(ck)
Average 1
12.61
22.8
23.0 26.0 10.82 0.594** 42
Air
Three Maximum 1 0.34 28.5 28.8 28.9 10.82 0.548** 42
Tempe-
days
rature
Minimum 1
15.56
17.5
17.8 22.9 10.82 0.686** 41
(TA)
Average 1
9.15
22.9
23.2 26.0 10.82 0.504** 43
Five Maximum 1 0.08 27.1 28.3 28.4 10.82 0.223 43
days
Minimum 1
0.31
18.7
19.0 19.1 10.82 0.623** 42
Table 5. Simulation of stem or air temperature with three parameters and three durations
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Agricultural Science
T 21.7
22.8 T
0.37
p
T 20 10.82 (
)(
)
( R=0.853**； n=66)
(9)
22.5 21.7 22.8 22.5
T 21.5
26.0 T
12.14
a
T 20 10.82 (
)(
)
( R=0.778**； n=85)
(10)
21.8 21.5 26.0 21.8
T 22.8
26.0 T
12.61
T 10.82 (
)(
)
A
( R= 0.594**； n=42)
(11)
23.0 22.8 26.0 23.0
3.4 Stem and air temperatures at 20 cm
Considering energy exchange and balance, stem and air temperatures in rice canopy were
connected with two energy sources: solar radiant energy and water heat energy. To avoid
trouble in actual operation, the chapter established effective statistic models as equations (12) and (13), which included the screen temperature (TA), cloud cover (N, from 1 to 10), and the water temperature at inflow (Tin) and outflow (Tout). Only by determining Tin and Tout, stem or air temperature at 20 cm could be calculated by screen temperature and cloud cover, which were both offered by the local weather station. It was helpful to estimate the damage of lower temperature weather and the effects of the adjusting measures in actual seed
production.
N ( T T )
N
in
out
p
T 20
(1
) T ( R＝0.812**, n＝46)
(12)
15.2
2
15.2 A
N ( T T )
N
in
out
a
T 20
(1
)
T ( R＝0.975**, n＝46)
(13)
34.9
2
34.9 A
3.5 Techniques by used plant temperature to safeguard sterile of Peiai64S
Peiai64S was a widely used TGMS in two-line hybrid rice breeding. Its fertility was
controlled by temperature during its sensitive stage. Its sterility usually fluctuated owing to the frequent fluctuation caused by monsoon, which resulted in damage to the seed purity of seed production in China southern rice area (Lu et al 2001, Yao et al 1995). The first so-called super hybrid rice in China, Liangyoupeijiu, which was released by the authors’ research
group, was popularized over 7 million ha, and has been the major planted rice for its largest planting area of China from 2002. However, in lower reaches of Yangtze River, in the past five years, there was twice lower temperature weather (daily averaged temperature for three days lower than 24°C) in August, during which was the sterility sensitive period of TGMS, that caused damage to the seed production of Liangyoupeijiu and other two-line hybrid rice.
It was a hidden problem for seed production of two-line hybrid rice. In the seed production practices of Liangyoupeijiu, it was found that when attacked by lower temperature weather, the seed purity exhibited a large difference even if the TGMS was in a same weather
condition, owing to the individual differences at landform and water treatment and so on.
Researches showed that the fertility of rice TGMS was affected directly by plant
temperature, which was infected by the microclimate of the field (Hu et al 2006). When attacked by lower temperature weather, by irrigating warmer water from river or deep pool, Plant Temperature for Sterile Alteration of Rice
179
plant temperature would be increased by 2°C, which was effective for safeguarding the
sterility of TGMS (Lu et al 2004, Zou et al 2005). So far, the forecast of sterile alteration is only based on the temperature information from weather station; there is lack of any study on
field microclimate or plant temperature, and especially no research has focused on the
temperature scale of plant or air around it. In researches of wheat, some researchers used plant temperature as the parameter for freeze injury and grain growing speed (Feng et al 2000, Liu et al 1992).
