11 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 Research on World Agricultural Economy http://ojs.nassg.org/index.php/rwae Assessment of Use of Improved Production Technologies among Goat Farmers in Abia State Nigeria Onu, S. E.*　Obinna, L. O.　Ufomba V. U Department of Agricultural Extension and Rural Development, Michael Okpara University of Agriculture, Umudike ARTICLE INFO ABSTRACT Article history Received: 22 April 2021 Accepted: 28 May 2021 Published Online: 31 May 2021 The study provided an empirical evidence on the use of improved goat production technologies among rural farmers in Abia State, Nigeria. The specific objectives of the study were to describe the socioeconomic char- acteristics of the respondent, ascertain the extent of use of improved goat production technologies, determine factors influencing use of improved goat production technologies and identify the constraint to access and use of improved goat production technologies in the study area. A multi-stage random sampling technique was adopted in selecting the sample size 120 respondents. Data for the study were collected through the use of question- naire. The data collected for the study were analysed with both descriptive and inferential statistics. The result of the socioeconomic characteristics revealed the mean age of the respondents was 43 years, majority 69.16% of the respondents were married, about 45% had secondary education, a mean household size of 6 persons, majority 66.67% were farmers, mean years of arming experience at 5.7 years, mean income of #102,000, mean farm size of 11 goats and majority (78.33%) of respondents were non- members of cooperative societies. The result on extent of use of improved goat pro- duction technologies, revealed that the respondents highly used most of improved goat production technologies as affirmed with the grand mean of x= 3.20. On constraint to use of improved goat production technologies, all the respondents 100% agreed that lack of access to credit was a constraint to use, 100% agrees on lack of credibility from source of technological in- formation, 99.2% agreed that they were afraid of taking risk, 93.3% agreed on difficulty in technology application among others. The OLS regression estimates of the influence of socioeconomic characteristics the respondents on the use of improved goat production technologies in the study area, revealed that age at 10%, education at 1%, household size at 1%, farming experience at 1%, farm size at 1%, income at 1% and access to credit at 5% were the determinants of use of improved goat production technologies in the study area and the null hypotheses rejected. In conclusion, greater use of available improved technologies will promote productivity, and therefore there is need for proper sensitization and awareness by relevant agencies. The study recommended that credit should be made available to farmers by relevant governmental and non- governmental agencies to increase the level of use of available improved technologies. Keywords: Use improved goat production technologies farmers 　1. Introduction Goats are among the main meat-producing animals in Nigeria, whose meat is one of the choicest and has high demand across the country. Besides meat, goats provide other products like milk, skin, fiber and manure. Nigeria, with over 3.9 million goats is one of the largest goat pro- ducing countries in Africa and playing a significant role *Corresponding Author: Onu, S. E.; samsononu@gmail.com 12 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 in livelihood and nutritional security as well as providing supplementary income to many marginal and resource poor farmers, (Food and Agricultural organization; FAO,: 2015). However, the productivity of goats under the traditional production system is very low owing to their maintenance under extensive system on natural vegetation and shrink- ing common grazing lands and tree lopping. Moreover, adoption of improved technologies and management prac- tices in the farmers’ flock is very low (Singh and Kumar, 2007). Adoption behaviour of goat farmers depends on knowledge, economic motivation, family education status, extension agency contact, social participation and income (Kumar et al., 2014). The adoption is low in important sci- entific practices due to lack of exposure, henceforth exten- sion agencies have to arrange training and demonstration programs of improved practices to goat keepers (Singh, 2017). Technology information usage on the other hand refers to the physical and mental acts involved in incorporating the improved technologies found, into the farmers exist- ing technology base. Technology use is an indicator of technology needs, because it leads an individual use the technology in order to meet his needs. Technology use is concerned with what happens with a technology once it has been obtained, and how it is applied to accomplishing a