Euphytica 125: 197–205, 2002.© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

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SPAD readings and associated leaf traits in durum wheat, barley andtriticale cultivars

Francesco Giunta∗, Rosella Motzo & Mauro DeiddaDipartimento di Scienze agronomiche e Genetica vegetale agraria, Facolta di Agraria, Universita di Sassari, Italy;∗Author for correspondence; e-mail: giunta@ssmain.uniss.it

Received 27 December 2000; accepted 7 September 2001

Key words: breeding, cereals, leaf area, leaf nitrogen concentration, SPAD readings, specific leaf area

Summary

SPAD readings may represent a useful screening criterion in breeding programs aimed at increasing the rate andduration of leaf photosynthesis. A two-year trial was conducted on 17 cultivars of durum wheat, 8 of triticale and18 of barley at two experiment stations in Sardinia, Italy, to evaluate the existence of genetic variation for SPADreadings, and to quantify the genetic associations between SPAD readings, area per leaf blade (LA), specific leafarea (SLA), leaf nitrogen concentration (LNC) and leaf nitrogen per unit of leaf surface (LN) in the period betweenbeginning of tillering and flag leaf appearance. Plants were grown at sufficient nitrogen fertilization. The averageSPAD reading of barley was 9–10 units lower than that of durum wheat and triticale. The combined ANOVAindicated that, in all the three species, the genotype by environment interaction variance associated with SPADreadings was lower than the genetic variance. In durum wheat and barley, SPAD readings exhibited a greater geneticvariance in comparison with LNC, LN and SLA. In durum wheat and triticale, SPAD readings were geneticallycorrelated with LN and SLA. Durum wheat differed from triticale because its genetic variation in SLA was notassociated with LA. A screening based on both SPAD readings and LA values should identify lines with goodphotosynthetic machinery that is not associated with low area per leaf blade.

Abbreviations: SPAD – [Soil-Plant Analysis Development] chlorophyll meter reading; LA – Area per Leaf blade;LNC – Leaf Nitrogen Concentration; SLA – Specific Leaf Area; LN – Leaf Nitrogen per unit of leaf surface

Introduction

High maximum net photosynthetic rates of singleleaves become critical where canopy structure and har-vest index have been optimised, provided that sink ca-pacity is not limiting (Nelson, 1988). Richards (2000)reported that wheat crops can be source limited dur-ing the late tillering and stem elongation phases, andthat these phases are critical in achieving high yieldpotentials. In contrast, during the subsequent periodfrom anthesis to grain filling, the rate of photosyn-thesis is mainly affected by the sink capacity of thecrop, although the capacity of the stem to store as-similates can reduce sink limitations to photosynthesis(Yang et al., 2000). However, the direct measurementof leaf photosynthetic rate in the field is time consum-

ing and weather dependent, hampering its utilisationas a screening criterion in segregating populations.Therefore, the development of alternative methods forestimating leaf activity in breeding programs could bevery useful.

Positive correlations between photosynthetic rateper unit area and chlorophyll content per leaf areahave been found in soybean (Hesketh et al., 1981)and wheat (Evans, 1983). Lines of wheat with highchlorophyll concentration, large semi-erect leaves andimproved green-area duration are currently utilised incrossing programs at CIMMYT, in order to increasethe source capacity (Reynolds et al., 1999). The leafchlorophyll concentration can be non-destructively es-timated through the SPAD-502 meter (Minolta Corp.,Ramsey, NJ) (Monje & Bugbee, 1992) originally

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utilised to optimise nitrogen management (Follett &Follett, 1992; Reeves et al., 1993; Fox et al., 1994).Later, SPAD readings have also been linearly correl-ated with maximum net photosynthetic rate in soybean(Ma et al., 1995) and in wheat (Gutiérrez-Rodríguezet al., 2000), and with discrimination against 13C inbarley (Araus et al., 1997). Working on rice, Laza etal. (1996) highlighted that SPAD readings providedgood estimates of maximum net photosynthetic ratewhen leaf nitrogen concentration was not limitingphotosynthesis.

The objectives of this work were: a) to evaluatethe existence and the amount of genetic variation forSPAD readings, leaf nitrogen concentration, leaf areaper blade, specific leaf area and leaf nitrogen perunit of leaf surface in a set of durum wheat, triticaleand barley cultivars; and b) to quantify the degree ofassociation among these traits in the three species.

