Improvement of coffee production performance via integrated lean and automated mechanization techniques

Abstract

Thailand is among high-ranking coffee-exporting countries in the world. This study investigated, analyzed and proposed ideas for improved production of coffee to meet the demands from the local and international market. Using data collected from site visits, observation and interviews, value stream mapping analysis was used to identify the activities in the coffee production process. Automated machine deployment was used to improve the performance of coffee production. From the findings, it was shown that the total cycle time could be reduced by more than 82 %, the profit of sorted cherry coffee could be increased by 80%, and the processing time of green bean coffee could be reduced by almost 97%. An automated color sorting machine was deployed to improve the production process. With integrated lean and automated mechanization techniques, farmers can gain several benefits and become more responsive to future customer demands.

Keywords:

• coffee
• value stream mapping
• automated machine
• production improvement
• processing time

1. Introduction

Hill tribe coffee in northern Thailand is popular nowadays. Unstable cherry coffee quality due to a labor shortage for color sorting and a lack of instability of green bean coffee in the drying process, affect the coffee production. Consequently, farmers focus on improving coffee production to be a leader in full-range manufacturing and product quality standards to boost business competitiveness and success in domestic and global markets (Kittichotsatsawat & Tippayawong, 2021). They seek to develop a production process that allows for cost reduction, process improvement, and, most importantly, the ability to react to the needs of customers. In order to increase the quality of coffee, after harvesting, those cherries could be sorted by color. A color sorter generally is a sorting machine for various agricultural products, such as grain, beans, corn seeds, and tomatoes (Goggi et al., 2006; Lee et al., 2020; Pasikatan & Dowell, 2003; Ruoyu et al., 2010). Moreover, not only could it be used explicitly with coffee beans (Kennedy et al., 2021), but it could also be applied to cherry coffee to solve the problem of different color shades after harvesting and add quality to the final product.

Over the years, farmers have focused on improving the hill tribe coffee process. No sorting out the cherry coffee is done before the drying process. Coffee producers initially sort out the colors of the coffee cherry with human labor before putting them into production. As a result, the production cost increases and raises quality concerns. Moreover, it is highly dependent on labor. Consequently, labor shortage is the main problem in the coffee harvesting season of hill tribe coffee in northern Thailand (Kittichotsatsawat et al., 2021), bringing about limitations in increasing the quality of coffee products. Improving quality of coffee may begin with color sorting before the blending and roasting processes. Therefore, adopting automated machine for sorting out cherry coffee could be very useful.

After the cherry coffee is sorted, it is processed in the drying step. Currently, the conventional drying process of cherry coffee is natural sun drying. It is processed from cherry coffee to green bean coffee by reducing the humidity in controlled conditions at 33–35 ^◦C for 30 days (720 h). However, if a dryer is used, the processed green bean coffee is done at 40–42^◦C with flow speed of 0.3–1.7 m/s. With the dryer, it can provide high-quality green bean coffee that matches international standard quality because the machine can control the conditions such as moisture content, temperature, time, and heat transfer rate (Salengke et al., 2019).

Value stream mapping (VSM) is one of the lean techniques to identify the activity in the process, which can categorize into three groups, including value added (VA), non-added value (NVA), and non-necessary value added (NNVA). There is used to identify activities and analyze problems in the process. For example, identify and improve the supply chain activity of Thai parboiled rice, identify risks in each process of the Hong Kong rice, identify the activity in the canned corn supply chain in order to evaluate the operational efficiency, identify the activity of production line to eliminate the waste activity for improving the environmental impact index, identify the production process activity to reduce the shortening delivery time in order reduce costs in the color industry, etc (Lam et al., 2015; Rohania & Zahraeea, 2015; Roosen & Pons, 2013; Thitijaroenpong, 2009; Wattanutchariya et al., 2016; Westin et al., 2013).

