carried out in a Bioer XP cycler (Bioer Technology Co. Ltd, Hangzhou, PR China) using PCR microplates (Axygen Inc., Union City, CA, USA). The total PCR reaction volume was 25 μl, comprising 2.0 μl of 40 ng genomic DNA, 2.5 μl 10 × PCR buffer (Tris with 15 mM MgCl2, conc. 10 × ), 2.5 μl deoxynucleotides, 2.0 μl primer, 0.33 μl Taq DNA polymerase (conc. 3 U/μl) (GeNei™ Bangalore Genei, Bangalore, India) and 15.67 μl sterile deionized water. The samples were subjected to the following thermal profile for amplification in a thermocycler: an initial extended step of denaturation at 94°C for 6 min followed by 32 cycles of denaturation at 94°C for 1 min, primer annealing at the respective annealing temperature for 1 min, primer extension at 72°C for 1 min and a final extension step at 72°C for 10 min. After amplification, the PCR tube was stored at 4°C until electrophoresis. The amplified products were electrophoresed on 3% agarose gels at 110 V in 1 × TAE buffer. The gels were stained with ethidium bromide and photographed using a G:Box XT4 gel documentation system (Syngene, Henry B. Gonzalez Convention Centre, Headquarter Hotel-Grand Hyatt, San Antonio).
Data analysis
The molecular weights of the bands were estimated using a standard 50 bp DNA ladder, and the homology of the bands was based on the distance of migration in the gels. STMS amplicons obtained from each entry were resolved as a single band on the agarose gel system and the dataset was used to conduct the analysis. The polymorphism information content (PIC) value for each STMS locus was calculated using the PIC calculator http://www.liv.ac.uk/~kempsj/pic.html (Jan, 2002). The PopGene Version 1.32 software (Yeh et al., Reference Yeh, Yang, Boyle, Ye and Mao2000) was used to estimate the various genetic diversity parameters. NTSYS-PC (version 2.02; Exeter Software, Setauket, NY, USA; Rohlf, Reference Rohlf2000) was used to prepare the dendrogram based on Nei's genetic distance.
Analysis of molecular variance (AMOVA) was carried out among groups, among populations within groups, and among individuals within populations in Arlequin 3.5 (Excoffier and Lischer, Reference Excoffier and Lischer2010) to determine the distribution of variation at different hierarchical levels. Population structure by AMOVA is based on an analysis of variance of gene frequencies, taking into account the number of mutational differences between molecular haplotypes. Fixation indices (Weir and Cockerham, Reference Weir and Cockerham1984) and population pairwise F ST (pairwise estimates of the correlation of alleles between populations) values were also computed using the above-mentioned software. Rcmd (R software) was used in the Arlequin version 3.5 software to generate a plot of pairwise F ST values. Population average pairwise differences were also computed using the software.
Morphological characterization
The rice landrace populations were also grown for morphological characterization in on-station field trials at the NBPGR Base Centre, Cuttack (Odisha), during the 2012 cropping season, and data were recorded on 24 qualitative morphological traits. The descriptors for qualitative traits were recorded at different stages of growth for all the ten landrace populations of rice, as per Bioversity-IRRI guidelines (2007). Data were also subjected to cluster analyses using the INDOSAT statistical package developed at the INDOSTAT Services, Hyderabad, India.
Results
Molecular diversity analyses
Fourteen STMS primer pairs were used for the molecular diversity analyses of the five named rice landraces, each representing both static and dynamic conservation (Table 2). A total of 64 alleles were detected in the ten populations of the five named landraces. The number of alleles ranged from 2 to 7, with an average of 4.57 alleles per locus. Of the 64 alleles, 60 were common and four were rare. Summary diversity of each landrace population is given in Table 3. The effective number of alleles and expected heterozygosity ranged from 1.08 to 1.25 and from 0.07 to 0.17, respectively, across the populations. The total number of alleles per landrace population ranged from 16 to 25, and the percentage of polymorphic loci ranged from 14.29 to 64.29 (Table 3). The PIC values ranged from 0.148 to 0.760, with a mean average value of 0.519 (Table 2).
Table 2 Repeat motif, number of alleles per locus (n), PIC and allele size range for all the 14 STMS loci
Table 3 Summary diversity of rice landrace populations based on 14 STMS markers
Na, observed number of alleles; Ne, effective number of alleles; I, Shannon's information index; He, expected heterozygosity; Nei He, Nei's expected heterozygosity; n, total number of alleles present; P, number of polymorphic loci; P%, percentage of polymorphic loci; S, static; D, dynamic.
Individual landrace pairwise distributions of allele frequencies under static and dynamic management are given in Table S1 (available online). The population structures of rice landrace populations under static and dynamic management were strikingly different, as revealed by the frequency of private alleles. The mean frequency of private alleles, under both static and dynamic management, was more than 50% (Table S1, available online). The AMOVA indicated a highest variation of 75.7% among populations within groups (static vs. dynamic) followed by that among individuals within populations (11.22%), among groups (6.66%) and within individuals (6.41%) (Table 4).