Materials and methods

The data were collected in a two-year trial (1997/98and 1998/99) on durum wheat (Triticum turgidumL. var. durum), triticale (× Triticosecale Wittmack)and barley (Hordeum vulgare L.) in Sardinia (Italy).Durum wheat and triticale were grown at the experi-ment stations of Ottava (41◦N; 8◦E; 80 m altitude) andSanta Lucia (40◦N; 8◦E; 15 m altitude), while barleywas grown only at Ottava. In the latter station the soilis a sandy-clay-loam with a depth of about 0.6 m dueto underlying layers of limestone (typic Xerochrepts),an average nitrogen content of 0.76�, and a C/N ratioof 12. At S.Lucia the soil is a clay-loam with a depthof about 2 m, an average nitrogen content of 0.40�and a C/N ratio of 12. The soil water content at fieldcapacity (– 0.02 MPa) on a weight basis is 22.4% atOttava and 31.4% at S.Lucia, whereas at –1.5 MPa itis 11.9 and 13.4%, respectively.

In both sites the climate is typically Mediterranean,with long-term averages of annual rainfall of 561 mmat S.Lucia and 538 mm at Ottava. Information on cropmanagement is in Table 1.

The plots measured 10 m2 each and were arrangedin a completely randomised block design with threereplications in all cases except for triticale at Ottava,with four replications. The trials were sown at a rateof 350 seeds per m2, using an 8-row planter with 0.17m-row spacing. Weeds were chemically controlled.

The cultivars utilised were a sample from the Na-tional Cultivar Trials for each of the three species that

are conducted each year at several locations in Italy.The two-year evaluation used 17 cultivars for durumwheat, 18 for barley and 8 for triticale (Table 2).

Heading was recorded when observed on morethan 50% of the tillers of the bordered areas of theplots. Plant height, with awns excluded, was meas-ured at three random locations per plot. Yield wascalculated on the basis of the whole plot, mechanicallyharvested. Average kernel dry weight was determinedon a sample of 1,000 kernels from each plot.

Observations on leaf characteristics were madeduring the period between the beginning of tilleringand flag leaf appearance. At each sampling, SPAD-502 readings were taken from the middle portion of theblades of 30 uppermost fully expanded leaf blades ofeach plot. On the same blades, the following paramet-ers were then determined: leaf area with a planimeter;dry weight after oven drying at 80 ◦C; and total leaf ni-trogen concentration (LNC, g N kg−1) by the Kjeldahltechnique.

Specific leaf area (SLA) was calculated by dividingleaf area by leaf weight (m2kg−1) for blades sampledwithin each plot. Leaf nitrogen (LN, g N m−2 leafarea) was calculated by dividing LNC by SLA.

ANOVA was performed for each species and yearby location combination. Since the error varianceswithin species were homogeneous at the Bartlett’s Test(Steel & Torrie, 1980), a combined ANOVA was per-formed for each species, in which each combinationyear by location was considered as a different envir-onment. Both cultivars and environments were con-sidered as random factors, as the cultivars utilised canbe considered a random sample of the Italian cultivarsof the three species. F test was performed according toMcIntosh (1983). Variance component estimates wereprovided by linear functions of the mean squares asindicated appropriate by the expectations of the meansquares (Johnson et al., 1955; Borojevic, 1990) repor-ted in Table 3. Negative estimates were assumed to bezero (Borojevic, 1990).

Finally, genetic, environmental and phenotypiccorrelations were calculated. Phenotipic correlationswere calculated from the phenotypic variances andcovariances. Environmental correlations come frommicro-environmental variations and represent residualcorrelations. Genetic correlations among those charac-ters that showed genetic variability were estimated asfollows:

rG(1,2) = covG(1,2)/[σ 2G(1) σ 2

G(2)]1/2

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Table 1. Dates of sowing and amount of fertiliser applied in the different experiments

Year Species Location Sowing Fertilisation (kg/ha)

date at sowing after sowing1

N P2O5 N

1997/98 Wheat Ottava 9-12-1997 80 90 32 (13/02)

