- Email: [email protected]
Modeling chickpea growth and development: Phenological development
Field Crops Research 99 (2006) 1–13 www.elsevier.com/locate/fcr
Modeling chickpea growth and development: Phenological development A. Soltani a,*, G.L. Hammer b, B. Torabi a, M.J. Robertson c, E. Zeinali a a
Department of Agronomy and Plant Breeding, Gorgan University of Agricultural Sciences, P.O. Box 386, Gorgan, Iran b School of Land and Food Sciences, The University of Queensland, Brisbane, Qld. 4072, Australia c CSIRO Sustainable Ecosystems, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Qld. 4067, Brisbane, Australia Received 25 July 2005; received in revised form 28 February 2006; accepted 28 February 2006
Abstract Quantitative information on temperature and photoperiod effects on development rate in chickpea (Cicer arietinum L.) is scarce. Data from a serially sown field experiment (2001–2003) on four cultivars was used to evaluate various approaches to phenology prediction. A range of functions describing the response of development rate to temperature and photoperiod was compared. Phenological data from numerous other field experiments across Iran were used for independent model evaluation. A multiplicative model that included a dent-like function for response to temperature and quadratic function for response to photoperiod was the most adequate at describing the response of development rate to temperature and photoperiod. The differences among cultivars for cardinal temperatures and critical photoperiod were small and a base temperature of 0 8C, lower optimum temperature of 21 8C, upper optimum temperature of 32 8C, ceiling temperature of 40 8C and critical photoperiod (below which development rate decreases due to short photoperiods) of 21 h were obtained. Inherent maximum rate of development and the photoperiod sensitivity coefficient characterized cultivar differences. The cultivars required 24.7–32.2 physiological days (i.e., number of days under optimum temperature and photoperiod conditions) from emergence to flowering, 8.2–12.0 from flowering to first-pod, 4.3 from first-pod to beginning seed growth and 30.3 from beginning seed growth to maturity. Differences among cultivars were not found for first-pod to beginning seed growth or for beginning seed growth to maturity. The phenology model developed using these findings gave good predictions of phenological development for a diverse range of temperature and photoperiod conditions across Iran. This model can be incorporated in simulation models of chickpea. # 2006 Elsevier B.V. All rights reserved. Keywords: Phenology; Development; Temperature; Photoperiod; Model; Chickpea
1. Introduction Chickpea (Cicer arietinum L.) is cultivated across the world in the Mediterranean basin, the near east, central and south Asia, east Africa, South America, North America and Australia. The time available for chickpea crops to produce adequate vegetative structures and then grain yield is often limited by hot or cold temperatures, rainfall distribution, or competition for use of land by other crops in rotation (Roberts et al., 1985; Smithson et al., 1985). To achieve good * Corresponding author. Tel.: +98 171 4420438; fax: +98 171 4420981/4420438. E-mail address: [email protected] (A. Soltani). 0378-4290/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2006.02.004
yield, crop duration must closely match the available growing season (Ellis et al., 1994). Phenological studies in chickpea have been limited. They have revealed that crop duration and especially timing of flowering is modulated strongly by genotype, temperature and photoperiod (Roberts et al., 1980, 1985; Summerfield et al., 1981; Ellis et al., 1994). Water stress may delay or stimulate crop development (Singh, 1991; Soltani et al., 1999), but a moderate level of water stress appears to have no direct effect on development (Robertson et al., 2002a). A linear increase in development rate towards flowering has been found for a temperature range of 10.8–29.3 8C and photoperiod range of 11–15.6 h d1 (van der Maesen, 1972; Roberts et al., 1980, 1985; Summerfield et al., 1981; Ellis
2
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
et al., 1994). These studies indicated that the critical photoperiod (below which development rate decreases due to short photoperiods) in chickpea should be longer than 15 h. Roberts et al. (1985) and Ellis et al. (1994) described the rate of development towards flowering as a function of mean temperature and photoperiod using an additive linear model, although Ellis et al. (1994) reported that environments with daily maximum temperature of 38 8C resulted in considerable delays to flowering. They did not test their model using independent data sets. Some researchers have challenged the use of the additive, linear model, which is based on mean temperature and photoperiod during the whole pre-flowering stage (Yin et al., 1997b; Carberry et al., 2001). Alternatively, a multiplicative model has been used to describe the nonlinear effects of temperature and photoperiod on crop development rate (e.g., Hammer et al., 1989; Grimm et al., 1993; Yin et al., 1997a). Accurate prediction of crop phenology is a major requirement for crop simulation models. The production and partitioning of dry matter in crop simulation models is regulated to a large extent by the timing of phenological stages. Soltani et al. (1999) used the thermal time concept to quantify phenological development in their chickpea model without inclusion of a photoperiod effect. This approach gives acceptable predictions for a limited range of sowing dates and latitudes. In subsequent studies, they limited the application of their model to spring-sown crops in NW Iran (e.g. Soltani et al., 2001). For a wider application of the model, the effect of photoperiod should be incorporated. Therefore, the objectives of this research were: (1) to find appropriate response functions for the effect of temperature and photoperiod on development rate, (2) to determine cardinal temperatures and photoperiods for development, (3) to determine which parameters characterize different responses in different cultivars, and (4) to develop and test a general phenology model for chickpea. The present paper is a part of a comprehensive study (Soltani et al., 2005, 2006a,b,c) aimed at improvement of chickpea simulation capabilities in our chickpea models (Soltani et al., 1999; Robertson et al., 2002a).
2. Materials and methods 2.1. Field experiments Four field experiments were conducted as a part of this study. Three of the four experiments were carried out at the Gorgan University of Agricultural Sciences Research Farm, Gorgan (368510 N, 548160 E and 100 m a.s.l.), Iran. The soil was a deep silty loam (fine-silty, mixed, active, thermic, Typic Calcixerolls) and all three experiments were conducted under well-watered conditions. The plots were irrigated after 60mm cumulative pan (class A) evaporation and irrigation amount was based on soil moisture depletion. Therefore, there was no effect of flooding or water deficit stresses.
The first experiment (Exp. 1) was a serially sown experiment that started in December 2001 and continued until August 2003. Four chickpea cultivars (Beauvanij, 9096c, Hashem and Jam—all kabuli-type) were sown at 11 sowing dates. These cultivars were selected from different geographical areas across Iran; 90-96c and Hashem are cultivated in the western part of the Caspian Sea Coast of Iran (Golestan province), Jam in the north-west (Azerbaijan province) and Beauvanij in the mid-west of the country (Kermanshah province). Sowing dates (day of year, DOY) were 12 December (346) 2001 and 15 January (15), 15 February (46), 17 March (76), 16 April (106), 18 May (138), 17 June (168), 16 August (228), 15 September (258), 14 October (287) and 12 November (316) 2002. The sowing dates employed do not necessarily reflect common practice, but were selected to create different temperature and photoperiod regimes. The experimental design was a single split plot with sowing dates as the main plot and genotypes the sub-plot, replicated four times. Plot size was 1.5 m 4 m. Plots were hand-seeded using row spacing of 25 cm. Target plant density was 50 plant m2 and two seeds were planted at correct spacing at 5 cm depth, and seedlings thinned to one in each position later. Stages of development of emergence (50% of plants with some parts at soil surface), flowering (50% of plants with one flower at any node, R1), first pod (50% of plants with 0.5 cm pod at one of the four upper nodes with unrolled leaf, R3), beginning seed growth (50% of plants with peas beginning to develop, R5), first maturity (50% of plants with one pod yellowed, R7) and full maturity (95% of pods have obtained their mature color, R8) were recorded every 1–2 days (Fehr and Caviness, 1977). During the experiment weeds were hand-controlled and several sprayings were carried out against Ascochyta blight. Hashem and 90-96c are resistant to the blight, but Beauvanij and Jam are susceptible and were badly affected. At the end, there were 9 sowing dates for Hashem, 10 for 90-96c, 7 for Beauvanij and 6 for Jam with reliable data. The second (Exp. 2) and third (Exp. 3) experiments were conducted in the growing seasons of 2002–2003 and 2003– 2004, respectively. For both experiments, factorial combinations of three sowing dates and four plant densities were the treatments. The experimental design was a randomized complete block design with four replicates. Again, a wide range of sowing dates was chosen to create different temperature and photoperiod regimes. In Exp. 2, sowing dates (DOY) were 5 January (5), 6 March (65) and 28 April (118) 2003 and the four plant densities were 15, 30, 45 and 60 plant m2. In Exp. 3, sowing dates were 6 December 2003 (340), and 20 January (20) and 20 March (79) 2004. The same plant densities as Exp. 2 were used. In both experiments, the chickpea cultivar Hashem, the local cultivar, was used. The fourth experiment (Exp. 4) was conducted at Gonbad Agricultural Research Station, Gonbad (348210 N, 558100 E and 37 m a.s.l.), Iran. The treatments and cultivar were
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
similar to Exps. 2 and 3. The experimental design was a single split plot with sowing dates as the main plot and plant densities the sub-plots, replicated four times. Sowing dates (DOY) were 7 December (341) 2002, 23 January (23) and 6 March (65) 2003. The field experiments (2, 3 and 4) were not conducted specifically for the objectives of the present study, but phenological stages were monitored every 2 days generating data sets suitable for model testing. In all experiments, daily maximum and minimum temperatures, sunshine hours and rainfall were measured at a standard weather station located a few meters (Gorgan) to a few hundred meters (Gonbad) from the experimental sites. Photoperiod for each day was calculated from latitude and calendar day and included allowance for civil twilight (solar angle 48; Keisling, 1982). This solar angle was selected based on the minimum illuminance requirement for chickpea of 6 lx (Summerfield and Roberts, 1987).
2.2. Modeling occurrence of flowering
2.2.1. Basic vegetative phase It is widely accepted that many crops require a minimum vegetative period during which they do not respond to photoperiod (Horie, 1994). This is commonly known as the basic vegetative phase. In a literature review on chickpea physiology, Saxena (1984) concluded that there were no indications of a pronounced juvenile phase in this crop. Therefore, in this study, it was assumed that all the genotypes respond to photoperiod immediately after emergence. Based on results of Soltani et al. (2006a), the rate of development from sowing towards emergence is determined according to a dent-like function on temperature with three linear segments, where four parameters (base, lower optimum, upper optimum and ceiling temperatures) define critical points. According to this function, rate of development is zero at temperatures lower than base temperature or higher than ceiling temperature. Between base and lower optimum temperatures, the rate increases linearly from zero to its maximum value. Lower and upper optimum temperatures define a plateau where the rate of development is constant and maximum. The rate of development again decreases linearly from upper optimum towards ceiling temperature. Cardinal temperatures for emergence were found to be 4.5 8C for base, 20.2 8C for lower optimum, 29.3 8C for upper optimum and 40 8C for ceiling temperature (Soltani et al., 2006a). It takes six physiological days (minimum number of days at optimum temperature) from sowing to emergence (sowing depth = 5 cm) (Soltani et al., 2006a). It has been reported that cultivated chickpeas have no or negligible vernalization response (Summerfield et al., 1989; Abbo et al., 2002). It was assumed that there is no or negligible vernalization response in all selected cultivars (Soltani et al., 2004).
3
2.2.2. Functions and parameters From emergence until flowering, a multiplicative relationship (Hammer et al., 1989; Horie, 1994) was used to compute development rate as a function of temperature and photoperiod: Rt ¼ Rmax f ðTÞ f ðPPÞ
(1)
where Rt is development rate on Day t, Rmax the maximum rate of development at optimum temperature and photoperiod, f(T) the temperature function, and f(PP) is the photoperiod function. The inverse of Rmax indicates minimum number of days from emergence to flowering at optimum temperature and photoperiod, or the physiological days requirement, from emergence to flowering ( f o hereafter). f(T) and f(PP) are calculated each day and have values between zero and one. The product of f(T) and f(PP) is called physiological day per calendar day on that day (PDt), i.e., PDt = f(T) f(PP). When f(T) and f(PP) are both equal to one, PDt is also equal to one and progress towards flowering is at its fastest rate. Starting at emergence, physiological days (PDt) are accumulated until a threshold of f o is reached. At this P time, P flowering is predicted to occur (i.e., PDt = f(T) f(PP) = 1/Rmax = f o). Alternatively, flowering is predicted to occur when cumulative valueP of development rates (Rt) reaches to a threshold of 1 (i.e., Rt = 1). In this study, three temperature functions and two photoperiod functions were selected due to their simplicity and the intuitive meaning of their parameters. The temperature functions were: Segmented function 8 T Tb > > > T T ; if Tb < T To > < o b (2) f ðTÞ ¼ Tc T ; if To < T < Tc > > > T T c o > : 0; if T Tb or T Tc Beta function #a 8 " > T Tb Tc T ððTc To Þ=ðTo Tb ÞÞ > > ; > > > < T o Tb T c T o f ðTÞ ¼ (3) if T > Tb and T < Tc > > > > 0; > > : if T Tb or T Tc And dent–like function 8 T Tb > > ; if Tb < T < To1 > > > T o1 Tb > > < Tc T ; if To2 < T < Tc f ðTÞ ¼ (4) Tc To2 > > > > 1; if To1 T To2 > > > : 0; if T Tb or T Tc where T is temperature, Tb the base temperature, To the optimum temperature, To1 the lower optimum temperature
4
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 1. (a) Functions used to describe the response of development rate to temperature. (b) Functions used to describe the response of development rate to photoperiod.