specific task. It is the final step in the technology seek- ing process. Goat rearing using improved management practices un- dertaken for maximization of returns from the enterprise was considered as ‘commercial goat farming’ in the pres- ent context. The entry of large farmers, who have better access to technical knowledge, resources and market, into this activity would help in realizing the potential of goat enterprise (Kumar, 2007). The trend of commercialization has especially been prominent in the Northern States of Nigeria, where demand for marketing is relatively better. Goat production can be singled out to be an ideal option for the South Eastern part of Nigeria, given the abundance of suitable rangelands and the accommodating climatic conditions in the area. In order to make the goat rearing a profitable enter- prise, technologies have been developed by the research institutions both at national and international level. Such improved practices developed have not been adopted by the farmers so far. Therefore, proper adoption of these improved practices by the goat farmers will be the only means to hasten further development in this sector. Improved technologies are various technical know- how for the promotion and development of agriculture. However in developing countries some of these technolo- gies have been rejected by rural farmers, giving rise to the need to examine technologies used by rural farmers in a particular locality so as to identify and meet their needs. Keeping in view the above facts, the present research was designed to study the Utilization level of improved goat farming technologies by goat farmers. Statement of problem Available statistics show that the supply of goat meat fell short of it's demand. Ijere (2012), asserts that while the average growth rate of the Nigerian population is be- tween 2.5 - 3.0%per annum, domestic food production lags behind at a growth rate less than 2% per annum, thereby creating food supply gap. The decreased output of agricultural produce over the years may not only be connected to deviations of farmers from improved recom- mended production technologies but also with lack of use of the existing improved production technologies leading to inefficiencies ( Ijere, 2012). Despite the multiple roles goats play in the livelihood of rural farmers and the economic growth of the country, they are still neglected by farmers and sources of credit. For efficient production in the goat production enterprise, a lot of improved technologies have be developed and transferred to the field for use. There is is little or no infor- mation on how farmers adopt and use these technologies, hence this study was conducted to investigate the use of improved goat production technologies among farmers in Abia state. Specific objectives of the study (1) describe the socioeconomic characteristics of the farmers in the area (2) ascertain the extent of use of improved goat produc- tion technologies (3) determine factors influencing use of improved goat production technologies (4) ascertain the constraints to use of existing improved technologies. Hypothesis H01: There is no significant difference between farmers socioeconomic characteristics and the extent of use of im- proved goat production technologies. 2. Methodology The study was conducted in Abia State. Abia State is located in the South-East agro-ecological zone of Nige- ria. According to National Population Commission, 2007 census report, Abia State has a population of 2,833,999 DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 13 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 people, made up of 1,454,195 males and 1,599,806 fe- males, and the population is predominately rural (62.25%) with only 37.75 % urban population. Abia State lies with- in Longitude 70 23E and 80 2E and Latitude 40 47N and 60 12N. The population of the study comprised of all the goat farmers in Abia State, Nigeria. A multi-stage random sampling technique was adopted in selecting the sample size 120 respondents. In the first stage, 2 Agricultural Blocks each were randomly selected from the 3 Agricul- tural Zones in Abia State making a total 6 Agricultural Blocks. In the second stage, 2 circles each were randomly selected from the 6 Agricultural Blocks making a total of 12 circles. In the third stage, 2 cells each were randomly selected from circles already selected making a total 24 cells. In the fourth stage five (5) goat farmers were ran- domly selected from each of the cells which gave a total of 120 respondents that were used for the study. The study made use of primary data. Data for the study were collect- ed through the use of questionnaire. Data were collected on all the specific objectives of the study. The data col- lected for the study were analysed with both descriptive and inferential statistics. All the specific objectives were analysed using descriptive statistic