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Good practice guidelines for biomass production studies, COST Action FP-0902, WG 2 Operations research and measurement methodologies

Authors:
COST Action FP-0902
WG 2 Operations research and measurement methodologies
GOOD PRACTICE
GUIDELINES FOR BIOMASS
PRODUCTION STUDIES
3
Editors: Magagnotti N., Spinelli R.
Contributors: Acuna M., Bigot M., Guerra S., Hartsough B., Kanzian
C., Kärhä K., Lindroos O., Magagnotti N., Roux S., Spinelli R., Talbot B.,
Tolosana E., Zormaier F.
Reviewers: Björheden R., Kellogg L., LeBel L.
is publication is supported by COST
COST Vademecum (Part A) - Pay-as-you-go System
v05/05/2010 Page 44 / 67
ESF provides the COST Oce through a European
Commission contract
COST is supported by the EU RTD Framework
Programme
PEFC/18-31-124
PEFC certified
This book is printed
on paper from
sustainably
managed forests and
controlled sources.
www.pefc.org
Graphic lay out: Comunicambiente.net
Illustrations: Giovanni Tribbiani
Printed by: Litotipograa Alcione S.r.l.
Published by:
CNR IVALSA
Via Madonna del Piano, 10
I-50019 Sesto Fiorentino (FI)
ITALY
www.ivalsa.cnr.it
“Good practice guidelines for biomass production studies”
Year of publication: 2012
ISBN 978-88-901660-4-4
4
COST- the acronym for European Cooperation in Science and Technology-
is the oldest and widest European intergovernmental network for
cooperation in research. Established by the Ministerial Conference in
November 1971, COST is presently used by the scientic communities
of 35 European countries to cooperate in common research projects
supported by national funds.
e funds provided by COST - less than 1% of the total value of the projects -
support the COST cooperation networks (COST Actions) through which,
with EUR 30 million per year, more than 30 000 European scientists are
involved in research having a total value which exceeds EUR 2 billion
per year. is is the nancial worth of the European added value which
COST achieves. A “bottom up approach” (the initiative of launching a
COST Action comes from the European scientists themselves), “à la
carte participation” (only countries interested in the Action participate),
“equality of access” (participation is open also to the scientic
communities of countries not belonging to the European Union) and
“exible structure” (easy implementation and light management of the
research initiatives) are the main characteristics of COST.
As precursor of advanced multidisciplinary research COST has a very
important role for the realisation of the European Research Area
(ERA) anticipating and complementing the activities of the Framework
Programmes, constituting a “bridge” towards the scientic communities
of emerging countries, increasing the mobility of researchers across
Europe and fostering the establishment of “Networks of Excellence”
in many key scientic domains such as: Biomedicine and Molecular
Biosciences; Food and Agriculture; Forests, their Products and Services;
Materials, Physical and Nanosciences; Chemistry and Molecular
Sciences and Technologies; Earth System Science and Environmental
Management; Information and Communication Technologies; Transport
and Urban Development; Individuals, Societies, Cultures and Health. It
covers basic and more applied research and also addresses issues of pre-
normative nature or of societal importance.
Web: http://www.cost.eu
5
Table of contents
1. Introduction 7
2. Background 8
3. Work measurement 9
3.1 Statistics in work measurement 9
3.2 Study types 10
4. Before you start 12
4.1 Study goal 12
4.2 Experimental design 12
4.3 Formulating a statistical model 15
4.4 Dening what to measure and how 16
4.4.1 Inputs 16
4.4.2 Outputs 17
4.4.3 Process variables 17
4.5 Practical rules 18
4.5.1 Safety 18
4.5.2 Ethics 19
5 Measurements in the eld 20
5.1 Measuring time input 20
5.1.1 Plot level 20
5.1.2 Shift level 21
5.1.3 Cycle level 21
5.1.4 Element level and work sampling 22
5.1.5 Units 27
5.1.6 Classication of time in forest studies 27
5.2 Measuring energy input 28
5.3 Measuring product output 30
5.3.1 Count 30
5.3.2 Solid Volume 30
6
5.3.3 Bulk volume 30
5.3.4 Fresh weight 31
5.3.5 Dry weight 32
5.4 Measuring energy output 32
5.5 Measuring quality output 33
5.5.1 Product quality 33
5.5.2 Stand impacts 33
5.5.3 Soil impacts 34
5.6 Measuring process variables 34
5.6.1 Physical environment 34
5.6.2 Organization 36
5.6.3 Technology 36
6 Data analysis 37
6.1 Descriptive statistics 37
6.2 Checking for outliers 38
6.3 Checking for normality 39
6.4 Data transformation 39
6.5 Making comparisons 39
6.6 Modelling 40
7. Conclusive notes 43
8. Relevant bibliography 45
Appendix 1 – Work science: denitions 47
Appendix 2 - Example of the main parameters
most capable of aecting harvesting performance 48
Appendix 3 - Classication of time
in forest work study (IUFRO 1995) 49
7
Good practice guidelines on biomass work
studies
1. Introduction
Forest Work Science is an important branch of Forest Science, which has
developed into an independent eld since the late 1920’s. Despite a strong
international cooperation within the forest engineering community, the
evolution of the discipline has inevitably generated local adaptations in
response to dierent work environments and individual preferences.
e mutual understanding once derived from common study methods
has largely been lost. As a result, there is now much misunderstanding
about time study methods, both at the theoretical and the practical
level. Ambiguity arises especially regarding the terminology, the units
of measure, the experimental design and the statistical treatment of
data. Hence, there is the need for a good practice guideline (GPG), which
must be simple and concise enough to encourage widespread adoption.
e guide should ensure comparability of results and repeatability of
experiments, both fundamental elements of the scientic method. In
turn, this will facilitate international network building and research
coordination, which are the ultimate goals of the EU COST Programme.
e purpose of this guide is to answer this need. is is a simple and
quick how-to guide that can help harmonize work study methods. It is
designed for the eld researcher who needs quick access to sound study
practice, even when lacking strong theoretical skills in work science and/
or statistics. Contrary to a scholarly book, this manual goes from practice
to theory and not the reverse. In fact, this manual does not replace the
many scholarly books dealing with operational studies and the related
statistical methods. Readers are encouraged to consult them, if they
8
wish to deepen their understanding of the subject. e reference section
contains a partial list of authoritative sources, which readers can use to
this very end.
2. Background
The origin of work studies is commonly credited to the paper “A
piece-rate system being a step toward partial solution of the labor
problem” published in 1895 by F.W. Taylor on the Transactions of the
American Society of Mechanical Engineers. Taylor was convinced that
for each task there was a quickest time in which it could be performed
by a “rst-class man”, without depleting his work capacity. e “rst-class
man” was the man best tted to perform that task, through natural and
acquired capabilities, including proper equipment. e quickest time was
referred to as the standard time, which could be determined through
scientic investigation and used for work management. Determining a
standard time was considered crucial to setting a fair piece rate and to
nd the “one best way” for performing a given task. Standard time was
subdivided in three main categories: 1 - time actually spent working; 2 -
time for overcoming fatigue (rest); 3 - time for overcoming delays. Complex
Scientic management has its roots in an exploitative era characterised by rapid industrialisation
9
tasks were simply treated as a sum of elemental tasks, and their standard
duration was considered as the sum of the standard duration of each
elemental task. at is where elemental time studies comes from. Some
of Taylor’s original concepts were criticised early on. Already in 1930,
the famous economist A.C. Pigou stated that everybody is continuously
learning and that there is no one best way to perform any given task.
Another common criticism is that the time to perform a complex task is
not necessarily the sum of the times to perform its elemental sub-tasks,
because there is often interaction between correlated sub-tasks, leading
to time economies or diseconomies. Regardless of critics, Taylor’s original
philosophy has shaped work management as a discipline. His concepts are
still echoed in modern work study techniques even 100 years after their
original formulation.
3. Work measurement
Work science has multiple goals, achieved with dierent types of
studies (Appendix 1). In this guide we are mostly interested with
work measurement. e objective of work measurement is to describe the
relationship between work inputs and work outputs, and the inuence
of process variables on that relationship. One may consider many types
of inputs and outputs, depending on the goal of the study. In a simple
work study, one may focus on mass output and time input. Energy is also
a very good choice for both input and output, especially when dealing
with energy biomass.
e direct relationship between product output and time input is called
productivity. e inverse is called time consumption (per unit product).
e variables aecting these relationships are many, and include such
factors as technology, work technique, operator skill and environmental
conditions. Some of these variables can be managed, while others are
passively received.
Determining the eect of process variables on the input-output
relationship has many practical uses, such as: setting work rates,
scheduling harvesting activities, and comparing technologies or work
methods.
3.1 Statistics in work measurement
U
nfortunately, process variables come in almost endless combinations,
which makes it dicult to determine the specic eect of the variable in
which we are interested (target variable). at is where statistics come into
play.
10
Good experimental design and statistical analysis of data allow contrast-
ing the eect of target variables against the general eect of all the other
variables combined (Figure 1). Target variables are often called “control-
lable factors”, on the assumption that what is known and predictable can
be managed, one way or the other. e other variables are called “nui-
sance variables”, and their eect “background noise”, or simply “nui-
sance”. Experimental design oers several techniques to dull background
noise, so that the target variable eects can emerge through proper sta-
tistical analysis.
Figure 1. A generic model of a work study system, aected by target and nuisance variables. e
lower tier shows the main strategies to dampen nuisance.
All variables can assume discrete nominal values (or levels - e.g. machine
A, B and C) or continuously changing numerical values. Variables of the
former type are commonly called “factors”, whereas variables of the lat-
ter type are called “covariates”.
3.2 Study types
Work measurement studies can be classied according to their scope, goals
and characteristics, with dierent study types normally requiring dierent
experimental designs and statistical techniques.
e scope of the study may be described after carefully dening the system
boundaries. In general a study can concern a single worker, a single machine
or a whole system.
As to goal, we can dierentiate comparative studies from modelling studies.
Comparative studies aim at determining if and how productivity or time con-
sumption is aected by two or more operational alternatives (e.g. machine
A vs. machine B). Basically, comparative studies try to disclose the eects of
xed “factors”. In contrast, modelling studies aim at determining the eect
11
of continuous variables, or “covariates” (e.g. tree size, extraction distance
etc.). Normally, xed eects are represented as nominal variables, whereas
covariates are represented as numerical variables. Studies often involve a
combination of comparative and modelling elements, with the specic goal
of the study normally determining how the study is classied (comparative
or modelling). For instance, modelling is often used in comparative studies
of the highly variable forest environment in order to enable comparisons to
be made under the same conditions (e.g. normalizing the comparison for
the same mean tree size).
