Determining Factors for Farmers' Decision in Crop Selection

(Case Study: Post-Harvest Tobacco Cropping Pattern and Red Chili

Cropping Pattern in Jember Regency)

MUHAMMAD FIRDAUS1, AHMAD SAUQI1, NANDA WIDANINGGAR2*, NELY SUPENI1,

FARID WAHYUDI1, SAIFUL AMIN1

1Faculty of Economics and Business,

Institute Technology and Science Mandala,

East Java 68121,

INDONESIA

2Faculty of Economics and Business,

Abdurachman Saleh University,

East Java 68312,

INDONESIA

*Corresponding Author

Abstract: - This research aims to analyze the determining factors in the selection of planting patterns for post-

harvest tobacco and red chili. The study was conducted in the Ambulu District and Wuluhan District, Jember

Regency, Indonesia, as both areas are centers for post-harvest tobacco and red chili cultivation. The research

utilized a sample size of 50 respondents from the Ambulu District and 50 respondents from the Wuluhan

District, totaling 100 respondents. The sampling technique employed was snowball sampling. Data analysis

tools included binary logit regression, R/C ratio, and independent t-test. The research findings indicate that the

determining variables influencing farmers' decisions in selecting planting patterns are land area, income, and

farming experience. Based on financial analysis, the income of post-harvest tobacco farmers is greater than that

of red chili farmers. Furthermore, there is a significant difference between the income of post-harvest tobacco

farmers and red chili farmers.

Key-Words: - Determining variables, Farming patterns, Income disparity, Agricultural development, Farmers’

decision.

5HFHLYHG-XQH5HYLVHG2FWREHU$FFHSWHG1RYHPEHU$YDLODEOHRQOLQH'HFHPEHU

1 Introduction

The focal point of national development is the

economic sector. Policy leadership will be the key

to any successful development strategy, particularly

if these efforts are to contribute to economic and

social transformation, [1]. This means that the

economic sector serves as the primary driver of

national development. The emphasis lies on the

interconnection between the economic, industrial,

and agricultural sectors. This implies that the

agricultural sector still plays a significant role in

Indonesia's economy. The agricultural added value

model shows that factors like education and skills,

economic growth, government spending, non-

farming business income, the number of people

living in rural areas, and technology have a

noticeable impact on the value generated by

agriculture, [2]. This is because agriculture remains

a major livelihood for a large portion of the

population, contributes significantly to the gross

domestic product, provides diverse food menus,

supports both upstream and downstream industries,

boosts farmers' income, and continues to foster

entrepreneurial opportunities, as well as contributes

a substantial amount of foreign exchange earnings,

[3].

The development of agriculture is aimed at

establishing resilient, advanced, and efficient

agricultural practices. The objective of agricultural

development is to increase production yield and

quality, enhance the income and living standards of

farmers, livestock breeders, and fishermen, expand

job opportunities and entrepreneurial prospects,

support industrial growth, and boost exports, [4].

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

353

Volume 21, 2024

While initially the focus of agricultural

development was on increasing rice commodity

production, now that Indonesia has achieved rice

self-sufficiency, [5], it is time to explore other

agricultural commodities. Among the range of food

commodities, the immediate goal is to achieve self-

sufficiency in horticulture and secondary crops

(palawija), as these commodities are crucial sources

of carbohydrates, particularly for achieving food

self-sufficiency, [6].

In efforts to enhance agricultural production and

farmers' income, the selection of crop types and

planting patterns throughout the year requires

careful attention and should be linked to marketing

strategies, [7]. If the goal is to implement an optimal

planting pattern that maximizes farmers' net income,

the choice of crop types and planting patterns should

be dynamically adjusted based on environmental

conditions, market demand, and prices.

The selection of crop types for each year is

highly important due to the dependence of crops on

their environment. By understanding the land's

environmental conditions, suitable crop types for

that land can be determined. Typically, several types

of crops are suitable for a piece of land. This offers

the advantage of choosing from various crop

alternatives for cultivation or engaging in

intercropping. Avoiding mismatches between crops

and their environment is crucial, as this can not only

decrease production but also lower the quality of

output, [8].

Jember Regency in East Java Province is an

agrarian area dominated by the agricultural sector.

This sector contributes 40% of the workforce

absorption. One of the reasons is its favorable

geographical conditions that support its growth and

development. This is evidenced by its significant

contribution to the Regional Gross Domestic

Product (GDP) at 26.55%, [9].

Tobacco is a distinctive, suitable, and dominant

crop in Jember Regency, [10], particularly in the

broader Besuki region. Many farmers cultivate this

crop, which yields high returns. In terms of

cultivation timing, Indonesian tobacco can be

categorized into two types: post-harvest (na oogst)

used as raw materials for the cigar industry, and

smallholder tobacco (voor oogst) which is tobacco

for raw materials of cigarettes, [11]. Post-harvest

tobacco is planted at the beginning of the dry season

and harvested at the start of the rainy season, while

smallholder is the opposite. The primary areas for

post-harvest tobacco cultivation are: Wuluhan,

Balung, Ambulu, Panti, Tempurejo, Jenggawah,

Rambipuji, Puger, Patrang, Sumbersari, Ajung, and

Mumbulsari Districts, [12].