while the hypothesis was tested using Ordinary Least Square regression model. The formula to compute the mean count to be used in this study is specified below. The mean ( ) is computed by multiplying the frequency (f) of the responses under each category by assigned value and dividing the sum (∑) of the product by (N) number of respondents to the partic- ular indicator as shown: X = ∑ N fx (3.1) Where, ∑ = Summation F = Frequency X = assigned scores to response category N = number of respondents X = Arithmetic mean H01: There is no significant relationship between farm- er’s socio-economic characteristics and the level of use of improved goat production technologies in the study area was tested using Ordinary Least Square regression model. Multiple regression helps to learn more about the rela- tionship between one dependent variable (Y) and two or more independent variables (X). It is used when we want to predict the value of a variable based on the value of two or more variables. It calculates a coefficient for each independent variable, as well as its statistical significance, to estimate the effect of each predictor on the dependent variable, with other predictors held constant. The OLS/Multiple regression expressed implicitly as follows. Y = f (X1 X2 X3 X4 X5 X6 X7,X8X9X10X11X12, X13, ei) …………………………….. (3.2) The four functional forms of OLS in explicit form is specified as; Linear Function Y = ß + ß1+x1+ ß2x2+ ……..…………………….ßnxn+ ei Exponential function Log Y = ß + ß1+x1+ ß2x2+..……………………..ßnxn + ei Semi-log function Y = ß0+ ß1lnx1 + ß2lnx2….............................… ßnlnxn + ei Cobb Douglas function Log Y = ß0+ ß1lnx1 + ß2lnx2 ………………… ßnlnxn + ei Where, Y = use of improved got production technologies (mean score) X1 = Age (years) X2 = Education level (Number of years spent in school) X3 = Marital status (1 = married, 0 = single) X4 = flock size (number of goats) X5 = farmers experience (years) X6 = household size (number of persons) X7 = Occupation ( Farming = 1, trading = 2, civil ser- vice = 3, artisan= 4) X8 = farm income (N) X9 = access to credit (yes=1 No = 2) X10= membership of cooperative (yes = 1, No = 0) e = error term 3. Results and Discussion 3.1 Socioeconomic characteristics of the respon- dents Table 1. Distribution of respondents based on their socio- economic characteristics Parameters Percentages Parameters Age(years) Farming Experience (years) 20-30 12.3 1-5 50.00 31-40 15.8 6-10 20.0 41-50 37.5 11-15 20.83 51-60 22.5 16-20 9.16 61-70 4.16 Mean 15.7 years Mean 42.8 years Farm income (N) Marital Status 10,000-50,000 10.83 Single 8.33 51,000 – 100,000 40.83 Married 69.16 101,000 – 150,000 41.67 Widow 16.66 151,000 – 200,000 6.67 Divorced 5.38 Mean 102,012.22 Level of Education Farm Size (number of goats) DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 14 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 Parameters Percentages Parameters Age(years) Farming Experience (years) No formal education 16.67 01 – 05 25 Primary 29.17 06 – 10 48.3 Secondary 45.0 11 – 15 25 Tertiary 9.16 16 – 20 1.7 Household Size (numbers) Mean 10.6 goats 2-5 45.0 Access to Credit 6-10 50.0 No 80.83 11-15 0.25 Yes 19.17 16-20 0.25 Mean 6.4 persons Primary Occupa- tion Farming 61.67 Trading 16.67 Civil service 16.66 Artisan 5.00 Source: Field Survey, 2019 Table 1 shows the distribution of respondents accord- ing to their age in the study area. The Table revealed that about 37.5% of the farmers were between the age range of 41-50 years, 22.5%were between 51-60 years 15.8% were between 31-40 years 12.3% were between 20-30 years and 4.16% were between 61-70 years. With the mean age of the farmers at 42.8 years, it implies that the respondents were still young, active and productive. The result agrees with the findings of Tiamiyu et al, (2009), that young farmers exhibit risk aversion and have higher tendency to adopt technologies that have long lag between investment and yield. The result showed that majority (69.16%) were mar- ried, 16.66% were widowed, 8.33% were single and 5.38% were divorced. The result indicates that married people are more involved in goat farming in the study area, this is in a bid to provide food and diversify sources of income which helps them to meet basic financial obligations like payment of school fees, rents, medical bills, purchase of seeds, fertilizers etc. The result revealed that most of the respondents were educated at different levels. A fairly large proportion 45% were educated at secondary school level, 29.17% had primary education, 16.67% had no formal education, and 9.16% had tertiary education. The high level of literacy among the respondents is expected to have a positive influence on their level of access and use of improved goat production technologies. The result agrees with the findings of Abdelmagid and Hassan;(2012); that educat- ed farmers are more receptive to advice from extension officers, deal