Factors influencing intensification of dairy production systems in Ethiopia

Abstract

Little is known of how dairy intensification driven by socioeconomic issues and dairy development efforts works as well as the challenges of changing production systems in developing countries, particularly in sub-Saharan Africa. A study was carried out to analyze factors determining intensification of dairy production systems and the present status of market-oriented smallholder dairy operations in Ethiopia. Data were collected through face-to-face interviews with 200 dairy farmers. The results revealed that 77% of respondents reared improved/crossbred dairy cows, 53.5% acquired good manure management besides crossbreeding, and 44% of the sampled rural households were involved in cultivating improved forage crops and crossbreeding practices. The binary logistic regression model output showed that herd size, farmland size, dairy training, and cooperative membership had significant effects on cultivating improved forages. Dairy production system, dairying experience, and herd size were significantly associated with rearing only crossbred dairy cows. Farmland size, dairy system, and awareness of manure handling were significantly associated with practicing good manure management. Further analysis of the extent of intensification indicated that mean daily milk yield per cow and household milk market share were significantly related to crossbreeding and manure management practices in combination. Thus, production systems–based dairy breeding and manure management, related input supply, and alternative formal marketing options are the key attributes of the intensification and improved productivity of smallholder dairy production that need to be considered while designing policy and intervention.

Keywords

intensive dairy production farm characteristics sustainable dairying smallholders marketable milk

Introduction

Most of the milk produced in the developing countries comes from small-scale dairy farms. Smallholder dairying is a cost-effective and key source of nutrition and income to 300 million farm families globally (Ogola and Kosgey, 2012; World Bank, 2006), playing an important role in alleviating poverty (Ahmed et al., 2004; Somda et al., 2005). Smallholder livestock production will need to intensify to provide higher value products and also to enhance food security and the need for animal protein (Cronin et al., 2014; FAO, 2011), particularly with the increasing demand for livestock products.

Ethiopia has huge potential to be one of the key countries of East Africa in dairy production (Staal et al., 2008). A number of smallholder dairy farms have emerged and become major milk providers to urban consumers. In the Ethiopian highlands, the Ada’a district is an area with a fast growing smallholder dairy production system and with strong milk marketing cooperative and private dairy processors (ILRI, 2005). The milkshed has also increased opportunities in the Addis Ababa market, where dairy industries and supermarkets are rapidly growing (Francesconi et al., 2010; Moti et al., 2013). Moreover, dairy producers in this area are using various improved production inputs and practices. These include crossbred dairy cows, concentrate feeds and crop residues, planted forages and manure in the zero-grazed/stall-fed system. Yet, there remain challenges of enhancing milk productivity within the ever-demanding socioeconomic, demographic, and ecological changes.

To this end, a strategy being promoted to support smallholder dairying is the intensification of dairy production through the use of improved agricultural technologies (Staal et al., 2008). Milk groups and cooperatives provide an environment suitable for dairy intensification by means of facilitating the dissemination of productivity enhancing technologies and also provide fluid milk marketing services (Chagwiza et al., 2016). However, there appears to be a lack of information on the limiting factors of the dairy intensification and on how barriers can be overcome in the context of smallholders. Few studies have analyzed emerging needs with respect to smallholder dairying, the farm characteristics influencing the intensification process, how producers can be able to respond to changing circumstances, and entry points for intervention to make the system sustainable.

This lack of evidence has hindered designing and implementing more attentive/contextual dairy development policies and intervention areas/programs in the intensified dairy regions of sub-Saharan Africa and other developing countries. Therefore, improved knowledge needs to be generated on the details and features of the present and future of smallholder dairy production. This requires detailed information on how dairy intensification works and the characteristics of evolving systems, including the influential factors, the strategies to be followed for managing the challenges of dairy production, status of intensification efforts, and the options of sustainable production. With this understanding, the objective of the study was to identify factors contributing to intensification of dairy production systems and their implications for sustainable dairying. The questions that this article addressed are as follows: how do dairy farm and household characteristics of smallholder dairying influence dairy intensification?; where, how, and at what level intensification is happening?; and who is participating and who is left out? These questions were answered using primary data from extensive interviews with selected dairy producers.

