6256 analysis of FARO 44 Rice Technologies Adoption among Farmers in Nigeria Ibrahim Mohammed1 AbstRACt The study examines the adoption of FARO 44 rice among Fadama project particpants. A multistage sampling technique was used to select 336 Fadama project farmers from three agricultural zones. Data collected were analysed using adoption scale and factor analysis as well as frequency and percentages. Majority of males were within the active age of 19-36years; married with farming experience of 16-20 years having 0.5-1ha of rice plot. Technologies such as improved seed recommended spacing; seed per hole; use of granular fertilizer were adopted by male respondents. Processing technologies adopted by male were only threshing and bagging. For storage technologies male respondents had adopted jute bags; rhumbus and silos while female respondents used only jute bags because it is cheaper and easy to handle. Factors constraining adoption were communication gap between farmers and facilitators; untimely delivery of inputs; transplanting too tedious and high cost of false bottom. It was concluded that majority of the technologies were at evaluation and trial stage for both male and female respondents. Keywords : Fadama Project; Rhumbus ; FARO; Rice; Factor analysis; Adoption; Nigeria 1 Sr. Lecturer, Department of Agricultural Extension and Rural Development, Federal University of Technology, Minna, Niger State, Nigeria INTRoDUCTIoN “FADAMA” is a Hausa name for irrigable land-usually low-lying plain underlay by shallow aquifers found along Nigeria’s major river system. The Fadama III Additional Financing a collaborative project of the World Bank, Federal and State Government which has been of immense benefit to farmers in Niger State in Nigeria. The project has greatly enhanced the capacity of farmers, increased their income, boosted their economy and made life more worthy of living (Ibrahim, 2016a). This project has helped to develop the farmers-managed irrigation scheme. Rice has long become a stable food in the Nigeria food chain. Nigeria no doubt, has natural endowment to be self-sufficient in rice production in less than 5years but has been impeded all a long by conflicting policies and import waivers which permitted large foreign owned rice processing mills to import brown rice from South East Asia thereby exporting badly needed jobs to those countries of import and increasing unemployment locally. Farming is not just an option in Niger State but Received : 01-06-2019; Accepted : 22-07-2019 Research Article Journal of Extension Education Vol. 31 No. 2, 2019 DOI:https://doi.org/10.26725/JEE.2019.2.31.6256-6263 6257 a necessity, considering the vast fertile land and other resources, the state can feed the entire West Africa (Ibrahim, 2016b). The most important determinants of the effectiveness of research results is the level of adoption of innovation that it generates, and on their profitability (Caswell, 2001). A common problem for many individuals and organization is how to speed up the rate of diffusion of a research program’s innovations The main objective of the study is to examine the factor analysis of adoption of FARO 44 rice among Fadama users group (FUGs), describe the socio-economic characteristics of the Fadama user groups and identify constraining factors hindering adoption of FARO 44 rice variety. METhoDoLoGY The study was conducted in Niger State of Nigeria. Out of twenty-five local governments that made up the state, three local governments namely Katcha (Zone I), Shiroro Zone (II) Wushishi (III) were purposively selected for the study. Their selection were based on the preponderance of Fadama User Groups (FUGs). Multi-stage sampling techniques were adopted for the study. In the first stage two production clusters were selected from each of the zones. In the second stage seven production groups were randomly selected from each of the production cluster and finally