Economic Values of Traits in the Breeding Goals Defined by Different Dairy Goat Breeders’ Associations in Kenya

doi:10.21203/rs.3.rs-3630506/v1

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Economic Values of Traits in the Breeding Goals Defined by Different Dairy Goat Breeders’ Associations in Kenya

https://doi.org/10.21203/rs.3.rs-3630506/v1

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published 23 Feb, 2024

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The current study aimed at development of breeding objectives and estimation of the economic values for traits of economic importance to various dairy goat breeders associations. This was achieved through the development of a bio-economic model to derive economic values for the selected traits. The input and output parameters used represented the average performance of Saanen, Toggenburg and Alpine breeds. The gross margin was estimated using the R software. Economic values for milk yield (MY, kg), pre-weaning survival rate (PrSr, %), post-weaning survival rate, (PoSR, %), doe survival rate, (DoSR, %), and kidding rate, (Kr, %) were estimated based on fixed flock size and fixed feed resources scenarios. The findings indicate varied gross margin across the breed associations; Saanen (KES 13,656.27), Toggenburg (KES 8,018.53) and Alpine (KES -129.96). In both scenarios, the economic values in selected traits were positive for the three breeds except for milk yield in Alpine, which was negative in both fixed flock and fixed feed resources. The positive economic values of selected traits indicated a unit increase in genetic merit of these traits. The developed bio-economic model was able to estimate the gross margin of Nyanza Dairy Goat Breeders Association (NDGA), Meru Dairy Goat Breeders Association (MDGA) and Dairy Goat Association of Kenya (DGAK) represented by Saanen, Toggenburg and Alpine breeds, respectively, reared under semi-intensive production system. The study offers an opportunity to evaluate genetic and economic merit of alternative strategies for dairy goats reared in semi-intensive production systems managed by various dairy goat breeders associations.

Bio-economic

Fixed feed

Fixed flock

Gross margin

Dairy goat products (milk and meat) offer consumers food products with nutritional, health, and environmental benefits, while by-products are the essential raw materials in different manufacturing industries (Herrero et al., 2013; Lipson et al., 2019; Ogola et al., 2010). In Kenya, milk from dairy goats is mainly used for home consumption. However, recent trends indicate a transition towards commercial goat milk value chains, especially in dry areas where environmental conditions hinder cattle-based dairy systems and in high potential areas where land sizes are continuously decreasing to sustainably support large ruminant dairy systems.

Various NGOs and bilateral development programmes introduced dairy goats in Kenya as a livelihood option through community-based breed improvement programmes. These programmes implemented crossbreeding schemes between the exotic breeds (Saanen, Toggenburg, Alpine, and Anglo-Nubian) and local goat breeds (Galla and Small East African) (Bett et al., 2011; Mburu et al., 2014) and resulted in different dairy goat genotypes kept by farmers (Peacock et al., 2011). The breeding objectives were mainly to improve the performance for both milk and meat production.

Breeding objectives development entails definition of traits that impact on profitability and estimation of their economic values, which should reflect the production and economic situation under which the animals are managed. In Kenya, the production environments under which the dairy goat genotypes are kept, are organized within the umbrella of various dairy goat associations. Although breeding objectives may be sensitive to production environments and production levels, they depend on the levels of inputs and outputs (Walmsley, 2021). Therefore, bio-economic modeling are critical tools in demonstrating appropriate biological and economic aspects of production systems for different breeds (Åby et al., 2012; Halasa et al., 2010). According to Nielsen et al. (2006), the bio-economic model also permits to explain the many complicated inter-relationships that arise between variables and genetic traits in the models.

Bio-economic models have been used to estimate economic values in various production systems for pigs (Mbuthia et al., 2015), dairy cattle (Wahinya et al., 2022), sheep (Gizaw et al., 2018), poultry (Okeno et al., 2013) and goats (Bett et al., 2007) in the tropics. However, economic values have not been estimated for dairy goat production considering various breeds and production units under semi-intensive production system. In addition, updating economic values to reflect the current and future production circumstances is paramount. The objective of the present study was to develop a bio-economic model and derive economic values for selected traits of Saanen, Toggenburg and Alpine dairy goat breeds reared under a semi-intensive production system managed under Nyanza Dairy Goat Breeders Association (NDGA), Meru Dairy Goat Breeders Association (MDGA) and Dairy Goat Association of Kenya (DGAK).

