The more cayenne pepper does not necessarily result in greater income, because the price of cayenne pepper affects revenue. Materials for consideration for farmers in making decisions to carry out cayenne pepper farming activities in addition to the technical aspects of how farmers allocate production factors to produce high production, the amount of production produced from the harvest, and also the economic aspect, namely the costs incurred by farmers to implement the activity. This study aims to analyze the factors that influence cayenne pepper farming and to analyze the total income of cayenne pepper farmers in Lakardowo Village, Jetis District, Mojokerto Regency. The research location was chosen purposively with a method of determining the location of the research which was determined deliberately based on the consideration that Lakardowo Village is a large chili producer center in the Mojokerto Regency area. The sample used in this research method is 43 farmers. The analysis in this study uses Partial Least Square (PLS) analysis and income analysis. The results of the study The factors that affect chili farming there is 1 factor that has a significant positive effect, namely Labor. While the 3 factors that do not have a significant effect are Land Area, Capital and Management Factors on chili farming. Factors - both factors that have no effect and influence must still be considered for increasing production and income of chili farming in Lakardowo Village, Jetis District, Mojokerto Regency. The marketing system has two forms of marketing channels for chili commodities in Lakardowo Village, Jetis District, Mojokerto Regency, from the two marketing channels seen from the value of marketing margins, farmer's share and marketing efficiency, marketing chain 2 provides a higher price share to farmers.
Key findings:
This study in Lakardowo Village, Mojokerto Regency, finds that while factors like labor significantly influence cayenne pepper farming, others like land area, capital, and management have no significant effect. Marketing channels also impact farmers differently, with chain 2 offering higher price shares. These factors should be considered to boost production and income.
What is known and what is new?
This study contributes by analyzing factors influencing cayenne pepper farming and income in Lakardowo Village, Mojokerto Regency. It finds labor significantly affects farming, while land area, capital, and management do not. Additionally, it highlights marketing channel differences, with chain 2 providing higher price shares. These insights provide valuable guidance for improving chili farming strategies and income in the area.
What is the implication, and what should change now?
The implication of this study is that farmers in Lakardowo Village should focus on optimizing labor allocation to improve cayenne pepper farming income. Additionally, they should consider the marketing channel that offers higher price shares, such as chain 2, to maximize their revenue. Changes in farming practices and marketing strategies based on these findings could lead to increased profitability for chili farmers in the area.
Indonesia is an agricultural country, where the agricultural sector has a large contribution to Gross Domestic Product (GDP). The agricultural sector consists of the agricultural sub-sectors of food crops and horticulture, plantations, livestock, marine and land fisheries, and forestry. One of the horticultural commodities that have high economic value in Indonesia is big red chili and cayenne pepper. These large red chili and cayenne pepper plants grow well in the highlands and lowlands, especially at the time of flowering they are not resistant to rain, because the flowers will fall easily, and are susceptible to damage due to extreme climate change [1].
East Java is the province with the largest cayenne pepper production in Indonesia, reaching 578.88 thousand tons in 2021. This amount contributes 41.75 percent to the national cayenne pepper production. Meanwhile, Central Java was in second place, contributing 12.93 percent with production reaching 179.29 thousand tons. Meanwhile, West Java contributed 9.91% with production reaching 137.46 thousand tons.
Lakardowo Village, Jetis District, Mojokerto Regency, East Java Province is one of the villages in Mojokerto Regency which is a center for producing cayenne pepper. Almost all farmers in Lakardowo Village plant cayenne pepper every year as an agricultural commodity that is relied upon as their main income.
Towards the end of the year until the beginning of the year, chili prices soared quite high reaching more than Rp. 100,000/kg, while at certain times the price could fall below Rp. 10,000/kg. This seasonal price fluctuation occurs almost every year and unsettles the chili consumer community. The spike in chili prices was caused by reduced supply, while demand was constant and continuous every day, even increasing in certain seasons. Farid and Subekti [2], stated that chili price fluctuations occur because chili production is seasonal, rain factors, production costs, and the length of distribution channels.
