Artificial incubation in family poultry farming systems in Brazil: technical feasibility and incubation performance

Abstract

Family poultry farming plays an important role in food security and income generation in rural Brazil; however, chick production in these systems still relies predominantly on natural incubation, which limits productivity and flock renewal. This study aimed to evaluate the feasibility and effectiveness of artificial incubation in family poultry farming systems by characterizing the socioeconomic profile and knowledge of rural producers regarding artificial incubation, implementing training and incubator distribution, and comparing incubation performance between rural farm and laboratory environments. The research was conducted in four regions of Minas Gerais, Brazil, and involved 87 family poultry farmers operating free-range production systems. Structured questionnaires were applied to characterize producers’ socioeconomic profiles, management practices, and knowledge regarding artificial incubation. From this group, 45 producers received theoretical and practical training (20 h) and were equipped with single-stage incubators with a capacity of 100 eggs. A total of 87 incubation trials were performed, including 45 trials on rural farms and 42 trials in laboratory environments. Producers were predominantly aged 50 years or older, had low formal education, and reported poultry farming as their main animal production activity. Although 74.7% were familiar with artificial incubation and recognized its advantages, reliance on broody hens (49.4%) and lack of egg sanitization (60.9%) were still common. There were no differences between farm and laboratory settings regarding hatchability (62.40 ± 2.19% vs. 62.87 ± 2.45%), hatchability of fertile eggs (80.09 ± 1.90% vs. 78.36 ± 2.38%), fertility (77.79 ± 1.81% vs. 80.42 ± 1.99%), stage-specific embryonic mortality, or overall incubation losses. These results demonstrate that family poultry farmers, when provided with appropriate training and technical support, are capable of achieving incubation outcomes equivalent to those obtained under controlled laboratory conditions. Artificial incubation is therefore a technically viable and scalable technology to improve chick availability, support flock renewal, and strengthen family poultry farming systems in Brazil.

Introduction

Family farming plays a central role in Brazil’s agricultural and social landscape. It encompasses a diverse population of smallholders and traditional communities who collectively manage 3.9 million rural establishments and occupy 23% of Brazil’s territory (Contag, 2023). This sector is responsible for 67% of rural employment and contributes 23% of the gross value of agricultural production, ranking eighth globally in food output (Sebrae, 2023).

Among the various activities within family farming, traditional poultry production stands out for its nutritional value, cultural relevance, and economic potential. Free-range, and native chickens, commonly raised in small-scale systems and backyard poultry production, provide affordable protein and serve as a source of supplemental income (Elkhoraibi et al., 2014; Gharib et al., 2023). However, chick production in these systems typically relies on natural incubation, which is limited by low chick output, exposure to environmental fluctuations, and reduced egg production due to hen brooding behavior (Mutombo, 2022; Oliveira and Santos, 2018).

Artificial incubation offers a controlled alternative that enhances hatchability and allows the production of a higher number of chicks within a shorter period. Its origins date back more than 3,000 years to rudimentary systems in ancient Egypt, where heat was provided by burning organic materials and eggs were manually turned (Boleli et al., 2016). Over time, technological advancements have led to sophisticated incubators capable of regulating temperature, humidity, ventilation, and egg turning (Franco et al., 2023). The primary barriers to its adoption include equipment costs, restricted access, and a lack of technical knowledge among producers.

This knowledge gap is significant because the success of artificial incubation is dependent on precise management. It requires strict control of physical factors, including temperature, humidity, egg turning, and ventilation. Deviations from optimal conditions can lead to embryonic mortality, poor chick quality, and reduced hatchability (Güz et al., 2020). In addition, egg quality, determined by shell integrity, egg weight, and breeder age, plays a crucial role in embryonic development and chick viability (Araújo et al., 2016; Biesek et al., 2023).

Despite its potential, there is a lack of scientific literature addressing the use of artificial incubation in extensive and family-based poultry systems. This gap hinders the dissemination of best practices and limits productivity gains among smallholders. Therefore, this study aims to bridge this technological gap through targeted extension programs by scientifically evaluating whether the provision of technical training and equipment can enable smallholders to overcome these barriers, successfully adopt artificial incubation, and increase the number of newly hatched chicks

This study aimed to evaluate the feasibility and effectiveness of artificial incubation in family poultry farming systems across four regions of Minas Gerais, Brazil. Specifically, it sought to (1) characterize the socioeconomic profile and knowledge of rural producers regarding artificial incubation, (2) implement training and equipment distribution, and (3) compare incubation outcomes between farm and laboratory environments. Based on this rationale, the hypothesis of this study was that, when provided with appropriate training and equipment, family poultry farmers can successfully manage the critical factors of artificial incubation, achieving hatchability and chick viability rates comparable to those obtained under controlled laboratory conditions.

Material and Methods

This study was conducted as part of a collaborative rural extension initiative involving four academic institutions in Minas Gerais, Brazil: the Federal University of Lavras (UFLA), the Federal University of Minas Gerais (UFMG), the State University of Minas Gerais (UEMG), and the Federal Institute of Minas Gerais, Bambuí campus (IFMG). Data collection was carried out in rural communities located in the municipalities of Bambuí, Igarapé, Ituiutaba, and Lavras, Minas Gerais, Brazil (Figure 1). All procedures involving human participants and animals were reviewed and approved by the Ethics Committee on Animal Use (CEUA) and the Research Ethics Committee (COEP) of the Federal University of Minas Gerais, Belo Horizonte, Brazil (protocol numbers 290/2023 and 6.410.205, respectively).

Fig. 1.

Location of the study areas in Minas Gerais, Brazil, highlighting the four regions where the study was conducted (1 = Ituiutaba; 2 = Bambuí; 3 = Lavras; 4 = Igarapé).