Material and methods

Research site

The study was conducted in the Ada’a district, East Shewa Zone of Oromia regional state. The district is located about 45 km southeast of the capital Addis Ababa. It lies between longitudes 38°51′ to 39°04′ East and latitudes 8°46′ to 8°59′ North covering a land area of 1750 km². The district is characterized by subtropical climate and receives 860 mm rainfall/annum. Mean annual temperatures range from about 8°C to 28°C (Alemayehu et al., 2012). These agroecological conditions provide a favorable environment for dairy production. There are high numbers of crossbred dairy cattle (indigenous × exotic breeds/mainly Holstein-Friesians) and other dairy development interventions in the district. Based on Workneh et al. (2004) and DAGRIS (2007), the indigenous cattle can be classified as Large East African Zebu/Arsi. Rural households have also been engaged in crop production, mainly teff (Eragrostis tef) and wheat as well as various types of pulses (WFED, 2014), which are also helpful as a local source of crop residue for livestock feed.

The formal milk market channels include cooperative and private milk processing companies. The Ada’a Liben Dairy and Dairy Products Marketing Association is ranked first out of the primary dairy cooperatives in Ethiopia. It was established in the town in 1997/1998 by 34 individuals (Azage, 2003). The private milk processing enterprises found in the district are Holland Dairy PLC and Genesis Farms. In addition, Sebeta Agro-Industry (MAMA milk from Addis Ababa) is also collecting milk in the area.

Sampling technique

This study employed an explanatory/ex-post-facto research design (Gay, 1976). Dairy producers in the area have participated in cooperative marketing and improved dairy practices (forage production, crossbreeding, and manure management), which have great potential to intensify dairy production systems in the study area. During the survey, there were 100 actively participating members of the dairy cooperative and they all were benchmarked and chosen for household survey purposively. Similarly, 100 nonmembers who sell milk to private processors were randomly selected from the lists of 300 dairy producers at milk collection centers. Accordingly, a total of 200 households from Ada’a dairy cooperative members and nonmembers were sampled for the study.

Data types and sources

The primary data included farm household characteristics: household head—age, religion, and level of education; family size; land holding; cattle herd size; experience in dairying; forage production; dairy cattle genotype; manure management; housing; water sources; daily milk yield; labor availability and utilization; and dairy training provision. The primary data were complemented with an in-depth review of secondary sources (analysis of documents) including reports and directives of line ministries, statistical reports, journal articles, books, and national policies.

Methods of data collection

The study employed both qualitative and quantitative methods to collect data through questionnaire household-level surveys/face-to-face interviews. It was supplemented with 15 key informant interviews of relevant government and private sectors, and direct observation of dairy farms. The questionnaire was pretested before administration. Enumerators (translators) who can speak the local language were selected and given a short training.

Statistical analyses

Data were entered, coded, and analyzed using the Statistical Package for Social Science (SPSS, 2011) software 20 and Stata 13. Descriptive statistics (means, standard deviation, frequency, and percent), t-tests, and binary logit regression were used to analyze the effect of different independent variables on the dependent variables. The logistic regression model analyzed the tendency of the relationship between explanatory variables (household socioeconomic characteristics) and the probability of milk producers’ involvement in intensified dairying. In this regard, the selected aspects of intensification (forage production, cross breeding, and manure management) were possibly varied among dairy farms and considered to be indicators of dairy intensification (the binary dependent variables). The logistic model was the model of choice to analyze the dichotomous variable. Colinearity was checked for independent variables before being included in the model. The fit of the model was also assessed. The positive or negative sign of the coefficient β indicates the direction of the relationship between a given independent variable and the dependent variable, while the odds ratio indicates the magnitude of change in the probability of the dependent variable event in case of a one-unit change in the independent variable (Hosmer and Lemeshow, 2000).

