A survey of Smallholder dairy cattle farmers in Tanzania: Farmer Demographic Characteristics and Basic Management Constraints

Understanding Smallholder Dairy Farming in Tanzania: A Cross-Sectional Survey of Farmer Demographics and Management Constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding Smallholder Dairy Farming in Tanzania: A Cross-Sectional Survey of Farmer Demographics and Management Constraints Athanas Ngou, Richard Laven, Timothy Parkinson, Isaac Kashoma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4993873/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted You are reading this latest preprint version Show more versions Abstract There has been a significant shift in the population of smallholder dairy cattle farmers in Tanzania, yet we lack current demographic data and information on key productivity constraints. This cross-sectional survey of 301 smallholder dairy cattle farmers across six regions aimed to gather demographic data and identify key farming constraints. Of the 301 households surveyed, 74% were headed by men, but in Njombe there was an equal number of women and men. Most respondents had primary education but had gone no further (55%); however, in Morogoro, 68% of farmers had been in secondary/university education. Across four regions (Njombe, Mbeya, Kilimanjaro and Arusha), herd size of 3–4 animals was most common (32–50%); however, in Morogoro and Tanga most herds had ˃4 animals (66% and 78%, respectively). Zero-grazing was the most common grazing system (75%), but tethering was predominant (68%) in Mbeya. Cash purchase was the most common means of obtaining the first cattle beast (66%), although a gift from a relative/friend (49%) was the most common source in Mbeya. High input costs (93%), unavailability of feed (71%), lack of land (68%) and diseases (62%) were the key identified constraints, while high breeding costs (96%), poor oestrus detection (89%), cows not displaying oestrus (79%) and lack of AI services (51%) were the key constraints to successful breeding. Despite the shared commonalities, demographic differences among regions call for fitting development strategies that address the specific needs of farmers in each region, rather than applying uniform solutions across Tanzania. Smallholder dairy cattle farmer household demographic farming constraints Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction Focused initiatives to improve the dairy cattle industry in Tanzania started in 1921 when the colonial government of the time introduced the first Holstein and Ayrshire cattle into Temeke, Dar es Salaam (Sumberg, 1997 ). The establishment of a few medium-to-large-scale settler dairy farms in the country's Northern and Southern highland regions followed this move. However, most of these farms were nationalised after independence, following the Arusha Declaration in 1967. This resulted in the dairy sector in the 1970s being dominated by governmental parastatal and state-owned farms, especially following the formation in 1975 of the Tanzanian Dairy Farming Company and Tanzania Dairies Ltd. The latter was responsible for processing and selling milk to consumers (Kurwijila et al., 1995 ; Mdoe & Wiggins, 1997 ). However, subsequent challenges, such as political interference with the milk price and the generally low productivity of the cattle, led to poor economic performance and the collapse of these governmental parastatal and state-owned farms in the early 1980s (Kurwijila et al., 1995 ; Mdoe & Wiggins, 1997 ). After this collapse, efforts to bridge the gap between Tanzanian milk production and demand for dairy products within Tanzania began. These efforts were led by private, religious and non-governmental organisations, all of which promoted smallholder dairy cattle farming (Kurwijila & Boki, 2003 ). Smallholder dairy cattle farming refers to a type of dairy farming typically practised on a small scale with a mean herd size of approximately 4 animals, ranging from 1 to 12 cattle, often involving unimproved genetics and rudimentary management by individual farmers or households (Alonso et al., 2014 ; Chagunda et al., 2004 ; McDermott et al., 2010 ; Nell et al., 2014 ; Njombe et al., 2011 ; Suleiman et al., 2016 ). These efforts were supported, at the government level, by the Tanzania Livestock Policy, which emphasised the promotion of smallholder dairy cattle farming (Mdoe & Wiggins, 1997 ). Initially, most smallholder dairy farms were owned by high and mid-ranking civil servants, with cows being kept in their owner’s place of residence (“backyard production“), with milk production being a supplementary, rather than a primary income source (Mdoe & Wiggins, 1997 ; Swai & Karimuribo, 2011 ). However, beginning in the 1990s, smallholder dairy cattle farming started experiencing rapid growth across Tanzania, with a change in the demographics of farmers, from producers for whom milk was a supplementary income, to producers for whom milk was the primary source of income (Limbu, 1999 ). The smallholder dairy sector thus currently provides significant income and employment across rural, peri-urban and urban areas of Tanzania, as well as being a valuable source of human nutrition in those areas (Gillah et al., 2012 ; Limbu, 1999 ; NBS & OCGS, 2021 ). Of the 33.9 million cattle in Tanzania, 33.8 million (99.6%) are kept by smallholder farmers, with only 142,000 (0.4%) in large-scale farms (i.e. farms that have more than 100 cattle) (Mbwambo et al., 2019 ; NBS & OCGS, 2021 ). Around 70% of the milk produced in Tanzania is from Indigenous/local Zebu cattle, with the remaining 30% from improved dairy cattle breeds (Brett, 2019 ). For smallholders, improved dairy cattle are generally crosses of European dairy breeds (e.g. Friesian, Ayrshire, and Jersey) with local Zebu, especially the Tanzanian Shorthorn Zebu, but also Boran and Sahiwal. These improved dairy cattle are concentrated in the rural areas of Tanga, Arusha, Kilimanjaro and Manyara (Northern Highland regions), Mbeya, Iringa and Njombe (Southern Highland regions) as well as in the Morogoro, Kagera and Dar es Salaam regions (Swai & Karimuribo, 2011 ). Initiatives to improve the productivity of the Tanzanian dairy industry, particularly smallholder farms, are ongoing (Chawala et al., 2019 ). These initiatives are a mixture of government-led programmes and external stakeholder-led programmes (with significant Tanzanian government support). They include long-running programmes such as the Dairy Development Program, and the Heifer Project International in Tanzania (HPI) Scheme (Msangya et al., 2015 ). Community Action Research Program (Pasape, 2022 ), AgResults Tanzania Dairy Productivity Challenge Project (2019–2024), and the newer countrywide program for the transformation of the livestock sector titled ‘Livestock Sector Transformation Plan (MoLF, 2022 ). However, despite the significant change in the population of smallholder dairy cattle farmers across Tanzania since the 1980s, there is limited information regarding the demographics of such farmers. Some localised surveys of smallholder dairy cattle farming have been undertaken, but these have generally been limited to one or two regions: Dar es Salaam (Kivaria et al., 2006 b), Morogoro (Gillah et al., 2013 ; Gillah et al., 2014 ), Tanga (Alonso et al., 2014 ), and Kilimanjaro and Arusha (Swai et al., 2014 ). Larger-scale surveys have been undertaken, but they did not report demographic data (Chawala et al., 2019 ) or had a limited analysis of such data (Mwambene et al., 2014 ). Additionally, most of these studies were conducted in urban and peri-urban areas. A better understanding of the demographics of smallholder dairy farmers (especially at the rural/regional level) is needed to help advisers and development agencies better understand who smallholder dairy farmers are, as well as their goals and challenges. Such understanding could be useful in targeting support for dairy farmers based on individual requirements (especially if structured at the regional level) rather than using a “one-size-fits-all” programme across Tanzania. Thus, as part of a larger study looking at the reproductive performance of cows on Tanzanian smallholder dairy farms, data on the demographic characteristics of smallholder dairy cattle farmers from thirteen districts across six different regions of Tanzania were collected. Alongside this, information on the constraints that the farmers perceived to be affecting their productivity was also collected. This paper aims to present this demographic and constraint data and to identify whether there are significant differences between regions in demographics and constraints reported by smallholder dairy farmers in six key dairying regions across Tanzania. 2. Methodology 2.1. Ethical considerations and approval This research was approved by the Ministry of Livestock and Fisheries through the Ethics Review Board of the Tanzania Livestock Research Institute (TALIRI) (reference number TLRI/RCC.21/007) of the United Republic of Tanzania. Permission letters were firstly provided by the office of the Regional Administrative Secretary from the six study regions, and then, from the Executive Directors of the respective District Council (DC), Town Council (TC), Municipal Council (MC) or City Council (CC) of the thirteen study districts of Tanzania mainland. A local veterinarian or livestock officer first introduced the interviewer (the first author did all interviews) to the farmer/respondent (usually the family head). The interviewer explained the reason for the visit. Thereafter, each respondent/farmer was given a written informed consent form for them to sign before participating in the questionnaire interview. If the interviewee was unable to read and write, another family member was called to approve and sign on their behalf. Results were anonymized and personal data were kept confidential. The specific consent of the relevant participants was obtained for any photographs used to illustrate this study. 2.2. Study area and study farm selection Information from smallholder dairy cattle farmers was gathered using a cross-sectional study design, from May 2022 to February 2023. Six regions of the Tanzania mainland were purposely selected, principally based on the proportion of improved dairy cattle. Three of these regions were in the Northern Highlands (Arusha, Kilimanjaro and Tanga), two in the Southern Highlands (Mbeya and Njombe) and one (Morogoro) in the Eastern zone. Within each region, district(s) were selected using a convenience sampling process with the help of local veterinarians/livestock officers (Figure 1). Within each district, convenience sampling was employed to identify study villages and the first study farm in each village (whose suitability was decided by the local veterinarian/livestock officer and interviewer). Snowball sampling was then employed to select other study farms in a particular village. Subjects could nominate as many further subjects as they wished with non-discriminative sampling being used until >50 respondents had been identified per region. No sample size calculations were undertaken; the number of farms visited was based on the number of farms that the authors believed could be visited within a district for over 2 weeks. (Insert Figure 1) 2.3. Field data collection A structured and pre-tested questionnaire was used to collect research data. Pre-testing was done in the Morogoro municipality capturing twenty-four smallholder farmers (who were not included in the final questionnaire) and eleven experts (veterinarians/livestock officers and researchers). The questionnaire was formulated so that all the questions were closed, and responses were entered into KoboToolbox (Cambridge USA) for subsequent data collation. Initial information collected from farmers included age, gender, dairy farming experience, involvement (full or part-time), education level, dependence on dairy farming (plus other household/farm income-generating activities) and decision-making process on the farm. Respondents were also asked about farm-related issues, such as the source of their first dairy animals, herd size, herd composition as well as breeding practices and preferences. The last section asked the respondents for their opinions regarding the key constraints that affected their farm, capturing information related to cattle health and reproduction, availability of veterinary and breeding services, availability of land and feed, and access to markets for their product. 2.4. Data management and analysis Data from the questionnaire were downloaded from KoboToolbox to Excel spreadsheets (Microsoft, Seattle, USA) before analysis using SPSS version 25 (IBM, Seattle, USA). Results are tabulated and presented as overall results and by region. Where the effect of region was thought to be of interest, a logistic regression was used to analyse the effect of region, with the response to a question being the dependent variable and region the only predictor variable. For most responses, multinomial logistic regression was used to evaluate the effect of region on the key outcome. If responses were clearly ordered, ordinal logistic regression was used, provided the proportional odds assumption was met. If this was not met, then a multinomial regression was used. Categories were merged for all analyses where totals were <10. For all analyses, except where stated, Tanga was used as the reference region, and the category with the highest frequency in the outcome was set as the reference category. Data from Arusha were included in the descriptive data but excluded from the analyses due to the small number of respondents. 