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Prediction of adaptability and yield stability of durum wheat genotypes from yield response in normal and artificially drought-stressed conditions
ELSEVIER
Field Crops Research 46 (1996) 71-80
Field Crops Research
Prediction of adaptability and yield stability of durum wheat genotypes from yield response in normal and artificially drought-stressed conditions P. Annicchiarico a,*, G. Mariani b a Istituto Sperimentale per le Colture Foraggere, 29 viale Piacenza, 1-20075 Lodi, Italy b Istituto Sperimentale per la Cerealicoltura, 176 via Cassia, 1-00191 Rome, Italy
Received 30 December 1994; accepted 26 October 1995
Abstract Adaptability, yield stability and yield reliability of durum wheat breeding lines are usually assessed through regional testing. The opportunity of partially substituting such testing by evaluation under normal and artificially drought-stressed rainfed conditions was investigated for a water-limited Italian region. Nine lines were grown at six sites for three seasons to assess adaptability across locations as Perkins and Jinks' slope of genotype regressions (/3), stability across environments as Shukla's stability variance (o-2), mean yield (Y), and Eskridge's reliability (R) from Y plus o- 2. Heterogeneity of genotype regressions explained 54% of genotype-location interaction variation. The fl values were strictly associated (r = - 0 . 9 9 ) with genotype scores on the first genotype-location interaction principal component (PC1), were not related to earliness of heading, and tended to negative correlation with plant stature that was hardly explainable in terms of resistance to lodging. Mean yield, PC1 score and rainfall of sites were correlated. The lines were also grown under normal and stress conditions at four sites for two seasons. The stress was established by placing metal channels between the rows that evacuated a portion of rainfall from the end of tillering stage onwards. Predictions of /3, o-2, Y and R, attempted respectively from slope of genotype-stress level interaction (/3p), /32, mean yield across conditions (Yp), and Yp plus /32, were assessed as genetic correlation. Predictions based on /3p and Ye computed over all test environments were all relatively good, whilst those based on data of individual seasons or locations were mostly inaccurate for o- 2, y and R. High-yielding sites could better predict Y and R. Two seasons' data from one such site showed correlations of 0.60, 0.53, 0.72 and 0.75 for prediction respectively of /3, o- 2, y and R. Evaluation of advanced breeding lines under normal and artificially stressed conditions at a high-yielding site may prove useful for reducing the number of lines promoted to subsequent regional testing a n d / o r restricting their regional testing to specific areas of adaptation. Keywords: Adaptation; Drought; Genotype-environmentinteraction; Selection, indirect; Triticum durum; Yield stability
1. Introduction A b o u t 75% of the world d u r u m wheat ( T r i t i c u m t u r g i d u m var. d u r u m ) g r o w i n g area is located in the
* Corresponding author. Fax: (39-371) 31853.
M e d i t e r r a n e a n basin (Srivastava, 1984). Rainfall in this region is characterized by large spatial and temporal variation, especially in spring. Drought stress, sometimes c o m b i n e d with heat stress, increases during this season and m a y b e c o m e severe during grain filling. The level of drought and its
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P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
timing interacts with genotype to produce large yield fluctuations (Ceccarelli, 1987). In order to stabilize yields, special emphasis has been placed on the attainment of high yield stability across differing levels of drought stress by regional breeding programmes aimed at wide adaptation (e.g. Srivastava et al., 1983; Duwayri et al., 1987). Specific adaptation to areas characterized, on the average, by either high or low levels of stress may alternatively be envisaged (Ceccarelli et al., 1987). Extensive regional testing is generally considered to be required for assessing adaptability a n d / o r yield stability of breeding lines, and for gaining additional information on the incidence of biotic and abiotic stresses in a given region. A multi-dimensional analytical approach such as the Additive Main effects and Multiplicative Interaction (AMMI) analysis (Gauch, 1992) is often required for effective description of adaptation patterns (Nachit et al., 1992; Annicchiarico and Perenzin, 1994). However, the uni-dimensional approach of joint regression analysis (Finlay and Wilkinson, 1963; Perkins and Jinks, 1968) may also prove adequate when a single environmental constraint such as soil moisture exerts an overwhelming influence on site mean yields (Seif and Pederson, 1978; Pecetti and Annicchiarico, 1993). The Shukla (1972a) stability variance can be recommended as a stability measure in a wide adaptation prospect, where the distinction between regression and deviation from regression components of variation provided by regression analysis is of limited value. Responses to locations for description of adaptation, and to environments (that is locations and seasons) for assessment of stability are those of practical interest for breeding (Becker, 1984; Annicchiarico and Perenzin, 1994). For selection purposes, the information on yield stability and mean yield can be combined into an index of yield reliability (Eskridge, 1990). This index estimates the lowest yield obtainable across environments at a P level chosen between 0.70 and 0.95. Variation among genotypes for drought tolerance has sometimes been evaluated in cereal crops by observing the yield response across contrasting levels of artificially managed water supply (e.g. Fischer and Maurer, 1978; Clarke et al., 1984; Acevedo et al., 1991). However, the opportunity to exploit the information provided by such trials for predicting
adaptability and yield stability of materials in a target region has hardly ever been thoroughly assessed, whereas the ability of predicting the yielding ability over a target region has only recently been investigated by Cooper et al. (1995). Pattern and extent of specific adaptation to drought-prone areas as expressed by genotype regression on site mean yield could be inferred respectively from the sign and the size of the genotype-drought stress level interaction effect. A negative indicator of stability as intended by Shukla's concept could be provided by the size of the interaction effect. A good prediction of adaptability, yield stability and mean yield over a target region, obviously possible only with reference to a region in which drought is a major environmental stress, would allow the breeder to substitute partially an extensive and costly regional testing by the cheaper alternative of evaluating breeding lines in only a few environments (locations a n d / o r seasons) under two levels of water supply. The objective of the present study was to test this hypothesis for durum wheat grown in a region comprising southern Italy and coastal areas of central Italy, in which terminal drought can be considered the main limiting factor to wheat yields (Vannella, 1987). 2. Material and methods 2.1. D a t a c o l l e c t i o n
The present study uses data collected as a part of a project aimed at identifying drought-tolerant, stable-yielding durum wheat lines to be used as donors in breeding programmes for semi-arid, Mediterranean environments (Mariani et al., 1991; Commission of the European Communities, 1993). Responses of nine genotypes well adapted to such environments, comprising both widely grown varieties and "elite" breeding lines constituted by different institutions taking part in the project, were considered. This germplasm was thought to include a large variation for earliness, plant stature and other adaptive traits. In a first set of trials, the genotypes were grown under rainfed conditions in a randomized complete block design with three replicates during three seasons starting from 1984-85 at six Italian locations, namely Rome (central Italy), Sassari (Sardinia), Fog-
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P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
gia (south-eastern Italy), Caltagirone, Cammarata and Catania (Sicily). These sites were chosen to adequately sample the main growing areas of the crop in the region. Such regional testing was designed to provide a reliable assessment of adaptability and yield stability. The genotypes were also grown under normal and managed drought stress conditions in eight environments comprising two seasons for each of four locations, namely 1984-85 and 1986-87 for Rome, 1986-87 and 1987-88 for Sassari, 1984-85 and 1985-86 for Caltagirone, and 1986-87 and 1987-88 for Cammarata. The stress was established by placing metal channels between the rows from the end of tillering stage onwards. The channels covered about two-thirds of the soil, but the proportion of rainfall actually shed was somewhat smaller since some was intercepted by overhanging leaves and diverted towards the bare soil. A slight inclination of the fields prevented leakage around the channels. On the average, the treatment caused a reduction of about 25% of total rainfall over the crop cycle, since 60% of rainfall occurred prior to the application of the channels. Normal and stressed conditions were arranged as main plots of a split-plot experimental design with three replicates, in which the genotypes were subplots. The data referred to as the normal condition in the latter set of trials were also included in the first trial data set whenever the environments were common to both sets. Plot size was 10 m 2 in both sets of trials. The experiments were managed according to the usual agronomic practices in each area. These practices included ploughing at 20-30 cm depth, fertilization rates ranging between 50 and 120 kg ha -t for N and between 60 and 80 kg ha ~ for P205, and one 2,4-D herbicide application for growing-season weed control. Grain yield of genotypes was recorded in all experiments. Heading date, and plant height after flowering were observed in the first set of trials at Rome and Caltagirone, Site rainfall from January to June was used for statistical analyses together with long-term temperature data.
2.2. Statistical analysis All analyses were conducted by SAS computer software (SAS Institute, 1990). An analysis of vari-
ance (ANOVA) including the factors "genotype", "location" and "season" was performed for grain yield data in the first set of trials. The second and the third factors were considered random. Genotype-location interaction variation was partitioned by a joint regression analysis describing genotype responses to site mean yield according to the ]3 statistic of Perkins and Jinks (1968). With reference to the Finlay and Wilkinson (1963) b statistic, ]3 is equal to ( b - 1). The heterogeneity of regression terms was tested on pooled deviations from regression. Heterogeneity of genotype regressions on site mean rainfall was verified. Adaptive responses were also depicted by AMMI analysis (Gauch, 1992). Testing of genotype-location principal component (PC) axes was performed by the Fort 2 test recommended by Cornelius (1993) for better control of Type I error rates. Yield stability of the genotypes was assessed by the Shukla (1972a) stability variance referring to all genotype-environment interaction effects. Comparison of each genotype with the most stable line was made through the test proposed by Shukla (1972b). Yield reliability (R) of the ith genotype was expressed by an index proposed by Eskridge (1990) as: R, =
-
-~
+ o-?
