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CROP BREEDING, GENETICS & CYTOLOGY

Blend Response and Stability and Cultivar Blending Ability in Oat

^a Dep. of Plant Pathology, Iowa State Univ., Ames, IA 50011
^b USDA-ARS Plant Science Research Unit, Dep. of Crop Science, Box 7620, North Carolina State Univ., Raleigh, NC 27695-7620

* Corresponding author (sarathom{at}iastate.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Genetic diversity in cropping systems can provide buffering against varying environmental conditions. Therefore, cultivar blends may have greater and more stable yields than their pure-line components. Optimization of cultivar blend development requires knowledge of the relative importance of pure-line yield potential, blend response, and cultivar interactions to blend yield. Grain yield and volume weight of oat (Avena sativa L.) pure-line cultivars and cultivar blends were measured in eight Iowa environments in order to compare their productivity and stability and to estimate genetic components of blend yields. In one experiment, five early-maturing cultivars were grown as pure lines and as all possible two- and three-way cultivar blends. In a second experiment, ten midseason-maturing cultivars were grown as pure lines and as all possible two-way blends. Grain yield was 3% greater (P < 0.05) and volume weight was 1% greater (P < 0.05) in blends than in pure lines in the early-maturity experiment; however, pure line and blends did not differ in the midseason-maturity experiment. Blends had more stable (P < 0.05) yields than pure lines in the early-maturity experiment only. Modified diallel analysis was used to partition the variation among two-way blends into general yielding ability (GYA) and true general competitive ability (TGCA) of each component genotype, and specific competing ability (SCA) interaction between blend components. General yielding ability variation was significant, whereas variation for neither TGCA nor SCA was significant. Oat genotype responses to blending were sufficiently consistent across blending partners that superior blends can be selected based on pure-line evaluations of early-maturing cultivars.

Abbreviations: GBA, general blending ability • GLM, general linear models • GYA, general yielding ability • SCA, specific competing ability • TGCA, true general competitive ability

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
OAT HECTARAGE IN THE USA has declined dramatically since 1950 (USDA-National Agricultural Statistics Service, 1998). Inclusion of oat in crop rotations, however, can enhance species diversity on farms and help to reduce weed and insect pests (Liebman and Dyck, 1993), increase soil quality and curb erosion (Gantzer et al., 1991), and stabilize farm incomes (Brummer, 1998). Because oat has value as feed for livestock, in human nutrition, and as a partial remedy for many production problems, methods to increase and stabilize oat grain yields are needed.

To minimize the adverse effects of environmental stresses on yield, plant breeders have attempted to develop cultivars that will perform reliably well across a range of years and sites (Evans, 1993; Allard and Bradshaw, 1964). Yield stability is the result of a crop's buffering capacity, that is, its ability to adapt to variable weather, insect, disease, weed, and soil conditions. Ways to improve a crop's buffering capacity include intra-population interplant buffering through mixing cultivars or genotypes, and individual intraplant buffering through maintenance of heterozygosity (Allard and Bradshaw, 1964). A cultivar blend can capitalize on the principle of intra-population buffering, because a mixture of genetically different plants may have a greater chance of successful adaptation across a range of environments than a genetically homogeneous population.

Smithson and Lenné (1996) reviewed the literature on cultivar blends in many crops and concluded that blends generally yield slightly more than pure lines, but their true benefits lie in disease control and stability. Blending can have significant positive effects on disease control (Mundt et al., 1995; Finkh and Mundt, 1992; Power, 1991), and can reduce yield losses caused by variability in soil quality (Trimble and Fehr, 1983). The usefulness of cultivar blends in oat, however, has not been established definitively. Pfahler (1965) reported that a small sample of cultivar blends had greater yield stability than the component pure lines. Frey and Maldonado (1967) found that cultivar blends had significantly higher yields than their component pure-line cultivars only when in more stressful environments. Shorter and Frey (1979), by contrast, found no difference between blend and pure-line performance.

