Journal of Horticultural Science & Biotechnology (2011) 86 (4) 353–358
Genetic diversity among and within watermelon (Citrullus lanatus) landraces in Southern Africa By C. MUJAJU1,2*, A. ZBOROWSKA1, G. WERLEMARK1, L. GARKAVA-GUSTAVSSON1, S. B. ANDERSEN3 and H. NYBOM1 1 Department of Plant Breeding and Biotechnology, Swedish University of Agricultural Sciences, Balsgård, 459 Fjälkestadsvägen, SE-291 95 Kristianstad, Sweden 2 Seed Services, Department of Research and Specialist Services, Ministry of Agriculture, Mechanization and Irrigation Development, Box CY550, Harare, Zimbabwe 3 Department of Agriculture and Ecology, Faculty of Life Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg, Denmark (e-mail:
[email protected]) (Accepted 23 February 2011) SUMMARY Genetic diversity in watermelon (Citrullus lanatus) was estimated among 213 seedlings from 22 accessions collected in Botswana, Namibia, South Africa, Zambia, and Zimbabwe. The accessions consisted of two types of watermelon landraces: sweet watermelon (C. lanatus var. lanatus) and cow-melon (C. lanatus var. citroides), also known as citron melon. In addition, three commercial varieties of C. lanatus var. lanatus from the USA were included for comparison. Ten simple sequence repeat (SSR; microsatellite) loci detected a total of 153 alleles. The polymorphic information content (PIC) ranged from 0.833 – 0.963, suggesting sufficient discriminatory power. Both a cluster analysis and a principal co-ordinate analysis produced two major clusters, one with the 13 cow-melon accessions and the other with the 12 sweet watermelon accessions. Within the sweet watermelon cluster, the three US cultivars grouped together with the Botswana accessions. Some of the other accessions also grouped according to their country of origin, but others did not. Within-accession diversity parameters showed that those sweet watermelon accessions found in traditional agrosystems were just as genetically variable as the cow-melon accessions.
C
itrullus lanatus is one of several species in the family Cucurbitaceae that are of particular importance for the inhabitants of Southern Africa. At present, the species is often divided into two varieties: C. lanatus var. lanatus (Thunb.) Matsum. & Nakai includes both sweet watermelon and ‘egusi’-type watermelon; whereas C. lanatus var. citroides (L. H. Bailey) Mansf. includes the ‘tsama’ type and the citroides group of watermelons (Dane and Liu, 2007), commonly referred to as cowmelons. ‘Egusi’ melons have seeds with a fleshy pericarp and are commonly found in West Africa. ‘Tsama’ melons and cow-melons (citron melons) are found predominantly in Southern Africa. The former have small, bitter fruit, while the latter have insipid fruit and are used as fodder melons, preserving melons, and cooking melons. Today, watermelon is cultivated throughout all the warmer parts of the World, with a global annual production approaching 100 million metric tonnes (MT). The largest producers are China, Turkey, Iran, the USA, Egypt, and Mexico (National Research Council, 2008). Watermelon is the fifth-most economically important vegetable crop, and it exhibits wide phenotypic diversity in terms of its growth habit, fruit traits, and resistance to biotic and abiotic stresses (Levi et al., 2004). For many years, watermelons have been a key part of subsistence agriculture in Southern Africa, *Author for correspondence.
