Introduction

Livestock production contributes for human protein, household income and employment generation [1]. A huge number of Bangladeshi people openly or implicitly concerned in livestock pursuits for bettering their living and domestic products, as well as to progress the village micro-economy. Livestock can reduce the level of poverty but researchers found still its low productivity, which is attributed by low quality of breeds, shortages of quality feeds, poor management practices, harsh environmental conditions, high incidence of diseases and parasites, etc. [2]. Tremendous efforts have been made by the agricultural and livestock institutions in Bangladesh to create and distribute proper livestock technologies to end users. Bangladesh Livestock Research Institute and the Directorate of Livestock Services have released better-quality livestock technologies but not reached to the farmers accurately and adoption level continued law yet, mainly because of faulty agricultural policies, poor institutional framework, unfavorable socioeconomic conditions of farmers and distorted consumer preferences.

Creation and diffusion of appropriate technologies would enhance livestock productivity. Dissemination or diffusion is a route by which created technologies are interconnected through particular means over time among the farmers of a community scheme [3]. Innovation diffusion model [4] stated that access to evidence is very essential for adoption and diffusion of technologies. Proper way of dissemination of technological innovations is beneficial to improvement of the small farm livestock production and family revenue [5]. Absence of available and adequate informal or formal diffusion routes of developed technologies is one of the major problems in Bangladesh. Technology adoption, as defined by [6], is the execution of information obtained for a particular innovation. Technology taking on is perceived as certain network to raise yield, enrich family earnings, minimize rural scarcity, and safeguard of foodstuff guarantee [7,8,9,10]. Adoption of newest know-hows is crucial to increase the profits of small farmers [11,12,13], generating comprehensive and reasonable gains to society [14, 15] and minimizing stress on renewable expected reserves [16, 17].

Empirical studies have shown that adoption on innovations and best management techniques influence farm performance in the long term but low level of application of modern technological innovations in the farm level. Also, it is essential to improve the socioeconomic condition of farmers at desired level successfully. On the other hand, there are some causes that regulate whether a smallholder will or will not take on a particular technology. Farmers anticipate some features like family background, economic status, farm individualities, technology traits, interaction with extension representatives, and farmers’ acquaintance of exact technologies to determine adoption of new technologies [18]. Precision farming is the associate sensor, information regularities, enriched equipment and informed managing to improve production by accounting for variability and suspicious within farming arrangements [19]. Precision livestock farming technology clearly has excessive prospective to produce additional value for a lot of investors making it potential to increase animal prosperity, well-being, effectiveness and the ecofriendly impression [20].

Well thoughtful dissemination and adoption will lead creator clusters, research establishments, and legislators in making judicious and notified outcomes about assigning resources for technology improvement. Therefore, the aim of the research was to investigate the dissemination and adoption of improved livestock technologies. Specific objectives were to (1) know the technological innovations by the researchers and approaches to its dissemination; (2) measure adoption levels of technologies among livestock farmers; (3) determine the factors influencing adoption of livestock innovations; (4) investigate the farmers’ behavioral precision toward the use of technologies.

Material and Methods

The main materials of this study were provided from questionnaires conducted by direct visits and interviews with livestock researchers (both husbandry and veterinarian), livestock farmers and conducting focus group discussions. Earlier research in the field, information from public institutions, attending different field days arranged by the researchers and subject-mater experts’ opinions were also included in the materials of this study. All the above activities and data collection were done from July 2016 to June 2017. Also, secondary data were collected from university, livestock research institute and published research reports and articles. Hard copy was collected from library and soft copy downloaded from website. Study designs and locations, sampling technique, data collecting procedure and analytical procedures are described as follows.

Study Design, Locations and Sampling

Data were collected through structured questionnaire in two phases. In the first phase of the research, researchers from Bangladesh Agricultural University and Bangladesh Livestock Research Institute were selected who invented or developed livestock technologies. In this stage, data were collected through structured questionnaire from 32 researchers. They were asked about the name, purpose and use of innovations; dissemination pathways and effectiveness of technology dissemination. Interview with researchers had done by the researcher himself. Name of the technological innovations and their dissemination pathways (e.g., by researchers (through their adaptive research), research institute, extension workers, training, field day, media, etc.) were recorded for each of the researchers. In addition to the above data, four focus group discussions (FGD) of livestock farmers were conducted in four different agro-ecological locations and interviewed to know the information of the innovated technologies dissemination. Each of the FGD cluster consists of 8 to 10 farmers, and they were formed by the concerns of upazila livestock officer and local leaders. The subject-matters of discussion were reached to the farmers before 2 weeks to conduct FGD for formulate comments. Participants of each of the FGDs were asked three questions: (1) are they get the information of the innovated technologies and use them, (2) what are the ways they receive the innovated technologies, and (3) what are the barriers to receive the innovations? Outcomes of the discussions, as well as the final conclusions, were recorded with the enumerator.

