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Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia

  • Imelda Imelda , Jangkung Handoyo Mulyo EMAIL logo , Any Suryantini and Masyhuri Masyhuri
Published/Copyright: May 16, 2023
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Open Agriculture
From the journal Open Agriculture Volume 8 Issue 1

Abstract

Over the past decade, smallholder rubber farming experienced significant issues related to risks of rainy season, rubber diseases, and price decreases. The risk exposure will reduce agricultural productivity, income, and sustainability. Farmers are expected to mitigate various risks by adopting the appropriate risk strategy. Efforts to support the risk strategy adoption are constrained by the lack of scientific research in rubber farming, especially for simultaneous risk strategy adoption. This study aims to identify the risk strategy adoption in rubber farming and analyse the determinant factors by considering farmers’ socioeconomic, rubber farm characteristics, and risk perception. The data were collected from 200 rubber farmers in West Kalimantan, Indonesia, and analysed using a multinomial logit model. The study results show that the most selected strategy was income diversification. The multinomial logit model indicates that farmers’ age, family members, rubber age, rubber clones, and rainy season risk perception positively affect the adoption of risk strategy. In contrast, experience, farm area, and farm distance have a negative effect. The results also indicate that family members, farm area, and farm distance variables significantly affect all choices of risk strategy adoption. The results of this study suggest several implications for government and policymakers in providing assistance and counselling, capital assistance, input access, and improving transportation, road access, and communication.

Keywords: multinomial logit model; risk perception; risk strategy adoption; rubber farmers

1 Introduction

Natural rubber is a primary Indonesian commodity contributing to national income and foreign exchange. Indonesia’s rubber export volume in 2020 reached 2.28 million tons, equivalent to US$ 3.01 billion [1]. Rubber plantations in several countries, such as Thailand, Indonesia, Vietnam, China, and Malaysia, are the primary source of income for smallholder farmers [2,3,4]. In Indonesia, 90.4% of rubber plantations are managed by 2.3 million small farmers, with an average area of 1.47 ha [1]. Indonesia has 3,726,173 ha of rubber area, which places Indonesia as the world’s largest owner of rubber area. However, regarding rubber production, Indonesia ranks second after Thailand [1], which means that the opportunity to develop rubber plantations in Indonesia remains promising and strategic efforts are still needed to increase productivity.

The agricultural sectors are very vulnerable to risk exposure [5]. Climate change is one of the significant problems in the agriculture sector that has received much attention in risk management studies [6,7,8,9,10,11,12]. The prolonged effect of climate change, especially the rainy season, is the leading cause of the decline in rubber production [13]. In rubber farming, the rainy season triggers disease attacks [14,15], such as leaf fall rubber disease and white root fungal disease. Rubber plantations are also vulnerable to price changes [16,17]. At the international level, the rubber price has always fluctuated, with a significant decline since 2011, which shows the vulnerability of rubber plantations that affects the decrease in income [18] and farmers’ welfare [19]. The decrease in rubber prices was not only a problem in Indonesia [16] but also occurred in other rubber-producing countries, including Thailand [20], Malaysia [3], India [18], China [21,22,23], and Sri Lanka [24]. The accumulation of several risks has threatened the smallholder farmers’ welfare and influenced their decision to adopt a risk strategy.

The Indonesian Ministry of Agriculture formulated policies for developing smallholder rubber plantations, such as providing input facilities, establishing and strengthening the Rubber Processing and Marketing Unit (RPMU), and replanting activities through rubber diversification with food crops and other plantation crops. At the farmer level, to reduce the risks and raise productivity, it is necessary to adopt the risk strategy [25,26]. Based on previous studies, the strategy options for rubber farming are plant disease prevention [2,27], income diversification [19,21], and group marketing through the RPMU [28]. Farmers can adopt the risk strategy and select a suitable strategy for their farming activities. They can adopt one or more strategies according to their physical and financial capabilities. The adoption of a simultaneous strategy at the same time is a common behaviour among farmers [29,30]. Since strategies vary according to objective (environmental) and subjective factors (risk perceptions), information on the farmers’ socioeconomic and farm characteristics is needed [31,32]. Adopting risk strategies is also related to the farmers’ risk perception [6,32,33,34,35,36]. Akhtar et al. [29] and Zhai et al. [37] confirm that risk perception plays an essential role in farmers’ decisions to adopt a risk strategy and must be considered in all studies related to risk management policies. Individuals unaware of the scope and threat of risk will ignore risk strategies, thus affecting agricultural activities [38,39]. Mahmood et al. [6] also stated that risk strategy adoption would not occur when farmers ignore the risk.

