Evaluating Sustainable Alternatives for Cocoa Waste Utilization Using the Analytic Hierarchy Process

Abstract

Cocoa production has emerged as an effective agricultural strategy to reduce conflict in Colombia, transitioning from coca to cocoa cultivation. While this shift has provided economic benefits, it has also resulted in the generation of substantial cocoa by-products. Although there are various alternative methods of utilizing these by-products, many farmers are unaware of them, and others lack the necessary tools to determine which alternative is the best to pursue. This study sought to explore sustainable options for cocoa waste utilization through the application of the Analytic Hierarchy Process (AHP). By employing technological surveillance, viable options for reusing cocoa residues were identified. The AHP results indicate that pellet production is a promising alternative for rural communities. It is also a potential source of energy that could address the community’s need for alternative energy sources. Initially, other energy production alternatives were not explored. However, in response to the AHP findings, this study also explored the use of cocoa waste combined with animal manure for energy generation through anaerobic digestion.

1. Introduction

Cocoa production stands out as an agricultural strategy for reducing conflict in Colombia. This has become especially clear since 2010, when the process of replacing coca crops with cocoa began [1]. Cocoa is profitable due to its high global demand, making it a suitable crop to replace illicit crops. This strategy has enabled many Colombian families to generate employment in rural areas and obtain legal economic income. In 2022, cocoa production reached 62,158 tons, with the department of Santander being the largest producer nationally with 36.8%, followed by Arauca with 16.9%, Antioquia with 8.3%, Tolima with 5.8%, Huila with 5.7%, and Nariño with 5.4% [2]. Tolima ranks fourth due to its favorable agroclimatic conditions, which give its crops a competitive advantage, facilitating their access to international markets [3].

Cocoa bean exports decreased from 11,309 tons in 2021 to 5721 tons by November 2022, a drop of 50.5%. However, exports of semi-processed and processed products increased by 21%. These figures reflect how, in recent years, the transformation of cocoa in Colombia has been promoted, generating greater income opportunities for entrepreneurs and the country. Nevertheless, this transformation has also increased the generation of cocoa fruit residues, which require proper disposal or comprehensive utilization for their valorization and the development of new intermediate or final products.

In rural areas, it is common for cocoa pod husks to be used as fertilizer for crops. However, excess husks in the soil can have negative effects, causing diseases in cocoa trees and phytosanitary problems, which can result in significant crop losses due to improper waste management and the proliferation of pests like the cocoa pod borer (CPB) [4]. Inadequate disposal of these residues raises environmental concerns due to the possible spread of diseases and bad odors [5,6]. Despite the existence of valuable applications for agricultural waste that can add significant value, many communities lack sufficient information on how to implement these applications effectively.

In contexts where multiple options are available, the decision-making process becomes intricate due to the diverse criteria that can affect the selection of the best alternative. Social, economic, technological, and environmental considerations often conflict, making it important to balance these competing factors when making decisions [7]. Multi-Criteria Analysis, also known as Multi-Criteria Decision Making (MCDM) or Multi-Criteria Decision Aid (MCDA) methods, addresses the challenge of making decisions that involve multiple criteria. These approaches can accommodate both quantitative and qualitative criteria and are characterized by conflicts among the criteria and challenges in designing or selecting alternatives [8]. In general, Multi-Criteria Decision Making (MCDM) methods are categorized into two main types: Multi-Objective Decision Making (MODM) and Multi-Attribute Decision Making (MADM). The key difference between these two categories lies in how alternatives are determined within the decision-making process. In MODM, alternatives are not predetermined, while in MADM, alternatives are predetermined and evaluated against a set of attributes [9]. In general, Multi-Criteria Analysis (MCA) is a good tool, particularly in areas and sectors where methods based on a single criterion prove inadequate, and where some impacts cannot be accurately quantified in monetary terms [10,11].