The chapter put forward a method to conclude the fertility of TGMS by stem or air
temperature at 20 cm height, which takes various factors including microclimate and
location of field into account. It is more direct and exact than the traditional method. In rice production, we can use it to estimate any field or representative plot of a large field, and to monitor directly the result of regulation for safeguarding seed production in two-line hybrid rice. The technique is: when attacked by lower temperature weather with average
temperature lower than 24°C during 5-15 days before TGMS heading, using infrared or
thermosensor temperature indicator to determine plant stem temperature at 20 cm height or air temperature around it at 02:00, 08:00, 14:00, 20:00 (or only 08:00 and 20:00) every day. If the averaged value is lower than the line of 22.8°C for stem temperature or air temperature is lower than 23.2°C, it implies that the TGMS will transform its sterility to fertility. For safeguarding its sterility, it is necessary to irrigate by warmer water higher than 25°C, and by depth of 15 cm, until the temperature is higher than the above index.
The present chapter also established a statistic model for stem or air temperature at 20 cm height. By the inflow and outflow water temperatures of any field, and screen temperature and cloud cover from the local weather station, stem or air temperature at 20 cm height can be concluded. For an application example, if one day, the average temperature and cloud
cover was 22°C and 9, respectively, and the actual water temperature of the field was 23°C, by (12) and (13), stem or air temperature at 20 cm height was calculated to be 22.55°C and 22.74°C, respectively, both of which were lower than the above temperature index. By
irrigating warmer water from river, the inflow and outflow water temperature were
measured as 26°C and 24°C, by (12) and (13), the stem and air temperature at 20 cm height was calculated to be 23.74°C and 24.23°C, respectively, since both are higher than the above temperature index, it will be effective for safeguarding the sterility of TGMS.
4. Abbreviation
Tp：plant temperature. Tp10, Tp20, Tp30, Tp40 denotes rice stem temperature at plant heights of 10cm, 20cm, 30cm and 40cm, respectively.
ΔTp: plant temperature difference between water irrigated and non-irrigated.
Ta20, Ta40 and Ta: air temperature at heights of 20cm, 40cm and 150cm, respectively.
Tw: water temperature.
Tin: temperature of inflow water.
Tout: temperature of outflow water.
Tw-A: temperature difference between water temperature and air temperature at height of
150cm.
LAI: leaf area index.
DL: distance between the last upper two leaves of rice plant.
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Agricultural Science
5. Notes
The most data were published in the journals of <Agricultural Sciences in China>, 2007, 6(11):1283-1290, and <Rice Science>, 2008, 15(3):223-231.
6. References
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Cheng W D, Yao H G, Zhao G P, Zhang G P. Application of canopy temperature in detecting
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Feng Y X, He W X, Rao M J, Zhong X L. Relationship between frost damage and leaf
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Ferchinger G N. Simulating surface energy fluxes and radiometric surface temperature for
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Hu N, Lu C G, Zou J S, Yao K M. Research on plant temperature of TGMS and its
application. Chin J Ecol, 2006, 25(5): 512-516. (in Chinese with English abstract) Leuning R. Leaf temperature during radiation frost, Part I. Observations. Agric Forest Meteor, 1988a, 42(2): 121-133.
Leuning R. Leaf temperature during radiation frost, Part II. A steady state theory. Agric Forest Meteor, 1988b, 42(2): 135-155.
Liao F M, Yuan L P. Study on the fertility expression of photo-thermo-sensitive genic male sterile rice Peiai64S at low temperature. Sci Agric Sin, 2000, 33(1): 1-9. (in Chinese with English abstract)
Li S P, Cai S Z, Fu X H. Effect of leaf temperature on differentiation and formation of Lichi floral buds and its control. Chin J Trop Crops, 1999, 20(4): 38-43. (in Chinese with English abstract)
Liu R W, Dong Z G. Effect of leaf temperature on grain milking in wheat. Chin J Agrom, 1992, 13(3)：1-5. (in Chinese with English abstract)
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rice, Rev Chin Agric Sci Techn, 2004, 6 (supple) : 41-44. (in Chinese with English abstract)
Lu C G, Zou J S, Hu N, Yao K M. Plant temperature for sterile alteration of a temperature sensitive genic male sterile rice, Peiai64S. Agric Sci China, 2007, 6(11):1283-1290.
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fertility of PTGMS rice in China. Sci Agric Sin, 1999, 32(4): 6-13. (in Chinese with English abstract)
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condition of winter wheat farmland ecosystem. Chin J Appl Ecol, 1997, 8(3): 332-334.
(in Chinese with English abstract)
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38(3): 269-277.
