Available online http://ojs.uma.ac.id/index.php/jotec
Moderating effects of agroecosystem on income generated from coffee farming management in Java Island of
Indonesia
Apri Kuntariningsih1) & Joko Mariyono2)
1) Program Studi Magister Manajemen, STIEPARI, Semarang
2) Program Studi Agribisnis, Universitas Diponegoro, Semarang
Received: December 28, 2022; Reviewed: February 21, 2023 Accepted: February 27, 2023 Abstract
In the tropical zone, the different altitudes alter the micro-climate, potentially affecting the performance of tropical crops. This study analyses the different agroclimatic zones resulting from different agroecosystems on the performance of smallholder coffee farming management. Primary data were obtained from interviews with 190 farmers running the coffee farming management in East and Central Java Provinces. The analytical model used a regression model that enables different agroecosystems to serve as moderating variables for agronomic aspects of coffee. The results show that the agroecosystem in East Java provided conducive growth of coffee. The income generated from coffee farming under the agroecosystem of East Java was higher than its counterpart. The agroecosystem of East Java enhanced land productivity, crop productivity, and the total production of coffee. Soil conditions and other micro-climate factors in the agroecosystem of East Java are influenced by active volcanoes, which are different from their counterpart surrounded by sleeping volcanoes. Extending the coffee plantation under the agroecosystem of East Java is recommended.
Keywords: economic aspects; central java; east java; smallholder
*Corresponding author:
E-mail: aprikunt@gmail.com
ISSN xxxx-xxxx (Online)
INTRODUCTION
As a tropical zone, Indonesia is one of the world's potential coffee producers and contributes significant market share.
At a national level, the farm size of coffee plantations in Indonesia in 2019 reached up to 1.2 million ha. Smallholder farmers dominate the coffee estate, with a total of up to 1.9 million farmers. Based on the farm size, 96.16% of the total acreage of coffee is held by farmers in rural areas, private companies own 2.04%, and the remaining 1.8% is under government management of state-owned enterprises (Directorate General of Plantation, 2020).
Coffee production is one of the businesses that is currently growing over the globe. Coffee-based businesses have been promising prospects, as studied Geibler et al. (2016) and Mishra (2013). It comprises long value chains that provide a good margin for every channel. The success of the coffee business starts with a good business model that pays attention to the value chain (Mishra, 2013). Ferreira et al. (2021) stated that going to a coffee shop is a lifestyle for people in urban areas. A good coffee business concerning the value chains will generate high profits. Lee and Bateman (2021) state that organic coffee is now gaining more attention in the international coffee business than conventional ones. Currently, both robusta and arabica coffee is developed organically for high profit.
The coffee plantation has roles as the source of income and job opportunities for people and is one of the sources of foreign exchange since the product is exported to some extent. The export of coffee plantations in Indonesia is the fourth largest export value, following rubber, palm oil, and cacao (Secretary General of Ministry of Trade, 2018).
In the last decade, the domestic demand for coffee has been rapidly increasing, especially as the raw material for the beverage. Coffee consumption has been seen as part of a lifestyle in people's
social existence. According to the Ministry of Agriculture (2016), coffee consumption in Indonesia in 2016 was 249,824 tons; in 2021, it is projected to increase by 48.06%, accounting for about 369,886 tons. The coffee price in Indonesia from 2014 to 2017 showed an increasing trend, with an average of 4.80% per year.
The production of coffee and its taste are compassionate with the microclimate of the agroecosystem (Rowe et al., 2022).
Different agroecosystems result in different characteristics of coffee beans, such as various specific single-origin coffee. In tropical areas of Indonesia, the climate condition, such as temperature, humidity, and sunlight, is almost uniform.
However, the altitude and geographical factors alter the microclimate, potentially influencing the coffee farm's performance.
This study has an objective to analyze the effect of different agroecosystems in two different areas on the performance of coffee farming.
RESEARCH METHODS
This study was carried out in East Java and Central Java. In East Java, the location covers the districts of Blitar, Kediri, Malang, Lumajang and Jember. The agroecosystem under these regions is highly influenced by the existence of the active volcanoes of Semeru, Bromo and Kelud. In Central Java, the site comprises the districts of Temanggung, Kendal, and Semarang. The agroecosystem under these regencies is highly affected by the existence of sleeping (inactive) volcanoes of Sundoro, Sumbing, and Ungaran.
Farmers in all areas of study grow coffee of Arabica and Robusta. The difference leads to different soil structures and fertility (Saputra et al., 2022). This study was conducted in 2019 by randomly selecting 180 farmers as representative respondents. Data were collected using the interview method.
