Home Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
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Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia

  • Abdul Muis Hasibuan EMAIL logo , Enny Randriani , Dani Dani , Tri Joko Santoso , Apri Laila Sayekti , Nur Kholilatul Izzah , Budi Martono , Meynarti Sari Dewi Ibrahim , Cici Tresniawati , Rita Harni , Syafaruddin Syafaruddin and Agus Wahyudi
Published/Copyright: April 25, 2023
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Open Agriculture
From the journal Open Agriculture Volume 8 Issue 1

Abstract

Small-scale coffee farming in Indonesia faces low productivity due to poor farming practices and low-quality planting materials. It highlights the need for improved coffee varieties that meet farmers’ preferences. Given the vulnerability of coffee farming to climate change, participatory breeding programs that involve collaboration between researchers and farmers to develop a climate-adapted variety are essential. This study used survey data from Gayo Highland, Aceh, Indonesia, to investigate farmers’ perception of the importance of climate-related attributes in a coffee variety, including those developed through a participatory breeding program, and the determinant factors. The result shows that farmers rated climate-related attributes as the least important (average score 0.36) compared to others, such as coffee productivity (1.57) and quality (1.22), resistance to pests and diseases (0.96), and input-use efficiency (0.57). This finding suggests a lack of awareness among farmers about the importance of climate issues in coffee farming. The estimation results also indicate that coffee extension activities have a negative association with farmers’ perceptions of the importance of climate attributes. This study recommends inclusive and targeted climate campaigns to increase farmers’ concern, awareness, and knowledge about the threats of climate change to coffee farming through strengthening advisory services.

Keywords: climate change; farmers’ preferences; local variety; climate training; extension services

1 Introduction

The development and adoption of superior plant varieties have been a focus of research worldwide in order to improve plant productivity, quality, and adaptability to various biotic and abiotic stresses such as pests, diseases, and climate shocks. However, the adoption of these superior varieties can be slow in some cases due to their incompatibility with the farming practices of local farmers [1]. Furthermore, farmers may not perceive the available superior varieties as meeting their preferences, leading to low adoption rates [2,3]. Involving farmers in the plant breeding process through collaboration with researchers can lead to the development of new superior varieties that are more readily accepted by farmers [4,5]. The knowledge and experience of farmers over a long period of time can give them confidence in the advantages of these varieties, encouraging widespread adoption.

Developing superior local varieties is particularly important for farmers cultivating perennial crops such as coffee. These crops have longer life cycles and face significant risks associated with initial investment costs and payback periods [6,7,8]. Perennial crops are also vulnerable to changing climatic conditions that can, directly and indirectly, impact productivity and yield quality [9,10,11,12]. Rising temperatures and extreme weather events, including climate variability, can directly affect crop production and quality [11,13]. Indirect effects include increased pest, disease, and weed attacks [14,15,16], reductions in pollination services [17], and declines in soil fertility and nutrition [18,19,20]. Local variety, which often performs better in marginal lands and variable climates [21,22], can be used to manage these risks [21,22].

One way to mitigate these risks is by identifying and developing climate-adapted attributes in a local variety. These attributes can include traits such as drought and heat tolerance, pest and disease resistance, and the ability to thrive in various agroecosystems [23]. By involving farmers’ participation in breeding and selecting for these attributes, farmers can increase the resilience of their crops and potentially improve their yields and profits [5,24]. Additionally, a climate-adapted local variety typically needs lower inputs such as pesticides and fertilizers [25], which can help reduce environmental impacts and improve the sustainability of the farming system. Therefore, the development and adoption of climate-adapted local variety can be a critical strategy for enhancing the resilience and profitability of perennial crop farming in the face of a changing climate.

In the context of coffee production, a climate-adapted local variety can also be used to increase productivity and quality, which are common issues for Indonesian coffee agribusiness, particularly for smallholder farmers [26,27]. The Directorate General of Plantation, Indonesian Ministry of Agriculture, noted that Indonesia’s coffee productivity in 2020 was only around 800 kg/ha, far below its potential of 2–3 tons/ha [28]. The low productivity and quality of Indonesian coffee have been attributed to various factors, including the use of low-yielding variety and inadequate maintenance and management practices [29]. For example, the national agricultural survey by Central Statistics Agency (BPS) in 2014 shows that about 96.7% of coffee farmers did not use certified coffee planting materials [30]. Utilizing local varieties with higher productivity and quality characteristics can help address these issues and improve the sustainability of coffee production in Indonesia.

