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Crop Science 40:742-756 (2000)
© 2000 Crop Science Society of America

CROP ECOLOGY, MANAGEMENT & QUALITY

A Five-Minute Field Test for On-Farm Detection of Pre-Harvest Sprouting in Wheat

John H. Skerritta and Russell H. Heywoodb

a Australian Centre for International Agricultural Research, GPO Box 1571, Canberra ACT 2601 Australia
b Quality Wheat CRC Limited, Private Bag 1345, North Ryde NSW 1670 and CSIRO Plant Industry, GPO Box 1600, Canberra ACT 2601 Australia

skerritt{at}aciar.gov.au


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 Materials and methods
 Results and discussion
 Conclusions
 REFERENCES
 
Field trials have shown that the extent of preharvest sprouting after rainfall can vary markedly between and within fields. Testing of grain from different fields or parts of fields before harvest would permit separate harvesting and binning of damaged grain from sound grain, and financial losses resulting from downgrading of the crop could be reduced. An immunochromatography method, based on detection of alpha-amylase isozymes, using specific antibodies was developed for field-level detection of preharvest sprouting in wheat (Triticum aestivum L.). In the test, alpha-amylase from ground grain is extracted with a salt solution and two drops of the extract is added to a zone on a disposable card. The result appears in less than 5 min. If the grain is sprouted, amylases in the samples become sandwiched between gold-labeled and immobilized antibodies, and a maroon band appears in the test window. Color intensity depends on the extent of weather damage, with good (negative) correlations between test color and Falling Number in large sets of samples comprising many cultivars. Precision is as good as or better than the Falling Number test. Methods for obtaining representative samples from a standing wheat crop were developed, and an extensive trial of the new method with farmers and elevator company staff was conducted in late 1998. Six wheat samples varying in Falling Number were tested blindly by a group of 75 farmers and grain handling company staff, with the vast majority obtaining correct results for each sample. The method should be suited for rapid screening on-farm prior to harvest, use at elevators, or as a rapid laboratory test.

Abbreviations: AH, Australian Hard • ELISA enzyme-linked immunosorbent assay • FN, falling number • IC, immunochromatography • P, polyclonal antibody • PH, Prime Hard wheat grade • M, monoclonal antibody


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
Materials and methods
 Results and discussion
 Conclusions
 REFERENCES
 
A SIGNIFICANT RISK FACTOR in winter cereal farming in certain environments is pre-harvest sprouting due to significant rain on ripe grain prior to harvest. The price paid to the grower is usually significantly reduced because the market value of the grain is lowered. This is because even mild sprouting affects the suitability of wheat for most food uses (Meredith and Pomeranz, 1985; Derera, 1989; Kruger, 1994). However, several factors, apart from the total rainfall received, can determine whether grain is actually sprouted. These include the cereal cultivar sown (Bingham and Whitmore, 1966; McCrate et al., 1981; Finney, 1985; Moss and Stiles, 1987), maturity stage, temperature patterns and rainfall during crop development (Takahashi, 1980; Gosling et al., 1981; Nielsen et al., 1984; Reddy et al., 1984; Mares, 1992), temperature, humidity and cloud cover after the rainfall event, and on-farm factors such as aspect, drainage, and soil type in each field or part of field (Medcalf et al., 1968; Lalluka, 1976; Mares, 1984). Thus, the extent of sprouting is very hard to predict. Visual estimation of the percentage of sprouted kernels is used by a number of agencies, including the U.S. Federal Grain Inspection Service. However, several studies have shown that visual estimation of sprouting can be unreliable (Mares, 1989), in part because much of the commercially relevant damage occurs before germination of the grain is visible (Jensen et al., 1984). Other factors, such as bleaching or staining of the crop are also not good quantitative indicators of sprouting, although they can be valuable in indicating crops that require testing.

The standard method for quantification of sprouting is the Falling Number test, a viscosity test in which the time required for a plunger to fall through a heated slurry of wholemeal and water in a large glass test tube is measured (Hagberg, 1960; Kruger, 1990). Although the method is simple in concept, the equipment is usually considered too expensive for the farmer market, and since it requires a special balance and sample grinder, the method is aimed at the mill laboratory. Since growers in many countries store at least some grain on farm, and grain is received at a very large number of elevators or storage sites, the unit cost of the Falling Number equipment means that it is not feasible to install it at each grain storage. Water quality and the fineness of grind also have important effects on Falling Number (Greenaway and Neustadt, 1967; Perten, 1967; McMaster and Derera, 1976). The method, which can only test one or two samples in a 10-min period, is also considered by many to be too slow for use in "real-time" monitoring of grain deliveries.

