Evaluation of Kartepe Village Production Patterns and Farmer Profiles

Abstract

The aim of this study was to provide necessary agricultural extension support while carrying out agricultural activities by revealing the production patterns and farmer profiles in the villages of the Kartepe district of the Kocaeli province. The farmers registered in the Farmer Registration System (FRS) were taken into account. A total of 260 farmers participated in a two-month study, answering a questionnaire during face-to-face interviews, and the data obtained were evaluated through performing statistical analyses. The data were interpreted by applying frequency tables and the Kruskal-Wallis test. A total of 89.6% of the farmers are male, 10.4% are female, 40.8% are 56 years old and over, and 34.6% are between 45 and 55 years old. In terms of education levels, 61.9%, the highest proportion, graduated from primary school, followed by 15.8% who graduated from secondary and high schools, those who graduated from university with a Bachelor/Associate Degree, and, finally, those who graduated with a postgraduate degree as well as those who are illiterate. A total of 69.2% of the farmers are retired, and 24.7% are workers. The land of 68.8% of the respondents is their own property, and the land of 11.9% of them is common land. This land ranges as follows: 32.7% consists of 1–10 acres and 11–20 acres, and 17.3% consists of 21–30 acres. More than half of the participants (51.9%) produce 4 tons or more of their product annually. The social security coverage of the respondents is as follows: 42.7% SSK, 21.2% Bagkur, and 16.5% Pension Fund. The analyses show that the differences in terms of occupation, land size, property status, number of workers and worker status, social security, the fight against diseases and pests, and the relationships between them are important.

1. Introduction

The agricultural sector has been affected by the increase in the world’s population, making it necessary to use existing agricultural production resources efficiently in order to feed this growing population. The agricultural sector has special importance particularly in developing countries in terms of both feeding these countries’ populations and economic development. For example, the agricultural sector has a very important role in the socio-economic development of Turkey, which is a developing country that has recently experienced mass immigration. Although there has been a significant decrease in the populations living in villages in recent years, a large proportion of these populations living in villages contribute to the economy with agricultural production activities. The proportion of people living in provincial and district centers, which was 93% in 2020, increased to 93.2% in 2021. The proportion of people living in towns and villages decreased from 7% to 6.8% [1].

Due to the COVID-19 pandemic experienced in recent years, countries have started to shut avenues in, and national agriculture is gaining importance due to export restrictions on agricultural products and foodstuffs. Meeting the increasing food demand depends on the importance given to the agricultural sector.

The need to fulfil the increasing food demand in an adequate and balanced way further increases the importance of the agricultural sector [2]. In order to increase productivity for agricultural production, it is necessary to transform production models and increase higher yields per unit area. Furthermore, information sources and the flow of information to the farmer are also important.

Many studies have been carried out to determine the information sources used by producers in their agricultural production activities and have reported that they use traditional, modern, and mixed information sources [3,4,5,6,7,8,9,10,11].

This study has great importance in terms of assessing the land structure of the farmers, the size of the land, and whether they are registered in the Farmer Registration System to determine the factors that affect agricultural production and in order to develop necessary propositions. Activities are carried out in almost every field of agricultural production in the villages of the Kartepe district, which was chosen as the research population. The results of this study will guide regions and farmers with similar structures in terms of production patterns.

It is important for farmers to obtain high yields and income in order for companies (farmers) to survive. Obtaining information from individuals and organizations on agricultural issues is necessary for the continuity of their family members’ work in the enterprise, and it is important to use new agricultural methods [12].

As an example, the federal government of Nigeria [13] has introduced various agricultural development schemes with the aim of encouraging youth participation and boosting food production and farmer income through the provision of agricultural infrastructure with inputs and effective extension at the federal level.

In terms of youth interest in agriculture, the authors [14] stressed that the perception of youth in terms of working in the agricultural sector is discouraging, despite the need to increase interest among the younger generation to pursue and advance careers in the agricultural sector. This is an important agenda that needs to be solved; if it is left unaddressed, within the next few years, the country will face a lack of human resources in the agricultural sector. This outcome can be avoided by offering a high income in the sector and providing capital support and consultation, as well as a conducive environment.

Such exhaustive packages can consist of supporting young farmers, including in terms of capacity building and providing subsidies, loans, and access to land in the European Union [15,16,17]. Programs that support young farmers should stop focusing only on the promotion of agricultural entrepreneurs and should also support others, including those who farm part time in Japan.

The Family Farming model has contrasts with the monoculture agroindustry due it favoring genetic, species, and land usage diversity in the agricultural landscape [18], while the monoculture agroindustry tends towards a more homogeneous landscape [19], reducing the diversity of the resources available to different species [20] when the environmental and ecological influence on the rural landscape are taken into account. Human activities are the biggest threat to the conservation of biodiversity [21]. The Family Farming model can be applied to the rural landscape; especially in tropical environments [22], it is helping to maintain the balance between productive systems and nature conservation [23].