(for dent-like function), To2 the upper optimum temperature (for dent-like function), Tc the ceiling temperature and a the shape parameter for beta function that determines the curvature of the function. Because of the low frequency of temperature greater than 40 8C, we fixed Tc at 40 8C when using the above functions. The ceiling temperature of 40 8C was chosen because it represents a biologically reasonable upper limit for developmental processes (e.g., Olsen et al., 1993; Yin et al., 1997a; Robertson et al., 2002a,b). Schematics of the functions are presented in Fig. 1a. The beta function as used here is very flexible and can mimic the quadratic, cubic and curvilinear (as presented by Hammer et al., 1989) functions.
Quadratic function 1; if PP Pc f ðPPÞ ¼ f ðPPÞ ¼ 1 PSðPc PPÞ2 ; if PP < Pc
Functions used for photoperiod were:
With the three functions for temperature and two functions for photoperiod, there were six combinations of functions used in Eq. (1). Each combination was fitted to data of each cultivar (see below) and root mean square of deviations (RMSD) and regression of predicted versus observed days from emergence to flowering were computed and compared.
Segmented function 1; if PP Pc f ðPPÞ ¼ 1 ðPc PPÞPS; if PP < Pc
(5)
(6) 1
where PP is photoperiod (h d ), Pc the critical photoperiod below which development rate decreases due to short photoperiod, and PS the photoperiod sensitivity coefficient. Schematics of the functions are presented in Fig. 1b. Ceiling photoperiod (Pce) below which phenological development is stopped ( f(PP) = 0; Fig. 1b) and plants do not flower can be obtained as Pce = [(PcPS 1)/ PS] for segmented function and as Pce = Pc (1/PS)0.5 for quadratic function.
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
2.2.3. Estimation procedure An optimization program, named DEVEL (Holzworth and Hammer, 1996) was used to estimate parameters of Eqs. (1)–(6) for each cultivar, using daily maximum and minimum temperatures and photoperiod data and observed dates of emergence and flowering. DEVEL uses the simplex optimization method and contains a library of eight options for temperature and photoperiod functions, ranging from linear response to logistic, which can be used separately or in combination to examine the independent and interactive effects of temperature and photoperiod. The user selects a function to describe the response being investigated and provides initial estimates of parameters and their bounds. DEVEL then proceeds to carry out an iterative optimization P Pthat minimizes residual sum of squares (SSE = [( Rt 1)2]), and provides optimized estimates of the parameters. The user repeats the process with alternative functions and selects the function that best statistically fits the temperature and photoperiod response. DEVEL also calculates the smallest and largest parameter estimate that result in SSE within 10% of the final, minimum value. This gives a form of error value to each of the parameters and is useful for comparison purposes. DEVEL includes algorithms for parameter profiling (McCullagh and Nelder, 1992) and jack-knifing (Efron, 1990; Jones and Carberry, 1994) that remove bias from the parameter estimates and introduce rigor into the parameter selection process (P.D. Jones and G.L. Hammer, pers. commun.). For the objective of this study, two modifications were made in DEVEL. Firstly, dent-like and beta functions for temperature and quadratic and negative exponential functions for photoperiod were included into DEVEL. Secondly, capability of using 3-hourly temperatures instead of mean daily temperature was incorporated for all temperature functions. DEVEL had this capability only for the segmented temperature function. Calculation of 3-hourly temperature was based on the method reported by Jones and Kiniry (1986) using cubic interpolation from minimum and maximum daily temperatures. It should be noted that in this study, temperature functions were applied in a time step of 3 h. As outlined by Birch et al. (1998) different starting conditions were tried to guard against the identification of local optima and to assess whether they converge to the same optimized value. Thus, the potential for the optimized estimates being artifacts of the starting conditions, or an erroneous selection from possible multiple solutions is minimized. We also used a linear, multiple regression model as proposed by Roberts et al. (1985) and Ellis et al. (1994) to describe relationship between development rate and temperature and photoperiod: 1 ¼ a þ bT þ cP f
(7)
5
where 1/f is the development rate towards flowering (inverse of time from emergence to flowering), a, b and c are the empirical regression coefficients, T the mean temperature and P is the mean photoperiod from emergence to flowering. 2.3. Modeling occurrence of post-flowering stages Post-flowering stages considered here are first-pod (R3), beginning of seed growth (R5) and maturity (R8). In the serially sown field experiment, all of the phases occurred during mid-spring to late summer due to the qualitative response of flowering to photoperiod in all the cultivars used (see Section 3.1). Therefore, it was not possible to obtain a wide environmental range for these phases and the temperature and photoperiod conditions experienced were somewhat restricted. It was assumed that the duration of these phenophases was controlled by temperature and the response function and the cardinal temperatures were the same as for the emergence to flowering interval. There is some experimental evidence supporting this assumption in chickpea. Roberts et al. (1980) reported that once flowers have appeared temperature is the most important environmental determinant of the length of reproductive period in all three cultivars that they evaluated. Sethi et al. (1981) studied phenological development of different chickpea cultivars from different maturity groups under natural and 24-h extended day length and reported that once flowering was induced, continuation of the extended day length treatment had no effect on the time of maturity. However, Turpin et al. (2003) reported postflowering development as sensitive to photoperiod in fababean, a long-day legume crop like chickpea. Based on the above assumptions, the physiological day requirements (cumulative values of f(T) from dent-like function—see Section 2.2.2) were calculated from R1 to R3, R3 to R5 and R5 to R8 for each cultivar-sowing date combination in the serially sown field experiment. These physiological day requirements were then analyzed and compared. 2.4. Model evaluation Data from Exps. 2–4 were used to evaluate model performance for predicting timing of R1, R3, R5 and R8 in cv. Hashem. We could not find other experimental data on timing of R3 and R5 according to measures used in the present study. To have a comprehensive, independent model evaluation, data on observed flowering and maturity dates were collected for the cultivars across Iran (Table 1). We only included data from irrigated conditions and rainfed conditions where drought effects appeared to be slight (based on yield data). Maximum and minimum temperatures were available for each experiment and daily photoperiod was calculated using latitude and day of the year as described in Section 2.1. Predicted days to flowering, R3, R5 and maturity were compared with observed data using RMSD and linear regression analysis.
6
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Table 1 Data sets for independent model testing Location and year
Latitude
Treatments
Reference
Many locations across Iran, 1970–1976 and 1995–2001 Bojnord, 1994/1995 Nishabur, 1994/1995 Nishabur, 1994/1995 Mashhad, 1995/1997 Mashhad, 1995/1996 Mashhad, 1995 Tabriz, 1994 Tabriz, 1995 Tabriz, 1998
32.17–38.138N
Genotype, planting date
37.478N 36.278N 36.278N 36.278N 36.278N 36.278N 38.138N 38.138N 38.138N
Genotype, Genotype Genotype, Genotype, Genotype, Genotype, Genotype, Genotype, Genotype,
Anon. (1971–1977, 1996–2002) Langary (1996) Hasanzadeh (1996) Rastgar (1997) Porsa et al. (2002) Goldani et al. (2000) Noroozzadeh (1996) Mohammadi (1995) Movahhedi (1996) Roozrokh et al. (2002)
plant density planting date and density planting date planting date plant density planting date and density irrigation, plant density irrigation, seed vigor
temperatures varied between 5.2 and 40.4 8C for maximum temperature and between 3.4 and 27.2 8C for minimum temperature (data not shown). Photoperiod ranged between 10.2 and 15.3 h (Fig. 2). The cultivars showed a qualitative long-day plant response as reported by Soltani et al. (2004). Briefly, crops sown in September, October, November and January flowered at nearly the same time in May (day of year 130); flowering was delayed up to 120 days when photoperiod decreased to 11–12 h (Fig. 2). Non-effective temperatures did not delay flowering for these sowing dates as crops sown in September accumulated twice as much
3. Results and discussion 3.1. Phenophase durations A wide range in the duration of crop phenophases resulted from the serially sown field experiment (Table 2). Variation in days to emergence and its relation to temperature have been discussed elsewhere (Soltani et al., 2006a). Duration from emergence to flowering varied as much as four- to fivefold (Table 2). For example, for Beauvanij this duration varied from 29 to 126 days and for Hashem from 36 to 227 days. For the period from emergence to flowering,
Table 2 The range and mean of observed duration (days) for specific phenophases of each of four chickpea cultivars in the serially sown field experiment Cultivar
Minimum
Maximum
Mean
Beauvanij Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
5.8 29.0 7.3 3.5 28.5 37.7
23.0 126.0 18.3 10.0 35.5 52.0
10.3 69.4 12.9 6.3 32.3 46.0
90-96c Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
5.8 37.0 7.3 3.0 18.0 33.8
23.2 220.1 17.0 7.0 24.3 51.3
10.7 118.3 10.8 5.2 20.6 45.2
Hashem Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
6.1 36.0 8.0 3.5 28.5 33.0
22.5 227.0 14.8 6.8 34.0 50.0
10.1 114.6 11.2 5.0 31.3 43.2
Jam Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
5.5 28.0 7.0 3.3 18.0 38.3
24.5 124.0 17.5 7.0 24.3 52.5
10.3 69.0 10.8 5.2 20.6 43.9
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 2. Flowering time of cv. Hashem sown at a range of sowing dates. Vertical, short lines and associated labels indicate sowing date and day of year. The thick line indicates the approximate time of flowering for all of these sowing dates (redrawn from Soltani et al., 2004).
thermal time (base temperature 0 8C) as those sown in January, although they flowered at the same time. This was the first report of a qualitative response of chickpea to photoperiod as flowering did not occur at photoperiods lower than a ceiling value. Previously, chickpea has generally been considered a quantitative long-day plant (van der Maesen, 1972; Roberts et al., 1980; Summerfield et al., 1981; Smithson et al., 1985; Verghis et al., 1999). Variation in the duration of the post-flowering phenophases was limited (Table 2). In cv. Hashem for example, 8– 15 days duration was observed for R1–R3, 4–7 days for R3– R5 and 29–34 days for R5–R8. This reduced variation resulted from the qualitative response of flowering to photoperiod in the cultivars evaluated. As a consequence of this qualitative response post-flowering stages occurred during mid-spring to late summer when there was much less variation in temperature and photoperiod. In the following sections we indicate how variation in durations of pre- and post-flowering phenophases is related to temperature and photoperiod by reporting results of model fitting. 3.2. Modeling duration to flowering Using iterative optimization procedures, such as simplex, there is no guarantee that the solution obtained is unique and optimal (Sinclair et al., 1991; Grimm et al., 1993; Yin et al., 1997a). There might be a series of solutions with similar SSE but different parameter estimates. In addition, the correlation between temperature and photoperiod under field conditions causes some compensation between parameters in studies like this one (Olsen et al., 1993). Defining parameter bounds (i.e., lower and upper limits allowed for each parameter), based on biological and physiological principles can assist in obtaining consistent estimates. However, the degree of difficulty is reduced if data sets covering a wide range of environmental conditions are included in the optimization (P.D. Jones and G.L. Hammer, pers. commun.). Considering these points, we had little problem in our experience using simplex optimization in DEVEL with these data sets. In nearly all cases, stable estimates were obtained with the first run of DEVEL.