General Model Description

In this study, bio-economic models were developed to include the biological traits and economic parameters of dairy goat genotypes reared in semi-intensive production systems in the tropics using the R software (R Core Team, 2021). The model reflected the gross margin of three breeding programmes for Saanen, Toggenburg and Alpine dairy goat breeds as a function of specific biological traits that needed to be genetically improved. The three breeding programmes were managed by Nyanza Dairy Goat Breeders Association (NDGA), Meru Dairy Goat Breeders Association (MDGA) and Dairy Goat Association of Kenya (DGAK) for Saanen, Toggenburg and Alpine, respectively. The model focused on each phase of the dairy goat life cycle from kid through weaning, grower, replacement to the breeding stage. The model was then expressed by grouping dairy goats into categories (kids, weaners, yearlings, replacement females, breeding does). The difference between the revenues and costs per doe per year determined by input and output variables was termed as gross margin. The main inputs included feed, husbandry (labour, treatment and disease control), marketing and reproduction costs which vary across the three breeds. The sources of revenues for different dairy goat breeds were derived from the sale of milk, surplus doelings and all the bucklings for breeding and culls of age does.

Breeding, Production, and Marketing System

The dairy goat breeds are managed under dairy goat breeders’ associations as community-based breeding programmes. For every breed, farmers are organized into groups of 15 farmers per group. Each group was given a buck that belongs to the association. The buck keeper took care of the buck and charged a fee for every mating. After one year, buck rotation between groups within each breed was done. Production variables used in the model for the three dairy goat breeders associations are shown in Table 1a and 1b.

Table 1

a Production variables used in the model for various dairy goat breeders associations representing Saanen, Toggenburg and Alpine breeds
Variable	Unit	Association¹
Variable	Unit	NDGA	MDGA	DGAK
Milk yield	Kg/day	2.53	1.7	1.83
Average daily gain kids	g/day	89.8	46.8	43.3
Average daily gain yearlings	g/day	68	67	62
Age at weaning	Months	3.6	3.4	3.3
Birth weight male	Kg	2.5	3	2.8
Birth weight female	Kg	2.33	2.65	2.55
Weaning weight	Kg	12.1	7.58	7.09
Yearling weight	Kg	27.5	27.5	25.5
Mature weight doe	Kg	39	34.8	35
Mature weight bucks	Kg	40	40	38
Associations¹, NDGA = Nyanza Dairy Goat Association, MDGA = Meru Dairy Goat Breeders Association and DGAK = Dairy Goat Association of Kenya

Table 1

b Production variables used in the model for various dairy goat breeders associations representing Saanen, Toggenburg and Alpine breeds
Variable	Unit	Association¹
		NDGA		MDGA	DGAK
Production Variables
Kidding rate²	Kids born per number of breeding does per year %	0.83	0.83			0.83
Pre weaning survival rate³	Kids surviving to weaning as a percentage of kids born(%)		64			64
Post weaning survival rate³	Kids surviving to one year as percentage of kids weaned (%)	78	78			78
Doe survival rate³	Does surviving as a percent of does present over years	54	54			54
Twinning rate	Number of kids born as twins as a percent of does	18²	30			35
Replacement survival rate	replacements survival as a percent of does or bucks present	0.9²	0.9			0.9
Associations¹, NDGA = Nyanza Dairy Goat Association, MDGA = Meru Dairy Goat Breeders Association and DGAK = Dairy Goat Association of Kenya
² Bett et al., (2007)
³Kiura et al., (2020)

The management variable used in the model for different Dairy Goat Breeders Associations representing Saanen, Toggenburg and Alpine breeds are presented in Table 2

Table 2

Management variables used in the model for various dairy goat breeders association representing Saanen, Toggenburg and Alpine breeds
Variables	Unit	Abbreviation	Association¹
			NDGA	MDGA	DGAK
Management variables
Age at mature weight	Months	AMW	24	24	24
Age of doe at first kidding	Years	AFK	2.1	2.2	1.7
lactation length	Months	LL	8.4	6.6	6.6
Kidding interval	Months	KI	11	8.7	8.5
Age at first mating	Years	Am	1.6	1.7	1.2
Age of selection replacement stock	Months	ARS	12	12	12
Age of selling surplus yearlings	Months	ASY	12	12	12
Kidding frequency	Average number of kiddings per doe per year	KF	1.5	1.5	1.5
Doe culling	Years	Dc	8.2	6.2	5.1
Energy content in pastures²	kJ/kg DM	Nep	4	4	4
Energy swill (kitchen waste)	MJ/kg DM	Nes	12	12	12
Associations¹, NDGA = Nyanza Dairy Goat Association, MDGA = Meru Dairy Goat Breeders Association and DGAK = Dairy Goat Association of Kenya

Table.3 shows the unit prices and cost variables used in the three different dairy goat breeders associations management. The variables represented the actual performance of the three breeds based on the household survey conducted using a structured questionnaire. In cases where these variables were not collected for the three breeds, other studies were considered (Bett et al., 2007; Kiura et al., 2020).

