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Cucurbit Genetics Cooperative Report 23:12-15 (article 4) 2000

Fruit Yield and Yield Component Correlations of Four Pickling Cucumber Populations

Christopher S. Cramer

Department of Agronomy and Horticulture, Box 30003, New Mexico State University, Las Cruses, NM 88003-0003

Todd C. Wehner

Department of Horticultural Science, North Carolina State University, Raleigh, NC 27695-7609

Introduction. Increased fruit yield has been one of the primary breeding objectives in the development of pickling cucumber cultivars (14). For purposes of selection, the most efficient trait for measurement of yield in once-over harvest in North Carolina is fruit number per plot (14). Smith et al. (12) found that fruit number had a higher heritability than fruit weight and the two were highly correlated. An alternative to direct selection for yield is to select for traits that are highly correlated with yield, but may have higher heritability. Those traits, often referred to as yield components, may include stem length, number of branches per plant, number of nodes per branch, time until first flowering, number of pistillate flower per node, and percentage of fruit set.

Yield components have been used to study fruit yields in vegetable crops such as cucumber (1, 3, 9, 11, 13, 18). In some instances, yield components have been positively correlated with yield and could be selected to improve yield. In cucumber, several strong correlations were observed between fruit yield and yield components. However, few of those studies involved genetically-diverse pickling cucumber populations. the objective of this study was to determine yield components that were strongly correlated with fruit yield in four U.S. pickling cucumber populations.

Methods. Four pickling cucumber populations, NCWPB, NCMBP, NCEP1, and NCH1, were developed at North Carolina State University (15, 16). The genetic variance for fruit yield, earliness, fruit quality, and disease resistance decreases while the mean for each trait improves from the NCWPB population to the NCMBP population, and to the NCEP1 and NCH1 populations (15, 16). After intercrossing, each population was selected using modified half-sib recurrent selection to improve fruit yield, earliness, and shape (17). For this study, eight (1995) and 4 (1996) families were taken randomly from the latest cycle of each population and self-pollinated in the greenhouse. S1 families were evaluated in a randomized complete block design with four replications in the spring and summer seasons of 1995 and 1996 at the Horticultural Crops Research Station, Clinton, NC. forty seeds were planted on raised, shaped beds on 27 April 1995 and 29 April 1996 for the spring season, and 13 July 1995 and 8 July 1996 for the summer season.

Plots of 3.1 m length were separated by 1.5 m alleys, with guard rows and 1.5 long end plots around the field. Recommended cultural practices for North Carolina were used throughout the experiment (10). Plots were thinned to 30 plants at first true leaf stage. Plants were harvested 5 September 1995 and 19, 22, or 23 August 1996 for the summer season. Time of harvest was when the check lines had reached 10% oversized (>51 mm in diameter) fruit stage (6). each plot was evaluated for number of branches, nodes, pistillate flowers, total fruit, oversized fruit, and cull (crooked- and nubbin-shaped) fruit. Plots were corrected to 30 plants each for plots with 16 to 30 plants (plots with fewer than 16 plants were considered missing to prevent biasing from stand correction). Plant stands were corrected to reduce mean differences in yield and components resulting from differences in stand. Pearson correlation coefficients between yield components and yield, among yield components, and among yield traits were determined. Since no statistical test for comparing the magnitudes of two correlations was available, a correlation (r) between yield components and total yield of 0.7 to 1.0 or -0.7 to -1.0 (r2 > 0.49) was considered strong, while a correlation of -0.69 to 0.69 was considered weak (2).

Results. The majority of correlations (96%) among yield components ranged from -0.69 to 0.69, defined as weak for this study (Table 1). In a similar study with slicing cucumbers, 85% of the correlations among yield components in the latest selection cycle were considered weak (3). The only strong correlation was a negative association between the percentage of pistillate nodes and the percentage of fruit set for the NCMBP population. This negative association could be explained by the phenomenon of fruit fruit inhibition observed for cucurbits. With first fruit inhibition, the development of other fruit is limited by the development of the first pollinated fruit 95). this phenomenon is thought to be caused by a limited amount of photosynthates, which can only support the growth of one fruit at a time (7). Thus, with first fruit inhibition, a plant with a high percentage of pistillate nodes would only be able to support a few fruit. As a result, the percentage of fruit set would be higher for a plant with a lower percentage of pistillate nodes.