7
The linear, additive model (Eq. (7)) gave a significantly worse fit than any of the six combinations of temperature and photoperiod functions used in the multiplicative model (Eq. (1)). The RMSD (26.2 days) for the linear, additive model was significantly higher and there was significant bias in the regression of predicted versus observed flowering time (data not shown). This was in contrast to findings of Roberts et al. (1985) and Ellis et al. (1994) who found good predictions with the additive model. However, the additive model is valid only for a restricted range of temperature and photoperiod, when temperatures are between base and optimum values and photoperiods vary between critical and ceiling values (Roberts et al., 1985; Ellis et al., 1994). Hence, the poor performance of the linear, additive model is likely due to the broad range of environmental conditions experienced in this study. This highlights the need for caution in extrapolating such models beyond limited ranges in key forcing factors. Comparison of different functions of temperature and photoperiod in modeling development rate using Eq. (1) indicated that there was no uniquely best function for each of temperature and photoperiod to predict flowering time in the cultivars studied. However, in some cases, the difference between functions was quite small. Amongst combinations of temperature and photoperiod functions examined using the multiplicative model (Eq. (1)), models containing (1) segmented functions for temperature and photoperiod, (2) dent-like function for temperature and segmented function for photoperiod, and (3) segmented function for temperature and quadratic function for photoperiod were discarded because, at least for one cultivar, they resulted in significant bias. The remaining models containing (1) beta function for temperature and quadratic function for photoperiod, (2) beta function for temperature and segmented function for photoperiod, and (3) dent-like function for temperature and quadratic function for photoperiod performed similarly with respect to RMSD and regression of predicted versus observed days from emergence to flowering (data not shown). However, the model that included the dent-like function for temperature and the quadratic function for photoperiod was more stable. Using this model the calculated cumulative number of physiological days from emergence to flowering was independent of temperature and photoperiod (Fig. 3). This indicates that the composite functions account well for temperature and photoperiod effects on rate of development. Other combinations of functions (data not shown) did not perform as well on this test of model generality. Various functions have been used by different researchers to describe the effect of temperature and photoperiod on development rate (e.g., Hammer et al., 1989; Horie, 1994; Piper et al., 1996; Yin et al., 1997a; Robertson et al., 2002a,b), because it is difficult to assert a general function for all crops and phenological stages. The dent-like function has been used to quantify effect of temperature on development rate (Piper et al., 1996), leaf appearance rate (Ritchie, 1991) and radiation use efficiency (Robertson et al., 2002a). The quadratic function used here is similar to
8
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 3. Calculated number of physiological days from emergence to flowering vs. (a) mean temperature and (b) mean photoperiod, during the same period. Data are pooled over cultivars.
that incorporated in the CERES-Wheat simulation model to describe the effect of photoperiod on development rate (Ritchie, 1991). Therefore, given its superior stability and no evidence supporting generality of other function forms, the dent-like function for temperature combined with the quadratic function for photoperiod were selected for use in our model of chickpea phenology. The adequacy of fit for this combination of functions is presented in Table 3 (Step 1) and Fig. 4 (Step 1). RMSD ranged between 6.5 and 14.8 days (i.e., 9 and 17% of the mean) among cultivars. There was no significant bias in the linear regression of predicted versus observed values. Estimates of cardinal temperatures and photoperiod parameters were 0.7 to 1.7 8C for Tb, 19.2– 20.9 8C for To1, 29.2–33.8 for To2, 20.5–21.9 for Pc, 0.0082– 0.0090 for PS and 22.7–39.2 for physiological days from emergence to flowering ( f o) (Table 4, Step 1). Fig. 5 presents profile plots for model fitting to data for cv. Beauvanij as an example. These plots show the response in SSE as each parameter was varied. In each case, there was an interval Table 3 Root mean square deviations (RMSD; days) and linear regression statistics (intercept (a), slope (b), and correlation coefficient (r)) for predicted vs. observed days from emergence to flowering for four chickpea cultivars. Results are given for three steps in model fitting, which relate to the number of parameters fitted (or fixed) across cultivars Function-cultivar
RMSD
a S.E.
b S.E.
r
Step 1: all model parameters (except Tc) fitted to individual cultivars Beauvanij 6.5 3.7 6.2 1.04 0.082 0.94 90-96c 10.0 3.2 4.6 1.04 0.041 0.97 Hashem 14.8 10.4 7.6 0.90 0.058 0.94 Jam 11.4 6.3 5.7 1.09 0.074 0.95 Step 2: only PS and fo fitted to individual cultivars Beauvanij 6.8 2.8 6.5 1.05 0.086 90-96c 9.6 1.4 4.6 0.98 0.042 Hashem 15.0 6.7 8.0 0.96 0.61 Jam 11.1 3.2 5.5 1.00 0.073
0.93 0.97 0.94 0.95
Step 3: only fo fitted to individual cultivars Beauvanij 4.8 0.4 6.1 90-96c 18.3 10.7 5.3* Hashem 17.1 5.7 8.1 Jam 10.8 2.1 4.5
0.93 0.97 0.94 0.96
0.98 0.081 1.20 0.047* 0.97 0.061 0.89 0.059
* Significant difference (P < 0.05) from 0 for a and significant difference from 1 for b.
over which SSE was minimized, indicating no redundancy in the number of parameters employed in the overall model. In the next step, we examined which parameters characterized different responses among cultivars. Initial estimates of Tb, To1, To2 and Pc were similar among cultivars (Table 4). Hence, the values of these parameters were fixed at 0 for Tb, 21 for To1, 32 for To2 and 21 for Pc, and DEVEL was run to estimate values for PS and f o only (Step 2 in Tables 3 and 4). There was no significant difference between the models fitted in Steps 1 and 2 (F = 1.01; P = 0.964) and there was no increase in RMSD between the 6-parameter to 2-parameter models. Further, regression of predicted versus observed days from emergence to flowering did not show any indication of change in bias (Table 3; Fig. 4). In the final step, the value of PS was fixed at 0.0085 for all cultivars and a 1-parameter model was fitted using DEVEL (Step 3 in Tables 3 and 4, and Fig. 4). While the difference between models fitted in Steps 1 and 3 was not significant (F = 1.28; P = 0.291), the simplification used in Step 3 resulted in significant increase in RMSD for cv. 90-96c (from 9.6 to 18.3 days) and cv. Hashem (from 15 to 17.1 days) and significant bias in prediction for cv. 90-96c (Table 3). Thus, it can be concluded that at least a 2-parameter model is required to account for cultivar effects and that the PS and f o parameters characterize genotypic differences in phenological development in response to temperature and photoperiod. For modeling purposes, the values of Tb, To1, To2, Tc and Pc can be fixed as indicated in Table 4, Step 2. Grimm et al. (1993), Piper et al. (1996) and Sinclair et al. (1991) for soybean, Carberry et al. (2001) for pigeonpea and Robertson et al. (2002b) for canola also obtained good prediction of flowering time with fixed values of cardinal temperatures. However, for rice, Yin et al. (1997a) reported that optimal temperature for development could not be fixed without loss of accuracy. However, they still obtained accurate predictions for 17 rice cultivars with fixed values for Tb and Tc. The value of Pc found in this study (21 h) is comparable to the value of 20 h found by Ritchie (1991) for wheat, a long day plant. Roberts et al. (1985) and Ellis et al. (1994) suggested that Pc of chickpea should be longer than 15 h. Furthermore, our finding of Pc = 21 is supported by observations in Australia that extending photoperiod from 16 to 19 h accelerated flowering in an early maturing
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
9
Fig. 4. Predicted vs. observed days from emergence to flowering for four chickpea cultivars for the three steps of model fitting as defined in Tables 3 and 4. Cultivars are Beauvanij, 90-96c, Hashem and Jam. Step 1, all model parameters (except Tc) fitted to individual cultivars; Step 2, only PS and fo fitted to individual cultivars; and Step 3, only fo fitted to individual cultivars.
chickpea cultivar (Amethyst) (M.J. Robertson, pers. commun.). However, Singh and Virmani (1996) incorporated a value of 11 h for Pc in their chickpea model. In practice this means no effect of photoperiod on development rate, because in the majority of chickpea growing areas around the world, and at the usual sowing dates, photoperiod is higher than 11 h. The results of this study do not support this finding.
Average values of f o for each cultivar across sowing dates are presented in Table 5. These values were calculated using the parameters estimates from Step 2 of model fitting (Table 4). They are similar to the values obtained during the optimization procedure, but allowed statistical comparison among cultivars as these estimates were obtained for all experimental treatments. The greatest value of f o (and, hence, lowest value of Rmax) was found for cv. 90-96c and
Table 4 Estimates of base temperature (Tb, 8C), lower optimum temperature (To1, 8C), upper optimum temperature (To2, 8C), ceiling temperature (Tc, 8C), critical photoperiod (Pc, h), photoperiod sensitivity coefficient (PS) and physiological days from emergence to flowering ( fo) for four chickpea cultivars. Results are given for three steps in model fitting, which relate to the number of parameters fitted (or fixed) across cultivars. Values for fixed parameters are indicated in parentheses Tc
Pc
PS
fo
Step 1: all model parameters (except Tc) fitted to individual cultivars Beauvanij 1.74 19.94 29.20 90-96c 0.70 19.15 29.39 Hashem 0.73 19.55 33.83 Jam 1.49 20.93 30.20
(40) (40) (40) (40)
20.46 20.92 21.07 21.87
0.00900 0.00823 0.00819 0.00900
27.1 32.9 28.5 22.7
Step 2: only PS and fo fitted to individual cultivars Beauvanij (0) (21) 90-96c (0) (21) Hashem (0) (21) Jam (0) (21)
(32) (32) (32) (32)
(40) (40) (40) (40)
(21) (21) (21) (21)
0.00877 0.00785 0.00845 0.00900
26.3 32.1 28.8 23.8
Step 3: only fo fitted to individual cultivars Beauvanij (0) 90-96c (0) Hashem (0) Jam (0)
(32) (32) (32) (32)
(40) (40) (40) (40)
(21) (21) (21) (21)
(0.0085) (0.0085) (0.0085) (0.0085)
26.6 30.7 29.1 25.7
Tb
To1
(21) (21) (21) (21)
To2
10
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 5. Profile plots of SSE (residual sum of squares) for the six parameters of the model fitted to data for cv. Beauvanij using DEVEL. Rmax is the maximum development rate, Tb the base temperature, To1 the lower optimum temperature, To2 the upper optimum temperature, Pc the critical photoperiod, and PS the photoperiod sensitivity coefficient.
the lowest value was found for cvs. Beauvanij and Jam (Tables 4 and 5). 3.3. Modeling duration of post-flowering phases Physiological days calculated for durations of postflowering stages in the field experiment are given in Table 5. Cultivars did not differ significantly in duration of R3–R5 or R5–R8, with mean values of 4.3 and 30.3 physiological Table 5 Mean physiological days from planting to emergence (PE), emergence to R1 (EF), R1–R3 (FP), R3–R5 (PS), R5–R8 (SM) and planting to R8 (PM) for four chickpea cultivars Genotype
PE
Beauvanij 90-96c Hashem Jam
5.6 6.1 6.4 6.5
Mean
6.3
EF a a a a
26.0 32.2 29.3 24.7 28.6
FP c a b c
12.0 9.0 8.2 9.2 9.5
PS a b b b
4.1 4.6 4.7 3.3 4.3
SM a a a a
29.4 31.1 30.3 30.2 30.3
PM a a a a
77.5 82.9 78.4 73.2
b a b c
days, respectively. For the R1–R3 interval, a significantly higher value (12.0 days) was found for cv. Beauvanij, but the other three cultivars (mean 8.8 days) did not differ (Table 5). Hence, difference among cultivars in duration from sowing to maturity (73.2–82.9 physiological days) was mainly related to differences in duration of the emergence-flowering interval. The physiological day requirements for the post-flowering phases (Table 5) are used in predicting duration of these phases. For example, progress toward R8 from R5 is calculated by integrating f(T), which is calculated using the dent-like function and the parameters values specified in Table 4 (Step 2). Starting from R5, R8 is predicted to occur when a threshold value is reached. The threshold value is the physiological day requirement for R5–R8 (30.3 days here), that is, the minimum number of days from R5 to R8 under optimal temperature conditions. 3.4. Model evaluation
78.6
Entries in each column followed by the same letter do not differ significantly (P < 0.05).
The phenology model of chickpea developed here is based on the concept of physiological day requirement (i.e.
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
11
Fig. 6. Simulated vs. observed days to stages R1, R3, R5 and R8 using independent data. Cultivars are Beauvanij, 90-96c, Hashem and Jam.
minimum duration in days at optimum temperature and photoperiod), which is considered more transparent and easier to understand than a thermal time target. However, the physiological day requirement is easily converted to a thermal time target using the fitted parameters and known forms of response functions. In our phenology model of chickpea, it takes six physiological days from sowing to emergence, based on a dent-like response function and its cardinal temperatures (See Section 2.2.1; Soltani et al., 2006a). From emergence to flowering, physiological day is calculated using a multiplicative model that incorporates response functions to temperature and photoperiod. The dent-like function is again used to quantify the effect of temperature with cardinal temperatures of 0 8C for Tb, 21 8C for To1, 32 8C for To2 and 40 8C for Tc. The influence of photoperiod is captured using a quadratic function with critical photoperiod of 21 h. It is assumed that photoperiod has no effect on development rate after flowering. For postflowering phases, the same response function and cardinal
temperatures as for emergence to flowering is applied. The post-flowering stages are predicted to occur after specified physiological day requirements. Results of the evaluation of the model using independent data sets (Table 1) are presented in Fig. 6 and Table 6. Data used for model evaluation came from different geographical areas across Iran and from spring- and winter-sown crops (Table 1). For cv. Jam all data came from spring-sown crops, while for other cultivars data from both spring- and wintersowings were included. To simulate phenological development for Exps. 2–4, data on phenological durations were averaged across plant densities. Differences among density treatments were less than 5 days (data not shown). Observed days to flowering ranged from 41 to 185 days. This range was 55–163 days for days to R3, 79–167 days for days to R5 and 78–228 days for days to R8. For the model evaluation based on comparison of simulated and observed duration from planting to specific phenological stages (Table 6), RMSD values were small in relation to the overall duration,
Table 6 Model evaluation based on comparison of simulated and observed duration from planting to specific phenological stages. For each stage, the number of observations (n), root mean square deviation (RMSD, d), mean duration (d), and linear regression statistics (intercept (a), slope (b), and coefficient of determination R2) are given Stage
Flowering (R1) First-pod (R3) First-seed (R5) Maturity (R8) *
n
82 9 6 84
RMSD
8.43 5.75 6.75 11.49
Significantly different (P < 0.05) from 1.