Selection and characterization of producers

A total of 87 family poultry farmers were recruited using a snowball sampling method. Inclusion criteria included active engagement in family farming, ownership of or access to backyard poultry production, free-range (“caipira”) poultry systems, and willingness to participate in training activities and incubation trials (Figure 2). Producers were interviewed individually using a structured and semi-structured questionnaire designed to characterize their sociodemographic profile, including sex, age, place of residence, family background in agriculture, marital status, number of children, ethnicity, educational level, income sources, household income, and main animal production activity (Supplementary Material). Ethnicity was self-reported according to the classification of the Brazilian Institute of Geography and Statistics (IBGE, 2022), including white, brown (pardo), black, and indigenous categories.

Fig. 2.

Methodological flowchart of the study design, illustrating participant selection, incubation procedures conducted under field and laboratory conditions and data collection. Created with Canva.com.

Knowledge and perception of artificial incubation

The questionnaire also included items related to producers’ knowledge, practices, and perceptions regarding artificial incubation. These variables included awareness of the term artificial incubation, prior experience with incubators, knowledge of the incubation period for chicken eggs, methods used to acquire chicks, egg cleaning or sanitization practices, type of nesting used by hens, occurrence of spoiled or non-viable eggs, and knowledge of the causes of egg spoilage. Additional questions addressed producers’ perceptions regarding the effectiveness of artificial incubation compared to natural brooding, its potential to improve chick production, and their interest in improving hatchability on their farms (Supplementary Material).

Information on the productive characteristics of poultry systems was obtained through the questionnaire and included flock size, number of roosters, average egg production (dozens per week), egg commercialization, chicken meat sales, and marketing channels. For analytical purposes, farms were stratified according to flock size into two categories: properties with fewer than 100 birds and properties with 101 birds or more. Farmers from all four regions participated in theoretical and practical training activities and egg handling procedures totaling 20 hours. Training covered incubator operation, egg handling, selection, sanitization, and storage procedures to standardize incubation practices across farms.

Incubation trials on farms and in laboratories

A total of 87 incubation trials were conducted, including 45 trials on farms and 42 trials in specialized laboratories at IFMG (Bambuí), UFMG (Igarapé), UEMG (Ituiutaba), and UFLA (Lavras). On-farm trials comprised 13 incubation procedures in Bambuí, 12 in Igarapé, and 10 each in Ituiutaba and Lavras, with an average of 95, 60, 66, and 50 eggs per incubation, respectively. Under laboratory conditions, 11 incubation procedures were conducted in Bambuí, 4 in Igarapé, 10 in Ituiutaba, and 11 in Lavras, with an average of 81, 45, 32, and 37 eggs per incubation, respectively.

On-farm and laboratory incubations were conducted after standardized training had been provided to all participating rural producers. The incubation activities were carried out over a six-month period, during which on-farm and laboratory incubations were performed simultaneously. Laboratory incubations were conducted using eggs supplied by the same producers who carried out the on-farm trials, ensuring consistency between incubation settings. For each incubation cycle, eggs were collected twice daily over five consecutive days. Producers were instructed to store the eggs in the lower compartment of a household refrigerator at an average temperature of 14 to 18°C for a maximum of five days prior to incubation. Eggs considered unsuitable for incubation, including those that were cracked, dirty, misshapen, double-yolked, or undersized were discarded. Selected eggs were sanitized by spraying them with a commercial disinfectant solution (Lysoform®, 5 mL/L of water) before incubation (Figure 2).

Each incubation cycle was treated as an independent experimental unit for statistical analysis. The incubations were conducted in different locations and periods. The study followed a completely randomized design, with incubation setting (farm or laboratory) was included as a fixed effect. All incubations were performed using 20 identical single-stage incubator model (IP 100D, Premium Ecológica®, Belo Horizonte, MG, Brazil) – five machines per region studied, with a maximum capacity of 100 chicken eggs. Incubators were equipped with automatic temperature and humidity control and automatic egg turning every 2 h. In field conditions, incubators were placed in well-ventilated areas protected from direct sunlight, whereas in university facilities they were maintained in closed rooms protected from light, with controlled ambient temperatures ranging from 22 to 24°C. Incubation temperature was set at 37.5°C with 60% relative humidity. From day 18 onward, temperature was reduced to 36.5°C to meet hatcher requirements. Eggs were placed horizontally. On-farm incubations were supervised by the producers after training, whereas laboratory incubations were supervised by the research team.

The objective of the statistical analysis was to compare incubation settings (farm vs laboratory). Regional effects were not included in the model, as the study was not designed to evaluate differences among municipalities.

Hatchery performance and embryonic mortality assessment

At day 21 of incubation, all unhatched eggs were opened for macroscopic evaluation of embryonic development. Fertility, hatchability, and embryonic mortality were assessed. Embryonic mortality was classified as early (1–7 days), intermediate (8–14 days), or late (15–21 days) according to Lourens et al. (2006). Additional loss categories included dead pipped embryos, live pipped embryos, live embryos without pipping, embryos with visible anomalies, and contaminated eggs. Hatchability was calculated as the proportion of hatched chicks relative to the number of incubated eggs. Hatchability of fertile eggs was calculated as the proportion of hatched chicks relative to fertile eggs. Fertility rates were expressed as percentages of incubated eggs. All loss categories were expressed as percentages of the total number of incubated eggs.

Statistical analyzes

Sociodemographic, productive, and perception data were analyzed using descriptive statistics, including absolute and relative frequencies for categorical variables. Associations between flock size (<100 vs ≥101 birds) and production structure or commercialization variables were evaluated using the chi-square test of independence. When expected cell frequencies were lower than five, Fisher’s exact test was applied. Statistical significance was set at P ≤ 0.05. For hatchery performance, each incubation trial was considered the experimental unit. Data were initially assessed for normality using the Shapiro–Wilk test (P<0.05) and for homogeneity of variances through visual inspection of residual plots.

When assumptions of normality were met, results were expressed as means ± standard error (SE), and comparisons between incubation sites were performed using a linear fixed-effects model. When normality assumptions were violated, comparisons between on-farm and laboratory incubation were conducted using the Mann–Whitney U test. A significance level of P ≤ 0.05 was adopted for all analyses.