3. Results At least 50 smallholder dairy cattle farmers were interviewed per region, except Arusha where only 16 farmers were interviewed. Fewer farmers in Arusha participated following the unavailability of local veterinarians and livestock officers, who were participating in the national livestock identification program, resulting in reluctance among farmers to participate. Overall, across the six regions, 301 farmers were recruited for the survey (Table 1). (Insert Table 1) 3.1. Smallholder dairy farmers’ household, family, and farm demographics Of the 301 households that participated in this study, 224 (74%) were headed by a father and 69 (23%) by a mother (Table 2). For the analysis of the effect of region, two categories were created (father and mother) with respondents who were recorded as ‘other’ combined with father or mother depending on their gender (male or female). There were differences between regions in who was the head of the household (Figure 2), with households in Njombe having much higher odds of having a female head of the household than households in Tanga (odds ratio (OR): 5.2, 95%CI: 2.8-13.1). (Insert Table 2) (Insert Figure 2) Most households (236/301; 78%) were monogamous, with the lowest proportion recorded in Njombe (41/54; 76%), and the highest in Arusha (14/16; 88%) (Figure 3). Additionally, in most households (248/301; 82%), the entire family was involved in the decision-making process, not just the head of the household. (Insert Figure 3) Total herd size ranged from 1 to 35 cattle, with the ‘3-4’ category being the mode herd size (90/301; 30%) (Table 2 and Figure 4). For the analysis of the effect of region, five categories of herd size were used: 1-2, 3-4, 5-6, 7-8 and ≥9. Ordinal logistic regression identified differences across regions in the proportion of farms in one of the higher herd size categories. Compared to Tanga, the odds of farms being in the higher herd size category were notably less in Kilimanjaro (OR: 0.3, 95%CI: 0.1-0.5), Mbeya (OR: 0.2, 95%CI: 0.08-0.3) and Njombe (OR: 0.1, 95%CI: 0.07-0.3). (Insert Figure 4) Of the 301 farms, 19 had no adult cows, 112 had no heifers, 98 had no calves and 249 farms had no breeding bulls. No effect of region on the proportion of farms with milking cows was found but compared to Tanga, farms in Njombe were less likely to have heifers (OR: 0.4, 95%CI: 0.2-0.8), and farms in Morogoro more likely to have bulls (OR: 2.6, 95%CI: 1.02-6.6). The proportion of farms with calves in Tanga was the highest of any region, with farms in Tanga having higher odds of having calves than farms in Njombe (OR: 0.1, 95%CI: 0.1-0.4), Mbeya (OR: 0.1, 95%CI: 0.1-0.4), Kilimanjaro (OR: 0.2, 95%CI: 0.1-0.6) and Morogoro (OR: 0.3, 95%CI: 0.1-1). Most respondents (201; 67%) reported having fewer than three people who took care of the dairy cattle on their farm (Table 2). As with herd size, there was an effect of region such that, with reference to Tanga, the odds of having more than two people actively participating on the farm was lower than in all other regions: Kilimanjaro (OR: 0.4, 95%CI: 0.2-0.8), Mbeya (OR: 0.3, 95%CI: 0.2-0.8), Morogoro (OR: 0.3, 95%CI: 0.1-0.6) and Njombe (OR: 0.3, 95%CI: 0.1-0.6). Cash purchase was the dominant (200; 66%) source of obtaining the first dairy cattle beast (Table 2). Regionally, this was true for all regions except for Mbeya, where a gift from a relative or friend was the most common source (Figure 5). For analysis of regional differences, data were merged into three groups: cash, gift and other (merging non-governmental organisation (NGO), bank and home-bred). Arusha was excluded from this analysis as there were no farms in the ‘other’ category. Relative to cash purchase, three regions had different odds of a gift being the source of their first cattle beast than respondents in Tanga: the odds were higher in Mbeya (OR: 2.7, 95%CI: 1.1-6.4) and were lower in Morogoro (OR: 0.3, 95%CI: 0.1-0.9) and Njombe (OR: 0.2, 95%CI: 0.05-0.8). For the ‘other’ category, the odds were lower in Kilimanjaro (OR: 0.04, 95%CI: 0.01-0.3), Mbeya (OR: 0.2, 95%CI: 0.1-0.8) and Morogoro (OR: 0.04, 95%CI: 0.01-0.3) relative to cash purchase than in Tanga. (Insert Figure 5) 3.2. Farmers/respondents and assistants/worker's demographic characteristics Most respondents were over 40 years of age (238/301; 79%), with the majority (155/301; 52%), being between 41 and 60 years. This was consistent across all regions (Figure 6 and Table 3). Using ordinal logistic regression with four age categories (i.e. ≤20, 21-40, 41-60 and ≥61), the odds of a farmer being in a higher age category were lower in Mbeya (OR: 0.4, 95%CI: 0.2-0.7) compared to Tanga. Experience in dairy farming was classified into 10-year blocks (≤10, 11-21, etc.). The highest proportion of respondents (118/301; 39%) had ≤10 years of experience. There was little difference between regions, except that ordinal logistic regression showed that the odds of being in a higher experience category were lower in Morogoro (OR: 0.49, 95%CI: 0.24-0.97) compared with Tanga. (Insert Figure 6) Most respondents (231/301; 77%) were involved full-time in dairy cattle farming (Table 3). The proportion was highest in Tanga (46/53; 87%) and lowest in Morogoro (41/57; 58%), with the odds of being a part-time farmer being higher in Morogoro (OR: 4.8, 95%CI: 1.8-12.4) than in Tanga. The most common level of education in respondents was primary level (7-14 years) (166/301; 55%) (Table 3 and Figure 7). For analysis of the effect of region, respondents who had not had a formal education were excluded. Compared to respondents from Tanga, the proportion in each education category was similar across all regions except for Morogoro, where respondents were more likely to have had a higher category of education (OR: 6.5, 95%CI: 3-13.9). (Insert Table 3) (Insert Figure 7) For the farm workers/assistants, the majority (157/203; 77%) were aged ≤30 years and had ≤5 years of experience (168/203: 83%), making them generally younger and less experienced than the main respondents (Table 3). Analysis of assistant demographics excluded data from Arusha as there were only 6 responses from that region. Compared to workers in Tanga, assistants from Mbeya (OR: 0.3, 95%CI: 0.12-0.77), Morogoro (OR: 0.2, 95%CI: 0.093-0.45), and Njombe (OR: 0.32, 95%CI: 0.09-0.56) all had lower odds of being in a higher age category. For experience, data were merged into four categories: <1 year, 1 to 5 years, 6 to years and ≥ 11 years. This analysis showed that assistants from Morogoro had lower odds of being in a higher experience category (OR: 0.18, 95%CI: 0.08-0.44) compared to those in Tanga, as did assistants in Kilimanjaro (OR: 0.37, 95%CI: 0.16-0.88). Most assistants were full-time workers (176/203; 87%), and most (182/203; 90%) had had only primary education. The education level of farm workers was similar across the study regions. 3.3. Smallholder dairy farmers’ sources of household income Although 77% of respondents reported full-time involvement with dairy farming, almost all households reported having other sources of income (282/301; 94%) (Table 4). Crop farming was the most common alternative, with 180/282 (64%) gaining at least some income from it. Conversely, only 37% (104/282) and 29% (81/282) of respondents were involved in employment or business, respectively. For analysis of regional differences in other income sources, two categories were created i.e., ‘Yes’ (representing major, moderate and minor) and ‘Not at all’. Excluding Mbeya and Arusha where there are no respondents for the ‘Not at all’ category, the odds for a farmer participating in crop farming were lower in Njombe (OR: 0.1, 95%CI: 0.01-0.9) than in Tanga. Further, the odds of a farmer relying on employment as the source of income were highest in Morogoro (OR: 5.2, 95%CI: 2.2-12.4) and lowest in Njombe (OR: 0.1, 95%CI: 0.04-0.4). Lastly, Mbeya had lower odds (OR: 0.2, 95%CI: 0.07-0.6) for its farmers depending on business as an income source compared to Tanga. Furthermore, farmers’ responses (major, moderate and minor, excluding ‘not at all’) for their involvement in other income-generation activities (i.e., crop farming, employment and business) apart from dairying, were further evaluated to determine their contribution to the household income. For that, responses were given score values i.e., 3=major, 2=moderate and 1=minor; where the total score was ≥6, then dairying was defined as not being the major source of income and defined as being the major source of income when the total score was ≤5. Based on this score, dairying was the major income source for 238/282 (84%) households. Logistic regression was used to evaluate the regional differences (excluding the Arusha region due to fewer respondents) of household dependence on other income sources. Concerning Tanga, only farmers from the Njombe had higher odds (OR: 4.9, 95%CI: 1.7-13.9) of non-dairying income being their major source of income. 3.4. Smallholder farmers’ dairy cattle management system and feed sources Across the 301 farms, 225 (75%) of respondents kept their dairy cattle under a zero-grazing/intensive system, in which forages were harvested daily and brought to the cattle in their shelter (Figures 8, 9, 10a, 10b and Table 5). This system dominated across all regions except Mbeya, where tethering at pasture (Figures 10a and 10b) was predominant (37/55; 68%). Across the different regions, the odds of semi-intensive farming (compared to intensive/zero grazing) were higher in Mbeya (OR: 12, 95%CI: 3.03-47.5) and Morogoro (OR: 6, 95%CI: 1.9-19.1) than in Tanga. (Insert Figure 8) (Insert Figure 9) (Insert Figure 10a and 10b) Sources of feed are shown in Table 5 and Figure 11. Natural pastures (i.e. naturally occurring grasses, legumes, and other species) ranked as the most used source of feed, with 294/301 (98%) respondents using at least some natural pasture, and 241 (80%) using it as the major source of feed. Most respondents gave at least some supplementary concentrate feeds to their cattle; only 10/301(3%) stated that they did not do so at all. (Insert Table 4) (Insert Table 5) Some feeds were used to a very limited extent including conserved forage, bought fodder and actual grazing; all of which were used by <25% of respondents. Across all regions, not at all was the most common response for those feeds (Table 5). Other more commonly used feed sources had regional variations. Cut forage was used by 32% of respondents. Of those, 54/97 (55%) were in Njombe, with most of those (51) reporting that it was a major source of feed (a category only used in Njombe). Fodder plots were used by 55% of respondents, but this varied appreciably by region, with respondents in Njombe and Mbeya having higher odds of reporting that they used fodder plots (at any level) than respondents in Tanga (OR: 3.8, 95%CI: 1.7–8.7, and OR: 8.3, 95%CI: 3.2–21.7, respectively), while respondents in Morogoro were less likely to report that they used fodder plots than those in Tanga (OR: 0.3, 95%CI: 0.1–0.7). Most respondents used crop residuals (only 4% did not) but the level of use varied markedly by region. Compared to Tanga, the odds of a respondent reporting moderate/major use of crop residuals (as opposed to minor/not at all) were much higher in Njombe and Mbeya (OR: 883, 95%CI: 88.9-8770 and OR: 25, 95%CI: 6.9-90.3, respectively). (Insert Figure 11) 3.5. Smallholder dairy farming constraints Farmers were asked to rank the important farming constraints (Table 6 and Figure 12), with high costs of inputs ranked as very highly significant by 229/301 (76%) respondents and by most farmers in all six study regions. Lack of enough land (150/301; 50%) was the second most important constraint, while unavailability of feed (24/301; 41%) was the third. For the assessment of regional variations, three ordinal categories were created i.e., High (encompassing ‘very highly significant’ and ‘important’), Moderate and Low (encompassing little importance and minor) and not at all (data from Arusha were excluded from this analysis because of the absence of data in one or more categories). Insufficient land was reported to be an important constraint by farmers in Kilimanjaro (OR: 3.2, 95%CI: 1.5–6.8), Morogoro (3.1, 95%CI: 1.4–7) and Njombe (2.1; 95%CI 1-4.5) than in Tanga. Availability of feed was regarded as of greater importance by farmers from Kilimanjaro (OR: 11.7, 95%CI: 4.6–29.8), Mbeya (OR: 3.4, 95%CI: 1.6–7.3), Morogoro (OR: 3.6, 95%CI: 1.6–7.8) and Njombe (OR: 2.7, 95%CI: 5.9–1.3) than those from Tanga. Lack of money to buy inputs was regarded as of greater importance in both Mbeya and Njombe (OR 7.32, 95%CI: 3.2–16.6 and 3, 95%CI: 1.4–6.2, respectively) than in Tanga, while the unpredictability of the milk market was regarded as of greater importance in Njombe (OR: 11.4, 95%CI: 0.2–0.7), Kilimanjaro (OR: 4.8, 95%CI: 2.2–10.4) and Mbeya (OR: 2.3, 95%CI: 1–5.2) than in Tanga. Concerning constraints for successful breeding, farmers in Njombe regarded the lack of a breeding service as more important than farmers in Tanga (OR: 9.5, 95%CI: 4.1–22.2). The importance of disease as a constraint was analysed using a multinomial regression, as the proportional odds assumption was not met (P<0.001). The odds of being in the low category compared to the higher category were consistent across regions. For the Moderate category, farmers in Kilimanjaro and Morogoro had lower odds of being in the Moderate rather than the High category when compared to farmers in Tanga (OR: 0.2, 95%CI: 0.1–0.1 and OR: 0.2, 95%CI: 0.1–0.7, respectively). (Insert Table 6) (Insert Figure 12) 3.6. Dairy breed selection criteria and breeding practice Preference for one or more breeds (Figure 13) was expressed by 160 (53%) respondents, whereas 59 (20%) had no specific preference (Table 7). Farmers in Njombe were more likely to report having a breed preference than farmers in Tanga (OR: 21.5, 95%CI: 4.7 - 97.6). Of those who expressed a breed preference, the focus was milk production (146/160: 91%), followed by easy handling (108/160: 68%), large body size (100/160: 63%) and ease of getting pregnant (37/160: 60%). (Insert Figure 13) Breeding methods are summarised in Table 8 and Figure 14. In general, the most frequently applied breeding method involved a mix of natural service and artificial insemination (AI) (138/301; 46%). At the regional level, AI alone was the most common method in Arusha (10/16; 63%), Kilimanjaro (34/66; 52%) and Tanga (29/53; 55%), while most farmers used a mix of AI and natural service in Morogoro (42/57; 74%) and Njombe (33/64; 61%), and natural service only predominated in Mbeya (53/55; 96%). Relative to Tanga, farmers were less likely to report using AI alone in Morogoro (OR: 0.2, 95%CI: 0.1-0.5) and Njombe (OR: 0.4, 95%CI: 0.2-0.9) (this analysis excluded Mbeya as no farmers in that region reported using AI alone). (Insert Figure 14) Of the 200 farmers who used natural service on some occasions, 180 answered the question about where they sourced bulls from. Overall, they largely (113/180; 63%) opted to hire bulls from nearby farms, with most of the rest using a combination of their own and their neighbours’ bulls (46/180, 26%). Only 17 (9%) reported exclusively using their bulls. Of the 240 farmers who used AI for at least some inseminations, 221 answered questions about AI use. Across all regions, most farmers reported receiving the service regularly (131/221; 59%) and most (179/221; 80%) reported that waiting time from informing the AI technician to the actual insemination ranged between seven and nine hours. (Insert Table 7) (Insert Table 8) In response to questions about the constraints around successful breeding (Figure 15 and Table 9), almost half of the respondents, 142/301(47%), indicated that the high cost of breeding was a major constraint, with 96% (289/301) identifying high breeding costs as being at least a minor constraint. This is a higher proportion than the effects of poor oestrus detection (89%, 269/301), cows not displaying oestrus (79%, 234/301) and unavailability of AI services (51%, 154/301). Constraints around breeding varied markedly across regions. Except for high breeding cost, compared to farmers in Tanga, farmers in Njombe were more likely to report that all the constraints listed in Table 9 were major/moderate constraints on their farm. The relevant OR were 43.3 (95%CI: 11.7–160) for unavailability of AI service, 4.4 (95%CI: 1.9–10.4) for unavailability of breeding bull, 5.6 (95%CI: 2.4–13.1) for bull being located at a distance, 6.5 (95%CI: 2.7–15.4) for poor oestrus detection, and 12.9 (95%CI: 4.5–16.2) for cows not showing heat. For Mbeya, farmers had higher odds (compared to Tanga) of reporting the unavailability of an AI service (OR: 44.4, 95%CI: 12–82.6), poor oestrus detection (OR 3; 95%CI: 1.3–6.9) and cows not showing heat (OR 6.4; 95%CI: 2.2–18.6) as major/moderate constraints. Despite having similar percentages of farmers using mixed breeding (see Table 7), farmers in Kilimanjaro were less likely to report bull availability and bull distance as major/moderate constraints than farmers in Tanga (OR 0.12; 95%CI: 0.03–0.6, and 0.3; 95%CI: 0.1–1, respectively), while farmers in Morogoro were more likely than farmers in Tanga to report that heat detection was a problem (OR 3.2; 95%CI: 1.4–7.5). (Insert Figure 15) (Insert Table 9) 4. Discussion 4.1. Smallholder dairy cattle household, family, and farm demographics To achieve sustainable development of smallholder dairy cattle farming in Tanzania, a proper understanding of the key participants involved is essential. This includes a thorough knowledge of