four females and four males were interviewed from each of the production groups. This gave a total of 336 respondents. Data were collected from the respondents using structure interview scheduled. Data collected were analyzed using descriptive statistics like mean and percentage. Adoption scale analysis was used to analyse the level of adoption of FARO-44 technologies. Seven point likert scale was adopted to ascertain level of adoption. The scale scores are as follows : unaware (0), aware (1), interest (2), evaluation (3), trial (4), accept (5), reject (6). Each item will therefore be computed by multiplying the frequency of each response pattern with its appropriate nominal value and dividing the sum with the number of respondents to the item. This is summarized with equation below. XS=∑ Where XS= Mean score ∑= Summation f=frequency n= Likert nominal value nr= number of respondents Any respondent who had mean score of three (3) or greater than mean score is said to adopted FARO 44 Technology for that item while any score below three (3) is said to have rejected the technology in question. Factor analysis procedure was employed with varimax rotation. The constraints were grouped using principal component analysis with iteration and varimax rotation method. The cut-off point constraint loading was within the range of 0.3-0.5. variables that load in more than one constraint will be discarded following Akinnagbe (2013) and Ibrahim (2016). fn nr Analysis of FARO 44 Rice Technologies Adoption among Farmers in Nigeria 6258 The Model is presented in equation…… (1) Y1= a11X1 + a12X2 + **********+a1nXn Y2= a21X1 + a22X2 + **********+a2nXn Y3= a31X1 + a32X2 + **********+a3nXn * * * Yn= an1X1 + an2X2 + **********+anmXn Where; Y1, Y2 ………… Y2 =Observed variable/ constraints to linkage / practice a1- an =Constraints to correlation coefficients; X1, X2, ……… Xn = Unobserved underlying factors constraining linkage practice FINDINGS AND DISCUSSIoN Table 1 shows that (64.3%) of males were in the age bracket of 19-36 years which is the active stage of life making it possible to withstand the rigor associated with the farming activities while only 41.7% of their female counterparts were in that age range. About 62.5% of the male respondents had secondary education while only 30.4% of the female counterparts had the same. This means that most of the female respondents were not allowed to continue with their secondary education because of marriage or other reasons. About 83.4% of male respondents had farming experience of 11-20 years while only 32.8% of their female counterparts had the same. This implies that with more experience in farming activities, farmers become less averse to the risk. All (100%) respondents were members of one cooperative or the other. This was possible because the sample was drawn from production clusters. Almost all 98% of the two categories of the respondents cultivated one hectare of land. which may probably be as a results of the Fadama III AF package. Majority 68.5% of male respondents had the house hold size of 6-10 persons while only (35.7%) of their female counterparts had same, probably because of Table 1 Distribution of Respondents according to Socio-economic Characteristics n=336 Sl. No. Socio-economic characteristics Male Female Pooled F % F % F % I Age (years) 1 1-18 - - 3 1.8 3 0.9 2 19-36 108 64.3 70 41.7 178 53.0 3 37-54 50 29.8 90 53.6 140 41.7 4 >54 10 6.0 5 3.0 15 4.5 II Marital status 1 Single 3 1.8 5 3.0 8 2.4 Journal of Extension Education 6259 the polygamy being practiced in most of the rural farm families in the rural communities. Effiong (2005) reported that a relatively large house hold size enhances the availability of Sl. No. Socio-economic characteristics Male Female Pooled 2 Married 165 98.2 155 92.3 320 95.2 3 Separated - - 4 2.3 4 1.2 4 Divorced - - 4 2.3 4 1.2 III Educational level 1 No schooling 3 1.8 25 14.9 28 6.0 2 Primary 55 32.7 