All statistical analyses were conducted using R software (2021). Hatchery performance was analyzed using the following fixed-effects model:

$${\mathrm{Y}}_{\text{ij}}=\mu +{\mathrm{T}}_{\mathrm{i}}+{\mathrm{e}}_{\text{ij}},$$

where Yᵢⱼ is the response variable, μ is the overall mean, Tᵢ represents the fixed effect of incubation site (farm or laboratory), and eᵢⱼ is the residual error.

Results

Results sociodemographic characteristics of producers

The sociodemographic profile of the interviewed producers is summarized in Table 1. Overall, most respondents were male (55.2%), although female participation was higher in Bambuí. In the remaining municipalities, male producers predominated. Regarding age, producers aged 50 years or older represented most of the sample (64.4%), followed by those aged 40–49 years. Younger age groups (≤39 years) accounted for a small proportion of respondents. This age profile was consistent across all municipalities, with the highest proportion of older producers observed in Ituiutaba (Table 1). Most participants resided in rural areas (80.5%), and rural residence predominated in all municipalities, reaching 100% in Ituiutaba. The majority of respondents reported being children of farmers, although this proportion varied among regions, with Ituiutaba showing an equal distribution between those who were and were not children of farmers (Table 1).

Table 1.

Sociodemographic profile of chicken producers in traditional rearing systems in four regions.

	Overall n (%)	Bambuí n (%)	Igarapé n (%)	Ituiutaba n (%)	Lavras n (%)
Gender
Female	39 (44.8)	14 (66.7)	10 (38.5)	6 (30.0)	9 (45.0)
Male	48 (55.2)	7 (33.3)	16 (61.5)	14 (70.0)	11 (55.0)
Age
Up to 29 years	7 (8.0)	3 (14.3)	2 (7.7)	0 (0.0)	2 (10.0)
30 to 39 years	7 (8.0)	3 (14.3)	1 (3.8)	1 (5.3)	1 (5.0)
40 to 49 years	17 (19.5)	5 (23.8)	5 (19.2)	2 (10.5)	5 (25.0)
50 years or older	56 (64.4)	10 (47.6)	18 (69.2)	16 (84.2)	12 (60.0)
Place of residence
Urban area	17 (19.5)	5 (23.8)	5 (19.2)	0 (0.0)	7 (35.0)
Rural area	70 (80.5)	16 (76.2)	21 (80.8)	20 (100.0)	13 (65.0)
Parent(s) engaged in farming
Yes	65 (74.7)	19 (90.5)	21 (80.8)	10 (50.0)	15 (75.0)
No	22 (25.3)	2 (9.5)	5 (19.2)	10 (50.0)	5 (25.0)
Marital status
Single	16 (18.4)	5 (23.8)	4 (15.4)	2 (10.0)	5 (25.0)
Married	62 (71.3)	13 (61.9)	18 (69.2)	17 (85.0)	14 (70.0)
Divorced	4 (4.6)	2 (9.5)	2 (7.7)	0 (0.0)	0 (0.0)
Widowed	5 (5.7)	1 (4.8)	2 (7.7)	1 (5.0)	1 (5.0)
Number of children
One	8 (9.2)	4 (19.0)	0 (0.0)	2 (10.0)	2 (10.0)
Two	29 (33.3)	6 (28.6)	7 (26.9)	8 (40.0)	8 (40.0)
Three	18 (20.7)	3 (14.3)	8 (30.8)	3 (15.0)	4 (20.0)
Four or more	14 (16.1)	2 (9.5)	7 (26.9)	3 (15.0)	2 (10.0)
None	18 (20.7)	6 (28.6)	4 (15.4)	4 (20.0)	4 (20.0)
Ethnicity
White	46 (52.9)	13 (61.9)	7 (26.9)	16 (80.0)	10 (50.0)
Black	10 (11.5)	1 (4.8)	8 (30.8)	1 (5.0)	0 (0.0)
Brown (pardo)	30 (34.5)	7 (33.3)	10 (38.5)	3 (15.0)	10 (50.0)
Indigenous	1 (1.1)	0 (0.0)	1 (3.8)	0 (0.0)	0 (0.0)

Bambuí (IFMG), Igarapé (UFMG), Ituiutaba (UEMG) and Lavras (UFLA).

Married producers predominated overall, followed by single, widowed, and divorced individuals. With respect to family composition, The most frequent category was having two children, followed by three children and no children. Ethnicity was predominantly self-reported as white, followed by brown (pardo), black, and indigenous, with differences in distribution across municipalities (Table 1).

Educational level and income characteristics are presented in Table 2. Incomplete elementary education was the most frequent educational level overall, followed by complete high school education. Educational attainment varied among municipalities, with incomplete elementary education being more frequently reported in Igarapé and Ituiutaba, while Bambuí showed a higher proportion of producers with complete high school education. Regarding income sources, the most common profile was the combination of agriculture and retirement benefits, followed by agriculture combined with self-employed activities and agriculture as the sole income source. Monthly household income was most frequently between one and two minimum wages, although higher income categories were also observed in Ituiutaba. Poultry production was identified as the main animal production activity overall, followed by cattle production, with cattle being predominant only in Ituiutaba (Table 2).

Table 2.

Educational level and income characteristics of chicken producers in traditional production systems in four regions.

