Nutritional Choice and Diet Diversity
Of
Sika Deer (Cervus nippon taiouanus)
In the Tropical Forest of Taiwan
INTRODUCTION
To understand the food selection of herbivores, both aspects of optimal nutritional
requirements and food diversity are essential. Freeland & Janzen (1974) did a
nutritional hypothetical work and demonstrate how herbivores avoided the toxic secondary
compounds in their daily diet. Pyke et al. (1977) also applied a mathematical model
to predict the optimization of dietary choices. However, there is a great need for
certain detailed empirical studies about specific nutrient distribution in plant
communities which herbivores selected as food, and how they adjust the nutritional
optimization to satisfy the other important consideration, such as the need of reproduction
and physically growth seasonally. In this paper, the optimization approach is adopted
to assess the daily and seasonal nutritive need in the deer's diet. This nutritional
model deals with the seasonal strategy of food plant selection of Sika Deer (Cervus
nippon) in Taiwan.
Ruminated ungulates favor different food plants to maximize the net intake of protein
and energy. For example, moose were found to be "energy maximizers" (Belovsky
1978). Formosan Sika deer, a generalist herbivore, inhabit the lowland plain (0-300
meters in altitude) of the tropical forest in Kenting, Taiwan. The area is full of
a variety of trees, shrubs and grass, where the strong wind effect leads to crimping
vines and wind-tolerant shrubs occupying the patchy rock areas, and forming a unique
food reserve. Formosan Sika Deer adopted a relatively flexible strategy to browse
the forage plants in different terrain and maintained a dynamic relationship with
the vegetation (Hu & Wang 1994). In this field study, the different plant intake
amount by deer and its specific nutrition intake spectrum (protein and energy) were
examined. It was also determined whether the real food preference was consistent
with the optimal foraging theory (Schoener 1971, Stephen & Krebs 1985), and the
empirical data explored in what degree the deer optimize its dietary choice to balance
the nutrition demands and resource constraints.
This research summarized the food selection principle and the foraging behavior
of Sika deer in South Taiwan peninsula based on a nutritive optimization model, which
will contribute to the conservation plan of this endangered species and ecosystem
management in the future. This paper focused on nutritional analysis of food plants
to understand how nutritive choices achieved the physiological requirement seasonally.
In addition to the nutrient analysis in lab and field observation, this study also
explored how the nutritive diversity and its changes over time supported the seasonal
needs of the deer population.
METHODS AND EMPIRICAL DATA
Data
The data in this research was collected in Area I (fig 5) of Sheting Natural Park,
Kenting National Park, Taiwan from April 1993 to Sept. 1994. Of the deer in this
area, there were 2 males and 4 females, which were radio-collared by S6 type activity
transmitters (Telonics Co.). Receivers and H-shaped antenna were used to track the
radio signal and follow the deer. Two collared male deer (Y42 and B6) and one sympatric
female deer group (B2 and its female group, including 3 females and 3 fawns) were
found easily to approach. This population was targeted to observe the detailed nutritive
intake intensively in a 10-hectare area. I observed their food habits and foraging
behavior from a relatively close distance (within 3 meters) with binoculars (Bushnell,
8x30). While locating the targeted deer, I recorded its behavior and food items every
half minute. The sampling methods depended upon individual and specific behavior:
in male deer, a focal sampling alone per hald minute was adopted; in female group,
a scan sampling consistently recorded each deer (Lehner 1979; Martin 1986, Hu et
al. 1994). The food plants selections were identified and the intake amount was estimated
by multiplying intake time with average intake
(g/min.) for that plant in a lab cafeteria test. Each month we observed 3-6 days
in the daytime (range from 6 a.m. to 6 p.m.). Totally we recorded the deer 47 days
over 1.5 years, and 184 feeding hours excluding resting intervals.
Study Area
Kenting National Park at the southernmost tip of Taiwan, Henchun peninsula. The
reintroduction park is a 120-hectare area and located at Sheting beside the Pacific
Ocean. The altitude ranges between 100 and 200 meters, and there are low hills and
a number of uplifted coral reefs scattering around the park. The north of Area I
where the targeted population lived has two short streams flowing northwest to southeast.
The seasonal streams in wet season and underground flow in dry season have a dramatic
difference in flow to influence the water source for wildlife and moisture retention.
