Vegetable Farmers' Perception of Production Risk Sources and

Environmental Aspects – Descriptive Statistical Analysis and

Multifactorial Linear Regression

ARIF MURRJA1, DENISA KURTAJ1, AGIM NDREGJONI2, LLAMBI PRENDI2

1Faculty of Economics and Agribusiness,

Agricultural University of Tirana,

ALBANIA

2Faculty of Business,

“Aleksander Moisiu” University Durrës,

ALBANIA

Abstract: - Farmers make decisions with incomplete information. Industrial producers can determine the

number of products they produce using different inputs. Farmers find it impossible. The paper aims to measure

farmers' perception of sources of production risk. For this purpose, a questionnaire was designed based on the

researched literature and the reality of the farms. We conducted face-to-face interviews with 260 farmers to

assess how they assess sources of production risk. We measured perception using a 1-to-5 Likert scale

psychometric rating.

From the descriptive statistical analysis, the perception of the farmers for the production risk is very high.

Also, the perception of the five sources of risk (drought, flooding, low temperature, non-quality factors of

production, and damage) varies from high to very high. While from the regression analysis, the statistically

significant variables are drought and flood. Their impact is 86% on production risk.

Key-Words: - Risk, source, event, perception, production, technical, management.

Received: March 12, 2023. Revised: June 17, 2023. Accepted: August 21, 2023. Published: September 11, 2023.

1 Introduction

Agricultural risks are a constant challenge for

farmers, with various types of risks to manage, [1],

[2], [3], [4], [5], [6], [7], [8], [9], [10], [11].

Researchers have categorized these risks into five

main categories, including production, market,

financial, legal, and human resource risks, [7], [8],

[9], [10], [11], [12], [13], [14], [15], [16], [17], [18],

[19], [20], [21]. When making decisions, farmers

must consider these different threats.

Farmers should be aware of five critical risks in

agriculture, [21]. These are the most significant

risks that can affect their farms. Our research

focuses on identifying sources of production risk in

agriculture. A critical threat to the production

process is the presence of pests that can reduce crop

yield and result in product loss. Production risks

stem from unsafe planting, growing, and producing

crops. The primary sources of production are bad

weather, pests, and diseases, [8], [9], [10], [14],

[22], [23], [24], [25], [26], [27], [28], the biological

cycle, [19], equipment breakdowns, globalization,

and free trade agreement, [19], [29], [30].

In agriculture, the products are diverse.

According to the direction of production, the farms

are: for the production of arable plants; vegetable

production; for production of fruit trees; fodder

production; zootechnical products; cattle products;

small products; and poultry products; the production

of fish and seafood, for the production of

ornamental plants; for the production of medicinal

plants, etc.

Our research investigates how farmers in the

Gur I Zi administrative unit in Shkodër

municipality, Albania, perceive production risk

sources. We have developed and tested a model that

links farm risks to resources and provides ways to

manage risks. The administrative unit has an area of

about 81.7 km2 and 11,800 inhabitants. About 3,000

to 3,100 families in this organizational unit deal

with agriculture and livestock. Agriculture is the

main activity. Farmers realize 42% of vegetable

production in this region, [31], [32], [33].

Currently, there are no existing studies

conducted in the region of Shkodra. We have

reviewed the latest research about vegetables in

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.77

Arif Murrja, Denisa Kurtaj,

Agim Ndregjoni, Llambi Prend

E-ISSN: 2224-3496

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Albania. They are qualitative and few. Our study

used a combination of qualitative and quantitative

analyses. Specifically, in issue 4.1, we presented a

descriptive qualitative based on farmers'

perceptions, followed in issue 4.2 by a quantitative

multifactorial regression analysis.

The search is unique in terms of the method

used. The research results will serve farmers, field

researchers, and local government, [8], [9], [10].

The research concludes by providing

recommendations for managing production risk

events. In conclusion, our research is innovative.

The paper will inspire the authors and other

researchers to research in the Guri I Zi

administrative unit, market risk, financial risk, legal

risk, and human resource risk in vegetable farms.

2 Literature Review

When conducting research, two primary concepts

are utilized risk and production risk. However, the

meaning of risk is often difficult to comprehend due

to its complexity and widespread usage.