	Overall n (%)	Bambuí n (%)	Igarapé n (%)	Ituiutaba n (%)	Lavras n (%)
Educational level
No formal education	2 (2.3)	0 (0.0)	0 (0.0)	0 (0.0)	2 (9.1)
Incomplete elementary education	31 (35.6)	3 (15.8)	14 (53.8)	10 (50.0)	4 (18.2)
Complete elementary education	8 (9.2)	1 (5.3)	0 (0.0)	3 (15.0)	4 (18.2)
Incomplete high school education	9 (10.3)	4 (21.1)	1 (3.8)	2 (10.0)	2 (9.1)
Complete high school education	19 (21.8)	8 (42.1)	5 (19.2)	2 (10.0)	4 (18.2)
Incomplete technical education	2 (2.3)	1 (5.3)	1 (3.8)	0 (0.0)	0 (0.0)
Complete technical education	2 (2.3)	0 (0.0)	0 (0.0)	1 (5.0)	1 (4.5)
Incomplete higher education	3 (3.4)	0 (0.0)	1 (3.8)	0 (0.0)	2 (9.1)
Complete higher education	11 (12.6)	2 (10.5)	4 (15.4)	2 (10.0)	3 (13.6)
Source of income
Agriculture and retirement	26 (29.9)	7 (33.3)	7 (26.9)	9 (45.0)	3 (15.0)
Agriculture and self-employed	21 (24.1)	5 (23.8)	2 (7.7)	4 (20.0)	10 (50.0)
Agriculture only	20 (23.0)	8 (38.1)	7 (26.9)	5 (25.0)	0 (0.0)
Retirement only	7 (8.0)	1 (4.8)	5 (19.2)	0 (0.0)	1 (5.0)
Outside rural activity	13 (14.9)	0 (0.0)	5 (19.2)	2 (10.0)	6 (30.0)
Main animal production
Bees	2 (2.3)	0 (0.0)	2 (7.7)	0 (0.0)	0 (0.0)
Cattle	28 (32.2)	8 (38.1)	4 (15.4)	13 (65.0)	3 (18.8)
Poultry	53 (60.9)	13 (61.9)	19 (73.1)	4 (20.0)	13 (81.3)
Sheep	0 (0.0)	0 (0.0)	0 (0.0)	1 (5.0)	0 (0.0)
Swine	3 (3.4)	0 (0.0)	1 (3.8)	2 (10.0)	0 (0.0)
Average household income (Minimum wages)1
Less than 1	2 (2.3)	1 (4.8)	0 (0.0)	0 (0.0)	1 (5.0)
1 to less than 2	34 (39.1)	11 (52.4)	11 (42.3)	2 (10.0)	10 (50.0)
2 to less than 3	21 (24.1)	5 (23.8)	7 (26.9)	2 (10.0)	7 (35.0)
3 to less than 4	17 (19.5)	3 (14.3)	4 (15.4)	9 (45.0)	1 (5.0)
4 to less than 5	4 (4.6)	1 (4.8)	1 (3.8)	1 (5.0)	1 (5.0)
5 or more	9 (10.3)	0 (0.0)	3 (11.5)	6 (30.0)	0 (0.0)

¹Brazilian minimum wage for the year 2024 (R$ 1,412.00; approximately US$ 285.00). Bambuí (IFMG), Igarapé (UFMG), Ituiutaba (UEMG) and Lavras (UFLA).

Variables related to producers’ knowledge, practices, and perceptions regarding artificial incubation are presented in Table 3, Table 4. Most producers reported being familiar with the term artificial incubation, although fewer than half had previously used an incubator. Awareness of artificial incubation was high in all municipalities, with high frequencies reported in Igarapé and Ituiutaba, while lower frequencies were observed in Lavras (Table 3). Knowledge of the incubation period for chicken eggs was widespread, with nearly all respondents indicating that they knew the correct duration. Among those who answered affirmatively, 21 days was the most frequently cited incubation period across all municipalities (Table 3). Natural brooding by hens was the most common method for chick acquisition overall, followed by the purchase of one-day-old chicks, use of incubators, and acquisition of adult chickens. In Ituiutaba, the purchase of one-day-old chicks was the most frequently reported method (Table 3).

Table 3.

Producers’ knowledge, incubation practices, and egg management in family poultry systems in four regions.

	Overall n (%)	Bambuí n (%)	Igarapé n (%)	Ituiutaba n (%)	Lavras n (%)
Aware of the term artificial incubation?
Yes	65 (74.7)	16 (76.2)	22 (84.6)	16 (80.0)	11 (55.0)
No	22 (25.3)	5 (23.8)	4 (15.4)	4 (20.0)	9 (45.0)
Have already used an incubator?
Yes	42 (48.3)	10 (47.6)	14 (53.8)	11 (55.0)	7 (35.0)
No	45 (51.7)	11 (52.4)	12 (46.2)	9 (45.0)	13 (65.0)
Knows how many days it takes to incubate chicken eggs?
Yes	83 (95.4)	20 (95.2)	23 (88.5)	20 (100.0)	20 (100.0)
No	4 (4.6)	1 (4.8)	3 (11.5)	0 (0.0)	0 (0.0)
How many days?
20	1 (1.2)	1 (5.0)	0 (0.0)	0 (0.0)	0 (0.0)
21	76 (90.5)	19 (95.0)	20 (87.0)	18 (90.0)	19 (95.0)
22	5 (6.0)	0 (0.0)	2 (8.7)	1 (5.0)	1 (5.0)
40	2 (2.4)	0 (0.0)	1 (4.3)	1 (5.0)	0 (0.0)
How do you acquire your chicks?
Uses broody hens	43 (49.4)	12 (57.1)	14 (53.8)	6 (30.0)	11 (55.0)
Uses an incubator	12 (13.8)	4 (19.0)	4 (15.4)	0 (0.0)	4 (20.0)
Buys one-day-old chicks	22 (25.3)	1 (4.8)	5 (19.2)	14 (70.0)	2 (10.0)
Buys adult birds	10 (11.5)	4 (19.0)	3 (11.5)	0 (0.0)	3 (15.0)
Performs any type of egg cleaning or sanitization?
Yes	34 (39.1)	6 (28.6)	15 (57.7)	6 (30.0)	7 (35.0)
No	53 (60.9)	15 (71.4)	11 (42.3)	14 (70.0)	13 (65.0)
Where hens lay their eggs?
Natural nests	77 (88.5)	18 (85.7)	24 (92.3)	19 (95.0)	16 (80.0)
Artificial nests	10 (11.5)	3 (14.3)	2 (7.7)	1 (5.0)	4 (20.0)
Have already seen spoiled/rotten/non-viable eggs?
Yes	85 (97.7)	21 (100.0)	26 (100.0)	20 (100.0)	18 (90.0)
No	2 (2.3)	0 (0.0)	0 (0.0)	0 (0.0)	2 (10.0)
Knows why eggs become spoiled?
Yes	60 (69.0)	12 (57.1)	20 (76.9)	20 (100.0)	8 (40.0)
No	27 (31.0)	9 (42.9)	6 (23.1)	0 (0.0)	12 (60.0)

