C1 1.916944a
B 1.447660 b
attr(,"class")
[1] "group"
> t=aov(dt~Lapdia,data=KL)
> summary(t)
Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 1.17 1.1657 5.285 0.0228 *
Residuals 164 36.18 0.2206
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
8 observations deleted due to missingness
> t1=LSD.test(t,"Lapdia")
> t1
$statistics
MSerror Df Mean CV 0.220586 164 2.078554 22.5958
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05
$means
dt std r LCL UCL Min Max Q25 Q50 Q75
B 2.005213 0.5082581 94 1.909562 2.100864 0.90 3.65 1.7000 1.925 2.31
C1 2.174306 0.4137048 72 2.065014 2.283597 1.25 3.50 1.9225 2.165 2.40
$groups
dt groups C1 2.174306 a
B 2.005213b
attr(,"class")
[1] "group"
> fix(KL)
> th=aov(Thanchinh~Lapdia,data=KL)
> summary(th)
Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 3.11 3.1125 4.157 0.0431 *
Residuals 164 122.79 0.7487
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
8 observations deleted due to missingness
> th1=LSD.test(th,"Lapdia")
> th1
$statistics
MSerror Df Mean CV 0.7487261 164 1.975904 43.7921
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05
$means
Thanchinh std r LCL UCL Min Max Q25 Q50 Q75 B 2.095745 0.8559614 94 1.919522 2.271968 1 5 2 2 3
C1 1.819444 0.8773582 72 1.618091 2.020798 1 4 1 2 2
$groups
Thanchinh groups B 2.095745 a
C1 1.819444b
attr(,"class")
[1] "group"
> fix(KL)
> c=aov(c50~Lapdia,data=KL)
> summary(c)
Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 133 133.4 4.616 0.0331 *
Residuals 164 4739 28.9
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
8 observations deleted due to missingness
> c1=LSD.test(c,"Lapdia")
> c1
$statistics
MSerror Df Mean CV 28.89721 164 13.29518 40.43279
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05
$means
c50 std r LCL UCL Min Max Q25 Q50 Q75 B 12.51064 5.134209 94 11.41585 13.60542 4 27 9 12 16
C1 14.31944 5.676307 72 13.06853 15.57036 4 29 10 14 18
$groups
c50 groups C1 14.31944 a
B 12.51064b
attr(,"class")
[1] "group"
> pt=aov(phth~Lapdia,data=KL)
> summary(pt)
Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 3.56 3.560 23.18 3.33e-06 ***
Residuals 164 25.19 0.154
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
8 observations deleted due to missingness
> pt1=LSD.test(pt,"Lapdia")
> pt1
$statistics
MSerror Df Mean CV 0.1536138 164 0.7771084 50.43518
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05
$means
phth std r LCL UCL Min Max Q25 Q50 Q75 B 0.6489362 0.4798621 94 0.5691154 0.728757 0 1 0 1 1
C1 0.9444444 0.2306689 72 0.8532405 1.035648 0 1 1 1 1
$groups
phth groups
C1 0.9444444 a
B 0.6489362 b
attr(,"class")
[1] "group"
> s=aov(sc~Lapdia,data=KL)
> summary(s)
Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 0.005 0.00471 0.106 0.745
Residuals 172 7.627 0.04435
> s1=LSD.test(s,"Lapdia")
> s1
$statistics
MSerror Df Mean CV 0.04434578 172 0.954023 22.0733
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05
$means
sc std r LCL UCL Min Max Q25 Q50 Q75 B 0.9494949 0.2200991 99 0.9077193 0.9912706 0 1 1 1 1
C1 0.9600000 0.1972788 75 0.9120034 1.0079966 0 1 1 1 1
$groups
sc groups C1 0.9600000 a
B 0.9494949 a
attr(,"class")
[1] "group"
2.4. C3 SICKLE LEAF GLUE IN LE THUY
> d=aov(stump_diameter~CTTN,data=LT13)
> summary(d)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 0.45 0.1484 0.339 0.797
Residuals 223 97.76 0.4384
16 observations deleted due to missingness
> library(agricolae)
> d1=LSD.test(d,"CTTN")
> d1
$statistics
MSerror Df Mean CV 0.4383911 223 2.642863 25.05279
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
stump_diameter | std | r LCL UCL | Min | Max Q25 | Q50 Q75 | |
CT 1 | 2.660294 | 0.6693347 | 34 2.436524 2.884065 | 1.72 | 4.81 2.2300 | 2.48 2.9675 |
CT 2 | 2.703971 | 0.5833105 | 68 2.545741 2.862200 | 1.59 | 4.30 2.2525 | 2.58 3.1425 |
CT 3 | 2.606264 | 0.6681844 | 91 2.469484 2.743043 | 1.37 | 5.43 2.1000 | 2.51 2.9300 |
DC | 2.601176 | 0.7784552 | 34 2.377406 2.824947 | 1.46 | 4.62 1.9950 | 2.53 2.9125 |
$groups | ||||||
CT 2 | stump_diameter 2.703971 | groups a | ||||
