Apple Inc. v. Samsung Electronics Co. Ltd. et al

Filing 999

Administrative Motion to File Under Seal filed by Samsung Electronics America, Inc.(a New York corporation), Samsung Electronics Co. Ltd., Samsung Telecommunications America, LLC(a Delaware limited liability company). (Attachments: #1 Proposed Order Granting Motion to Seal, #2 Samsung's Opposition to Apple's Motion to Exclude Testimony of Samsung's Experts, #3 Declaration of Joby Martin in Support of Samsung's Opposition, #4 Exhibit A to the Martin Declaration, #5 Exhibit B to the Martin Declaration, #6 Exhibit C to the Martin Declaration, #7 Exhibit D to the Martin Declaration, #8 Exhibit E to the Martin Declaration, #9 Exhibit F to the Martin Declaration, #10 Exhibit G to the Martin Declaration, #11 Exhibit H to the Martin Declaration, #12 Exhibit I to the Martin Declaration, #13 Exhibit J to the Martin Declaration, #14 Exhibit K to the Martin Declaration, #15 Exhibit L to the Martin Declaration, #16 Exhibit M to the Martin Declaration, #17 Exhibit N to the Martin Declaration, #18 Exhibit O to the Martin Declaration, #19 Exhibit P to the Martin Declaration, #20 Exhibit Q to the Martin Declaration, #21 Exhibit R to the Martin Declaration, #22 Exhibit S to the Martin Declaration, #23 Proposed Order Denying Apple's Motion to Exclude Testimony of Samsung's Experts)(Maroulis, Victoria) (Filed on 5/31/2012)

EXHIBIT S

Review of Agricultural Economics—Volume 28, Number 1—Pages 111–131
DOI:10.1111/j.1467-9353.2006.00276.x

Management and Risk
Characteristics of Part-Time
and Full-Time Farmers in Norway

The objective of this exploratory study was to provide empirical insight into how different
categories of farmers perceive and manage risk. The data originate from a questionnaire of
dairy and crop farmers in Norway. The associations between part-time and full-time farming and farm and farmer characteristics, farmers’ goals and future plans, risk perceptions,
and risk management responses were examined with simple t- and chi-square tests, as well
as with logistic regression. The results indicate that full-time and part-time farmers’ goals,
risk perceptions, and management strategies differ significantly. Policy makers and advisers should consider these differences when developing policies and recommendations for
the different types of farmers.

A

n increasing number of Norwegian farm families have off-farm employment. In 2002, about 61% worked off-farm. Norwegian farms are small compared with those in many developed countries and farm income represents on
Gudbrand Lien is senior researcher, Norwegian Agricultural Economics Research Institute, Oslo, Norway and senior researcher, Eastern Norway Research Institute, Lillehammer, Norway.
Ola Flaten is senior researcher, Norwegian Agricultural Economics Research Institute,
Oslo, Norway.
Anne Moxnes Jervell is director, National Institute for Consumer Research, Oslo,
Norway.
Martha Ebbesvik is senior adviser, Norwegian Centre for Ecological Agriculture,
Tingvoll, Norway.
Matthias Koesling is senior adviser, Norwegian Centre for Ecological Agriculture,
Tingvoll, Norway.
Paul Steinar Valle is chair, Institute of Livestock Medicine, The Norwegian School of
Veterinary Science, Oslo, Norway.

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Gudbrand Lien, Ola Flaten, Anne Moxnes Jervell,
Martha Ebbesvik, Matthias Koesling,
and Paul Steinar Valle

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average, a relatively small and decreasing part of the farm-family household
income. In 2001, only 23% of the average total household income (for holder
and spouse/cohabitant) came from agriculture, forestry, and fishing. By contrast, in 1992, the income from the primary industries amounted to 27% of total household income (Statistics Norway). Similar developments are found in
many developed countries (e.g., Hill; Andersson, Ramamurtie, and Ramaswami).
For example, Mishra et al. reported that more than 94% of U.S. total farm
household income was derived from off-farm sources in 2000, up from 62% in
1987.
Studies within a wide range of approaches and disciplines have examined characteristics and motivations that explain part-time and full-time farming. A number of studies examining time allocation in farm households have adapted theory
from “new household economics” (Becker) to the special case of the agricultural
household model (e.g., Huffman). Results of these studies include: (a) the characteristics of those participating in off-farm employment and the factors affecting labor supply (hours worked) in off-farm activities (Weersink, Nicholson, and
Weerhewa; Woldehanna, Oude Lansink, and Peerlings), (b) the association between education and off-farm work (e.g., Huffman), (c) the effect of differences
in and variability of incomes/wealth between agriculture and other occupations
(e.g., Mishra and Goodwin; Andersson, Ramamurtie, and Ramaswami; Fall and
Magnac), (d) whether part-time farming is a stable adjustment, a way to full-time
farming, or a way out of agriculture (e.g., Kimhi), and (e) survival strategies and
diversification on marginal farms (Meert et al.).
Combining part-time farming activities with wage labor is a diversification
strategy that may contribute more than on-farm diversification to household income stability. Studies of Norwegian farming households indicate that consumption is more affected by wage than farming income (Sand). Similar results are
shown for other countries and for the relation between wage income and business income in general (e.g., Carriker et al.).
Part-time and full-time farmers are to different degrees financially dependent
on farming income. Because the two groups have chosen different livelihood
strategies, it seems likely that there will be differences in their perceptions of
risk in farming and how they cope with it. Information is lacking about farmers’ risky environment and their reactions to it, and especially about differences
between part-time and full-time farmers. Some studies (e.g., Wilson, Dahlgran,
and Conklin; Martin; Patrick and Musser; Meuwissen, Huirne, and Hardaker;
Hall et al.) have examined how farmers in general perceive and manage risk.
The empirical relationships between risk attitudes, management, and part- and
full-time farming choices have not, as far as we know, been explored in earlier
studies.
Policy makers, farm advisers, and researchers need more practical insights into
the likely differences between full-time farmers and the large number of part-time
farmers in order to provide better advice and to develop more sharply targeted
policies. This exploratory and descriptive study aims to fill part of this gap by
providing recent empirical information about part-time and full-time farmers’
characteristics, including risk perceptions and responses, but also farm and operator characteristics, and farming goals.

