performance evaluation of integrated farmers scheme

ISBN: 978-978-34560-5-7

AKSUJAEERD 1 (1): 1 – 10, 2018 AKSU Journal of Agricultural Economics, Extension and Rural Development. © Department of Agricultural Economics and Extension, Akwa Ibom State University, AKSU, Nigeria, December.

PERFORMANCE EVALUATION OF INTEGRATED FARMERS SCHEME IN AKWA IBOM STATE, NIGERIA Ene, Ukeme1 and Asa, Ubong1. Department of Agricultural Economics and Extension University of Uyo, Uyo, Nigeria Corresponding author: [email protected]

Abstract This study assessed the performance of Integrated Farmers’ Scheme in Akwa Ibom State Nigeria. The research study was necessary to evaluate the objectives of the IFS over a period of time to ascertained the impact of the scheme in promoting and dignifing farming as a business among youths in the study area. The specific objectives of the study were to; compare the difference in output levels between beneficiaries and non-beneficiaries of the scheme; ascertain the beneficiaries’ level of satisfaction with the scheme; and determine the number of beneficiaries’ whose farming business are still functioning in the scheme. A multi-stage sampling procedure was used to select two hundred and twenty eight (228) beneficiaries and seventy five (75) non-beneficiaries for the study. Data obtained were analyzed using z-test and Spearman’s rho rank correlation. The z-test analysis result showed a significant difference between output levels of beneficiaries and non-beneficiaries on the scheme, since the calculated t-values for crop (2.64) and livestock (2.94) enterprises was greater than the critical t-value (2.41). Majority of the beneficiaries were satisfied with the integrated farmers’ scheme with 8 items out of 11 and with a mean score of 2.50 computed from 4-point likert scale. The findings revealed that majority of the beneficiaries’ farming businesses are still functioning with 69.3%, 94.4%, 83.6% and 65.4% in each of the batches used for the study respectively. It is recommended, among others, that the scheme take cognizance of the needs of the beneficiaries with respect to the volume of loan given to the beneficiaries; the methods of loan disbursement, as well as loan repayment methods, since most of the respondents expressed dissatisfaction with the volume of loan given by the scheme.

Key words: Performance Evaluation, Integrated Farmers Scheme, Output Enterprises, Level of Satisfaction. Introduction Agriculture contributes immensely to the Nigeria economy in the provision of food for the increasing population, supply of raw materials to industries, major source of employment and generation of foreign exchange earnings (FAO, 2006). Nigeria is basically an agro-based economy with abundant land and water resources to enhance agricultural development. Agricultural sector in the 1960s contributed up to 64% of the total GDP of Nigeria, but gradually declined to 48% in the 1970s, but today agriculture contributes about 24.43% to Nigeria’s GDP NBS, (2016).During the oil boom, there was a diverse agro-ecological weather conditions that support a variety of farming activities (Olajide and Tijani, 2009). However, successive administrations over the years neglected agriculture and failed to

diversify the economy away from being dependent on the capital-intensive oil sector. Apart from neglegence of the agricultural sectors, there was also little or no motivation to the youth across the country to enable them engage effectively in agrarian activities. Nigeria, has the potentials to return to its previous position in agriculture if adequate attention is given. Ebong (2014) ascertained that there is a great potential in agricultural sector if well developed, for it’s capable of contributing more to the Gross Domestic Product. In recent times, the call for youth involvement in agriculture across the nation has lingered and is becoming stronger now based on the current socio-economic hardship, ageing farming population, food insecurity and unemployment in the country. A major concern of the Federal and State governments in Nigeria is how to

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Ene, Ukeme. and Asa, Ubong

tackle the problem of unemployment among the youths. Various regions in Nigeria have designed and executed self-empowerment schemes to enhance the economic empowerment of youths, (Umeh and Odo, 2008). One of such schemes is the Akwa Ibom State Integrated Farmers Scheme. This scheme was established within the Ministry of Agriculture and Natural Resources by an Edict of 1998 and subsequently signed into law by the state government of Akwa Ibom State to reduce dependence on government for employment, create and sustain new jobs and reduce frustration among our young people. This scheme is one of the strategies of the state government to involve the youths in agriculture to reduce dependency on government and reduce unemployment through tapping the vast agricultural potentials in the state. The State government in an attempt to encourage youths involvement in agricultural production, empowered the youths with credit facilities between (N300,000.00 - N500,000.00) to each beneficiary to invest in meaningful agricultural enterprises like crop, livestock and fisheries production which settled quite a number of unemployed graduates in the State over time. The problem today in a developing Nation like Nigeria is that youth unemployment rates in the formal economy is high because of the following factors; educational opportunities beyond the fourth grade cannot be accessed by half the population, and where there is a clear mismatch between the skills that schools and universities teaches and the ones that employers want, or where the growth of the country’s economy has trouble keeping up with the rapid growth of its youth population. Such would describe the plight of today’s youth in many developing countries especially, countries where corruption, is the key to success, (Integrated Regional Information Network, 2007). In response to this dilemma, government agencies have become increasingly interested in supremes’ workforce, programmes development strategies, that is, “livelihood developments” especially for young people from marginalized backgrounds.The multifaceted benefits of IFS include economic benefits, in terms of increased food production; social function in terms of provision of employment opportunities for the excess labor force displaced from other sectors AKSUJAEERD

in the society (FAO, 2004). The main objectives of IFS is to ameliorate the suffering of the youth as a result of unemployment and the philosophy guiding it’s establishment is the eradication of poverty and unemployment at the grass root level. Furhermore, IFS seeks to enhance the; identification and recruitment of dynamic youths that are trainable and have the desire to make a career in farming, get better educated and youthful segment of our society to farm and do it as means of livelihood; identify profitable farm business ventures in crops and attract investment in these enterprises; promote and dignify farming as a business; provide self-employment to graduates of various levels of education etc (AKSIFS, 2003). However, the main objective is to sustainably increase the output of IFS beneficiaries and at the long run, the loan can be repaid. Many credit institutions do often face challenges in the repayment of loans, especially in the agricultural sector due to poor performance. Etukumoh, (2010) reported low repayment of loans (6.9%) obtained by the IFS beneficiaries implying that repayment of loans depends on the performance output in farm production and income.The integrated farming system has revolutionized conventional farming, livestock, horticulture, agro-industry and allied activities (Chan, 2006). This could be crop-fish integration, livestockfish integration, crop- livestock, fish and other enterprises, (Tokrisha, 2006). The benefit of integrated farming cannot be over emphasized (Ugwumba and Orji, 2006). IFS has been confirmed to reduce cost of production and thus increase farmers productivity, income, nutrition and overall welfare, (Tokrisha, 2006); if properly adopted as investment in agriculture, IFS will improve the personal savings and health of farmers. It is on this basis that the study investigated the performance of the scheme so far, so as to determine the difference in output between IFS beneficiaries and non-beneficiaries in the study area.The specific objectives of the study were to; compare the difference in output levels between beneficiaries and nonbeneficiaries of the scheme; ascertain the beneficiaries’ level of satisfaction with the scheme; determine the number of beneficiaries’

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Performance evaluation of integrated farmers scheme whose farming businesses are still functioning in the scheme.

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terrain, the alluvial plains (mangrove and flood plains), the beach ridge sands and the rolling sandy plains. The topography is a gently undulating plain being part of the coastal plain sands of Calabar Formation. Agriculture is the highest economic activity in the State and contributes a large percentage to its income that is second only to petroleum. The inhabitant engage in farming as a way of augmenting and supplementing family income

Methodology Study Area The study was conducted in Akwa Ibom State, Nigeria. Akwa Ibom State is located in SouthEast agro-ecological zone with 31 Local Government Areas. Akwa Ibom State is bounded by Abia State in the North, Rivers State in the West, Cross River State in the East and the Atlantic Ocean in the South and lies between latitude 40 331 and 50 351 North and longitude 70 351 and 80 251 East. Akwa Ibom lies within the humid tropical with annual precipitation ranges from 2000-3000mm per annum. The climate is characterized by two seasons, namely, the wet or rainy season and the dry season. The region is flat and low-lying, but three major physiographic units can be identified from the

Population and Sampling Procedure A multi-stage samplingtechnique was used to obtain data for the study. First, Four (4) out of six batches were purposively selected. The selected batches were the first, third, forth, and sixth batches.This was to ensure that batches that collected N300, 000.00 and N500, 000.00 had an equal chance of being part of the sample. Second, proportionate random sampling of four (4) selected batches was carried out.

