Correlation of photothermal quotient with spring wheat yield

The effect of photothermal quotient at anthesis and maturity on kernel m, kernel weight (KW) and kernel yield were examined in experiments involving three different genotypes such as: Chakwal-50, Wafaq-2001 and GA-2002 sown at five different sowing times (Planting windows) for two years (2008-09 and 2009-10) at the National Agricultural Research Center (NARC) Islamabad. Photothermal quotient (PTQ) was calculated at anthesis (PTQ1) and maturity (PTQ2) for two years. A direct relationship was observed between photothermal quotients, kernel m, kernel weight and kernel yield for all planting windows and genotypes. The maximum value (213.15) of photothermal quotient was recorded when crop was grown on 28th Oct, 2008 (PW2) during the first year and minimum value was observed (143.82) for PW5 (25th Dec, 2009) during the second year. Decrease in photothermal quotient was observed from PW2 to PW5, whereas correlation analysis depicted positive and significant association with individual kernel weight, kernel m and kernel yield with PTQ. The correlation coefficient (r) between kernel m and PTQ1 was 0.5103, while the correlation between PTQ2, individual kernel weight (0.6375) and yield (0.6507) exhibited significance. The outcomes of photothermal quotient and the relationship established through these experiments between kernel m, KW and kernel yield and PTQ may be used as a road map to minimize yield losses and select genotypes and managements according to availability of climatic resources.


INTRODUCTION
Light, water, temperature, rainfall and solar radiation are important factors influencing crop biodynamism.Temperature being the determinant factor for crop efficiency, its contribution towards crop growth is well known among the researchers in the form of Growing Degree-Days (GDD), but GDD does not explain the light requirement of a crop.However, light which is a pack of energy (photon) also contributes significantly towards crop growth.Therefore, it is necessary to study the combined effect of *Corresponding author.E-mail: ahmadmukhtar@uaar.edu.pk.

Abbreviations:
GDD, Growing degree-days; PTQ, photothermal quotient; KM, kernels m -2 ; KW, kernel weight; PW, planting windows.temperature and light on crop in the form of photothermal quotient (PTQ).Egli (1998) documented that yield of crops and its variation is totally dependent upon climatic factors.Similarly, Slafer and Rawson, (1994) reported that the most determinant factor which is positively correlated with yield is kernel number per unit area.Temperature and solar radiations are main factors which determine grain numbers in crops (Jeuffroy and Chabanet, 1994).Similarly, seed abortion/sterility in different crops is mainly because of extreme heating or cooling (Giuliani et al., 1997).Extreme high temperature during a limited time interval reduces the kernel numbers.However, another determinant factor which affect kernel numbers is solar radiation provided all other resources are available at optimum level.Therefore, photothermal quotient was calculated to study the combined effect of temperature and solar radiation on kernel number in wheat (Abbate et al., 1995).
Photothermal quotient is the light and heat requirement for a plant.Kernel yield is directly related to the photothermal quotient.Monasterio et al. (1994) reported that photothermal quotient assess the main effect of radiation on kernel per meter square (KM) via a linear relationship with crop growth and that of temperature, which affect plant growth rate through direct relationship.Solar radiation and temperature manipulate plant process in a different way but their collective results on yield could be presented by the photothermal quotient.Photothermal quotient can be defined as the ratio of total solar radiation in MJm -2

day
-1 to the mean daily temperature minus a base temperature (4.5°C for Spring Wheat).In wheat, it is assumed that temperature and radiation are the driving forces for crop development rate and final production.Therefore, it is essential to recognize physiological and morphological behavior of various wheat genotypes in relation to temperature and solar radiation.Study done in the past has explained the value and arithmetical importance of PTQ to forecast and demonstrate wheat yields.Nalley et al. (2009) re-identified PTQ to improve the explanatory power of statistical models used to explain KM and boost understanding of climate's impact on the production of crop.Elucidation and investigation were also improved by disaggregating photothermal quotient into split variables that is, solar radiation and temperature.A 1 MJm -2 d -1 rise in solar radiation increased KM by 1.25%, whereas a 1°C rise in temperature decreased KM by 2.8%.The PTQ is basically a key of growth per unit time and assumes that development rate is directly proportional to average temperature (Nix, 1976).The collective effects of solar radiation and temperature on number of seed have been studied in grain crops, using a simple quotient calculated for the critical period in which the number of seed is defined by Cantagallo et al. (1997).Similarly, Khichar and Niwas (2007) concluded direct relationship with PTQ and growth of wheat under different planting systems.However, Loomis and Amthor (1996) documented that crop growth and yield are derived from photosynthesis, therefore dependent on receipt and capture of solar radiation.
Solar radiation provides light required for seed germination, leaf expansion, growth of stem and shoot, flowering, fruiting and thermal conditions for the physiological functioning of the plant.It is important to understand physiological and morphological behavior of various wheat genotypes in relation to ambient factors especially temperature and radiation.Hence studies are needed to understand and quantify the crop response and its relationship with varied combination of temperature and solar radiation to workout suitable planting windows for wheat.This is only possible if the canopies are tested under varying circumstances of temperature and radiation.The easiest way to do this is to sow the crop under varying sowing times.Wheat genotypes were monitored for temperature and radiation regimes at anthesis and maturity.This helped to determine the effect of temperature and solar radiation on yield that can be illustrated by PTQ.
The specific objective of this study is to revise the association of kernels m -2 (KM), Kernel weight (KW) and yield with photothermal quotient across planting windows and years.To provide variable environmental situations during the periods of anthesis and maturity, research was conducted using different genotypes sown in field conditions at five different planting dates for two successive years.