In the second phase of the research, four ecologically representative study locations were selected from North-Central region of Bangladesh to investigate the livestock farmer’s adoption of technologies. They were the Fulbaria upazila in Mymensingh district as a rural plain land area, Jhinaigathi upazila in Sherpur district as a hilly area, Dewangonj upazila in Jamalpur district as a low land (river flooded) area and Mymensingh Sadar as a peri-urban area. A two-stage sampling (village and livestock farmer) procedure was conducted. In order to select villages, three villages from each of the rural locations were selected randomly from the list of total number of villages in each upazila collected from upazila livestock offices. Approximately 150 livestock households (at least one cattle owner or poultry farm having at least 80 chicks) per village based on livestock statistics were found in upazila offices. Mymensingh sadar upazila consisted 21 administrative wards of which five wards at the outer-side of the city were considered as peri-urban location where aggregated population of livestock households were 268. In the second stage of sampling, 15 households (10% of the population households) were selected from each village using simple random sampling technique and nine households were randomly selected from each administrative ward. Finally, 180 livestock households were found to collect primary data in four agro-ecological zones.

Collection of Field Data

Data were collected through oral interview of the selected household heads using a structured questionnaire conducted by a trained enumerator. Supervision was made by the researcher (investigator) and also made direct contract with the farmers for interview. Both the data collection questionnaires were developed considering the earlier research works, using extensive review of literature and informal contacts with subject-matter experts’ opinions. Mostly categorical responses of the researchers and farmers were recorded, whether or not they applied one or more listed innovations, experiences of farmers in technology uses, farmers insights of their capability to use technology and they were asked whether their behavior toward use of livestock technologies. Interviews were conducted mostly at the sites of the farmers’ operations and in some cases at the farmers’ home. A small number of farmers were hesitates to interview or disclose the information. Once they were agreed to interview after politely convince them by describing the importance of the research. Sometimes, village leader accompanied the researcher to establish a trustworthy and friendly atmosphere.

Categorization of Variables and Measurement

Data analyses of the first phase of research were done earlier. The name of technological innovations, major purpose of innovations and their dissemination pathways were listed. Then, the researchers were categorized according to the purpose and ways of innovated technology dissemination and expressed as percentage. The contributory roles of these channels in disseminating available livestock technologies and their effectiveness were analyzed. Each of the technologies was tally marked and adoption level was deliberate by number of farmers who adopted that innovated technology divided by total number of farmers of that item and expressed as percentage. Figure was performed with Microsoft Excel.

Sex, age, schooling, land holdings, household size, household income, farming experience, sources of information and study locations were comprised as categorical variables rather than continuous variables in the logistic regression specification. The categories of these variables were defined as follows: Sex (0 = female, 1 = male); Age (0 < 30 years, 1 = 30–49 years, 2 = 50 years and above); Education (0 = illiterate or primary, 1 = secondary education, 2 = higher secondary and above); Household size (0 = small (1–4), 1 = medium (5–8), 2 = large (> 8)); Land holdings (0 =  < 0.05 ha, 1 = 0.05–0.19 ha, 2 = 0.20 ha and more); Annual household income (low < Tk.1,20,000, medium = Tk.1,20,000–3,00,000, high > Tk.3,00,000; Experience in livestock farming (0 =  < 10 years, 1 = 10–24 years, 2 = 25 years and more); Sources of information (0 = no information or information from friends/neighbor/relatives, 1 = contact with researchers or livestock offices/field day/training, 2 = extension agents); Locations (0 = peri-urban location, 1 = rural plain land, 2 = hilly land, 3 = river-flooded low land).

Livestock farmers were classified as adopters and non-adopters according to scores achieved by each individual. The technologies were weighted as score 2 for crossbred cattle (any number of cattle) owner, beef cattle, red Chittagong cattle, indigenous FAnGR conservation and poultry owners; and score 1 for each of the other innovations used by a farmer. For example, a farmer had four crossbred cattle and he used open nucleus breeding system, FMD vaccine and mobile veterinary service, then his obtained scores was (2 + 1 + 1 + 1), i.e., 5. A farmer was considered as an adopter, who obtained scores at least 4, otherwise he was considered as a non-adopter.