Studies on risk strategies received much attention in the agricultural sector, such as food crops [40,41,42,43], fisheries [26], and the integration of crop livestock [44]. Previous studies in rubber farming focus only on the intercropping system to mitigate the risks [23,45]. This study offers novelty since it discusses the simultaneous adoption of several risk strategies using an econometric approach. In addition, this study also highlights the three main risks in rubber farming, such as the rainy season, rubber disease, and price decrease, while previous studies only focused on one type of risk [19,46,47]. Therefore, this research was carried out to fill the study gap and enrich the literature.

This study aims to identify (1) farmers’ decisions in risk strategy adoption and (2) the determinant factors in risk strategy adoption. This study is essential because understanding risk strategy adoption can assist the government and policymakers develop programs to mitigate the risks and lead to sustainable rubber farming. Using farmers’ socioeconomics characteristics, rubber farm characteristics, and risk perceptions as variables also help the government to design policy programs based on the variables significantly affecting risk strategy adoption. The article is structured as follows: Section 1 contains a research introduction. Section 2 contains methods, including the study area, sampling design, and data analysis using a multinomial logit model. The results and discussion are provided in Section 3, which contains information about descriptive statistics of independent variables, risk strategy adoption, and the determinant factors of risk strategy adoption. Section 4 gives the research conclusions and recommendations.

2 Methods

2.1 Study area and sampling design

The study area was determined purposively in West Kalimantan, Indonesia’s largest rubber production centre, especially on Kalimantan Island. In this region, rubber farming became a primary source of income for 283,023 smallholder farmers but the farming activity experienced a problem of low productivity [1,48]. The data collection was conducted with a multistage sampling method. In the first stage, we determined the districts with the highest rubber area and production in West Kalimantan, such as Sanggau, Sintang, and Sambas. In the second stage, we determined the subdistricts as a centre for rubber production and the existence of RPMU in the area, such as Balai, Parindu, Sepauk, and Sajad (Figure 1). Finally, we selected 200 sample farmers using simple random sampling. The interviews were conducted using questionnaires regarding the decisions of farmers to adopt the risk strategy.

Figure 1 Map of the research location. Source: Developed from ArcGIS.
Figure 1

Map of the research location. Source: Developed from ArcGIS.

2.2 Data analysis

Farmers have different perception and decisions when faced the risks. Farmers must decide to adopt or not adopt the risk strategy, and when they decide to adopt, they can consider adopting one or more risk strategies. Our survey shows rubber farmers generally apply three risk strategies: plant disease control, income diversification, and group marketing. From the three risk strategy options, we can generate eight choices of risk strategies described in Table 1.

Table 1

Alternatives choices of risk strategy

Choices Risk strategy Total farmers Percentage
1 No strategy adoption 22 11.00
2 Income diversification 72 36.00
3 Plant disease control + income diversification 26 13.00
4 Income diversification + group marketing 36 18.00
5 Plant disease control + income diversification + group marketing 44 22.00
Risk strategies that were excluded from the analysis
6 Plant disease control 0 0
7 Group marketing 0 0
8 Plant disease control + group marketing 0 0

Three categories of risk strategy alternatives (plant disease control, group marketing, and plant disease control + group marketing) were excluded from the analysis because no farmer adopted the strategy in those categories. Finally, we have five choices for the alternatives of risk strategy. Based on the knowledge that rubber farmers can adopt a combination of risk strategies and adopting one strategy can affect the adoption of other strategies, we consider analysing the risk strategy adoption in the multinomial logit model. This model has been extensively used in the previous literature discussing the adoption of multiple risk strategies [37,41,42,49,50,51]. The analysis of multinomial logit models provides more interpretations, inferences, and data that can enhance the comprehension of risk strategy adoption.

