Coffee Areas Research Articles

The Effect of Land Area and Labor on Production of Coffee in Indonesia

Indonesia is an agricultural country where most of the population works in the agricultural sector. Therefore, the agricultural sector contributes significantly to its economic growth. This study aims to determine the effect of land area and labor production of coffee in Indonesia. The study uses secondary data. Data analysis technique in this study is multiple linear regression with data panels in 32 provinces of high production coffee in Indonesia from 2017 until 2021. Dependent variable is coffee production and the independent variables are land and labor. Results show that land area has a positive and significant effect on coffee production. It means that raising the land area significantly increases the coffee production. However, labor has a positive but not significant effect on coffee production. This study is expected to help Indonesian policy-makers for drafting policies to increase coffee production in Indonesia. Keywords: coffee production, land area, labor, panel

Increasing Robusta Coffee Production Through a Rejuvenation Agrotechnology System in The Politeknik Negeri Jember Collection Garden

Robusta coffee is more produced because of the wider area of Arabica coffee. However, coffee productivity is not offset by plant maintennace. Pruning coffee plants is one way of rejuvenating coffee plants that coffee farmers can do to maintain their productivity a part from replanting. This study was conducted to determine the effect of agrotechnology rejuvination on robusta coffee clones BP 42, BP 234, BP 409, and BP 534 in the collection garden of Politeknik Negeri Jember which are no longer productive so with a 100% rejuvenation system for each clone is carried out. The research method was carried out by observation of coffee plants and shade plants, Foccus Group Discussion (FGD), training and assistance in the implementation of pruning. Temperature and humidity observations are carried out every 2 days in the morning, afternoon and evening with 3 repetitions. The results showed that the rejuvenation method was said to be successful, because the four clones namely BP 42, BP 234, BP 409, and BP 534 produced good shoot growth. The average good growth of shoots was found in BP 409 and BP 534 clones of 100%, followed by BP 42 and BP 234 clones of 94.4%.

The Effect of Using Fast Roast and Slow Roast Roasting Techniques on the Chemical and Organoleptic Characteristics of Robusta Coffee Beans (Coffea robusta L.)

One of the commodities with the highest economic value in the plantation sector in Indonesia is coffee. In 2016 there was a significant difference between the area of robusta and arabica coffee, with the area of robusta coffee being superior. The wide difference between robusta and arabica coffee plantations is because robusta coffee is considered a potential opportunity in developing the community's economy compared to arabica coffee with a particular market. To improve the taste or quality of coffee production, one of the factors is the roasting process. Currently, the demand for coffee production is increasing with the need for maximum quality and taste of coffee beans, so the processing of coffee plants must be very concerned and improved. Therefore this study aims to determine the roasting technique with a roasting machine and its effect on improving the quality and taste of coffee beans that are popular with consumers. This study used a Split Plot Design (SPD) using the basic pattern of a completely Randomized Design consisting of 2 levels. The first factor is the roasting time as the main plot with four levels. Then the second factor is the roasting temperature with two levels. Each factor was repeated four times so there were 32 experimental units. The results of this study indicate that in the roasting process using slow roast and fast roast techniques, roasting time affects all observation parameters, namely moisture content, pH value, brix value, flavor, aroma, and caffeine. However, the roasting temperature only affects moisture content, brix value, and caffeine.

Chemical Characterization of Coffee from Several Region of Indonesia (Cafein value, pH and Total Acid)