Table 3

Unit prices and cost variables used in the various dairy goat breeders’ associations management representing Saanen, Toggenburg and Alpine breeds
Variable¹	Unit	Association²
Variable¹	Unit	NDGA	MDGA	DGAK
Milk marketing cost	KES/kg	5	5	5
Milking labour cost	KES/kg	10	10	10
labour cost	KES/head per month	500	500	500
Mineral cost	KES/head per month	200	300	250
Dipping cost	KES/head per year	100	200	200
Veterinary costs	KES/head per year	100	100	100
Helminth control costs	KES/head per year	120	120	120
Slaughter animal's marketing cost	KES/head	200	200	200
Milk price	KES/kg	62	59	22
Meat price	KES/kg	500	500	500
Roughage price	KES/kg DM	5	5	5
Swill price	KES/kg DM	5	5	5
Breeding buck price	KES/buck	11,381	11,242	7,152
Breeding doe price	KES/doe	10,229	10,524	10,412
Consumable meat	meat weight(100/carcass weight)100	60	60	60
¹All costs and prices in Kenya shillings (KES), US$ 1 = 117.55
²Association, NDGA = Nyanza Dairy Goat Association, MDGA = Meru Dairy Goat Breeders Association and DGAK = Dairy Goat Association of Kenya

Management of the three breeds considered ectoparasite and endoparasite control, veterinary services and labour as the standard management practices. For all the breeds, spraying was done once a month, while the endoparasite control was done after every three months for all the categories.

Marketing was an on-farm activity, where culled does and milk were sold at farm-gate prices. For the breeding doelings and bucklings, the association set a fixed price. The sale of culled does was influenced by the prevailing market price of meat or the buyer/middleman. However, the marketing costs included transport (for milk to the collection point and for animals to the slaughterhouse) and market levies. The marketing costs were calculated according to Kosgey et al. (2003) as the sum of the average costs earned after buying live animals to the selling of its carcass.

Specific Model Description

The total annual gross margin of the flock was calculated as a function of k biological traits (which will be genetically improved) with the related costs and revenues values as described previously (Bett et al., 2007) but with some alteration as follows:

$$\mathbf{G}\mathbf{M}\varvec{k}=\sum _{\varvec{i}=1}^{\varvec{k}}\varvec{n}\varvec{i}\left(\varvec{R}\varvec{i}-\varvec{C}\varvec{i}\right)\varvec{t}\varvec{i}$$

where n_i is the number of expressions for trait i in a year, R_i is the revenue per unit per expression of trait i (t_i), and C_i is the cost per unit per expression of trait i (t_i).

The following are biological traits that controls revenues and costs for the three dairy goat breeds; milk yield (MY), pre-weaning survival rate (PrSR), post-weaning survival rate (PoSR), kidding rate (KR) and doe survival rate (DoSR).

Calculation of Revenues

All three breeds derive their income from milk, surplus breeding doelings, all breeding bucklings, and does culled for age. The model did not take into account sources of income from manure, skins, insurance, or status. Therefore, the study's definition of gross margin was "gross margin as the difference between income and cost per doe per year."

Multiple gross margin functions have been created for the evaluation of the gross margin of the flock for the various dairy goat breeders associations and economic values (EVs) for the traits in the breeding objective were obtained using the partial budgeting approach. Economic values for the traits considered were calculated under two scenarios with some modification: fixed flock-size and fixed feed scenario as described previously (Kosgey et al., 2003; Kahi & Nitter, 2004). The fixed flock-size scenario assumed that the flock size remains the same due to inadequate economic resources, farm size, structure, and management and not because of constraints from feed resources.

