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Published in Agron J 98:588-595 (2006)
DOI: 10.2134/agronj2005.0211
© 2006 American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA
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Wheat

Delayed Harvest Effect on Soft Red Winter Wheat in the Southeastern USA

Dianne Farrera, Randy Weisza,*, Ronnie Heinigera, J. Paul Murphya and Michael H. Pateb

a Dep. of Crop Science, North Carolina State Univ., Raleigh, NC 27695-7620
b Bay State Milling Co., 55 Franklin St., Winona, MN 55987

* Corresponding author (randy_weisz{at}ncsu.edu)

Received for publication July 15, 2005.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Harvest of soft red winter wheat (Triticum aestivum L.) in the southeastern USA can be delayed because of inclement weather or other unforeseen problems. Our objectives were to determine the impact of delaying harvest beyond grain ripeness (135 g kg–1 grain moisture content) on yield, test weight, grain protein, and 20 milling and baking quality parameters, and to determine if these impacts were correlated with environmental conditions occurring between grain ripeness and harvest. In 2001 and 2002, a total of six trials were conducted where treatments consisted of a timely harvest at grain ripeness and a delayed harvest, 8 to 19 d later. Yield was reduced by up to ~900 kg ha–1 due to delayed harvest, with yield losses negatively related to total precipitation and positively related to minimum daily temperatures (R2 = 0.99) during the delay interval, indicating that dry and warm environments increased yield losses. Test weight reductions up to ~115 kg m–3 were seen and were linearly related to the number of precipitation events (r2 = 0.93) between harvests. Grain protein was not affected by delayed harvest. Of the milling and baking quality parameters measured, grain and flour falling number, clear flour percentage, grain deoxynivalenol (DON), and farinograph breakdown times were negatively affected by delayed harvest. Lower falling numbers and higher levels of DON are consistent with the high humidity and rainfall typical of the southeastern USA wheat harvest and are problematic for millers. Decreased farinograph breakdown times can be a problem for bakers.

Abbreviations: DON, doxynivalenol • MTI, mixing tolerance index


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
WINTER WHEAT reaches physiological maturity and maximum dry matter yield when the grain is about 37% moisture (Calderini et al., 2000), but at that moisture content the grain is too soft to combine. Wheat grain is usually combine ready (sometimes referred to as "harvest ripe" or "grain ripe") when the grain has dried to between 13 and 15% moisture. Wheat harvest, however, can be delayed beyond grain ripeness because of inclement weather, machinery failure, or other unforeseen problems. Harvest delays due to rain and subsequent periods of high humidity can be especially problematic in the soft red winter wheat production areas of the southeastern USA.

In many wheat-producing areas, harvest delay is known to result in yield reductions due to hail damage, lodging, or shattering. Shattering occurs when the spikelets or grains fall from the plant. In Canadian hard wheats, shattering can be especially problematic when environmental conditions lead to larger seed size (Clarke, 1981), and yield losses up to 17% have been attributed to shattering after delayed harvest (Clarke and DePauw, 1983). In the mid-Atlantic USA, when harvest was delayed by 21 d after the grain was ripe, soft red winter wheat yields were reduced by an average of 10% (Johnson et al., 1980).

Wheat test weight can be affected by harvesting after the grain is ripe. Test weight, a measure of grain weight per unit volume, is composed of two components: the packing efficiency of the grain, and the density of the individual kernels. Packing efficiency is dependent on genotype, while kernel density is primarily affected by environment (Yamazaki and Briggle, 1969). A test weight of 747 kg m–3 or above is considered representative of good soft wheat grain quality (USDA–ARS Soft Wheat Quality Lab., 2004) and producers can be penalized financially when test weights fall below this critical value. At physiological maturity, hard spring wheat test weight is relatively low and increases to a maximum at grain ripeness (Gan et al., 2000). Gan et al. (2000) also demonstrated that when harvest was delayed beyond grain ripeness, spring wheat test weight begins to decrease, and they measured loses up to 50 kg m–3, which appeared to be related to rewetting and drying of the grain. Rewetting Canadian hard wheat also resulted in a test weight reduction (Pushman, 1975) and Swanson (1941) showed that this reduction in hard wheat test weight after wetting and drying cycles was due to a roughening of the bran coat.