A survey method was used in this research. The research was conducted by using a population sample and developing a questionnaire as the primary data- collecting instrument (Singarimbun and effendi, 1989). The respondents of coffee farmers were determined by a three-stage cluster random sampling method – determining district, determining sub- districts, the sample of villages and hamlets, and finally, farmers. This study's total number of respondents was 180, distributed 100 farmers in East Java and 80 farmers in Central Java.
Primary data were collected from coffee farmers, which included cost information related to farm management.
Data collection was conducted by observation, and direct interviews with coffee farmers based on the questionnaire prepared beforehand. The data analysis method was a quantitative method of multiple linear regression.
Following Soekartawi (2003), the factors influencing Robusta coffee farmers were analyzed using multiple linear regression, with the following mathematic formulation as follows:
(1)
where IC is income (IDR/year), LD is land (ha), NT is number of trees, LS is lifespan (year), PR is productions (kg/year), VI is value of inputs (IDR/year), FC is fixed cost (IDR/year), AG is agroecosystem (1= East Java, 0=otherwise), is error terms, and are
coefficient to be estimated.
The regression model specified in equation (1) indicates that the agroecosystem serves as a shifter of income. However, the agroecosystem is hypothesized to have a moderating effect on land, the number of trees, the lifespan of the crop, and production. To analyze the moderating effect of the agroecosystem,
then the model of regression is developed as follows:
where the moderating effect of agroecosystem on land, number of trees, lifespan and production is represented by
, , , and , respectively.
The formal hypothesis in this study is formulated as follows:
H0: , for i=1, 2,…, 6; j=1, 2,…,
5
H1: the H0 is untrue
Moreover, coefficient determination (R2), statistic test F test, and t-test were applied. The model of regression was run using STATA ver.13 (StataCorp, 2013).
RESULTS AND DISCUSSION
Description of coffee farming in the study areas
Coffee farmers usually harvest coffee cherries from July to August every year. The standard measure for coffee maturity is marked by a change in the colour of the coffee fruit skin to red or reddish yellow. However, some coffee farmers harvest their coffee when it is partially ripe. Several things cause this, but the main thing is the consideration of the urgent economic needs of the family.
Coffee is harvested when it is ripe. This harvest has the following advantages.
Among others, easy process to peel off; the ratio of the weight of coffee beans to the weight of fresh coffee is high; the beans are pithy so that the bean size is enormous;
fast coffee drying time; good physical quality and flavour. Harvesting unripe
coffee where the fruit is still green or yellow, harvesting coffee whose fruit is too ripe (black fruit), or harvesting the fruit in an unhealthy condition will cause the low physical quality of the beans, and the taste is unfavourable.
The coffee marketed by farmers based on post-harvest handling (shape) can be classified into two types: cherries and green beans. Marketing in the form of coffee logs if the coffee fruit is not subject to post-harvest handling, or in other words, after harvesting, it can be directly marketed to buyers, who are generally collector traders. Meanwhile, the marketing of coffee in the form of coffee beans is carried out by farmers when there is post-harvest processing. Processing coffee from coffee cherries into coffee beans is made by dry processing.
Coffee farmers mostly carry out the dry processing process, and this is because, in general, the capacity of the coffee farmers to do is small, and it is easy to do even though it only uses simple equipment. In dry processing, after the coffee fruit is harvested, it is dried immediately. Coffee fruit must be dried immediately to avoid undergoing chemical processes that can reduce product quality.
Peeling the flesh of the fruit, peeling the horn skin, and peeling the epidermis are done after the coffee becomes dry. Peeling the dry coffee fruit skin aims to separate the coffee beans from the fruit skin, horn skin and epidermis. Peeling the horn skin was conducted using a pulper, stripping the epidermis using a huller.
This condition supports the opinion of Najiyati and Danarti (2004) that the difference between dry and wet processing is the treatment using water. Wet processing uses water for peeling and washing the coffee cherries, while dry processing, after the coffee cherries are harvested immediately dried and then stripped.
The different forms of coffee marketed by farmers have different consequences for post-harvest processing costs. Farmers who market in the form of coffee beans have a higher variable cost than farmers who market in the form of coffee logs. The gap is IDR 12,444,500/ha/yr compared to IDR 10,320,00/ha/year. The high difference is due to post-harvest processing costs for coffee marketed as coffee beans. Post- harvest processing costs refer to costs for drying and milling or stripping the coffee skin. The production input value in the form of fertilizer (mainly manure) in coffee farming is absolutely lower than the cost of labour. This is because coffee farmers still need to optimize fertilization according to the recommendations of the related technical agencies.