the features of the farmers and their farms, locality, and farming practices. In this survey, it was found that most farms/households (74%) were headed by men. This is consistent with other recent surveys, such as that of Kashoma and Ngou ( 2023 ) who reported, based on a survey of farms across 17 districts, that 68% of households were headed by men. However, both of these percentages are much lower than previous reports, e.g. the 90% reported by Kivaria et al. ( 2006 b), in the Dar es Salaam region and the 93% reported by Swai et al. ( 2014 ) in Kilimanjaro and Arusha. These findings indicate that there has been an increase in the number of women involved in smallholder dairy farming. One positive reason for this increase is the focus of NGOs on the empowerment of women-led households. This was most apparent in Njombe, where almost equal numbers of men and women were household heads, consistent with the report by Msangya et al. ( 2015 ), that 53% of the households in Njombe supported by the Heifer in Project International Trust were led by women. The other, less positive, reason for the increased involvement of women in smallholder dairying is likely to be the rural-urban migration of men searching for better wages, therefore, by default, leaving women to manage the household and its livestock (Ojango et al., 2017 ). Smallholder dairy farming in Tanzania is generally collaborative, with household heads including other household members (spouse and/or children) in decisions related to farming. In most regions, a herd size of 3 to 4 animals was the most common (Swai et al., 2014 ). However, in both Tanga and Morogoro, the most common herd size was > 4 animals. The somewhat larger herd size reported in Tanga and Morogoro may reflect, at least in part, the proximity of these regions to Dar es Salaam, which is the biggest milk market in the country. However, the rapid increase in the population in the Dar es Salaam region in the recent past (Lupala, 2021 ) has not been reflected in recent increases in the size of farms in either Morogoro (Nkya et al., 1999 ) or Tanga (Zylstra et al., 1995 ), so market size is probably not the sole factor. The similarity of farm size across most regions is also reflected in the number of people/farms actively involved in farming, with 1–2 people actively involved in the majority (67%) of farms. Tanga had both larger herds and more active people/farms, with 3–4 people per farm being the most common category, probably reflecting increased involvement of family members as previously reported (Swai et al., 2005a ). Across the six study regions, cash purchase was the principal (66%) source of a household’s first dairy cattle beast, consistent with the 72% reported by Swai et al. ( 2014 ) in Kilimanjaro/Arusha. The only region where cash purchase was not the principal source was Mbeya, where a gift from a family member or friend was as common as cash purchase (49 vs 45% of respondents, respectively). This process, known as ‘kufufya’ by Nyakyusa speakers, involves an individual giving a heifer or cow (which could be pregnant or non-pregnant) to a relative or friend. When that animal calves, the original owner takes back the milking cow if the newborn calf is female, or if it is a bull calf, the new owner keeps the cow (until it produces a female calf) but shares the milk it produces with the original owner. This process has been popularised using the Kiswahili slogan “Kopa Ng’ombe lipa Ng’ombe” (meaning: borrow a cow, pay a cow). Similar techniques have been used by NGOs (Kayunze et al., 2001 ; Msangya et al., 2015 ), with the recipient of a cow raising female offspring before passing on the younger animals to a new family/owner (De Vries ( 2012 ); “Passing on the Gift”. Likewise, Kopa Ng’ombe lipa Ng’ombe, Passing on the Gift has been a regionally specific process with only Tanga and Mbeya reporting that a significant proportion of respondents got their first cow from an NGO. 4.2. Farmers/respondents and attendants/workers' demographic characteristics Most respondents (51%) in this survey were aged between 41 and 60 years, with 39% having < 10 years of farming experience. According to Swai et al. ( 2014 ), only 11% of their respondents had < 10 years of experience. The higher figures in this survey, even in the regions studied by Swai et al. ( 2014 ), strongly suggest that there have been a significant number of new entrants into dairy farming over the last 10 years. This appears to be particularly the case in Njombe and Morogoro as both regions had ≥ 50% of respondents with < 10 years’ experience. As expected, farm assistants were younger (77% were < 30 years of age) and less experienced (83% had < 5 years of experience) than farm owners. For both groups, primary education was the highest education level (55% for farm owners, 66% for assistants) and dairy farming was a full-time occupation (77 vs 87%, respectively). Having a majority of smallholder farmers and assistants with at least primary education signifies that they are more likely to adopt and apply modern farming techniques to improve their productivity. 4.3. Smallholder dairy cattle households’ sources of income Across all respondents, very few (6%) relied solely on dairy cattle for their income. However, even for those who relied on other household income sources (i.e., employment, crop farming and business), the majority (84%) of them still depended on smallholder dairying as their major income source (except the Njombe region). According to Swai et al. ( 2014 ), dairying was the major source of income for only 32% of respondents in Kilimanjaro and Arusha regions, while Kashoma and Ngou ( 2023 ) reported 56.4%. This shows an increase in the number of households relying on smallholder dairying year-by-year. This trend can be expected to result in a stable household economy, providing sustainable income and improved nutrition. Furthermore, it indicates significant growth in the sector, leading to improved food security, economic development, and increased employment opportunities for many Tanzanians. Therefore, this study suggests that the government and other stakeholders support smallholder dairy cattle farmers in achieving increased and sustainable productivity. 4.4. Smallholder dairy cattle grazing system and feed sources. There were marked differences across regions in the source of non-dairy income. In Morogoro, 64% of respondents had moderate/major income from employment. This may be related to the proximity of institutions like Sokoine University and access to government-related jobs. Gillah et al. ( 2013 ) reported that 19% of farmers in Morogoro were government employees and 15% were retired officers. For the remaining regions, crop farming was the principal alternative source of income. In most regions it was listed as a moderate/minor source of income by respondents (64–84% depending on region); however, in Njombe it was a major source of income for 52% of respondents. This indicates that smallholder dairy cattle farming is expanding beyond urban and peri-urban areas, traditionally managed by government officers, to rural areas, where crop farmers are increasingly adopting these practices. The involvement of crop farmers in smallholder dairy cattle farming was also recorded in the previous studies (Kashoma & Ngou, 2023 ; Swai et al., 2014 ). Zero grazing was the predominant system in all study regions (67 to 99% of respondents) except for Mbeya (only 16% of respondents). Zero grazing or ‘cut-and-carry’ uses natural pastures, with fresh forages being obtained from roadsides, highways, and non-cultivated areas such as communal lands, uncultivated fields, communal swamp areas and communal grazing areas (Swai & Karimuribo, 2011 ; Urio, 1986 ). Tethering was the predominant system in Mbeya (68% of farmers) which may reflect the longer periods in the Mbeya region (Busokelo district in particular) when grazing is available since the area is a cooler highland area (770 to 2865 metres above sea level) which receives 1500 to 2700 mm rainfall per year (Makala, 2017 ; Nyunza & Mwakaje, 2012 ). The current survey evaluated the use of eight on-farm feed sources. The survey identified five types of feed sources: 1) commonly used feed sources with limited variability across regions (i.e. concentrates, and natural pastures); 2) commonly used feed sources with large differences in level of use by region (crop residuals); 3) rarely used feed sources with > 75% farms reporting no use and > 90% of farms no more than minor use (i.e. grazing, bought fodder and conserved feeds); 4) feed sources that are rarely used in most regions, but commonly used in one or more regions (e.g. cut fodder from outside); and 5) feed sources that vary markedly across and within regions (e.g. fodder plots in farm). These regional differences in feeding practices need to be considered when developing support programs. Furthermore, these results also show that it would be too simplistic to assume that all farms within a region use the same feed sources at the same time and in the same way, such that, even at the regional level, feed supply will require multifactorial solutions to achieve success. The use of crop residuals as livestock feed could have many benefits: cost savings (crop residuals are more affordable), availability (mostly are locally available) and greater utilization of agricultural by-products. Such practice can help to guarantee sustainable agricultural practices and more efficient resource management within the community of smallholder dairy cattle farming in countries like Tanzania. 4.5. Dairy breed selection criteria and breeding practice Only 53% of farmers across the six regions had a breed preference. Surprisingly, the proportion of farmers from Njombe who had a preference was much higher than this average (at 96%). The reason for this difference is unclear. Despite the low proportion of respondents who had a specific breed preference, most respondents (91%) expressed their opinions on the cattle attributes they desired. Unsurprisingly, high milk production is the primary reason for their choices, as highlighted in the previous surveys done in Tanzania (Chawala et al., 2019 ; Gillah et al., 2014 ; Swai & Karimuribo, 2011 ). This preference (and the preference for large body size especially in Morogoro, Tanga and Mbeya) probably reflects the marketing of ‘high yielding’ cows to smallholders rather than the direct experience of the respondents of such cows playing an important role in improving farm incomes (Chung, 2024 ). The breeding method was very dependent on the region, reflecting the generally poor AI infrastructure across much of Tanzania and the centralisation of the AI service at the National Artificial Insemination Centre (NAIC) in Arusha. Regions that predominantly used AI were generally close to the NAIC, with regions further away using a combination of natural insemination and AI. Nevertheless, across the six regions, 80% of farmers used at least some AI. For five of the six regions, > 90% of farmers reported using some AI, with only Mbeya having the majority of farmers using bulls only (96%). Centralization of the AI centre in Tanzania could be the reason for the irregular availability of semen and other consumables like liquid nitrogen to farmers located away from NAIC. Similarly, as reported previously by Mwanga et al. ( 2019 ) in their study on factors influencing breeding decisions by smallholder dairy farmers in Sub-Saharan African countries. Also, irregularities in the supply of IA consumables by Kashoma and Ngou ( 2023 ). In another report (Kanuya et al., 2014 ), over 60% of farmers in Morogoro and Tanga reported experiencing challenges related to AI delivery systems. Though there are some imported doses of semen by private organizations (Katjiuongua, 2014 ; Ogutu et al., 2014 ), plans are underway to improve the available semen production by involving public-private partnerships and development partners (Ogutu et al., 2014 ). Further, Kusiluka et al. ( 2006 ) reported a lack of breeding services for smallholder dairy cattle farmers in the eastern zone of Tanzania. The cost of AI varies with distance from the NAIC. The proximity to the AI centre means that AI services can be offered to farmers at a relatively low cost (i.e. between 15,000 and 25,000 TZs per insemination (~ US $ 6–10) for first insemination). Nevertheless, irrespective of distance, AI was seen by many respondents as being expensive. The lack of a good AI infrastructure in Tanzania probably limits productivity on dairy farms, since AI confers significant advantages over natural mating in terms of genetic gain and disease control (Parkinson & Morrell, 2019 ). Increased use of AI could be greatly beneficial for smallholder productivity, reducing the risk of disease from the use of untested local bulls and improving production efficiency and longevity, especially if the focus is not just on increasing milk production. Furthermore, to sustain productivity, emphasis must be placed on improving the management and nutrition of the animals. This is because inadequate nutrition and management of animals may result in reduced milk yields, increased health disorders and most importantly, impaired reproductive ability (Van Saun, 1991 ). The high cost and unreliability of AI service were among the constraints highlighted in the previous studies from Tanzania (Kanuya et al., 2014 ) and in sub-Saharan African countries (Mwanga et al., 2019 ). 