Similarly, the mechanization technique is utilized to improve and enhance productivity and control the quality standard of products in agricultural products, which can communicate between machines and people through advanced machine technology, such as robotics and automation machines, including the transfer component, conveyor, and auto vehicle machine, which must be used to install and configure the sensor (Kamilaris & Prenafeta-Bold, 2018). Furthermore, it can monitor and track information related to manufacturing, such as raw materials and data storage, as well as integrate all value chains into the system, such as Wireless Sensor Networks (WSN), Cloud computing, Internet of Things (IoT), Image processing, Convolutional Neural Networks (CNN), Remote sensing, Artificial Neural Networks (ANN), and blockchain, among other things (Kittichotsatsawat et al., 2021). A color sorter is a popular automated machine used to classify and sort agricultural items such as grain, corn, rice products, tomato, and coffee bean (Chaitanya et al., 2019; Jie et al., 2011; Pearson, 2009; Zhong et al., 2014). Meanwhile, the dryer machine is utilized to reduce the coffee beans’ moisture content to control the quality of products to gain the high-quality after dryer-processed green bean coffee (Alves et al., 2017; Pintana et al., 2016; Thanompongchart et al., 2016, 2017; Tippayawong et al., 2008, 2009, 2009).

It is clear that mechanization techniques have the potential to be helpful in coffee production. It can tackle the difficulties of unstable cherry coffee quality and a lack of stability of green bean coffee in the drying process by deploying a color sorter and a dryer throughout the coffee production. To the authors’ knowledge, no previous study was conducted to employ these methods in coffee production (automated mechanization and lean improvement techniques). So, in this study, process improvement of coffee production with lean and mechanization techniques was proposed.

2. Materials and methods

2.1. Exploratory study of coffee production process

A thorough understanding of the entire coffee production process was need to find opportunities for improvement. Data on the coffee production process and factors that significantly affect the product quality were gathered through interviews with 15 farmers, 15 coffee producers, and one entrepreneur as part of the value chain activities. The purpose of this research was to assess and improve the activities and efficiency of coffee production in northern Thailand. The coffee production framework includes farmers, production processors and entrepreneur. The coffee process was sketched through a lean technique in which value stream mapping (VSM) was used to identify each step of the coffee process activity and examine the problem through three categories: value added (VA), non-value added (NVA), and necessary non-value-added activity (NNVA). It was used to identify each stage of the coffee-producing process. After data collection, the data was analyzed using VSM to determine the coffee process. VA, NVA, and NNVA were recognized as the overall actions in the coffee process. After that, the current state mapping of the process of coffee production was created.

Mechanization is advancing and contributing to greater efficiency (ODI, 2016). Subsequently, machines were utilized in coffee production sectors to improve operational efficiency and create value in the business. It was expected to increase opportunities and productivity in the production process while decreasing costs, resources, and management. These tools are considered to be relatively new for agricultural production, especially coffee production.

2.2. Application of lean technique

VSM was applied for identifying the VA, NVA, and NNVA activities in the current state of the coffee process. Then, the future state was created as a new flow chart or mapping. Finally, a way to improve the coffee production efficiency was suggested.

VSM is a lean manufacturing tool that attempts to map your process from supplier to customer by highlighting product and information flows and identifying delays and non-value-adding processes. Moreover, VSM is one of the most popular tools for identifying and categorizing supply chain activities, focusing on the flow of materials and information required to deliver a product to a consumer, which categorizes supply chain activities into three major types: Value Added (VA), Non-Value Added (NVA), and Necessary-Non-Value Added (NNVA).

VSM was applied to a Thai parboiled rice supply chain, showing that the supply chain could be reduced from 34 to 12 activities (32% reduction) (Wattanutchariya et al., 2016). It was also used to identify risks at various stages in the Hong Kong rice supply chain in order to ensure supply chain performance (Lam et al., 2015) and to evaluate the operational efficiency of the canned corn supply chain to group activities ranging from seed procurement to export transportation (Thitijaroenpong, 2009). Westin et al. (2013) discussed how VSM was used to identify and eliminate waste in order to improve production flow through analysis and sourcing inspection. Furthermore, VSM was used to improving the environmental impact index using an integrated VSM technique to identify activities in the production line (Rohania & Zahraeea, 2015; Roosen & Pons, 2013). This tool could be used by production staff to quantify environmental impacts at the process level and aggregated waste disposal documents for the entire value chain. Moreover, VSM proved as a helpful technique for shortening delivery time and lowering production costs in the color industry, reducing delivery time from 8.5 to 6 days, representing a 30% and 46% reduction, respectively. Previous research revealed that VSM was mainly used to analyze companies with a defined scope of the production line. VSM has never been used to analyze or classify activities throughout an agricultural product ‘s supply chain from upstream (farmers) to downstream (customer for the farmer’s coffee beans).

