Financial Risk Analysis - Case study Guri I Zi in the Municipality of
Shkodër in Albania
DENISA KURTAJ1, TEUTA ÇERPJA2,a, ARIF MURRJA3,b,*
1Faculty of Economics and Agribusiness,
Agricultural University of Tirana,
ALBANIA
2Faculty of Economics, Business, and Development,
European University of Tirana,
ALBANIA
3Faculty of Economics and Agribusiness,
Agricultural University of Tirana,
ALBANIA
aORCiD: 0000-0002-5845-6145
bORCiD: 0000-0002-6794-8782
*Corresponding Author
Abstract: - Vegetable farmers operating in Guri I Zi, located in the Shkodra district, meet 42% of the vegetable
market demand in the region. To identify the most important financing risks faced by these farmers when
searching for financial resources, a study was conducted to analyze the financing risks related to their activity.
The study used descriptive analysis and multiple regression analysis techniques to determine the main factors
influencing the financing risks of these farmers. The study found that farmers perceived low profits, excessive
debt, and high-interest rates as critical financing risks. However, the multifactorial analysis revealed that low
earnings were statistically insignificant, while excessive debt and high interest rates were statistically
significant. Regression analysis showed a strong correlation between financial risk, excessive debt, and high
interest rates at 86%. The main objective of the study was to make farmers aware of the importance of financial
risks.
Key-Words: - Financial risk, identification, analysis, entrepreneurship, agriculture, multifactorial regression.
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1 Introduction
Farmers are exposed to risks in production, market,
financing, legality, and human resources (See
Figure 1). The study focuses on financial risk.
Farmer enterprises face the inability to secure loans
from banks, especially in the case of female and
small-scale entrepreneurs, [1], [2], [3]. The lack of
bank credit is associated with unpredictable changes
in adverse climate conditions, [4], [5], [6],
damaging their production through CO2 emissions,
rainfall variations, droughts, frosts, etc., [4]. Such a
situation is also found in Albania. Albanian farmers,
unable to access bank loans, are forced to seek
financial resources from alternative institutions with
high interest rates. Therefore, an analysis of
financial risk in farmer entrepreneurship is
necessary to identify and address financing
challenges.
In developing countries, agriculture plays a
significant role in economic development, [4], [5].
In Albania, agriculture constitutes approximately
1/5 of the Gross Domestic Product (GDP), [7], [8].
However, as a developing country, Albanian
agriculture faces numerous challenges that require
attention from the central government and local
authorities, [8]. Financial risks for farmers include a
lack of financial resources, low-profit margins, high
production costs, and high levels of debt. This study
focuses on the administrative unit of Guri I Zi in the
Shkodër district, where climatic conditions are
favorable for vegetable production, [8].
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.8
Denisa Kurtaj, Teuta Çerpja, Arif Murrja
E-ISSN: 2224-3496
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This research is unique because there is a
scarcity of studies on vegetable production. The
study aims to identify financial risks and assess the
most significant risks for farmers in the study area.
This investigation could help farmers address
financial difficulties and provide valuable
information for researchers and other interest
groups, such as customers, suppliers, and public
institutions.
The findings of this study could encourage local
and central authorities to provide financial support
to farmers. Informed institutions can develop
appropriate policies and programs that favor
entrepreneurial farmers. As such, this study has
several beneficiaries. Entrepreneurs will have a
valuable guide for managing financial risks, while
researchers and academics will have new data
sources for their research. Meanwhile, responsible
state institutions will have valuable information to
undertake supportive policies in the agricultural
sector.
Research in Albania, as a developing country,
adds value to the agricultural economy, [9]. Another
objective of the study is to understand the financial
risks associated with increasing investments in
vegetable farms in the Shkodër area. The growth of
farm entrepreneurship in this region will have
positive effects on improving product quality at low
costs, thus satisfying one of the most important
consumer demands.
2 Literature Review
Agricultural production is threatened by numerous
risk events. The trend of today's studies in
agricultural businesses is the research of five main
risks or, five general risks or five big risks, [8], [9],
[10]. These risks are production risk, market risk,
financial risk, legal risk, and human resources risk
(See Figure 1).
Fig. 1: Five major farm risks
Source: [8]
Theoretically, these risks have been explained
by several authors, [10], [11], [12], [13], [14], [15],
[16], [17], [18], [19], [20], [21], [22], [23], [24].
International studies have analyzed the challenges
and risks faced by farmers in various contexts of the
agricultural industry. In premodern Iceland, primary
concerns were related to weather variability and
human diseases, [25]. In the Netherlands, the focus
has been on livestock farming, with conclusions
identifying price volatility as the primary risk,
followed by epidemic animal diseases and farmer
deaths, [26]. In the Caribbean and Pacific Islands,
fruit and vegetable farmers primarily face marketing
and production risks, [27].