CT 1 | 2.660294 | a | ||||
CT 3 | 2.606264 | a | ||||
DC | 2.601176 | a | ||||
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Tc Ch C Scientific Research Work on the Ministry of Science and Technology; Training of Cadres for the Ministry of Science and Technology; Organizing the National Conference on Science and Technology in the Central Highlands/City -
Identify Rating Levels and Rating Scales
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zt2a3gstourism,quan lan,quang ninh,ecology,ecotourism,minh chau,van don,geography,geographical basis,tourism development,science
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of the islanders. Therefore, this indicator will be divided into two sub-indicators:
a1. Natural tourism attractiveness a2. Cultural tourism attractiveness
b. Tourist capacity
The two island communes in Quan Lan have different capacities to receive tourists. Minh Chau Commune is home to many standard hotels and resorts, attracting high-income domestic and international tourists. Meanwhile, Quan Lan Commune has many motels mainly built and operated by local people, so the scale and quality are not high, and will be suitable for ordinary tourists such as students.
c. Time of exploitation of Quan Lan Island Commune:
Quan Lan tourism is seasonal due to weather and climate conditions and festivals only take place on certain days of the year, specifically in spring. In Quan Lan commune, the period from April to June and from September to November is considered the best time to visit Quan Lan because the cultural tourism activities are mainly associated with festivals taking place during this time.
Minh Chau island commune:
Tourism exploitation time is all year round, because this is a place with a number of tourist attractions with diverse ecosystems such as Bai Tu Long National Park Research Center, Tram forest, Turtle Laying Beach, so besides coming to the beach for tourism and vacation in the summer, Minh Chau will attract research groups to come for tourism combined with research at other times of the year.
d. Sustainability
The sustainability of ecotourism sites in Quan Lan and Minh Chau communes depends on the sensitivity of the ecosystems to climate changes.
landscape. In general, these tourist destinations have a fairly high level of sustainability, because they are natural ecosystems, planned and protected. However, if a large number of tourists gather at certain times, it can exceed the carrying capacity and affect the sustainability of the environment (polluted beaches, damaged trees, animals moving away from their habitats, etc.), then the sustainability of the above ecosystems (natural ecosystems, human ecosystems) will also be affected and become less sustainable.
e. Location and accessibility
Both island communes have ports to take tourists to visit from Van Don wharf:
- Quan Lan – Van Don traffic route:
Phuc Thinh – Viet Anh high-speed boat and Quang Minh high-speed boat, depart at 8am and 2pm from Van Don to Quan Lan, and at 7am and 1pm from Quan Lan to Van Don. There are also wooden boats departing at 7am and 1pm.
- Van Don - Minh Chau traffic route:
Chung Huong high-speed train, Minh Chau train, morning 7:30 and afternoon 13:30 from Van Don to Minh Chau, morning 6:30 and afternoon 13:00 from Minh Chau to Van Don.
f. Infrastructure
Despite receiving investment attention, the issue of infrastructure and technical facilities for tourism on Quan Lan Island is still an issue that needs to be resolved because it has a direct impact on the implementation of ecotourism activities. The minimum conditions for serving tourists such as accommodation, electricity, water, communication, especially medical services, and security work need to be given top priority. Ecotourism spots in Minh Chau commune are assessed to have better infrastructure and technical facilities for tourism because there are quite complete and synchronous conditions for serving tourists, meeting many needs of domestic and foreign tourists.
