Factors Affecting the Profitability of Poultry Egg Production in Southern Togo: A Quantile Regression Approach

The study was conducted in Southern Togo. Located between the Republic of Ghana to the west and the Republic of Benin to the east, Togo is a narrow strip of land ranging from 50 to 150 km in width. The country covers 56,600 km² and stretches approximately 600 km from Burkina Faso in the north to the Atlantic Ocean in the south, lying between latitudes 6°–11° N and longitudes 0°–1°4 E. Togo has two main climatic zones: a humid tropical climate and a subequatorial climate. The southern part of the country is characterized by a subequatorial climate with two rainy seasons and two dry seasons.

Data were collected between January and March 2025. A pre-tested, structured questionnaire was used to interview 269 farmers who were members of farmer groups working with the Togo Institute for Technical Advice and Support (ICAT-TOGO). Secondary data were obtained from online sources and relevant institutions. The primary data included age, gender, marital status, educational level, production system, access to credit, and household characteristics. Farmers were also asked about total revenue, egg production, and the costs, prices, and quantities of key inputs such as labor, medications, day-old chicks, and feed.

The variables selected to capture the production and socioeconomic characteristics of the farmers, along with their expected signs, are described in Table 1.

The profitability of chicken-egg production in the study area was assessed by analyzing production costs and returns. Gross margin analysis is commonly used to evaluate business efficiency by comparing enterprises or alternative production strategies (Olorunwa, 2018). Budget analysis involves examining and interpreting the components of production costs and revenues, as shown below:

Net Income (Ni) of the i^th producer is defined as the difference between Total Revenue (TR_i) and Total Cost (TC_i). This equation represents the farmer’s profit after all production costs have been deducted from income generated through sales (Equation 1): (1) NIi=TRi−TCi {NI_i} = {TR_i} - {TC_i}

Equation 2 expresses Total Revenue (TR_i) as the product of the price of eggs and other outputs (P_i) and the quantity sold (Q_i): (2) TRi=PricePi*Quantity sold Qi {TR_i} = {\rm{Price}}\left( {{P_i}} \right)*{\rm{Quantity}}\;{\rm{sold}}\;\left( {{Q_i}} \right)

Equation 3 defines Total Cost (TC_i) as the sum of Total Variable Costs (TVC_i) and Total Fixed Costs (TFC_i): (3) TCi=TVCi+TFCi {TC_i} = {TVC_i} + {TFC_i} Where:

• NI_i – Net Income accrued to i^th farmer from egg sales (CFA)

• TR_i – Total Revenue realized from egg production (CFA)

• TC_i – Total Cost incurred by i^th farmer (CFA)

• TVC_i – Total Variable Cost (CFA)

• TFC_i – Total Fixed Cost (CFA)

• P_i – Price per unit of output (CFA)

• Q_i – Total quantity of eggs sold/produced by the i^th farmer.

Quantile regression has several advantages over the Ordinary Least Squares method (OLS), including its ability to handle outliers and provide robust estimates when the distribution of the dependent variable is skewed. It was used in the study to assess the impact of sociodemographic variables and poultry-specific characteristics on the farm income of poultry egg producers. Quantile regression is also suitable for addressing data heterogeneity because it examines the conditional distribution of the response variable beyond the mean. As noted by Cade et al. (1999), focusing solely on mean effects can understate, overstate, or entirely miss important distributional changes.

The ordinary least squares model is given in Equation (4): (4) Yi=β0+β1X1i+β2X2i+…+βnXni+εi {Y_i} = {\beta _0} + {\beta _1}{X_{1i}} + {\beta _2}{X_{2i}} + \ldots + {\beta _n}{X_{ni}} + {\varepsilon _i} Where ε_i are independently and identically distributed with mean zero and constant variance. Let F(y)=P(Y ≤ y) denote the cumulative distribution function of a random variable Y.

According to Koenker and Bassett Jr. (1982), the τ^th quantile of Y is defined as: Qyτ=infy=Fy≥τ {Q_y}\left( \tau \right) = \left\{ {inf \left( y \right) = F\left( y \right) \ge \tau } \right\} Where τ ∈ [0, 1].

