Yield Components in Cucumber: An Overview

Christopher S. Cramer

Department of Agronomy and Horticulture, MSC 3Q, Box 30003, New Mexico State University,
Las Cruces, NM 88003-0003

Todd C. Wehner

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

Additional index words. correlation, Cucumis sativus, Cucurbitaceae, heterosis, indirect selection, path analysis, path coefficients, planting density

Abstract. Yield components have been used in many horticultural crops to select for fruit yield indirectly. This paper summarizes the results from several studies on the correlation between yield and yield components, and on the value of yield components in selecting for fruit yield of pickling and slicing cucumbers (Cucumis sativus L.). For the cucumber populations studied, yield components were weakly correlated with fruit yield; strong correlations were only observed for certain yield component­population combinations. Use of yield components to improve fruit yield would not have an advantage over direct selection in most cucumber populations. Selection for improved yield resulted in no change in yield component means, and maintained weak correlations (<|0.7|) between components and yield or weakened strong correlations (|0.7|). Planting density affected yield and yield component means greatly, and correlations between the two traits. No heterosis was detected for yield and yield components in pickling cucumber. Additional studies on the heritability of yield components and the value of other yield components may improve the situation, but currently there is no value in yield components in selection for improved fruit yield in cucumber.

We gratefully acknowledge the assistance of Tammy L. Ellington, Rufus R. Horton, Jr., Jinsheng Liu, Nischit V. Shetty, Joel L. Shuman, and S. Alan Walters.

 

Fruit yield in many crops is quantitatively inherited, with low heritability and many genes involved in its control. Thus, yield is difficult to improve using direct selection, requiring large, replicated field trials. Indirect selection using a second trait that is highly correlated with the trait of interest has potential to help the selection process. In order for indirect selection to be better than direct selection, the indirectly selected trait must have a higher heritability than the trait of interest. For example, suppose fruit yield has a heritability of 0.25, if the indirectly selected trait has a correlation with yield of 0.70 then the heritability of that trait must be at least 0.36 (based on multiplication of 0.25 and 0.70) for indirect selection to be effective. In addition, the selection methods used for indirect selection should require no more resources than direct selection or indirect selection would be too costly.

Even so, yield components have been used successfully to improve yield in horticultural crops.

Examples of yield components include number of harvests, plants per hectare, number of branches per plant, number of nodes per branch, number of fruit per node, number of pistillate flowers, percentage marketable yield, percentage early yield, and percentage culls. Yield measurement in cucumber, and many horticultural crops, is different from that in many agronomic crops, where yield is measured at plant or fruit maturity. In cucumber, fruit are harvested at an immature stage in multiple harvests. Yield components have been studied in blueberry (Vaccinium corymbosum L.) (Siefker and Hancock, 1986), cucumber (AbuSaleha and Dutta, 1988; Solanki and Shah, 1989; Prasad and Singh, 1994a, 1994b; Yin and Cui, 1994; Zhang and Cui, 1994), strawberry (Fragaria ¥ananassa Duchesne) (Hancock et al., 1984), and tomato (Lycopersicon esculentum Mill.) (McGiffen et al., 1994). In tomato, McGiffen and coworkers (1994) concluded that factors such as increased

 

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number of flowers, reduced flower abortion, and greater fruit load could increase marketable yield by enhancing total fruit set, increasing maturity (decreasing the percentage of green fruit), and decreasing the total number of culls.

The partitioning of yield into components provides several advantages for improving yield. Component analysis allows the design of efficient selection strategies for improving yield (Moser and Frey, 1994). Specifically, the analysis of yield components may indicate the component traits that contribute to specific breeding objectives (McMullan et al., 1988). Yield component analysis also may provide insight for making yield gains (Payne et al., 1986). Finally, genes for a specific yield component useful for increasing yield can be identified using yield component analysis (Frey, 1988; Moser and Frey, 1994; Rasmusson, 1987).