90 53.6 145 43.1 3 Secondary 105 62.5 51 30.4 156 46.4 4 Tertiary 5 3.0 2 1.2 7 2.1 IV Membership of cooperative 1 Member 168 100 168 100 336 100 2 Non-member - - - - - - V Farming experience 1 <5 - - 7 4.2 7 2.1 2 5-10 20 11.9 89 53.0 109 32.4 3 11-15 50 29.8 35 20.8 85 25.3 4 16-20 90 53.6 20 12.0 110 32.7 5 21-25 6 3.6 15 9.0 21 6.3 6 26-30 2 1.2 2 1.2 4 1.2 VI Farm size (ha) 1 0.5-1.0 165 98.2 166 98.8 331 98.5 2 1.1-1.5 3 1.8 2 1.2 5 1.4 VII household size - - 1 0-5 50 29.8 105 62.5 155 46.1 2 6-10 115 68.5 60 35.7 175 52.0 3 11-15 3 1.8 3 1.8 6 1.8 4 >15 - - - - - - VIII occupation 1 Full time farmer 165 98.2 128 98.2 293 87.2 2 Part time farmer 3 1.8 40 23.8 43 12.8 Analysis of FARO 44 Rice Technologies Adoption among Farmers in Nigeria 6260 labour. This implies that adoption cost, risk perception labour requirement and human capital requirements are definitely reduced. Level of FARO 44 Variety Adoption Technologies The results show that recommended improved rice seed had the highest frequency of adoption with a score of 93 for the male famers followed by 66 for recommended Table 2 Frequency Distribution of Male and Female respondents by Stages of Adoption of FARO 44 Rice Production, Processing and Storage Technologies Sl. No. TEc Una- -ware Aware Interest Evalu ation Trial Ado- -ption Reje- -cted Adoption Mean Score I Production Technologies M F M F M F M F M F M F M F M F 1 IS 0 0 12 19 20 38 13 25 30 32 93 54 0.0 0.0 4.0 3.4 2 TP 12 0 25 29 26 26 25 43 50 41 30 29 0.0 0.0 2.4 3.1 3 DP 31 0 35 25 37 39 30 53 35 30 0.0 21 0.0 0.0 2.0 2.9 4 TD 0 0 30 15 25 25 20 22 44 25 49 44 0.0 0.0 3.3 2.7 5 S 45 36 38 45 27 29 33 44 20 14 5 0.0 0.0 0.0 1.8 1.9 6 RS 0 0 7 6 20 15 35 45 40 35 66 67 0.0 0.0 3.8 3.8 7 SPH 0 0 45 58 25 35 27 25 28 20 43 10 0.0 20 3.0 2.7 8 PB 45 50 54 60 25 30 24 28 20 0.0 0.0 0.0 0.0 0.0 1.4 1.2 9 FAG 0 0 12 30 17 25 25 45 35 33 79 35 0.0 0.0 3.9 3.1 10 FAS 0 0 35 45 47 44 42 37 25 27 19 15 0.0 0.0 2.7 2.5 11 WCM 0 45 35 40 27 33 47 22 33 28 26 0.0 0.0 0.0 2.9 1.7 12 MBS 45 40 35 25 25 37 20 27 15 19 28 20 0.0 0.0 2.1 1.5 13 WM 0 0 15 17 25 47 30 38 40 34 58 32 0.0 0.0 3.6 3.1 14 FAL 0 38 45 40 40 39 30 27 23 24 10 0.0 20 0.0 2.6 1.8 15 FAS 0 44 55 36 45 45 25 21 10 22 13 0.0 20 0.0 2.3 1.6 16 H 0 45 35 38 28 40 45 32 33 13 7 0.0 20 0.0 3.1 1.7 17 R 0 32 35 41 45 34 37 27 31 34 20 0.0 0.0 0.0 2.7 1.9 II Processing Technologies 1 T 0 0 25 29 15 35 25 82 20 12 83 10 0.0 0.0 3.7 2.4 2 FB 45 0 25 20 35 15 15 75 25 35 10 23 13 0.0 2.2 3.2 3 DS 42 0 35 35 25 25 27 70 19 20 20 18 0.0 0.0 2.0 2.3 spacing of 20 cm by 20 cm. This means that male respondents want to optimize the space and maximize outputs. Recommended quantity of granular fertilizer application had a score of 79. This implies that respondents attach value to granular fertilizer than any other production inputs in the study area apart from improved rice seed. This may probably be attributed to the role fertilizer plays in increasing the output of the farmers. Journal of Extension Education 6261 Source: Field survey, 2017 Where; TEC= Technologies ranging from 1-26 Production Technologies I.S (Improve seed)25kg of FARO 44/ ha; T.P (Time of planting) (June) D. P(Depth of planting) 3-4cm T.D(Touch down) (pre- emergence herbicide) S(Solito) (post emergence herbicides);R.S (Recommended spacing)20cm by 20cm SPH(Seed per hole)4-5 seed P. B (Puddling and bonding FAG (Fertilizer application “granular”) first dose (NPK 15: 15: 15: 4 bags); FA (Fertilizer application) second dose (Urea 46:0:0 2bags); W.C (weed control measure) MBS(Methods of bird scaring) WM(Water management)FA (Fertilizer application) “liquid” first dose (NPK 2liters, Boron 2liters; FA(Fertilizer application second dose (Urea liquid 2liters); H. (Harvesting) R.(Recoup) 25% Processing Technologies T (Threshers) UFB (Use of False bottom) for per boiling; DS. (Drying slabs) D. (De-stoner) MG. (Measurement gauge)B. Bagging. Storage Technologies 23. JB (Jute bag) R.