The economic values under the fixed flock-size scenario were calculated as follows:

$$\mathbf{E}\mathbf{V}\mathbf{f}\mathbf{i}\mathbf{x}\mathbf{e}\mathbf{d} \mathbf{f}\mathbf{l}\mathbf{o}\mathbf{c}\mathbf{k} \mathbf{s}\mathbf{i}\mathbf{z}\mathbf{e}=\left[\frac{\varvec{\delta }\varvec{R}-\varvec{\delta }\varvec{C}}{\varvec{\delta }\varvec{t}}\right]$$

where EVfixed flock size are the economic values per unit change in the trait of interest, δR is the marginal changes in revenues after 1% increase in the trait of interest and δC is the marginal changes in costs after 1% increase in the trait of interest, and δt the marginal change in trait after 1% increase. The economic values under fixed feed resources simulated a situation where the flock is constant due to an inadequate amount of feed resources. Feed scarcity is the most limiting factor in smallholder production systems in the tropics. The EV assuming a fixed feed resource was calculated as follows:

$$\mathbf{E}\mathbf{V} \mathbf{f}\mathbf{i}\mathbf{x}\mathbf{e}\mathbf{d} \mathbf{f}\mathbf{e}\mathbf{e}\mathbf{d} \mathbf{r}\mathbf{e}\mathbf{s}\mathbf{o}\mathbf{u}\mathbf{r}\mathbf{c}\mathbf{e}=\left(\frac{\varvec{\delta }\varvec{R}-\varvec{\delta }\varvec{C}}{\varvec{\delta }\varvec{t}}\right)-\left(\frac{\varvec{\delta }\mathbf{F}\mathbf{I}}{\varvec{\delta }\varvec{t}}\times \frac{\mathbf{P}\varvec{d}}{\mathbf{F}\mathbf{I}}\right)$$

where FI is the feed intake per doe before genetic improvement, δFI is the marginal change in feed intake per doe after genetic improvement (kg DM), P_d is the profit per doe per year before genetic improvement.

Revenues, costs and gross margin for the three breeds representing dairy goat breeders’ associations are presented in Table 4. The gross margin for farmers under the breeders’ association keeping Saanen and Toggenburg was KES 13,656.27 and KES 9,705.75 per doe per year, respectively, while the breeder association keeping Alpine had KES − 129.96 per doe per year.

Table 4

Overall gross margin for the various dairy goat breeders’ associations representing Saanen, Toggenburg and Alpine breeds
Association	Revenue (KES)	Costs (KES)	Gross margin (KES)
NDGA	26,541.08	12,884.82	13,656.27
MDGA	17,724.28	9,705.75	8,018.53
DGAK	10,027.63	10,157.59	-129.96
NDGA = Nyanza Dairy Goat Association, MDGA = Meru Dairy Goat Breeders Associations and DGAK = Dairy Goat Association of Kenya

Economic values of selected traits for NDGA, MDGA and DGAK representing Saanen, Toggenburg and Alpine breeds, respectively are presented in Table 5.

Table 5

Economic values per unit increase in genetic merit of traits for all the three breeds in two scenarios.
Scenario	Association	Milk yield	Pre-weaning survival rate	Post weaning survival rate	Doe survival rate	Kidding rate
Fixed flock	NDGA	45.88	85.99	63.62	76.20	59.79
	MDGA	36.05	95.67	70.86	80.20	66.59
	DGAK	-3.55	76.30	53.70	65.44	50.46
Fixed feed	NDGA	18.46	33.72	20.73	39.02	19.48
	MDGA	22.18	55.99	38.30	51.98	35.99
	DGAK	-3.31	76.92	54.21	65.88	50.94
NDGA = Nyanza Dairy Goat Association, MDGA = Meru Dairy Goat Breeders Associations and DGAK = Dairy Goat Association of Kenya

The values are presented in two scenarios (fixed flock and fixed feed scenarios). All the economic values in the fixed flock scenario were higher than in the fixed feed situation. In both scenarios, the economic value for milk yield in Alpine breed was negative (KES − 3.55 and KES − 3.31). In fixed flock circumstances, Toggenburg had higher economic values in most of the traits apart from milk yield. In comparison with the other breeds, Alpine had higher economic value in pre-weaning survival rate (KES 76.92), post-weaning survival rate (KES 54.21), doe survival rate (KES 56.88) and kidding rate (KES 50.94) under fixed feed situation.

A bio-economic model was developed to indicate Nyanza Dairy Goat Breeders Association, Meru Dairy Goat Breeders Association and Dairy Goat Association of Kenya representing Saanen, Toggenburg and Alpine breeds, respectively. Economic values were derived from the models with gross margin estimated per doe per year. On average, gross margin for the association keeping Saanen was more when compared with Alpine and Toggenburg breeds. Revenue, cost, and gross margin increased with the intensity of production. The greater production cost in the Saanen breed was most likely attributable to higher inputs to sustain higher output level compared to lower inputs in other breeds. The revenue was higher in the Saanen breed because of higher productivity and the price of outputs. The higher price of breeding bucks could indicate higher demand for Saanen breeding bucks than the supply. Alpine breed had the lowest revenue and negative gross margin, and this probably because of low the price of both milk and breeding bucks when compared with the other breeds.

