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CROP PHYSIOLOGY & METABOLISM

Photoperiod and Temperature Responses in Early-Maturing, Near-Isogenic Soybean Lines

Eastern Cereal and Oilseed Research Centre (ECORC), Agric. & Agri-Food Canada, Ottawa, Ontario, Canada, K1A 0C6

* Corresponding author (coberer{at}em.agr.ca)

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
While photoperiod responses have been studied in soybean [Glycine max (L.) Merr.] isolines, identification of temperature and photo–thermal responses are lacking in early-maturing soybean. This study was conducted to quantify photoperiod and temperature responses of early-maturing soybean. Six ‘Harosoy’ isolines with different combinations of alleles at the E1, E3, E4, and E7 loci were grown in growth cabinets with 10-, 12-, 14-, 16-, and 20-h photoperiods and with either 18 or 28°C constant temperature. Under the most inductive conditions (10 and 12 h, 28°C), all isolines flowered in about 26 d. Under the least inductive conditions (20 h, 28°C), there was a 50 d difference in flowering time between the early- and late-flowering isolines. Interestingly, the late-flowering isolines flowered earlier under cool than under warm temperatures. A mathematical model was developed to quantify the effects of temperature and photoperiod on days to first flower. This model related the rate of phenological development from planting to flowering to temperature, photoperiod and the interaction between temperature and photoperiod. The equation was integrated analytically, resulting in an inverse time (1/time) equation, or numerically resulting in the development of a Growing Photothermal Day (G_PTD) similar to a heat unit. The model had a base temperature (5.8°C) below which the rate of phenological development was zero, a critical or base photoperiod (13.5 h) below which photoperiod had no effect, and two genetic coefficients, one of which varied with isoline. The isoline photoperiod sensitivity coefficient was linearly related to the number of dominant (late flowering, photoperiod sensitive) alleles. The model fit the observed data well (R² = 0.96).

Abbreviations: MG, maturity group • SED, standard error of a difference

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
SIX LOCI have been reported to control time to flowering and maturity in soybean: E1 and E2 (Bernard, 1971), E3 (Buzzell, 1971), E4 (Buzzell and Voldeng, 1980), E5 (McBlain and Bernard, 1987), and E7 (Cober and Voldeng, 2001). In each case, late flowering and maturity is partially dominant and early flowering and maturity is recessive. In this report, we will refer to these alleles as dominant or recessive for simplicity. The dominant alleles condition late flowering and maturity primarily by detecting non-inductive photoperiods and should be called photoperiod-sensitivity alleles (Upadhyay et al., 1994; Cober et al., 1996). To identify and study these alleles, near-isogenic lines have been developed by backcrossing the commercial cultivars Clark or Harosoy to sources of alternative alleles (Bernard et al., 1991; Voldeng and Saindon, 1991; Voldeng et al., 1996).

The E1, compared with the e1 allele, delayed flowering greatly with estimates of 16 to 23 d delay under field conditions (Bernard, 1971; McBlain et al., 1987; Cober et al., 1996). The E3, compared with the e3 allele, delayed flowering 4 to 5 d under field conditions (McBlain et al., 1987; Cober et al., 1996). The E4, compared with the e4 allele, delayed flowering 1 to 6 d under field conditions (Saindon et al., 1989; Cober et al., 1996). The E7, compared with the e7 allele, delayed maturity 5 d under field conditions (Cober and Voldeng, 2001).

It has been known for some time that cooler temperatures as well as longer photoperiods delay time to flowering in soybean (Garner and Allard, 1930). This means that cultivars tend to be adapted to narrow ranges in latitude and a great deal of work has been done relating phenological development to temperature and photoperiod. This past work can be divided into two main groups. In the first group of studies, the rate of phenological development was related to temperature and photoperiod functions which were added together. This results, after integration, in inverse time (one divided by the number of days in the period) being equal to additive functions of the mean temperature and mean photoperiod for the phenological period under consideration (e.g., time from sowing to first flower). Linear regression was used to determine genetic coefficients in these equations (Hadley et al., 1984; Constable and Rose, 1988; Summerfield et al., 1993; Upadhyay et al., 1994; Perry et al., 1987). In the study by Upadhyay et al. (1994), determining genetic coefficients was extended to the allele level by studying temperature and photoperiod effects on different combinations of alleles at the E1, E2, and E3 loci in ‘Clark’ soybean isolines.