Based on its experimental characteristics a study can be dened as either
“observational” or “experimental”. In an observational study, inuencing
variables cannot be controlled. at may result in a rather weak study de-
sign, which will provide indicative rather than conclusive evidence about
the eect of target variables. Many forest work studies are observational
in character, yet they oer valuable insights into the studied processes and
nd their way to the scientic press. In contrast, experimental studies in-
volve a stronger capacity to control process variables, with levels that can
be suitably arranged in a strong experimental design. e insights obtained
from these studies are stronger and much more reliable than those obtained
from observational studies. Simulated environments oer an ideal oppor-
tunity to conduct the perfect experiment. Nevertheless, researchers playing
with experiments must be careful not to build an experimental design that
is too articial to reect real operational conditions.
Performing a work study is a complex job, involving several steps ( Figure 2).
Figure 2 – Flowchart of the dierent steps required for a work study
12
4. Before you start
4.1 Study goal
A clear study goal will guide researchers through all steps of a good study.
e exact denition of this goal is aected by: 1) specic problem to
solve or knowledge to acquire, 2) foreseen use of study results and 3)
available resources. For instance, the need for accuracy will dier be-
tween a low-budget study aimed at obtaining a rough and ready estimate
of expected performance and a large-scale experimental study designed
to produce reliable guidelines for ocial use. However, it is of the out-
most importance to spend both time and consideration to formulate the
goal, so that it meets both the expectation of the end user and the avail-
able budget. A clear goal statement is the foundation of a good study. A
hypothesis statement is often part of the goal statement.
4.2 Experimental design
Experimental design is the process of planning a study to meet speci-
ed objectives. Planning an experiment properly is very important in
order to ensure that the right type of data and a sucient sample size
are available to answer the research questions of interest as clearly and
eciently as possible.
Once the goal has been dened and the hypothesis formulated, the re-
searcher is ready to draw his/her experimental design. e design must
fulll two conditions. First, the nuisance variables should be controlled
eciently and logically. Second, the design should lead to simple analy-
sis, since it is the experimental design that decides how the collected
data should be analyzed statistically.
In order to know what nuisance to control, an important step in the ex-
perimental design is to list all conceivable nuisance variables. Nuisance
can be handled by the following methods: constant keeping, randomiza-
tion and inclusion. By constant keeping the study is conducted under
constant conditions - e.g. only chipping logs of a given size and species,
which corresponds to one given nuisance level (where log size represents
a nuisance variable in the study). Randomization consists in randomly
allocating the treatments to the dierent levels of nuisance – e.g. ran-
domly allocating the piles of similar, but not identical, material to be
chipped. Inclusion is also called experimental control and consists of
treating nuisance variables as additional target variables, measuring
their levels and associating them to the corresponding levels recorded
for productivity, time consumption or any other response variables.
Box 1 – Example of goal statement and hypothesis statement
e goal of this study was to compare the technical and economic
performance of terrain chipping and roadside chipping, applied to
short-rotation biomass plantations. e null hypothesis was that
there was no signicant dierence in the performance of the two
work systems, when applied to short-rotation plantations.
13
4. Before you start
4.1 Study goal
A clear study goal will guide researchers through all steps of a good study.
e exact denition of this goal is aected by: 1) specic problem to
solve or knowledge to acquire, 2) foreseen use of study results and 3)
available resources. For instance, the need for accuracy will dier be-
tween a low-budget study aimed at obtaining a rough and ready estimate
of expected performance and a large-scale experimental study designed
to produce reliable guidelines for ocial use. However, it is of the out-
most importance to spend both time and consideration to formulate the
goal, so that it meets both the expectation of the end user and the avail-
able budget. A clear goal statement is the foundation of a good study. A
hypothesis statement is often part of the goal statement.
4.2 Experimental design
Experimental design is the process of planning a study to meet speci-
ed objectives. Planning an experiment properly is very important in
order to ensure that the right type of data and a sucient sample size
are available to answer the research questions of interest as clearly and
eciently as possible.
Once the goal has been dened and the hypothesis formulated, the re-
searcher is ready to draw his/her experimental design. e design must
fulll two conditions. First, the nuisance variables should be controlled
eciently and logically. Second, the design should lead to simple analy-
sis, since it is the experimental design that decides how the collected
data should be analyzed statistically.
In order to know what nuisance to control, an important step in the ex-
perimental design is to list all conceivable nuisance variables. Nuisance
can be handled by the following methods: constant keeping, randomiza-
tion and inclusion. By constant keeping the study is conducted under
constant conditions - e.g. only chipping logs of a given size and species,
which corresponds to one given nuisance level (where log size represents
a nuisance variable in the study). Randomization consists in randomly
allocating the treatments to the dierent levels of nuisance – e.g. ran-
domly allocating the piles of similar, but not identical, material to be
chipped. Inclusion is also called experimental control and consists of
treating nuisance variables as additional target variables, measuring
their levels and associating them to the corresponding levels recorded
for productivity, time consumption or any other response variables.
Box 1 – Example of goal statement and hypothesis statement
e goal of this study was to compare the technical and economic
performance of terrain chipping and roadside chipping, applied to
short-rotation biomass plantations. e null hypothesis was that
there was no signicant dierence in the performance of the two
work systems, when applied to short-rotation plantations.
ese relationships are then used to correct the analysis. Depending on
the characteristics of the nuisance variable included in the analysis, one
will talk about “blocking” (for variables assuming xed levels) or “intro-
ducing a co-variate” (for continuous variables).
Experimental design addresses the questions of how to combine the
study treatments with the possible methods of nuisance control in such
a manner that no treatment is systematically favored.
Comparative studies will normally use some kind of factorial design.
e general similarities in factorial designs are that a certain number
of repetitions are conducted for each combination of treatments and
blocks (Figure 3). It is advisable to aim for equally many repetitions for
all combinations, i.e. a balanced design. at facilitates simple and valid
analysis. However, a balanced design is not compulsory in the analysis,
and especially not so with large number of repetitions. e capability to
accommodate for unbalance is very helpful, since unbalance often occurs
due to unforeseen events during the experiment.
A similar approach will be followed in modelling studies, with the dier-
ence that the focus is on how measurable and/or quantiable nuisance
factors of interest inuence one or many controllable factors (treat-
ments). us, instead of exerting passive statistical control on the nui-
sance variables, the ones of interest should be actively selected so they
vary within a predened range. To improve analysis, the number of ob-
servations should be balanced within the range of variation.
Each repetition of the same experiment is also called an observational
unit, sample or replicate. e reason for observing several units that re-
ceive the same treatment is the expected variation in both response to
treatments and in measurements. us, it is crucial to dene the obser-
vational unit (i.e. what should be replicated) and the required number of
repetitions. is can be calculated based on information about: expected
mean value, sample variation and desired (statistical) accuracy.
14
e procedure for such calculations varies with the type of experimental
design, and can be found in standard statistical textbooks. However, we
include the example below as a reference. e equation is used as a basis
for determining sample size in harvesting studies conducted at the cycle
level (Murphy 2005):
number of replications = t2 * V/(E*Mean /100)2 [1]
where: t = Student’s t-value (= 1.96 for a 95% condence interval t2 = 3.842)
V = expected variance of work cycle time
E = level of precision required (e.g. 5%,)
Mean = expected mean of work cycle time
Although not formed by any kind of natural law, the precision level is
generally set to 5%, which means that the researcher is willing to accept
a 5% risk that the hypothesis is incorrectly evaluated.
e expected mean value and variation is less easy to quantify in ad-
Figure 3 –Design of an experiment for comparing terrain chipping vs. roadside chipping in a short-
rotation poplar plantation. Plots are allocated randomly to the two treatment levels (i.e terrain
chipping = blank plots; roadside chipping = plots with a forwarder symbol). e experiment is
blocked for two main clone types, i.e. Monviso and AF2. It is therefore a factorial 2 x 2 design,
where each of the 4 treatments is repeated 6 times (i.e. total of 24 replications). Hence the design
is balanced.
15
vance of the study. Ideally, pre-studies could be used to provide appro-
priate information for the calculation. Otherwise, this information can
be obtained from previous similar studies. In fact, the decision about
the number of repetitions is often based on educated guesses. However,
this is related with the risk of having too few observations for detecting
any dierences (large variation compared to the size of the treatment
eect) – or to spend excessive resources on too many observations (very
limited variation and large treatment eect). Within a given experimen-
tal design, the sum of the samples (observational units) in all treatment
combinations results in the total number of samples to be studied. is
number is an important consideration in determining whether or not
the stipulated experimental design will t the study budget. If too re-
source demanding, alternative designs will have to be considered and/
or the number of samples will have to be decreased. us, the actual ex-
perimental design will not only be the result of a preferred accuracy in
statistical analysis, but also of the budget constraints.
4.3 Formulating a statistical model
Since it is the experimental design that decides how the collected data
should be analyzed statistically, it is natural that the researcher should
be aware of what statistical methods and models should be used for the
Box 2 – Experimental design: example 1
We want to determine the dierence between two chipper models
(= one treatment with two levels) operated by three dierent
operators (= one blocking factor with three levels) under identical
(i.e. similar) conditions. erefore, each operator will work with
each chipper, so that we shall have 2 x 3 = 6 combinations. is is a
factorial design. We decide to conduct 5 repetitions per combination,
so that the total number of samples will be 6 x 5 = 30. e order in
which we shall distribute samples should be random. So operators
shall switch between machines randomly, until we have completed 5
repetition of each of the 6 operator x machine combinations. Under
real conditions, a pure random sampling might be inconvenient, so
that one may assign operator x machine combination randomly, but
conduct the ve repetitions sequentially for each combination. is
procedure is formally incorrect, but it is often accepted if one can
nd other measures to mitigate the error deriving from sequential
repetition, or if it can be rationally explained why it is expected that
such violation of good practice does not represent a main source of
error.