)~/2
(1)
where Yi a n d o'i 2 represent, respectively, the genotype mean yield and Shukla's stability variance across environments, o-~ is obtained as the difference between the mean square of environment and that of genotype-environment interaction divided by number of genotypes, and Zu ,o is the percentile from the standard normal distribution for which the cumulative distribution function reaches a P level equal to ( 1 - oe). P = 0.75 was considered adequate for the region. Therefore, R estimated the lowest yield obtainable in three environments out of four. The genotypes were also compared for heading date and plant height across environments. Relationships between these traits and adaptability and stability statistics were assessed by simple correlation analysis. Relationships were also investigated between mean yield, ordination on PC axes and climatic variables of locations. The ANOVA and testing of effects for the second set of trials were generally performed as indicated by Gomez and Gomez (1984, p. 339) for a split-plot
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
74
Table 1 Regional testing of nine d u r u m w h e a t g e n o t y p e s in Italy. Analysis of variance for grain yield in six locations a n d three seasons, with partitioning of g e n o t y p e - l o c a t i o n interaction b y (1) joint regression analysis and (2) A M M I analysis Source o f variation
Degrees of f r e e d o m
M e a n squares
G e n o t y p e (G) E n v i r o n m e n t (E) L o c a t i o n (L) Season (S) LXS G×E G× L 1. Regressions Deviations 2. PC1 PC2 PC3 GXS G×L×S Pooled error
8 17 5 2 10 136 40 8 32 12 10 8 16 80 288
3.20 44.48 70.44 19.08 36.57 0.85 1.76 4.75 1.01 3.71 1.89 0.46 0.82 0.40 0.20
** ** n0 ~ ** ** ** ** ** ** ** n~ * **
ns, * , * * = not significant and significant at P < 0.05 a n d < 0.01, respectively; PC = principal component.
design repeated in different environments. Only, the factor "environment" was considered random. The interaction effect of each genotype with droughtstress level was described in terms of linear component (/3p) of that interaction. Where YNi and YN indicate the individual and the mean yield value under normal conditions and Ysi and Ys indicate the
individual and the mean yield value under stress
conditions, /3p for the ith genotype was: /3pi =
(YNi-- YN) -(Ysi-- rs)
(2)
rN-le s
Difference from zero of /3p values was assessed by testing the significance of mean square of genotype individual contributions to genotype-stress level interaction sum of squares according to Snedecor and Cochran (1967, p. 352), using the genotype-stress level-environment interaction as the error term. /3p and its absolute value were used to predict respectively /3 and the square root of Shukla's stability variance in the former set of trials. Yield reliability of the genotypes was assessed according to Eq. 1 by assigning the same values to tr 2 and Z0 -4). Mean yield and Shukla's stability variance across environments for the genotypes were then substituted in Eq. 1 by mean yield across stress levels (Yp) and squared /3p values standardized to same mean and variance as o-i2. The ability of adaptability, yield stability, mean yield and yield reliability of the genotypes obtained from response to managed drought stress, to predict the information issued by extensive regional testing was assessed in terms of genetic correlation (%) computed as the intra-class correlation between observed and predicted values, as devised by Yamada et al. (1988). Difference from zero of rg values was tested according to Snedecor and Cochran (1967, p. 294). Whilst regression coefficients are normally
Table 2 Regional testing o f nine d u r u m w h e a t g e n o t y p e s in Italy. M e a n a n d range of seasonal rainfall (January to June), m e a n daily m a x i m u m temperature in spring and m i n i m u m temperature in winter, grain yield, and g e n o t y p e - l o c a t i o n interaction principal c o m p o n e n t (PC) scores o f the experimental locations Location
Caltagirone Cammarata Catania Foggia Rome Sassari
Rainfall a ( m m )
Maximum b
Minimum b
Yield (t h a - 1)
Mean
Range
temp. (°C)
temp. (°C)
Mean
Range
245 253 269 282 510 285
151-424 218-302 105-566 250-345 400-729 197-398
22.0 22.1 23.4 23.9 22.7 22.1
5.9 5.6 8.5 4.6 5.5 4.6
3.75 2.91 3.88 2.69 5.30 4.08
3.03-4.31 2.37-3.69 2.35-4.86 0.81-3.75 4.53-5.91 2.64-5.03
a A c r o s s seasons of the trials. b L o n g - t e r m values.
PC 1
PC 2
0.37 0.70 0.27 0.63 - 1.94 - 0.03
0.25 - 0.04 - 0.47 0.00 -0.04 0.30
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P. Annicchiarico, G. Mariani/ Field Crops Research 46 (1996) 71-80
distributed (Snedecor and Cochran, 1967), Shukla's stability variance was approximated to normal distribution by taking its square-root prior to its correlation with the absolute value of /3p. Genetic correlations were performed over the eight test environments as well as for a smaller number of environments to assess the predictive ability of data from single seasons, locations and environments.
Rome
Foggia
mm
mm
1= 25O
m ~ ~-~o
~-
i
' •
15o
Ill
loo
5o
3. R e s u l t s a n d d i s c u s s i o n
3.1. Regional testing
The ANOVA results of regional yield testing are given in Table 1. Genotypes and environments differed significantly, whereas locations and seasons did not. Variation among locations appeared, however, very large (Table 2). The lack of statistical significance of this factor was due to the large location-season interaction effect that represented its error term (Table 1). Seasonal values of yield and rainfall were rather variable but tended to be fairly small at Cammarata and Foggia, and high at Rome (Table 2). The correlation between site mean yield and average rainfall ( r = 0.86, P < 0.05) confirmed that yields in the region can be considerably affected by drought stress. The presence also of heat stress could justify the response of Foggia, characterized by very low yield, moderately low rainfall and high temperatures in spring (Table 2). In order to provide some indication of the variation in level and timing of drought stress across the region, winter and spring monthly totals of rainfall and potential evapotranspiration are reported in Fig. 1 for the contrasting sites of Foggia and Rome in the cropping season 1984-85, in which both site mean yields and climatic features were fairly representative for the locations. The data provide a sound basis for comparison because there was no marked difference in soil water-holding capacity and mean heading date between the sites. The relatively high rainfall in June occurred too late to counterbalance the effects of previous terminal stress, fairly severe already at heading time at Foggia. All interaction terms that include the factor "genotype" were significant (Table 1). The estimates of variance components were 0.151, 0.023 and 0.067 t ha -~ respectively for genotype-location,
Jan Feb Mar Apt May Jun
Jan Feb Mar Apt May
Jun
Fig. 1. Monthly totals of rainfall (open histogram) and potential evapotranspiration (black square) in winter and spring 1995 at Rome and Foggia. The arrows indicate mean heading date.
genotype-season and genotype-location-season interactions. The much larger variance of genotype-location compared with genotype-season interaction justifies specific adaptation as a goal for local breeding programmes. This is in agreement with previous studies on durum (Boggini et al., 1992) and bread wheat (Annicchiarico and Perenzin, 1994), which also indicated southern Italy and coastal areas of central Italy as possibly distinct macro-environments for the breeding activity. Heterogeneity of genotype regressions on site yield was significant and accounted for 54% of the genotype-location interaction sum of squares (Table 1), a proportion high enough to regard the regression model as adequate for description of adaptive responses (Romagosa and Fox, 1993). PC1 and PC2 in the AMMI analysis were also significant and explained respectively 63% and 27% of the interaction variation (Table 1). Site scores on these PC axes are reported in Table 2. The ordination on PC1 was closely associated with mean yield (r = - 0 . 9 2 ) and rainfall (r = -0.97), whereas PC2 scores tended to correlate with daily minimum temperatures in winter (r = 0.80, P < 0.06). Genotype regressions on site rainfall differed at P < 0.08. The ordination of genotypes for /3 value was almost coincident with those for PC1 score and slope of regression on site rainfall (Table 3). The whole of these findings confirm that genotype-location interactions were mainly related to genotype variation for response to the level of soil moisture and, as such, could be summarized fairly well by an uni-dimensional analytical model. A more
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
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Table 3 Regional testing of nine d u r u m w h e a t g e n o t y p e s in Italy. Correlation coefficients between m e a n grain yield, Perkins a n d Jinks' /3 statistic estimated across six locations, square root o f S h u k l a ' s stability variance estimated across 18 e n v i r o n m e n t s (o-), first (PC1) and second (PC2) g e n o t y p e - l o c a t i o n interaction principal c o m p o n e n t score, slope of linear regression on rainfall o f locations (bR), and h e a d i n g date a n d plant height across two locations Variables
Yield
/3
o-
Yield PCI PC2 bR H e a d i n g date Plant height
-0.74 * - 0.18 0.75 * - 0.04 - 0.67 *
0.74 * -0.99 * * 0.02 0.98 * * 0.18 - 0.66
- 0.56 0.34 0.00 - 0.54 0.31 0.06
*, * * = not significant a n d significant at P < 0.05 and < 0.01, respectively.