The logistic model is of the form

Y = Ln [P_{i} / (1 - P_{i})] = β_{0} + β_{1} X_{1 i} + β_{2} X_{2 i} + ... + β_{k} X_{k i}

where the subscript i means the ith observation in the sample. P is the probability that a dairy farmer implements dairy intensification for an observed set of variables and (1 − P) is the probability that a farmer does not join in the intensification. β ₀ is the intercept term and β ₁, β ₂,…, β_k are the coefficients of the independent variables X ₁, X ₂,…, X_k .

Definition of factors used in the Logit model.

Variable	Description
Dependent variables
Cultivated improved forage^a	1 = yes; 0 = no
Reared improved/crossbred dairy stock	1 = yes, 0 = crossbred and indigenous cows
Practiced good manure management^b	1 = good/better, 0 = fair/satisfactory
Independent variables
Dairy cooperative membership	1 = member, 2 = nonmember
Cow genotypes kept	1 = crossbred dairy cow, 2 = crossbred and local
Household head age	1 = 24–34, 2 = 35–44, 3 = 45–54, 4 = ≥55
Dairy as a major income source	1 = yes, 2 = no
Experience in dairy farming	1 = ≤5 years, 2 = 6–10 years, 3 = >10 years
Dairy training given	1 = yes, 2 = no
Dairy production systems	1 = peri-urban, 2 = rural, 3 = urban
Education	1 = illiterate, 2 = primary, 3 = junior to secondary, 4 = tertiary/diploma and above)
Awareness on proper manure handling	1 = yes, 2 = no
Cattle herd size, farmland size and dairy labor supply were continuous variables

^aForage production (mainly Vetch: Vicia dasycarpa and Oats: Avena sativa) took place in rural dairy system.

^bDairy producers with separate cattle housing, cemented/concrete floor type, biogas production, regular cleaning of cowshed (≥3 times/day), and having a manure pit were considered as proper/good manure management (Falvey and Chantalakhna, 1999; FAO and IDF, 2011). Otherwise, fair/satisfactory, that is, separate housing, without cemented floor (soil and stone), no biogas (dung cake making), and cleaning cowshed <3 times/day.

Results

Analysis of factors influencing intensification of dairy production

Forage production

Forty-four percent of the sampled rural households cultivated improved forage plants (Figure 1 and Online supplementary Appendix). The statistical model output showed that cooperative membership, herd size, farmland size, and dairy training were significantly associated with improved forage production. This indicated that the probability of participating in forage production was positively and significantly influenced by herd size (OR = 1.210; 95% CI), land holding size (OR = 2.990; 95% CI), and dairy cooperative membership (OR = 6.731; 95% CI). However, dairy training provision negatively and significantly affected the probability of cultivating improved forage crops (OR = 0.036; 95% CI; Table 1).

Figure 1.

Percentage of dairy households and their intensive farm management strategies versus production systems.

Table 1.

Maximum likelihood estimates of the dairy intensification model (forage production).

Variables	B	SE	p Value	Odds ratio
Cooperative membership (2)	1.907	0.823	0.021^a	6.731
Cow genotype (1)	−0.739	0.879	0.400	0.477
Major income source (2)	0.774	0.958	0.419	2.168
Dairy experience (2)	0.003	1.13	0.998	1.003
Dairy experience (3)	1.227	1.04	0.238	3.411
Household head age (2)	−1.046	1.04	0.316	0.351
Household head age (3)	−0.457	1.07	0.669	0.633
Household head age (4)	−1.977	1.22	0.104	0.138
Dairy training (2)	−3.336	1.24	0.007^a	0.036
Cattle herd size	0.191	0.06	0.001^a	1.210
Farmland size	1.095	0.463	0.018^a	2.990
Labor supply	0.439	0.39	0.265	1.551
Constant	−6.577	2.06	0.001	0.001

^aStatistically significant at p < 0.05; 1.00 reference category.