The climate is subtropical with a rainy season and typhoons in summer; mean rainfall
exceeds 1900 mm during May-Sept. per year and annual rainfall averages about 2200
mm (Central Weather Bureau, Taipei, Taiwan). About 90% of rainfall is during the
wet season (May-Sept.); and there is severe drought in the dry season (Oct.-April)
since the evaporation rate is higher than the precipitation rate.
The vegetation of this whole area is seasonal forest, and is represented by a mosaic
of grassland, shrub, and Acacia forest. The early migration cultivation and the long-term
browsing effect of cattle in the pasture resulted in the formation of the patchy
habitats. Of the whole area, the most area was occupied by dominant vegetation, such
as semi-deciduous forest, Acacia forest on the hill slope and evergreen sclero-phyllous
forest upon reef rock. Under the canopy, there are scattered native plants (e.g.
Acanthus & Verbena) with the invaded exotic legume (Leucaena glauca). The territory
of the focus deer population within Area I was approximately 10 hectares, which included
several ponds, a field climate station, and an abandoned house, which was utilized
by deer as shelter.
Nutritional Analysis and Intake Calculation
To calculate the daily intake amount of protein and energy, I used the general standard
equations of total nutrient intake (Hudson & White 1991). The net protein and
energy intake amount can be written as:
p_total = p1+p2+p3+...
c_total = c1+c2+c3+... (1)
where P is the daily total intake amount of protein (g/day), C is the daily total
intake amount of energy (calorie/day); Pi is daily protein intake amount (g/day)
contributed from a specific plant species i, Ci is daily energy intake (cal/day)
contributed from the plant species i; ti is the observed feeding time (in minutes)
for plant i; ri is the feeding rate (g/min) for that food plant, which was indirectly
measured in the short-term captive periods of wild population (n=43 in Area I) by
providing the natural food and observing its intake condition to estimate the foraging
consumption rate in lab in June 1993.
To evaluate the food quality and its nutritive values, we conducted a test for 49
plants of primary food items and dominant vegetation species. Energy (cal/g), protein
(%), and ADF (acid detergent fiber, %) for certain forage plants were sampled in
summer (August 1993) and winter (February 1994) respectively. The nutritive examinations
were done by the nutrition analysis lab in the National Husbandry Examination Center,
Tainan, Taiwan. In equation (1), pi is the protein concentration (%, g of protein/total-g)
and ci is the energy concentration (calorie/g) for each specific food plants, which
information was obtained from the above nutritional analysis lab (Table 1).
Step 1. The basic nutritional values (protein [p] and energy [c])
Within limited budget, we tested both favorable and unfavorable food plants as
many as possible (table 1). I denoted each plant an identification code starting
with its vegetation group: A(tree), C(vine), D(shrub), E(herb) and F(grass). Totally
we obtained the protein values (% in weight base) for 33 plants (fig 10), and energy
content (kcal/kg) for 22 plants (fig 9). When we separated those plants dichotomously
with natural gap (fig 7), we got a direct sense some plants were important in nutrition,
some not (table 6).
Step 2. Conduct a monthly behavioral observation about the foraging time (min.) toward
each plant (Ti value)
Kuo (1994) studied the same deer population and obtained informative data for total
foraging time. He found out there was no significant difference in activity pattern
between day and night. I only observed the foraging behavior in day time, but calculated
an estimated average foraging time for a particular plant per day. I also extracted
the feeding (including walking, searching and intaking) and true intake ratio from
my observations (table 5).
Step 3. Consider seasonal time constraint (T*)
Kuo (1994) estimated a daily foraging ratio of 24 hours, which changed over seasons.
Generally seasonal time constraint (T*) ranged from 14.3% (rut period in male deer)
to 50.6% (wet season) of 24 hours. By integrating step 2 & 3, we estimated how
many minutes deer feed on a specific plant per day (table 4).
Step 4. Physical intake rate (r) determines the nutrient intake rate (a,b) partially
Our model didn't deal with the protein and energy concentration (p,c) directly.
However, we need to know the specific nutrient intake rate: a (g/min.) is protein
intake rate and b (kcal/min.) is energy intake rate , which are equal to physical
foraging rate times nutrient values (p,c) (see equation 1 & 2). If we lacked
information of the certain plant's physical intake rate (r), we just extrapolated
and referred the value from the same vegetation group to make it up.