Until now there has been no concrete solution from the government to control the spike in chili prices, except to monitor prices and imports of chilies from abroad. Chili in the last October 2021 period jumped very sharply. The increase in chili imports was recorded at 1.774%. Chili imports in October 2021 were recorded at 44,591,583 kilograms (kg). This number increased by 1.774% compared to October 2020 which was only 2,378,576 kg. Compared to the previous month, chili imports also rose 887.46% from 4,515,794 kg in September 2021. Meanwhile, the value of chili imports in October 2021 was recorded at US$ 5,359,072. This value increased by 15.89% compared to October 2020.
Efforts to reduce the spike in chili prices are to continue to provide an adequate supply of chili in the market by planting chilies throughout the season, including during the rainy season. Farmers are usually reluctant to plant chilies in the rainy season because the risk of crop failure is quite high.
The production of more cayenne pepper does not necessarily result in greater income, because the price of cayenne pepper affects revenue. The selling price of cayenne pepper at the farmer level is still low, causing the income received by farmers is also low. Materials for consideration for farmers in making decisions to carry out cayenne pepper farming activities in addition to the technical aspects of how farmers allocate production factors to produce high production, the amount of production produced from the harvest, and also the economic aspect, namely the costs incurred by farmers to carry out the activity. Because even though production has increased but cannot be marketed, it will be futile efforts to increase production [3].
It is known that agricultural products from farmers, especially horticultural commodities, are easily damaged or cannot last long. Therefore, planting planning and the process of delivering harvests from farmers (producers) to markets (consumers) must be considered. Apart from being a meeting place for traders and buyers for transactions, markets are important for agricultural products. The purpose of building a market is to provide a place or means for producers to deliver their products to consumers through marketing agencies, so that the market is the final consumer and or organizational consumer, namely consumers who have tangible needs and desires as a demand for the product.
This study aims to analyze the factors that influence cayenne pepper farming and the income of cayenne pepper farmers in Lakardowo Village, Jetis District, Mojokerto Regency.
The research location was chosen purposively with a method of determining the location of the research which was determined deliberately based on the consideration that Lakardowo Village, Jetis District, Mojokerto Regency is a center for producing large cayenne pepper commodities in the Mojokerto Regency area and has been cultivated for quite a long time. In addition, almost all farmers cultivate chili during the chili planting season. The population in this study were farmers and traders in Lakardowo Village, Jetis District, Mojokerto Regency, the highest production of cayenne pepper was the total population of 1085 farmers. According to Sugiyono (Sugiyono, P. D. 2009) [4] the sample is part of the number and characteristics possessed by the population. Purposive sampling method was carried out to select cayenne pepper farmers who would be used as respondents' sources. Determination of the number of samples is carried out using the Slovin formula as follows:
N
n = -------------------
1 + Ne2
Information:
n = Number of Samples
N = Total Population
e = Error Tolerance Limit (error tolerance) 15% (0.15)
So:
1085
n = ------------------- = 42.7
1 + 1085(0,15)2
Based on the above calculations, the researchers determined that the sample members used in this research method were 43 farmers.
To analyze the factors that influence the cayenne pepper farming. By making a questionnaire where the results of filling out the questionnaire will be analyzed by grouping based on variables and scoring with a Likert scale. The data analysis technique in this study used Partial Least Square (PLS). PLS is a Structural Equation Modeling (SEM) equation model with an approach based on variance or component-based structural equation modeling. According to Ghozali & Latan (Ghozali, I., & Latan, H. 2015) [5], the purpose of PLS-SEM is to develop a theory or build a theory (prediction orientation). PLS is used to explain whether there is a relationship between latent variables (prediction). PLS is a powerful analytical method because it does not assume current data with a certain scale measurement, the number of samples is small [5]. PLS-SEM analysis consists of two sub-models, namely the measurement model or the outer model and the structural model or the inner model.
The marketing channel of cayenne pepper is observed starting from chili farmers by calculating the percentage supply of cayenne pepper from farmers to the final consumer. The marketing channel will describe the marketing channel map. The analysis of the marketing channel of cayenne pepper in Lakardowo Village, Jetis District, Mojokerto Regency is carried out by identifying the marketing institutions that make up the marketing channel. Marketing institutions play a role in the process of distributing cayenne pepper from farmers to final consumers.