The correlations between yield and its components at the latest cycles exhibited a high percentage (85%) of weak correlations (Table 1). All of the strong correlations between fruit yield and yield components were positive. For both the NCMBP and NCH1 population, the number of branches per plant exhibited a strong positive correlation with the number of total and marketable fruit per plant (Table 1). The differences in correlations among populations might be attributed to germplasm used to form each population. LJ90430, a multi-branched, multi-fruiting accession of C. sativus var, hardwickii, was used in the formation of all population except the NCEP1 population (15, 16). With that germplasm in each population, the NCWBP, NCMBP, and NCH1 populations might be expected to exhibit strong correlations between the number of branches and total fruit per plant. With over 1000 breeding accessions used in the formation of the NCWBP population, the hardwickii germplasm originally in this population was highly diluted, and this probably accounted for the lack of strong correlations between branch and fruit number per plant. Thus, both the NCMBP and NCH1 populations would still be exhibiting the strong correlation between branch and fruit number resulting from the C. s. var. hardwickii germplasm used in their development.

The percentage of pistillate nodes of the NCH1 population was positively correlated with the number of total, marketable, and early fruit per plant (Table 1). This relationship may be associated with C.S. var. hardwickii germplasm used in the development of this population. furthermore, the NCH1 contains more of this germplasm than the other three populations. Selection for an increased percentage of pistillate nodes in this population may have promise for increasing the number of fruit produced per plant. This selection may result in more gain in yield than direct selection for yield per se if heritability for the percentage of pistillate nodes is higher than heritability for yield. Narrow-sense heritability of fruit number in cucumber has been reported to be between 0.00 and 0.25 for most populations 11, 12). Narrow sense heritability for gynoecy in cucumbers was also low (0.20 to 0.25) with the variance in sex expression being mainly dominance variance (11). Sex expression in cucumbers is primarily governed by three major loci, a, F, and G (8). The minimum number of effective factors involved in sex expression has been reported to be five (11). Thus, even with a strong correlation between the percentage of pistillate nodes and the number of fruit per plant, indirect selection for yield based upon the percentage of pistillate nodes may or may not be advantageous for improvement of yield for once-over harvest.

Regarding correlations among yield traits, the total number of fruit per plant was positively correlated with the number of marketable fruit for each population (Table 1). Therefore, selection for an increased number of fruit per plant should also increase the number of marketable fruit per plant. A strong, positive correlation also existed between total and marketable fruit number when these populations were tested at a low plant density (4). Total fruit number was positively correlated with the number of early fruit per plant for the NCWBP and NCH1 population (Table 1). Selection for increased yield in these populations would increase the number of early-maturing fruit produced per plant. The number of early fruit per plant was positively correlated with the number of marketable fruit per plant for the NCWBP and NCH1 populations (Table 1). In both of these populations, selection for an increased number of marketable fruit per plant also would increase the number of early-maturing fruit per plant. the NCH1 population also exhibited a strong, positive correlation between marketable and early fruit number when the population was grown at a reduced planting density (4).

Table 1. Correlation coefficientsz among yield components (branches per plant, nodes per branch, percentage of pistillate nodes, percentage of fruit set), between yield components and yield traits (total, marketable and early yield per plant), and among yield traits for the latest cycle in each population.

Trait
Nodes/branch
Pistillate nodes (%)
Fruit set (%)
Fruit number per plant
Total
Marketable
Early
NCWBP Cycle 5
Branches/plant
0.00
00.25
0.61*
-0.01
0.06
-0.04
Nodes/branch
0.05
-0.29
0.38
0.21
0.06
Pistillate nodes (%)
0.16
0.51
0.32
0.36
Fruit set (%)
0.15
0.20
0.24
Total fruit number
0.92***
0.80***
Marketable fruit number
0.87***
NCMBP Cycle 10
Branches/plant
0.04
-0.50
0.16
0.72**
0.76***
0.07
Nodes/branch
0.31
-0.61*
0.44
0.26
-0.40
Pistillate nodes (%)
-0.75**
-0.20
-0.43
-0.17
Fruit set
0.02
0.21
0.55*
Total fruit number
0.91***
0.24
Marketable fruit number
0.35
NCEP1 Cycle 9
Branches/plant
0.58*
0.05
Nodes/branch
0.34
0.30
Pistillate nodes (%)
0.34
-0.61*
-0.08
0.57*
-0.19
-0.18
Fruit set (%)
-0.58*
-0.38
0.21
0.37
0.31
Total fruit number
0.02
-0.14
0.97***
0.53*
Marketable fruit number
0.43
0.61*
NCH1 Cycle 10
Branches/plant
-0.54*
0.41
-0.28
0.74**
0.72**
0.59*
Nodes/branch
-0.26
-0.05
-0.26
-0.21
-0.13
Pistillate nodes (%)
-0.59*
0.79***
0.77***
0.77***
Fruit set (%)
-0.29
-0.29
-0.40
Total fruit number
0.99***
0.83***
Marketable fruit number
0.82***