where the farmers rely on the fruit, especially during times of drought. This region has been proposed as a centre-of-origin for watermelon, and both the domesticated and wild populations encompass a wide range of morphological variation. Thus, farmers use watermelons in various ways, depending on local customs and traditions. The flesh can be used as a dessert or to make a meal for cooking. Roasted melon seeds are also consumed. Insipid watermelons are used as an animal feed. In the Kalahari desert, wild watermelons represent an important source of both food and water. In contrast, there have been few, or no crop improvement programmes targeted at commercialisation. Recently, the frequent recurrence of droughts in this region has encouraged the cultivation of watermelons as a staple food, substituting for maize in marginalised areas. With the possible impacts of future climate change, the development of more droughttolerant watermelon varieties may become critical. In order to exploit the phenotypic potential of watermelon, insights into the level and distribution of genetic diversity become crucial. Until now, the application of molecular markers to assess genetic diversity in watermelon germplasm from Southern Africa has been limited mainly to material classified as part of the US Plant Introductions (PIs), and elite lines or cultivars. In addition, morphological diversity has been described in landraces in Namibia (Maggs-Kölling et al., 2000), and molecular diversity has been studied in
Assessment of genetic diversity in watermelon
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TABLE I Description of SSR loci used and PIC values Primer pair (Forward/Reverse) MCPI-07-M13F MCPI-07-R MCPI-28-M13F MCPI-28-R MCPI-03-M13F MCPI-03-R MCPI-32-M13F MCPI-32-R MCPI-21-M13F MCPI-21-R MCPI-12-M13F MCPI-12-R MCPI-33-M13F MCPI-33-R MCPI-13-M13F MCPI-13-R MCPI-14-M13F /2MCPI-14-R MCPI-37-M13F MCPI-37-R Mean
SSR Motif
AT (ºC)
AN
Range of fragment sizes
FIS
HO
HE
PIC
(AAG)9
55
6
232 – 255
–0.798
0.988
0.550
0.833
(AAG)9
55
9
240 – 289
0.564
0.140
0.328
0.889
(TG)8
55
9
212 – 254
0.382
0.143
0.232
0.889
(AAG)5(ATC)8
55
10
245 – 282
0.065
0.439
0.479
0.900
(AG)11
55
14
176 – 234
0.506
0.188
0.381
0.929
(AAG)7N69(AT)26
55
16
214 – 332
0.406
0.208
0.350
0.936
(AG)8N173(TA)8
55
17
244 – 292
0.306
0.293
0.422
0.941
(AG)25
55
19
184 – 235
0.327
0.394
0.586
0.947
(AAT)15
55
26
218 – 291
0.411
0.246
0.418
0.962
(AAT)9
55
27
160 – 270
0.272
0.354
0.486
0.963
–
0.244
0.340
0.422
0.919
–
–
15.3
*SSR motifs (markers) described by Joobeur et al. (2006). AT, annealing temperature; AN, total number of polymorphic alleles for each primer pair present in accessions; FIS, allele-specific F-statistics for all accessions; Ho, observed heterozygosity; HE, expected heterozygosity; PIC, polymorphic information content.
landraces in Zimbabwe (Mujaju et al., 2010). The present study investigates the level of genetic diversity in watermelons across several countries within the proposed centre-of-origin, using simple sequence repeats (SSRs; microsatellites) to facilitate improved genetic exploitation and conservation strategies.
MATERIALS AND METHODS Breeding methods and common production systems In Southern Africa, watermelon cultivation is especially important in drought-prone, semi-arid areas with an annual rainfall below 650 mm. Most of these semi-arid areas are places where traditional agriculture is practiced, as well as providing the sources from which material in the National Gene Banks of Southern Africa have been collected for ex-situ conservation. Even though there are only limited conventional breeding efforts in Southern Africa, numerous local varieties have been developed by farmers through selection in their fields over time and space. Watermelon is generally classified as a cross-pollinated species and has been shown to exist as both monoecious and andromonoecious populations which practice a mixture of selfing and outcrossing for reproduction (Ferreira et al., 2000). Every season, farmers select and sow seeds of the different watermelon types used for cooking (i.e., citron melon or cow-melon) or eating fresh (i.e., sweet watermelon). The seeds can be selected from a single fruit, or a sample is taken from several fruit from the same field (Minsart et al., 2010). In Africa, some watermelons are never planted. After a field has been cultivated, some plants are left to self-sow in the following season (National Research Council, 2008). This is especially true of those wild and semi-wild forms used as emergency food, feed, and for their seed. In general, there has been no further breeding of this material once it has been taken from the farmers’ fields into the National Gene Banks.
