The Need and Acceptability of Cassava Starch Residue as an Alternative Energy Source in Poultry Feeds: Farmers and Consumers Perspectives in the Ashanti Region of Ghana

This study explores the potential of cassava starch residue (CSR) as a sustainable alternative energy source in poultry feeds in the Ashanti Region of Ghana. With rising maize prices and supply challenges impacting poultry production, CSR offers a promising substitute. Employing a mixed-methods approach, data were collected from 150 respondents including 50 poultry farmers and 100 consumers using semi-structured questionnaires across selected districts. Descriptive statistics, chi-square tests and logistic regression were applied to assess CSR awareness, acceptance, and its perceived impact on profitability and feed efficiency. Results indicate that while overall awareness of CSR is low (22%), farmers with higher education and larger farm capacities show a greater propensity to adopt CSR, primarily due to its potential cost-effectiveness and sustainability benefits. The analysis further highlights that demographic factors such as age and educational background significantly influence perceptions regarding CSR. Notably, chi-square tests showed significant associations between education and CSR awareness (χ² = 45.66, df= 2, p= 1.21e−10), cost-effectiveness (χ² = 26.31, df= 6, p= 0.0002), and overall profitability (χ² = 57.65, df = 6, p= 1.35e−10). Predicted probability models further indicated that farmers aged 26 and above and those with tertiary education had a higher likelihood of adopting CSR, while smaller-scale operations (50–500 birds) were less inclined. The study concludes that targeted educational and policy interventions are crucial to enhance CSR adoption, reduce reliance on maize, and improve the economic viability of poultry production in Ghana. 

Introduction

The poultry industry is essential to Ghana’s agricultural economy since it creates jobs, revenue, and food security. For an expanding population, the industry offers an essential source of animal protein. For many smallholder farmers across the country, it provides a means of subsistence [1]–[3]. However, the high cost and erratic availability of conventional energy sources, mainly maize, pose a continuous barrier to Ghana’s poultry production’s sustainability and profitability [4]. A major contributor to production costs is maize, a staple in poultry feed [5]. Poultry producers’ profitability is directly impacted by increases in maize prices, which are frequently caused by weather patterns, conflicting needs from human consumption, and reliance on imports [6].

This dependence on maize makes the poultry value chain vulnerable, which impedes the industry’s expansion and the consumer’s capacity to afford poultry goods. It is essential to investigate sustainable and alternative energy sources for chicken feed in light of these difficulties. Cassava, a readily available and widely cultivated root crop in Ghana, presents a promising opportunity. Cassava starch residue, a by-product of starch extraction, is often underutilized despite its potential as an energy source for livestock [7]. CSR is rich in carbohydrates, primarily starch, which can be converted into readily available energy by poultry [8].

Poultry feed typically comprises energy sources, protein sources, vitamins, and minerals, with energy sources constituting the largest proportion of the feed [9]. In Ghana, maize is the predominant energy source in poultry diets [5]. Other commonly used energy sources include wheat, rice bran, and sorghum, although their inclusion levels are generally lower compared to maize [10].

Maize’s popularity as a poultry feed ingredient stems from its palatability, high energy content, and relatively balanced nutrient profile [8]. However, the heavy reliance on maize for poultry feed production in Ghana presents several challenges which include high and fluctuating prices, competition from human consumption and import dependence [5], [11], [12]. These challenges highlight the vulnerability of relying heavily on maize as the primary energy source in poultry feed. This situation underscores the urgent need to explore alternative, readily available, and cost-effective energy sources to enhance the sustainability and competitiveness of the poultry industry in Ghana.

Cassava starch residue (CSR) is a valuable resource, especially as an energy source in animal feed. It consists of the fibrous portion of the cassava root, separated during starch extraction. CSR is rich in carbohydrates, primarily in residual starch and fiber, which can be used by poultry as an energy source. The fiber fraction consists of cellulose, hemicellulose, and lignin, which are less digestible but contribute to gut health. CSR is generally low in protein content, requiring supplementation with protein sources [13]. It contains minerals like calcium, phosphorus, and potassium, but can be limited by anti-nutritional factors like tannins, saponins, and cyanogenic glycosides. Proper processing techniques, such as drying, fermentation, or enzymatic treatment, are crucial to reduce these compounds and improve CSR’s nutritional value [14], [15].