MATERIALS AND METHODS
The study was conducted at NARC, Islamabad, Pakistan, latitude 33° 42/ N and longitude 73° 10/ E during 2008-09 and 2009-10.Field experiments were laid out using Randomized Complete Block Design, treatments consisted of three wheat genotypes (Chakwal-50, Wafaq-2001 andGA-2002) sown on five planting windows, experiments were replicated four time in 4.5 x 10 m plot with row spacing of 25 cm.The sowing was done on five different planting windows (PW) for two years that is, 2008-09 and 2009-10 and these windows were ; PW1(Sowing on 20-10-2008 and 23-10-2009), PW2 (sowing on 28-10-2008 and 05-11-2009), PW3 (sowing on 05-11-200805-11- and 19-11-200905-11- ), PW4 (sowing on 19-11-200805-11- and 27-11-2009) and PW5 (sowing on 05-12-2008 and 10-12-2009).Sowing was done by hand drill using seed rate of 50 kg/ha.Prior to sowing, a particular field was fallowed during summer and was ploughed once with soil inverting implement and thereafter thrice with tractor mounted cultivator.Recommended doses of fertilizer nitrogen and phosphorus 100 kg/ha was added with the last ploughing in the form of urea and DAP.The data (regarding weather) that prevailed during the study period were collected from the weather station located at NARC.The data included maximum and minimum daily air temperature (in °C), precipitation (mm) and sunshine duration (hours) (Figure 7).The photothermal quotient was calculated on a daily basis with the following formula: Where, T is the daily mean temperature [(max + min) / 2] and PTQ is expressed as MJ m -2 day -1 °C-1 (Monasterio et al., 1994).
For a given level of solar radiation, highest photothermal quotient is attained at 10°C and reduces linearly to zero at a temperature of 4.5°C.The value of 10°C was launched to allow the emergence of daytime cold and was attained from Vankeulen and Seligman (1987), who described a decline in maximum gross absorption rate of an individual leaf at this temperature.The following PTQ's were generated for individual genotypes by averaging the daily PTQ for different periods: PTQ1 from 20 days before heading to 10 days after heading; PTQ2 from heading + 10 days to heading +40 days (Monasterio et al., 1994).All genotypes at maturity were harvested from an area of one meter square through four replications and five planting windows.Kernel number, kernel weight and kernel yield were calculated.Data was analyzed by using STATGRAPHICS XVI and correlation was determined.