A detailed explanation of the analytical model for this study is depicted in the research flowchart in Figure 2. The multinomial logit model was utilised to determine the determinant factors of risk strategy adoption because the dependent variable contains multiple outcomes [37,42]. The base category for the multinomial logit model is the first category (no strategy adoption). The probability of farmers with characteristics x adopting the risk strategies P can be detailed as follows:

(1) P ( Y = m | X ) = exp ( X β m ) 1 + ∑ h = 1 M exp ( X β h ) , m = 1 , 2 , … , 5 ,

where Y indicates the dependent variable, referring to one of five possible options (1 = no adoption, 2 = income diversification, 3 = plant disease control + income diversification, 4 = income diversification + group marketing, and 5 = plant disease control + income diversification + group marketing); β indicates the regression coefficients related to risk strategy option m in the multinomial logit model; and X indicates the independent variables influencing the adoption of risk strategies: socioeconomic characteristics, rubber farm characteristics, and farmers’ risk perception. Socioeconomic characteristics variables in the analysis are mainly based on previous studies, such as farmers’ age [31,52,53,54], education [52,53,55], experience [42,53,56], and family members [55]. We also include farm characteristics, such as farm area [31,42,52,53], farm distance [23], and the use of high-yielding seeds [54]. This study adds rubber age as an independent variable because rubber is an annual crop. Rubber trees start to produce latex at 5 years and have optimal production at 15–20 years. The growth period of rubber plants will certainly affect farming performance and farmers’ decisions in adopting risk strategies. Finally, adding farmer risk perception as an independent variable is reasonable because rubber farmers face various risks affecting rubber productivity and risk strategy adoption [13,19]. Khan et al. [7] and Adnan et al. [52] also state that risk perception can determine the adaptive ability of farmers toward risk.

Figure 2 Developed from previous studies and survey result.
Figure 2

Developed from previous studies and survey result.

The parameters used to measure risk perception are based on farmers’ perceptions of the severity and frequency of each risk source using a Likert scale [57,58,59]. Farmers are asked to score 1 (very low) to 5 (very high) based on their understanding and experience of risk situations in past years. Next, the results of the perception scores on severity and frequency are summed up to obtain the total score. Farmers perceive risk as low if the total score is 2–5 and high if the total score is 6–10. Next, for the multinomial logit model, we coded 1 for high-risk perception and 0 for low-risk perception.

Before running the model, we conduct a multicollinearity test using the variation inflation factor (VIF) and tolerance. The VIF for all independent variables is below 10, and a tolerance above 0.1 indicates no multicollinearity in the model. Furthermore, all independent variables expected to influence the adoption of risk strategies are included in the multinomial logit analysis using Stata software. Only variables with a significant effect are discussed in the next section.

3 Results and discussion

3.1 Descriptive statistics

The descriptive statistics from the independent variables are described in Table 2.

Table 2

Summary of independent variables

Independent variables Description and measurement Mean Std. Dev.
Age of farmer Continuous, age of the farmer in years 43.85 9.17
Education Continuous, education level of the farmer in years 8.20 2.43
Family members Continuous, family members of the household in persons 4.46 1.03
Experience Continuous, farming experience in years 17.34 7.45
Rubber area Continuous, rubber farm area in hectare 1.49 0.50
Age of rubber Continuous, age of rubber trees in years 13.08 4.00
Farm distance Continuous, distance from rubber farm to the village centre in kilometres 2.32 1.11
Rubber clone Binary, 1 if the farmer uses rubber clone and 0 otherwise 0.76 0.43
Rainy season Binary, 1 if the farmer perceived the rainy season as high risk in rubber farming and 0 otherwise 0.81 0.40
Rubber disease Binary, 1 if the farmer perceived rubber disease as high risk in rubber farming and 0 otherwise 0.69 0.46
Price decrease Binary, 1 if the farmer perceived price decrease as high risk in rubber farming and 0 otherwise 0.75 0.43

The mean age of farmers was 44 years, with minimum and maximum ages of 25 and 75 years, respectively. The mean of education and farming experience was 8 and 17 years, respectively. The mean of family members was four people, and each household had 1.5 ha of private rubber area. Most farmers (76%) applied rubber clones with an average age of 13 years and in the mature trees category (6–30 years). Furthermore, the average distance from the farm to the village centre was 2.3 km.