16
chosen design. us, it is important to formulate the statistical model
that will be used to analyze that data. If hypothesis and experimental de-
sign are well aligned and the experiment includes few treatments, blocks
and co-variates, this is quite straight-forward. For instance, a design
with one treatment under constant conditions would have the statistical
model of:
yij = µ + αj +εij [2]
in which y is the response variable for observation i within treatment
level j (e.g. time consumption for a given log chipped by chipper A), µ is
the grand mean (total mean value), α is the main eect of the treatment
j, and ε is the random error. is model would be evaluated in a one-way
analysis of variance (ANOVA). If the study employs statistical control of,
for instance, dierences in log sizes, a co-variate is added to the model,
according to
yij = µ + αj +b×xij +εij [3]
where b is the slope of the co-variate x. is model would be evaluated in
a one-way analysis of co-variance (ANCOVA).
4.4 Dening what to measure and how
e object of work studies is the relationship between work inputs and
work outputs, and its reaction to the eects of process variables. A well
planned and implemented work study will determine the inputs, the
outputs and the process variables, trying to dene their possible rela-
tionships with statistical methods. In particular, a work study will re-
quire that all of the following objects are measured:
4.4.1 Inputs
Being the characterizing element of work studies, time is obviously the
very rst object to be measured in a time study, e.g. the time input per
cycle, per cycle element or per plot. Another crucial input to be measured
Box 3 – Experimental design: example 2
When aiming to model the inuence of log size on chipping
productivity for a given machine, the design would be to dene a
range of log sizes to be studied and the total number of logs to be
chipped. Ideally, the number of logs should be spread evenly along
the log size range (instead of having 95% small logs, 2% medium
sized logs and 3 % large logs).
17
is the energy used by the process under study. is is especially impor-
tant when the object of the process is the manufacturing of an energy
product – such as biomass fuel.
4.4.2 Outputs
As work is assumed to produce outputs, these outputs should be deter-
mined with sucient accuracy for productivity studies, since productiv-
ity is dened as output divided by input. Outputs are both quantity and
quality, equally essential to evaluating any work method and/or technol-
ogy. In forest work, quality concerns two separate entities: product and
environment.
Product quality is evaluated by comparing actual product characteristics
with market specications. In the case of fuel chips, for instance, these
are: moisture content, particle size distribution, contamination level etc.
e environmental quality of a given work technique is generally dened
by its stand and soil impacts. Hence, the main outputs of a forest har-
vesting process are: product quantity, product quality, environmental
quality.
4.4.3 Process variables
Process variables that may aect time consumption and/or productivity
should be determined with accuracy, both in comparative and modelling
studies. In the former, determining work conditions is crucial to ensure
that all alternative treatments are applied under the same (controlled)
conditions. In the latter, the validity of any model will depend on the ac-
curate determination of the aecting independent variables.
Not all these objects must be measured in every study: object inclusion
and measurement accuracy will be tailored to the goal of the study, and
to the resources allocated to conduct it.
At the outset, the research team should dene the goal of the particu-
lar study and determine the variables that must be recorded in order to
meet these objectives.
If the study involves modelling, the researchers should generate a ma-
trix of the relevant dependent variables (such as cycle time elements and
production) and independent variables, anticipating which of the latter
may inuence the former. In Appendix 2 readers can nd an example of
the many variables that could be explored, when studying a range of dif-
ferent harvesting techniques.
18
4.5 Practical rules
4.5.1 Safety
Safety is the rst and foremost requisite for all work activities, coming
before productivity and environmental quality. Field researchers should
always make sure that they are not exposing themselves and others to
unnecessary risk. ey
have the legal and social
obligation to comply with
all safety requirements. If
they are working in an en-
vironment still relatively
casual about safety proce-
dures, their obligation is
also moral, because they
can set an example that
will help introduce a safe-
ty culture where this is
badly needed. Regardless
of how condent we are
in our capacities, many
operators look up to us
because of our education
and status and we should
set a useful example that
may save lives. Whenever
entering an operation, re-
searchers shall always:
- wear high-visibility
clothing (jacket or vest);
- wear a hard hat (with hearing protectors and visor when needed);
- ask the operator/s about the work routine, the safe zones and the risk
zones, so that the researcher will always stay away from risk zones and
inside the safe zones. Whenever losing sight of the researcher, the op-
erator should stop work immediately;
- agree with the operator/s on a system of communication, so as to
quickly and unambiguously transmit urgent information (e.g. radio
phones);
- abstain from drinking alcohol and/or taking any drugs that may impair
Commercial GPS-tracking black-box unit
19
one’s alertness and judgment;
- and, more generally speaking, be compliant with any other require-
ments coming from regulations or internal rules adopted by the com-
panies in charge of the forest operations.
Special attention should be paid when climbing into and out of contain-
ers to collect chip samples, as container edges are tall and slippery. When
climbing is necessary, that should be done with caution, using the steps
normally tted on most containers.
Work at a landing can often be observed from a xed station, including
the researcher’s own car, appropriately parked in a safe zone where it
does not hinder the operation. is can oer much relief under rainy
and/or cold weather conditions.
When studying felling, processing or harvesting machinery, a safe dis-
tance should be maintained in order to minimize the risk of injury in
case of uncontrolled tree fall or saw chain breakage. In certain forest
stands, safe distance may make it impossible for the researcher to ob-
serve the operation in such a detail as required for the study. e only
safe place within the safe zone is in the machine cab, and it may happen
that the researcher rides in the cab together with the operator. However,
that it is not advisable unless the cab has been designed to take a pas-
senger. Otherwise, the eventual passenger may not t inside the internal
survival volume remaining after a possible roll-over or impact. In such
instances, researchers should consider remote data collection, as allowed
by video-recorders, on-board computers and commercial GPS-tracking
black-box units.
Field study researchers work outdoors and should take all precautions
required by outdoor work, including: wearing appropriate clothing (com-
fortable, rainproof, warm, fresh - according to need); wear work boots
or similar shoes; carry their own supply of water and food, as needed;
use insect repellent or carry an appropriate weapon if harmful insects
or animals may cause danger or discomfort; and get the appropriate vac-
cinations for any diseases that can be contracted in the specic work
environment (tick-borne encephalitis, tetanus etc.).
4.5.2 Ethics
Work studies often represent an intrusion into the personal working
space of individuals, crews, and enterprises. Studies can be initiated
by companies that wish to know more about their own operations, by
machine manufacturers that wish to test or enhance their design, or by
20
researchers who wish to investigate one or more aspects of machine or
system performance in an applied setting. Studies are almost never initi-
ated by workers themselves.
Scientic management has its roots in an exploitative era characterised
by rapid industrialisation and a need to quantify eciencies and costs.
While work studies are important, the integrity of the personal work
space is protected by collective agreements, legislation and common re-
spect.
rough work studies, the researcher unavoidably gains insight into the
physical and intellectual capacities of the subjects involved. In motor-
manual work this is explicit in the form of heart and lung performance
measurement. In mechanised operations it might be the subjects’ deci-
sion making or concentration ability.
is short passage cannot deal with the complexities of labour law and
human rights in the 34 participating countries. It is a simple reminder
that researchers should be familiar with the legal and ethical framework
within which they operate. Work studies should be founded on dialogue,
trust and condentiality. Subjects should be made aware of the purpose,
methodology and intended use of the results. eir consent should be
obtained beforehand. e eld of ethics also includes the relationship
within your own study team and with other research teams. ese are
treated extensively in the USDA Code of Scientic Ethics, which provides
an excellent example and is freely available on the Internet http://www.
fs.fed.us/rm/analytics/ethics.htm
Similar attention must be paid to the relationship with the customer and
all subjects involved in the study, and to the possible condentiality obli-
gations imposed on sensitive data.
5 Measurements in the eld
5.1 Measuring time input
Time consumption measurements oer dierent resolution depending
on whether they are conducted at the shift, cycle or elemental level.
5.1.1 Plot level
In plot level measurement the observation unit consists of a single plot,
like those described in Figure 3. Hence, all the time input necessary for
harvesting the plot is cumulated. Time input is measured directly by the
researcher observing the operation, or automatically by appropriate sen-
21
sors connected to a data logger. Data can also be recorded manually by a
cooperative operator appropriately instructed.
5.1.2 Shift level
Shift level measurement implies that the observation unit consists of a
whole shift, whose duration and organization should always be indicated
(e.g., 8 hours total time, including 6 hours of actual work and two hours
of maintenance). Shift level measurements are conducted manually or
automatically. In the former case, operators are given data collection
forms and are instructed to note daily on the forms data such as: date,
place, job type, starting and ending hour, estimated output (turns, trees,
m3 etc.), fuel consumption and any major delays.
e cause and estimated duration of all delays should also be noted.
Much of the same information can be collected automatically through
appropriate sensors, connected to a data logger. Most dedicated har-
vesters are already tted with the necessary equipment to capture these
data, for the purpose of operational optimization and cost control. Shift
level measurement is generally the main technique used for long-term
follow-up studies aimed at determining machine utilization, long-term
productivity and incidence of delays.
5.1.3 Cycle level
In cycle level measurement, the observation unit is a single work cycle
(e.g. the felling of a tree, the forwarding of a load etc.). Compared to
shift-level measurement, cycle level measurement oers more detail and
can help describe the work pro-
cess with much more accuracy. It
also helps identify the variability
of a work process very quickly.
Individual relationships can be
isolated that could be dicult to
pinpoint with shift level meas-
urements. A number of dier-
ent tools can be used for manual
measurements of time consump-
tion at cycle level, including:
standard wristwatch, stopwatch,
stopwatch board, or hand-held
computer. All these instruments
can determine the time elapsed
Time study board
22
between the start and the end of a previously dened work cycle, and
this value is noted on paper or voice recorder by the researcher, or stored
in the computer memory. Time consumption can also be captured auto-
matically, if an action or a sequence of actions dening the start and the
end of a work cycle can be identied by appropriate sensors. ese can
transfer captured data to the storage of an on-board computer (if tted)
or to an add-on external storage. e MultiDAT system developed by FP
Innovations (formerly FERIC) and distributed by Castonguay Electron-
ique Inc. in Canada is an example of a proven automated data collection
system. Furthermore, modern eet control and management systems
oer similar capabilities and could be used for the purpose of collecting
time and motion data.
5.1.4 Element level and work sampling
Element level measurement consists of splitting the work cycle into func-
tional steps (elements) and then recording time consumption separately
for each of them. is allows the work process to be described in more de-
tail, which may contribute to a better understanding of process dynam-
ics. In particular, the benets of elemental measurement are: 1) indicating
which specic process steps take more time, so that specic improvement
measures will primarily target these steps; 2) separating eective work time
from delay time, since these
two categories have dierent
internal variability and could
be modelled in dierent ways;
3) separating functional ele-
ments that react to dierent
work characteristics, so that
more accurate sub-models can
be developed.