Comparisons of blends and pure lines can vary among samples of cultivars because of genotypic variation for contributions to blend performance. Gizlice et al. (1989) used a modified diallel analysis to characterize specific genotypic contributions to blend response. In this analysis, variation among blends was partitioned into general blending ability (GBA) and SCA variances. These effects are analogous to the general and specific combining abilities estimated from diallel analyses of single-cross hybrids in maize (Zea mays L.; Sprague and Tatum, 1942). Gizlice et al. (1989) demonstrated that if pure-line components are evaluated in the same experiment as the blends, then GBA can be partitioned into two components, GYA and TGCA. The GYA represents the innate yielding ability of a cultivar grown as a pure line, and the TGCA is the additional mean competitive response of a cultivar calculated as the difference between the GBA and the GYA. If either TGCA or SCA effects are significant, blend yields would be expected to be significantly different than the average of their two component cultivars. Cultivars with positive TGCA effects are those that capitalize on or contribute to the stabilizing and buffering effects of blending.

Choosing the most efficient strategy for evaluating and selecting genotypes for use in blends requires knowledge of the relative importance of GYA, TGCA, and SCA effects in blends. If SCA effects are important, then superior blend combinations cannot be predicted on the basis of average blend responses of component cultivars or cultivar pure-line performance. In this case, as many combinations of genotypes as possible should be evaluated in order to have a good probability of identifying the best blends. If SCA effects are not important but variation for TGCA effects among cultivars exists, then superior blends can be developed by mixing genotypes with superior average blending responses. In this case, the most efficient breeding procedure would be to evaluate genotypes in blend combinations with a subset of tester genotypes and to select those genotypes with best average blending ability for use in blends. Finally, if blends are superior to pure lines but SCA effects are unimportant and variation among genotypes for TGCA effects is lacking, then superior blends can be identified simply on the basis of pure-line evaluations. Whereas Shorter and Frey (1979) performed a diallel analysis of oat blends and found that SCA effects were not important, they did not partition GBA into GYA and TGCA to identify optimal breeding procedures for blends. Furthermore, most of the genotypes in their study were selected from the same population, resulting in a limited sample of the genetic diversity available for use in oat cultivar blends. Estimation of the relative importance of GYA, TGCA, and SCA effects in a genetically broader sample of modern oat cultivars is needed to determine if blending current oat cultivars is warranted to enhance productivity or stability and to identify the most efficient method of identifying superior blends.

We investigated the effects of blending modern oat cultivars on grain yield and volume weight means and stabilities in two experiments. The objectives of these experiments were (i) to determine whether blend yields and volume weights were greater than those of pure lines, (ii) to compare yield stability of blends and pure lines, and (iii) to identify the genotypic sources of blend response in oat cultivar blends with the goal of providing breeders with a method for selecting effective cultivar combinations.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Experimental Design and Observations
Two separate experiments were performed to evaluate early-maturing and midseason-maturing cultivars of oat. To simplify the mechanical harvesting of plots, blends were developed by mixing cultivars of the same maturity class. In the first trial, five early-maturing cultivars (Dane, Don, Horicon, Sheldon, and Starter) were grown as pure lines, all possible two-cultivar blends, and all possible three-cultivar blends for a total of 25 entries. In the second trial, 10 midseason-maturing cultivars (Blaze, Burton, Chaps, Jerry, Jim, Newdak, Ogle, Prairie, Premier, and Rodeo) were evaluated as pure lines and as all two-way cultivar blends. With the addition of one experimental check line, IAR56-5, there were 56 entries in the midseason-maturity experiment. Blends were developed by compositing equal numbers of seeds of each component line and mixing thoroughly before planting.