Mean Observed
Predicted
111 110 124 153
112 115 129 155
a
b
R2
1.64 0.396 1.62 7.91
1.043* 1.041 1.054 1.074*
0.98 0.99 0.99 0.96
12
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
being 8.4 days for time to flowering, 5.8 days for R3, 6.8 days for R5, and 11.5 days for R8. However, there was a slight but significant model bias (P = 0.05) for days to flowering and days to R8. This reflected the high precision level associated with these phases as a result of the large number of observations (82 and 84, respectively) (Table 6). This bias was not significant at P = 0.01 and did not appear practically important (see Fig. 6). Therefore, we conclude that our model accounts well for the effects of temperature and photoperiod on development rate in chickpea and its performance is acceptable for widespread application.
4. Conclusion Our results indicated that: (1) A multiplicative model that included a dent-like function for response to temperature and a quadratic function for response to photoperiod was the most adequate model to describe development rate in chickpea over a broad of temperature and photoperiod conditions. (2) Differences among cultivars in cardinal temperatures and the critical photoperiod for response functions were small and Tb of 0 8C, To1 of 21 8C, To2 of 32 8C, Tc of 40 8C and Pc of 21 h could be used in modeling for all cultivars in the study. (3) Cultivar differences in development rate were characterized by differences in the inherent maximum rate of development (Rmax = 1/f o) and the photoperiod sensitivity coefficient (PS). (4) Cultivars differed significantly in their physiological day requirement from emergence to flowering (25–32 days) and from R1 to R3 (8.2–12 days) but not for R3– R5 (4.3 days) and R5–R8 (30.3 days). (5) The phenology model developed for chickpea gave reasonable predictions of phenological development across Iran. This model can be used in simulation models of chickpea over a diverse range of temperature and photoperiod conditions. Acknowledgement Special thanks to the Agricultural Production Systems Research Unit, Toowoomba, Australia for hosting AS for his sabbatical period.
References Abbo, S., Lev-Yadun, S., Galway, N., 2002. Vernalization of wild chickpea. New Physiol. 154, 695–701. Anonymous, 1971–1977. Annual report of 1970–1977 on grain legumes. Tehran Univ., School of Agric., Karaj, Iran (text in Persian, tables in English).
Anonymous, 1996–2002. Annual report of 1995–2001. Dryland Agricultural Research Institute, Maragheh, Iran (text in Persian, tables in English). Birch, C.J., Hammer, G.L., Rickert, K.G., 1998. Temperature and photoperiod sensitivity of development in five cultivars of maize from emergence to tassel initiation. Field Crops Res. 55, 93–107. Carberry, P.S., Ranganathan, R., Reddy, L.J., Chauhan, Y.S., Robertson, M.J., 2001. Predicting growth and development of pigeonpea: flowering response to photoperiod. Field Crops Res. 69, 151–162. Efron, B., 1990. The Jackknife, the Bootstrap and other Resampling Plans. SIAM, Philadelphia. Ellis, R.H., Lawn, R.J., Summerfield, R.J., Qi, A., Roberts, E.H., Chay, P.M., Brouwer, J.B., Rose, J.L., Yeates, S.J., Sandover, S., 1994. Towards the reliable prediction of time to flowering in six annual crops. V. Chickpea. Exp. Agric. 30, 271–282. Fehr, W.R., Caviness, C.E., 1977. Stages of soybean development, Iowa State Univ. Coop. Ext. Serv. Spec. Rep. 80. Goldani, M., Bagheri, A.R., Nezami, A., 2000. Effect of sowing date on yield and yield components of chickpea in Mashhad. J. Agric. Sci. Natur. Resour. (Gorgan) 7 (1), 68–77 (text in Persian with English abstract). Grimm, S.S., Jones, J.W., Boote, K.J., Hesketh, J.D., 1993. Parameter estimation for predicting flowering date of soybean cultivars. Crop Sci. 33, 137–144. Hammer, G.L., Vaderlip, R.L., Gibson, G., Wade, L.J., Henzell, R.G., Younger, D.R., Warren, J., Dale, A.B., 1989. Genotype-by-environment interaction in grain sorghum. II. Effects of temperature and photoperiod on ontogeny. Crop Sci. 29, 376–384. Hasanzadeh, S., 1996. Evaluation morphological traits affecting grain yield in some chickpea genotypes. MS Thesis, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (text in Persian with English abstract). Holzworth, D., Hammer, G.L., 1996. DEVEL: a crop development modeling tool. In: Agricultural Production Systems Research Unit, Toowoomba, Australia. Horie, T., 1994. Crop ontogeny and development. In: Boote, K.J., Bennett, J.M., Sinclair, T.R., Paulsen, G.M. (Eds.), Physiology and Determination of Crop Yield. ASA, CSSA, and SSSA, Madison, USA, pp. 153–180. Jones, P.N., Carberry, P.S., 1994. A technique to develop and validate simulation models. Agric. Syst. 46, 427–442. Jones, C.A., Kiniry, J.R., 1986. CERES-Maize: a simulation model of maize growth and development. Texas A&M Univ. Press, College Station, TX. Keisling, T.C., 1982. Calculation of the length of day. Agron. J. 74, 758–759. Langary, M., 1996. Effect of plant density on yield and yield components in chickpea under rainfed conditions of the north Khorasan. MS Thesis, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (text in Persian with English abstract). McCullagh, P., Nelder, J.A., 1992. Generalized Linear Models, 2nd ed. Chapman and Hall, London. Mohammadi, S., 1995. Effect of plant density and sowing date on growth and yield of chickpea. M.S.Thesis, Faculty of Agriculture, Univ. of Tabriz, Tabriz, Iran (text in Persian with English abstract). Movahhedi, M., 1996. Growth analysis of two chickpea cultivars under various planting densities and irrigation regimes. M.S. Thesis, Faculty of Agriculture, Univ. of Tabriz, Tabriz, Iran (text in Persian with English abstract). Noroozzadeh, S., 1996. Effect of plant density on yield and yield components in two chickpea genotypes under Mashhad environmental conditions. MS Thesis, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (text in Persian with English abstract). Olsen, J.K., McMahan, C.R., Hammer, G.L., 1993. Prediction of sweet corn phenology in subtropical environments. Agron. J. 410–415. Piper, E.L., Boote, K.J., Jones, J.W., Grimm, S.S., 1996. Comparison of two phenology models for predicting flowering and maturity date of soybean. Crop Sci. 36, 1606–1614. Porsa, H., Bagheri, A.R., Nezami, A., Mohammadabadi, A.A., Langari, M., 2002. Evaluation winter sowing for chickpea under rainfed conditions of
A. Soltani et al. / Field Crops Research 99 (2006) 1–13 the north Khorasan. J. Agric. Sci. Technol. (Mashhad) 16 (1), 83–91 (text in Persian with English abstract). Rastgar, J., 1997. Growth analysis and yield of chickpea cultivars under different sowing date and density. MS Thesis, Faculty of Agriculture, Tabriz University, Tabriz, Iran (text in Persian with English abstract). Ritchie, J.T., 1991. Wheat phasic development. In: Hanks, J., Ritchie, J.T. (Eds.), Modeling Plant and Soil Systems. American Society of Agronomy, Madison, Wisconsin, pp. 31–54. Roberts, E.H., Hadley, P., Summerfield, R.J., 1985. Effect of temperature and photoperiod on flowering in chickpeas (Cicer arietinum L.) Ann. Bot. 55, 881–892. Roberts, E.H., Summerfield, R.J., Minchin, F.R., Hadley, P., 1980. Phenology of chickpea (Cicer arietinum L.) in contrasting aerial environments. Exp. Agric. 16, 343–360. Robertson, M.J., Carberry, P.S., Huth, N.I., Turpin, J.E., Probert, M.E., Poulton, P.L., Bell, M., Wright, G.C., Yeates, S.J., Brinsmead, R.B., 2002a. Simulation of growth and development of diverse legume species in APSIM. Aust. J. Agric. Res. 53, 429–446. Robertson, M.J., Watkinson, A.R., Kirkegaard, J.A., Holland, J.F., Potter, T.D., Burton, W., Walton, G.H., Moot, D.J., Wratten, N., Farre, I., Asseng, S., 2002b. Environmental and genotypic control of time to flowering in canola and Indian mustard. Aust. J. Agric. Sci. 53, 793–809. Roozrokh, M., Ghassemi-Golezai, K., Javanshir, A., 2002. The relationship between seed vigor and growth and yield in chickpea. Seed Seedling J. (Karaj) 18 (2), 43–54 (text in Persian with English abstract). Saxena, N.P., 1984. Chickpea. In: Goldsworthy, P.R., Fisher, N.M. (Eds.), The Physiology of Tropical Field Crops. Wiley, New York, pp. 207– 232. Sethi, S.C., Byth, D.E., Gowda, G.L.L., Green, J.M., 1981. Photoperiodic response and accelerated generation turnover in chickpea. Field Crops Res. 4, 215–225. Sinclair, T.R., Kitani, S., Hinson, K., Bruniard, J., Horie, T., 1991. Soybean flowering date: linear and logistic models based on temperature and photoperiod. Crop Sci. 31, 786–790. Singh, P., 1991. Influence of water-deficits on phenology, growth and drymatter allocation in chickpea. Field Crops Res. 28, 1–15. Singh, P., Virmani, S.M., 1996. Modeling growth and yield of chickpea (Cicer arietinum L.) Field Crops Res. 46, 41–59. Smithson, J.B., Thompson, J.A., Summerfield, R.J., 1985. Chickpea (Cicer arietinum L.) In: Summerfield, R.J., Roberts, R.E. (Eds.), Grain Legume Crops. Collins, London, pp. 312–390. Soltani, A., Ghassemi-Golezani, K., Rahimzadeh-Khooie, F., Moghaddam, M., 1999. A simple model for chickpea growth and yield. Field Crops Res. 62, 213–224.
13
Soltani, A., Khooie, F.R., Ghassemi-Golezani, K., Moghaddam, M., 2001. A simulation study of chickpea crop response to limited irrigation in a semiarid environment. Agric. Water Manage. 49, 225–237. Soltani, A., Torabi, B., Zeinali, E., Sarparast, R., 2004. Response of chickpea to photoperiod as a qualitative long-day Plant. Asian J. Plant Sci. 3 (6), 705–708. Soltani, A., Robertson, M.J., Torabi, B., Yousefi-Daz, M., Sarparast, R., 2006a. Modeling seedling emergence in chickpea as influenced by temperature and sowing depth, Agric. For. Meteorol., in press Soltani, A., Robertson, M.J., Mohammad-Nejad, Y., Rahemi-Karizaki, A., 2006b. Modeling chickpea growth and development: leaf production and senescence. Field Crops Res. 99, 14–23. Soltani, A., Robertson, M.J., Manschadi, A.M., 2006c. Modeling chickpea growth and development: nitrogen accumulation and use. Field Crops Res. 99, 24–34. Soltani, A., Torabi, B., Zarei, H., 2005. Modeling crop yield using a modified harvest index-based approach: application in chickpea. Field Crops Res. 91, 273–285. Summerfield, R.J., Ellis, R.H., Roberts, E.H., 1989. Vernalization in chickpea: fact or artifact? Ann. Bot. 64, 599–603. Summerfield, R.J., Minchin, F.R., Roberts, E.H., Hadley, P., 1981. Adaptation to contrasting aerial environments in chickpea (Cicer arietinum L.) Trop. Agric. 58, 97–113. Summerfield, R.J., Roberts, E.H., 1987. Effects of illuminance on flowering in long- and short-day grain legumes: a reappraisal and unifying model. In: Atherton, J.G. (Ed.), Manipulation of Flowering. Butterworths, London, pp. 203–223. Turpin, L.E., Robertson, M.J., Haire, C., Bellotti, W.D., Moore, A.D., Rose, I., 2003. Simulating fababean development, growth and yield in Australia. Aust. J. Agric. Res. 54, 39–52. van der Maesen, L.J.G., 1972. Cicer L. a monograph of the genus with special reference to chickpea (Cicer arietinum L.), its ecology and cultivation, Communication of the Agricultural University, Wageningen 72-10. Verghis, T.I., McKenzie, B.A., Hill, G.D., 1999. Phenological development of chickpea in Canterbury New Zealand. N. Z. J. Crop Hort. Sci. 27 (3), 249–256. Yin, X., Kropff, M.J., Horie, T., Nakagawa, H., Centeno, H.G.S., Zhu, D., Goudriaan, J., 1997a. A model for photothermal responses of flowering in rice. I. Model description and parameterization. Field Crops Res. 51, 189–200. Yin, X., Kropff, M.J., Nakagawa, H., Horie, T., Goudriaan, J., 1997b. A model for photothermal responses of flowering in rice. I. Model evaluation. Field Crops Res. 51, 189–200.
