Phytocenologic relationships in pasture stands
for foal and their multifunctionality in marginal conditions.
Maršálek,M., Čermák,
B., Klimeš, F., Kobes, M., Lád, F., Frelich, J.: The Agricultural
Faculty of South Bohemian University in České Budějovice.
Abstract
Yield, botanic composition and
chemical analyses of grass and herbage were measured in seven localities with
different altitudes between 250 and 800 metres above sea level. Grass and
herbage are the most natural and optimal feedstuff for cattle. Grazing
management should notably regulate the pasture composition, i.e. support
dominance of soft stoloniserous strains of grasses and decrease occurrence of
weed and less value strain of gramineous grasses.
Key words: pasture, foal agrobotanical structure,
nutrients, mouse.
Introduction
. Hence, the role of livestock as an integrated
part of the organic production systems and its importance for future
development differs considerably between countries and regions in the EU(Klimes
et al 2001, Čermák et al 2004, Holoubek,Čermák 2004). .
The pasture nutritional
parameters varied in depending of agrobotanical structure, type
of fenophase parameters,
agrotechnological service with management of
fertiliser, conditions of pasture stands, climate conditions and other
conditions. Long time utilisation and management of pasture area can increased
of nutrients parameters and their
stability through vegetations.
After
pasture cycle the rest of pasture must be to cut or mulch The balance between
production yield and nutrients norms for animals colud be with good managewment
of pasture area organised (JURŠÍK, TRÁVNÍČEK,
DRGÁČ, 2001 cit.Čermák2004). In czech Mountains
conditions the pasture sezone is 140-155 days. In opposite Teslík (1996)
reported the economical parameters are by near 200 days of pasture sezone. For
this condition is early of spring and late of outum the animals transport to
the pasture area and ad the nutrients with the conservate feed as silage and
concentrate. The aditional of minerals components depend of the evaluation of
the feed ration in observed farms.
For
the cows withaut milk production 0,7-1
ha of pasture are per 1 cow with calf by
low intensity. For high intensity the 1200-1800 kg live weight per 1 ha can
calculated.
(LOUDA, MRKVIČKA, STÁDNÍK,
2001 cit.Čermák2004).
Material and Methods
In 200-2006 7 different experimental locations
were chosen in the Sumava Mountains area between 250 to 900 m. Three farms had
dairy cows, 2 farms had beef cattle on pasture, and 2 farms had a combined beef
and dairy herd. During summer, all cattle were grazing. Dairy cows were
supplemented with concentrate and hay according to their milk production.
During winter, the cattle on all farms were fed with silage. The pasture yield,
botanic structure of grass, clover and other plants and the quality of animal
product were monitored.
The samples of forages were analysed for content of DM, ash and crude protein (CP). Ash-free NDF, ADF and ADL were determined using a Fibertec analyzer (Fibertec System M). NDF was determined according to Van Soest et al. (1991). An overnight pretreatment with a-amylase (A6380, Sigma) at 38 °C according to Ferreira et al. (1983) was followed by addition of sodium sulfite and a heat stable a-amylase (Termamyl, Novo Nordisk, Denmark) during NDF boiling. ADF and ADL were analysed according to Van Soest et al. (1991). CP was analysed according to the Kjeldahl method (AOAC, 1990). Ash was determined after combustion at 525 oC (AOAC, 1990).
Results and discussion
The
many hundrets of samples are taken out of different pasture stands. The first
part of samples evaluation is sumarised
in grapf 1
Graph 1
The similarity of pasture goups (content of agrobotanical groops –
grasses, trefoils, other plants) in observed pasture stands after 4 years of
utility of pratotechniques with the pasture in optimal structure
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OPS
P 2x / NPK
P 3x / NPK P 2x / 0
P 4x / NPK
P 3x / 0
P
4x / 0
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0,1
0,2 0,3 0,4
0,5 0,6 0,7
0,8 0,9 1,0 s
Legend :
s
= similarity of richness species, OPS =
fictive pasture with optimal agrobotanical structure, Pn x =
number of pasture cycle per year,
NPK =
fertiliser N+PK/ha
0 =
non fertilized area
Foal farm deviding in group in different altitude
|
Group |
Level
of altitude |
farms |
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I. |
Till 250 m.l.m. |
ŠCHK Kubišta, hřebčín Equus-Kinský |
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II. |
250 – 300 m.l.m. |
Luka-Týn, hřebčín Albertovec, Padělky |
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III. |
300 – 400 m.l.m. |
Hřebčín Suchá, ZH Písek |
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IV. |
400 – 500 m.l.m. |
ZH Tlumačov |
|
V. |
over 500 m.l.m. |
Horní Město-Skály |
- Hřebčín Equus – Kinský, n.v. 206 m
- ŠCHK Kubišta, n. v. 235 m
- Padělky, n. v. 251 m
- Hřebčín Albertovec, n. v. 260 m
- Luka – Týn, n. v. 266 m
- Hřebčín Suchá, n. v. 347 m
- ZH Písek, n.v. 378 m
- ZH Tlumačov, n. v. 460 m
- Horní Město (Skály), n. v. 675 m
The optimum structure of agrobotanical groups could be: grasses 60-65%, trefoils 20-25%, other
plants 10-15% (Klimeš et al 2001, Kadlec et al 2002,
Čermák et al. 2004).