In the field of multi-criteria decision-making (MCDM), various methods are commonly employed, each offering unique advantages and facing specific limitations. Among the most widely used is the Analytic Hierarchy Process (AHP), valued for its ability to decompose complex decisions into a hierarchical structure, facilitating the integration of both qualitative and quantitative criteria [12]. Another popular method is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which has a straightforward approach to ranking alternatives based on their proximity to an ideal solution [13]. Additionally, PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) stands out for its effectiveness in managing outranking problems, making it particularly useful in situations with conflicting criteria. Its visual tools, such as GAIA planes, provide decision-makers with an intuitive means of analyzing alternatives, enhancing the overall decision-making process [14].

Among multi-criteria decision-making (MCDM) methods, the Analytic Hierarchy Process (AHP) has been widely applied in the fields of agroindustry and agriculture, proving to be an important tool for decision-making in complex scenarios involving multiple criteria. In the agroindustrial sector, AHP has been used to optimize supply chain management and to select the most suitable technologies for processing agricultural products. For example, AHP has been employed to prioritize alternative production technologies, considering factors such as cost, efficiency, and environmental impact [15].

Similarly, in the agricultural sector, AHP has been used to support decisions related to crop selection and resource allocation. It has also been applied to determine the most appropriate crop rotation systems, considering soil conditions, water availability, and market demand [16]. Additionally, in the area of sustainable agriculture, AHP has been used to evaluate and prioritize water management strategies that balance economic viability with environmental sustainability [17].

The widespread use of AHP is grounded in its greater flexibility in handling subjective judgments compared to other methods, such as TOPSIS or PROMETHEE. This is a significant feature when working with criteria defined by local communities.

The utilization of cocoa waste can be approached using the AHP methodology, which can help optimize decision-making to maximize environmental, economic, and social benefits. The Analytic Hierarchy Process (AHP) is a structured technique used for organizing and analyzing decisions. AHP helps decision-makers set priorities and make optimal choices by breaking down complex decisions into a series of pairwise comparisons, then synthesizing the results to include both qualitative and quantitative criteria [18]. The utilization of cocoa waste can be addressed using the AHP methodology, which aids in optimizing decision-making to maximize environmental, economic, and social benefits. This approach not only enhances waste management efficiency but also promotes sustainable practices and improves the quality of life for the communities involved.

Recent studies have highlighted the utilization of cocoa waste in the production of biofuels in solid, liquid, and gaseous forms. According to Lu et al. (2018), the cocoa pod husk constitutes 75% of the total weight of the cocoa fruit. It is the main byproduct generated from the cocoa process and, due to its lignocellulose content, can be used as a biofuel source [19]. For example, the use of cocoa pod husks in biochar fuel production at relatively low pyrolysis temperatures resulted in a bioresource with a calorific value of 17.8 MJ/kg and a high potassium content, resembling lignite [20]. Furthermore, the pyrolytic conversion of cocoa pod husks at different temperatures (300 to 600 °C) produced a bio-oil with a calorific value of 36.23 MJ/kg, similar to diesel [21]. On the other hand, cocoa residues are a good source for bioethanol production. Fermentation using Zymomonas mobilis with an initial microorganism dose of 14% v/v achieved an alcohol content of 10.62% on the 8th day of the process [22] (Billah et al., 2020). Additionally, some research has shown that anaerobic digestion could be an effective process for treating cocoa waste despite its low biodegradability (0.41) due to its high lignin content [23] (Antwi et al., 2019). In these cases, chemical and hydrothermal pretreatments have been used to improve biogas performance [23,24]. Moreover, Rojas et al. (2020) demonstrated at a laboratory scale the production of hydrogen from cocoa mucilage using dark fermentation processes [25]. The maximum hydrogen production was achieved under mesophilic conditions because the hydrolysis stage was longer. In this context, cocoa waste has the potential to be a biomass source for biofuels due to its chemical composition. However, the small quantity of cocoa waste produced in agricultural units may pose a limitation for these technologies.