In the second group of studies, the rate of phenological development was related to temperature and photoperiod functions, which were multiplied together (Major et al., 1975; Grimm et al., 1993; Grimm et al., 1994; Piper et al., 1996a; Piper et al., 1996b). Analytical integration was difficult for multiplicative functions and usually a photothermal unit was derived which was, in reality, a simple type of numerical integration. Temperature and photoperiod were used to calculate a daily or hourly value for each function. These values were then multiplied and the products summed over the phenological period. These sums should be constant for a given cultivar over locations and years (Stewart et al., 1998). Genetic coefficients in these functions were determined by a non-linear least squares method such as the downhill simplex method used by Grimm et al. (1993).

The objectives of this study were to: quantify photoperiod and temperature responses of early-maturing soybean isolines by both the growing degree day and inverse time concepts; show how the two methods are related; and develop a soybean photothermal unit for estimating the period from sowing to first flower. We will also quantify the effects of different combinations of alleles at the E loci on time to first flower in Harosoy isolines.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Growth Cabinet Experiments
The genotypes used in this study were near-isogenic lines, with different combinations of photoperiod-sensitivity alleles at the E1, E3, E4, and E7 loci, developed in a backcrossing program with Harosoy as the recurrent parent (Table 1). All these lines are homozygous e2e2 e5e5 at the other photoperiod-sensitivity loci. The E6 gene symbol has been assigned in a study which is not yet published. Isolines with an OT prefix were developed (OT93-28, OT94-41, Voldeng et al., 1996; OT89-5, Voldeng and Saindon, 1991) at the Eastern Cereal and Oilseed Research Center, Ottawa, ON, and isolines with an L prefix were developed and described by Bernard et al. (1991). This study was conducted in growth cabinets. Seeds were germinated in vermiculite and seedlings were transplanted into 13-cm standard pots filled with regular phytotron potting mix (3 parts loam: 2 parts vermiculite: 1.5 parts peat moss: 1 part crushed brick).

The experiment was designed as a 5 by 2 factorial, for the main plot treatments of photoperiod and temperature, with six isolines in split plots. Individual temperature-photoperiod combinations were assigned randomly to growth cabinets. Two pots (one plant per pot) of each of the six isolines were grown within each cabinet for each replicate. Two replications were made over time for a total of 4 plants of each isoline for each temperature-photoperiod combination. The date of the first open flower was recorded for each plant. Temperature, photoperiod, and genotypes were considered fixed effects. The error term used in the F-test for the main plot effects, of temperature, photoperiod and temperature x photoperiod, was replication x temperature x photoperiod. The GLM procedure of SAS (SAS Institute Inc., Cary, NC) was used for analysis of variance.

Theoretical Considerations for Modeling Flowering Time
Summerfield et al. (1991) proposed that rate of development with time (dR/dt) for soybean from sowing to flowering could be expressed as the following function of temperature (T):

(1)

where a and b* were genetic coefficients. With the addition of a base temperature T_B), below which the rate of development was zero, Eq. [1] was rearranged to

(2)

There was also an assumption of an upper threshold temperatureT_C above which rate of development was constant and is usually set at 30°C for soybean (Summerfield et al., 1991). If Eq. [2] is realistic, a value of 30° for T_C will not influence results in this study. Integrating Eq. [2] resulted in

(3)

where f was the number of days from sowing to first flower and T_A was the average daily temperature for this period. Since R was assumed to increase from zero to 1 during this time, Eq. [3] becomes

(4)

For short-day plants such as soybean, time to flowering is extended when photoperiod (P) exceeds a critical level (P_C) and the rate of development can be expressed as

(5)

where P - P_C cannot be negative. The coefficient c_T in Summerfield et al. (1991) is a genetic constant. In this study, c_T is the following function of temperature:

(6)

where b, c, and d are genetic coefficients and T_D is a temperature that we set to 28°C which will be explained in more detail below. Eq. [6] (c_T) is similar to an equation used by Constable and Rose (1988). The restriction on c_T is that it can not be positive.