Elemental measurement is
conducted with the same in-
struments listed in the pre-
vious paragraph. When very
short elements must be cap-
tured, one may resort to video
recording: elemental time con-
sumption is then measured in
the lab, using the slow/pause
function and the time stamp
of the video recorder. In all
Collecting time data
23
cases, it is crucial that the actions marking the beginning and the end of
each functional step be clearly dened and described, so that the study can
be interpreted and eventually replicated by fellow researchers. If more ac-
tions occur at the same time, they will overlap.
In this case, we shall have three options, depending on the goal of the
study: 1) we may record their separate durations; 2) we may dene a new
combination element; 3) we may decide for a priority system that allocates
the overlap time to one of the two separate activities. Let us consider the
case of a feller-buncher handling cut trees while rolling on its tracks. If we
need to separate the two activities, we shall ask a colleague for assistance so
that two separate persons will time the two functions separately.
Box 4 - Subdivision of cycle time into functional elements
As an example, one may consider subdividing a chipper cycle (dened
as the process of lling up a container of known volume) into the
following time elements:
- Moving the chipper along the wood pile or between adjacent wood
piles. Starts when the outriggers are lifted o the ground and
ends when they are rmly positioned into the ground at the next
chipping station.
- Parking the container near to the chipper. Starts when the chipper
is still, waiting for the container to be placed by its side and ends
when the chipper begins chipping again.
- Chipping. Starts when the rst wood load is moved to the chipper
infeed and ends when no more wood is being fed to the chipper.
- Other work. Any other work process (e.g. piling, handling wood
with the loader etc.)
- Delays. Any interruption of the work process (See next box).
When subdividing a work cycle into functional steps it is important
to resist the temptation of producing too many time elements, since
that may detract from recording accuracy, increase the possibility of
errors and complicate experiment replication by others.
It is important to remember the purpose of the specic study and the
main goal of elemental breakdown, which is the separation of process
steps that are dierently aected by dierent independent variables
and/or require dierent improvement measures.
Separating more elements that are similarly aected by the same
variables and/or improvement measure will produce no practical
benets. It is also helpful to look at other studies for ideas on
elemental breakdown and on relevant variables to measure.
24
e same result will be obtained by videotaping the operation and then
playing the tape twice, so that one person can record the two separate
times. Otherwise, we can dene a special combination element (e.g. “han-
dle and roll”) to contain overlap time.
Finally, we can allocate overlap time to either the “handle” or the “roll”
Box 5 - Delays
Delays are interruptions of the work process and are commonly
subdivided into three main categories depending on their origin.
Mechanical delays are caused by the need to service or repair the
machine used for performing the work task. Personal delays are
interruptions caused by the operator, and include rest breaks.
Operational delays are related to organizational causes, such as a
poor balance between the chipper and the supporting units (waiting)
or an excessive concentration of machines on the same track (trac),
work planning and site reconnaissance.
A fourth delay type is represented by interruptions of the work cycle
caused by the study itself (study delays). ese are generally excluded
from the analysis. e subdivision between evitable and inevitable
delays requires a subjective judgment and should be discouraged.
e main problem with delays is their large variability, due to erratic
occurrence.
A reliable estimate of delay time (or overall time including delays) will
require a very large number of replications and a comparably long
observation time. erefore, two main solutions have been devised
to overcome this problem: 1) including into the study only those
delay events that fall within a maximum duration limit (e.g. 10 or 15
minutes); 2) excluding delays from data recording and accounting for
delay time through specic delay coecients applied to productive
time. e rst strategy tends to underestimate the incidence of
delays.
For example, long-term studies of chipping operations have shown
that delay events shorter than 15 minutes represent over 80% of
the occurrences, but only 32% of the total delay time. e second
strategy is based on conducting a long-term follow-up study of the
operation or on combining a large number of detailed time studies
into a larger data pool, in order to extract the long-term incidence
of delay time (delay coecient). is should also be expressed as a
percent of productive work time, and not of worksite time, since the
latter form is awed by inter-correlation.
25
function. e function with priority will be the one clearly appearing in the
study, whereas the other function will be “masked”.
e description of time elements should clearly report which functions
may overlap, and which will have priority when overlap occurs. Priority
will be attributed on the basis of the study goal. If for instance our feller-
buncher study aimed at predicting track wear by determining how much
time a feller-buncher spent moving, then the “roll” function should receive
priority and the “handle” function would be “masked” whenever the two
occurred together.
Elemental time is recorded with two main techniques: continuous timing
and snap-back timing. With continuous timing, the time of each element
shift is noted, and the duration of each time element is calculated by sub-
Box 6 - Handhelds computers
Time study data can be collected with handheld computers running
dedicated time study software.
ese computers are often
ruggedized, so that
they can withstand
the outdoor forest
environment. Dierent
machines are used by
dierent groups, but
the most common are:
the Husky Hunter (and
subsequent models)
running the dedicated
Siwork3 time study
software, which is
still very popular in
many English-spea
king
countries, as well as in Denmark, France and Italy; the Latschbacher
family of eld computers, widespread in Austria and Germany; the
Rufco 900 in Finland. All these machines are relatively old and at
times they present interface problems with modern laptops, as most
of them use serial connection ports that have virtually disappeared
from new personal computers. Modern potential replacements are
new portable machines such as Allegro, Psion, Ranger and Toughbook.
Interfacing potential, software availability, reliability and battery life
are the main parameters to consider when choosing a handheld for
time study purposes.
Husky Hunter hand-held computer
26
tracting two time marks (i.e. the time when the function was completed
minus the time at which it was initiated). is technique requires back-cal-
culation but is the only viable option when using a wristwatch for timing.
Snap-back timing consists in restarting from zero at each element shift.
at is done using the “lap” function available on most stopwatches. e
advantage of this method is that one does not need any calculations to ob-
tain the net elemental time.
Work sampling (also known as frequency study) is another technique for
measuring the elemental breakdown of time consumption. It consists of
observing the process at xed or random intervals, and noting in which
of the previously-dened functional steps the work team is engaged in
that specic moment. At the end of the study, the researcher will obtain
a total time (duration of the study) and a relative frequency1 of the dif-
ferent functional steps – which is one of the outputs expected of any
elemental time study.
e advantage of work sampling is that it allows one researcher to follow
more teams at a time, by organizing a sequence of observation intervals
for the dierent teams. e disadvantage is that work sampling does not
oer any information about cycle duration, since the observation inter-
val cannot be synchronized with the variable duration of the work cycle.
In fact, one should carefully avoid the synchronization of observation
interval with cyclic work, which would return a biased representation of
cycle time distribution.
Irregular sampling intervals are preferable to regular intervals, because
they exclude the accidental synchronization with cyclic elements. Work
sampling is often used for quantifying equipment and people interaction
delays within a working team or work system.
1 whence the alternative denition of “frequency study”
Box 7 – Hawthorne eect
It is a well-known phenomenon, where workers modify their
behavior just because they know that they are being studied. is
may determine performance increases (or decreases) that are not
caused by the technical changes introduced with the experiment.
e Hawthorne eect may introduce a signicant bias in short-term
work measurements. For this reason, reliable productivity levels are
best determined with long-term follow-up studies, or by analyzing
long-term production statistics.
27
5.1.5 Units
Time consumption is generally measured in hours, minutes and seconds,
depending on the resolution of the study. Occasionally, very short pro-
cess steps can be measured in smaller units, like the tenth of a second.
Work studies are often conducted with clocks that measure minutes and
centiminutes – i.e. hundredths of a minute – instead of minutes and sec-
onds. at is a compromise aimed at transforming into a quasi-decimal
system the traditional sexagesimal time measurement system. It elects
the minute as the most representative unit and breaks it into hundredths,
in order to simplify the eventual data processing (by allowing decimal
calculation). It is a very eective measure, even in a time when comput-
ers can easily transform sexagesimal records into decimal records, and
the reverse. However, the second is the only SI unit for time measure-
ment, although the hour and the minute have been ocially accepted
for use with the International System2. Hence, time study data can be
reported in scientic publications in any of these three units, whereas
the use of centiminutes (cmin) could be rightly opposed by reviewers.
In that case, an acceptable formulation could be min*10-2 or 1/100 min
5.1.6 Classication of time in forest studies
Time consumption can be subdivided and/or grouped according to the
role of the specic work steps within the whole process. A number of
classications have been produced over time, generally deriving from
the work of the Nordic Research Council and the American Pulp and Pa-
per Association. Most previous classication eorts have already been
consolidated and partly harmonized within IUFRO, and the best syn-
thesis is still oered by the IUFRO Forest Work Study Nomenclature,
published in 1995. In Section D, the IUFRO document presents a clear
and comprehensive classication of time in forest work study.
at same classication is reported in Appendix 3 of the present manual,
and adopted for the purposes of our good practice guideline without any
further changes.
Some scholars question the subdivision into time elements, because of
their possible inter-correlation. Correct statistical theory requires that
variables are independent from each other. Hence the statistical treat-
ment of separate time elements would be incorrect if these were found
to be inter-correlated. In that case, it would only be correct to analyze
2 Tab. 6, Page 105 of e International System of Units ( S I ) 1998, 7th edition 1998, Organisation
Intergouvernementale de la Convention du Mètre.
28
total time consumption as a whole. However, elemental time studies are
still popular and useful.
5.2 Measuring energy input
Reliable measurements of the energy used for the supply of energy bio-
mass are crucial to the compilation of Life Cycle Analysis (LCA) studies,
and ultimately to the formulation of policy suggestions. e work-study
researcher is eminently well situated to provide accurate data on energy
inputs. Direct energy consumption is normally measured by recording
fuel consumption and then converting it to energy units through a con-
stant that represents the energy content of the fuel.
Fuel consumption studies can be carried out at various levels of resolu-
tion, depending on the goal of the study. Table 1 lists the main tech-
niques, with their pros and cons.
Any essential fuel consumption study should always provide at least the
following data:
- Engine model, make, year of manufacture and displacement (cm3);
- Litres or kilograms of fuel used for the duration of the study;
- Amount of biomass produced for the duration of the study.
Additional information could include: duration of the study, engine in-
formation relating to emissions (Euro standard or American tier sys-
tem), etc.