RESULTS
Photothermal units for anthesis and maturity have shown significant differences among genotypes and environments.Genotype Chakwal-50 captured maximum photothermal units (217.66MJm -2 day -1 ) than the other two genotypes during 2008-09.The results showed significant differences among wheat genotypes for photothermal units from anthesis to maturity.Among planting windows, decline in PTQ units was observed from PW2 to PW5 during both growing years.In the second growing season, overall drop in photothermal units were observed for all planting windows.The PW5 accumulated minimum photothermal units at maturity.Kernel per meter square is a strong determinant of yield and it needs to be managed properly.As in our study, planting windows were used as management option in both years therefore KM was significantly affected due to planting windows.Highest KM was recorded for PW3 for Chakwal-50 in 2008-09 while minimum was noted for PW3 in case of GA-2002 during second growing year that is, 2009-10.The interactive effect among planting windows and PTQ2 on KW (g) resulted in a linear relationship (Figure 2).The results showed that since Chakwal-50 accumulated maximum PTQ in PW1 as compared to Wafaq-2001, it yielded significantly higher in 2008-09, but for PW4 and PW5, KW decreased in genotypes due to minimum PTQ available to crop for maturity.In general, a declining trend was noted for KW in 2009-10.
Kernel yield and photothermal quotient at maturity have strong correlation with linear regression model for yield (y= -70.4x +4277); R2=0.59 among genotypes and planting windows for both years that is, 2008-09 and 2009-10 (Figure 3).Similarly, coefficient of determination (R2) for PTQ at maturity (PTQ2) depicted 86% variability in yield mainly affected due to PTQ2.The results revealed that PTQ and kernel yield have a strong direct relationship, if PTQ increases there will be an increase in yield while drop in PTQ yielded negative trend (Figure 3).There was a significant difference in time for accumulation of PTQ between five planting windows in both years.Additionally, the number of kernel m -2 was significantly associated (r2=0.698)with planting windows in both years while PTQ2 was negatively associated (r2=-0.6356)with PW's in year 2008-09 and 2009-10 (Figure 1).The link between number of kernel m -2 and photothermal quotient at anthesis determine the number of fertile florets at anthesis and the efficacy of these florets for kernels production.
The association between PTQ at maturity and KW among PW's (Figure 2) have accounted for a significant positive correlation with R2=0.758 for KW and R2=0.889 for photothermal quotient.Maximum KW was recorded for PW3 in year 2008-09 while minimum for PW5.This variable trend in both years among PW's may be due to differential availability of photothermal quotient by genotypes springing up from emergence till maturity.Similarly, Ahmed et al. 7871 another determinant which affected KW was photothermal unit available to genotypes at anthesis (PTQ1).Kernel yield produced in relation to photothermal quotient among planting windows have attributed a significant difference.The highest kernel yield was recorded in PW2 while lowest was observed from PW5 for the year 2008-09.However, in 2009-10 the output of kernel yield was significantly low.Correlation coefficient between environment and planting window followed a negative trend (r = -0.5612)while behavior of genotypes among two environments was highly significant (r = -0.4016).Similarly, photothermal quotient at anthesis (PTQ1) and PTQ2 (photothermal quotient at maturity) accounted for a negative correlation (-0.5264 and -0.7101, respectively) among two environments that is, 2008-09 and 2009-10 (Table 1).The relationship value of -0.7654 for KM was observed in both environments, while for KW and yield the correlation coefficient accounted was -0.7945 and -0.4258, respectively.These negative values elaborate that KM, KW and yield decreased significantly from one environment that is, 2008-09 to another environment .The correlation between planting window and genotype, photothermal quotient at anthesis (PTQ1), photothermal quotient at maturity (PTQ2) KM, KW and kernel yield have been recorded negatively (-0.7016, -0.6722, -0.6356, -0.0834 and -0.4275, respectively).This negative correlation illustrates the fact that yield exhibited a decreasing trend among planting windows (PW1 to PW5).

DISCUSSION
Kernel per meter square was the determinant factor for variation in kernel yield across planting windows (Slafer and Rawson, 1994;Egli, 1998) which was significantly affected due to photothermal quotient at anthesis and maturity (Figure 1 and 5), hence results of the present study are similar to the above conclusion.Moreover, significant positive correlation was recorded between kernel m -2 with PTQ1 and PTQ2 (0.5103 and 0.6474).The decrease in kernel m -2 among planting windows may be due to high temperature around heading.This phenomenon is similar to findings of Dawson and Wardlow (1989).They reported that high temperature during the pre-heading stage can minimize pollen viability, resulting in less number of kernels per spike.Among genotypes, Chakwal-50 accumulated the highest PTQ (217.66) in second planting window (Figure 4 and 5) which resulted in the highest yield.Thus, these results correspond with the results of Monasterio et al. (1994).Similarly kernel weight and photothermal quotient accounted for a linear positive trend as shown in Figure 2. The highest kernel weight was recorded in PW3 during 2008-09 as compared to 2009-10 and similar findings were reported by Khichar and Niwas (2007)   all other resources are available at optimum level.
Yield was strongly correlated with PTQ1 and PTQ2 (Table 1).The highest yield was obtained from PW2 showing strong association with photothermal quotient (Figure 3 planting windows and environment 2009-10 may be because of unavailability of optimum environmental conditions.Similar findings were reported by Abbate et al. (1995).The association of photothermal quotient at matu-rity with kernel weight and kernel yield among planting windows and genotypes followed a positive trend (Figure 4 and 6).The results closely agree with that of Poggio et al. (2005).Positive correlation existed with the findings of    within the abstracted outcomes of previous research about the relationships of crop development with environmental variables, which has been used as an optimization procedure for crop yield (Dumoulin et al., 1994).However, work-like association of photothermal quotient with kernel developments and yield components needs to be documented in order to build a comprehensive model between photothermal quotient and crop growth and development.

Conclusion
Based upon experimental results, it may be accounted that yield is directly proportional to the PTQ provided all resources are available optimally.Climate change can affect this value at different phonological stages of crop due to day by day rise in temperature and significant drop in rainfall.Management practice such as planting windows option may be a tactical measure for the adjustment of PTQ's according to varying climate of experimental site.Adaptability of wheat genotype based upon its relationship with PTQ knowledge for a particular climate zone can be recommended to minimize losses in yield due to extreme climatic events.

Figure 1 .
Figure 1.PTQ1 and number of kernel per meter square area of wheat genotypes at five sowing windows during two environments (2008-09 and 2009-10).

Figure 7 .
Figure 7. Climatic parameters of the experimental site