This study is grounded in the pressing need to address the challenges posed by the rising cost of traditional poultry feed ingredients in Ghana and to explore the potential of cassava CSR as a sustainable alternative. By addressing critical aspects such as the perceptions, concerns, adoptions and expectations of key stakeholders within the poultry and cassava value chains. Therefore, this survey sought to explore the awareness and acceptability of the use of cassava by-products, particularly, CSR among poultry farmers and consumers.

Research Methodology

Study Area

The research was conducted in some selected districts of the Ashanti Region of Ghana. According to the General Report Volume 3A Ghana 2021 Population and Housing Census published by the Ghana Statistical Service (GSS), 5,440,463 people are living in the Ashanti region, which has an area of 24,389 square kilometers. Between latitudes 5.50 N and 7.46 N and longitudes 0.15 W and 2.25 W, it is located. There are forty-three districts in the region. The primary employment of the region’s inhabitants is agriculture, which includes cash crops like cocoa, coffee, oil palm, citrus, cashew, and mango, as well as food crops like cassava, plantain, rice, yam, cocoyam, maize, and vegetable production, livestock, and poultry farming [16]. Transportation, sales, and technical and professional employment are examples of additional economic activity. The main industries in the area include manufacturing, wholesale, mining, and agriculture. The working population in the region is primarily employed by the public and private formal sectors. The importance of industry and agriculture contributes to the Ashanti Region’s dynamic economy and diverse population. This region was chosen due to its significant poultry production activities ensuring the representation of diverse stakeholder perspectives and capturing variations in poultry production systems [17]. Fig. 1 shows the map of the Ashanti region with its 43 districts.

Fig. 1. Map of the ashanti region with all the districts [18].

Research Design

The study adopts a mixed-methods approach, combining both quantitative and qualitative data collection and analysis techniques.

Study Population and Sampling

The study population comprised 150 respondents in the Ashanti Region including poultry farmers (50) and consumers (100). A multi-stage sampling technique was employed to select participants. First, a purposive sampling was used to select ten districts which are Atwima Nwabiagya North, Atwima Nwabiagya South, Atwima Kwanwoma, Adansi South, Ejisu-Juaben Municipal, Atwima Mponua, Kwabre East, Afigya Kwabre North, Bakwai Municipal and Kwabere with high poultry farming activities. Second, cluster random sampling was used to select at least five poultry farms within each selected district/area. Third, for consumer perspectives, a simple random sampling was employed to select 100 consumers who patronize poultry products at local markets. The mathematical models employed for the sampling of both poultry farmers and consumers are detailed in the following formula:

(1) $$\begin{array}{rl}& Purposivesamplingtoselectd\\ & =10districtsoutofD=43\end{array}$$

Cluster sampling within each selected district:

(2) $$\begin{array}{rl}& Totalnumberofpoultryfarmerssampled\left(n_f\right)\\ & =d\times f=10\times 5=50\end{array}$$

where f is the number of poultry farmers selected per district.

(3) $$\begin{array}{r}{S}_{f=}\bigcup _{i=1}^d{C}_i\end{array}$$

where, C_i represents the cluster of five randomly selected farmers in district i, and the total sampled farmers S_f is the union of all selected clusters.

For consumer sampling, assuming equal probability for all, if the total population of consumers in these 10 districts is Nc, and you sample nc = 100, the probability of selecting any one consumer is given by:

(4) $$\begin{array}{r}P(C_i)\frac{n_c}{N_c}\end{array}$$

Mathematical representation using an indicator function is given as:

(5) $$\begin{array}{r}{S}_c=\{C_1,C_2,\dots ,C_{100}\},C_{i\sim \bigcup (1,N_c)}\end{array}$$

where C_i follows a uniform distribution over the total consumer population N_c.

The overall combined model for both poultry farmers ($X_f$) and consumers ($X_c$) sampled respectively, is expressed as:

(6) $$\begin{array}{r}{X}_f=\sum _{i=1}^{10}\sum _{j=1}^5{F}_{ij},X_c=\sum _{i=1}^{100}{C}_i\end{array}$$

where Fij represents the jtℎ poultry farmer in district i, and Ci represents a randomly chosen consumer

Data Collection

Semi-structured questionnaires were administered randomly to capture data on participants representing a mix of selected samples of poultry farmers and consumers across the study districts in the region. The questionnaire explored in-depth stakeholder demographics, perceptions, concerns, and expectations regarding CSR utilization in poultry feed. Before the collection of data, each participant gave their informed consent. Participants were made aware of the goals of the study, that participation was voluntary, and that they could leave the study at any moment without incurring any fees. Throughout the whole research procedure, confidentiality and anonymity were upheld. The questionnaires were pretested using farmers and consumers who did not take part in the primary survey to ascertain the validity and reliability of the information. The pertinent outcome of the pretesting was used to modify the questionnaires.