The synthesis of a number of hydrolytic enzymes is markedly increased during grain germination, and several methods for quantification of these enzymes, especially alpha-amylases (which are very active early in germination) have been used to determine the extent of preharvest sprouting in wheat (Barnes and Blakeney, 1974; Derera, 1989; Kruger, 1990). Indeed, the action of alpha-amylases on a heated wholemeal slurry is the basis of the Falling Number test. In recent research, we developed an alternative method, based upon the use of antibodies specific for alpha-amylase, which used small antibody-coated tubes to provide a simple color readout. Results from this ELISA (enzyme-linked immunosorbent assay) method, which took less than 10 min to perform, correlated well with Falling Number, and the method was suitable for use in field situations (Verity et al., 1999). The antibody-based method was similar in precision to the Falling Number test, and test performance was essentially independent of wheat cultivar. However, the ELISA method had some significant shortcomings. Once the amylase enzymes were extracted from the grain, the method required several addition steps and the tubes had to be washed with a saline solution to separate antigen (amylase)-bound antibody-enzyme conjugate from unbound conjugate before addition of a color developer. Critically, the color developed in the test was affected by the ambient temperature and the result was not stable to storage. While this variation could be accounted for by performing standards in each test, this required the use of many additional tests in the application of the procedure.

Immunochromatography (IC) is an immunoassay test format that has recently been utilized extensively for medical testing in remote situations (e.g., detection of infectious diseases in developing countries, Torlesse et al., 1997) and in home or clinician office situations (home-use pregnancy or ovulation tests, e.g., Kasahara and Ashihara, 1997). The principal advantage of the method is that the chromatography step causes the separation of antibody-bound and unbound (free) substances, such that a separate washing step in the method is not required. In addition, when the method is used with directly labeled antibodies (e.g., gold labeled), the requirement for a separate color development step using an enzyme substrate–chromogen system is also avoided. Thus, in the simplest IC tests, it is possible to use only a single step, namely addition of the test substance to a test card. Another advantage of using gold-labeled antibodies is that the test result is permanent. Despite these potential advantages, the IC method has not been previously applied to on-farm testing.

This paper describes the development of a simple IC test for detection of pre-harvest sprouting in wheat and barley (Hordeum vulgare L.) grains, through the quantification of alpha-amylases in test samples. After within-laboratory validation, the method was validated through a collaborative trial in which test samples were assessed for sprouting by farmers and some elevator staff. In order to establish protocols for field sampling of grain, the test was used to quantify the extent of within- and between-field variation in sprouting at five Australian and one New Zealand test sites. The method now forms the basis of a commercial test kit for farm or elevator use, registered as the WheatRite test (QWIP Proprietary Limited, of Sydney, Australia).


   Materials and methods
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
Results and discussion
 Conclusions
 REFERENCES
 
Antibodies to Alpha-Amylase; Preparation of Immunochromatography Devices
The preparation and specificity characteristics of antibodies to alpha-amylases were described in a previous paper (Verity et al., 1999). Two antibody combinations, which functioned in sandwich ELISA and provided ELISA results that correlated with Falling Number, were selected for further study. These were a polyclonal antibody to both high and low pI alpha-amylase isozymes (P) and a monoclonal antibody (1H5-1F12) to high pI alpha-amylase isozymes (M) (Verity et al., 1999). Antibodies (1 µL of 1 mg/mL) were either dried in a narrow band on the nitrocellulose test strip (5-µm pore size, Millipore, Bedford, MA) or adsorbed to 40-µm-diam colloidal gold particles (British Biocell International, Cardiff, UK) by the method of Slot and Geuze (1985). A paper pad with dried gold particles was mounted on one side of a test card, while a nitrocellulose strip containing antibody was mounted on the other side of the card. The two sides of the card (76 by 64 mm) were folded together to start the test. Antibody combinations that were assessed in detail were P (immobilized) + P (gold labeled) and P (immobilized) + M (gold labeled). Other antibody combinations described in Verity et al. (1999) were also assessed, but these gave either no color response in the test [e.g., M (immobilized) + M (gold)] or poorer responses than these two combinations.

Performance of the Test
The immunochromatography test was performed as follows (Fig. 1A, B) . Wheatmeal (0.5 g) was extracted with 6 mL of 85 mM NaCl by hand shaking for 15 s; previous research (Verity et al., 1999) had shown that this method extracts amounts of amylase enzyme equivalent to longer, mechanical shaking in buffer solutions. The extract (50 µL) was applied to a filter paper pad containing gold colloid coated with one of the test antibodies. Phosphate-buffered saline, pH 7.4—0.05% (v/v) polysorbate 20 (60 µL) was added to a second pad mounted on a test card on the side opposite to the pad containing the gold-labeled antibody (Fig. 2) , and positioned below a short (18 mm long by 4 mm wide) nitrocellulose strip. This nitrocellulose strip contained two distinct zones of antibody. The lower (test) zone, 6 mm from the base of the nitrocellulose strip, contains a stabilized antibody to alpha-amylase. The upper (reference) zone, 10 mm from the base of the nitrocellulose strip, contains goat anti-mouse or anti-rabbit antibodies complementary to the host species of the antibody used in the gold conjugate. The test is started by closing the card, bringing into contact the pad containing the gold-labeled anti-amylase antibodies (complexed with any amylase present in the wheat extract) with the pad containing the buffer and the nitrocellulose strip (Fig. 1B). This strip also contacts a paper pad used to encourage capillary movement of complexes of amylase antigen and antibody-coated gold up the membrane.