Both experiments were grown during 1998 and 1999 at Ames (central Iowa), Nashua (northeastern Iowa), Crawfordsville (east central Iowa), and Lewis (west central Iowa). Soils at each location were: Nicollet silty loam (fine-loamy, mixed, superactive, mesic Aquic Hapludoll) at Ames, Readlyn loam (fine-loamy, mixed, mesic Aquic Hapludoll) at Nashua, Mahaska silty clay loam (fine, smectitic, mesic Aquic Argiudoll) at Crawfordsville, and Marshall silty clay loam (fine-silty, mixed, superactive, mesic Typic Hapludoll) at Lewis. The experimental designs were a 5 x 5 square lattice for the early-maturity experiment and a 7 x 8 rectangular lattice for the midseason experiment. There were three replications of each experiment within each environment. Plots were 3.72 m² and consisted of four rows, each spaced 30 cm apart. Plots were sown at a rate of 1000 seeds per plot.

Flowering date was recorded at Ames as the number of days after planting when 50% of the panicles in each plot were fully emerged. Reactions to natural infections of crown rust (Puccinia coronata Corda var. avenae W.P. Fraser and Ledingham) were rated twice at 1-wk intervals within the 2 wk following mean heading date of all entries at Ames and Nashua in both years. Ratings were based on a nine-point combined scale of incidence and severity (Holland et al., 1998). Plots were machine-harvested and grain yield (kg ha^-1) and volume weight (kg m^-3) were measured on every plot.

Statistical Analysis
Analyses of variance for each trait within and across environments were obtained using the SAS procedure general linear models (GLM; SAS Institute, 1985). Crossover genotype-by-environment interactions were identified as instances in which the difference between two genotypes' mean values was significantly (P < 0.05) positive in at least one environment and significantly negative in at least one other environment. A genotype's stability for yield and volume weight was estimated using Shukla's (1972) measure of genotype-by-environment interaction variance (²_i),

[1]

where

[2]

and p is the number of genotypes, q is the number of environments, X_ij is the observed mean value of genotype i in environment j, and is the overall mean. Grain yield and volume weight adaptabilities over environments were measured using Lin and Binns' (1988) superiority statistic, P_i, which is defined as

[3]

where X_ij is the mean value of the ith cultivar grown in the jth environment, M_j is the maximum mean response in the jth environment, and n is the number of environments tested. Sampling variances for P_i and ²_i estimators were obtained with the jackknife procedure (Weir, 1996). Average stability and superiority of blends and pure lines were compared using a one-way analysis of variance in SAS procedure GLM (SAS Institute, 1985), in which the variation among entries within each group was used to test the significance of the mean differences.

Diallel analyses of yield and volume weight in both experiments were performed for blend response and blend performance per se according to Gizlice et al. (1989). Models from Federer et al. (1982) were used to describe our results for blends:

[4]

and for pure lines:

[5]

In this model, Y_hij is a mean value for a blend of genotypes i and j over replications at one environment, µ is the general mean effect, _h is the hth environmental effect, _i is the deviation of the ith pure-line genotype from the mean of all pure lines (two times GYA), _i/2 is the TGCA of the ith genotype (in blends), _ij is the SCA of genotypes i and j when grown together, and _hij is a genotype-by-environment interaction effect. TGCA effects were estimated from the analysis of blend response. The genotype-by-environment effect was further partitioned into GYA-by-environment, TGCA-by-environment, and SCA-by-environment effects. Mean squares for each effect were calculated using the SAS procedure GLM (SAS Institute, 1985). Mean separations were based on Fisher's protected LSD at P < 0.05.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Effects of Blending on Grain Yield and Volume Weight
Average grain yields and volume weights varied among the eight environments (Table 1). Highest yields were observed at Ames in 1999, with average yields of 4248 kg ha^-1 for the early-maturity experiment and 4787 kg ha^-1 for the midseason-maturity experiments. The environment with the lowest yields was Crawfordsville in 1998, where the average yields were only 1867 kg ha^-1 in the early-maturity experiment and 1762 kg ha^-1 in the midseason-maturity experiment. Mean volume weights ranged from 433 to 464 kg m^-3 and 450 to 551 kg m^-3 across environments in the early and midseason-maturity experiments, respectively. Crown rust infection was observed in all environments, but disease reaction scores did not differ among pure-line cultivars in any environment in which they were measured. Thus, it is unlikely that crown rust resistance contributed to blend responses in this study.