S.Lucia 15-12-1997 – – 90 (09/01)

Triticale Ottava 10-12-1997 80 90 –

S.Lucia 12-12-1997 92 – –

Barley Ottava 16-12-1997 36 92 40 (12/02)

1998/99 Wheat Ottava 9-12-1998 80 90 –

S.Lucia 23-11-1998 82 92 –

Triticale Ottava 9-12-1998 82 92 –

S.Lucia 24-11-1998 82 92 –

Barley Ottava 28-12-1998 36 92 40 (06/03)

1 Application date in brackets.

Table 2. Cultivars utilised and average values and range of variation in yield, kernel weight, headingdate and height of their means calculated within each environment

Cultivars Yield Kernel Heading Height

weight

(t ha−1) (mg) (days) (cm)

Durum wheat1

Ofanto, Gianni, Giemme, Rusticano average 6.24 48.2 105 89

Tresor, Duilio, Bronte, Colosseo, Iride, minimum 3.49 37.0 95 75

Simeto, Parsifal, Fortore, Durfort, maximum 9.03 56.8 118 108

Creso, Ciccio, Italo, San Carlo

Triticale2

Antares, Mizar, Catria, Noe, Magistral, average 5.86 40.9 102 118

Cume, Trica, Rigel minimum 3.10 25.7 87 97

maximum 7.35 52.5 121 140

Barley3

Asso, Federal, Samson, Baraka, average 5.60 39.8 113 93

Balkan, Diomede, Canoro, Abondant, minimum 2.33 33.1 99 68

Fjord, Arda, Alfeo, Kelibia, Gotic, maximum 7.47 48.9 126 107

Sereno, Amillis, Sonora, Passport,

Formula

1 All the cultivars are semi-dwarf.2 All the cultivars are spring types except for Noe and Magistral.3 All the cultivars are winter types except for Formula; 11 are polistic, 7 distic.

where covG(1,2), σ 2G(1) and σ 2

G(2) represent the ge-netic covariance between, and genetic variances of,traits 1 and 2, respectively. Genetic covariance es-timates were obtained from the matrix of the sumof products and the sum of squares of the com-bined ANOVA (proc MANOVA option PRINTE andPRINTH, SAS institute, 1987). Mean product expect-ations are analogous to the mean square expectationsand thus estimates of the genetic covariance com-

ponent between two traits were derived in the samefashion as for the corresponding variance component.

The significance of phenotypic correlations wastested by a t-test. The problem of significance of ge-netic correlations is more complex, as they are not dir-ectly estimated but derived from analysis of variancesand covariances, so no direct test of the significance ofgenetic correlations is available.

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Results and discussion

Environments and the genetically determined responseto them are reflected in the yield, morphological andphenological data reported in Table 2. Average head-ing date for durum wheat (15 April) was intermedi-ate between those of triticale (12 April) and barley(23 April). Cultivar grain yield ranged from a min-imum of 2.3 t ha−1 for barley to a maximum of 9.0t ha−1 for durum wheat. The tallest cultivars werethose of triticale (maximum 140 cm), whereas durumwheat produced the heaviest kernels (48 mg on av-erage). These data are within the range of variationreported for these cereal crops in Mediterranean en-vironments, highlighting the good adaptation of thecultivars utilised.

Area per leaf blade (LA) averaged over cultivarsand environments was almost double in triticale anddurum wheat than in barley (Table 4). This result is indisagreement with that reported by Lopez-Castañedaet al. (1995), who attributed the vegetative vigour ofspring cultivars of barley to their great leaf area. Thewinter habitus of the Italian cultivars used in our trialwas probably the reason for these contrasting results.A particularly high genetic variance was estimated forLA in triticale (Table 5), due to the presence of thetwo winter cvs Noè and Magistral, characterised bysmaller leaves in comparison with the spring types.