Modeling chickpea growth and development: Phenological development A. Soltani a,*, G.L. Hammer b, B. Torabi a, M.J. Robertson c, E. Zeinali a a
Department of Agronomy and Plant Breeding, Gorgan University of Agricultural Sciences, P.O. Box 386, Gorgan, Iran b School of Land and Food Sciences, The University of Queensland, Brisbane, Qld. 4072, Australia c CSIRO Sustainable Ecosystems, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Qld. 4067, Brisbane, Australia Received 25 July 2005; received in revised form 28 February 2006; accepted 28 February 2006
Abstract Quantitative information on temperature and photoperiod effects on development rate in chickpea (Cicer arietinum L.) is scarce. Data from a serially sown field experiment (2001–2003) on four cultivars was used to evaluate various approaches to phenology prediction. A range of functions describing the response of development rate to temperature and photoperiod was compared. Phenological data from numerous other field experiments across Iran were used for independent model evaluation. A multiplicative model that included a dent-like function for response to temperature and quadratic function for response to photoperiod was the most adequate at describing the response of development rate to temperature and photoperiod. The differences among cultivars for cardinal temperatures and critical photoperiod were small and a base temperature of 0 8C, lower optimum temperature of 21 8C, upper optimum temperature of 32 8C, ceiling temperature of 40 8C and critical photoperiod (below which development rate decreases due to short photoperiods) of 21 h were obtained. Inherent maximum rate of development and the photoperiod sensitivity coefficient characterized cultivar differences. The cultivars required 24.7–32.2 physiological days (i.e., number of days under optimum temperature and photoperiod conditions) from emergence to flowering, 8.2–12.0 from flowering to first-pod, 4.3 from first-pod to beginning seed growth and 30.3 from beginning seed growth to maturity. Differences among cultivars were not found for first-pod to beginning seed growth or for beginning seed growth to maturity. The phenology model developed using these findings gave good predictions of phenological development for a diverse range of temperature and photoperiod conditions across Iran. This model can be incorporated in simulation models of chickpea. # 2006 Elsevier B.V. All rights reserved. Keywords: Phenology; Development; Temperature; Photoperiod; Model; Chickpea
1. Introduction Chickpea (Cicer arietinum L.) is cultivated across the world in the Mediterranean basin, the near east, central and south Asia, east Africa, South America, North America and Australia. The time available for chickpea crops to produce adequate vegetative structures and then grain yield is often limited by hot or cold temperatures, rainfall distribution, or competition for use of land by other crops in rotation (Roberts et al., 1985; Smithson et al., 1985). To achieve good * Corresponding author. Tel.: +98 171 4420438; fax: +98 171 4420981/4420438. E-mail address: [email protected] (A. Soltani). 0378-4290/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2006.02.004
yield, crop duration must closely match the available growing season (Ellis et al., 1994). Phenological studies in chickpea have been limited. They have revealed that crop duration and especially timing of flowering is modulated strongly by genotype, temperature and photoperiod (Roberts et al., 1980, 1985; Summerfield et al., 1981; Ellis et al., 1994). Water stress may delay or stimulate crop development (Singh, 1991; Soltani et al., 1999), but a moderate level of water stress appears to have no direct effect on development (Robertson et al., 2002a). A linear increase in development rate towards flowering has been found for a temperature range of 10.8–29.3 8C and photoperiod range of 11–15.6 h d1 (van der Maesen, 1972; Roberts et al., 1980, 1985; Summerfield et al., 1981; Ellis
2
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
et al., 1994). These studies indicated that the critical photoperiod (below which development rate decreases due to short photoperiods) in chickpea should be longer than 15 h. Roberts et al. (1985) and Ellis et al. (1994) described the rate of development towards flowering as a function of mean temperature and photoperiod using an additive linear model, although Ellis et al. (1994) reported that environments with daily maximum temperature of 38 8C resulted in considerable delays to flowering. They did not test their model using independent data sets. Some researchers have challenged the use of the additive, linear model, which is based on mean temperature and photoperiod during the whole pre-flowering stage (Yin et al., 1997b; Carberry et al., 2001). Alternatively, a multiplicative model has been used to describe the nonlinear effects of temperature and photoperiod on crop development rate (e.g., Hammer et al., 1989; Grimm et al., 1993; Yin et al., 1997a). Accurate prediction of crop phenology is a major requirement for crop simulation models. The production and partitioning of dry matter in crop simulation models is regulated to a large extent by the timing of phenological stages. Soltani et al. (1999) used the thermal time concept to quantify phenological development in their chickpea model without inclusion of a photoperiod effect. This approach gives acceptable predictions for a limited range of sowing dates and latitudes. In subsequent studies, they limited the application of their model to spring-sown crops in NW Iran (e.g. Soltani et al., 2001). For a wider application of the model, the effect of photoperiod should be incorporated. Therefore, the objectives of this research were: (1) to find appropriate response functions for the effect of temperature and photoperiod on development rate, (2) to determine cardinal temperatures and photoperiods for development, (3) to determine which parameters characterize different responses in different cultivars, and (4) to develop and test a general phenology model for chickpea. The present paper is a part of a comprehensive study (Soltani et al., 2005, 2006a,b,c) aimed at improvement of chickpea simulation capabilities in our chickpea models (Soltani et al., 1999; Robertson et al., 2002a).
2. Materials and methods 2.1. Field experiments Four field experiments were conducted as a part of this study. Three of the four experiments were carried out at the Gorgan University of Agricultural Sciences Research Farm, Gorgan (368510 N, 548160 E and 100 m a.s.l.), Iran. The soil was a deep silty loam (fine-silty, mixed, active, thermic, Typic Calcixerolls) and all three experiments were conducted under well-watered conditions. The plots were irrigated after 60mm cumulative pan (class A) evaporation and irrigation amount was based on soil moisture depletion. Therefore, there was no effect of flooding or water deficit stresses.
The first experiment (Exp. 1) was a serially sown experiment that started in December 2001 and continued until August 2003. Four chickpea cultivars (Beauvanij, 9096c, Hashem and Jam—all kabuli-type) were sown at 11 sowing dates. These cultivars were selected from different geographical areas across Iran; 90-96c and Hashem are cultivated in the western part of the Caspian Sea Coast of Iran (Golestan province), Jam in the north-west (Azerbaijan province) and Beauvanij in the mid-west of the country (Kermanshah province). Sowing dates (day of year, DOY) were 12 December (346) 2001 and 15 January (15), 15 February (46), 17 March (76), 16 April (106), 18 May (138), 17 June (168), 16 August (228), 15 September (258), 14 October (287) and 12 November (316) 2002. The sowing dates employed do not necessarily reflect common practice, but were selected to create different temperature and photoperiod regimes. The experimental design was a single split plot with sowing dates as the main plot and genotypes the sub-plot, replicated four times. Plot size was 1.5 m 4 m. Plots were hand-seeded using row spacing of 25 cm. Target plant density was 50 plant m2 and two seeds were planted at correct spacing at 5 cm depth, and seedlings thinned to one in each position later. Stages of development of emergence (50% of plants with some parts at soil surface), flowering (50% of plants with one flower at any node, R1), first pod (50% of plants with 0.5 cm pod at one of the four upper nodes with unrolled leaf, R3), beginning seed growth (50% of plants with peas beginning to develop, R5), first maturity (50% of plants with one pod yellowed, R7) and full maturity (95% of pods have obtained their mature color, R8) were recorded every 1–2 days (Fehr and Caviness, 1977). During the experiment weeds were hand-controlled and several sprayings were carried out against Ascochyta blight. Hashem and 90-96c are resistant to the blight, but Beauvanij and Jam are susceptible and were badly affected. At the end, there were 9 sowing dates for Hashem, 10 for 90-96c, 7 for Beauvanij and 6 for Jam with reliable data. The second (Exp. 2) and third (Exp. 3) experiments were conducted in the growing seasons of 2002–2003 and 2003– 2004, respectively. For both experiments, factorial combinations of three sowing dates and four plant densities were the treatments. The experimental design was a randomized complete block design with four replicates. Again, a wide range of sowing dates was chosen to create different temperature and photoperiod regimes. In Exp. 2, sowing dates (DOY) were 5 January (5), 6 March (65) and 28 April (118) 2003 and the four plant densities were 15, 30, 45 and 60 plant m2. In Exp. 3, sowing dates were 6 December 2003 (340), and 20 January (20) and 20 March (79) 2004. The same plant densities as Exp. 2 were used. In both experiments, the chickpea cultivar Hashem, the local cultivar, was used. The fourth experiment (Exp. 4) was conducted at Gonbad Agricultural Research Station, Gonbad (348210 N, 558100 E and 37 m a.s.l.), Iran. The treatments and cultivar were
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
similar to Exps. 2 and 3. The experimental design was a single split plot with sowing dates as the main plot and plant densities the sub-plots, replicated four times. Sowing dates (DOY) were 7 December (341) 2002, 23 January (23) and 6 March (65) 2003. The field experiments (2, 3 and 4) were not conducted specifically for the objectives of the present study, but phenological stages were monitored every 2 days generating data sets suitable for model testing. In all experiments, daily maximum and minimum temperatures, sunshine hours and rainfall were measured at a standard weather station located a few meters (Gorgan) to a few hundred meters (Gonbad) from the experimental sites. Photoperiod for each day was calculated from latitude and calendar day and included allowance for civil twilight (solar angle 48; Keisling, 1982). This solar angle was selected based on the minimum illuminance requirement for chickpea of 6 lx (Summerfield and Roberts, 1987).
2.2. Modeling occurrence of flowering
2.2.1. Basic vegetative phase It is widely accepted that many crops require a minimum vegetative period during which they do not respond to photoperiod (Horie, 1994). This is commonly known as the basic vegetative phase. In a literature review on chickpea physiology, Saxena (1984) concluded that there were no indications of a pronounced juvenile phase in this crop. Therefore, in this study, it was assumed that all the genotypes respond to photoperiod immediately after emergence. Based on results of Soltani et al. (2006a), the rate of development from sowing towards emergence is determined according to a dent-like function on temperature with three linear segments, where four parameters (base, lower optimum, upper optimum and ceiling temperatures) define critical points. According to this function, rate of development is zero at temperatures lower than base temperature or higher than ceiling temperature. Between base and lower optimum temperatures, the rate increases linearly from zero to its maximum value. Lower and upper optimum temperatures define a plateau where the rate of development is constant and maximum. The rate of development again decreases linearly from upper optimum towards ceiling temperature. Cardinal temperatures for emergence were found to be 4.5 8C for base, 20.2 8C for lower optimum, 29.3 8C for upper optimum and 40 8C for ceiling temperature (Soltani et al., 2006a). It takes six physiological days (minimum number of days at optimum temperature) from sowing to emergence (sowing depth = 5 cm) (Soltani et al., 2006a). It has been reported that cultivated chickpeas have no or negligible vernalization response (Summerfield et al., 1989; Abbo et al., 2002). It was assumed that there is no or negligible vernalization response in all selected cultivars (Soltani et al., 2004).
3
2.2.2. Functions and parameters From emergence until flowering, a multiplicative relationship (Hammer et al., 1989; Horie, 1994) was used to compute development rate as a function of temperature and photoperiod: Rt ¼ Rmax f ðTÞ f ðPPÞ
(1)
where Rt is development rate on Day t, Rmax the maximum rate of development at optimum temperature and photoperiod, f(T) the temperature function, and f(PP) is the photoperiod function. The inverse of Rmax indicates minimum number of days from emergence to flowering at optimum temperature and photoperiod, or the physiological days requirement, from emergence to flowering ( f o hereafter). f(T) and f(PP) are calculated each day and have values between zero and one. The product of f(T) and f(PP) is called physiological day per calendar day on that day (PDt), i.e., PDt = f(T) f(PP). When f(T) and f(PP) are both equal to one, PDt is also equal to one and progress towards flowering is at its fastest rate. Starting at emergence, physiological days (PDt) are accumulated until a threshold of f o is reached. At this P time, P flowering is predicted to occur (i.e., PDt = f(T) f(PP) = 1/Rmax = f o). Alternatively, flowering is predicted to occur when cumulative valueP of development rates (Rt) reaches to a threshold of 1 (i.e., Rt = 1). In this study, three temperature functions and two photoperiod functions were selected due to their simplicity and the intuitive meaning of their parameters. The temperature functions were: Segmented function 8 T Tb > > > T T ; if Tb < T To > < o b (2) f ðTÞ ¼ Tc T ; if To < T < Tc > > > T T c o > : 0; if T Tb or T Tc Beta function #a 8 " > T Tb Tc T ððTc To Þ=ðTo Tb ÞÞ > > ; > > > < T o Tb T c T o f ðTÞ ¼ (3) if T > Tb and T < Tc > > > > 0; > > : if T Tb or T Tc And dent–like function 8 T Tb > > ; if Tb < T < To1 > > > T o1 Tb > > < Tc T ; if To2 < T < Tc f ðTÞ ¼ (4) Tc To2 > > > > 1; if To1 T To2 > > > : 0; if T Tb or T Tc where T is temperature, Tb the base temperature, To the optimum temperature, To1 the lower optimum temperature
4
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 1. (a) Functions used to describe the response of development rate to temperature. (b) Functions used to describe the response of development rate to photoperiod.