One of the meanings is that risk is uncertainty,

[15], [21], [34]. Risk is favorable for someone and

unfavorable for someone else. Misfortune is often

associated with risk, [15], [21]. Risk is usually

measured by considering both its consequences and

probability, [21]. Investing in the market from an

entrepreneurial perspective is about

entrepreneurship, [15], [21].

Production risk arises from the unpredictable

natural growth processes of crops and livestock.

Many factors can affect the quantity and quality of

goods, including weather, disease, pests,

technology, free trade agreements, and

globalization, [8], [9], [10], [19], [29], [30].

There are numerous studies on risk in

agriculture, but the need for other studies continues

for geographical, economic, and time reasons, [8],

[9], [10]. Unforeseen events with significant impacts

continue to occur to farmers, suggesting that risk

has changed over time, [35], [36]. The challenges to

the agricultural sector are many. These challenges

make risk management in agriculture more critical

than ever, [37], [38], [39], [40]. However, whether

farmers' exposure to risks has increased over time

remains an open question, [41].

From what we presented above, our research

hypothesis is as follows:

The hypothesis: Risk events, such as drought,

floods, temperatures, non-quality production factors,

and diseases/pests, severely affect production risk.

There is a risk of decreased production or yield

due to factors outside the farmer's control, such as

weather and technology, which may result in losses,

[42].

Studies show that farmers' perspectives are

greatly affected by their gender, age, family

situation, farm size, and desire to make a living,

[43]. In Albania, vegetable farmers in the district of

Korça have rated financial risk high, followed by

marketing, political/legal, human resources, and

production risks. Farmers in Albania face

production risks such as low yield and poor quality

due to such problems as soil salinity, pests, diseases,

and unsuitable seeds and seedlings. During the last

few years, Albania's lack of human resources has

become a critical risk for agriculture, [44]. The

reasons for the lack of human resources in the

agriculture sector in Albania are migration and

emigration in the last three decades. In the 90s of

political upheaval (transition from the centralized

socialist system to the market economy system), the

Albanian society emigrated mainly to Greece and

Italy. Meanwhile, there was a massive displacement

of the agricultural population in the urban regions,

specifically in Tirana (the capital) and Durrës (the

economically important region), [45], [46]. Even

today, in the Western Balkans, Albania is among the

countries with the greatest emigration needs in the

countries of the European Union, the United States

of America, Canada, and Australia. Therefore,

human resources in the agricultural sector are in a

critical situation, [47], [48].

The country's challenging climate further

exacerbates these concerns, [49], [50], [51], [52],

[53]. Climate change has a more significant negative

impact on smallholder farmers in Albania, [52],

highlighting the need for management to understand

the consequences of climate change and for

government-led interventions to help farmers, [52],

[54], [55].

Figure 1 presents a visual view of the research

problem, the formulation of the proposed

hypothesis, the selection of data, the methodology to

verify the hypothesis, conclusions, and

recommendations. The arrow shows the role of the

government in the development of agriculture.

Without the care of the government, there is no

development of agriculture.

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Arif Murrja, Denisa Kurtaj,

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Fig. 1: Conceptual framework of the study

Source: Authors' elaboration

3 Materials and Methods

3.1 Turning Concepts Into Statistical

Variables

As we have emphasized above, the concepts of our

model are two: (i) Risk; and (ii) Sources of risk.

These concepts are more divided explicitly as

follows (Table 1):

Table 1. Concepts of the model

I) Risk

II. Sources

Production

risk (Y)

1) Drought (X1)

2) Flood (X2)

3) Very high/low temperatures (X3)

4) Non-quality factors of production (X4)

5) Disease/pest (X5)

Dependent

variable

Independent variables

Source: Authors' elaboration

Table 2 shows how we have translated abstract

ideas into quantifiable variables for the study.

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: Authors' elaboration

3.2 Data Collection

We conducted a study that surveyed 260 farmers

and collected primary statistical data. We evaluated

their perceptions using the Likert scale, which

ranged from 1 to 5, and the results are presented in

Table 3, and Table 4. Table 3 presents the farmer's

perceptions of the five main risks, and Table 4

presents the farmers' perceptions of the five

production risk events taken in the study.