Table 1: Proportionate selected batches of the beneficiaries on (10%) Batches

No of beneficiaries

No of respondents (10%)

1st batches

153

15

3rd batches

310

31

4th batches

620

62

6th batches 1200 Total 2283 Source: Integrated Farmers Scheme Office

120 228

Thirdly, random selection of 10% from each of beneficiaries. The selected zones were; Abak, the selected batches, giving a total of (228) Ikot Ekpene and Uyo. Five farmers from each of beneficiaries as the sample size. Three zones out the local government areas in each zones were of six Agricultural Development Programme randomly selected. (ADP) zones were selected to choose nonTable 2: Number of AKADEP registered farmers (non-beneficiaries of IFS) ADP Zone Uyo Ikot Ekpene Abak Total Source: AKADEP, 2015

Number of Registered Farmers 25 25 25 75

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This was to ensure that the selected local government area from each zones also have an equal chance of being selected based on the number of registered farmers, giving a total sample size of 25 respondents, respectively. In all, a total of seventy five (75) registered farmers for non-beneficiaries’ were selected.

d = Difference in the ranked values of X and Y n = Number of paired variables (X and Y) Results and Discussion Differences in Output Levels (kg) between Beneficiaries and Non-beneficiaries of Integrated Farmers’ Scheme The distribution of respondents based on the output of their crop enterprise is presented in Table 3. The Table shows that an average quantity of Cassava tubers, Plantain, Pineapple and Vegetables from the output of beneficiaries was greater than that of the non-beneficiaries with (3.2-1.7), (26.3-21.1), (55.6-28.3) and (20.3-12.7), respectively. This could be attributed to the advantage of loan enjoyed by the beneficiaries of the scheme. The Table further reveals that the average value of the output in crop enterprises of the beneficiaries of integrated farmers’ scheme in Akwa Ibom State were N33, 657.77 while the average value of output in crop enterprise of non-beneficiaries was N20, 269.58. The result shows that the average output value of the beneficiaries is greater than that of non-beneficiaries. This is due to the Integrated Farmers’ Scheme intervention of giving loans to its beneficiaries to encourage them to be engaged in commercial farming which enhanced their crop production activities. This result agrees with FAO (2004) which states that providing loans to farmers is thought to be an effective measures of stimulating and increasing farm production output.

Model Specification The formular for student’s z-test used for testing is as follows: The Z- test model for hypothesis is stated thus,

Z cal 

X1  X 2 S2 X 2 S 2 X1  n1n2

…………Equation (1)

where X1 = Mean output of beneficiaries X2 = Mean output of non-beneficiaries S2X1 = Variance for output of beneficiaries S2X2 = Variance for output of non-beneficiaries n1 = number of sampled beneficiaries n2 = number of sampled non-beneficiaries The formular for Spearman’s rho correlation used for testing is as follows: =1−

(

)

………………….Equation

(2)

Where: rxy = correlation coefficient Table 3: Distribution of respondents based on output of crop enterprises S/N Items

1

Cassava tubers(tons/ha)

Beneficiaries Non- beneficiaries Average price Average Total value Average Total value per unit quantity quantity N15,135.3 3.2 N48,432.96 1.7 N25,730.01

2

Plantain (bunches)

N670.00

26.3

N17,621.00 21.1

N14,137.00

3

Pineapple (kg/ha)

N260.90

55.6

N14,506.04 28.3

N7,383.57

4

Vegetables (kg/ha, waterleaf) Total

N2,663.60

20.3

N54,071.08 12.7

N33,827.72

N134,631.08

N 81,078.3

N33,657.77

N 20,269.58

Mean Field survey, 2015 AKSUJAEERD

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A z-test analysis of the difference between the output levels between beneficiaries and nonbeneficiaries, as shown in Table 4.4, reveals that a significant difference exists between the output levels of beneficiaries and non-beneficiaries of Integrated Farmers’ Scheme both in crop enterprises and livestock enterprises since the calculated t-values for both enterprises are greater than the critical t-value (2.41). The result reveal that the Integrated Farmers’ Scheme, as an intervention programme in the state, had a positive effect on crop and livestock enterprises output levels of beneficiaries of the Scheme. The result agrees with Onumadu and Inyang (2015) who reported that agricultural output of beneficiaries of Integrated Farmers’ Scheme project in Akwa Ibom State is higher than the output of non-beneficiaries of the Scheme, and that the Scheme has therefore had an appreciable positive effect on the beneficiaries output.

Distribution of respondents based on output of livestock enterprises The distribution of livestock enterprises outputs of beneficiaries and non-beneficiaries of Integrated Farmers’ Scheme during the period of this study is shown in Table 4. The average quantity in livestock output of beneficiaries and non-beneficiaries was different as that of the beneficiaries was more than the nonbeneficiaries in areas of Fishery (212.7-122.8), Broilers (237.5-171.8), Layers (307.4-157.2), Goatry (2.8-2.5) and Piggery (3.6-2.4).The result clearly shows the advantages granting loans for investment in agricultural productivity. The average value of output of the beneficiaries is N128, 316.37 while the average value of the output of non-beneficiaries is N110, 755.56. The finding shows that the average value of output of the beneficiaries is greater than that of nonbeneficiaries corroborating the result of Table 3.

Table 4: Distribution of respondents based on output of livestock enterprises S/N Items

Beneficiaries

Non-beneficiaries

1

Fishery (kg)

Average (N) Average Total value price/unit quantity(u nit) N706.7 212.7 N150,315.09

Average quantity

Total value (N)

122.8

N123,622.76

2

Broiler (kg)

N909.30

237.5

N215,958.78

171.8

N156,217.74

3

Layers (Eggs in crates)

N556.50

307.4

N171,068.10

157.2

N87,481.80

4

Goatry (size)

N9,800.00

2.8

N27,440.00

2.5

N24,500.00

5

Piggery (size)

N21,333.30

3.6

N76,799.88

2.4

N51,199.92

Total

N641,581.85

N443,022.22

Mean

N128,316.37

N110,755.56

Source: Field survey, 2015

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Table 5: z-test Analysis of differences in output levels between beneficiaries and Non-beneficiaries of integrated farmers’ scheme in Akwa Ibom State Crop Enterprises Livestock Enterprise Groups Df Zcal Critical t Decision Df Zcal Critical t Decision Beneficiaries 8 2.64 2.41 Significant 8 2.94 2.41 Significant Non-beneficiaries Note: df = degree of freedom, zcal = calculated t, level of significance = 0.05. Computed from field survey data, 2015 Table 6: Anaysis of Mean, Square of std daviations, Degree of freedom, zcal and Decision for Crop Group Mean Variance or N DF z-cal tDecision Sq of std dev critical Beneficiaries

N134,631.08 1.77

228

Non-beneficiaries

N20,269.58

75

1.6

8

2.64

2.41

Significant

Source: Field survey, 2015 Table 7: Anaysis of Mean, Square of standard deviations, Degree of freedom, zcal and Decision for Livestock Group Mean Variance or N DF z-cal tDecision Sq of std dev critical Beneficiaries

N128,316.37 1.89

228

Non-beneficiaries

N110,755.56 2.00

75

8

2.94

2.41

Significant

Source: Field survey, 2015 Beneficiaries Satisfaction with Integrated Farmers Scheme In Table 8 an item analysis of the Beneficiaries’ Satisfaction Scale (BSS) revealed that the beneficiaries showed satisfaction in 8 out of the 11 items BSS. The mean scores of 2.50 computed from a 4-point likert scale serves as the cut-off point between satisfaction and dissatisfaction of the items. The items beneficiaries showed satisfaction most were;. “I am satisfied with the new skills I have learnt as a beneficiary of the Scheme” ( = 3.32), “I am satisfied with the output of my crops/livestock enterprises as a beneficiary of the scheme” ( = 3.20) and “I am satisfied with the trainings I have received from the Scheme ( = 3.09). Training of the beneficiaries of the Scheme is an important objective of the Integrated Farmers’

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Scheme, and a significant factor that engenders satisfaction among stakeholders in the agricultural sector (Ibrahim, Muhammad, Yahaya and Luka, 2008). The Scheme also offered technical help to the beneficiaries of the Scheme and FAO (2010) stated that quality of technical help influence beneficiary satisfaction in agricultural projects. However, the beneficiaries showed dissatisfaction in three items in the beneficiaries’ satisfaction scale namely: “the loan I obtained from the scheme was sufficient to help improve my farming business ( = 1.00), the Scheme has improved food security in the state ( = 1.09) and “I am satisfied with the monitoring and evaluate activities of the officials of the Scheme “(180).

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Table 8: Item analysis of beneficiaries’ satisfaction with Integrated Farmers’ Scheme in Akwa Ibom State, Nigeria Statements Mean Remark 1

I am satisfied with the output from my crops/livestock enterprises as a beneficiary of the scheme 3.20 Satisfied 2 The profit from my enterprise have increased since I Satisfied became a beneficiary of the Scheme 3.01 3 I am satisfied with the trainings I have received from the 3.09 Satisfied Scheme 4 I am satisfied with the Scheme as a channel of improving 2.62 Satisfied my income generating activities 5 The loan I obtained from the Scheme was sufficient to help 1.00 Dissatisfied improve my farming business 6 The Scheme has helped reduced poverty among youths in 3.00 Satisfied the state 7 I am satisfied with the monitoring and evaluation activities 1.80 Dissatisfied of the official of the scheme 8 My farm business has expanded due to being beneficiary of 3.00 Satisfied the Scheme 9 The scheme has improved food security in the state 1.09 Dissatisfied 10 I am satisfied with the new skills I have learnt as a 3.32 Satisfied beneficiary of the scheme 11 The scheme has reduced unemployment among youths in 2.96 Satisfied the state Source: Field survey, 2015 Most of the beneficiaries desired an increase in to loan facilities granted to the farmers by the the loan package beneficiaries in order for them government. to increase their production capacities, which the The correlation analysis result of the relationship officials of the Scheme turned down based on between the beneficiaries’ satisfaction and their the state government’s policy/ directive. The perceived effectiveness of Integrated Farmers’ mean of the summated score of the BSS is 25 Scheme in Akwa Ibom State. The Table reveals which served as a cut-off point between a high positive significant relationship between satisfaction and dissatisfaction levels of the beneficiaries’ satisfaction with the Scheme beneficiaries of the Scheme (Oladele and Mabe, and their perceived effectiveness of the Scheme 2010). The Table reveals that 62.3% of the at 1%. level (r = 0.68 p< 0.01). This implies a beneficiaries of the Scheme were satisfied with direct relationship between the two variables the Scheme while 37.7% were dissatisfied with signifying that an increase in the beneficiaries’ the scheme. The high level of satisfaction of satisfaction with the Scheme is directly related beneficiaries of the Scheme could be attributed to an increase in their perception of the to the positive effect of the Scheme on their effectiveness of the Scheme in the study area. outputs as depicted. According to Umar, Elias et al., (2015) reported that the perceived Ofeikwu, Shuaibu, Dunaya and Tambari (2015) economic return from being a beneficiary of an a high level of satisfaction of farmers on a agricultural programme is directly related to the commercial farming programme was attributed beneficiaries’ satisfaction with such programmes }}}}}}}}}}}}

in developing countries.

