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Extraction method: principal component analysis; rotation method: Varimax Kaiser normalization Table 2 Rotated component matrix and component score coefficient matrix of factor analysis agement of the initial flock.

Table 2 Extraction method: principal component analysis; rotation method: Varimax Kaiser normalization Table 2 Rotated component matrix and component score coefficient matrix of factor analysis agement of the initial flock.

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Extraction method: principal component analysis; rotation method: Varimax Kaiser normalization *1% significant level Table 1 Extraction communalities of factors (main benefits) A factor analysis was used to classify the primary benefits of managing small ruminants in northern Ghana. Ten (10) dimensions of benefits derived from small ruminant pro- duction are presented in Table 1. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.633, which justifies the application of factor analysis to the data. The outcome of the Bartlett’s test of sphericity suggests that the relationship that exists among farmers’ reasons for rearing small ruminants is strong. The test also shows a cu- mulative percentage variance of 51.77% of the components adopted in the study. The extraction coefficients indicate the amount of variance generated by each variable that is produced by the factors in the factor loading results. Hair Jr et al. (1998) recom- mended a factor loading of at least 0.335 as significant for a sample size between 200
Extraction method: principal component analysis; rotation method: Varimax Kaiser normalization *1% significant level Table 1 Extraction communalities of factors (main benefits) A factor analysis was used to classify the primary benefits of managing small ruminants in northern Ghana. Ten (10) dimensions of benefits derived from small ruminant pro- duction are presented in Table 1. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.633, which justifies the application of factor analysis to the data. The outcome of the Bartlett’s test of sphericity suggests that the relationship that exists among farmers’ reasons for rearing small ruminants is strong. The test also shows a cu- mulative percentage variance of 51.77% of the components adopted in the study. The extraction coefficients indicate the amount of variance generated by each variable that is produced by the factors in the factor loading results. Hair Jr et al. (1998) recom- mended a factor loading of at least 0.335 as significant for a sample size between 200
Note: Within rows, means (least square mean values) of components with different superscripts are significantly different at P < 0.05 on a t-test of means. Exchange rate: US$1 was equivalent to Gh¢1.9 From Table 3, the data depict an insignificant difference between the annual costs of external inputs for the two agro-ecological zones. Both zones have low annual input costs because the animals are managed under the traditional free-range system of
Note: Within rows, means (least square mean values) of components with different superscripts are significantly different at P < 0.05 on a t-test of means. Exchange rate: US$1 was equivalent to Gh¢1.9 From Table 3, the data depict an insignificant difference between the annual costs of external inputs for the two agro-ecological zones. Both zones have low annual input costs because the animals are managed under the traditional free-range system of
Note: Within rows, means (least square mean values) of components with different superscripts are significantly different at P < 0.05 on a t-test of means. Exchange rate: US$1 was equivalent to Gh¢1.9 at the time of the study Table 4 Gross margin component (Gh) across agro-ecologies to the greater number of sheep and/or goat sold and the increase in stock size through
Note: Within rows, means (least square mean values) of components with different superscripts are significantly different at P < 0.05 on a t-test of means. Exchange rate: US$1 was equivalent to Gh¢1.9 at the time of the study Table 4 Gross margin component (Gh) across agro-ecologies to the greater number of sheep and/or goat sold and the increase in stock size through
From Table 5, the financing benefit of sheep and goats in subsistence households is Gh¢75 in GS and Gh¢69 in SS per household per annum. The insurance role is Aggregate (total net) benefit
From Table 5, the financing benefit of sheep and goats in subsistence households is Gh¢75 in GS and Gh¢69 in SS per household per annum. The insurance role is Aggregate (total net) benefit
Table 5 Socioeconomic benefits and aggregate net benefit from small ruminants for two agro- ecological zones per household Note: Within rows, means (least square means values) of components with different superscripts are significantly different at P < 0.05 on a t-test of means. Exchange rate: US$1 = Gh¢1.9 estimated at Gh¢84 in GS and Gh(69 in the SS zone. The total socioeconomic func- tions of the animals, therefore, are Gh 158.46 and Gh¢137.84 in Guinea savannah and Sudan savannah zones, respectively. The benefits represent 27% in Guinea savannah and 29% in Sudan savannah zones of the aggregate net benefit from sheep and goat production (Fig. 2). Even though the estimated values of the socioeconomic products are not different for the two agro-ecological zones, the annual aggregate net benefits of sheep and goats are significantly different for the two regions. The differences mainly came from the higher overall non-market contribution of small ruminant products in the GS agro-ecological zone.
Table 5 Socioeconomic benefits and aggregate net benefit from small ruminants for two agro- ecological zones per household Note: Within rows, means (least square means values) of components with different superscripts are significantly different at P < 0.05 on a t-test of means. Exchange rate: US$1 = Gh¢1.9 estimated at Gh¢84 in GS and Gh(69 in the SS zone. The total socioeconomic func- tions of the animals, therefore, are Gh 158.46 and Gh¢137.84 in Guinea savannah and Sudan savannah zones, respectively. The benefits represent 27% in Guinea savannah and 29% in Sudan savannah zones of the aggregate net benefit from sheep and goat production (Fig. 2). Even though the estimated values of the socioeconomic products are not different for the two agro-ecological zones, the annual aggregate net benefits of sheep and goats are significantly different for the two regions. The differences mainly came from the higher overall non-market contribution of small ruminant products in the GS agro-ecological zone.
Received: 9 May 2019 Revised: 2 February 2021 Accepted: 18 April 2021 Published online: 10 May 2021
Received: 9 May 2019 Revised: 2 February 2021 Accepted: 18 April 2021 Published online: 10 May 2021
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Related topics:
AgricultureAgricultural Economics and Food SecurityGross margin
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