Nanggroe Aceh Darussalam Province (Aceh) is one of the leading coffee producers in Indonesia. In 2019, this province had a 125,000 ha coffee plantation, which places this province as the fourth largest coffee producer in Indonesia after South Sumatera, Lampung, and North Sumatera Provinces (Figure 1). However, for Arabica coffee, Aceh is the largest producer with a total production of 65.8 thousand tons, which is mainly produced in Gayo Highland which covers Aceh Tengah and Bener Meriah Regencies [28]. This province is also known as a specialty coffee producer, which has received several international acknowledgments and certifications. As a result, Aceh’s coffee has become an exclusive supply base for many coffee firms with high competition [31].

Figure 1 Study sites.
Figure 1

Study sites.

In line with the performance of coffee plantations in Indonesia, coffee farming in Aceh is also characterized by poor farm management, which causes low productivity and is exacerbated by climate change issues [32,33]. A study by Mahyuda et al. [34] shows that the use of recommended varieties in Gayo Highland is moderate with low fertilizer applications, which might contribute to low productivity. Farmers mostly plant the recommended varieties, i.e., Gayo 1 and Gayo 2 [34], released through a participatory breeding program a decade ago [35]. However, the potential yield of the two varieties is relatively low, at approximately 1.1 tons/ha [35]. On the other hand, there is an unreleased local variety, Ateng Super, with a potential yield of 1.7 tons/ha. Because of the high yield and many advantages for many attributes, this “local variety” has started to interest farmers to adopt in the last few years. Based on the field observations and assessments by the research team from Indonesian Industrial and Beverage Crops Research Institute, the “Ateng Super” local variety has good adaptability to the environment in the Gayo Highland area, and the yield can achieve 2.7 tons/ha. Also, it shows better performances compared to Gayo 1 and Gayo 2 varieties, so it was proposed to be released as a superior local variety of Gayo 3 and has been approved by the Indonesian authority [36].

This study aims to assess the farmers’ perception of the importance of climate-related attributes of local coffee varieties resulting from a participatory breeding program in Gayo Highland, Aceh, Indonesia. Using data from a survey of a sample of smallholder coffee farmers in the program, this study also attempted to understand the determinant factors associated with the farmers’ perceptions. By understanding the perceived performance of local varieties in this context, this study can provide insights into developing and promoting superior local varieties that are more readily accepted by farmers. In addition, it can be used to design or promote the relevant climate-related programs or strategies for Arabica coffee development, especially in the study sites.

2 Materials and methods

2.1 Study sites and data collection

This study is a part of a research project entitled “The acceleration of the releasing local Arabica coffee variety through the participatory breeding program.” The project was conducted in Gayo Highland, i.e., Bener Meriah and Aceh Tengah Regency, Nanggroe Aceh Darussalam Province, Indonesia (Figure 1). The project covered three altitude categories, i.e., 900, 1,300, and 1,500 meter above sea level (m asl), in order to capture the variation of coffee growth and yield performance within the different altitudes. Following the project area, this study uses data from a survey of 37 coffee farmer’s households participating in the research project. The selected respondents covered three altitudes, two regencies, and five subdistricts (Table 1).

Table 1

Selected respondents

No. Altitude (m asl) Regency Subdistrict No. of respondents (Household)
1 900 Bener Meriah Gajah Putih and Pintu Rime Gayo 10
2 1,300 Aceh Tengah Bebesan 9
Kebayakan 9
3 1,500 Bener Meriah Permata 9

Data collection was conducted from September to October 2021. The research team interviewed the household head using a structured questionnaire to capture information on household sociodemographic characteristics, coffee farming practices (the use of input and labor), and the perception related to the objective of this study.

2.2 Data analysis

2.2.1 Farmers’ perception

The primary data obtained from the survey were analyzed descriptively. The respondents’ sociodemographic characteristics are compared between regencies instead of altitude as the supporting services such as extension/training are mainly provided by the local government. This study analyzes the importance of attributes of three local coffee varieties, namely, Gayo 3 variety (local coffee variety released through farmers’ participatory breeding process in 2022) and Gayo 1 and 2 (existing varieties that are widely used by farmers in the survey sites, which also released through the similar process in 2010) as a comparison.

In this analysis, the evaluation of attribute importance is done by a score ranging from −2 = not important, −1 = slightly important, 0 = moderately important, 1 = important, and 2 = very important. Mapping was performed using the mean score, using this scale range of 0.8. With this scale range, the level of importance of the value of each attribute can be formulated as follows: (−2.00) to (−1.21) = not important, (−1.20) to (−0.41) = slightly important, (−0.40) to (0.39) = moderately important, (0.40) to (1.19) = important, and (1.20) to (2.00) = very important. Moreover, the level of farmers’ perception of a variety of attribute performance can be categorized as (−2.00) to (−1.21) = very low, (−1.20) to (−0.41) = low, (−0.40) to (0.39) = moderate, (0.40) to (1.19) = high, and (1.20) to (2.00) = very high.

