Traits associated with dry edible bean (Phaseolus vulgaris L.) productivity under diverse soil moisture environments

Transcription

1 Euphytica 133: , Kluwer Academic Publishers. Printed in the Netherlands. 339 Traits associated with dry edible bean (Phaseolus vulgaris L.) productivity under diverse soil moisture environments Amare Abebe Shenkut & Mark A. Brick Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, U.S.A.; ( author for correspondence) Received 9 July 2002; accepted 10 June 2003 Key words: breeding, common bean, drought stress, productivity, Phaseolus vulgaris L., seed yield Summary Two experiments were conducted in the Rift Valley, Ethiopia (8 N and 39 E) to determine associations between eight plant traits and seed yield, and to obtain estimates of narrow sense heritability for the traits. Experiment I evaluated seven dry edible bean cultivars/lines at two locations to simulate different soil moisture stress, including, Debre Zeit (non-stress) and Dera (moderate-stress). Experiment II evaluated 25 cultivars/lines in three environments including, Melkassa early planted (non-stress), Melkassa late planted (high-stress), and Dera (moderate-stress). A randomized-complete-block design with three replicates was used in both experiments. Plant traits evaluated were seed yield, pods plant 1, seeds pod 1, 100 seed weight, root dry weight, hypocotyl diameter, plant biomass, plantheightand daysto flowering. Plant traits that were significantlyassociatedwith seed yield were included in a stepwise-regression model to determine which trait or combination of traits provided the best model to estimate seed yield in each environment. An analysis of variance was conducted to test main effects and interactions between plant traits and environments. Significant variation among lines occurred for seed yield and all plant traits in both experiments. Strong positive correlations were observed between plant biomass and seed yield in all environments. Seed yield and pods plant 1 were also highly associated in four of the five environments. Stepwise regression models indicated that the combination of pods plant 1 and plant biomass consistently contributed to seed yield prediction, while other traits did not. Because both plant biomass and pods plant 1 had moderate to high narrow sense heritability estimates and low GE interactions, they should be useful as indirect selection criteria to improve and stabilize seed yield in a breeding program. Abbreviations: ANOVA analysis of variance; GE genotype by environment interaction; GY genotype by year interaction; GYE genotype by year by environment interaction; R 2 coefficient of determination Introduction Dry edible bean is the major protein and cash crop for many farmers in the Rift Valley, East Africa, where seasonal rainfall is erratic and soil moisture deficit often limits production. Dry bean production varies greatly from year to year and region to region in Ethiopia, with the national average ranging from 600 to 700 kg ha 1 (CSA, 1992). To alleviate the risk of drastic yield reduction due to insufficient seasonal rainfall, bean varieties that are stable in yield performance across soil moisture stress and non-stress environments are needed. To improve and stabilize seed yield under diverse environmental conditions, dry bean breeders need to identify plant traits that are positively associated with seed yield and have minimal interactions with environments. These traits can then be used as effective selection criteria to improve seed yield in an applied breeding program. Many studies have demonstrated genetic variation for seed yield among bean cultivars/lines under soil moisture stress and non-stress environments (Robins & Domingo, 1956; Acosta-Gallegos & Kohashi- Shibata, 1989; White & Singh, 1991; Abebe, 1994;

2 340 Singh, 1995; Graham & Ranalli, 1997; Schneider et al., 1997; Ramirez-Vallejo & Kelly, 1998; Barron et al., 1999; Singh, 2001). Because seed yield is a complex trait that is highly influenced by the environment, selection programs based solely on seed yield often fail to increase yield. Indirect selection methods based on yield components have been suggested for selection strategies to improve yield of cereal crops, and to determine associations with seed yield (Grafius, 1964; Rasmusson, 1987; Frey et al., 1988; McMullan et al., 1988; Terán & Singh, 2002). Yield components of dry beans have also been studied to determine their associations with seed yield. Singh (1995) reported low, negative correlations between dry bean seed yield and seed weight in both non-stress and stress environments. His results suggest that correlations between seed yield and seed weight were inconsistent across environments. Inconsistent correlations