Q_y(τ) represents the inverse of the distribution function of the response variable. Therefore, the quantile function in Equation (5) can be written as: (5) Yi=β0τ+β1τX1+β2τX2+…+βnXn+εi {Y_i} = {\beta _0}\left( \tau \right) + {\beta _1}\left( \tau \right){X_1} + {\beta _2}\left( \tau \right){X_2} + \ldots + {\beta _n}{X_n} + {\varepsilon _i}

The conditional quantile function is given by Equation (6): (6) Qτε=β0τ+β1τX1+β2τX2+…+βnτXn+ui {Q_{\left( {{\tau \over \varepsilon }} \right)}} = {\beta _0}\left( \tau \right) + {\beta _1}\left( \tau \right){X_1} + {\beta _2}\left( \tau \right){X_2} + \ldots + {\beta _n}\left( \tau \right){X_n} + {u_i} φ_t = Q_(τ)ε_t is identically distributed with mean zero and variance one. This can be written more compactly as Equation (7): (7) Qyiτε=XiTiβiτ {Q_{yi}}\left( {{\tau \over \varepsilon }} \right) = X_i^Ti{\beta _i}\left( \tau \right) Where: (8) X=(1,X0… … … … .Xi)T X = {({\it 1},\;{X_{\it 0}} \ldots \; \ldots \; \ldots \; \ldots \;.{X_i})^T}

The conditional cumulative probability of (Y_i) is given by Equation (9): (9) PrYi≤qXi/Xi=x=τ Pr\left( {{Y_i} \le q\left( {{X_i}} \right)} \right)/{X_i} = x = \tau

The minimization problem to be solved is: (10) EYi−qXiτXi=x min E(|Yi−fxiτXi=x)f∈Lu \matrix{{E\left( {\left| {{Y_i} - q\left( {{X_i}} \right)\tau } \right|{X_i} = x} \right)min \,E(|{Y_i} - f\left( {{x_i}} \right)\left| \tau \right|{X_i} = x)} \cr {f \in L\left( {\rm{u}} \right)} \cr }

The τ^th quantile regression estimator β^i {\hat \beta _i} , which minimizes the objective function over β_τ, is defined in Equation (11): (11) Qβτ=∑yi>XiβτnτYi−XiT+∑yi<Xiβτ(1−τ)Yi−XiTβτ {Q_{\left( {{\beta _\tau }} \right)}} = \sum\nolimits_{{y_i} > {X_i}{\beta _\tau }}^n {\left( \tau \right)\left| {{Y_i} - X_i^T} \right|} + \sum\nolimits_{{y_i} < {X_i}{\beta _\tau }} {\left( {1 - \tau } \right)\left| {{Y_i} - X_i^T{\beta _\tau }} \right|}

Here, τ|e_i| and (1 − τ)|e_i| represent the asymmetric penalties for under-prediction and over-prediction, respectively, where 0 < τ < 1. The quantile regression coefficients can be estimated using linear programming.

The explicit functional form is presented in Equation (12): (12) QTY/X=x=XTτ 0<τ<1 {Q_T}\left( {Y/X = x} \right) = {X^T}\left( \tau \right)\;0 < \tau < 1 Where:

• Y – average annual income earned by the ith farmer from egg production

• X₁ − X_n – socio-demographic characteristics of farmers and poultry-specific attributes

• B_τ – marginal change in the τ^th quantile resulting from a marginal change in X.

The dependent and independent variables used in this study are presented in Table 1.

Table 2 presents the distribution of poultry egg farmers by flock size. The mean flock size was 745 birds. Nearly half of the respondents (47.96%) had flocks of 500–1000 birds, with an average flock size in this group of approximately 746 birds. Flocks of 1001–2000 birds accounted for 28.62% of respondents, while larger flocks were less common: 12.64% of farmers had flocks of 2001 to 3000 birds, and 10.78% managed more than 3000 birds.

This distribution indicates that most poultry egg farmers in the surveyed area are small-scale operations.

As shown in Table 3, only 4.8% of respondents were younger than 30, while 47.6% were aged 40–50. The majority of respondents were male (94.1%), with females representing 5.9%. Most respondents were married (88.5%), and 11.5% were single. Table 5 shows that households had an average of four members, ranging from zero to nine. Among respondents who were full-time farmers, 65.8% relied on farming as their main source of income. Regarding educational attainment, 36.8% had received training in livestock husbandry, 3.3% had no formal education, 12.6% had completed elementary school, 32.3% had completed secondary school, and 14.9% had completed further education.

Experience in chicken farming varied widely: 29% had less than five years of experience, 36.8% had five to ten years, 16% had ten to fifteen years, 8.2% had fifteen to twenty years, and 10% had more than twenty years (Table 4). In terms of production, 66.2% of farms raised Isa Brown chickens, while 17.8% raised Leghorn. Of those with access to finance, 84% relied on personal resources, and only 16% used credit services. Additionally, only 39.4% of respondents had access to extension services, whereas 60.6% did not (Table 4).