more with technical recommendations that require litracy, are rational in their choice of technologies rather than developing a negative attitude towards new technologies and that education have a positive and signif- icant influence on adoption. The result revealed that a large proportion (50%) of the respondents had 6-10 persons in their household 45% had 2-5 persons in their household 2.5% had 11-15persons in their household and another 2.5% had 16-20 persons in their household. The mean household size is 6.4 per- sons, which implies that there is enough persons in most household to provide family labour in the goat production enterprise. From a prior expectation, availability of family labour reduces labour cost, increase productivity and net profit. The result indicates that majority 61.67% of respon- dents are famers, 16.67% are traders, 16.66% are civil servants while 5% are artisans. Goat production are un- dertaken by farmers majorly as they see it as an invest- ment and insurance that provide income to meet seasonal purchases of seeds, fertilizers and other inputs in times or seasons of crop failure and fall in prices of crops (Mahama 2012). Also a major characteristics of livestock production system is its integration into crop production system by farmers, where the droppings serve as manure and help to replenish soil fertility while crop residues are been used in feeding the goats (Dube, 2015). The result shows that a large proportion 50% of the re- spondents had 1-5 years of experience, 20.83% had 11-15 years, experience, 20% had 6-10 years, experience, while 9.17% had 16-20 years, experience. The result revealed that the farmers had 5.7 mean years of experience. The implication is that a large proportion of farmers are new in goat farming, are zealous and are willing to access and use improved goat production technologies. This result agrees with the findings of Chilot et al; (2009); that farm- ing experience does not matter or is inversely related to adoption of improved technologies. The result revealed that a fair proportion 41.67% were within the monthly income of ₦101,000 - ₦150,000, 40.83% were within the income range of ₦51,000 - ₦100,000, 10.83% were within the income range of ₦10,000 - ₦50,000 while 6.67% were within the income range of ₦151,000 - ₦200,000. No respondent had above ₦201,000 income. The study revealed that the respondents had a mean income of ₦ 102,012.22, and the implication is that the respondent had a relatively moderate level of income. The result showed that a fairly large proportion 48.3% had 6-10 goats, 25% had 1-5 goats another 25% had 11- 15 goats and 1.7% had 16-20 goats. The mean farm size is 11 goats. The result implies that farmers in the study area were mainly smallholder farmers. The finding is plausible because farm size is a determinant of technology DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 15 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 adoption. The result agrees with the of (Djana, 2011), and (FAO, 2013), that most farmers are peasants and operate at subsistence level. The result indicates that majority 78.33% 0f respon- dents were non -members of cooperative societies, while 21.67% were members of cooperative societies. Being a member of cooperative society affords farmers the op- portunity of sharing technological information, thereby creating awareness, enhancing understanding of existing technologies, Akinola as cited by Simon; (2012); creates access to available technologies which in turn leads effi- ciency and higher productivity. The result revealed that majority 80.83% of respon- dents had no access to credit while19.17% had access to credit. It is expected that access to credit will help farmers to increase their farm size, hire labour, purchase needed inputs, equipment and adopt necessary technologies Ab- doulaye et al; (2014) Inadequate capital and poor access to credit from credit institutions are major reasons why farmers still operate at subsistence level (Adunni Sanni; 2008). 3.2 Extent of use of improved goat production technologies The result revealed a grand mean of 3.20 implying a high level of use of the improved goat production tech- nologies. The result revealed that the respondents used the slated floor system (x = 3.65), vaccination (x =3.34), goats raised on plateforms (x = 3.36), flushing of does (x =3.36), fostering of kids (x =3.33), formulation of concentrates (x =3.29), colostrum feeding (x =3.29), feeding goats with concentrate (x =3.28), farm fumigation and disinfection (x =3.28), changing of bucks (x =3.27), crosss breeding (x =3.08), odour transfer (x =3.00), dipping (x =2.93), De- worming (x =2,92), giving mineral supplement (x =2.92). The result implied that respondents in the study area made use of the improved goats production technologies lead- ing to higher productivity and generation of income. This result disagrees