D. Miller - PDF American Society of Sugar Cane Technologists

USDA-ARS Sugarcane Field Station, Canal Point, FL 33438, USA

Q. W. Li

Sugar Industry Research Institute, Guangzhou, Peoples' Republic of China

ABSTRACT

The two main objectives of this study were to determine the relationships between six environmental factors and time of floral emergence (TFE) in sugarcane (Saccharum spp.), and to explore the feasibility of forecasting flowering time of some important varieties used as parents with the goal of seeing what factors could be manipulated by breeders. The data included the natural TFE of six cultivars used as parents (CP 72-1210, CP 65-357, CP 70-1133, CP 72-370, CP 74-383, and CP 72-356) and six climatic factors from 1969 through 1991. The climatic factors were: average minimum temperature in October, average minimum temperature from November 1 to November 15, total rainfall in October, rainy days in October, total rainfall from August through September, and rainy days from August through September. Three models were fitted: the general model included all six climatic factors and two reduced models obtained by stepwise regression. Reduced model B contained variables that were significant at P < 0.15 while model C contained only variables significant at P <

0.05. General and reduced models across varieties were significant at P < 0.01. General and reduced models were also calculated for each variety. This study showed that the average minimum temperature in October had the largest effect on flowering dates, followed by the number of rainy days in October. Thus, one would expect that increasing night temperatures and applying sprinkler irrigation in October would cause earlier flowering.

INTRODUCTION

Photoperiod, temperature, and rainfall are important environmental factors that affect sugarcane flowering. Since photoperiod is fixed at any given latitude and date, many researchers have studied the qualitative relationships between temperature and rainfall and the flowering process.

Photoperiodic induction has been shown to occur in Florida from late September to early October.

Environmental influences that occur after that would have primary effect on time of tassel emergence (7). The optimum night temperature for floral development was reported to be around 73.4°F (10).

The optimum daytime temperature was about 82.4°F (3). Temperatures below 69.8°F were shown by Clements and Awada, 1967 and Nuss and Brett, 1977 to delay panicle growth and emergence.

Temperatures exceeding 87.8°F at induction time reduced flowering intensity (3; 5). Temperatures below 64.4°F have been shown to prevent floral induction (4; 6).

Low rainfall has also been shown to reduce the intensity of flowering (1; 12; 15). They also showed that in areas where photoperiod and temperature seldom inhibit flowering, the variation in intensity of flowering between years was primarily the result of differences in annual rainfall. The strong correlation between rainfall and flowering intensity has been used in Hawaii and other locations for prediction of flowering intensity (9; 15).

Although temperature and rainfall have very important effects on time of flowering, their relative importance may change, depending on the location, timing, and varieties being grown. So, it is necessary for every sugarcane breeding station to develop the relationships between these two factors in different developmental stages and their effect upon TFE. The main objectives of this

research were to determine the relationships between climatic factors and TFE and to develop prediction equations for forecasting the TFE of several important parental cultivars.

MATERIALS AND METHODS

Six major parental cultivars and six climatic factors were chosen for multiple linear regression analysis (Table 1). The six cultivars (CP 65-357, CP 70-1133, CP 72-356, CP 72-370, CP 72-1210, and CP 74-383) were chosen for use in this study since at least 14 years data were available on the first date the clone was used for crossing. All tassels, used in this study were produced from plants grown in 10 gallon pots in a soil mixture of a ratio of 2 parts muck (euic, hyperthermic Typic Medisaprist) to 1 part washed builder's sand. Plants were started as one bud seed pieces in the greenhouse the last week of January. They were transplanted into the pots on outside growing racks the first week of April. Plants were watered with an irrigation system. Soil moisture should not have been a limiting factor. Plants were fertilized five times at 3 week intervals starting in mid-April with a total pot of 25g N, 10.9g P, 16.5g K plus the micronutrients 0.06g Cu, 0.16g Zn, 0.17g Mn, 0.07g B, and 0.38g Fe. The last application of fertilizer was made the first week of July.