Several researchers have examined the possibility of using yield components to improve fruit yield in cucumber. In 1994, Yin and Cui analyzed the relationship between early cucumber yield and 15 important traits in cucumber using stepwise regression. Their data suggested early yield was mainly composed of days from sowing (DFS) to first staminate flowering on 50% of plants, DFS to first pistillate flowering, DFS to first pistillate flowering on 50% of plants, fruiting percentage on the main stem, fruiting branch percentage, fruit harvested in early stage, and mean weight per fruit. They did not, however, determine the correlations between those traits and yield or suggest a selection strategy for improving fruit yield. In another study, Zhang and Cui (1994) divided early cucumber yield into four groups based on biological significance, early yield components, morphological traits, growth period factors, and yield physiological factors. They determined that yield physiological factors and morphological traits directly affected early yield.

Increased yield has been an objective of many cucumber breeding programs. Yield in cucumber is quantitatively inherited and has low heritability, making progress difficult. Narrow-sense heritability for fruit yield has been reported to be 0.02 to 0.88 depending on the trait and method of measurement (Wehner, 1989). Heritability of yield during intermediate trials has been 0.07 to 0.25

depending on population and environment tested (Smith et al., 1978). El-Shawaf and Baker (1981) estimated heritability of yield to be 0.00 to 0.56 with crosses among selected inbred lines and testing a single environment. Smith and coworkers (1978) and Strefeler and Wehner (1986) observed higher heritability estimates for diverse populations developed by crossing hundreds of lines than for uniform populations developed by crossing only a few, elite lines. With those low heritability values, indirect selection of more heritable traits might be beneficial for increasing cucumber fruit yield.

Materials and methods

Latest cycle correlation. In Fall 1994, we initiated several studies in both pickling and slicing cucumbers to examine the correlations between yield components and fruit yield in the hopes of selecting particular yield components for improving fruit yield (Cramer and Wehner, 1998a, 1998b). Four pickling and four slicing cucumber populations that differed in their genetic diversity and mean yield performance (Wehner, 1997, 1998a, 1998b) were selected. Those populations were developed and improved using modified half-sib recurrent selection to improve fruit yield, earliness, and shape of the population (Wehner and Cramer, 1996a, 1996b). Half-sib families were selected in the spring season based on a simple weighted index (SWI), weighted 70% toward yield and 30% toward quality (Wehner and Cramer, 1996a, 1996b). Four families were chosen at random from each population­cycle combination in 1995 and 1996 and self-pollinated in the greenhouse. The S1 families were tested using a randomized complete block design with four replications in each of two seasons (spring, summer) in each of two years (1995, 1996) (Cramer and Wehner, 1998a, 1998b).

Plots 3.1 m long were harvested once-over by hand at the 10% oversize (>51 mm in diameter for pickles and >60 mm in diameter for slicers) stage as recommended by Miller and Hughes (1969) for optimum yield. Each plot was evaluated for number of branches, nodes, pistillate flowers, total, early, and marketable fruit, and fruit shape. Plots with low stand count were corrected (Cramer and

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Wehner, 1998f) and correlations were calculated using PathSAS (Cramer et al., 1998). Correlations of 0.70 to 1.00 (positive or negative) were considered to be strong, while correlations of ­0.69 to 0.69 were considered weak.

Selection and components. We were also interested in the effect of selection on yield and yield component means, yield component correlations, yield trait correlations and the correlations between yield components and yield (Cramer and Wehner, 1998a, 1998b). Three cycles of selection (early, intermediate, late) were chosen from each population (Cramer and Wehner, 1998a, 1998b). Four families were chosen at random from each population­cycle combination, self-pollinated in the greenhouse and tested in the field as described in the previous section. Cycles of selection were subplots, and populations were whole plots.

Low planting density and components. Additional studies were conducted to determine the effect of low planting density on trait means and correlations (Cramer and Wehner, 1998d, 1998e). Four S1 families from three cycles of selection were tested at two planting densities: 6,450 (low) or 64,500 (normal) plants per hectare. Plots were arranged with density as whole plots, populations as subplots, and cycles as sub-subplots. All other testing procedures were similar to those described for the latest cycle correlation study.

Heterosis and components. A separate study was used to study the correlation between heterosis and yield components for four pickling cucumber families (Cramer and Wehner, 1998c). Six pickling cucumber inbreds were hybridized to form F1, F2, BC1A, and BC1B generations (Cramer and Wehner, 1998c). The families were similar to those used by Ghaderi and Lower (1977). The experiment was a randomized complete block design with four replications in each of two seasons during 1996 in a split-plot arrangement with families as whole plots and generations as subplots (Cramer and Wehner, 1998c). All other testing procedures were similar to those procedures described for the latest cycle correlation study.