complex, bi-dimensional AMMI model was needed in a previous study on bread wheat genotypes grown in a region also including northern Italy and inland central Italy, where responses to winter cold and late frosts proved as important as those to soil moisture for adaptation patterns (Annicchiarico and Perenzin, 1994). The preference given hereafter to regression on site yield for description of adaptability is justified by its biological and statistical consistency with the current assessment of response to managed drought stress, and the greater ease of ordinating
locations in the region according to mean yield rather than to PC scores. The /3 value of the genotypes is reported in Table 4. The line D 3415 was better adapted to low-yielding locations while Karel and W4267 showed the opposite trend. The adaptive responses determined substantial genotype-environment interactions of the cross-over type for these genotypes passing from high- to low-yielding sites (Table 4). A positive correlation was found between /3 and overall mean yield (Table 3). Such correlation is rather common and mostly attributable to a scale effect that leads differences among genotypes to be larger in highthan in low-yielding environments (Becker and LEon, 1988). This also seemed to be the case in the present study, given the correlation between location mean yield and within-location standard deviation of genotype mean yields ( r = 0.91, P < 0.05). Yield stability and reliability of the genotypes is also given in Table 4. Various lines proved less stable than Messapia. This genotype could combine the highest mean yield with the highest stability across environments, and can be recommended as a valuable germplasm source for local breeding programmes. The absence of correlation between stability and mean yield (Table 3) further indicated that these traits are not incompatible. Materials showing specific adaptation were generally characterized also
Table 4 Regional testing o f nine d u r u m w h e a t g e n o t y p e s in Italy. G r a i n yield (t h a - I ) , overall (Y), at the high-yielding site o f R o m e (YH), and across the l o w - y i e l d i n g sites of C a m m a r a t a and F o g g i a (YL), Perkins and Jinks' /3 statistic estimated across six locations, S h u k l a ' s stability variance (o- 2) a n d E s k r i d g e ' s yield reliability ( R , t h a - l ) estimated across 18 environments, a n d h e a d i n g date (HD, days f r o m April 1) a n d plant height (PH, c m ) across two locations Genotype
y a
Amedeo Capeiti 8 Creso D3415 Karel M 104 Messapia Vespro W4267
3.51 3.44 3.72 3.50 3.93 3.97 4.14 3.81 3.89
a b c d
b b ab b ab ab a ab ab
y~ a
YL a
4.38 4.45 5.47 3.69 6.17 5.88 5.74 5.84 6.05
2.61 2.56 2.75 3.13 2.77 2.75 3.12 2.80 2.70
b b a b a a a a a
Separation of the top (a) and b o t t o m (b) *, * * = different f r o m zero at P < 0.05 *, * * = different f r o m M e s s a p i a at P < Estimate o f the lowest yield obtainable
b b ab a ab ab a ab ab
/3 b
O"2 c
-0.24 -0.23 0.07 -0.74 * * 0.33 * 0.25 0.05 0.20 0.31 *
0.29 0.16 0.33 0.81 0,36 0,16 0.06 0,22 0.15
** ** ** **
*
R d
HD a
PH a
2.58 2.54 2.78 2.45 2.98 3.07 3.27 2.89 2.99
25.1 23.6 34.5 27.4 27.8 22.1 24.0 29.7 30.7
109 108 78 95 82 87 90 83 81
b b a
b b
ranking m e a n s at P < 0.05 a c c o r d i n g to the N e w m a n - K e u l s test. a n d < 0.01, respectively. 0.05 and < 0.01, respectively. across e n v i r o n m e n t s at P = 0.75 on the basis o f S h u k l a ' s stability variance.
a a b b
b b
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
by low yield stability, as indicated by the correlation ( r = 0.80, P < 0.05) between absolute value of /3 and the square-root of Shukla's stability variance. A remarkable exception was represented by Creso, of which the low stability (Table 4) was mostly accounted for by its interaction with seasons and by already known specific adaptation patterns which are independent from mean yield of locations (Boggini et al., 1992). Although not the lowest yielding, D 3415 ranked as the least reliable because of its low yield stability (Table 4). The variation among genotypes in earliness of heading and plant height was rather large (Table 4). Earliness, which is often the main trait related to specific adaptive responses (Wallace et al., 1993), showed no correlation with /3 values (Table 3). Indeed, only for W4267 a relationship could be established between its lateness and its positive response to more favourable locations. Therefore, escape from drought appeared less important than tolerance to drought as an adaptive trait in these materials. The occurrence of adaptive responses not predicted from earliness reinforces the importance of multi-environment testing for detection of such responses. Taller genotypes tended to be specifically adapted to low-yielding sites (Table 3). This response could be accounted for by higher susceptibility to lodging in favourable environments, or greater drought tolerance via greater translocation of stem reserves to grains under stress, when photosynthetic capacity is reduced (Austin, 1989). The latter hypothesis is supported by the fact that no severe lodging occurred at any experimental site. Finally, neither heading time nor plant height were related to yield stability, whilst plant stature was inversely correlated with overall mean yield (Table 3). 3.2. Response to managed drought stress The ANOVA results for the genotypes grown under normal and artificially stressed conditions in eight environments can be summarized as follows. The environments differed ( P < 0.01) for mean yield across stress levels. Those in Rome and Sassari, averaging about 4 t ha 1, and those of Caltagirone and Cammarata, averaging about 3 t ha - l , could be ranked as fairly high yielding and low yielding,
77
Table 5 Response to managed drought stress of nine durum wheat genotypes over eight environments in Italy. Grain yield (t ha - 1 ) in normal rainfed ( I N ) and stress (Ys) conditions, slope of linear components of genotype-stress level interaction ( t i p ) , and Eskridge's yield reliability ( R e, t ha - I ) estimated from mean yield across stress levels and tip Genotype
YN a
Ys a
[~p b
Rp c
Amedeo Capeiti 8 Creso D3415 Karel M104 Messapia Vespro W4267
3.64 b 3.70 b 3.44 b 3.51 b 4.07 a 4.16a 4.06 a 3.92 a 3.77 ab
2.93 b 3.05 b 2.70 b 3.18 a 3.27 a 3.55a 3.50 a 3.18 a 2.99 b
0.04 -0.10 0.10 -0.53 * * 0.24 -0.02 -0.12 0.17 0.21
2.49 2.62 2.35 2.38 2.92 3.11 3.00 2.79 2.58
a Separation of the top (a) and bottom (b) ranking means at P < 0.05 according to the N e w m a n - K e u l s test. b *, * * = different from zero at P < 0.05 and < 0.01, respectively, following F test of genotype contribution to g e n o t y p e stress level interaction sum of squares. Estimate of the lowest yield obtainable across environments at P = 0.75 on the basis of predicted Shukla's stability variance.
respectively. Conditions also differed ( P < 0 . 0 1 ) , their mean yield being 3.97 and 3.28 t ha-~ for the normal and stressed respectively. The yield reduction due to the stress, which averaged about 17%, was somewhat less than expected, probably because the application of the metal channels, while shedding most of the spring rainfall, also reduced evaporation of water stored in the soil from winter rainfall. The effects of genotypes and genotype-stress level, genotype-environment and stress level-environment interactions were also significant ( P < 0.05). The occurrence of interaction for the last term was probably due to differences among environments for rainfall patterns and, to some extent, for time of application of the stress. Genotype-stress level-environment interaction was not significant ( P < 0.10), indicating that no major inconsistency took place between environments for yield response of the genotypes across stress levels. The mean value of such responses, as described by /3p values, is reported in Table 5 together with genotype mean yield in normal and stressed conditions. D3415 was significantly better adapted to the drier conditions and less stable for yield across stress levels. Its yield reliability, as estimated from mean yield across conditions (Yp)
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
78
Table 6 Comparison of estimates of adaptability, yield stability, mean yield and yield reliability obtained from regional testing and from response to managed drought stress over various sets of environments, for nine durum wheat genotypes. Data are coefficients of genetic correlation (rg) for Perkins and Jinks' /3 statistic across 6 locations and square root of Shukla's stability variance (o-), mean yield ( Y ) and Eskridge's reliability ( R ) across 18 environments with slope of linear components of genotype-stress level interaction (/3p), absolute value of tip, mean yield across stress levels (Yp), and reliability ( R p ) estimated from lip and /3p, respectively Environments
/3 - / 3 p
Overall c Within seasons ~ Within locations e Caltagirone Cammarata Rome Sassari Single environments f
0.81 0.58 0.59 0.60 0.79 0.60 0.35 0.44
a o- - [/3p[ a y _ yp a R - Rp ab 0.74 0.50 0.31 0.13 0.29 0.53 0.30 0.24
0.74 0.57 0.40 0.04 0.21 0.72 0.64 0.30
0.77 0.62 0.41 0.04 0.17 0.75 0.69 0.32
a Coefficients > 0.59, > 0.66 and > 0.79 differ from zero at P < 0.10, < 0.05 and < 0.01, respectively. b Estimate of the lowest yield obtainable across environments at P = 0.75 on the basis of Shukla's stability variance. c tip and Yp computed over eight environments. Average of rg values for three seasons; tip and Yp computed over two locations for 1984-85 and 1987-88 seasons, and over three locations for 1986-87 season. e Average of rg values for four locations; /3p and Yp computed over two seasons for each location. f Average of % values for eight environments.