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Table 9: Level of satisfaction of beneficiaries of Integrated Farmers’ Scheme in Akwa Ibom State Satisfaction status BSS satisfaction score Frequency Percentage Dissatisfied < 25 Satisfied  25 Total Source: Field Survey, 2015

86 142 228

37.7 62.3 100

Table 10: Correlation analysis of the relationship between the beneficiaries’ satisfaction and their perceived effectiveness of the IFS in the state Satisfaction Effectiveness Satisfaction Correlation coefficient 1.000 0.681*** Sig. (2-tailed) 0.000 N 228 228 Effectiveness Correlation coefficient 0.681*** Sig. (2-tailed) 0.000 N 228 Note: *** = Correlation is significant at the 0.01 level Source: SPSS version 20.0 computer printout The perceived effectiveness of the Integrated Farmers’ Scheme by the beneficiaries is influenced by the perceived economic return from being a beneficiary of the scheme which in turn affects the beneficiaries’ satisfaction with the Scheme. Irani and Scherler (2002) also reported that there is a relationship between respondents’ satisfaction and their perceived effectiveness in their study on” job satisfaction as an outcome measure of the effectiveness of an agricultural communications academic program”. Beneficiaries who’s farming business are still functioning in the scheme The distribution of the beneficiaries based on who’s farming businesses were still functioning at the time of this study. The table reveals that among the Batch I beneficiaries, 69.3% of them

still had a functioning agricultural business, 94.4% of Batch 2 beneficiaries still have a functioning businesses, 83.6% of Batch 3 beneficiaries still had a functioning business, and 65.4% of Batch 4 beneficiaries still had functioning agricultural businesses. The findings revealed that majority of the beneficiaries continued functioning in agricultural businesses based on the training and assistance they received from the scheme thereby indicating that the scheme positively impacted them. However, some of the beneficiaries did not continue with agricultural businesses and this could be attributed to factors such as; psychological factors, Government policy induced factors, insufficient funds and environmental factors. Abdul-lateef and Sharifah (2015) share similar views.

Table 11: Distribution of beneficiaries based on number of those still functioning in (IFS) farming businesses Batches Number of beneficiaries that Number of beneficiaries still % of those still completed training functioning in the IFS business functioning in the business Batch 1 150 104 69.3% Batch 3 307 290 94.4% Batch 4 615 514 83.6% Batch 6 1120 733 65.4% Source: IFS office, 2015

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Performance evaluation of integrated farmers scheme Conclusion The study evaluated the performance of Integrated Farmers’ Scheme in Akwa Ibom State, Nigeria by comparing the output levels of beneficiaries and non-beneficiaries of the Scheme. The study reveals that a significant difference exists between the crop and livestock enterprises output levels of beneficiaries and non-beneficiaries of the Scheme as the output levels of the beneficiaries was significantly higher than that of the non-beneficiaries of the Scheme. The result revealed that a high positive significant relationship exists between the beneficiaries’ satisfaction with the Scheme and their perceived effectiveness of the Scheme at 1%. level (r = 0.68 p< 0.01). The findings revealed that majority of the beneficiaries continued functioning in agricultural businesses based on the training and assistance they received from the scheme thereby implying that, the scheme has a positive impact on them. In conclusion that the Integrated Farmers’ Scheme of Akwa Ibom State had a positive impact on the crop and livestock enterprises’ output levels of the beneficiaries of the Scheme.

References Abdul-lateef, A. L. and Sharifah, N. R. (2015). Identifying the causes of decline in youth participation in agricultural empowerment program of youth integrated training farm malete, Kwara State. Journal of Research on Humanities and Social Sciences, 5 (7): 194201. Chan, G. L., (2006).Integrated Farming System: What Does Integrated Farming Do. Available at http:www.actahort.org/books/ 655-36htm.Retrieved 2016. Ebong, V. O. (2014). Analysis of Agribusiness Performance of Poultry Farmers of Integrated Farmers Scheme in Akwa Ibom State, Nigeria: Department of Agricultural Economics and Extension, University of Uyo. Sci-Afric Journal of Scientific Issues, 2(6): 274-283. Etukumoh, E. (2010). Agricultural Loan Default among Farmers in Nigeria. The Case of Akwa Ibom State Integrated Farmers Scheme: Unpublished M.Sc. Dissertation, Department ofAgricultural Economics and Extension, University of Uyo, Nigeria.

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Recommendations Based on the findings of the study, the following recommendations are made: It is recommended that the Scheme should take cognizance of the needs of beneficiaries in respect to the volume of loan given to the beneficiaries, the methods of loan disbursement/repayment, since most of the respondents expressed dissatisfaction with the volume of the loan given by the Scheme as well as the loan disbursement/repayment, methods used by officials of the Scheme.It is recommended that officials of the Scheme should make their monitoring and evaluation services more effective/efficient since most of the beneficiaries expressed dissatisfaction with the monitoring/evaluation activities of officials of the Scheme.The technical assistance given to the beneficiaries of the Scheme should be improved to ensure that such assistance meet the needs of the beneficiaries and even further improve the output levels of beneficiaries. This is due to the fact that most of the beneficiaries perceive the technical assistance aspect of the Scheme as being ineffective, as this will help the beneficiaries businesses to stand the test of time. Elias, A., Nohmi, M., Yasunobu, K., and Ishida, A. (2015). Farmers’ Satisfaction with Agricultural Extension service and its Influencing Factors: A Case Study in North West Ethiopia, Journal of Agricultural Science Technology, 18 (1): 39-53. FAO, (2004). FAO Declare war on farmers not Hunger: Grain, 16 June 2004. Grain;org. FAO, (2010). Beneficiary Satisfaction and Impact Assessment of ISFP TCP Projects: a Global Synthesis, Office of Knowledge, Exchange, Research and Extension, Rome, Food and Agriculture Organization. Ibrahim, H., Muhammad, D. M., Yahaya, H. and Luka, E. G. (2008). Role perception and job satisfaction among extension workers in nasarawa state, nigeria, Production Agriculture Technology Journal, 4 (1): 62 – 70. Integrated Regional Information Networks IRIN (2007). Youth in Crisis: Coming of Age in the 21st Century. New York: United Nation. 14: 61-76.

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Irani, T. and C. Sherler (2002). Job Satisfaction as an Outcome Measure of the Effectiveness of an Agricultural Communications Academic program, Journal of Agricultural Education, 43 (1): 12 – 23. Lerner, R. M. (2005).Promoting Positive Youth Development– Theoretical and Empirical Bases.Sciences of Adolescent Health and Development, National Research Council of Institute of Medicine. Washington DC.National academics of Science (6th Ed) pp. 1-17. National

Population Commission Report (2006).Population and Development Review, Government of Nigeria, p. 209.

National Research Council (NRC), (2005).Growing Up Global: Changing Transitions to Adulthood in Developing Countries. WashingtonDC. National Research Council. National Youth Development Policy and Strategic Plan of Action Federal Republic of Nigeria, (2001). Oladele, O. I. and L. K. Mabe (2010). Identifying the Component Structure of Job Satisfaction by Principal Component Analysis among Extension Officers in North West province, South Africa, Journal of Agriculture and Rural Development in the Tropics and subtropics, 111 (2): 111 – 117. Oladeji, J.O., Oyedokun, A. O. and Bankole, M. B. (2005).Youth Activities and Constraints to Community Development in Akoko North, Ondo State, Nigeria.Journal of Agricultural Extension. 13 (1): 28-34. Onwekwusi G. C. and E. O. Effiong (2002).Youth Empowerment in Rural Areas through Participation in Rabbit production. A Case

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of Akwa Ibom State NigeriaNigeria Journal of Rural Sociology, 2 (4):95-99. Onumadu, F. N., Inyang, N. U., (2015). Analysis of Effect of Integrated Farmers’ Scheme Project on Beneficiaries Farm Output in Akwa Ibom State, Nigeria.International Journals of Information and Communication Research, 5 (1): 8-12. Tokrisha, R., (2006).Integrated Livestock Fish Farming Systems in Thailand. Available at http:www.fad.org/doerep/004/ac155c/AC15 5E13.htm.Retrieved 2014. Ugwumba, C. O. A. and Orji, E. C.(2005).Traditional Farming System and it Effect on Farm Cash Income in old Ngikoka Local Government Area of Anambra State: Nigeria. AmericanEurasianJournal of Agriculture andEnvironmental Science, 8 (1): 21-24. Umar, S., Ofeikwu, P. O., Shuaibu, H., Duniya, P. K. and Tambari, I. W. (2015). Growth Enhancement Support Scheme (GESS) Among Farm Families in Kaduna State Nigeria. Journal of Agricultural Extension Profession. 19 (1): 110-121. Umeh, G. N. and Odo, B.I., (2008). Profitability of Poultry Production among School Leavers in Anaocha Local Government Area of Anambra State Nigeria. Nigeria Journal of Animal Production, 2 (9): 76-80 United

Nations (2005): Report on World Urbanization Prospects.Department of Economics and Social Affair. New York, United Nations.