Dairy genotypes

Seventy-seven percent of dairy farmers were identified as rearing only crossbred dairy cows, while 23% of the dairy households had both crossbred and indigenous cows (Figure 1 and Online supplementary Appendix). The model result revealed that dairy production system, dairying experience, and cattle herd size significantly influenced the likelihood of keeping crossbred cows. This showed that urban dairy producers were more likely to have intensified dairying through rearing only crossbred dairy stock (OR = 49.9; 95% CI). The participation in only crossbreeding dairy program was less likely in rural dairy production system (OR = 0.150; 90% CI). Dairying experience positively and significantly affected the likelihood of keeping crossbred cows (OR = 5.78; 90% CI). The probability of keeping improved dairy cows decreased by 77% as cattle herd size increased by one unit (OR = 0.765; 95% CI; Table 2).

Table 2.

Maximum likelihood estimates of the dairy intensification model (dairy genotypes reared).

Variables	B	SE	p Value	Odds ratio
Dairy system (2)	−1.895	0.96	0.049^a	0.150
Dairy system (3)	3.911	1.76	0.026^b	49.93
Cooperative membership (2)	1.130	0.94	0.230	3.09
Major income source (2)	−1.084	0.92	0.241	0.338
Dairy experience (2)	1.109	0.85	0.194	3.03
Dairy experience (3)	1.754	1.02	0.085^a	5.78
Household head education (2)	0.781	0.87	0.368	2.184
Household head education (3)	0.338	0.80	0.673	1.402
Household head education (4)	0.034	1.69	0.984	1.034
Dairy training (2)	−0.447	0.79	0.571	0.639
Labor supply	−0.669	0.41	0.104	0.512
Farmland size	−0.068	0.37	0.853	1.070
Herd size	−0.268	0.08	0.000^c	0.765
Constant	5.134	1.63	0.002	169.64

^aStatistically significant at p < 0.1; 1.00 reference category.

^bStatistically significant at p < 0.05; 1.00 reference category.

^cStatistically significant at p < 0.01; 1.00 reference category.

Manure management

More than 50% of dairy farms (53.5%) practiced good manure management (Figure 1 and Online supplementary Appendix). The model output revealed that dairy production systems, farmland size and awareness of manure handling negatively and significantly related to manure management. This showed that dairy producers having relatively more land (OR = 0.591; 95% CI) and those with no awareness of manure handling (OR = 0.312; 95% CI) were less likely to intensify dairying through practicing good/better manure management. Likewise, the probability of participating in good manure management practice was lower in rural dairy production system (OR = 0.244; 90 CI; Table 3).

Table 3.

Maximum likelihood estimates of the dairy intensification model (manure management).

Variables	B	SE	Sig.	Odds ratio
Dairy system (2)	−1.409	0.72	0.052^a	0.244
Dairy system (3)	0.778	0.43	0.069^a	0.459
Cow genotype (1)	0.332	0.57	0.560	1.394
Awareness of manure handling (2)	−1.165	0.38	0.002^b	0.312
Dairy experience (2)	0.220	0.51	0.663	1.247
Dairy experience (3)	0.218	0.52	0.678	1.243
Cooperative membership (2)	0.258	0.38	0.496	1.295
Farmland size	−0.526	0.31	0.091^a	0.591
Cattle herd size	0.063	0.04	0.106	1.065
Labor supply	0.048	0.19	0.806	1.049
Constant	0.676	0.97	0.485	1.967

^aStatistically significant at p < 0.1; 1.00 reference category.

^bStatistically significant at p < 0.01; 1.00 reference category.

Discussion

The emphasis of the present study was to identify factors that determine intensification of dairy production systems.