Bambuí (IFMG), Igarapé (UFMG), Ituiutaba (UEMG) and Lavras (UFLA).

Table 4.

Producers’ perceptions of incubator use and chick production in four regions.

	Overall n (%)	Bambuí n (%)	Igarapé n (%)	Ituiutaba n (%)	Lavras n (%)
For hatching eggs, it is better to use?
Hen	13 (14.9)	2 (9.5)	4 (15.4)	4 (20.0)	3 (15.0)
Incubator	62 (71.3)	19 (90.5)	16 (61.5)	16 (80.0)	11 (55.0)
Do not know	12 (13.8)	0 (0.0)	6 (23.1)	0 (0.0)	6 (30.0)
Artificial incubation can improve chick production?
Yes	72 (82.8)	21 (100.0)	21 (80.8)	18 (90.0)	12 (60.0)
No	3 (3.4)	0 (0.0)	0 (0.0)	1 (5.0)	2 (10.0)
No opinion	12 (13.8)	0 (0.0)	5 (19.2)	1 (5.0)	6 (30.0)
Would like to improve chick hatchability on the farm?
Yes	79 (90.8)	21 (100.0)	23 (88.5)	19 (95.0)	21 (84.0)
No	8 (9.2)	0 (0.0)	3 (11.5)	1 (5.0)	4 (16.0)

Bambuí (IFMG), Igarapé (UFMG), Ituiutaba (UEMG) and Lavras (UFLA).

Most producers reported not performing egg cleaning or sanitization, with higher frequencies reported in Igarapé, where sanitization was more frequently reported. Eggs were predominantly laid in natural nests in all municipalities. Nearly all respondents reported having observed spoiled or non-viable eggs, and most indicated that they were aware of the causes of egg spoilage, although lower frequencies of this knowledge were observed in Lavras (Table 3).

When asked about preferences for hatching methods, most producers indicated artificial incubation as superior to natural brooding by hens. In addition, the majority believed that artificial incubation could improve chick production on their farms and expressed a desire to improve hatchability, indicating a strong interest in adopting improved incubation practices (Table 4).

Productive characteristics of the poultry systems, stratified by flock size, are presented in Table 5. Significant associations were observed between flock size and production structure variables. Larger flocks (≥101 birds) were significantly associated with the number of roosters (P < 0.001) and egg production levels (P = 0.002). In addition, commercialization patterns were associated with flock size, with significant associations observed for egg sales (P = 0.032), meat sales (P = 0.023), and place of sale (P = 0.049). Most farms maintained up to five roosters in their flocks, particularly among properties with fewer than 100 birds. In contrast, farms with 101 birds or more more frequently maintained 11–20 roosters. Egg production was most commonly between 2.6 and 10 dozen eggs per week, especially among smaller flocks, whereas larger flocks more frequently produced higher weekly egg volumes. More than half of the producers reported selling eggs, with higher frequencies observed among farms with 101 birds or more. Chicken meat sales were less frequent overall and occurred mainly on an occasional basis, particularly among larger flocks. Most products were sold directly at the producer’s home, regardless of flock size, although farms with larger flocks presented a wider distribution of marketing channels, including retail trade (Table 5).

Table 5.

Production structure and product commercialization in family poultry farms according to flock size in four regions.

Variable	Category	Total n (%)	<100 birds n (%)	≥101 birds n (%)	P-value1
Number of roosters in the flock					<0.001
	<1	9 (10.3)	0 (0.0)	9 (52.9)
	Up to 5	63 (72.4)	60 (85.7)	3 (17.6)
	6–10	9 (10.3)	9 (12.9)	0 (0.0)
	11–20	5 (5.7)	1 (1.4)	4 (23.5)
	>21	1 (1.1)	0 (0.0)	1 (5.9)
Egg production (dozens/week)					0.002
	<1	2 (2.3)	2 (2.6)	0 (0.0)
	Up to 2.5	14 (16.1)	14 (18.4)	0 (0.0)
	2.6–10	43 (49.4)	41 (53.9)	2 (18.2)
	11–20	18 (20.7)	13 (17.1)	5 (45.5)
	21–30	5 (5.7)	4 (5.3)	1 (9.1)
	>31	5 (5.7)	2 (2.6)	3 (27.3)
Egg sales					0.032
	Yes	49 (56.3)	39 (51.3)	10 (90.9)
	No	29 (33.3)	29 (38.2)	0 (0.0)
	When available	9 (10.3)	8 (10.5)	1 (9.1)
Chicken meat sales					0.023
	Yes	25 (28.7)	24 (31.6)	1 (9.1)
	No	42 (48.3)	38 (50.0)	4 (36.4)
	When available	20 (23.0)	14 (18.4)	6 (54.5)
Place of product sales					0.049
	Do not sell	28 (32.2)	28 (36.8)	0 (0.0)
	At home	36 (41.4)	31 (40.8)	5 (45.5)
	Home delivery	2 (2.3)	1 (1.3)	1 (9.1)
	Retail trade	5 (5.7)	3 (3.9)	2 (18.2)
	At home and home delivery	8 (9.2)	7 (9.2)	1 (9.1)
	At home and retail trade	8 (9.2)	6 (7.9)	2 (18.2)

¹Associations between flock size and categorical variables were assessed using Fisher’s exact test due to low expected frequencies in some cells. Properties with fewer than 100 chickens totaled 70, whereas those with 101 chickens or more totaled 17. Statistical significance was set at P ≤ 0.05.