Empirical Models: Binary Logistic Regression and Precision Index

In order to analyze the factors affecting the adoption of livestock technologies by farmers, probability model does not allow the usage of an ordinary least square (OLS) technique as the dependent variables are qualitative in nature and hence logistic regression model was used. Effect of technology adoption was determined by comparing socioeconomic factors (those having influence on technology adoption or non-adoption) between adopters and non-adopters. Binary logistic regression analysis was used to determine the socioeconomic factors affecting adoption of livestock. Including coefficients, odds ratios (ratio of the probabilities of two mutually exclusive outcomes) were derived to evaluate how likely the explanatory variables influence farmer’s attitude to adopt technologies. Technology adopted or non-adopted was the dependent variable (dichotomous: adopter = 1 and non-adopter = 0), and selected socioeconomic factors were the independent variables. All of the independent variables were categorical and higher categories for each variable were compared with lower category (reference category). In this case, it can be shown that how much more likely or less likely to be the higher category compared to the lower category. Statistical Package of Social Science (SPSS) for Version 20 was used for analysis purpose. The logistic model was as follows:

$$Y = Logit \left( p \right) = Ln \left( {\frac{p}{1 - p}} \right) = \alpha + Z^{\prime}\delta$$

where \(Y\) took the value of 1 if the farmer adopted innovated technologies (obtained score 5) and 0 otherwise, p is the probability that farmer adopted innovated technologies, 1- p is the probability that farmer does no adopted innovated technologies, α is a constant term, Z is a vector of independent variables and the \(\delta\) s are logistic coefficients to be estimated.

Ten key elements [21, 22] were used for farmer’s behavioral precision on agree to livestock technology adoption. They were internalization, differentiation, enrolment, legitimation, activation, skill set workability, interactional workability, relational integration, reconfiguration and systemization. A 5-point Likert Scale was used to measure behavioral precision of the respondents. All the declarations were set at random to escape subjects’ bias in stating their view. Each respondent was questioned to specify his/her boldness against each declaration along a 5-point scale, i.e., ‘strongly agree,’ ‘agree,’ ‘no opinion,’ ‘disagree’ and ‘strongly disagree.’ Weights allocated to these responses were 4, 3, 2, 1 and 0, respectively. The total score of a defendant was calculated by adding up the weights for answers against all the 10 declarations. The precision score for each declaration was computed by employing precision index (PI) and it was computed by employing the following formula [23]:

$$Precision\; Index \left( {PI} \right) = 4SA + 3A + 2.NO + 1.DA + 0.SDA$$

where SA = Total number of defendants stating their behavior ‘strongly agree’ for the statement, A = Total number of defendants stating their behavior ‘agree’ for the declaration, NO = Total number of defendants stating their behavior ‘no opinion’ for the declaration, DA = Total number of defendants stating their behavior ‘disagree’ for the declaration, SDA = Total number of defendants stating their behavior ‘strongly disagree’ for the declaration.

Results and Discussion

Dissemination of the Innovated Technologies

Using innovative technologies, farmers can increase animal production efficiency and profitability and hence livestock researchers in Bangladesh have been developed a large number of technologies. Most of these technologies were developed for research, academic and policy formulation and industrial purposes and not used sufficiently in the field level but some of them have moderate use. Some of the technologies are being used by various institutions in Bangladesh. The technologies innovated/developed by the livestock researchers are shown in Table 1. Technologies were disseminated through adaptive research, field trial, field day, training and extension services. Also, farmers received technologies by visit to the institutes or Government offices. Most of the researchers claimed that the innovated technologies were disseminated through adaptive research or field trial (78.1%) and through training to the farmers (75%), whereas 56.3% claimed that technologies were disseminated through extension workers (Fig. 1). About 43.8% farmers received technologies by visit to the institutes or upazila livestock office, 37.5% disseminated by research institutes directly. About 37.5% and 25% innovated technologies were disseminated by field day (participatory technology development demonstrated to the community) and media (radio, television, newspaper, monthly or weekly magazine), respectively, whereas 21.9% and 12.5% by FGD and sub-group activities (clustering farmers and farmer-farmer cross visit), respectively. It is noted that a technology was disseminated to the farmers by multiple numbers of ways.