Table 6 presents the main expenses associated with chicken egg production. Animal feed was the largest cost component, accounting for 74.65% of total variable costs (TVC). Paid labor cost 2,960,665.428 CFA francs (5,294 USD), representing 12% of TVC. Medication and veterinary care accounted for 2.37%, while day-old chicks represented 7.33% of TVC. Transportation contributed 2.02% to the variable cost structure.

Egg sales were the primary source of revenue, accounting for 86.51% of total revenue (TR), with an average value of 25,327,202.01 CFA francs (45,598 USD). Culled laying hens generated an average of 3,841,542.565 CFA (6,876 USD), or 13.12% of TR. Overall, poultry egg production in the study area was profitable, with an average net income of 4,682,305.115 CFA francs (2,374 USD). Farmers earned 0.19 CFA francs for every CFA franc invested, corresponding to a return on investment (ROI) of 19%. The net income ratio of 0.159 indicates that 15.9% of total revenue remained as net profit after covering all costs.

Table 7 presents the results of the quantile regression used to identify the factors affecting the agricultural income of egg farmers. Three quantile models – at the 25th, 50th, and 75th quantiles – were computed. The model’s performance was evaluated based on economic theory and econometric criteria, including the F statistic, the significance of variable coefficients, and pseudo-R² values. The F statistic was highly significant at the 1% level, indicating that the explanatory variables collectively had a statistically significant effect on agricultural revenue.

Each model demonstrated a reasonable fit, with pseudo-R² values of 0.71, 0.72, and 0.70 at the 25th, 50th, and 75th quantiles, respectively. This indicates that the independent variables explained 71–72% of the variation in agricultural revenue across different points in the income distribution.

Egg prices emerged as the most influential factor across all quantiles, with strong and consistent coefficients (0.870 at τ = 0.25, 0.861 at τ = 0.50, and 0.867 at τ = 0.75), highlighting how farmers’ earnings increase substantially with higher egg prices. Revenue from slaughtered chickens also had a significant impact, particularly at the lowest quantile (coef = 0.125 at τ = 0.25). Waste recovery had a small but positive effect.

Among input variables, animal feed cost was the most significant, with coefficients of 0.466 (t = 11.68) at τ = 0.25, 0.436 (t = 9.88) at τ = 0.5, and 0.494 (t = 10.84) at τ = 0.75, indicating a strong relationship between feed costs and farmers’ financial performance.

Herd size coefficients were close to zero but highly significant across all quantiles (t > 4.7), suggesting that larger herds are associated with higher revenues.

Some factors were significant only at specific quantiles. At τ = 0.75, breed type became important (coef = 0.046), indicating that high-performing breeds benefit the most profitable producers. Depreciation of poultry houses was significant at τ = 0.50 (coef = 0.046), suggesting that infrastructure quality matters for farmers at the median income level.

A few cost-related and financial factors had negative effects at higher quantiles. Credit availability was significantly negative at τ = 0.50 (coef = −0.084), and labor costs were significantly negative at τ = 0.75 (coef = −0.046), indicating potential inefficiencies associated with these factors.

Tables 3, 4, and 5 show that most layer farmers are middle-aged, consistent with findings from Nigeria (Adeyonu, 2016; Osinowo and Tolorunju, 2019). Farmers in this age group are generally economically active and receptive to adopting agricultural innovations to increase production. The highly uneven gender distribution – dominated by men – reflects a regional pattern in southern Togo, where men participate more actively in commercial poultry production (Tchaye and Yovo, 2023). The predominance of married respondents suggests stable family structures that may help ensure adequate labor availability. Large households (Table 5) often contribute family labor, reducing dependence on hired workers and lowering production costs, a relationship also noted by Johnson et al. (2021).

Regarding education, a significant proportion of farmers had at least a secondary schooling or training in animal husbandry. Education and training are essential for understanding modern management practices, and previous studies have shown that farmer education enhances productivity (Luvhengo et al., 2018). Farmers’ experience levels varied, although many had five to ten years of practice. Experience tends to improve technical efficiency and managerial skills, though its effects may depend on age and educational attainment (Ojo, 2003).

The strong preference for the Isa Brown breed (66.2%) over the Leghorn breed (17.8%) indicates a regional shift toward high-productivity breeds to meet increasing consumer demand in West Africa (Ihou et al., 2025). However, challenges remain. Only 16% of respondents had access to credit services, highlighting financial constraints that hinder investment and growth. Similarly, only 39.4% of farmers had access to extension services, suggesting limited technical support for many farmers. As emphasized by Soviadan et al. (2024), access to finance and extension services play a critical role in improving productivity and promoting technology adoption.