and tip, was the lowest, although its Yp was higher than that of Capeiti 8 and Amedeo (Table 5). The broad sense heritability coefficients of yield in normal and stressed conditions were generally similar (data not reported). Genetic correlations assessing the ability of response to artificially managed drought stress to predict genotype performance from regional testing are reported in Table 6. Given the relatively small number of genotypes, even coefficients significant just at P < 0.10 were considered to be of some indicative value. Predictions based on tip and Yp values computed over all test environments could be rated as good for adaptability and moderately good for the remaining traits. Predictions from data of individual seasons or locations were mostly inaccurate for yield stability, mean yield and yield reliability. However, large differences for predictive value emerged among
locations, particularly with reference to mean yield and yield reliability. Better prediction of these traits was clearly a feature of high-yielding sites, i.e. Rome and, to a lesser extent, Sassari. Most probably, prediction of genotype mean yield in these locations was more accurate because site mean yield, averaged across stress levels, approached the level of overall mean yield in regional testing; conversely, the same prediction carried out in low-yielding locations was biased by genotype-environment interaction effects which tended to favour drought-tolerant materials. In addition, high-yielding sites can better predict overall mean yield in the presence of the mentioned correlation between location mean yield and within-location standard deviation of genotype mean yields, which confers a greater weight to performance at such sites. This correlation is frequent in multi-locational testing of cereals in water-limited regions (Yau, 1991). In accordance with the present findings, prediction of genotype mean yield over an Australian region, attempted from artificial environments differing by sowing date and levels of irrigation and fertilization, was satisfactory when it was based on data from a few favourable environments (Cooper et al., 1995). For prediction of mean yield, data from Rome and Cammarata had the advantage of being less biased by genotype-environment interactions associated with the level of winter cold, given the PC2 score close to zero of these sites (Table 2). The predictive ability of sites for yield reliability was strongly affected by that for mean yield due to the close relationship between these variables at the fairly low P level chosen for reliability estimation. Predictions based on data of individual environments were generally poor (Table 6). Given the lack of major inconsistencies between environments for yield response of the genotypes, the generally observed increase of predictive ability at an increasing number of environments over which fls was computed was probably due to the relatively high error which affected fls estimates in individual environments. Evaluation of breeding materials under artificially stressed conditions in order to reduce multi-environment testing is frequently advocated for biotic stresses (Eisemann et al., 1990). Artificial environments differing for cultural practices proved useful for reproduction of adaptive patterns in carrots (Dowker et
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
al., 1978) and for identification of high-yielding materials over a target region in wheat (Cooper et al., 1995). The present results indicate that evaluation under normal and artificial drought stress conditions can be envisaged both in a wide and a specific adaptation prospect to reduce the need of testing across a region where drought is a major stress. This may prove useful especially for the evaluation of advanced breeding lines, in order to reduce the number of lines promoted to subsequent regional testing a n d / o r restrict their regional testing to specific areas of adaptation. However, data from at least two environments (seasons or locations) should always be used, and a high-yielding test site is recommended for a reliable prediction of genotype mean yield and yield reliability. Prediction of yield stability is more difficult than that of adaptability, probably because of the greater influence of occasional stresses other than drought on the response of genotypes for the former trait. For application of the stress, metal channels have the advantage, compared with rain shelters (Acevedo et al., 1991), of not altering the pattern of distribution of rainfall while reducing its amount. However, the channels should be installed very early in the cropping season to induce a high stress. Response to irrigation is a cheaper alternative that may also work well for predictions, but is appropriate to sites where rainfall is low and varies little across seasons.
Acknowledgements The study was undertaken within the framework of the project "Durum wheat: breeding for higher and more stable yield in semi-arid Mediterranean zones" jointly funded by E.E.C.-Agrimed and the Italian Ministry of Agriculture and Forestry. The contribution of Drs. G. Boggini, F. Calcagno, S. Cassaniti, M. Deidda, G. Di Prima and G. Wittmer as those responsible in the Institutions co-operating in the research work is gratefully acknowledged.
References Acevedo, E., Craufurd, P.Q., Austin, R.B. and P6rez-Marco, P., 1991. Traits associated with high yield in barley for low-rainfall environments. J. Agric. Sci., Camb., 116: 23-36.
79
Annicchiarico, P. and Perenzin, M., 1994. Adaptation patterns and definition of macro-environments for selection and recommendation of common wheat genotypes in Italy. Plant Breeding, 113: 197-205. Austin, R.B., 1989. Maximising crop production in water-limited environments. In: F.W.G. Baker (Editor), Drought Resistance in Cereals. CAB International, Wallingford, pp. 13-26. Becker, H.C., 1984. Analysis of genotype × environment interactions with partitioning of environmental effects into effects of locations and years. Vortr. Pflanzenziichtg., 7: 209-218. Becker, H.C. and L6on, G., 1988. Stability analysis in plant breeding. Plant Breeding, 101: 1-23. Boggini, G., Annicchiarico, P., Longo, A. and Pecetti, L., 1992. Produttivith e adattamento di nuove costituzioni di frumento duro (Triticum durum Desf.). Riv. Agron., 26: 482-488. Ceccarelli, S., 1987. Wide adaptation. How wide? Euphytica, 40: 197-205. Ceccarelli, S., Nachit, M.M., Ortiz Ferrara, G., Mekni, M.S., Tahir, M., van Leur, J. and Srivastava, J.P., 1987. Breeding strategies for improving cereal yield and stability under drought. In: J.P. Srivastava, E. Porceddu, E. Acevedo and S. Varma (Editors), Drought Tolerance in Winter Cereals. J. Wiley and Sons, Chichester, pp. 101-114. Clarke, J.M., Townley-Smith, T.F., McCaig, T.N. and Green, D.G., 1984. Growth analysis of spring wheat cultivars of varying drought resistance. Crop Sci., 24: 537-541. Commission of the European Communities, 1993. Durum wheat. In: P. Crossa-Raynaud, B. Hubert and G. Rossetto (Editors), Agrimed Research Programme - Analysis and Evaluation of Results. Commission of the European Communities, Brussels, pp. 25-27. Cooper, M., Woodruff, D.R., Eisemann, R.L., Brennan, P.S. and DeLacy, I.H., 1995. A selection strategy to accomodate genotype-by-environment interaction for grain yield: managed-environments for selection among genotypes. Theor. Appl. Genet., 90: 492-502. Cornelius, P.L., 1993. Statistical tests and retention of terms in the Additive Main effects and Multiplicative Interaction model for cultivar trials. Crop Sci., 33:1186-1193. Dowker, B.D., Jackson, J.C. and Phelps, K., 1978. Variation studies in carrots as an aid to breeding. VI. Genotype-environment interactions in contrasting field environments. J. Hortic. Sci., 53: 131-137. Duwayri, M., Tell, A.M. and Shqaidef, F., 1987. Breeding for improved yield in moisture-limiting areas: the experience of Jordan. In: J.P. Srivastava, E. Porceddu, E. Acevedo and S~ Varma (Editors), Drought Tolerance in Winter Cereals. J, Wiley and Sons, Chichester, pp. 163-169. Eisemann, R.L., Cooper, M. and Woodruff, D.R., 1990. Beyond the analytical methodology - better interpretation and exploitation of genotype-by-environment interaction in breeding. In: M.S. Kang (Editor), Genotype-Environment Interaction and Plant Breeding. Louisiana State University, Baton Rouge, pp. 287-300. Eskridge, K.M., 1990. Selection of stable cultivars using a safetyfirst rule. Crop Sci., 30: 369-374. Finlay, K.W. and Wilkinson, G.N., 1963. The analysis of adaptation in a plant breeding programme. Aust. J. Agric. Res., 14: 742-754.
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Fischer, R.A. and Maurer, R., 1978. Drought resistance in Spring wheat cultivars. I. Grain yield responses. Aust. J. Agric. Res., 29: 897-912. Gauch, H.G., 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier, Amsterdam, 278 pp. Gomez, K.A. and Gomez, A.A., 1984. Statistical Procedures for Agricultural Research, second edition. J. Wiley and Sons, Chichester, 680 pp. Mariani, G., Blanco, A., Boggini, G., Calcagno, F., Cassaniti, S., Deidda, M., Di Prima, G., Galterio, G., D'Egidio, M.G., Nardi, S., Pasquini, M., Wittmer, G., 1991. Frumento duro: ricerche per una maggiore e pih regolare resa nelle zone mediterranee semi-aride. Attivitg 1985-1989 - Rapporto finale. Istituto Sperimentale per la Cerealicoltura, Rome, 76 pp. Nachit, M.M., Nachit, G., Ketata, H., Gauch Jr., H.G. and Zobel, R.W., 1992. Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat. Theor. Appl. Genet., 83: 597-601. Pecetti, L. and Annicchiarico, P., 1993. Grain yield and quality of durum wheat landraces in a dry Mediterranean region of northern Syria. Plant Breeding, 110: 243-249. Perkins, J.M. and Jinks, L.L., 1968. Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity, 23: 339-356. Romagosa, I. and Fox, P.N., 1993. Genotype × environment interaction and adaptation. In: M.D. Hayward, N.O. Bosemark and I. Romagosa (Editors), Plant Breeding: Principles and Prospects. Chapman & Hall, London, pp. 373-390. SAS Institute, 1990. SAS/STAT User's Guide. Version 6, fourth edition. SAS Institute, Cary, 943 pp.
Seif, E. and Pederson, D.G., 1978. Effect of rainfall on the grain yield of spring wheat, with an application to the analysis of adaptation. Aust. J. Agric. Res., 29: 1107-1115. Shukla, G.K,, 1972a. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity, 29: 237-245. Shukla, G.K., 1972b. An invariant test for the homogeneity of variances in a two-way classification. Biometrics, 28: 10631072. Snedecor, G.W. and Cochran, W.G., 1967. Statistical Methods. The Iowa State University Press, Ames, 593 pp. Srivastava, J.P., 1984. Durum wheat - its world status and potential in Middle East and North Africa. Rachis, 3: 1-8. Srivastava, J.P., Jana, S., Gautam, P.L. and Niks, R.E., 1983. Parallel selection: an approach to increase grain yield and stability. In: S. Sakamoto (Editor), Proc. Sixth Int. Wheat Genet. Symp. Kyoto University, Kyoto, pp. 725-733. Vannella, S., 1987. Influenza dell'andamento climatico sulle rese del frumento. Riv. Agron., 21 (Suppl. 4): 223-229. Wallace, D.H., Baudoin, J.P., Beaver, J., Coyne, D.P., Halseth, D.E., Masaya, P.N., Munger, H.M., Myers, J.R., Silbernagel, M., Yourstone, K.S. and Zobel, R.W., 1993. Improving efficiency of breeding for higher crop yield. Theor. Appl. Genet., 86: 27-40. Yamada, Y., Itoh, Y. and Sugimoto, I., 1988. Parametric relationships between genotype × environment interaction and genetic correlation when two environments are involved. Theor. Appl. Genet., 76: 850-854. Yau, S.K., 1991. Need of scale transformation in cluster analysis of genotypes based on multi-location yield data. J. Genet. Breed., 45: 71-76.