Based on the importance of each attribute and the level of coffee variety attributes performance perceived by farmers, this study expanded the analysis to understand the farmers’ attitudes toward coffee varieties by using Fishbein multi-attribute analysis [37]. This model focuses on the attitude formed by a person toward particular objects, which are Gayo 3, Gayo 1, and Gayo 2 varieties. Fishbein’s model formulation [37] is as follows:

(1) A j = ∑ k = 1 n b ijk e ijk ,

where A j is the overall respondent’s attitude toward coffee variety j , b ijk is the strength of the respondent ( i ) toward attribute k for coffee variety j , e ijk is the importance evaluation of attribute k by respondent i for variety j , and n is the number of analyzed coffee attributes.

This study evaluated 11 coffee variety attributes, namely, productivity, taste, size of coffee bean, resistance to cherry pod borer pest, resistance to leaf rust disease, resistance to the nematode, the efficiency of fertilizer use, adaptability to drought, adaptability to high precipitation, adaptability to long rainy season period, and early harvesting age. The attributes are selected based on the literature review and discussion with coffee stakeholders. An attribute with the highest attitude value is perceived as the most important by farmers. Meanwhile, the farmer’s overall attitude toward the object was compared between the varieties Gayo 1, Gayo 2, and Gayo 3, where the variety with the highest attitude value is considered to have better performance.

2.2.2 Econometric method

In order to understand the factors associated with the farmers’ perception of the importance of climate-related attributes in the coffee variety, this study conducted the analysis using the ordered logit model (OLM). This model is suggested by Hoffmann [38] for Likert-scale data as used in this study. Thus, the standard OLM was applied for the perceived importance of the climate attributes as follows:

(2) y i * = δ 1 V i + δ 2 W i + δ 3 Z i + ∈ i ,

where y i * denotes the perceived importance of the attribute of adaptability to climate events by the respondent i . Following the study by Cameron and Trivedi [39], the perception of the importance of the attributes is defined by y i = k if α k − 1 < y i * ≤ α k , where k = 1 (not important), …, k = 5 (very important), m = 5, α 0 = − ∞ , and α m = ∞ . Then,

(3) Pr [ y i = k ] = Pr [ α k − 1 < y i * ≤ α k ] = Pr [ α k − 1 < x i ′ δ + u i ≤ α k ] = Pr [ α k − 1 − x i ′ δ < u i ≤ α k ] = F ( α k − x i ′ δ ) – F ( α k − 1 − x i ′ δ ) ,

where F is the cumulative distribution function of u i , and vector x comprises the variables of farmer’s characteristics, agricultural assets, social capitals, and others. The ordered logit coefficients ( δ 1 − 3 ) are interpreted as the expected change of variable level ( y i * ) by its respective regression coefficient in the ordered log-odds scale as a response to the increase of one unit in predictors ( V i , W i , and Z i ) , while the other variables in the model are held constant. The OLM was estimated using the rms package in the R statistical program [40].

3 Results

3.1 Respondent characteristics

Table 2 presents the sociodemographic characteristics of the respondents. The average age of the total respondents is 37.58 years, which means that the coffee farmers are still at productive ages. The average age in Bener Meriah regency is 35.68 years, and that in Aceh Tengah is 39.78 years. However, there is no statistically significant difference in the age of household heads in the two regencies at 5% levels. The education level is relatively high; more than half of the respondents graduated from senior high school, and 27% of the respondents graduated from university. In comparison, based on a national 2014 survey by the Indonesian Statistical Agency, more than 70% of coffee farmers’ formal education in Indonesia is less than 6 years (elementary school) [30]. In addition, the experience of respondents in coffee farming is also relatively good, with an average of 15.38 years. Farmers in Aceh Tengah Regency have longer experience compared to those in Bener Meriah.