Forage production

The effects of household factors (cattle herd size, farmland size, and cooperative membership) on improved forage production could be explained as follows: First, those farmers with larger herd size (both crossbred and indigenous cattle) were motivated for planting forages on their relatively better land holdings though there were competing land requirements for crop and dairy production in the rural system. Secondly, there was also private dairy processing plants collecting milk from noncooperative members in this dairy production system as an important emerging marketing channel in case of limited access by the dairy cooperative. Therefore, dairy services including marketing and training provision are the limiting factors in forage production besides the household resource endowments (cattle herd, farm land).

The current study is consistent with the findings by Mapiye et al. (2006) and Hassen (2014) that the intensity of practicing improved forage production was influenced by size of dairy cattle ownership and farm size. Similarly, a study on smallholder dairying in Uganda indicated that farmers with fewer or no improved cows and/or local cows were less likely to use improved forage technology (IFT). There was also a significant and negative relationship between farm size and use of IFT (Martínez-García et al., 2016; Turinawe et al., 2012). Training, demonstrations, and educational tours can improve knowledge of farmers about legume-based technologies (Mapiye et al., 2006). Membership in a farmers’ association did not significantly influence forage production in Southeastern Tunisia (Chebil et al., 2009). On the contrary, it was reported that membership of farmer groups had a significant and positive influence on use of IFT in smallholder dairying in Uganda (Turinawe et al., 2012).

The studied dairy farmers allocated 0.13 ha for forage cultivation (8% of farm land) though most of the farmers (56%) had more than average land size (1.54 ha) (see Online supplementary Appendix). Shortage of seeds and extension services were limits to a shift toward intensive feeding. If these were addressed, land might have been used more efficiently to plant forage crops through appropriate cultivating strategies. Otherwise, the use of grain crop by-products and outsourced feed concentrates will continue to be the major feed resources for foreseeable future. Both crop residues (mainly wheat straw) and feed concentrates (mainly wheat bran, oil seed cake, and poultry litter) were provided to lactating cows, which need to be complemented with forage crops to replace some of the low quality roughage, costly feed concentrate, and unavailability of grazing land. Therefore, greater participation of farmers in the production of improved forage crops needs to be promoted, including leguminous forage on less fertile border plots of farmland and that can be integrated with soil and water conservation structures in areas with poorly drained land. For instance, in an experimental station, vetch can optimize both the biological and economic response of dairy cows when supplemented at the rate of 50% replacement of a formulated concentrate mix (Getu et al., 2010). Moreover, improving the supply of good quality fodder, particularly when linked to the provision of improved (exotic or crossbred) dairy animals, has the potential to increase milk production, and hence family incomes and nutrition, dramatically (Wambugu et al., 2006). In this regard, farmer-to-farmer extension and demonstration at the Farmer’s Training Center could be successful methods to promote improved forages. Continued demonstration of the social, economic, and environmental benefits of improved forages can help achieve institutional change (Rao et al., 2015).

Dairy genotypes

In relation to dairy genetics, the result indicated that most of the crossbred dairy cows were reared by (peri)-urban dairy producers, who had more dairying experience than the rural/mixed crop-dairy production systems. The peri-urban dairy producers had begun dairying by only rearing crossbred dairy stock due to market access and land pressure. Although, 25.5% of peri-urban and 49.7% of urban dairy producers had taken dairy training at the beginning, most of them enriched their dairy operations through greater years of experience. Indigenous cattle are also important resource in the moderately intensive rural dairy system as a source of draft power for staple crop farming. Hence, production systems, associated size of cattle holding, and dairying experience of households are important factors in a crossbreeding program.