In Lithuania, studies have shown a high level of
production risk, particularly due to non-productive
inputs and plant diseases, [28]. In Slovakia,
marketing risk emerged as a priority, followed by
natural disasters and contract non-compliance, [15].
In India, major challenges include marketing risks,
unfavourable weather, and delays in veterinary
services, [29]. In Chile, climate phenomena, price
fluctuations, and currency exchange rates are
significant concerns, [30].
In the United States, production risks, market
risks, and financial risks outweigh personal or legal
risks, [21]. Other studies in the United States have
concluded that non-climatic resources pose more
concerns than climatic ones, [31]. In Norway,
uncertainty about expected earnings, fear of
inability to continue payments to the state, and debt
repayment are considered the primary sources of
risk, [32]. In Pakistan, the main concern is frequent
changes in agricultural policies, followed by
agricultural equipment prices and the absence of
agricultural cooperatives, [33].
In Turkey, low-income risk, diseases, and
professional inadequacies are significant concerns,
[31], [34]. In Kosovo, studies have shown a wide
range of risk factors, including legal, financial,
market, human resources, and production risks, [35],
[36], [37].
Different risk factors are found in different
fields of production. Different factors are also found
at different times. Therefore, risk events must be
studied for each enterprise and at any time.
Two more studies were done in Guri I Zi in
2023. The first study analyzes the regressive
relationship of production risk with the events that
affect this process and it was concluded that
vegetable farms were threatened by flooding and
drought, [8].
This study focuses on financing risk. Farmers
face financial risk, which is related to the way of
financing and the financial condition of the farm.
Farm
Risk
1.
Production
risk
2.
Market
risk
3.
Financial
risk
4.
Legal
risk
5.
Human
resouces
risk
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Farm activity needs liquid funds to finance
operations, pay suppliers, loans, and other financial
obligations, [23], [26], [38], [39], [40]. Financial
risk occurs when money is borrowed to finance the
farm business. This risk can be caused by
uncertainty about future interest rates, a lender's
willingness and ability to continue to provide funds
when needed, and the farmer's ability to generate the
income needed to repay the loan, [41], [42], [43],
[44].
Thereby, we want to prove the hypothesis:
H1: The financial events of low profits,
excessive debts, and rising loan interest rates have
serious impacts on financial risk.
The financial risk factors perceived by farmers,
in studies in the intensive poultry production
industry in Kosovo, follow the trend of damages,
which means the perception is consistent with the
value of damages. Financial risk events have a large
standard deviation (€46,900), but a small dispersion
of 33%, [35].
3 Materials and Methods
3.1 Definition of Statistical Concepts
The main variables of our study are "Financial risk"
as a dependent variable and "Financial risk sources"
as independent variables (Table 1).
Table 1. Concepts of the model
Financial risk(Y)
1) Low profits (X1)
2) Excessive debts (X2)
3) Increase in loan interest (X3)
Dependent variable
Independent variables
Source: Authors' elaboration
3.2 Qualitative Evaluation of Variables
In this study, a risk assessment method was applied,
based on a rating scale from 1 to 5, known as the
Likert scale. This method is widely known and used
in numerous studies, especially in risk analysis
within the agricultural context, [45], [46], [47], [48].
Assessments were done to identify and evaluate the
three most significant financial risk events in
vegetable farms. The assessment technique and
outcomes are reported in Table 2 of the study. This
assessment approach assists in identifying and
analyzing the financial risk in the context of
agricultural enterprise, providing a basis for taking
further steps to manage and reduce this risk for
farmers.
Table 2. Turning concepts into variables
Method of measurement
Assessment
1-Very low risk
1-260
2-Low risk
261-520
3-Average risk
521-780
4-High risk
781-1,040
5-Very high risk
1,041-1,300
Source: [8]
3.3 Preliminary Survey Preparation
Based on the literature, [8], [18], [26], [49], [50],
and the specific situation of vegetable farms in the
administrative unit "Guri I Zi," a questionnaire with
three open-ended questions was developed. The
study included 3,500 farmers from the area. The
inability to survey all farmers led to the selection of
a sample as follows, [51], [52], [53].
Where Z = 1.96; p =0.5; q = 0.5 and e = 0.05, n0
is calculated:
In our case, the population consists of 3,500
farmers and we can slightly reduce it, [51], [53].
Where n is the sample size and N is the
population size equal to 3,500.
The sample size of the study is:
3.4 Survey, Data Collection and Analysis
To assess how farmers perceive the impact of three
financial risk factors, 260 farmers were individually
interviewed. The interviews were conducted
randomly, ensuring that each farmer had an equal
chance of representation. Their responses were
initially recorded in Excel and then elaborated in
Table 4 for reference and further analysis.