For the better understanding of pasture samples in different nutrients evaluation is the
structure of some carbohydrates especially the
spectra of NDF and ADF is necessary to evaluate for feed rations. For the cows after calving is 28% of
NDF and 21% of ADF in feed rations recommended.
In the following tables (1-2) and
the average of their parameters are presents. In graph 4 the original dry
matter each samples of analyzed results and predicted method are compared. From
this graphs we can see that the data spectra in zone average, minimum and
maximum can find. Each pasture stand are with similar
method evaluated (Koukolová et.al.2004).
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L |
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M1x/0 |
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K1x/0 |
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K2x/0 |
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K2x/NPK |
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P2x/0 |
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P2x/NPK |
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[n] 0 5 10 15 20
Legend :
L =
grasslands with out utilization, M
= mulch, K = cut, P
= pasture
1x; 2x =
frequency of utilization, NPK
= fertilized grassland, 0= with out fertilization grassland, n =
number of observations of Microtus arvalis per 100 m2
EVALUATION
OF QUALITY PASTURE STANDS IN 2005 YEAR
may 2005
Table n.1
|
Sample |
Lab.drymatter. |
orig.dry matter. |
NL |
SNL |
fet |
ash. |
CF |
ADF |
NDF |
BNLV |
DEK |
|
number |
% |
% |
% |
% |
% |
% |
% |
% |
% |
% |
MJ/kg |
|
1 |
87,94 |
14,65 |
18,56 |
12,71 |
2,64 |
10,73 |
20,19 |
26,05 |
46,2 |
47,88 |
10,45 |
|
2 |
88,18 |
10,38 |
18,56 |
12,71 |
3,00 |
11,68 |
19,23 |
25,98 |
38,7 |
47,53 |
10,43 |
|
3 |
88,78 |
14,79 |
16,50 |
11,30 |
2,18 |
10,45 |
24,10 |
32,25 |
54,61 |
46,77 |
10,17 |
|
4 |
88,76 |
14,25 |
16,28 |
11,15 |
2,45 |
10,18 |
20,45 |
28,45 |
44,43 |
50,64 |
10,38 |
|
5 |
88,78 |
13,32 |
16,36 |
9,83 |
3,86 |
10,81 |
20,41 |
26,47 |
40,68 |
50,56 |
10,40 |
|
6 |
88,86 |
13,33 |
19,35 |
13,25 |
2,32 |
9,09 |
21,47 |
27,96 |
42,84 |
47,77 |
10,58 |
|
7 |
89,38 |
14,89 |
16,81 |
11,51 |
2,34 |
11,45 |
20,27 |
28,64 |
41,76 |
49,13 |
10,25 |
|
8 |
88,66 |
14,29 |
16,11 |
11,03 |
2,39 |
10,03 |
20,81 |
27,97 |
41,19 |
50,66 |
10,37 |
|
9 |
88,62 |
16,83 |
26,18 |
17,93 |
3,17 |
10,76 |
18,95 |
22,75 |
37,53 |
40,94 |
10,89 |
|
10 |
88,74 |
17,44 |
20,22 |
13,85 |
3,29 |
11,96 |
16,57 |
25,31 |
36,51 |
47,96 |
10,62 |
|
11 |
88,26 |
23,83 |
19,70 |
13,49 |
2,73 |
7,36 |
19,63 |
25,15 |
49,49 |
50,58 |
10,92 |
|
12 |
89,24 |
25,49 |
14,01 |
9,59 |
1,92 |
7,44 |
23,43 |
30,19 |
58,15 |
53,20 |
10,40 |
|
13 |
87,32 |
24,94 |
20,35 |
13,93 |
2,38 |
11,26 |
15,34 |
21,48 |
29,03 |
50,67 |
10,65 |
|
14 |
88,60 |
26,58 |
18,77 |
12,85 |
2,09 |
10,69 |
19,74 |
26,92 |
35,96 |
48,71 |
10,42 |
|
17 |
87,80 |
18,07 |
15,15 |
10,37 |
2,47 |
12,55 |
21,36 |
27,84 |
39,63 |
48,47 |
10,02 |
|
18 |
88,88 |
29,23 |
11,13 |
7,62 |
2,05 |
10,71 |
26,10 |
32,85 |
53,55 |
50,01 |
9,80 |
|
19 |
88,36 |
23,25 |
13,97 |
9,56 |
2,68 |
10,95 |
21,91 |
27,21 |
44,04 |
50,49 |
10,16 |
|
20 |
88,40 |
24,55 |
14,26 |
9,76 |
2,46 |
9,91 |
21,71 |
29,52 |
46,42 |
51,66 |
10,27 |
|
21 |
88,76 |
19,88 |
18,34 |
12,56 |
2,62 |
12,03 |
16,67 |
23,29 |
38,15 |
50,34 |
10,44 |
|
22 |
88,36 |
18,22 |
19,22 |
13,16 |
2,54 |
10,29 |
16,52 |
22,41 |
34,19 |
51,43 |
10,68 |
|
23 |
88,20 |
22,21 |
18,96 |
12,98 |
2,71 |
9,14 |
20,58 |
24,66 |
45,8 |
48,61 |
10,64 |
|
24 |
89,26 |
23,28 |
14,22 |
9,74 |
2,66 |
10,50 |
21,64 |
27,61 |
37,42 |
50,98 |
10,23 |
Data are in 100% dry matter.