Coffee contains an active compound derived from the alkaloid group called caffeine. The content of active chemical compounds and various chemical properties of coffee are influenced by the area of the coffee is grown, the height of the plantation land, the type of coffee, and the processing process. In this research, a quantitative analysis of the chemical compounds and chemical properties of coffee from various regions in Indonesia was carried out with the aim of determining the caffeine content and acidity level (pH) as well as the total acid contained. This parameter is important to analyze due to it could influence the level of consumer acceptance. The method used in this research is the Completely Randomized Design (CRD) method using one research factor, the origin of the coffee area including Aceh coffee, Papua coffee, Wonosalam coffee, Sidikalang coffee and Ngawi coffee with the parameters observed being caffeine content, pH value and total titrated acid. The results showed that the caffeine content in Arabica Aceh, Excelsa Ngawi, Arabica Papua, Robusta Sidikalang and Excelsa Wonosalam meets the provisions of SNI 01-3542-2004, not more than 2% from 0.2% - 1.78%, the pH of these five coffee regions has significant differences from 5.69-6.55. These pH value has still considered safe for human consumption. Furthermore, the total acid value of these five types of coffee ranges from 0.97 to 1.36, with the total acid value of Sidikalang robusta being different from coffee from other regions.

Dynamics of Changes in Land Use in the National Coffee Development Area in Bantaeng Regency

Establishing a national area for coffee development is a step forward by the government to improve the economy of farming communities. However, five years after its enactment, it shows that things differ from the desired program objectives. There are widespread changes in land use by farmers as a result of the decreasing productivity of coffee plants. This study aims to identify changes in land use and predict changes in the future. We used the Cellular Automata (CA)–Markov model and secondary data to verify the results of this study. This study found that the change from coffee land cultivation to clove land cultivation has changed the agricultural landscape in the coffee area in the last five or seven years and is projected to continue to experience significant growth in the next three years. The trend of changing coffee land cultivation to clove cultivation is a form of farmer resilience towards a livelihood that is considered more profitable. The socio-economic factors of the farming community are a serious problem. The demands of household living needs are in a position that must be hastened without careful consideration of risks in the future. Cultivation knowledge, climate change, biophysical land conditions and socio-economic conditions are essential variables that encourage farmers' behaviour in changing the landscape of their plantations.

Inferring multiple coffee flowerings in Central America using farmer data in a probabilistic model

Coffee (Coffea arabica L.) is a climate-sensitive crop; rainfalls may trigger flowering event occurrences, and extreme rainfall during a flowering day can cause considerable yield reductions. Multiple flowering events can occur in the span of 12 months; the number varies from year to year. This paper introduces a Bayesian network model capable of inferring coffee flowering events in coffee areas in the Pacific Region of Central America based on observed data for coffee flowering and precipitation. The model structure was determined based on expert knowledge, and the model parametrization was learned from 53 years of data registered in the region. Data from four farms in the region were used for model validation. The model's performance in the inference of flowering intensity was good (spherical payoff of 0.78 out of maximal 1.00), and the model was able to depict expected behaviors for single and multiple flowerings. Further, comprehensive new details on the dynamics of multiple flowerings within a crop season were obtained, e.g., that a large flowering event tends to occur more quickly (8 to 10 days) after rain than a small flowering (10 to 13 days). We believe that this Bayesian network model has the potential to evolve and support the development of agricultural index-based insurance to deal with yield losses due to extreme rainfall during flowering. The use of longer farm records for model building may also serve to increase farmers' trust in the reliability of the tool.

Decision Support System Selection of Land Suitable for Coffee Types Using the SMART Method (Case Study of the Jember Cocoa Coffee Research Center)

Coffee is one of Indonesia's mainstays in economic matters, coffee cultivation in Indonesia will produce 774.60 thousand tons of coffee in 2021. This means that this could be a lucrative opportunity. Not only that, the existence of coffee shops also began to be popular in many areas. The majority of these coffee shops utilize local farmers' products. Decision Support Systems are part of a computer-based (including knowledge-based) information system that is used to support decision-making within an organization or company. DSS can also be regarded as a computer system that processes data into information in making decisions on specific semi-structured problems. Decision support systems aim to provide information, guide, provide predictions, and direct solution options to users of information so they can make better decisions. the purpose of the research conducted is to develop a decision support system using the Simple Multi Attribute Rating Technique (SMART) method to determine the right coffee area and according to the time of planting at Jember Cocoa Coffee Research Center. The system successful performance of the features in the system, admin and user can use a decision support system for selecting coffee grounds according to their type.