Marketing margin is the difference between the price received by farmers and the price paid by consumers. To analyze the price data marketing used is the price at the farmer level (producer) and the price at the consumer level, can be systematically formulated as follows:
Mpi= Pri – Pfi
Mpi = Bi + Ki
Ki = Mpi - Bi
Information:
Mpi = Red Chili Marketing Margin (Rp/Kg)
Pri = selling price of red chili at the trader level (Rp/Kg)
Pfi = Red chili selling price at farmer level (Rp/Kg)
Bi = Total costs incurred by marketing agencies (B1, B2, B3Bn)
Ki = Total profit affected by marketing agency (K1, K2, K3,…Bn)
So that:
To calculate the farmer's share or the share received by the producer, the formula according to Soekartawi (2005) [6] is used as follows:
Fs = Share received by farmers (%)
Pfi = Price received at farmer level (Rp/Kg)
Pri = Price received at the merchant level (Rp/Kg)
To calculate marketing efficiency (Ep), in general, the formula according to Soekartawi (Soekartawi. 2005) [6] can be used, namely:
Information:
EP = Marketing efficiency (%)
TBP = Total marketing costs (Rp/Kg)
TNP = Total production value, namely: Purchase price to the final consumer (Rp/Kg)
The lower the ratio of the total cost to the total value of the product, the more efficient the marketing system and the higher the ratio of the total cost to the total value of the product, the marketing system is inefficient.
Factors Affecting Chili Farming:
Outer Model:
Convergent Validity:
Convergent validity of the measurement model with reflexive indicators can be seen from the correlation between item scores/indicators and construct scores. Individual reflective measure is said to be high if it has a correlation of more than 0.70 with the construct to be measured.
Table 1: Average Variance External
Variable | AVE . value |
Land Area (X 1 ) | 0.954 |
Capital (X 2 ) | 0.898 |
Labor (X 3 ) | 0.943 |
Management Factor (X 4 ) | 0.894 |
Chili Farming (Y) | 0.856 _ |
Source: primary data analysis, 2022 |
The minimum value to declare that reliability has been achieved is 0.50. The results of the AVE test in this study were X1 to X4 valued at 0.894 to 0.954 and Y was worth 0.856, the AVE value had exceeded 0.05. So that it meets the standard of the AVE value, namely the average extract variant with a value > 0.5 is used as a determinant of convergent validity. So if < 0.5 then it is not convergently valid. Table 1:
Discriminant Validity:
Discriminant validity indicators can be seen in the cross loading between the indicators and their constructs. Variables with low loading values indicate that the indicator does not have a good effect on reflective indicators > 0.7, while the formative variables of each variable provide the largest contribution to the construct. Variables with low loading values indicate that the indicator does not have a good effect on reflective indicators > 0.7, while the formative variables of each variable provide the largest contribution to chili farming. In this study, the correlation value of the variable with the indicator is greater than the correlation value with other constructs. Thus, all constructs or latent variables already have good discriminant validity, where indicators in the performance indicator block are better than indicators in other blocks.
Composite Reliability and Cronbach's Alpha:
Table 2: Composite Reliability and Cronbach's Alpha
Variable | Cronbach's Alpha | rho_A | Composite Reliability |
Land Area (X 1 ) | 0.9 84 | 0.984 | 0.988 _ |
Capital (X 2 ) | 0.977 _ | 0.979 _ | 0.981 _ |
Labor (X 3 ) | 0.970 _ | 0.973 _ | 0.980 _ |
Management Factor (X 4 ) | 0.9 41 | 0.945 | 0.962 _ |
Chili Farming (Y) | 0.916 _ | 0.919 _ | 0.947 _ |
Source: primary data analysis, 2022 |
A construct is declared reliable if it has a composite reliability value above 0.70 and Cronbach's alpha above 0.60. From the results of the SmartPLS output above, all constructs have a composite reliability value above 0.70 and Cronbach's alpha above 0.60. So it can be concluded that the construct has good reliability.
Analysis of Variant (R2):
Information | R Square | R Square Adjusted |
Chili Farming | 0.804 _ | 0.783 _ |
Source: primary data analysis, 2022 |
From the model, the coefficient of determination (R2) is 0.804. This shows that 80.4% of the variations in chili farming variables can be explained by the variables of land area, capital, labor and management factors, while the remaining. 6% is explained by other variables not included in the model.