Management and Risk Characteristics of Farmers in Norway

113

Conceptual Framework

Figure 1. Elements of Van Raaij’s model of a firm’s decision-making
environment

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Many studies have been carried out as bases for testable hypotheses about differences between part-time and full-time farmers. For example, results show that
part-time farmers are younger, have higher education, and smaller farms (e.g.,
Mishra and Goodwin; Woldehanna, Oude Lansink, and Peerlings). However, examinations of differences between part-time and full-time farmers’ perceptions
and management of risk are virtually absent in earlier comparative studies, which
makes it hard to develop firm hypotheses. An exploratory approach was considered appropriate as research design in this study, though certainly not as a
replacement for testable hypotheses.
Van Raaij’s model of the firm’s decision-making environment is useful to study
the relationship between farm and personal characteristics, risk perceptions, and
management responses (e.g., Wilson, Dahlgran, and Conklin). Van Raaij’s model
is a framework for research on economic behavior, where the perceived economic
environment determines the individual’s economic behavior with subjective wellbeing as its consequence.
Figure 1 presents the groups of variables used in our research design. First, P →
E/P describes how farm, farmers’ goals, and other personal variables (P) impact
farmers’ perceptions of risk factors (E/P). Second, the relationship P → E/P → B
reflects how the farm/personal variables and risk perceptions influence economic
behavior (B), i.e., their risk management strategies. Off-farm work is a personal
characteristic (i.e., P), but is also a strategy to cope with risk (i.e., B). As pointed
out by Wilson, Dahlgran, and Conklin, a personal variable (e.g., part-time vs. fulltime farming) influences economic behavior (e.g., risk management). However,
the off-farm risk management decision also alters the personal characteristics. In
other words, the impact may also be P ↔ E/P ↔ B, and it is often impossible to
prove which way the causation flows.
Within this framework, a range of possible empirical differences between parttime and full-time farmers can be explored, and the results may generate hypotheses for future research. A difference that may be explored is if independence in
their work is expected to be a more important goal for full-time than part-time
farmers. And since the two groups of farmers have different livelihood strategies,
part-time farmers may rank the strategy “off-farm diversification” higher than
full-time farmers. Further, since part-time farmers receive part of their income
off-farm, farm income stability may be less important to them than to full-time
farmers. These examples illustrate the wide range of issues that can be explored
in our empirical analysis within this research design.

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Materials

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The data reported here were collected as a part of a larger questionnaire of
risk and risk management in farming. The Norwegian Agricultural Authority
(SLF) has a register of farmers who receive support payments, which includes the
total population of farmers in Norway. Based on the 2001 applications, there were
more than 17,800 dairy farmers (including 325 organic) and more than 15,600 crop
farmers (202 organic). From this SLF register, 850 crop and 862 dairy farmers were
sampled. Conventional farmers were selected using simple random sampling,
while all organic dairy and crop farmers received the questionnaire. The survey
was sent out in January 2003. We were informed that 34 of these farmers had
quit farming, reducing the number of possible respondents to 1,678. After two
reminders, 1,033 farmers returned the questionnaire for an effective response rate
of 62%.1
Because of small herd sizes in Norway, dairy farms were defined as having more
than five dairy cows. Crop farms were defined as having more than 1 hectare (ha)
grain, or more than 0.5 ha of potatoes, or more than 0.2 ha of intensive crops
(vegetables, fruit, or berries). Dairy farms that also met the cropping criteria were
specifically excluded from the crop group.
The survey consisted of questions related to (a) farmers’ perceptions of sources
of risk, (b) farmers’ perceptions of various risk management strategies, (c) farmers’ goals and future plans, and (d) characteristics of the farm and farmer. Most
questions were of the closed type, many in the form of seven point Likert-type
scales. The questionnaire was pre-tested in sessions with farmers, and refined
over several stages based on the comments and suggestions received.
The distinction between full-time and part-time farmers was based on a question that asked respondents if the holder and the spouse (cohabitant) were employed off-farm. If yes, they were asked to report their percentage of off-farm
position(s). In the analysis, we have chosen to define a part-time farm as a holding where a single farmer (i.e., unmarried or noncohabitant) or a farmer and
the partner have at least a 15% off-farm work position. By this classification, we
have defined “dual career” households as full-time farms for example, when one
partner has a less than 15% position off-farm and the other works full-time offfarm. After deleting all respondents that failed to answer the part-time question,
we were left with 394 crop farms (169 full-time and 225 part-time farms) and
467 dairy farms (386 full-time and 81 part-time).
Respondents with off-farm work were asked to score six reasons for off-farm
work on a Likert-type scale from 1 (not important) to 7 (very important). From
a list of 14 farming goals ranging from profit maximization to social contact, the
respondents were asked to select up to five as most important. The farmers also
indicated their future plans for their holding (within a five-year perspective), by
selecting one or several of nine options (such as no changes, downsize, exit, or
expand).
The survey presented 33 sources of risk for dairy farmers and 25 risk management strategies. Similarly, crop farmer respondents considered 22 sources of risk
and 23 risk management strategies. Farmers were asked to score each source of
risk on a Likert-scale from 1 (no impact) to 7 (very high impact) to express its
potential impact on their farm’s economic performance. Farmers also indicated

Management and Risk Characteristics of Farmers in Norway

115

Methods
Data examined in this study were collected as part of a larger survey of
Norwegian farmers (Koesling et al.; Flaten et al.). Organic farmers were heavily
over-represented in the sample versus their actual share of Norway’s population.
Further, our survey sample was not completely representative of the regional and
farm size distribution of Norwegian dairy and crop farming. In all analyses, the
survey data were weighted with respect to organic/conventional farming systems, regions and farm size, to give results that are as representative as possible
for dairy and crop farming in Norway.
As the first step of the analysis, farmers’ and farm characteristics, goals, risk
perceptions, and strategies were summarized and compared. Mean values obtained for part-time and full-time farmers were compared using standard t-tests
for metric (quantitative) variables and chi-square tests for nonmetric (qualitative) variables. Strictly speaking, Likert-type scales are ordinal. In this study, a
cardinal interpretation was undertaken. The scale was treated as a continuous
variable (Hair et al.; Spicer), making it possible to use standard parametric (multivariate) statistical procedures (e.g., Patrick and Musser; Meuwissen, Huirne, and
Hardaker).
Any combined effect of variables that may reflect differences in characteristics
between part-time and full-time farmers may be overlooked in bivariate analyses
(Spicer). We used regression analysis to gain a more complete picture of differences between part-time and full-time farmers in goals, risk sources, and risk
management strategies (figure 1). Data reduction techniques were used to reduce
the numbers of factors in the regressions (Hair et al.).
We used common factor analysis to summarize the information about risk
perceptions and risk management strategies in a reduced number of factors/
variables. Factor analysis also reduced multicollinearity problems in subsequent
regressions. Factor solutions with different numbers of factors were examined
before structures were defined, in order to have the most representative and parsimonious set of factors (Hair et al.). Orthogonal (varimax) rotation was used to
obtain factor solutions that were easier to interpret. Standardized factor scores for
each farmer and factor were saved for subsequent multivariate analyses.