Data Analysis

The statistical techniques employed in the study included descriptive statistics, inferential tests, and multivariate analysis using R version 4.3.2 (R Core Team, 2023) in investigating acceptance and perception of the use of CSR in poultry feed as a source of alternative energy. Frequencies and percentages were used in describing and explaining survey data. The chi-square test was used in testing for association between categorical factors such as educational level, farm size, and awareness of CSR based on the model:

(7) $$\begin{array}{r}{\mathrm{\chi }}^2=\mathbb{\Sigma }\frac{{(O-E)}^2}{E}\end{array}$$

where O is the observed frequency and E is the expected frequency. Significant results at p < 0.05 indicated strong associations between factors like education level and CSR awareness.

Cramér’s V, a correlation heatmap, was used in testing for the strength of association between categorical factors, and a strong association between factors such as cost-effectiveness, efficiency in terms of feed, and profitability in terms of poultry production. A Cramér’s V correlation matrix was used to measure the strength of relationships between categorical variables:

(8) $$\begin{array}{r}V=\sqrt{\frac{x^2}{n(k-1)}}\end{array}$$

where n = Total sample size and k = minimum of (rows - 1) or (columns - 1).

Predicted probabilities were calculated in testing for the adoption of CSR and use of CSR in farms and consumption in terms of demographics and operational factors such as age, level of education, and farm size modeled as an example:

(9) $$\begin{array}{r}P(Y=\text{Strongly Agree})=f(Age,education,FarmSize)\end{array}$$

Results and Discussion

Poultry Farmers Socioeconomic Data

Table I displays the socioeconomic data of the respondents. Of the 50 respondents, 39 were male (78%), making them the majority of farmers. This higher percentage of males dominating poultry farmers is a result of the way farming activities are conducted. Equally, a survey conducted by Quaye et al. [19] asserted that a higher population of the poultry farmers enclave in the Kumasi metropolis is dominated by males. The remaining 11 respondents (22%) of the total were women. Given the survey’s location, it is not surprising to see that Christianity accounts for 46 (92%) of respondents’ religious affiliation, with Muslims making up the minority at four (8%). In support of this, Ghana Statistical Service [20] national population census found that the majority of Ghanaians are Christians, with the majority of them living in the southern regions of the country.

Table I. Descriptive Characterization of Poultry Farmers Responses

Variables	Number of respondents (n = 50)	Percentages(%)
a. Demographic characteristics of farmers
Sex
Male	39	79
Female	11	22
Religion
Christian	46	92
Muslim	4	8
Education
Non-formal	2	4
Secondary	14	28
Tertiary	34	68
Age
Below 18	1	2
18–26	2	4
26–35	21	42
Above 35	26	52
b. Composition of animals, Farm size, and feed dynamics
Type of Production
Broilers	3	6
Layers	34	68
Both	13	26
Farm size
50–500 birds	7	14
500–1000	8	16
1000–5000	17	34
5000–10000	13	26
Above 10000	5	10
Source of feed
Prepared from the farm	40	80
Bought from feed mills	8	16
Both	2	4
Nutritional Integrity of Feed
Every batch of feed	3	6
Occasionally	32	64
At regular intervals	1	2
When there is a change in performance	12	24
Cost of Energy Source
Cheap	0	0
Very cheap	0	0
Moderate	0	0
Expensive	12	24
Very Expensive	38	76
Availability of Energy Source
Always Available	24	48
Available most of the time	19	38
Occasionally Available	5	10
Rarely Available	2	4
Meeting the Nutritional Demand of Birds
Yes	49	98
No	1	2