A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements

Understanding a crop’s productive potential is crucial for optimizing resource use in agriculture, encouraging sustainable practices, and effectively planning planting and preservation efforts. Achieving precise and tailored management strategies is equally important. However, this task is particularly challenging in coffee cultivation due to the absence of accurate productivity maps for this crop. In this article, we created a multicriteria model to estimate the productive potential of coffee trees based on the observation of landscape elements that determine environmental fragility (EF). The model input parameters were slope and terrain shape data, slope flow power, and orbital image data (Landsat 8), allowing us to calculate the NDVI vegetation index. We applied the model developed to coffee trees planted in Bambuí, Minas Gerais, Brazil. We used seven plots to which we had access to yield data in a recent historical series. We compared the productivity levels predicted by the EF model and the historical productivity data of the coffee areas for the years 2016, 2018, and 2020. The model showed a high correlation between the calculated potential and the annual productivity. We noticed a strong correlation (R2) in the regression analyses conducted between the predicted productive potential and the actual productivity in 2018 and 2020 (0.91 and 0.93, respectively), although the correlation was somewhat weaker in 2016 (0.85). We conclude that our model could satisfactorily estimate the yearly production potential under a zero-harvest system in the study area.

Analisis Skalogram: Studi Kasus Pengembangan Kopi Mamasa Berbasis Kesesuaian Lahan dan Sarana Fasilitas Penunjang di Kabupaten Mamasa

To optimize Mamasa coffee production, a land use model that is appropriate to its designation is needed. The actual coffee area in Mamasa Regency reached 33,589.79 ha with a total of 20,980 farmer heads of households. This means that a large number of Mamasa people depend on Mamasa coffee. Analyze the land suitability of Mamasa coffee in relation to the regional hierarchy based on the availability of coffee supporting facilities in Mamasa Regency. Data analysis used the land suitability analysis method combined with a scalogram, to see contextually the process of intensification and development of Mamasa coffee supporting facilities. The results of this study indicate that most of the land for the business of managing Arabica and Robusta coffee plants is dominantly constrained by the physical conditions of the area where around 84.5% of the land is not suitable (N) with dominant slopes with erosion hazards (eh), root media (rc) and water availability (wa) in several open locations. In addition, the availability of coffee supporting facilities in Mamasa Regency is dominant in hierarchy 3 of 88%, while hierarchy 2 is only 9% and hierarchy 1 is 3%. This means that the facilities in Mamasa Regency in order to support coffee development related to processing facilities to marketing are still minimal. The Government of Mamasa Regency needs to pay attention to the improvement of coffee supporting facilities, especially in areas that are suitable land for Mamasa coffee, so that the intensification of coffee production in the upstream can be optimized and increased marketing in the downstream.

KEANEKARAGAMAN TUMBUHAN PAKU (PTERIDOPHYTA) DI KAWASAN WISATA AIR TERJUN KALIBENDO BANYUWANGI

Kalibendo Waterfall located in the plantation area of coffee, rubber, and clove in Kampunganyar Village, Banyuwangi Regency, Indonesia. The area has a height up to 825 meters with choppy topography, and a type B climate on the Schmidt-Ferguson scale so that it is a suitable environment for ferns (Pteridophyta). In the last five years, the Kalibendo waterfall area has been developed as a tourist area so that it can potentially affect the diversity of Pteridophyta. This research aims to study the diversity, abundance of species, and the importance index of Pteridophyta in the Kalibendo waterfall. This is a quantitative descriptive research with cruise methods along 650 meters and purposive sampling technique. Results of the study found 4 Ordo, 9 Families, and 20 Pteridophyta Species scattered in the ecosystem of clove, rubber, and river stands. The abundance of Pteridophyta individuals in the Kalibendo Banyuwangi waterfall tourist area was 1029 individuals. The species with the highest abundance value was Pyrrosia piloselloides with 389 individuals, and the lowest was Woodwardia radicans with 2 individuals. The fern species that had the highest importance index (INP) is Pyrrosia piloselloides as much as 53.67%, KR 37.85%, and FR 15.83%. The highest value of H' Pteridophyta is located in the river ecosystem. The highest value of E' Pteridophyta is located in the river ecosystem which shows an even distribution of species at that location. Kata kunci: air terjun, keanekaragaman, pteridophyta, wisata