Compared with research on the effects of delayed harvest on hard wheat test weight, very little has been reported for soft red winter wheat. In Indiana, delaying harvest by 45 d resulted in a test weight loss of about 49 kg m–3 in soft red winter wheat, and these losses were related to the frequency of wetting and redrying (Pool et al., 1958). In Arkansas, Lloyd et al. (1999) found that reductions in soft red winter wheat test weight with delayed harvest varied from year to year and generally were less than those reported for hard wheat.

The effects of delayed harvest on wheat milling and baking quality is generally assumed to be less important than those found for yield and test weight. Pearling index, a measure of kernel hardness, has been shown to increase in response to grain wetting or delayed harvest (Swanson, 1941; Pool et al., 1958; Johnson et al., 1980). When soft red winter wheat harvest was delayed up to 45 d after grain ripeness in Indiana, there was no change in protein or ash percentage, and only small changes in flour viscosity, dough mixing characteristics, and cookie size (Pool et al., 1958). In similar experiments in Maryland (Johnson et al., 1980), a decrease in ash percentage and increase in pearling index but no other changes in milling or baking characteristics of soft red winter wheat were observed with delayed harvest.

An exception to the generalization that wheat milling and baking characteristics are little affected by delayed harvest can occur when grain is exposed to high humidity, as can frequently happen in the southeastern USA. Under these conditions, wheat grain can sprout in the field. Falling number is a measure of {alpha}-amylase enzyme activity, with a lower falling number indicating higher activity (Gooding and Davies, 1997) and a potential for sprouting. Canadian hard wheats windrowed near physiological maturity and allowed to weather for different periods of time all had lower falling numbers (Christensen and Legge, 1984). Baking products made from Australian hard wheats subjected to laboratory wetting showed severe defects and increased {alpha}-amylase enzyme activity (Edwards et al., 1989). Another possible outcome of exposure to high humidity after grain ripeness is increased growth of Fusarium graminearum (Schwabe) and consequently elevated levels of DON in the grain and milled products (Murray et al., 1998).

In general, little information on the effects of delayed harvest is available for soft red winter wheat, and no previous work on this topic has been reported for the southeastern USA, where high temperatures, frequent rainfall, and prolonged periods of high humidity are common at the time of grain ripeness. In this light, our primary objective was to determine the impact of delayed harvest on grain yield, test weight, grain protein, and milling and baking qualities of soft red winter wheat in the southeastern USA. A second objective was to determine if changes to the grain and grain quality caused by delayed harvest could be correlated with environmental conditions occurring after grain ripeness.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Research Locations and Experimental Design
Experiments were conducted across six site-years throughout North Carolina in 2001 and 2002. Specific locations were the Circle Grove Seed Farm in Belhaven, NC, in 2001 (B2001), the Piedmont Research Station near Salisbury, NC, in 2002 (P2002), the Cunningham Research Station near Kinston, NC, in 2001 and 2002 (C2001 and C2002), and the Tidewater Research Station near Plymouth, NC, in 2001 and 2002 (T2001 and T2002). Taxonomic classification of the soils at these sites is shown in Table 1. At each site-year, a randomized complete block design was implemented with two treatments, a "timely harvest" when the grain first reached 135 g kg–1 moisture and a "delayed harvest" 8 to 19 d later (Table 2), and five (B2001, C2001, and T2001), six (T2002), 12 (C2002), or 13 (P2002) replications.


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  		Table 1. Site-year abbreviation, location, soil series, and soil taxomomic classification for each experimental location.

 

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  		Table 2. Site-year, cultivar, tillage method, seeding rate, plot size, row spacing, and the dates of planting, timely harvest (first harvest) and delayed harvest (second harvest) of soft red winter wheat.

 
Agronomics
Soft red winter wheat cultivars, tillage, seeding rates, plot sizes, row spacing, and dates of planting and harvest for each site-year are shown in Table 2. Two soft red winter wheat cultivars, ‘Pioneer 26R61’ (P 26R61) and ‘Coker 9704’ (C 9704) were used. Cultivar C 9704 is a medium-early heading wheat that at the time this research was conducted had moderate susceptibility to powdery mildew (Blumeria graminis DC Speer) and to leaf rust (Puccinia triticina Eriks.; Bowman, 2003). Cultivar P 26R61 is also a medium-early heading wheat that at the time this research was conducted was moderately resistant to powdery mildew and had "good" resistance to leaf rust (Bowman, 2003). There was no available data on resistance to glume blotch [Stagonospora nodorum (Berk.)] or Fusarium graminearum (Schwabe) for these cultivars.