The use of waste in the cocoa supply chain to produce energy production not only adds value to the waste but also enhances the quality of life for community members. Some technologies can add value to these solid residues by transforming them into renewable energy through thermochemical processes such as combustion [26] and biochemical processes such as anaerobic digestion [27]. Combustion, which transforms biomass into heat in the presence of oxygen, may be limited by economic and technical factors at the local level [28], as it requires physical conditioning of the residue, such as drying and pelletizing. However, there are other processes that can produce energy with minimal costs. Anaerobic digestion (AD) is a biochemical process in which a microbial consortium, in the absence of oxygen, promotes the transformation of available organic matter into by-products such as biogas and nutrient-rich fluids [29]. By using low-cost technologies like biodigesters [30], the organic load and environmental impact of the residues can be reduced, generating valuable by-products that can improve the socioeconomic conditions of rural communities in low-income countries [31]. Therefore, the proper use of cocoa fruit residues and other residues generated in production units not only benefits soil health but also boosts the economy of producers.

This study conducts an analysis in a cocoa-producing community in Tolima, examining their harvesting and post-harvesting techniques. Subsequently, a hierarchical analysis is applied to identify alternative uses for the waste. Based on the results of the AHP, a thermochemical energy production alternative was prioritized. However, in this initial analysis, other types of energy generation were not considered relevant and, therefore, were not included in the AHP evaluation. Nevertheless, in response to the AHP findings, this study also explored, in parallel, the potential of cocoa husks and other residues produced in the production units as sources for biochemical energy generation, aiming to utilize organic waste. The study demonstrates an application for utilizing cocoa waste in energy generation through biodigesters.

2. Methodology

2.1. Characterization of the Production Process in the Region

A survey was designed and administered to farmers from the productive units in a community in Tolima with the aim of investigating agricultural practices throughout the cocoa production process. The survey focused on elements related to the cultivation, management, and handling of the products and by-products generated, as well as the stages and conditions of the cocoa transformation process carried out by the surveyed farmers.

For the survey design, the categorical analysis technique was employed, evaluating three specific categories: general information on cocoa cultivation, general practices and conditions of cocoa processing, and the identification of weaknesses in cocoa cultivation practices among farmers.

2.2. Analytical Hierarchy Process (AHP) Method

The Analytical Hierarchy Process (AHP) is a multi-criteria decision analysis (MCDA) method that uses ratio scales to measure performance on the considered criteria and the importance of these criteria [25]. The process can be divided into three main stages:

Hierarchical structure: In this stage, the criteria for evaluating the alternative solutions to the problem are defined.

Pair criteria comparison: In this stage, systematic comparisons between criteria and alternatives are made. Each comparison sets a relative value for each one of the criteria based on its importance.

Priority calculation: In this stage, a mathematical process is used to calculate the relative priorities of the criteria and alternatives based on the scores obtained in the comparison matrices. The result is a hierarchy of priorities that allows you to make informed and objective decisions.

In this study, the AHP method was used to determine which alternative methods of reutilizing cocoa wastes could have the most impact on the San Bernardo community. To identify alternatives for the use of residues of different cocoa wastes, technological surveillance was applied. The technological surveillance (TV) methodology developed for the exploration of alternatives was carried out using the technique of [32], which includes implementing a comprehensive network approach supported by the UNE 166006:2018 standard [33,34]. This standard considers communication to be one of the most important aspects of the management system and its use for the creation, storage, distribution, and utilization of information [35,36].

The Technological Surveillance model was designed to be divided into five sections (Figure 1).

The first phase, called Identify, is used to determine the technologies to monitor, the information needs, and the key monitoring factors of interest to carry out a study that will help us design an effective strategy for each case. The second step, called Searching, consists of defining the search equations and the specialized databases to be used for the collection of information. This step allows the capture of relevant information and determines keywords and the different search parameters such as the period. After that, the step called Analysis corroborates that the information obtained in previous steps corresponds to the main focus of the search. In this case, the required information must respond to the requirements to identify physical and chemical procedures implemented to utilize cocoa by-products.