Table 4 AMOVA design and results
Fixation indices: F IS= 0.64; F SC= 0.81; F CT= 0.07; F IT= 0.94.
The pairwise estimates of F ST revealed very high significant population differentiation, which ranged between 0.68 and 0.89 (Fig. 1; Fig. S1 (available online)). High population differentiation was observed even between populations of the same named landrace under static and dynamic management. This shows that the populations share limited genetic diversity among them. Even no pairs of a named landrace population representing static and dynamic conservation grouped together in the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram (Fig. S2, available online) based on Nei's genetic distance.
Fig. 1 Population pairwise F ST estimates of rice landraces (S and D indicate static and dynamic conservation status).
Analyses of adaptive variations
Minor variations were observed both within and among the landraces under static and dynamic management. All the ten landraces analysed for qualitative traits were non-scented and had medium green-coloured leaf blades. They were monomorphic for traits such as the shape of the ligule (two-cleft), attitude of branches in the panicle (drooping) and panicle: secondary branching (sparse) (Table S2, available online). There were distinguishable differences within and between the landraces for traits such as the colour of basal leaf sheath, auricle, collar, stigma, lemma epiculus and pericarp. Traits such as the attitude of the leaf blade and flag leaf, which is very well correlated with the yield in rice crops, displayed significant variations, which were helpful in distinguishing diversity within landraces. The colour of the ligule ranged from white to purple, which further indicated differences within the landraces (Table S2, available online). With regard to culm strength, an important trait for judging the lodging resistance of a plant, among the accessions in the Kalakadamba landrace, the accession representing static conservation had strong culm compared with on-farm managed populations. The population representing the dynamic management of the Narada landrace had awn, whereas the other landraces were awnless (Table S2, available online).
The data on some important qualitative traits were subjected to a cluster analysis (Fig. S3, available online). Three groups were formed, with the Narada (static) landrace clustering separately. The diversity pattern did not follow a set pattern, and the same named landraces clustered separately and even did not match the pattern observed in the molecular diversity analyses.
Discussion
The genotypes of different rice landrace populations representing static and dynamic conservation were different for most of the STMS loci analysed in the present study. Population differentiation based on F ST measures was very high even for the same named landrace. Some recent studies have assessed the population genetic structure of named rice landraces (Pusadee et al., Reference Pusadee, Jamjod, Chiang, Rerkasem and Schaal2009; Kumar, Pandey et al., Reference Kumar, Bisht and Bhat2010; Sreejayan et al., Reference Sreejayan Kumar, Varghese, Jacob and Thomas2011; Pandey et al., Reference Pandey, Bisht and Bhat2012) and found very high partitioning of total variability among individual populations.
Variation in the diversity pattern of the same named landrace under dynamic and static conservation was also observed for qualitative traits (Fig. S3, available online). Even the clustering pattern based on a few important qualitative morphological traits did not match with the pattern observed in the molecular diversity analyses (Fig. S2, available online). The only similarity in the clustering pattern was observed for the Narada landrace under static management, which was quite distinct from all the other landrace populations in clustering carried out based on molecular data and a few important qualitative characters. Therefore, consistency in farmer naming needs to be duly validated. Furthermore, the variation in static and dynamic conservation for qualitative traits could not be conclusively discussed as the differences might be due to sampling variations. The greater population differentiation in the molecular diversity analyses could be attributed to genetic drift.
Far fewer studies have been carried out on the multilocus structure of landrace populations, i.e. the extent to which genetic variants at one locus are correlated in occurrence with variants at another locus. Such a structure arises from selection, genetic drift or fragmentation of the population and is retained through selection, isolation and the lack of migration, and restrictions on outcrossing and genetic recombination (Brown, Reference Brown and Brush2000).
In diversity assessment, the importance of variations due to adaptation and molecular diversity has long been argued. In the present study, it was observed that under on-farm management while the particular attributes, characters or adaptations of a population apparently persisted over generations in different named landrace populations, the underlying genotypes changed substantially. New alleles or combinations may arise and increase in frequency at the expense of other alleles that have disappeared (Tables S2, available online). As has been argued by Brown (Reference Brown and Brush2000), in situ strategies fail to preserve all the extant biodiversity at the gene level. As better alleles or combinations arise and enjoy selective advantage, others thereby will be less fit and decline. This is the cost of evolutionary substitution and the price paid for allowing evolution to continue.
The above-mentioned findings can also be argued in support of the neutral theory of molecular evolution (Kimura, Reference Kimura1968). Kimura's neutral theory claims that the overwhelming majority of evolutionary changes at the molecular level is caused not by selection acting on advantageous mutants, but by random fixation of selectively neutral or very nearly neutral mutants through the cumulative effect of sampling drift under continued input of new mutations (Kimura, Reference Kimura1991).