August 2005 Table
n.2
|
Sample |
Lab.drymatter. |
orig.dry matter. |
NL |
DNL |
fet |
ash. |
CF |
ADF |
NDF |
BNLV |
DEK |
|
number |
% |
% |
% |
% |
% |
% |
% |
% |
% |
% |
MJ/kg |
|
1 |
90,12 |
23,51 |
12,25 |
8,17 |
2,01 |
11,11 |
24,58 |
31,31 |
48,21 |
50,05 |
9,60 |
|
2 |
89,66 |
22,42 |
12,52 |
8,35 |
1,73 |
11,71 |
25,10 |
32,31 |
48,22 |
48,94 |
9,49 |
|
3 |
90,18 |
28,18 |
10,33 |
6,89 |
1,63 |
8,98 |
31,39 |
36,52 |
63,33 |
47,67 |
9,43 |
|
4 |
90,54 |
25,87 |
12,95 |
8,63 |
1,88 |
9,63 |
28,82 |
34,03 |
59,72 |
46,72 |
9,61 |
|
5 |
89,80 |
24,49 |
13,92 |
9,28 |
2,36 |
9,82 |
23,65 |
31,05 |
42,72 |
50,25 |
9,90 |
|
6 |
89,82 |
29,94 |
11,47 |
7,65 |
2,04 |
8,39 |
30,98 |
37,77 |
55,66 |
47,12 |
9,62 |
|
7 |
90,16 |
43,98 |
8,14 |
5,42 |
1,62 |
7,05 |
32,19 |
40,59 |
64,11 |
51,00 |
9,52 |
|
8 |
89,74 |
26,51 |
9,89 |
6,59 |
1,85 |
7,39 |
33,20 |
42,53 |
57,82 |
47,67 |
9,55 |
|
9 |
89,46 |
22,36 |
11,64 |
7,76 |
1,84 |
11,73 |
25,04 |
33,76 |
48,12 |
49,75 |
9,47 |
|
10 |
89,90 |
21,79 |
10,51 |
7,01 |
2,12 |
9,96 |
25,97 |
31,4 |
53,61 |
51,44 |
9,61 |
|
11 |
90,66 |
41,84 |
8,23 |
5,48 |
2,49 |
5,93 |
27,54 |
36,62 |
65,18 |
55,81 |
9,94 |
|
12 |
90,20 |
41,60 |
7,71 |
5,14 |
1,96 |
5,41 |
29,15 |
36,15 |
65,15 |
55,77 |
9,85 |
|
13 |
89,66 |
29,88 |
15,67 |
10,45 |
1,92 |
9,92 |
24,17 |
29,27 |
47,51 |
48,32 |
9,88 |
|
14 |
90,06 |
15,01 |
14,79 |
9,86 |
2,28 |
9,74 |
24,82 |
32,29 |
44,85 |
48,37 |
9,89 |
|
15 |
90,90 |
28,41 |
11,82 |
7,88 |
2,02 |
8,53 |
27,17 |
32,27 |
56,42 |
50,46 |
9,76 |
|
16 |
90,38 |
20,66 |
12,96 |
8,64 |
2,10 |
9,13 |
27,05 |
32,39 |
55,10 |
48,76 |
9,76 |
|
17 |
89,46 |
17,89 |
13,93 |
9,29 |
2,38 |
12,07 |
22,22 |
29,39 |
38,14 |
49,40 |
9,71 |
|
18 |
90,00 |
22,50 |
9,80 |
6,53 |
2,36 |
11,53 |
25,23 |
29,17 |
48,88 |
51,08 |
9,47 |
|
19 |
89,49 |
20,34 |
10,5 |
7,00 |
2,49 |
13,00 |
22,92 |
27,11 |
40,06 |
51,09 |
9,44 |
|
20 |
90,08 |
24,02 |
11,21 |
7,47 |
2,55 |
10,35 |
25,58 |
31,84 |
51,86 |
50,31 |
9,67 |
|
21 |
89,40 |
14,11 |
13,04 |
8,69 |
2,46 |
12,68 |
22,42 |
29,57 |
38,10 |
49,4 |
9,60 |
|
22 |
89,28 |
17,85 |
12,69 |
8,46 |
2,41 |
11,76 |
19,08 |
28,89 |
40,01 |
54,06 |
9,82 |
|
23 |
89,64 |
23,89 |
11,12 |
7,41 |
2,13 |
9,23 |
26,32 |
35,32 |
55,11 |
51,20 |
9,71 |
|
24 |
89,80 |
22,45 |
12,35 |
8,23 |
2,43 |
8,13 |
27,84 |
32,07 |
57,23 |
49,25 |
9,86 |
Data are in 100% dry matter.