All trials received 34 kg N ha–1 preplant and 112 kg N ha–1 at growth stage 25 (Zadoks et al., 1974) in the form of urea–NH4NO3 (UAN, 30% N) or NH4NO3 (34% N) fertilizer. Lime and fertilizer rates other than N followed standard recommendations for North Carolina based on annual soil tests (Hardy et al., 2002; Crozier et al., 2004). Pre- and postemergence herbicides were applied as needed (York, 2004), and weed management was excellent at all site-years.

Data Collection
Air temperature, total precipitation, number of precipitation events (Fig. 1 ), relative humidity, and wind speed data for days between the timely and delayed harvest at each trial were obtained from the State Climate Office of North Carolina website (North Carolina State University, 2004). Thirty-year and trial-year growing season mean temperature and precipitation data occurring between the months of October and July were obtained from the same source.


Figure 1
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  		Fig. 1. Daily mean maximum and minimum temperature and daily total precipitation measured between the first (timely) and second (delayed) harvests of soft red winter wheat at each of six trial locations.

 
Grain yield and moisture were measured using a HarvestMaster grain gauge (Juniper Systems, Logan, UT) attached to either a Massey Ferguson MF-8 or Gleaner K2 combine (AGCO Corp., Duluth, GA). Yields were adjusted to 135 g kg–1 moisture. Grain samples of 0.45 to 3.0 kg were taken from each plot for analysis of test weight, grain protein, and milling and baking quality. Grain yield data was excluded from analysis at C2001 because of a mechanical malfunction, but a representative grain sample was taken for test weight and grain protein analyses.

Test weight was determined on a volume weight basis with a DICKEY-john Grain Analysis computer (model GA C2000, DICKEY-john Corp., Auburn, IL). Grain samples taken at harvest were subsampled (~85 g) for total N analysis determined by combustion using a CHN analyzer (McGeehan and Naylor, 1988) at Waters Agriculture Laboratories (Camilla, GA). Total N was converted to grain protein by multiplying by 5.83 (Kent and Evers, 1994).

Grain samples were pooled across replications to provide 5.0 kg for milling and baking quality analysis, except for C2001 where the total grain sample was insufficient. Grain samples were pooled across all replications for B2001 and T2001. Grain samples were pooled across each half of the replications for C2002, P2002, and T2002, creating two pooled replicates in each of these trials.

Laboratory and Statistical Analysis
Midstate Mills, Newton, NC, performed milling and baking quality analyses following the standards of the American Association of Cereal Chemists (AACC International, 2000). Quality parameters measured were: kernel weight, grain falling number, grain DON (a laboratory error resulted in loss of grain DON data from P2002), patent flour, clear flour, extractable flour, flour moisture, flour protein, flour falling number, flour DON, and cookie spread. Patent and clear flour are parts of the extractable flour, which are used to blend specialized milling grades for specific end uses (Pyler, 1952). Rheological properties of the dough were also examined. To test the physical properties of the dough, a farinograph (C.W. Brabender, Hackensack, NJ) recorded the farinograph flour absorption, development time, stability time, mixing tolerance index (MTI), and breakdown time, all components of the dough structure (Bloksma and Bushuk, 1988). Additionally, an alveograph (Chopin by Seedburo Equipment Co., Chicago, IL) recorded the alveograph overpressure, extensibility, curve configuration, and work, which measures the resistance and extensibility of a dough (Bloksma and Bushuk, 1988).

Analysis of variance using PROC MIXED in SAS Version 8 (SAS Inst., Cary, NC) was used to evaluate significance of treatment and interaction effects. Trial (the combination of year, location, cultivar, and tillage system) and harvest date were treated as fixed effects and replications were treated as random effects. Least square mean separations were employed for testing differences between and among treatments. Simple linear and quadratic regression and stepwise determination of multiple regressions of grain yield, test weight, grain protein, and milling and baking qualities on environmental factors were determined using PROC REG. For multiple regression, the forward entry stepwise method was used, with P = 0.05 as the critical value for allowing new variables to enter the model.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Environmental Conditions
The 30-yr growing season mean temperature and total precipitation at the locations used in this study were 13.6°C and 85.8 cm, respectively. The 2001 growing season mean temperature and total precipitation were 13.5°C and 52.4 cm, respectively, which was drier than the 30-yr mean. The 2002 mean temperature and total precipitation were 10.4°C and 92.7 cm, respectively, which was cooler and wetter than either the 30-yr mean or the 2001 growing season.