Tobacco farming significantly contributes to the

national economy. Furthermore, it provides a larger

workforce compared to other agricultural

commodities and generates higher income for

farmers, [13]. However, the prospects for tobacco

farming are uncertain due to regulations on

"smoking bans" which are government regulations

aimed at reducing tobacco consumption, and the

uncertain prospects for tobacco farming could be

influenced by the impact of these policies on the

tobacco market and the livelihoods of farmers.

Understanding the relationship between crop

pattern selection and profitability is a crucial aspect

of agricultural management. This study aims to

provide scientific information on: (1) the

determining variables of crop pattern selection, and

(2) the income difference between post-harvest

tobacco planting patterns compared to non-post-

harvest tobacco planting patterns. As a comparative

benchmark, horticultural crops with the highest

economic value, specifically red chili peppers, were

chosen, based on the findings of, [14], which

indicated that red chili pepper cultivation had the

highest spread across 19 out of 31 districts.

Moreover, according to, [15], this large red chili

cultivation could yield higher income compared to

other horticultural crops.

2 Methods

This research was conducted in the Ambulu and

Wuluhan Districts of Jember Regency, Indonesia.

The selection of these research locations was

deliberate, taking into consideration that these two

areas have different cropping patterns. Some

farmers adopt the post-harvest tobacco planting

pattern, while others follow the red chili pepper

planting pattern. According to, [16], both Ambulu

and Wuluhan Districts are centers for post-harvest

tobacco as well as red chili pepper cultivation in

Jember Regency. Farmers plant post-harvest

tobacco and red chili peppers in the second planting

season. Therefore, in the research locations, there

are two planting patterns: rice -> post-harvest

tobacco -> corn; and rice -> red chili peppers ->

corn. Rice and corn farming efforts are assumed to

yield the same results, so only the comparison

between post-harvest tobacco and red chili peppers

is analyzed. Economically, these crops are key

income sources for local farmers and boost the

regional economy. Post-harvest tobacco is a

traditional cash crop, supporting many households.

In addition, red chili peppers have gained

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

354

Volume 21, 2024

popularity, contributing to local income and the

food industry.

Socially, these crops hold cultural significance,

shaping community identities. Tobacco farming is

deeply rooted in local traditions, while red chili

peppers are integral to local cuisine and

celebrations. Environmentally, there is a growing

focus on sustainable practices. While tobacco

farming has raised environmental concerns, red chili

pepper cultivation offers an eco-friendlier

alternative. Therefore, farmers are increasingly

adopting sustainable approaches.

This study employs the snowball sampling

technique. Snowball sampling is a sampling method

that starts with a small number of participants and

then expands, [17]. The total sample size in this

research consists of 100 respondents, with 50

respondents being post-harvest tobacco farmers and

50 respondents being red chili farmers. The data

sources include both primary and secondary data.

Primary data refers to information collected directly

from the field, [18], [19] and is obtained from

firsthand sources, such as individuals, typically

gathered through interviews or questionnaire

responses conducted by the researcher, [20]. Data

collection methods involve using primary data

obtained through observation, questionnaires, and

interviews with farmers. Secondary data sources are

official documents issued by institutions such as the

Central Bureau of Statistics (BPS) and the

Department of Agriculture for Food Crops,

Horticulture, and Plantations. The research focuses

on determining the variables that influence farmers'

decisions in choosing their agricultural

commodities. The data analysis tools employed in

this study include logistic binary regression analysis

and independent t-test.

2.1 Logistic Regression Binary Analysis

Regression analysis is a technique used to test the

presence or absence of an influence between one

variable and another variable, expressed in the form

of a regression equation, [16]. Ordinary regression

analysis cannot be used to model the relationship

between a binary response variable and multiple

predictor variables. One approach that can be used

to address this issue is logistic regression analysis.

Logistic regression is a data analysis technique

that employs mathematics to uncover the

relationship between two data factors. It then uses

this relationship to predict the value of one factor

based on the other. Predictions typically yield

limited outcomes, such as yes or no, [21].

The statistical analysis used in this research is

logistic regression analysis. Logistic regression

analysis tests whether the probability of the

dependent variable occurring can be predicted by

the independent variables. Logistic regression

analysis does not require a normal distribution in the

independent variables, [22]. Therefore, logistic

regression analysis does not necessitate classic

assumption tests, such as tests for normality,

heteroskedasticity, and classical assumptions on the

independent variables.