Step 5. Is the comparison of two-plants model appropriate and effective?
To keep this model simple, there is an assumption by comparing the top two plants
to represent the major nutritive intake source of deer, where we calculate the fitness
function and make judgements. The result demonstrated: the plant species a1 always
showed up in the primary food list (table 5), except 4 months in total 40 monthly
observations. The ratios occupied by the top two plants ranged from 25.4% to 70.7%
per observation. If we calculated frequency that foraged 40+% intake from the top-2-plant
system, there were 82.5% (37/40) observations available and it seemed effective enough
to give us some sense of food selection behavior: Sika deer usually focused on a
limited number of important plants to compose their daily diets (table 5).
Step 6. Broad diet spectrum vs. specific major food
Our model only considers the main resource of nutritive intake, especially limited
in protein and energy. However, there are a lot of reasons deer don't favor some
high-nutritional food, for example plants a15, a33, d1 and d18 (fig 12). The possible
reasons might depend on our incomplete knowledge of wildlife about digestive mechanism
and rumen toxicology (see discussion). By eliminating some doubtable toxic food plants
(which were not favorable for deer, e.g. d1, d18, a33), we focused on a reasonable
set with a series of nutritive values (fig 8).
Step 7. Identify the plant a1 as the yearly primary food
Since major food plants have to be abundant enough to contribute to the daily diet,
we only compare the relationship between the primary food (a1) and other submajor
food in step 5 list (e1, a4. f1). Even though we don't know exactly how parameters
of fitness function behaves, and nutritive constraint values (P*,C*), we can compare
the intake amount (estimated by the foraging time) to test the model prediction:
will deer always choose the plant with better protein and energy? The scatter plots
(fig 13,14,15) show us a clear tendency: plant a1 wins! To somewhat degree, deer
foraged plant a1 heavily, discarding which plant was the other submajor food. The
extent of clusters by feeding on plant a1 varied over seasons.
RESULTS
Exotic Plant (a1) as a Rich-Nutrient Deer Food
Plant a1 (Leucaena glauca (L.) Benth.) is an exotic legume from South America, which
was introduced into Taiwan for fuel and paper pulp. After World War II, this species
was no longer of economic interest and neutralized in secondary forest and disturbed
land in South Taiwan, where the climate was similar to its native habitat. This legume
has a better nitrogen-fixing ability due to its symbiotic rhizo-bacteria in the root
system. Both in dry and wet seasons, the legume a1 became the most favorable deer
food due to its excellent nutritional characteristics, including nutrient concentrations
of protein and energy (p,c) and the respondent intake rates (a,b) (fig 8, table 1).
Plant a1 was the primary source of food plant yearly for the Sika deer to obtain
both protein and energy, which appeared in the primary food list (90% occurrence,
only 4 observation records didn't show up as a major food, table 4) and led deer
spent a significant time to search and feed it. Since deer could intake enough nutrients
through feeding a1, the predominant exotic species, the deer also chose a variety
of submajor food plants to meet the other physiological and favorable requirements
(table 5), and maximized the fitness, growth rate, survivorship or fecundity. To
summarize the major (a1) and submajor forage plants, the deer spent most of their
daily feeding time to intake these 2-3 primary species (table 5). In this case, our
two-plant diet model can properly work with 80% of the Sika deer data, which a1 and
the other submajor plants contributed to the most forage amount daily (39.5-70.7%).
According to our simple model theoretically, it implies the deer would spend much
more time in foraging plant a1 as a nutrition optimal choice, rather than considering
e1, a4, and f1, which all are secondarily important food sources. While the deer
focused on searching and feeding a1 or e1 (fig 13 for e1) as the daily major food,
the relative intake amount by foraging from a4 and f1 were significant low (fig 14
for a4, fig 15 for f1).
What's Going with the Widespread and Exotic Plant
A1 and Recovery Sika Deer?
If the recovery deer population can adapt this widespread and exotic plant well
as a qualified food, deer will take advantage in the invaded lowland patches (lower
than 300 meters elevation) without a serious concern about the limit of food supply.
Excluding the basic nutritional requirement, we still need to check the other factors
of carrying capacity and deer conservation, such as the balance of minor elements,
human population density, habitat fragmentation, dispersal corridor and land use
in human-dominant ecosystem. We need an integrated program to combine all useful
information to assess the potential habitat in the short-coming future.