Plant material and DNA extraction Seeds from 22 watermelon accessions, representing the two major forms of watermelon (i.e., sweet watermelon and cow-melon), were obtained independently from Namibia, Botswana, South Africa, Zambia, and Zimbabwe (Figure 1). In addition, three commercial varieties of sweet watermelon were obtained from the Harris Moran Seed Company (Davis, CA, USA) for comparison. All the African accessions were obtained from local gene banks, except for Botswana, where the two accessions were obtained directly from farmers. Seeds were germinated in a greenhouse at 25°C at Balsgård, Sweden.A total of 243 plants (approx. ten plants
FIG. 1. A schematic map of the SADC region showing the five countries in which the total of 22 (circled) accessions were collected. The map shows their close proximity to the Kalahari and Namib deserts, regarded as a centre-of-origin of watermelon. In addition, three commercial watermelon varieties ‘(Sugarbaby’; ‘Crimson Sweet’, and ‘Charleston’) were obtained from the USA.
C. MUJAJU, A. ZBOROWSKA, G. WERLEMARK, L. GARKAVA-GUSTAVSSON, S. B. ANDERSEN and H. NYBOM per accession) were chosen for this study. DNA was extracted from young leaf tissue (10 mg) using the Qiagen DNeasyTM Plant Mini Kit (QIAGEN AB, Sollentuna, Sweden) following the manufacturer’s protocol. DNA concentrations and sizes were estimated visually using the DNA low mass ladderTM (Invitrogen Life Technologies, Carlsbad, CA, USA) by electrophoresis in 2% (w/v) agarose gels and stained in 0.5 µg ml–1 ethidium bromide. PCR analysis The PCR protocol for the SSR primers selected followed Mujaju et al. (2010). Ten SSR primer pairs (Table I), originally described by Joobeur et al. (2006), were chosen, based on previous screening of African watermelon DNA at the Department of Agricultural Sciences, University of Copenhagen, Denmark. To separate the DNA fragments and determine their sizes, all primer-pairs were fluorescently labelled at their 5´end with either FAM (primers MCP1-07, MCP1-13, MCP1-21, MCP1-32, and MCP1-37) or HEX (primers MCP1-03, MCP1-12, MCP1-14, MCP1-28, and MCP133). The PCR products were separated and analysed using capillary gel electrophoresis on an ABI 3130XL Genetic DNA Analyser (Applied Biosystems, Foster City, CA, USA). The sizes of the amplified products were calculated based on an internal standard (500ROXTM Size Standard; Applied Biosystems) using GeneMarker® Software Version 1.85 (SoftGenetics, State College, PA, USA). A manual “binning step” was included to assign all detected alleles to repeat unit equivalents (GarkavaGustavsson et al., 2008). Data analysis For single-locus evaluations of the SSR data, all SSR fragments were scored as allele sizes at each locus. The polymorphic information content (PIC) values for each locus were then calculated according to the formula: PIC = 1 – ∑Pi2 where Pi was the frequency of the i-th allele (Smith et al., 1997). Data for all ten SSR loci in the 243 plants from the 25 accessions were then collated into multi-locus profiles of allele size, and the resultant data matrix was used for subsequent analyses. The programme, GenAlEx 6 (Peakall and Smouse, 2006) was used to calculate the percentage of polymorphic alleles within each accession, the allele-specific F-statistics (FIS), the expected heterozygosity (HE), the observed heterozygosity (HO), and Shannon’s index of diversity (I). Testing short allele dominance was carried out by regression of allelespecific FIS statistics on allele sizes. A significant negative slope indicated that short allele dominance may be suspected (Wattier et al., 1998). GST values for genetic differentiation among accessions were calculated according to the formula: GST = (HT – HS)/HT where HT was the total genetic diversity, and HS was the mean within-accession diversity (Nei, 1977). Analyses of molecular variance (AMOVA), to estimate the partitioning of genetic variation at different
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levels, between sweet watermelons and cow-melons, between and within accessions, and between and within the USA and the five African countries, were calculated using Arlequin Version 3.0 (Excoffier et al., 2005). AMOVA calculations yielded an independent estimate (ST) of accession differentiation for comparison with Nei’s GST. Levels of similarity among and within accessions were also investigated using multivariate methods. A Nei’s genetic similarity matrix generated by GenAlEx 6 was used as an input matrix to construct a UPGMA cluster analysis with NTSYS-pc Version 1.80 (Rohlf, 1993). The distortion effect was estimated using a cophenetic correlation analysis. As a means to verify groups derived from the cluster analysis, and being more useful for data that lack a strong hierarchical structure, a principal co-ordinate analysis (PCO) was computed in GenAlEx 6.