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  		Fig. 1 A. Principle of the test; B. Flow chart indicating test steps

 


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  		Fig. 2 Test card (open) showing inner surfaces and pads ready for addition of extract and buffer

 
Scanning of the Cards
A Hewlett Packard Scanjet 6100C scanner was used with Deskscan II data acquisition and Phoretics 1D software (Phoretics International, Newcastle, UK). The nitrocellulose test strip was dried and mounted onto white cartridge paper. Strips were scanned in the gray scale, and the reflectance of the center 75% of the strip used for measurements. After subtraction of background reflectance from a zone between the test and reference bands, the areas under the reflectance profiles for both test and reference bands were integrated.

Grain Samples
For initial evaluation, a set of 14 grain samples was selected, comprising eight Australian hard wheat cultivars (Hartog, Cunningham, Pelsart, Banks, Sunco, Sunstate, Perouse, and Janz), collected from elevators at Miles and Roma in southern Queensland, Australia, during the 1996 harvest. These samples (with Falling Numbers from 100–403 s), were ground with a Jupiter mill and analyzed for alpha-amylase by the standard and rapid tube assays. Results reported in an earlier paper demonstrated that similar amounts of enzyme are extracted with this mill and other cereal mills (e.g., a cyclone mill, Udy, Fort Collins, CO) and a domestic coffee grinder (Verity et al., 1999). In addition, 36 samples of three hard cultivars (Vulcan, Sunland, and Halberd) and two soft cultivars (Matong and Tincurrin), ranging in Falling Number between 85 and 423 grown at Narrabri, New South Wales, Australia, and subjected to controlled wetting, were milled on a Falling Number mill with a 0.8-mm screen, and analyzed to further examine the influence of cultivar on the test results. These cultivars contained hard and soft wheat types of diverse protein contents (8–15% protein) and end-use types. Samples were stored at 4°C before testing, to minimize storage-induced changes in Falling Number. Each sample was tested in the immunochromatography test, the ELISAs described by Verity et al. (1999), and the standard Falling Number method for wholemeal (ground with a Falling Number 3100 mill), and determined in duplicate (AACC, 1983).

Field Sampling
Grain was sampled from 60 fields in different Australian and New Zealand localities, states, and seasons [Roma, Queensland (October 1996); Liverpool Plains, New South Wales (December 1996); Tara, Queensland (October 1997); Southland, New Zealand (February 1998); Emerald, Queensland (September 1998); Ravensthorpe, Western Australia (December 1998)] to obtain a widely different range of field sizes, management types, and cultivars. All fields sampled had received at least 50 mm of rainfall in the 5- to 10-d period before sampling; while the fields primarily represented sprouted ones, some did not have depressed Falling Numbers. Some details of the fields tested are shown in Tables 1A and 1B .


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  		Table 1 Within-field variation in pre-harvest sprouting: (A) fields exhibiting within-field variation over 30 Falling Number units; and (B) fields exhibiting within-field variation under 30 Falling Number units{dagger}{ddagger}§

 
There were two main aims of the sampling research. First, we needed to develop and validate crop sampling methods. For example, it was important to establish the minimum sample size that would provide valid test results. Samples of 5, 10, 20, or 50 spikes were collected from 3 to 10 plants within a 10-m2 area. Duplicate samples of spikes were also taken from neighboring sites (approximately 2 m apart) at 6 to 9 locations within 17, 22, 22, and 5 fields, respectively. Samples were threshed and ground and the Falling Number was determined. Second, it was important to analyze the degree of variation within and between fields. Apart from comparing the degree of sprouting between fields on the same farm or in the same area, the emphasis was on within-field sampling. Three types of experiment were performed.

1. Samples were taken at 4 to 10 places within each field. For some small fields (up to 20 ha), this represented samples 10 m in from the center of each edge of the field. With most other fields, samples were either collected from corners and the center of each edge, and one to three sites in the center of the field or samples were collected along a "W" pattern in the field.

2. Assessment of the variation in sprouting results between repeat samples from sites approximately 2 to 3 m apart.

3. Establish the variation in sample results when returning to similar areas of field (estimated from memory only, and disregarding visual cues such as footprints), 1 to 2 h after collection of the initial sample. In each of Cases 1, 2, and 3, mean and standard deviations of Falling Numbers were calculated.

Samples that were lodged, green, or double sown (headlands) were not collected; in addition, samples were collected at least 10 m in from the edge of the crop. The aim of the experiments was not to establish the environmental factors responsible for pre-harvest sprouting, but rather to establish strategies for sampling of fields that may be sprouted.

Collaborative Trial of Immunochromatography Test
Kits containing test cards, buffer extraction tubes and solution, sample scoops, and droppers were provided to 79 farmers and grain handling company staff, along with a color chart with sample results corresponding to samples of Falling Numbers 350, 300, 250, and 150. About half of the respondents had observed a demonstration of the method. The participants assessed the performance of known standards of Falling Numbers 350, 300, 250, and 150. These standards correspond to key FN cut-off values (in seconds) in Australian grain trading, namely for Prime Hard (>350), Australian Hard (300–349), 2H (2nd Quality Hard, 250–299), General Purpose (150–249), and Feed Grain (<150 Falling Number). We did not provide more severely damaged standards, since detection of mild damage (low levels of sprouting) is the most important. In addition, the accuracy and precision of test results obtained with six test samples (Falling Numbers withheld from participants) were assessed. Finally, the participants were asked to comment on ease of use of the test.