Multidecadal evaluation of changes in coffee-growing areas using Landsat data in Central Highlands, Vietnam

Vietnam is the world’s second-largest coffee exporter. Information on coffee-growing areas is thus important for production estimation. This research developed an approach for coffee mapping and change detection using Landsat and DEM data in Central Highlands, Vietnam. The data were processed for 1995, 2005, and 2020 using random forests (RF). The mapping results, verified with reference data, showed overall accuracy and Kappa coefficient higher than 86.9% and 0.74, respectively. These findings were reaffirmed by a close relationship between the satellite-based coffee area and official statistics, with the root mean squared percentage error (RMSPE) and mean absolute percentage error (MAPE) greater than 9.9% and 7.6%, respectively. From 1995 to 2020, the newly planted coffee area increased by 135,520 ha, due to the conversion of forests to coffee plantations. Such distributions of coffee areas and decadal changes in farming activities are critically useful for policymakers to formulate successful crop planning strategies.

Trends in Area, Production, and Productivity of Coffee in Chikkamagaluru District of Karnataka, India

Coffee is a significant commodity crop worldwide, and Karnataka, an Indian state with coffee-growing regions such as Chikkamagaluru, Kodagu, and Hassan, is a major producer. Chikkamagaluru, also known as the Coffee Land of Karnataka, is the primary location for Arabica coffee production and cultivation of various other spice crops such as areca nut, pepper, cardamom, vanilla, lime, clove, and cinnamon. Despite the importance of coffee production, coffee growers encounter multiple challenges in cultivating and yielding high-quality coffee. Therefore, researchers have explored issues associated with coffee production and yield and suggested feasible solutions. To make informed decisions, it is essential to analyze the productivity and production of the coffee-growing region. This study used 25 years of coffee time series data obtained from the Coffee Board of India, Bengaluru, from 1995-1996 to 2019-2020, to investigate the coffee-growing region's problems. The data was analyzed using linear (linear, cubic) and nonlinear (exponential, logistic, and Gompertz) growth models. The results showed that the cubic model provided the best fit for the Chikkamagaluru district's coffee-growing region. Meanwhile, the linear and Gompertz models were the best fit for coffee output and productivity, respectively. The study revealed a decrease in Chikkamagaluru coffee productivity over the study period, despite an increase in the coffee-growing area.

Allometric models to estimate the individual leaf area of arabica coffee: an approach from frequentist and Bayesian statistics

Allometric models to estimate the individual leaf area of arabica coffee: an approach from frequentist and Bayesian statistics

Multiple causations of hospitalizations due to mycoses in a Brazilian region: from anthropogenic to climate factors

Fungal infections are a public health problem. Mycoses are neglected, some are related to regions with low human development and under the influence of climatic conditions. The study aimed to analyze a series of hospitalizations due to mycoses (HM) that occurred in Minas Gerais, Brazil, from 1998 to 2018, as outcomes of anthropic and climate variability. We performed predictive analyses using Spearman’s rank correlation and logistic regression modeling to determine the correlation between HM and social, demographic, economic, epidemiological, and climate factors in Minas Gerais, Brazil. Data were obtained from national databases from 1998 to 2018. We found significant correlations between HM and population size, demographic density, health facilities (negative correlations), rural population, and coffee area harvested or cultivated (positive correlations), indicating that living in small urban centers is a strong predictor. We find weak but statistically significant associations between HM and meteorological conditions. Interestingly, the outbreak of hospitalizations matched La Niña events in the municipality with the highest frequency of hospitalization due to mycoses in Minas Gerais. Complex interactions among social, demographic, economic, and climatic factors drive mycoses dynamics. This work provides evidence that climatic and human activities influences hospitalizations due to mycoses in a Brazilian region.