According to the literature, VSM methods were potential tools for supply chain improvement in the food industry. Additionally, they assisted in improving efficiency in production procedures and instructions, which affected the required supply chain and reduced the problems in the organization. This study employs VSM to analyze and identify supply chain activities in order to identify coffee supply chain activity in northern Thailand.

2.3. Application of mechanization in the production system management

After data analysis from upstream to downstream, starting from the harvest of cherry coffee to the green bean coffee, the coffee production process was improved by applying the color sorter and dryer machines. These were utilized to improve the cherry coffee and green bean coffee by controlling the quality of the product.

2.3.1. Color sorter machine

Smart factory or smart manufacturing is the combination of the process and organization of all parts. The components of the intelligent factory, such as automatics, robots, internet, cyber-physical system, big data, advanced device, sensors, etc., are the automatic work process. These technologies, when combined, can be integrated with human labor, paper documentation, big data analysis and organization management (Pereira & Romero, 2017). The smart machine can communicate between machines and humans, via the sensors and tools programmed by humans based on advanced technology such as robotics and automation machines (Kamilaris & Prenafeta-Bold, 2018). Moreover, the smart machines can track and monitor the production information such as raw materials, data storage, etc., as well as integrating all components in the value chains by utilizing artificial intelligence in the production process, for example, Wireless Sensor Networks (WSN), Cloud computing, Internet of Things (IoT), Image processing, Convolutional Neural Networks (CNN), Remote sensing, Artificial Neural Networks (ANN) and blockchain (Kittichotsatsawat et al., 2021).,

Currently, the coffee business is interested in using smart machines in its operations, and the popular machines for color and defect separation are the color sorter. Interestingly, this machine can sort colors and defects from agricultural products to reduce time and control the stable quality of cherry coffee classified by a human. In addition, color sorter was utilized to classify and sort out the productivity in agriculture products, such as, grain, corn, rice products, tomato, coffee bean, etc (Chaitanya et al., 2019; Jie et al., 2011; Pearson, 2009; Zhong et al., 2014). According to the study of the color sorter, it was found the factors affected the quality of the coffee cherry were the process of color detection, light intensity sensor, speed of conveyor belt, and air pressure. Moreover, these factors influence the quality of the product.

2.3.2. Drying machine

The purpose of water removal is to prolong the shelf life of food by reducing its water activity which inhibits microbial growth and enzyme activity. In addition, reducing the weight and volume of food also minimizes storage costs and transportation convenience for consumers. The drying mechanism is when hot air or air blows over the surface of wet food, heat is transferred to the surface of the food, and it evaporates with the latent heat of vaporization. The water vapor diffuses through the air film and is carried away by the moving hot air. This condition will cause the vapor pressure at the surface of the food to be lower. The steam pressure in the food affects to steam pressure difference. The inner layer of food has a high steam pressure and gradually decreases. When the food layer approaches the dry air, this difference causes pressure to expel water from the food (Nielsen, 2010).

In order to reduce the moisture content of the coffee beans, the moisture content of dried coffee beans should not exceed 12 % w.b (Gautz et al., 2008). Drying at 60 °C affected the quality of coffee in storage to deteriorate (Coradi et al., 2007). While drying temperature at 45 °C, including air speed did not influence on the quality of the hulled, dried coffee beans. Interestingly, accelerating of drying rate by increasing the temperature to reach the desired temperature has negatively affect to the coffee’s texture (Alves et al., 2017; Pintana et al., 2016; Thanompongchart et al., 2016, 2017; Tippayawong et al., 2008, 2009, 2009).

According to the study of the dryer machine, it can be used to control the quality of coffee instead of drying them naturally. The farmers can plan production and forecast consumer demand in the long term. In addition, if smart machine is introduced to help in the production system, it will be able to compete more with other competitors.

After a color sorter was utilized in the coffee production, it was found that the factors that affected the quality of the coffee cherry were the process of color detection, light intensity sensor, speed of the conveyer belt, and air pressure. These factors influence the quality of the product. Drying was found to affect the control of the quality of green bean coffee, if the farmer can manage and control the drying conditions effecting, they can improve production efficiency and obtain the benefit of their production sustainability.