The environmental conditions of interest were those that occurred between the two harvest dates (Fig. 1). Trials C2001, T2001, and T2002 had the fewest days and precipitation events between harvests, which resulted in lower total precipitation, compared with the other trials (Fig. 1). Trials B2001 and P2002 had similar numbers of days and precipitation events between harvests, although P2002 had the highest total precipitation and the largest precipitation event between harvests. Trial C2002 had the most days and precipitation events between harvests.

Grain Yield, Test Weight, and Grain Protein
Trial, harvest date, and trial x harvest date had statistically significant effects on grain yield (Table 3). Trials T2001, C2002, and T2002 had decreases in grain yield with delayed harvest, while Trials P2002 and B2001 exhibited no difference between the first and second harvests (Table 4). The greatest yield loss percentage occurred in T2002, followed by T2001. Both of these trials had delayed harvests of 8 d and also had the fewest precipitation events (Fig. 1, Table 4). Trial C2002 also sustained a high yield loss percentage and had the longest delay between harvests and the most precipitation events. Trials B2001 and P2002 also had long delays between harvests and a higher number of precipitation events than T2002 and T2001, but yields did not differ between harvests (Fig. 1, Table 4).


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  		Table 3. Analysis of variance results for the effects of trial, harvest date, and their interaction on soft red winter wheat grain yield, test weight, and grain protein. The number of degrees of freedom (df) for each source of variation, and the model coefficients of variation (CV) are also shown.

 

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  		Table 4. Least square mean estimates for soft red winter wheat grain yield and test weight for the timely harvest (first harvest) and delayed harvest (second harvest) for six trials. The number of days between harvests (Days) and the percent change is also shown.

 
Simple linear and quadratic regressions were performed for yield differences between harvests (averaged across replications) vs. mean temperature, the mean daily maximum and minimum temperatures, total precipitation, total number of precipitation events, mean daily wind speed, mean daily relative humidity, and days between harvests. A relationship was found only between yield differences and total precipitation (r2 = 0.90, Fig. 2 ). At trials with low rainfall (<2 cm), yield loses were the greatest (~900 kg ha–1) compared with trials with 5 cm of precipitation (P2002), where yields did not differ between harvests (Table 4, Fig. 2). Based on stepwise multiple regression analysis of yield differences vs. the weather variables, a relationship between yield differences, total precipitation (partial R2 = 0.90), and mean minimum daily temperature (partial R2 = 0.09) was found (R2 = 0.99) such that:

Formula 1[1]
where Y is the change in yield (kg ha–1), Ptot is the total precipitation (cm), and Tmin is the mean minimum daily temperature (°C) between harvests. Equation [1] is consistent with warmer and drier conditions after grain ripeness resulting in greater yield losses with delayed harvest.


Figure 2
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  		Fig. 2. Change in soft red winter wheat grain yield between the timely and delayed harvests vs. total precipitation between harvests across five trials.

 
Trial, harvest date, and trial x harvest date were significant for reduction in test weight (Table 3). Trial C2002 had the greatest reduction in test weight (Table 4) and also had the greatest number of days and precipitation events between harvests. Trial T2002 had no significant reduction in test weight, despite having similar days between harvests and number of precipitation events compared with C2001 and T2001, where reductions in test weight did occur. Trial C2001 test weights at each harvest date were above the 747 kg m–3 standard, while at B2001, T2001, and C2002, test weights were above the 747 kg m–3 standard at the timely harvest, but fell below standard at delayed harvest (Table 4). Trials P2002 and T2002 were conducted in the wetter and cooler 2002 growing season and exhibited test weights consistently below the minimum standard across harvest dates.

There were no linear or quadratic relationships between reductions in test weight and mean temperature, mean daily minimum or maximum temperatures, total precipitation, mean daily wind speed, or mean daily relative humidity. There was, however, a relationship (r2 = 0.93) between the number of precipitation events and the change in test weight between harvests (Fig. 3 ). The more precipitation events, the greater the loss in test weight. Not surprisingly, precipitation events were highly correlated with the number of days between harvests (r = 0.94) and consequently there was also a linear relationship between change in test weight and days between harvests but with a lower r2 of 0.84. These data suggest that a longer interval between grain ripeness and harvest increased the opportunity for more precipitation events and wetting and drying cycles, and caused a corresponding reduction in test weight.