From downloaded information, the step called Valorization takes care of carrying out a depuration process that consists of exclusively extracting papers related to the products obtained from cocoa wastes. Some papers focus on the cocoa process and others on products from cocoa liquor. According to their content, papers were grouped into categories according to their stated alternative uses for cocoa wastes. In this way, analysis of the literature of leading authors and their contributions of research to produce valorization of cocoa wastes was conducted.

Finally, within the fourth phase, called Results Intelligence, the information about the articles was stored in a structured, categorized, and classified way. All of this allows the definition of alternatives to be analyzed in the Analytical Hierarchy Process (AHP) method.

2.3. Experimental Set-Up for Biogas Production of Cocoa Pond by Anaerobic Co-Digestion

The anaerobic co-digestion of cocoa pond shells with raw cow manure was evaluated in two concentration ratios: 50% C–50% M and 70% C–30% M. The assays were performed in amber batch reactors of 250 mL, with a load volume of 160 mL, at room temperature (25 ± 2 °C, mesophilic range). A blank (cow manure content alone) was carried out to correct endogenous methane production. Biogas production was measured daily for 77 days. In all cases, the batch reactors were run in triplicate. The results were expressed as mean values and standard deviations. Standard statistical procedures were used, including a standard deviation, mean averaging, and absolute differences. The significance of the variance test was determined using variance analysis, with a significant level of 0.05 (p ≤ 0.05) considered significantly different.

Cocoa pond shells and cow manure were collected from the communities of rural areas of Ibagué-Tolima (Colombia). Cocoa pond shells were chopped with a food processor into sections of 1 to 5 mm. These were stored in plastic bags at 4 °C for about 10 days. Cow manure was stored in a plastic bottle of 2 L for about 3 days at room temperature and in the absence of oxygen.

Biogas production was performed using water displacement equipment, with a measure range of 1 to 100 mL of biogas, and reported in a standard temperature and pressure (STP) condition. This equipment was built taking into account the principles of fluid pressure and the model suggested by Ojikutu and Osokoya (2014) and Esposito et al. (2012) [37,38].

Composite samples were analyzed for various physicochemical parameters, as Total Solids (TS), Volatile Solids (VS), moisture, and ash, according to Standard Methods for the Examination of Water and Wastewater [39].

For the kinetic model, the modified Gompertz model was applied to adjust the experimental biogas production (Equation (1)). This model has been applied in modeling methane and biogas production in the anaerobic digestion process of lignocellulose material [40,41]

$$\beta \left(t\right)={\beta }_0·exp\left\{-exp\left[{\displaystyle \frac{{\mu }_{m}e}{{\beta }_0}}\left(\lambda -1\right)+1\right]\right\}$$

(1)

where β(t) is the cumulative biogas yield (mL/gSV), β₀ is the final biogas production (mL/gSV), μ_m refers to the maximum biogas production rate (mL/gSV.d), λ is the lag phase time (d), e is equal to 2.72, and t means the anaerobic digestion time (d). Furthermore, the parameters (μ_m and λ) were estimated using a non-linear square method of Matlab R2019A (lsqnonlin function) for all tests.

3. Results and Discusion

3.1. Characterization of the Production Process in the Region

Colombian farmers follow the necessary steps to ensure high-quality cocoa pods, relying on experience and observation rather than standardized methods. After the harvest, the pods are opened, mucilage is extracted, and the fermentation process begins, typically taking several days. Once the beans reach the desired moisture level, they are dried under indirect sunlight and stored in sacks. Among the farmers surveyed, 60% are directly involved in farming, while 40% hold administrative roles. Cocoa trees range in age from 7 to 30 years, with newer trees under two years old. Cocoa is cultivated on plots ranging from 3.25 to 15 hectares, with productive areas between 0.5 to 4.5 hectares. The most common cocoa varieties are Criollo, Clonados, CCN51, and Hybrid/Trinitario. Peak harvests occur in June and November, while August and September are less favorable. The lack of irrigation systems leaves 80% of participants vulnerable to drought-related losses, and land slippage during the rainy months is also a significant challenge.