RESULTS A total of 153 SSR alleles were observed, with a PIC index ranging from 0.833 – 0.963 (Table I). The size range of these alleles differed considerably among loci (23 bp, MCPI-07; 49 bp, MCPI-28; 42 bp, MCPI-03; 37 bp, MCPI-32; 58 bp, MCPI-21; 118 bp, MCPI-12; 48 bp, MCPI-33; 51 bp, MCPI-13; 73 bp, MCPI-14; and 110 bp, MCPI-12). A highly significant positive correlation was found between the PIC value and the number of alleles at each locus (Pearson correlation coefficient r = 0.907; P < 0.001). PIC values were also positively correlated with the range of allele sizes (r = 0.678; P < 0.05). Several different estimators of within-accession variation were calculated (Table II). The percentage of polymorphic alleles varied from 70% – 100% for both the cow-melon (13) and sweet watermelon (12) accessions, whereas HO varied from 0.15 – 0.42, and from 0.25 – 0.53 for cow-melon and sweet watermelon, respectively. All loci, except MCP1-07, showed higher values for HE (0.31 – 0.62 for cow-melon, and 0.33 – 0.61 for sweet watermelon) than for HO. The difference was statistically significant (P < 0.05) for all loci, except MCP1-32, when HO and HE values for the different accessions were compared with pair-wise t-tests. Short allele dominance does, however, not appear to be the reason. Regression analysis of FIS values, with respect to allele sizes, revealed that the slope was not significantly different from zero (slope = 0.0054; r = 0.230; P = 0.268). Shannon index values varied from 0.51 – 1.17, and from 0.53 – 1.11 for cow-melon and sweet watermelon, respectively. Some accessions showed comparatively high values for within-accession variation with all or most of the estimators used, especially cow-melons NM1567 and ZM5798, and sweet watermelons NAM1610 and ZW20. The three cultivars from the USA showed comparatively low values for all parameters, except HO (Table II). Analysis of molecular variance (AMOVA) within and among the 25 accessions revealed that 31% of the total variation resided between the two major groups (i.e., cow-melon and sweet watermelon), 25% between accessions within groups, and 44% within accessions (Table III). The overall GST for between-accession differentiation was 0.44, slightly lower than the AMOVA ST value of 0.48. Separate calculations for
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Assessment of genetic diversity in watermelon TABLE II Within-accession genetic variation in watermelons collected in six countries
Accession Code*
Actual Accession No.