The binary logistic regression equation in this

study is as follows:

Y = a + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6

+ β7X7 + β8D8 + β9D9 + e

Where:

Y = Farmer's decision probability (1 = post-harvest

tobacco planting pattern, 0 = other than post-harvest

tobacco planting pattern, specifically red chili

planting pattern)

a = Constant

X1 = Land area (hectares)

X2 = Income (Rupiah)

X3 = Capital availability (Rupiah)

X4 = Farmer's age (years old)

X5 = Education (years)

X6 = Farming experience (years)

X7 = Number of dependents (people)

D8 = Land ownership (1 = owned, 0 =

sharecropping)

D9 = Government policy (1 = supportive, 0 = other)

β1 through β9 = Regression coefficients

E = Error

Logistic regression analysis includes several

tests, namely: Overall Model Fit Assessment,

Goodness of Fit Test, Nagelkerke’s R Square

(Coefficient of Determination), and Partial Test

(Wald Test), [22]. Overall model fit is used to

determine if all independent variables affect the

dependent variable. If the p-value (sig) ≤ 0.05, then

overall, the independent variables are statistically

significant in explaining the dependent variable. The

log-likelihood test is used to assess the model's

fitness. If the log-likelihood value for block number

0 is greater than the log-likelihood value for block

number 1, then the regression model is considered

good. The goodness of fit test is utilized to evaluate

the suitability or appropriateness of the logit model

used (Nagelkerke’s R Square, Hosmer, and

Lemeshow’s Test, and Classification Plot).

Meanwhile, Odds Ratio is employed to understand

the extent to which farmers' decisions are influenced

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

355

Volume 21, 2024

by factors affecting the decision. Wald test or partial

test is conducted to determine the individual effect

of variables by comparing the Wald statistic value to

the chi-square distribution. If the p-value (sig) ≤

0.05, then the independent variable significantly

affects the dependent variable.

2.2 R/C Ratio Analysis

The Return/Cost (R/C) ratio analysis in this study is

used to determine the income of the post-harvest

tobacco planting pattern and the red chili planting

pattern. The R/C ratio measures operational

efficiency, which is the comparison between

business revenue (R) and total cost (TC). Farming is

considered efficient (profitable) if the R/C value is

greater than 1, [23], [24].

Mathematically, the R/C ratio is formulated as

follows:

R/C = TR/TC

Where:

R/C = Return per Cost Ratio (total revenue / total

cost)

TR = Total Revenue

TC = Total Cost

Total revenue for a company (producer) is the

product of the unit price of the product and the

quantity of products sold, [23], [24]. Thus, total

revenue is obtained using the formula:

TR = P x Q

Where:

TR = Total Revenue

Q = Total Quantity (Number of Products)

P = Price of the Product

Cost from the perspective of a producer or

supplier encompasses all expenses incurred by the

producer to produce a product. To calculate the total

cost expended, the following mathematical formula

is used, [3], [25]:

TC = TFC + TVC

Where:

TC = Total Cost

TVC = Total Variable Cost

TFC = Total Fixed Cost

2.3 Independent t-test

The independent t-test is employed to determine the

difference in income between post-harvest tobacco

farmers and red chili farmers using the SPSS

software application. The population variances of

income from the post-harvest tobacco planting

pattern and the red chili planting pattern can be

assessed through the significance (sig) value in

Levene’s test, [22]. If the sig value is > 0.05, then

the population variances are assumed to be equal

(equal variances assumed). Conversely, if the sig

value is ≤ 0.05, then the variances are assumed to be

different (equal variances not assumed). Thus, the

research hypotheses are as follows:

H0: There is no significant difference in income

between post-harvest tobacco farmers and red chili

farmers.

H1: There is a significant difference in income

between post-harvest tobacco farmers and red chili

farmers.

3 Results and Discussion

3.1 Factors Influencing Farmers’ Decision in

Choosing Planting Patterns

Farmers’ decisions in choosing planting patterns are

influenced by various factors. The factors strongly

suspected to affect farmers’ decisions in selecting

planting patterns in this study are analyzed using a

binary logistic regression model. This analysis aims

to examine the likelihood of variables such as land

area, income, capital, farmer’s age, education,

farming experience, number of family dependents,

land ownership, and perception of government

policy affecting farmers’ decisions, coded as (1) if

farmers choose the post-harvest tobacco planting

pattern and (0) if farmers do not choose the post-

harvest tobacco planting pattern (red chili planting

pattern). The results of this analysis can be seen in

Table 1.

Based on the Omnibus Test of Model

Coefficients which is used to assess whether the

combined set of independent variables has a

statistically significant influence on farmers'

decisions regarding planting patterns, with a

significance value of 0.000 < 0.05, it can be

concluded that collectively, the examined

independent variables are capable of influencing

farmers’ decisions in selecting planting patterns and

can be included in the model. Meanwhile, from the

results of the log-likelihood test, it is observed that

the log-likelihood value for block number 0

(83.167) is higher than the log-likelihood value for

block number 1 (26.138), indicating that the logistic

regression model being used is good.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

356

Volume 21, 2024

The Nagelkerke R Square (R2) value obtained is

quite high at 0.807. This demonstrates that 80.7% of

farmers’ willingness or decisions in selecting

planting patterns can be explained by factors such as

land area, income, capital, farmer’s age, education,

farming experience, number of family dependents,

land ownership, and government policy, while the

remaining 19.3% is influenced by other factors

outside the model.