with the finding of Mahama (2012), that farmers are not willing to adopt new or improved tech- nologies due to their small size of holding and financial challenges associated with new technologies. Table 2. Mean rating of respondents based on the extent of use of improved goat production technologies Extent of use of improved goat production technologies Very often Often Rarely Never ∑ƒx Slated floor system 78(312) 42(126) 0(0) 0(0) 438 3.65 Goat raised on platforms 65(260) 35(105) 18(36) 2(2) 403 3.36 Formulation of concentrates 35(140) 85(255) 0(0) 0(0) 395 3.29 Feeding goats with concentrates 47(188) 60(180) 13(26) 0(0) 394 3.28 Giving mineral supplement 30(120) 60(180) 20(40) 10(10) 350 2.92 Identification of does on heat 25(100) 73(219) 11(22) 11(11) 352 2.93 Cross breeding 27(108) 46(138) 38(76) 9(9) 555 3.08 Vaccination 65(260) 41(123) 14(28) 0(0) 411 3.43 Flushing of does 65(260) 35(105) 18(36) 2(2) 403 3.36 Colostrum feeding 35(140) 85(255) 0(0) 0(0) 395 3.29 Farm fumigation or disinfection 47(188) 60(180) 13(26) 0(0) 394 3.28 Deworming 30(120) 60(180) 20(40) 10(10) 350 2.92 Dipping 25(100) 73(219) 11(22) 11(11) 352 2.93 Are you aware that bucks (male goats) are to be changed at recommended intervals 60(240) 40(120) 12(24) 8(8) 392 3.27 Fostering of kids 40(160) 80(240) 0(0) 0(0) 400 3.33 Odour transfer 40(160) 60(180) 20(40) 0(0) 360 3.00 Total mean 51.27 Grand mean 3.20 Source: Field Survey, 2019 DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 16 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 3.3 Factors influencing use of improved goat pro- duction technologies Table 3. OLS regression estimates of the socio-economic determinants of use of improved goat production technol- ogies in the study area Variables Linear Exponen- tial Semi-Log + Double Log (Constant) -2338.142 (-0.032) 8.980 (9.566)*** 103387.027 (4.714)*** 11.173 (4.507)*** Age -194.886 (-0.255) 0.007 (0.681) -37351.323 (-0.972) -.773 (-1.779)* Marital status -26405.1 (-3.097)*** -0.303 (-2.794)** -28338.511 (1.236) -0.417 (1.361) Years of Education 3244.229 (1.805)* 0.064 (2.501)** -34888.386 (1.151) 1.149 (3.355)*** Household size -302.356 (-0.122) 0.005 (0.172) 1376.132 (0.106) .068 (3.461)*** Farming experience 1950.902 (1.983)** 0.010 (0.832) 14972.501 (4.160)*** .089 (3.767)*** Farm size 0.054 (0.247) 1.766E-6 (0.633) 8394.982 (0.767) .048 (3.390)*** Monthly income 0.422 (2.071) 1.823E-6 (0.705) 474.305 (2.037)** .113 (5.768)*** Access to Credit -0.057 (-0.637) 1.967E-6 (1.740)* 4482.591 (4.112)*** .288 (2.60)** Cooperative member- ship 39594.605 (0.651) 0.659 (0.473) 29725.679 (1.363) .637 (0.588) R-Square 0.685 0.655 0.616 0.765 R Adjusted 0.618 0.609 0.597 0.733 F – ratio 14.710*** 11.711*** 12.27*** 16.144*** Field Survey, 2019 Key: * Significance at 10%, ** Significance at 5%, *** Significance at 1% ***, + = Lead Equation and the values in bracket are the t-value The result in Table 3 showed the Ordinary Least Square regression estimates of the socio-economic determinants of extent of use of improved goat production technologies in the study area. Four functional forms of multiple re- gressions were analyzed and Double-log functional form was selected based on magnitude of the R2 value, number of significant variables and F- ratio. The R2 (coefficient of multiple determination) value was 0.765 which implied that 76.5% of the total observed variations in the depen- dent variable (Y) were accounted for while 23.5% of the variation was due to error. F–statistics was significant at1% indicating the fitness of the model used. The coefficient of age was statistically significant at 10% and negatively related extent of use of improved goat production technologies in the study area. This implies that as the age of farmers’ increase, their extent of use of improved goat production technologies decreases. This inverse relationship implies that the age of the farmers’ increase, their extent of use of improved goat production technologies in the study area decrease. The result is in agreement with Effiong et al (2014) who found age to be negatively signed to output indicating that the farmers output decreases as the farmer’s age increases. The coefficient of education was positively related and statistically significant at 1% level of probability. The result implied that an increase in the level of education of the respondents in the study area will lead to a corre- sponding increase extent of use of improved goat produc- tion technologies in the study area. The result conforms to the researchers a prior expectation that education enhance farmers’ awareness, access to market as well as enhances extent of use of improved goat production technologies. Abudu et al., (2014) reported that increase in education of farmers positively influenced access, participation and adoption of improved agricultural practices. This is en- couraging because Imonikhe (2010) states that