4.6. Smallholder dairy cattle farming constraints Two areas of constraints were examined: general constraints and specific breeding-related constraints. High costs of inputs were identified as the leading constraint in the general constraints section of the survey, with 76% of all farmers reporting it as a highly significant constraint (range: 65–95% by region). In general, high input costs seemed to be perceived as a more important constraint than high breeding costs alone. In all regions, the proportion of farmers who reported high input costs as a highly significant constraint was higher than the proportion who reported high breeding costs as a major constraint. High cost of inputs was also reported previously: concentrates and veterinary drugs (Kivaria et al., 2006 b) in Dar es Salaam and northern Tanzania (Swai et al., 2014 ). Furthermore, in all regions, the most common response about high input costs was that it was a highly significant constraint, whereas, for breeding costs, the most common response varied between major and moderate, depending on the region. Insufficient land and feed were also seen as highly significant/important constraints by most respondents, except in Tanga, where most respondents (51% and 59%, respectively) thought that they were of minor to moderate importance. This regional difference may reflect the high number of swamp areas and abandoned sisal plantations in the Tanga region areas from which farmers can reliably obtain forage. The cost of breeding was seen as a major constraint by a high proportion of respondents in all regions (> 80%), with no evidence that being near the NAIC made that cost less of a constraint. This was also highlighted previously that farmers are unable to pay for the AI service due to high costs and irregularities in the AI delivery system (Kashoma & Ngou, 2023 ). Irregularities in the AI delivery system were also reported to be a challenge for smallholder dairying improvement in Rwanda (Rugwiro et al., 2021 ). In contrast, distance from the NAIC did affect whether the lack of an AI service was an important constraint, with the regions close to Arusha reporting no or minor concerns and the regions far from Arusha reporting major/moderate concerns. Indeed, concerns about reproductive constraints were particularly prominent in Njombe, with all constraints (other than cost) being ranked as more important in Njombe than in Tanga. The pattern for Mbeya was similar but the differences were less marked. This probably reflects the different patterns of breeding in the two regions – natural service only predominates in Mbeya, while mixed and AI only predominate in Njombe. More reliance on AI leads to more concerns – again highlighting the importance of improving the AI infrastructure in dairying regions remote from Arusha. The regional differences in the relative importance of constraints again highlight the importance of developing regional solutions to improve smallholder dairy farm productivity, while the variability in the relative importance of constraints across farms within a region highlights the importance of understanding what is driving those differences at the farm level. Previous studies on smallholder dairying constraints in Tanzania include inadequate, seasonal and irregular forage availability in Arusha, Kilimanjaro (Swai et al., 2014 ) and Dar es Salaam (Kivaria et al., 2006 b). Also, the high cost and poor conception/pregnancy rates achieved by AI (Kanuya et al., 2014 ) was considered by farmers from Tanga and Morogoro to be a significant disincentive to breed by AI. 5. Conclusion The primary objective of this study was to conduct a large-scale survey to understand the unique characteristics of smallholder dairy cattle farmers in Tanzania, and to identify regional differences that might impact the development of the dairy farming industry. The findings of this study revealed significant regional differences in demographic factors such as gender, age and farming experience, reflecting the structure of the dairy industry and the availability of alternative income streams in different regions. These regional differences underscore the importance of tailoring interventions to farmers' specific needs and preferences in each locality rather than assuming a uniform approach will be effective. This study also highlighted some areas that require attention within the specific agro-economic context of different regions in Tanzania. Addressing these issues will form the foundation for developing regionally tailored and sustainable solutions. A ‘one size fits all’ approach risks undermining the success of herd-improvement programs or other initiatives by failing to consider the needs of farmers in different regions. The sustainable development of Tanzania’s smallholder dairy farming industry requires targeted and flexible strategies that consider these disparities. This includes a thorough understanding of the farmer's characteristics, preferences and constraints before implementing any programs. Such considerations will enhance the likelihood of success for initiatives introduced by the government, NGOs or other stakeholders. Lastly, this study serves as the baseline for future research and action. Further qualitative research is recommended to explore the underlying reasons behind these regional differences, which were beyond the scope of this study, to refine strategies further and ensure the long-term success of the interventions aimed at improving the dairy industry in Tanzania. Abbreviations Ltd – Limited NBS - National Bureau of Statistics OCGS - Office of the Chief Government Statistician MoLF – Ministry of Livestock and Fisheries TALIRI - Tanzania Livestock Research Institute DC - District Council TC - Town Council MC - Municipal Council CC - City Council USA – United States of America IMB - International Business Machines CI - Confidence Interval OR - Odds Ratio Declarations Funding The Massey University Foundation Scholarship supported this work. Competing interests The authors declare that they have no competing interests. Authors' contributions All authors contributed to the study design and conception. AN did the data collection under the guidance of IK and RL. Data analysis and interpretation were done by AN, RL and TP. The first draft of this manuscript was written by AN, and all authors commented on the versions of the manuscript. All authors read and approved the final document. Data availability The generated and analysed datasets used in this study are available from the corresponding author upon acceptable request. Ethics approval This research was approved by the Ministry of Livestock and Fisheries through the Ethics Review Board of the Tanzania Livestock Research Institute (TALIRI) (reference number TLRI/RCC.21/007) of the United Republic of Tanzania. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Authors consent to the publication of this article and participants permission for publication was achieved where appropriate. Competing interests The authors declare that they have no competing interests. Acknowledgements The authors would like to thank Massey University, New Zealand for their invaluable assistance and support during the research process. 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Advantages and disadvantages of artificial insemination. In D. E. Noakes, Parkinson, T. J., & England, G. C. (Ed.), Veterinary Reproduction and Obstetrics (10th ed., pp. 746-777). WB Saunders. Pasape, L. (2022). A Review of Success Factors Behind Community Action Research Program (CARP): A Case of Experiences from Smallholder Dairy Farmers of Lushoto in Tanzania. Journal of Participatory Research Methods, 3(2). https://doi.org/https://doi.org/10.35844/001c.37544 Rugwiro, P., Pascal, N., Manirahaba, E., Aphrodis, T., Abijuru, J., Nizeyimana, B., Habumugisha, D., & Uwumukiza, D. (2021). Assessment of challenges associated with artificial insemination service delivery in dairy cattle in Rwanda. International Journal of Veterinary Sciences and Animal Husbandry, 6, 45-57. https://doi.org/10.22271/veterinary.2021.v6.i3a.373 Suleiman, T., Mdegela, R., & Karimuribo, E. (2016). Characteristics of dairy farming and its effect on milk production: a case study of Unguja island of Zanzibar, Tanzania. Livestock Research for Rural Development, 28(10), 174. Retrieved July 15, 2024, from https://lrrd.cipav.org.co/lrrd28/10/sule28174.html Sumberg, J. (1997). Policy, milk and the Dar es Salaam peri-urban zone: a new future for an old development theme? Land Use Policy, 14(4), 277-293. https://doi.org/https://doi.org/10.1016/S0264-8377(97)00020-3 Swai, E., Karimuribo, E., Schoonman, L., French, N., Fitzpatrick, J., Kambarage, D., & Bryant, M. (2005a). Description, socio-economic characteristics, disease management and mortality dynamics in smallholder's dairy production system in the coastal humid region of Tanga, Tanzania. Livestock Research for Rural Development, 17(4). Retrieved July 15, 2024, from https://www.lrrd.cipav.org.co/lrrd17/4/swa17041.htm Swai, E., Mollel, P., & Malima, A. (2014). Some factors associated with poor reproductive performance in smallholder dairy cows: the case of Hai and Meru districts, Northern Tanzania. Livestock Research for Rural Development, 26(6), 1-2. Retrieved July 15, 2024, from https://lrrd.cipav.org.co/lrrd26/6/swai26105.htm Swai, E. S., & Karimuribo, E. D. (2011). Smallholder Dairy Farming in Tanzania. Outlook on AGRICULTURE, 40(1), 21-27. https://doi.org/10.5367/oa.2011.0034 Urio, N. A. (1986). On-farm research on utilization of crop residues by smallholder dairy farmers in Hai district, Tanzania. Towards Optimal Feeding of Agricultural Byproducts to Livestock in Africa, 17. Van Saun, R. J. (1991). Dry Cow Nutrition: The Key to Improving Fresh Cow Performance. Veterinary Clinics of North America: Food Animal Practice, 7(2), 599-620. https://doi.org/https://doi.org/10.1016/S0749-0720(15)30785-4 Zylstra, L., Lyimo, C., & Rutamu, I. (1995). Milk production and Marketing in Tanga Region: Efficiency of farmer co-operatives versus private sector. Conference Proceedings presented at the Workshop on Strategies for Market Orientation of Small-Scale Milk Producers and their Organizations, Morogoro (Tanzania), p20-24, March 1995. Retrieved July 15, 2024, from https://www.fao.org/4/X5661E/x5661e0f.htm. Tables Tables 1 to 9 are available in the Supplementary Files section. Supplementary Files ListofTablesFarmersDemographicsandConstraints.docx Cite Share Download PDF Status: Under Review Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4993873","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":421987156,"identity":"2b7cef09-5bad-41b1-ac47-e830a91cfa91","order_by":0,"name":"Athanas Ngou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYNCCCtK1nCFZB2MbKar5Z7dffMw7b5u8fP/hYxIMNXYM5vwH8GuRuHOm2Jh3223DDTfS0iQYjiUzWDYQ0MJwIydNGqiFcYMEj5kEA9sBBoODDfh1yIO1zLltP7///DcJhn9ALYcJWGJwI/2YNG/D7cSGAzlsEoxtQC3HCGgxvJHDbDjn2O1koF+MLRL7knkMCIW43I30hw/e1Ny2nd9/+OGND9/s5AzOHyCgh4HHAMZikUgAcgmpBwL2BzAW8wcilI+CUTAKRsEIBABLY0SmuIKIogAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6448-3908","institution":"Massey University School of Veterinary Science","correspondingAuthor":true,"prefix":"","firstName":"Athanas","middleName":"","lastName":"Ngou","suffix":""},{"id":421987157,"identity":"7af4be2e-6ba7-41a6-996a-f073ebfdfab1","order_by":1,"name":"Richard Laven","email":"","orcid":"","institution":"Massey University School of Veterinary Science","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Laven","suffix":""},{"id":421987158,"identity":"acb5e7ba-d6fb-455f-aa44-cf2eab95c404","order_by":2,"name":"Timothy Parkinson","email":"","orcid":"","institution":"Massey University School of Veterinary Science","correspondingAuthor":false,"prefix":"","firstName":"Timothy","middleName":"","lastName":"Parkinson","suffix":""},{"id":421987159,"identity":"d79b1247-0e6b-4b8b-afb4-523f4491cab5","order_by":3,"name":"Isaac Kashoma","email":"","orcid":"","institution":"Sokoine University of Agriculture College of Veterinary Medicine and Biomedical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Kashoma","suffix":""},{"id":421987160,"identity":"0e236534-aa4d-4775-9d19-276384dbbe74","order_by":4,"name":"Danny Donaghy","email":"","orcid":"","institution":"Massey University INR: Massey University School of Agriculture and Environment","correspondingAuthor":false,"prefix":"","firstName":"Danny","middleName":"","lastName":"Donaghy","suffix":""}],"badges":[],"createdAt":"2024-08-29 01:29:40","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-4993873/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4993873/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77365074,"identity":"d82595ba-9ab8-4ffc-9229-276041ee3fd2","added_by":"auto","created_at":"2025-02-20 15:47:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":864053,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Tanzania showing study regions: Southern Highland regions (Mbeya, Iringa and Njombe), Northern Highland regions (Arusha, Kilimanjaro and Tanga) and Morogoro. (\u003cstrong\u003eKey:\u003c/strong\u003e DC - District council, TC - Town council, MC - Municipal council and CC - City council).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/a161848fc4c5182e814c9b42.png"},{"id":77365079,"identity":"9382ec56-9644-4b6b-ae1e-731872725182","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14768,"visible":true,"origin":"","legend":"\u003cp\u003eGender of the head of the household of Tanzania smallholder dairy farms.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/f31e45c17380dce93d70b17c.png"},{"id":77365080,"identity":"6c0a4073-bb42-43d5-9fd0-feef8543e834","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15452,"visible":true,"origin":"","legend":"\u003cp\u003eComparison across six regions of Tanzania on family type in smallholder dairy farmers’ households.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/ee20e674333deb1257e9c10e.png"},{"id":77365083,"identity":"60ceaa6e-7c83-407d-bb0d-9fdfeb7f49a3","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18986,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of cattle per smallholder dairy farms across the study regions in Tanzania.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/0c0a5510ffd69206cadad8e6.png"},{"id":77365085,"identity":"ae55ab68-1b5c-46e0-b249-30a070d4d56f","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14583,"visible":true,"origin":"","legend":"\u003cp\u003eSource of first dairy animal across smallholder cattle farms/households in Tanzania.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/71d76923dbe37a1a7280722b.png"},{"id":77365086,"identity":"7b1b4645-3921-4f64-980a-26f89bc28690","added_by":"auto","created_at":"2025-02-20 15:47:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23976,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of ages of smallholder dairy cattle farmers across the study regions of Tanzania.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/6388710c0096d0210cc71752.png"},{"id":77365089,"identity":"e79c7f99-b279-47b1-a768-6cf04ea71f4b","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":16530,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum education level of smallholder dairy cattle farmers across study regions in Tanzania.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/5af37417ca0163efdc12df31.png"},{"id":77365090,"identity":"203c6f95-1fd8-4d87-aff0-2c19b5542188","added_by":"auto","created_at":"2025-02-20 16:03:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":18182,"visible":true,"origin":"","legend":"\u003cp\u003eDairy cattle management systems among the smallholder dairy farms across the study regions of Tanzania mainland.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/742fc76aeb6a1ee12bbc803b.png"},{"id":77365092,"identity":"58b50668-e460-4a7d-aab0-004826c4bd41","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2454112,"visible":true,"origin":"","legend":"\u003cp\u003eDairy cow tethered for grazing by a smallholder dairy farmer.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/67b51c6afcdb68f48159d781.png"},{"id":77365094,"identity":"933fc3bd-0e97-46e6-baae-cc6620b035b8","added_by":"auto","created_at":"2025-02-20 15:55:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1746331,"visible":true,"origin":"","legend":"\u003cp\u003eSmallholder dairy cattle farmers undertaking cut and carry farming: a) carrying the cut forages, b) spreading cut forages in the feeding trough.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/6260fbf0f1a26c58da215f11.png"},{"id":77365097,"identity":"4669dfa5-1c2e-4e98-b1e3-012a3c01d31a","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":22514,"visible":true,"origin":"","legend":"\u003cp\u003eDairy cattle feed sources for smallholder dairy farmers in different regions of Tanzania.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/beeef78b13b495521848c25d.png"},{"id":77365098,"identity":"6a394dbf-f394-4508-8fdc-916b46d1c404","added_by":"auto","created_at":"2025-02-20 15:55:02","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":31190,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral constraints for successful smallholder dairy cattle farming in Tanzania.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/e22c1fd48cb4505df40f9adc.png"},{"id":77365101,"identity":"ddc25358-f82c-4ab7-8405-dc82ae8216ce","added_by":"auto","created_at":"2025-02-20 15:47:01","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":14295,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral criteria expressed by smallholder dairy cattle farmers in Tanzania when selecting their preferred dairy cattle breed.