Table 1 Innovated livestock technologies and their sources and adoption levels by the small farm holders
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Fig. 1
figure 1

Different pathways of dissemination of the innovated technologies and the proportion of each way opined by the researchers

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According to the opinion of researchers, farmers were benefited in livestock production activities as well as income and employment generation by using the transferred technologies. The most important of them were the improvement of milk production and reproductive performance of cows. Through transferring beef cattle/goat production and meat processing technologies, employment and income generation and protein supply to human, as well as food security, are also improving. Development of animal recording system, seed animal production, open nucleus breeding system and indigenous FAnGR conservations are being used by various organizations in Bangladesh. Unconventional feed used as a chicken feed, that reduces the cost of production and improves meat quality of chicken would certainly improve the farmers’ livelihood and contribute to ensure food security of Bangladesh. Using the technology BAU-Bro chicken and single cell protein farmers will get better profit and reduce import of chicks and protein concentrate. According to the opinion of veterinarians, innovated vaccines would be commercialized to disseminate to the farmers; medicinal plants, probiotic and organic acid could be alternative to antibiotic in poultry feed; and using ‘Mobile Vet Service’ by farmers calf mortality would be reduced.

Technology generation and dissemination are important components but observation and documented studies indicate that spreading attitudes of livestock technologies (especially, training, visit and media approaches) had not very successful and this finding agrees with the finding of [24]. Newly innovated poultry technologies and disease control technologies were used in limited extent in the field due to lack of extension works. The FGD findings indicate that the farmers received common information like use of crossbred cattle, improved local breed, FMD vaccine, poultry farm management and its feeding systems mostly from extension officers and some extent from researchers and neighbors or friends. They heard about a few names of the other innovations but they had no sufficient knowledge on them and they could not rely on them to use in the point of benefits. Answer of the second question (dissemination ways), which was the main objective of the FGD, was not successful as they were not familiar on the improved innovations. Finally, participants were asked to state the barriers of their access in livestock innovations. Participants of two FGDs (hilly and river-flooded land) expressed that there has very limited excess to extension workers, researchers or other sources of information. Participants of other two FGDs expressed that educational knowledge, lack of money and ethics in use of innovations were the major disadvantages and hindered the effective dissemination.

Level of Adoption of Technologies at Farm Level

The adoption level of crossbred cattle and red Chittagong cattle was 40.5% and 22.7%, respectively. FMD vaccine trivalent developed by Bangladeshi scientists was used by 23.9% cattle farmers and low cost feed formulation with unconventional feed ingredients for poultry was moderate use (10–15%) in the farm levels. Eight innovations like beef fattening technology, indigenous FAnGR conservation, peri-urban dairy farming, use of organic acids and probiotic on poultry feed, use of safe feed additives as alternatives to antibiotics for poultry, Killed Fowl cholera vaccine, Killed ND vaccine and Killed HS vaccine were adopted by 5–10% of the farmers. Adoption level of the remaining technologies by the selected farmers was below 5% (Table 1). Most of the innovated technologies were collected from Bangladesh Agricultural University (BAU) and others from Bangladesh Livestock Research Institute (BLRI) and different NGOs (Table 1). Also, the innovated technologies were classified based on the type of farming and type of farmers (male or female) and shown in Table 2. There are nine technologies were adopted by the poultry farmers. Rest of the technologies were adopted by the dairy and beef cattle farmers where health-related technologies were common for both type of farming. Ten technologies were used either by male or female farmers and the remaining by the male alone.

Table 2 Classification of the technologies based on the type of farming and farmers
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The technologies crossbred cattle, red Chittagong cattle and FMD vaccines had wide application in the study areas. Most of the farmers wanted to acquire crossbreed cattle, but had not done so because they were prevented from adopting crossbred dairying for some reasons, especially for lack of money and difficulties in farm management. The results interpret that breeding-related technologies had the higher level of awareness of the farmers, while health-related technologies were less known. This results may be due to farmers gave more emphasis on production for higher profit rather than expensive health treatments. Secondly, farmers know more about production-related farming knowledge from extension officers, relatives, neighbors and media but usually these media do not transfer knowledge on health-related technologies. The overall level of adoption by farmers is low and it can be attributed to a number of factors. First of all, researchers developed the technologies mainly for academic and research purposes and advise the farmers in some extent in their research area only. Usually, developed innovations were not patented or included with the Government policies. As a result, there has no extension activities of these newly innovated technologies and farmers did not come into contact with them. Secondly, farmers had low education or knowledge and attitude toward change or taking risk may be the principal constraints to adoption. Thirdly, the awareness level among the respondents was also low and with deficiency of training in the appropriate use of the know-how, it is not too surprising that adoption is also low.