This paper analyses agricultural mechanization vehicles, products sold, agricultural support, pests in agricultural lands, and methods of struggling and describes the distribution of farmers in the Kartepe district. This paper highlights producer profiles and income levels after working and struggling in the field along with the economic and social opportunities for the farmers.

The aim of this study is to determine the profile of farmers fighting against plant diseases and pests in the field and to obtain and sell excess products with appropriate irrigation methods.

Study area and Data

Turkey’s annual population growth rate is 12.7‰ (12 per thousand) according to 2021 data [1], and the demand for agricultural products has increased in terms of quantity, quality, and feeding the increasing population. The population and annual population growth rate of Turkey between 2007 and 2021 are given in

Table 1. Turkey’s Population and Annual Population Growth Rate, 2007–2021.

Population, Annual Population Growth Rate, Number of Provinces, Districts, Towns, Villages, and Population Density, 2007–2021
Year	Population	Annual Growth Rate of Population (‰)	Number of Provinces	Number of Districts (1)	Number of Towns and Submunicipalities	Number of Villages (2)	Population Density
2007	70,586,256	-	81	850	2294	34,438	92
2008 (3)	71,517,100	13.1	81	892	1981	34,349	93
2009	72,561,312	14.5	81	892	1978	34,367	94
2010	73,722,988	15.9	81	892	1977	34,402	96
2011	74,724,269	13.5	81	892	1977	34,425	97
2012	75,627,384	12.0	81	892	1977	34,434	98
2013 (4)	76,667,864	13.7	81	919	394	18,214	100
2014	77,695,904	13.3	81	919	396	18,340	101
2015	78,741,053	13.4	81	919	397	18,362	102
2016	79,814,871	13.5	81	919	397	18,373	104
2017	80,810,525	12.4	81	921	396	18,380	105
2018	82,003,882	14.7	81	922	386	18,275	107
2019	83,154,997	13.9	81	922	386	18,280	108
2020	83,614,362	5.5	81	922	386	18,287	109
2021	84,680,273	12.7	81	922	387	18,288	110

Source: The results of Address Based Population Registration System (ABPRS), 2007–2021 TUİK, 2022. (https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2021-45500, accessed on 2 August 2022). Source: The results of Address Based Population Registration System (ABPRS), 2007–2021 TSI, 2022. Population and number of provinces, districts, municipalities, and villages are determined according to the administrative attachment, legal entity, and name changes recorded in the National Address Database (NAD) by the General Directorate of Civil Registration and Nationality (GDCRN) in accordance with the related regulations and administrative registers. (1) Province centers (central districts) are not included in the number of districts. (2) Villages in which there is no residing population according to ABPRS are included in the number of villages. (3) The main reason for the major differences in the number of districts, towns, and villages compared to the previous year is the administrative division changes regulated by Law No. 5747. (4) The main reason for the major differences in the number of districts, towns, and villages compared to the previous year is the administrative division changes regulated by Law No. 6360.

.

If the demographic structure of Kocaeli is examined, the population has increased by 36,183 compared to the previous year’s population of 2,033,441 according to 2021 statistics, consisting of 1,027,775 men and 1,005,666 women. This means 50.54% are men and 49.46% are women. Kocaeli has a surface area of 3623 km², and people are living in a density of 561 people per square kilometer [24]. The province welcomes immigration and has high population movements. The researched population has similar characteristics to each other in terms of demographic structure of the district and the province. The demographic structure of Kartepe in relation to the research is given in

Table 2. Population Details of Kartepe.

Affiliated Province	Kocaeli
Population	Quantity	%
Total Population	131,416	100
Total Male Population	66,828	50.85
Total Female Population	64,588	49.15
District Center Population	131,416	100
Number of Neighborhoods in the Province	32	100

Source: Kartepe population, Kocaeli 2022–2022, https://www.nufusune.com/kartepe-ilce-nufusu-kocaeli, accessed on 2 August 2022 [25].

[25].

Kartepe was chosen as the study population because it is one of the districts where agricultural activities are intensely carried out due to its location. Kartepe is a district where tourism and industry are concentrated, as well as agriculture. Fruit growing, greenhouse cultivation, poultry, sheep and cattle breeding, and landscape and ornamental plants are cultivated as agricultural activities in the region. Ten (10) villages/towns were selected as the research population (Sultaniye, Pazarçayırı, Örnekköy, Karatepe, Balaban, Nusretiye, Şirinsulhiye, Avluburun, Eşme Ahmediye and Ketenciler villages). According to Metropolitan Law No. 6360, which was adopted in 2012 and entered into force on 30 March 2014 (Union of Municipalities of Turkey, 2014; The Grand National Assembly of Turkey, 2012), the villages were transferred to the status of neighborhood.