Incubation results

The average incubation performance results from rural farms and laboratory settings, stratified by municipality, are presented as descriptive statistics in Table 6, Table 7. The overall mean results across municipalities are shown in Table 8. No significant differences were observed between incubation environments for hatchability (62.40 ± 2.19% vs. 62.87 ± 2.45%; P = 0.457), hatchability of fertile eggs (80.09 ± 1.90% vs. 78.36 ± 2.38%; P = 0.506), or fertility (77.79 ± 1.81% vs. 80.42 ± 1.99%; P = 0.102). Similarly, embryonic mortality rates at early (3.30 ± 0.38% vs. 4.69 ± 0.87%; P = 0.550), intermediate (3.11 ± 0.36% vs. 3.54 ± 0.60%; P = 0.912), and late (5.33 ± 0.53% vs. 6.34 ± 0.89%; P = 0.761) developmental stages did not differ between farms and laboratories.

Table 6.

Incubation results measured on rural farms.

	Bambuí	Igarapé	Ituiutaba	Lavras
Variables (%)	Mean ± SE	Mean ± SE	Mean ± SE	Mean ± SE
Hatchability	66.62 ± 3.43	57.08 ± 5.40	58.13 ± 4.36	67.58 ± 3.52
Hatchability of fertile	84.26 ± 2.83	71.33 ± 5.10	84.20 ± 2.06	81.09 ± 2.83
Fertility	78.74 ± 2.36	79.89 ± 4.00	68.91 ± 4.64	82.90 ± 2.33
Early mortality	2.49 ± 0.61	4.74 ± 0.93	2.57 ± 0.47	3.37 ± 0.81
Intermediate mortality	2.84 ± 0.53	3.70 ± 0.77	2.80 ± 0.64	3.08 ± 1.03
Late mortality	4.34 ± 0.74	6.59 ± 1.10	5.40 ± 1.31	5.04 ± 1.14
Dead pipped	1.09 ± 0.30	1.67 ± 1.16	0.86 ± 0.38	0.75 ± 0.32
Live pipped	0.15 ± 0.10	0.76 ± 0.45	0.50 ± 0.26	1.98 ± 0.57
Live without pipping	0.50 ± 0.33	4.03 ± 2.26	0.27 ± 0.18	0.59 ± 0.40
Anomaly	0.56 ± 0.19	0.00 ± 0.00	0.37 ± 0.25	0.00 ± 0.00
Contaminated	0.16 ± 0.11	1.16 ± 0.60	0.48 ± 0.26	0.41 ± 0.28

Values are expressed as mean ± standard error (SE). Bambuí (IFMG), Igarapé (UFMG), Ituiutaba (UEMG) and Lavras (UFLA).

Table 7.

Incubation results under laboratory conditions in university facilities.

	Bambuí	Igarapé	Ituiutaba	Lavras
Variables (%)	Mean ± SE	Mean ± SE	Mean ± SE	Mean ± SE
Hatchability	59.81 ± 3.80	57.78 ± 1.57	71.55 ± 3.11	60.42 ± 6.04
Hatchability of fertile	81.63 ± 3.10	64.32 ± 3.48	88.03 ± 1.71	71.42 ± 5.45
Fertility	72.55 ± 3.44	90.28 ± 2.95	81.10 ± 2.65	84.07 ± 3.98
Early mortality	2.65 ± 0.37	7.78 ± 1.76	2.11 ± 0.65	7.96 ± 2.30
Intermediate mortality	3.44 ± 0.91	2.78 ± 1.47	3.15 ± 0.68	4.28 ± 1.61
Late mortality	5.94 ± 1.38	5.28 ± 1.60	3.50 ± 1.10	9.70 ± 2.02
Dead pipped	0.81 ± 0.35	0.56 ± 0.56	0.20 ± 0.20	0.13 ± 0.13
Live pipped	0.28 ± 0.20	3.61 ± 1.39	0.00 ± 0.00	0.00 ± 0.00
Live without pipping	0.00 ± 0.00	7.78 ± 3.60	0.00 ± 0.00	0.86 ± 0.46
Anomaly	0.09 ± 0.09	0.00 ± 0.00	0.00 ± 0.00	0.00 ± 0.00
Contaminated	0.10 ± 0.01	4.72 ± 1.78	0.58 ± 0.39	0.70 ± 0.38

Values are expressed as mean ± standard error (SE). Bambuí (IFMG), Igarapé (UFMG), Ituiutaba (UEMG) and Lavras (UFLA). Data are presented descriptively; no statistical comparisons among regions were performed.

Table 8.

Incubation results of hatchings conducted on rural farms and in laboratories.

	Farms	Laboratories	P-value
	Mean ± SE (%)	Mean ± SE (%)
Hatchability	62.40 ± 2.19	62.87 ± 2.45	0.457
Hatchability of fertile	80.09 ± 1.90	78.36 ± 2.38	0.506
Fertility	77.79 ± 1.81	80.42 ± 1.99	0.102
Early mortality	3.30 ± 0.38	4.69 ± 0.87	0.550
Intermediate mortality	3.11 ± 0.36	3.54 ± 0.60	0.912
Late mortality	5.33 ± 0.53	6.34 ± 0.89	0.761
Dead pipped	1.12 ± 0.33	0.40 ± 0.14	0.109
Live pipped	0.80 ± 0.21	0.49 ± 0.24	0.346
Live without pipping	1.41 ± 0.64	1.13 ± 0.55	0.691
Anomaly	0.24 ± 0.08	0.03 ± 0.03	0.777
Contaminated	0.56 ± 0.19	0.93 ± 0.33	0.203

Values are expressed as mean ± standard error (SE). Means were compared by T test (P<0.05).