Figure 3
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  		Fig. 3. Change in soft red winter wheat grain test weight between the timely and delayed harvests vs. the number of precipitation events between harvests across six trials.

 
Mean levels of grain protein in each trial were typical for the region (Bowman, 2004), ranging from 108 g kg–1 in trial T2002 to 131 g kg–1 at C2002 (Table 5). Trial was the only factor contributing variability in grain protein (Table 3). There was no evident trend to explain differences in grain protein between trials. Consistent with nonsignificant harvest date and harvest date x trial effects, linear, quadratic and multiple regression analyses with weather factors were not conducted for grain protein.


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  		Table 5. Least square mean estimates for soft red winter wheat grain protein and selected milling and baking quality parameters for each of six trials.

 
Milling and Baking Quality
Variation due to trial, harvest date, and the interaction of these effects were not significant for nine of the 20 milling and baking quality parameters examined (Table 6). These included flour moisture, protein, and DON; cookie spread; farinograph flour absorption, development time, and MTI; and alveograph overpressure and work.


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  		Table 6. Analysis of variance results for the effects of trial, harvest date, and their interaction on 20 soft red winter wheat milling and baking quality parameters. The number of degrees of freedom (df) for each source of variation, and the model coefficients of variation (CV) are also shown.

 
Only trial had an effect on kernel weight, patent and extractable flour, farinograph stability time, and alveograph extensibility and curve configuration (Tables 5 and 6); delayed harvest did not affect these parameters. It is possible that some of the differences in kernel weight between trials may have been due to a cultivar effect because C 9704 kernel weight data was only available for T2002. While this possibility cannot be ruled out, there was kernel weight variation among the remaining trials where P 26R61 was evaluated (Table 5). The trial effect for patent and extractable flour could be explained by a year effect with the 2001 trials (B2001 and T2001) significantly different than the 2002 trials (C2002, P2002, and T2002; Table 5). Farinograph stability, alveograph extensibility, and alveograph curve configuration effects across trials revealed no apparent trend to describe the among-trial differences (Table 5).

Trial and harvest date were significant sources of variation for flour falling number (Table 6). With wetter and cooler conditions in the 2002 to 2003 harvest period than the 2001 to 2002 harvest, flour falling numbers were different for B2001 and T2001 than for T2002, C2002, and P2002 (Table 5). The mean flour falling number for a delayed harvest (315 s) was significantly reduced compared with a timely harvest (358 s). Because of the nonsignificant interaction between trial and harvest date for flour falling number, indicating that the weather between harvests had no effect, differences in flour falling number between harvests were not regressed with environmental conditions.

Harvest date was the only significant source of variation for clear flour (Table 6). Delayed harvest resulted in 18% clear flour compared with a timely harvest value of 19.8%. Because of the nonsignificant interaction between trial and harvest date for clear flour, indicating that weather conditions between harvests had no effect, differences in clear flour between harvests were not regressed with environmental variables.

Harvest date, trial, and trial x harvest date were significant for grain falling number (Table 6). There was a significant reduction in grain falling number between harvest dates only in trial P2002 (Table 7) and this was responsible for the trial x harvest date interaction and possibly the harvest date and trial main effects. Trial P2002 experienced the highest total precipitation and highest mean daily relative humidity, two environmental variables that could have initiated germination in the ripe grain. Nevertheless, an analysis of the whole dataset found no significant linear, quadratic, or multiple regression relationships between differences in grain falling number between harvests and weather conditions.


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  		Table 7. Least square mean estimates of grain falling number, grain deoxynivalenol (DON), and farinograph breakdown time of soft red winter wheat at a timely harvest (first harvest) and delayed harvest (second harvest) for each of five trials.

 
Harvest date and trial x harvest date were significant for grain DON content (Table 6). An increase in the grain DON content between the two harvest dates was observed in all trials except T2001 (Table 7). Trial B2001 had the largest increase in DON (2.31 mg kg–1), followed by T2002, with the next largest increase in DON (0.90 mg kg–1). Trial C2002 had the smallest increase in DON levels (0.35 mg kg–1). There were no significant linear, quadratic, or multiple regression relationships between increases in grain DON and weather conditions between harvests.