According to the recommendation of related technical agencies, the dosage of manure used for mature coffee plants is 14,000 kg/ha/year. However, coffee farmers only use manure for an average of 587.98 kg/ha/year. This is due to the need for more manure availability in the coffee farming area and the relative lack of understanding of farmers about the benefits of organic fertilizers for the coffee production process.
The total revenue of coffee farmers who market their coffee products in the form of coffee cherries is IDR 22,174,000/year/5,09 m2, while the total revenue of coffee farmers who market their coffee in the form of coffee beans is IDR 29,462,000/year/5,39 m2. The revenue component comes from the production output multiplied by the selling price per unit weight. This is per the opinion of Suratiyah (2006), which states that gross income or income is all income obtained from farming during one period calculated from sales. The amount of production influences the revenue value of coffee farmers in Temanggung Regency, the form of the product being marketed,
and the selling price of the coffee weight unit.
The production of coffee for each farmer is different. This is partly due to the different scales of coffee farming, the coffee plants' age, and the varying care of coffee plants. Coffee prices may vary due to the different forms of coffee marketed, the quality of the coffee, and the different marketing channels chosen by farmers.
The quality of coffee also affects the marketing price of coffee. For example, Coffee beans with a moisture content above 12% are slightly lower than coffee beans with a moisture content of 12%, where the price difference is around IDR 500/kg. Farmers sell coffee to collectors and wholesalers, and if the price in the market is not much different, farmers usually choose to sell to collector traders (Listyati, 2017).
The income of coffee farmers who sell their coffee beans (green beans) is higher than that of coffee farmers who market their products in the form of coffee logs. This is in line with the results of Saragih's research (2019), which showed that farmers who carry out primary processing (processing red logs into unhulled coffee) get higher income and are significantly different from farmers who sell red logs. For this reason, efforts are needed so those coffee farmers are motivated to process their products into coffee beans to increase the added value of their farming. Hariyati (2014) states that the highest driving factor in developing processed coffee products in Sidomulyo Village, Silo District, Jember Regency is the high motivation of farmers. The income of coffee farmers who market their products in the form of coffee beans has a more excellent value than that of coffee farmers
who market their products in the form of coffee logs. The results illustrate that the role of post-harvest processing of coffee products can increase the added value of coffee farming.
Most farmers sell their produce in the form of ground coffee (bean coffee) to collector traders who have collaborated for a long time, while a small proportion of farmers sell in the form of coffee cherries.
Soekartawi (2005) states that the level of technology application in agriculture is one factor that determines the level of production and income of agricultural businesses.
Theoretically, many factors influence high and low income. These factors have a direct or indirect effect on farmers' income. Some of the factors include the farm area, the age of the plant, the amount of production, fixed production costs, variable production costs, and the shape of the marketed coffee product.
Based on Table 1, the factors of farm area, age of coffee plants, production of coffee logs, fixed production costs, variable production costs, and agroecosystem simultaneously significantly affect coffee farmers (F-sig. <
0.01). At the same time, the coefficient of determination (R2) is 0.87, which means that the variation in independent factors of 87% can explain the variations in coffee farmers' income. In contrast, the remaining was explained by other factors excluded from the regression equation model.
Table 1. Estimated coefficients of factors affecting income (equation 2)
Model Unstandardized
Coef. Standardized Coef.
t-value Sig.
Value Std. Error Beta
Constant 225,453 11,236 -- 20.065 0.000
LD - 0.059 0.009 -0.606 -6.781 0.000
NT 1.531 0.365 0.309 4.192 0.000
LS 0.100 0.013 0.947 7.437 0.000
PR 1.2E-006 0.000 0.047 0.640 0.524
VI -1.9E-005 0.000 -0.427 -2.616 0.010
FC 0.006 0.0018 0.002 1.321 0.271
AG 17,997 6,002 0.376 2.999 0.000
AG*LD 0.002 0.004 0.291 1.970 0.021
AG*NT 0.103 0.039 0.466 2.641 0.000
AG*LS 0.011 0.091 0.234 0.121 0.326
AG*PR 0.021 0.009 0.181 2.333 0.002
#Obs 180
R2 0.87
F 49.338
Source: analysis of primary data
There are two coefficients: an unstandardized coefficient and a standardized coefficient that does not have a constant because the regression line crosses the original point. Standardization (standardized beta) is applied to eliminate differences in the unit of measure applied to independent variables whose reality is not the same (heterogeneous). According to Ghozali (2007), if the size of the independent variables is not the same, then it is better if the regression equation interpretation uses the standardized beta.