Table 2

Characteristics of coffee farmers

No. Variable Total Bener Meriah Aceh Tengah P-value
No. observations (respondents) 37 19 18
1 Age of household head (years) 37.68 35.68 39.78 0.263
2 Education of household head 0.138
– Elementary school 5.40% 5.26% 5.56%
– Junior high school 13.51% 15.79% 11.11%
– Senior high school 54.05% 68.42% 38.49%
– University 27.03% 10.53% 44.44%
3 Coffee farming experience (year) 15.38 11.79 19.17 0.021**
4 Household member 3.51 2.84 4.22 0.000***
5 Member of coffee farmers’ group (1 if yes, 0 otherwise) 0.84 0.95 0.72 0.048**
6 Member of cooperative (1 if yes, 0 otherwise) 0.22 0.26 0.17 0.482
7 Coffee training (1 if yes, 0 otherwise) 0.59 0.42 0.78 0.029*
8 Coffee area (ha) 0.95 0.95 0.78 0.012**
9 Agricultural land (ha) 0.99 1.19 0.78 0.004***

“*,” “**,” and “***” significant at 10, 5, and 1% probability levels, respectively, computed by a two-sided t-test for continuous variables and Mann–Whitney (Wilcoxon) test for dummy variables.

The average agricultural land owned is 0.99 ha, while the average coffee area is 0.95 ha. The coffee and agricultural land ownership in Bener Meriah is significantly higher than in Aceh Tengah. In terms of the involvement of coffee farmers in a farming support system, 84% of the respondents are members of farmer groups, and 22% are members of cooperatives. About 59% of the respondents reported that they had received training/extension on coffee cultivation. If compared between the regencies, Bener Meriah is better in the involvement of the respondents in a farmer group but lower participation in coffee training/extension activities.

3.2 Importance and performance of Arabica coffee attributes

In order to understand farmers’ attitudes toward coffee varieties, we analyze farmers’ perceptions of the importance of the attributes attached to the varieties. Of the 11 attributes analyzed, respondents considered five attributes very important: early harvesting age, productivity, coffee bean size, resistance to pod borer pest, and taste (Table 3). Meanwhile, there are three important attributes: resistance to leaf rust disease, resistance to nematodes, and efficient use of fertilizers. Attributes related to climate change, such as adaptability to prolonged dry seasons/droughts, adaptability to heavy rainfall, and adaptability to long rainy seasons period, are considered moderately important attributes by the respondent farmers.

Table 3

Level of importance of Arabica coffee superior variety attributes perceived by farmers in Aceh Province

Attributes Score Total Mean score ( e i ) Category
−2 −1 0 1 2
Productivity 0 0 0 16 21 58 1.57 Very important
Taste 0 0 4 21 12 45 1.22 Very important
Coffee bean size 0 0 2 16 19 54 1.46 Very important
Resistance to pod borer pests 0 0 1 21 15 51 1.38 Very important
Resistance to leaf rust disease 0 1 6 22 8 37 1.00 Important
Resistance to nematodes 0 4 5 19 9 33 0.89 Important
Efficient use of fertilizers 0 4 13 15 5 21 0.57 Important
Adaptability to droughts 0 3 20 12 2 13 0.35 Moderately important
Adaptability to heavy rainfall 0 0 24 12 1 14 0.38 Moderately important
Adaptability to long rainy seasons period 0 5 22 8 2 7 0.19 Moderately important
Early harvesting age 0 0 0 3 34 71 1.92 Very important

Table 4 shows the performance of three Arabica coffee varieties available at the study sites. Almost all of the climate adaptability-related attributes are perceived to have a moderate performance, except for adaptability to the drought of Gayo 1 varieties. It implies that farmers perceived that Gayo 1 has high adaptability to drought. Other than climate attributes, Gayo 1 has five attributes perceived to have a high performance (productivity, taste, coffee bean size, resistance to leaf rust disease, and efficient use of fertilizers), two attributes that have moderate performance (resistance to pod borer pests and resistance to nematodes), and an attribute that has low performance (early harvesting age). Gayo 2 only has two attributes that are perceived to have a high performance (taste and coffee bean size), five moderate performance attributes (productivity, resistance to pod borer pests, resistance to leaf rust disease, resistance to nematodes, and efficient use of fertilizers), and a low-performance attribute (early harvesting age). Gayo 3 has four very high-performance attributes (productivity, taste, coffee bean size, and early harvesting age), three attributes with high performance (resistance to pod borer pests, resistance to leaf rust disease, and efficient use of fertilizers), and an attribute with moderate performance (resistance to nematodes).