From the results of farmers’ risk perceptions, it was found that farmers perceive the rainy season, rubber diseases, and price decrease as high risks, with a percentage of 81, 69, and 75%, respectively. Farmers perceive the rainy season as a high risk because the rainy season reduces tapping days and time, resulting in a decrease in latex yields. The rainy season with high rainfall intensity and long duration will affect tapping activities and rubber productivity [60,61,62]. When it rains, rainwater will fall on the trees and flow down the trunks and branches, which then enter the tapping bowl so that the tapping results are wasted because they are mixed with rainwater. Generally, farmers can tap 4–5 times a week, but during the rainy season, farmers can only tap 2–3 times a week.

Regarding rubber plant diseases, farmers perceive leaf fall and white root fungus as high risks because they cause a decrease in production. Rubber leaf fall disease causes up to 75–90% loss of leaves and a reduction of 25–45% in production [63]. White root fungus disease can cause decreased productivity and death of rubber trees [64,65,66]. Furthermore, farmers perceive rubber price decrease as high risk because output prices are the primary stimulus for farmers to increase the quality and quantity of latex. Consequently, when farmers receive lower prices, farmers are not motivated to improve the quantity or quality of production. The decline in global rubber prices since 2011 has impacted farmers’ income and investment ability, decreasing purchasing power and diverting income to other sources of livelihood [18,19].

3.2 Risk strategy adoption

The farmers’ decisions to adopt risk strategies are presented in Figure 3. The risk strategy adoption varies depending on the farmer’s physical ability, knowledge, and financial condition. The study’s main findings are information about risk strategy adoption and determinant factors to generate policy implications that mitigate risks and maintain the sustainability of rubber farming. Based on the five existing alternatives of risk strategy, the most chosen strategy was income diversification (36%). This strategy is the most widely applied in the agricultural sector [36,52,67,68,69]. The income diversification strategy can be applied through on-farm or non-farm diversification. Farmers can adopt on-farm diversification through the intercropping system of rubber trees and other crops, such as tea, corn [23], coffee, vanilla, and nutmeg [18]. Previous studies found that the implementation of farm diversification can increase farm profits [24], create jobs [45], and maintain the functions of the land ecosystem [18,20,70]. This strategy also benefits rubber farmers by reducing the climate change effect and fluctuations in rubber prices [24,36,71]. Adopting agricultural diversification also plays an essential role in forming a sustainable agricultural resistant to climate change [36] and can increase the food security of farmer households [10].

Figure 3 Farmers’ risk strategy adoption in rubber farming.
Figure 3

Farmers’ risk strategy adoption in rubber farming.

In addition to the on-farm diversification, farmers can diversify their income through non-farm activities, namely as construction workers, traders, village officials, and other non-farm jobs. This strategy is helpful for farmers, especially when there is a decrease in rubber prices. Farmers who apply this strategy must divide their time with farm activities. By contrast, farmers relying only on agricultural activity are more flexible in planning and implementing risk strategies [6]. Several previous studies found that the adoption of non-farm diversification contributes to an increase in income [24,72,73], food security [74], and farmers’ welfare [75].

Another strategy carried out by rubber farmers is rubber disease control. This strategy indicates that farmers were aware that rubber tree diseases could reduce rubber production and cause the death of rubber trees [2]. One way to control plant diseases is to use pesticides and weed control with herbicides. Weed control is essential in preventing plant diseases because weeds become hosts for rubber plant pests and diseases [76]. In addition, early detection of plant diseases is also essential in preventing and controlling plant diseases [77]. The weakness of this strategy is the high costs that farmers have to incur in plant disease control [27] and weed control [76], making it challenging for farmers to adopt this strategy.

The following strategy is group marketing through RPMU. This strategy can help farmers sell their rubber directly to the factory, not through the collectors [16,70]. Farmers’ membership in RPMU will increase their opportunities to obtain information, knowledge, and guidance from fellow members, extension workers, and related agencies. It aligns with previous studies, which stated that farmer groups contribute to increasing awareness and risk mitigation skills [6,78,79].