Table 1. Results of Logit Analysis on Factors Influencing Farmers’ Decisions

Variable

S.E

Wald

Sig.

Exp (B)

Land area (X1)

-22.339

9.086

6.046

.004*)

.200

Income (X2)

.000

6.491

.001*)

1.000

Capital availability (X3)

.000

.039

.743

1.000

Farmer’s age (X4)

-.126

.067

.151

.037*)

1.026

Education (X5)

-.300

.259

1.345

.246

.700

Farming experience (X6)

-.255

.096

7.047

.008*)

.750

Number of dependents (X7)

1.331

.692

3.701

.054

3.784

Land ownership (D8)

.922

2.064

.200

.655

2.514

Government policy (D9)

-1.639

2.104

.607

.436

.194

Constant

1.366

5.729

.054

.816

3.802

Omnibus Test of Model Coefficients

.000

Chi-Square Count

57.051

Chi-Square Table

18.307

Chi-Square Table

3.841

-2 Log Likelihood Block Number =0

83,167

-2 Log Likelihood Block Number =1

26,138

Hosmer and Lemeshow Test

.975

Nagelkerge R Square

.807

Overall Percentage

92.2

The accuracy of the predictive model can be

concluded based on the Overall Percentage Model

value, which is 92.2%. This leads to the conclusion

that the logistic regression model being used is good

as it surpasses the 80% threshold. In the Hosmer and

Lemeshow Test, the significance value of the model

is 0.975, where this value is greater than 0.05.

Hence, it can be stated that the used model is

capable of explaining or fitting the data.

Land area (X1) significantly influences farmers’

decisions in selecting planting patterns. This is

because, according to the farmers, a larger

cultivated land area leads to greater production

opportunities. These findings are in line with

previous research, [26], [27], [28], which state that

land area significantly affects farmers’ decisions. A

larger land area encourages farmers to engage in

farming activities, resulting in higher production

yields.

Income (X2) significantly affects farmers’

decisions in selecting planting patterns. This is due

to the respondents’ belief that the post-harvest

tobacco planting pattern provides higher economic

value or income compared to other planting

patterns. This aligns with the research findings by,

[29], indicating that post-harvest tobacco farming

can yield a net profit of 141.59 million Rupiah per

hectare. In contrast, red chili cultivation is reported

to yield the highest profits in Jember Regency, [15];

however, according to, [30], the net profit per

hectare for red chili cultivation is only 126.68

million Rupiah. Under the assumption of ceteris

paribus, meaning the same planting seasons (season

1 for rice and season 3 for corn) yield equal farming

results.

The variable of capital availability (X3) does not

significantly influence farmers' decisions in

selecting planting patterns. This is because farmers'

capital is not only in the form of cash but also

includes natural resources. [24], states that farming

capital can consist of land, agricultural

tools/materials, and credit/cash. Farmers possess

capital in the form of agricultural machinery (farm

equipment) and leftover fertilizer from previous

seasons. These findings are consistent with research

by, [31], which shows that key determinants that

affect the selection of cropping patterns include the

farmer's age, proximity of the farmland to the

farmer's home, distance from the farmland to

processing facilities, and the maturity of the crops.

Farmer’s age (X4) does not significantly affect

farmers’ decisions in selecting planting patterns.

This is because, according to the farmers, both post-

harvest tobacco and red chili planting patterns entail

similar risks. Additionally, the ability to engage in

farming activities is not solely dependent on age but

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

357

Volume 21, 2024

also the skills, diligence, and determination of the

farmers themselves. These findings align with

research by, [32], which indicates that age is not a

significant consideration for farmers in their farming

activities. Both young and elderly farmers are

equally capable of engaging in farming effectively.

Education (X5) does not significantly influence

decisions in selecting planting patterns. This is due

to the relatively uniform and limited variability in

farmers’ education levels in the research area, which

suggests minimal impact on planting pattern

choices. Respondent farmers engaged in post-

harvest tobacco farming are not heavily reliant on

high levels of education but rather on traditional

knowledge passed down through generations and

shared experiences with other farmers in their

community. These findings are consistent with

research by, [26], [33].

Farming experience (X6) significantly influences

farmers’ decisions in selecting planting patterns.

This is because, for the most part, farmers in the

research location rely solely on their experience

when making choices and cultivating their crops.

Additionally, farmers with more experience tend to

make farming decisions more quickly. This

contrasts with previous research, such as the

findings of, [32].