Further disclosed, the advantage of using standardized beta is that it can eliminate differences in the unit of measure in the independent variable. From the results of the regression equation, the factors of production of farm area, age of coffee plants, production of coffee logs, variable production costs, and the agroecosystem partially significant effect on the income of coffee farmers in the study sites (t-sig. <
0.05). Only the fixed production cost factor has no significant effect on the income of coffee farmers in Temanggung Regency (t- sig. > 0.05).
The land area factor for coffee farming has a significant effect. It has a negative correlation with the income of coffee farmers, which means that if the land factor as a medium for coffee farming is increased, the area is greater (greater than 0.53 ha of average land area).
Assuming the other factors are considered constant, it will reduce the income value of coffee farmers and vice versa. This happens because the land area factor as a factor of production has reached its optimal point
(diminishing returns), so if the additional land area is done, it will result in a more significant increase in production costs compared to the increase in revenue. In turn, it will reduce the value of coffee farmers' income. Robusta. According to Soekartawi (1994), the law of diminishing returns can illustrate the relationship between production and one production factor with the assumption that other production factors are considered constant.
The age of coffee plants has a significant effect. It has a positive correlation to the income of coffee farmers, which means that if the age of the coffee plant is longer than the average plant age and assuming other factors hold it constant, it will increase the income value of coffee farmers. This condition implies that the longer the age of the coffee plant, the more it has a positive role in increasing production. It will also affect the increase in the income of coffee farmers. This condition reflects that the average age of coffee plants is still in a productive condition or it is yet to be time for replanting.
The production of log coffee has a significant effect. It has a positive correlation to the income of coffee farmers, which means that the production of coffee logs is greater than the average production. With the assumption that other factors are considered constant, it will increase the income value of coffee farmers. This means that increasing the production of coffee logs will increase the value of revenue more significantly than
the cost of production so that the income of coffee farmers increases significantly.
Fixed production costs have no significant effect on the income of coffee farmers, which means that regardless of the number of fixed production costs, it does not affect the income of coffee farmers. This follows the theory of production costs that the number of production costs remains independent of the amount of product produced so that the production costs do not affect the income of coffee farmers.
Variable production costs have a significant effect and have a negative correlation to the income of coffee farmers, which means that the variable production costs are more significant than the average and assume different factors. Others are considered constant; it will reduce the income value of coffee farmers. This condition occurs because the variable production costs positively correlate to the scale of farming or coffee production.
However, if the increase in variable production costs is more significant than the increase in farm income, then the variable production costs negatively correlate with the income of coffee farmers.
The agroecosystem has a significant effect and positively correlates to coffee farmers' income, meaning that coffee farming under the agroecosystem in Eas Java generated higher income than in Central Java. Furthermore, the agroecosystem significantly moderates land, tree and production. This condition means that East Java's agroecosystem is conducive to coffee farming. The agroecosystem in East Java brought about the land and tree of coffee becoming productive, and the production of coffee is higher than those in Central Java. The agroecosystem of coffee in East Java is surrounded by many active volcanoes, such as Semeru, Bromo, Kelud, and Welirang. The volcanoes regularly release materials influencing soil structure and
fertility (Saputra et al., 2022). This condition potentially affects the performance of coffee farming.
CONCLUSION
Coffee farming has been widely grown in Indonesia. This crop is one of the commodities that has been internationally tradeable. At a household level, coffee farming is an income source for smallholder estate crops in Java, Indonesia.
This study concludes that the factors that influence income are the area of farming land, age of coffee plants, production of coffee, variable production costs, and the agroecosystem partially having a significant effect on the income of coffee farmers. Notably, the agroecosystem in East Java positively moderates the agronomic aspects of coffee farming so that the productivity of land and coffee plants improves, and the production of coffee increases, ultimately enhancing farmer income. It should be recommended that coffee farming development be well planned in the areas where active volcanoes provide benefits for farming.
However, the location should be precautionarily selected because there is a potential disaster when the volcanoes erupt.
BIBLIOGRAPHY
Badan Pusat Statistik Provinsi Jawa Tengah. (2016).
Jawa Tengah Dalam Angka 2015. Badan Pusat Statistik Jawa Tengah, Semarang.
Direktorat Jenderal Perkebunan. (2016). Statistik Perkebunan Indonesia 2015-2017.
Kementerian Pertanian. Jakarta.
Direktorat Jenderal Perkebunan. (2016). Road Map Komoditi Kopi Tahun 2015 - 2045.