The proportions of dead pipped embryos (1.12 ± 0.33% vs. 0.40 ± 0.14%; P = 0.109), live pipped embryos (0.80 ± 0.21% vs. 0.49 ± 0.24%; P = 0.346), and live embryos without pipping (1.41 ± 0.64% vs. 1.13 ± 0.55%; P = 0.691) were also statistically comparable between incubation environments. Low frequencies of embryos with anomalies (0.24 ± 0.08% vs. 0.03 ± 0.03%; P = 0.777) and egg contamination (0.56 ± 0.19% vs. 0.93 ± 0.33%; P = 0.203) were observed in both incubation settings, with no significant differences between farms and laboratories. Overall, these results indicate that incubation performance under rural farm conditions was equivalent to that achieved in laboratory environments (P > 0.05).

Discussion

This study provides original and applied evidence on the feasibility of artificial incubation within family poultry farming systems under Brazilian rural conditions. Brazil is a country of continental dimensions, characterized by pronounced regional heterogeneity in climate, infrastructure, socioeconomic conditions, and poultry production systems. In this context, the present work represents an important initial step toward establishing a replicable methodological framework that can be adopted in other Brazilian states to better understand family poultry farming and the use of artificial incubators by small-scale producers nationwide. The scarcity of studies integrating socioeconomic characterization, rural extension actions, and biological incubation outcomes in family poultry systems highlights the relevance of this contribution to Brazilian poultry science, particularly regarding the sustainable use and conservation of locally adapted chicken populations.

The sociodemographic profile observed in this study provides essential context for interpreting the profile of backyard poultry production’s farmers, management practices and incubation outcomes. The predominance of older producers suggests that family poultry farming remains strongly associated with traditional rural livelihoods rather than being driven by younger generations or specialized commercial interests (Cunha et al., 2018). This may be partially explained by the financial returns from family poultry are insufficient to maintain the interest of younger generations, who are increasingly drawn to urban labor to improve the household income. Consequently, such dynamics may be associated with the historical loss of the generational labor and culture associated with family poultry farming. This profile differs from national agricultural trends described by Cattafesta et al. (2020), indicating that poultry keeping may persist as a culturally rooted activity maintained by more experienced farmers.

Backyard poultry production in the United States, as described by Elkhoraibi et al. (2014), is predominantly a small-scale, lifestyle-oriented activity motivated by self-consumption, animal welfare concerns, and perceived egg and meat quality. Although flock sizes are generally small and owners report relatively good bird health, important gaps in disease awareness and biosecurity practices have been identified, particularly regarding highly transmissible diseases. When contrasted with Brazilian family poultry systems, both similarities and structural differences emerge. In Brazil, family poultry keeping is more directly linked to income supplementation and food security rather than lifestyle choice (Cunha et al., 2018), often occurring under greater resource constraints and more limited access to technical assistance. Nonetheless, in both contexts, restricted veterinary support and limited technical guidance are reported. Therefore, international evidence reinforces the importance of targeted extension strategies, accessible training programs, and biosecurity education to strengthen flock health, productivity, and sustainability in Brazilian family poultry systems.

In the present study, male participation slightly predominated among family poultry producers. This pattern appears to differ from reports in African village poultry systems, which are traditionally characterized by a central female role in poultry management (Dinka et al., 2010; Guèye, 2000; Guèye, 2005; Mutombo, 2022). Gender roles in poultry production are shaped by complex social, cultural, and economic factors that vary across regions. A similar predominance of male respondents was reported by Cunha et al. (2018) in dairy farms, where more than 95% of producers interviewed were men. However, in that study, poultry production was typically considered a secondary or tertiary activity, and daily care of chickens and pigs was frequently performed by women, whereas men were primarily responsible for cattle and horses. These findings suggest that the individual identified as the “producer” in surveys may not necessarily correspond to the person responsible for routine poultry management within the household. Therefore, the gender distribution observed in the present study should be interpreted with caution, as intra-household labor division may not be fully captured by the designation of a single respondent. Such dynamics highlight the importance of inclusive extension strategies to ensure equitable access to training and technological resources for all genders within family poultry systems.

Low formal education levels, combined with diversified income sources and modest household incomes, reflect the structural vulnerability of family poultry systems in Brazil. Nevertheless, the low proportion of producers without formal education indicates a baseline literacy level that favors the effectiveness of technical training. This suggests that, despite financial constraints, targeted educational interventions can bridge the technological gap, allowing producers to confidently adopt new management practices and enhance their operational resilience. Similar patterns have been reported in other developing contexts, where poultry farming functions primarily as a complementary income source rather than a sole livelihood (Mutombo, 2022; Zemelak et al., 2016). These characteristics reinforce the importance of low-cost, knowledge-based technological interventions, such as artificial incubation, that align with the economic realities of smallholders.

Producers demonstrated substantial theoretical awareness regarding artificial incubation, including knowledge of the incubation period and recognition of the perceived advantages of incubators over broody hens for chick production. However, a discrepancy between knowledge and routine practice was evident. Despite widespread recognition of the benefits of artificial incubation and a strong expressed desire to improve hatchability, traditional practices such as reliance on broody hens, lack of egg sanitization, and use of natural nests remained predominant. Similar gaps between awareness and adoption have been reported in smallholder poultry systems globally (Ajayi et al., 2020; Kassu and Beyero, 2015).

This intention–action gap suggests that access to information alone is insufficient to ensure sustained adoption of new technologies. Although nearly half of the producers had previously used an incubator, consistent incorporation into routine production remained limited into their production systems. This pattern may be explained by economic constraints, limited technical confidence, or unsuccessful prior experiences, emphasizing the need for continuous technical support rather than one-time training interventions (Ajayi and Koledoye, 2020; Guèye, 2002).