Cocoa farmers follow traditional practices for harvesting, fermentation, and processing. They prioritize pod maturity during harvest, with only 20% classifying beans by size. Fermentation completion is judged by appearance, duration, texture, temperature, and smell, without pH measurement. Drying takes 3–15 days depending on climate, with wood being the most common container material. About 40% of farmers outsource roasting, shelling, and grinding, while the rest rely on traditional methods using wood stoves and steel containers, guided by visual cues. Grinding usually requires two to three passes through manual or electric mills. Key challenges include a lack of potable water, reliance on low-tech post-harvest methods that hinder traceability, and the absence of a gas pipeline, which complicates some post-harvest processes.

3.2. Technological Surveillance and AHP Method

3.2.1. Search for Alternatives for the Use of Cocoa Waste

Table 1. Equation used in databases for technological surveillance.

Equation (1)	Cocoa OR mucilage AND uses
Equation (2)	Cocoa And agribusiness OR harness OR pod husk
Equation (3)	Wastes And cocoa AND uses
Equation (4)	Cocoa OR cacaota AND Uses
Equation (5)	Cocoa OR Pod husk AND uses

shows the searching equations we used in databases. After carrying out the search in different databases with each of the equations described, a total of 1310 articles was obtained, of which only those whose publication date fell between the years 2015 and 2020 were included. It was also filtered by the type of document (scientific articles) and the type of access, resulting in 425 documents, of which those that were adapted to the case study valorization of cocoa residues in rural populations were selected. It is important to highlight that Latin American countries lead publications on the utilization of cocoa waste. In countries like Colombia, research on this topic has increased as many illicit crops have been replaced with cocoa crops.

3.2.2. Alternatives from Cocoa Waste

Once the documents were obtained, a list of uses for waste in the cocoa production process was compiled, presenting alternatives to add value to the by-products. This information was then filtered based on the alternatives that could be implemented in the community, focusing on cocoa pod husks and mucilage, which according to community interviews are the most problematic waste products. Five alternatives were selected that use mucilage or cocoa pod husks and are suitable for the community’s conditions: pellets, flour, pectin, vinegar, and alcoholic beverages.

3.2.3. Selection Criteria

The selection criteria were determined according to the context and needs of the community. The selection criteria are detailed below:

• Availability: This criterion evaluates the availability of raw material, referring to the amount of waste generated. Since the evaluated alternatives are only produced from two types of waste: mucilage and cocoa pod husks, the highest score was given to products with a high content of cocoa pod husks, as this is the most generated waste in the studied community.
• Implementation: This criterion evaluates the equipment needed to produce each product, aiming for low investment requirements. To evaluate this, we identified the necessary equipment for each alternative through previously conducted technology surveillance. The alternatives were rated on a scale from 1 to 5, with 1 being the lowest investment and 5 the highest. The ranking of these alternatives was done with consideration of the limited resources available to the community involved in the study (

  Table 2. Equipment used in the manufacturing of each selected alternative and their respective score on the Saaty scale.

  Alternative	Equipment	Value
  Pellets	Mill, sieve, hydraulic press, electronic caliper, digital scale, compression machine	2
  Pectin	Dehydrator, mill	5
  Flour	1.5 mm sheet, dehydrator	1
  Wine	Alcohol meter, bottles, chemical supplies for fermentation	3
  Vinegar	Yeast, bottles, chemical supplies for fermentation	4

  ).
• Commercialization: This criterion was evaluated through two sub-criteria: price and demand. For the price, products that generate the most profit upon sale were considered. This profit is assumed based on the market price value. The prices of the products were obtained through a search in various physical and online markets for similar or identical products available in the market (

  Table 3. Marketing Price in (COP).

  Alternative	Commercialization Price (COP)	Value
  Pellets	5000	3.5
  Pectin	36,000	1
  Flour	25,000	2
  Wine	5000	3.5
  Vinegar	1700	5

  ). For the evaluation of this criterion, the prices of each product are organized from highest to lowest, with 1 being the highest price and 5 being the lowest price.

To determine demand, companies that commercialize products identical or similar to the proposed alternatives were contacted to obtain information about the average national consumption (

Table 4. Annual consumption of the selected alternatives.