Table 4

Perceived performance of Arabica coffee variety attributes

Attributes Gayo 1 Gayo 2 Gayo 3
Mean score ( b i 1 ) Category Mean score ( b i 2 ) Category Mean score ( b i 3 ) Category
Productivity 0.54 High 0.14 Moderate 1.41 Very high
Taste 1.03 High 0.64 High 1.30 Very high
Coffee bean size 1.14 High 0.68 High 1.24 Very high
Resistance to pod borer pests 0.38 Moderate 0.21 Moderate 0.65 High
Resistance to leaf rust disease 0.43 High 0.04 Moderate 0.57 High
Resistance to nematodes 0.32 Moderate 0.11 Moderate 0.27 Moderate
Efficient use of fertilizers 0.76 High 0.25 Moderate 0.95 High
Adaptability to droughts 0.43 High 0.11 Moderate 0.19 Moderate
Adaptability to heavy rainfall 0.30 Moderate 0.21 Moderate 0.24 Moderate
Adaptability to long rainy seasons period 0.24 Moderate 0.11 Moderate 0.30 Moderate
Early harvesting age −0.43 Low −0.75 Low 1.68 Very high

Based on the level of importance and performance of each attribute of three Arabica coffee varieties, as shown in Tables 3 and 4, the farmers’ attitude toward the varieties is analyzed using the Fishbein multi-attribute method. The results indicate that respondents perceive Gayo 3 to have the highest score of 11.27 (Table 5 and Figure 2). The score far surpassed Gayo 1 and Gayo 2 with scores of only 4.91 and 1.26, respectively. These findings indicate that from the perspective of farmers’ perception, Gayo 3 has more advantages than Gayo 1 and Gayo 2.

Table 5

Multi-attribute Fishbein analysis for farmers’ attitude toward Arabica coffee varieties in Aceh Province

No. Attributes Importance levels ( e i ) Gayo 1 Gayo 2 Gayo 3
b i 1 A 1 b i 2 A 2 b i 3 A 3
1 Productivity 1.57 0.54 0.85 0.14 0.22 1.41 2.20
2 Taste 1.22 1.03 1.25 0.64 0.78 1.30 1.58
3 Coffee bean size 1.46 1.14 1.66 0.68 0.99 1.24 1.81
4 Resistance to pod borer pests 1.38 0.38 0.52 0.21 0.30 0.65 0.89
5 Resistance to leaf rust disease 1.00 0.43 0.43 0.04 0.04 0.57 0.57
6 Resistance to nematodes 0.89 0.32 0.29 0.11 0.10 0.27 0.24
7 Efficient use of fertilizers 0.57 0.76 0.43 0.25 0.14 0.95 0.54
8 Adaptability to droughts 0.35 0.43 0.15 0.11 0.04 0.19 0.07
9 Adaptability to heavy rainfall 0.38 0.30 0.11 0.21 0.08 0.24 0.09
10 Adaptability to long rainy seasons period 0.19 0.24 0.05 0.11 0.02 0.30 0.06
11 Early harvesting age 1.92 −0.43 −0.83 −0.75 −1.44 1.68 3.22
Total score 4.91 1.26 11.27
Figure 2 Farmers’ attitude map toward coffee variety attributes.
Figure 2

Farmers’ attitude map toward coffee variety attributes.

In more detail, according to the results presented in Table 5 and Figure 2, Gayo 3 is perceived to be better for almost all attributes than Gayo 1 and Gayo 2. There are three attributes where Gayo 1 is perceived as better than Gayo 2 and Gayo 3, namely, resilience to nematodes, adaptability to drought, and adaptability to high rainfall. For the attributes of early harvesting age and productivity, Gayo 3 has a very significant advantage. Meanwhile, in terms of taste, farmers perceive Gayo 3 as having a higher taste, followed by Gayo 2 and Gayo 1. Even though Gayo 3 is perceived as the best variety in the survey sites, Gayo 1 has more advantages for climate-related attributes than Gayo 2 and Gayo 3. Since the climate-related attributes are perceived as the lowest importance, and the farmers’ attitude is calculated as equation (1), the final attitudes ( A j ) for climate attributes are lower than other attributes for all varieties, except for the early harvesting age of Gayo 1 and Gayo 2.

3.3 OLM estimation

The estimation results for the farmers’ perception of the importance of climate-related attributes of the Arabica coffee variety are presented in Table 6. The estimation results indicate that the farmers in Bener Meriah Regency have a higher probability of perceiving all climate-related attributes as more important than those in Aceh Tengah Regency. First, for the importance of adaptability to drought attributes, farmers who plant Gayo 1 variety decrease the probability of farmers perceiving this attribute to be more important. Larger household size increases the probability of farmers perceiving the higher importance of a variety’s adaptability to drought. The opposite association resulted from the agricultural land, where the more extensive agricultural land has a negative relationship with the importance of this attribute. The cooperative membership also shows a similar association.