Field Crops Research 46 (1996) 71-80
Field Crops Research
Prediction of adaptability and yield stability of durum wheat genotypes from yield response in normal and artificially drought-stressed conditions P. Annicchiarico a,*, G. Mariani b a Istituto Sperimentale per le Colture Foraggere, 29 viale Piacenza, 1-20075 Lodi, Italy b Istituto Sperimentale per la Cerealicoltura, 176 via Cassia, 1-00191 Rome, Italy
Received 30 December 1994; accepted 26 October 1995
Abstract Adaptability, yield stability and yield reliability of durum wheat breeding lines are usually assessed through regional testing. The opportunity of partially substituting such testing by evaluation under normal and artificially drought-stressed rainfed conditions was investigated for a water-limited Italian region. Nine lines were grown at six sites for three seasons to assess adaptability across locations as Perkins and Jinks' slope of genotype regressions (/3), stability across environments as Shukla's stability variance (o-2), mean yield (Y), and Eskridge's reliability (R) from Y plus o- 2. Heterogeneity of genotype regressions explained 54% of genotype-location interaction variation. The fl values were strictly associated (r = - 0 . 9 9 ) with genotype scores on the first genotype-location interaction principal component (PC1), were not related to earliness of heading, and tended to negative correlation with plant stature that was hardly explainable in terms of resistance to lodging. Mean yield, PC1 score and rainfall of sites were correlated. The lines were also grown under normal and stress conditions at four sites for two seasons. The stress was established by placing metal channels between the rows that evacuated a portion of rainfall from the end of tillering stage onwards. Predictions of /3, o-2, Y and R, attempted respectively from slope of genotype-stress level interaction (/3p), /32, mean yield across conditions (Yp), and Yp plus /32, were assessed as genetic correlation. Predictions based on /3p and Ye computed over all test environments were all relatively good, whilst those based on data of individual seasons or locations were mostly inaccurate for o- 2, y and R. High-yielding sites could better predict Y and R. Two seasons' data from one such site showed correlations of 0.60, 0.53, 0.72 and 0.75 for prediction respectively of /3, o- 2, y and R. Evaluation of advanced breeding lines under normal and artificially stressed conditions at a high-yielding site may prove useful for reducing the number of lines promoted to subsequent regional testing a n d / o r restricting their regional testing to specific areas of adaptation. Keywords: Adaptation; Drought; Genotype-environmentinteraction; Selection, indirect; Triticum durum; Yield stability
1. Introduction A b o u t 75% of the world d u r u m wheat ( T r i t i c u m t u r g i d u m var. d u r u m ) g r o w i n g area is located in the
* Corresponding author. Fax: (39-371) 31853.
M e d i t e r r a n e a n basin (Srivastava, 1984). Rainfall in this region is characterized by large spatial and temporal variation, especially in spring. Drought stress, sometimes c o m b i n e d with heat stress, increases during this season and m a y b e c o m e severe during grain filling. The level of drought and its
0378-4290/96/$15.00 © 1996 Elsevier Science B,V. All rights reserved SSDI 0 3 7 8 - 4 2 9 0 ( 9 5 ) 0 0 0 8 7 - 9
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timing interacts with genotype to produce large yield fluctuations (Ceccarelli, 1987). In order to stabilize yields, special emphasis has been placed on the attainment of high yield stability across differing levels of drought stress by regional breeding programmes aimed at wide adaptation (e.g. Srivastava et al., 1983; Duwayri et al., 1987). Specific adaptation to areas characterized, on the average, by either high or low levels of stress may alternatively be envisaged (Ceccarelli et al., 1987). Extensive regional testing is generally considered to be required for assessing adaptability a n d / o r yield stability of breeding lines, and for gaining additional information on the incidence of biotic and abiotic stresses in a given region. A multi-dimensional analytical approach such as the Additive Main effects and Multiplicative Interaction (AMMI) analysis (Gauch, 1992) is often required for effective description of adaptation patterns (Nachit et al., 1992; Annicchiarico and Perenzin, 1994). However, the uni-dimensional approach of joint regression analysis (Finlay and Wilkinson, 1963; Perkins and Jinks, 1968) may also prove adequate when a single environmental constraint such as soil moisture exerts an overwhelming influence on site mean yields (Seif and Pederson, 1978; Pecetti and Annicchiarico, 1993). The Shukla (1972a) stability variance can be recommended as a stability measure in a wide adaptation prospect, where the distinction between regression and deviation from regression components of variation provided by regression analysis is of limited value. Responses to locations for description of adaptation, and to environments (that is locations and seasons) for assessment of stability are those of practical interest for breeding (Becker, 1984; Annicchiarico and Perenzin, 1994). For selection purposes, the information on yield stability and mean yield can be combined into an index of yield reliability (Eskridge, 1990). This index estimates the lowest yield obtainable across environments at a P level chosen between 0.70 and 0.95. Variation among genotypes for drought tolerance has sometimes been evaluated in cereal crops by observing the yield response across contrasting levels of artificially managed water supply (e.g. Fischer and Maurer, 1978; Clarke et al., 1984; Acevedo et al., 1991). However, the opportunity to exploit the information provided by such trials for predicting
adaptability and yield stability of materials in a target region has hardly ever been thoroughly assessed, whereas the ability of predicting the yielding ability over a target region has only recently been investigated by Cooper et al. (1995). Pattern and extent of specific adaptation to drought-prone areas as expressed by genotype regression on site mean yield could be inferred respectively from the sign and the size of the genotype-drought stress level interaction effect. A negative indicator of stability as intended by Shukla's concept could be provided by the size of the interaction effect. A good prediction of adaptability, yield stability and mean yield over a target region, obviously possible only with reference to a region in which drought is a major environmental stress, would allow the breeder to substitute partially an extensive and costly regional testing by the cheaper alternative of evaluating breeding lines in only a few environments (locations a n d / o r seasons) under two levels of water supply. The objective of the present study was to test this hypothesis for durum wheat grown in a region comprising southern Italy and coastal areas of central Italy, in which terminal drought can be considered the main limiting factor to wheat yields (Vannella, 1987). 2. Material and methods 2.1. D a t a c o l l e c t i o n
The present study uses data collected as a part of a project aimed at identifying drought-tolerant, stable-yielding durum wheat lines to be used as donors in breeding programmes for semi-arid, Mediterranean environments (Mariani et al., 1991; Commission of the European Communities, 1993). Responses of nine genotypes well adapted to such environments, comprising both widely grown varieties and "elite" breeding lines constituted by different institutions taking part in the project, were considered. This germplasm was thought to include a large variation for earliness, plant stature and other adaptive traits. In a first set of trials, the genotypes were grown under rainfed conditions in a randomized complete block design with three replicates during three seasons starting from 1984-85 at six Italian locations, namely Rome (central Italy), Sassari (Sardinia), Fog-
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P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
gia (south-eastern Italy), Caltagirone, Cammarata and Catania (Sicily). These sites were chosen to adequately sample the main growing areas of the crop in the region. Such regional testing was designed to provide a reliable assessment of adaptability and yield stability. The genotypes were also grown under normal and managed drought stress conditions in eight environments comprising two seasons for each of four locations, namely 1984-85 and 1986-87 for Rome, 1986-87 and 1987-88 for Sassari, 1984-85 and 1985-86 for Caltagirone, and 1986-87 and 1987-88 for Cammarata. The stress was established by placing metal channels between the rows from the end of tillering stage onwards. The channels covered about two-thirds of the soil, but the proportion of rainfall actually shed was somewhat smaller since some was intercepted by overhanging leaves and diverted towards the bare soil. A slight inclination of the fields prevented leakage around the channels. On the average, the treatment caused a reduction of about 25% of total rainfall over the crop cycle, since 60% of rainfall occurred prior to the application of the channels. Normal and stressed conditions were arranged as main plots of a split-plot experimental design with three replicates, in which the genotypes were subplots. The data referred to as the normal condition in the latter set of trials were also included in the first trial data set whenever the environments were common to both sets. Plot size was 10 m 2 in both sets of trials. The experiments were managed according to the usual agronomic practices in each area. These practices included ploughing at 20-30 cm depth, fertilization rates ranging between 50 and 120 kg ha -t for N and between 60 and 80 kg ha ~ for P205, and one 2,4-D herbicide application for growing-season weed control. Grain yield of genotypes was recorded in all experiments. Heading date, and plant height after flowering were observed in the first set of trials at Rome and Caltagirone, Site rainfall from January to June was used for statistical analyses together with long-term temperature data.
2.2. Statistical analysis All analyses were conducted by SAS computer software (SAS Institute, 1990). An analysis of vari-
ance (ANOVA) including the factors "genotype", "location" and "season" was performed for grain yield data in the first set of trials. The second and the third factors were considered random. Genotype-location interaction variation was partitioned by a joint regression analysis describing genotype responses to site mean yield according to the ]3 statistic of Perkins and Jinks (1968). With reference to the Finlay and Wilkinson (1963) b statistic, ]3 is equal to ( b - 1). The heterogeneity of regression terms was tested on pooled deviations from regression. Heterogeneity of genotype regressions on site mean rainfall was verified. Adaptive responses were also depicted by AMMI analysis (Gauch, 1992). Testing of genotype-location principal component (PC) axes was performed by the Fort 2 test recommended by Cornelius (1993) for better control of Type I error rates. Yield stability of the genotypes was assessed by the Shukla (1972a) stability variance referring to all genotype-environment interaction effects. Comparison of each genotype with the most stable line was made through the test proposed by Shukla (1972b). Yield reliability (R) of the ith genotype was expressed by an index proposed by Eskridge (1990) as: R, =
-
-~
+ o-?