The climatic factors chosen for study were: average daily minimum temperature from November 1 to 15 (X1), average minimum temperature during October (X2), total rainfall in October (X3), total rainfall from August through September (X4), number of rainy days in October (X5), and number of rainy days in August and September (X6). Data were analyzed using SAS, (1988) stepwise multiple regression analysis to determine the effect of these six variables on flowering time. General models for all cultivars and individual models for each cultivar were fitted. All complete models were reduced by stepwise regression until the remaining models were significant at P < 0.01 level. Two reduced models (B&C) were fitted. The B model included factors that were significant at P < 0.15. The C model only included factors that were significant at P < 0.05. The analysis was also done using a stepwise regression in SAS to identify independent variables that were significant at P = 0.15, 0.05, and 0.01, respectively.

RESULTS AND DISCUSSION

If one compares the mean low temperature (Table 1) in October 66.8°F and the first half of November 61.7°F with that reported in the literature (10; 4; 6; and 3), very little flowering would be expected and it would be much delayed. Yet, sugarcane consistently produces tassels under natural conditions at Canal Point.

The importance of variables differed among varieties (Table 2). The general model was significant and accounted for 46% of variation in flowering time. When the general model was reduced by stepwise multiple regression, the reduced model still accounted for 45% of the variation in flowering time. The three variables significant in the reduced model were: average minimum temperature in October (X2), total rainfall in October (X3), and total rainfall from August through September (X4). Based on the reduced general model (C), an average 1°F decrease in the minimum temperature in October (X2) would delay the flowering date by 2.87 days. An increase of 1 rainy day in October would delay the flowering date by 0.77 days. If total rainfall in August and September increased by 1 inch the average date of flowering would be decreased by 1.25 days. Based on these data, it is apparent that there are additional factors that play an important role in floral initiation, inflorescence development, and tassel emergence.

When reduced models are considered for the individual cultivars, (X2) the average minimum temperature in October was the only variable significant for all cultivars, and as the temperature decreased, flowering was delayed. As number of rainy days in October (X4) increased, flowering was also delayed in CP 65-357, CP 72-356, and CP 74-383. An increase in the total rainfall in October delayed flowering in CP 72-370 and CP 74-383. An increase in the total rainfall in August and

September (X5) accelerated the time of flowering in CP 72-356. A decrease in the minimum temperature between November 1 and 5 delayed flowering only in CP 70-1133.

Table 1. Climatic factors and flowering dates of six varieties.

Average Min. Total Rainfall Rainy Days Flowering Dates(Y1) Year Temperature Oct. Aug.- Oct. Aug.-

Nov. Oct. Sept. Sept. CP CP CP CP CP CP 1-15 65- 72- 72- 74- 70- 72-

357 1210 356 383 1133 370 X1 X2 X3 XB X4 X6

°F in. Julian calendar 1991 63.3 64.4 2.8 13.5 14 27 325 339 332 325 337 351 1990 57.6 65.1 3.6 8.1 5 23 340 351 354 340 346 1989 57.6 62.0 3.3 18.0 10 31 347 375 347 347 355 375 1988 63.0 65.6 0.2 14.4 5 30 350 357 364 347 344 1987 66.0 66.9 6.2 8.4 6 16 344 347 356 348 336 347 1986 69.1 68.5 2.3 10.5 8 33 350 339 328 335 328 328 1985 63.1 70.2 3.7 17.4 9 26 330 337 326 330 330 326 1984 61.1 67.3 0.7 12.1 5 26 334 355 341 338 339 331 1983 59.2 69.2 14.2 10.9 14 29 336 336 332 336 332 342 1982 63.8 67.2 5.0 14.7 10 32 337 344 341 326 333 333 1981 60.1 68.0 0.4 18.6 3 30 348 343 341 341 336 341 1980 64.4 68.3 1.4 22.0 6 27 342 344 332 330 342 344 1979 65.0 67.0 3.5 16.1 7 31 332 341 344 341 332 337 1978 64.1 68.7 4.5 18.4 10 32 331 335 331 328 333 1977 61.0 63.8 1.4 20.1 4 33 348 355 343 341 343 339 1976 61.0 64.3 0.3 10.5 3 25 357 354 359 344 1975 63.2 68.0 4.4 13.5 12 25 335 344 337