education enhances farmers’ ability to make accurate and meaning- ful management decision. The coefficient of coefficient of house size was pos- itively related and statistically significant at 1% level of probability. This result of implies that an increase in household size will result to a corresponding increase in the extent of use of improved goat production technol- ogies in the study area. The increase of household size suggests that more family labour would be readily avail- able since relatively large household size is an obvious advantage in terms of labour supply, where wage rate is relatively costly (Nwaobiala, 2013). The coefficient of farming experience was significant at 1% and positively related to extent of use of improved goat production technologies in the study area. The result implied that a unit increase in the years of farming will lead to an increase in the extent of use of improved goat production technologies in the study area. In agreement with this result, Onu and Maduka (2017) also found that farming experience has shown to enhance the participa- tion increasing agricultural output. The coefficient of annual farm size was statistically sig- nificant at 1% and positively related to the extent of use of improved goat production technologies in the study area. This result implies that a unit increase in the farmers’ farm size will lead to a corresponding increase in the extent of use of improved goat production technologies in the study area. The coefficient of income was statistically significant at 1% and it is positively related to extent of use of improved goat production technologies in the study area. This im- plies that a unit increase in income will lead to an increase in extent of use of improved goat production technologies in the study area. This may be attributed to the fact that an increase in income will enable the farmers to adopt new farming strategies, buy new equipment, ease transporta- tion and improves investment into the enterprise. DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 17 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 The null hypothesis which stated that there is no sig- nificant relationship between farmer’s socio-economic characteristics and the extent of improved goat production technologies in the study area was therefore rejected at 5% alpha level and concluded otherwise. 3.4 Constraints to use of improved goat produc- tion technologies Table 4. Distribution of respondents based on the con- straints to use of improved goat production technologies Constraints to use of improved goat production tech- nologies Frequen- cy Percentage The technologies expensive to adopt 92 76.67 Procedures were difficult to understand 67 58.83 Lack of veterinary experts around you 73 60.83 Technologies were against your cultural or religious beliefs 91 75.83 Lack of access to credit 120 100.0 Lack of credibility from source 120 100.0 Farm size is small 104 86.67 Difficulty in applying technology 112 93.3 Lack of technical support 69 57.5 You were afraid of taking risk 119 99.2 Source: Field Survey, 2019 Multiple responses recorded Table 4 showed Distribution of respondents based on the constraints to use of improved goat production technologies. On constraint to use of improved goat pro- duction technologies, all the respondents 100% agreed that lack of access to credit was a constraint in the use of improved technologies. Access to credit is expected to increase the adoption (use) of new technologies if the funds are not channeled to other household activities. The result implied that lack of access to credit is a major factor militating against the use of improved agricultural tech- nologies. The result agrees with the findings of Abdoulaye et al (2014), Aduani Sanni (2008), and Kasana et al (2010) that access to credit have a positive influence on adoption of new technologies. Another 100% agreed that lack of credibility from source of technological information is another reason why they don’t use or adopt improved agricultural practices. This result implies that sources of agricultural information or technologies are not honest. Lack of credibility may be in form of lack of follow up service, failure of technology to solve required problem e.t.c. The partnership between farmers and sources of technology must be enhanced and participatory approach must be used, to ensure that farm- ers are fully involved, Chambers et al; (2009). Again, a large proportion 99.2% agreed that they were afraid of taking risk. Risk refers to imperfect knowledge of the future. It talks about chances of occurrence events that leads to failure. The result implies that farmers were afraid of investing in the new technologies for fear of failure and loss of finance. This result disagrees with the finding of Tiamiyu et al (2009) that young farmers ex- hibit lower risk aversion and that older farmers are more likely to