Food Habit
This research included 151 food plant species from 62 families, including 5 species
of pteridophyte, 116 species of dicotyledon and 30 species of monocotyledon. That
deer fed proportionally more on woody species than herbaceous species (Chi-square,
p<0.05) indicated that deer at Sheting area tended to be browsers, rather than
grazers.
Nutritive Change between Summer and Winter
We observed the favorable and unfavorable forage plants by deer browsing, and analyzed
their nutritional contents (Table 2). In tropical Taiwan, the hot and humid summer
is the growth season for deer and plants. Generally speaking, the quality and quantity
of forage plants match the physiological needs of Sika deer synchronously.
1. In nutrient content of heat energy, most summer forages were better than winter
forages. The only exceptions were a4 and e1.
2. In protein concentration, most summer forages were better than winter forages.
There were exceptions of a1, a6 and d1.
3. ADF method is to estimate lignified nitrogen, lignin and cellulose by extracting
plant tissue with strong acid solutions. ADF method is also widely used as an easy
method to measure the fiber in a feed (Van Soest 1982). In ADF testing, the plants
in early autumn (Aug.1993) had more fiber content than spring (Feb.1994).
Nutritive Diversity of Forage Plants
The preference and intake amount of the forage plant primarily reflect its nutritive
values and the anti-herbivore mechanisms with either physical or chemical agents.
Some rich-nutrition plants were not browsed by deer due to its strong taste and toxic
components, such as a33 & d18 (table 2). I also listed the relative secondary
compounds which had significant amount in plant to detect.
1. The rich-energy forages were a33 (unfavorable acacia, predominant vegetation),
d1 (unfavorable citrus, native fence tree), a1 (much favorable, especially leaf and
fruit, exotic and neutralized legume as fodder plant), a15 (unfavorable mahogany,
native fence tree), and d18 (unfavorable verbena, shelter shrub). The poor-energy
forages were a4 (favorable mulberry) and c14 (favorable in specific season) (Fig
9).
2. The rich-protein forages were a1 (much favorable legume), a33 (unfavorable acacia),
d18 (unfavorable verbena), a41(unfavorable sandalwood), and d1 (unfavorable citrus).
The poor-protein forages included grasses (f1, f2) (Fig 10).
3. The rich-fiber (ADF) forages were e49 (unfavorable agave, exotic fiber plant
as cable raw material in World War II), c9 (favorable spurge vine), e38 (unfavorable
lily), a15 (unfavorable mahogany), and grasses (f1, f2). The poor-fiber forages
were a1 (very favorable legume), a4 (favorable mulberry), a6 (favorable loquat) and
c17 (favorable dog bane) (Fig 11).
4. Most plants in our survey had energy content from 3.6-4.2 kcal/kg, and protein
content from 7-15% (Fig 12).
DISCUSSION
Nutritional Change over Time
Nutrition of deer forages varies with the physiological state and development of
forage plants. Even though seasonal change of nutrients in tropical Taiwan was not
obvious, we deduced that new growth in early summer had more protein content than
the matured leaves in winter, because the young leaves accumulated protein in protoplasm
to grow. In winter or dry season, forage tended to have low nutritive value. In
summer or wet season, the forage yield was at its maximum, and the nutritive values
were higher. In fall, the leaves predominated with hydrolysis, which lignified the
cell wall and increased fiber, so the ADF value increased in August.
Nutritive Diversity in Plants to Fulfill the Foraging Need
Forage quality or digestibility varies between plants, seasons, and locations (Klein
1962). The effective factors are climate (light, temperature, moisture), soil quality,
species variation, and terrain difference (exposure, altitude, slope). There was
a positive correlation between nitrogen (protein) content of forages and their nutritive
quality, while a negative correlation exists between fiber content and nutritive
quality. Both nitrogen (protein) and fiber are reliable indicators of forage quality
(Klein 1965). Basically, the deer most favorite forages tended to have higher energy
or protein, and have less fiber content. On the other hand, the unfavorable forages
tended to more fiber which the deer avoided. We found, for some reason, deer didn't
feed on the rich-nutrient (energy or protein) forages as we supposed. Those plants
had a somewhat strong taste (e.g. d1, a15, a41) or certain defensive substances (e.g.
d18, a33)(table 3).