(for dent-like function), To2 the upper optimum temperature (for dent-like function), Tc the ceiling temperature and a the shape parameter for beta function that determines the curvature of the function. Because of the low frequency of temperature greater than 40 8C, we fixed Tc at 40 8C when using the above functions. The ceiling temperature of 40 8C was chosen because it represents a biologically reasonable upper limit for developmental processes (e.g., Olsen et al., 1993; Yin et al., 1997a; Robertson et al., 2002a,b). Schematics of the functions are presented in Fig. 1a. The beta function as used here is very flexible and can mimic the quadratic, cubic and curvilinear (as presented by Hammer et al., 1989) functions.
Quadratic function 1; if PP Pc f ðPPÞ ¼ f ðPPÞ ¼ 1 PSðPc PPÞ2 ; if PP < Pc
Functions used for photoperiod were:
With the three functions for temperature and two functions for photoperiod, there were six combinations of functions used in Eq. (1). Each combination was fitted to data of each cultivar (see below) and root mean square of deviations (RMSD) and regression of predicted versus observed days from emergence to flowering were computed and compared.
Segmented function 1; if PP Pc f ðPPÞ ¼ 1 ðPc PPÞPS; if PP < Pc
(5)
(6) 1
where PP is photoperiod (h d ), Pc the critical photoperiod below which development rate decreases due to short photoperiod, and PS the photoperiod sensitivity coefficient. Schematics of the functions are presented in Fig. 1b. Ceiling photoperiod (Pce) below which phenological development is stopped ( f(PP) = 0; Fig. 1b) and plants do not flower can be obtained as Pce = [(PcPS 1)/ PS] for segmented function and as Pce = Pc (1/PS)0.5 for quadratic function.
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
2.2.3. Estimation procedure An optimization program, named DEVEL (Holzworth and Hammer, 1996) was used to estimate parameters of Eqs. (1)–(6) for each cultivar, using daily maximum and minimum temperatures and photoperiod data and observed dates of emergence and flowering. DEVEL uses the simplex optimization method and contains a library of eight options for temperature and photoperiod functions, ranging from linear response to logistic, which can be used separately or in combination to examine the independent and interactive effects of temperature and photoperiod. The user selects a function to describe the response being investigated and provides initial estimates of parameters and their bounds. DEVEL then proceeds to carry out an iterative optimization P Pthat minimizes residual sum of squares (SSE = [( Rt 1)2]), and provides optimized estimates of the parameters. The user repeats the process with alternative functions and selects the function that best statistically fits the temperature and photoperiod response. DEVEL also calculates the smallest and largest parameter estimate that result in SSE within 10% of the final, minimum value. This gives a form of error value to each of the parameters and is useful for comparison purposes. DEVEL includes algorithms for parameter profiling (McCullagh and Nelder, 1992) and jack-knifing (Efron, 1990; Jones and Carberry, 1994) that remove bias from the parameter estimates and introduce rigor into the parameter selection process (P.D. Jones and G.L. Hammer, pers. commun.). For the objective of this study, two modifications were made in DEVEL. Firstly, dent-like and beta functions for temperature and quadratic and negative exponential functions for photoperiod were included into DEVEL. Secondly, capability of using 3-hourly temperatures instead of mean daily temperature was incorporated for all temperature functions. DEVEL had this capability only for the segmented temperature function. Calculation of 3-hourly temperature was based on the method reported by Jones and Kiniry (1986) using cubic interpolation from minimum and maximum daily temperatures. It should be noted that in this study, temperature functions were applied in a time step of 3 h. As outlined by Birch et al. (1998) different starting conditions were tried to guard against the identification of local optima and to assess whether they converge to the same optimized value. Thus, the potential for the optimized estimates being artifacts of the starting conditions, or an erroneous selection from possible multiple solutions is minimized. We also used a linear, multiple regression model as proposed by Roberts et al. (1985) and Ellis et al. (1994) to describe relationship between development rate and temperature and photoperiod: 1 ¼ a þ bT þ cP f
(7)
5
where 1/f is the development rate towards flowering (inverse of time from emergence to flowering), a, b and c are the empirical regression coefficients, T the mean temperature and P is the mean photoperiod from emergence to flowering. 2.3. Modeling occurrence of post-flowering stages Post-flowering stages considered here are first-pod (R3), beginning of seed growth (R5) and maturity (R8). In the serially sown field experiment, all of the phases occurred during mid-spring to late summer due to the qualitative response of flowering to photoperiod in all the cultivars used (see Section 3.1). Therefore, it was not possible to obtain a wide environmental range for these phases and the temperature and photoperiod conditions experienced were somewhat restricted. It was assumed that the duration of these phenophases was controlled by temperature and the response function and the cardinal temperatures were the same as for the emergence to flowering interval. There is some experimental evidence supporting this assumption in chickpea. Roberts et al. (1980) reported that once flowers have appeared temperature is the most important environmental determinant of the length of reproductive period in all three cultivars that they evaluated. Sethi et al. (1981) studied phenological development of different chickpea cultivars from different maturity groups under natural and 24-h extended day length and reported that once flowering was induced, continuation of the extended day length treatment had no effect on the time of maturity. However, Turpin et al. (2003) reported postflowering development as sensitive to photoperiod in fababean, a long-day legume crop like chickpea. Based on the above assumptions, the physiological day requirements (cumulative values of f(T) from dent-like function—see Section 2.2.2) were calculated from R1 to R3, R3 to R5 and R5 to R8 for each cultivar-sowing date combination in the serially sown field experiment. These physiological day requirements were then analyzed and compared. 2.4. Model evaluation Data from Exps. 2–4 were used to evaluate model performance for predicting timing of R1, R3, R5 and R8 in cv. Hashem. We could not find other experimental data on timing of R3 and R5 according to measures used in the present study. To have a comprehensive, independent model evaluation, data on observed flowering and maturity dates were collected for the cultivars across Iran (Table 1). We only included data from irrigated conditions and rainfed conditions where drought effects appeared to be slight (based on yield data). Maximum and minimum temperatures were available for each experiment and daily photoperiod was calculated using latitude and day of the year as described in Section 2.1. Predicted days to flowering, R3, R5 and maturity were compared with observed data using RMSD and linear regression analysis.
6
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Table 1 Data sets for independent model testing Location and year
Latitude
Treatments
Reference
Many locations across Iran, 1970–1976 and 1995–2001 Bojnord, 1994/1995 Nishabur, 1994/1995 Nishabur, 1994/1995 Mashhad, 1995/1997 Mashhad, 1995/1996 Mashhad, 1995 Tabriz, 1994 Tabriz, 1995 Tabriz, 1998
32.17–38.138N
Genotype, planting date
37.478N 36.278N 36.278N 36.278N 36.278N 36.278N 38.138N 38.138N 38.138N
Genotype, Genotype Genotype, Genotype, Genotype, Genotype, Genotype, Genotype, Genotype,
Anon. (1971–1977, 1996–2002) Langary (1996) Hasanzadeh (1996) Rastgar (1997) Porsa et al. (2002) Goldani et al. (2000) Noroozzadeh (1996) Mohammadi (1995) Movahhedi (1996) Roozrokh et al. (2002)
plant density planting date and density planting date planting date plant density planting date and density irrigation, plant density irrigation, seed vigor
temperatures varied between 5.2 and 40.4 8C for maximum temperature and between 3.4 and 27.2 8C for minimum temperature (data not shown). Photoperiod ranged between 10.2 and 15.3 h (Fig. 2). The cultivars showed a qualitative long-day plant response as reported by Soltani et al. (2004). Briefly, crops sown in September, October, November and January flowered at nearly the same time in May (day of year 130); flowering was delayed up to 120 days when photoperiod decreased to 11–12 h (Fig. 2). Non-effective temperatures did not delay flowering for these sowing dates as crops sown in September accumulated twice as much
3. Results and discussion 3.1. Phenophase durations A wide range in the duration of crop phenophases resulted from the serially sown field experiment (Table 2). Variation in days to emergence and its relation to temperature have been discussed elsewhere (Soltani et al., 2006a). Duration from emergence to flowering varied as much as four- to fivefold (Table 2). For example, for Beauvanij this duration varied from 29 to 126 days and for Hashem from 36 to 227 days. For the period from emergence to flowering,
Table 2 The range and mean of observed duration (days) for specific phenophases of each of four chickpea cultivars in the serially sown field experiment Cultivar
Minimum
Maximum
Mean
Beauvanij Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
5.8 29.0 7.3 3.5 28.5 37.7
23.0 126.0 18.3 10.0 35.5 52.0
10.3 69.4 12.9 6.3 32.3 46.0
90-96c Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
5.8 37.0 7.3 3.0 18.0 33.8
23.2 220.1 17.0 7.0 24.3 51.3
10.7 118.3 10.8 5.2 20.6 45.2
Hashem Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
6.1 36.0 8.0 3.5 28.5 33.0
22.5 227.0 14.8 6.8 34.0 50.0
10.1 114.6 11.2 5.0 31.3 43.2
Jam Sowing to emergence Emergence to flowering Flowering to first-pod (R1–R3) First-pod to beginning seed growth (R3–R5) Beginning seed growth to maturity (R5–R8) Flowering to maturity (R1–R8)
5.5 28.0 7.0 3.3 18.0 38.3
24.5 124.0 17.5 7.0 24.3 52.5
10.3 69.0 10.8 5.2 20.6 43.9
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 2. Flowering time of cv. Hashem sown at a range of sowing dates. Vertical, short lines and associated labels indicate sowing date and day of year. The thick line indicates the approximate time of flowering for all of these sowing dates (redrawn from Soltani et al., 2004).
thermal time (base temperature 0 8C) as those sown in January, although they flowered at the same time. This was the first report of a qualitative response of chickpea to photoperiod as flowering did not occur at photoperiods lower than a ceiling value. Previously, chickpea has generally been considered a quantitative long-day plant (van der Maesen, 1972; Roberts et al., 1980; Summerfield et al., 1981; Smithson et al., 1985; Verghis et al., 1999). Variation in the duration of the post-flowering phenophases was limited (Table 2). In cv. Hashem for example, 8– 15 days duration was observed for R1–R3, 4–7 days for R3– R5 and 29–34 days for R5–R8. This reduced variation resulted from the qualitative response of flowering to photoperiod in the cultivars evaluated. As a consequence of this qualitative response post-flowering stages occurred during mid-spring to late summer when there was much less variation in temperature and photoperiod. In the following sections we indicate how variation in durations of pre- and post-flowering phenophases is related to temperature and photoperiod by reporting results of model fitting. 3.2. Modeling duration to flowering Using iterative optimization procedures, such as simplex, there is no guarantee that the solution obtained is unique and optimal (Sinclair et al., 1991; Grimm et al., 1993; Yin et al., 1997a). There might be a series of solutions with similar SSE but different parameter estimates. In addition, the correlation between temperature and photoperiod under field conditions causes some compensation between parameters in studies like this one (Olsen et al., 1993). Defining parameter bounds (i.e., lower and upper limits allowed for each parameter), based on biological and physiological principles can assist in obtaining consistent estimates. However, the degree of difficulty is reduced if data sets covering a wide range of environmental conditions are included in the optimization (P.D. Jones and G.L. Hammer, pers. commun.). Considering these points, we had little problem in our experience using simplex optimization in DEVEL with these data sets. In nearly all cases, stable estimates were obtained with the first run of DEVEL.