)~/2
(1)
where Yi a n d o'i 2 represent, respectively, the genotype mean yield and Shukla's stability variance across environments, o-~ is obtained as the difference between the mean square of environment and that of genotype-environment interaction divided by number of genotypes, and Zu ,o is the percentile from the standard normal distribution for which the cumulative distribution function reaches a P level equal to ( 1 - oe). P = 0.75 was considered adequate for the region. Therefore, R estimated the lowest yield obtainable in three environments out of four. The genotypes were also compared for heading date and plant height across environments. Relationships between these traits and adaptability and stability statistics were assessed by simple correlation analysis. Relationships were also investigated between mean yield, ordination on PC axes and climatic variables of locations. The ANOVA and testing of effects for the second set of trials were generally performed as indicated by Gomez and Gomez (1984, p. 339) for a split-plot
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74
Table 1 Regional testing of nine d u r u m w h e a t g e n o t y p e s in Italy. Analysis of variance for grain yield in six locations a n d three seasons, with partitioning of g e n o t y p e - l o c a t i o n interaction b y (1) joint regression analysis and (2) A M M I analysis Source o f variation
Degrees of f r e e d o m
M e a n squares
G e n o t y p e (G) E n v i r o n m e n t (E) L o c a t i o n (L) Season (S) LXS G×E G× L 1. Regressions Deviations 2. PC1 PC2 PC3 GXS G×L×S Pooled error
8 17 5 2 10 136 40 8 32 12 10 8 16 80 288
3.20 44.48 70.44 19.08 36.57 0.85 1.76 4.75 1.01 3.71 1.89 0.46 0.82 0.40 0.20
** ** n0 ~ ** ** ** ** ** ** ** n~ * **
ns, * , * * = not significant and significant at P < 0.05 a n d < 0.01, respectively; PC = principal component.
design repeated in different environments. Only, the factor "environment" was considered random. The interaction effect of each genotype with droughtstress level was described in terms of linear component (/3p) of that interaction. Where YNi and YN indicate the individual and the mean yield value under normal conditions and Ysi and Ys indicate the
individual and the mean yield value under stress
conditions, /3p for the ith genotype was: /3pi =
(YNi-- YN) -(Ysi-- rs)
(2)
rN-le s
Difference from zero of /3p values was assessed by testing the significance of mean square of genotype individual contributions to genotype-stress level interaction sum of squares according to Snedecor and Cochran (1967, p. 352), using the genotype-stress level-environment interaction as the error term. /3p and its absolute value were used to predict respectively /3 and the square root of Shukla's stability variance in the former set of trials. Yield reliability of the genotypes was assessed according to Eq. 1 by assigning the same values to tr 2 and Z0 -4). Mean yield and Shukla's stability variance across environments for the genotypes were then substituted in Eq. 1 by mean yield across stress levels (Yp) and squared /3p values standardized to same mean and variance as o-i2. The ability of adaptability, yield stability, mean yield and yield reliability of the genotypes obtained from response to managed drought stress, to predict the information issued by extensive regional testing was assessed in terms of genetic correlation (%) computed as the intra-class correlation between observed and predicted values, as devised by Yamada et al. (1988). Difference from zero of rg values was tested according to Snedecor and Cochran (1967, p. 294). Whilst regression coefficients are normally
Table 2 Regional testing o f nine d u r u m w h e a t g e n o t y p e s in Italy. M e a n a n d range of seasonal rainfall (January to June), m e a n daily m a x i m u m temperature in spring and m i n i m u m temperature in winter, grain yield, and g e n o t y p e - l o c a t i o n interaction principal c o m p o n e n t (PC) scores o f the experimental locations Location
Caltagirone Cammarata Catania Foggia Rome Sassari
Rainfall a ( m m )
Maximum b
Minimum b
Yield (t h a - 1)
Mean
Range
temp. (°C)
temp. (°C)
Mean
Range
245 253 269 282 510 285
151-424 218-302 105-566 250-345 400-729 197-398
22.0 22.1 23.4 23.9 22.7 22.1
5.9 5.6 8.5 4.6 5.5 4.6
3.75 2.91 3.88 2.69 5.30 4.08
3.03-4.31 2.37-3.69 2.35-4.86 0.81-3.75 4.53-5.91 2.64-5.03
a A c r o s s seasons of the trials. b L o n g - t e r m values.
PC 1
PC 2
0.37 0.70 0.27 0.63 - 1.94 - 0.03
0.25 - 0.04 - 0.47 0.00 -0.04 0.30
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P. Annicchiarico, G. Mariani/ Field Crops Research 46 (1996) 71-80
distributed (Snedecor and Cochran, 1967), Shukla's stability variance was approximated to normal distribution by taking its square-root prior to its correlation with the absolute value of /3p. Genetic correlations were performed over the eight test environments as well as for a smaller number of environments to assess the predictive ability of data from single seasons, locations and environments.
Rome
Foggia
mm
mm
1= 25O
m ~ ~-~o
~-
i
' •
15o
Ill
loo
5o
3. R e s u l t s a n d d i s c u s s i o n
3.1. Regional testing
The ANOVA results of regional yield testing are given in Table 1. Genotypes and environments differed significantly, whereas locations and seasons did not. Variation among locations appeared, however, very large (Table 2). The lack of statistical significance of this factor was due to the large location-season interaction effect that represented its error term (Table 1). Seasonal values of yield and rainfall were rather variable but tended to be fairly small at Cammarata and Foggia, and high at Rome (Table 2). The correlation between site mean yield and average rainfall ( r = 0.86, P < 0.05) confirmed that yields in the region can be considerably affected by drought stress. The presence also of heat stress could justify the response of Foggia, characterized by very low yield, moderately low rainfall and high temperatures in spring (Table 2). In order to provide some indication of the variation in level and timing of drought stress across the region, winter and spring monthly totals of rainfall and potential evapotranspiration are reported in Fig. 1 for the contrasting sites of Foggia and Rome in the cropping season 1984-85, in which both site mean yields and climatic features were fairly representative for the locations. The data provide a sound basis for comparison because there was no marked difference in soil water-holding capacity and mean heading date between the sites. The relatively high rainfall in June occurred too late to counterbalance the effects of previous terminal stress, fairly severe already at heading time at Foggia. All interaction terms that include the factor "genotype" were significant (Table 1). The estimates of variance components were 0.151, 0.023 and 0.067 t ha -~ respectively for genotype-location,
Jan Feb Mar Apt May Jun
Jan Feb Mar Apt May
Jun
Fig. 1. Monthly totals of rainfall (open histogram) and potential evapotranspiration (black square) in winter and spring 1995 at Rome and Foggia. The arrows indicate mean heading date.
genotype-season and genotype-location-season interactions. The much larger variance of genotype-location compared with genotype-season interaction justifies specific adaptation as a goal for local breeding programmes. This is in agreement with previous studies on durum (Boggini et al., 1992) and bread wheat (Annicchiarico and Perenzin, 1994), which also indicated southern Italy and coastal areas of central Italy as possibly distinct macro-environments for the breeding activity. Heterogeneity of genotype regressions on site yield was significant and accounted for 54% of the genotype-location interaction sum of squares (Table 1), a proportion high enough to regard the regression model as adequate for description of adaptive responses (Romagosa and Fox, 1993). PC1 and PC2 in the AMMI analysis were also significant and explained respectively 63% and 27% of the interaction variation (Table 1). Site scores on these PC axes are reported in Table 2. The ordination on PC1 was closely associated with mean yield (r = - 0 . 9 2 ) and rainfall (r = -0.97), whereas PC2 scores tended to correlate with daily minimum temperatures in winter (r = 0.80, P < 0.06). Genotype regressions on site rainfall differed at P < 0.08. The ordination of genotypes for /3 value was almost coincident with those for PC1 score and slope of regression on site rainfall (Table 3). The whole of these findings confirm that genotype-location interactions were mainly related to genotype variation for response to the level of soil moisture and, as such, could be summarized fairly well by an uni-dimensional analytical model. A more
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Table 3 Regional testing of nine d u r u m w h e a t g e n o t y p e s in Italy. Correlation coefficients between m e a n grain yield, Perkins a n d Jinks' /3 statistic estimated across six locations, square root o f S h u k l a ' s stability variance estimated across 18 e n v i r o n m e n t s (o-), first (PC1) and second (PC2) g e n o t y p e - l o c a t i o n interaction principal c o m p o n e n t score, slope of linear regression on rainfall o f locations (bR), and h e a d i n g date a n d plant height across two locations Variables
Yield
/3
o-
Yield PCI PC2 bR H e a d i n g date Plant height
-0.74 * - 0.18 0.75 * - 0.04 - 0.67 *
0.74 * -0.99 * * 0.02 0.98 * * 0.18 - 0.66
- 0.56 0.34 0.00 - 0.54 0.31 0.06
*, * * = not significant a n d significant at P < 0.05 and < 0.01, respectively.