One important factor that shapes farmers’ opinions is education. The majority of poultry farmers (68%) had tertiary education, followed by those with secondary education (28%) and non-formal education (4%). The modal age of respondent farmers was between the ages of 26 and 35 (42%) and over 35 (52%). The age groups of 26 to 35 and over 35 years old showed a higher frequency of farmers being very youthful. This suggests that the poultry sector has a vibrant, youthful, and energetic workforce, but also suggests that, given the right business environment, the industry has promising potential in its available workforce [21]. The presence of relatively young people in the sector is in itself a motivation to other young professionals and the unemployed population to venture or invest in the industry, should the success of existing farmers be evident. This could be a positive step in addressing the troubling issue of rising unemployment and also help to boost general productivity and investor confidence in the industry. Only three respondents (5%) raised only broilers, while 34 respondents (68%) kept layers, and 13 respondents (26%) kept both broilers and layers. The low percentage of farmers producing only broilers could be explained by the lack of readily available markets and the incapacity of local farmers to compete with the large importation of frozen chicken products from foreign countries [22]. It is estimated that Ghana imports more than 350 billion cedis worth of chicken products annually for local consumption [23], [24]. Given the comparatively expensive production cost of broiler production in the country, farmers find it difficult to compete with the price of imported chicken products consequently, demotivating farmers to enter into broiler production. This has compelled many poultry farmers to focus mainly on layer production, where egg production and sales of spent layers, post-production cycle, can compensate for the production cost and still yield some profit if operations run smoothly.

The response on farm size is also presented in Table I shows that 17 respondents (34%) had a farm capacity between 1000–5000 and 13 of them (26%) had a capacity between 5000–10000 birds making them medium to commercial scale farmers. As a result of the farm sizes which make them medium to commercial scale, the majority of the farmers prepare their feed on-farm. This is confirmed by 80% of the respondents who admitted to preparing their feed on the farm.

Given the relatively increased cost of energy-source feed ingredients nowadays, it is not surprising to witness that 38 responses (76%) indicated that energy-source feed ingredients are very expensive and about 25% of the total feed cost is attributed to the percentage of energy-source inclusion in the feed. Therefore, a supplementary or alternative energy source has become necessary to reduce the total cost of feed and increase the marginal profit after the production cycle [25], [26]. In terms of availability, 48% of the respondents admit that the conventional energy source is always available while 38% of them admit that it is available, most of the time. Out of the 50 respondents, 49 (98%) indicated that the conventional energy source meets the energy requirements of the birds.

Poultry Farmers Perception Responses

Sixty-six (66%) of the respondents admitted that they are aware of and know other energy sources, as shown in Table II. However, 33 (66%) of the respondents admitted not having knowledge of CSR while only 17 (34%) of them admitted to having a little knowledge of CSR. In terms of CSR adoption and acceptability by farmers, 54% of the respondents are willing to adopt its usage as a supplementary energy source based on the condition that its energy content is high enough to meet the energy requirements of birds. Twenty-two (22%) of the respondents are willing to accept CSR usage also based on availability and accessibility while the remaining 18% are based on affordability.

Table II. Descriptive Perception Responses of Poultry Farmers

Variables	Number of respondents	Percentage
a. Awareness of other energy sources and knowledge of cassava starch residue
Awareness of other energy sources
Yes	33	66
No	17	34
Cassava Starch Residue awareness
Yes	11	22
No	39	78
Cassava by-product use in diet
Yes	11	22
No	39	78
b. Acceptability of cassava starch residue as an alternative energy source
Conditions for CSR usage
High energy content	27	54
Available and Accessible	11	22
Affordable	9	18
Increase productivity	2	4
Equal in cost and quality	1	2
Productivity improvement
Strongly agree	1	2
Agree	11	22
Neutral	32	64
Disagree	6	12
Cost effectiveness
Strongly Agree	6	12
Agree	34	68
Neutral	9	18
Disagree	1	2
Feed efficiency
Strongly Agree	2	4
Agree	14	28
Neutral	27	54
Disagree	7	14
Overall profitability
Strongly Agree	2	4
Agree	35	70
Neutral	10	20
Disagree	3	6

Correlation Heatmap Between Farmers Categorical Variables

The Cramér’s V values, which evaluate the degree of correlation between pairs of categorical variables, are shown in a heatmap (Fig. 2). Strong associations were observed among certain variables. Overall profitability and feed efficiency show strong correlations with other variables, as indicated by their high Cramér’s V values. Likewise, poultry productivity improvement shows strong relationships with factors like conditions for cassava byproduct usage and cost-effectiveness. This implies that productivity and profitability outcomes of the poultry industry may be markedly influenced by the important dependencies shown by these interactions [27]. Conversely, variables such as sex and educational level, which typically showed low Cramér’s V values with other variables, show weaker relationships. This implies that technical or operational aspects of chicken production, including feed efficiency or profitability, may be less affected by demographic factors [28]. Furthermore, several variable groups showed moderate relationships with one another, such as those about feed and energy sources. Within certain sectors, like feed and energy considerations, these clustered patterns imply interdependence.

Fig. 2. Heatmap of Cramér’s V correlation between farmers categorical variables.