Exploring the cooling effect of shading for climate change adaptation in coffee areas

Rising air temperatures are the main reason for the expected reduction in land suitability for coffee cultivation under climate change in Central America. One of the reasons farmers use shade trees is to create a cooler microclimate in coffee plantations located in warming areas; therefore, adjusting the shade levels could alleviate future high temperatures. Even though data on expected climatic changes are available, no studies have addressed the cooling potential of shading in coffee production systems. In this study, we use regional climate information (RCP 4.5) and a simple shade model to explore the potential of shading as an adaptation practice in the coffee areas in Central America. A model was developed to estimate the required shade levels for Coffea arabica L. based on mean air temperature. Modeled and observed shade data were compared. Results indicate that compared to 2000, an overall increment of 23 ± 18% of shading would be required to alleviate the warming conditions by 2050. The shading will be more beneficial to coffee areas at medium and high altitudes than to areas at low ones. Also, the number of coffee areas that require dense shade levels (shading > 60%) may double by 2050. This would lead to a boost in tree biomass (carbon content) but also increase the competition for the coffee plants and consequently affect coffee yields. Trade-offs between adaptation, mitigation, and productivity objectives are expected in the coffee areas in the future.

Financial Feasibility of Arabica Coffee Plantation Business Through Partnership Pattern in Solok Regency, West Sumatra

Lembah Gumanti District has the largest area of Arabica coffee in Solok Regency, with a production of 2588.8 tons in 2019. Some problems farmers face in developing Arabika coffee farming include a lack of knowledge of cultivation techniques and low selling price of coffee (cherry) beans at the farm gate. The Solok Radjo Cooperative emerged to solve farmers' problems and is willing to help develop Arabica coffee plantations in partnership. This study aims to: (1) describe the implementation of partnerships in Arabica coffee farming and (2) analyze the feasibility of arabica coffee farming with partnership pattern. A survey method was used involving 40 sample farmers selected using simple random sampling. Data analysis was carried out qualitatively to determine the implementation and benefits obtained by the partnering parties (farmers and Solok Radjo Cooperative). Quantitative analysis is intended to determine the financial feasibility of developing Arabica coffee with a partnership pattern. In the partnership system, the Solok Radjo Cooperative provides farmers assistance to use superior seeds, socialization of Arabica coffee cultivation and harvesting techniques, and willingness to buy coffee beans (cherry) from farmers with higher prices than local collectors. The benefit obtained by the Solok Radjo Cooperative is the guaranteed supply of Arabica coffee production with better quality. Arabica coffee plantation business through this partnership is feasible with a Net BC ratio of 2.43.

Development of Coffee Production in Kepahiang

The purpose of this study is to increase the income of coffee farmers in Kepahiang Regency, increase promotion and branding of coffee in Kepahiang Regency at the national and international levels, increase the knowledge of farmers and field workers about coffee cultivation technology farmers. This research includes descriptive research, which explains factual phenomena conceptually. The factual findings from the research are verified by existing theoretical concepts. The approach taken was a survey of a number of respondents who were selected based on certain criteria. The unit of analysis used in this study was coffee farmers, stakeholders, and related stake holders as well as the local government of coffee-producing districts, coffee business actors/traders in Kepahiang district. Optimizing the cooperation network, both the government and related stockholders; Optimizing priority programs for superior coffee production using special economic zones; Following up on the MOU with KPTIK and Bengkulu Provincial Government to make Bengkulu a national coffee area; Fighting for Bengkulu to get HAKi to increase regional competitiveness; Increased production of superior coffee already has sufficient value in the eyes of consumers;

Cultivation, Yield, Income, and Tree Diversity in a Coffee Field in the Eastern Ghats of Andhra Pradesh, India