Economic values for the three breeds were derived under two scenarios (fixed flock size and fixed feed), characterizing the dairy goat smallholder production systems in Kenya. In smallholder systems, animals are kept in small flocks with unstable numbers; therefore, assuming the fixed flock size may not be accurate. Under both scenarios, economic values were higher in fixed flock for the three breeds in all the selected traits. Although, Alpine had the lowest revenue, under fixed feed resources, the high economic values were recorded compared with the other breeds. This is contrary to the study of Bett et al. (2007) on Kenya dual-purpose goats that the economic values were higher under fixed feed resource situation.

In dairy production systems, milk yield is positively associated with growth and survival of kids. The number of animals available for replacement and sale may therefore be as a result of milk yield improvement. Bennison et al. (1997) reported the significance of animal survival in connection to milk and meat production in a production system. In this study, under both scenarios, negative economic values were derived for milk yield in Alpine breed, implying that genetic improvement of milk yield would result in a decrease in the gross margin of Alpine production. It is worth noting, that farmers who kept Alpine breed changed their breeding objective from milk yield to producing breeding animals according to Waineina et al. (2021). Farmers changing breeding objective has been reported before, in that farmers might perceive some traits found to be very important in the breeding program proposal as of secondary significance (Bett et al., 2007). Survival rate PrSr, PoSR, and DoSR traits recorded high economic values in both situations. This is an indication that genetic improvement on those traits would influence positively on profitability of Alpine breed production.

In this study, a higher survival rate would result in a higher revenue from the sale of milk, breeding bucklings, surplus does after selection for replacement, and cull does. This has been reported in other species (dairy cattle), that improving the survival rate has a positive impact in milk revenue per cow from a good number of mature cows and the sale of surplus heifers (Kahi & Nitter, 2004). An increase by one unit in genetic merit of the survival traits could lead to more profit per doe per year. In all the breeds, improvement of survival in early stage in life (PrSr trait) resulted in higher profits than the same improvement in survival later in life (DoSR trait). The presence of a large population of young animals in all the breeds could lead to an increase of profit per doe per year through the sale of more breeding animals.

The results of this study indicate that the developed bio-economic model was able to estimate the gross margin of Saanen, Toggenburg and Alpine breeds representing the dairy goat breeders’ associations reared under semi-intensive production system. The model derived the economic values per unit increase due to one unit change in the genetic merit of the selected trait keeping the other traits constant. All of the selected traits with positive economic value suggest a unit increase in genetic worth of these traits. Therefore, all those traits can be included in the breeding programme for the improvement of Saanen, Alpine, and Toggenburg breeds reared under semi-intensive production system in the tropics.

Acknowledgements Special thanks to the African Development Fund (AfDB), USAID and the Government of Kenya under the Kenya Climate Smart Agriculture Project (KCSAP CGS/CRGs-AD-2019) for their financial support.

Author contribution All authors contributed to conception and design of this study, data collection, analysis and writing of the manuscript were performed by Ruth Waineina. Ngeno Kiplangat, Evans Ilatsia and Tobias Okeno read and approved the manuscript.

Funding This work was supported by The Kenya Climate Smart Agriculture Project (KCSAP) jointly supported by Government of Kenya and World Bank (reference number, KCSAP CGS/CRGs-AD-2019).

Data availability The datasets generated during and/or analyzed during the current study are not publicly available due to the lead institution (KALRO) policies but are available from the corresponding author on reasonable request.

Animal Rights The National Commission of Science and Technology under the permit No: NACOSTI/P/19/69981/29118 and Kenya Institute of Primate Research under the permit No: ISERC/03/2020 approved the materials and procedures of this study.

Conflict of interest The authors declare no conflict interests.

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You are reading this latest preprint version

Economic Values of Traits in the Breeding Goals Defined by Different Dairy Goat Breeders’ Associations in Kenya

Status:

Journal Publication

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Abstract

Introduction

Materials and Methods

General Model Description

Breeding, Production, and Marketing System

Specific Model Description

Calculation of Revenues

Results

Discussion

Declarations

References

Status:

Journal Publication

Version 1