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/ef60b9678567853e472ace60.png"},{"id":77365102,"identity":"d580e6cd-792b-4f00-b034-6dc26decb743","added_by":"auto","created_at":"2025-02-20 16:03:01","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":15842,"visible":true,"origin":"","legend":"\u003cp\u003eBreeding methods applied by smallholder dairy farmers across the study regions in Tanzania; the use of artificial insemination (AI), just natural breeding (bull only), or a combination (both).\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/14d79db6e39b6e32fe9b92d3.png"},{"id":77365105,"identity":"1b6d3133-b155-44de-a149-856199fe7991","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":26489,"visible":true,"origin":"","legend":"\u003cp\u003eConstraints to successful breeding in smallholder dairy cattle farming in Tanzania\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/b770b6922a4ca43fd69b1441.png"},{"id":77365072,"identity":"d781f3de-92f4-4186-a6c7-9981be166dbe","added_by":"auto","created_at":"2025-02-20 16:11:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6977568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/60202107-3831-4ba3-810b-f08048a3284f.pdf"},{"id":77365075,"identity":"8cbcbd6d-2272-4d33-ba2c-6ab19a3a5640","added_by":"auto","created_at":"2025-02-20 15:39:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3418420,"visible":true,"origin":"","legend":"","description":"","filename":"ListofTablesFarmersDemographicsandConstraints.docx","url":"https://assets-eu.researchsquare.com/files/rs-5795828/v1/9fc211fb05cdc68c482845c6.docx"}],"financialInterests":"","formattedTitle":"Understanding Smallholder Dairy Farming in Tanzania: A Cross-Sectional Survey of Farmer Demographics and Management Constraints","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFocused initiatives to improve the dairy cattle industry in Tanzania started in 1921 when the colonial government of the time introduced the first Holstein and Ayrshire cattle into Temeke, Dar es Salaam (Sumberg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The establishment of a few medium-to-large-scale settler dairy farms in the country's Northern and Southern highland regions followed this move. However, most of these farms were nationalised after independence, following the Arusha Declaration in 1967. This resulted in the dairy sector in the 1970s being dominated by governmental parastatal and state-owned farms, especially following the formation in 1975 of the Tanzanian Dairy Farming Company and Tanzania Dairies Ltd. The latter was responsible for processing and selling milk to consumers (Kurwijila et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Mdoe \u0026amp; Wiggins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). However, subsequent challenges, such as political interference with the milk price and the generally low productivity of the cattle, led to poor economic performance and the collapse of these governmental parastatal and state-owned farms in the early 1980s (Kurwijila et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Mdoe \u0026amp; Wiggins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter this collapse, efforts to bridge the gap between Tanzanian milk production and demand for dairy products within Tanzania began. These efforts were led by private, religious and non-governmental organisations, all of which promoted smallholder dairy cattle farming (Kurwijila \u0026amp; Boki, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Smallholder dairy cattle farming refers to a type of dairy farming typically practised on a small scale with a mean herd size of approximately 4 animals, ranging from 1 to 12 cattle, often involving unimproved genetics and rudimentary management by individual farmers or households (Alonso et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chagunda et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; McDermott et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nell et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Njombe et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Suleiman et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These efforts were supported, at the government level, by the Tanzania Livestock Policy, which emphasised the promotion of smallholder dairy cattle farming (Mdoe \u0026amp; Wiggins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInitially, most smallholder dairy farms were owned by high and mid-ranking civil servants, with cows being kept in their owner\u0026rsquo;s place of residence (\u0026ldquo;backyard production\u0026ldquo;), with milk production being a supplementary, rather than a primary income source (Mdoe \u0026amp; Wiggins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Swai \u0026amp; Karimuribo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, beginning in the 1990s, smallholder dairy cattle farming started experiencing rapid growth across Tanzania, with a change in the demographics of farmers, from producers for whom milk was a supplementary income, to producers for whom milk was the primary source of income (Limbu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The smallholder dairy sector thus currently provides significant income and employment across rural, peri-urban and urban areas of Tanzania, as well as being a valuable source of human nutrition in those areas (Gillah et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Limbu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; NBS \u0026amp; OCGS, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf the 33.9\u0026nbsp;million cattle in Tanzania, 33.8\u0026nbsp;million (99.6%) are kept by smallholder farmers, with only 142,000 (0.4%) in large-scale farms (i.e. farms that have more than 100 cattle) (Mbwambo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; NBS \u0026amp; OCGS, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Around 70% of the milk produced in Tanzania is from Indigenous/local Zebu cattle, with the remaining 30% from improved dairy cattle breeds (Brett, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For smallholders, improved dairy cattle are generally crosses of European dairy breeds (e.g. Friesian, Ayrshire, and Jersey) with local Zebu, especially the Tanzanian Shorthorn Zebu, but also Boran and Sahiwal. These improved dairy cattle are concentrated in the rural areas of Tanga, Arusha, Kilimanjaro and Manyara (Northern Highland regions), Mbeya, Iringa and Njombe (Southern Highland regions) as well as in the Morogoro, Kagera and Dar es Salaam regions (Swai \u0026amp; Karimuribo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInitiatives to improve the productivity of the Tanzanian dairy industry, particularly smallholder farms, are ongoing (Chawala et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These initiatives are a mixture of government-led programmes and external stakeholder-led programmes (with significant Tanzanian government support). They include long-running programmes such as the Dairy Development Program, and the Heifer Project International in Tanzania (HPI) Scheme (Msangya et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Community Action Research Program (Pasape, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), AgResults Tanzania Dairy Productivity Challenge Project (2019\u0026ndash;2024), and the newer countrywide program for the transformation of the livestock sector titled \u0026lsquo;Livestock Sector Transformation Plan (MoLF, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, despite the significant change in the population of smallholder dairy cattle farmers across Tanzania since the 1980s, there is limited information regarding the demographics of such farmers. Some localised surveys of smallholder dairy cattle farming have been undertaken, but these have generally been limited to one or two regions: Dar es Salaam (Kivaria et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb), Morogoro (Gillah et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gillah et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Tanga (Alonso et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Kilimanjaro and Arusha (Swai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Larger-scale surveys have been undertaken, but they did not report demographic data (Chawala et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or had a limited analysis of such data (Mwambene et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, most of these studies were conducted in urban and peri-urban areas. A better understanding of the demographics of smallholder dairy farmers (especially at the rural/regional level) is needed to help advisers and development agencies better understand who smallholder dairy farmers are, as well as their goals and challenges. Such understanding could be useful in targeting support for dairy farmers based on individual requirements (especially if structured at the regional level) rather than using a \u0026ldquo;one-size-fits-all\u0026rdquo; programme across Tanzania. Thus, as part of a larger study looking at the reproductive performance of cows on Tanzanian smallholder dairy farms, data on the demographic characteristics of smallholder dairy cattle farmers from thirteen districts across six different regions of Tanzania were collected. Alongside this, information on the constraints that the farmers perceived to be affecting their productivity was also collected. This paper aims to present this demographic and constraint data and to identify whether there are significant differences between regions in demographics and constraints reported by smallholder dairy farmers in six key dairying regions across Tanzania.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthical considerations and approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Ministry of Livestock and Fisheries through the Ethics Review Board of the Tanzania Livestock Research Institute (TALIRI) (reference number TLRI/RCC.21/007) of the United Republic of Tanzania. Permission letters were firstly provided by the office of the Regional Administrative Secretary from the six study regions, and then, from the Executive Directors of the respective District Council (DC), Town Council (TC), Municipal Council (MC) or City Council (CC) of the thirteen study districts of Tanzania mainland. A local veterinarian or livestock officer first introduced the interviewer (the first author did all interviews) to the farmer/respondent (usually the family head). The interviewer explained the reason for the visit. Thereafter, each respondent/farmer was given a written informed consent form for them to sign before participating in the questionnaire interview. If the interviewee was unable to read and write, another family member was called to approve and sign on their behalf. Results were anonymized and personal data were kept confidential. The specific consent of the relevant participants was obtained for any photographs used to illustrate this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudy area and study farm selection\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformation from smallholder dairy cattle farmers was gathered using a cross-sectional study design, from May 2022 to February 2023. Six regions of the Tanzania mainland were purposely selected, principally based on the proportion of improved dairy cattle. Three of these regions were in the Northern Highlands (Arusha, Kilimanjaro and Tanga), two in the Southern Highlands (Mbeya and Njombe) and one (Morogoro) in the Eastern zone. Within each region, district(s) were selected using a convenience sampling process with the help of local veterinarians/livestock officers (Figure 1). Within each district, convenience sampling was employed to identify study villages and the first study farm in each village (whose suitability was decided by the local veterinarian/livestock officer and interviewer). Snowball sampling was then employed to select other study farms in a particular village. Subjects could nominate as many further subjects as they wished with non-discriminative sampling being used until \u0026gt;50 respondents had been identified per region. No sample size calculations were undertaken; the number of farms visited was based on the number of farms that the authors believed could be visited within a district for over 2 weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Figure 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eField data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured and pre-tested questionnaire was used to collect research data. Pre-testing was done in the Morogoro municipality capturing twenty-four smallholder farmers (who were not included in the final questionnaire) and eleven experts (veterinarians/livestock officers and researchers). The questionnaire was formulated so that all the questions were closed, and responses were entered into KoboToolbox (Cambridge USA) for subsequent data collation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitial information collected from farmers included age, gender, dairy farming experience, involvement (full or part-time), education level, dependence on dairy farming (plus other household/farm income-generating activities) and decision-making process on the farm. Respondents were also asked about farm-related issues, such as the source of their first dairy animals, herd size, herd composition as well as breeding practices and preferences. The last section asked the respondents for their opinions regarding the key constraints that affected their farm, capturing information related to cattle health and reproduction, availability of veterinary and breeding services, availability of land and feed, and access to markets for their product.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData management and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the questionnaire were downloaded from KoboToolbox to Excel spreadsheets (Microsoft, Seattle, USA) before analysis using SPSS version 25 (IBM, Seattle, USA). Results are tabulated and presented as overall results and by region. Where the effect of region was thought to be of interest, a logistic regression was used to analyse the effect of region, with the response to a question being the dependent variable and region the only predictor variable. For most responses, multinomial logistic regression was used to evaluate the effect of region on the key outcome. If responses were clearly ordered, ordinal logistic regression was used, provided the proportional odds assumption was met. If this was not met, then a multinomial regression was used. Categories were merged for all analyses where totals were \u0026lt;10. For all analyses, except where stated, Tanga was used as the reference region, and the category with the highest frequency in the outcome was set as the reference category. Data from Arusha were included in the descriptive data but excluded from the analyses due to the small number of respondents.\u0026nbsp;\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"3. Results","content":"\u003cp\u003eAt least 50 smallholder dairy cattle farmers were interviewed per region, except Arusha where only 16 farmers were interviewed. Fewer farmers in Arusha participated following the unavailability of local veterinarians and livestock officers, who were participating in the national livestock identification program, resulting in reluctance among farmers to participate. Overall, across the six regions, 301 farmers were recruited for the survey (Table 1).