between yield components and seed yield across stress and nonstress environments were also reported by Ramirez- Vallejo & Kelly (1998). Nienhuis & Singh (1988) showed positive correlations between seed yield and yield components of dry bean, and Westermann & Crothers (1977) demonstrated that seed yield was highly correlated with pod number plant 1, but not seed number pod 1 or seed weight among bean cultivars. Westermann & Crothers (1977) results implied that the pod number plant 1 had a greater influence on seed yield than seed number pod 1 or seed weight. The lack of consistent associations between yield components and seed yield suggests that individual yield components are not always practical to develop varieties with stable performance across a broad range of environments. A more thorough basis for predicting seed yield should involve the development of a model using a combination of plant traits, including traits other than yield components, that accounts for the variation in seed yield (Hocking, 1976; Willman et, al., 1987). To be useful as selection criterion, traits should be highly correlated with seed yield, have low genotype by environment interactions (GE) and highly heritable (Yuan, et al., 2002; Rao et al., 2002). Heritability estimates reported for dry bean traits differ among authors due to differences among environments, methods used to estimate heritability, and genotypes used to calculate the estimates. Heritability estimates for seed yield ranged from zero to 0.18 according to Zimmerman et al. (1984b), from 0.26 to 0.80 according to Singh (1995) and from 0.16 to 0.46 according to Ramirez-Vallejo & Kelly, Estimates of heritability for plant traits such as pods plant 1, Table 1. Soil organic carbon (OC), nitrogen (N), available phosphorous (P), and ph at the three test sites Location OC N P ph % % ppm Debre Zeit Melkassa Dera seeds pod 1, and seed weight were high according to Zimmerman et al. (1984a) and Ramirez-Vallejo & Kelly (1998), and moderate to low according to Nienhuis & Singh (1988). Because heritability estimates are of major importance in a breeding program, reliable measurements are essential to interpret the value of the trait to a selection program. To improve the precision in heritability estimates, exact confidence intervals can be used as suggested by Knapp (1985). The objectives of this research were to identify plant traits in dry bean that are highly correlated with seed yield, have low GE interactions, and high estimates of narrow sense heritability across a diverse set of soil moisture-stress environments. This information should identify plant traits that can be used as selection criteria to develop bean varieties adapted to environments where seasonal rainfall is erratic. Materials and methods Two experiments were conducted in the Rift Valley, Ethiopia (8 N and 39 E) in 1992 and The experiments were conducted at three locations including, Debre Zeit, Melkassa and Dera, located 1650, 1550 and 1500 masl, respectively. The Debre Zeit and Dera sites were in farmers fields, and the Melkassa site was at the Melkasa Research Center (MRC), Institute of Agricultural Research, Nazreth, Ethiopia. In general, Debre Zeit, Melkassa, and Dera are considered non-stress, intermediate-stress, and high soil moisture stress environments, respectively, based on the amount of seasonal rainfall at each location. Debre Zeit had higher mean rainfall, lower mean maximum, and minimum air temperatures than Melkassa and Dera during the study. Historically soil moisture deficit is not a limitation to crop production at Debre Zeit, while it occurs occasionally at Melkassa and often at Dera. The soil textures at Debre Zeit, Melkassa, and Dera experimental sites are clay, sandy loam, and clay loam

3 341 Table 2. Twenty five cultivars/lines tested in experiment I and II, origin, seed color, mean 100 seed weight, plant height, days to flowering, and days to maturity over three locations (Melkassa Early Planting, Dera, and Melkassa Late Planting) Varieties Origin Seed color 100 seed Plant Days to Days to weight height flowering maturity g cm No. No. Harold MAD Pink G2816 MAD Cream Ex Rico 23 MAM White Rosa MAD Pink A 422 MAD Brown speckled AND 338 ANNG Red speckled New 590 MAD Pinto A 410 MAD Cream ICA ANNG Cream AND 197 ANNG Red speckled G5059 MAM Cream V8025 MAM Black G 4446 MAD Dark cream BAT 477 MAM Cream PAN 133 MAM White Olathe MAD Pinto EMP 175 MAM White BAT 388 1C MAM White Aguacaliente MAD Cream G4830 MAM Black Viva MAD Pink EMP 105 MAM Dark red A 154 MAM Cream BAT 798 MAM Black G 5201 MAM Black Mean LSD Cultivars/lines tested in both experiment I and II. MAD = Middle America, Race Duango, MAM = Middle America, Race Mesoamerican; ANNG = Andean, Race Nueva