Determinants Influencing the Likelihood of Technology Adoption

The logistic results provided significant value of χ2 (18) = 62.945, p < 0.01. Also, the results stated 29.5% (Cox and Snell R2) and 39.5% (Nagelkerke R2) variance in use of livestock technologies and overall proportion predicted was 80.0%. Additionally, an insignificant value for the goodness-of-fit test (Hosmer and Lemeshow) χ2 (8) = 14.827, p > 0.10 were obtained. The sign and values of β (coefficients) indicate increase or decrease in adoption level. The column exp(β) in Table 3 gives the exponential of expected value of β.

Table 3 Logit estimate of issues affecting the adoption of livestock technologies by the farmers (N = 180)
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Adoption of technologies was negatively associated with farmer’s age group; farmers of age group 30–49 years were 39.1% less likely and farmers of age group 50 years and more were 56.2% less likely to adopt technologies compared to farmers of age group less than 30 years. Level of education has a positive impact on adoption of livestock technologies; farmers having higher secondary or more education (β = 1.810) were significantly (p < 0.01) better users of technologies compared to illiterate or primary educated farmers. Adoption of technologies was positively associated with farmer’s family size; farmers with family size (5–8) were 1.288 times more likely and more than 8 family members were 1.291 times more likely to be adopting technologies compared with the family size (1–4). Farmers having medium and higher household income were positively (β = 1.049 and β = 1.545, respectively) and significantly (p < 0.05) associated with adoption of livestock technologies. The odds ratio implies that other things being kept constant, adoption of technologies increases by 2.854 times more likely by the farmers of middle income and 4.686 times more likely by the farmers of high income, respectively, compared to low-income farmers. Farmers who had more than 0.2 hectare of land were 1.778 (β = 0.576) times more likely to use technologies and who had more than 1 hectare of land were 1.942 (β = 0.664) times more likely to use technologies compared to landless farmers; however, those were not significant (p > 0.05). Similarly, livestock farming experience had a optimistic association with adoption of technologies and more than 25 years experienced farmers were 3.59 times more probable to adopt technologies compared to less than 10 years of experience.

The β coefficients against contact farmers/field day and extension workers were 0.466 and 1.20, respectively, which implies that the farmers who received information from these two sources were higher technology adopters compared to the farmers who did not receive information or received information from friends, neighbors and relatives. The farmers who had contact with extension agents were significantly (p < 0.05) higher adopters than the farmers who did not receive information or received information from friends, neighbors and relatives. On the other hand, the farmers who received information from contact with government offices or researchers or participated in the field day were 1.594 times and the farmers who had contact with extension agents were 3.32 times more likely to be adopting livestock technologies compared to the farmers who did not receive information or received information from friends, neighbors and relatives. The β coefficients of rural plain land, hilly land and river-flooded land were −0.482, −1.774 and −1.906, respectively, which implies that the farmers of all these three locations were lower technology adopters compared to the farmers of peri-urban location. Adoption score was expressively lower in hilly area and river-flooded area (p < 0.01) than adoption score in peri-urban location. On the other hand, odds ratios of these three locations were 0.618, 0.170 and 0.149, respectively, which interpret that farmers in rural plain areas were 40.2%, farmers in hilly areas were 83% and farmers in river-flooded areas were 85.1% less probable to adopt livestock know-how likened to the farmers in peri-urban locations.

The variable sex had the positive coefficients which implies that male farmers are more likely to adopt the technologies as compared to female-headed farmers, which agrees the findings of [25] and meets the prior expectation. The probability of households adopting technologies decreases with increasing their age, as newer farmers are willing to bear more risk than their older counterparts. Newer farmers have more intention to adopt technology than older farmers [26,27,28] and age has adverse effect on adoption. Household heads having at least secondary education had more intention to adopt livestock technologies, which confirms education is helpful to adopt technologies. Mentality of educated farmer demonstrates higher attitude toward innovations [25], while education may foster technology adoption [6]. Size of land owned had influenced the household decision to adopt advanced technologies positively as compared to landless farmers but marginal farmers were more likely to adopt technologies compared with small landholdings because enough land is not required for livestock farming. Usually, farmers of larger landholdings depend on crop cultivation and they rear small number of cattle and or poultry birds for household consumption. But farmers having marginal landholdings tried to engage in business, otherwise in livestock farming and they used advanced technologies for more income as a livelihood purpose.