The study aimed to better understand farmers'
varied perceptions of financial risks in the
agriculture industry, with an emphasis on how they
understand and handle these risks. We sought to get
in-depth information directly from farmers through
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one-on-one interviews so that we were able to
understand their challenges and points of view.
Randomly selecting interviewees aimed to
minimize bias and ensure a diverse representation of
farmers from various backgrounds and farm sizes.
This approach aimed to get an in-depth
understanding of the financial risk environment in
the study area's agricultural community.
Farmers' perceptions of financial risk indicators
were documented in Excel, which made data
administration and analysis easier. This allowed us
to find patterns, trends, and correlations in the data.
The data tabulation that followed in Table 3 offered
an organized structure for referring to and carefully
analyzing the gathered data.
Table 3. Farmers' Responses on the perceptions of
Financial Risk events
Financial risk events
Likert rating
1
2
3
4
5
Earnings lower than expected
0
0
0
100
160
Excessive debts
15
25
70
90
60
Increase in loan interest
0
40
70
140
10
Financial risk
15
25
30
110
80
Source: Authors' elaboration
The survey data were collected and processed in
the Excel program. Then they were analyzed in the
Gretl program. The theoretical multiple linear
regression model specification is:
Y = a + bX1 + cX2 + dX3 + e (5)
3.5 Statistical Model Estimation
Multifactorial regression analysis is a widely used
method in social and economic sciences to
understand complex relationships among different
variables, [54], [55], [56], [57], [58], [59]. This
method is important in socio-demographic research
and financial risk analysis due to its ability to
identify relationships and effects among various
factors.
In the case of studies on financial risk,
multifactorial regression analysis can be used to
understand how other factors, such as financial
events, impact the financial risk of a subject.
Through the use of the coefficient of determination
(R2) and the Pearson correlation coefficient, this
method can show how much of the changes in
independent variables (such as financial events) are
explained by changes in dependent variables (such
as financial risk).
In our research, Fisher's F-test and critical
values of Fisher's were used to assess the statistical
significance of the model. Furthermore, the
statistical significance of variables was determined
through the p-value (P-value), allowing us to
understand which factors have a significant impact
on the financial risk of farmers in the context of our
study.
Overall, multifactorial regression analysis
provides a consistent tool for assessing and
analyzing the influence of different factors, helping
us better understand the dynamics and complexity
of market risk in the context of our scientific
research.
4 Problem Solution
In another study, the perception of vegetable
farmers in this area about the five main risks was
measured and evaluated according to the Likert
scale, [8]. The data are shown in Table 4 and Figure
2.
Table 4. Farmers' perception of the five main
risks on the farm
Segment
The five
main risks
Perceptions
1,041-
1,300
Production
risk
1,220
(i) Very high
1,041-
1,300
Marketing
risk
1,080
(ii) Very high
781-
1040
Financial
risk
995
(iii) High
781-
1040
Human
resources risk
850
(v) High
521-
780
Legal
risk
670
(iv) Average
Source: [8]
Fig. 2: Farmers' perception of the five main risks
Source: [8]
According to Table 4 and Figure 2, the financial
risk is rated the third in terms of importance, after
the production risk and marketing risk, followed by
the human resources risk, and finally the legal risk.
4.1 Descriptive Analysis of Financial Risk
The perception of suggested sources of financial
risk is presented in Table 5 and Figure 3.
1220
1080
995
670
850
0 500 1000 1500
Production risk
Marketing risk
Financial risk
Legal risk
Human resources risk
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Table 5. The importance of the market risk variables
Seg
ment
Source of financial
risk
Perception
[1041-
1300]
Earnings lower than
expected
1 200
(i) Very important
[781-
1040]
Excessive debts
935
(ii) Important
[781-
1040]
Increase in loan
interest
900
(iii) Important
Source: Authors' elaboration
Fig. 3: The importance of financial risk variables
Source: Authors' elaboration
As we see from Table 5 and Figure 3, low
profits are perceived as very important, followed by
excessive debts and high loan interest rates.
The perception of 260 farmers in percent for
lower profits is presented in Figure 4.
Fig. 4: Perception of low profits
Source: Authors' elaboration
Regarding the perception of risk for low profits
among 260 surveyed farmers, 38% or 100 farmers
evaluate it with a high impact, and 62% or 160
farmers evaluate it with a very high impact.
The perception of 260 farmers in percent about
excessive debts is presented in Figure 5.
Fig. 5: Perception of excessive debts
Source: Authors' elaboration
Regarding the perception of excessive debts by
260 surveyed farmers, 6% or 15 farmers evaluate it
with very low impact, 10% or 25 farmers evaluate it
with low impact, 27% or 70 farmers evaluate it with
medium impact, 34% or 90 farmers rate it as high
impact, and 23% or 60 farmers rate it as very high
impact.