1974 61.1 65.0 2.1 13.0 7 34 357 1973 61.6 67.4 3.4 10.0 11 29 330 1972 63.0 67.0 1.7 4.3 5 21 340 1971 63.0 67.0 8.1 11.9 15 31 328 1970 54.0 67.4 3.8 15.3 11 34 341 1969 56.3 68.9 8.4 13.9 14 37 332

Mean 61.7 66.8 3.7 13.7 8 29 340 347 342 338 337 341 S.D. ±3.3 ±1.9 ±3.1 ±4.2 ± 4 ±5 _±9 ±10 ± 1 ±7 ±1 ±12

research were to determine the relationships between climatic factors and TFE and to develop prediction equations for forecasting the TFE of several important parental cultivars.

MATERIALS AND METHODS

The climatic factors chosen for study were: average daily minimum temperature from November 1 to 15 (X,), average minimum temperature during October (X2), total rainfall in October (X3), total rainfall from August through September (X4), number of rainy days in October (X6), and number of rainy days in August and September (X6). Data were analyzed using SAS, (1988) stepwise multiple regression analysis to determine the effect of these six variables on flowering time. General models for all cultivars and individual models for each cultivar were fitted. All complete models were reduced by stepwise regression until the remaining models were significant at P <, 0.01 level. Two reduced models (B&C) were fitted. The B model included factors that were significant at P < 0.15. The C model only included factors that were significant at P < 0.05. The analysis was also done using a stepwise regression in SAS to identify independent variables that were significant at P = 0.15, 0.05, and 0.01, respectively.

RESULTS AND DISCUSSION

September (X6) accelerated the time of flowering in CP 72-356. A decrease in the minimum temperature between November 1 and 5 delayed flowering only in CP 70-1133.

Table 1. Climatic factors and flowering dates of six varieties.

Year

1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972 1971 1970 1969 Mean S.D.

Average Min.

Temperature Nov.

1-15

63.3 57.6 57.6 63.0 66.0 69.1 63.1 61.1 59.2 63.8 60.1 64.4 65.0 64.1 61.0 61.0 63.2 61.1 61.6 63.0 63.0 54.0 56.3 61.7 4.3.3

Oct.

F 64.4 65.1 62.0 65.6 66.9 68.5 70.2 67.3 69.2 67.2 68.0 68.3 67.0 68.7 63.8 64.3 68.0 65.0 67.4 67.0 67.0 67.4 68.9 66.8

±1.9+

Total Rainfall Oct.

x

3 Aug.- Sept.

in.

2.8 3.6 3.3 0.2 6.2 2.3 3.7 0.7 14.2 5.0 0.4 1.4 3.5 4.5 1.4 0.3 4.4 2.1 3.4 1.7 8.1 3.8 8.4 3.7 L3.1

13.5 8.1 18.0 14.4 8.4 10.5 17.4 12.1 10.9 14.7 18.6 22.0 16.1 18.4 20.1 10.5 13.5 13.0 10.0 4.3 11.9 15.3 13.9 13.7 +.4.2

Rainy Oct.

14 5 10 5 6 8 9 5 14 10 3 6 7 10 4 3 12 7 11 5 15 11 14 8

± 4 Days

Aug.- Sept.

x

27 23 31 30 16 33 26 26 29 32 30 27 31 32 33 25 25 34 29 21 31 34 37 29

±5

Flowering Dates(Y,) CP

65- 357

325 340 347 350 344 350 330 334 336 337 348 342 332 331 348 357 335 357 330 340 328 341 332 340

±9 CP 72- 1210

339 351 375 357 347 339 337 355 336 344 343 344 341 335 355 354 344

347

± 1 0 CP 72- 356

-Julian 332 354 347 364 356 328 326 341 332 341 341 332 344 331 343 359 337

342

± 1 1 CP 74- 383

CP 70- 1133

calendar 325 340 347 347 348 335 330 338 336 326 341 330 341 341

338 _±7

337 346 355 336 328 330 339 332 333 336 342 332 328 343 344

337

±7 CP 72- 370

351 375 344 347 328 326 331 342 333 341 344 337 333 339

341

±12

Table 2. General and varietal multiple regression models of climatic factors {X,)1/ on the flowering time (Y,) in six varieties using from 14 to 23 years data.