Table 6

OLM estimation for the perceived importance of climate attributes of Arabica coffee variety

Variables Adaptability to drought Adaptability to heavy rainfall Adaptability to long rainy season
y = 3 −7.769 — 6.609
(6.518) — (6.237)
y = 4 −16.001** −6.886 −0.259
(7.673) (7.054) (5.802)
y = 5 −21.227** −12.044 −3.487
(8.488) (7.957) (5.933)
Dummy Regency (1 if Bener Meriah) 10.799*** 3.062* 4.731***
(3.480) (1.673) (1.773)
Dummy Gayo 1 (1 if respondent plants Gayo 1 variety) −2.299* −0.577 −2.124*
(1.213) (1.182) (1.119)
Dummy Gayo 2 (1 if respondent plants Gayo 2 variety) −1.206 −0.511 1.517
(2.563) (2.752) (2.776)
Age 0.144 0.148 −0.015
(0.104) (0.119) (0.083)
Education 0.397 0.607 −0.530
(0.845) (0.929) (0.837)
Household size 1.253* 0.415 0.398
(0.675) (0.736) (0.534)
Experience 0.012 −0.073 −0.012
(0.080) (0.100) (0.078)
Farmers group membership (1 if yes) 1.521 0.418 −0.537
(1.188) (1.641) (1.129)
Cooperative membership (1 if yes) −3.688** −7.493 0.100
(1.813) (21.384) (1.479)
Coffee training/extension (1 if yes) 0.148 −3.628** −2.934*
(1.366) (1.735) (1.695)
Agricultural area (ha) −2.994* −1.497 −1.197
(1.606) (1.720) (1.319)
Coffee shade maintenance (1 if yes) 0.917 0.094 1.463
(1.207) (1.400) (1.243)
Pseudo-R 2 0.771 0.658 0.708
L.R. 41.997 26.314 36.219

***p < 0.01; **p < 0.05; *p < 0.1; standard errors are in parentheses.

Second, farmers involved in coffee training/extension have a lower probability of higher importance of adaptability to heavy rainfall attributes. Finally, for the adaptability to the long rainy season, farmers who plant Gayo 1 and are involved in coffee training/extension have a negative association with the importance of this attribute. An interesting finding from the OLM is that the farmers’ social capitals, i.e., cooperative membership and involvement in coffee training/extension, negatively affect farmers’ concern about the importance of climate-related attributes.

4 Discussion

4.1 Perceived importance of climate-related attributes

Gayo Highland is predicted to be one of the most affected Arabica coffee-producing zones by climate changes. Based on Schroth et al.’s [13] model, the suitable area for Arabica coffee in Aceh potentially decreases by 90% in 2050 as the impact of climate change, so the climate issues should obtain massive attention from the coffee stakeholders, especially farmers and the decision makers. Considering the real threat of climate change to coffee farming in the study area, the results show that coffee farmers’ concerns about climate issues are relatively low. It is indicated by their perception of climate-related attributes of Arabica coffee varieties as having the lowest importance compared to other attributes. The attributes of adaptability to droughts, heavy rainfall, and long rainy seasons are perceived as less important than other attributes such as productivity, taste, harvesting age, resistance to pests and diseases, and input efficiency (Table 3). These results imply that farmers might prioritize the attributes that directly impact their household income in the short term. For example, better productivity, taste, and harvesting age could increase farm revenues directly, while resistance to pests and diseases and efficient input use are essential to decrease farm costs. This finding is in line with that of Arsil et al. [41], where rice farmers in Indonesia tend to prioritize the perceived technology attributes contributing to economic return as the primary consideration for adopting system of rice intensification technology. It implies that farmers will tend to adopt a variety with a better performance of those attributes and less preference for climate-adaptable variety traits.

This finding, however, indicates the low awareness of the farmers regarding climate change issues. Given the adverse negative impact of climate change on coffee farming, such as declining yield, losing suitable areas, and increasing pests and diseases attack [11,13,42], this low awareness potentially leads to the unwillingness to adapt to climate issues. Many studies have shown that the farmers’ concern/awareness of climate issues is an important step in the farmers’ adaptation strategies (e.g., [43,44]). Indeed, an adaptable variety is not the only coping strategy for climate change issues; however, the use of climate adaptive variety is acknowledged as one of the most important strategies (e.g., [45,46]). Hence, it is important to educate coffee farmers about climate issues, including the importance of climate variables in coffee farming, especially those attached to the coffee variety attributes.