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Bank (2007).Youth and Development: Investing in the Next GenerationDevelopment outreach. Washington DC: World Bank.

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ISBN: 978-978-34560-5-7 AKSUJAEERD 1 (1): 11 – 14, 2018 AKSU Journal of Agricultural Economics, Extension and Rural Development. © Department of Agricultural Economics and Extension, Akwa Ibom State University, AKSU, Nigeria, December

ASSESSMENT OF AGROCHEMICALS UTILIZATIONAMONG RURAL FARMERS IN EBONYI STATE, NIGERIA Samuel E. Esheya Department of Agricultural Education, Ebonyi State College of Education Ikwo Corresponding author: [email protected] Abstract This paper assessed the utilization of agrochemicals among rural farmers in Ebonyi State of Nigeria. Simple random sampling technique was adopted to select 240 crop and livestock farmers from the thirteen Local Government Areas of the state through oral interview schedule. Data were collected on the types of agrochemicals, affordability of agrochemicals and factors that encourage/discourage its utilization by rural farmers in Ebonyi state. Analysis of data was done using descriptive statistical tools. Results of the study show that majority of the respondents use herbicides (92.3%) and insecticides (84.6%) as both were the most affordable agrochemicals in the study area. Affordability, ease of use, reduction of human drudgery and yield improvement were the notable factors that encouraged agrochemicals utilization in the study area while high cost, illiteracy/ignorance, health hazards and soil pollution discouraged its use by some rural farmers. Thus, provided that proper safeguards are observed, the utilization of agrochemicals by crop and livestock farmers is a sine qua non for improved agricultural productivity in Ebonyi State and beyond. Key words: Assessment, Utilization, Agrochemicals, Rural farmers, Productivity. Introduction Agrochemicals are chemical products used to manage an agricultural ecosystem in a farming area to protect crops and livestock from pests, diseases and enhance performances. According to Unswort (2010), agrochemicals include: fertilizers, liming and acidifying agents, soil conditioners, pesticides, and chemicals used in animal husbandry, such as antibiotics and hormones. These include pesticides (insecticides, fungicides, nematicides), herbicides, synthetic fertilizers, hormones, chemical growth agents, and concentrated raw animal manure. The utilization of agrochemicals for crop and livestock production has been critical to the development of agriculture in Nigeria as it has reduced drudgery and increased yields significantly (Brown, 2014). Thus, there is increased demand for and usage of agrochemicals for agricultural production in Ebonyi state. However, the clarion call for the optimization of agrochemicals utilization among rural farmers in Ebonyi state is hindered by the fact that some of these chemicals cause substantial environmental and ecological damage, greatly reducing their benefits. Some agrochemicals are toxic, and agrochemicals in

bulk storage may pose significant environmental and health risks, particularly in the event of accidental spills (Forget, 2003). Jeyaratham (2015) opined that in many countries, use of agrichemicals is highly regulated, since the much desired commercialization of agriculture and increase in productivity per hectare cannot be achieved without agrochemical utilization among rural farmers. Adebayo and Fato (2016) maintained that a functional and result based agricultural system still remains the best and fastest means of empowering and transforming the lives of the rural poor farmers, who constitute a large percentage of the Nigerian population. For an innovative based agricultural production, rural farmers must be encouraged to adopt effective utilization of agrochemicals in agricultural production especially in areas of soil nutrient improvement, livestock feed formulation, weed and pest control, as well as prevention of post harvest losses (Soule, 1991). The cardinal objective of this study was to assess the utilization of agrochemicals among rural farmers in Ebonyi state. The specific objectives were to; identify the common

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Agrochemicals utilization among rural farmers

agrochemicals utilized by the rural farmers; determine the affordability of agrochemicals; and describe the factors that encourage/discourage the utilization of agrochemicals among rural farmers in Ebonyi state of Nigeria Methodology Study area This study was carried out in Ebonyi state of Nigeria. Ebonyi State is an inland south-eastern state of Nigeria, populated primarily by Igbos. Its capital and largest city is Abakaliki. It is one of the six new states in Nigeria created in 1996; Ebonyi was created from the old Abakaliki division of Enugu State and old Afikpo division of Abia State. The state which is situated in the South-eastern part of the country shares boundaries with Benue to the north, Enugu to the northwest, Abia to the south-east and Cross River to the east. The state has a total land area of 5,533 square kilometres and has an estimated population of 1739136 people (NPC, 2006). About 89 percent of the total population live in the rural areas. Agriculture is the major occupation of the people and almost all the families farm either as primary or secondary occupation. The ecological zone of the state favours the growing of tree crops, roots and tubers, cereals and vegetables. These crops are grown in small holder plots usually in mixtures of at least two crops. The main food crops are rice, yam, cassava, cocoyam, potatoes, maize and vegetables. The farmers however rely heavily on rainfall for crop farming. Sample Size and Sampling Technique Ebonyi state is made up of thirteen Local Government Areas namely: Abakaliki, Afikpo

North, Afikpo South, Ebonyi, Ezza North, Ezza South, Izzi, Ikwo, Ishielu, Ohaozara, Ohaukwu, Onicha and Ivo. Simple random sampling technique was used to select twenty farmers from each Local Government Area. This gave a sample size of 240 farmers from a population of about 18, 411 farmers who are registered members of All Farmers Association of Nigeria in Ebonyi state. Both primary and secondary data sources were used for this study. Primary data were obtained through field survey using a well-structured questionnaire; while secondary data were obtained from Ebonyi State Agricultural Development Programme, Ebonyi State Ministry of Agriculture and other publications relevant to the study. Descriptive statistics such as tables, percentage, mean and standard deviation were used for data analysis Results and discussion Types of agrochemicals used by rural farmers The different types of agrochemicals commonly used by rural farmers are presented in Table 1: Herbicides, Insecticides, Fungicides, Nematicides, Rodenticides and Acaricides. The term pesticides are commonly used to refer to all of these groups of agrochemicals at once. This result shows that majority of the respondents (92.3%) use herbicides, followed by insecticides (84.6%), fungicides (61.7%) and nematicides (50.8%). Findings equally show that rodenticides (34.5%) and acaricides (28.1%) were not in popular use by rural farmers in the study area. Thus, rural farmers in the study area basically use two types of agrochemicals: herbicides and insecticides for control of weeds and insects respectively

Table 1: Types of agrochemicals used by rural farmers Name of Agrochemical Frequency Herbicides 223 Insecticides 203 Fungicides 148 Nematicides 121 Rodenticides 82 Acaricides 68 Source: Field Survey, 2018.

Percentage 92.3 84.6 61.7 50.8 34.5 28.1

The level of affordability of the different types of agrochemicals by rural farmers in the study

Affordability of agrochemicals AKSUJAEERD

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Samuel, E. area is contained in Table 2. Following the result obtained in Table 2, herbicides (81.7%) and insecticides (90.0%) are the two most affordable agrochemicals in the study area. The high demand for these chemicals makes them always available and come in various brand names, sizes and prices. This increased supply in response to high demand makes them readily

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available and affordable to the rural farmers when compared to its usefulness in the farm. Results further indicate that majority of the respondents, 61.1% for nematicides, 100.0% for rodenticides and 71.3% for acaricides made no response regarding affordability of the aforementioned agrochemicals.

Table 2: Affordability of agrochemicals by rural farmers Pesticides Level of Affordability Very affordable Affordable Not affordable No response Herbicides 114(47.5) 82(34.2) 44(18.3) Insecticides 139(57.9) 77(32.1) 24(10.0) Fungicides 48(20.0) 79(32.9) 103(42.9) Nematicides 18(7.5) 21(8.8) 53(22.1) 148(61.6) Rodenticides 240(100.0) Acaricides 9(3.8) 34(14.1) 26(10.8) 171(71.3) Source: Field Survey, 2018. (Numbers in parenthesis are percentages). Encouraging/discouraging factors of agrochemicals utilization Generally, the level of acceptability and utilization of agrochemicals for farm production is determined by many factors and vice versa. Table 3 shows some of the factors that encourage and/or discourage the use of agrochemicals among rural farmers in Ebonyi state. According to the results contained in Table 3, affordability (87.9%), ease of usage (76.3%), reduction in human drudgery (96.3%), increase in farm size (81.6%) and improvement in farm yield (74.2%) were the factors that positively influenced usage. However, only 38.8% of the respondents were of the opinion that agrochemical utilization is an encouraging factor in farm produce storage. Majority of the

Total 240(100.0) 240(100.0) 240(100.0) 240(100.0) 240(100.0) 240(100.0)

respondents (61.2%) opined that agrochemicals interfere with the natural morphology of crops and livestock thereby shortening their shelf lives. Besides, the respondents equally identified factors such as illiteracy/ignorance (90.0%), attendant health hazards (80.0%), associated pollution (98.8%) and effects on non-target organisms (72.1%) as responsible for discouraging agrochemical utilization among rural farmers in the study area. They however disagreed with the proposition that high cost of agrochemicals (35.0%) and weeds/pest resistance (28.3%) were among the discouraging factors of agrochemical utilization among rural farmers in Ebonyi State.