Varieties Model Multiple Regression Models R2

General2' A3' Y = 546-0.27X1-2.67X2* * +0.60X3-1.13X4* *-0.03X5-0.12X6

Model C Y = 5 3 9 - 2 . 8 7 X2* * + 0 . 7 7 X3* * - 1 . 2 5 X4* *

0 . 4 6#* 0.45* •

CP65-357 A Y = 429 + 0.15Xr1.50X2 + 0.79X3-2.21X4**-0.32X5 + 0.78X6

CP65-357 B Y = 420-1.22X2+-1.65X4 + 0.51X6+

CP65-357 C Y = 353-1.61X4**

0.62**

0.58**

0.43**

CP70-1133 A Y = 539-0.82Xr2.13X2**-0.03X3-0.22X4 + 0.39X5-0.42X6

CP70-1133 C Y = 534-0.87X,*-2.13X2**

0.86* * 0 . 7 7 * '

CP72-1210 A Y = 597-0.70X1-3.02X2**-0.12X3-0.52X4 + 0.14X5-0.88X6

CP72-1210 C Y = 592-3.67X2**

0.71 • • 0 . 6 2 ' *

CP72-356 A Y = 559 + 0.13Xr2.93X2**+0.84X3-1.96X4**-0.61X5-0.34X6

CP72-356 C Y = 534-2.48X2**-1.57X4**-1.00X5*

0.74**

0 . 7 1 "

CP72-370 A Y = 6 7 1 - 0 . 5 8 Xr4 . 2 8 X2* * + 0 . 7 0 X3 + 0.55X4 + 0.73X5-0.88X9

CP72-370 B Y = 647-4.63X2** + 1.03X3+

CP72-370 C Y = 618-4.14X2**

0.77**

0.64**

0.58**

CP74-383 A Y = 467+0.22Xr1.96X2+1.42X3-2.05X4**-0.17X5-t-0.03X6

CP74-383 C Y = 468-1.81X2* + 1.41X3*-2.00X4**

0.64* * 0.62**

1 / : Y Stands for time of flowering;

X, for average minimum temperature from November 1 to November 1 5 ; X2 for average minimum temperature in October;

X3 for total rainfall in October;

X4 for rainy days in October;

X6 for total rainfall from A u g u s t through September; and X6 for rainy days from August through September.

2/: General model was obtained by analyzing all data together.

3/: A stands for the complete model; B and C for the reduced models.

4/: +, * and ** show the significance at the 0.15, 0.05 and 0.01 levels, respectively.

The B model includes all variables that were significant at the P< 0.15 while the C model includes only those variables significant at P<. 0.05 or 0.01.

An explanation of the above results might be as follows: Most varieties are in the early stages of panicle development in October (7). The transition stages from a vegetative to floral growing point are very sensitive to temperature and moisture stress. In Canal Point, the mean daytime temperatures ranged from 90.1+ 1.5, 86.3± 1.5, to 81.7+_ 2.5°F in September, October, and the first half of November, respectively, and are suitable for floral induction (within the range reported by Clements and Awada, 1967), but the mean night temperatures are less than the optimal 73.4°F described by Nuss (1980). The cultivars were planted in cans and received water by an automatic watering system, therefore soil moisture was not a limiting factor. However, it is possible that the relative humidity was too low to meet the needs for panicle development. Amin et al. (1971) reported that plants misted during the day produced more than twice as many tassels as ones given the same photoperiod treatment but not misted.