Tests of Association of Poultry Farmers Variables

Fig. 3 depicts the test of association of the educational level and farm size distribution of poultry farmers by some perception variables. From the chart (Fig. 3A), it can be seen that the majority of the farmers with higher educational counts (χ² = 45.66; df = 2; p-value = 1.207e−10) are not aware of CSR as an alternative or supplementary energy source. This observation is significantly higher in poultry tertiary farmers with tertiary education compared to those with secondary education. Fig. 3B shows the relationship between farmers’ educational levels and the response to the cost-effectiveness of using CSR in poultry feed. It can be seen that farmers with tertiary and secondary education agree that the usage of CSR in poultry diets can be cost-effective (χ² = 26.31; df = 6; p-value = 0.0002), which is significantly more than farmers with non-formal education. Fig. 3C also represents farmers education and overall profitability. From the chart, individuals with higher educational levels (tertiary and secondary) agree that CSR usage will impact the overall profitability of the production due to its cost-effectiveness (χ² = 57.65; df = 6; p-value = 1.351e−10). The results reveal that despite a high educational background, farmers may not have received information regarding alternative sources of feeds, and a lack of information dissemination in academic and expert agricultural training programs could have been an issue. Likewise, educated farmers can comprehend the economy of feeds and have a desire to include alternative ingredients in an effort to maximize production costs [29]. Fig. 3D shows the farm size distribution by type of poultry production. Farmers who kept layers only had the highest farm size distribution and a higher farm capacity. Individuals with farms of 1000–5000 birds had the highest count. Farms with only broilers as a type of poultry production had the least farm size (χ² = 52.55; df = 8; p-value = 1.319e−08). This distribution can be understood through the prolonged rearing period of layers, for whom a steadier and more long-term feed scheme is preferable compared to broilers, whose rearing period is shorter and can possibly react less sensitively towards fluctuations in feed price [30]. As per the report, farm size and farm type both have an important role in deciding farmers’ options for alternatives such as CSR.

Fig. 3. Test of associations among categorical variables.

Predicted Probabilities of Poultry Farmers Opinions

The predicted probabilities of various farm sizes as impacted by the age and educational background of farm operators are shown in Fig. 4 for the three different types of poultry farms: layers, broilers, and mixed farms (both layers and broilers). Age is a significant factor in influencing farm size probabilities across all panels. Smaller farms (50–500 birds) are more likely to employ younger farm operators, particularly those with secondary or nonformal education. However, as age increases, there is a slow shift towards farms with larger capacity, especially for those who have completed higher education. This pattern implies that managing larger poultry farms may be made easier by experience or access to resources accumulated over time. Farm size probabilities are strongly influenced by the operator’s educational level. Among those with nonformal education, smaller farms continue to be the most common across all age groups, suggesting limited room for growth. On the other hand, the distribution is more varied, among secondary-educated operators with medium-sized farms (1000–5000 birds) becoming more prevalent as age increases. Among tertiary-educated operators, the shift is most pronounced, with larger farms (>10,000 birds) having the highest probabilities, especially for older individuals. This pattern suggests that higher education is associated with better farm management skills, access to financing, or entrepreneurial skills needed to expand operations [31]. Additionally, the type of farm, along with age and education, influences farm size. Medium and large broiler farm sizes increase with age, especially among tertiary-educated operators. In layer farms, younger operators prefer smaller farm sizes, but older, tertiary-educated individuals prefer larger farm sizes. Mixed farms (both) are more likely to remain small or medium-sized, with limited expansion of production, independent of the operator’s age or education level. This insight underscores the importance of tailored interventions, such as education and training programs, to support growth in the poultry sector.

Fig. 4. Predicted probabilities of various farm sizes by other variables.

Consumers

Demographic Characteristics

The demographic distribution of sampled consumers is demonstrated in Fig. 5. The respondents were dominated by the male gender (60%) (Fig. 6B), predominantly of the Christian religion (95%) (Fig. 6C). A significant percentage of the respondents had up to Tertiary (79%) education (Fig. 6A) while the lowest percentage was observed in consumers with non-formal education. Furthermore, the majority of the consumers were above 35 years of age (43%), followed by the age group of 9–25 (33%) (Fig. 6D).

Fig. 5. Demographic characteristics of consumers.

Fig. 6. Heatmap of Cramér’s V correlation between consumers categorical variables.