Granada. (Calceric-cambisol, FAO classification), respectively. Soil organic carbon (OC%) nitrogen (N%), available phosphorus and ph for the three locations are shown in Table 1. Experiment I evaluated seven dry edible bean cultivars/lines (Table 2) for seed yield and eight plant traits at Debre Zeit and Dera in 1992 and The non-stress environment at Debra Zeit received 560 and 629 mm seasonal rainfall (June through October) in 1992 and 1993, and the moderate-stress environment at Dera received 345 and 384 mm in 1992 and 1993, respectively. The seven cultivars/lines were hand planted in experimental units that consisted of four rows 5 m long, spaced 0.60 m apart. Seeds were spaced 0.10 m apart within the row. A randomizedcomplete-block design with three replicates was used. Plots were hand weeded throughout the growing seasons. Only, the middle two rows in each experimental plot were used for data collection. Experiment II was conducted to obtain independent estimates of narrow sense heritability and associations between plant traits and seed yield using a broader group of cultivars and lines (Table 2). All cultural and experimental practices were similar to Experiment I. Twenty-five dry bean cultivars/lines were tested in three environments during The cultivars/lines were composed of the same seven lines in Experiment I, plus an additional 18 lines selected

4 342 from local adaptation trials. The three environments included two at Melkassa, classified as non-stress and high-stress environments, and one at Dera classified as a moderate-stress environment. The non-stress and high-stress environments at Melkassa were achieved by modification of planting date. The optimum sowing date for dry bean at Melkassa Research Center is prior to the onset of the rainy-season, which normally begins during June or early July and terminates during mid to late August. The early trial was planted 5 July and the late trial planted 5 August. The earlyplanted trial received 528 mm of rainfall during the growing season, while the late-planted trial received 384 mm. The early trial did not undergo soil moisture deficit during the critical pod set and seed fill stages of growth, while the late trial suffered soil moisture deficit during these periods. The Dera trial received 420 mm of rainfall during the growing season. Days to flower, plant height, seed yield, and seed weight were recorded based on measurements and observations made on the entire middle two rows of each experimental unit. Flowering date was recorded by visual observations, and date to first flower was considered when 50% of the plants in the center two rows had one open flower. Plant height was measured at harvest maturity in each experimental unit as the distance from the soil surface to the top of the canopy. Seed yield was calculated from the entire middle two rows, which were hand harvested and threshed. Harvested seeds were dried at room temperature to 13% moisture prior to weighing. Five hundred seeds from the yield of each experimental unit were used to determine 100 seed weight. The remainder of plant traits and biomass were measured from a 0.5 m linear sub-section of the middle two rows two weeks before harvest. Each linear section was chosen within the experimental units based on even plant uniformity and distribution and contained 5 plants. Plants within the area were excavated with a shovel to a depth of approximately 0.30 m. The soil was carefully removed from the roots by gently shaking and each plant was dissected into root, hypocotyl (the plant stem from the soil surface to the first true leaf scar), shoot, and pods. Traits evaluated included, root dry weight, hypocotyl diameter, seeds pod 1 and pods plant 1. Hypocotyl diameter was measured on each plant with a vernier caliper immediately after plants were removed from the soil. All plant parts, excluding seeds, were oven dried for 24 hours at 100 C prior to weighing. Total biomass was obtained by adding the dry weight of roots and above ground plant parts. Narrow sense heritability estimates for all traits were calculated using the components of variance method (Hallauer & Miranda, 1988). Heritability estimates for each trait were based on plot means from variance components derived from the ANOVA across environments (Tables 3, 4). Heritability estimates for days to flower and plant height were based on measurements made on the entire middle two rows of each experimental unit. Estimates for seed yield, pods plant 1, seeds pod 1, 100 seed weight, root dry weight, hypocotyl diameter, and plant biomass were based on observations made on the 0.5 m sub-section of each experimental unit. Lines (genotypes) were treated as random effects and locations as fixed effects in Experiments I and