complex, bi-dimensional AMMI model was needed in a previous study on bread wheat genotypes grown in a region also including northern Italy and inland central Italy, where responses to winter cold and late frosts proved as important as those to soil moisture for adaptation patterns (Annicchiarico and Perenzin, 1994). The preference given hereafter to regression on site yield for description of adaptability is justified by its biological and statistical consistency with the current assessment of response to managed drought stress, and the greater ease of ordinating
locations in the region according to mean yield rather than to PC scores. The /3 value of the genotypes is reported in Table 4. The line D 3415 was better adapted to low-yielding locations while Karel and W4267 showed the opposite trend. The adaptive responses determined substantial genotype-environment interactions of the cross-over type for these genotypes passing from high- to low-yielding sites (Table 4). A positive correlation was found between /3 and overall mean yield (Table 3). Such correlation is rather common and mostly attributable to a scale effect that leads differences among genotypes to be larger in highthan in low-yielding environments (Becker and LEon, 1988). This also seemed to be the case in the present study, given the correlation between location mean yield and within-location standard deviation of genotype mean yields ( r = 0.91, P < 0.05). Yield stability and reliability of the genotypes is also given in Table 4. Various lines proved less stable than Messapia. This genotype could combine the highest mean yield with the highest stability across environments, and can be recommended as a valuable germplasm source for local breeding programmes. The absence of correlation between stability and mean yield (Table 3) further indicated that these traits are not incompatible. Materials showing specific adaptation were generally characterized also
Table 4 Regional testing o f nine d u r u m w h e a t g e n o t y p e s in Italy. G r a i n yield (t h a - I ) , overall (Y), at the high-yielding site o f R o m e (YH), and across the l o w - y i e l d i n g sites of C a m m a r a t a and F o g g i a (YL), Perkins and Jinks' /3 statistic estimated across six locations, S h u k l a ' s stability variance (o- 2) a n d E s k r i d g e ' s yield reliability ( R , t h a - l ) estimated across 18 environments, a n d h e a d i n g date (HD, days f r o m April 1) a n d plant height (PH, c m ) across two locations Genotype
y a
Amedeo Capeiti 8 Creso D3415 Karel M 104 Messapia Vespro W4267
3.51 3.44 3.72 3.50 3.93 3.97 4.14 3.81 3.89
a b c d
b b ab b ab ab a ab ab
y~ a
YL a
4.38 4.45 5.47 3.69 6.17 5.88 5.74 5.84 6.05
2.61 2.56 2.75 3.13 2.77 2.75 3.12 2.80 2.70
b b a b a a a a a
Separation of the top (a) and b o t t o m (b) *, * * = different f r o m zero at P < 0.05 *, * * = different f r o m M e s s a p i a at P < Estimate o f the lowest yield obtainable
b b ab a ab ab a ab ab
/3 b
O"2 c
-0.24 -0.23 0.07 -0.74 * * 0.33 * 0.25 0.05 0.20 0.31 *
0.29 0.16 0.33 0.81 0,36 0,16 0.06 0,22 0.15
** ** ** **
*
R d
HD a
PH a
2.58 2.54 2.78 2.45 2.98 3.07 3.27 2.89 2.99
25.1 23.6 34.5 27.4 27.8 22.1 24.0 29.7 30.7
109 108 78 95 82 87 90 83 81
b b a
b b
ranking m e a n s at P < 0.05 a c c o r d i n g to the N e w m a n - K e u l s test. a n d < 0.01, respectively. 0.05 and < 0.01, respectively. across e n v i r o n m e n t s at P = 0.75 on the basis o f S h u k l a ' s stability variance.
a a b b
b b
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by low yield stability, as indicated by the correlation ( r = 0.80, P < 0.05) between absolute value of /3 and the square-root of Shukla's stability variance. A remarkable exception was represented by Creso, of which the low stability (Table 4) was mostly accounted for by its interaction with seasons and by already known specific adaptation patterns which are independent from mean yield of locations (Boggini et al., 1992). Although not the lowest yielding, D 3415 ranked as the least reliable because of its low yield stability (Table 4). The variation among genotypes in earliness of heading and plant height was rather large (Table 4). Earliness, which is often the main trait related to specific adaptive responses (Wallace et al., 1993), showed no correlation with /3 values (Table 3). Indeed, only for W4267 a relationship could be established between its lateness and its positive response to more favourable locations. Therefore, escape from drought appeared less important than tolerance to drought as an adaptive trait in these materials. The occurrence of adaptive responses not predicted from earliness reinforces the importance of multi-environment testing for detection of such responses. Taller genotypes tended to be specifically adapted to low-yielding sites (Table 3). This response could be accounted for by higher susceptibility to lodging in favourable environments, or greater drought tolerance via greater translocation of stem reserves to grains under stress, when photosynthetic capacity is reduced (Austin, 1989). The latter hypothesis is supported by the fact that no severe lodging occurred at any experimental site. Finally, neither heading time nor plant height were related to yield stability, whilst plant stature was inversely correlated with overall mean yield (Table 3). 3.2. Response to managed drought stress The ANOVA results for the genotypes grown under normal and artificially stressed conditions in eight environments can be summarized as follows. The environments differed ( P < 0.01) for mean yield across stress levels. Those in Rome and Sassari, averaging about 4 t ha 1, and those of Caltagirone and Cammarata, averaging about 3 t ha - l , could be ranked as fairly high yielding and low yielding,
77
Table 5 Response to managed drought stress of nine durum wheat genotypes over eight environments in Italy. Grain yield (t ha - 1 ) in normal rainfed ( I N ) and stress (Ys) conditions, slope of linear components of genotype-stress level interaction ( t i p ) , and Eskridge's yield reliability ( R e, t ha - I ) estimated from mean yield across stress levels and tip Genotype
YN a
Ys a
[~p b
Rp c
Amedeo Capeiti 8 Creso D3415 Karel M104 Messapia Vespro W4267
3.64 b 3.70 b 3.44 b 3.51 b 4.07 a 4.16a 4.06 a 3.92 a 3.77 ab
2.93 b 3.05 b 2.70 b 3.18 a 3.27 a 3.55a 3.50 a 3.18 a 2.99 b
0.04 -0.10 0.10 -0.53 * * 0.24 -0.02 -0.12 0.17 0.21
2.49 2.62 2.35 2.38 2.92 3.11 3.00 2.79 2.58
a Separation of the top (a) and bottom (b) ranking means at P < 0.05 according to the N e w m a n - K e u l s test. b *, * * = different from zero at P < 0.05 and < 0.01, respectively, following F test of genotype contribution to g e n o t y p e stress level interaction sum of squares. Estimate of the lowest yield obtainable across environments at P = 0.75 on the basis of predicted Shukla's stability variance.
respectively. Conditions also differed ( P < 0 . 0 1 ) , their mean yield being 3.97 and 3.28 t ha-~ for the normal and stressed respectively. The yield reduction due to the stress, which averaged about 17%, was somewhat less than expected, probably because the application of the metal channels, while shedding most of the spring rainfall, also reduced evaporation of water stored in the soil from winter rainfall. The effects of genotypes and genotype-stress level, genotype-environment and stress level-environment interactions were also significant ( P < 0.05). The occurrence of interaction for the last term was probably due to differences among environments for rainfall patterns and, to some extent, for time of application of the stress. Genotype-stress level-environment interaction was not significant ( P < 0.10), indicating that no major inconsistency took place between environments for yield response of the genotypes across stress levels. The mean value of such responses, as described by /3p values, is reported in Table 5 together with genotype mean yield in normal and stressed conditions. D3415 was significantly better adapted to the drier conditions and less stable for yield across stress levels. Its yield reliability, as estimated from mean yield across conditions (Yp)
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Table 6 Comparison of estimates of adaptability, yield stability, mean yield and yield reliability obtained from regional testing and from response to managed drought stress over various sets of environments, for nine durum wheat genotypes. Data are coefficients of genetic correlation (rg) for Perkins and Jinks' /3 statistic across 6 locations and square root of Shukla's stability variance (o-), mean yield ( Y ) and Eskridge's reliability ( R ) across 18 environments with slope of linear components of genotype-stress level interaction (/3p), absolute value of tip, mean yield across stress levels (Yp), and reliability ( R p ) estimated from lip and /3p, respectively Environments
/3 - / 3 p
Overall c Within seasons ~ Within locations e Caltagirone Cammarata Rome Sassari Single environments f
0.81 0.58 0.59 0.60 0.79 0.60 0.35 0.44
a o- - [/3p[ a y _ yp a R - Rp ab 0.74 0.50 0.31 0.13 0.29 0.53 0.30 0.24
0.74 0.57 0.40 0.04 0.21 0.72 0.64 0.30
0.77 0.62 0.41 0.04 0.17 0.75 0.69 0.32
a Coefficients > 0.59, > 0.66 and > 0.79 differ from zero at P < 0.10, < 0.05 and < 0.01, respectively. b Estimate of the lowest yield obtainable across environments at P = 0.75 on the basis of Shukla's stability variance. c tip and Yp computed over eight environments. Average of rg values for three seasons; tip and Yp computed over two locations for 1984-85 and 1987-88 seasons, and over three locations for 1986-87 season. e Average of rg values for four locations; /3p and Yp computed over two seasons for each location. f Average of % values for eight environments.