3.2.1.4. Determine assessment levels and assessment scales
Corresponding to the levels of each criterion, the index is the score of those levels in the order of 4, 3, 2, 1 decreasing according to the standard of each level: very attractive (4), attractive (3), average (2), less attractive (1).
3.2.1.5. Determining the coefficients of the criteria
For the assessment of DLST in the two communes of Quan Lan and Minh Chau islands, the students added evaluation coefficients to show the importance of the criteria and indicators as follows:
Coefficient 3 with criteria: Attractiveness, Exploitation time. These are the 2 most important criteria for attracting tourists to tourism in general and eco-tourism in particular, so they have the highest coefficient.
Coefficient 2 with criteria: Capacity, Infrastructure, Location and accessibility . Because the assessment area is an island commune of Van Don district, the above criteria are selected by the author with appropriate coefficients at the average level.
Coefficient 1 with criteria: Sustainability. Quan Lan has natural and human-made ecotourism sites, with high biodiversity and little impact from local human factors. Most of the ecotourism sites are still wild, so they are highly sustainable.
3.2.1.6. Results of DLST assessment on Quan Lan island
a. Assessment of the potential for natural tourism development
For Minh Chau commune:
+ Natural tourism attractiveness is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined as average (2 points) and the coefficient is quite important (coefficient 2), then the score of Capacity criterion is 2 x 2 = 4.
+ Exploitation time is long (4 points), the most important coefficient (coefficient 3) so the score of the Exploitation time criterion is 4 x 3 = 12.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is assessed as good (3 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 3 x 2 = 6 points.
The total score for evaluating DLST in Minh Chau commune according to 6 evaluation criteria is determined as: 12 + 4 + 12 + 4 + 4 + 6 = 42 points
Similar assessment for Quan Lan commune, we have the following table:
Table 3.3: Assessment of the potential for natural ecotourism development in Quan Lan and Minh Chau communes
Attractiveness of self-tourismof course
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
CommuneMinh Chau
12
12
4
8
12
12
4
4
4
8
6
8
42/52
Quan CommuneLan
6
12
6
8
9
12
4
4
4
8
4
8
33/52
b. Assessment of the potential for humanistic tourism development
For Quan Lan commune:
+ The attractiveness of human tourism is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined to be large (3 points) and the coefficient is quite important (coefficient 2), then the score of the Capacity criterion is 3 x 2 = 6.
+ Mining time is average (3 points), the most important coefficient (coefficient 3) so the score of the Mining time criterion is 3 x 3 = 9.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points.
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is rated as average (2 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 2 x 2 = 4 points.
The total score for evaluating DLST in Quan Lan commune according to 6 evaluation criteria is determined as: 12 + 6 + 6 + 4 + 4 + 4 = 36 points.
Similar assessment with Minh Chau commune we have the following table:
Table 3.4: Assessment of the potential for developing humanistic eco-tourism in Quan Lan and Minh Chau communes
Attractiveness of human tourismliterature
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Quan CommuneLan
12
12
6
8
9
12
4
4
4
8
4
8
39/52
Minh CommuneChau
6
12
4
8
12
12
4
4
4
8
6
8
36/52
Basically, both Minh Chau and Quan Lan localities have quite favorable conditions for developing ecotourism. However, Quan Lan commune has more advantages to develop ecotourism in a humanistic direction, because this is an area with many famous historical relics such as Quan Lan Communal House, Quan Lan Pagoda, Temple worshiping the hero Tran Khanh Du, ... along with local festivals held annually such as the wind praying ceremony (March 15), Quan Lan festival (June 10-19); due to its location near the port and long exploitation time, the beaches in Quan Lan commune (especially Quan Lan beach) are no longer hygienic and clean to ensure the needs of tourists coming to relax and swim; this is also an area with many beautiful landscapes such as Got Beo wind pass, Ong Phong head, Voi Voi cave, but the ability to access these places is still very limited (dirt hill road, lots of gravel and rocks), especially during rainy and windy times; In addition, other natural resources such as mangrove forests and sea worms have not been really exploited for tourism purposes and ecotourism development. On the contrary, Minh Chau commune has more advantages in developing ecotourism in the direction of natural tourism, this is an area with diverse ecosystems such as at Rua De Beach, Bai Tu Long National Park Conservation Center...; Minh Chau beach is highly appreciated for its natural beauty and cleanliness, ranked in the top ten most beautiful beaches in Vietnam; Minh Chau commune is also home to Tram forest with a large area and a purity of up to 90%, suitable for building bridges through the forest (a very effective type of natural ecotourism currently applied by many countries) for tourists to sightsee, as well as for the purpose of studying and researching.