The three species provided a range in specific leafarea (SLA) (Table 4) within which Criswell & Shibles(1971), Loomis & Connor (1992) and Richards (2000)reported a negative linear relationship with photosyn-thetic rate. Significant differences in SLA among cul-tivars were found within wheat and triticale (Table 5),but not within barley, which was the species with thelowest average SLA. The higher SLA reported for bar-ley by Lopez-Castañeda et al. (1995) in comparisonwith wheat is again probably a consequence of thedifferent habitus of the cultivars utilised, and hence ofthe different area per leaf blade. In fact, differences inSLA among the three species in our study were associ-ated with concomitant variations in LA (r = 0.56∗∗, n =43), whereas no significant relationship was detectedbetween SLA and mass per leaf blade, in spite of thewide variation in the latter character (49–141 mg ofdry matter per leaf blade).

As the objective of the experiment was concernedwith yield potential, nitrogen was applied in orderto avoid limitations to yield. This was reflected inthe average values of 52 and 47 g kg−1 of total leafnitrogen concentration (LNC) of durum wheat and trit-

icale, respectively (Table 6). These values are wellabove the critical level of 36 g kg−1 suggested by Foxet al. (1994) in winter bread wheat as the thresholdfor vegetative phase below which a response of grainyield to nitrogen fertilisation is expected. In barley,on the contrary, the average LNC was equal to 35 gkg−1, consistent with the lower LNC reported forthis species in comparison with wheat by Bishop &MacEachern (1971).

Such differences in LNC among species weregreatly reduced when the leaf nitrogen was expressedper unit of leaf surface (LN, g N m−2 of leaf surface),because of the high dependence of the leaf extensionprocess on nitrogen concentration (Novoa & Loomis,1981). According to Gastal and Nelson (1994), leafextension rate of tall fescue is closely associated withnitrogen concentration of the basal 3-mm segment ofthe leaf blade, whereas little nitrogen is deposited dur-ing cell maturation. As a consequence, nitrogen perunit leaf surface in the leaf blade is a quite conservat-ive quantity. For all cultivars within the three speciesLN was greater than 1.6 g m−2 (Table 6), the valueabove which the maximum photosynthetic rate is notnitrogen limited (Sinclair & Horie, 1989). In wheatand triticale, the genetic component of variance wasgreater for LN than for LNC (Table 5), whereas nogenetic variation was estimated in barley for these twoparameters.

Associated with the lower LNC, the average SPADreading of barley was proportionally lower by 9–10units than in durum wheat and triticale (Table 6). Thevariation in SPAD readings when the three specieswere considered together was indeed positively relatedto LNC (r = 0.88∗∗, n = 43, y = 0.5571x + 18.183). Thestrength of this relationship improved when SPAD wasmultiplied by SLA (r = 0.94∗∗, n = 43, y = 21.61x –52.174) as proposed by Peng et al. (1993) to cope witheffects of leaf thickness on variation in the relationshipbetween SPAD meter readings and LNC observed byCampbell et al. (1990).

For each species, SPAD readings varied within arange that, in soybean, was associated with differencesin photosynthetic rate (Ma et al., 1995). In the caseof durum wheat, SPAD readings ranged from 42.5 to50.6, a much wider range than that between 37.0 and42.8 within which a significant association with netphotosynthetic rate of bread wheat was reported byGutiérrez-Rodríguez et al. (2000). The greatest ge-netic variance for SPAD readings was estimated indurum wheat (Table 5). In this species SPAD read-ing was the trait which exhibited the greatest genetic

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Table 3. Form of variance and covariance analysis with mean squares and mean productsexpected composition

Source of variation Degrees of Composition of Composition of

freedom1 mean squares2 mean products

Environments l – 1

Reps in environments l(r – 1)

Lines g σ 2e + rσ 2

gl + rlσ 2g cove +rcovgl + rlcovg

Lines × environments (g – 1)(l – 1) σ 2e + rσ 2

gl cove + rcovgl

Error l(r – 1)(g – 1) σ 2e cove

1 l, r and g symbolise number of environments, replications per environment and lines, respect-ively.2 subscripts e, gl, g indicate residual, g × l interaction and lines, respectively.