Alternative	Annual Consumption	Reference	Value
Pellets	2,486,122.92 T/year	Castañeda & Arciniegas, 2019 [42]	5
Pectin	450–550 T/year	Pectinas de Colombia SAS	2
Flour	0.02 T/year	Harinas del Valle, 2015 [43]	1
Wine	12,888,000 L/year	Empresa Productora de Vino	4
Vinegar	10,000,000 L/year	Sierra Morena Zapatca	3

). The alternatives were rated on a scale from 1 to 5, based on the average national consumption, with 1 being the lowest consumption and 5 the highest.

• Environmental Impact: This criterion evaluates the degree of environmental impact generated by the studied residues on the soil. Farmers do not adequately dispose of cocoa pod husks, placing them in areas near the cultivation sites [44]. Due to their high lignin content, the decomposition rates of the matter are low [45], leading to the accumulation of residual biomass on the plantations. This accumulation can cause the spread of vectors [46], diseases in the crops, and a reduction in soil quality [47]. Mucilage and the fermentation process generate leachates that cause the leaching of bases such as calcium, magnesium, aluminum, potassium, and sodium, reducing soil pH and soil stability due to increased porosity [48]. Additionally, the interviewed cocoa farmers stated that “mucilage is a harmful liquid for the plants where this residue falls, to the point of causing the vegetation to disappear if not treated”. For this reason, higher scores were given to alternatives that utilized a greater proportion of mucilage.

Once the criteria were defined, a comparison matrix was created. This matrix compares the criteria with each other based on Saaty’s scale [31]. The comparison was performed by experts in value addition processes.

Table 5. Comparison Matrix of Established Criteria.

Criteria	Availability	Investment	Price	Demand	Environmental Impact
Availability	1	0.11	0.14	0.14	1
Investment	9	1	3	3	5
Price	7	0.33	1	0.14	3
Demand	7	0.33	7	1	3
Environmental Impact	1	0.2	0.33	0.33	1
Total	25	1.98	11.48	4.62	13

shows the comparison matrix of the criteria. After obtaining this comparison, the matrix was normalized, and the priority vector was determined.

After this comparison, the selection criteria for each alternative are evaluated. Using the results from these matrices, the weight for each criterion and the average weight value/priority vector are determined (

Table 6. Weight by criterion and priority vector of the AHP model.

Criteria	Weight Criterion	Weight Criterion/Priority Vector
Availability	0.22	5.10
Investment	2.52	5.84
Price	0.84	5.27
Demand	2.06	6.83
Environmental Impact	0.35	5.44
Average		5.70

).

With the average obtained from Table 6, the consistency ratio (CR) is determined.

$$CR={\displaystyle \frac{CI}{RI}}$$

where CI is the consistency index and RI is the randomness index, which are calculated using the following equations:

$$CI={\displaystyle \frac{P-n}{n-1}}$$

where P is the average of the weights with the priority vector and n number of criteria.

$$RI={\displaystyle \frac{1.98(n-2)}{n}}$$

where n is the number of criteria.

The consistency index allows measuring the degree of consistency between the paired opinions provided by the evaluators. The degree of consistency should be <0.1 to proceed with the AHP process.

Table 7. Consistency Index, Randomness Index, and Consistency Ratio.

$CI={\displaystyle \frac{5.70-5}{5}}$ $CI=0.17$

$RI={\displaystyle \frac{1.98(5-2)}{5}}$ $RI=1.19$

$CR={\displaystyle \frac{0.17}{1.19}}$ $CR=0.1$

shows the calculation of CI, RI, and CR.

Since the consistency ratio is less than 0.1, it is possible to calculate the weighted score of the alternatives with the selection criteria.

Table 8. Prioritization matrix of the proposed alternatives for the utilization of cocoa waste.