The number of family dependents (X7) does not

significantly affect farmers' decisions in selecting

planting patterns. This is because, according to the

farmers, seeking input from their children as family

dependents is unnecessary in their farming

activities. Farmers only discuss and decide with

their spouses when choosing farming commodities.

The presence of family dependents or the number of

family members only affects the availability of labor

within the family. This aligns with the viewpoint of,

[31], stating that farmers' decision-making has little

to no significant impact on planting pattern choices.

The dummy variable of land ownership (D8)

does not significantly affect farmers' decisions in

selecting planting patterns. This is because farmers

in the research area tend to focus on commercial

aspects, choosing crops that offer the highest

income potential, regardless of land ownership

status. These findings are in line with the results of

previous research, [29], [30], which state that

Tembakau Na Oogst farming provides higher profits

compared to horticulture, [15]. After selecting the

commodity, according to, [3], farmers tend to

maximize their production capacity by efficiently

managing their farming practices in terms of

techniques and resource utilization.

The dummy variable of government policy (D9)

does not significantly affect farmers' decisions in

selecting planting patterns. Farmers in their farming

activities do not consider the policy restrictions on

tobacco circulation. Post-harvest tobacco farmers do

not take this government policy into account.

According to the respondent farmers, whether or not

there is a policy restricting tobacco circulation, they

will still engage in tobacco farming. This is because,

besides their proficiency in growing tobacco, they

also believe that tobacco has historically been a

flagship commodity and continues to yield

satisfactory results. The land of post-harvest tobacco

farmers is of the highest class (Class A). While it

can be used for other crops, tobacco is still

perceived as the best-performing commodity.

3.2 Farmers' Probability of Choosing the

Post-Harvest Tobacco Planting Pattern

According to the logistic regression analysis results,

the magnitude of the independent variables'

influence can be determined, where the significant

determinant factors are land area, income, and

farming experience. These probabilities are

indicated by the Exp(B) values in Table 1.

The Exp(B) value for the land area variable is

found to be 0.200, indicating that for every 1-

hectare increase in land area, the likelihood of

farmers adopting the post-harvest tobacco planting

pattern increases by 20%, assuming all other factors

remain constant. This result aligns with the research

hypothesis, suggesting that as the farming area

expands, the likelihood of higher production

increases. This is presumed to impact farmers'

inclination to engage in farming. These findings

correspond with previous studies, [26], [34],

indicating that a larger farming area leads to a

greater number of crops cultivated by farmers,

potentially resulting in increased production.

The income variable provides farmers with a

likelihood of 1.000 toward their decision, indicating

that every 1 Indonesian Rupiah increase in income

raises the likelihood of farmers engaging in farming

by 100%, assuming all other factors remain

constant. This research result aligns with the

hypothesis that higher farmer income leads to a

stronger inclination to engage in farming. This

occurs because when farmers earn more, they see

farming as financially rewarding and sustainable.

With improved financial stability, they invest in

better seeds, equipment, and expansion, making

farming more attractive and profitable. Increased

income also helps them address challenges like

weather or pests, strengthening their commitment to

farming as a viable profession. This also

corresponds with the research of, [35], which states

that farmers' decisions in farming take into

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

358

Volume 21, 2024

consideration the potential for higher income from

farming activities.

The farming experience variable offers farmers a

likelihood of 0.750 toward their decision, indicating

that each additional year of farming experience

increases the likelihood of farmers engaging in

farming by 75.0%, assuming all other factors remain

constant. This result aligns with the research

hypothesis that longer farming experience enhances

farmers' willingness to engage in farming. This

research finding also corresponds with the study by,

[36], suggesting that farmers with more extensive

experience tend to make quicker and more accurate

decisions due to their enhanced farming skills and

abilities.

3.3 Difference in Income between Post-

Harvest Tobacco Farmers and Red

Chili Farmers

Financial analysis of post-harvest tobacco and red

chili farming using the R/C ratio can be observed in

Table 2. Based on Table 2, it is clear that there is a

notable income difference of 42.26% between the

income of post-harvest tobacco farmers and non-

tobacco (red chili) farmers. This difference is

attributed to the fact that post-harvest tobacco

farming tends to generate higher income compared

to red chili farming.

Table 2. Analysis of Post-Harvest Tobacco and Red

Chili Farming

Farming

Classification

Unit

Post-Harvest

Tobacco

Red Chili

Total Farming

Costs (average)

27,042,469

53,532,193

Farming

Revenues

(average)

168,633,671

180,210,264

Farming Income

(average)

141,591,202

126,678,071

R/C ratio

6.24

3.37

The research location is known for its post-

harvest tobacco cultivation and is strictly not

allowed to be directly adjacent to horticultural

crops. This is because post-harvest tobacco

cultivation requires dry land, while horticultural

crops require sufficient water availability.