   Results and discussion
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
Conclusions
 REFERENCES
 
Relationship between Band Intensity and Falling Number
Within 1 to 2 min after closing the test card, a front of antibody-coated gold particles moved up the strip past the observation window. The reference band was immediately apparent, and in samples of Falling Number less than 250 s, the test band was also immediately apparent. At 5 min color development, there was a very good (and roughly linear) relationship between band intensity and Falling Number when 14 wheat samples (Roma and Miles, Qld, 1996 harvest) were analyzed in five separate experiments. Samples of Falling Number 350 and above either produced no band (P + M) test, or a consistently faint band (P + P test). Thus, the band intensity values were similar for wheats of Falling Numbers 350, 375, and 400; this result is expected, since each of the samples contained similarly low levels of amylase and the differences in Falling Number are thought to mainly arise from differences in sample paste viscosity (Perten, 1964; Finney, 1985; Moss and Stiles, 1987).

There are two potential methods for analyzing the data. Either the reflectance of the test band can be measured alone (Fig. 3A) , or the reflectance of the reference band (produced by the reaction of antibody-coated gold particles) could also be measured, and the ratio calculated between the test and the reference bands (see Fig. 3B). Both approaches produced similar relationships with Falling Number, with an approximately linear relationship between either reflectance and Falling Number (linear regression r2 = 0.954, Fig. 3A) or reflectance ratio and Falling Number (linear regression r2 = 0.970, Fig. 3B).



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  		Fig. 3 Between-run precision of the immunochromatography tests; relationship between reflectance (P + P test), reflectance ratio (P + M test) and Falling Number. Data are mean ± SD for 14 hard wheat samples from Roma, Qld, analyzed on five separate days

 
Precision and Method of Data Analysis
Between-Run Precision
This was assessed by analyzing 14 wheat samples, in five separate experiments performed three times. The precision was slightly greater (especially for samples with Falling Numbers below 250) when the test reflectance ratio was used (Fig. 3B), rather than simply test reflectance (Fig. 3A). When expressed in Falling Number units, the imprecision estimates from the IC test were typically 10 to 30 units. The precision was highest in the critical region of 250 to 350 Falling Number, and was similar to (or perhaps slightly better than) the imprecision of the Falling Number machine test in the hands of an experienced operator (Tipples, 1971; Kruger, 1990; Verity et al., 1999). The data show that samples with critical cut-off values of FN 350, 300, 250, 200, and 150 could be reliably distinguished.

Within-Run Precision
Within-assay precision was assessed by performing 5 analyses on single grain extracts of FN 154, 275, and 382 in a single experiment for both antibody combinations. Within-assay precision was very high for both tests (Fig. 4) .



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  		Fig. 4 Within-run precision of the immunochromatography tests; reflectance values for samples of FN 154, 275 and 382 for both test formats. Data are mean ± SD for samples analyzed five times on the same day

 
Assay Time
The tests were run for 3, 5, 7, and 30 min. Samples of six different Falling Numbers across the likely test range were evaluated in two or three experiments. It was possible to obtain useable results with a 3-min test run, although in some cases the background intensities were higher (the cards had not entirely cleared) and the signal was lower. It was, thus, hard to distinguish some samples above a Falling Number of 300 seconds. While very acceptable results were obtained for the 5-min run time, there was a slight increase in band intensity at 7 min and a more significant increase at 30 min (Fig. 5A) . Because the intensity of the reference band also increased with time, the effect of assay time on the reflectance ratio was less marked (Fig. 5B). Results for both antibody combinations were similar, indicating that any difference in antibody kinetics was not seen after 5 min. The color was higher when the tests were run to completion (30 min), without unacceptable background appearing in the P + M test in samples with Falling Number greater than 350 s. Since there are advantages in the test being as fast as possible, a standard 5-min test time was chosen.



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  		Fig. 5 Effects of test time on A. reflectance and B. reflectance ratio for the P + P test. Data are means ± range for duplicate tests on six hard wheats from Roma, Qld

 
Potential Cultivar Differences in Sample Performance
A set of samples that were as diverse as possible was selected, containing particular hard and soft wheats. These wheats would not in commercial practice be grown or received at a single site, but do represent extremes of quality type, with hard and soft-grained cultivars that differ in starch characteristics. Overall, highly significant correlations (r = 0.921 and 0.926) were obtained for a linear regression between Falling Number and reflectance for both tests. Similar reflectance (see Fig. 6) or reflectance ratio versus Falling Number relationships were seen for each test format and wheat cultivar, although there was a tendency for Halberd to have high reflectances for a given Falling Number and Matong to have low reflectances. Independent Rapid ViscoAnalyser (Newport Scientific, of Sydney Australia) experiments on the wholemeals demonstrated that the inherent paste viscosities differed (Verity et al., 1999). Since Falling Number measures a combination of amylase activity caused by weather damage and inherent paste viscosity, there are slight but significant differences between cultivars in the relationship between Falling Number and enzyme activity/levels (as are measured by this test) (Medcalf et al., 1968; Barnes and Blakeney, 1974; Finney, 1985).