Defensive Substances as an Anti-Herbivore Mechanism
The resisting substances of plants include lignin, cutin, and many secondary compounds.
The function of those substances is to resist wind, disease, and herbivore's browsing.
They reduce the nutritive value of the forage plant (Van Soest 1982), and avoid deer's
over-browsing as to reserve the essential resource for plant growth and reproduction.
Secondary compounds, such as alkaloids, glycosides, saponins, tannin, terpenes,
phenols, etc., have a clear defensive rather than metabolic role. They have a metabolic
source and are often stored in fresh and vulnerable tissues that are not defended
by physical structures like lignins and silica. Secondary compounds may always have
a greater effect on browsers (e.g. Sika Deer in Kenting) than grazers, because of
its high concentration and local distribution in plant parts (Table 2). For example,
wood plants and vines tend to be more highly defended towards browsers. (Bryant 1991)
About the secondary compounds:
1. Alkaloids
Alkaloids are the heterocyclic nitrogenous compounds that exhibit the inhibitory
activity towards digestion. In our study area, there were certain rich-alkaloid plants:
a1 (much favorable), a23 (favorable), and d18 (unfavorable). Although d18 (verbena)
also had better protein and energy, but deer seemed to merely treat it as a shelter
plant and didn't feed on it. Sometimes the specific d18 fruit attracted deer to browse,
but deer always were careful not to forage the other parts. We believed the reason
might be its high level of alkaloids, glycosides and saponins. We do not understand
how the deer's digestive system is able to absorb nutrients with high alkaloid contents,
such as plant a1 and a23.
2. Essential Oils
Essential oils represent a diverse group of organic substances in plants that are
volatile and soluble in organic solvents. They have low molecular weight such as
ester, ether, phenols and terpenes and also exhibit antimicrobial activity. When
those plants are browsed, rumen organisms would change or make some adjustments (Schwart
1980).
Legume (e.g. a1) is the essential forage for deer either in captivity or in the
wild. Legume also has terpenoid throughout the plant body, which includes saponins
and steroids (table 2) and would be somewhat toxic, although rumen bacteria might
have ability to partially detoxify them. Some terpenoids also have potential to cause
stable foam formation and to promote RBC hemolysis (Van Soest 1982).
We observed that deer primarily fed on a1 legume either in dry or wet seasons, and
obtained high energy and protein contents from this quality forage. Deer may regulate
the intake amount with different feeding patterns and behavioral controls to avoid
its overdosed terpenoids toxicity.
3. Tannin
Tannin may be effective as toxins, rather than a defensive compound by its protein
precipitating ability (Robbins 1987). Because of the high content of tannin in a33
(acacia), Sika Deer didn't browse this predominant vegetative plant even though it
was high in energy and protein contents. In addition, other forages with more or
less tannin content might be browsed in a limited dose (a23, d3), or the deer would
avoid tasting the highly protective plant parts with tannin, such as a1 bark and
a6 fruit (table 3).
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Table 2. Plant List and the Examination of Nutritive
Contents
(Nutritive test date: S=summer samples in Aug. 1993; W=winter samples in Feb. 1994)
ID scientific names test browsing common
of forage plant date amount name
Tr
a1. Leucaena glauca (L.) Benth. (leaf, fruit) W,S ++++ legume
a2. Macaranga tanarius (L.) Muell.-Arg. W,S +++ spurge
a3. Pittosporum pentandrum (Blanco) Merr. W,S ++ (Pittosporaceae)
a4. Broussonetia papyrifera (L.) L'Herit. ex Vent. W,S +++ mulberry
a6. Eriobotrya deflexa (Hemsl.) Nakai W,S ++ rose - loquat
a8. Trema orientalis (L.) Blume W,S + elm
a15. Aglaia formosana (Hayata) Hayata W - mahogany
a23. Terminalis catappa L. W ++ (Combrefaceae)
a31. Ficus wightiana Wall. ex Benth. W + mulberry - ficus
a33. Acacia confusa Merr. W - legume - acacia
a41. Champereia manillana (Blume) Merr. W + sandalwood
a43. Bambusa dolichoclada Hayata W + bamboo
Vines
c1. Ipomoea obscura (L.) Ker-Gawl. W +++ morning glory
c3. Passiflora suberosa L. W,S ++ passionflower
c4. Trachelospermum gracilipes Hook. f. W ++ dos bane
c7. Gymnema alternifolium (Lour.) Merr. W +++ milkweed
c9. Mallotus repandus (Willd.) Muell.-Arg. W ++ spurge
c12. Merremia gemella (Burm. f.) Hall. f. W ++ morning glory
c14. Piper kawakamii Hayata W,S + (Piperaceae)
c15. Parsonia laevigata (Moon) Alston W + dog bane
c17. Trachelospermum jasminoides (Lindl.) LemaireW + dog bane
Shrubs
d1. Murraya paniculata (L.) Jack. W,S - rue / citrus
d3. Ardisia cornudentata Mez W + (Myrsinaceae)
d4. Pandanus odoratissimus L. f.