Figure 3.1: Thenmala Forest Bridge (India) Source: https://www.thenmalaecotourism.com/(August 21, 2019)
3.2.2. Using SWOT matrix to evaluate Quan Lan island tourism
General assessment of current tourism activities of Quan Lan island is shown through the following SWOT matrix:
Table 3.5: SWOT matrix evaluating tourism activities on Quan Lan island
Internal agent
Strengths- There is a lot of potential for tourism development, especially natural ecotourism and humanistic ecotourism.- The unskilled labor force is relatively abundant.- resource environmentunpolluted, still
Weaknesses- Poorly developed infrastructure, especially traffic routes to tourist destinations on the island.- The team of professional staff is still weak.- Tourism products in general
quite wild, originalintact
general and DLST in particularalone is monotonous.
External agents
Opportunity- Tourism is a key industry in the socio-economic development strategy of the province and Van Don economic zone.- Quan Lan was selected as a pilot area for eco-tourism development within the framework of the green growth project between Quang Ninh province and the Japanese organization JICA.- The flow of tourists and especially ecotourism in the world tends toincreasing
Challenge- Weather and climate change abnormally.- Competition in tourism products is increasingly fierce, especially with other localities in the province such as Ha Long, Mong Cai...- Awareness of tourists, especially domestic tourists, about ecotourism and nature conservation is not high.
Through summary analysis using SWOT matrix we see that:
To exploit strengths and take advantage of opportunities, it is necessary to:
- Diversify products and service types (build more tourism routes aimed at specific needs of tourists: experiential tourism immersed in nature, spiritual cultural tourism...)
- Effective exploitation of resources and differentiated products (natural resources and human resources)
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attr(,"class")
[1] "group"
> h=aov(tree_height~CTTN,data=LT13)
> summary(h)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 0.15 0.05006 0.965 0.41
Residuals 223 11.56 0.05186
16 observations deleted due to missingness
> h1=LSD.test(h,"CTTN")
> h1
$statistics
MSerror Df Mean CV 0.0518596 223 0.9486344 24.00577
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
tree_heightstd r | LCL UCL | Min | Max | Q25 | Q50 | Q75 | |
CT 1 0.9008824 | 0.2211042 34 | 0.8239185 0.9778462 | 0.60 | 1.55 | 0.725 | 0.875 | 1.0375 |
CT 2 0.9652941 | 0.2092023 68 | 0.9108725 1.0197158 | 0.53 | 1.33 | 0.800 | 0.940 | 1.1400 |
CT 3 0.9401099 | 0.2336783 91 | 0.8930658 0.9871540 | 0.43 | 1.92 | 0.795 | 0.940 | 1.1000 |
DC 0.9858824 | 0.2525400 34 | 0.9089185 1.0628462 | 0.60 | 1.47 | 0.805 | 0.940 | 1.2300 |
$groups
tree_height groups DC 0.9858824 a
CT 2 0.9652941a
CT 3 0.9401099a
CT 1 0.9008824a
attr(,"class")
[1] "group"
> dt=aov(canopy_diameter~CTTN,data=LT13)
> summary(dt)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 0.351 0.11708 1.73 0.162
Residuals 223 15.095 0.06769
16 observations deleted due to missingness
> dt1=LSD.test(dt,"CTTN")