Fuel is a main energy input
29
Measurement
resolution
Technology / Method Pros & Cons
Continuous Onboard ow meter (factory
tted)
• Onboard ow meter (tted for
research)
Coupled with electronic impulse
from hydraulic valve bank, it al-
lows consumption analysis on
separate work elements, e.g
boom movement, driving, chip-
ping.
Requires relatively sophisticated
equipment
Operation or
Shift level
• Onboard ow meter
Standard ow meter on new
machines provides accurate
current and shift level data.
• Pump-tted ow meter
Flow meter on an electric
or manual pump is used to
record fuel volume during re-
fuelling.
• Scale
Scale can be used to weigh
fuel before refuelling
Shift or operation level data can
provide more robust informa-
tion, evening out erratic peaks.
Requires less intensive observa-
tion / data management
Short term – retain machine op-
erator enthusiasm
Lose individual work elements in
the analysis
Motivation - often requires that
operator must record and log
data alone
Daily or weekly • Onboard ow meter
• Pump tted ow meter
• Scale
Similar to above, but less fre-
quent measurement is required.
Reduced accuracy in relation to
outputs
Not easy to keep operator moti-
vated to ll forms - serious cor-
ruption of data if error or omis-
sion occurs
Monthly, quar-
terly or yearly
Typically based on data ob-
tained from
• Onboard ow meter
Fuel issued to machine (ac-
counting system must iden-
tify machines)
Fuel purchase details from
bulk supplier (for single ma-
chine)
Total periodic fuel consump-
tion averaged by machines
Often reasonably accessible data
(due to accounting laws)
Robust data covering a wide
range and depth of observations
Cannot be used to develop spe-
cic models on operations
Risk of data loss or corruption
Table 1 – Techniques to measure fuel consumption
30
5.3 Measuring product output
Productivity studies require that time consumption be associated with a
product output, in order to determine the following relationships: Pro-
ductivity = Product output/Time input; Specic time consumption =
Time input/Product output. Specic energy use = Energy input/Product
output. Product output can be measured using the dierent units listed
below.
5.3.1 Count
Output can be measured just by counting the units produced, such as
trees, logs, bundles, grapple loads or container loads. Unit count is a very
approximate measure, which makes sense only when the units have a
regular standard size, which can be easily quantied into mass or volume
gures.
5.3.2 Solid Volume
Solid Volume is a very reliable entity for estimating biomass output.
Once the measurement technique and the eventual inclusion/exclusion
of bark is dened, the measurement is relatively robust. Solid volume can
be measured with caliper and logger’s tape, using many dierent tech-
niques (Huber, Smalian etc.). Otherwise, it can be determined using the
harvester measurement system, provided this is correctly calibrated. An-
other method to estimate solid volume consists of using volume tables,
which return tree volume as a function of tree diameter and tree height.
In this case, eld measurements are limited to the diameter at breast
height (DBH) of the trees to be processed, and to a certain number of
tree heights, necessary to develop a diameter-height curve. However, all
these methods will produce solid volume estimates for the stem and the
main branches only, excluding the volume of smaller branches and twigs.
at can be accounted for by using an empirical biomass expansion fac-
tor (BEF), which increases the stem volume estimate by a certain per-
centage, reecting the contribution of smaller branches3. However, BEFs
will only provide approximate estimates, partly defeating the benet of
using solid volume as the reference for estimating biomass output.
5.3.3 Bulk volume
Bulk Volume (or loose volume) is the volume physically occupied by a
3 See: Teobaldelli M., Somogyi Z., Migliavacca M., Usoltsev V. 2009 Generalized functions of
biomass expansion factors for conifers and broadleaved by stand age, growing stock and site index.
Forest Ecology and Management 257: 1004-1013.
31
certain quantity of biomass. is unit is often used with small logs, re-
wood and chips. Loose volume is very easy to determine, since it just
takes a tape to measure the volume of the log stack or the internal vol-
ume of the chip container.
Loose volume can be converted into solid volume or weight by using
appropriate coecients, which should be estimated case by case with
sampling. Although very easy to use, loose volume oers a somewhat ap-
proximate estimate, since the actual product mass will vary with the size
and the form of the individual elements forming the stack or the pile.
Furthermore, dierent chippers may “pack” chips with a dierent power,
thus producing a more or less compact load, even for the same particle
size distribution. Finally, loose volume can be determined with good ac-
curacy only if the stack or the container has a regular shape, whereas de-
termining the loose volume of chip piles may be dicult and will return
approximate estimates.
5.3.4 Fresh weight
Fresh weight (or green weight) is considered the most direct measure-
ment of actual mass output. However, its correct determination requires
the use of accurate scales, often unavailable in the surroundings of the
study site. In that case, loads can be scaled at delivery (they generally
are) and their weight can be transmitted to the researcher, providing
that each load is clearly and unambiguously identied. As an alterna-
tive, one can use portable scales of dierent types, applied to the loader
Plate scales used for axle weighing
32
boom or installed on large metal plates and used for axle weighing. Both
methods can oer good results, providing that the plates are correctly cali-
brated, that they are placed on solid level ground and that all axles are at
the same level when weighing.
5.3.5 Dry weight
Dry weight oers a better representation of true product value compared
to fresh weight, because it excludes the inevitable contribution of water
to mass output. Dry weight is an indirect measure obtained from fresh
weight, after determining moisture content. Existing European stand-
ards dene the methods for sampling (EN 14778), sample preparation
(EN 14779) and moisture content determination (EN 14774). e ac-
curacy of dry weight estimates will be aected by the errors accumulated
during sampling.
5.4 Measuring energy output
Energy Content is another indirect measure of output value, and it has
the merit of indicating the actual value for the end user, which simpli-
es communications with plant engineers (who will call it “lower heating
value”).
Energy content is obtained by multiplying dry weight for an appropriate
energy density coecient, then subtracting the energy absorbed by the
water inside the product. A typical example for hardwoods would be the
Box 8
- Which units should one use to measure product output?
at is indeed a big question. All units have their pros and cons, and
may be adopted depending on the goals and the circumstances of the
study. Whenever indirect measures are provided (i.e. dry weight and
energy content) it is essential that the researcher reports: the methods
used for estimating them; the values actually measured for fresh
weight and moisture content; the equations and parameter values used
in the energy calculations. An indication of variability of the direct
measurements would also be useful.
Please notice that the term “weight” applied here is formally inaccurate:
what we are really measuring is a physical quantity dened as mass.
However, understanding with managers and operators will be easier if
we use the term weight, rather than mass. Hence, it is convenient to use
“mass” in scientic papers and “weight” in everyday speech. After all,
language is a convention.
33
following one, which returns Giga Joule per metric tonne:
GJ/t = dry weight, t * 18.5 GJ/t – water weight, t * 2.5 GJ/t
is estimate is indirect and based on coecients, and its accuracy is
aected by the reliability of the coecients and the eventual error with
moisture content determination.
5.5 Measuring quality output
Job quality reects on product quality and environment quality, the latter
dened as the impact on the stand and the forest soil. In fact, environmen-
tal quality is a complex concept, going far beyond a simple determination
of direct stand and soil impacts. However, determining the full extent of
environmental impacts exceeds the scope of simple work studies.
5.5.1 Product quality
Product quality will be estimated in dierent ways, depending on tar-
get specications. For the manufacturing of logs, measurement accu-
racy and supercial damage could be important quality indicators. e
former will be checked with tape and caliper, the latter through visual
inspection, or by capturing and processing digital pictures with image
analysis software in order to estimate surface damage with more accu-
racy. In the case of chips, the actual work process can impact product
quality especially for what concerns contamination and particle size dis-
tribution. Contamination with soil and stones can be estimated visually,
or by separating wood and contaminants (manually or with the help of
a solvent) and determining the weight ratio. Particle size distribution
should be determined according to European Standard EN 15149.
5.5.2 Stand impacts
Stand impacts are generally determined by inspecting the residual
stand after harvest, in order to detect and catalogue any eventual dam-
age caused to residual trees and/or advanced regeneration. Inspection
is generally conducted on sample plots of varying shape and size. e
number and size of sample plots will be determined as a function of sam-
ple variability and desired accuracy. Tree damage is generally attributed
a severity class, often related to wound size, type, position and depth.
Supercial wounds smaller than 10 cm2 are often neglected, since they
do not seem to aect tree health, growth rate or wood quality4.
4 Whitney R. 1991. Q uality of eastern white pine 10 years after damage by logging. For. Chronicle
67: 23–26.
34
5.5.3 Soil impacts
Soil impacts are determined in a number of dierent ways, from very
simple to very sophisticated. We may want to stick with the simple
methods, assuming that the sophisticated methods will only be deployed
for studies specically devoted to the analysis of logging disturbance,
and belong to soil science more than to work science. For our purposes,
simple visual inspection could be enough, and it can be conducted sci-
entically with a standard method, such as that described by McMahon
(1995), which is rapid and easy. According to this method, the harvest
site is covered by a regular grid of inspection points with a mesh size suf-
cient to obtain the desired sampling intensity, on the basis of expected
sample variability and desired accuracy. en each point is visually in-
spected and attributed a predetermined disturbance class. As a result,
one will obtain a reliable estimate for the frequency of dierent visible
soil disturbance phenomena.
5.6 Measuring process variables
Process variables that may aect time consumption or productivity
should be determined as accurately as possible, both in comparative and
modelling studies. Such variables can be grouped in the following three
large categories.
5.6.1 Physical environment
Terrain and forest characteristics have a major eect on work perfor-
Time study on a biomass operation
35
mance, and can be described by a number of dierent indicators, gener-
ally pertaining to the elds of forest mensuration and topography. For
the specic purpose of forest operations, terrain characteristics can also
be described using the Swedish Terrain Classication System5, which is
widely adopted in Scandinavia, as well as in Ireland and in the United
Kingdom6 – with local variations. e system is simple, and the original
manual oers reference pictures for the evaluator. It produces a single
synthetic indicator capable of describing slope gradient, terrain rough-
ness and ground bearing capacity.
When describing the physical environment it is important to distinguish
between those variables that are essential for the study and those that
are not, although potentially useful. e failure of many attempts at de-
veloping general data collection protocols is likely to rest in the over-
abundant requirements of such protocols, which put an unacceptable
burden on the researcher, often constrained by budget or time limita-
tions. erefore, it may be better to restrain data collection to those vari-
ables that are most likely to aect the performance of the work process
under examination. For instance, there is little need to dene ground
slope or residual stand density if the study concerns a chipper working
at a landing.