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their perceived importance of each risk management strategy on a Likert-scale
from 1 (not relevant) to 7 (very relevant).
Additional information about the production systems was obtained through
merging the questionnaire survey data with two available databases: the SLFregister of farmers’ support payments, which includes each farmer’s stocking
and cropping details, and the dairy cow health and production records registered
in the Norwegian Herd Recording System.
The analyses were carried out separately for dairy and crop farmers mainly
because part-time dairy farmers inevitably have a heavier daily on-farm workload than part-time crop farmers. While the majority of crop producers combine
farming with off-farm work, there are fewer part-time dairy farmers. Because
combining off-farm occupation with farm work will probably have widely different implications for dairy and crop operations, the division was made to enable
the differences to be highlighted.

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Motivations for Off-Farm Work
The most important motivations for off-farm work, independently of crop or
dairy farming system, were to increase the total household income and to get a
more reliable and stable income, both with average scores of about 6.3 (figure 2).
These results are in accordance with a comparative study of dairy farm families
in New York and Ontario (Weersink, Nicholson, and Weerhewa). The Weersink,
Nicholson, and Weerhewa study supports our results that social contact was not
among the main motivations for working off-farm. Barlett also found that the main
reason for off-farm work was in response to the higher variability associated with
farm income.
There were, however, differences in motivation between dairy and crop farmers, the latter ranked both future job opportunities (p < 0.10) and utilizing idle
family labor (p < 0.01) significantly higher than dairy farmers. The differences may
be related to the large amount of labor required in a dairy operation throughout
the year, so that the enterprise does not lend itself so well to part-time farming. Cropping operations, in contrast, are more seasonal. The need to do something other than farming scored low as a motivational factor in both farming
systems.

Descriptive Analysis
Key Farmer and Farm Characteristics
Table 1 compares the main farmer and farm characteristics and shows that
there are significant differences. Compared with full-time dairy farmers, for example, part-time dairy farmers were younger (p < 0.001), worked less on the farm
(p < 0.05), had more years of schooling (p < 0.001), and the main farm operator
was more frequently a woman (p < 0.01). Part-time crop farmers were younger

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Some 20–40% (depending on the group) of the respondents did not answer
one or more relevant questions about sources of risk or management responses.
In cases with missing data, most of the respondents failed to answer only a few
items. If remedies for missing data are not applied, any observations with missing
values are omitted. Using only complete observations can produce bias in the
results unless observations are missing completely at random. There is also a loss
of precision as the sample size is reduced (Hair et al.). To deal with missing data,
in the factor analyses we deleted a few cases lacking more than 40% of the risk
source variables or 50% of the risk management strategies variables. For the rest,
missing data points were replaced with the mean value of that variable based on
all valid responses in the group (dairy or crop).
Associations between part-time and full-time farmers (dependent variables)
and independent variables were analyzed using binary logistic regressions. Independent variables included farm and farmer characteristics, goals and future
plans, in addition to the standardized scores obtained from the factor analyses of
risk sources and risk responses. No multicollinearity problems were detected in
the regression models. The logistic regression models were complete, but to save
space, only the significant variables are reported.

Management and Risk Characteristics of Farmers in Norway

117

Figure 2. Part-time crop (n = 225) and dairy (n = 81) farmers’ main
reasons for off-farm work. Weighted average score of responses ranking for each reason. Significance level in parenthesis, based on independent samples t-test between crop and dairy farmers. Values are
from a Likert-type scale with 1 being the least important and 7 the
most important

Work with something else

5.0
3.0

Get a more reliable and
stable income

1.0
Future job opportunities
(P < 0.10)

Social contact

Utilize idle family labor
(P < 0.01)
(p < 0.001) than their full-time colleagues, were more frequently unmarried
(p < 0.01), spent significantly less time working on the farm (p < 0.001), had
more general education, but less frequently received agricultural education
(p < 0.01), and had less farmland (p < 0.01). Part-time crop farmers had less land
in potatoes, vegetables, fruits and berries than full-time crop farmers (p < 0.01).
These results are consistent with previous studies (e.g., Mishra and Goodwin;
Woldehanna, Oude Lansink, and Peerlings).

Farmers’ Goals
Full-time farmers ranked producing high-quality food as the most important
goal and reliable and stable income second (table 2). Part-time dairy farmers
ranked reliable and stable income first and having time for the family second.
Unlike dairy farmers, part-time crop farmers differed less from full-time farmers.
Instead of income stability, however, part-time crop farmers ranked the goal to
improve the farm for the next generation as the second most important. Producing high quality food was more important for full-time than for part-time dairy
farmers (p < 0.001). As expected, “independence” was ranked higher by full-time
than part-time crop farmers (p < 0.05). Sustainable and environmentally sound
farming (landscape preservation included) was ranked higher among part-time

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Increase the total
household income
7.0

Crop farmers
Dairy farmers

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Table 1. Weighted average farmer and farm characteristics for
full-time and part-time dairy and crop farmers

Farmer and Farm
Characteristics

Crop

Full Time

Part Time

Full Time

Part Time

386
48.1
84
2.06
9
57
5/73/22
26
22.7

81
43.0∗∗∗
86
1.84∗
23∗∗∗
55
12/76/12∗∗
19
21.5

169
52.8
90
1.41
26
61
7/72/21
61
24.3
9

225
47.6∗∗∗
78∗∗
0.65∗∗∗
44∗∗∗
47∗∗
7/80/13
65
18.3∗∗
4∗∗

14.5

13.4

Note: Weighted average farmer and farm characteristics marked with asterisks show that the
characteristics of full-time and part-time dairy and crop farmers, respectively, are significant different
at (∗ )p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001, based on independent samples t-test (for metric
values) and chi-square-test (for nonmetric values).
a Data (2002) from the Norwegian Agricultural Authority.
b Principal person(s) in charge for farm management: woman, man, split between two or more
persons.
c Measured as a dummy variable where 1 denotes central location and 0 denotes otherwise.

farmers than among both full-time dairy (p < 0.01) and crop farmers (p < 0.10). It
seems that part-time farmers are concerned about preserving the landscape, but
perhaps full-time farmers do so unconsciously. The data also show an association
between education level (which is highest among part-time operators) and the
importance assigned to environmental issues.
Profit maximization was ranked rather low by all groups of respondents. However, on average, part-time farmers ranked this goal somewhat higher than fulltime farmers, and significantly (p < 0.05) so in dairy production. One reason may
be that part-time farmers have a higher opportunity cost of farm labor than fulltime farmers. Faced with low farm incomes, the part-time farmer may be inclined
to work more off-farm.
In our study, having a reliable and stable farm income was less important for
part-time than full-time crop farmers (p < 0.10). We also found that stable income
was more important for dairy than crop farmers. This may be because dairy
farmers have more control over the production process since cropping is more
dependent on weather and growing conditions. Risk-averse farmers may also
choose to go into dairying rather than cropping, since more stable income is
obtained from dairying.
Our results support earlier studies (e.g., Bergevoet et al.; Gasson et al.; Willock
et al.) reporting that farmers have several goals and see farming as more than a
way to make money.

