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Birundu et al. (2016) Assessing the possibility of incorporating Japanese small-scale logging systems into forest operations in Kenya
1. Assessing the possibility of
incorporating Japanese small-scale
logging systems into forest
operations in Kenya
Birundu Abednego Osindi*1, Yasushi Suzuki2, Jun’ichi Gotou2, Hirotaka Nagai2, Yoshifumi
Hayata2, Shin Yamasaki3, Toshihiko Yamasaki3
1Graduate School of Integrated Arts and Sciences, Kochi University, Nankoku 783-8502, Japan
2Faculty of Agriculture and Marine Science, Kochi University, Nankoku 783-8502, Japan
3Kochi Prefectural Forest Technology Centre, Kami 782-0078, Japan
07/09/2016 1
FORMEC 2016 – From Theory to Practice: Challenges for Forest Engineering September 4 – 7, 2016, Warsaw, Poland
2. Contents
07/09/2016 2
Part 1:
Introduction
Part 2:
Feasibility of small-scale mechanized logging in Kenya
Conclusions and recommendations
Source: https://thumb1.shutterstock.com/display_pic_with_logo/98072/427528480/stock-photo-kenya-flag-combined-with-japan-flag-
427528480.jpg
3. 07/09/2016 3
44 million
•80% dependency on
forestry for energy
127 million
•Alternative sources of
energySource: National Geographic
http://prepperscore.nationalgeographic.com/media/images/ddp-population.png
7 %
•Kenyan Constitution- 10%
67 %
•Global requirement of
10 % (CBD COP 9)
KENYA JAPAN
Source: Green Eternity
http://www.greenternity.com/assets/OAK.png
Forests- integral role in daily life
Need for conservation and management
Part 1: Introduction
Both countries exhibit forms of forest and forest
product utilization
Need to investigate the comparative advantage of
Japan
4. 07/09/2016 4
Part 2: Feasibility of mechanized logging in Kenya
Introduction
Source: Takimoto and Yovi (2003)
Manual tree harvesting in Kenya; chain saw
felling and transportation by human labour
Cost and productivity comparison
Assessing the feasibility of small-scale
mechanized logging in Kenya
5. 07/09/2016 5
Research Methodology
Small-scale logging sites:
1) Mr. Okamoto private
forest (October 2015)
2) Kochi Prefectural Forest
Technology Centre (May
2016)
Mini-forwarder
Data Obtained
Time study of work elements
Log parameters
Machinery and labor hourly
costs
Manual logging productivity
Equations derived to calculate productivity and costs of manual
and mini-forwarder logging systems
8. 07/09/2016 8
0 100 200 300 400 500
Site 1
Site 2
Average cycle time (s)
Sites
Move Empty
Lateral Empty
Hooking
Lateral Loaded
Move Loaded
Offloading
Maneuver
Others
I. Average cycle times
Results and discussion
Site 1= Mr. Okamoto forests(17 cycles recorded)
Site 2=Kochi Prefectural Forest Technology Center
(3 cycles recorded)
Main line logging
Lateral logging
9. 07/09/2016 9
Definition Formula
Parameter Values
a b
T1 Move Empty Time (s) a1x1+b1 0.2046 18.298
T2 Lateral Empty Time (s) a2x2
b
2 1.0975 1.37
T3 Lateral Loaded Time (s) a3x2
b
3 0.4314 1.9362
T4 Move Loaded Time (s) a4x1+b4 0.2168 39.092
Tc Constant Logging Time(s) 69.6 - -
Tf Total Forwarding Time (s) a5x3+b5 3.9967 942
Definition of elements in the regression equations
Four regression equations (T1, T2, T3, and T4) were obtained to determine how
distance of the main line and lateral movements affects the total cycle times and
productivity.
Total time per logging cycle (s), Tlg=T1+T2+T3+T4+Tc
X=Distance
10. 07/09/2016 10
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 20 40 60
Productivity(m3/crewhour)
Main Logging Distance, x1 (m)
Vhi1
Vav1
Vlo1
Vhi2
Vav2
Vlo2
Vhi3
Vav3
Vlo3
Vhi, Vav, Vlo=ranges of mini-forwarder volumes
1,2 and 3=0m, 10m, 20m, lateral logging
distances, x2, respectively
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 50 100 150
Prductivity(m3/person-day)
Distance (m)
Productivity
(m3/man-
day): Difficult
Productivity
(m3/man-
day):
Medium
Productivity
(m3/man-
day): Easy
Source: Umeda et. al (1982)
II. Effect of logging distance on productivity
Regression equations of the field data were obtained and combined with other
formula to calculate the productivity of mini-forwarder logging.