Table 3: Factors that encourage/discourage agrochemical utilization Encouraging factors Frequency/% Discouraging factors Affordability 211(87.9) High cost Ease of use 183(76.3) Illiteracy/ignorance Reduce human labour 231(96.3) Health hazards Boosts farm size 196(81.6) Pollution Improves yield 178(74.2) Non-target effects Encourages product 93(38.8) Weeds/pests resistance storage Source: Field Survey, 2018. Figures in parenthesis are percentages

Frequency/% 84(35.0) 216(90.0) 192(80.0) 237(98.8) 173(72.1) 68(28.3)

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Agrochemicals utilization among rural farmers

Conclusion The utilization of agrochemicals for agricultural production has many benefits as well as some unfortunate consequences. Nowadays, much attention is being paid to the development of organic methods of enhancing soil fertility, crop/livestock performance, product preservation and pests/diseases control. Unfortunately, economically effective alternatives to most uses of agrochemicals have not yet been discovered (Pimentel, 2002). Consequently, modern agricultural industries will continue to rely heavily on the use of agrochemicals to manage their problems of soil fertility, soil quality, crop/livestock References Adebayo, O. A. & Fato , B.F. (2016). Agrochemicals Usage among Arable Crop Farmers in Ibarapa Area of Oyo State. Proceedings of 21st Annual National Conference of the Agricultural Extension Society of Nigeria, Held at the University of Ibadan, Ibadan, 16th-21st April, 2016. Andreu, V. & Pico, Y. (2004). Determination of pesticides and their degradation products in soil: critical review and comparison of methods. Trends Anal Chemistry: 23(10– 11):772–789. Brown I. (2014). UK Pesticides Residue Committee Report. 2004. (available online:http://www.pesticides.gov.uk/uploade dfiles/Web_Assets/PRC/PRCannualreport20 14.pdfalso available on request). Forget G. (2003). Balancing the need for pesticides with the risk to human health. In: Forget G, Goodman T, de Villiers A, editors. Impact of Pesticide Use on Health in Developing Countries. 1993. IDRC, Ottawa: 2

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improvement, post-harvest/storage losses and weeds/pests control. Thus, there is need to develop health education packages based on knowledge, attitude and practices and to disseminate them within the rural communities so as to minimise cost and human exposure to agrochemicals. Besides, awareness creation through agricultural research and extension linkages on the types, utilization, importance, affordability and safety measures on agrochemicals utilization for improved agricultural productivity in Ebonyi state of Nigeria should be pursued by government at all levels.

Jeyaratnam J.(2015). Health problems of pesticide usage in the third world.B M J.2015;42:505. [PMC free article][PubMed] Muller,

F. (2000).Agrochemicals: Composition, Production, Toxicology, Applications. New York: VCH Publishing.

NPC (2006).National Population Census Report 2006. Pimentel, D. (2002). Environmental and Economic Costs of Pesticide Use.Bioscience 41: 402409. Spearks, D. L(2012). Environmental Soil Chemistry. 2nd ed. New York: Academic Press. Soule, J. D. & Piper, J. K. (1991).Farming in Nature's Image: AnEcological Approach to Agriculture. Washington, DC: Island Press. Unsworth, J. (2010). History of Pesticides Use.International Union of Pure and Applied Chemistry. New York: Academic Press.

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ISBN: 978-978-34560-5-7

AKSUJAEERD 1 (1): 15 – 24, 2018 AKSU Journal of Agricultural Economics, Extension and Rural Development. © Department of Agricultural Economics and Extension, Akwa Ibom State University, AKSU, Nigeria, December.

CORRELATES ANALYTICS OF SKILL COMPETENCIES AMONG YOUNG ADULTS; EXPERIENCES FROM FACULTY OF AGRICULTURE, AKWA IBOM STATE UNIVERSITY, OBIO AKPA Jemimah Timothy Ekanem1, Emem Bassey Inyang2 and Glory Edwin Bassey 1 1

Department of Agricultural Economics and Extension, Faculty of Agriculture, Akwa Ibom State University, Obio Akpa Campus, Nigeria 2 Department of Agricultural Economics and Extension, Faculty of Agriculture, University of Uyo, Uyo, Akwa Ibom State, Nigeria Corresponding author: [email protected], 08060790069

Abstract Competency in skill acquisition training programmes introduced into educational institutions could be an effective strategy for poverty reduction in developing economies. Relating these training programmes to the situational contexts of beneficiaries has been very elusive in the literature. Using the descriptive and relational survey design, this study analysed the correlates of skill competencies among trainees who benefitted from the 2016 Students’ Industrial Work Experience Scheme (SIWES) in Akwa Ibom State University (AKSU). It specifically assessed the incidence and levels of skill competency among the trainees and analysed the influence of trainees’ social and economic variables on their level of competency in vocational skills. Data were obtained from the 62 trainees that participated in 2016 SIWES using a well-designed and validated questionnaire. Descriptive Statistics, Incidence index, composite index and binary logistic regression were the analytical tools used in the study. Results showed that, only 44.9% of trainees were competent in farming skills pooled from the eight subsectors of agricultural production, majority (55.1%) of the trainees were not competent in the practical skills they were exposed to. Again, from a follow up analysis the trainees who affirmed to be competent had low competency in 17 skills, average competency in 33 skills and high competency in 27 skills. The binary logistic regression results showed that trainees’ gender, age, marital status, and ownership of farm(s) were also reliable and significant predictors of skill competencies in the study area, χ2 (12) = 19.156, p < .0005. These factors should be seen by development institutions/agencies as viable and potent factors in the design, formulation of contents and implementation of vocational skills acquisition training programmes for young adult learners if the programme is well intentioned. Keywords: Correlates, Competency, Skills, Young Adults, SIWES, AKSU Introduction The strategic thrust towards agricultural development in Nigeria is to boost productivity, commercialization, and competitiveness of agricultural businesses by transforming small holder agriculture from subsistence to an innovative, commercially oriented and a modern sector (Brana, 2008). This requires that farmers and potential farmers be empowered and made to acquire competencies in farming skills to enable them effectively engage in production and value addition activities such as product processing, branding and farm level quality improvements. Wisconsin Cooperative

Extension (2002) viewed competency as having sufficient knowledge, attitudes and skills that can contribute to excellence in extension education programmes. Encarta (2009) defines competence as the ability to do something well, measured against a standard especially ability acquired through experience or training.Competency can therefore be seenasadequate quantity of knowledge, skill and ability to accomplish a particular task or purpose. Reports have proven that efforts aimed at equipping young adults with competencies in agricultural business undertakings can produce far reaching effects on the economy of any nation, especially, that of closing the income

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Correlates analytics of skill competencies among young adults

inequality gap and reduction in the rate of unemployment, (Subramaniam, 2010;Nawai and Shariff, 2011; Mahmood and Hanafi, 2013). When people acquire competencies in agricultural production enterprises, their selfconfidence, self-esteem and their ability to participate in entrepreneurial decision-making at household and community levels is boosted, (Ebong and Asodike, 2011; Ikegwuet al 2014; Nwanaka and Amaehule, 2011). Globally, agricultural skill acquisition programs introduced into educational institutions were meant to provide the level of education or knowledge needed to exploit entrepreneurial opportunities in the field of agriculture which could help the economic development of such countries (Emaikwu, 2011; Shane, 2003), and studies have shown that competencies in such skillsare the most critical factors in the utilization of entrepreneurship opportunity for self-employment (Ekpe et al., 2015). Training in agricultural vocations was found to have positive effect on entrepreneurial activities in France (Brana, 2008); Germany (Stohmeyer, 2007) and in Malaysia (Samian and Buntat, 2012). Competencies in agricultural-related skillsoccasioned by university education could lead to business opportunities and impact on entrepreneurship since training and/or education produce prior experience which leads to preparedness for entrepreneurial activity (Shane, 2003). However, Rufai et al (2013) and Dasmani (2011) found that agricultural science graduates were denied employment because they possessed low skills and low self-confidence required by industries since they had no industrial exposures while in school. A review of the literature reveals diverse reports with regards to the extent to which agricultural internship programmes have achieved its laid down objectives. Oladele, et al (2011) conducted a study on the effectiveness of Field Practical Training (FPT) for competence acquisition among students in Botswana’s College of Agriculture. In their findings, students reported that their level of competencechanged from not competent to competent in 31out of the 47 tasks that were examined. These were predominantly inthe areas AKSUJAEERD

of soil and crop production and animalscience while most activities related to farmengineering were not popular in the competentrating by the students. Oloruntoba (2008) reportedsimilar findings that the farm practical year programme improved the competence of students of the University of Agriculture, Abeokuta in many agricultural tasks. This confirms the expectations that the practical training programme reinforces the theory from the class and thus, helps in preparing better graduates for future employment world. However, Oladele, et al (2011) also found students reporting that they were not competent before and even after undergoing the FPT programme in four of the agricultural practical skills examined. This indicates that the FPT programme was not solely responsible for their change in competency levels. Attempts have also been made by Students’ Industrial Work Experience Scheme (SIWES)since 2014 to address the competency needs of young adults in practical agricultural skills by exposing young adults in Akwa Ibom State University to vocational skills acquisition programme geared towards the provision of practical trainings. The scheme has undertaken this to ensure the formal training and competency of young adults on practical agricultural skills through work-oriented functional literacy to capture skills in crop production, livestock rearing and management, soil fertility management, aquaculture, farm mechanization, and agricultural economics and extension services. The rationale for Students’ Industrial Work Experience Scheme (SIWES) is for young school leavers to integrate theoretical knowledge with real working environments and put them into practice. The trainees must therefore posses some level of skill competencies to attest to the effectiveness of the programme. However, the essence of these vocational skills acquisition programmes is yet to be felt at both individual and community levels especially within the state. This assertion is due to the increasing depth of poverty, unemployment, illiteracy and general underdevelopment in the state.

