Study area

This study was conducted in the Mpwapwa District of the Dodoma region in four villages: Idilo, Igovu, Mjini, and Mazoe nje (Fig. 1). The Dodoma regional headquarters is 120 km from Mpwapwa District, which is 120 km from Dodoma. The district has a dry savanna climate characterized by an average temperature of 27 °C. It has a short rainy season from December to April, ranging between 600 and 700 mm per annum. This district was selected because it is dominated by the production of local and blended goats by the Tanzania Livestock Research Institute.

Fig. 1
figure 1

The four study villages in the Mpwapwa District, Dodoma

Vegetation

The natural plains of Dodoma are characterized by wooded areas, open grasslands, and little or no tree or brush cover. Dodoma ground cover consists of forested grasslands and thickets of bushes (URT 2014). During the dry season, the bush is leafless and dry, but comes to life during the rainy, when the entire countryside turns vivid green (Kayombo, et al. 2020). The remainder of the territory is covered by woodlands, with the highest concentrations in hills (URT 2014). Dry savanna shrub-thicket areas with scattered trees and grassland patches interrupted by trees and shrubs comprise the vegetation. Common plant species include Bussea massaiensis, Commiphora coerulea, C. ugogensis, C. africana, Acacia tortilis, A. senegal, Maerua decumbens, Combretum apiculatum, Grewia forbesii, Brachystegia spiciformis, Sclerocarrya birrea, Julbernardia globiflora, Delonix elata, Markhamia acuminate, Euphorbia candelabrum, and Terminalia sericea as indigenous taxa, mixed with exotics such as Peltophorum pterocarpum and Tamarindus indica (Kayombo et al. 2020).

Study design and sampling procedure

A cross-sectional study design was used to collect secondary data. Secondary data were obtained from records kept at the Tanzania Livestock Research Institute, district livestock officers, and livestock keepers. Secondary data include reports and documents stored at the centre over the previous 10 years (2010–2020). Records from the secondary data and documents were extracted for each year. The number of kids born per goat (doe) was determined using the secondary data: birth type (single child or twin) and growth performance (weight, length, and size). The information was mainly dependent on that collected by the TARILI from their farms. This was done to avoid ambiguity in the objective that could be created if the data included goats kept by local communities.

Sample size

Productivity animal experiment: quantity of milk produced per day per goat

To understand the potential of blended goats in milk production, five blended goats and five local breeds were randomly selected from farmers, specifically among the lactating does for milk production assessment. These goats were monitored for 3 weeks (21 days) where milking was performed twice a day in the morning (6:00 pm–7:00 pm) and evening (6:00 pm–7:00 pm). This made a total of 420 samples. The sample size was calculated with a 95% confidence level, a standard deviation of 0.5, and a confidence interval (margin of error) of 5% (Kibuacha 2021). A sample size of 384.16 was required.

Productivity animal experiment: meat productivity in terms of weight and length

Goats were randomly selected by the researchers with the assistance of a livestock keeper from the four villages. At least 40 goats were recruited from each village for this study. Equal numbers of blended and locally bred goats were used in this study. There were 170 goats in the goat breed category (blended, n = 85; local, n = 85). The weight and length of the selected goats were measured using a weight balance and tape, respectively.

Number of individuals born and mortality rate of kids from 2010 to 2020

A longitudinal survey was applied for sampling goats to determine the individuals born and the mortality rate for 10 years from 2010 to 2020. Records kept for 10 years from 2010 to 2020 were collected from TALIRI and livestock district officers because they are properly kept. It was determined to collect 730 samples. The sample size was determined at a 95% confidence level, a standard deviation of 0.5, and a confidence interval (margin of error) of 5% (Kibuacha 2021). In this regard, a total of 384.16 sample size. Also, the average population of blended goats in Mpwapwa is 5500 (Mruttu et al. 2016), which demanded a sample size of 384 at a confidence interval (margin of error) of 5%.

Data collection process

Productivity in terms of milk produced by goats: the quantity of milk collected from each goat was measured in litres to determine the quantity produced per milking session. In addition, other factors such as diseases encountered by farmers, management practices, and grazing systems (zero grazing and free range) were also recorded during data collection.

Productivity in terms of meat produced by goats in terms of weight and length of the goat was measured for each selected goat. Apart from the length, the measurement of girth was also taken into consideration. Other information recorded for the selected goats included sex, age, management practices, diseases, and other associated factors.

Furthermore, data to understand the number of individuals born and the mortality rate of kids from 2010 to 2020 was collected. The number of kids born and those who died in each year were recorded. In addition, the cause of death, such as diseases for kids that died, weight, birth type (single, twin, or triplets), sex, and girth, was recorded.

Data analysis

Differences in growth performance, mortality rate, milk production rate, and calving type (single or twin) between local and blended goats were analysed using the Mann-Whitney U test. A non-parametric statistical test was used because the data were not normally distributed with a kurtosis of less than 0.49. The influence of different factors, such as disease and management, on goat productivity was determined using a generalized linear model (GLM). Specifically, linear regression models were used to find a linear relationship between weight as a continuous variable and the predictors (diseases, management, and birth type). That is, lm (dependent variable (weight in Kg) ~ response variable (predictor valuable), data). The analysis model used for productivity in terms of meat (weight) was Yijklmo = ×β + mi + Bj + Dk + Btl + Hm + Gn + eijklm.

where Yijklmo = live weight of nth goat (KG), β = the general mean, mi = fixed effect of management system (i = 1, 2), Bj = fixed effect of breed of goats (j = 1, 2), Dk = fixed effect of diseases of goats (k = 1, 2,3,4), Btl = fixed effect of birth type of goats (j = 1, 2, 3), Hm = fixed effect of height of goat (m = 1, 2, 3 … nth), Gn = fixed effect of girth of goat (n = 1, 2, 3 … nth), and eijklm = the random residue effect.

The analysis model used for productivity in terms of milk production was Yijk = ×α + bi + nj + Dk + eijk.

whereas Yijk = amount of milk produced for nth goat in millilitres, α = the general mean, bi = fixed effect of breed of goats (i = 1, 2), nj = fixed effect of the number of kids born per nth year (j = 1, 2,3 …10th), Dk = fixed effect of the cause of death in goats (diseases) (j = 1, 2, 3, 4), and eijk = the random residue effect.

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