In terms of the likelihood of Arabica coffee variety adoption, Fishbein multi-attribute analysis was used to identify and explain the perception and level of respondent trust in Arabica coffee varieties [47,48]. The results indicate that the local variety resulting from a participatory breeding program (Gayo 3) is potentially more preferred than older varieties (Gayo 1 and Gayo 2), as this variety has a much higher value for almost all attributes. It implies that the attributes of Gayo 3 have addressed the farmers’ concerns and preferences regarding Arabica coffee’s improved variety traits so that it potentially increases the farmers’ acceptance [49,50,51]. This finding also implies that the participatory breeding process can develop coffee varieties highly compatible with farmers’ preferences and improve the conventional breeding method [52].

4.2 Determinant factors of perceived importance of climate attributes

Many studies show that farmers’ characteristics have essential roles in farmers’ behaviors (e.g., [53,54]). The characteristics such as age, education, farm experience, social capital, and others could determine their farming practice decisions, including responding to the changing business environment. However, the estimation results of OLM show that sociodemographic variables such as age, education, and experience have no statistically significant relationship with the perceived importance of climate-related attributes of a coffee variety. This finding is in line with that of Hasibuan et al. [43], where sociodemographic variables are less influential than advisory services, such as farmers’ institutions and extension activities, in shaping farmers’ perceptions of climate issues.

In this study, agroecological aspects are represented by a regency dummy variable. This variable may cover altitude, local climate, local government programs, and other related factors. As stated in the previous section, the OLM estimation indicates that there is a statistical difference in the perceived importance of climate-related attributes between the two regencies, meaning that geographical aspects have a significant association with the perceived importance of climate-related attributes of the Arabica coffee variety. This finding, however, supports the previous studies where the geographical aspects are associated with spatial heterogeneity that could lead to a different perception of the farmers, especially that related to climate issues [43,55,56].

While the low concern of farmers toward climate issues, the regression estimations find the unexpected association of farmers’ support systems with the farmers’ perception of the importance of climate attributes. Since the farmers’ concern about climate issues is closely associated with their climate adaptation practices (e.g., [46,57]), the low perceived importance of climate attributes on coffee varieties is expected to be addressed by the farmers’ support systems such as training/extension or institutional services. Previous studies show that farmers’ awareness of climate issues can be improved by providing climate training and extension to the farmers [43,58]. The possible explanations for this finding are as follows: first, the coffee training/extension might be too focused on the coffee farming technology, which may ignore the subject of climate issues. The ignorance of climate issues in a farmers’ services program is often founded in developing countries as they may be too focused on productivity enhancement target, so that it needs a strategy to shift the training/extension program from crop production based into climate-smart practices, which could increase the farmers’ awareness to climate issues [59]. One of the strategies is attaching climate information in the training/extension module as a part of the farm education program [60]. Second is the low capacity of the extension workers regarding climate issues. In many developing countries, including Indonesia, the availability of extension workers is very limited. Even though an extension agent only has a limited competency, it is very common if each extension staff is demanded to assist farmers with all issues [61,62,63]. As a result, the information delivered to the farmers may not be accurate. Hence, it is crucial to improve the capacity of extension workers regarding climate issues.

5 Conclusions

This study finds that coffee farmers in the Gayo Highland region have a low perception of the importance of climate-related attributes. This may be due to a lack of awareness about the negative impacts of climate change on coffee farming. This low perception of the importance of climate-related attributes may hinder the adoption of adaptation strategies such as the use of climate-adaptable coffee varieties. The main determinant factors of the perceived importance of climate attributes among farmers are agroecological aspects and supporting services. Sociodemographic characteristics, such as age, education, and experience, have no statistically significant relationship with the perceived importance of climate attributes. Farmers’ support systems, such as coffee training/extension and cooperative membership, have a negative relationship with the perceived importance of climate attributes. Therefore, decision makers and extension services need to prioritize the education and awareness of coffee farmers about the impacts of climate change on coffee farming and the importance of adaptable coffee varieties. Improving the capacity of extension workers is also essential, especially related to climate issues. The participatory breeding process may also be an effective method for creating varieties that are more compatible with farmers’ preferences and needs. By increasing awareness of the importance of climate-adaptable traits and providing resources for farmers to participate in the breeding of coffee varieties, it may be possible to improve the long-term sustainability and resilience of coffee farming in the region.

This study has several limitations that may be important to consider for future research. First, the analysis only uses the farmers’ perspective to value the climate-related attributes of Arabica coffee varieties available on the study site. Comparing the farmers’ perception with others’ perspectives, i.e., researchers, extension workers, the coffee industry, and other stakeholders, may provide a better understanding of the importance of climate-related traits of a local coffee variety. Second, this study elicits the perceived importance of climate-related attributes using the 5-point Likert scale. More advanced measurements of the perceived importance (i.e., best–worst scaling) may provide better information, especially in comparing the importance of each attribute.

tel: +62-815-1411-7032

Acknowledgments

The authors gratefully acknowledge the support and participation of local farmers, local government, enumerators, and the Gayo 3 research team.