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Number of farms
Age of the farmera
Marital status (% married)
Farm labor units (man-years)
Education, BS or higher (%)
Agricultural education (%)
Management responsibility (%)b
Location (%)c
Farmland (ha)a
Potatoes, vegetables, and fruit
(% of farmland)
Number of dairy cowsa

Dairy

Management and Risk Characteristics of Farmers in Norway

119

Table 2. Weighted percentage of responses ranking each goal among
the top five
Dairy
Farmers’ Goals

Full
Time

Part
Time

Rank
P t.a

Full
Time

Rank
F t.a

Part
Time

Rank
P t.a

68
66
49
43

46∗∗∗
70
43
51

3
1
4
2

60
56
45
42

1
2
4
5

55
48(∗ )
34∗
49

1
4
6
3

33

41

6

46

3

54

2

30

27

9

13

11

11

11

27

43∗∗

4

37

6

24

29

8

18

22
20
17
3
2
1

31(∗ )
22
27∗
1
5∗∗∗
1

7
11
9
13
12
13

30
20
21
8
2
1

45(∗ )

5

10

24

7

7
9
8
12
13
14

24
17
24
4
6(∗ )
3

7
10
7
13
12
14

Note: Weighted percent of responses for each goals marked with asterisks show that the goals of
full-time and part-time dairy and crop farmers, respectively, are significant different at (∗ )p < 0.10,
∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001, based on chi-square-test. Ranked by decreasing importance
for full-time dairy farmers.
a Ranking by part-time (P t.) dairy farmers (column five), full-time (F t.) crop farmers (column seven)
and part-time (P t.) crop farmers (column ten), respectively.

Perceptions of Risk Sources
Table 3 shows the rating of risk sources and whether they differ significantly
among the groups. The risk sources are presented in order of decreasing importance for full-time dairy farmers. All groups ranked institutional risks (such as
uncertainty about the continuation of government support payments, changes in
the dairy quota system, or changes in tax policy) as important sources of risk. The
importance of institutional risks may reflect the somewhat unpredictable changes
in Norwegian farm policies and regulations, together with external pressures for
deregulation and associated fears of farm support cuts. The finding should also be
linked to Just’s proposal that longer-term swings (e.g., lasting changes in agricultural policy) represent a much greater risk to farmers than year-to-year variability
in payoffs. Only the downside consequences of long-term changes are likely that
to be sufficiently prolonged to cause farm failure. A Finnish study also found that

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Produce high quality food
Reliable and stable income
Independence
Time for family, living
quality for children
Improve the farm for next
generation
Have possibility to some
leisure
Sustainable and
environmentally sound
farming
Reduce debt, become free
of debt
Continue to be a farmer
Work with animals/crops
Maximize profit
Increase equity
Social contacts
Higher private
consumption

Crop

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Review of Agricultural Economics

Table 3. Weighted mean score and t-tests for full-time and part-time
dairy and crop farmers for sources of risk
Dairy
Risk Sourcesa

Part
Time

5.92

5.70

5.80
5.79
5.53
5.49
5.47
5.20
5.11
5.11
5.01

Rank Full Rank
P t.b Time F t.b

Part
Time

Rank
P t.b

2

5.31

4

5.58(∗ )

3

5.71
5.53
5.19(∗ )
4.70∗∗∗
5.41
5.20
5.24
5.40
5.02

1
3
8
11
4
7
6
4
9

5.43

3

5.62

2

5.28
4.93
4.97

5
8
7

4.75∗∗∗
4.29∗∗∗
4.85

4.97
4.90
4.74

4.89
4.34∗∗
4.65

10
16
12

4.57

9

4.53

8

4.67
4.64
4.52
4.35
4.34
4.32
4.32
4.32
4.27
4.26
4.21
4.14
4.13
3.73
3.52
3.17
3.15

4.43
4.48
4.20
4.29
4.09
4.34
3.91(∗ )
3.55∗∗∗
4.34
4.10
4.10
4.49
4.00
3.54
3.56
3.15
3.37

15
14
20
19
23
16
25
27
16
21
21
13
24
28
26
30
29

4.00
4.54
4.27

14
10
11

3.69
4.45
4.06

14
9
11

3.79
5.10

15
6

3.94
4.79∗

12
6

3.52
5.98
4.19
5.71

16
1
12
2

3.84(∗ )
5.96
3.32∗∗∗
5.48

13
1
15
4

3.12
2.95
3.05
3.16

18
20
19
17

2.57∗∗
3.14
2.85
3.24

20
17
19
16

2.73

2.69

31

2.70

21

2.90

18

2.35
2.32

2.47
2.38

32
33

2.58
2.42

22
23

2.57
2.51

20
22

7
10
5

Note: Weighted mean score (1 = no dependency, 7 = very high dependency) for full-time dairy farmers,
part-time dairy farmers, full-time crop farmers, and part-time crop farmers. Weighted mean numbers
marked with asterisks show that the mean scores of full-time and part-time dairy and crop farmers,
respectively, are significant different at (∗ )p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001, based on
independent samples t-test.
a
Ranked by decreasing importance for full-time dairy farmers.
b
Ranking by part-time (P t.) dairy farmers (column four), full-time (F t.) crop farmers (column six) and
part-time (P t.) crop farmers (column eight), respectively.