Therefore, post-harvest tobacco crops must be

separated from horticultural crops, allowing for

income differences among farmers with the same

land area. The strict separation between post-harvest

tobacco cultivation, requiring dry land, and

horticultural crops, which depend on ample water

supply, is enforced due to their differing land and

water requirements, preventing direct adjacency to

maintain crop quality and yield, leading to income

disparities among farmers with similar land sizes in

the region. To determine the income disparity

between post-harvest tobacco farmers and post-

harvest farmers, an independent sample t-test is used

for analysis. The results of this analysis are

presented in Table 3.

Table 3. Results of Independent Sample T-test

Analysis of Farm Income

Farm Income

Levene’s Test

for quality

of variances

(Sig.)

t-test for

Equality

of Mean

(Sig.)

Equal Variances

Assumed

.001*

.003

Equal Variances Not

Assumed

.004*

Mean of Post-Harvest

Tobacco Farm

141,591,202

Mean of Red Chili

Farm

126,678,071

The population variances of income between

post-harvest tobacco farmers and red chili farmers

can be observed in Levene's test for quality of

variances, where the significance value is 0.001 <

0.05, indicating that the population variances of

income for post-harvest tobacco farmers and red

chili farmers differ significantly. The significant

income disparity among farmers can be seen from

the unequal variances not assumed in the t-test for

equality of means, where the significance value is

0.004 < 0.05, indicating a significant difference

between the income of post-harvest tobacco farmers

and red chili farmers. Although both post-harvest

tobacco and red chili crops are suitable for

cultivation in the location, it turns out that post-

harvest tobacco remains a high-value economic

crop. This result aligns with previous studies, [29],

[30], which state that the post-harvest tobacco

commodity tends to have high-income levels, and

only a few commodities can match such income

levels, even though post-harvest tobacco carries

high risks as well.

4 Conclusion

The results of this study indicate that the

determining variables for farmers' decisions in

choosing planting patterns are land area, income,

and farming experience. In financial analysis, the

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

359

Volume 21, 2024

income of post-harvest tobacco farmers is higher

than that of red chili farmers. In addition, there is a

significant difference in income between post-

harvest tobacco farmers and red chili farmers.

The study's findings provide valuable

recommendations for both the government and

farmers to enhance the agricultural sector. For the

government, it is crucial to design targeted policies

that support increased income opportunities for

farmers, considering factors like land area, income

levels, and farming experience. These policies

should encompass better access to agricultural

resources, education, training programs, and

improved market access. Such measures can

empower farmers to diversify their crops, adopt

modern farming techniques, and explore value-

added agricultural activities. Additionally,

bolstering rural infrastructure and facilitating market

access can further enhance their profitability.

Farmers, on the other hand, should focus on

income-enhancing strategies tailored to their

resources and expertise. This includes diversifying

crops, optimizing financial management, staying

updated on agricultural advancements, conducting

market research, collaborating with experts and

peers, and effectively managing risks. The

incorporation of environmental considerations and

sustainable practices is also essential. By combining

government support with informed decision-

making, both parties can work together to develop

more effective crop selection and planting

strategies, ultimately contributing to a more resilient

and profitable agricultural landscape.

Acknowledgement:

The authors would like to thank the Directorate of

Research, Technology, and Community Service

from the Ministry of Education, Culture, Research,

and Technology of the Republic of Indonesia, which

provided research grants, namely Higher Education

Excellence Basic Research (Penelitian Dasar

Unggulan Perguruan Tinggi/PDUPT) in 2022-2024.

References:

[1] Prasetyo, P. E. (2019). Role of

entrepreneurial culture as the driver of

economic growth. International Journal of

Economics and Financial Issues, Vol.9(3),

pp.237.

[2] Bashir, A., Susetyo, D. (2018). The

relationship between economic growth,

human capital, and agriculture sector:

Empirical evidence from Indonesia,

International Journal of Food and

Agricultural Economics (IJFAEC), Vol.6,

1128-2019–554, pp.35–52.

[3] Firdaus, M. (2017). Agribusiness

Management Manajemen Agribisnis, 3rd ed.,

Vol. 6. Yogyakarta: Bumi Aksara.

[4] Mubyarto. (1991). Introduction to

Agricultural Economics (Pengantar Ekonomi

Pertanian). Jakarta: LP3ES.

[5] Davidson, J. S. (2018). Then and Now:

Campaigns to Achieve Rice Self-Sufficiency

in Indonesia. (Bijdragen Tot de Taal-, Land-

En Volkenkunde), Journal of the Humanities

and Social Sciences of Southeast Asia,

Vol.174(2–3), pp.188–215.

[6] Sastraadmadja, E. (1989). Indonesian

Agricultural Economics (Ekonomi Pertanian

Indonesia). Bandung: Angkasa.

[7] Mackie, J. A. C. (1985). The Changing

Political Economy of an Export Crop: The

Case of Jember’s Tobacco Industry. Bulletin

of Indonesian Economic Studies, Vol.21(1),

pp.113-138.