Results and discussion

Latest cycle correlation. Based on the results from our studies, indirect selection of yield

components to improve fruit yield would not be advantageous for all cucumber populations tested. In addition, not all yield components will result in yield improvement. For the majority of populations and yield components tested, the correlations between yield and its components were weak at the latest cycle of selection. For example, the percentage fruit set was weakly correlated with total yield for the latest cycle of selection for most populations. Thus, selection based upon percentage fruit set would have not advantage over direct selection for yield in those populations.

Selection for a particular component does, however, show promise for increasing yield in several populations. In the NCH1 population, selection for percentage of pistillate nodes (more gynoecious) would increase the number of total and marketable fruit per plant (Cramer and Wehner, 1998a). The indirect selection for yield in this population through the percentage of pistillate nodes may result in more gain in yield than selection for yield itself. As mentioned previously, fruit yield in cucumbers has low to moderate heritability (Smith et al., 1978). The heritability of percentage pistillate nodes appears to be quantitative, although gynoecious vs. monoecious sex expression is controlled primarily by two genes, gy and F, with several modifiers (Pierce and Wehner, 1990). Although the total number of genes conferring fruit yield in cucumber is not known, the number of genes controlling sex expression is most likely fewer than the genes controlling yield. Thus, the percentage pistillate nodes may be more heritable than fruit yield.

In addition to the correlations between yield and its components, the correlations among yield traits, and among yield components were examined in our studies. For most populations, selection for increased total number of fruit resulted in a similar increase in the number of marketable fruit (Cramer and Wehner, 1998a, 1998b). Several of those populations already exhibited increases in total and marketable yield with selection (Wehner and Cramer, 1996a, 1996b). In addition to marketable yield, selection for an increase in total fruit per plant would also increase the number of early fruit per plant in the NCH1 pickle

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population and several slicer populations (Cramer and Wehner, 1998a, 1998b). Most correlations between marketable and early yield were weak. However, for the NCWBP population, selection for marketable fruit would increase the number of early fruit (Cramer and Wehner, 1998a). With regard to the correlations among yield components, most correlations were weak. When strong correlations did exist, they varied over populations. For example, an increase in the number of branches per plant of the NCH1 population would decrease the percentage fruit set (Cramer and Wehner, 1998a). In addition, selection for increased number of branches per plant would reduce the number of nodes per branch of an elite slicer population (Cramer and Wehner, 1998b).

One difficulty in recommending a particular yield component for yield improvement is the germplasm being used for improvement. The populations used in our studies were dramatically different in their genetic diversity, mean performance, growth habits, and trait variations (Cramer and Wehner, 1998d; Wehner, 1997, 1998a, 1998b). The populations were chosen in order to examine a diversity of germplasm. We hoped to find correlations that were consistent over all populations. Genetic differences between populations resulted, however, in differences in correlations between populations and made generalizations difficult to make (Cramer and Wehner, 1998a, 1998b).

Another difficulty in selecting yield components for improved yield is the change in correlations between components and yield over seasons (Cramer and Wehner, 1998a, 1998b). We tested populations in both the spring and summer seasons of 1995 and 1996. When averaged over years, some correlations varied in their strength depending upon the growing season. In North Carolina, there is a large difference in environmental conditions between spring and summer seasons. Breeding lines are normally selected in the spring season, but resulting cultivars would be grown in both the spring and summer seasons. Yield improvement has been observed in the nonselected environment (Wehner and Cramer, 1996a, 1996b).

Selection and components. For pickle and slicer populations, selection for improved yield

had little or no effect on yield component means and their correlation with yield. In numerous instances, the changes with selection in correlations between yield components and yield did not correlate with similar changes in yield or yield component means over selection. With regards to the correlations between yield and its components, selection either weakened strong correlations or maintained weak correlations. In many of the populations, strong correlations were observed between yield and its components at the early cycle of selection, but were not observed at the latest cycle. For the NCBA1 slicer population, the correlation between fruit set and total yield weakened with selection (Cramer and Wehner, 1998b). In addition, the correlation of early yield with branches per plant and nodes per branch also weakened from cycle 0 to cycle 10 for a medium-based pickle population (Cramer and Wehner, 1998a). The results obtained in our studies suggest that selection acts to improve yield by weakening the correlations between yield and its components. In this way, yield can be improved independent of yield components and one particular component does not have a strong influence on yield and/or a particular component does not have a stronger influence than another component.