*, **, *** Significant at P< 0.05, 0.01, 0.01, respectively.
Z Strong correlations (bold) were considered to be 0.7 to 1.0 and -0.7 to -1.0.  

Literature Cited

  1. AbuSaleha and O.P. Dutta. 1988. Interrelationship of yield components in cucumber. Veg. Sci. 15:79-85.
  2. Cramer, C.S. 1997. Specific combining ability for fruit yield and shape, yield and yield components of cucumber (Cucumis sativus L.) populations improved using recurrent selection. Ph.D. Diss. North Carolina State Univ., Raleigh, North Carolina.
  3. Cramer, C.S. and T.C. Wehner. 1998a. Fruit yield and yield component means and correlations of four slicing cucumber populations improved through six to ten cycles of recurrent selection. J. Amer. Soc. Hort. Sci. 123:388-395.
  4. Cramer, C.S. and T.C. Wehner. 1998b. Fruit yield and yield components of cucumber populations grown at low plant density. In: J.D. McCreight (ed.) Cucurbitaceae '98, evaluation and enhancement of cucurbit germplasm, p. 277-285. ASHS Press, Alexandria, VA.
  5. McCollum, J.P. 1934. Vegetative and reproductive responses associated with fruit development in the cucumber,. Memoir 163, Cornell Univ. Agric. Exp. Sta., Ithica, NY.
  6. Miller, C.H. and G.R. Hughes. 1969. Harvest indices for pickling cucumbers for once-over harvested systems. J. Amer. Soc. Hort. Sci. 94: 485-487.
  7. Pharr, D.M., S.C. Huber and H.N. Sox. 1985. Leaf carbohydrate status and enzymes of translocate synthesis in fruiting and vegetative plants of Cucumis sativus L. Plant Physiol. 77: 104-108.
  8. Pierce, L.K. and T.C. Wehner. 1990. Review of genes and linkage groups in cucumber. HortScience 25:605-615.
  9. Prasad, V.S.R.K. and D.P. Singh.1994. Genetic association and interrelationship between yield components in cucumber. J. Maharshtra Agric. Univ. 19:147-148.
  10. Schultheis, J.R. 1990. Pickling cucumbers. N.C. State Ag. Extension. Horticulture Information Leaflet No. 14-A.
  11. Serquen, F.C., J. Bacher, and J.E. Staub. 1997/ Genetic analysis of yield components in cucumber at low plant density. J. Amer. Soc. Hort. Sci. 122: 522-528.
  12. Smith, O.S., R.L. Lower and R.H. Moll. 1978. Estimates of heritiblities and variance components in pickling cucumbers. J. Amer. Soc. Hort. Sci. 103L 222-225.
  13. Solanki, S.S. and A. Shah. 1989. Path analysis of fruit yield components in cucumber. Prog. Hort. 21:322-324.
  14. Wehner, T.C. 1989. Breeding for improved yield in cucumber. In. J. Janick (ed.), Plant Breeding Reviews 6:323-359., AVI Press, Stamford, CT.
  15. Wehner, T.C. 1997. Three pickling cucumber populations: NCWBP, NCMBP, and NCEP1. HortScience 32: 941-944.
  16. Wehner, T.C. 1988. Two special cucumber populations: NCH1 and NCBA1. HortScience 33:766-768.
  17. Wehner, T.C. and C.S. Cramer. 1996. Gain for pickling cucumber yield and fruit shape using recurrent selection. Crop Sci.36: 1538-1544.
  18. Yin, M. and H. Cui. 1994. Analysis of component traits for early yield in cucumber. Cucurbit Genet. Coop. Rpt. 17: 27-29.
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Page citation: Wehner, T.C., Cucurbit Genetics Cooperative;
Created by T.C. Wehner and T. Ng, 1 June 2005; design by C.T. Glenn;
send questions to T.C. Wehner; last revised on 15 May, 2013