Assuming R goes from zero to one during time to first flower, Eq. [5], when integrated under constant environmental conditions and combined with Eq. [6], results in

(7)

where f is the number of days from sowing to first flower and P_A is the average photoperiod. This integration only applies for constant environmental conditions. If the interactive term, d(T_A - T_D)(P_A - P_C), is neglected, then average values of T_A and P_A could be used, assuming linear effects of temperature and photoperiod, in a varying environment. For these conditions, however, T_A, P_A, and f are interdependent and an iterative procedure must be used to solve for f (Summerfield et al., 1993). It is also unclear what happens when T exceeds T_C or falls below T_B. It is also important to note that in an environment with varying temperatures and/or photoperiods, Eq. [7] does not follow from Eq. [5] and [6].

The Growing Degree Day (G_DD) (Shakewich, 1995) uses these same concepts but in a different way where the emphasis is on accumulating G_DDs. The rate of change of G_DD from sowing to first flower is expressed as

(8)

Normally, we use the simple trapezoidal method to integrate Eq. [8] resulting in

(9)

or more simply

(10)

since t is a time step of one day. Integrating Eq. [8] formally from sowing to flowering results in

(11)

which demonstrates that the accumulation of G_DDs should be a constant at different temperatures for the same genotype and shows how G_DDs are related to the Summerfield et al. (1991) inverse time concept. When photoperiod has an effect on development rate, a Growing Photothermal Day (G_PTD), following the same reasoning, can be expressed as

(12)

To test this theory, we fit the growth cabinet data (time to first flower at two temperatures and five photoperiods for six isolines of soybeans) to Eq. [7] solving for T_B, b, c, and d using a non-linear least squares fitting algorithm (Marquardt, 1963). We solved for T_C indirectly using a sensitivity analysis because the fit was relatively insensitive to T_C compared with the other unknowns. The reason for this will be evident from the results. We then calculated an average value for G_PTD using these coefficients and recalculated time to flowering for each treatment and isoline by summing over the number of days for each treatment until the average value was reached.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
There were significant effects of temperature, photoperiod, and a temperature x photoperiod interaction for days to first flower. The effects of genotype and the interactions, genotype x photoperiod, genotype x temperature, and genotype x temperature x photoperiod were all significant for days to first flower (Table 2). Under the most inductive conditions of short photoperiod and warm temperature, there were no differences among isolines and all lines flowered in about 26 d. In less inductive photoperiods, differences between isolines became apparent. In the warm temperature and 20 h photoperiod there was a 50 d difference in flowering time between the earliest- and latest-flowering isoline. All isolines responded to photoperiod under the least inductive conditions (warm temperature, 20-h photoperiod), indicating that the earliest-flowering isoline in this study (OT94-47) is not photoperiod insensitive and that more photoperiod-sensitivity alleles are present in this genotype. The lack of differences between isolines under the most inductive conditions agrees with reports of Upadhyay et al. (1994) and Cober et al. (1996) and supports the contention of both groups that these flowering genes function to detect photoperiod and should be called photoperiod-sensitivity genes.

Equation [7] was fitted to the observations of time to first flower to quantify temperature and photoperiod responses. The values of the nine coefficients solved for by the least squares method are approximately an order of magnitude larger than their standard errors (Table 3). Even though only two temperatures were used in this study, the fitting procedure calculated a base temperature of 5.8°C, a reasonable number for northern soybean varieties [Maturity Group (MG) 0 and 00]. The base photoperiod (P_C) was 13.5 h and was obtained by differentiating the polynomial in Fig. 1 and setting the differential to zero. This compares with 14.5 h used by Jones et al. (1989) for northern varieties (MG 0 and 00) of soybean and agrees with 13.5 h reported for Harosoy by Grimm et al. (1993). Eq. [7] fit the data quite well (Fig. 2 and 3) with an R² of 0.96 and only a small bias as indicated by the regression line in Fig. 3. The model assumed that b, T_C, and T_B were constants because of the common genetic background (Harosoy) of the isolines.