Cow-melon (CWM) NM844 NAM844 NM852 NAM852 NM1567 NAM1567 NM1612 NAM1612 NM1625 NAM1625 NM1634 NAM1634 NM1686 NAM1686 SA4190 SA4190 SA4384 SA4384 ZM4929 ZM4929 ZM5798 ZM5798 ZW29 CM29 ZW36 CM36 Mean – Sweet watermelon (SWM) USSUG Q20289 – Sugarbaby USCRM Q35081 – Crimson Sweet USCHL Q49226 – Charleston BT1 BOT1 BT2 BOT2 NM1610 NAM1610 SA1124 SA1124 SA4186 SA4186 ZM007 SS007 ZM025 SS025 ZW02 CM2 ZW20 CM20 Mean – Mean of Means –
NPL
%PL
HO
HE
I
10 10 10 10 10 10 10 10 10 10 10 10 10 –
70.00% 90.00% 100.00% 90.00% 100.00% 80.00% 70.00% 80.00% 90.00% 100.00% 100.00% 100.00% 80.00% 88.46% (3.17%)
0.37 (0.13) 0.42 (0.11) 0.37 (0.11) 0.39 (0.09) 0.34 (0.11) 0.31 (0.10) 0.15 (0.10) 0.36 (0.11) 0.33 (0.11) 0.40 (0.11) 0.32 (0.11) 0.27 (0.10) 0.27 (0.10) 0.33 (0.03)
0.35 (0.09) 0.48 (0.08) 0.62 (0.05) 0.42 (0.07) 0.45 (0.07) 0.31 (0.08) 0.30 (0.08) 0.46 (0.10) 0.43 (0.08) 0.51 (0.06) 0.52 (0.06) 0.36 (0.06) 0.33 (0.08) 0.43 (0.02)
0.63 (0.17) 0.95 (0.20) 1.17 (0.16) 0.76 (0.13) 0.85 (0.17) 0.58 (0.14) 0.51 (0.15) 0.89 (0.20) 0.80 (0.15) 0.92 (0.12) 0.95 (0.14) 0.66 (0.11) 0.57 (0.13) 0.79 (0.04)
10 10 10 10 10 13 10 10 2 8 10 10 – –
70.00% 80.00% 90.00% 100.00% 90.00% 100.00% 100.00% 100.00% 70.00% 80.00% 100.00% 100.00% 90.00% (3.48%) 89.20% (2.30%)
0.44 (0.13) 0.33 (0.12) 0.32 (0.10) 0.48 (0.08) 0.25 (0.10) 0.25 (0.08) 0.28 (0.09) 0.39 (0.12) 0.25 (0.11) 0.26 (0.09) 0.41 (0.09) 0.53 (0.10) 0.35 (0.03) 0.34 (0.02)
0.36 (0.09) 0.33 (0.09) 0.34 (0.06) 0.46 (0.06) 0.41 (0.06) 0.61 (0.03) 0.44 (0.07) 0.42 (0.07) 0.35 (0.08) 0.37 (0.09) 0.45 (0.06) 0.51 (0.07) 0.42 (0.02) 0.42 (0.02)
0.60 (0.14) 0.54 (0.15) 0.59 (0.10) 0.86 (0.13) 0.68 (0.10) 1.11 (0.07) 0.83 (0.12) 0.73 (0.11) 0.53 (0.13) 0.65 (0.17) 0.76 (0.11) 0.85 (0.13) 0.73 (0.04) 0.76 (0.03)
*The first two letters of the accession code denote the country of origin (US, United States; BT, Botswana; NM, Namibia; SA, South Africa; ZM, Zambia; and ZW, Zimbabwe. The numbers refer to the accession number or name. Other acronyms or symbols used: NPL, number of plants sampled; %PL, percentage polymorphic alleles; HO, observed heterozygosity; HE, expected heterozygosity; I, Shannon’s index. Standard errors are indicated in parenthesis showing the sampling error of the mean.
differentiation among cow-melon and among sweet watermelon accessions, respectively, produced slightly lower values for the former (GST = 0.30; ST = 0.35) compared to the latter (GST = 0.33; ST = 0.38). Partitioning the variation into countries revealed that 15% of the total variation resided within countries, 34% between accessions within countries, and 50% within accessions. The cophenetic correlation between the genetic similarity matrix and the cluster analysis was 0.92, suggesting an overall good-fit between the matrix and the cluster analysis. Both the cluster analysis (UPGMA TABLE III Partitioning of genetic variation using GST and AMOVA based on the SSR data with no prior grouping of accessions, grouping into the two major forms (cow-melon or sweet watermelon), or grouping by the six countries of origin Source of variation Partitioning all accessions GST ST Partitioning between the two major forms (cow-melon and sweet watermelon) Between group diversity (AMOVA) Between accessions within groups (AMOVA) Within accession diversity (AMOVA) Partitioning within each major form Cow-melon GST ST Sweet watermelon GST ST Partitioning by six countries Between country diversity (AMOVA) Between accessions within countries (AMOVA) Within accession diversity (AMOVA) * Significant at P < 0.01.