  1. Funding information: This study is a part of the research project funded by the collaboration of the Indonesian Industrial and Beverage Crops Research Institute, Ministry of Agriculture with Dinas Pertanian dan Perkebunan, Nanggroe Aceh Darussalam Province, in order to accelerate the releasing local varieties of Gayo 3.

  2. Author contributions: AMH – questionnaire development, data analysis, conceptualization, manuscript writing; ER, DD, NKI, BM, MSDI, CT, RH – data collection, data analysis, manuscript writing; TJS, SS – data analysis, manuscript writing, supervision; ALS, AW – questionnaire development, data analysis, manuscript writing.

  3. Conflict of interest: The authors state no competing interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-09-15
Revised: 2023-03-06
Accepted: 2023-04-04
Published Online: 2023-04-25

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2022-0197
Keywords for this article
climate change; farmers’ preferences; local variety; climate training; extension services
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BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. The impact of COVID-19 pandemic on business risks and potato commercial model
  3. Effects of potato (Solanum tuberosum L.)–Mucuna pruriens intercropping pattern on the agronomic performances of potato and the soil physicochemical properties of the western highlands of Cameroon
  4. Machine learning-based prediction of total phenolic and flavonoid in horticultural products
  5. Revamping agricultural sector and its implications on output and employment generation: Evidence from Nigeria
  6. Does product certification matter? A review of mechanism to influence customer loyalty in the poultry feed industry
  7. Farmer regeneration and knowledge co-creation in the sustainability of coconut agribusiness in Gorontalo, Indonesia
  8. Lablab purpureus: Analysis of landraces cultivation and distribution, farming systems, and some climatic trends in production areas in Tanzania
  9. The effects of carrot (Daucus carota L.) waste juice on the performances of native chicken in North Sulawesi, Indonesia
  10. Properties of potassium dihydrogen phosphate and its effects on plants and soil
  11. Factors influencing the role and performance of independent agricultural extension workers in supporting agricultural extension
  12. The fate of probiotic species applied in intensive grow-out ponds in rearing water and intestinal tracts of white shrimp, Litopenaeus vannamei
  13. Yield stability and agronomic performances of provitamin A maize (Zea mays L.) genotypes in South-East of DR Congo
  14. Diallel analysis of length and shape of rice using Hayman and Griffing method
  15. Physicochemical and microbiological characteristics of various stem bark extracts of Hopea beccariana Burck potential as natural preservatives of coconut sap
  16. Correlation between descriptive and group type traits in the system of cow’s linear classification of Ukrainian Brown dairy breed
  17. Meta-analysis of the influence of the substitution of maize with cassava on performance indices of broiler chickens
  18. Bacteriocin-like inhibitory substance (BLIS) produced by Enterococcus faecium MA115 and its potential use as a seafood biopreservative
  19. Meta-analysis of the benefits of dietary Saccharomyces cerevisiae intervention on milk yield and component characteristics in lactating small ruminants
  20. Growth promotion potential of Bacillus spp. isolates on two tomato (Solanum lycopersicum L.) varieties in the West region of Cameroon
  21. Prioritizing IoT adoption strategies in millennial farming: An analytical network process approach
  22. Soil fertility and pomelo yield influenced by soil conservation practices
  23. Soil macrofauna under laying hens’ grazed fields in two different agroecosystems in Portugal
  24. Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic
  25. Properties of paper coated with Prunus serotina (Ehrh.) extract formulation
  26. Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
  27. Molecular and phenotypic markers for pyramiding multiple traits in rice
  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
  31. Optimization of extraction using surface response methodology and quantification of cannabinoids in female inflorescences of marijuana (Cannabis sativa L.) at three altitudinal floors of Peru
  32. Production factors, technical, and economic efficiency of soybean (Glycine max L. Merr.) farming in Indonesia
  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
  37. Analysis of trade potential and factors influencing chili export in Indonesia
  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
  39. Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia
  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
  44. Effects of salt stress on growth of Quercus ilex L. seedlings
  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
  46. Indicators of swamp buffalo business sustainability using partial least squares structural equation modelling
  47. Effect of arbuscular mycorrhizal fungi on early growth, root colonization, and chlorophyll content of North Maluku nutmeg cultivars
  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
  50. The improvement of horticultural villages sustainability in Central Java Province, Indonesia
  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
  69. Effect of integrate water shortage and soil conditioners on water productivity, growth, and yield of Red Globe grapevines grown in sandy soil
  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
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