3. Results and discussions

3.1. Process improvement by VSM

VSM was performed to examine the entire process, from harvesting cherry coffee to the transportation and activities in the coffee production process, including time spent in each phase. The VSM of the coffee cherry production process had 23 steps, from harvesting cherry coffee on the farm to packing bales in the northern coffee roasting factory. However, the major cause of unpredictability in the process was discovered to be related to the raw material supply and productivity after-harvest stage. Finally, the information was gathered through various methods, such as interviews, site visits, and questionnaires, as shown in Figure 1.

Figure 1. The current state VSM of the cherry coffee production process (Kittichotsatsawat & Tippayawong, 2021).

After the coffee process activity was identified, the NVA and NNVA activities were shown in the VSM mapping process. However, three points were found; (1) The step before harvesting of cherry coffee activity due to unpredictable productivity each year resulted in inconsistent demand, supply, and limited trade opportunities. (2) The sorting step of cherry coffee by manual labor was a time-consuming process, leading to inconsistent quality of cherry coffee and high labor costs. (3) The drying process was uncertain due to moisture content fluctuation of coffee after drying, affecting the quality of green bean coffee.

Table 1 shows the current states. It can be seen that almost 70% of total time were VA. The majority of VA activity was from harvesting cherry coffee, sorting out cherry coffee and drying activities which had serious risks and uncontrollable factors. The NVA and NNVA were around 22% and 9%, respectively. However, the ratio of NVA and NNVA was about 30%, activities including the coffee production process and transportation. If the coffee production process and transportation time are reduced, the total cycle time in supply chain will be decreased.

Table 1. The activities of current state are categorized and summarized

Type of activities	Current State
	No. of Activities	%	Time (min)	%
Value Added (VA)	16	69.6	50,625	97.0
Non-Added Value (NVA)	5	21.7	102	0.2
Non-Necessary Value Added (NNVA)	2	8.7	1,445	2.8
Total	23	100	52,172	100

After identifying the activities in the coffee process, mechanization was applied to the process to improve all unnecessary activities and reduce time and cost. The VA activities were improved and the time of VA were decreased. Coffee production process could be created as shown in Figure 2. The time of sorting out cherry coffee by labor could be reduced from 1,440 to 48–240 min (97–83%), and the time of the drying process from 43,200 to 1,440 min (97%).

Figure 2. Mechanization applied to improve the performance of the future state.

From Figure 2, the harvesting of the cherry coffee process, the sorting out of cherry coffee by labor, and the drying process were found to be bottlenecks. For the coffee production improvement, deployment of the color sorter and dryer machine could solve some of the problems.

Table 2 compares the activities of the current state and improved performance. The result showed that the performance of VA could significantly be improved. The production time of the supply chain could be reduced from 52,172 to 9,212 min, more than 82 % reduction in the total cycle time.

Table 2. Improved performance of the coffee production process by VSM

Type of activities	Improved State			% Reduction
	Time (min) (To-Be)	Time (min) (As-Is)	Time reduction (min)
Value Added (VA)	50,625	7,665	42,960	84.85
Non-Added Value (NVA)	102	102	-	-
Non-Necessary Value Added (NNVA)	1,445	1,445	-	-
Total	52,172	9,212	42,960	82.34

3.2. Process improvement by mechanization

3.2.1. Color sorter machine

An automated color sorter was deployed to sort the cherry coffee with different color shades to increase the efficiency of cherry coffee production. The camera was used to detect the difference in color shade based on the CCD sensor and CIELAB system. The cherry coffee was separated. Coffee with distinct color was transported via individual conveyor belt. Next, the cherry coffee was crushed by the air compressor. Finally, farmers or entrepreneurs can obtain cherry coffee with the same color separating into red, yellow, or green.

Once the color sorter machine was adopted in place of manual labor, it was shown that the capacity of sorting out cherry coffee increased from 200–300 kg/8 h a day or 25 to 37.5 kg/person/h to 150–500 kg/h/one color sorter. The sorter’s capacity was increased by 83–93% per one color sorter, reducing labor costs from 8–10 THB/kg to 2–3 THB/kg. This was 77–80% labor cost reduction per kg product. A simple economic analysis from the initial cost includes three parts, i) sorting cost, ii) break-even point, and iii) profit and benefit.

3.2.1.1. Sorting cost

Price of the color sorter machine = 2,000,000 THB (Planned/Preventive maintenance, n = 10 years).