The perception of 260 farmers in percent for the
increase in loan interest rates is presented in Figure
6.
Fig. 6: Perceptions of the increase in loan interest
rates
Source: Authors' elaboration
Regarding the perception of risk in the increase
in loan interest rates, among 260 farmers surveyed,
15% or 40 farmers evaluate it with low impact, 27%
or 70 farmers evaluate it with medium impact, 54%
or 140 farmers evaluate it with high impact, and 4%
or 10 farmers rate it with very high impact.
According to the data in Table 3 and the
statistical descriptions in Figures 4, 5, and 6, the
individual perceptions of 260 farmers regarding the
four financial risk events are considerably different.
They have a higher perception of low profits.
Diverse is the perception of excessive debts and
high loan interest rates.
4.2 Analysis of Statistical Results
The reliability of the questionnaire was assessed
according to Cronbach's alpha, [60].
For our study:
Alfa Cronbach is 90%, meaning that the
questionnaire results are reliable.
1200
935
900
0 500 1000 1500
Earnings lower than…
Excessive debts
Increase in loan…
38%
62%
6%
10%
27%
34%
23%
15%
27%
54%
4%
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4.2.1 Multivariate Regression Analysis
The analysis of the results from Table 6 indicates a
statistically significant relationship between
variables X2 and X3 and the variable Y. Variables
X2, representing excessive debt, and X3, determining
the increase in credit interest rates, are significant at
the level of statistical significance with a p-value
<0.01 and p-value <0.05 respectively. This suggests
that changes in excessive debts and credit interest
rates have a considerable impact on variable Y.
On the other hand, the variable X1, which
represents low profits, does not show a statistically
significant relationship with variable Y, having a p-
value >0.05. This means that changes in low profits
do not have a significant or obvious impact on
variable Y in this analysis.
These findings contribute to a better
understanding of the relationships and impacts
between the studied variables, enhancing the
understanding of the dynamics of their changes in
the context of variable Y.
Table 6. P-Value of variables
Coefficient
Std. Error
t-ratio
p-value
const
0.690025
0.324703
2.125
0.0345
**
X1
−0.148118
0.104201
−1.421
0.1564
X2
0.876412
0.0624497
14.03
<0.0001
***
X3
0.194322
0.0983481
1.976
0.0492
**
Table 7. Fisher's Critical value and correlation
Mean dependent var
3.830769
S.D. dependent var
1.143310
Sum squared resid
45.21117
S.E. of regression
0.420245
R-squared
0.866458
Adjusted R-squared
0.864893
F (3, 256)
553.6665
P-value(F)
1.4e-111
In addition to the significance of the variables,
we also assess the overall significance of the model,
where the current value of the Fisher statistic
exceeds the critical value of the Fisher statistic, thus
accepting hypothesis H1. The adjusted R-squared
coefficient indicates that 86% of the financial risk is
determined by variables X2 and X3 (Excessive debt
and High credit interest rate) (See Table 7). After
excluding the insignificant variable,
The regression equation takes the form:
Y = 0.690025 + 0.876412X2 + 0.194322X3
(8)
From the regression equation, we observe that
the relationship between financial risk and the
independent variables X2 (Excessive debt) and X3
(High credit interest rate) is positively correlated.
5 Conclusions and Recommendations
Albania enjoys very suitable climatic conditions for
agricultural development. In the two previous
studies, the main production risks have been
identified, which include floods, drought, and
market risks, where high competition was found to
be the most important, [8]. This is an important step
in understanding the challenges faced in this area.
But the financial risk is also one of the five main
risks of the farm (production risk, market risk, legal
risk, and human resources risk). Therefore, in this
study, we have analyzed the financial risk. We
suggest to researchers in the future to analyze the
legal risk and the risk of human resources. This will
enable farmers to be aware of all risk events in this
venture.
In our statistical analysis, it was found that the
perception of financial risk consists of excessive
debts and high interest rates of loans. Only two
variables (X2 and X3) are statistically significant.
Hypothesis H1 is partially accepted. To be more
objective, statistical analysis has shown that
excessive debts have a high impact, while interest
rates have a small impact. The percentage of high
debts and interest together is 86%.
To cope with these risks, one suggestion is to
focus on subsidy schemes, seeking support from
local authorities and even from larger institutions
such as the European Union, [61]. It is important to
emphasize that the identified risks, especially in the
financial aspect, have a significant impact on the
field of production and can have significant
consequences on the sustainability of the farm.
References:
[1] Van de Zande, G.D., Amrose, S., Donlon, E.,
Shamshery, P., Winter V, A. G. (2023).
Identifying Opportunities for Irrigation
Systems to Meet the Specific Needs of
Farmers in East Africa. Water 16, 75,
https://doi.org/10.3390/w16010075.