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Changes in government support
payments
Changes in tax policy
Milk price variability
Milk quota policy
Animal welfare policy
Meat price variability
Changes in consumer preferences
Injury, illness, death of operator(s)
Cost of operating inputs
Nondomestic epidemic animal
diseases
Domestic epidemic animal diseases
Forage yield uncertainty
Other government laws and
regulations
Fire damages
Cost of capital equipment
Technical failure
Meat production variability
Changes in technology
Marketing/sale
Legislation in production hygiene
Production diseases
Cost of credit (interest rate)
Crop prices variability
Family member’s health situation
Crop yields variability
Milk yield variability
Hired labor cost and availability
Credit availability
Family relations
Availability and cost of leased
farmland
Additional organic farming
payments
Organic farming laws/regulations
Price premiums organic products

Full
Time

Crop

Management and Risk Characteristics of Farmers in Norway

121

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changes in agricultural policy were the most important risk source for farmers
(Sonkkila).
Price variability was the highest ranked source of risk among crop farmers.
Milk price variability was ranked third by dairy farmers. Crop producers
ranked crop yield variability higher than dairy producers ranked milk yield
variability. Crop yield variability may be of greater importance because output is
highly influenced by weather while milk yields are somewhat stable.
All groups ranked availability and cost of hired labor, credit, and leased farmland, as well as family relations and “organic” risk sources (laws and regulations,
price premiums, and farm payments) low as sources of risk (table 3). The low score
for organic sources is due to the small numbers of organic farmers in Norwegian
agriculture.
Full-time dairy farmers rated milk quota policy (p < 0.10), animal welfare policy
(p < 0.001), forage yield uncertainty (p < 0.01), legislation in production hygiene
(p < 0.10), and production diseases (p < 0.001) as more important risk sources than
part-time dairy farmers. There was a negative association between risk related to
animal welfare policy and increasing education level. The greater importance
attached to animal welfare policy by full-time farmers may reflect the higher
education level among part-timers.
Full-time crop farmers regarded changes in consumer preferences (p < 0.001),
injury, illness and death of operator(s) (p < 0.001), marketing/sale (p < 0.05),
family members’ health situation (p < 0.001), and hired labor cost and availability
(p < 0.01) as most important. Some of these findings may reflect the fact that fulltime crop farmers do more farm work and had more farmland than part timers.
Full-time farmers’ incomes also are normally more dependent on farm output than
part-time farmers. Further, since the full-time crop producers had more potatoes,
vegetables and fruit than their part-time colleagues (table 1), marketing/sales
often will be more important. The greater vegetable and fruit production also
made full-time crop farmers more dependent on availability of seasonal rented
labor and their own health situation in labor-intensive harvesting seasons.
Common factor analysis was applied to the risk source variables of the dairy
and crop sub-samples separately (table 3) to reduce the number of variables in
subsequent binary logistic regression analyses.
The number of variables for the dairy risk source data was reduced from 33 to 6
(the last columns of appendix A). Some 50.2% of the total variance was accounted
for, a satisfactory amount in social sciences (Hair et al.). The factors were labeled
(a) dairy, that loads significantly from a variety of dairy production variables;
(b) institutional, consist of a wide collection of public payment and government
legislation variables; (c) organic, which has extremely high loadings of the three
variables specific for organic farming; (d) human resources, with heavy loadings
of health and family variables; (e) credit, with high loadings of the interest rate
and credit availability; and ( f ) market, which involves high loadings of changes
in consumer preferences and marketing.
Of the 22 risk sources presented for crop producers, the factor analysis resulted
in six factors (the first columns of appendix A) that explained 56.2% of the total
variation. The factors were labeled (a) institutional, with high loadings for public
payments and government variables, and input prices; (b) organic, where the
three specific external risks for organic farming had high loading; (c) human

122

Review of Agricultural Economics

resources, includes both health risk of the operator and the family, uncertainty
about the family, hired labor and fire; (d) credit, with high loadings for credit cost
and availability; (e) crop, with crop prices and crop yields variability having high
loadings; and (f ) market, involving significant loadings for changes in consumer
preferences and marketing. The factor scores from these factor variables were
used in subsequent multivariate analysis.

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Perceptions of Risk Management Strategies
Having good liquidity, preventing/reducing livestock and crop diseases and
pests (for dairy farmers and crop farmers, respectively), buying farm business
insurance and personal insurance, and producing at lowest possible cost were
strategies generally perceived as highly relevant (table 4). In recent studies of
farmers in other countries, the same financial management strategies were also
perceived among the most important (Meuwissen, Huirne, and Hardaker; Hall
et al.; Harwood et al.), even though the national risk environments are different.
Farmers in our study generally did not perceive organizing the farm as a corporation, possessing off-farm investments, and having surplus machinery capacity
as important risk management strategies.
While full-time dairy farmers did not consider off-farm work as an important
risk strategy, part-time farmers scored it higher (p < 0.001). Further, compared to
their full-time colleagues, part-time dairy farmers ranked off-farm investments
(p < 0.001), surplus machinery capacity (p < 0.001), solvency (p < 0.05), and
storage (p < 0.01) as relatively more important strategies to deal with risk, but
ranked buying farm business insurance (p < 0.10) lower. Full-time crop farmers ranked off-farm work low as a risk management strategy, while it was the
top-rated strategy for part-time farmers. Full-time crop farmers attached much
greater importance than their part-time colleagues to good liquidity (p < 0.05),
use of risk-reducing technologies (irrigation, etc.) (p < 0.001), cooperative marketing (p < 0.05), use of economic consultancies (p < 0.10), enterprise diversification
(p < 0.001), and use of production contracts (p < 0.001). Full-time crop farmers
might rank risk-reducing technologies and production contracts higher because
they produce more vegetables and fruit. On-farm diversification was also important for full-time farmers, perhaps since their main source of income is the
farm.
Common factor analysis was applied to the risk management variables for
dairy and crop farmers separately, in order to reduce the number of variables
for use in subsequent regressions (table 4). The factor analysis identified seven
interpretable and feasible dairy factors (the last columns of appendix B), accounting for 44.4% of the variance. Labels and interpretations of the factors are
(a) flexibility, which includes on-farm strategies to enhance flexibility (storage
included) and use of price contracts; (b) consultancy, with high loadings for veterinarian, agronomy/nutrition, and economic consultancies; (c) disease prevention,
with high loadings of prevention/reduction of pests and diseases in crops/forage
and livestock; (d) insurance, which has heavy loadings for insurance contracts;
(e) diversification, which includes off-farm investments, off-farm work, on-farm
diversification, and collecting more information; (f ) financial, including financial
aspects of the farm business (solvency, liquidity, and production costs); and

Management and Risk Characteristics of Farmers in Norway

123

Table 4. Weighted mean score and t-tests for full-time and part-time
dairy and crop farmers for risk management strategies
Dairy
Risk Management Strategiesa

Part
Time

Rank
P t.b

Full
Time

Rank
F t.b

Part
Time

Rank
P t.b

6.44
6.33

6.43
6.29

1
2

6.46

1

6.21∗

2

6.08

5.82(∗ )