	Availability	Investment	Price	Demand	Environmental Impact	Result
Pellets	0.30	0.26	0.50	0.50	0.05	0.36
Pectin	0.30	0.50	0.03	0.13	0.05	0.28
Flour	0.04	0.07	0.07	0.03	0.43	0.08
Wine	0.06	0.13	0.26	0.07	0.43	0.15
Vinegar	0.30	0.03	0.13	0.26	0.05	0.13
Priority Vector	0.04	0.43	0.16	0.30	0.06

shows the overall rating of the AHP process for the selected alternatives evaluated against the criteria.

From the application of the AHP method, it can be observed that the Pellets alternative has the highest-ranking score, meaning it is the most beneficial alternative according to the established selection criteria.

3.3. Potential Use of Cocoa Waste as an Energy Source

Pellet production is a promising alternative due to its potential use in energy production, which would address the community’s need for alternative energy sources. Initially, other energy production alternatives were not explored. Nevertheless, in response to the AHP findings, this study evaluated the possibility of producing biofuel that utilizes not only cocoa waste but also farm animal manure (pigs, cows, rabbits, etc.). This option proves to be doubly beneficial for the community, as using this type of fuel for basic needs such as cooking, which is still done with firewood today, would positively impact the health and well-being of the inhabitants. Therefore, an experimental design has been developed for the anaerobic co-digestion of cocoa waste and farm animal manure to determine their potential for biogas production.

Two different ratios of cocoa pond shell (C) with cow manure raw (M) (50/50 and 70/30, respectively) were monitored daily to determine their biogas production. The biogas curves are shown in Figure 2. In addition, statistical analyses of the data showed a significant difference (p < 0.05) between the ratios tested.

Table 9. Physical characteristics of substrates utilized. SV and Ash values in relation with dry matter.

Substrate	Moisture (%)	SV (g/100 g)	Ash (g/100 g)
Cocoa pond shell (Co)	87.09 ± 0.96	88.87 ± 0.82	11.13 ± 0.62
Cow manure (M)	82.42 ± 1.73	77.02 ± 6.93	22.98 ± 5.73

shows the physical characteristics of substrates utilized. The volatile solids fraction is high in both substrates, indicating a significant amount of organic matter potentially available for conversion into biogas by anaerobic microorganisms.

The results of the anaerobic co-digestion of the cocoa pond showed that the 70/30 concertation ratio produced slightly more biogas than the other one. However, both tests were positive for gas production. In general, the constituents originating from cocoa pond shells presented favorable characteristics for anaerobic biodegradation, with volatile solids between 63.2 and 92.6%. Similar results were obtained by Suhartini, et al. (2021) in their research on the co-digestion of cocoa pods with cocoa leaves for enhanced biogas production [48].

Overall, according to different authors, the key factors for the anaerobic digestion of lignocellulosic biomass could include the material composition, inoculum, temperature, TS content, carbon/nitrogen (C/N) ratio, particle size of the feedstock, concentration of inhibitors (e.g., ammonia), alkalinity of the system, and pretreatment methods [23,27,29,49].

These cocoa residues present an elevated lignocellulosic content. For example, Antwi et al. (2019) observed values of 26% cellulose and 21% lignin [23], and Acosta et al. (2021) detected 42.7% cellulose and 6.14% lignin [27]. These characteristics of lignocellulosic biomass are correlated with biogas yield; the correlation is positive with cellulose content and negative with lignin content [50]. In fact, these assays presented a major increase in biogas production when the cocoa pond was increased. This may be due to the higher substrate content and the choice of microorganisms used in the assays. In addition, the co-digestion strategy helps us to achieve a nutrient balance, an ideal carbon/nitrogen (C/N) ratio, which provides a synergistic effect to improve biogas yields [48,51]. Moreover, Acosta et al., (2021) observed that the key contribution of manure co-digestion with cocoa seems to be the provision of buffering capacity, a source of nutrients, and potential key microorganisms [27]. According to Hagos et al. (2017), anaerobic co-digestion is the most used strategy to increase biogas production due to the lignocellulosic composition of cocoa waste, which has a complex chemical composition with recalcitrant characteristics, unfavorable to microbial activity [52].