Trial and trial x harvest date were significant for farinograph breakdown time (Table 6). At Trials B2001 and T2002, breakdown time decreased with delayed harvest (Table 7). At all other trials, there were no differences in breakdown times between harvest dates. There were no significant linear, quadratic, or multiple regression relationships between reductions in farinograph breakdown time and weather conditions between harvest dates.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Grain Yield, Test Weight, and Grain Protein
Johnson et al. (1980) reported average yield losses of 10% when soft red winter wheat harvest was delayed by 21 d in the mid-Atlantic region of the USA. In our study, we found that yield losses of nearly 20% were possible with only 8 d between harvests (see Table 4). These higher yield losses occurred in trials with the lowest total precipitation between harvests (Fig. 2). Stepwise regression indicated that, in addition to total precipitation, temperature was also an important factor (Eq. [1]). The chances of shattering increase as grain dries, and hot dry weather facilitates this drying process. Consequently, it may be that the highest yield losses reported here, primarily associated with trials that had hotter and drier weather, were the result of increased shattering in the field.

In this study, the test weight reductions associated with delayed harvest were higher than those reported for soft red winter wheat in other growing regions. In Arkansas, Lloyd et al. (1999) saw no reduction in soft red winter wheat test weight when harvest was delayed for 21 d in 1996, and a relatively small reduction in 1997 (1.4–5.4%). In our study, delayed harvest of 8 to 19 d caused reductions in test weight of 3.3 to 14.4% in all but one trial (Table 4) and these reductions were highly related (r2 = 0.93) to the number of precipitation events. This suggests that grain wetting and drying cycles were important factors contributing to reductions in test weight.

In contrast to yield and test weight, of which both were more highly affected by delayed harvest than previous research in other soft red winter wheat production areas, grain protein was not affected by delayed harvest. This is consistent with reports by Pool et al. (1958) and Lloyd et al. (1999), who also saw no change in grain protein with delayed harvest in Indiana and Arkansas, respectively.

Milling and Baking Quality
Of the 20 milling and baking quality parameters investigated, only grain and flour falling number, grain DON levels, clear flour, and farinograph breakdown time showed significant effects associated with delayed harvest. Flour falling number decreased with delayed harvest and grain falling number decreased at P2002 with delayed harvest. This indicated increased {alpha}-amylase enzyme activity, although sprouting was not visibly evident. Enzyme activity may be increased with warm temperatures (Hagemann and Ciha, 1987), but a relationship between grain or flour falling number and temperature was not evident. As grain is exposed to increased levels of moisture, the grain can imbibe water, begin germination, and activate {alpha}-amylase (Gooding and Davies, 1997). This may be what happened at Trial P2002 with grain falling number, where precipitation amounts were high and grain falling number decreased significantly between harvests. The observed decrease in flour falling number was probably the result of a complex combination of temperature, increased moisture through grain wetting, and time associated with delayed harvest.

A critical level for DON in grain is 2 mg kg–1 according to local mill standards and 1 mg kg–1 for flour according to the Food and Drug Administration (2005). When these toxins are present in milling and baking products, they can change the flavors of foods and pose a potential health risk (Woloshuk et al., 1995). At three out of five trials where DON data was available, delayed harvest resulted in higher DON levels (Table 7). At two of those trials, delaying harvest caused grain DON levels to exceed the critical value of 2 mg kg–1.

A decrease in clear flour could be an indication of a decrease in grain carbohydrates with delayed harvest. Average clear flour decreased between harvests by nearly 2%, and these reductions might be linked to increased respiration or {alpha}-amylase enzyme activity, which with time would result in starches being broken down to sugars.

Farinograph breakdown time is a measure of the dough's ability to retain its structure with time in the mixing process (Bloksma and Bushuk, 1988). Decreases in breakdown time with delayed harvest (Table 7) could indicate changes in the protein/carbohydrate ratio in the grain (Bloksma and Bushuk, 1988).

Previous research from the midwestern or mid-Atlantic USA showed little to no effect of delayed harvest on soft red winter wheat milling and baking qualities (Johnson et al., 1980; Pool et al., 1958). Our findings are generally in agreement with these reports, but a few differences are noteworthy. Consistent with high humidity at the time of wheat harvest in the southeastern USA, delayed harvest did have an effect on grain and flour falling numbers and on grain DON levels, both of which can be significant problems for millers. Additionally, the reductions in farinograph breakdown time associated with delayed harvest at some trials were substantial, and this could be a significant problem for the baking industry.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 





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