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The unemployment rate in Akwa Ibom has experienced steady increase above 21% since the 21st century (Ngwu 2003). One fundamental problem with formal education and training programmes is the top-bottom approach in their design and delivery process (Egunyomi and Ekom 2010). This approach tends to make well intentioned programmes look like imposition on the people with far reaching negative effects. The dilemma that this situation creates is whether these vocational skills acquisition programmes normally have any bearing with the social, cultural and economic contexts of their beneficiaries. There is doubt whether at all, the social factors like literacy level, marital status, age and gender of target participants are considered in the skill acquisition planning process.

(1971); and Ezeano (2010) have investigated factors related to the adoption of improved farm practices and the isolated variables include farmer age, education, years of experience, social and tenure status, agro-climate, location, farm size, credit, and characteristics of the innovation itself such as relative advantage, compatibility, complexity, divisibility and communicability, techniques of communication, amount of participation and the use of traditional culture. In their study on farmers’ characteristics and agricultural productivity in Kenya, Saina et al. (2012) established that secondary school agricultural education enables young farmers to have a broader capacity, be more effective, self reliant, resourceful and capable of solving farming problems thereby improving their productivity.

Studies have been carried out on gender division of labour, explaining the relationship between gender and vocations. Citing one of such studies by Murdock (1987), Holborn and Haralambos (2008) reported that vocations such as hunting, lumbering and mining are predominantly male roles, while cooking, water carrying, making and preparing of clothes (modern sewing, tailoring and fashion designing) are largely female roles. Gender sensitivity in lifelong learning has received the nod of many international agencies in development (Egunyomi and Ekom 2010). Writing on gender and its place in determining the type of skill acquisition programme for beneficiary groups, Stromquist (2002) stated that “gender is an element of social relationship that operates at multiple levels. It affects every day interactions, public institutions, work and the household”.

This study was motivated by the scarcity of studies that have analysed the social and economics dynamics as correlates to agricultural/vocational skill competencies among young adults, especially, in African region. What is the competency level of the trainees in the 2016 SIWES programme organized by Faculty of Agriculture, Akwa Ibom State University? Would male trainees be more competent in farming skills than their female counterparts? Would there be variations in the agricultural skills competencies between married and unmarried trainees? Will the age of the trainees determine their competency in the skills-oriented internship programme they participated? How does parents’ occupation and trainees’ knowledge of O’level Agricultural science influence their skill competencies in the internship programme? Findings from this study will provide answers to these questions. Development programme agencies and vocational skills institutions/implementers as well as the literature will be enriched. Specifically, the study assessed the level of skill competency of the trainees in the2016 internship programme organized by Faculty of Agriculture, Akwa Ibom State University.

The same doubt applies with the cultural factors like language, values, norms and beliefs systems of the people. Also, it is not clear whether the economic context (parents’ occupation and employment status) have been given adequate consideration in the process of designing such programmes. Onu (1991) observed that innovation uptake is dependent on the capacity of the user to access innovation and later use it. This capacity, he observed, is dependent on certain cultural, socio-economic, personal, political and geographical variables. Many researchers like Welsch, (1965); William et al.,

Research hypothesis Trainees’socio-economic characteristics (gender, age, marital status, parents’ occupation, knowledge of O’level Agricultural Science and

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ownership of farm) do not significantly predict their level of competency in vocational skills. Methodology The study was conducted in Akwa Ibom State using the 2016 Students’ Industrial Work Experience Scheme (SIWES) students of Akwa Ibom State University. Structured questionnaire was used to collect data from the trainees who participated in the SIWEStraining programme. List of participating trainees was obtained from the SIWES Coordinator. Allthe 62 students who participated in the2016 SIWES were used as respondents for the study. The instrument for data collection was developed with the help of the list of competencies the trainees are expected to acquire in each of the sub-sector of the scheme.The instrument was divided into two parts to satisfy the objectives of the study. Part 1 extracted information on the personal characteristics of the trainees such as gender, age, marital status, occupation of the parent, and if they offered Agricultural science in secondary school, while the second part generated data on trainees’ perceived competencies. The levels of competency were measured by asking trainees to indicate their perceived levels of competency in performing specific farming skills acquired from the SIWES programme using a 3point Likerttype rating scale with the following categories: 3 = very competent; 2 = competent; and 1 = not competent. The survey questionnaire was subjected to face and content validity and suggestions were incorporated into the final version of the questionnaire.Incidence index and Composite indexanalytics were used to diagnosethe incidence and level of competencyof the traineeson agricultural production skills respectively while binary logistic regression was deployed to test the hypothesis of the study. To identify the key characteristics that could predict the trainees’ skills competency, a dichotomous variable indicating whether the trainee was competent or not was first computed. That is,1, if trainee is competent and 0 if otherwise.

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We then used a Logistic regression model, given by Logit (P) = ln[p/1-p]=β0 +β1X₁+ β2X₂+ β3X₃+ β4X₄+ β5X₅+ β6X6+ e, whereP denoted the probability that the trainees were competent X1= Age of trainees (in years) X2= Sex of the trainees, X3= marital status, X4= Parents/Guardians Occupation, X5= Knowledge of O’level Agricultural science, X6= Ownership of farm β0-β6represent the beta coefficients e = error term Results and discussion A.Socio-economic characteristics of respondents The results presented in Table 1 reveals that majority (74.19%) of the respondents were female while 25.81% were male and students in the age bracket of 21-25 years constituted the majority of the study population represented by 62.90% while those above 30 years were 1.61%. This implies that majority of the students were young and agile and therefore could handle practical agricultural activities without much difficulties. Also, almost all the respondents (93.55 %) were single with only 6.45% being married. Most of the respondents had parents and guardians who were civil servants as against 12.90% who were farmers. Again, majority (66.13%) of the respondents’ parents owned a farm. The assumption here is that, students can acquire skills due to their exposure to the farming activities of their parents and this can place them on an advantaged position above their colleagues on the programme. Findings also revealed that almost all the respondents (98.39%) offered agricultural science in their secondary school. This could be the reason they opted for the study of agriculture at the university level. The implication is that, most skills will not appear totally strange to the respondents by reason of this earlier knowledge acquired.

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Table 1: Distribution of socio-economic characteristics of respondents Characteristics Gender Male Female Age (years) 16-20 21-25 26-30 30 years and above Marital Status Single Married Parents/Guardians Occupation Trader Contractor Civil Servant Farmer Self Employed Parents/Guardians Ownership of farm Yes No Did you offer Agricultural Science inSecondary School Yes No Source: Field Survey, 2017

Frequency

Percentage (%)

16 46

25.81 74.19

2 39 20 1

3.23 62.90 32.26 1.61

58 4

93.55 6.45

6 2 30 8 16

9.68 3.23 48.39 12.90 25.81

41 21

66.13 33.87

61 1

98.39 1.61

B. Incidence index analysis of practical skill competency among the trainees Trainees were exposed to various practical activities across the various sub-sectors of agricultural production. For ease of explanation and meaningful discussions, pooled incidence of competencies on skills they have acquired from the various sub-sectors of agricultural production is presented on Table 1 below. The trainees were more competent (competency incidence = 0.1384)in livestock management skills which made up 31.1% composition of the total skills the trainees were exposed to during the internship programme, implying that in relative terms, 13.84% of the trainees were competent in livestock management skills as indicated in the superscript. This was followed by crop production skills which made up 14.29% of total skills with competency

incidence of 0.07033. Agricultural economics skills were next most achieved skills with competence incidence of0.05064. The sub-sector made up 9.09% of total skills learnt. In a nutshell, only 44.9% of trainees were competent in farming skills pooled from the eight subsectors of agricultural production, 55.1 % were not competent. This finding corroborates Oladele, et al (2011) who also found students reporting that their level of competencechanged from not competent to competent in 31out of the 47 tasks that were examined. These were predominantly inthe areas of soil and crop production and animalscience while most activities related to farmengineering were not popular in the competentrating by the students.They also found students reporting that they were not competent before and even after undergoing the training programme in four of

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the agricultural practical skills examined. They observed that the training programme was not

solely responsible competency levels.

for

their

change

in

Table 2: Distribution of the Trainees based on Incidence Index Analysis of Practical Skill Competency Skills Category Frequency of skill Percentage items Pooled Skills items Composition (%) Competency Incidence index Livestock management 24 31.17 0.1384 a Crop production skills 11 14.29 0.07033b Soil management skills 9 11.69 0.04096f Aquaculture skills 5 6.49 0.02903h Poultry production 5 6.49 0.02919g Farm mechanization 8 10.39 0.04552d Agric economic skills 7 9.09 0.05064c Agric extension skills 8 10.39 0.04516e Total 77 100 0.44923 Source: Computed from field survey, 2017 C. Level of skill competency of the trainees This sub-section sought to estimate the proportion of practical skills that the trainees had attained competency based on probabilistic value that ranged from 0.0 to 1.0, which were derived from the responses of the trainees. The probabilistic value of 0.0 to 1.0 implies low to high. The distribution pattern of Practical Skills Competency (PSC) index of all the sampled trainees were analyzed for meaningful interpretation using broadly categorized three ranges; low, average and high. The trainees were distributed across the three categories of PSC index. As shown in Table 2 below, the PSC index column invariably depicts the percentage of competency among the population if the index value is multiplied by 100. Thus, PSC index range of 0.00-0.399 being interpreted as low level of practical skills competency implies that the traineeshad less than or 39.9 percent competency in 17 practical skills constituting 22.1% of all the requisite skills otherwise 0.399 index. This is regarded as low level of skills competency.