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 1)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.1. Smallholder dairy farmers’ household, family, and farm demographics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOf the 301 households that participated in this study, 224 (74%) were headed by a father and 69 (23%) by a mother (Table 2). For the analysis of the effect of region, two categories were created (father and mother) with respondents who were recorded as ‘other’ combined with father or mother depending on their gender (male or female). There were differences between regions in who was the head of the household (Figure 2), with households in Njombe having much higher odds of having a female head of the household than households in Tanga (odds ratio (OR): 5.2, 95%CI: 2.8-13.1).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 2)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 2)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMost households (236/301; 78%) were monogamous, with the lowest proportion recorded in Njombe (41/54; 76%), and the highest in Arusha (14/16; 88%) (Figure 3). Additionally, in most households (248/301; 82%), the entire family was involved in the decision-making process, not just the head of the household.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 3)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTotal herd size ranged from 1 to 35 cattle, with the ‘3-4’ category being the mode herd size (90/301; 30%) (Table 2 and Figure 4). For the analysis of the effect of region, five categories of herd size were used: 1-2, 3-4, 5-6, 7-8 and ≥9. Ordinal logistic regression identified differences across regions in the proportion of farms in one of the higher herd size categories. Compared to Tanga, the odds of farms being in the higher herd size category were notably less in Kilimanjaro (OR: 0.3, 95%CI: 0.1-0.5), Mbeya (OR: 0.2, 95%CI: 0.08-0.3) and Njombe (OR: 0.1, 95%CI: 0.07-0.3).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 4)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOf the 301 farms, 19 had no adult cows, 112 had no heifers, 98 had no calves and 249 farms had no breeding bulls. No effect of region on the proportion of farms with milking cows was found but compared to Tanga, farms in Njombe were less likely to have heifers (OR: 0.4, 95%CI: 0.2-0.8), and farms in Morogoro more likely to have bulls (OR: 2.6, 95%CI: 1.02-6.6). The proportion of farms with calves in Tanga was the highest of any region, with farms in Tanga having higher odds of having calves than farms in Njombe (OR: 0.1, 95%CI: 0.1-0.4), Mbeya (OR: 0.1, 95%CI: 0.1-0.4), Kilimanjaro (OR: 0.2, 95%CI: 0.1-0.6) and Morogoro (OR: 0.3, 95%CI: 0.1-1).\u0026nbsp;\u003c/p\u003e\u003cp\u003eMost respondents (201; 67%) reported having fewer than three people who took care of the dairy cattle on their farm (Table 2). As with herd size, there was an effect of region such that, with reference to Tanga, the odds of having more than two people actively participating on the farm was lower than in all other regions: Kilimanjaro (OR: 0.4, 95%CI: 0.2-0.8), Mbeya (OR: 0.3, 95%CI: 0.2-0.8), Morogoro (OR: 0.3, 95%CI: 0.1-0.6) and Njombe (OR: 0.3, 95%CI: 0.1-0.6).\u0026nbsp;\u003c/p\u003e\u003cp\u003eCash purchase was the dominant (200; 66%) source of obtaining the first dairy cattle beast (Table 2). Regionally, this was true for all regions except for Mbeya, where a gift from a relative or friend was the most common source (Figure 5). For analysis of regional differences, data were merged into three groups: cash, gift and other (merging non-governmental organisation (NGO), bank and home-bred). Arusha was excluded from this analysis as there were no farms in the ‘other’ category. Relative to cash purchase, three regions had different odds of a gift being the source of their first cattle beast than respondents in Tanga: the odds were higher in Mbeya (OR: 2.7, 95%CI: 1.1-6.4) and were lower in Morogoro (OR: 0.3, 95%CI: 0.1-0.9) and Njombe (OR: 0.2, 95%CI: 0.05-0.8). For the ‘other’ category, the odds were lower in Kilimanjaro (OR: 0.04, 95%CI: 0.01-0.3), Mbeya (OR: 0.2, 95%CI: 0.1-0.8) and Morogoro (OR: 0.04, 95%CI: 0.01-0.3) relative to cash purchase than in Tanga.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 5)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.2. Farmers/respondents and assistants/worker's demographic characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMost respondents were over 40 years of age (238/301; 79%), with the majority (155/301; 52%), being between 41 and 60 years. This was consistent across all regions (Figure 6 and Table 3). Using ordinal logistic regression with four age categories (i.e. ≤20, 21-40, 41-60 and ≥61), the odds of a farmer being in a higher age category were lower in Mbeya (OR: 0.4, 95%CI: 0.2-0.7) compared to Tanga.\u0026nbsp;\u003c/p\u003e\u003cp\u003eExperience in dairy farming was classified into 10-year blocks (≤10, 11-21, etc.). The highest proportion of respondents (118/301; 39%) had ≤10 years of experience. There was little difference between regions, except that ordinal logistic regression showed that the odds of being in a higher experience category were lower in Morogoro (OR: 0.49, 95%CI: 0.24-0.97) compared with Tanga. \u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 6)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMost respondents (231/301; 77%) were involved full-time in dairy cattle farming (Table 3). The proportion was highest in Tanga (46/53; 87%) and lowest in Morogoro (41/57; 58%), with the odds of being a part-time farmer being higher in Morogoro (OR: 4.8, 95%CI: 1.8-12.4) than in Tanga.\u003c/p\u003e\u003cp\u003eThe most common level of education in respondents was primary level (7-14 years) (166/301; 55%) (Table 3 and Figure 7). For analysis of the effect of region, respondents who had not had a formal education were excluded. Compared to respondents from Tanga, the proportion in each education category was similar across all regions except for Morogoro, where respondents were more likely to have had a higher category of education (OR: 6.5, 95%CI: 3-13.9).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 3)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 7)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFor the farm workers/assistants, the majority (157/203; 77%) were aged ≤30 years and had ≤5 years of experience (168/203: 83%), making them generally younger and less experienced than the main respondents (Table 3). Analysis of assistant demographics excluded data from Arusha as there were only 6 responses from that region. Compared to workers in Tanga, assistants from Mbeya (OR: 0.3, 95%CI: 0.12-0.77), Morogoro (OR: 0.2, 95%CI: 0.093-0.45), and Njombe (OR: 0.32, 95%CI: 0.09-0.56) all had lower odds of being in a higher age category. For experience, data were merged into four categories: \u0026lt;1 year, 1 to 5 years, 6 to years and ≥ 11 years. This analysis showed that assistants from Morogoro had lower odds of being in a higher experience category (OR: 0.18, 95%CI: 0.08-0.44) compared to those in Tanga, as did assistants in Kilimanjaro (OR: 0.37, 95%CI: 0.16-0.88). Most assistants were full-time workers (176/203; 87%), and most (182/203; 90%) had had only primary education. The education level of farm workers was similar across the study regions.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.3. Smallholder dairy farmers’ sources of household income\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAlthough 77% of respondents reported full-time involvement with dairy farming, almost all households reported having other sources of income (282/301; 94%) (Table 4). Crop farming was the most common alternative, with 180/282 (64%) gaining at least some income from it. Conversely, only 37% (104/282) and 29% (81/282) of respondents were involved in employment or business, respectively. For analysis of regional differences in other income sources, two categories were created i.e., ‘Yes’ (representing major, moderate and minor) and ‘Not at all’. Excluding Mbeya and Arusha where there are no respondents for the ‘Not at all’ category, the odds for a farmer participating in crop farming were lower in Njombe (OR: 0.1, 95%CI: 0.01-0.9) than in Tanga. Further, the odds of a farmer relying on employment as the source of income were highest in Morogoro (OR: 5.2, 95%CI: 2.2-12.4) and lowest in Njombe (OR: 0.1, 95%CI: 0.04-0.4). Lastly, Mbeya had lower odds (OR: 0.2, 95%CI: 0.07-0.6) for its farmers depending on business as an income source compared to Tanga.\u0026nbsp;\u003c/p\u003e\u003cp\u003eFurthermore, farmers’ responses (major, moderate and minor, excluding ‘not at all’) for their involvement in other income-generation activities (i.e., crop farming, employment and business) apart from dairying, were further evaluated to determine their contribution to the household income. For that, responses were given score values i.e., 3=major, 2=moderate and 1=minor; where the total score was ≥6, then dairying was defined as not being the major source of income and defined as being the major source of income when the total score was ≤5. Based on this score, dairying was the major income source for 238/282 (84%) households. Logistic regression was used to evaluate the regional differences (excluding the Arusha region due to fewer respondents) of household dependence on other income sources. Concerning Tanga, only farmers from the Njombe had higher odds (OR: 4.9, 95%CI: 1.7-13.9) of non-dairying income being their major source of income.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.4. Smallholder farmers’ dairy cattle management system and feed sources\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAcross the 301 farms, 225 (75%) of respondents kept their dairy cattle under a zero-grazing/intensive system, in which forages were harvested daily and brought to the cattle in their shelter (Figures 8, 9, 10a, 10b and Table 5). This system dominated across all regions except Mbeya, where tethering at pasture (Figures 10a and 10b) was predominant (37/55; 68%). Across the different regions, the odds of semi-intensive farming (compared to intensive/zero grazing) were higher in Mbeya\u0026nbsp;(OR: 12, 95%CI: 3.03-47.5) and Morogoro\u0026nbsp;(OR: 6, 95%CI: 1.9-19.1)\u0026nbsp;than in Tanga.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 8)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 9)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 10a and 10b)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSources of feed are shown in Table 5 and Figure 11. Natural pastures (i.e. naturally occurring grasses, legumes, and other species) ranked as the most used source of feed, with 294/301 (98%) respondents using at least some natural pasture, and 241 (80%) using it as the major source of feed. Most respondents gave at least some supplementary concentrate feeds to their cattle; only 10/301(3%) stated that they did not do so at all.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 4)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 5)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSome feeds were used to a very limited extent including conserved forage, bought fodder and actual grazing; all of which were used by \u0026lt;25% of respondents. Across all regions, not at all was the most common response for those feeds (Table 5). Other more commonly used feed sources had regional variations. Cut forage was used by 32% of respondents. Of those, 54/97 (55%) were in Njombe, with most of those (51) reporting that it was a major source of feed (a category only used in Njombe). Fodder plots were used by 55% of respondents, but this varied appreciably by region, with respondents in Njombe and Mbeya having higher odds of reporting that they used fodder plots (at any level) than respondents in Tanga (OR: 3.8, 95%CI: 1.7–8.7, and OR: 8.3, 95%CI: 3.2–21.7, respectively), while respondents in Morogoro were less likely to report that they used fodder plots than those in Tanga (OR: 0.3, 95%CI: 0.1–0.7). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003eMost respondents used crop residuals (only 4% did not) but the level of use varied markedly by region. Compared to Tanga, the odds of a respondent reporting moderate/major use of crop residuals (as opposed to minor/not at all) were much higher in Njombe and Mbeya (OR: 883, 95%CI: 88.9-8770 and OR: 25, 95%CI: 6.9-90.3, respectively).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 11)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.5. Smallholder dairy farming constraints\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFarmers were asked to rank the important farming constraints (Table 6 and Figure 12), with high costs of inputs ranked as very highly significant by 229/301 (76%) respondents and by most farmers in all six study regions. Lack of enough land (150/301; 50%) was the second most important constraint, while unavailability of feed (24/301; 41%) was the third.\u0026nbsp;\u003c/p\u003e\u003cp\u003eFor\u0026nbsp;the assessment of regional variations, three ordinal categories were created i.e., High (encompassing ‘very highly significant’ and ‘important’), Moderate and Low (encompassing little importance and minor) and not at all (data from Arusha were excluded from this analysis because of the absence of data in one or more categories).\u0026nbsp;Insufficient land was reported to be an important constraint by farmers in Kilimanjaro (OR: 3.2, 95%CI: 1.5–6.8), Morogoro (3.1, 95%CI: 1.4–7) and Njombe (2.1; 95%CI 1-4.5) than in Tanga. Availability of feed was regarded as of greater importance by farmers from Kilimanjaro (OR: 11.7, 95%CI: 4.6–29.8), Mbeya (OR: 3.4, 95%CI: 1.6–7.3), Morogoro (OR: 3.6, 95%CI: 1.6–7.8) and Njombe (OR: 2.7, 95%CI: 5.9–1.3) than those from Tanga.\u0026nbsp;\u003c/p\u003e\u003cp\u003eLack of money to buy inputs\u0026nbsp;was regarded as of greater importance in both Mbeya and Njombe (OR 7.32, 95%CI: 3.2–16.6 and 3, 95%CI: 1.4–6.2, respectively) than in Tanga, while the unpredictability of the milk market was regarded as of greater importance in Njombe (OR: 11.4, 95%CI: 0.2–0.7), Kilimanjaro (OR: 4.8, 95%CI: 2.2–10.4) and Mbeya (OR: 2.3, 95%CI: 1–5.2) than in Tanga. Concerning constraints for successful breeding, farmers in Njombe regarded the lack of a breeding service as more important than farmers in Tanga (OR: 9.5, 95%CI: 4.1–22.2).\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe importance of disease as a constraint was analysed using a multinomial regression, as the proportional odds assumption was not met (P\u0026lt;0.001). The odds of being in the low category compared to the higher category were consistent across regions. For the Moderate category, farmers in Kilimanjaro and Morogoro had lower odds of being in the Moderate rather than the High category when compared to farmers in Tanga (OR: 0.2, 95%CI: 0.1–0.1 and OR: 0.2, 95%CI: 0.1–0.7, respectively).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 6)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 12)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.6. Dairy breed selection criteria and breeding practice\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePreference for one or more breeds (Figure 13) was expressed by 160 (53%) respondents, whereas 59 (20%) had no specific preference (Table 7). Farmers in Njombe were more likely to report having a breed preference than farmers in Tanga (OR: 21.5, 95%CI: 4.7 - 97.6). Of those who expressed a breed preference, the focus was milk production (146/160: 91%), followed by easy handling (108/160: 68%), large body size (100/160: 63%) and ease of getting pregnant (37/160: 60%).