var. sinensis (Warb.) Kanehira W + (Pandanaceae)
d18. Lantana camara L. W - verbena
d28. Hibiscus taiwanensis Hu W - mallow
Herbs
e1. Hypoestes cumingiana Benth. & Hook. W,S ++++ acanthus
e2. Stachytarpheta jamaicensis (L.) Vahl. W + verbena
e38. Ophiopogon formosanum Ohwi W + lily
e47. Lygodium japonicum (Thunb.) Sw. W ++ fern (Schizaeaceae)
e49. Agave sisalana Perr. ex Enghlm. W + agave
e51. Cyperus alternifolius L.
subsp. flabelliformis (Rottb.) KukenthalW + sedge
Grasses
f1. Miscanthus floridulus (Labill.) Warb.
ex Schum. & Laut. W,S +++ grass
f2. Imperata cylindrica (L.) Beauv.
var. major(Nees)Hubb.ex Hubb. & VaughanW,S ++ grass
Table 3. Distributions of second compounds in deer
forage plants in Taiwan
(substance distributions in plant parts: lf=leaf, fr=fruit, sd=seed, bk=bark,al=all
plant, rt=root, st=stem)
ID (second compounds class) distributed parts - specific substances, local concentration
(if available)
Trees
a1 (alkaloids)lf,fr-mimosine, leucenol (saponins)lf sd-sitosterol4.6% (tannin)bk6.25%
sd lf
a3 (saponins)al-hederagenin,barrigenol (tannin)al 0.4% al-edergenin
a6 (glycosides)lf,sd-amygdalin1.4% (saponins)lf sd (tannin)lf fr 0.17% (others)
lf-As 0.012%(fresh);0.022%(old) fr-pectin 1.03% sd-HCN 0.004% fr-Na, K 0.22%, Fe
0.02%
a23 (alkaloids)al,fr-trigonelline (tannin)al bk 6.26% rt 7.77% (others)al-Mg(much),
K(rare,3.87%) al-phytosterol fr-glucosazon, pentosan al-pyridine 0.1%(K salt,
etc)
a31 (glycosides)lf-flavonic glycoside, coumarin (others)rt,st-phenoid
a33 (tannin)al:5-16%(age=10-30 yr)
Vines
c1 (glycosides)sd-pharbitin 2.0%, gibberellin glucoside 7%
c3 (others)al-theine, leucodelphinidin
c4 (glycosides)al (others)al-acetamide, inositol dimethyl ether,arctin
c7 (others)al-sarcostin,metaplexigenin,benzoylramanone,deacylcynanchogenin,utendin,pergularin,lanoxin
c14 (others)al-piperidine, piperidene, futoenone, futoxide
c17 (glycosides)al-flaronglycoside (others)al-lanoxin, tracheloside, acetamide, inositol
dimethyl ether
Shrubs
d1 (others)bk-mexoticinlf-isopentyl limettin, exoticin, heptamethoxy flavone, cadinene,
flavone, coumarin, engenol, imperatorin, murralogin, meranzin, imurrangatin, noracrolnlcine
d3 (tannin)al (others)al-bergennin, querectin, myricitrin
d4 (others)lf-phenol
d18 (alkaloids)al (glycosides)al-lanoxin (saponins)al (others)al-flavoid
d28 (glycosides)fl-anthocyanin,f lavonic glycoside
Herbs
e2 (glycosides)al (others)al-phenol 0.0028%
e47 (others)al-phenol
Grass
f2 (glycosides)al (others)al- K 0.75%