In this study we analyse coffee cultivation, yield, revenue and tree diversity in coffee forest regimes in Eastern Ghats of Andhra Pradesh. The coffee growing regions in India are mainly confined to the traditional South Indian states that are Karnataka, Kerala and Tamil Nadu and partly in non-traditional regions are Andhra Pradesh and Odisha. While to a smaller extent in North-Eastern states. The production of coffee has an impact on the national and international economies and is the second-most traded commodity in the world. Total area under coffee plantations in Andhra Pradesh was about 4010 ha. The Average yield of Andhra Pradesh coffee is roughly 363.9026MT per year and the average revenue is 629.158(in lakhs) per year. Deforestation and the loss of biodiversity are two effects of the expansion of coffee farming. However, shade-grown coffee has been hailed as a way to safeguard tropical biodiversity. If promoted, shade-grown coffee can help preserve some biodiversity, but it can't match a natural forest's whole range of ecological advantages. The coffee cultivated in Andhra Pradesh is mainly shade grown coffee. In this study we also focused on tree species diversity, we analysed data on tree species diversity from 20 field plots (0.1 ha) in coffee area of, Eastern Ghats, Andhra Pradesh, India. We also analyse diversity indices evenness indices species richness indices and Density, frequency, basal area and IVI also calculated. Forest ecosystems can be protected against rising levels of disruption by increasing the diversity of tree species. The variety of tree species may also be directly impacted by anthropogenic disturbance.

Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

Monitoring the spatial variability of agricultural variables is a main step in implementing precision agriculture practices. Active optical sensors (AOS), with their instrumentation directly on agricultural machines, are suitable and make it possible to obtain high-frequency data. This study aimed to evaluate the potential of AOS to map the spatial and temporal variability of coffee crop yields, as well as to establish guidelines for the acquisition of AOS data for sensing the sides of a coffee plant, allowing the evaluation of large commercial fields. The study was conducted in a commercial coffee area of 10.24 ha, cultivated with the Catuaí 144 variety. Data collection was performed with six Crop Circle ACS 430 sensors (Holland Scientific, Lincoln, NE, USA) and two N-Sensor NG sensors (Yara International, Dülmen, Germany). Seven field expeditions were made to collect data using the optical sensors during 2019 and 2021, obtaining data during the flowering, fruit-filling and fruit maturation phases (pre-harvest), and post-harvest. The results showed that the different faces of the same plant present a different Pearson’s correlation coefficient (r) to its yield, obtained with a yield monitor on the harvester. The face with the highest exposure to solar radiation presented a slightly higher correlation to yield (−0.34 ≤ r ≤ −0.17) when compared with the face with less exposure (−0.27 ≤ r ≤ −0.15). In addition, it was observed that the vegetation indices measured at the beginning of the coffee cycle (before the rainy season that starts in October) present a positive correlation to the coffee yield of that same year (0.73 ≤ r ≤ 0.91). On the other hand, this relationship is changed after the beginning of the rain season, at which time the vegetation index increases abruptly, inverting the correlation with the yield after that (−0.93 ≤ r ≤ −0.77). Furthermore, it was observed that, due to the biennial nature of coffee production, the vegetation index acquired at a specific time has an inverted relationship when compared with the yield of that year and to the yield of the following (or previous) year.

Vernacular Names and Genetics of Cultivated Coffee (Coffea arabica) in Yemen

While Ethiopia and South Sudan are the native habitats for Coffea arabica, Yemen is considered an important domestication center for this coffee species as most Arabica coffee grown around the world can be traced back to Yemen. Furthermore, climatic conditions in Yemen are hot and extremely dry. As such, Yemeni coffee trees likely have genetic merits with respect to climate resilience. However, until recently, very little was known about the genetic landscape of Yemeni coffee. The Yemeni coffee sector identifies coffee trees according to numerous vernacular names such as Udaini, Tufahi or Dawairi. However, the geographical landscape of these names and their correlation with the genetic background of the coffee trees have never been explored. In this study, we investigated the geographic occurrence of vernacular names in 148 coffee farms across the main coffee areas of Yemen. Then, we used microsatellite markers to genotype 88 coffee trees whose vernacular name was ascertained by farmers. We find a clear geographical pattern for the use of vernacular coffee names. However, the vernacular names showed no significant association with genetics. Our results support the need for a robust description of different coffee types in Yemen based on their genetic background for the benefit of Yemeni farmers.