Table 3. Farmers' perception of the five main

risks in agriculture

Farm risks

Likert rating

Production risk

320

900

Marketing risk

460

525

Financial risk

440

400

Legal risk

200

195

240

Human resources risk

330

320

125

Source: Authors' elaboration

Table 4. Farmers' responses on the perceptions

of production risk events

Production risk events

Likert rating

Drought

100

270

440

Flood

360

850

Very high/low

temperatures

460

575

Non-quality factors of

production

180

440

200

Disease/pest

360

725

Source: Authors' elaboration

3.3 The Methodology Used

In this research, we have combined descriptive

statistical analysis (qualitative perceptual analysis)

with multifactorial regression statistical analysis

(quantitative analysis). These data are been from

direct meetings with farmers. These data are first

entered in Excel. Then their processing was done in

the SPSS program.

The variables were connected through the

multiple linear regression model. Here's how the

model is presented:

Yi=a+bX1+cX2+… Xn+e

We compared the actual Fisher (Ff) with the

critical Fisher (Fk) to determine whether the model

was statistically significant. Sig./(P-value)

determines the statistical significance of the

dependent variable. R2 is the coefficient of

determination, which indicates how much of the

dependent variable is determined by the independent

variable. Pearson's Correlation Coefficient shows

the relationship between variables.

Identifying the problem and studying

the literature

Hypothesis and data

collection

Methodology and

hypothesis verification

Conclusions

Recommendations

The role of

government

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4 Problem Solution

To address the issue, we analyzed the farmers'

perceptions through descriptive analysis and

conducted a multifactorial regression analysis for

quantitative analysis.

4.1 Descriptive Analysis

First, we present the farmers' perception of the five

main risks in agriculture and then the perception of

production risk events.

4.1.1 Descriptive Analysis of the Five Main Risks

Table 5 and Figure 2 present the farmers' responses

to the five main risks. In the first column of Table 5

are the assessment segments according to Table 2,

in the second column are the five main risks, and in

the third column are the farmers' perceptions of each

risk. These perceptions are the summaries of the

perceptions according to the Likert scale in Table 3.

In column four are the evaluations in question

according to the evaluation method in Table 2.

Table 5. 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: Authors' elaboration

Fig. 2: Farmers' perception of the five main risks

Source: Authors' elaboration

From the above, we find that farmers have a very

high perception of production risk, followed by

marketing risk. They have a high perception of

financial risk, followed by human resources risk.

They have an average perception of legal risk.

4.1.2 Descriptive Analysis of Five Production

Risks

Table 6 and Figure 3 present the farmers' responses

to the five production risk events taken in the study.

In the first column of Table 6 are the assessment

segments according to Table 2, in the second

column are the five main risks, and in the third

column are the farmers' perceptions of each risk.

These perceptions are the summaries of the

perceptions according to the Likert scale in Table 3.

In column four are the evaluations in question

according to the evaluation method in Table 2.

Table 6. Importance of production risk variables

Segme

The source of

production risk

Perceptions

1,041-

1,300

Flood

1,210

(i) Very high

Disease/pest

1,160

(ii) Very high

Very high/low

temperatures

1,125

(iii) Very high

781-

1,040

Non-quality factors

of production

860

(v) High

Drought

860

(iv) High

Source: Authors' elaboration

Fig. 3: Importance of production risk variables

Source: Authors' elaboration

From the above, we conclude that farmers

highly perceive floods, followed by diseases/pests

and temperature fluctuations. They have a high

perception of non-quality factors and droughts.

4.2 Analysis of Statistical Results

Initially, all independent variables underwent

testing. As found in Table 7 (Appendix), especially

in columns 5 and 6, the variables low temperatures,

non-quality production factors, and diseases and

pests are statistically insignificant (Sig. or P-

value/statistical significance is above 0.05).

Specifically, in Table 7 (Appendix), we read Sig. or

P-value is greater than 0.05. For low/high

temperatures, it is 0.968. For non-quality production

factors, it is 0.152. For diseases/pests, it is 0.813.

In conclusion, H1 will be accepted for the

variables "drought" and "flooding" and rejected for

"non-quality inputs", "high/low temperatures", and

"diseases/pests".

1220

1080

995

670

850

0 500 1000 1500

Production risk

Marketing risk

Financial risk

Legal risk

Human resources risk

860 1210

1125

860 1160

0 500 1000 1500

Drought

Flood

Very high/low…

Non-quality factors of…

Disease/pest

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The regression model continues with the

drought and flood variables, which are statistically

very significant (almost 100% significance). Their

statistical significance is for droughts Sig., or the P-

value is 0.001, and for floods Sig., or the P-value is

0.00. (Table 8, Appendix).