7
The linear, additive model (Eq. (7)) gave a significantly worse fit than any of the six combinations of temperature and photoperiod functions used in the multiplicative model (Eq. (1)). The RMSD (26.2 days) for the linear, additive model was significantly higher and there was significant bias in the regression of predicted versus observed flowering time (data not shown). This was in contrast to findings of Roberts et al. (1985) and Ellis et al. (1994) who found good predictions with the additive model. However, the additive model is valid only for a restricted range of temperature and photoperiod, when temperatures are between base and optimum values and photoperiods vary between critical and ceiling values (Roberts et al., 1985; Ellis et al., 1994). Hence, the poor performance of the linear, additive model is likely due to the broad range of environmental conditions experienced in this study. This highlights the need for caution in extrapolating such models beyond limited ranges in key forcing factors. Comparison of different functions of temperature and photoperiod in modeling development rate using Eq. (1) indicated that there was no uniquely best function for each of temperature and photoperiod to predict flowering time in the cultivars studied. However, in some cases, the difference between functions was quite small. Amongst combinations of temperature and photoperiod functions examined using the multiplicative model (Eq. (1)), models containing (1) segmented functions for temperature and photoperiod, (2) dent-like function for temperature and segmented function for photoperiod, and (3) segmented function for temperature and quadratic function for photoperiod were discarded because, at least for one cultivar, they resulted in significant bias. The remaining models containing (1) beta function for temperature and quadratic function for photoperiod, (2) beta function for temperature and segmented function for photoperiod, and (3) dent-like function for temperature and quadratic function for photoperiod performed similarly with respect to RMSD and regression of predicted versus observed days from emergence to flowering (data not shown). However, the model that included the dent-like function for temperature and the quadratic function for photoperiod was more stable. Using this model the calculated cumulative number of physiological days from emergence to flowering was independent of temperature and photoperiod (Fig. 3). This indicates that the composite functions account well for temperature and photoperiod effects on rate of development. Other combinations of functions (data not shown) did not perform as well on this test of model generality. Various functions have been used by different researchers to describe the effect of temperature and photoperiod on development rate (e.g., Hammer et al., 1989; Horie, 1994; Piper et al., 1996; Yin et al., 1997a; Robertson et al., 2002a,b), because it is difficult to assert a general function for all crops and phenological stages. The dent-like function has been used to quantify effect of temperature on development rate (Piper et al., 1996), leaf appearance rate (Ritchie, 1991) and radiation use efficiency (Robertson et al., 2002a). The quadratic function used here is similar to
8
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 3. Calculated number of physiological days from emergence to flowering vs. (a) mean temperature and (b) mean photoperiod, during the same period. Data are pooled over cultivars.
that incorporated in the CERES-Wheat simulation model to describe the effect of photoperiod on development rate (Ritchie, 1991). Therefore, given its superior stability and no evidence supporting generality of other function forms, the dent-like function for temperature combined with the quadratic function for photoperiod were selected for use in our model of chickpea phenology. The adequacy of fit for this combination of functions is presented in Table 3 (Step 1) and Fig. 4 (Step 1). RMSD ranged between 6.5 and 14.8 days (i.e., 9 and 17% of the mean) among cultivars. There was no significant bias in the linear regression of predicted versus observed values. Estimates of cardinal temperatures and photoperiod parameters were 0.7 to 1.7 8C for Tb, 19.2– 20.9 8C for To1, 29.2–33.8 for To2, 20.5–21.9 for Pc, 0.0082– 0.0090 for PS and 22.7–39.2 for physiological days from emergence to flowering ( f o) (Table 4, Step 1). Fig. 5 presents profile plots for model fitting to data for cv. Beauvanij as an example. These plots show the response in SSE as each parameter was varied. In each case, there was an interval Table 3 Root mean square deviations (RMSD; days) and linear regression statistics (intercept (a), slope (b), and correlation coefficient (r)) for predicted vs. observed days from emergence to flowering for four chickpea cultivars. Results are given for three steps in model fitting, which relate to the number of parameters fitted (or fixed) across cultivars Function-cultivar
RMSD
a S.E.
b S.E.
r
Step 1: all model parameters (except Tc) fitted to individual cultivars Beauvanij 6.5 3.7 6.2 1.04 0.082 0.94 90-96c 10.0 3.2 4.6 1.04 0.041 0.97 Hashem 14.8 10.4 7.6 0.90 0.058 0.94 Jam 11.4 6.3 5.7 1.09 0.074 0.95 Step 2: only PS and fo fitted to individual cultivars Beauvanij 6.8 2.8 6.5 1.05 0.086 90-96c 9.6 1.4 4.6 0.98 0.042 Hashem 15.0 6.7 8.0 0.96 0.61 Jam 11.1 3.2 5.5 1.00 0.073
0.93 0.97 0.94 0.95
Step 3: only fo fitted to individual cultivars Beauvanij 4.8 0.4 6.1 90-96c 18.3 10.7 5.3* Hashem 17.1 5.7 8.1 Jam 10.8 2.1 4.5
0.93 0.97 0.94 0.96
0.98 0.081 1.20 0.047* 0.97 0.061 0.89 0.059
* Significant difference (P < 0.05) from 0 for a and significant difference from 1 for b.
over which SSE was minimized, indicating no redundancy in the number of parameters employed in the overall model. In the next step, we examined which parameters characterized different responses among cultivars. Initial estimates of Tb, To1, To2 and Pc were similar among cultivars (Table 4). Hence, the values of these parameters were fixed at 0 for Tb, 21 for To1, 32 for To2 and 21 for Pc, and DEVEL was run to estimate values for PS and f o only (Step 2 in Tables 3 and 4). There was no significant difference between the models fitted in Steps 1 and 2 (F = 1.01; P = 0.964) and there was no increase in RMSD between the 6-parameter to 2-parameter models. Further, regression of predicted versus observed days from emergence to flowering did not show any indication of change in bias (Table 3; Fig. 4). In the final step, the value of PS was fixed at 0.0085 for all cultivars and a 1-parameter model was fitted using DEVEL (Step 3 in Tables 3 and 4, and Fig. 4). While the difference between models fitted in Steps 1 and 3 was not significant (F = 1.28; P = 0.291), the simplification used in Step 3 resulted in significant increase in RMSD for cv. 90-96c (from 9.6 to 18.3 days) and cv. Hashem (from 15 to 17.1 days) and significant bias in prediction for cv. 90-96c (Table 3). Thus, it can be concluded that at least a 2-parameter model is required to account for cultivar effects and that the PS and f o parameters characterize genotypic differences in phenological development in response to temperature and photoperiod. For modeling purposes, the values of Tb, To1, To2, Tc and Pc can be fixed as indicated in Table 4, Step 2. Grimm et al. (1993), Piper et al. (1996) and Sinclair et al. (1991) for soybean, Carberry et al. (2001) for pigeonpea and Robertson et al. (2002b) for canola also obtained good prediction of flowering time with fixed values of cardinal temperatures. However, for rice, Yin et al. (1997a) reported that optimal temperature for development could not be fixed without loss of accuracy. However, they still obtained accurate predictions for 17 rice cultivars with fixed values for Tb and Tc. The value of Pc found in this study (21 h) is comparable to the value of 20 h found by Ritchie (1991) for wheat, a long day plant. Roberts et al. (1985) and Ellis et al. (1994) suggested that Pc of chickpea should be longer than 15 h. Furthermore, our finding of Pc = 21 is supported by observations in Australia that extending photoperiod from 16 to 19 h accelerated flowering in an early maturing
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
9
Fig. 4. Predicted vs. observed days from emergence to flowering for four chickpea cultivars for the three steps of model fitting as defined in Tables 3 and 4. Cultivars are Beauvanij, 90-96c, Hashem and Jam. Step 1, all model parameters (except Tc) fitted to individual cultivars; Step 2, only PS and fo fitted to individual cultivars; and Step 3, only fo fitted to individual cultivars.
chickpea cultivar (Amethyst) (M.J. Robertson, pers. commun.). However, Singh and Virmani (1996) incorporated a value of 11 h for Pc in their chickpea model. In practice this means no effect of photoperiod on development rate, because in the majority of chickpea growing areas around the world, and at the usual sowing dates, photoperiod is higher than 11 h. The results of this study do not support this finding.
Average values of f o for each cultivar across sowing dates are presented in Table 5. These values were calculated using the parameters estimates from Step 2 of model fitting (Table 4). They are similar to the values obtained during the optimization procedure, but allowed statistical comparison among cultivars as these estimates were obtained for all experimental treatments. The greatest value of f o (and, hence, lowest value of Rmax) was found for cv. 90-96c and
Table 4 Estimates of base temperature (Tb, 8C), lower optimum temperature (To1, 8C), upper optimum temperature (To2, 8C), ceiling temperature (Tc, 8C), critical photoperiod (Pc, h), photoperiod sensitivity coefficient (PS) and physiological days from emergence to flowering ( fo) for four chickpea cultivars. Results are given for three steps in model fitting, which relate to the number of parameters fitted (or fixed) across cultivars. Values for fixed parameters are indicated in parentheses Tc
Pc
PS
fo
Step 1: all model parameters (except Tc) fitted to individual cultivars Beauvanij 1.74 19.94 29.20 90-96c 0.70 19.15 29.39 Hashem 0.73 19.55 33.83 Jam 1.49 20.93 30.20
(40) (40) (40) (40)
20.46 20.92 21.07 21.87
0.00900 0.00823 0.00819 0.00900
27.1 32.9 28.5 22.7
Step 2: only PS and fo fitted to individual cultivars Beauvanij (0) (21) 90-96c (0) (21) Hashem (0) (21) Jam (0) (21)
(32) (32) (32) (32)
(40) (40) (40) (40)
(21) (21) (21) (21)
0.00877 0.00785 0.00845 0.00900
26.3 32.1 28.8 23.8
Step 3: only fo fitted to individual cultivars Beauvanij (0) 90-96c (0) Hashem (0) Jam (0)
(32) (32) (32) (32)
(40) (40) (40) (40)
(21) (21) (21) (21)
(0.0085) (0.0085) (0.0085) (0.0085)
26.6 30.7 29.1 25.7
Tb
To1
(21) (21) (21) (21)
To2
10
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
Fig. 5. Profile plots of SSE (residual sum of squares) for the six parameters of the model fitted to data for cv. Beauvanij using DEVEL. Rmax is the maximum development rate, Tb the base temperature, To1 the lower optimum temperature, To2 the upper optimum temperature, Pc the critical photoperiod, and PS the photoperiod sensitivity coefficient.
the lowest value was found for cvs. Beauvanij and Jam (Tables 4 and 5). 3.3. Modeling duration of post-flowering phases Physiological days calculated for durations of postflowering stages in the field experiment are given in Table 5. Cultivars did not differ significantly in duration of R3–R5 or R5–R8, with mean values of 4.3 and 30.3 physiological Table 5 Mean physiological days from planting to emergence (PE), emergence to R1 (EF), R1–R3 (FP), R3–R5 (PS), R5–R8 (SM) and planting to R8 (PM) for four chickpea cultivars Genotype
PE
Beauvanij 90-96c Hashem Jam
5.6 6.1 6.4 6.5
Mean
6.3
EF a a a a
26.0 32.2 29.3 24.7 28.6
FP c a b c
12.0 9.0 8.2 9.2 9.5
PS a b b b
4.1 4.6 4.7 3.3 4.3
SM a a a a
29.4 31.1 30.3 30.2 30.3
PM a a a a
77.5 82.9 78.4 73.2
b a b c
days, respectively. For the R1–R3 interval, a significantly higher value (12.0 days) was found for cv. Beauvanij, but the other three cultivars (mean 8.8 days) did not differ (Table 5). Hence, difference among cultivars in duration from sowing to maturity (73.2–82.9 physiological days) was mainly related to differences in duration of the emergence-flowering interval. The physiological day requirements for the post-flowering phases (Table 5) are used in predicting duration of these phases. For example, progress toward R8 from R5 is calculated by integrating f(T), which is calculated using the dent-like function and the parameters values specified in Table 4 (Step 2). Starting from R5, R8 is predicted to occur when a threshold value is reached. The threshold value is the physiological day requirement for R5–R8 (30.3 days here), that is, the minimum number of days from R5 to R8 under optimal temperature conditions. 3.4. Model evaluation
78.6
Entries in each column followed by the same letter do not differ significantly (P < 0.05).
The phenology model of chickpea developed here is based on the concept of physiological day requirement (i.e.
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
11
Fig. 6. Simulated vs. observed days to stages R1, R3, R5 and R8 using independent data. Cultivars are Beauvanij, 90-96c, Hashem and Jam.
minimum duration in days at optimum temperature and photoperiod), which is considered more transparent and easier to understand than a thermal time target. However, the physiological day requirement is easily converted to a thermal time target using the fitted parameters and known forms of response functions. In our phenology model of chickpea, it takes six physiological days from sowing to emergence, based on a dent-like response function and its cardinal temperatures (See Section 2.2.1; Soltani et al., 2006a). From emergence to flowering, physiological day is calculated using a multiplicative model that incorporates response functions to temperature and photoperiod. The dent-like function is again used to quantify the effect of temperature with cardinal temperatures of 0 8C for Tb, 21 8C for To1, 32 8C for To2 and 40 8C for Tc. The influence of photoperiod is captured using a quadratic function with critical photoperiod of 21 h. It is assumed that photoperiod has no effect on development rate after flowering. For postflowering phases, the same response function and cardinal
temperatures as for emergence to flowering is applied. The post-flowering stages are predicted to occur after specified physiological day requirements. Results of the evaluation of the model using independent data sets (Table 1) are presented in Fig. 6 and Table 6. Data used for model evaluation came from different geographical areas across Iran and from spring- and winter-sown crops (Table 1). For cv. Jam all data came from spring-sown crops, while for other cultivars data from both spring- and wintersowings were included. To simulate phenological development for Exps. 2–4, data on phenological durations were averaged across plant densities. Differences among density treatments were less than 5 days (data not shown). Observed days to flowering ranged from 41 to 185 days. This range was 55–163 days for days to R3, 79–167 days for days to R5 and 78–228 days for days to R8. For the model evaluation based on comparison of simulated and observed duration from planting to specific phenological stages (Table 6), RMSD values were small in relation to the overall duration,
Table 6 Model evaluation based on comparison of simulated and observed duration from planting to specific phenological stages. For each stage, the number of observations (n), root mean square deviation (RMSD, d), mean duration (d), and linear regression statistics (intercept (a), slope (b), and coefficient of determination R2) are given Stage
Flowering (R1) First-pod (R3) First-seed (R5) Maturity (R8) *
n
82 9 6 84
RMSD
8.43 5.75 6.75 11.49
Significantly different (P < 0.05) from 1.