The present study is in line with results reported by various authors. According to Staal et al. (2002) and Dehinenet et al. (2014), dairy farming experience was positively related to the keeping of crossbred dairy cattle. Adoption of improved dairy cow technologies was negatively associated with size of livestock ownership (Moll et al., 2007). However, our finding is contrary to that reported by Tebug et al. (2014) in that crossbred cattle rearing was independent of herd size and duration of dairy farming. In general, crossbreeding local cattle with higher yielding exotic dairy breeds is an important tool for intensifying smallholder farming (Tulachan et al., 2002). Therefore, structured crossbreeding programs are needed to effectively run crossbreeding and also to retain purebred local breeds (FAO, 2007; Staal and Kaguongo, 2003), which is useful in maintaining genetic diversity in terms of production systems and a changing climate.

In the present study, 80.5% of respondents used artificial insemination (AI) service. The remaining 17% used bull service and 1% used both types of mating in the rural dairy system. In this regard, breeding or reproduction problems were among the major challenges faced by dairy producers that varied across production systems. These problems included the need for repeated breeding, the birth of male calves, lack of superior breeding stock, and irregularity of the AI service (see Online supplementary Appendix). To this end, most (54.8%) smallholder dairy producers purchased their foundation dairy stock from private/local sources and raised replacement heifers on their farms, which calls for a reliable and known genotype source of improved breeding stock (genetic improvement program). The existence of both crossbred and indigenous cattle herds maintain genetic diversity, which may contribute to sustainability on the rural farms, while the (peri)-urban producers need to focus on the strategy of increasing yield per animal (keeping the most productive crossbred dairy cows only) and associated issues to address the environmental concerns.

Manure management

Intensified dairying through practicing good/better manure management was implemented by dairy producers who had a smaller land holdings and better awareness of dairy manure handling, mainly in the (peri)-urban dairy production systems. The limited land resource and awareness in these intensive farming systems motivated dairy producers to manage manure in a better way. Similar observations were made by other studies. Training (awareness) of household heads had a significant effect in manure management through biogas adoption (Mwirigi et al., 2009; Nguyen et al., 2015) in Kenya and Vietnam. Knowledge on composting in improving soil productivity (fertility) affected the use of manure in Malawi (Mustafa-Msukwa et al., 2010). In a nutshell, intensification increases the need for technical knowledge and services (Kristjanson et al., 2014). The farming system (zero grazing), size of farm, and management of animal manure (biogas use) were also significantly related according to a study by Mwirigi et al., (2009).

In this study, 25% of dairy producers reported that they had manure handling problems, which varied across production systems. The practice of stall feeding, the availability of water source (mainly hand-pumped well), necessity of fertilizer for crops, and firewood shortage in the rural production system provide future prospects for biogas digester technology, which would have environmental and economic benefits. Some urban dairy farmers were also using effective microorganisms with feed to prevent odor, which were supplied by a private company. According to Worley and Wilson (2011), anaerobic digestion (biogas production) is also one solution to the odor control. Overall, adequate technical skills on planning manure waste management need to be adopted by smallholder dairy producers to handle the manure-related problems effectively in the stall-feeding system. Moreover, barn floors of some dairy farms need renovation, new biogas digesters need to be introduced, and existing ones require close follow-up and maintenance to utilize this essential resource (manure). The Ethiopian government’s recognition of urban agriculture as an enterprise creating jobs is a good start. However, the urban municipality or local authorities need to allocate land and establish accessible “dairy parks” in the suburban areas, which will encourage improved management and investment in sustainable peri-urban dairying. This is supported by FAO (2013), where a number of community-based units or dairy parks were set up in China, where smallholders keep and milk their cows.