> dt1
$statistics
MSerror Df Mean CV 0.067692 223 1.100441 23.64297
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
canopy_diameter | std r | LCL | UCL Min Max Q25 Q50 Q7 | |
CT 1 | 1.071471 | 0.2543814 34 | 0.9835399 | 1.159401 0.67 1.68 0.915 1.035 1.300 |
CT 2 | 1.158676 | 0.2402045 68 | 1.0965001 | 1.220853 0.75 1.83 0.950 1.145 1.300 |
CT 3 | 1.084615 | 0.2810666 91 | 1.0308677 | 1.138363 0.48 2.03 0.845 1.090 1.305 |
DC | 1.055294 | 0.2452115 34 | 0.9673634 | 1.143225 0.45 1.65 0.935 1.050 1.187 |
$groups
canopy_diameter | groups | |
CT 2 | 1.158676 | a |
CT 3 | 1.084615 | a |
CT 1 | 1.071471 | a |
DC | 1.055294 | a |
attr(,"class")
[1] "group"
> th=aov(main_trunk~CTTN,data=LT13)
> summary(th)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 1.34 0.4463 0.403 0.751
> th1=LSD.test(th,"CTTN")
> th1
$statistics
MSerror Df Mean CV 1.106854 223 2.550661 41.247
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
main_trunkstd r LCL UCL | Min | Max | Q25 | Q50 | Q75 | |
CT 1 2.411765 | 1.0478737 34 2.056201 2.767328 | 1 | 5 | 2 | 2.0 | 3 |
CT 2 2.588235 | 1.0683408 68 2.336814 2.839657 | 1 | 6 | 2 | 3.0 | 3 |
CT 3 2.527473 | 0.9468152 91 2.310134 2.744811 | 1 | 5 | 2 | 3.0 | 3 |
DC 2.676471 | 1.2725681 34 2.320907 3.032034 | 1 | 6 | 2 | 2.5 | 3 |
$groups
main_trunk groups DC 2.676471 a
CT 2 2.588235a
CT 3 2.527473a
CT 1 2.411765a
attr(,"class")
[1] "group"
> c=aov(bough_50_cm~CTTN,data=LT13)
> summary(c)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 2.78 0.9261 1.251 0.292
Residuals 223 165.09 0.7403
16 observations deleted due to missingness
> c1=LSD.test(c,"CTTN")
> c1
$statistics
MSerror Df Mean CV 0.7403125 223 1.903084 45.21158
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
size_50_cmstd r | LCL | UCL | Min | Max | Q25 Q50 | Q75 | |
CT 1 1.647059 | 0.7739060 34 | 1.356269 | 1.937849 | 0 | 4 | 1 2 | 2 |
CT 2 1.985294 | 0.9540588 68 | 1.779674 | 2.190914 | 1 | 6 | 1 2 | 2 |
CT 3 1.934066 | 0.8406637 91 | 1.756321 | 2.111811 | 0 | 4 | 1 2 | 3 |
DC 1.911765 | 0.7926804 34 | 1.620975 | 2.202555 | 1 | 4 | 1 2 | 2 |
$groups
bough_50_cm groups CT 2 1.985294 a
CT 3 1.934066a
DC 1.911765a
CT 1 1.647059a
attr(,"class")
[1] "group"
> pt=aov(part_body~CTTN,data=LT13)
> summary(pt)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 0.47 0.1560 0.814 0.487
Residuals 223 42.71 0.1915
16 observations deleted due to missingness
> pt1=LSD.test(pt,"CTTN")
> pt1
$statistics
MSerror Df Mean CV 0.1915366 223 0.2555066 171.2868
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
bodystd r LCL | UCL | Min | Max | Q25 | Q50 | Q75 | |
CT 1 0.2058824 | 0.4104256 34 0.05797222 | 0.3537925 | 0 | 1 | 0 | 0 | 0 |
CT 2 0.2647059 | 0.4444566 68 0.16011762 | 0.3692941 | 0 | 1 | 0 | 0 | 1 |
CT 3 0.2307692 | 0.4236593 91 0.14035918 | 0.3211793 | 0 | 1 | 0 | 0 | 0 |
DC 0.3529412 | 0.4850713 34 0.20503104 | 0.5008513 | 0 | 1 | 0 | 0 | 1 |
$groups body | groups | ||||||
DC 0.3529412 CT 2 0.2647059 CT 3 0.2307692 CT 1 0.2058824 | aaa a |
attr(,"class")
[1] "group"
> s=aov(song_chet~CTTN,data=LT13)
> summary(s)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 0.423 0.14103 2.321 0.0759 .