Table 4. Grand mean, environmental and cultivar means in the three species for area per leaf blade and specific leaf area (SLA)

Durum wheat Triticale Barley

Area per Area per Area per

leaf blade SLA leaf blade SLA leaf blade SLA

(cm2) (m2kg−1) (cm2) (m2kg−1) (cm2) (m2kg−1)

Grand mean 19.3 23.2 22.9 20.7 12.2 19.0

Environmental means

Ottava 1998 17.2 23.8 Ottava 1998 27.2 19.5 Ottava 1998 13.1 17.0

Ottava 1999 17.1 23.5 Ottava 1999 10.4 21.1 Ottava 1999 10.5 21.0

S.Lucia 1998 27.9 20.0 S.Lucia 1998 29.7 19.1

S.Lucia 1999 15.1 25.6 S.Lucia 1999 19.2 24.1

SE1 0.25 0.22 0.42 0.30 0.27 0.39

Cultivar means

Bronte 20.4 23.1 Antares 20.0 20.0 Abondant 12.2 17.3

Ciccio 19.1 23.2 Catria 28.4 21.9 Alfeo 12.6 22.3

Colosseo 22.3 25.1 Cume 23.7 22.3 Amillis 10.7 19.9

Creso 17.2 23.9 Magistral 16.5 18.6 Arda 13.1 21.0

Duilio 19.4 22.0 Mizar 18.8 20.2 Asso 14.6 16.6

Durfort 19.6 23.5 Noe 15.7 20.2 Balkan 12.5 17.9

Fortore 21.4 22.1 Rigel 24.3 22.4 Baraka 9.5 19.0

Gianni 18.7 22.6 Trica 25.7 22.2 Canoro 11.9 20.7

Giemme 19.9 23.0 Diomede 15.9 18.1

Iride 19.6 23.8 Federal 15.2 18.2

Italo 18.1 24.0 Fjord 8.3 18.4

Ofanto 19.0 22.5 Formula 11.0 19.5

Parsifal 19.9 23.1 Gotic 14.3 19.4

Rusticano 18.1 22.8 Kelibia 9.3 18.0

Sancarlo 18.6 21.8 Passport 11.2 20.5

Simeto 19.6 22.9 Samson 12.6 18.9

Tresor 17.5 25.2 Sereno 11.4 20.1

Sonora 12.6 16.9

SE 0.64 0.46 1.28 0.72 1.20 1.02

1 SE, standard error of the mean, calculated from the residual MS for the environments and from the G×E interaction MS for the cultivars.

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Table 5. Analysis of variance and estimates of variance components and of their percentage on the total variance for five leaf traits.The environments consist of the different combinations of years and locations.

Source of variation df Area per leaf blade SLA LNC LN SPAD reading

MS σ 2 % MS σ 2 % MS σ 2 % MS σ 2 % MS σ 2 %

Durum wheat

Replications 8 21 31 6 0.19 16.1

Environment (E) 3 1708∗∗ 282∗∗ 2721∗∗ 1.31∗ 471.7∗∗Cultivar (G) 16 20∗∗ 1.27 25.6 11∗∗ 0.7 22.9 18∗ 0.8 10.9 0.15∗∗ 0.01 14.7 55.0∗∗ 3.94 39.1G × E 48 5∗ 0.63 12.7 3 ns 0.0 0.4 8∗ 1.0 13.4 0.06∗∗ 0.01 15.9 7.8∗ 0.83 8.2Error 128 3 3.07 61.7 2 2.5 76.7 5 5.4 75.8 0.03 0.03 69.4 5.3 5.31 52.7Total 203 4.97 3.2 7.1 0.05 10.07

Triticale

Replications 10 21 5 49 0.22 14.6

Environment (E) 3 1762∗∗ 134∗∗ 1640∗∗ 2.02∗∗ 164.0∗∗Cultivar (G) 7 272∗∗ 15.37 58.8 25∗ 1.1 20.6 40ns 1.1 5.3 0.66∗∗ 0.03 20.8 25.8∗ 1.03 13.6G × E 21 26∗∗ 5.15 19.7 8∗∗ 1.3 25.6 23ns 1.1 5.3 0.18∗ 0.02 14.9 9.3ns 0.90 11.9Error 67 6 5.63 21.5 3 2.8 53.8 18 18.3 89.4 0.09 0.09 64.3 5.7 5.66 74.5Total 108 26.15 5.2 20.5 0.14 7.60