II. Years were treated as a random effect in Experiment I. Narrow sense heritability was calculated as follows: h 2 = σ 2 g /(σ 2 g + σ 2 e/ry + σ 2 gy/ Y) Where: σ 2 g = genetic variance due to lines (genotypes), σ 2 e = experimental error variance, r = number of replicates, Y = number of years and σ 2 gy =genotype year interaction. Exact confidence intervals were calculated according to Knapp (1985). To compute GE interactions for seed yield and plant traits, a combined analysis of variance across years and environments (locations) was performed according to McIntosh (1983). Environments were considered fixed effects, while genotypes and years were considered random effects. Data from the entire middle two rows of each experimental unit was used to calculate correlations with seed yield, seed weight, plant height and days to flower. Data from the 0.5 m linear sub-section of each experimental unit was used to calculate correlations with pods plant 1, seeds pod 1, root dry weight, hypocotyl diameter, and plant biomass. Phenotypic correlations between seed yield and plant traits were calculated as plot means over two years within environments in Experiment I, and within environments for Experiment II. To determine plant traits that accounted for the largest proportion of variation among lines across environments, stepwise multiple regression procedures as described by Kleinbaum et al. (1988) were performed using PC-SAS (SAS Institute, 1988). Plant traits were used as independent variables and seed yield as the dependent variable. After the first independent variable is added to the model, stepwise regression adds those variables that are not auto-

5 Table 3. Mean squares for seed yield and eight plant traits among seven dry bean lines/cultivars (genotypes) grown in two environments during 1992 and 1993 at Debre Zeit (non-stress) and Dera, (moderate-stress) in Experiment I Sources of df Seed yield Pods Seeds 100 Seed Days to Biomass Plant Hypocotyl Root dry kg ha 1 plant 1 pod 1 weight flower g/plant height diameter weight No. No. g No. cm mm g Year (Y) 1 41,513, ,046 1, Environment (E) 1 55,515,656 3, , Y E 1 4,761, Reps/ Y E 8 970, Genotype (G) 6 1,569, G E 6 380, G Y 6 892, G E Y 6 410, Pooled error , Mean CV ,,and implies significance at p<0.05, p<0.01 and p<0.001, respectively. Table 4. Mean squares for seed yield and eight plant traits among twenty-five dry bean lines/cultivars (genotypes) grown in 1993 at Melkassa early planting (non-stress environment), Melkassa late planting (high-stress environment), and Dera (moderate-stress environment) from Experiment II Sources of df Seed yield Pods Seeds 100 Seed Days to Biomass Plant Hypocotyl Root dry kg ha 1 plant 1 pod 1 weight flower g/plant height diameter weight No. No. g No. cm mm g Rep/E , Environments (E) 2 52,110, , Genotype (G) 24 1,657, , G E , , Pooled error , , , Mean CV% ,,and implies significance at p<0.05, p<0.01 and p<0.001, respectively. correlated and significantly improve the model. Variables that do not produce an F statistic at p < 0.05 level when added to the model are dropped. Since there is no single criterion to always select the best model, three selection criteria including Mallows Cp selection (Mallows, 1967), adjusted R 2, and multiplepartial F test, as reviewed by Hocking (1976), were used to compare full and restricted models in the stepwise regression summary. The Cp selection criterion was used to decide how many variables to put in the best model, where Cp values near p +1, where p is the number of variables in the model, are considered optimum. The F test was used to compare the partial R 2 of the variables in the best model. The model with the highest R 2 as a single trait or in combination with other traits, generated by stepwise regression methods, was then considered as the best model for prediction of seed yield in the respective environment. The statistical information, Cp, partial R 2, multiple-partial F test, and adjusted R 2 were generated by procedures of SAS (1988). Results and discussion The analyses of variance for seed yield and plant traits among the cutivars/lines in Experiments I and II are shown in Tables 3 & 4, respectively. Significant variation among cultivars/lines occurred for seed yield and all plant traits evaluated in both experiments. In Ex-