Residuals 239 14.523 0.06077
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
> s1=LSD.test(s,"CTTN")
> s1
$statistics
MSerror Df Mean CV 0.06076742 239 0.9341564 26.38857
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
deadstd | r LCL | UCL | Min | Max | Q25 | Q50 | Q75 | |
CT 1 1.0000000 | 0.0000000 | 34 0.9167184 | 1.0832816 | 1 | 1 | 1 | 1 | 1 |
CT 2 0.9189189 | 0.2748228 | 74 0.8624678 | 0.9753700 | 0 | 1 | 1 | 1 | 1 |
CT 3 0.9009901 | 0.3001650 | 101 0.8526700 | 0.9493102 | 0 | 1 | 1 | 1 | 1 |
DC 1.0000000 | 0.0000000 | 34 0.9167184 | 1.0832816 | 1 | 1 | 1 | 1 | 1 |
$groups dead | groups | |||||||
CT 1 1.0000000 DC 1.0000000 CT 2 0.9189189 CT 3 0.9009901 | aa ab b |
attr(,"class")
[1] "group"
> sk=aov(sk_tong~CTTN,data=LT13)
> summary(sk)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 5.3 1.778 0.339 0.797
Residuals 223 1169.7 5.245
16 observations deleted due to missingness
> sk1=LSD.test(sk,"CTTN")
> sk1
$statistics
MSerror Df Mean CV 5.245419 223 5.482555 41.7741
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
sk_tongstd | r LCL UCL Min | Max | Q25 | Q50 | Q75 | |
CT 1 5.545000 | 2.316262 | 34 4.770962 6.319038 2.28 | 12.97 | 4.0500 | 4,930 | 6.6075 |
CT 2 5.693529 | 2.016537 | 68 5.146202 6.240857 1.84 | 11.21 | 4.1325 | 5,260 | 7.2125 |
CT 3 5.354835 | 2.311448 | 91 4.881705 5.827966 1.07 | 15.12 | 3.6100 | 5,040 | 6.4700 |
DC 5.340000 | 2.693313 | 34 4.565962 6.114038 1.40 | 12.31 | 3.2525 | 5,095 | 6.4150 |
$groups sk_tong | groups | |||||
CT 2 5.693529 CT 1 5.545000 CT 3 5.354835 DC 5.340000 | aaaaa | |||||
attr(,"class") [1] "group" |
> fix(LT13)
> v=aov(litter_fall~CTTN,data=LT13)
> summary(v)
Df Sum Sq Mean Sq F value Pr(>F)
CTTN 3 0.00134 0.0004463 0.363 0.78
Residuals 223 0.27390 0.0012282
16 observations deleted due to missingness
> v1=LSD.test(v,"CTTN")
> v1
$statistics
MSerror Df Mean CV 0.001228234 223 0.1254185 27.94339
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
litter_fallstd r LCL | UCL | Min Max Q25 | Q50 Q75 | |
CT 1 0.1261765 | 0.03481681 34 0.1143321 | 0.1380209 | 0.08 0.24 0.1000 | 0.12 0.1400 |
CT 2 0.1288235 | 0.03098047 68 0.1204483 | 0.1371988 | 0.07 0.21 0.1075 | 0.12 0.1525 |
CT 3 0.1232967 | 0.03537278 91 0.1160568 | 0.1305366 | 0.06 0.27 0.1000 | 0.12 0.1400 |
DC 0.1235294 | 0.04155189 34 0.1116850 | 0.1353738 | 0.06 0.23 0.0900 | 0.12 0.1400 |
$groups
litter_fall groups CT 2 0.1288235 a
CT 1 0.1261765a
DC 0.1235294a
CT 3 0.1232967a
attr(,"class")
[1] "group"
> v=aov(litter_fall~CTTN,data=LT23)
> summary(v)
Df Sum Sq Mean Sq F value Pr(>F) CTTN 3 0.0865 0.02884 9.273 7.43e-06 ***
Residuals 268 0.8335 0.00311
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
27 observations deleted due to missingness