Barley

Replications 4 7 0.5 16 0.06 2.8

Environment (E) 1 282∗∗ 437∗∗ 2952∗∗ 33.22∗∗ 870.4∗∗Cultivar (G) 17 25∗∗ 3.12 40.3 14ns 0.9 9.8 17ns 0.01 0.0 0.41ns 0.001 0.0 19.9∗∗ 2.64 26.4G × E 17 6ns 0.82 10.6 9ns 0.1 0.8 22∗ 3.5 23.7 0.43ns 0.05 52.6 4.1ns 0.001 0.0

Error 59 4 3.80 49.1 8 8.4 89.4 11 11.3 76.3 0.28 0.28 47.4 7.4 7.36 73.6Total 98 7.74 9.4 14.8 0.33 10.00

∗ Significant at 5% level of probability; ∗∗, significant at the 1% level of probability; ns, non significant.1 The value was negative.

variance estimate in comparison to all the other traitsconsidered. In all species the genetic variance estimatefor SPAD readings was greater than the GxE interac-tion variance estimate. In barley, SPAD reading andLA were the only traits with a significant genotypicvariance and a relevant genetic component estimate.

Positive phenotypic and genetic correlationsbetween SPAD readings and LN were calculatedwithin durum wheat and triticale, whereas no signi-ficant correlation was found between SPAD readingsand LNC within species (Table 7).

In wheat and triticale higher SPAD readings andhigher LN were both phenotipically and geneticallyassociated with thicker leaves (lower SLA). Since leafthickness was not genetically associated with LNC, itcan be argued that variation in SPAD and LN amonggenotypes grown under non limiting nitrogen levels isassociated with SLA. According to this correlations,the negative relationship between SLA and net as-similation rate discussed by Richards (2000), couldbe attributed to the lower amount of photosyntheticmachinery (LN, SPAD) per unit leaf area.

Durum wheat differed from triticale because its ge-netic variation in SLA was not associated with LA,notwithstanding the quite large phenotypic variationin both LA (17.2–22.3 cm2) and SLA (21.8–25.2 m2

kg−1). This means that, in durum wheat, a selectionfor high values of LN and SPAD does not necessarilyimply a reduction in LA. This result is consistent withthat of Nelson & Sleper (1983) who, working on tallfescue, showed that rate of photosynthesis per unit leafarea tends to be largely independent of selection forleaf size.

In contrast, although SPAD readings for barleyshowed both genotypic and genetic variance, no signi-ficant relationship existed between SPAD readings andthe other traits considered. The only significant associ-ation was the negative phenotypic correlation betweenSLA and LN. It was caused by environmental factors,due to the absence of genetic variation in LN amongthe cultivars utilised.

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Table 6. Grand mean, environmental and cultivar means in the three species for nitrogen dry weight, specific leaf nitrogen and SPAD readings