adopt innovation as a result of accumulated knowledge, capital and experience. Again about 93.3% agreed that difficulty in technology application served as a constraint in the use of improved technologies. The re- sult implied that the respondents lack the technical know how to handle the innovation. This result is in agreement with the findings of Simon (2006) that farmers require certain level of literacy in handling technical recommen- dation. Again 86.67% of the respondent agreed that small farm size was the constraints to use of improved technologies. The result implies that small farm size is a dis incentive to technology adoption. Most of the respondents are peasants and operate at a subsistence level which conforms with the findings of Djana,(2011). Another 76.67% agreed that cost adopting technolo- gies posed as a constraint to improved goat technology use. The result implied that most of the farmers could not afford the technologies as a result of high cost Gertrude; (2011). A major characteristics of Nigerian farmers is that they are poor and leave poor capital base FAO (2013). Cost may not always be in terms of money or financial benefit, but if what the farmer is expected to give up is less than what he is to gain, Okoosi, (2009). Furthermore, 75.83%, of the respondents agreed that technologies were against their cultural and religious be- lieve. The result implies that the respondents did not use technologies that were against their cultural and religious believes. For technologies to be adopted it must be com- patible with the existing values, norms and experience of the user. This findings together previous findings from others researchers has led to the formulation of demand driven extension by Government and other agencies, ac- cording to Getrude (2011). About 60.83% of the respondents agreed that lack of veterinary experts around them was one of the constraints to use of improved technologies , the result implies that the respondents did not use technologies that required the expertise of veterinarians because of their none availabil- ity in the study area. This finding have a dire implication DOI: http://dx.doi.org/10.36956/rwae.v2i2.382 18 Research on World Agricultural Economy | Volume 02 | Issue 02 | June 2021 Distributed under creative commons license 4.0 in the health management of flock. There is inflated cost of animal health service delivery which most times are unavailable, Getrude, (2011). Huge financial burden is incurred by farmers in an effort to manage diseases within their flock. Bester et al (2010). Again 58.83% of respondents agreed that difficulty in understanding procedures of technology was a constraint to use of improved goat production technologies. The re- sult implies that the technologies were complex for a large proportion of the farmers to understand. Technologies that are too complex are not readily adopted by farmers and this conforms to the findings of Djana, (2011). Finally, 57.50% of respondents agreed that lack of technical sup- port was a constraint in the use of improve goat technol- ogy, the result implied that farmers at one or the other in the adoption process required technical support from tech- nology developers or extension officers. This assistance can be provided through individual and group training. Lack of technical support may lead to failure in usage of a technology. 4. Conclusion and Recommendations The study provided an empirical evidence on use of improved goat production technologies in the study area. It could be inferred from the study that the respondents, highly utilized the available improved production technol- ogies. Furthermore, some factors (poor financial status, poor educational background, small size of holding, lack of access to credit, lack of technical support, etc.), served as serious constraint to use of improved goat production technologies and some of these factors, are beyond the control of rural farmers. In conclusion, greater use of available improved technologies will promote productivi- ty, and make goat production a profitable enterprise. Based on the findings of the study, the following rec- ommendations were made; (1) Credit should be made available to farmers by rele- vant governmental and non- governmental agencies to in- crease the level of use of available improved technologies. (2) Agricultural development programmes (ADPs) should provide necessary technical support to the farmers when needed. References [1] Abdelmagid, S.A. and Hassan, F.K. (2012). Factors aff-ecting the adoption of wheat production technol- ogy in the Sudan: Quarterly Journal of International Agriculture 35(4): 325 – 337. [2] Adunni Sanni, S. (2008). Animal Traction: An Un- derused Low External Input Technology among Farming Communities in Kaduna State, Nigeria. Tropicultura 26(1): 48-52. [3] Ani, A.O. and Undiandeye, U.C. 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