View larger version (11K): [in this window] [in a new window]   Fig. 1. Sensitivity study on the critical photoperiod (PC) above which photoperiod extends the time to first flower. The standard error of estimate (SEE) of the model (Eq. [7]) fit to the data is plotted against PC. The solid line is Y = 51.41 - 7.128X + 0.2642X2 with a minimum at 13.5 h (R2 = 0.98).

View larger version (22K): [in this window] [in a new window]   Fig. 2. Days to first flower as a function of photoperiod at two temperatures (28 and 18°C) for five photoperiods and six isolines of soybean. Lines are model calculations. Error bars are standard errors of the mean. Photoperiods were 10, 12, 14, 16 and 20 h but some data are offset by plus or minus 0.2 h for clarity.

View larger version (16K): [in this window] [in a new window]   Fig. 3. Observed versus calculated days to first flower. The solid line is the 1:1 line and the broken line is the regression, Y = 2.018 + 0.9542X with an r2 of 0.96.

View larger version (25K): [in this window] [in a new window]   Fig. 4. The photoperiod-temperature interaction function (cT) as it varies with temperature for the six isolines.

The photoperiod coefficient (c) at 28°C varied linearly with the number of dominant alleles (Fig. 5). The addition of E1, E3, or E4 alleles to a E7 background produced equivalent results. This contrasts with the results of Upadhyay et al. (1994) where E1 had a larger effect than E2 or E3. Similarly, to Upadhyay et al. (1994), the addition of a third dominant allele did not increase photoperiod sensitivity. Upadhyay et al. (1994) grouped isolines into three similarly-responding photoperiod-sensitivity cohorts containing 0 or 1, 1 or 2, and 2 or 3 dominant alleles for use in their model. We simply used the number of dominant alleles and when we put this linear equation into the model, Eq. [7] changed from

(14)

to

(15)

where N was the number of dominant alleles. With the use of the number of dominant alleles in the model, the coefficient of determination decreases from 0.96 to 0.89. Obviously, the c coefficients were not perfectly correlated to the number of alleles. In particular, the change in photoperiod sensitivity tended to be above average from one to two dominant alleles with a small, insignificant change associated with adding the third dominant allele (Fig. 5; Table 3). There were no significant differences between coefficients in the group of isolines with two dominant alleles (Table 3).

View larger version (13K): [in this window] [in a new window]   Fig. 5. Photoperiod sensitivities determined by the values of the photoperiod coefficients (c), as a function of the number of dominant alleles. The regression (solid line) is Y = -0.002138 - 0.0008123X with an r2 of 0.90.

In summary, these data showed that isolines with various combinations of late- and early-flowering alleles flowered similarly under inductive short photoperiods. Long, non-inductive photoperiods resulted in delayed flowering where the number of days of flowering delay depended on the number of dominant alleles. An interesting temperature-photoperiod interaction was noted where the late-flowering isolines, under long days, flowered earlier in cool conditions than in warm conditions. It was possible to model the photo-thermal response of these lines on an isoline basis or more simply on a number-of-dominant-alleles basis. Both the inverse time (rate of progress to flowering) and the G_PTD (Growing Photothermal Day) models fit the data well. The use of isolines in quantifying and modeling flowering response may allow development of genetic coefficients for alleles compared with the current practice of developing genetic coefficients for individual cultivars. Untested combinations of alleles may be used to verify the model. The model may be useful to predict desirable combinations of alleles for new agronomic production systems where temperatures and photoperiods are different from conventional production systems.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
ECORC Contribution no. 001518.

Received for publication May 2, 2000.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 

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