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 13)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBreeding methods are summarised in Table 8 and Figure 14. In general, the most frequently applied breeding method involved a mix of natural service and artificial insemination (AI) (138/301; 46%). At the regional level, AI alone was the most common method in Arusha (10/16; 63%), Kilimanjaro (34/66; 52%) and Tanga (29/53; 55%), while most farmers used a mix of AI and natural service in Morogoro (42/57; 74%) and Njombe (33/64; 61%), and natural service only predominated in Mbeya (53/55; 96%). Relative to Tanga, farmers were less likely to report using AI alone in Morogoro (OR: 0.2, 95%CI: 0.1-0.5) and Njombe (OR: 0.4, 95%CI: 0.2-0.9) (this analysis excluded Mbeya as no farmers in that region reported using AI alone).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 14)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOf the 200 farmers who used natural service on some occasions, 180 answered the question about where they sourced bulls from. \u0026nbsp;Overall, they largely (113/180; 63%) opted to hire bulls from nearby farms, with most of the rest using a combination of their own and their neighbours’ bulls (46/180, 26%). Only 17 (9%) reported exclusively using their bulls.\u003c/p\u003e\u003cp\u003eOf the 240 farmers who used AI for at least some inseminations, 221 answered questions about AI use. \u0026nbsp;Across all regions, most farmers reported receiving the service regularly (131/221; 59%) and most (179/221; 80%) reported that waiting time from informing the AI technician to the actual insemination ranged between seven and nine hours.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 7)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 8)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn response to questions about the constraints around successful breeding (Figure 15 and Table 9), almost half of the respondents, 142/301(47%), indicated that the high cost of breeding was a major constraint, with 96% (289/301) identifying high breeding costs as being at least a minor constraint. This is a higher proportion than the effects of poor oestrus detection (89%, 269/301), cows not displaying oestrus (79%, 234/301) and unavailability of AI services (51%, 154/301). Constraints around breeding varied markedly across regions. \u0026nbsp; Except for high breeding cost, compared to farmers in Tanga, farmers in Njombe were more likely to report that all the constraints listed in Table 9 were major/moderate constraints on their farm. The relevant OR were 43.3 (95%CI: 11.7–160) for unavailability of AI service, 4.4 (95%CI: 1.9–10.4) for unavailability of breeding bull, 5.6 (95%CI: 2.4–13.1) for bull being located at a distance, 6.5 (95%CI: 2.7–15.4) for poor oestrus detection, and 12.9 (95%CI: 4.5–16.2) for cows not showing heat. For Mbeya, farmers had higher odds (compared to Tanga) of reporting the unavailability of an AI service (OR: 44.4, 95%CI: 12–82.6), poor oestrus detection (OR 3; 95%CI: 1.3–6.9) and cows not showing heat (OR 6.4; 95%CI: 2.2–18.6) as major/moderate constraints. Despite having similar percentages of farmers using mixed breeding (see Table 7), farmers in Kilimanjaro were less likely to report bull availability and bull distance as major/moderate constraints than farmers in Tanga (OR 0.12; 95%CI: 0.03–0.6, and 0.3; 95%CI: 0.1–1, respectively), while farmers in Morogoro were more likely than farmers in Tanga to report that heat detection was a problem (OR 3.2; 95%CI: 1.4–7.5).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Figure 15)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Insert Table 9)\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Smallholder dairy cattle household, family, and farm demographics\u003c/h2\u003e \u003cp\u003eTo achieve sustainable development of smallholder dairy cattle farming in Tanzania, a proper understanding of the key participants involved is essential. This includes a thorough knowledge of the features of the farmers and their farms, locality, and farming practices.\u003c/p\u003e \u003cp\u003eIn this survey, it was found that most farms/households (74%) were headed by men. This is consistent with other recent surveys, such as that of Kashoma and Ngou (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who reported, based on a survey of farms across 17 districts, that 68% of households were headed by men. However, both of these percentages are much lower than previous reports, e.g. the 90% reported by Kivaria et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb), in the Dar es Salaam region and the 93% reported by Swai et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in Kilimanjaro and Arusha. These findings indicate that there has been an increase in the number of women involved in smallholder dairy farming. One positive reason for this increase is the focus of NGOs on the empowerment of women-led households. This was most apparent in Njombe, where almost equal numbers of men and women were household heads, consistent with the report by Msangya et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), that 53% of the households in Njombe supported by the Heifer in Project International Trust were led by women. The other, less positive, reason for the increased involvement of women in smallholder dairying is likely to be the rural-urban migration of men searching for better wages, therefore, by default, leaving women to manage the household and its livestock (Ojango et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSmallholder dairy farming in Tanzania is generally collaborative, with household heads including other household members (spouse and/or children) in decisions related to farming. In most regions, a herd size of 3 to 4 animals was the most common (Swai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, in both Tanga and Morogoro, the most common herd size was \u0026gt;\u0026thinsp;4 animals. The somewhat larger herd size reported in Tanga and Morogoro may reflect, at least in part, the proximity of these regions to Dar es Salaam, which is the biggest milk market in the country. However, the rapid increase in the population in the Dar es Salaam region in the recent past (Lupala, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) has not been reflected in recent increases in the size of farms in either Morogoro (Nkya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) or Tanga (Zylstra et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), so market size is probably not the sole factor. The similarity of farm size across most regions is also reflected in the number of people/farms actively involved in farming, with 1\u0026ndash;2 people actively involved in the majority (67%) of farms. Tanga had both larger herds and more active people/farms, with 3\u0026ndash;4 people per farm being the most common category, probably reflecting increased involvement of family members as previously reported (Swai et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross the six study regions, cash purchase was the principal (66%) source of a household\u0026rsquo;s first dairy cattle beast, consistent with the 72% reported by Swai et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in Kilimanjaro/Arusha. The only region where cash purchase was not the principal source was Mbeya, where a gift from a family member or friend was as common as cash purchase (49 vs 45% of respondents, respectively). This process, known as \u0026lsquo;kufufya\u0026rsquo; by Nyakyusa speakers, involves an individual giving a heifer or cow (which could be pregnant or non-pregnant) to a relative or friend. When that animal calves, the original owner takes back the milking cow if the newborn calf is female, or if it is a bull calf, the new owner keeps the cow (until it produces a female calf) but shares the milk it produces with the original owner. This process has been popularised using the Kiswahili slogan \u0026ldquo;Kopa Ng\u0026rsquo;ombe lipa Ng\u0026rsquo;ombe\u0026rdquo; (meaning: borrow a cow, pay a cow). Similar techniques have been used by NGOs (Kayunze et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Msangya et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with the recipient of a cow raising female offspring before passing on the younger animals to a new family/owner (De Vries (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); \u0026ldquo;Passing on the Gift\u0026rdquo;. Likewise, Kopa Ng\u0026rsquo;ombe lipa Ng\u0026rsquo;ombe, Passing on the Gift has been a regionally specific process with only Tanga and Mbeya reporting that a significant proportion of respondents got their first cow from an NGO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Farmers/respondents and attendants/workers' demographic characteristics\u003c/h2\u003e \u003cp\u003eMost respondents (51%) in this survey were aged between 41 and 60 years, with 39% having\u0026thinsp;\u0026lt;\u0026thinsp;10 years of farming experience. According to Swai et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), only 11% of their respondents had\u0026thinsp;\u0026lt;\u0026thinsp;10 years of experience. The higher figures in this survey, even in the regions studied by Swai et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), strongly suggest that there have been a significant number of new entrants into dairy farming over the last 10 years. This appears to be particularly the case in Njombe and Morogoro as both regions had\u0026thinsp;\u0026ge;\u0026thinsp;50% of respondents with \u0026lt;\u0026thinsp;10 years\u0026rsquo; experience.\u003c/p\u003e \u003cp\u003eAs expected, farm assistants were younger (77% were \u0026lt;\u0026thinsp;30 years of age) and less experienced (83% had\u0026thinsp;\u0026lt;\u0026thinsp;5 years of experience) than farm owners. For both groups, primary education was the highest education level (55% for farm owners, 66% for assistants) and dairy farming was a full-time occupation (77 vs 87%, respectively). Having a majority of smallholder farmers and assistants with at least primary education signifies that they are more likely to adopt and apply modern farming techniques to improve their productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Smallholder dairy cattle households\u0026rsquo; sources of income\u003c/h2\u003e \u003cp\u003eAcross all respondents, very few (6%) relied solely on dairy cattle for their income. However, even for those who relied on other household income sources (i.e., employment, crop farming and business), the majority (84%) of them still depended on smallholder dairying as their major income source (except the Njombe region). According to Swai et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), dairying was the major source of income for only 32% of respondents in Kilimanjaro and Arusha regions, while Kashoma and Ngou (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported 56.4%. This shows an increase in the number of households relying on smallholder dairying year-by-year. This trend can be expected to result in a stable household economy, providing sustainable income and improved nutrition. Furthermore, it indicates significant growth in the sector, leading to improved food security, economic development, and increased employment opportunities for many Tanzanians. Therefore, this study suggests that the government and other stakeholders support smallholder dairy cattle farmers in achieving increased and sustainable productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Smallholder dairy cattle grazing system and feed sources.\u003c/h2\u003e \u003cp\u003eThere were marked differences across regions in the source of non-dairy income. In Morogoro, 64% of respondents had moderate/major income from employment. This may be related to the proximity of institutions like Sokoine University and access to government-related jobs. Gillah et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported that 19% of farmers in Morogoro were government employees and 15% were retired officers. For the remaining regions, crop farming was the principal alternative source of income. In most regions it was listed as a moderate/minor source of income by respondents (64\u0026ndash;84% depending on region); however, in Njombe it was a major source of income for 52% of respondents. This indicates that smallholder dairy cattle farming is expanding beyond urban and peri-urban areas, traditionally managed by government officers, to rural areas, where crop farmers are increasingly adopting these practices. The involvement of crop farmers in smallholder dairy cattle farming was also recorded in the previous studies (Kashoma \u0026amp; Ngou, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Swai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZero grazing was the predominant system in all study regions (67 to 99% of respondents) except for Mbeya (only 16% of respondents). Zero grazing or \u0026lsquo;cut-and-carry\u0026rsquo; uses natural pastures, with fresh forages being obtained from roadsides, highways, and non-cultivated areas such as communal lands, uncultivated fields, communal swamp areas and communal grazing areas (Swai \u0026amp; Karimuribo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Urio, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Tethering was the predominant system in Mbeya (68% of farmers) which may reflect the longer periods in the Mbeya region (Busokelo district in particular) when grazing is available since the area is a cooler highland area (770 to 2865 metres above sea level) which receives 1500 to 2700 mm rainfall per year (Makala, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nyunza \u0026amp; Mwakaje, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The current survey evaluated the use of eight on-farm feed sources. The survey identified five types of feed sources: 1) commonly used feed sources with limited variability across regions (i.e. concentrates, and natural pastures); 2) commonly used feed sources with large differences in level of use by region (crop residuals); 3) rarely used feed sources with \u0026gt;\u0026thinsp;75% farms reporting no use and \u0026gt;\u0026thinsp;90% of farms no more than minor use (i.e. grazing, bought fodder and conserved feeds); 4) feed sources that are rarely used in most regions, but commonly used in one or more regions (e.g. cut fodder from outside); and 5) feed sources that vary markedly across and within regions (e.g. fodder plots in farm). These regional differences in feeding practices need to be considered when developing support programs. Furthermore, these results also show that it would be too simplistic to assume that all farms within a region use the same feed sources at the same time and in the same way, such that, even at the regional level, feed supply will require multifactorial solutions to achieve success.