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  		Fig. 6 Cultivar influences on test performance: Relationship between Falling Number and reflectance (P + P test) for samples of five diverse wheat cultivars

 
Relationship between Immunochromatography Test Results and ELISAs for Amylase
The results obtained with the set of 36 artificially sprouted hard and soft wheats were related to quantification of either total alpha-amylases (P + P test) or the high isoelectric point isozymes (P + M test) using the tube ELISA assay described in Verity et al. (1999). The linear correlation coefficients between ELISA results for both the reflectance and reflectance ratio were high: P + M test (reflectance r = 0.937, reflectance ratio r = 0.944), and P + P test (reflectance r = 0.906, reflectance ratio r = 0.909). In each case, a parabolic fit of the data was slightly better than a linear fit, since at low Falling Numbers (i.e., under 150), the incremental change in reflectance response for the immunochromatography test format was lower than the ELISA response for amylase. This is not of major practical significance, since grain at the very low Falling Numbers is already excluded from most food or premium end-uses.

Effect of Assay Temperature
In addition to within–between assay precision, potential variation of assay performance at different temperatures will be the major factor in determining whether standards would need to be run in each test run. The tests were run with 14 hard-grained samples on each of two separate days, after equilibration of the reagents for 1 to 2 h at several temperatures (4, 16, 24, 30, and 37°C). To mimic the effects of performing the assay at the different ambient temperatures, all components, including the grain samples and saline extractant, were equilibrated at the indicated temperatures. There was a trend for both test formats that the reflectance (see Fig. 7) and reflectance ratios were highest at 30°C. Although the mean values at 30°C were consistently higher for samples with Falling Numbers below 250, this difference was statistically significantly different from the results at 16, 24, and 37°C in only a few cases. These temperatures probably cover the range encountered in most situations during the summer or fall harvest season, so the lack of major difference in this range is encouraging. The reflectance of the test samples was decreased by 60% at 4°C. If temperatures this low are encountered, accurate results could be obtained, merely by analysis of the set of standards provided with the kit at the low ambient temperature. Since the temperature-reflectance profile suggests that the differences are not statistically significant across the broad range of room and field temperatures, it should be possible to avoid routinely running standards with each set of samples. While the temperature profile and precision profile result suggest that standards do not have to be run with each sample set, it would be advisable to analyze a calibration set of three or four samples daily.



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  		Fig. 7 Effect of ambient temperature on assay performance; data are means ± SD for reflectance values from the P + M assay for 14 hard wheat samples from Roma, Qld

 
Stability of Materials
Unlike most other methods for grain analysis, the current technique uses antibodies, which are biological materials. It was thus important to determine the stability of these reagents. An accelerated stability trial was performed in which cards were stored at 4 or 37°C for up to 4 wk; the materials were then transferred to room temperature for 1 h and the performance of the reagents assessed. Data shown in Fig. 8A and 8B are from two experiments in which three replicates of wholemeals of each of three different Falling Numbers were tested after 1, 14, 21 and 28 d storage at the temperatures indicated. There was excellent storage stability for both test formats with little or no decline in reflectance upon extended storage. In addition, there was no evidence that the background absorbances obtained with an unsprouted wheat sample (FN 382) changed upon storage. Industry convention is that proven stability of biologicals for 7 d at 37°C is indicative of storage stability for over a year in refrigeration (Kirkwood, 1977), and the present results demonstrate stability for over a month at 37°C. The reagents in the current immunochromatography format were more stable than those in our amylase ELISA (Verity et al., 1999); for ELISAs, stability for 1 wk at 37°C is considered excellent (Khatkhatay et al., 1993). A final point is that unlike ELISA, where the colored product is typically in a liquid form and may fade in the minutes or hours after test completion, the colored product of the immunochromatography test is in a dry form and is stable for many months after test completion. This is important because the original test record can be preserved for reference in the event of farmer dispute of grain payments.



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  		Fig. 8 Stability of antibody-coated test cards at 37°C. Data are reflectance values for A. P + P test, B. P + M test at day 1 (unstored) and after storage for 7, 21, and 28 d

 
Collaborative Trial of the Test
Seventy-nine trial participants returned responses, and of those, 75 could be analyzed. Eighteen of the responses were from grain handlers or processors, the rest were from individual farmers. Four responses could not be evaluated since they did not contain the data required or the participants indicated that they had used an incorrect test method. All but three of the 75 participants indicated that the results obtained by testing the four standards agreed with the sample results provided in the color chart; they could clearly differentiate results produced by samples with 350, 300, 250, and 150 Falling Numbers by eye. The three that obtained some disagreement commented that the color on the cards was darker than the chart; this could be due to not stopping the test at 5 min, and thus the color on the test cards continuing to develop. A further two participants obtained darker colors for the 250 standard than on the card.