6 344 Table 5. Correlation coefficients between seed yield and plant traits of seven dry bean lines/cultivars grown under non-stress environment (Debre Zeit) and moderate-stress environment (Dera) in Experiment I, and twenty five lines/cultivars grown under a non-stress environment (Melkassa early planting), moderate-stress environment (Dera), and high-stress environment (Melkassa late planting) in Experiment II Plant traits Experiment I (n = 21) Experiment II (n = 75) Non-stress Moderate-stress Non-stress Moderate-stress High-stress Pods plant 1 (No.) Seeds pod 1 (No.) Root dry weight (g) Hypocotyl dia. (mm) Total biomass (g/plant) Plant height (cm) Days to flowering (No.) seed weight (g) ,,and implies significance at p<0.05, p<0.01 and p<0.001, respectively. periment I, the three-way interaction among genotypes (cultivars/lines = g), environments (E), and years (Y) was only significant (p <0.05) for seed yield and pods plant 1. The GY and GE interactions were significant for seed yield, seeds weight, days to flower, and plant height. Plant traits including seeds pod 1, biomass and hypocotyl diameter did not have GE interactions, hence genotype performance was consistent across environments and years for these traits. In Experiment II, GE interactions were significant for all traits except days to flower, hypocotyl diameter, and root dry weight. These results indicate that among the traits studied only hypocotyl diameter did not interact with the environment in either experiment. At a more conservative error rate (p <.010), pods plant 1 and root dry weight did not interact with environments in either experiment. Plant traits that have consistent performance among lines across environments, should provide good selection criteria that would contribute to stability in performance across a range of environments, assuming they are positively correlated with seed yield and sufficient genetic variability is available. Phenotypic correlations between seed yield and plant traits in each of the five environments are shown in Table 5. Positive correlations were observed between biomass and seed yield across environments. Pods plant 1 was also correlated with seed yield in all environments except the non-stress environment in Experiment II. Seeds pod 1, root dry weight, hypocotyls diameter, plant height, and days to flower were correlated with yield in some environments, but not consistently across environments. Westernmann & Crothers (1977) also reported that seed yield was associated with pods plant 1 and Ramirez-Vallejo & Kelly (1998) found strong correlations between seed yield and above ground biomass. Our results clearly indicate that pods plant 1 and plant biomass were consistently correlated with seed yield across a range of environments, thus should be good candidates as selection criteria to improve seed yield in a breeding program. Seed yield is a function of many plant traits and their inter-relationships. The relationship between the individual traits and yield was used to develop a regression model to identify traits most closely associated with yield. In each environment, plant traits that were significantly associated with yield were included in stepwise-regression procedures to determine the model that best estimated seed yield. In the nonstress environment in Experiment I, five traits were correlated with yield including, pods plant 1, hypocotyl diameter, plant biomass, plant height, and days to flowering (Table 5). However, only pods plant 1, plant biomass and plant height contributed significantly to the multiple regression model to predict yield (Table 6). Combined, these three traits accounted for 65% of the variation in seed yield. The remaining traits did not improve the model. In the moderate-stress environment, traits including pods plant 1, hypocotyl diameter, biomass, and plant height were correlated with seed yield (Table 5). Pods plant 1 alone accounted for 44% of the variation in yield (Table 6). When plant biomass was added to the model, the model accounted for 50% of the variation in yield. The addition of other traits to the model did not improve the fit. In the non-stress environment in Experiment II, only plant biomass and root dry weight contributed

7 Table 6. Summary result from the multiple regression models for the regression of seed yield on plant traits in non-stress and moderate-stress environments in Experiment I, and non-stress, moderate-stress and high-stress environments in Experiment II Environments Mean yield Plant traits Partial R 2 Cp F P>F Model kg/ha R 2 Experiment I Non-stress (Debre Zeit) 2892 Pods plant Biomass Plant height Moderate-stress (Dera) 1266 Pods plant Biomass Experiment II Non-stress (Melkassa, Early Planting) 2541 Biomass Root dry wt Moderate-stress (Dera) 1718 Biomass Days to flower Pods plant High-stress (Melkassa Late Planting) 896 Biomass Pods plant Plant height Table 7. Mean values for seed yield and plant traits, narrow sense heritabilities (h 2 ) and confidence limits among dry bean lines grown in the Rift valley, Ethiopia. Calculations based on 7 lines/cultivars in two environments for two years for Experiment I and 25 lines/cultivars grown at three