Higher night temperatures (not beyond 71 -74°F) in conjunction with more rainy days in October should promote panicle development and earlier flowering. The canes are in later stages of panicle development early in November. Only the flowering of CP 70-1133 was delayed by lower night temperatures between November 1 and 15. Varieties left outside flowered later than varieties moved onto the railcart system where night temperatures are controlled between 72-75°F (data not shown).

Average flowering dates for the warm treatments were 2 to 3 days earlier than for the same cultivar left at ambient temperature in the can area. Other results at Canal Point suggest that sprinkling growing plants in October might help promote panicle development and make many varieties flower earlier and at higher intensities. In 1991, two of four noble canes (Akoki 22 and S. officinarum 8095) were sprinkled three times daily in October. They produced tassels on all stalks and flowered much earlier than usual. S. officinarum 8095 flowered on October 30, and was the first variety in the photoperiod house to flower. In some countries, sprinkling was also found to be effective in promoting flowering of some poor-flowering varieties, including some noble canes (15; 13; 8; 2). Additional data under controlled conditions is needed to study the effects of sprinkling on tassel development and time of emergence.

The coefficients of determination (R2) of the reduced models were fairly high and significant at P < 0.01, ranging from 0.43 to 0.77 (Table 2). So, the reduced models should provide reasonably good predictions of flowering dates. For some cultivars, such as CP 70-1133, CP 72-356, CP 72- 1210 and CP 74-383, both reduced models were the same. The reduced C model for CP 72-1210 and CP 72-370 included only average minimum temperatures in October (X2). If the average minimum temperature in October was increased by 1°F the C model predicts that TFE would be advanced by 3.7 and 4.1 days, respectively. The C model for CP 65-357 included only rainy days in October (X4). For each additional rainy day in October, the TFE would be advanced by 1.6 days. The reduced model for CP 70-1133 included two factors (minimum temperature in October and from November 1 to November 15). For each 1 °F increase in minimum temperature over the period October 1 to November 15, the flowering date of CP 70-1133 would be advanced by 3.0 days.

The six factors chosen for regression analysis are believed to be the most important ones affecting flowering at Canal Point. Other climatic factors such as the number of cloudy days or light intensity might also play an important role in the flowering response, and be important in the prediction of flowering dates. Also, as more data are accumulated, prediction models can be adjusted to provide more accurate prediction of the TFE of sugarcane varieties. There is also limited evidence (data not shown) that these data could be used to predict heavy flowering years in commercial fields. Prediction of high flowering intensity could become important if flowering suppressors become important in the Florida sugar industry.

REFERENCES

1. Alexander, W. P. 1924. A report on tasseling. Hawaii. Plant. Rec. 28:133-151.

2. Amin, M. H., E. S. Kassem, N. M. Bayoumi, and Z. A. Menshawi. 1971. Growth and flowering of sugarcane in relation to photoperiod and air humidity. Proc. ISSCT 14:348-353.

3. Clements, H. F. and M. Awada. 1967. Experiments on the artificial induction of flowering in sugarcane. Proc. ISSCT 12:795-812.

4. Coleman, R. E. 1963. Effect of temperature on flowering of sugarcane. Int. Sugar J. 65:351 - 353.

5. Ellis, T. 0., J. F. Van Breeman and G. Arceneaux. 1967. Flowering of sugarcane in relation to maximum temperature during the induction period. Proc. ISSCT 12:790-794.

6. Gosnell, J. M. 1973. Some factors affecting flowering in sugarcane. Proc. South. Afr. Sugar Technol. Assn. 47:144-147.

7. James, N. I. and J. D. Miller. 1971. Shoot Apex development in early-, mid-, and late- season flowering sugarcane clones. Proc. ISSCT 14:334-347.

8. Menshawi, Z. A. 1977. Floral induction of sugarcane during the spring and summer months at Hawamdieh, Egypt. Proc. ISSCT 16:137-146.

9. Moore, P. H. 1987. Physiology and control of flowering. Copersucar International Sugarcane Breeding Workshop: 103-128.

10. Nuss, K. J. 1980. Effects of photoperiod and temperature on initiation and development of flowers in sugarcane. Proc. ISSCT 17:486-493.