A further six ground-grain samples were provided to trial participants without indication of their sprouting status; participants were required to run the test and interpret the results with respect to the test card (Table 2) . The approximate Falling Numbers of these samples, mean and standard deviations of the estimated FN from the trial responses were: Samples A and C (blind duplicates)—actual FN 202 ± 21, reported FN 224 ± 55 (A) and 238 ± 52 (C); Sample B—actual FN 430 ± 18, reported FN 347 ± 9; Sample D—actual FN 110 ± 19, reported FN 133 ± 23; Samples E and F (blind duplicates)—actual FN 310 ± 16, reported FN 300 ± 31 (E) and 303 ± 31 (F). In each case, the mean of the results obtained was close to the expected mean. Although the users were asked in the trial instructions to estimate the FN of the sample, the vast majority of participants called the unknowns as being equivalent to one of the four standards. In other words, Samples A and C (FN 202) were described by the farmers as being of either 250 or 150; very few farmers estimated an intermediate number. The test is designed to give very little or no band color at a Falling Number of 350, thus any sample of FN 350 or over would give the same result. For this reason, Sample B (FN 430) provided an average result of 347 because most farmers called it as 350. Sample D had a Falling Number of 110, but about half the farmers reported it as FN 150, this being the lowest standard provided in the kit. A significant number indicated a value of 100. In 26 cases where farmers reported the result as "significantly under 150", we have assumed this to be 100 for the purpose of statistical calculations. Data obtained for Samples E and F were particularly close to the actual result.


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  		Table 2 Estimated Falling Numbers obtained for unknown samples in a trial of the test method by 75 farmers and grain handlers. Values shown are numbers of participants obtaining a particular value (or range of values)

 
The implications of the call by the trial participants for possible downgrading of the wheat were analyzed. Sample A and C was actually downgraded from the premium Australian Prime Hard (APH) grade (FN greater than or equal to 350) by weather damage. Seventy-four of 75 correctly called Sample A and 75 of 75 correctly called Sample C as downgraded from APH. Sample B was correctly described as APH (FN > 350) by 67 of 75; a further seven respondents described the sample as being in the FN 325 to 345 range. Sample D was downgraded from Australian Standard White wheat (FN greater than or equal to 300) by weather damage. Seventy-five of 75 accurately described the samples as downgraded. Samples E and F were duplicate subsamples of Australian Hard (AH) grade wheat, of insufficient FN and inappropriate cultivar to be classified in the Australian Prime Hard grade. Seventy-one of 75 accurately recorded Sample E as insufficient Falling Number for PH; 68 of 75 accurately called the FN of the sample as appropriate for AH grade. Sixty-six of 75 accurately recorded Sample F as of insufficient FN for PH grade; eight of the nine responses which incorrectly recorded the FN as 350 were from the same company. There were very few unexplained incorrect results. One farmer obtained a 350 FN result for Sample A (FN 202), while two farmers obtained FN 150 or below for Samples E and F (FN 310). We cannot exclude accidental swapping of some samples. The participants were asked whether the ease of use of the kit was high, moderate, or low. Seventy-one of 75 described the ease of use as high.

Development of Field Sampling Methods
Sample Size
Integral to the ability to use the test on-farm (as well as with elevator-harvested grain) was the need to develop appropriate methods for sampling of spikes from the standing wheat crop. Sampling is especially critical, since depression of Falling Number from sound grain levels can either be caused by a small number of highly sprouted grains or milder germination of a larger number of grains (Jensen et al., 1984). Although there is significant anecdotal evidence that the degree of sprouting varies between plants and within fields, there are little published data on this phenomenon, other than information on differential cultivar susceptibility to sprouting. It was thus important to establish the effects of sample size on sprouting. Falling Numbers were calculated from duplicate samples of 5, 10, 20, or 50 spikes taken from neighboring sites at six to nine locations within up to 22 fields, respectively.

The mean Falling Numbers for the fields fell into two clear groups: (i) seven fields with mean Falling Numbers of 145 to 184 (moderately sprouted) and (ii) 15 fields with Falling Numbers of 264 to 351 (unsprouted to mildly sprouted). These were analyzed separately (Table 3) , since it has been shown that variation in Falling Number even among small samples of sound grain samples is minimal. Two types of data were evaluated. First, the calculated FN from the mean of the duplicate samples of 5, 10, and 20 spikes was determined, and within-field variation in this parameter expressed as a standard deviation (of six to nine duplicate measurements per field). The average SD values were similar for different sample sizes for the high Falling Number group of fields, but for the moderately sprouted lower FN group of fields, the SD values were much higher for five spikes than sets of 10 or 20 spikes. This would suggest that any assessment of within-field variation in FN using 5-spike samples could provide inflated estimates of imprecision. Second, the average difference in calculated FN between each of the individual sets of duplicates (range) was determined for each spike sample size (Table 3). Again, there was little variation in the ranges for the different sample sizes of the high FN set, but for the moderately sprouted lower FN set the average range was somewhat higher for the 5-spike sets than the 10- or 20-spike set. Taken together, the results suggest that samples of 10 or more spikes provide estimates of Falling Numbers that are much less subject to sampling variation than 5-spike samples. Duplicate sets of 50-spike samples were also taken from seven to nine places in five of the moderately sprouted (lower FN) fields, but the imprecision estimates (e.g., 25 FN SD of within-field means of duplicates) were no better than those obtained with 10 or 20 spikes.