Value 0.44 0.48*
31%* 25%* 44%*
0.30 0.35* 0.33 0.38* 15%* 34%* 50%*
dendrogram not shown) and the principal co-ordinate analysis (Figure 2) resulted in the two major groups of cow-melon and sweet watermelon. Within the sweet watermelons, accessions from the USA and Botswana grouped together, whereas the remaining accessions showed variable levels of country-based differentiation. Similarly, some cow-melon accessions grouped according to country, whereas others did not.
DISCUSSION Evaluation of SSR markers In recognition of their abundance, high degree of polymorphism, reliability, repeatability and co-dominant inheritance (Weising et al., 2005), simple sequence repeat (SSR) markers were chosen to study genetic diversity in watermelons at a regional level. The ten SSR primer pairs produced an average of 15.3 alleles per locus, with an average PIC value of 0.92. In a previous study on watermelon accessions in Zimbabwe, the average PIC value was somewhat lower (0.79; Mujaju et al., 2010), possibly due to the lower overall variability among samples. Considerably lower average PIC values were reported when three accessions of West African watermelon, C. lanatus oleaginous-type, were studied using nine SSR loci (PIC = 0.37; Minsart et al., 2010), as well as in a study on Korean watermelon varieties (PIC = 0.34; Kwon et al., 2007). In the latter two studies, sampling was conducted on a comparatively restricted geographical scale and, in the former case, targeted only a single type of watermelon. A slightly higher PIC value (0.53) was reported in a study involving the construction of a watermelon BAC library and identification of SSRs anchored to the melon or Arabidopsis genomes (Joobeur
C. MUJAJU, A. ZBOROWSKA, G. WERLEMARK, L. GARKAVA-GUSTAVSSON, S. B. ANDERSEN and H. NYBOM
FIG. 2. Two-dimensional plot of 25 watermelon accessions using principal co-ordinate (PCO) analysis of the SSR data. CWM refers to the cowmelons accessions and SWM to the sweet watermelon accessions. The first two letters of the accession code denote the country of origin (US, United States; BT, Botswana; NM, Namibia; SA, South Africa; ZM, Zambia; and ZW, Zimbabwe) and the numbers are the accession number or name. The axes are principle component 1 (PC 1) and principle component 2 (PC 2).
et al., 2006). The positive correlations that existed between PIC value and the number of alleles per locus, as well as between the PIC value and the allele size range, indicates that SSR (microsatellite) loci with a large range of alleles show greater variation (Dixit et al., 2010). Genetic diversity in watermelon All multivariate analyses (AMOVA, cluster analysis, and PCO) were clearly able to discriminate between sweet watermelons and cow-melons. Previous SSRbased studies revealed a similar level of differentiation (Jarret et al., 1997; Kwon et al., 2007; Mujaju et al., 2010). Differentiation between these two major groups has also been reported in a number of studies using dominantlyinherited molecular and biochemical markers (Navot and Zamir, 1987; Jarret et al., 1997; Levi et al., 2000; 2001a,b; 2005). There was significant differentiation between accessions in our study, when calculated across all accessions and when calculated within each of the two main groups (i.e., cow-melons and sweet watermelons). When calculated across all watermelon accessions, estimates of among-accession differentiation (ST = 0.48; GST = 0.44) were higher than the values obtained for wild populations of annual (FST = 0.40) or short-lived perennial species (FST = 0.31), or for mixed breeding (FST = 0.26) or outcrossing species (FST = 0.22), as reported by Nybom (2004). The existence of two strongly differentiated forms within our accessions possibly accounted for the discrepancy observed. At least in Zimbabwe, these two forms of watermelon are usually cultivated in separate parts of the field or farm, thereby promoting their relative genetic isolation. The average within-accession HE value (0.42) calculated across all watermelon accessions was higher than the values for the three US commercial cultivars. The latter may have experienced more stringent selection procedures in US breeding programmes. They were also rather similar to each other, according to the PCO. Apparently, commercial watermelon varieties have diverged into small populations (Dane and Liu, 2007), and successive selection during domestication may have further reduced genetic diversity within and among