2. Material and Method

The study was carried out in Kartepe, which is an industrial and agricultural city in the Marmara Region. The geographical location of Kartepe district is based on the borders of Sapanca Lake and Sakarya in the east; the Köseköy settlement area in the west; the Sakarya border in the south and southeast, including Kartepe Mountain in the south and southeast; Başiskele district in the southwest and south; and İzmit District in the north. The southern part of Kartepe is formed by the middle part of the Samanlı Mountains, which extend in the east-west direction and form a high mass. This section, which constitutes the water area between the Gulf of Izmit and Lake Iznik, and part of the Sakarya River, consists of plateaus at various heights. The district’s area is 269 km² [26].

The main material of this study consists of all the villages (10 villages) in the Kartepe District, and the data of the 2019 production period of 260 agricultural enterprises operating in the villages. The data were obtained through mutual interviews with the producers, using a questionnaire designed for the purpose. The questionnaire was prepared for collecting data for the research and made use of previous studies on the subject and the general and agricultural characteristics of the region. Mentioned [27,28,29] studies were mainly used in the preparation of the questionnaire used in the research. The questions were technically open- and closed-ended.

The research area was determined by taking into account the employees of Kocaeli Provincial and District Directorate of Agriculture and the available statistics. According to the agricultural structure of Kocaeli province and the number of producers registered in the Farmer Registration System (FRS), there are ten villages/towns in Kartepe district, which are thought as best reflect of the region’s production patterns and have sufficient product variety [30]. These were chosen as the research objects. On a voluntary basis, the farmers who could be reached and who agreed to provide information were interviewed. The data were collected by conducting a face-to-face survey with 260 farmers between September and October 2019, using the Simple Random Sampling Method with the farmers registered in the FRS in these 10 villages. The obtained data were evaluated by executing the Kruskal-Wallis test and the Chi-square test.

The Kruskal-Wallis test is a model that shows the non-parametric equivalent of one-way analysis of variance [31]. Tukey’s test is used for multiple non-parametric comparisons [32]. Categories were created for the data obtained as they were named and sorted. There are observed values and expected values for each category. Therefore, the values calculated using the equation below show the chi-square distribution:

$${\chi }^2={\displaystyle \sum }_{i=1}^k\frac{{\left(f-f^{\prime }\right)}^2}{f\prime }$$

Here, k is number of categories (classes), f is the observed frequency for each class, and f’ is the expected frequencies [33]. When the variables are independent, (r − 1)(c − 1) shows a Chi-square distribution with degrees of freedom. r is the number of levels of the row variable, and c is the number of levels of the additionally classified column variables [34]. One of the measures is used to determine the significance of the relationship or dependency between two categorical variables, and these are given below as Cramer’s V statistic [35,36]:

$$V=\sqrt{\frac{{\chi }^2}{N\left\{\min \left(r-1\right),\left(c-1\right)\right\}}}$$

The correlation (independence) coefficient or Cramer V effect size is reported in order to evaluate the magnitude of the relationship between the categories. Values of 0.10, 0.30, and 0.50 indicate low, medium, and advanced relationships, respectively, in the Cramer V statistic [26]. The V coefficient takes values between −1 and +1. If the coefficient is 0, there is no relationship between the variables (independent); 1 means that there is a full positive relationship between the variables, and −1 means that there is a full negative relationship between the variables (dependency). The data were analyzed in the SPPS (25.0 version) statistical package program.

3. Research Findings and Discussion

The research was carried out to determine the production pattern and farmer profiles in the villages of Kartepe district of Kocaeli province, and the findings were evaluated as a result of the statistical analysis. The demographic and socio-economic characteristics of the farmers are given in

Table 3. Demographic and socio-economic characteristics of farmers.