1.1) Color sorter machine

If n = 10 years, the planned/preventive maintenance is 2,000,000/10×12 = 17,000 THB/month.

If n = 5 years, the planned/preventive maintenance is 2,000,000/5×12 = 34,000 THB/month.

1.2) Electricity cost of coffee sorting machine (Color Sorter)

Electricity cost = 4,000 THB/month (48,000 THB/year)

1.3) Labor cost

Labor cost 300 THB/day (8 hrs.) x 25 = 7,500 THB/month (90,000 THB/year).

1.4) Sorter color machine capacity

The capacity of color sorter per month (25 days) = 30,000–100,000 kg/month.

1.5) Expense

N = 5 years, the planned/preventive maintenance is 34,000 × 4,000 × 7,500 = 455,000 THB. Thus, sorting/kg = 45,000/(30,000 and 100,000) = 0.46–1.51 THB/kg.

The labor can sort out the color shade of cherry coffee at about 8–10 THB/kg/person. However, in 1 year crop, the coffee will bear fruit for an estimated 5–6 months per year. Thus, the cost from sorting out by labor is (8–10 THB/kg) x (30,000 to 100,000 kg) x 6 months = 1,444,000–6,000,000 THB/year. In contrast, if the farmer uses the color sorter instead of the labor, the cost will reduce by 0.46–1.51 THB/kg, which means that the sorting cost will decrease by 84.90–94.25%. In addition, a capacity color sorter can operate 3 to 4 rounds per day. Hence, if the sorting machine is available, the farmer can earn extra income by providing sorting service to other coffee producers.

3.2.1.2. Break-even point

In summary, switching from manual labor to a color sorter machine can reduce the cost of sorting cherry coffee from 8–10 THB/kg to 0.46–1.51 THB/kg, a decrease of 7.54–8.49 THB/kg. If manual labor is used, the farmers can sort out 235,571 to 265,251 kg of cherry coffee in a month. However, with a daily production of 1,000 to 1,500 kg, the payback period for the color sorter machine would be 177 to 235 days. The color sorter machine has a capacity of sorting 4 rounds per day, with a total of 1,200 to 4,000 kg of cherry coffee.

• 2.1) The capacity for sorting the cherry coffee using the labor in one year; they can sort out up to 60 - 90 tons/per year (the cost for sorting is about 8 to 10 THB/kg).

• 2.2). The capacity for sorting the cherry coffee using the color sorter machine is 360 - 1,200 tons per year (the cost of sorting is about 0.46 - 1.51 THB/kg).

4. Profit and benefit

• 3.1) The capacity of community enterprise can produce per year: 90 tons x 1,000 kg x 8 THB = 720,000 THB.

• 3.2) If the other suppliers use the color sorter to sort out the cherry coffee, it can calculate as 500 tons x 1,000 kg x 2 THB/kg = 1,000,000 THB.

Thus, the farmers or entrepreneurs obtain 720,000 + 1,000,000 = 1,720,000 THB. If they invest 2,000,000/1,720,000 THB, they will gain the payback within 1.16 years.

4.1. Dryer machine

In summary, the implementation of a drying machine in the coffee drying process has several benefits:

1. Reducing drying time, increasing production volume by 20% (from 100 tons to 120 tons per year), resulting in an increase in income of 3,000,000 THB per year.

2. Reducing the loss of dried kernels by 5% (from 5 tons per year) with a value of 750,000 THB per year.

3. Allowing for optimum drying conditions, promoting the production of unique high-quality products and increasing sales by at least 10%.

4. Helping produced stable quality products and increasing customer confidence, helping maintain a customer base in a competitive coffee market.

Adopting mechanization to the coffee production process can improve the quality and the accuracy of coffee to match customer needs. Finally, it can lead to increased efficiency and productivity for farmers and entrepreneurs and improve their profits.

It was high time for the coffee production process to be mechanized to improve the efficiency of coffee process activity to match customer needs. It is noted that this study focused on small coffee farmers in northern Thailand. There are numerous coffee farms in Thailand who are still short of knowledges and experiences in managing the process effectively. Therefore, this study places a strong emphasis on coffee production management and looks into developing towards highly efficient production processes. The findings from this case study can be applied, modified and used in larger coffee farms.