[2] Manuela, J., Rivera-Ferreband, G. M., López-
i-Gelats, F. (2024). Contributions of a
feminist perspective to the analysis of farm
viability: the livelihoods reproduction
framework. The Journal of Peasant Studies,
Vol. 51, No. 1, 185–211,
https://doi.org/10.1080/03066150.2023.22105
00.
[3] Durga N., Schmitter, Claudia Ringler, C.,
Mishra, Sh., Manuel S. Ofosu, M. A., Paul
Pavelic, P., Hagos, F., Melaku, D., Verma,
Sh., Minh, Th., Matambo, C. (2024). Barriers
to the uptake of solar-powered irrigation by
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
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Denisa Kurtaj, Teuta Çerpja, Arif Murrja
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Volume 20, 2024
smallholder farmers in sub-Saharan Africa: A
review. Energy Strategy Reviews 51 (101294),
https://doi.org/10.1016/j.esr.2024.101294.
[4] Jha, P., Chinngaihlian, S., Upreti, P., Handa,
A. (2023). A machine learning approach to
assess implications of Climate Risk Factors on
Agriculture: The Indian case. Climate Risk
Management, (41) 100523,
https://doi.org/10.1016/j.crm.2023.100523.
[5] Sargani, G. R., Shen, Y,. Liu, Y., Joyo, M. A.,
Liu, J., Jiang, Y., and Zhang, H. (2023). Farm
risks, livelihood asset allocation, and
adaptation practices in response to climate
change: A cross country analysis. Front.
Environ. Sci. 10:1066937, doi:
10.3389/fenvs.2022.106693.
[6] Kalogiannidis, S., Papadopoulou, C.-I.,
Loizou, E., Chatzitheodoridis, F. (2023). Risk,
Vulnerability, and Resilience in Agriculture
and Their Impact on Sustainable Rural
Economy Development: A Case Study of
Greece. Agriculture, 13, 1222,
https://doi.org/10.3390/agriculture13061222.
[7] Tomorri, I., Domi, S., Çera, G., Remzi Keco,
R., Kapaj, I. (2024). Examination of the
Importance and Level of Application of
Digitization in the Rural Sector, the Case of
Albania, WSEAS Transactions on Business
and Economics, vol. 21, pp. 528-543,
https://doi.org/10.37394/23207.2024.21.44.
[8] Murrja, A., Kurtaj, D., Ndregjoni, A., Prendi,
L. (2023). Vegetable farmers' perception of
production risk sources and environmental
aspects – Descriptive statistical analysis and
multifactorial linear regression. WSEAS
Transactions on Environment and
Development, vol. 19, pp. 826-835,
https://doi.org/10.37394/232015.2023.19.77.
[9] Lushi, I., Çera, G., Murrja, A. & Ujkani, S.
(2023). Linking farmers’ bargaining power in
trade to their plans for future economic
activities. Southeast European Journal of
Economics and Business. 18 (2), p. 173-185,
https://doi.org/10.2478/jeb-2023-0026.
[10] Melyukhina, O. (2011). Risk Management in
Agriculture in The Netherlands, OECD Food,
Agriculture and Fisheries Papers, No. 41,
OECD Publishing, Paris,
http://dx.doi.org/10.1787/5kgj0d5lqn48-en.
[11] Drollette, S. A. (2009). Understanding
Agricultural Risk. AG/ECON/2009-01RM,
[Online].
https://extension.usu.edu/apec/files/uploads/ag
ribusiness-and-food/risk-management/risk-
management-factsheets/Managing-
Production-Risk.pdf (Accessed Date:
February 21, 2024).
[12] Crane, L., Gantz, G., Isaacs, S., Jose, D.
Sharp, R. (2013). Introduction to Risk
Management, [Online].
http://extensionrme.org/Pubs/Introduction-to-
Risk-Management-ENGLISH.pdf (Accessed
Date: February 21, 2024).
[13] Schaffnit-Chatterjee, C. (2010). “Risk
management in agriculture” Towards market
solutions in the EU, pp. 3-4; 17-19, [Online].
https://www.farm-
d.org/app/uploads/2019/05/Deutsche-Bank-
Research-Risk-Management-in-Agriculture-
091710.pdf (Accessed Date: February 21,
2024).
[14] Hassan, A., A. Farooq, M. Ishaq, G. Sadiq
andA. Nawaz (2023). Risk and coping
strategies of vegetable smallholder farmers in
Khyber Pakhtunkhwa of Pakistan. Sarhad
Journal of Agriculture, 39(2): 440-451,
10.17582/journal.sja/2023/39.2.440.451.
[15] Jankelova. N., Masar, D., Moricova, S.
(2017). Risk factors in the agriculture sector.