5

6.01

3

5.96

4

5.88

5.93

3

5.87

6

5.89

5

5.84
5.75
5.55

5.81
5.51
5.53

6
10
9

5.92
5.74
5.16

5
7
10

5.49
5.43

5.84∗
5.71(∗ )

4
8

5.94
6.14

4
2

5.89
5.98

5
3

5.36
5.32
5.16

5.36
5.36
5.06

11
11
13

5.44
4.77

8
14

5.43
4.36∗

8
17

4.74

4.75

15

4.52

18

4.59

13

4.69
4.57
4.52

4.78
4.50
4.28

14
17
19

5.36
4.56
4.29

9
17
19

5.18
4.49
3.91(∗ )

10
14
20

4.15
4.10
4.05
3.72
3.67
3.31
3.23
2.44
2.19

4.66∗∗
4.31
3.65(∗ )
3.97
5.72∗∗∗
4.06∗∗∗
3.25
3.77∗∗∗
2.60∗

16
18
23
21
7
20
24
22
25

4.27
4.99
5.04
4.82
4.73
3.77
4.61
3.10
2.65

20
12
11
13
15
21
16
22
23

3.97
4.23∗∗∗
4.39∗∗∗
4.60
6.33∗∗∗
3.82
4.41
3.77∗∗∗
2.46

19
18
16
12
1
21
15
22
23

5.81
5.24∗∗∗
4.95

7
9
11

Note: Weighted mean score (1 = not important, 7 = very important) for full-time dairy farmers,
part-time dairy farmers, full-time crop farmers, and part-time crop farmers. Weighted mean numbers
marked with asterisks show that the mean scores of full-time and part-time dairy and crop farmers,
respectively, are significant different at (∗ )p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001, based on
independent samples t-test.
a Ranked by decreasing importance for full-time dairy farmers.
b Ranking by part-time (P t.) dairy farmers (column four), full-time (F t.) crop farmers (column six)
and part-time (P t.) crop farmers (column eight), respectively.

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Liquidity—keep cash in hand
Prevent/reduce livestock
diseases
Buying farm business
insurance
Produce at lowest possible
cost
Buying personal insurance
Risk reducing technologies
Use of agronomic/nutritional
consultancies
Solvency—debt management
Prevent/reduce crop diseases
and pests
Small gradual changes
Cooperative marketing
Use of veterinarian
consultancies
Shared ownerships of equip.,
joint operations
Asset flexibility
Keeping fixed costs low
Use of economic
consultancies
Storage
Enterprise diversification
Production contracts
Collecting information
Off-farm work
Surplus machinery capacity
Product and market flexibility
Off-farm investments
Organize the farm as a
corporation

Full
Time

Crop

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Review of Agricultural Economics

Multivariate Analysis
Table 5 presents significant results from the binary logistic regression for dairy
and crop farmers. Compared to full-time dairy farmers, part-time dairy farmers
(at 5% significance level) were younger; were more frequently married/in partnership; gave higher importance to the goals of sustainable and environmentally
sound farming, and to improving the farm for the next generation.
Compared to full-timers, part-time dairy farmers considered downsizing the
farm operation as a more important strategic direction; viewed human risk as less
important; and considered consultancy, insurance and fixed cost sharing as less
important strategies to manage risk. Further, disease prevention, diversification
(including off-farm investments, off-farm work, on-farm diversification, etc.), and
financial aspects were more important for part-time than full-time dairy farmers.
Compared to full-time farmers (at 5% significance level), part-time crop farmers
were younger, more frequently single, worked less on the farm, invested more
off the farm, had a higher household income, regarded the goals of independence and sustainable and environmentally sound farming lower, and planned
more frequently to downsize the farm operation and less frequently to diversify
with more enterprises over the next five years. They were less concerned about
risk sources such as human resources and crop price and yield variability, but
more concerned about credit risks. They regarded consultancy as a less important
strategy to manage risk than did full-time crop farmers.
In general, there was consistency between the partial statistical analyses and
the regression analyses. Unlike the bivariate analyses, the regression analyses
showed no significant differences between part-time and full-time dairy farmers’
off-farm investment strategy. Further, we found no significant differences between
part-time and full-time crop producers’ education levels and the importance they
assigned to maximizing profit. Surprisingly, the regression results indicated that
sustainable and environmentally sound farming was more important for full-time
than part-time crop farmers, the opposite results of the bivariate analysis.
It is also surprising that both groups of part-time farmers plan more frequently to downsize the farm operation, compared to their full-time colleagues. The

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(g) fixed cost sharing, which has high loadings for shared ownership of equipment
and joint operations.
Of the 23 risk management strategies presented for the crop producers, the
factor analysis identified six factors (the first columns of appendix B) which accounted for 40.1% of the variance. Labels and interpretations of the crop factors
are (a) consultancy, which includes heavy loadings for consultancy, storage, and
joint operation; (b) flexibility, with high loadings for product, market, and asset
flexibility; (c) insurance, where farm business and personal insurance dominate;
(d) low cost, which includes producing at lowest possible cost, preventing or
reducing crop diseases and pests, and risk-reducing technologies; (e) financial,
including mainly solvency and liquidity; and (f ) diversification, which includes
mainly off-farm work and joint operations.
The differences in derived factors for crop and dairy farmers were small. In
other words, the crop and dairy farmers seem to use much of the same strategies to
manage risk. This finding may indicate fairly similar underlying factor structures
among management responses of farmers across the two farm types.

Management and Risk Characteristics of Farmers in Norway

125

Table 5. Dairy and crop farmers, results of multiple logistic
regressions. Binary dependent variable is part-time (=1) or full-time
farmer (=0)
Dairy
Independent Variables

Df
Pseudo R2 adj
a

Param.a

Sign. Lev.b

Param.a

Sign. Lev.b

−0.59
0.90
0.51

∗∗

−0.47
−0.82

∗∗

∗
∗

( )

∗

(∗ )

−0.31

( )

−0.30

∗

−0.38
−0.81
0.67

∗∗∗

0.91

∗

∗∗∗

−0.68
−0.54

∗∗

( )

0.38

(∗ )

0.79

∗∗∗

0.88

∗∗∗

0.85

∗

−0.28

∗∗

∗

∗

−0.30
0.35
−0.29
0.95
0.45
−0.38

∗∗∗

325
0.60

∗∗∗

∗

0.99
−0.52

∗

∗

(∗ )

−0.31
0.41
−0.33

∗∗

−0.31

∗

∗∗
∗∗

∗
∗∗
∗∗

0.23
0.23

(∗ )
(∗ )