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  		Table 3 Effect of varying number of spikes in a sample on test precision

 
Twenty spikes of the cultivars tested in this study provided 15 to 50 g of grain. Even this quantity is smaller than the optimal size of sample in published studies of precision of the Falling Number test with harvested elevator-delivered grain (Tipples, 1971). However, we do not recommend the use of 50 spike samples. First, our results did not show a significant improvement in precision, and second, we believe that it would be impractical to require a farmer to collect, thresh, and grind 50-spike samples, especially if a hand-operated thresher and grinder is used. Farmers consulted during the trials of the method appeared to be prepared to use 20-spike samples. Twenty-spike samples have been used for the remainder of the study.

Between-Field Variation
Earlier workers reported that factors such as cultivar sown, seeding density, time of sowing (and thus maturity), soil type, drainage, and aspect affect the degree of sprouting after rain (Bingham and Whitmore, 1966; Ringlund, 1983; Strand, 1983). While it was not our aim to re-investigate the relative importance of these parameters, we had sampled multiple fields on 12 of the farms that had wheat with mild to significant sprouting in at least one field (i.e., FN < 350 for the Australian Prime Hard varieties, Farms A, E–L) and FN < 300 for the other cultivars. Results from these farms (means and SD of calculated FN determined at six to nine sites within each field) are shown in Table 4 . In five of the 12 farms, the same cultivars were sampled from two or three fields, and in each case, while there was evidence of mild sprouting, the mean calculated FN values did not significantly differ between fields. In five of the seven other farms, there were statistically significant differences between fields in calculated Falling Number. In several of these cases, assuming that the grain could otherwise have had the necessary protein content and physical grain characteristics to attract a premium, there would have been large differences in financial returns from the grain. For example, on Farm E, the Batavia grain would not have been downgraded because of weather damage, while the two other fields would have been downgraded two or three classification levels. On Farm G, the second Perouse field would not have been downgraded because of weather damage, the first Perouse field and the Cunningham field would have been downgraded by a single classification level, while the Pelsart grain would have been downgraded three classification grades. These examples reinforce how downgrading losses can be minimized by testing and the separate harvesting and storage of grain from sprouted and unsprouted fields.


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  		Table 4 Between-field variation in pre-harvest sprouting

 
Effect of Minor Variation in Sampling Site Within-Field
A significant consideration is whether the measured FN from a sample of 20 spikes from a 4-m2 area is representative of the sprouting in that part of the field. These studies were carried out in mild to moderately sprouted (FN 150–330) and significantly sprouted fields (FN < 150; actual FN values below 150 were not determined in this experiment). In one set of experiments, a second sample was taken over a neighboring 4-m2 block. If plants in a particular neighboring 4-m2 block were visibly different (e.g., immature, frosted, or stained by fungi), a different neighboring block was sampled. The mean difference between FN values at 22 pairs of neighboring mild-moderately sprouted sites was only 17 ± 8. In addition, in all 36 pairs of neighboring sites where the FN of the first site was less than 150, the neighboring sample also gave a FN of under 150. These results suggest that sampling of a small block of spikes should provide results that are representative of a neighboring set of spikes.

In the second set of experiments, after an initial sampling of six to nine places within the field, the sampler returned to approximately the same site 1 to 2 h later (in large fields this was up to 200 m distant from the initial point of sampling). There was slightly greater variation between sites in this experiment, but the variation was still somewhat less than the variation across the entire field. For the 22 pairs of mild-moderately sprouted sites, the mean difference was 22 ± 16 (22 sites); in the more severely sprouted sites (FN < 150), neighboring sample FN values were usually under 150 (23/36 sites). In 10 of the other 13 cases, the second site was less than 20 FN units higher than the first site. There were three sites where the second sample was significantly greater in Falling Number (by 55, 77, and 135 units).

Within-Field Variation
The fields were ranked according to the extent of within-field variation in FN, with about 30% of the fields being rather non-uniform. Seventeen fields with SD of mean FN greater than 40 are listed in Table 1A and 44 more uniform fields are listed in Table 1B. In most of the non-uniform fields, there was an apparently non-random distribution of Falling Number values, with the lower FN (more sprouted) wheat tending to be at one end or corner of the field. The most striking difference between the two groups of fields was that the fields with variable sprouting were often sloped. Eight of 17 fields in Table 1A were described as sloping (gradient > 5%), and one with a slight slope (gradient 2–5%). In contrast, only three of 44 of the more uniform fields in Table 1B were sloping with a further eight of 44 having a slight slope or undulating. There was a consistent trend for the upper ends of the fields to be less sprouted. For example, the upper end of Field 38 had samples with Falling Numbers of 304, 279, 260, and 400, while the lower end had samples with Falling Numbers of 157, 147, and 190. Samples from the upper end of Field 24 had FN of 311, 240, and 206, while the lower end had FN of 163, 108, 172, 172, and 146). Samples from the upper end of Field 35 had FN 278 and 240 and lower end FN of 158 and 178. These trends may arise because the upper areas would typically be better drained, have greater breeze circulation, and depending on their aspect, could receive drying afternoon sun.