Agric. Econ. – Czech, 63, 2017 (6): 247–258.
doi: 10.17221/212/2016-AGRICECON.
[16] Kahan, D. (2013). Managing Risk in Farming,
[Online].
https://www.fao.org/uploads/media/3-
ManagingRiskInternLores.pdf (Accessed
Date: February 21, 2024).
[17] Komarek, A. M., De Pinto, A., Smith, V. H.
(2020). A review of types of risks in
agriculture: What we know and what we need
to know. Agricultural Systems, 178, 102738,
https://doi.org/10.1016/j.agsy.2019.102738.
[18] Melyukhina, O. (2011). Risk Management in
Agriculture in New Zealand, OECD Food,
Agriculture and Fisheries Papers, No. 42,
OECD Publishing, Paris,
http://dx.doi.org/10.1787/5kgj0d3vzcth-en.
[19] OECD (2008a). An Assessment of Risk
Exposure in Agriculture: A Literature Review.
Working Party on Agricultural Policies and
Markets, [Online].
https://one.oecd.org/document/TAD/CA/APM
/WP(2008)22/FINAL/en/pdf (Accessed Date:
February 21, 2024).
[20] Sciabarrasi, M. (2024). The Big Five Risk
Faced by Farmers, [Online].
https://nevegetable.org/big-five-risks-faced-
farmers (Accessed Date: February 21, 2024).
[21] Thomas, G. (2018). Risk management in
agriculture, SB 18-46, [Online].
https://bprcdn.parliament.scot/published/2018/
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
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72
Volume 20, 2024
7/4/Risk-management-in-
agriculture/SB%2018-46.pdf (Accessed Date:
February 21, 2024).
[22] Thompson, N.M., Bir, C., Widmar, N.J.O.
(2019). Farmer perceptions of risk in 2017.
Agribusiness 35, 182–199,
https://doi.org/10.1002/agr.21566.
[23] Ullah, R., Shivakoti, G.P., Zulfiqar, F.,
Kamran, M.A. (2016). Farm risks and
uncertainties: sources, impacts, and
management. Outlook Agric. 45, 199–205,
https://doi.org/10.1177/0030727016665440.
[24] Martin, S., McLeay, F. (1998). The diversity
of farmers’ risk management strategies in a
deregulated New Zealand environment. J.
Agric. Econ. 49, 218–233.
[25] Harwood, J., Heifer, R., Coble, K., Perri, J.,
Somwaru, A. (1999). Management Risk in
Farm,
http://dx.doi.org/10.22004/ag.econ.34081.
[26] Meuwissen, M.P.M., Huirne, R.B.M.,
Hardaker, J.B., 2001. Risk and risk
management: an empirical analysis of Dutch
livestock farmers. Livest. Prod. Sci. 69, 43–
53, https://doi.org/10.1016/S0301-
6226(00)00247-5.
[27] Angelucci, F., Conforti, P. (2010). Risk
management and finance along value chains
of Small Island Developing States. Evidence
from the Caribbean and the Pacific. Food
Policy 35, 565–575,
https://doi.org/10.1016/j.foodpol.2010.07.001.
[28] Girdziute, L., Slavickiene, A., Vaitkevicius, S.
(2014). Integrated risk assessment at
Lithuanian farms. Transform. Bus. Econ. 13,
143–154.
[29] Chand, S., Narayan, P., Chaudhary, K.R.
(2018). Sources of risks in livestock
production and their management strategies in
northern India. Indian J. Anim. Sci. 88, 612–
619,
https://doi.org/10.1016/j.foodpol.2010.07.001.
[30] Lobos, G., Schnettler, B., Mena, C.,
Ormazábal, Y., Cantillana, J. C., Retamales, J.
B. (2018). Perception of risk sources by
Chilean blueberry producers. Rev. Bras.
Frutic. 40, https://doi.org/10.1590/0100-
29452018248.
[31] Lane, D., Chatrchyan, A., Tobin, D., Thorn,
K., Allred, S., Radhakrishna, R. (2018).
Climate change and agriculture in New York
and Pennsylvania: risk perceptions,
vulnerability and adaptation among farmers.
Renewable Agriculture and Food Systems 33,
197–205,
https://doi.org/10.1017/S1742170517000710.
[32] Flaten, O., Lien, G., Koesling, M., Valle, P.S.,
Ebbesvik, M. (2005). Comparing risk
perceptions and risk management in organic
and conventional dairy farming: empirical
results from Norway. Livestock Production
Science, Vol. 95, Issues 1–2, pp.11-25,
https://doi.org/10.1016/j.livprodsci.2004.10.0
14.
[33] Iqbal, M. A., Ping, Q., Zafar, M. U., Abbas,
A., Bashir, M. K., Ali, A., Kousar, R. (2018).