Livestock farming skill had an optimistic association with adoption of technologies which implies that farmers who had longer years of experience in farming had adopted improved technologies than those who had the lower years of experience in farming. Farmers having higher experience in livestock farming may develop the confidence in handling the risk and skills in technology use. Results of significantly positive association between extension services and adoption of technologies agree with the findings of [29] who found that extension contact is to be significantly (p < 0.10) and positively correlated with the adoption decision of livestock farmers. This means extension services may play prime roles in technology broadcasting and their adoption at the farmers level in Bangladesh. Farmers in peri-urban area were more motivated to use livestock technologies because they are usually landless and concentrate feeds, crossbreeding technologies and other materials for intensive livestock farming are available therein. Also, farmers in this area had more advantages to receive training and motivation by extension workers as well as field day, exhibition, print media, etc., and hence they were able to use most of the technologies like vaccine, artificial insemination, peri-urban dairy farming, beef fattening technology, etc. On the other hand, Fulbaria upazila (defined as rural plain land area), where researchers fixed some contact farmers, arranged field days, animal-exhibitions, connect with NGOs, etc., as a result, some of the farmers in this area get usable technology facilities. Researchers also arranged training, field day and extension works in the hilly remote area but in small scale. A large number of farmers adopted crossbred cattle and more common technologies hearing from friends, relatives, neighbors and different media in Dewangonj upazila defined as a river-flooded low land. But the socioeconomic conditions of the farmers in the latest two locations are poor and farmers usually rear indigenous cattle based on green grass only and hence improved livestock technologies are rarely used.

Farmers’ Behavioral Precision toward Livestock Technologies

Farmers’ behavior using precision toward technology was investigated by knowing the extent of their opinion against 10 statements. Precision index (PI) for each declaration had organized in rank sequence according to their degree of view. Precision index was found to vary from 252 to 476 for all farmers (Table 4). Farmers understand the values and benefits, and importance of latest technology (internalization) got the 1st grade among the declarations where 97 farmers (53.9%) agreed with this opinion. Most of the farmers did not adopt the technologies sufficiently but they believe it was right for them to be involved (rank 2). Systemization and skill set workability were ranked 3 and 4, respectively. Only 52 farmers (28.9%) agreed that they had understanding the use of technology differs from existing practices which was ranked 5. The statements enrolment, interactional workability and reconfiguration obtained lower scores for farmers’ behavioral precision.

Table 4 Farmers’ behavior precision toward livestock technologies (statement-wise score of the farmers)
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Really, farmers understand values and benefits of new technologies but not adopt sufficiently. So, they decide to use stocked tools but precision technology should be more evidence based. Most of the farmers did not agree with the statement relation with integration, activation, reconfiguration and interactional workability obtained 6th, 7th, 8th and 9th rank, respectively. This means that farmers had low confidence in use of technologies, technology did not make their work easier, they did not try to change the technology to suit their way of working and they were not willing to implementation of the technologies. Very limited enrolment by farmers who contended that livestock rearing varied on their attitude and skill, available resources and money, interaction with the animals as well as extension personnel for motivation to buy and use technologies. Expenses incurred in obtaining the technology were considered challenging and this produced them to reflect whether it would enhance farm value [30]. Thus, use of precision technology is important to be aware of how these factors touch farmers’ behavior.

Conclusion

The findings interpret that the technology transfer through visits and media were very limited and adoption level of breeding-related technologies was better but newly innovated and disease control technologies were very low extent. Thus, government offices and research institutes should take initiative by arranging frequent demonstration workshops among the farmers with newly developed technologies. Also, they should realize farmer’s demand and capability to implement technologies. They should widen and monitor the livestock extension services for prompt transfer of technologies. Also, they should make strategy for disseminate the information to the farmers on time through print or electronic format or clustering the farmers, especially in the rural areas. Consciousness and practice of innovated technologies by farmers are tremendously low in the investigated area, especially in the hilly and low-lying areas. Thus, identifying the root causes of lower rates of adoption by further investigation may help to undertake better strategies for technology developers. Results also suggested that the level of education and household income of the farmers and extension services are influencing the likelihood for increasing adoption of livestock technologies. Thus, rigorous extension service is crucial to motivate and transform knowledge to the farmers on adoption of livestock technologies. The results of the farmers’ behavioral precision explain farmers had low assurance in use of technologies and were not keen to apply them. Thus, it is essential to work with them to embed capable knowledge in the design and execution of technology uses. Further, a much more detailed investigation is also required on adoption and insights of farmers toward latest technologies.