3.3 Determinant factors of risk strategy adoption

This study uses a multinomial logit model to explore the determinant of risk strategy adoption. Table 3 shows the parameter estimate and relative risk ratio for the multinomial logit models. The results show the log-likelihood ratio chi-square is equal to 267.130 with a probability of 1% and pseudo-R 2 of 0.440. All the results indicate that both models fit. Multinomial logit models stated that the farmers’ age, family members, rubber age, rubber clone, and rainy season risk perception positively affect the risk strategy adoption. In contrast, experience, rubber area, and farm distance have a negative effect.

Table 3

Parameter estimate and the relative risk ratio of the multinomial logit model

Variables Exp. sign Risk strategies adopted by the farmers
Income diversification (Y = 2) Plant disease control + income diversification (Y = 3) Income diversification + group marketing (Y = 4) Plant disease control + income diversification + group marketing (Y = 5)
Coeff. Std. err RRR Coeff. Std. err RRR Coeff. Std. err RRR Coeff. Std. err RRR
Age of the farmer + 0.054ns 0.061 1.055 −0.081ns 0.090 0.922 0.193** 0.079 1.213 0.054ns 0.078 1.056
Education + 0.044 ns 0.139 1.045 −0.203ns 0.201 0.816 −0.190ns 0.201 0.827 −0.286ns 0.199 0.751
Family members + 1.065*** 0.408 2.899 1.500*** 0.563 4.483 2.165*** 0.558 8.713 2.912*** 0.569 18.399
Experience − −0.209*** 0.079 0.811 −0.012ns 0.111 0.988 −0.467*** 0.114 0.267 −0.308*** 0.106 0.735
Rubber area − −2.578*** 0.949 0.076 −5.834*** 1.256 0.003 −4.655*** 1.204 0.009 −7.070*** 1.244 0.0009
Age of rubber + 0.339*** 0.119 1.405 −0.0005ns 1.490 0.999 −0.966ns 1.162 0.908 −0.249ns 0.164 0.779
Farm distance − −0.659* 0.381 0.517 −1.811*** 0.520 0.163 −1.222*** 0.446 0.295 −1.167*** 0.453 0.311
Rubber clone + 1.303* 1.048 3.680 1.186ns 1.298 3.273 −0.638ns 1.195 0.528 −0.139ns 1.224 0.869
Rainy season + 1.306ns 0.841 3.692 2.426* 1.246 11.319 1.398ns 1.349 4.046 2.149* 1.301 8.572
Rubber disease + −1.305ns 1.029 0.271 −0.932ns 1.199 0.394 −1.594ns 1.244 0.203 −1.749ns 1.199 0.174
Price decrease + −0.659ns 1.017 0.517 1.574ns 1.330 4.827 0.149ns 1.172 1.161 −0.091ns 1.170 0.913
Constant −0.475ns 3.293 0.622 9.399ns 4.562 12084.8 4.411ns 4.527 82.316 9.744** 4.457 17056.8
LR chi2 267.130***
Log-likelihood −169.952
Pseudo R 2 0.440
Reference category: no strategy adoption (Y = 1).

*** P < 0.01; ** P < 0.05; * P < 0.1; Coeff – coefficient; ns – not significant.

Following studies from Coffey and Schroeder [41], Abdur Rashid Sarker et al. [42], and Gebru et al. [80], the coefficients of the multinomial logit model are interpreted based on the log of the relative risk ratio (or odds ratio) in a specific category relative to the reference category. A positive coefficient implies a positive influence on the probability of adopting a risk strategy compared to not adopting a strategy and vice versa. Relative risk ratio is more frequently used and more straightforward to interpret than logit coefficients, so this study focuses on discussing relative risk ratios [81]. Relative risk ratios are explained as the influence of independent variables on the relative risk of adopting a risk strategy relative to no strategy adoption as the reference category. The next interpretation is focused on the coefficient’s sign (positive or negative) and the relative risk ratio of each independent variable that has a significant effect.