Mean Observed
Predicted
111 110 124 153
112 115 129 155
a
b
R2
1.64 0.396 1.62 7.91
1.043* 1.041 1.054 1.074*
0.98 0.99 0.99 0.96
12
A. Soltani et al. / Field Crops Research 99 (2006) 1–13
being 8.4 days for time to flowering, 5.8 days for R3, 6.8 days for R5, and 11.5 days for R8. However, there was a slight but significant model bias (P = 0.05) for days to flowering and days to R8. This reflected the high precision level associated with these phases as a result of the large number of observations (82 and 84, respectively) (Table 6). This bias was not significant at P = 0.01 and did not appear practically important (see Fig. 6). Therefore, we conclude that our model accounts well for the effects of temperature and photoperiod on development rate in chickpea and its performance is acceptable for widespread application.
4. Conclusion Our results indicated that: (1) A multiplicative model that included a dent-like function for response to temperature and a quadratic function for response to photoperiod was the most adequate model to describe development rate in chickpea over a broad of temperature and photoperiod conditions. (2) Differences among cultivars in cardinal temperatures and the critical photoperiod for response functions were small and Tb of 0 8C, To1 of 21 8C, To2 of 32 8C, Tc of 40 8C and Pc of 21 h could be used in modeling for all cultivars in the study. (3) Cultivar differences in development rate were characterized by differences in the inherent maximum rate of development (Rmax = 1/f o) and the photoperiod sensitivity coefficient (PS). (4) Cultivars differed significantly in their physiological day requirement from emergence to flowering (25–32 days) and from R1 to R3 (8.2–12 days) but not for R3– R5 (4.3 days) and R5–R8 (30.3 days). (5) The phenology model developed for chickpea gave reasonable predictions of phenological development across Iran. This model can be used in simulation models of chickpea over a diverse range of temperature and photoperiod conditions. Acknowledgement Special thanks to the Agricultural Production Systems Research Unit, Toowoomba, Australia for hosting AS for his sabbatical period.
References Abbo, S., Lev-Yadun, S., Galway, N., 2002. Vernalization of wild chickpea. New Physiol. 154, 695–701. Anonymous, 1971–1977. Annual report of 1970–1977 on grain legumes. Tehran Univ., School of Agric., Karaj, Iran (text in Persian, tables in English).
Anonymous, 1996–2002. Annual report of 1995–2001. Dryland Agricultural Research Institute, Maragheh, Iran (text in Persian, tables in English). Birch, C.J., Hammer, G.L., Rickert, K.G., 1998. Temperature and photoperiod sensitivity of development in five cultivars of maize from emergence to tassel initiation. Field Crops Res. 55, 93–107. Carberry, P.S., Ranganathan, R., Reddy, L.J., Chauhan, Y.S., Robertson, M.J., 2001. Predicting growth and development of pigeonpea: flowering response to photoperiod. Field Crops Res. 69, 151–162. Efron, B., 1990. The Jackknife, the Bootstrap and other Resampling Plans. SIAM, Philadelphia. Ellis, R.H., Lawn, R.J., Summerfield, R.J., Qi, A., Roberts, E.H., Chay, P.M., Brouwer, J.B., Rose, J.L., Yeates, S.J., Sandover, S., 1994. Towards the reliable prediction of time to flowering in six annual crops. V. Chickpea. Exp. Agric. 30, 271–282. Fehr, W.R., Caviness, C.E., 1977. Stages of soybean development, Iowa State Univ. Coop. Ext. Serv. Spec. Rep. 80. Goldani, M., Bagheri, A.R., Nezami, A., 2000. Effect of sowing date on yield and yield components of chickpea in Mashhad. J. Agric. Sci. Natur. Resour. (Gorgan) 7 (1), 68–77 (text in Persian with English abstract). Grimm, S.S., Jones, J.W., Boote, K.J., Hesketh, J.D., 1993. Parameter estimation for predicting flowering date of soybean cultivars. Crop Sci. 33, 137–144. Hammer, G.L., Vaderlip, R.L., Gibson, G., Wade, L.J., Henzell, R.G., Younger, D.R., Warren, J., Dale, A.B., 1989. Genotype-by-environment interaction in grain sorghum. II. Effects of temperature and photoperiod on ontogeny. Crop Sci. 29, 376–384. Hasanzadeh, S., 1996. Evaluation morphological traits affecting grain yield in some chickpea genotypes. MS Thesis, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (text in Persian with English abstract). Holzworth, D., Hammer, G.L., 1996. DEVEL: a crop development modeling tool. In: Agricultural Production Systems Research Unit, Toowoomba, Australia. Horie, T., 1994. Crop ontogeny and development. In: Boote, K.J., Bennett, J.M., Sinclair, T.R., Paulsen, G.M. (Eds.), Physiology and Determination of Crop Yield. ASA, CSSA, and SSSA, Madison, USA, pp. 153–180. Jones, P.N., Carberry, P.S., 1994. A technique to develop and validate simulation models. Agric. Syst. 46, 427–442. Jones, C.A., Kiniry, J.R., 1986. CERES-Maize: a simulation model of maize growth and development. Texas A&M Univ. Press, College Station, TX. Keisling, T.C., 1982. Calculation of the length of day. Agron. J. 74, 758–759. Langary, M., 1996. Effect of plant density on yield and yield components in chickpea under rainfed conditions of the north Khorasan. MS Thesis, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (text in Persian with English abstract). McCullagh, P., Nelder, J.A., 1992. Generalized Linear Models, 2nd ed. Chapman and Hall, London. Mohammadi, S., 1995. Effect of plant density and sowing date on growth and yield of chickpea. M.S.Thesis, Faculty of Agriculture, Univ. of Tabriz, Tabriz, Iran (text in Persian with English abstract). Movahhedi, M., 1996. Growth analysis of two chickpea cultivars under various planting densities and irrigation regimes. M.S. Thesis, Faculty of Agriculture, Univ. of Tabriz, Tabriz, Iran (text in Persian with English abstract). Noroozzadeh, S., 1996. Effect of plant density on yield and yield components in two chickpea genotypes under Mashhad environmental conditions. MS Thesis, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (text in Persian with English abstract). Olsen, J.K., McMahan, C.R., Hammer, G.L., 1993. Prediction of sweet corn phenology in subtropical environments. Agron. J. 410–415. Piper, E.L., Boote, K.J., Jones, J.W., Grimm, S.S., 1996. Comparison of two phenology models for predicting flowering and maturity date of soybean. Crop Sci. 36, 1606–1614. Porsa, H., Bagheri, A.R., Nezami, A., Mohammadabadi, A.A., Langari, M., 2002. Evaluation winter sowing for chickpea under rainfed conditions of
A. Soltani et al. / Field Crops Research 99 (2006) 1–13 the north Khorasan. J. Agric. Sci. Technol. (Mashhad) 16 (1), 83–91 (text in Persian with English abstract). Rastgar, J., 1997. Growth analysis and yield of chickpea cultivars under different sowing date and density. MS Thesis, Faculty of Agriculture, Tabriz University, Tabriz, Iran (text in Persian with English abstract). Ritchie, J.T., 1991. Wheat phasic development. In: Hanks, J., Ritchie, J.T. (Eds.), Modeling Plant and Soil Systems. American Society of Agronomy, Madison, Wisconsin, pp. 31–54. Roberts, E.H., Hadley, P., Summerfield, R.J., 1985. Effect of temperature and photoperiod on flowering in chickpeas (Cicer arietinum L.) Ann. Bot. 55, 881–892. Roberts, E.H., Summerfield, R.J., Minchin, F.R., Hadley, P., 1980. Phenology of chickpea (Cicer arietinum L.) in contrasting aerial environments. Exp. Agric. 16, 343–360. Robertson, M.J., Carberry, P.S., Huth, N.I., Turpin, J.E., Probert, M.E., Poulton, P.L., Bell, M., Wright, G.C., Yeates, S.J., Brinsmead, R.B., 2002a. Simulation of growth and development of diverse legume species in APSIM. Aust. J. Agric. Res. 53, 429–446. Robertson, M.J., Watkinson, A.R., Kirkegaard, J.A., Holland, J.F., Potter, T.D., Burton, W., Walton, G.H., Moot, D.J., Wratten, N., Farre, I., Asseng, S., 2002b. Environmental and genotypic control of time to flowering in canola and Indian mustard. Aust. J. Agric. Sci. 53, 793–809. Roozrokh, M., Ghassemi-Golezai, K., Javanshir, A., 2002. The relationship between seed vigor and growth and yield in chickpea. Seed Seedling J. (Karaj) 18 (2), 43–54 (text in Persian with English abstract). Saxena, N.P., 1984. Chickpea. In: Goldsworthy, P.R., Fisher, N.M. (Eds.), The Physiology of Tropical Field Crops. Wiley, New York, pp. 207– 232. Sethi, S.C., Byth, D.E., Gowda, G.L.L., Green, J.M., 1981. Photoperiodic response and accelerated generation turnover in chickpea. Field Crops Res. 4, 215–225. Sinclair, T.R., Kitani, S., Hinson, K., Bruniard, J., Horie, T., 1991. Soybean flowering date: linear and logistic models based on temperature and photoperiod. Crop Sci. 31, 786–790. Singh, P., 1991. Influence of water-deficits on phenology, growth and drymatter allocation in chickpea. Field Crops Res. 28, 1–15. Singh, P., Virmani, S.M., 1996. Modeling growth and yield of chickpea (Cicer arietinum L.) Field Crops Res. 46, 41–59. Smithson, J.B., Thompson, J.A., Summerfield, R.J., 1985. Chickpea (Cicer arietinum L.) In: Summerfield, R.J., Roberts, R.E. (Eds.), Grain Legume Crops. Collins, London, pp. 312–390. Soltani, A., Ghassemi-Golezani, K., Rahimzadeh-Khooie, F., Moghaddam, M., 1999. A simple model for chickpea growth and yield. Field Crops Res. 62, 213–224.
13
Soltani, A., Khooie, F.R., Ghassemi-Golezani, K., Moghaddam, M., 2001. A simulation study of chickpea crop response to limited irrigation in a semiarid environment. Agric. Water Manage. 49, 225–237. Soltani, A., Torabi, B., Zeinali, E., Sarparast, R., 2004. Response of chickpea to photoperiod as a qualitative long-day Plant. Asian J. Plant Sci. 3 (6), 705–708. Soltani, A., Robertson, M.J., Torabi, B., Yousefi-Daz, M., Sarparast, R., 2006a. Modeling seedling emergence in chickpea as influenced by temperature and sowing depth, Agric. For. Meteorol., in press Soltani, A., Robertson, M.J., Mohammad-Nejad, Y., Rahemi-Karizaki, A., 2006b. Modeling chickpea growth and development: leaf production and senescence. Field Crops Res. 99, 14–23. Soltani, A., Robertson, M.J., Manschadi, A.M., 2006c. Modeling chickpea growth and development: nitrogen accumulation and use. Field Crops Res. 99, 24–34. Soltani, A., Torabi, B., Zarei, H., 2005. Modeling crop yield using a modified harvest index-based approach: application in chickpea. Field Crops Res. 91, 273–285. Summerfield, R.J., Ellis, R.H., Roberts, E.H., 1989. Vernalization in chickpea: fact or artifact? Ann. Bot. 64, 599–603. Summerfield, R.J., Minchin, F.R., Roberts, E.H., Hadley, P., 1981. Adaptation to contrasting aerial environments in chickpea (Cicer arietinum L.) Trop. Agric. 58, 97–113. Summerfield, R.J., Roberts, E.H., 1987. Effects of illuminance on flowering in long- and short-day grain legumes: a reappraisal and unifying model. In: Atherton, J.G. (Ed.), Manipulation of Flowering. Butterworths, London, pp. 203–223. Turpin, L.E., Robertson, M.J., Haire, C., Bellotti, W.D., Moore, A.D., Rose, I., 2003. Simulating fababean development, growth and yield in Australia. Aust. J. Agric. Res. 54, 39–52. van der Maesen, L.J.G., 1972. Cicer L. a monograph of the genus with special reference to chickpea (Cicer arietinum L.), its ecology and cultivation, Communication of the Agricultural University, Wageningen 72-10. Verghis, T.I., McKenzie, B.A., Hill, G.D., 1999. Phenological development of chickpea in Canterbury New Zealand. N. Z. J. Crop Hort. Sci. 27 (3), 249–256. Yin, X., Kropff, M.J., Horie, T., Nakagawa, H., Centeno, H.G.S., Zhu, D., Goudriaan, J., 1997a. A model for photothermal responses of flowering in rice. I. Model description and parameterization. Field Crops Res. 51, 189–200. Yin, X., Kropff, M.J., Nakagawa, H., Horie, T., Goudriaan, J., 1997b. A model for photothermal responses of flowering in rice. I. Model evaluation. Field Crops Res. 51, 189–200.