In addition to correlations weakening with selection, several instances were observed in which correlations became stronger or remained strong with selection for improved yield. For the NCH1 pickle population, selection for improved yield did not weaken the strong, positive correlation between branches per plant and total fruit number per plant (Cramer and Wehner, 1998a). This strong, positive correlation between branches per plant and total yield might be related to the germplasm used to form that population: LJ 90430, a multibranched, multifruiting accession of C. sativus var. hardwickii (Wehner, 1998b). Plants of this breeding line develop a large photosynthate capacity with multiple branches and small leaves, after which simultaneous, multiple-fruit development occurs (Ramirez et al., 1988). With this breeding line, there is a strong, positive association between the number of branches produced and the number of fruit produced. With this same population, the correlation between percentage

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correlation between fruit set and fruit weight, and an increased negative correlation between the number of nodes per branch and total fruit weight. In addition, inbreeding depression was associated with a weakening of the strong negative correlations between the number of nodes per branch and total fruit weight. Those correlations were only associated with heterosis and inbreeding depression for one particular cross and should not be applied for all crosses in which heterosis for yield may be observed. This study did not observe widespread heterosis or inbreeding depression for yield in cucumber as previously reported (Ghaderi and Lower, 1979).

Conclusions

In conclusion, indirect selection of yield components for yield improvement should only be used for particular populations and yield components. The majority of correlations between yield and its components in pickling and slicing cucumber populations were weak. Selection for improved yield resulted in no change in yield component means but maintained weak correlations between yield and its components, or weakened the strong correlations. Planting density altered both yield and yield component means and strong correlations between yield and its components.

Heterosis for fruit yield in pickling cucumber hybrids was rarely expressed, making associations between yield components and heterosis difficult to determine. More work needs to be done in order to determine heritability of yield components, other potential yield components and yield measures, and better evaluation methods.

Currently, the heritability of most cucumber yield components is unknown. In addition, other yield components, such as plants/ha, nodes per plant, average internode length, number of pistillate flowers per node, and other measures of yield (such as fruit weight and value), should be studied for correlations between yield and its components. For yield component selection to be used in cultivar development, better evaluation methods for measuring yield components must be developed. In addition, other plant types, such as determinate, compact, and little leaf, may have different relationships between yield components and yield.

pistillate nodes and total and marketable yield per plant was weak initially but became positive with selection. The strengthening of the correlation may have resulted from a decrease in the percentage of pistillate nodes of the population from cycle 0 to cycle 10 (Cramer, 1997). With fewer pistillate nodes being produced, yield per plant is more dependent upon those nodes for fruit production. Hence the stronger correlation between the two traits.

Selection also altered the correlation among yield components. For the NCBA1 slicer populations tested in the summer season, all correlations among yield components, except the correlation between pistillate nodes and nodes per branch, were strong (positive or negative) at cycle 0 and weakened from cycle 0 to cycle 8. In addition, the number of branches per plant was weakly correlated with fruit set at cycle 0 and the two traits became negatively correlated by cycle 10 for a special pickle population (Cramer and Wehner, 1998a).

Low planting density and components. With regards to the effects of planting density on yield component means and their correlation with yield, yield component means varied dramatically between planting densities and the correlation between yield components and yield were stable over densities for some populations and were unstable over planting densities for other populations (Cramer and Wehner, 1998d, 1998e). Weak correlations were particularly consistent over densities whereas stronger correlations varied between densities (Cramer and Wehner, 1998d). Lower planting densities can be used for testing yield components of a particular population, if the relationship between correlations at low and normal planting densities are known. Otherwise, a normal planting density of 64,500 plants/ha should be used to select for improved components of yield.

Heterosis and components. For the heterosis study, very little heterosis was observed for yield components or yield in the crosses studied, which made significant correlations between yield and yield components difficult to interpret (Cramer and Wehner, 1998c). When heterosis for fruit yield was observed, it was associated with a decreased

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