As expected, the farmers’ age had a positive and significant influence on the adoption of income diversification + group marketing strategies, with a probability of 5%. The relative risk ratio implies that if other variables are constant, an increase of 1 year in farmers’ age will increase 1.213 times the possibility of farmers adopting the strategy of income diversification + group marketing relative to no strategy adoption. In previous research, a positive influence of farmers’ age on the risk strategy adoption has also been found [52,53,82]. Older farmers will generally adopt a strategy they believe is less risky and will consider adopting income diversification + group marketing strategies. It is seen that elderly farmers do not consider adopting the plant disease control strategy due to their lack of physical ability. A previous study by Adnan et al. [52] also implies that older farmers have more experience dealing with risky situations and will choose the less risky strategy.

The family member was found to have a positive and significant influence on the adoption of income diversification, plant disease control + income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies at significant levels of 1%. The relative risk ratio implies that if other variables are constant, an increase of one person in family members will increase 2.899, 4.483, 8.713, and 18.399 times the possibility of farmers adopting income diversification, plant disease control + income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies relative to no strategy adoption. The highest value of the relative risk ratio on adopting plant disease control + income diversification + group marketing strategies also indicates that an increase in family members increases the probability of all risk strategies adoption. This result is similar to our prior expectation and the previous research that argues the positive influence of family members on risk strategy adoption [55,83,84]. The family members reflect the additional family workforce that can assist in implementing strategies, for example, controlling plant diseases, crop diversification, seeking work outside the agricultural sector, and conducting group marketing through RPMU. Farmers can save input costs by using family labour to reduce the additional costs incurred in risk strategy adoption. More family members indicate additional labour that opens opportunities to adopt the risk strategy [6,30,85,86]. More specifically, studies from Ullah and Shivakoti [55] and Khan et al. [83] stated that a larger family size signifies more labour assistance to adopt on-farm and off-farm diversification strategies. A study by Ahsan [84] added that senior family members have more opportunities to do business in the non-farm sector. Furthermore, the analysis found that the family member variable significantly affects all choices of risk strategy adoption. This study suggests farmers involve their families in rubber farming activities or adopt risk strategies. The government and related stakeholders are also expected to assist and counsel farmers and their family members to motivate them to carry out farming activities or adopt risk strategies.

Experience had a negative and significant effect on the adoption of income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies with a probability of 1%. The results of the relative risk ratio argued that in ceteris paribus, an increase of 1 year in experience will decrease 0.811, 0.267, and 0.735 times the possibility of farmers adopting income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies relative to no strategy adoption. This result indicates that experienced farmers are less likely to adopt risk strategies. The farmers want to focus on rubber farming and are less motivated to invest in other crops or businesses. They also have experience in rubber marketing and good relationships with collectors, so they are not involved in group marketing. These findings confirmed our expectations and previous studies that found the negative effect of farm experience on risk strategy adoption [26,53,87]. Furthermore, Zhai et al. [37] explained that experienced farmers prefer traditional farming concepts and find it challenging to adopt the technology. Ullah et al. [53] found that experienced farmers were generally focused on farming and reluctant to do other businesses. A case study by Min et al. [87] also stated that experienced farmers prefer to specialise in rubber farming and not adopt an income diversification strategy.

The rubber area was found to have a negative and significant influence on the adoption of income diversification, plant disease control + income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies with a probability of 1%. The relative risk ratio implies that in ceteris paribus, an increase of 1 ha in the rubber area will decrease 0.076, 0.003, 0.009, and 0.0009 times the possibility of farmers adopting income diversification, plant disease control + income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies relative to no strategy adoption. This result is in line with our expectations and previous studies that found the negative influence of farm areas on risk strategy adoption [30,52,68]. Farmers with larger areas need extra time and effort to maintain and tap the rubber tree, making it challenging to adopt the risk strategy. Lu et al. [30] found that the large farm area significantly reduces off-farm diversification adoption. Adnan et al. [52,68] also noted that farmers with larger farm areas are less motivated to adopt strategies since their assets are more solid and secure, allowing them to take higher risks. Conversely, farmers with small areas are more vulnerable to risk and tend to adopt strategies to minimise losses. They prefer to apply strategies related to their agricultural activities. In this research, they adopt plant disease control, income diversification, and group marketing, either individually or simultaneously. The results show that the rubber farm area significantly affects all choices of risk strategy adoption. This study suggests that the government and stakeholders can provide capital assistance and input access, especially to farmers with small farm areas, which will assist them in carrying out rubber farming and adopting a risk strategy.