[8] Indriani, Y. H. (1993). Crop and Land

Selection According to Environmental

Conditions and Market Demands (Pemilihan

Tanaman dan Lahan Sesuai Kondisi

Lingkungan dan Pasar). Jakarta: Penebar

Swadaya.

[9] Badan Pusat Statistik. (2023). Jember

Regency Central Statistics Bureau (Badan

Pusat Statistik Kabupaten Jember). Badan

Pusat Statistik, [Online],

https://jemberkab.bps.go.id/ (Accessed Date:

March 26, 2023)

[10] Farhan, M., Susilowati, S. H., Hariono, B.

(2021). Structural model of main problems to

developing agribusiness tobacco-based non-

smoking diversified products in Jember.

Food and Agricultural Sciences: Polije

Proceedings Series, Vol.3(1), pp.33-41.

[11] Rondhi, M., Wardhono, A., Prakoso, B.

(2010). Instututional Arrangement of

Indonesian Tobacco Farming: An Approach

to Farming, Post-Harvest Processing and

Trading. 2010 International Conference on

Institutional Economics. April 17-18, 2010,

Jinan, China.

[12] Jember Regency Horticulture and Plantation

Food Crops Service. (2021). Strategic Plan

2021-2026 (Dinas Tanaman Pangan

Hortikultura dan Perkebunan Kabupaten

Jember. Rencana Strategis 2021-2026),

[Online],

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

360

Volume 21, 2024

https://ppid.jemberkab.go.id/storage/dokume

opd/GgnyeN65nkjUpHAxwyE7eSlwM9Nyy

YnUicKuiMJm.pdf (Accessed Date: March

26, 2023)

[13] Padmo, S., & Djatmiko, E. (1991).

Socioeconomic Study of Tobacco Tembakau

Kajian Sosial Ekonomi. Yogyakarta: Aditya

Media.

[14] Firdaus, M., & Suherman. (2016). Central

Horticulture in Paddy Fields in Jember

Regency (Sentra Hortikultura Lahan Sawah

di Kabupaten Jember). National Seminar on

Research Results and Community Service.

[15] Firdaus, M., Suherman. (2019). Horticultural

Farm Business Priorities in Jember. East

African Scholars Journal of Economics,

Business and Management, Vol.2(5).

[16] Firdaus, M. (2019). Econometrics: An

Applied Approach Ekonometrika Suatu

Pendekatan Aplikatif (3rd ed.). Jakarta:

Bumi Aksara.

[17] Sugiyono. (2008). Research Methods in

Education: (Quantitative, Qualitative, and

R&D Approaches) [Metode Penelitian

Pendidikan: (Pendekatan Kuantitatif,

Kualitatif dan R & D)]. Bandung: Alfabeta.

[18] Hasan, M. I. (2002). Key Material in

Research Methodology and Its Application

(Pokok-Pokok Materi Metodologi Penelitian

dan. Aplikasinya). Jakarta: Penerbit Ghalia

Indonesia.

[19] Nazir, M. (2013). Research Methods

(Metode Penelitian). Jakarta: Bumi Aksara.

[20] Umar, H. (2013). Research Methods for

Theses and Dissertations (Metode Penelitian

Untuk Skripsi dan Tesis), 2nd ed. Jakarta:

Rajawali Pers.

[21] Trihendi, C. (2007). An In-Depth Analysis of

Regression Analysis: Effective Strategies for

Conducting Causal Relationship Analysis

(Kupas Tuntas Analisis Regresi, Strategi Jitu

Melakukan Analisis Hubungan Causal).

Yogyakarta: Andi.

[22] Ghozali, I. (2013). Application of

Multivariate Analysis with IBM SPSS 23

Program (Aplikasi Analisis Multivariate

dengan Program IBM SPSS 23). Semarang:

Badan Penerbit Universitas Diponogoro.

[23] Soekartawi. (2006). Farm Business Analysis

(Analisis Usahatani). Jakarta: UIP Press.

[24] Suratiyah, K. (2015). Agricultural Science

(Ilmu Usahatani). Jakarta: Penebar Swadaya.

[25] Firdaus, M. (2020). The Basics of Project

Evaluation (Dasar-dasar Evaluasi Proyek).

Jember: Mandala Press.

[26] Syahputra, N., Mawardati, M., Suryadi, S.

(2017). Analysis of Factors Affecting

Farmers' Choice of Planting Patterns in Cash

Crops in Paya Palas Village, Ranto

Peureulak District, East Aceh Regency

(Analisis Faktor yang Mempengaruhi Petani

Memilih Pola Tanam pada Tanaman

Perkebunan di Desa Paya Palas Kecamatan

Ranto Peureulak Kabupaten Aceh Timur).

Agrifo: Malikussaleh University

Agribusiness Journal, Vol.2(1), 41.

[27] Rahma, R. N., Rondhi, M., Suwandari, A.