locations in one year for Experiment II Plant traits Experiment I Experiment II Mean h 2 Confidence limit (95%) Mean h 2 Confidence limit (95%) Upper Lower Upper Lower Seed yield (kg ha 1 ) Pods plant 1 (No.) Seeds pod 1 (No.) Root dry weight (g plant 1 ) Hypocotyl dia. (mm) Biomass (g plant 1 ) Plant height (cm) Days to flower (No.) seed weight (g) to the model (Table 6). This was the only model that did not include pods plant 1. In the moderate-stress environment, plant biomass, days to flower, and pods plant 1 contributed to yield prediction and accounted for 42% of the variation. In the high-stress environment, plant biomass alone accounted for 53% of the variation in gain yield, however both pods plant 1 and plant height improved the combined model to account for 60% of the variation. In summary, plant biomass was included as a significant component of the model to predict seed yield in all five models, and pods plant 1 in four of the five models. Because the stepwise regression model included both plant biomass and pods plant 1 in four of the five models, these traits are not highly inter-correlated, thus should make them useful as simultaneous selection criteria to improve seed yield. One must use caution with plant biomass as it is known to be negatively correlated with harvest maturity, and should only be used within adapted germplasm as a selection criterion. In this study, all cultivars/lines were well adapted to the test environ-

8 346 ment and days to harvest maturity only varied from 72 to 84 days (Table 2). To use plant traits as indirect selection criteria for seed yield, they must have high heritability. Table 7 shows narrow sense heritability estimates for traits measured among the lines in Experiments I and II. Narrow sense heritability estimates were significantly different from zero for all traits except seed yield in both Experiments. Low and/or non-significant estimates for seed yield have been reported by others including, Chung and Stevenson (1973), Conti (1985), Motto et al. (1978), Sarafi (1978), Nienhuis & Singh (1988), Singh (1995), and Schneider et al. (1997). The low heritability estimates for seed yield point out the difficulty in making progress to improve seed yield across diverse environments if selection for seed yield alone is used as the sole selection criterion. Low heritablitiy estimates may also be due to the lack of genetic variation for seed yield among lines used in this study. In general, heritability estimates were high for seed weight and days to flower, and intermediate for pods plant 1, seeds pod 1, plant biomass, plant height, hypocotyl diameter, and root dry weight. All significant estimates for plant traits were greater than 0.40, indicating that sufficient genetic variability occurred among lines for these traits, and they should be easier to alter through selection than seed yield per se. Traits such as plant biomass and pods plant 1, that accounted for the largest proportion of variation in seed yield, were highly associated with yield across environments and should provide breeders with selection criteria that are both easy to identify and useful for improving dry bean seed yield in regions that have diverse soil moisture regimes such as the Rift Valley in Ethiopia. Conclusions In both experiments, estimates of heritability for seed yield were not different from zero and the magnitudes of the GE interactions were high. These results support the findings by others that selection for seed yield as the sole selection criterion in a single environment may not result in improved seed yield across a range of environments. A better method to improve seed yield across a range of environments may be to use traits that are highly associated with seed yield and stable across environments as indirect selection criterion. A stepwise regression model showed that the combination of pods 1 plant and plant biomass were consistently useful to predict seed yield, while other traits did not contribute to the model. Further, both plant biomass and pods plant 1 had higher narrow sense heritability estimates and lower GE interactions than seed yield. Because plant biomass and pods plant 1 can be more easily evaluated on a visual scale compared to other yield components, they should be useful as selection criterion for breeding programs with limited resources. Acknowledgements We gratefully acknowledge the support of the Eastern Africa Bean Improvement Program, the Institute for Agricultural Research, Ehthiopia, and the International Center for Tropical Agriculture, Cali, Colombia for providing resources and germplasm to conduct this research. References Acosta-Gallegos, J.A. & J. Kohashi-Shibata, Effect of water stress on growth and yield of intermediate