Extent of dairy intensification

The extent of dairy intensification measured as milk supplied by producers revealed that mean daily milk yield and associated milk sales were significantly related with crossbreeding and manure management practices in combination, particularly in (peri)-urban dairy production systems (Table 4). In other words, dairy farmers, who practiced crossbreeding and good manure management supplied greater volumes of milk to dairy processing plants than the nonpracticed group. Hence, crossbreeding has a positive effect on milk production depending on levels of genetic makeup and management practices employed. Good manure management is also useful in sustaining the health and comfort of dairy stock and subsequently enhanced milk yield. The greater volume of milk produced in these systems was also attributed to increasing levels of intensification, greater proportion of crossbred dairy cows, better utilization of feed concentrates, greater dairy experience, the provision of alternative (government and private) veterinary and artificial insemination services, and access to information. The use of locally available inputs such as indigenous cattle for crossbreeding and by-products (concentrate feed and crop residues) may contribute to the socioeconomic sustainability of dairy practices. However, the government livestock extension service is not in a position to respond to the changes in dairy production systems, which calls for robust intervention and strengthening of the extension service to enhance management of the systems, mainly through building the capacity of dairy farmers.

Table 4.

Means and standard deviations of dairy parameters against the improved dairy management practices.^a

Mean milk yield and improved dairy stock	Dairy management practices
	Crossbreeding and good manure management			Crossbreeding and forage production
	Yes	No	p Value	Yes	No	p Value^b
Daily milk yield/stock (liters) (mean ± SD)	15.0 ± 4.33	13.0 ± 4.42	0.04^c	13.60 ± 3.95	13.93 ± 4.48	0.726
Daily milk yield/farm (l)	28.58 ± 16.06	20.69 ± 15.86	0.001^d	22.60 ± 18.92	25.2 ± 16.05	0.460
Milk sold/household/day (l)	26.75 ± 16.65	19.69 ± 15.57	0.002^d	20.58 ± 15.57	23.83 ± 16.63	0.357
Number of improved dairy stock	8.08 ± 5.01	5.76 ± 4.29	0.001^d	7.00 ± 6.15	6.99 ± 4.63	0.991

^aNumber of observations = 200.

^bNonsignificant.

^c p < 0.05.

^d p < 0.01.

Conclusions

This study examined the factors associated with the participation of market-oriented smallholder dairy producers in intensification practices at various levels. There were fair to moderately increasing levels of intensification through forage production, crossbred dairy stock holding, and manure management in the rural and (peri)-urban dairy production systems. Herd size, farmland size, dairy training, and cooperative membership had significant effects on cultivating improved forages. Dairy production system, dairying experience, and herd size were significantly associated with rearing only crossbred dairy cows. Farmland size, dairy system, and awareness of manure handling were significantly associated with good manure management. Hence, present dairy intensification in smallholder dairying in Ethiopia necessitates fulfilment of crossbreeding and manure management by increased attention to systems of production. This in turn is significantly connected with relatively greater milk sales, which lead to positive outcomes (income source) for the low or medium-input dairy households. To this end, policy and dairy extension support need to be in place for smallholder dairy producers to better engage with intensive dairying; improve milk productivity; remain in farming and endorse the full potential to contribute to dairy food demand and livelihoods while minimizing ecological pressure. Controlled crossbreeding (AI or bull and supply of improved breeding stock) and planned manure management could help to meet the challenges and make the dairy production sustainable at farm level. Further research is required on the performance and challenges of formal milk marketing channels in different dairy production systems.

Supplementary material

Supplemental Material, Appendices - Factors influencing intensification of dairy production systems in Ethiopia

Supplemental Material, Appendices for Factors influencing intensification of dairy production systems in Ethiopia by Habtamu L Didanna, Ashenafi Mengistu Wossen, Tadesse Kuma Worako, and Berhanu Kuma Shano in Outlook on Agriculture

Footnotes

Acknowledgements

The authors would like to thank the dairy farmers in Ada district for their cooperation during the study period. Drs Getaw Tadesse, Moti Jaleta, and Jacob Ricker-Gilbert also deserve thanks for their helpful comments to the article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study, part of the PhD thesis research of the first author, was partially financed by Addis Ababa and Wolaita Sodo Universities.

Supplementary material

Supplementary material for this article is available online.

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