Farm risk sources and their mitigation: a case
of cotton growers in Punjab. Pak. J. Agric.
Sci. 55, 677–684,
http://dx.doi.org/10.21162/PAKJAS/18.7070.
[34] Karadas, K., Birinci, A. (2018). Identification
of risk factors affecting production of
beekeeping farms and development of risk
management strategies: a new approach. Rev.
Bras. Zootec. 47,
https://doi.org/10.1590/rbz4720170252.
[35] Murrja, A., Ndregjoni, A., Kapaj, I., Maloku,
S., Kapaj, A. (2022). Financial Risk Analysis
in the Intensive Poultry Growth in The
Republic of Kosovo. International Journal of
Economics and Finance Studies, 14 (03), 366-
387, doi: 10.34111/ijefs. 20220078.
[36] Murrja, A., Ndreca, P., Maloku, S., Meço, M.
(2023). Analysis of Production Risk in
Intensive Chicken Farms – the Case of
Kosovo. Folia Oeconomica Stetinensia, 23(2),
300–316, https://doi.org/10.2478/foli-2023-
0032.
[37] Ndregjoni, A., Murrja, A., Prendi, L. (2023).
Analysis of Legal Risk in Farms of Intensive
Chicken Production - The Case of Kosovo.
WSEAS Transactions on Environment and
Development, vol. 19, pp. 655-667, doi:
10.37394/232015.2023.19.64.
[38] Saqib, S. E., Ahmad, M.M., Panezai, S., Ali,
U. (2016). Factors influencing farmers’
adoption of agricultural credit as a risk
management strategy: The case of Pakistan.
Int. J. Disaster Risk Reduct, 17, 67–76,
http://dx.doi.org/10.1016%2Fj.ijdrr.2016.03.0
08.
[39] Adeyonu, A. G., Otunaiya, A. O., Oyawoye,
E. O., & Okeniyi, F. A. (2021). Risk
perception and risk management strategies
among poultry farmers in southwest Nigeria.
Cogent Social Sciences, 7(1), 1891719,
https://doi.org/10.1080/23311886.2021.18917
19.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.8
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E-ISSN: 2224-3496
73
Volume 20, 2024
[40] Wauters, E., de Mey, Y., van Winsen, F., Van
Passel, S., Vancauteren, M., Lauwers, L.
(2015). Farm household risk balancing:
implications for policy from an EU
perspective. Agric. Finance. Rev. 75, 450–
468, https://doi.org/10.1108/AFR-04-2015-
0017.
[41] Wauters, E., Van Winsen, F., De Mey, Y.,
Lauwers, L. (2014). Risk perception, attitudes
toward risk and risk management: evidence
and implications. Agric. Econ.–Czech 60,
389–405, doi: 10.17221/176/2013-
AGRICECON.
[42] Glemarec, Y. (2017). Addressing the gender-
differentiated investment risks to climate-
smart agriculture. Aims Agric. Food 2 (1), 56–
74. https://doi.org/10.3934/agrfood.2017.1.56.
[43] Imami, D., Skreli, E. (2022). Agricultural Risk
A rapid assessment and recommended
mitigation mechanisms, [Online].
https://www.researchgate.net/publication/366
878182_Agricultural_Risk_A_rapid_assessme
nt_and_recommended_mitigation_mechanism
s (Accessed Date: February 21, 2024).
[44] Ndem, C.N., Osondu, C. K. (2018). Risk
sources and management strategies among
cassava farmers in Abia State, Nigeria.
Scientific papers series management. Econ.
Eng. Agric. Rural Dev. 18, 267–276, [Online].
https://managementjournal.usamv.ro/pdf/vol.1
8_1/Art36.pdf (Accessed Date: February 21,
2024).
[45] Arhin, I., Yeboah, E., Liu, X., X Liu, A.,
Chen, X., & Li, X. (2024) Integrating farmers’
perception of sustainable agricultural
technologies towards the development of
sustainable tea production in China,
International Journal of Agricultural
Sustainability, 22:1, 2303886,
https://doi.org/10.1080/14735903.2024.23038
86.
[46] Dossou-Yovo, R. E., Aminou Arouna, A.,
Benfica, R., Mujawamariya, G., Yossa, R.
(2024). A participatory framework for
prioritizing climate-smart agriculture
innovations in rice-based systems: A case
study of Mali. Smart Agricultural Technology
(7) 100392,
https://doi.org/10.1016/j.atech.2023.100392.
[47] Bannor, K. R., Oppong-Kyeremeh, H., Amfo,
B., Kuwornu, J. K. M., Kyire, S. K. C.,
Amponsah, J. (2023). Agricultural insurance
and risk management among poultry farmers
in Ghana: An application of discrete choice
experiment. Journal of Agriculture and Food
Research (11) 100492,
https://doi.org/10.1016/j.jafr.2022.100492.