dry bean (Phaseolus vulgaris) cultivars. Field Crops Res 20: Abebe, A., Evaluation of methods for screening drought tolerant dry bean lines in the Rift Valley of Ethiopia. Ph.D. dissertation, Colorado State Univ., USA. Barron, E., R.J. Pasini, D.W. Davis, D.D. Stuthman & P.H. Graham, Response to selection for seed yield and nitrogen (N2) fixation in common bean(phaseolus vulgaris L.). Field Crops Res 62: Chung, J.H. & E. Stevenson, Diallel analysis of the genetic variation in some quantitative traits in dry bean. Agric Res 16: CSA, Central Statistical Authority, Agricultural Sample Survey 1989/90: Results on Area, Production and Yield of Major Crops by Sector and Season. Statistical Bulletin 104. Addis Ababa, Ethiopia. Conti, L., Conclusive results of a selection programme for obtaining a dwarf bean (Phaseolus vulgaris L.) resistant to some viruses and characterized by agronomical qualities. Genet Agr 39: Frey, K.J., J.K. McFerson & C.V. Branson, A procedure for one cycle of recurrent selection per year with spring-sown small grains. Crop Sci 28: Grafius, J.E., A geometry for plant breeding. Crop Sci 4: Graham, P.H. & P. Ranalli, Common bean (Phaseolus vulgaris L.). Field Crops Res 53: Hallauer, A.R. & J.B. Miranda, Quantitative Genetics in Maize Breeding. 2nd edn. Iowa State University Press, Ames. Hocking, R.R., The analysis and selection of variables in linear regression. Biometric 32: Kleinbaum, D.G., L.L. Kupper & K.E. Muller, Applied Regression and other Multivariable Methods. 2nd edn. PWS-Kent Pub. Co., Boston, MA. Knapp, S.J., W.W. Stroup & W.M. Roos, Exact confidence intervals for heritability on a progeny mean basis. Crop Sci 25:

9 347 Mallows, C.L., Some comments on Cp. Technometrics 15: McIntosh, M.S., Analysis of combined experiments. Agron J 75: McMullan, P.M., P.B.E. McVetty & A.A. Urquhart, Dry matter and nitrogen accumulation, redistribution, and their relationship to grain yield and grain protein in oats. Can J Plant Sci 68: Motto, M., G.P. Soressi & F. Sallamini, Seed size inheritance in a cross between wild and cultivated common beans (Phaseolus vulgaris L.). Genetica 49: Nienhuis, J. & S.P. Singh, Genetics of seed yield and its components in common bean (Phaseolus vulgaris L.) of Middle-America origin. Plant Breed 101(2): Ramirez-Vallejo, P. & J.D. Kelly, Traits related to drought resistance in common bean. Euphytica 99: Rao, M.S.S., B.G. Mullinix, M. Rangappa, E. Cebert, A.S. Bhagsari, V.T. Sapra, J.M. Joshi & R.B. Dadson, Genotype environment interactions and yield stability of food-grade soybean genotypes. Agron J 94: Rasmusson, D.C., An evaluation of ideotype breeding. Crop Sci 27: Robins, J.S. & C.E. Domingo, Moisture deficits in relation to the growth and development of dry beans. Agron J 48: Sarafi, A., A yield-component selection experiment involving American and Iranian cultivars of common bean. Crop Sci 18: 5 7. SAS Institute, SAS Users Guide: Statistics. SAS Institute, Cary, NC. Singh, S.P., Breeding for seed yield. In: A. Van Schoonhoven & O. Voysest (Eds.), Common Beans: Research for Crop Improvement, pp C.A.B. Int., Wallingford, U.K. & CIAT, Cali, Colombia. Singh, P.S., Selection for water stress tolerance in interracial populations of common bean. Crop Sci 35: Singh, S.P., Broadening the genetic base of common bean cultivars. Crop Sci 41: Schneider, K.A., R. Rosales-Serna, F.J. Ibarra-Perez, B. Cazares- Enriquez, J.A. Acosta Gallegos, P. Ramirez-Vallejo, N. Wassimi & J.D. Kelly, Improving common bean performance under drought stress. Crop Sci 37: Terán, H. & Shree P. Singh, Comparison of sources and lines selected for drought resistance in common bean. Crop Sci 42: Westermann, D.T. & S.E. Crothers, Plant population effects on the seed yield components of bean. Crop Sci 17: Willman, M.R., F.E. Below, R.J. Lambert, A.E. Howey & D.W. Mies, Plant traits related to productivity of maize. II. Development of multiple traits models. Crop Sci 27: White, J.W. & S.P. Singh, Breeding for adaptation to drought. In: A. Van Schoonhoven & O. Voysest (Eds.), Common Beans: Research for Crop Improvement, pp C.A.B. Int., Wallingford, U.K. & CIAT, Cali, Colombia. Yuan, J., V.N. Njiti, K. Meksem, M.J. Iqbal, K. Triwitayakorn, M.Y.A. Kassem, G.T. Davis, M.E. Schmidt & D.A. Lightfoot, Quantitative trait loci in two soybean recombinant inbred line populations segregating for yield and disease resistance. Crop Sci 42: Zimmerman, M.J., J.G. Waines & K.W. Foster, 1984a. A heritability and correlation study of grain yield, yield components, and harvest index of common bean on sole crop and in intercrop. Field Crops Res 9: Zimmerman, M.J., J.G. Waines & K.W. Foster, 1984b. Heritability of grain yield of common bean in sole crop and in intercrop with maize. Crop Sci 24(4):

10