Gender	Value	Ratio (%)
Male	233	89.6
Female	27	10.4
Total	260	100.0
Age Groups
15–25	3	1.2
26–35	15	5.8
36–45	46	17.7
46–55	90	34.6
56+	106	40.8
Total	260	100.0
Education
Non-literate	5	1.9
Literate	4	1.5
Primary School	161	61.9
Middle School	41	15.8
High School	41	15.8
University (Associate Degree/Undergraduate)	6	2.3
Master/Doctorate	2	0.8
Total	260	100.0
Number of individuals living in the family
2–3	51	19.6
4–5	139	53.5
6–7	45	17.3
7+	25	9.6
Total	260	100.0
Job
Farmer	180	69.2
Agricultural Engineer	1	0.4
Self-Employment	13	5.0
Teacher	2	0.8
Other (Worker, Retired)	64	24.7
Total	260	100.0
Land Ownership Status
None	3	1.2
Property	179	68.8
Rent	8	3.1
Partner	19	7.3
Property-Rent	17	6.5
Property-Partner	31	11.9
Property-Rent-Partner	1	0.4
Rent-Partner	2	0.8
Total	260	100.0
Size of Agricultural Land
1–10 Acre	85	32.7
11–20 Acre	85	32.7
21–30 Acre	45	17.3
31–40 Acre	22	8.5
41+	23	8.8
Total	260	100.0

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When Table 3 is examined, 233 (89.6%) of the 260 respondents are male and 27 (10.4%) are female. Moreover, 106 (40.8%) are 56 years old and over, and 90 (34.6%) are in the 46–55 age group. As a summary, the majority of the participants are 46 years old and over, and Torun (2011) found similar results in a study which he conducted in 2011 [9].

There are 161 (61.9%) primary school graduates, the highest number of participants, followed by 41 individuals (15.8%) who are secondary and high school graduates, respectively. The number of university (Bachelor/Associate Degree), postgraduate, literate, and illiterate people are in the minority. Of the 260 participants, 180 (69.2%) were farmers, and 64 (24.7%) were retired and workers. Participation of retired and workers, especially farmers, is higher. While 68.8% (179 people) of the survey respondents own their land, 11.9% of them have common properties. It was observed that 32.7% of them had 1–10 acres and 11–20 acres of land, and 17.3% had 21–30 acres. It has been determined that more than half of the participants (51.9%) produce 4 tons of product or more annually. Moreover, 42.7% of the respondents’ social security coverage is SSK, 21.2% is Bagkur, and 16.5% is from a Pension Fund. In fact, the respondents have worked or are working in another job in addition to agricultural activities closely related to the location of the region. The fact that Kocaeli and its working areas are an industrial zone can be considered the most important reason why many of the farmers have social security.

Information sources on agricultural production were mostly obtained from the Provincial/District Directorate of Agriculture (40.8%). This is followed by 31.2% of their own knowledge. More than half of the participants (51.5%) are members of the Agricultural Credit Cooperative, 8.5% are members of other cooperatives, and 40% are not members of any structure. The organizational structure in Kartepe includes ten (10) cooperatives, one (1) agricultural chamber, and two (2) Agricultural Credit Cooperativesl and the total number of organizations is thirteen (13). The distribution of cooperatives according to their functions is: nine (9) Agricultural Development Cooperatives and one (1) Irrigation Cooperative. Although membership to a cooperative is high in quantity, it shows a decrease in real terms compared to previous years. In another study conducted by [9] in the same region in 2011, membership to a cooperative was 87%. The most important reason for this rate decreasing to 60% within 10 years is unsatisfactory agricultural incomes, the increase in input prices, and that membership to a cooperative is unnecessary

In the study, the Kruskal-Wallis test was used to reveal and make sense of the differences in the views and behaviors of the farmers in different categories.

Hypothesis:

H0. 

F₁ = F₂ = … = F_k = F⁰ versus

H1. 

At least one F_i is not equal to F⁰ [37].

Variables that were checked in this study for whether the difference is significant or not are listed below:

• The Professions of Farmers;
• Land Ownership Status;
• Agricultural Land Size;
• The amount of product obtained;
• Employee Status;
• Number of Workers employed;
• Whether there is social security or not;
• Whether or not the opinions and thoughts of some variables differ.

Kruskal-Wallis test was performed on these variables.

The results of the Kruskal-Wallis test in the evaluation of the differences between the participants’ opinions according to their profession are given in

Table 4. Kruskal-Wallis test results (according to profession).

Profession	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	2.250	0.690	ns
Who are the products sold to	5.221	0.265	ns
How many of the manufactured products are sold	20.920	0.001	***
Which cooperatives are they a member of	1.437	0.838	ns
Satisfaction with government incentives	6.651	0.156	ns
Which agricultural benefit are they are receiving support from	5.088	0.278	ns
Amount of agricultural support received	8.496	0.075	ns
Disease in farmlands	4.467	0.347	ns
Pests in agricultural lands	1.221	0.875	ns
Methods of struggling	3.456	0.485	ns
Chemical usage	0.892	0.926	ns
Irrigation methods	14.684	0.005	**

Note: ** and *** indicate statistical significance at p < 0.01, and p < 0.001, respectively. ns: non-significance. Profession: Farmer, agricultural engineer, self-employment, teacher, other (worker, retired).