View this table: [in this window] [in a new window]   Table 2. Mean heading date, grain yield, volume weight, Shukla's genotype-by-environment stability variance (2i), and Lin and Binns' (1988) adaptability parameter (Pi) for five early-maturing pure-line oat cultivars and 10 two-way and 10 three-way cultivar blends evaluated at eight Iowa environments.

View this table: [in this window] [in a new window]   Table 3. Heading date, grain yield, volume weight, Shukla's genotype-by-environment stability variance (2i), and Lin and Binn's adaptability parameter (Pi) for 10 midseason-maturing pure-line oat cultivars and 45 two-way cultivar blends evaluated at eight Iowa environments.

Averaged over environments, blend volume weights were greater than pure-line volume weights in the early-maturity experiment, but not in the midseason-maturity experiment (Tables 1–5). The entry with the highest ranking for volume weight in the early-maturity experiment was a pure line, Starter (Table 2), but Starter was not different than the highest ranked blend, Horicon/Starter. In the midseason-maturity experiment, the pure line with the highest volume weight, Jerry, was not different from the best blends, Blaze/Jerry and Jerry/Premier (Table 3). In both experiments, the blend with the highest volume weight included the best pure line, suggesting that blend performance was largely determined by pure-line performance. Because the highest-ranked entries for volume weight in both experiments were pure-line cultivars, and because grain uniformity may be important to farmers who market their grain for milling for human consumption, blending may have unfavorable effects on volume weight. Farmers wishing to use blends should grow cultivar blends that will produce sufficient grain uniformity to satisfy their marketing needs.

Two- and three-component blend yields and volume weights were compared in the early-maturity experiment. No difference existed between the two types of blends for either grain yield or volume weight (Tables 2 and 4). The mean yields of two- and three-way blends were 3321 and 3356 kg ha^-1, respectively, and the mean volume weights of two- and three-way blends were 476 and 475 kg m^-3, respectively. Increasing the number of cultivars included in a blend increases genetic diversity, but it did not affect blend performance. Frey and Maldonado (1967) obtained similar results from comparisons of two to six component blends in oat, and Clay and Allard (1969) concluded that barley (Hordeum vulgare L.) blends containing up to 10 components were no better than two-way blends.

Effects of Blending on Stability
Three of the ten possible comparisons of pure-line cultivars in the early-maturity experiment exhibited significant crossover genotype-by-environment interactions for grain yield. In the midseason-maturity experiment, among 45 pure-line cultivar comparisons, 10 comparisons of yield and 6 of volume weight exhibited significant crossover genotype-by-environment interactions. The existence of numerous significant genotype-by-environment interactions among a set of elite cultivars within the target set of Iowa oat-growing environments suggests that improvements in yield stability, as well as yield potential, are desirable. We used two statistics to measure the stability or adaptability of the entries in our experiment to determine if blending cultivars results in improved stability of grain yield or volume weight.

Shukla's stability variance (Shukla, 1972) is an unbiased estimate of the genotype-by-environment interaction variance for a given genotype. According to Shukla (1972), cultivars with smaller genotype-by-environment interaction variances (²_i) are more stable. On average, Shukla's genotype-by-environment stability variance was smaller for blends than pure lines in the early-maturity experiment, but not the midseason-maturity experiment (Tables 2 and 3). Although volume weight stability was not significantly better in blends than in pure lines, the blend with the smallest Shukla's variance for volume weight in the early-maturity experiment (Dane/Starter) was significantly more stable than the most stable pure line (Sheldon) (Table 2).