> v1=LSD.test(v,"CTTN")
> v1
$statistics
MSerror Df Mean CV 0.00310996 268 0.2072794 26.90427
$parameters
test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05
$means
litter_fallstd r LCL UCL Min Max | Q25 | Q50 Q75 | |
CT 1 0.1867164 | 0.05214746 67 0.1733026 0.2001303 0.07 0.33 | 0.15 | 0.18 0.21 |
CT 2 0.1992500 | 0.05566116 80 0.1869743 0.2115257 0.11 0.34 | 0.16 | 0.19 0.24 |
CT 3 0.2338667 | 0.06573685 75 0.2211884 0.2465450 0.10 0.40 | 0.19 | 0.23 0.27 |
DC 0.2078000 | 0.04272790 50 0.1922723 0.2233277 0.12 0.31 | 0.18 | 0.20 0.23 |
$groups
litter_fall groups CT 3 0.2338667 a
DC 0.2078000 b
CT 2 0.1992500 bc
CT 1 0.1867164 c
attr(,"class")
[1] "group"
III. ANALYSIS OF WINDSHIELDING CAPACITY OF FOREST BELT
> md=escalc(n1i = N,n2i = N,m1i=Vt,m2i = Vs,sd1i=st,sd2i=ss,data=CG,measure =" SMD",append=TRUE)
> summary(md)
Models | Year | Dairung | N | Vt | st | Vs | ss | E | so | K | sk | |
1 | Stick | 2008 | Bach dan | 1 | 3.10 | 0.00 | 2.70 | 0.00 | 0.13 | 0.00 | 1.10 | 0.00 |
2 | Stick | 2008 | Corrosion resistant glue | 2 | 3.35 | 0.21 | 2.90 | 0.14 | 0.14 | 0.01 | 1.15 | 0.07 |
3 | Theory & Hung | 2005 | Corrosion resistant glue | 3 | 5.50 | 0.00 | 1.27 | 0.21 | 0.77 | 0.04 | 4.43 | 0.67 |
4 | LA | 2018 | Glue is glue | 116 | 2.62 | 0.53 | 1.24 | 0.77 | 0.53 | 0.27 | 4.00 | 4.96 |
5 | Lieu | 2017 | Glue is glue | 9 | 7.16 | 0.11 | 5.53 | 0.81 | 0.23 | 0.11 | 1.31 | 0.19 |
6 | Stick | 2008 | glue is a stick | 6 | 3.40 | 0.17 | 2.65 | 0.20 | 0.22 | 0.04 | 1.28 | 0.04 |
7 | Theory | 2004 | glue is a stick | 14 | 2.19 | 1.53 | 0.78 | 0.22 | 0.49 | 0.28 | 3.02 | 2.55 |
8 | Stick | 2008 | Klt + Kll | 1 | 3.70 | 0.00 | 2.50 | 0.00 | 0.32 | 0.00 | 1.50 | 0.00 |
9 | Stick | 2008 | Wall glue | 1 | 3.00 | 0.00 | 2.20 | 0.00 | 0.27 | 0.00 | 1.40 | 0.00 |
10 | Stick | 2008 | Casuarina | 4 | 3.40 | 0.29 | 2.35 | 0.29 | 0.31 | 0.03 | 1.48 | 0.05 |
11 | Theory | 2004 | Casuarina | 41 | 4.84 | 1.28 | 2.18 | 0.91 | 0.54 | 0.17 | 2.51 | 1.03 |
12 | Theory & Hung | 2005 | Casuarina | 1 | 5.50 | 0.00 | 1.60 | 0.00 | 0.71 | 0.00 | 3.40 | 0.00 |
13 | Stick | 2008 | AD Xoan | 2 | 3.40 | 0.14 | 2.75 | 0.07 | 0.19 | 0.01 | 1.25 | 0.07 |
you for what you see
1 NA NA NA NA NA NA NA 2 1.4226 1.2530 1.1194 1.2709 0.2038 -0.7713 3.6165 3 22.7288 43.7165 6.6118 3.4376 0.0006 9.7698 35.6878 4 2.0810 0.0266 0.1630 12.7655 <.0001 1.7615 2.4005 5 2.6854 0.4225 0.6500 4.1312 <.0001 1.4113 3.9594 6 3.7286 0.9126 0.9553 3.9031 <.0001 1.8563 5.6010 7 1.2524 0.1709 0.4134 3.0298 0.0024 0.4422 2.0626
8 NA NA NA NA NA NA NA
9 NA NA NA NA NA NA NA 10 3.1450 1.1182 1.0574 2.9742 0.0029 1.0725 5.2176 11 2.3727 0.0831 0.2883 8.2305 <.0001 1.8077 2.9378
12 NA NA NA NA NA NA NA 13 3.3134 2.3723 1.5402 2.1512 0.0315 0.2946 6.3322