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As per Table 4, the opinions and thoughts on “How much of the produced products are sold” and “Irrigation methods” differed significantly according to the professions of the participants (p < 0.001 and p < 0.01). The difference in “How many of the manufactured products are sold” was between the self-employed farmers and other (worker-retired) farming professions. The difference in irrigation methods is due to the difference between other (worker-retired) farming professions. These results show an important factor in accessing educational information resources and using information correctly.

The Kruskal-Wallis test results for the evaluation of the differences between the opinions of the participants according to the ownership status of the land are given in

Table 5. Kruskal-Wallis test results (by land ownership).

By Land Ownership	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	4.326	0.633	ns
Who are the products sold to	4.006	0.668	ns
How many of the manufactured products are sold	18.342	0.005	**
Which cooperatives are they a member of	2.159	0.829	ns
Satisfaction with government incentives	9.077	0.169	ns
Which agricultural benefits are they receiving support from	11.602	0.071	ns
Amount of agricultural support received	9.124	0.167	ns
Disease in farmlands	4.626	0.593	ns
Pests in agricultural lands	5.692	0.459	ns
Methods of struggling	4.456	0.615	ns
Chemical usage	10.317	0.112	ns
Irrigation methods	8.728	0.189	ns

Note: ** indicate statistical significance at p < 0.01. ns: non-significance. Profession: Farmer, agricultural engineer, self-employment, teacher, other (worker, retired). By land ownership: Property, rent, partner, property-rent, property-partner, property-rent-common, rent-partner.

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As in Table 5, the opinions and thoughts on “How many of the produced products are sold” differed significantly according to the land ownership of the participants (p < 0.01). This difference was between rental-lease property, rental-property joint, and property-lease property groups. The opinions on other issues did not differ significantly according to the professions of the individuals. According to these results, factors such as the ownership of the land by the farmer and the sale of the produced product are important factors in terms of acquiring farming as a profession. The Kruskal-Wallis test results for the evaluation of the differences between the opinions and thoughts of the participants in terms of the size of the agricultural land are given in

Table 6. Kruskal-Wallis test results (Change according to the size of the agricultural land).

Change According to the Size of the Agricultural Land	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	7.617	0.107	ns
Who are the products sold to	10.652	0.031	*
How many of the manufactured products are sold	31.392	0.001	***
Which cooperatives are they members of	22.159	0.001	***
Satisfaction with government incentives	0.964	0.915	ns
Which agricultural benefits are they receiving support from	4.700	0.320	ns
Amount of agricultural support received	11.965	0.018	*
Disease in farmlands	10.022	0.040	*
Pests in agricultural lands	3.098	0.542	ns
Methods of struggling	9.611	0.048	*
Chemical usage	14.467	0.006	**
Irrigation methods	5.511	0.239	ns

Note: *, **, and *** indicate statistical significance at p < 0.05, p < 0.01, and p < 0.001, respectively. Ns: non-significance. Change according to the size of the agricultural land: 1–10 acres, 11–20 acres, 21–30 acres, 31–40 acres, over 41 acres.

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As in Table 6, participant opinions on “Who are the products sold to”, “How many of the products are sold”, “Which cooperatives are they members of”, “Amount of agricultural support received”, “Disease in farmlands”, “Methods of struggling” and “Chemical usage” showed significant differences according to the size of the agricultural land of the participants (p < 0.05, p < 0.001 and p < 0.01). The results of the multiple comparison test are listed below:

According to “Who are the products sold to”:

• 1–10 acres–31–40 acres;
• 21–30 acres–31–40 acres;

According to “How many of the products are sold”:

• 1–10 acres–over 41 acres;
• 1–10 acres–21–30 acres;
• 1–10 acres–31–40 acres;
• 11–20 acres–31–40 acres;

According to “Which cooperatives are they a member of”:

• 1–10 acres–31–40 acres;
• 1–10 acres–21–30 acres;
• 1–10 acres–11–20 acres;

According to “Amount of agricultural support received”:

• 1–10 acres–over 41 acres;
• 1–10 acres–21–30 acres;

According to “Disease in farmlands”:

• 21–30 acres–31–40 acres;
• 11–20 acres–31–40 acres;

According to “Methods of struggling”:

• 21–30 acres-over 41 acres;
• 31–40 acres-over 41 acres;

According to “Chemical usage”:

• 1–10 acres–21–30 acres;
• 1–10 acres–11–20 acres.

The difference between the land size groups is important. It has been revealed that the size of the land is an important factor in the production patterns, the amount of production, the marketing and sale of the products, and the organization (membership in cooperatives).

The results of the Kruskal-Wallis test for the evaluation of the differences between the participants’ opinions in terms of the amount of product gained are given in

Table 7. Kruskal-Wallis test results (Change according to product change).