In this case, the measurement may include average piece size (easily ob-
tained by counting the number of pieces needed to ll a container of
known volume or weight), landing surface, tree species and tree part
(branches, logs, whole trees etc.), which have already been shown to have
a signicant eect on chipping performance. Other forest compartment
data can help describe the general background of the experiment and
are welcome if they come for free, but can also be omitted without much
prejudice to the quality of the research.
5 Berg S. 1992 Terrain classication system for forestry work. Skogsarbeten, Kista, Sweden. 28
6 UK Forestry Commission. 1995. Terrain classication. Technical Note 16/95. 5 p.
Box 9
- Measuring extraction distance
Extraction distance is a key independent variable in biomass extraction
studies. Distance can be measured with several instruments, including:
tape measure, hip chain, pacing, laser range-nder, machine odometer,
GPS, map coordinates. It is always very important to indicate how
distance was determined and if the distance reported is the map
distance or the actual slope distance. It is also important to specify
whether extraction occurred uphill or downhill.
36
In general, one should record with greatest accuracy all those charac-
teristics of the physical environment that could be used as independent
variables in the eventual data analysis, such as tree size in felling studies
or extraction distance in forwarding studies.
5.6.2 Organization
Operation layout and work organization have a strong inuence on pro-
ductivity and time consumption. In general, it is enough to provide a
simple description of how the whole operation is organized, how many
units and crew members are involved, and what are their specic tasks.
If the work organization generates specic risk for operator safety (e.g.
interference between operators), it may be useful to identify this risks
and suggest solutions.
Operator experience, skills and motivation have a major impact on pro-
ductivity and time consumption. “Operator eect” has been shown to af-
fect productivity for up to 40%, which accounts for the gap between the
inexperienced and the very experienced operator. Ideally, work studies
should be conducted with many dierent operators, in order to integrate
operator variability into the study design. is often conicts with the
time and the nancial constraints of most research projects. Operator
rating would oer a practical way to deal with operator variability, and
could be conducted with a number of dierent methods, often accurately
codied. Unfortunately, no current method oers both easy application
and objective evaluation, so that operator rating is either too complex or
too subjective. For this reason, most researchers have discarded operator
rating, and prefer to use general habilitation criteria, based on opera-
tor background. at leads to the exclusion of any operators considered
inexperienced, unwilling, clumsy or slow. Evaluation is done by exam-
ining the operator work history, interviewing the operator and his/her
colleagues and supervisors, and observing the operator at work. ese
precautions will not prevent operator eect from causing some variabil-
ity in the results, but are most likely to contain the eventual error within
acceptable limits.
e payment system can have a strong eect on operator motivation and
help explain eventual inconsistencies between similar studies. Ideally, a
study should provide suitable information on the compensation system
(i.e. hourly rate, piece rate etc.) for comparison purposes.
5.6.3 Technology
All studies should integrate a full description of the technology being
37
tested, such as machine make, model, type, power, year of manufacture.
is information can be easily collected during work pauses, by reading
the machine ID tag and interviewing the operator. Depending of the
study goal other information such as vehicle weight, number of wheels,
tire size and loader outreach would be useful.
6 Data analysis
Data analysis is closely related to experimental design, since a
specic design will generally be geared to a specic data analysis
technique. Hence, we are back to the two main study types: comparative
and modelling. In fact, statistics oer many dierent techniques, most
of which could be used for analyzing time study data. Here we shall
consider only the most popular techniques, frequently the simplest to
use. In general, data analysis will move through the following steps.
6.1 Descriptive statistics
e very rst step in data analysis is describing the data. is is done
through synthetic indicators that express the distribution of data and
go under the general name of descriptive statistics. Essential descrip-
Figure 4 – Box Plot displaying the results of the comparison of terrain vs. roadside chipping in a
short-rotation poplar plantation.
38
tive statistics are: mean, standard deviation or standard error, minimum
and maximum. It is often useful to add the lower and upper quartiles, as
well. Descriptive statistics will be updated at the end of the analysis, if
the data have been purged of the eventual outliers. Data distribution can
also be described graphically using Box Plots, which display the median
(central line), the range and the 10th, 25th, 75th and 90th percentiles (Fig.
4). Box Plots are especially useful for displaying potential outliers (any
dots much further away from the 10th and 90th percentile lines).
6.2 Checking for outliers
e data pool should be checked for outliers. e easiest way to do that
is by extracting average and standard deviation for each data string, and
checking how these values match expected gures for that given data
type. Evident mismatches should arouse suspicion. For instance, if the
average felling cycle has a duration of 40 seconds, it would be reasonable
to suspect that a record indicating a duration of 4000 seconds is faulty.
en, this record should be extracted and examined for possible errors
(e.g. erroneous inclusion of more cycles in the same record, transcription
error, unwarranted inclusion of delay events etc.). A further method to
detect possible outliers is to plot the data and just look for any points
that seem unreasonably o the charts. Finally, there are formal outlier
tests, essentially based on the criteria of “distance from the mean” and
“distance from the nearest neighbor”. Among these tests are the Grubbs’
Test for the detection of single outliers and the Tietjen-Moore Test for
the detection of multiple outliers. Standard or modied Z-scores can also
be used for the detection of potential outliers. Suspected outliers should
only be removed from the data pool if there is an objective reason (proof
of error) to justify their exclusion. Otherwise we might be tampering
with the data. When in doubt, a good strategy could be that of retaining
the potential outliers and adopting a robust statistical technique that
will not be unduly aected by outliers (outlier accommodation).
Box 10 - Statistical packages
A number of dierent statistical packages can be used for analyzing the
data collected in forest work studies, and among them some of the most
common are: SAS, SPSS, Minitab, R for Excel. ey oer similar results
but vary in cost and user-friendliness. R for Excel is a good tool, with
a large capability and is freely available to all, although it takes some
learning before it can be used correctly. e others are available at a
price (from very moderate to high) and are generally quicker to master.
39
6.3 Checking for normality
Before analysis, data should be checked for normality by drawing a fre-
quency distribution graph. If the data is normally distributed, then we
can use parametric statistics; otherwise, we need to apply non-paramet-
ric statistics.
6.4 Data transformation
If data is not normally distributed, one could also try to alter its distribu-
tion through mathematical operations performed on each observation.
is procedure is called data transformation and it has the purpose of
bringing data distribution closer to normality. Common transforma-
tions are: square-root transformation for count data, logarithm trans-
formation for size data and arcsine transformation for percent data.
Transformed numbers are then used in the planned statistical tests. Test
results must be reconverted to the original through back-transforma-
tion, by applying the inverse of the mathematical operation originally
performed.
6.5 Making comparisons
e statistical signicance of any dierence of mean values returned by
comparative trials can be checked with dierent statistical tests, depend-
ing on the number of treatments being compared, the relationships be-
tween repetitions in the treatments (paired or not) and the distribution
of the data. If the data are normally distributed, the best way to analyze
a typical factorial experiment will be through the technique called Anal-
ysis of Variance (ANOVA). e ANOVA table will provide information
Eect DF SS % F-Value P-Value Power
min per
odT
Treatment
Clone
Interaction
Residual
1
1
1
20
840.157
52.628
13.606
37.184
89%
6%
1%
4%
451.90
28.31
7.32
<0.0001
<0.0001
0.0136
1.00
1.00
0.74
MJ per
odT
Treatment
Clone
Interaction
Residual
1
1
1
20
9297.51
3789.88
1155.08
3853.81
51%
21%
6%
21%
48.25
19.67
5.99
<0.0001
0.0003
0.0237
1.00
0.99
0.64
kg CO2
per odT
Treatment
Clone
Interaction
Residual
1
1
1
20
1.71
0.96
0.3
1.057
42%
24%
7%
26%
32.46
18.15
5.59
<0.0001
0.0004
0.0282
1.00
0.99
0.61
Table 2 – ANOVA table calculated with the data obtained from the experiment represented in Figure 3
40
about the statistical signicance and the strength of the eects derived
from the treatments under analysis. Table 2 shows a typical ANOVA ta-
ble calculated for the data obtained from the factorial experiment repre-
sented in Figure 3.
e ANOVA table taken as an example shows that treatment type (i.e.
terrain chipping vs. roadside chipping) has a strong (89% of total SS) and
signicant eect (p <0.0001) on time consumption expressed in min-
utes per oven-dry tonne. Clone type also has a signicant (p <0.0001)
yet minor (6%) eect on specic time consumption. Please note that the
percent value for each given eect is simply obtained by dividing the
sum of squares (SS) for that eect by the total sum of squares. Also note
that you can be interested in other types of input-output relationships
than time consumption: in biomass energy studies it is also important to
quantify energy use eciency (MJ per tonne) and GHG emission levels
(kg CO2 per tonne).
In general, when we are checking the eect of one single variable (e.g.
only treatment or only clone) we can conduct a standard t-test, if the
variable being tested can assume only two levels (i.e. either roadside
chipping or terrain chipping). If the variable can assume more than
two levels, we can use one of the ANOVA post-hoc tests, such as Fish-
er PLSD, Schee, Bonferroni-Dunn, Tukey-Kramer etc. If data do not
follow a normal distribution, the above tests will be replaced by their
non-parametric equivalents, such as the Mann-Whitney test (two-way
comparison, unpaired), the Wilcoxon Signed Rank test (two-way com-
parison, paired) or the Kruskal-Wallis test (more than two treatments).
ere are also other non-parametric tests that could be used to the same
purposes, and the mention of specic tests made above is for reference
only, without being exclusive. If the test includes both xed eects and
co-variates, then the ANOVA should be replaced by the similar technique
called ANCOVA (Analysis of Co-Variance).
6.6 Modelling
e statistical signicance of suspected relationships can be tested
through regression analysis. e most commonly used regression type
is ordinary least square regression. is technique is used to calculate
an equation capable of representing the relationship between a depend-
ent variable (typically time consumption) and one or more independent
variables.
e predicting capacity of the equation is described by the coecient
of determination (R2), which indicates the percent of the total variation
41
explained by the numerical relationship just produced. A regression with
R2 = 0.8 will explain 80% of the total variation in the data pool, and will
indicate a good predictor. Several indicators describe whether the eect
of a given independent variable is statistically signicant, and among
Box 11 - Caveats
Statistics are a specialist eld and foresters are not always too versed or
interested in mathematics (although some are really good at it). Hence
there is a risk of making fundamental mistakes with data analysis. We
list some of the most common mistakes encountered when examining
forest harvesting studies, so that you may avoid them and get your
manuscript through peer-reviewing with as little damage as possible.