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(in Chinese with English abstract)
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Xie J H, Huang P B, Huang P J, Long Z Y, Jiang D X. Comparison of climatic factors between farming fields and meteorological station in early autumn seed production of two-line hybrid rice. Hyb Rice, 2001, 16(3): 32-35. (in Chinese with English abstract) Xu M L, Zhou G Q. Studies on thermo-sensitive part of Peiai64S relating to its fertility
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Yao K M, Chu C S, Yang Y X, Sun R L. A preliminary study of the fertility change
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Zhao L X, Jing J H, Wang S T. Studies on water transports in the soil-plant-atmosphere continuum on Weibei rainfed highland, Shanxi Province-Effects of ecological
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Section 5
Crop Protection
9
Infrared Spectroscopy Applied to
Identification and Detection of Microorganisms
and Their Metabolites on Cereals
(Corn, Wheat, and Barley)
Cécile Levasseur-Garcia
Université de Toulouse, Institut National Polytechnique de Toulouse, Ecole d’Ingénieurs de Purpan, Département Sciences Agronomiques et Agroalimentaires, Toulouse,
France
1. Introduction
Over the last several years, mycotoxins, which are metabolites secreted by fungi, have been the subject of numerous studies. These eukaryotes play a major ecological role in the life cycle of plants. Indeed, for some fungi, the role of saprophyte places them at the heart of ecosystem dynamics [Alexopoulous et al., 1996].
Some 350 mold species produce a large range of secondary metabolites (over 300, of which
~30 are toxic) [Fremy et al., 2009] and represent a potential danger for animal and human health and cause significant losses for the cereals industry [Le Bars et al., 1996]. The effects of mold are well illustrated by decreases in crop and livestock yields, public health
problems, or write-offs on the international cereal market [Le Bars, et al., 1996]. The United Nations Food and Agriculture Organization estimates annual global losses from mycotoxins
at 1 billion tons of foodstuffs [Fao, 2001]. The primary organisms impacted by mycotoxins are plants. Currently, about 25% of agricultural crops worldwide are contaminated by these metabolites [Charmley et al., 2006].
In response to these significant economic and health risks, global non-tariff barriers (i.e., specific food-safety standards imposed on imported products) were erected to control
commercial trade based on the mycotoxic quality of foodstuffs. These measures generate
significant economic and material losses for countries that export contaminated foodstuffs, either because their cargo is refused or because of a reduction in prices. To limit these consequences, farmers and the food industry strive to reduce the presence of mycotoxins in their products. Therefore, producers and processors are searching for alternative analytical methods to determine, in a quick, simple, and inexpensive manner, the risk of their products containing fungi or mycotoxins. The use of infrared spectroscopy—a mature technology—to
monitor foodstuffs could respond to this need.
In this chapter, we focus on mycotoxins found mainly in wheat, barley, and corn and that
have been studied in the international literature; namely, deoxynivalenol, fumonisins, and aflatoxin B1.
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Agricultural Science
2. Advantages of using infrared spectroscopy to manage fungal and
mycotoxic risk for wheat, barley, and corn
Fungus can be detected by microbiological methods involving visual, microscopy, and
microbial-cultural methods.
Conventional methods of mold detection are based on direct observation by eye or by
microscope of thalli, contaminated foodstuffs, or microbial cultures. These methods are time consuming and require viable samples and a good deal of expertise. Counting methods are
difficult to apply to fungi because, during their reproduction, a spore generates a mycelium that can in turn divide itself into tens of individuals. Furthermore, a fungal contamination may not be visible at the surface of grains [Hirano, et al., 1998, Pearson, et al., 2001].
Other methods are based on molecular biology or on the detection of antigens specific to
given molds. Organisms, either dead or alive, can be detected by the polymerase chain
reaction (PCR) by copying a large number of DNA sequences that are originally present in
small quantities (with a multiplicative factor on the order of 109). By amplifying certain genes of toxigenic strains, PCR serves as a tool to determine the risk. Various researchers have tested PCR to detect Fusarium contamination in corn [Jurado et al., 2006; Jurado et al., 2005; Nicolaisen et al., 2009]. These methods are rapid, sensitive, and can be automated.
They are good qualitative methods (e.g., good selectivity) but offer only average precision in quantitative terms (they are called “semiquantitative”). These techniques are thus very
reliable, provided the fungal strain to be detected is known beforehand, and so are used as referential methods. With such methods, a grain is deemed of suitable microbiological
quality if less than 10 000 germs of the storage flora per gram of grain are detected.