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The result has also shown that the traineesacquiredbetween 40to 69.9 percent of competency in 33 practical skills constituting42.9 percent of the total practical skills they were exposed to.This signified average level of competencywhile PSC index of 0.70-1.00 implies that the trainees were 70 to 100 percent competent in 27 practical skills constituting35.1 percent of the total requisite skills experienced during the internship programme and were described as high level of competencyin practical skills. It can therefore be summarized that the trainees had low competency in 17 skills, average competency in 33 skills and high competency in 27 skills. Deductively, the moderate level of skills competency portrayed by the trainees depicts that they need more effective trainings so they could be professionally equipped to be selfemployed and to contribute meaningfully to economic development of the state and the nation at large. Figure 1below gives a clearer description of the levels of practical skills competency.

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Table 3: Distribution based on the trainees level of practical skills competency Skills Competency (PSC) Skills Competency index range Frequency index range interpretation of competent skills 0.00-0.399 Low 17 0.40-0.699 Average 33 0.70-1.00 High 27 Total 77 Source: Computed from Field survey, 2017

Percentage of competent skills 22.1 42.9 35.1 100.0

100 100 77 80 60

42.9 33

40 17 20

0

27

22.1

Low

Average

35.1

High

0 0.00-0.399

0.40-0.699

0.70-1.00

Total

Skills Competency index range interpretation Frequency Percentage of competent skills

Figure 1: Trainees level of Practical Skills Competency D. Results of hypothesis testing Ho: Trainees’ socio-economic characteristics (gender, age, marital status, parents’ occupation, knowledge of O’level Agricultural Science and ownership of farm) do not significantly predict their level of competency in vocational skills. Results as earlier shown in Table 2 convincingly indicated that majority (55.07 %) of the trainees were not competent on the practical agricultural skills they were exposed to during the internship programme, although results in Table 3 reported moderate to high level of competency for those that ascertained they were competent in the practical skills. This became a source of worry to the researchers. Could the huge perception of incompetency among the trainees be explained by the variations in the socio-economic attributes of the trainees? A binary logistic regression was performed to ascertain the effects of socio-economic characteristics of the trainees on their competency likelihood.Social variables

like the trainees’ gender, age, marital status and acquisition of knowledge of O’level agricultural science were examined while parents’ occupation and ownership farms were examined as economic variables. The logistic regression model as shown in Table 4 was statistically significant in predicting competency in vocational skills, χ2(12) = 19.156, p < .0005. The model explained 38.5% (Nagelkerke R2) of the variance in skills competency and correctly classified 91.1% of cases (-2 Log likelihood = 53.680). Males were 2.759 times more likely to exhibit skill competency than females (Exp B= 2.759). Increasing age was associated with an increased likelihood of exhibiting skill competency as each unit increase in the trainees who were 30 years and above led to increase in the odds of exhibiting skill competency by 12.259 compared to trainees within 21 -25 and 26 to 30 years of age whose unit increment led to odds of increasing skill competency by 4.934 and 1.410

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respectively (see Table 4). The table revealed that being single was associated with a reduction in the likelihood of exhibiting skill competencies by 0.346. Similarly, not owning a farm was associated with a reduction in the likelihood of exhibiting skill competencies by 0.501 (see Table 4). Therefore, gender, age, marital status, and ownership of farm reliably and significantly contributed to the model while parents’ occupation and knowledge of O’level Agricultural science had no influence on the prediction of skill competencies in the study area. Findings above have logically added areas of development intervention focus relating that the independent variables that contributed to the model are viable and potent factors in the design, formulation of contents and implementation of vocational skills acquisition training programmes for young adult learners. Their non-consideration in the process of programme design and implementation could therefore lead to their unimpressive impact on target beneficiaries.The result of findings above corroborated the studies by Fasokun (1998), Nyong and Oladipo (2003) that since jobs are

becoming multi-skilled, training by formal education for employed and unemployed will respond to the social contexts of all learners. Furthermore, the result of the study affirms Stufflebeam (2003) context, input, process and product (CIPP) framework for planning programme evaluation. In this case, knowing the gender and gender roles through situation analysis within their social context could provide sufficient information that can guide programme design and implementation strategies that will result in impressive programme outcomes that meet the needs of participants.The significant influence of marital status to indices of vocational skills competency may be linked to the fact that knowledge of this status will assist planners to know not only their family needs but the timing of the training programmes. It is important that intervention agencies know and understand through the process of needs survey the social dynamics of vulnerable groups. Programme type, goals, objectives, input materials, implementation strategies and time can be determined accurately through such in-depth knowledge.

Table 4: Estimated Coefficients of the Socio-economic Factors affecting Skill Competencies among Trainees Variables B SE(B) Exp(B) p-value Remark Gender Female Reference Male 1.015 .919 2.759 .020 Significant Age 16 -20 Reference 21-25 1.596 .570 4.934 .003 Significant 26-30 .543 .179 1.410 .002 Significant 30 and above 2.506 7.71 12.259 .000 Significant Marital status Married Reference Single -.060 1.409 .346 .002 Significant Parents’ occupation -21.353 14638.55 .000 .999 knowledge of O’level agric -21.555 40192.933 .000 1.000 Sc. Ownership of farm Yes Reference No -.691 .759 .501 .033 Significant Source: Computed from field survey 2016. χ2 (12) = 19.156, -2 Log likelihood = 53.680, Nagelkerke R2=.385. Significant at .05 level of probability

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Conclusion The study investigated the influence of some socio-economic variables on vocational skills competenciesamongyoung adults. From the results of the data analysis and the implications of these results, it is concluded that socioeconomic characteristics of economically depressed people have significant influences on vocational skills competencies that can address their situation.

Recommendation Accordingly, development intervention institutions/agencies like Students’ Industrial Works Experience Scheme (SIWES) should seek to always survey the socioeconomic circumstances of the people prior to programme design and implementation. Situation analysis offers an opportunity for the intervention agencies, government and target beneficiaries to jointly participate in identifying, assessing and prioritizing the needs of the people against their social, cultural and economic relevance.

References Brana, S. (2008). Microcredit in France: Does gender matter? 5th Annual ConferenceNice.European Microfinance Network.

among Small-Scale Farmers in SouthEastern Nigeria. Agronomical Nigeriana, Vol. 9, No. 1 &2; 280-282.

Dasmani, A. (2011). Challenges facing technical institutions graduates in practical skill acquisition in the upper east region of Ghana.Asia-Pacific Journal of Corporative Education, Hamilton, New Zealand, 12 (2), 67-77. Ebong, J. M. and Asodike, J. D. (2011). Skill preferences of participants of skill acquisition program in Rivers State, Nigeria. British Journal of Humanities and Social Science, 3 (1), 128-136. Egunyomi, D. A. and Ekom, O. O. (2010).Comparative Analysis of Influence of Some Socio-economic Variables on Vocational Skills Acquisition Programmes for Adult Learners in South-South, Nigeria.J Economics, 1 (1): 33-43 Ekpe, I. Razak, R. C., Ismail, M., & Abdullah, Z. (2015). Entrepreneurial Skill Acquisition and Youth’s Self-Employment in Malaysia: How Far? Mediterranean Journal of Social Sciences, Vol 6 No 4. Emaikwu, S. O. (2011). Integrating entrepreneurship skill acquisition in the university curriculum for national development Journal of Research in Education and Society, 2 (3), 40-48 Encarta, (2009).Microsoft R Student 2009 DVD. Redmond WA: Microsoft Corporation. Retrieved May 27, 2009 from http//www.microsoft.com/Encarta. Ezeano, C.I. (2010). Constraints to Sweet Potato Production, Marketing and Utilization

Fasokun T.O. (1998). Adult education strategies for promoting indigenous knowledge and skills. In: M Omolewa, EE Osuji, A Oduaran (Eds.): Prospects and Renewal:The State of Adult Education Research in Africa. Dakar: UNESCO, pp. 285 – 292. Holborn M,andHaralambos M (2008). Sociology: Themes and Perspectives. London: Harper Collins Publishers Ltd. Ikegwu, E. M., Ajiboye, Y. O., Aromolaran, A. D., Ayodeji, A. A. & Okorafor, U. (2014). Human empowerment through skill acquisition: Issues, impacts and consequences- A nonparametric view. Journal of Poverty, Investment and Development- An open accessInternational Journal, 5 (1), 94-101. Mahmood, R. and Hanafi, N. (2013). Entrepreneurial orientation and business performance of women-owned small and medium enterprises in Malaysia: Competitive advantage as a mediator. International Journal of Business and Social Science , 4 (1), 82-90. Nawai, N. and Shariff, M. N. M. (2011).The importance of micro financing to the microenterprises development in Malaysia's experience.Asian Social Science , 7 (12), 226-238. Ngwu PCN (2003). Non-formal Education: Concepts and Practices. Enugu: Filladu Publishing Company.

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Nwanaka, C.R & Amaehule, S. (2011). Skill acquisition: Imperative for business studies educators among secondary schools in Rivers State. Mediterranean Journal of Social Sciences, 2 (7), 37-43.

students in Malaysian higher education through workplace experience. 3rd International Conference on Business and Economic Research (3rd ICBER 2012) Proceedings, pp.1545- 1556, held on 12-13 March 2012 at Golden Flower Hotel, Bandung, Indonesia.

Nyong, E. E. and Oladipo, E. (Eds.) (2003). Creating an Enabling Environment for the Sustainable Development of the NigerDelta Region. Port Harcourt:NDDC. IX – X.

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Oladele, O. I., Subair, S. K. and Thobega, M. (2011).Effectiveness of field practical training for competence acquisition among students of Botswana College of Agriculture.African Journal of Agricultural Research Vol. 6(4), pp. 923-930

Stohmeyer, R. (2007). Gender gap and segregation in self-employment: On the role of field of study and apprenticeship training. Germany: German Council for Social and Economic Data (RatSWD).