F-Square Uji Test:
The F-Square test is to find out the goodness of the model. The results of the F test can be seen in the following table. Table 4:
Table 4: F-Square . Test Results
Information | F – Square |
Land Area (X 1 ) | 0.118 _ |
Capital (X 2 ) | 0.051 _ |
Labor (X 3 ) | 0.893 _ |
Management Factor (X 4 ) | 0.0 77 |
Source: primary data analysis, 2022 |
Based on the results of the calculations for the variables of capital, land area and management factors have a value of 0.051; 0.118 and 0.077 this value is greater than 0.02 then it is declared weak. While the value of the labor variable is 0.893, which is greater than 0.35, then the labor variable is stated to be large at the structural level.
Measurement Model:
Measurement Model is part of SmartPls 3 which specifies indicators (observed variables) for each construct variable, and calculates the reliability value for the construct, or a model that explains the operationalization of research variables into measurable indicators expressed in the form of path diagrams and or certain mathematical equations [7].
Figure 1: Results of the Structural Model of Factors Affecting Chili Farming
To assess the significance of the prediction model in testing the structural model, it can be seen from the path coefficient and p values between the independent variables to the dependent variable in the Path Coefficient table at the SmartPLS 3 output below:Table 5
Table 5: Path Coefficient and P Values
Variable | Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T Statistics (IO/STDEVI) | P Values |
X 1 → Y | 0.342 _ | 0.325 _ | 0.195 _ | 1.752 | 0.080 _ |
X 2 → Y | -0. 180 | -0. 153 | 0.1 48 | 1.212 | 0.226 _ |
X 3 → Y | 0.631 _ | 0.622 _ | 0.122 _ | 5.188 | 0.000 |
X 4 → Y | 0.176 _ | 0.1 78 | 0.091 _ | 1.936 | 0.053 _ |
Source: primary data analysis, 2022 |
Marketing System Analysis:
Marketing Agency:
Marketing Function:
Marketing Channel:
Marketing Margin:
Marketing Channel I | |||
No. | Description | Unit | Score |
1 | Farmer |
|
|
| Total cost | Rp/Kg | 22.893 |
| Selling price | Rp/Kg | 35,000 |
2 | Collecting Merchant |
|
|
| Total cost | Rp/Kg | 3,643 |
| Selling price | Rp/Kg | 40,000 |
| Marketing Margin | Rp/Kg | 5,000 |
3 | Agent or Distributor |
|
|
| Total cost | Rp/Kg | 1,000 |
| Selling price | Rp/Kg | 42,000 |
| Marketing Margin | Rp/Kg | 2,000 |
4 | Retailer |
|
|
| Total cost | Rp/Kg | 1,230 |
| Selling price | Rp/Kg | 45,000 |
| Marketing Margin | Rp/Kg | 3,000 |
| Total Margin |
| 10,000 |
Marketing Channel II | |||
1 | Farmer |
|
|
| Total cost | Rp/Kg | 22.893 |
| Selling price | Rp/Kg | 35,000 |
2 | Collecting Merchant |
|
|
| Total cost | Rp/Kg | 1,215 |
| Selling price | Rp/Kg | 38.000 |
| Marketing Margin | Rp/Kg | 3,000 |
3 | Retailer |
|
|
| Total cost | Rp/Kg | 1,230 |
| Selling price | Rp/Kg | 40,000 |
| Marketing Margin | Rp/Kg | 2,000 |
| Total Margin |
| 5,000 |
Data analyzed, 2022 |
Farmer's Share:
Marketing channel | Farmer Level Price (Rp/Kg) | Prices at Retailers (Rp/Kg) | Farmer's Share (%) |
I | 35,000 | 45,000 | 77.78 |
II | 35,000 | 40,000 | 87.5 |
Source: Data analyzed, 2022 |
Marketing Efficiency:
marketing chain 1: farmers - village collectors - agents or distributors - retailers - consumers;
marketing chain 2: farmers – village collectors – retailers – consumers.
The authors declare that they have no conflict of interest.
No funding sources
The study was approved by the Institutional Ethics Committee of UPN Veterans East Java
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