The study also highlighted three main challenges in the coffee production, which included inconsistent productivity, prolong period of sorting out cherry coffee by manual labor, and uncertainty of coffee bean after natural drying. It was also shown that VSM and mechanization could be applied to mitigate these issues and improve the efficiency of the coffee production.

Color sorters are specialized machines used in agriculture and food processing facilities to inspect, sort, and grade agricultural products based on their color, shape, or other optical characteristics. For example, color sorters are commonly used to sort grains such as rice, wheat, barley, and maize based on color, size, and foreign material content (Wu et al., 2016). Moreover, it can be employed to remove damaged or discolored seeds like soybeans, sunflower seeds, and sesame seeds (Wu et al., 2022). It was utilized to detect and ensure uniformity in color and quality of almonds, cashews, and pistachios (Lunadei et al., 2013). Color sorters were employed in the grading of vegetables process in order to remove damaged or spoiled items, such as tomatoes and potatoes (Rajkumar et al., 2022) and classify the color and size of raisins and dried apricots (Falconer et al., 2006).

In summary, color sorters are valuable tools in the agricultural and food processing industries, ensuring the quality, safety, and consistency of a wide range of agricultural products. They help improve product quality, reduce waste, enhance processing efficiency, and contribute to the overall food supply chain’s reliability and safety.

Drying plays a significant role in agriculture, particularly in the post-harvest phase, by reducing the moisture content of agricultural products to improve their shelf life, reduce spoilage, and maintain quality. It was utilized to rice, corn, apples, apricots, grapes, almonds, cashews, peanuts, sunflower seeds, sesame seeds to reduce their moisture content (Brar & Danyluk, 2018; Dalipagic & Elepu, 2014; Gragasin et al., 2004; Kaymak-Ertekin & Gedik, 2004). It was used to preserve flavor and aroma in herbs and spices and onions and garlic (Kaymak-Ertekin & Gedik, 2005; Verboloz et al., 2020). It was utilized in tobacco leaves, forage and hay, tea leaves, herbs and medicinal plants (Condorí et al., 2020; Undersander & Saxe, 2013; Xie et al., 2014; Yuliana et al., 2022) and fish and seafood industry (Desnanjaya et al., 2023).

It contributes to the efficient post-harvest handling and preservation of these products, ensuring these reach consumers in good condition while extending their shelf life for market distribution or further processing.

5. Conclusion and recommendations

This research work aimed to analyze and improve the coffee production in SMEs. The study utilized VSM and mechanization to improve the coffee production for the community enterprises of hill tribe in northern Thailand. From the VSM, the coffee production could be improved by cutting the VA activities. It was shown that farmers could reduce VA time in sorting out cherry coffee by labor and drying process. Moreover, the coffee production performance could be increased by reducing the total cycle time, increasing the profit of sorting out the cherry coffee by reducing labor costs from 8–10 THB/kg to 2–3 THB/kg, and reducing the drying time of the green bean by 97% which affect to the increasing production volume by 20%, which could be beneficial in reducing operating costs and cycle time. It was suggested that entrepreneurs should plan the demand, including emphasized demand forecasting, agriculture, and production planning. A better arrangement could bring about better fulfillment of customer demand and transportation frequency. However, transportation time was not investigated in this study. Transportation time reduction in coffee production was focused on for further study. Moreover, this study could be more interesting if coffee production costs are considered.

The limitation of the color sorters primarily relies on visual criteria, such as color, shape, and optical characteristics. They may not be suitable for sorting products based on other factors like size, weight, or chemical composition. In some cases, it is unable to detect internal defects. High-quality color sorting machines can be expensive and affect significant investment for (SMEs). Dryer machines will consume energy for the drying process, leading to extra operational costs and environmental concerns. Over-drying or uneven drying can affect the quality of agricultural products, causing product shrinkage, loss of flavor, or reduced nutritional value.

For future work, advanced technologies may be employed for color sorters. It may incorporate more advanced sensors, such as hyperspectral imaging and multispectral cameras, to detect a wider range of product characteristics beyond color. More precise sorting based on factors such as internal defects and chemical composition may be possible. Dryer machines will likely incorporate energy-efficient technologies, such as heat recovery systems and improved insulation, to reduce energy consumption and operational costs. Smart dryer machines may be developed that automatically adjust drying parameters based on product characteristics and environmental conditions to obtain future sustainability.

Disclosure statement

No potential conflict of interest was reported by the author(s).