Overall, the socioeconomic characteristics of farmers influence their production capacity, willingness to adopt new technologies, and ability to manage production risks. Expanding access to credit and extension services is likely to improve sectoral performance, productivity, and long-term sustainability.

Feed costs (74.65%) remain the most significant obstacle to profitability, as they directly affect production efficiency (Table 6). This aligns with studies showing that feed typically accounts for more than 60% of total poultry costs (Arif and Shafi, 2021). Labor constitutes the second-largest component (12%). Medium-sized farmers often rely on hired labor due to their larger flock sizes and more commercial orientation, which increases labor costs. Although labor costs are often lower than feed costs, they still play a substantial role in determining overall profitability (Adaszyńska-Skwirzyńska et al., 2025).

Expenditure on pharmaceuticals and veterinary care (2.37%) indicates that farmers prioritize flock health to reduce mortality and maintain productivity. Preventive and curative health management practices are essential for sustaining long-term profitability. Similarly, the cost of day-old chicks (7.33%) highlights their importance in the cost structure. This finding aligns with Chawke et al. (2021), who reported that chick purchasing accounted for approximately 12% of variable costs in broiler production in India, though this proportion varies by region depending on farming practices, input prices, and production systems.

The majority of revenue (86.51%) was generated from egg sales, confirming that egg production is the primary purpose of keeping laying hens. Revenue from culled birds, while less substantial (13.12%), still provides an important supplementary income stream that supports cash flow. Positive profitability indicators (ROI = 0.19; net income ratio = 0.159) and the positive net income recorded demonstrate that poultry egg production is financially viable in the study area. These findings are consistent with previous research showing that poultry farming remains profitable despite increasing input costs, as observed in broiler industries in Poland (Adaszyńska-Skwirzyńska et al., 2025) and Nigeria (Ebukiba et al., 2023).

Although profitability is evident, the ratios also indicate room for improvement. Enhancing efficiency – particularly through reducing feed costs, improving flock management, increasing access to high-quality inputs, and strengthening credit or extension services – could further raise ROI and overall profitability. Therefore, while the results highlight the economic potential of poultry egg production, they also point to the need for improved production practices and supportive policies to ensure long-term sustainability.

Table 7 shows that egg prices are the most significant factor influencing agricultural income across all quantiles. This finding aligns with Chen (2019) and Mubarok et al. (2024), who noted that egg prices – shaped by market dynamics, feed costs, and seasonal fluctuations – directly determine the profitability of poultry enterprises. The relatively uniform coefficients across quantiles indicate that both low-income and high-income producers benefit from favorable egg prices.

Waste recovery and income from culled birds provide additional revenue, though smaller in magnitude. For low-income farmers, however, these supplementary income sources play an important role in maintaining financial stability.

On the input side, feed prices emerged as the most influential factor, underscoring the central role of feed costs in the poultry sector. Although the positive coefficients may appear counterintuitive, they suggest that farmers facing higher feed prices are those typically operating larger, more commercial, or more productive farms – allowing them to remain profitable despite cost increases. This structural relationship reflects global trends in industrial poultry systems, where feed cost efficiency is a key determinant of competitiveness.

Herd size was also significant, supporting the idea that larger flock sizes are associated with higher income. This aligns with findings of Khan et al. (2022) and Arulnathan et al. (2024), who argue that greater production capacity enhances productivity and profitability in commercial poultry systems. Breed type becomes significant only at higher quantiles, indicating that high-income producers benefit more from superior genetics, such as improved laying rates and feed conversion. Likewise, the significance of housing depreciation at the median quantile suggests that infrastructure quality matters particularly for mid-level producers seeking to advance into the top income category. These results are consistent with Aldieri et al. (2021), who emphasize that improved technologies and infrastructure enhance technical efficiency.

Conversely, the negative and significant labor cost coefficients at higher quantiles indicate diminishing returns to hired labor. As farms expand, increased wage expenditures or inefficient labor management may reduce profitability. Similarly, the negative effect of credit availability suggests challenges related to loan use or repayment. According to Karlan et al. (2014), the issue often stems not from a lack of credit itself but from uninsured risks, poor loan targeting, or burdensome repayment conditions that impede rather than enhance farm profitability.

Overall, the results show that although market conditions and scale expansion contribute positively to income, inefficiencies in labor use and credit management can constrain profitability among the most advanced producers. Enhancing farmers’ financial literacy, improving labor management, and increasing access to risk-mitigation tools – such as insurance or subsidized credit – could therefore significantly strengthen economic outcomes.