The in situ approach to conserving landraces undoubtedly holds several advantages over ex situ strategies. In view of the existing findings, a few hypotheses towards which research should be directed could be made (Brown, Reference Brown and Brush2000). The important areas of research on in situ conservation of landrace diversity on farm in the context of population structure may include allelic richness and genotypic diversity, specific adaptations, localized divergence, diversity to meet temporal and environmental adaptations, and continuance of crop evolutionary processes. On-farm populations have the capacity to support a much greater number of rare alleles and of different (multilocus) genotypes than accessions in genebanks (Marshall, Reference Marshall, Brown, Clegg, Kahler and Weir1989). Diversity itself confers long-term population fitness because it helps populations to cope with variable environments. Landrace populations of crops have survived centuries of selection for reliable production in subsistence agriculture, yielding a definite, known but probably limited benefit to the farmers who grow them (Frankel et al., Reference Frankel, Brown and Burdon1995). Presumably, they are selected for resilience and stability, though of modest productivity. The in situ strategy conserves the crop evolutionary processes (mutation, migration, recombination and selection). It provides scope for ongoing evolution, particularly in response to environmental changes and pathogen and pest pressures fluctuating in numbers and genetic composition. The key variables include (i) genetic diversity within populations, which is the essential raw material for evolution; (ii) breeding system variation (such as changes in outcrossing rate); (iii) variation in resistance in space and time, related to pest pressure and diversity; and (iv) the dynamics of seed systems, persistence and migration. Furthermore, regeneration of ex situ collections is currently considered a serious and enormous challenge (Brown et al., Reference Brown, Brubaker and Grace1997). Viability is inevitably lost at rates depending on the resources for and the management regimens of such collections. The task is to regenerate accessions without incurring genetic drift or genetic shift (Breese, Reference Breese1989).
Diversity conserved on farm is subject to a range of forces and is likely to be in a dynamic state. As yet, the data are far too limited to assess the various factors – human, biological, edaphic or climatic – to determine the requirements for optimal outcomes. The challenge is to plan for the assessment of these factors in relation to changes in genetic structure over time. Population biology research for in situ conservation thus needs to be both descriptive and hypothesis testing in order to guide technical improvement and management of landrace populations (Brown, Reference Brown and Brush2000).
Of the five rice landraces, two populations of the Narada landrace representing static and dynamic conservation grouped separately in the UPGMA dendrogram and Principal Components Analysis (PCA). One probable reason could be that farmers often are not consistent in naming and describing landraces. It is important to investigate across the area of study that the same named landraces are genetically similar. As the populations of the same named rice landraces in the present study were distinct genetically and highly differentiated, for population genetic parameters and also for monitoring allele loss/change over time and space, one needs to collect these landraces at periodic intervals from the same household. The landraces also need to be under continuous cultivation to make precise comparisons. Clarification on what constitutes a landrace at each scale (village, community and region) is the first step towards defining the amount and distribution of crop diversity maintained by farmers (Jarvis et al., Reference Jarvis, Myer, Klemick, Guarino, Smale, Brown, Sadiki, Sthapit and Hodgkin2000).
It was observed that all these rice landraces were under continuous cultivation for several years. As long as farmers themselves find it in their own best interests to grow these populations, both farmers and society will benefit at no extra cost to anyone (Jarvis et al., Reference Jarvis, Myer, Klemick, Guarino, Smale, Brown, Sadiki, Sthapit and Hodgkin2000). Furthermore, varietal dynamics based on average area and households growing the landrace are important for its on-farm management. The landraces can be categorized as common or rare based on average area and average number of households planting them. All the populations analysed in the present study were categorized as locally common as these were grown in more fields of above-average sizes. Qualset et al. (Reference Qualset, Damania, Zanatta, Brush, Maxted, Ford-Lloyd and Hawkes1997) suggested that small land holdings isolate landrace populations from one another, thus reducing the generation of new genetic material by natural recombination. In common with biogeography theory (MacArthur and Wilson, Reference MacArthur and Wilson1967), they say that without human management, the genetic diversity in small ‘patches’ of crops would suffer genetic drift and inbreeding depression. They suggest that human inputs may offset these processes, as isolated populations can rely on seed exchange and farmer selection for the introduction of new genetic material (Louette et al., Reference Louette, Charrier and Berthaud1997). However, if two populations are isolated, they will diversify genetically to a greater extent and therefore interpopulation diversity will increase.
The population structure of named landraces in traditional farming would help decide the optimum number of landrace populations to be collected and conserved ex situ and level of adaptive variations in niche environments including biotic and abiotic stresses. The complementarity of in situ and ex situ conservation is based on the recognition that crop genetic resources involve more than the alleles and genotypes of crop populations. Besides the genetic raw material of landraces, crop genetic resources also comprise related species, agroecological interrelationships and human factors (Brush, Reference Brush and Brush2000). For the on-farm conservation of crop landraces, the traditional cultures and cropping systems that grow and use such populations are fundamental aspects of the habitats to which they are adapted. The systems shape their present genetic structure and determine the changes within landrace populations (Brown, Reference Brown and Brush2000).
Supplementary material
To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S1479262113000440
Acknowledgements
The authors thank the Director, NBPGR, New Delhi, India, for providing the facilities for the study. They also thank the Indian Agricultural Research Institute, New Delhi, India, for the scholarship provided to the senior author (Logapriyan) for PhD degree.