Change According to Product Change	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	2.819	0.589	ns
Who are the products sold to	7.304	0.121	ns
How many of the manufactured products are sold	78.101	0.001	***
Which cooperatives are they members of	5.605	0.231	ns
Satisfaction with government incentives	7.045	0.134	ns
Which agricultural benefits are they receiving support from	6.304	0.178	ns
Amount of agricultural support received	5.294	0.258	ns
Disease in farmlands	7.718	0.102	ns
Pests in agricultural lands	5.764	0.217	ns
Methods of struggling	3.643	0.456	ns
Chemical usage	7.476	0.113	ns
Irrigation methods	8.839	0.065	ns

Note: *** indicates statistical significance at p < 0.001. ns: non-significance. Change according to product change: 1 tons, 2 tons, 3 tons, 4 tons, 5 tons.

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As per Table 7, it has been determined that the opinions about “How many of the produced products are sold” show significant differences according to the amount of product obtained by the participants (p < 0.001).

These differences are formed between:

• 0–3 tons;
• 0–4 tons;
• Over 0–4 tons;
• Over 2–4 tons;
• Over 1–4 tons;
• Over 3–4 tons;
• Over 4–4 tons.

There was no significant difference in their views on other issues. The difference here is that according to the amount of production, the place and method of marketing and selling products also change. Excess obtained product directs the producer to the market, while a low production amount directs the producer to domestic consumption.

The results of the Kruskal-Wallis test for the evaluation of the differences between the participants’ opinions in terms of worker status are given in

Table 8. Kruskal-Wallis test results (Change in workers status).

Change in Workers Status	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	7.299	0.121	ns
Who are the products sold to	12.298	0.012	*
How many of the manufactured products are sold	55.409	0.001	***
Which cooperatives are they members of	8.956	0.062	ns
Satisfaction with government incentives	10.737	0.030	*
Which agricultural benefits are they receiving support from	4.493	0.343	ns
Amount of agricultural support received	3.773	0.438	ns
Disease in farmlands	2.356	0.663	ns
Pests in agricultural lands	3.574	0.467	ns
Methods of struggling	4.184	0.382	ns
Chemical usage	8.248	0.083	ns
Irrigation methods	9.33	0.053	ns

Note: * and *** indicate statistical significance at p < 0.05 and p <0.001, respectively. ns: non-significance. Change in workers status: no workers, permanent workers, seasonal workers, family manpower, other.

.

Differences in “who are the products sold to":

• No workers-seasonal workers;
• No workers-others;
• Family manpower-others;
• Family manpower-seasonal workers;

Difference in “How many of the manufactured products are sold”:

• No workers-seasonal workers;
• No workers-others;
• Family manpower-others;
• Family manpower-seasonal workers;

Difference in “Satisfaction with government incentives”:

• No workers-seasonal workers;
• No workers-Family Manpower.

It can be clearly seen that the employment status of workers varies depending on the size of the land, the product obtained, and the incentives received, and it can significantly affect the production pattern.

The results of the Kruskal-Wallis test for the evaluation of the differences between the participants’ opinions in terms of the number of workers employed are given in

Table 9. Kruskal-Wallis test results (Change in number of workers).

Change in Number of Workers	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	3.667	0.598	ns
Who are the products sold to	4.069	0.539	ns
How many of the manufactured products are sold	3.667	0.597	ns
Which cooperatives are they members of	4.575	0.470	ns
Satisfaction with government incentives	4.813	0.439	ns
Which agricultural benefits are they receiving support from	10.691	0.058	ns
Amount of agricultural support received	10.417	0.064	ns
Disease in farmlands	6.379	0.271	ns
Pests in agricultural lands	3.783	0.581	ns
Methods of struggling	16.572	0.005	**
Chemical usage	3.764	0.584	ns
Irrigation methods	9.556	0.089	ns

Note: ** indicates statistical significance at p < 0.01. ns: non-significance. Change in number of workers: 1, 2, 3, 4, 5, 5+.

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According to Table 9, it has been determined that the opinions on the subject of “Methods of struggling" differ significantly according to the number of workers (p < 0.001). These differences were between groups with 5–0, 5–3, more than 5–5, 5–2, more than 4–5, 4–2, 1–2, and 0–2 according to the number of workers. The result shows that there is a need for more labor in the periods when there are jobs needed, affecting the production pattern and the type of product produced and the struggle against diseases and pests. It can be said that there may be changes in the number of workers from time to time, but it is important to determine that there is a greater need for a larger labor force during busy periods. The results of the Kruskal-Wallis test for the evaluation of the differences between the participants’ opinions in terms of social security status are given in

Table 10. Kruskal-Wallis test results (Change in social security status).