[48] Girma, Y., Berhanu K., and Amsalu Bedemo,
A. (2023). Risk Aversion and Perception of
Farmers about Endogenous Risks: An
Empirical Study for Maize Producers in Awi
Zone, Amhara Region of Ethiopia. Journal of
Risk and Financial Management 16: 87,
https://doi.org/10.3390/jrfm16020087.
[49] Stantcheva, S. (2023). How to Run Surveys:
A Guide to Creating Your Own Identifying
Variation and Revealing the Invisible. Annual
Review of Economics, [Online].
https://www.annualreviews.org/doi/10.1146/a
nnurev-economics-091622-010157 (Accessed
Date: March 11, 2024).
[50] Olivera, M. D., Zhengb, Y., Naylorb, A. L.,
Murtagha, M., Waldronb, S., Peng, T. (2020).
How does smallholder farming practices and
environmental awareness vary across village
communities in the karst terrain of southwest
China? Agriculture, Ecosystems and
Environment (288) 106715,
https://doi.org/10.1016/j.agee.2019.106715.
[51] Okoye, P. U., Okolie, K. C., and Odesola, I.
A. 2022. Risks of Implementing Sustainable
Construction Practices in the Nigerian
Building Industry. Construction Economics
and Building, 22:1, 21–46,
https://doi.org/10.5130/AJCEB.v22i1.742.
[52] Israel, Glenn D. (1992). Determining Sample
Size. Fact Sheet PEOD-6. Florida
Cooperative Extension Service, Institute of
Food and Agricultural Sciences, University of
Florida, [Online].
https://www.psycholosphere.com/Determinin
g%20sample%20size%20by%20Glen%20Isra
el.pdf (Accessed Date: February 21, 2024).
[53] Cochran, W.G., (1977). Sampling techniques.
3rd Ed. New York: John Wiley & Sons Inc,
[Online].
https://fsapps.nwcg.gov/gtac/CourseDownloa
ds/IP/Cambodia/FlashDrive/Supporting_Docu
mentation/Cochran_1977_Sampling%20Tech
niques.pdf (Accessed Date: February 26,
2024).
[54] Ndong, O. G., Villerd, J., Cousin, I., Therond,
O. (2023). Using a multivariate regression tree
to analyze trade-offs between ecosystem
services: Application to the main cropping
area in France. Science of The Total
Environment,
https://doi.org/10.1016/j.scitotenv.2020.14281
5.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.8
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E-ISSN: 2224-3496
74
Volume 20, 2024
[55] Masoud, M., El Osta, M., Alqarawy, A.,
Elsayed, S., Mohamed Gad, M. (2022).
Evaluation of groundwater quality
for agriculture under different conditions
using water quality indices, partial least
squares regression models, and GIS
approaches. Applied Water Science (2022) 12:
244, https://doi.org/10.1007/s13201-022-
01770-9.
[56] Fan, D., He, W., Smith, W. N., Drury, C. F.,
Jiang, R., Grant, B. B., Shi, Y., Song, D.,
Chen, Y., Wang, X., He, P., & Zou, G.
(2022). Global evaluation of inhibitor impacts
on ammonia and nitrous oxide emissions from
agricultural soils: A meta-analysis. Global
Change Biology, 28, 5121–5141,
https://doi.org/10.1111/gcb.16294.
[57] Issahaku, G., and Abdulai, A. (Adoption of
climate-smart practices and its impact on farm
performance and risk exposure among
smallholder farmers in Ghana. Australian
Journal of Agricultural and Resource
Economics, 64, pp. 396–420,
https://doi.org/10.1111/1467-8489.12357.
[58] Osmani M., Kambo A., (2019). Efficiency of
apple small-scale farming in Albania-a
Stochastic Frontier Approach, New Medit, 18
(2): pp. 71-88,
http://dx.doi.org/10.30682/nm1902d.
[59] .Osmani M., Kambo, A. (2019). Small-
scale apple farmers’ willingness to invest –
the case of Korça region farmers in Albania.
Acta Universitatis Agriculturae Et
Silviculturae Mendelianae Brunensis,
https://doi.org/10.11118/actaun201967010309
[60] Warrens, M. J. (2015). Some Relationships
Between Cronbach's Alpha and the Spearman-
Brown Formula. Journal of Classification,
32(1), 127-137, doi: 10.1007/s00357-015-
9168-0.
[61] Brazhnyk, L., Ivanyuta, V., Kravchenko, T.,
Kozak, O., Los, O., & Pavliuk, S. (2023).
Harmonization of Agricultural Policy and
Economic Security of the EU States. WSEAS
Transactions on Environment and
Development, vol. 19, pp. 1-10,
https://doi.org/10.37394/232015.2023.19.1.
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