The
data from the stands in 2006 year has
the some differences as in the year 2005. This results in graphs numer 1-4 are
presents. Low representation of
clover was noticed when surveying the quality of grazing lands almost at all grazing
land grazed by colts in 2005 and 2006. Only localities from number 5 to 8 showed to
have optimal ratio of clover, grass and herbs representation.
Graph n.1
Graph
n.2
Graph n.3
Graph n.4
Table 4. Basic statistical evaluation , F-test a T-test of growth characteristic in comparison with growinght standard ST4 in differt
|
Alitude m.over.m. |
n |
awerage |
sx |
Var,
coeficient Vk |
Min |
Max |
F-test |
t-test |
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|
1. Do 250 |
44 |
3,93 |
1,25 |
31,8 |
2 |
6 |
9,559+++ |
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|
2. 250-300 |
36 |
5,58 |
1,32 |
23,65 |
1 |
7 |
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|
3. 300-400 |
85 |
4,15 |
1,45 |
34,94 |
2 |
7 |
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4. 400-500 |
47 |
4,19 |
1,59 |
38,01 |
1 |
6 |
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|
5. 500-700 |
44 |
5,02 |
1,62 |
32,18 |
2 |
7 |
Breeding stations located above 600 m altitude
above the sea level had higher content of dry matter. More significant
differences among the grazing lands in different altitudes were not revealed.
The tendency of healthier foal in high altitude was observed.
Conclusions
The EU-Regulation provides a
framework ensuring that the living conditions for organic livestock are better
than the minimum conditions in conventional systems, but this does not
necessarily ensure a higher level of animal health and food safety. The most important
source of variation is the farm management. The implementation of a high animal
health status often requires additional skills and the use of additional
resources (labour, time, investments etc.). Limited availability of these
resources and structural problems impede efforts to improve the status of
animal health at the farm level. When faced with conflicting aims and resource
limitations farmers do not always give the highest priority to animal health.
This can have a negative impact on food quality and safety. Hence, there is a
need for preventive strategies that are closely related to the specific farm
systems to improve animal health and food safety in organic systems throughout
EU. In Czech republic the area of
permanent grasslands rapidly increased. It is in opposite with the decreased
number of animals. The other field of pasture utilisation could be find. The
multifunctional utilisation for wild animals, agroturism and sports is not rather evalued.
Low representation of clover was noticed when
surveying the quality of grazing lands almost at all grazing land grazed by
colts in 2005. Only localities from number 5 to 8 showed to have optimal ratio of
clover, grass and herbs representation. Breeding stations located above 600 m
above the sea level had higher content of dry matter. More significant
differences among the grazing lands in different altitudes were not revealed.
Čermák, B. a kol.: Vliv kvality krmiv na produkci a zdravotní nezávadnost mléka a masa.
Klimeš, F., Střeleček, F., Čermák, B., Hrabě, F.,
Tetter, M.: Methodological
Aspects in the Study of Species Richness and Diversity in Species of Grasslands.
Collection of Scientific Papers, Faculty of Agriculture in Č.
Budějovice, Series for Crop Sciences, 18., 2001 (2): 91– 98
Koukolová, V., Kobes, M., Homolka, P.,
Čermák, B.: Stanovení
degradovatelnosti NDF paastevní píce IN VITRO metodami a jejich
ověření metodou IN SITU na kanylovaných kravách.
In:Sborník
anotací z mezinárodní konference XXI. Dni
živočíšnej fyziologie Košice 23-24.9.2004, s. 51.
This project by
MSM 6007665806 was supported.
Contact
adress:Doc.Ing.B.Čermák,CSc.
e-mail: cermak@zf.jcu.cz