- Productivity is a derived unit (output/time) and as such it is very
unwieldy. Averaging the individual productivity values for a number of
observations will return a value that will be dierent from the sum of
output values divided by the sum of time consumption values, due to
the skew in the distribution of single observations. Hence, it is always
preferable to use time consumption in all calculations, since this
gure is more stable and theoretically more appropriate. Productivity
values are calculated in the very end of analysis, by inversing time
consumption values. For instance, if time consumption is 0.2 hours/
m
3
, the productivity is 1/0.2=5 m
3
/hour.
- In regression analysis, the predictors must be linearly independent,
i.e. it must not be possible to express any predictor as a linear
combination of the others. Basically, collinear variables contain
information about the dependent variable and are redundant. Such
redundancy will confound the individual eects of the variables, thus
weakening their predicting capacity.
- e use of polynomial equations to describe machine performance or
any work related phenomenon is considered illogical by most foresters,
and such equations should not be used just because they provide
better “ts” than other models. In fact, the form of the mathematical
model should be based on what is known about the mechanics of the
process. In many cases, linear models are also inappropriate, except
as rst-order approximations over limited ranges of the independent
variables. For example, a linear model for forwarder travel time as a
function of distance is consistent with the mechanics of forwarder
travel. On the other hand, a linear model for number of trees
accumulated by a feller buncher as a function of average tree size is
guaranteed to fail as tree size increases.
42
them the p-value is most commonly used. is value is always individu-
ally associated to each independent variable, and it can be interpreted
as the probability that the eect described by the equation can happen
by chance. Hence, a very low p-value (< 0.05) is a good criterion for the
inclusion of a variable in the regression equation. Beside R2, the analysis
of residuals can provide useful information on model quality.
When handling more than one independent variable, multiple linear re-
gression is applied. By denition, this technique works best with inde-
pendent variables that change linearly. Non-linear variables can be lin-
earized by appropriate transformation, for instance by raising them to
Box 12 - Reporting
To ensure quality, clarity and repeatability, a report should include at
least the following elements:
- introduction and background of the study, leading to problem
statement
- clear and direct goal statement (e.g. e goal of this study was to…)
- description of the system under study, including a denition of system
boundaries
- description of site conditions
- description of the experimental design, including number of
replications and total study duration
- description of the techniques employed for statistical analysis
-
denition of time concept used (what kind of delays were
included etc.)
- denition of observational level used (shift level, cycle level etc.)
- denition of cycles and/or time elements (with break points and
priority levels as needed)
- denition of output units and all required calculations for estimating
indirect outputs
- description of measurement methods
- description of results, and comparison with the results from other
similar studies
- inferences that can be made from the results of the study
- report of known constraints or limitations with the study and/or
generalization of results
Use simple and clear language. No scientic report ever won the Pulitzer
prize, so it is not worth trying now. If necessary, get the document
revised by a professional language editor. Be concise, avoid redundant
tables and gures.
43
the power of 2, if they show a quadratic behavior.
Regression equations can also be used to compare treatments. To this
purpose, one of the treatments will be taken as the base case and the
others will be congured just as other independent variables, reporting
the value 0 or 1 respectively for the absence or the presence of the spe-
cic treatment.
For example, if we have a yarder working alternatively with standard and
radio-controlled chokers, then we can add the variable “radio-controlled
chokers”. is variable is set to 1 when the radio-controlled chokers are
used, and to 0 when standard chokers are used. Since it indicates a treat-
ment dierence, this variable is dened as an “indicator” variable. In
fact, the indicator variable is not a truly continuous variable (such as
yarding distance), and for this reason it is also called a “Dummy” vari-
able.
However dummy variables work well and their use in work studies is ac-
cepted, widespread and very eective 7. Models should be veried and
validated. A complete validation process normally includes several steps,
but here we can recommend at least two of them: internal verication
and independent validation. Internal verication consists of using the
model to replicate some of the observations inside the data pool used for
its construction.
e same predictor values will be input into the model and the predicted
value will be compared with the actual one, using statistical analysis to
detect if the eventual dierence is statistically signicant. Independent
validation is a very similar process, whereas the observations being rep-
licated come from outside the original data pool used to calculate the
model. For this purpose, one may try and obtain data from other stud-
ies, or partition the study data pool into two subsets, one of which will
be used for model construction and verication, and the other for model
validation.
7. Conclusive notes
Much has been written about work studies, and this short guide can
neither summarize all the knowledge on the subject, nor replace the
many scholarly books that represent the foundations of work science. In
fact, this guide only aims at providing a common platform for all people
7 For a better explanation of the signicance, the justication for use and the benet obtained
from dummy variables, see Olsen et al. 1998.
44
approaching forest biomass work studies, so that misunderstandings
can be avoided and communication improved. Ultimately, the success
of this eort will depend on the contribution of all people involved in
the COST Action, and on their adoption of this guide as the reference
for their future work. We believe that this handbook is simple, clear and
comprehensive enough for practical use in forest biomass work studies.
What is more, this guide does not give prescriptions on how to do things,
but rather oers insights on what could be done, leaving everyone free to
develop their own specic approach to the work. Harmonization is not
standardization. ere is no need for everyone to do the same thing in
the same way. at is contrary to academic freedom and progress. What
we need to do is to understand what everyone has done, so that we can
track back the process to the original elements and eventually “translate”
the results. It is unrealistic to think that everyone should speak the same
“scientic language”. On the contrary, it is more practical to develop
a “dictionary” that will allow eective communication regardless of
language. at is the main purpose of this GPG, which oers advice, not
directions, hoping that this advice can be useful and – only if useful –
adopted by those who will read it.
45
8. Relevant bibliography
A
number of dierent manuals and articles are available on the subject,
so that it may be dicult and confusing to compile an exhaustive
bibliography on work studies. However, the following texts may provide
essential information on time studies, and their reading is strongly
recommended:
Bergstrand K.G. 1991. Planning and analysis of forestry operation stud-
ies. Skogsarbeten Bulletin n. 17, 63 pp.
Björheden R., Apel K., Shiba M., ompson M.A. 1995. IUFRO Forest
work study nomenclature. Swedish University of Agricultural Science,
Dept. of Operational Eciency, Garpenberg. 16 p.
Björheden, R. 1991. Basic time concepts for International comparisons
of time study reports. Journal of Forest Engineering 2: 33–39.
Day R. 1975. How to write a scientic paper. ASM News, 41: 486-494.
Gullberg T. 1995. Evaluating operator-machine interactions in compara-
tive time studies. International Journal of Forest Engineering, 7, 1, 51-
61.
Harstela P. 1988. Principle of comparative time studies in mechanized
forest work. Scandinavian Journal of Forest Research n.3: 253-257.
Howard A. 1989. A sequential approach to sampling design for time
studies of cable yarding operations. Canadian Journal of Forest Research
19: 973–980.
ILO Guidelines for labour inspection in forestry Geneva, International
Labour Oce, 2006 ISBN 92-2-118081-6 (print) ISBN 92-2-118082-4
(web) Labour inspection, forestry, ILO Convention, comment, applica-
tion. 04.03.5
Lindroos, O., 2010. Scrutinizing the theory of comparative time studies
with operator as a block eect. International Journal of Forest Engineer-
ing 21: 20-30.
McMahon S. 1995. A survey method for assessing site disturbance.
Project Report 54. Logging Industry Research Organisation, New Zea-
land, 16 pp.
Murphy G. 2005 Determining sample size for harvesting cost estima-
tion. New Zealand Journal of Forestry Science 35: 166-169.
Nuutinen Y., Väätäinen K., Heinonen J., Asikainen A., Röser D. 2008 e
46
accuracy of manually recorded time study data for harvester operation
shown via simulator screen. Silva Fennica 42: 63-72.
Olsen E., Hossain M., Miller M. 1998. Statistical Comparison of Meth-
ods Used in Harvesting Work Studies. Oregon State University, Forest
Research Laboratory, Corvallis, OR. Research Contribution n° 23. 31 p.
Samset I. 1990. Some observations on time and performance studies in
forestry. Communications of the Norwegian Forest Research Institute,
n. 43.5, 80p.
Spinelli R., Visser R. 2009. Analyzing and estimating delays in wood
chipping operations. Biomass and Bioenergy 33: 429-433.
Zerga, J. E. 1944. Motion and time study: a résumé and bibliography.
Journal of Applied Psychology 28: 477-500.
47
Appendix 1 – Work science: denitions
Work science is the branch of knowledge associated with work and its
measurement, including: the work itself, man at work, the machines,
tools and other equipment employed in work and the organization and
methods of work.
Work study is the systematic study of technical, psychological, physio-
logical, social and organizational aspects of work. It provides for critical
examination of existing and proposed ways of doing work. Work study
is based on objective, unbiased observation and analysis. It is applied to
establish or improve the eciency of production.
Organization study is the systematic and critical analysis of organiza-
tional structures and relationships, in order to describe and improve the
organization.
Method study is the systematic and critical analysis of ways of doing
work, in order to make improvements.
Work measurement is the application of techniques designed to meas-
ure: 1) the input of resources into the productive process, 2) the meth-
ods and motions of work and 3) the output of production. For man at
work the measurement may include: time consumption, movements and
working motions, physical and mental workload etc. For machines and
tools: time consumption, wear, movements and maneuvers, energy con-
sumption etc. In addition to this, it is common to include descriptions of
the work object (tree size etc), the work environment (terrain, weather
etc) and the quantity and quality of production.
Time study is the measurement, classication and subsequent system-
atic and critical analysis of time consumption in work, with the purpose
of eliminating useless time consumption.
Motion study is the systematic and critical analysis of working mo-
tions with the purpose of describing the motions, eliminating useless
motions, and arranging the remaining motions in the best sequence for
performing the operations.
48
Operation Options Cycle parameters Units Stand/operational parameters Units
Felling and/
or processing
Motor-
manual
Mechanized
Tree size:
- unit volume/weight
- dbh or other dimensions
- number of trees per cycle (if
more than one)
Species
m
3
– dry t (or
green tons at a
given moisture
content) tree n°
cm
nr trees•cycle
-1
Species code
Felling type (clear cut,
systematic, selective thinning)
Felling intensity
Stand type (Even-aged high
forest, coppice, etc.)