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cultivars, compared to the original watermelon crops. Even lower values were reported for West African watermelon C. lanatus oleaginous-type (HE = 0.19; HO = 0.13; Minsart et al., 2010), which possibly represents a narrow genetic background. In our study, the mean value for within-accession HO was slightly higher for sweet watermelons (0.35) than for cow-melons (0.33), whereas the mean values for HE and the Shannon index were slightly higher for cow-melons (HE = 0.43; I = 0.79) than for sweet watermelons (HE = 0.42; I = 0.73). Contrary to previous studies, where higher levels of genetic diversity have been reported within C. lanatus var. citroides compared to C. lanatus var. lanatus (Navot and Zamir 1987; Jarret et al. 1997), this study revealed comparable levels of variability. HO values were lower than HE values in the majority of our watermelon accessions, with positive values for FIS, indicating a lack of heterozygosity. Since we found no evidence for short allele dominance, the observed heterozygote deficiency may be a result of the breeding system. In addition to outcrossing, watermelons can also self-pollinate. Therefore, accessions may sometimes have been derived from pollinations among related or less diverse plants (the Wahlund effect). AMOVA demonstrated a significant differentiation between countries (15%; P < 0.001), but this may largely have been due to the uneven distribution of the two major groups of watermelon accessions between countries. In contrast, UPGMA clustering, as well as PCO, showed considerable overlap among accessions from the different countries. Interestingly, the two sweet watermelon accessions from Botswana were grouped adjacent to the three sweet watermelon cultivars from the USA, suggesting that the latter may have originated from that area. A recent study on genetic diversity among landraces collected on-farm in one Zimbabwean village showed that the diversity of watermelon landraces was linked to different cultivation practices, the level of seed exchange, and to associated traditional beliefs (Mujaju et al., unpublished data). Trade between relatives living in neighbouring countries, for example, leads to enhanced seed exchange and gene flow, while continued selection and cultivation based on traditional beliefs enhances the differentiation between accessions over time. Implications for genetic conservation in Southern Africa Ex-situ conservation: While ex-situ conservation is static, it can preserve the germplasm of a wide range of crop species for decades, independent of their respective status for cultivation and use in the field, provided that the viability of the propagation material can be maintained. Unfortunately, the rate of seed germination was extremely low, or non-existent, in a large number of the samples obtained for our study. Insufficient seed viability may have biological causes, but it can also result from technical problems during seed storage, such as frequent electricity outages. Although conservationists are expected to intervene only when there is a significant deleterious change in genetic diversity (Maxted et al., 2002), loss of propagule viability becomes a potential threat to genetic diversity, especially for the seeds of sweet watermelon varieties which need to be constantly monitored and checked. Conservationists or genebank
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Assessment of genetic diversity in watermelon
managers need to consider establishing regeneration programmes for watermelon accessions of low viability, to avert loss of diversity and, consequently, the long-term adaptive potential of crop populations. On-farm conservation: On-farm conservation is mostly linked to the maintenance of landraces through farmers’ cultivation practices, and it is dynamic, as landraces continue to evolve. In Southern Africa, on-farm conservation strategies are embedded in traditional agricultural systems where agro-biodiversity is still important in subsistence agriculture (Maxted et al.,
2002). Maggs-Kolling et al. (2000) observed a grouping of watermelon forms in Namibia according to their cultural uses. The management of these accessions in farmers’ fields in different countries is critical and should be promoted since it is closely associated with cultural and cultivation practices. The authors are indebted to the various National Plant Genetic Resource Centres within the SADC Region for the plant material used, and acknowledge research funds from the Nordic-SADC Plant Genetic Resources Centre Network Programme, Lusaka, Zambia.
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