According to Candia-García et al. (2018), the Gompertz model is useful to explain the lag time and sigmoidal growth curve of biogas production from the anaerobic digestion of lignocellulosic biomass [40]. Solid lines (Figure 2) show the model’s results with a squared correlation coefficient (R2) close to unity, indicating a good fit in describing the biogas production in our assays.

Table 10. Gompertz model parameters.

Co/M Ratio	Final Biogas Production (β0), mL/gSV	Maximum Biogas Production Rate (µm), mL/gSV.d	Lag Phase Time (λ), d	Squared Correlation Coefficient (R2)
50/50	127.7	9.12	0	0.96
70/30	134.7	2.14	1.4	0.90

shows the Gompertz model’s parameters as obtained in the simulations. The lag phase time (λ) was short, with values of 0 and 1.4 d for cocoa pond shell/cow manure raw ratios of 50/50 and 70/30, respectively. Thus, microorganisms adapted quickly, synthesizing the necessary enzymes in anaerobic conditions. According to Kim et al. (2020) [53], in the anaerobic digestion of organic waste, a long lag phase can be caused by acidification due to the initial accumulation of volatile fatty acids (VFA) and a high VFA to volatile solids ratio. Our results indicate that co-digestion of cocoa pond shell and cow manure achieves a positive equilibrium with the substrate mixture, as evidenced by the short lag phase observed. The difference in the lag phase between the two treatments could be attributed to the amount of carbohydrates, which is higher in the 70/30 treatment.

The maximum biogas production rate (m) was 9.12 and 2.14 mL/gSV.d and the final biogas production (0) was 127.7 and 134.7 mL/gSV, for 50/50 and 70/30, respectively. These results revealed that the 70/30 ratio could achieve better biogas production than the 50/50 ratio. Where cocoa pond shell was the primary substrate, biogas production was higher and faster, possibly because the anaerobic process was less sensitive to inhibition caused by intermediate compounds such as volatile fatty acids. Indeed, the 50/50 curve exhibited a stepwise pattern, indicating the presence of distinct and sequential hydrolysis processes among the substrates utilized. Generally, anaerobic co-digestion enhances biogas production when the components are combined in optimal ratios.

Indeed, a lower lag phase time and a higher maximum biogas production rate can mean a faster startup and a higher efficiency of anaerobic degradation [40]. Moreover, the co-digestion of cocoa waste and cow manure could significantly enhance biogas production rate when a suitable pretreatment is applied [23].

The results present the potential of cocoa residues for energy conversion through anaerobic co-digestion with raw cow manure. Anaerobic digestion (AD) is one of the most promising biotechnologies to degrade and convert lignocellulosic waste into bioenergy [29]. These findings are of key importance in the design and operation of anaerobic digestion reactors that have one agricultural residue as feedstock. Moreover, it can reduce the organic load and residue impact on the environment [49]. In fact, the digestate, a byproduct of anaerobic digestion, acts as a natural soil amendment, reducing the need for synthetic fertilizers [40]. Futhermore, AD technology represents an alternative method of reducing greenhouse gas emissions and improving the socioeconomic conditions of rural communities in low-income countries [31,54].

4. Conclusions

The application of the AHP methodology effectively identified the most promising alternatives for cocoa waste utilization, such as pellet production for energy generation. By prioritizing alternatives based on criteria relevant to the community, the method facilitates decision-making that aligns with local needs and resource availability.

The incorporation of not only economic criteria but also social and environmental factors into the AHP methodology ensured that the prioritization not only could optimize resource use and reduce environmental impact but also address the socioeconomic needs of the community.

Obtaining energy through anaerobic co-digestion using cocoa residues is possible and presents reasonable biogas yields. Indeed, raw cow manure is a suitable co-substrate in the co-digestion of cocoa waste, and both residues are common on agricultural farms.

The incorporation of anaerobic digestion into cocoa waste management strategies not only optimizes waste reuse for renewable energy but also offers socioeconomic benefits to rural communities, making it a viable and sustainable solution for agricultural waste valorization.