Change in Social Security Status	Kruskal-Wallis	p-Value	Significance
What equipment is used in agricultural mechanization vehicles	6.735	0.241	ns
Who are the products sold to	6.136	0.293	ns
How many of the manufactured products are sold	26.242	0.001	***
Which cooperatives are they members of	4.422	0.490	ns
Satisfaction with government incentives	3.430	0.634	ns
Which agricultural benefit are they receiving support from	13.684	0.018	*
Amount of agricultural support received	14.068	0.015	*
Disease in farmlands	9.310	0.097	ns
Pests in agricultural lands	13.120	0.022	*
Methods of struggling	14.479	0.013	*
Chemical usage	6.571	0.255	ns
Irrigation methods	13.732	0.017	*

Note: * and *** indicate statistical significance at p < 0.05 and p < 0.001, respectively. ns: non-significance. Social security status: Pension fund, SSK, Bagkur, private social security, others, no social security.

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As per Table 10, the opinions of the participants on “How many of the produced products are sold”, “Which agricultural benefit are they receiving support from”, “Satisfaction with government incentives”, “Pests in agricultural lands”, “Methods of struggling”, and “Irrigation methods” showed significant differences according to the size of the land (p < 0.001 and p < 0.05).

The differences in “How many of the produced products are sold”:

• SSK-No social security;
• SSK-Pension Fund;
• SSK-Private social security;
• SSK-others;
• Bagkur-Private social security;
• Bagkur-others;

The differences in “Methods of struggling”:

• Bagkur-SSK;
• Bagkur-No social security;
• Bagkur-Private social security;

The differences in “Which agricultural benefit are they receiving support from”:

• SSK-others;
• Pension fund-others;
• Pension fund-Private social security;
• Bagkur-Pension fund;
• Pension fund-No social security;

The differences in “Satisfaction with government incentives”:

• Bagkur-Private social security;
• Pension fund-Private social security;
• Pension fund-other;
• Pension fund-No social security;

The differences in “Pests in agricultural lands and struggling methods”:

• Private social security-No social security;
• Bagkur-Private social security;
• Pension fund-Private social security;
• SSK-Private social security;

The differences in “Irrigation methods”,

• Pension fund-other;
• No social security-other;
• SSK-other;
• Bagkur-other;
• Pension fund-Bagkur.

Social security is a very important factor in the jobs and professions chosen. People want health insurance and a job where they can receive a pension in old age. According to study, being an SGK member paves the way for many jobs, and most of the participants in agricultural production work in other jobs: agricultural production is a side job and an additional income.

One study shows that the considered responses from farmers who reported that they had sheep scab in their flocks expressed a significantly higher level of agreement with the statement ‘Do scab outbreaks cause emotional stress to the affected farmer?’ (Kruskal-Wallis H statistic) [38]. The obtained results of another study demonstrated that the variables (migration status, income sources, training, number of varieties, seed sources, types of varieties) included in the technical efficiency model are important in explaining the levels and variations in agricultural production in Sri Lanka [39]. The results of the Kruskal-Wallis test indicate that the perceived level of poultry farmers and interest in the COVID-19 pandemic according to age, gender, educational status, training in poultry farming, and experience of the poultry farmers and types of poultry farming were highly significant at 1% and 5% [40]. Some farmers are willing to sacrifice some profit to engage in environmental stewardship, although that is a sign of strong intrinsic attachment to their farms [41]. In another study, farms with agritourism performed better than those that perform direct marketing and specialization not only in terms of income but also labor productivity. The labor productivity of the group of farmers using direct marketing was worse. This shows that the regional differences are coincidental [42].

Chi-square test results

The relationship between diseases and pests in agricultural land, irrigation methods, types of chemicals used against these diseases and pests, and control methods were analyzed with the chi-square test. If the relationship between the variables with the chi-square test is significant, the Cramer V coefficient was calculated for the significance measure of this relationship. The results of the Chi-square test, which determines the relationship between the variables, are in

Table 11. Chi-square test results.