The age of rubber positively and significantly influences the adoption of income diversification strategy at a significance level of 1%. The relative risk ratio implies that if other variables are constant, an increase of 1 year in rubber age will increase 1.405 times the possibility of farmers adopting an income diversification strategy relative to no strategy adoption. It is reasonable because the old rubber trees are more susceptible to plant disease, which can reduce latex production and farm income [88]. As a result, farmers must find other income sources by adopting on-farm or non-farm diversification.

Furthermore, the distance from the farm area to the village centre negatively and significantly affects the adoption of income diversification, plant disease control + income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies with a significant level of 10 and 1%. The relative risk ratio implies that if other variables are constant, an increase of 1 km in farm distance will decrease 0.517, 0.163, 0.295, and 0.311 times the possibility of farmers adopting income diversification, plant disease control + income diversification, income diversification + group marketing, and plant disease control + income diversification + group marketing strategies relative to no strategy adoption. The results of previous studies also stated a negative effect between the farm distance to the village centre and the adoption of an income diversification strategy [6,23]. The convenience of farmers in accessing market information, access to counselling, and farming management will assist farmers in making decisions, especially in adopting proper risk management practices to address risks and uncertainties in the agricultural sector [89,90]. The farm’s location far from the city centre will make it difficult for farmers to adopt the risk strategy because the main village is a centre of input suppliers, output markets, and information access [51,91]. Several previous studies have explained the role of the distance between the farm and the city centre from different aspects. Mahmood et al. [6] highlight the farm distance as a determinant of the farmers’ convenience in obtaining inputs and increasing the possibility of planning and adopting the strategy. Bishu et al. [32] confirmed that longer farm distances would hinder farmers’ participation in the risk strategy adoption because it requires additional transportation and communication costs. The long distance from the rubber farm to the village centre will make it difficult for farmers to perform maintenance and tapping. Farmers perform latex tapping at least once every 2 days or more, depending on weather conditions at the time of tapping. Hence, the distance of the rubber farm is essential for farmers, especially when transporting rubber yield production. Furthermore, Khan et al. [83] stated that open access to information and communication could motivate farmers to adopt an income diversification strategy. Mulwa et al. [90] explained that easy access to informal sources from fellow farmers or traders could increase the probability of risk strategy adoption. The location of farms far from the city centre, damaged roads, and the lack of transportation facilities will hinder farmers from carrying out farming activities and implementing risk strategies [92]. The results of the analysis show that the distance variable has a significant effect on all choices of risk strategy adoption. This study suggests the government’s role in improving communication, transportation, and road access infrastructure to make it easier for farmers to perform farm maintenance, weeding, latex tapping, and other farming activities.

At a significance level of 10%, using rubber clones positively and significantly affects adopting an income diversification strategy. The relative risk ratio implies that if other variables are constant, using rubber clones will increase 3.680 times the possibility of farmers adopting income diversification relative to no strategy adoption. Farmers who use rubber clones show an adaptive attitude towards technology, which increases the possibility of farmers adopting risk strategies, especially income diversification strategies. This study’s results align with previous studies that found the positive effect of improved varieties on risk strategy adoption [54,92]. Furthermore, Rajak [93] also stated that using new varieties resistant to illnesses became one of the best management practices in the agriculture sector. The use of rubber clones at the research site is mostly PB 260. Razar et al. [94] explained the superiority of clone PB 260, which shows high latex production even under low input conditions. Therefore, the government and related stakeholders are needed to motivate and facilitate farmers using rubber clones, for example, by providing rubber clone seeds, input facilities, counselling, and assistance.