(2019). Factors Affecting Planting Patterns

and Farmer Income under Various Water

Availability in the Karanglo Irrigation Area

(Faktor-Faktor yang Mempengaruhi Pola

Tanam dan Pendapatan Petani pada Berbagai

Ketersediaan Air di Daerah Irigasi

Karanglo). UNEJ E-Proceeding.

[28] Saputro, M. W. A., Lestari, E. &

Permatasari, P. (2022). Analysis of the

Influence of Farmer Decision Factors in

Choosing Tobacco Cultivation in the Selo

District, (Boyolali Regency Analisis

Pengaruh Faktor-faktor Keputusan Petani

dalam Memilih Budidaya Tembakau di

Kecamatan Selo Kabupaten Boyolali).

Seminar Nasional Dalam Rangka Dies

Natalis Ke-46 UNS Tahun 2022, Vol.6(1).

[29] Firdaus, M., Suherman, Wahyudi, F., Sauqi,

A., Widaninggar, N. (2022). Cost Profit

Volume Analysis of Big Chili Farming In

Jember Regency. International Journal of

Economics, (Business Dan Accounting

Research) (IJEBAR), Vol.6(2), pp.831-835.

[30] Firdaus, M., Widaninggar, N., Wahyudi, F.,

Sauqi, A., Suherman. (2022). Feasibility

Study of Na-Oogst Tobacco in Jember

District. Jurnal Ekonomi dan Bisnis Digital,

Vol.1(2), pp.83-88

[31] Rosalia, E., Karyani, T. (2020). Factors

Influencing Coffee Farmers in the Village of

Pulosari, Pangalengan Sub-District, in

Choosing between Monoculture and

Polyculture Farming Systems (Faktor-Faktor

yang Mempengaruhi Petani Kopi di Desa

Pulosari Kecamatan Pangalengan dalam

Memilih Sistem Pola Tanam Monokultur

Dan Polikultur). Mimbar Agribisnis: Jurnal

Pemikiran Masyarakat Ilmiah Berwawasan

Agribisnis, Vol.6(2), pp.486.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

361

Volume 21, 2024

[32] Aryanti, T., Affandi, M. I., Lestari, D. A. H.

(2018). Determining Factors in Farmers'

Decision-Making for Tomato Farming

Patterns and Income in Tanggamus Regency

(Faktor-Faktor Penentu Keputusan Petani

Dalam Pola Tanam dan Pendapatan

Usahatani Tomat di Kabupaten Tanggamus).

Diaspora: Eksakta, Vol.1(1).

[33] Permana, S. N. (2007). Internal and External

Factors Affecting Farmers in Choosing

Profitable Farming Patterns (Faktor-faktor

Internal dan Eksternal yang Mempengaruhi

Petani dalam Memilih Pola Tanam Yang

Menguntungkan). In Wawasan Tridharma

No. 2 Tahun XX September 2007.

[34] Sudjarmoko, B. (2010). Factors Affecting

Profitability and Efficiency in Smallholder

Coconut Plantations (Faktor-Faktor yang

Mempengaruhi Tingkat Keuntungan dan

Efisiensi pada Usaha Rakyat Perkebunan

Kelapa). Sosiohumaniora, Vol.12(1), pp.57.

[35] Evayanti, N., Rusmadi, Ratina, R. (2004).

Socioeconomic Factors Influencing Farmers'

Decision to Engage in Pineapple Farming in

Sungai Merdeka Village (Faktor-faktor

Sosial Ekonomi yang Memengaruhi

Keputusan Petani Mengusahakan Usahatani

Nenas di Desa Sungai Merdeka). EPP,

Vol.1(1), pp.17-21.

[36] Suhendrik, Wicaksono, I. A., Utami, D. P.

(2013). Keputusan Petani Dalam Melakukan

Soybean Farming (Glycine max L. Merrill)

in Pituruh Subdistrict, Purworejo Regency

(A Case Study of the Integrated Crop

Management Field School for Soybean in

Pituruh Subdistrict) [Usahatani Kedelai

(Glycine max L merill) di Kecamatan

Pituruh Kabupaten Purworejo (Studi Kasus

Sekolah Lapangan Pengelolaan Tanaman

Terpadu Kedelai Di Kecamatan Pituruh)].

Jurnal Surya Agritama, Vol.2(2), pp.1-12.

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

This research was funded by the Directorate of

Research, Technology, and Community Service, the

Ministry of Education, Culture, Research, and

Technology of the Republic of Indonesia through

research grants, namely Higher Education

Excellence Basic Research (Penelitian Dasar

Unggulan Perguruan Tinggi/PDUPT) in 2022-2024.

Conflict of Interest

The authors have no conflicts of interest to declare.

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

_US

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.31

Muhammad Firdaus, Ahmad Sauqi,

Nanda Widaninggar, Nely Supeni,

Farid Wahyudi, Saiful Amin

E-ISSN: 2224-2899

362

Volume 21, 2024