Correlation Heatmap Between Consumer Categorical Variables

Fig. 6 depicts a heatmap showing the strength of the relationship between categorical variables employed in the analysis, using Cramér’s V as a measure of correlation. Educational level has a high relationship with characteristics such as age, gender, and religion, as demonstrated by the red or orange cells in the corresponding row and column. This shows that demographic factors such as age, gender, and religion have a significant impact on educational success. Variables such as CSR sustainability, circular economy, and waste reduction have substantial connections (red cells), implying that there is a close alignment in respondents’ opinions towards sustainability-related factors, indicating that they are likely part of a shared perspective or belief system. Consumption of cassava and concern about chickens have a moderate to significant relationship with educational level, CSR awareness, and CSR diet consumption by chickens. This means that awareness and concern for sustainability-related behaviours may differ depending on the educational level and other demographic characteristics of the respondent [33].

Tests of Association of Consumer variables

Figs. 7 and 8 represent consumers educational level vs circular economy and sustainability. From the chart, it can be seen that consumers with tertiary education had the highest distribution of responses agreeing or disagreeing with the perception statements: “CSR contribution to the circular economy and CSR is more sustainable’’ across the different educational levels. The least was observed in non-formal education (χ² = 35.237, df = 4, p-value = 4.153e−07). This suggests that their educational background had an influence on their opinion about CSR contribution to the circular economy and sustainability of CSR. Higher education levels are more involved in conversations about sustainability and circular economy principles, resulting in higher awareness and readiness to embrace alternative resources such as CSR. Targeted awareness campaigns and educational initiatives may help the general public understand and appreciate CSR as a sustainable resource, especially in terms of waste reduction, resource efficiency, and cost-effectiveness.

Fig. 7. Test of association between educational level and circular economy.

Fig. 8. Test of association between educational level and sustainability.

Fig. 9 shows how educational attainment influences individuals’ views on the statement ‘‘CRS can reduce agricultural waste’’ (χ² = 29.976, df = 4, p-value = 4.95e−06). It can be inferred that tertiary education shapes how individuals perceive and address issues related to the environment. This suggests that more educated individuals are more aware of the environmental impact of agricultural waste.

Fig. 9. Test of association between educational level and agricultural waste reduction of CSR.

Predicted Probabilities of Consumers Opinions

Fig. 10 represents a summary and interpretation of the patterns observed in the plots by predicted probabilities of individuals agreeing or disagreeing with the statement that “Reduction of grain competition is important” across different age groups and educational levels. The probability of individuals selecting “Strongly agree” (purple line) rises sharply across all educational levels as the age increases. This suggests that older individuals, irrespective of their educational background, are more likely to strongly agree with the importance of reducing grain competition. Conversely, the probability of selecting “Disagree” (red line) diminishes significantly with age, indicating that younger individuals or those in earlier age groups are less likely to disagree. Individuals with non-formal education exhibit a more pronounced transition from “Neutral” (green) or “Agree” (blue) to “Strongly agree” at younger age thresholds than other groups. In the secondary education group, the change between agreement levels occurs slightly later in the age spectrum, indicating a slower but comparable shift towards stronger agreement. Among those with tertiary education, the tendency is similar, but agreement levels look more equally distributed throughout ages, indicating a greater consensus as age increases. Overall, older people are less likely to strongly disagree or remain neutral. The results show a high link between age and agreement on the need to lower grain competition. Educational background determines the rate and form of this shift, with more educated populations showing a progressive increase in agreement. These trends indicate that older people, particularly those with formal education, may be more aware of or concerned about the implications of grain rivalry, which is most likely influenced by their knowledge or life experiences [32].

Fig. 10. Predicted probabilities of reduction of grain competition by the use of CSR.

Conclusion

This study demonstrates that cassava starch residue offers a viable alternative energy source for poultry feed, potentially reducing feed costs and maize dependency in Ghana. Despite overall low awareness with only 22% of respondents familiar with cassava starch residue, poultry farmers with higher education and larger-scale operations showed a strong propensity for adoption, as evidenced by significant associations between education, cost-effectiveness, and profitability. These results underscore cassava starch residue’s potential to enhance the sustainability and economic viability of poultry production. We recommend implementing targeted educational programs to increase cassava starch residue awareness, particularly among small-scale and less-educated poultry farmers. Additionally, policy support should facilitate further research on optimizing cassava starch residue processing and formulation, ensuring improved nutritional performance. Collaborative efforts between cassava processors and poultry producers are essential to drive widespread cassava starch residue adoption and secure long-term benefits for the industry.