One disadvantage of Shukla's stability measure is that it does not provide any information on the magnitude of yield of the cultivars. A cultivar that has a constant response to environments may be very stable, but if it is consistently lower yielding, it is not useful to the producer. Lin and Binns' (1988) adaptability parameter (P_i) compares the yields of test cultivars with the greatest-yielding cultivar within each location in the experiment, rather than with the mean yield of all cultivars. Smaller values of P_i reflect greater adaptability of an entry across environments. The difference between Shukla's stability variance and Lin and Binn's adaptability parameter is demonstrated by their correlations with mean yield in this study. In the midseason-maturity experiment, Shukla's variance had no correlation with mean yield (r = -0.039, P = 0.78), demonstrating that a cultivar's yield potential has little to do with its stability. Lin and Binns' adaptability parameter, however, was highly negatively correlated with grain yield (r = -0.964, P < 0.0001) because the adaptability parameter measures the magnitude as well as the consistency of yield across environments. Blends were significantly better adapted than pure lines on average, according to Lin and Binns' adaptability parameter for yield in the early-maturity trial, but were not different than pure lines in the midseason experiment (Tables 2 and 3).

Competing Ability Analysis
Our results indicated that oat yields can sometimes be increased through the blending of particular cultivars. However, all blends were not better than all pure lines, and blend response was not consistent among all blends, so methods for identifying superior blends would be helpful to farmers and plant breeders. Comparison of the relative importance of variation in GYA, GBA, TGCA, and SCA effects can guide breeding efforts aimed at developing improved cultivar blends. SCA and TGCA are measures of blending performance that exclude innate pure-line yielding effects (GYA). In our experiments, SCA effects were not significant (Tables 4 and 5), indicating that statistical interactions between cultivars in blends were not important. This lack of SCA simplifies the identification of superior blends because it makes expensive evaluations of all possible blend combinations unnecessary.

Variation among TGCA effects was also not significant in both experiments (Tables 4–7). This implies that no cultivar has a general blending ability that is significantly better or worse than others. The average of cultivar TGCA effects is equal to half the mean blend response. The significance of blend response in the early-maturity experiment indicates that the mean TGCA effect of early-maturity cultivars was greater than zero; nevertheless, there was no significant variation among cultivar TGCA effects. This implies that while early-maturity cultivars in general responded positively to blending, the positive blend response was sufficiently consistent among cultivars that superior blend components can be selected efficiently on the basis of pure-line evaluations. Testing of oat blends is not necessarily required to identify superior blend components. Further evidence of this was that the significant pure-line GYA and GBA effects for grain yield and volume weight in the early- and midseason-maturity experiments (Tables 6 and 7) were highly correlated (r = 0.85, P < 0.01 for effects on yield in the early-maturity experiment, and r = 0.95, P < 0.0001 for effects on yield in the midseason-maturity experiment). Finally, blends of the greatest-yielding pure lines within each experiment (Dane/Horicon in the early-maturity experiment and Chaps/Rodeo in the midseason-maturity experiment) were not significantly different than the best blends within each experiment for yield (Tables 2 and 3).

View this table: [in this window] [in a new window]   Table 7. General blending ability (GBA), general yielding ability (GYA), and true general competing ability (TGCA) effects for yield and volume weight of 10 midseason-maturing spring oat cultivars grown as pure lines and all possible two-way blends in eight Iowa environments. i = the deviation of the ith pure-line genotype from the mean of all pure lines, and i/2 = the TGCA of the ith genotype.

View this table: [in this window] [in a new window]   Table 6. General blending ability (GBA), general yielding ability (GYA), and true general competing ability (TGCA) effects for yield and volume weight of five early-maturing spring oat cultivars grown as pure lines and all possible two-way blends in eight Iowa environments. i = the deviation of the ith pure-line genotype from the mean of all pure lines, and i/2 = the TGCA of the ith genotype.

	ACKNOWLEDGMENTS

 
This work was supported in part by a competitive grant from the Leopold Center for Sustainable Agriculture and by the Department of Agronomy, Iowa State University.