(Rhumbus) WH(Ware house) S : Sale 85% to off takers. Factors Constraining Adoption of FARO 44 among Respondents Table 4 shows factor matrix on adoption constraints. Factors base on variable loading were used; four factors were identified and named. Factor one (1) were economic related factors, (2). policy related factor; cultural related factors (3) and attitude related factors (4). Items that loaded high in factor 1, (economics related constraints), included Poor relationship between farmer/facilitator and desk officers (eigen value=.373); Poor monitoring and evaluation (eigen value =.327); Difficulty in raising counterpart fund (eigen value=.354); In ability to recoup 25% of the total harvest (eigen value=.301); Farmers cum herdsmen Sl. No. TEc Una- -ware Aware Interest Evalu ation Trial Ado- -ption Reje- -cted Adoption Mean Score 4 D 30 0 45 15 25 18 30 25 20 30 18 80 0.0 0.0 2.1 3.8 5 MG 0 0 45 47 35 40 25 33 20 19 15 17 28 12 2.9 2.6 6 B 0 0 12 55 18 25 25 35 45 28 68 15 0.0 10 3.8 2.4 III Storage Technologies 1 JB 0 0 27 30 29 38 15 37 37 30 60 33 0.0 0.0 3.4 3.0 2 R 0 0 20 38 35 20 32 30 43 45 38 35 0.0 0.0 3.2 2.6 3 WH 30 30 35 25 30 28 20 20 25 18 18 20 10 27 2.4 2.8 4 S 30 38 25 20 20 26 15 22 30 29 20 19 28 14 3.0 2.5 Analysis of FARO 44 Rice Technologies Adoption among Farmers in Nigeria 6262 clash (eigen value = .302), High cost of false bottom (eigen value=.486);Items that loaded high in factor 2, (policy related constraints), is Untimely delivery of inputs(eigen value= .783). while for cultural related factors were; Transplanting is too tedious (eigen value= .413); poor saving culture (eigen value.335); while for attitude related factors are wide commutation gap between the famers and facilitators (eigen value.796) and Liquid fertilizer not effective (eigen value. 460). Table 4 Factors Constraining Adoption of FARO 44 technologies Sl. No. Variables Factor 1 Factor 2 Factor 3 Factor 4 Remarks 1 Business plan not in line with farmers demand - - -.032 .025 D 2 Poor relationship between farmer/ facilitator and desk officers .373* .134 .242 .040 S 3 Poor monitoring and evaluation .327* .109 .282 .204 S 4 Wide Communication gap between the famers and facilitators .149 .035 .065 .796* S 5 Untimely delivery of inputs .161 .783* .039 .077 S 6 Germination percentage is low -.431* .041 .192 .042 S 7 Difficulty in raising counterpart fund -.354* .020 .204 .045 S 8 Liquid fertilizer not effective -.079 .050 .045 .460* S 9 Transplanting is too tedious .164 .066 .413* .158 S 10 Insufficient rain fall -.066 - -.126 - D 11 Problem of qualee bird .014 - .163 - D 12 Incidence of gall midge .175 - .168 - D 13 Problem of iron toxicity .290 .0665 .107 -.145 NS 14 Inability to recoup 25% of the total harvest .301* .261 .061 -.032 S 15 Low pricing by the off takers .080 .049 .159 .007 NS 16 Language barrier .025 .103 .060 .298 NS 17 Poor saving culture .103 .055 .335* -.137 S 18 Farmer cum herdsmen clash .302* .079 .078 .058 S 19 High cost of milling machine - .276 - .007 D Journal of Extension Education 6263 Key: D= Discarded, S=Significant NS= Not significant Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. *Significant CoNCLUSIoN It is concluded that male farmers attached more value to adoption of production technologies than processing technologies while female respondents had adopted most of the processing technologies than production technologies. Moreso, recommended spacing of 20 cm by 20 cm had the highest percentage (74%) of adoption from the male respondents while Solito (post emergence herbicide) had the highest percentage (28%) of rejection from female respondents. Majority of the technologies were at evaluation and trial stages for both male and female respondents. The study recommends strengthening of the communication process among all the stakeholders. REFERENCES Akinnagbe, O.M. (2010). 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