Four of the other fields with uneven sprouting had visible signs of frost damage, weathering, or patchiness; only one of the less variable fields (the second most uneven one) had visible weathering. Although persisting humidity within the crop has been associated with sprouting susceptibility (Clarke et al., 1984; Nielsen et al., 1984), we could not establish whether this was a factor in the fields sampled in this study, as similar proportions of fields with poorly drained areas were represented in both sets of data. While we did not carry out sufficient targeted sampling of spikes from neighboring wet and dry areas, it was noteworthy that where we did do this (Fields 44 and 13) the grain from the poorly drained areas was much more sprouted. It would thus seem advisable to avoid harvesting areas with poor drainage until subsamples have been tested by either the Falling Number method or the WheatRite test for sprouting.

How Much Sampling Needs to Be Undertaken?
The results described above demonstrate that grain from a 20-spike sample can provide a reasonably reliable estimate of the extent of sprouting in a part of a field. Although 30% of fields had significant within-field sprouting variation, the fact that 70% of fields have relatively uniform Falling Numbers suggests that it should be feasible to collect a small number of spike samples from a standing crop and gain an estimate of the condition of the field. While there were more small fields in the more uniform set, large fields were represented among both the variable and uniform fields. We thus conclude:

1. When sprouting is present, fields that are reasonably flat and with similar soil types and drainage throughout usually have relatively uniform weather damage. At least two, and ideally four well-separated samples from each field should provide a reasonable picture of the level of damage present in these circumstances.

2. Where sprouting is present, fields that have significant areas that are either sloping or show signs of crop discoloration may exhibit within-field variation in sprouting that should be sampled at a greater number of sites. We have preliminary evidence that areas with poor drainage or that are tree-sheltered should also be tested separately.


   Conclusions
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 Conclusions
REFERENCES
 
A simple, 5-min field method has been developed for estimation of pre-harvest sprouting. There were good correlations between FN and the intensity of the test result; the only minor differences in the Reflectance/Falling Number responses for very different varieties arise because Falling Number also measures differences in inherent starch paste viscosity. The ability of the IC test format to produce a graded response with variable FN was unexpected, as virtually all other applications of IC testing are qualitative "yes-no" assays. Precision of the test is similar to, or slightly better than, the Falling Number test, especially if used with a suitable reflectance reader. While precision is slightly higher when a reflectance ratio (of the intensity of the test band to that of a control band) is used, raw reflectance is usually quite satisfactory. A purpose-designed reflectance reader for the test cards is currently under evaluation. Test results were similar at a range of ambient temperatures (between 16 and 37°C). It should be possible to monitor reproducibility by performing checks of standards daily, rather than within each run. The storage stability of the test cards is excellent, suggesting that they could be shipped to users and kept for extended periods at room temperature. Both of the antibody combinations assessed performed very well; the P + M format may be slightly preferred since samples above FN 350 consistently produce a colorless zone in the test. The test formats currently provide positive results for samples above FN 350; if lower test sensitivity is required to quantify Falling Numbers in the range closer to 60, this can be simply achieved by reducing the quantity of wheat extract applied to the cards (1998, unpublished results). An extensive industry trial of the method demonstrated that it is reliable in the hands of non-laboratory staff, and despite the fact that results were scored visually rather than with an instrument. It was important to carry out such a trial. For example, despite immunochromatography-based home-pregnancy tests being widely available, there has been considerable concern about data quality. Two large studies have shown that these tests worked much better in hands of lab workers than in those of the public, usually because participants did not follow test instructions correctly (Daviaud et al., 1993; Bastian et al., 1998).

The extent of preharvest sprouting can vary significantly within and between different fields. Our field research has shown that the variation is non-random, and that in many cases after moderate harvest rains, only one field or one end of a field is affected. Even after heavy rains, whole fields or major parts of fields can regularly be free from weather damage. There may also still be possibilities for some separate harvesting and binning of grain of different degrees of damage in order to maximize the quantity of grain at higher grades. There would be significant economic gain by harvesting the sound and damaged grain separately. Depending on farm size, the savings from downgrading per farm could be in the range of tens of thousands of dollars. The farmer may need to sample and test all fields where damage is expected. Research has consistently shown that estimates of Falling Number on the basis of visual assessment alone are unreliable as a basis for commercial decisions. Sound grain should be harvested first to avoid storage or harvesting with damaged grain, or the risk of damage by subsequent rain. Our field research demonstrated that about 70 to 80% of fields surveyed suffered relatively uniform weather damage that would have made it unlikely that differential–partial harvesting of individual fields would provide a significant economic gain. However, major differences are often found between fields, and this provides a major financial incentive for testing. This is because the cultivar sown and time of sowing can have major effects on the level of sprouting after rain.


   ACKNOWLEDGMENTS
 
The authors are grateful to the over 100 individual farmers who participated in the trial and provided access to their farms for grain sampling. We especially acknowledge the assistance of the Qld Grain Growers' Association, GrainCo Ltd, GrainCorp Ltd, Cooperative Bulk Handling (Western Australia) Ltd, and assistance of Ross Hansen and Stewart Armstrong of New Zealand Crop and Food.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 Conclusions
 REFERENCES
 
This work was done at CSIRO Plant Industry/ Quality Wheat CRC.

Received for publication August 23, 1999.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 Conclusions
 REFERENCES
 





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