The regression equation is Y=X1+X2, where X1

is drought and X2 is flood.

Table 9 (Appendix) summarizes the model

taken in the study for production risk. The

correlation coefficient R indicates a strong

relationship between the dependent and independent

variables. The coefficient of determination R2 shows

that 86% of the variance caused by production risk

is explained by drought and flooding (Table 10,

Appendix).

The independent variables, drought, and

flooding, are not related to each other. Pearson's

Correlation Coefficient is equal to 1. It indicates the

positive relationship of Y with X1 and X2. (Table 11,

Appendix).

5 Conclusions and Recommendations

Based on our descriptive statistical analysis, it turns

out that farmers perceive the five main risks from

medium to very high (Table 5). They exhibit a very

high perception of production risk, which was the

focus of our research. For five production risk

events, their perception is very high for three

sources such as flood, disease/pest, and high/low

temperature. For the other two events (non-quality

factors and drought), the perception is the same and

is rated high (Table 6).

But, the regression analysis results present us

with a different situation. Droughts and floods are

statistically significant factors. Their impact on the

risk of vegetable production is 86%. The other three

independent variables, such as low temperature,

non-quality factors of production, and pest control,

are not statistically significant. So we conclude that

the perception of farmers does not match the results

of the regression analysis.

A 2002 study recommended that farmers

promote integrated pest management strategies

because of growing concerns about the harmful

effects of pesticides on the environment, human

health, and plant and wildlife life, [44]. Another

2022 study recommends farmers do soil and water

analysis before they invest, use chemicals to reduce

salinity (but at a high cost), increase funding to

protect plants, and buy certified seedlings, [56]. The

economy depends on the environment as it uses

natural resources for production and generates waste

in various forms. Research indicates that continuing

this trend could result in significant climate change,

the depletion of natural resources, and harm to the

ecosystem, [57].

Production risk is one of the most critical risks

for vegetable farmers. The five production risk

events show that the perception does not match the

regression analysis. Even because the risk

perception for low temperatures, substandard

production factors and defects, and pests vary from

high to very high, they are statistically insignificant.

Farmers should focus on drought and floods. These

two events are statistically significant. The negative

impact of these two events cannot be managed and

prevented by farmers. Government intervention is

necessary to reduce damage from floods and

drought. These two sources of vegetable production

risk require strategic investments.

Institutional support and transparency are

necessary to guarantee the advanced development of

agriculture. The countries of the European Union

provide this support through the Common

Agricultural Policies, [58].

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Appendix

Table 7. Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Correlations

Std. Error

Beta

Zero-order

Partial

Part

(Constant)

.574

.181

3.168

.002

Drought

.099

.034

.176

2.883

.004

.757

.178

.067

Flood

.850

.044

.873

19.258

.000

.924

.770

.449

Very Low temperatures

-.002

.051

-.003

-.040

.968

.697

-.003

-.001

Non quality factors of

production

-.031

.021

-.080

-1.437

.152

.795

-.090

-.034

Diseases/pest

-.012

.052

-.017

-.237

.813

.710

-.015

-.006

a. Dependent Variable: Production Risk

Table 8. Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Std. Error

Beta

(Constant)

.703

.121

5.811

.000

Drought

.069

.020

.123

3.408

.001

Flood

.807

.035

.830

22.977

.000

a. Dependent Variable: Production Risk

Table 9. ANOVAa

Model

Sum of Squares

Mean Square

Sig.

Regression

47.645

23.822

790.991

.000b

Residual

7.740

257

.030

Total

55.385

259

a. Dependent Variable: Production Risk

b. Predictors: (Constant), Flood, Drought

Table 10 -Model Summary

Model

R Square

Adjusted R

Square

Std. Error of the

Estimate

Durbin-Watson

.927a

.860

.859

.174

.215

a. Predictors: (Constant), Flood, Drought

b. Dependent Variable: Production Risk

Table 11. Correlations

Production Risk

Drought

Flood

Pearson Correlation

Production Risk

1.000

.757

.924

Drought

.757

1.000

.764

Flood

.924

.764

1.000

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.77

Arif Murrja, Denisa Kurtaj,

Agim Ndregjoni, Llambi Prend

E-ISSN: 2224-3496

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Volume 19, 2023

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed to the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.77

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Agim Ndregjoni, Llambi Prend

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Volume 19, 2023