New approaches are based on detecting constituents and fungal metabolites. Such approaches exploit the fact that molds have specific characteristics that distinguish them from other eukaryotes. These characteristics include the regulation of certain enzymes, the synthesis of lysine amino acid by a particular metabolic route, extremely structural characteristics (e.g., the Golgi apparatus), and genetic characteristics (e.g., haploid). From among these attributes, two types of compounds can be used as indicators of a fungal contamination.
The secreted compounds are synthesized compounds such as soluble carbohydrates (e.g.,
disaccharide trehalose and polyhydric alcohols such as mannitol or arabitol) or products of the metabolization of complex carbons such as volatile aldehydes, alcohols, ketones, spores, primary metabolites, secondary metabolites (i.e., volatile compounds). The last item gives rise to the characteristic fungal odor and is often detected by an electronic nose. For
nonvolatile compounds, other tools such as infrared spectroscopy seem better suited.
The structural compounds of mold can also be used for their detection. The main
polysaccharides of the cell wall of mold are the α et β (1-3) glucans, as well as chitin.
Ergosterol is a component of fungal cell membranes.
Chitin may absorb infrared light, making it useful for infrared spectroscopy [Nilsson, et al., 1994; Roberts et al., 1991]. The main inconvenience in using this component as an indicator of fungal contamination is that chitin is not limited to fungi; it is found in insects, diatoms, arachnids, nematodes, crustaceans, and several other living organisms [Muzzarelli, 1977]. In addition, it may take different forms, each of which requires a specific detection technique.
Roberts et al. [Roberts, et al., 1991] estimates the quantity of mold on barley by detecting this molecule but also detects glucans by near-infrared spectroscopy.
Infrared Spectroscopy Applied to Identification and Detection
of Microorganisms and Their Metabolites on Cereals (Corn, Wheat, and Barley)
187
Ergosterol, however, is more specific to mold. This molecule, which may still be called
provitamin D2, is a C24-methylated sterol (and is part of the subgroup of organic
compounds that are soluble in lipids) and is found in the cell membranes of yeasts and
filamentous fungi. This molecule is not found in animal cells [Verscheure et al., 2002] and is in the minority among the sterols found in higher plants [Pitt et al., 1997] and insects [Weete, 1980]. Griffiths et al. [Griffiths et al., 2003] demonstrated that ergosterol is the primary sterol found in molds: it represents 95% of the total sterols, with the remaining 5% being
ergosterol precursors from Leptosphaeria maculans. This specificity makes this molecule a potential tracer of fungal activity. It is generally agreed that the ergosterol content of grains must be less than a given threshold; the limit for corn is 8 µg/g.
3. Infrared spectroscopy to detect fungal and mycotoxic contamination of
wheat, barley, and corn
3.1 Background and methods
The first application of infrared spectroscopy to detect microorganisms dates from the 1950s
[Miguel Gomez et al., 2003]. In these applications, the spectrometers were calibrated depending on the method of dosing the fungi or mycotoxins. In the 1980s, Fraenkel et al. [Fraenkel et al., 1980] and Davies et al. [Davies et al., 1987] published their first works on the detection by infrared spectroscopy of fungal contamination ( Botrytis cinerea and Alternaria tenuissima), but the application of this tool to detecting mold really grew in the 1990s. This growth was due to the fact the existing agronomic models required collecting a significant amount of data in the field, making this approach unsuitable for routine use. In addition, industry required nondestructive techniques to assess the health safety of crops. Therefore, several research teams used infrared spectroscopy to detect mold and mycotoxins on cereals, which could be done concomitantly
with the quantification of other parameters such as protein content, humidity, etc.
One method proposed to determine the fungal or mycotoxin content is to quantify the total fungal biomass. Toward this end, ergosterol is used as a fungus marker [Castro et al., 2002; Saxena et al., 2001; Seitz et al., 1977; Seitz et al., 1979]. Very often, this type of study is coupled with a study of the mycotoxin content and fungal units (colony-forming units or
CFU). Indeed, the quantity of fungi is not proportional to the quantity of mycotoxins; it is possible to have small quantities of fungi but large quantities of mycotoxins, and vice versa.
Indeed, fungi may disappear after secreting its toxins, either because of the evolution of the mycoflora or because of the application of chemical treatments. In addition, certain strains are more toxic than others. Two conclusions exist from the work on this subject: some
researchers find a correlation between the mycotoxin content, the ergosterol content, and/or the fungal units [Lamper et al., 2000; Le Bouquin et al., 2007; Mi