\u003c/p\u003e \u003cp\u003e The use of crop residuals as livestock feed could have many benefits: cost savings (crop residuals are more affordable), availability (mostly are locally available) and greater utilization of agricultural by-products. Such practice can help to guarantee sustainable agricultural practices and more efficient resource management within the community of smallholder dairy cattle farming in countries like Tanzania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Dairy breed selection criteria and breeding practice\u003c/h2\u003e \u003cp\u003eOnly 53% of farmers across the six regions had a breed preference. Surprisingly, the proportion of farmers from Njombe who had a preference was much higher than this average (at 96%). The reason for this difference is unclear. Despite the low proportion of respondents who had a specific breed preference, most respondents (91%) expressed their opinions on the cattle attributes they desired. Unsurprisingly, high milk production is the primary reason for their choices, as highlighted in the previous surveys done in Tanzania (Chawala et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gillah et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Swai \u0026amp; Karimuribo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This preference (and the preference for large body size especially in Morogoro, Tanga and Mbeya) probably reflects the marketing of \u0026lsquo;high yielding\u0026rsquo; cows to smallholders rather than the direct experience of the respondents of such cows playing an important role in improving farm incomes (Chung, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe breeding method was very dependent on the region, reflecting the generally poor AI infrastructure across much of Tanzania and the centralisation of the AI service at the National Artificial Insemination Centre (NAIC) in Arusha. Regions that predominantly used AI were generally close to the NAIC, with regions further away using a combination of natural insemination and AI. Nevertheless, across the six regions, 80% of farmers used at least some AI. For five of the six regions, \u0026gt;\u0026thinsp;90% of farmers reported using some AI, with only Mbeya having the majority of farmers using bulls only (96%). Centralization of the AI centre in Tanzania could be the reason for the irregular availability of semen and other consumables like liquid nitrogen to farmers located away from NAIC. Similarly, as reported previously by Mwanga et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in their study on factors influencing breeding decisions by smallholder dairy farmers in Sub-Saharan African countries. Also, irregularities in the supply of IA consumables by Kashoma and Ngou (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In another report (Kanuya et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), over 60% of farmers in Morogoro and Tanga reported experiencing challenges related to AI delivery systems. Though there are some imported doses of semen by private organizations (Katjiuongua, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ogutu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), plans are underway to improve the available semen production by involving public-private partnerships and development partners (Ogutu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Further, Kusiluka et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) reported a lack of breeding services for smallholder dairy cattle farmers in the eastern zone of Tanzania.\u003c/p\u003e \u003cp\u003eThe cost of AI varies with distance from the NAIC. The proximity to the AI centre means that AI services can be offered to farmers at a relatively low cost (i.e. between 15,000 and 25,000 TZs per insemination (~\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e6\u0026ndash;10) for first insemination). Nevertheless, irrespective of distance, AI was seen by many respondents as being expensive. The lack of a good AI infrastructure in Tanzania probably limits productivity on dairy farms, since AI confers significant advantages over natural mating in terms of genetic gain and disease control (Parkinson \u0026amp; Morrell, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Increased use of AI could be greatly beneficial for smallholder productivity, reducing the risk of disease from the use of untested local bulls and improving production efficiency and longevity, especially if the focus is not just on increasing milk production. Furthermore, to sustain productivity, emphasis must be placed on improving the management and nutrition of the animals. This is because inadequate nutrition and management of animals may result in reduced milk yields, increased health disorders and most importantly, impaired reproductive ability (Van Saun, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The high cost and unreliability of AI service were among the constraints highlighted in the previous studies from Tanzania (Kanuya et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and in sub-Saharan African countries (Mwanga et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Smallholder dairy cattle farming constraints\u003c/h2\u003e \u003cp\u003eTwo areas of constraints were examined: general constraints and specific breeding-related constraints. High costs of inputs were identified as the leading constraint in the general constraints section of the survey, with 76% of all farmers reporting it as a highly significant constraint (range: 65\u0026ndash;95% by region). In general, high input costs seemed to be perceived as a more important constraint than high breeding costs alone. In all regions, the proportion of farmers who reported high input costs as a highly significant constraint was higher than the proportion who reported high breeding costs as a major constraint. High cost of inputs was also reported previously: concentrates and veterinary drugs (Kivaria et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb) in Dar es Salaam and northern Tanzania (Swai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, in all regions, the most common response about high input costs was that it was a highly significant constraint, whereas, for breeding costs, the most common response varied between major and moderate, depending on the region. Insufficient land and feed were also seen as highly significant/important constraints by most respondents, except in Tanga, where most respondents (51% and 59%, respectively) thought that they were of minor to moderate importance. This regional difference may reflect the high number of swamp areas and abandoned sisal plantations in the Tanga region areas from which farmers can reliably obtain forage.\u003c/p\u003e \u003cp\u003eThe cost of breeding was seen as a major constraint by a high proportion of respondents in all regions (\u0026gt;\u0026thinsp;80%), with no evidence that being near the NAIC made that cost less of a constraint. This was also highlighted previously that farmers are unable to pay for the AI service due to high costs and irregularities in the AI delivery system (Kashoma \u0026amp; Ngou, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Irregularities in the AI delivery system were also reported to be a challenge for smallholder dairying improvement in Rwanda (Rugwiro et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, distance from the NAIC did affect whether the lack of an AI service was an important constraint, with the regions close to Arusha reporting no or minor concerns and the regions far from Arusha reporting major/moderate concerns. Indeed, concerns about reproductive constraints were particularly prominent in Njombe, with all constraints (other than cost) being ranked as more important in Njombe than in Tanga. The pattern for Mbeya was similar but the differences were less marked. This probably reflects the different patterns of breeding in the two regions \u0026ndash; natural service only predominates in Mbeya, while mixed and AI only predominate in Njombe. More reliance on AI leads to more concerns \u0026ndash; again highlighting the importance of improving the AI infrastructure in dairying regions remote from Arusha.\u003c/p\u003e \u003cp\u003e The regional differences in the relative importance of constraints again highlight the importance of developing regional solutions to improve smallholder dairy farm productivity, while the variability in the relative importance of constraints across farms within a region highlights the importance of understanding what is driving those differences at the farm level. Previous studies on smallholder dairying constraints in Tanzania include inadequate, seasonal and irregular forage availability in Arusha, Kilimanjaro (Swai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Dar es Salaam (Kivaria et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb). Also, the high cost and poor conception/pregnancy rates achieved by AI (Kanuya et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was considered by farmers from Tanga and Morogoro to be a significant disincentive to breed by AI.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe primary objective of this study was to conduct a large-scale survey to understand the unique characteristics of smallholder dairy cattle farmers in Tanzania, and to identify regional differences that might impact the development of the dairy farming industry. The findings of this study revealed significant regional differences in demographic factors such as gender, age and farming experience, reflecting the structure of the dairy industry and the availability of alternative income streams in different regions. These regional differences underscore the importance of tailoring interventions to farmers' specific needs and preferences in each locality rather than assuming a uniform approach will be effective.\u003c/p\u003e \u003cp\u003eThis study also highlighted some areas that require attention within the specific agro-economic context of different regions in Tanzania. Addressing these issues will form the foundation for developing regionally tailored and sustainable solutions. A \u0026lsquo;one size fits all\u0026rsquo; approach risks undermining the success of herd-improvement programs or other initiatives by failing to consider the needs of farmers in different regions. The sustainable development of Tanzania\u0026rsquo;s smallholder dairy farming industry requires targeted and flexible strategies that consider these disparities. This includes a thorough understanding of the farmer's characteristics, preferences and constraints before implementing any programs. Such considerations will enhance the likelihood of success for initiatives introduced by the government, NGOs or other stakeholders. Lastly, this study serves as the baseline for future research and action. Further qualitative research is recommended to explore the underlying reasons behind these regional differences, which were beyond the scope of this study, to refine strategies further and ensure the long-term success of the interventions aimed at improving the dairy industry in Tanzania.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eLtd \u0026ndash; Limited\u003c/li\u003e\n \u003cli\u003eNBS - National Bureau of Statistics\u003c/li\u003e\n \u003cli\u003eOCGS - Office of the Chief Government Statistician\u003c/li\u003e\n \u003cli\u003eMoLF \u0026ndash; Ministry of Livestock and Fisheries\u003c/li\u003e\n \u003cli\u003eTALIRI - Tanzania Livestock Research Institute\u003c/li\u003e\n \u003cli\u003eDC - District Council\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTC - Town Council\u003c/li\u003e\n \u003cli\u003eMC - Municipal Council\u003c/li\u003e\n \u003cli\u003eCC - City Council\u003c/li\u003e\n \u003cli\u003eUSA \u0026ndash; United States of America\u003c/li\u003e\n \u003cli\u003eIMB - International Business Machines\u003c/li\u003e\n \u003cli\u003eCI - Confidence Interval\u003c/li\u003e\n \u003cli\u003eOR - Odds Ratio\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Massey University Foundation Scholarship supported this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study design and conception. AN did the data collection under the guidance of IK and RL. Data analysis and interpretation were done by AN, RL and TP. The first draft of this manuscript was written by AN, and all authors commented on the versions of the manuscript. All authors read and approved the final document.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe generated and analysed datasets used in this study are available from the corresponding author upon acceptable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Ministry of Livestock and Fisheries through the Ethics Review Board of the Tanzania Livestock Research Institute (TALIRI) (reference number TLRI/RCC.21/007) of the United Republic of Tanzania.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors consent to the publication of this article and participants permission for publication was achieved where appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Massey University, New Zealand for their invaluable assistance and support during the research process. Also, we acknowledge the support obtained during proposal preparation and data collection from the Sokoine University, Tanzania. We also acknowledge the regional, district, and village veterinary/livestock officers, as well as the smallholder dairy cattle farmers from the study sites visited during this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNgou et al.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlonso, S., Toye, P. G., Msalya, G., Grace, D., \u0026amp; Unger, F. (2014). Smallholder dairy farming in Tanzania: Farming practices, animal health and public health challenges and opportunities. Retrieved July 15, 2024, from https://www.slideshare.net/ILRI/smallholder-dairy-tanzania\u003c/li\u003e\n\u003cli\u003eBrett, C. (2019). A new way to boost smallholder dairy productivity in Tanzania. world bank. org/voices. 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Retrieved July 15, 2024, from https://www.fao.org/4/X5661E/x5661e0f.htm. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 9 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Smallholder dairy cattle farmer, household, demographic, farming constraints","lastPublishedDoi":"10.21203/rs.3.rs-4993873/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4993873/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere has been a significant shift in the population of smallholder dairy cattle farmers in Tanzania, yet we lack current demographic data and information on key productivity constraints. This cross-sectional survey of 301 smallholder dairy cattle farmers across six regions aimed to gather demographic data and identify key farming constraints. Of the 301 households surveyed, 74% were headed by men, but in Njombe there was an equal number of women and men. Most respondents had primary education but had gone no further (55%); however, in Morogoro, 68% of farmers had been in secondary/university education. Across four regions (Njombe, Mbeya, Kilimanjaro and Arusha), herd size of 3\u0026ndash;4 animals was most common (32\u0026ndash;50%); however, in Morogoro and Tanga most herds had ˃4 animals (66% and 78%, respectively). Zero-grazing was the most common grazing system (75%), but tethering was predominant (68%) in Mbeya. Cash purchase was the most common means of obtaining the first cattle beast (66%), although a gift from a relative/friend (49%) was the most common source in Mbeya. High input costs (93%), unavailability of feed (71%), lack of land (68%) and diseases (62%) were the key identified constraints, while high breeding costs (96%), poor oestrus detection (89%), cows not displaying oestrus (79%) and lack of AI services (51%) were the key constraints to successful breeding. Despite the shared commonalities, demographic differences among regions call for fitting development strategies that address the specific needs of farmers in each region, rather than applying uniform solutions across Tanzania.\u003c/p\u003e","manuscriptTitle":"Understanding Smallholder Dairy Farming in Tanzania: A Cross-Sectional Survey of Farmer Demographics and Management Constraints","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-02-20 15:38:56","doi":"10.21203/rs.3.rs-4993873/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical 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