276
0.66

∗∗∗

Coefficients for dummy variables are unstandardized, all others are standardized.
Variables significant at (∗ )p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01 and ∗∗∗ p < 0.001. Only significant variables are
shown. Parameter estimates for the complete models are available from the authors.
c
Measured as dummy variable where 1 denotes married/partner and 0 otherwise.
d
Measured as a dummy variable where 1 denotes formal schooling beyond secondary school and 0
denotes secondary school education or less.
e
Measured as a dummy variable where 1 denotes agricultural education and 0 denotes otherwise.
f
Measured as a dummy variable where 1 denotes off-farm investments the last five years and 0 denotes
otherwise.
g
Measured as a dummy variable where 1 denotes farm income ≥ NOK 100,000 (≈US$14,700) and 0
denotes otherwise.
h
Measured as a dummy variable where 1 denotes household income ≥ 350,000 NOK and 0 otherwise.
i
Measured as a dummy variable where 1 denotes the farmer mentioned the goal or future plan as
important and 0 denotes otherwise.
j
Factor score variables from the factor analysis for each farmer are used.
b

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Farmer and farm
Age of the farmer
Marital statusc
Educationd
Agricultural educatione
Farm labor units (man-years)
Off-farm investmentf
Farm incomeg
Household incomeh
Goalsi
Maximize profit
Independence
Sustain. and environmentally
sound farming
Improve the farm for
next generation
Future plansi
Downsize the farm operation
Diversify, with one/several
farm enterprises
Risk sourcesj
Human resources
Credit
Crop
Risk strategiesj
Consultancy
Disease prevention
Insurance
Diversification
Financial
Fixed cost sharing

Crop

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Review of Agricultural Economics

Conclusions
There is little published information about differences in how part-time and
full-time farmers perceive and manage risk. This study revealed that full-time
and part-time farmers’ goals, risk perceptions, and risk management strategies
differ significantly. Further, compared to full-time farmers, part-time farmers plan
more frequently to downsize their farm operations, which may be a necessity to
cope with multiple job situations. Policy makers and advisers should consider
the differences in goals, management, and risk characteristics between part-time
and full-time farmers when developing policies and recommendations. That parttime farmers differ from full-time farmers, for example, in considering off-farm
work as a highly relevant strategy to cope with risk and to obtain a more reliable
and stable income as an important motivation for off-farm work is important
in that connection. We could then expect farm-income stabilization to be of less
concern for part-time than for full-time farmers, but the two groups do not differ
significantly in their perceptions of government payments and output price risks.
Advisers should distinguish between part-time and full-time farmers, since, e.g.,
the first group may consider on-farm diversification less important.
Since the results showed that several risk factors are important to all farmers,
it would be helpful if those advising farmers could provide more and better
information to enable their clients to make better-informed judgments about the
risks they face. Also, farm management consultants and advisers should make
greater use of modern decision analysis tools that incorporate the main sources
of risk.

Acknowledgments
The authors are grateful to Joseph F. Hair, Jr. for helpful suggestions on ways to analyze the questionnaire data and to the reviewers, co-editor Colin Carter and J. Brian Hardaker, for valuable comments on
earlier versions of this manuscript. They gratefully acknowledge financial support from the Research
Council of Norway. The authors thank the farmers participating in the survey for their cooperation
and willingness to answer the questionnaire.

Endnote
1 The sampling strategy used, the high response rate and the weighting schemes used (see later in
text) imply that the samples should be representative for the farmer populations. Note, however, that
the nonrespondents (38%) may introduce selection biases in the analysis of the questions, which are
not accounted for in results presented.

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expected options for most farmers are growth, consolidation, or exit. A Belgian
study has, however, found that off-farm employment very rarely led to cessation
of agricultural work (Meert et al.). For many part-time farmers, the downsizing
option may be necessary to cope with multiple job situations. There are several
explanations for the finding that younger farmers participate more frequently in
the off-farm labor market. One is that entering farmers often have an off-farm
education and experience before taking over the farm. An extra job may also
contribute to financing farm investments (Meert et al.), and younger farm families can often get help on the farm from the older generation (Jervell). The age
differences between part-time and full-time farmers may also indicate, however,
that younger farmers increasingly expect to combine farming and off-farm work
(table 1).

0.08
0.11
0.67
0.62
0.44
0.23
0.20
0.10
0.17
0.49
0.26
0.59
0.70
0.28
0.14
0.03
−0.03

0.03
0.04
0.10
0.15
0.19
0.07
0.15
0.89
0.94
0.26
0.76
0.06
0.04
−0.03
0.07
0.22
0.17

2
0.11
0.13
0.12
0.18
0.01
0.08
0.24
0.09
0.09
0.15
0.05
0.05
0.14
0.67
0.73
0.52
0.40

3
0.09
0.08
0.11
0.27
0.30
0.74
0.81
0.16
0.08
−0.03
0.12
0.05
0.04
0.01
0.05
0.39
0.37

4
0.10
0.10
0.17
0.13
0.09
0.09
−0.01
−0.04
−0.01
0.06
0.01
0.07
0.06
0.08
−0.02
0.08
0.06

5
0.79
0.66
0.01
0.05
0.16
0.11
0.04
0.10
0.04
0.13
−0.04
0.03
0.08
0.14
0.16
0.07
0.12

6
0.12
0.17
0.27
0.33
a
0.21
0.22
0.03
0.05
0.13
0.13
−0.10
0.03
0.12
0.17
a
a

1
0.16
0.07
0.35
0.35
a
0.16
0.06
0.03
0.07
0.41
0.08
0.54
0.57
0.16
0.13
a
a

2

4
0.21
0.03
0.13
0.14
a
0.12
0.10
0.06
0.06
0.16
0.12
0.22
0.38
0.67
0.57
a
a

3
0.02
0.20
−0.07
−0.02
a
0.06
0.19
0.87
0.90
0.27
0.81
−0.02
−0.03
0.04
0.13
a
a

Dairy Farmersc

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Changes in consumer preferences
Marketing/sale
Cost of operating inputs
Cost of capital equipment
Changes in technology
Cost of credit (interest rate)
Credit availability
Price premiums organic products
Organic farming laws/regulations
Other government laws and regulations
Additional organic farming payments
Changes in gover. support payments
Changes in tax policy
Injury, illness, death of operator(s)
Family member’s health situation
Family relations
Hired labor cost and availability

1

Crop Farmersb

Appendix A. Crop and dairy farming, joint varimax rotated factor loading for sources of risk