The productive characteristics of the surveyed farms further highlight the relevance of improving reproductive efficiency. Larger flocks tended to maintain more roosters and were more actively engaged in egg commercialization, particularly through direct farm-gate sales. It is possible that maintaining larger flocks increases the producers' selling power, and the resulting higher profits facilitate the acquisition of more animals or better reproductive conditions. Ultimately, this dynamic strengthens the reproductive base and fosters solid, positive productive cycles. This informal marketing structure, widely described in family poultry systems (Ali, 2003; Ajayi et al., 2020), depends heavily on consistent chick availability to sustain flock size and generate surplus production. Consequently, improvements in hatchability and chick output through artificial incubation directly affect both household food security and income generation.

The central biological finding of this study was the no significant differences in incubation performance between on-farm and laboratory environments. Hatchability, fertility, and embryonic mortality rates were statistically comparable across incubation settings, demonstrating that, when supported by appropriate training and standardized procedures, family farmers can achieve outcomes equivalent to those obtained under controlled laboratory conditions. This result challenges the prevailing assumption that incubation under rural or smallholder conditions is inherently inferior due to environmental instability and infrastructural limitations (Ori, 2011).

The success of small-scale incubation is closely linked to pre-incubation management, specifically egg storage and sanitization. Proper storage conditions, maintaining temperatures below 18°C and avoiding prolonged periods before setting, are critical to preserving embryo viability and preventing a decline in hatchability (Tainika et al., 2024). In the present study, the adherence to standardized storage protocols on the farms likely mitigated the typical fluctuations found in rural settings. Furthermore, egg sanitization serves as the first line of defense against vertical and horizontal pathogen transmission (Costa et al., 2022). While large-scale operations rely on synthetic fumigants, smallholders require more accessible and cost-effective alternatives. The exclusion of blood-stained eggs and the proper cleaning of floor eggs, practices learned by the producers, may have contributed to improved values observed in this study.

The fertility and hatchability rates observed in this study are comparable to those reported for backyard, native, guinea fowl, and free-range poultry populations in other regions (Allanah et al., 2014; Araújo et al., 2023; Desha et al., 2015; Kumar, 2010). Embryonic mortality patterns were similar between farm and laboratory incubations, with low contamination rates suggesting that basic sanitary practices, once adopted, can be effective even under non-industrial conditions. Nevertheless, the presence of early embryonic losses indicates that further improvements in egg storage, selection, and environmental control remain necessary (Biesek et al., 2023; Nowaczewski et al., 2022). These results reinforce the concept that strict industrial-level standardization may not be strictly necessary to achieve satisfactory incubation outcomes in family poultry systems (Chaiban et al., 2020). Instead, the consistent application of simple, well-defined management practices, supported by training and supervision, appears capable of supporting reproductive performance under smallholder conditions.

One important limitation of this study was the initial reluctance of farmers to engage in the project. This hesitation is closely associated with a culturally rooted cautious attitude that forms part of the traditional code of conduct within the Brazilian rural population, particularly when external interventions or new technologies are introduced (Azevedo, 2018; Cunha et al., 2018). A second limitation involved the need to adapt technical communication to the educational and practical realities of family farmers. Ensuring that producers clearly understood the relevance of artificial incubation and consistently adhered to the methods taught and demonstrated demanded repeated guidance, simplified explanations, and close follow-up throughout the implementation phase.

From a broader perspective, this study suggests that artificial incubation can function as a strategic tool for strengthening family poultry farming in Brazil. By improving chick availability, artificial incubation supports flock renewal, enhances income opportunities, and may contribute to the conservation of locally adapted chicken populations that are increasingly threatened by genetic replacement. Importantly, the methodological approach employed here provides a foundation for future investigations across different Brazilian regions and also other parts of world, contributing to a more comprehensive representative understanding of family poultry systems.

Conclusion

Family poultry production in the evaluated municipalities is characterized by traditional, small-scale systems managed mainly by older male rural producers with limited formal education and income. Although natural brooding predominates, producers demonstrate knowledge of basic incubation principles and strong awareness of artificial incubation, which is widely perceived as superior and desirable for improving hatchability and chick output. Overall, the results highlight a favorable context for the adoption of artificial incubation. With adequate technical support and extension efforts, this technology has the potential to enhance reproductive efficiency, increase productivity, and promote sustainable flock renewal in Brazilian family poultry farming.

Regarding the comparison between on-farm incubations conducted by producers and those performed under controlled laboratory conditions, the similar hatchability, fertility, and embryonic mortality rates observed in both settings support the reliability of artificial incubation under rural conditions. These findings indicate that, when accompanied by appropriate training and standardized procedures, family poultry producers can successfully manage the critical factors of incubation and achieve performance comparable to that obtained in laboratory environments. Overall, the study provides evidence that artificial incubation is a viable and technically feasible tool for strengthening Brazilian family poultry systems.

CRediT authorship contribution statement

Kimberly Cristina Abreu Alves: Writing – original draft, Investigation, Formal analysis, Data curation. Márcio Gilberto Zangeronimo: Writing – original draft, Supervision, Formal analysis, Conceptualization. Renata Ribeiro Alvarenga: Writing – original draft, Investigation, Conceptualization. Otoniel Félix Souza: Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation. Henrique Carneiro Lobato: Writing – review & editing, Investigation, Formal analysis, Data curation. Julia Macedo Fernandes Oliveira: Investigation, Formal analysis, Data curation. Adriana Gonçalves Silva: Visualization, Investigation, Formal analysis, Data curation. Julyana Machado Silva Martins: Visualization, Supervision, Formal analysis, Data curation. Kalu Chaves de Paula: Visualization, Investigation, Formal analysis, Data curation. Adriano Geraldo: Visualization, Supervision, Formal analysis, Conceptualization. Leonardo Jose Camargos Lara: Writing – review & editing, Conceptualization. Itallo Conrado Sousa Araújo: Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Funding acquisition, Conceptualization.

Disclosures

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.