11. Nuss, K. J. and P. G. C. Brett. 1977. Artificial induction of flowering in a sugarcane breeding programme. Proc. South Afr. Genet. Soc. 6:54-64.

12. Pereira, A. R., V. Barbieri and N. A. Villa Nova. 1983. Climatic conditioning of flowering induction in sugarcane. Agric. Meterol. 29:103-110.

13. Rohring, P. E., T. 0. Ellis, and G. Arceneaux. 1959. Microclimatic modification by mist sprays within polyethylene enclosure in relation to flowering of sugarcane. Proc. ISSCT 10:794-801.

14. SAS Institute. 1988. SAS user's guide: Statistics Version 6.03 ed. SAS Inst. Cary, NC.

15. Yeu, W. K. 1980. Studies of flowering of sugarcane in the south of Hainan, China Proc.

ISSCT 17:1301-1306.

RECURRENT SELECTION FOR COLD TOLERANCE OF SUGARCANE P. Y. P. Tai and J. D. Miller

USDA-ARS Sugarcane Field Station Canal Point, Florida 33438

ABSTRACT

Many commercial sugarcane cultivars do not have the desired level of cold tolerance for subtropical regions, but clones of Saccharum spontaneum from northern latitudes or high altitudes can show considerable tolerance to low temperatures. Attempts to transfer cold-tolerant genes from these S. spontaneum clones to commercial cultivars by using interspecific hybridization have not been successful because of the loss of cold tolerance during nobilization. Experiments were initiated to determine the cold-tolerance performance obtained after a first or second-cycle of recurrent selection.

Clones obtained from progenies of backcrosses and polycrosses of commercial sugarcane x S.

spontaneum hybrids and from biparental crosses among the more cold hardy commercial cultivars were evaluated under natural freeze conditions at Gainesville, Florida. Cold tolerance ratings were based on estimated green leaf area following natural freezes. Results from a two-year test indicated that heritability of cold tolerance was low in both populations. The frequency of clones with better cold tolerance than that of the commercial check, NCo 310, was 8% for the commercial cultivar x commercial cultivar population and 22.2% for the backcross-polycross population. While backcross- polycross populations showed a higher frequency of cold-tolerant clones, these clones were low in sugar and high in fiber. The recurrent selection technique of using a combined gene pool of commercial cultivars and S. spontaneum may help prevent the loss of the cold-tolerant genes during the process of nobilization and result in new commercial cultivars with improved cold tolerance.

INTRODUCTION

Noble sugarcane originated in the tropical Pacific islands, probably in New Guinea (2).

However, modern commercial cultivars derived from noble canes are grown in subtropical regions where they are sometimes subjected to cold weather and are well adapted to warm climates and freezes (3,10,11). Some clones of S. spontaneum collected from Turkestan, Northern Iran, and Pakistan show considerable cold tolerance (3,10). Attempts to transfer the cold-tolerant genes from S. spontaneum to commercial cultivars through interspecific hybridization have not been successful due to the loss of this character during the process of nobilization (3,4,5). However, some progress has been made by using interspecific hybridization to produce superior cold-tolerant breeding stocks (8,12).

Cold tolerance of sugarcane has been classified into three categories: resistance of leaves and buds to frost damage, resistance of mature stalks to freezing and subsequent deterioration, and the ability to ratoon after a severe winter (1,9). The resistance of leaves to frost damage was genetically controlled (1). Tai and Miller (15) demonstrated that genotype x location and genotype x year interactions had significant effects on cold tolerance of sugarcane, and that heritability of this trait was very low (H = 26%). The gain from selection for cold tolerance is expected to be small due to the number of locations and years required to accurately evaluate the clonal phenotypes.

Recurrent selection is a population improvement method that intermates superior progeny of one population to produce an improved second population. The process is repeated until the desired level of improvement occurs (7). In sugarcane, Breaux (6) reported that sugar content can be successfully increased by recurrent selection. No information is available regarding the improvement of cold tolerance through recurrent selection.

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