Durum wheat Triticale Barley

LNC LN SPAD LNC LN SPAD LNC LN SPAD

(g kg−1) (g m−2 reading (g kg−1) (g m−2 reading (g kg−1) (g m−2 reading

Grand mean 51.9 2.3 46.3 47.3 2.3 47.3 35.2 1.9 37.4

Environmental means

Ottava 1998 53.6 2.3 44.0 Ottava 1998 40.8 2.1 44.8 Ottava 1998 41.2 2.6 40.6

Ottava 1999 57.2 2.4 49.0 Ottava 1999 57.6 2.8 50.0 Ottava 1999 30.8 1.5 35.0

S.Lucia 1998 41.2 2.1 48.8 S.Lucia 1998 42.5 2.2 46.3

S.Lucia 1999 55.7 2.2 43.3 S.Lucia 1999 53.3 2.2 49.5

SE1 0.33 0.03 0.32 0.8 0.1 0.4 0.5 0.1 0.4

Cultivar means

Bronte 51.3 2.2 44.4 Antares 48.7 2.5 48.4 Abondant 36.1 2.3 35.5

Ciccio 51.9 2.2 45.9 Catria 49.1 2.3 48.0 Alfeo 36.9 1.7 37.0

Colosseo 50.9 2.0 42.5 Cume 45.6 2.0 45.6 Amillis 35.9 1.8 39.7

Creso 51.4 2.2 44.7 Magistral 47.2 2.6 50.6 Arda 37.8 1.8 36.9

Duilio 52.4 2.4 45.7 Mizar 49.5 2.5 47.5 Asso 39.6 2.8 38.0

Durfort 52.4 2.3 46.6 Noe 51.8 2.7 46.8 Balkan 34.6 2.1 39.6

Fortore 50.2 2.3 46.6 Rigel 48.3 2.2 47.5 Baraka 37.5 2.1 39.9

Gianni 50.8 2.3 45.0 Trica 48.3 2.2 47.0 Canoro 35.1 1.8 36.2

Giemme 54.3 2.4 46.5 Diomede 35.0 2.0 37.6

Iride 51.8 2.2 48.1 Federal 36.9 2.3 38.7

Italo 53.1 2.2 44.4 Fjord 37.9 2.2 40.3

Ofanto 52.2 2.3 49.9 Formula 35.4 1.9 40.1

Parsifal 49.5 2.1 46.3 Gotic 37.2 2.1 38.7

Rusticano 52.4 2.3 47.8 Kelibia 36.1 2.1 39.0

Sancarlo 53.4 2.4 47.8 Passport 36.3 1.8 35.9

Simeto 52.9 2.3 50.6 Samson 33.7 2.0 37.4

Tresor 51.5 2.1 43.7 Sereno 34.3 1.7 36.6

Sonora 32.7 2.2 33.8

SE 0.8 0.1 0.8 1.2 0.1 0.8 1.2 0.3 0.8

1 SE, standard error of the mean calculated from the residual MS for the environments and from the GxE interaction MS for the cultivars.

Table 7. Simple phenotypic (p), genetic (g) and environmental (e) correlation coefficients

Durum wheat Triticale Barley

SPAD LA SLA LN SPAD LA SLA LN SPAD LA SLA LN

LA p –0.08 –0.34 –0.31

g –0.14 –0.44 –0.47e 0.30 0.08 0.51

SLA p –0.57 –0.06 –0.73 0.84 –0.03 –0.16

g –0.65 –0.15 –1.02 1.07 –0.10 –0.32e –0.04 0.31 0.08 0.43 –0.16 –0.10

LN p 0.59 –0.13 –0.88 0.55 –0.84 –0.89 0.11 0.29 –0.84

g 0.71 –0.17 –0.93 0.58 –1.07 –0.95 – – –

e 0.14 –0.22 –0.79 0.29 –0.07 –0.56 0.29 0.33 –0.86

LNC p 0.34 –0.30 –0.17 0.60 –0.03 –0.34 –0.23 0.63 0.39 0.01 0.06 0.42

g 0.38 –0.53 –0.05 0.42 –0.62 –0.61 –0.66 0.91 – – – –

e 0.14 0.09 –0.03 0.59 0.49 0.38 0.16 0.62 0.32 0.35 –0.08 0.49

Phenotypic correlations above 0.48 for wheat, 0.71 for triticale and 0.47 for barley are significant at the 0.05 probability level.

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Conclusions

The existence of genetic variance and the low G×Einteraction variance of SPAD readings in the threespecies, in a range of conditions in which nitrogenwas not limiting growth, make feasible the utilisationof the SPAD meter in breeding programs as indirectselection criterion, when the source capacity is to beimproved. The fact that this genetic variance is presentin a set of high yielding, well-adapted cultivars, makesthis result even more interesting. Breeders routinelycross agronomically acceptable types, which are of-ten represented by elite lines or by currently cultivatedcultivars.

The absence of genetic correlation between SPADreadings and LA in durum wheat implies the possib-ility to select for a higher SPAD reading, and a likelyhigher amount of photosynthetic machinery per unitarea, without reducing the leaf area needed to lightinterception, in a period in which a low leaf area indexcan limit the crop growth rate. This lack of correla-tion should be an essential pre-requisite to select lineswith inherent differences in photosynthetic potential.The negative correlations between LA and CO2 ex-change rate per unit leaf area may in fact be one of thecauses of the lack of consistent correlations betweenphotosynthesis per unit leaf area and yield reviewed byBhagsari & Brown (1986). In this respect, a screeningbased on both SPAD readings and LA values wouldhelp discriminate lines in which good photosyntheticmachinery is not associated with a low leaf area.

Acknowledgements

The authors thank Dr A.J. Condon and the unknownreferee for their very helpful comments and sugges-tions.

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