The rainy season risk perception was found to positively and significantly affect the adoption of plant disease control + income diversification and plant disease control + income diversification + group marketing strategies with a probability of 10%. The relative risk ratio implies that if other variables are constant, perceiving the rainy season as high risk will increase 11.319 and 8.572 times the possibility of farmers adopting plant disease control + income diversification and plant disease control + income diversification + group marketing strategies relative to no strategy adoption. Individual perceptions are essential in adopting strategies to mitigate rubber farming risks [87]. Our survey found that most respondents perceive the rainy season as high risk due to increased rainfall and rainy days in past years. Farmers find it difficult to carry out farm maintenance and latex tapping in the rainy season, which decreases production and farmers’ income. This condition motivates farmers to adopt the plant disease control + income diversification strategy. Previous studies also found the positive influence of high-risk perception on risk strategy adoption [7,29,31,37,52,53,91,95]. Khan et al. [7] found that risk perception mainly affects agricultural productivity, investment, and decision-making. Asravor [31] and Irham et al. [91] also explained that understanding farmers’ risk perception is essential to adopt the appropriate risk strategy. Farmers with high-risk perceptions tend to minimise the risks by adopting the appropriate strategies [37,53]. Furthermore, Mulwa et al. [90] state that farmers should pay attention to climate change conditions to identify and adopt the appropriate risk strategy. Adaptation to climate change can be improved through increased access to information on weather and climate [36,92]. These findings provide recommendations to the government and stakeholders to increase farmers’ adaptation capacity by providing information about climate change.

This study has limitations in several aspects, namely, the risk strategy adoption focused on plant disease control, income diversification, and group marketing. In future studies at different research locations, the possibility of farmers adopting other risk strategies can be explored, thus further enriching the literature. In addition, the multinomial logit model used in this study can be further enriched by adding other independent variables that influence the risk strategy adoption.

4 Conclusion and recommendations

Understanding risks in agriculture is essential for farmers to ensure that the decision to adopt the risk strategy will give positive results. This research focuses on the rubber production centre in West Kalimantan, Indonesia. However, the research method can be applied to future studies in other regions and countries, mainly in rubber-producing countries. The study results indicated that most rubber farmers (36%) adopt an income diversification strategy to mitigate the risks. Furthermore, farmers are also considering adopting multiple strategies, such as plant disease control + income diversification (13%), income diversification + group marketing (18%), and plant disease control + income diversification + group marketing (22%).

The study results indicate that the age of farmers, family members, age of rubber trees, rubber clones, and rainy season risk perception positively affect the adoption of risk strategy. In contrast, experience, farm area, and farm distance have a negative effect. We focus on policy implications on three variables significantly affecting all choices of risk strategy adoption, such as family members, rubber area, and farm distance. Family members positively affect all choices of risk strategy adoption, so this study suggests that the government and related stakeholders can provide assistance and counselling to farmers and their family members to motivate them to adopt risk strategies. The rubber area and farm distance variables negatively affect all risk strategy choices. This study suggests that the government and related stakeholders can provide capital assistance and input access to farmers with small farm areas to improve farmer performance in rubber farming activities and adopt risk strategies. Regarding farm distance, infrastructure improvements in communication, transportation, road access, and information provision are needed to adopt risk strategies effectively. The role of government and relevant stakeholders is also essential to improve adaptive capacity through assistance to farmers in applying rubber clones, replanting old rubber trees, and providing information about climate change.

Moreover, this study contributes to helping farmers make decisions and provides valuable information for assessing risk strategies. For the government and policymakers, this study contributes to identifying policy programs for developing rubber farming and adopting risk strategies. Future research could include and identify additional explanatory variables and the possibility of other risk strategy adoption to improve model predictability in understanding the risk strategy adoption and enrich the literature.

Acknowledgments

The authors are grateful to the Indonesian Ministry of Education, Culture, Research, and Technology for giving a doctoral scholarship and research support. Furthermore, we value the intelligent remarks and suggestions made by our anonymous reviewers.

  1. Funding information: This study was financed with sources of Indonesian Ministry of Education, Culture, Research, and Technology.

  2. Author contributions: II: conceptualisation, formal analysis, investigation, methodology, validation, visualisation, writing – original draft, writing – review and editing. JHM: conceptualisation, formal analysis, methodology, validation, visualisation, writing – review and editing. AS: conceptualisation, methodology, visualisation, writing – review and editing. MM: conceptualisation, methodology, visualisation, writing – review and editing.

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

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

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Received: 2023-03-09
Revised: 2023-03-30
Accepted: 2023-04-01
Published Online: 2023-05-16

© 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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  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
https://doi.org/10.1515/opag-2022-0196
Keywords for this article
multinomial logit model; risk perception; risk strategy adoption; rubber farmers
Creative Commons
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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