Weather conditions
Terrain class /Aver. slope
Felling type code
Trees•ha
-1
, m
3
•ha
-1
, t•ha
-1
Stand type code
Weather code
Terrain class code, % slope
Extraction Forwarding
Skidding
Yarding
Species / product sizes
Load type (Whole trees,
branches and tops, bundles,
stump wood, roundwood)
Payload
Extraction distances:
- Access/main road unloaded trip
- Strip road unloaded trip
- Access/main road loaded trip
- Strip road loaded trip
- Loading distance
Slope, if signicant
- Access/main road unloaded trip
- Strip road unloaded trip
- Access/main road loaded trip
- Strip road loaded trip
Species code
Load code
m
3
– dry or
humid tonnes/
cycle (trip)
No. of bundles
m
m
m
m
m
± % slope
± % slope
± % slope
± % slope
Terrain class /Aver. slope
Felling intensity
Biomass and/or roundwood
removals
If Y, bundle or pile size.
Time in pile at harvesting
before extraction
Biomass average moisture, if
signicant
Distance between strip roads
Access/main roads density
Terrain class code, % slope
Trees•ha
-1
, m
3
•ha
-1
, t•ha
-1
Y/N
Trees•pile
-1
, m
3
•pile
-1
, dry
(or green) t•pile
-1
% m.c. (with indication of
dry or wet basis)
m
m•ha
-1
Comminution Shredding
Chipping
Species / material sizes
Load type (Whole trees,
branches and tops, bundles,
stump wood, roundwood)
Species code
Load code
Machine type
Engine power
Infeeding system (Crane –
type, capacity -, front loader,..)
Communution mechanism
Landing size
Landing organisation
Transport (truck) type/s
Machine type code
HP – kW
r/min
Feeding system code - data
m
2
Code – draft scheme
Truck type code/s –
payload/s.
Appendix 2 - Example of the main parameters most capable of
aecting harvesting performance
49
Appendix 3 - Classication of time in forest work
study (IUFRO 1995)
50
© COST Oce, 2012
No permission to reproduce or utilise the contents of this book by any means is
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be cited as:
COST Action FP-0902 - “Good practice guidelines for biomass production
studies”
Please note: exceptions must be justied in writing by the Action Chair/MC.
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51
Published by:
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“Good practice guidelines for biomass production studies”
Year of publication: 2012
ISBN 978-88-901660-4-4
... In addition, the requirement for the work cycles to have a consistently measurable duration was also considered when future time studies are carried out. The work cycles defined using the work items are described in terms of their routine repetition in terms of elemental (basic) working times (Magagnotti and Spinelli, 2012). The start and end points of each work cycle (one stump biomass harvesting) were determined based on observation of a long and numerous operations. ...
... In this study, method studies were carried out within the scope of work study for the purposes of work evaluation such as work efficiency, cost analysis, work improvement, etc., as well as information for pre-work planning and organization such as job definition, knowing the volume of work in advance, determining the requirements of the work, knowing the required work and machine power qualifications. In the literature, method studies are carried out with similar purposes and methods to standardize the works (Kanawaty, 1992;Spinelli and Magagnotti, 2012). ...
... However, firstly, the listing of the work performed and the flow order of the work elements representing the work system are in accordance with the work study techniques encountered in the literature (Kanawaty, 1992;Yıldırım, 1989). The selection, naming, starting and break points (measuring points for time studies) of the work slots are also in line with the time components and methods to determine them (FAO, 1999;Athenassiadis et al., 2011;Magagnotti and Spinelli, 2012;Eker, 2015). ...
Chapter
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The aim of this study is to present the working methodology of the processes and procedures followed in the harvesting of stump biomass, and in this context, to recognize and evaluate the harvesting and supply chain of stump biomass. The rationale for this study is based on some information gaps such as the lack of sufficient information on how the stump harvesting is carried out, which has been carried out especially in Turkish pine tree species for various reasons, especially in areas reserved for industrial plantations, which have become widespread in the last 10 years; the work and processes and the sequence of harvesting and subsequent transportation are not systematically revealed; and it is not clear which technology is used in the supply of thisbiomass. The objectives of this study are to reveal what the process of procurement of stump biomass consists of, the work and processes carried out in the stand and on the roadside, the work and processes related to the transportation time, and the order in which these are carried out and with which type of labor force.
... In agriculture, the integration input-output volumes, and operation-influencing factors. These studies aim to deduce productivity rates, which are key production metrics, and to formulate prediction models (Björheden 1991;Magagnotti et al. 2012;She et al. 2018). However, short-term direct field observations are expensive and time-consuming, making the automation of data collection a desirable solution for various harvesting methods (Talbot et al. 2017). ...
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This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency.
... Time studies are used to determine productivity and utilization by analyzing the amount of time spent on individual work tasks. Thereby, the influence of different factors on the machine performance can be studied, and unnecessary time consumption can be avoided (Björheden 1991;Magagnotti et al. 2012;She et al. 2018). Productivity is often measured in volume or mass per delay-free productive machine hour (PMH 0 ) or productive machine hour including delays shorter than 15 min (PMH 15 ). ...
... Felling and bucking can be done manually, mechanically, or with the use of automated harvesters and feller-bunchers. Productivity evaluation and time studies are two popular methods used to investigate the economics of felling operations (Magagnotti et al., 2012). ...
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In Poplar plantations, manual tree harvesting techniques (using chainsaws) are still utilized, especially on gentle terrain or for smaller trees, where automated felling may not be a possible or safe option. The most important variables affecting the productivity of motor-manual felling, a global survey show that terrain slope, understory density, distance to trees, and diameter at breast height (DBH) are the most major variables influencing the productivity of manual tree felling. In harvesting activities like felling, delimbing, and bucking, skilled workers are required. Chainsaw use is associated with timber harvesting activities including felling, delimbing, and bucking in forest areas. The chainsaw felling hourly output was 0.4546 m3/h, the delimbing hourly production was 0.482 m3/h, and the bucking hourly production was 0.753 m3/h. As tree DBH increased, chainsaw productivity also increased. In the present study, the utilization rate and chain saw productivity hours (PMH) were 8.25 hours and 71%, respectively. Regression models' calibration and validation procedures for many aspects of tree utilization were used to develop the mathematical models between the above-mentioned process included in utilization as dependent variables and both DBH and total height of trees as independent variables. The coefficient of determination was used to test the efficiency of the calibration and validation of the developed models in the estimation of the dependent variables, the most acceptable Equation No. 11 with a 91.86 coefficient of determination(R2). Planning for sustainable harvesting can be influenced by the primary suggestions for improving motor-manual felling productivity.
... time study such as plot level, shift level and work cycle) are often used to measure the mass output and time input to determine the productivity of a harvesting machine. Work productivity of harvesting machines can be impacted by various factors including technology type, environmental conditions, stand characteristics, operator skills and working methods (Magagnotti et al. 2012). ...
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Forest fires are one of the most severe incidents that can destroy large areas of forest lands and cause substantial damages to the nearby residential areas and farmlands. Human efforts to reduce the likelihood of starting forest fires include prescribed burning, mechanical fuel load reduction and livestock grazing. Following natural disasters in forest areas, a salvage recovery operation is usually conducted that may cause some environmental impacts. This article aimed to review the literatures to provide a summary of different types of machines and working methods that have been studied in different countries. The review results showed that the most common method for mechanical fuel load reduction was mechanised cut-to-length using a harvester and a forwarder. This method has been widely applied in Canada, USA, Europe, and Oceania. In some countries (e.g. Australia and USA) the whole tree method is also applied for mechanical fuel load reduction. This method was often conducted using feller-bunchers, grapple skidders and chippers/grinders. The majority of mechanical fuel load reduction projects produced the extra fibre which could be used for bioenergy purposes. The number of salvage recovery studies was limited compared with studies on mechanical fuel load reduction. Salvage recovery operations applied a cut-to-length and whole tree harvesting methods. Windthrown trees were extracted to the roadside using heavy size forwarders or grapple skidders in flat/moderate terrains while tower yarders were applied in steep terrains. Detailed information on work productivity is described in this article which can be of use to the academic and industrial users.
... Several good guidelines have been presented for reporting studies on forest operations (e.g. [118,119]). ...
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Purpose of Review The aim of this literature review was to bring together the most relevant and recent research information on the fuel consumption and CO2 emissions caused by the fully mechanized cut-to-length (CTL) harvesting machinery when applied to industrial roundwood. A specific aim of this review was to describe the effect of different independent variables on fuel consumption in fully mechanized CTL wood-harvesting operations. Recent Findings The review showed that the engine power of CTL forest machines accounts for most of the variance in the hourly fuel consumption of both harvesters and forwarders. We underline that the cubic-metre-based fuel consumption of CTL forest machines is correlated to the same factors that affect work productivity. Among all influencing factors, the average stem size, removal intensity and silvicultural treatment have the strongest effect on the fuel consumption per m³ incurred with felling-processing, whereas forwarding distance, removal intensity and payload size are the main drivers of fuel consumption per m³ as incurred with extraction. Further influencing factors are soil type (mineral soil or peatland), use of tracks, assortment type and machine size. Together with those factors, the role of the machine operator remains crucial and is dependent on two separate skills: the capacity to achieve high productivity, and that to apply fuel-saving driving techniques. Summary The easiest way to reduce the carbon footprint of CTL harvesting machines is to increase the productivity of the harvesting work, for example by giving machine operator-specific training to utilize more efficient work methods and economic energy-efficient driving techniques. Furthermore, several other measures to reduce the carbon footprint of CTL harvesting operations were discussed in this review. Finally, we recommend that all essential variables that have a significant impact on the productivity of harvesting work, fuel consumption and CO2 emissions are reported in study papers in the future.
... Work efficiency is defined using time study method where transport time (including productive and delay times) and work output (volume or weight of transported wood) are measured (Griliches, 1998;Magagnotti et al., 2012;Heinimann, 2021;). Different factors can impact the productivity of a truck, such as transport distance, load weight, road types, and truck configurations. ...
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... Despite the prevalence of accurate fuel consumption gauges in modern harvesting equipment, accurate information about actual fuel consumption during harvesting is difficult to find (Athanassiadis et al., 1999). Other fuel monitoring and measurement techniques for harvesting operations have been suggested but these techniques are yet to be embraced by logging contractors (Acuna et al., 2012). Most logging contractors use common rules of thumb or existing spreadsheets in determining the rates of fuel consumption by machines and harvesting systems during operations (Smidt and Gallagher, 2013;Greene et al., 2014;Kenny et al., 2014). ...
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