Kementerian Pertanian. Jakarta.
Direktorat Jenderal Perkebunan. (2017). Pedoman Teknis Pengembangan Tanaman Kopi Tahun 2018. Kementerian Pertanian. Jakarta
Ekowati, T, Sumarjono, D., Setiyawan H. dan Prasetyo, E. (2014). Buku Ajar Usahatani.
UPT UNDIP Press Semarang, Semarang.
Ferreira, J., Ferreira, C. and Bos, E. (2021). Spaces of consumption, connection, and community:
Exploring the role of the coffee shop in urban lives. Geoforum. Vol. 119, pp. 21–29.
Geibler, J.V., Cordaro. F., Kennedy, K.., Lettenmeier, M. and Roche, B. (2016). Integrating resources efficiency in business strategies: a mixed-method approach for environmental life cycle assessment in the single-serve coffee value chain. Journal of Cleaner Production, Vol. 115, pp. 62-74
Ghozali, I. (2007). Aplikasi Analisis Multivariate dengan Program SPSS. Edisi keempat. UNDIP Press, Semarang.
Hariyati, Y. (2014). Pengembangan produk olahan kopi di Desa Sidomulyo Kecamatan Silo Kabupaten Jember. J. Agriekonomika, Vol. 3 No. 1, pp. 81-91.
Lee, Y and Bateman, A., (2021). The competitiveness of fair trade and organic versus conventional coffee based on consumer panel data. Ecological Economics, Vol. 184, No 106986, pp. 1-18.
Listyati, D., Sudjarmoko, B. Hasibuan, A.M and Randriani, E. (2017). Analisis usaha tani dan rantai tata niaga kopi robusta di Bengkulu.
Jurnal Tanaman Industri dan Penyegar, Vol. 4 No. 3, pp. 145-154.
Mishra, A. (2013). Business model for Indian retail sector: The Cafe Coffee Day case in conversation with V.G. Siddharta, Chairman, Coffee Day. IIMB Management Review, Vol 25, pp. 160-170
Najiyati and Danarti. (2006). Kopi (Budidaya dan Penanganan Lepas Panen). Edisi Revisi.
Penerbit Penebar Swadaya, Jakarta.
Rowe, R.L., Prayogo, C., Oakley, S., Suprayogo, D., Mc Namara, N.P. (2022). Improved coffee management by farmers in state forest plantations in Indonesia: An experimental platform, land, 11(5), p. 671.
Saputra, D.D., Sari, R.R., Hairiah, K., Suprayogo, D., van Noordwijk, M. (2022). Recovery after volcanic ash deposition: vegetation effects on soil organic carbon, soil structure and infiltration rates. Plant and Soil,,Vol. 474 No.
1-2, pp. 163–179.
Saragih, J. R., (2019). Pendapatan dan nilai tambah pengolahan primer kopi arabika di Desa Sait Buttu Saribu, Kecamatan Pamatang Sidamanik, Kabupaten Simalungun. Journal of Regional and Rural Development Planning, Vol. 3 No. 1, pp. 1-10.
Saria, C.P.M and Fitria, J. (2018). Faktor-Faktor yang mempengaruhi pendapatan petani kopi di Kecamatan Bintang Kabupaten Aceh Tengah (Studi Kasus Desa Wakil Jalil). Jurnal Ekonomi Pertanian, Vol. 1 No. 2, pp. 57-64.
Singarimbun, M. and S. Effendi. (1989). Metode Penelitian Suvei. Penerbit LP3ES, Jakarta.
Soekartawi, (1994). Teori Ekonomi Produksi;
Dengan Pokok Bahasan analisis Fungsi Cobb- Douglas. Penerbit Raja Grafindo Persada, Jakarta.
Soekartawi, (2003). Prinsip Ekonomi Pertanian.
Penerbit Rajawali Press, Jakarta.
Soekartawi. (2005). Agribisnis Teori dan Aplikasinya. Penerbit Raja Grafindo Persada, Jakarta.
StataCorp. (2013). Stata: Release 13 Statistical Software. College Station, StataCorp.
Suratiyah, K. (2006). Ilmu Usahatani. Penerbit Penebar Swadaya, Jakarta.
Wahyu, R.E., and Suwandari, A. (2012). Faktor- faktor yang mempengaruhi pendapatan dan prospek usahatani kopi rakyat di Desa Sumberbulus Kecamatan Ledokombo Kabupaten Jember. Jurnal Sosial Ekonomi Pertanian, Vol. 6 No. 3, pp. 43-52.