Received for publication August 14, 2000.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 

• Allard, R.W., and A.D. Bradshaw. 1964. Implications of genotype-environmental interactions in applied plant breeding. Crop Sci. 4:503–508.[Free Full Text]
• Brummer, E.C. 1998. Diversity, stability, and sustainable American agriculture. Agron. J. 90:1–2.[Abstract/Free Full Text]
• Clay, R.E., and R.W. Allard. 1969. A comparison of the performance of homogeneous and heterogeneous barley populations. Crop Sci. 9:407–412.
• Evans, L.T. 1993. Crop evolution, adaptation, and yield. Cambridge University Press, Cambridge.
• Federer, W.T., J.C. Connigale, J.N. Rutger, and A. Wijesinha. 1982. Statistical analyses of yields from uniblends and biblends of eight dry bean cultivars. Crop Sci. 22:111–115.
• Finkh, M.R., and C.C. Mundt. 1992. Stripe rust, yield, and plant competition in wheat cultivar mixtures. Phytopathology 82:905–913.
• Frey, K.J., and U. Maldonado. 1967. Relative productivity of homogeneous and heterogeneous oat cultivars in optimum and suboptimum environments. Crop Sci. 7:532–535.[Abstract/Free Full Text]
• Gantzer, C.J., S.H. Anderson, A.L. Thompson, and J.R. Brown. 1991. Evaluation of soil loss after one hundred years of soil and crop management. Agron. J. 83:74–77.[Abstract/Free Full Text]
• Gizlice, Z., T.E. Carter, J.W. Burton, and T.E. Emigh. 1989. Partitioning of blending ability using two-way blends and component lines of soybean. Crop Sci. 29:885–889.[Abstract/Free Full Text]
• Holland, J.B., D.V. Uhr, D. Jeffers, and M.M. Goodman. 1998. Inheritance of resistance to southern corn rust in tropical-by-corn-belt maize populations. Theor. Appl. Genet. 96:232–241.
• Liebman, M., and E. Dyck. 1993. Crop rotation and intercropping strategies for weed management. Ecol. Applic. 3:92–122.
• Lin, C.S., and M.R. Binns. 1988. A superiority measure of cultivar performance for cultivar x location data. Can. J. Plant Sci. 68:193–198.
• Mundt, C.C., L.S. Brophy, and M.S. Schmitt. 1995. Choosing crop cultivars and cultivar mixtures under low versus high disease pressure: a case study with wheat. Crop Prot. 14:509–515.
• Pfahler, P.L. 1965. Environmental variability and genetic diversity within populations of oats (cultivated species of Avena) and rye (Secale cereale L.). Crop Sci. 5:271–275.[Free Full Text]
• Power, H.G. 1991. Virus spread and vector dynamics in genetically diverse populations. Ecology 82:232–241.
• SAS Institute. 1985. SAS user's guide: Statistics. 5th ed. SAS Inst., Cary, NC.
• Shorter, R., and K.J. Frey. 1979. Relative yields of mixtures and monocultures of oat genotypes. Crop Sci. 19:548–553.[Abstract/Free Full Text]
• Shukla, G.K. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237–245.[Web of Science][Medline]
• Smithson, J.B., and J.M. Lenné. 1996. Varietal mixtures: a viable strategy for sustainable productivity in subsistence agriculture. Ann. Appl. Biol. 128:127–158.[Web of Science]
• Sprague, G.F., and L.A. Tatum. 1942. General vs. specific combining ability in single crosses of corn. J. Am. Soc. Agron. 34:923–32.
• Trimble, M.W., and W.R. Fehr. 1983. Mixtures of soybean cultivars to minimize yield loss caused by deficiency chlorosis. Crop Sci. 23:691–694.[Abstract/Free Full Text]
• USDA-National Agricultural Statistics Service. 1998. Iowa agricultural statistics. [Online]. [1 p.] Available at http://www.nass.usda.gov/ia/ [cited June 2000; modified 20 April 2001; verified 22 May 2001]. USDA-NASS, Washington, DC.
• Weir, B.S. 1996. Genetic Data Analysis II. Sinnauer Associates, Inc., Sunderland, MA.

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This Article Abstract Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Helland, S. J. Articles by Holland, J. B. Search for Related Content PubMed Articles by Helland, S. J. Articles by Holland, J. B. Agricola Articles by Helland, S. J. Articles by Holland, J. B. Related Collections Intercropping Systems Other Grain Crops