0.02
0.02
0.33
0.32
a
0.73
0.62
0.13
0.09
0.12
0.02
0.18
0.14
−0.02
0.05
a
a

5

Continued

0.54
0.56
0.31
0.14
a
0.00
0.05
0.06
0.13
−0.06
0.04
0.14
0.18
0.13
0.07
a
a

6

Management and Risk Characteristics of Farmers in Norway
127

0.28
0.47
a
a
a
a
a
a
a
a
a
a
a
0.16
0.29

1
0.04
0.09
a
a
a
a
a
a
a
a
a
a
a
−0.08
0.04

2
0.54
0.27
a
a
a
a
a
a
a
a
a
a
a
0.18
−0.01

3
0.24
0.24
a
a
a
a
a
a
a
a
a
a
a
0.14
−0.01

4

Crop Farmersb

0.11
0.21
a
a
a
a
a
a
a
a
a
a
a
0.78
0.73

5

1
0.25
0.27
0.50
0.62
0.58
0.42
0.23
0.31
0.66
0.36
0.29
0.65
0.34
a
a

6
−0.06
−0.01
a
a
a
a
a
a
a
a
a
a
a
0.12
0.14
0.23
0.35
0.04
0.15
0.09
0.14
0.49
0.43
0.24
0.58
0.56
0.18
0.41
a
a

2

4
0.63
0.36
0.27
0.24
0.47
0.44
0.17
0.13
0.07
0.06
0.07
0.17
0.07
a
a

3
0.09
0.11
0.03
0.11
0.08
0.11
0.20
0.34
0.12
−0.02
0.15
0.07
−0.04
a
a

Dairy Farmersc

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Factor loadings >|0.30| are in bold.
“a” means that the variable is deleted from the factor analysis because of low factor loadings and low communality or farm-type conditionality.
b Factors 1–6 are institutional, organic, human resources, credit, crop, and market.
c Factors 1–6 are dairy, institutional, organic, human resources, credit, and market.

Fire damages
Technical failure
Forage yield uncertainty
Production diseases
Domestic epidemic animal diseases
Nondomestic epidemic animal diseases
Animal welfare policy
Legislation in production hygiene
Milk yield variability
Milk price variability
Milk quota policy
Meat production variability
Meat price variability
Crop yield variability
Price variability

Appendix A. Continued

0.22
0.26
0.22
0.12
0.16
0.16
−0.19
−0.14
0.18
0.14
0.03
0.14
0.13
a
a

5

0.03
0.10
0.27
0.00
0.02
0.08
−0.27
−0.25
0.09
0.27
0.14
0.24
0.28
a
a

6

128
Review of Agricultural Economics

0.05
a
0.05
0.12
0.18
0.00
0.13
0.39
0.58
a
0.12
0.09
0.59
0.36
0.30
−0.04
0.17
−0.02
a
0.04
0.10
0.39
−0.05

0.55
a
0.64
0.56
0.53
0.25
−0.09
0.14
0.12
a
0.52
−0.19
−0.05
0.44
0.44
0.05
0.21
0.11
a
0.10
0.12
0.16
0.46

2

0.00
−0.02
0.14
0.16
−0.01
0.17
0.18
a
0.73
0.78
0.07
0.15

0.07
a
0.04
0.00
−0.04
0.07
0.04
0.12
−0.01
a
0.04

3
0.00
a
0.07
0.09
0.06
0.58
0.69
0.26
0.13
a
0.01
0.17
0.02
0.13
−0.05
0.16
−0.04
0.12
a
0.08
0.03
−0.03
−0.03

−0.04
−0.23
0.06
0.13
0.38
0.65
0.49
a
0.19
0.07
0.00
0.09

5

0.00
a
0.06
0.17
0.16
0.17
0.10
0.28
0.26
a
0.11

4

1

0.06
0.15
0.43
0.37
0.13
0.17
0.13
−0.04
0.02
0.09
a
a

−0.11
a
−0.02
0.06
0.10
0.00
0.15
0.15
0.08
a
0.67
0.39
0.14
0.04
0.07
0.09
0.00
−0.11
a
−0.01
0.03
−0.04
−0.05

0.17
0.09
−0.08
0.46
0.46
−0.02
0.06
0.58
0.65
0.20
0.09

6

0.03
0.05
0.29
0.06
0.15
0.37
0.20
0.10
0.19
0.14
a
a

0.67
0.65
0.55
0.06
0.17
0.20
−0.10
0.02
−0.03
0.01
0.21

2

0.13
−0.14
0.11
0.04
0.39
0.41
0.67
0.67
0.12
0.19
a
a

−0.04
0.19
0.20
0.27
0.23
0.15
0.08
0.01
−0.04
0.23
0.11

3

5
0.14
−0.03
0.04
−0.10
−0.11
−0.16
0.10
0.10
0.18
−0.02
0.16
0.54
0.68
0.27
0.27
−0.02
0.00
0.14
−0.09
−0.08
0.10
a
a

4
0.16
0.03
0.17
0.06
0.12
0.15
−0.01
0.00
−0.03
0.06
0.01
−0.04
0.03
0.09
0.04
0.18
0.11
0.05
0.30
0.70
0.69
a
a

Dairy Farmersc

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Factor loadings >|0.30| are in bold.
“a” means that the variable is deleted from the factor analysis because of low factor loadings and low communality or farm-type conditionality.
b
Factors 1–6 are consultancy, flexibility, insurance, low cost, financial, and diversification.
c
Factors 1–7 are flexibility, consultancy, disease prevention, insurance, diversification, financial, and fixed cost sharing.

Use of economic consultancies
Use of veterinarian consultancies
Use of agron./nutritional consultancies
Production contracts
Storage
Liquidity—keeping cash in hand
Solvency—debt management
Asset flexibility
Product and market flexibility
Keeping fixed costs low
Shared ownerships of equip.,
joint operations
Off-farm work
Off-farm investments
Collecting information
Enterprise diversification
Produce at lowest possible cost
Risk reducing technologies
Prevent/reduce livestock diseases
Prevent/reduce crop diseases and pests
Buying farm business insurance
Buying personal insurance
Organize the farm as a corporation
Cooperative marketing

1

Crop Farmersb

6

0.10
−0.11
−0.07
−0.09
0.29
0.01
0.08
0.26
0.06
0.08
a
a

0.01
0.08
0.01
−0.09
0.11
0.60
0.63
0.22
0.03
−0.01
−0.07

Appendix B. Crop and dairy farming, joint varimax rotated factor loading for risk management strategies

0.12
−0.03
0.09
0.10
0.11
0.14
−0.01
0.16
−0.07
0.10
a
a

0.15
0.03
0.00
−0.11
−0.27
0.00
−0.09
0.11
0.17
0.28
0.81

7

Management and Risk Characteristics of Farmers in Norway
129

130

Review of Agricultural Economics

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