Mini-forwarder logging productivity Manual logging productivity
Productivity, P=3600xV/T
V=Log Volume
T=Total logging time (s)
11. 07/09/2016 11
0
20
40
60
80
100
120
0 2 4 6 8 10
TotalResultantCost(USD/m3)
Labor Cost (USD/ Person-hour)
Mini-forwarder Manual
Intersection Point
At current Kenyan labor cost
of 0.6 USD/person-hour,
resultant costs for manual and
mini-forwarder are 14.8
USD/m3 and 36.5 USD/m3Resultant cost= cost incurred per operation
A. Relationship between labor cost and resultant cost
III. Operational efficiency of mechanized and manual
Logging
12. 07/09/2016 12
z (resultant cost)= x/y
At a labor cost of 5.8
USD/person-hour,
resultant cost for both
systems is equal at 51.3
USD/m3
In both labor cost
values, mini-forwarder
productivity (0.52
m3/crew hour) is higher
than that of manual
logging (0.14 m3/crew
hour)
0.0
0.2
0.4
0.6
0 10 20 30
Manual
Mini-forwarder
Series2
Hourly cost x (USD/crew-hour)
Productivityy(m3/crew-hour)
z = 51.3
z = 40 USD/m3z = 10 z = 20
z = 80
A
B
B. Relationship between costs and productivity
13. 07/09/2016 13
III. Effect of Kenyan economic growth on labor costs
Kenya was the 3rd fastest growing
economy in the world in 2015
Among top 10 middle-income
countries in Africa
Outcome= increased wages, thus
higher labor costs
Source: Kenya Vision 2030
Source: Robinson J. (2015).
Kenya
14. Conclusions and recommendations
07/09/2016 14
Manual logging is still the most feasible logging method in
Kenya, but as the economy grows, it will be favorable to adopt
small-scale mechanized logging such as mini-forwarders in its
forestry.
There should be further studies
to propose possible adjustments
that can be made to such logging
systems before they are introduced
into Kenyan forestry.
Source: Mr. Katagiri (Okayama Prefecture Forest Centre)
17. 07/09/2016 17
Gears
Runni
ng
Speed
Forward
1 km/h 1.62
2 km/h 2.69
3 km/h 4.26
4 km/h 6.56
Reverse
1 km/h 1.5
2 km/h 2.5
Minimum Rotation
Diameter
m
1.65
Gradeability Degrees 25
Winch
Line Pull
Speed of
the Drum
Fro
nt
1 m/sec 0.33
2 m/sec 0.67
3 m/sec 1.04
Bac
k
1 m/sec 0.31
2 m/sec 0.62
Pulling Force kN (kgf) 2.9 (300)
Drum Capacity of Wire Rope m 8@8mm
Editor's Notes
Forests play an integral part in daily livelihoods of people and the wellbeing of the environment, ranging from provision of fuel, construction materials, and other environmental services.
Therefore, there is a need for concerted efforts to manage, conserve and utilize them in a sustainable manner.
Kenya has a forest cover of 7% compared to Japan’s 67%
This figure is be low the Kenyan constitutional requirement of at least 10% forest cover
80% of Kenya’s 44 million ppl depend of forests as a source of energy, while Japan’s 127 million mostly depend of electric sources of energy.
The fact that both countries exhibit various forms of forests utilization call for an investigation to find out what Japan does better, and if Kenyan forestry can learn lessons from it.
Productivity for manual logging was obtained from Umeda et. Al (1982)
The major focus of this analysis were the costs of the two systems
It is evident that at the current Kenyan labour cost of 0.5 USD/man-hour, the resultant cost of manual logging is much lower than that of the mini-forwarder,
However, after the intersection point, the resultant cost of manual logging becomes higher than that of the mini-forwarder,
As you can notice the cost values of both systems are equal at the intersection point. Therefore, the “solver” function in Microsoft Excel was used to determine these cost values which were 5.8 USD/man-hour for labour cost and 51.3 USD/m3 for resultant cost,
An x-y-z coordinate system was used to better understand the relationship between the obtained costs and productivity,
At the equal resultant cost of 51.3 USD/m3, the productivity of mini-forwarder logging is way much higher than that of manual logging,
This means that an a mini-forwarder can be comfortably introduced in Kenya at such labour cost of 5.8 USD/man-hour,
As explained in part 2, the Kenyan economy is rapidly growing and this might create better conditions for introduction of small-scale mechanization in Kenya forestry.
The Kenyan govt. has a mega project called Vision 2030 that aims at transforming the country into a fully middle-income country by the year 2030
Kenya is one of fastest developing economies in the world, in addition to being one of the top 10 middle income countries in Africa.
The outcome of this is improved livelihoods, meaning ppl can be able to afford alternative sources of energy like electricity thus avoid depending on forests; and also the forestry sector will benefit from availability of subsidies and other tariffs that enhance forestry operations.