Variables	Chi-Square	df	p	$\mathit{V}$
Disease in agricultural land-Irrigation methods	94.744	70	0.026	0.270
Pests in agricultural land-Irrigation methods	79.192	60	0.049	0.247
Disease in agricultural land-Usage of chemicals	270.525	154	0.001	0.308
Disease in agricultural land-Type of chemicals	284.854	132	0.001	0.316
Disease in agricultural land-Struggling Methods	170.383	84	0.001	0.330
Pests in agricultural land-Struggling Methods	75.559	72	0.131	0.234

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According to Table 11, as a result the Chi-square test analyses for the relationships between all variables, except for the relationship between “Pests in the agricultural land-struggling method”, it was found that the relationship between the variables was not independent (p < 0.05 and p < 0.001). The disease variable in agricultural land is dependent on irrigation method, and this relationship is significant (p < 0.05). The V coefficient determines its dependency as 0.270, and there is a moderate relationship. The pest variable in agricultural land is not dependent on the irrigation method. According to this dependence, V = 0.247, and the relationship is moderate. Chi-square values for disease-type of chemicals used in agricultural land and pests-type of chemicals used in agricultural land are 270,525 and 284,854, respectively. The V coefficients for both were 0.308 and 0.316, respectively, and it was seen that there was a strong relationship between them. Disease in agricultural land is dependent on the method of control. According to this, Chi-square = 170,383 and V = 0.330. Pests in agricultural land are not independent of the struggling method (Chi-square = 75.559, p > 0.05).

In a study conducted in the Marmara Region, it was reported that the majority of young farmers are engaged in farming together with another business or that their farming income is not enough to address their concerns about the economic future. It has been determined that young farmers continue their production activities with the risk of economic sustainability in agricultural production, where there is no regular income guarantee and government incentives have not yet become fully widespread. The general concern of new young peasant farmers is economic sustainability. Here, the perception of the “romantic” countryside becomes fragile, and daily economic concerns and expectation anxiety about the future are triggered in every moment and remain uncertain [43]. In this case, retired people and people from other professions, apart from farmers, were also engaged in farming.

In another case, according to Bursa irrigation unions, more than half of the farmers (57%) are in the 46–65 age group. It shows that the farmers’ lands are more intensively cultivated between the 11–20-acre (41%) and 21–30-acre (26%) land sizes [44]. The results of said study are partially similar to the results of this study in this aspect. In a study conducted on farmers in Igdır, it was determined that farmers generally want to receive more product support. In the same way, producers with large lands want new farming techniques to be brought to them and adopted [45]. As a result of the questionnaire applied to 72 people between the ages of 18 and 40 in Gaziantep and Şanlıurfa provinces, 88.9% of young farmers stated that they would continue agricultural production, and 39% of young farmers tend to migrate from rural to urban areas [28]. In this study, the number of young farmers in the specified age range is low.

4. Conclusions and Recommendations

A scientific methodology for the analysis of participant perception to determine the farmer profile was shown using a case study of Kartepe village in Kocaeli province, a metropolitan region of Turkey.

After a literature review, a number of factors were identified, and a survey was conducted to determine farmers’ perceptions towards these factors. The Kruskal-Wallis test was adopted to reveal the difference in farmers’ perceptions, and factors from various socioeconomic subgroups were used. A number of conclusions can be drawn based on the study’s results.

Based on the findings of this study, different perceptions of how much of the manufactured product is sold and irrigation methods were formed according to the occupation of the participants. Land ownership presented different views on how much of the manufactured product is sold. The size of the agricultural land has caused perception differences in factors such as to whom the product is sold, how much of the produced product is sold, which cooperatives farmers are a member of, the amount of agricultural support, plant diseases, control methods, and chemical use. It was seen that product changes created different perceptions on how much product was sold based on the results of the Kruskal-Wallis test. It has been revealed that to whom the product is sold, how much of the product is sold, and satisfaction with government incentives create statistically different perceptions according to worker status. It was observed that the change in the number of workers caused different perceptions in the methods of combating plant diseases and. Finally, factors such as how much of the products are sold, agricultural benefits, agricultural support, pests in agricultural lands, methods of struggling, and irrigation methods have created different perceptions according to their social security status.

The following recommendations should be considered based on these results.

It is necessary to provide social security to those engaged in agricultural production, to consolidate agricultural lands, to make changes in the inheritance law, and to prevent the fragmentation of lands.

In addition, the need for a sufficient workforce, the production pattern, the education level, the choice of profession or job, the ownership of land, the sense of trust, and incentives to encourage production must be addressed.

Encouraging and supporting farmers to be organized, taking into account regional diseases and pests and soil structure while determining the production pattern, and delivering the necessary information to the farmer on time will increase the quality of production and facilitate the marketing of the products. Supporting farmers, who are one of the main actors of regional development, in all aspects, such as agricultural production, making farming a viable profession, and facilitating the farmer’s access to the information they need, will increase agricultural investments and change the producer profile in the agricultural field.

It is important to give the necessary support to everyone who is engaged in farming for the continuity of agricultural production and to feed the growing population. In addition, for the continuity of agricultural activities, the villages converted into neighborhood status with the Law No. 6360 should be brought back to the village status, and the legal rights brought by the public legal entity should be returned to the villages.

That means more production will be achieved in agriculture and the necessary marketing and export will create the expected economic returns for countries who implement this situation.