and tip, was the lowest, although its Yp was higher than that of Capeiti 8 and Amedeo (Table 5). The broad sense heritability coefficients of yield in normal and stressed conditions were generally similar (data not reported). Genetic correlations assessing the ability of response to artificially managed drought stress to predict genotype performance from regional testing are reported in Table 6. Given the relatively small number of genotypes, even coefficients significant just at P < 0.10 were considered to be of some indicative value. Predictions based on tip and Yp values computed over all test environments could be rated as good for adaptability and moderately good for the remaining traits. Predictions from data of individual seasons or locations were mostly inaccurate for yield stability, mean yield and yield reliability. However, large differences for predictive value emerged among
locations, particularly with reference to mean yield and yield reliability. Better prediction of these traits was clearly a feature of high-yielding sites, i.e. Rome and, to a lesser extent, Sassari. Most probably, prediction of genotype mean yield in these locations was more accurate because site mean yield, averaged across stress levels, approached the level of overall mean yield in regional testing; conversely, the same prediction carried out in low-yielding locations was biased by genotype-environment interaction effects which tended to favour drought-tolerant materials. In addition, high-yielding sites can better predict overall mean yield in the presence of the mentioned correlation between location mean yield and within-location standard deviation of genotype mean yields, which confers a greater weight to performance at such sites. This correlation is frequent in multi-locational testing of cereals in water-limited regions (Yau, 1991). In accordance with the present findings, prediction of genotype mean yield over an Australian region, attempted from artificial environments differing by sowing date and levels of irrigation and fertilization, was satisfactory when it was based on data from a few favourable environments (Cooper et al., 1995). For prediction of mean yield, data from Rome and Cammarata had the advantage of being less biased by genotype-environment interactions associated with the level of winter cold, given the PC2 score close to zero of these sites (Table 2). The predictive ability of sites for yield reliability was strongly affected by that for mean yield due to the close relationship between these variables at the fairly low P level chosen for reliability estimation. Predictions based on data of individual environments were generally poor (Table 6). Given the lack of major inconsistencies between environments for yield response of the genotypes, the generally observed increase of predictive ability at an increasing number of environments over which fls was computed was probably due to the relatively high error which affected fls estimates in individual environments. Evaluation of breeding materials under artificially stressed conditions in order to reduce multi-environment testing is frequently advocated for biotic stresses (Eisemann et al., 1990). Artificial environments differing for cultural practices proved useful for reproduction of adaptive patterns in carrots (Dowker et
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
al., 1978) and for identification of high-yielding materials over a target region in wheat (Cooper et al., 1995). The present results indicate that evaluation under normal and artificial drought stress conditions can be envisaged both in a wide and a specific adaptation prospect to reduce the need of testing across a region where drought is a major stress. This may prove useful especially for the evaluation of advanced breeding lines, in order to reduce the number of lines promoted to subsequent regional testing a n d / o r restrict their regional testing to specific areas of adaptation. However, data from at least two environments (seasons or locations) should always be used, and a high-yielding test site is recommended for a reliable prediction of genotype mean yield and yield reliability. Prediction of yield stability is more difficult than that of adaptability, probably because of the greater influence of occasional stresses other than drought on the response of genotypes for the former trait. For application of the stress, metal channels have the advantage, compared with rain shelters (Acevedo et al., 1991), of not altering the pattern of distribution of rainfall while reducing its amount. However, the channels should be installed very early in the cropping season to induce a high stress. Response to irrigation is a cheaper alternative that may also work well for predictions, but is appropriate to sites where rainfall is low and varies little across seasons.
Acknowledgements The study was undertaken within the framework of the project "Durum wheat: breeding for higher and more stable yield in semi-arid Mediterranean zones" jointly funded by E.E.C.-Agrimed and the Italian Ministry of Agriculture and Forestry. The contribution of Drs. G. Boggini, F. Calcagno, S. Cassaniti, M. Deidda, G. Di Prima and G. Wittmer as those responsible in the Institutions co-operating in the research work is gratefully acknowledged.
References Acevedo, E., Craufurd, P.Q., Austin, R.B. and P6rez-Marco, P., 1991. Traits associated with high yield in barley for low-rainfall environments. J. Agric. Sci., Camb., 116: 23-36.
79
Annicchiarico, P. and Perenzin, M., 1994. Adaptation patterns and definition of macro-environments for selection and recommendation of common wheat genotypes in Italy. Plant Breeding, 113: 197-205. Austin, R.B., 1989. Maximising crop production in water-limited environments. In: F.W.G. Baker (Editor), Drought Resistance in Cereals. CAB International, Wallingford, pp. 13-26. Becker, H.C., 1984. Analysis of genotype × environment interactions with partitioning of environmental effects into effects of locations and years. Vortr. Pflanzenziichtg., 7: 209-218. Becker, H.C. and L6on, G., 1988. Stability analysis in plant breeding. Plant Breeding, 101: 1-23. Boggini, G., Annicchiarico, P., Longo, A. and Pecetti, L., 1992. Produttivith e adattamento di nuove costituzioni di frumento duro (Triticum durum Desf.). Riv. Agron., 26: 482-488. Ceccarelli, S., 1987. Wide adaptation. How wide? Euphytica, 40: 197-205. Ceccarelli, S., Nachit, M.M., Ortiz Ferrara, G., Mekni, M.S., Tahir, M., van Leur, J. and Srivastava, J.P., 1987. Breeding strategies for improving cereal yield and stability under drought. In: J.P. Srivastava, E. Porceddu, E. Acevedo and S. Varma (Editors), Drought Tolerance in Winter Cereals. J. Wiley and Sons, Chichester, pp. 101-114. Clarke, J.M., Townley-Smith, T.F., McCaig, T.N. and Green, D.G., 1984. Growth analysis of spring wheat cultivars of varying drought resistance. Crop Sci., 24: 537-541. Commission of the European Communities, 1993. Durum wheat. In: P. Crossa-Raynaud, B. Hubert and G. Rossetto (Editors), Agrimed Research Programme - Analysis and Evaluation of Results. Commission of the European Communities, Brussels, pp. 25-27. Cooper, M., Woodruff, D.R., Eisemann, R.L., Brennan, P.S. and DeLacy, I.H., 1995. A selection strategy to accomodate genotype-by-environment interaction for grain yield: managed-environments for selection among genotypes. Theor. Appl. Genet., 90: 492-502. Cornelius, P.L., 1993. Statistical tests and retention of terms in the Additive Main effects and Multiplicative Interaction model for cultivar trials. Crop Sci., 33:1186-1193. Dowker, B.D., Jackson, J.C. and Phelps, K., 1978. Variation studies in carrots as an aid to breeding. VI. Genotype-environment interactions in contrasting field environments. J. Hortic. Sci., 53: 131-137. Duwayri, M., Tell, A.M. and Shqaidef, F., 1987. Breeding for improved yield in moisture-limiting areas: the experience of Jordan. In: J.P. Srivastava, E. Porceddu, E. Acevedo and S~ Varma (Editors), Drought Tolerance in Winter Cereals. J, Wiley and Sons, Chichester, pp. 163-169. Eisemann, R.L., Cooper, M. and Woodruff, D.R., 1990. Beyond the analytical methodology - better interpretation and exploitation of genotype-by-environment interaction in breeding. In: M.S. Kang (Editor), Genotype-Environment Interaction and Plant Breeding. Louisiana State University, Baton Rouge, pp. 287-300. Eskridge, K.M., 1990. Selection of stable cultivars using a safetyfirst rule. Crop Sci., 30: 369-374. Finlay, K.W. and Wilkinson, G.N., 1963. The analysis of adaptation in a plant breeding programme. Aust. J. Agric. Res., 14: 742-754.
80
P. Annicchiarico, G. Mariani / Field Crops Research 46 (1996) 71-80
Fischer, R.A. and Maurer, R., 1978. Drought resistance in Spring wheat cultivars. I. Grain yield responses. Aust. J. Agric. Res., 29: 897-912. Gauch, H.G., 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier, Amsterdam, 278 pp. Gomez, K.A. and Gomez, A.A., 1984. Statistical Procedures for Agricultural Research, second edition. J. Wiley and Sons, Chichester, 680 pp. Mariani, G., Blanco, A., Boggini, G., Calcagno, F., Cassaniti, S., Deidda, M., Di Prima, G., Galterio, G., D'Egidio, M.G., Nardi, S., Pasquini, M., Wittmer, G., 1991. Frumento duro: ricerche per una maggiore e pih regolare resa nelle zone mediterranee semi-aride. Attivitg 1985-1989 - Rapporto finale. Istituto Sperimentale per la Cerealicoltura, Rome, 76 pp. Nachit, M.M., Nachit, G., Ketata, H., Gauch Jr., H.G. and Zobel, R.W., 1992. Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat. Theor. Appl. Genet., 83: 597-601. Pecetti, L. and Annicchiarico, P., 1993. Grain yield and quality of durum wheat landraces in a dry Mediterranean region of northern Syria. Plant Breeding, 110: 243-249. Perkins, J.M. and Jinks, L.L., 1968. Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity, 23: 339-356. Romagosa, I. and Fox, P.N., 1993. Genotype × environment interaction and adaptation. In: M.D. Hayward, N.O. Bosemark and I. Romagosa (Editors), Plant Breeding: Principles and Prospects. Chapman & Hall, London, pp. 373-390. SAS Institute, 1990. SAS/STAT User's Guide. Version 6, fourth edition. SAS Institute, Cary, 943 pp.
Seif, E. and Pederson, D.G., 1978. Effect of rainfall on the grain yield of spring wheat, with an application to the analysis of adaptation. Aust. J. Agric. Res., 29: 1107-1115. Shukla, G.K,, 1972a. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity, 29: 237-245. Shukla, G.K., 1972b. An invariant test for the homogeneity of variances in a two-way classification. Biometrics, 28: 10631072. Snedecor, G.W. and Cochran, W.G., 1967. Statistical Methods. The Iowa State University Press, Ames, 593 pp. Srivastava, J.P., 1984. Durum wheat - its world status and potential in Middle East and North Africa. Rachis, 3: 1-8. Srivastava, J.P., Jana, S., Gautam, P.L. and Niks, R.E., 1983. Parallel selection: an approach to increase grain yield and stability. In: S. Sakamoto (Editor), Proc. Sixth Int. Wheat Genet. Symp. Kyoto University, Kyoto, pp. 725-733. Vannella, S., 1987. Influenza dell'andamento climatico sulle rese del frumento. Riv. Agron., 21 (Suppl. 4): 223-229. Wallace, D.H., Baudoin, J.P., Beaver, J., Coyne, D.P., Halseth, D.E., Masaya, P.N., Munger, H.M., Myers, J.R., Silbernagel, M., Yourstone, K.S. and Zobel, R.W., 1993. Improving efficiency of breeding for higher crop yield. Theor. Appl. Genet., 86: 27-40. Yamada, Y., Itoh, Y. and Sugimoto, I., 1988. Parametric relationships between genotype × environment interaction and genetic correlation when two environments are involved. Theor. Appl. Genet., 76: 850-854. Yau, S.K., 1991. Need of scale transformation in cluster analysis of genotypes based on multi-location yield data. J. Genet. Breed., 45: 71-76.