Oloruntoba A (2008) Agricultural Students’ Perceptions of Farm Practical Year Programme at University of Abeokuta, Nigeria. Agricultural Conspectus Scientificus, 73(4): 245-252.

Stromquist N.P. (2002). Poverty and schooling in the life of girls in Latin America.Adult Education and Development, IIZ/DVV 59: 15 – 34. Studies, Working Paper No. 174.

Onu, D.O. (1991). Communication and adoption of improved soil conservation technology by small scale farmers in Imo state of Nigeria. Journal of West Africa Farming Systems Research Network, 2. Rufai, A., Abdulkadir, M & Abdul, B. (2013). Technical vocational education (TVE) institutions and industries partnership: Necessity for graduates’ skills acquisition. International Journal of Scientific and Research Publications, 3 (4), 1-4. Saina, E. K., Kathuri, N. J., Rono, P. K., Kipsat, M. J. & Sulo, T. (2012). Food security in Kenya: the impact of building rural farmers capacity through agriculture education in secondary school. Journalof emerging trends in education research studies, 3(3), 338-345. Samian, S. S. and Buntat, Y. (2012). Selfemployment: Perceptions among deaf

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S. (2003). A general theory of entrepreneurship: The individualopportunity nexus. UK: Edward Elgar.

Stufflebeam DL (2003). Models of evaluation and evaluation forms as alternative frameworks for planning evaluation studies. American Evaluation Association Conference.The Evaluation Centre, Western Michigan University. Subramaniam, T. (2010).Micro enterprise and employment creation among the youths in Malaysia.Jati , 15, 151-166. Welsch, D.E. (1965). Response of Economic Incentives by Abakaliki farmers in Nigeria.Journal of Farm Economics, Vol. 47, No. 4. William, S.K.T. and Williams, C.E. (1971). Farmers contact with Agricultural Extension Services in Western States of Nigeria. Bulletin of Rural Economics and Sociology Vol.6, No. 1, Pp. 48, 59. Wisconsin Cooperative Extension (2002). Competencies for Extension Faculty and Academic Staff in Community Based Educator Roles. Wisconsin: University of Wisconsin Publication.

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ISBN: 978-978-34560-5-7

AKSUJAEERD 1 (1): 25–35, 2018 AKSU Journal of Agricultural Economics, Extension and Rural Development. © Department of Agricultural Economics and Extension, Akwa Ibom State University, AKSU, Nigeria, December

FACTORS INFLUENCING CASSAVA PRODUCTION AMONG FARM HOUSEHOLDS IN ORUK ANAM LOCAL GOVERNMENT AREA, AKWA IBOM STATE, NIGERIA 1

Offor, Offiong Samuel* and Ukpong, Akwaowo Clifford

1

Department of Agricultural Economics and Extension, Faculty of Agriculture, Akwa Ibom State University, Obio Akpa Campus, Nigeria Corresponding author: [email protected]

Abstract The study focused on factors that influence Cassava production in Oruk Anam Local Government Area of Akwa Ibom State. The study assessed the socio economic profiles of cassava farmers in Oruk Anam Local government area, examined the factors that influence cassava production among food crop farming households and ascertained the productivity levels of cassava farmers in the study area. The study employed descriptive statistics and regression analysis to analyze the data. The results show that,majority of the respondents (71.4%) involved in cassava production in the study area were male and only 28.6% were female, the mean age stood at 48 years. Furthermore, results showed 68.6% were married. The mean years of farming experience was 23 years. The household size distribution of cassava farmers in the study area revealed that majority (65.7%) had 6 to 10 members while 11.4% of the total respondents had family sizes ranging from 1-5 and the average household size of 9 persons was obtained for all respondents in the study area. All independent variables had a positive relationship with cassava output except age, fertilizer and planting materials which had a negative relationship with output. Age had a negative and insignificant relationship with cassava production, educational background of the farmers was also insignificant but had a positive coefficient of 54.4. Household size had a positive relationship and was significant at 5%. Farm size also had a positive relationship and was significant at 10%. Farming experience also showed a significant and positive relationship with cassava output. Access to credit had a positive and significant at 10%. Farmers should be encouraged in cassava production by making single digit loans available to them. Also, input acquisition centers should be located close to the farmers where they can easily access planting materials and other agricultural inputs. Keywords: Cassava, Farm households, Factors, Multiple regression Introduction Cassava (Manihot esculenta crantz) is a perennial crop that is grown throughout the tropical low land. It is regarded as a benchmark for food security in the sub-Saharan Africa. It is ranked second to maize in terms of calorie intake (FAOSTAT, 2009). Cassava is grown primarily for itsenlarged storage roots, which are used for human consumption, following a variety of traditional processing methods including boiling, roasting, processing into flour, and fermentation (Salick, Cellinese and Knapp, 1997; Hillocks, 2002). It plays an important dietary role in the diets of almost 1 billion people worldwide (Prochnik et al., 2012). In some regions, particularly in Africa and Brazil, the foliage may also be harvested for human consumption and animal feed, providing supplemental dietary protein (Hillocks, 2002). Cassava is also grown for industrial purposes, such as the production of starch and for

fermentation into ethanol (El-Sharkawy, 2004; Adelekan, 2010). In Nigeria, cassava is mainly produced for home consumption and for sale in the markets and, the bulk of it is produced by small-scale farmers. Nigeria’s production accounts for 19% of the world output and 34% of Africa’s output (Okoro et. al., 2005). In order to increase Nigeria’s foreign exchange, cassava is regarded as one of the non-oil export crops. Nigeria is the largest cassava producing country in the world with an annual estimate of 39 million tones (Central Bank of Nigeria, 2003). Nigeria’s production accounts for 19% of the world output and 34% of Africa’s output (Okoro et al., 2005). According to Nweke et. al. (2002) eighty percent of Nigerians eat a cassava meal at least once a week and majority eats cassava at least once a day; hence it plays a

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Factors influencing cassava production among farm households

major role in the country’s food security. As a crop whose by-products have a wide array of uses, cassava is the most important food crop for Nigeria by production quantity next to yam, which is the most important food crop by value (FAOSTAT, 2012). The country has consistently been ranked as the world’s largest producer of cassava since 2005. Cassava is also seen to have a high povertyreduction potential for Nigeria due to its low production cost (Nweke, 2004, FAO 2005). Egesi et al., (2006), argued that cassava has been transformed from a reserve commodity for support in times of famine into a rural staple, and subsequently a cash crop. Recognizing Nigeria’s tremendous agricultural potentials, the Government has accepted the view that there should be a resolve to make agriculture the main stream of the economy which will boost the production of cassava – a staple food crop in Nigeria. This is why the successive Governments has invested in agriculture by introducing various agricultural revolutionary programmes, like; Commodity Marketing and Development Company, the Presidential Initiative on Cassava Production to mention but a few (Oyegbami et. al., 2010). However, despite the significant role of agriculture in our National economy, Food is still imported; its productivity level still remains slow compared with the result of the productivity in the past decades (FMARD, 2001). Studies have established that one of the major causes of poverty in Nigeriais the decline in agricultural productivity (Eboh et. al.2006; Ayoola, 2009). Productivity improvements in agriculture could provide one of the routes out of poverty for Nigeria and Cassava crop transformation is the greatest poverty fighter among food crops in Nigeria )Nweke, 2004; Fakoyode et. al., 2008). Okoli and Umebali (2008) observed that for these rural farmers to improve their well-being and meet their food requirements, their poverty must be reduced or curbed.

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However, small scale farmers use low-level production techniques due to some factors that militate against cassava production business. Some of these factors include, inaccessibility to credit facility, illiteracy, small farm size, inadequate access to agricultural information like market product prices, input prices, high interest rates and poor market and rural road networks inadequate funds for production, processing, storage, marketing as well as improvement in the cassava value chain, Capital, unfavorable prices and shortage in labour availability, pest and diseases among others (Kuye, 2015). Literature review Empirical studies have been carried out on factors that influence cassava production in Nigeria. Kuye (2015), carried out a comprehensive analysis of performance of Bank of Agriculture and selected commercial banks in enhancing cassava production by farmers in South-south Nigeria (2009-2013), results showed that numerous factors tend to militate against cassava production in Nigeria such as inadequate farmland, land fragmentation, poor soil fertility, poor/marginal soil, pests and diseases attack, use of local varieties, excess rainfall, unfavorable market prices, unavailability of research result, lack of functional cooperatives, inadequate extension services, inadequate funds, unattractive price, labour shortage, land acquisition issues, pest and disease infestation, soil water pollution, scarcity and high cost of fertilizer, high cost of agrochemicals, air pollution, scarcity of improved varieties, drought, crop destruction by cattle and stream/river pollution. Atagher and Okorji (2014) assessed the factors that influence productivity among cassava women farmers in Benue State, Nigeria. The study findings revealed that farming experience, years of education, the use of improved cassava stem cuttings and access to credit significantly influenced the productivity levels of cassava women farmers in the study area. . Respondents identified poor pricing of output, lack of credit, poor soils, processing problems, lack of good market infrastructure, labour supply problems, and high transport costs as factors militating against successful cassava production in the study area. Page 26

Offor, O., and Ukpong, C. Ogunleye (2017), compared profitability and efficiency of cassava production among government and nongovernment assisted farmers association in Osun State, Nigeria. Data were collected using a multistage sampling procedure and analyzed with the aid of descriptive statistics, stochastic frontier and budgetary analyses. The results showed that members of government-assisted farmers’ associations had better access (100%) to credit (e.g. production credit) compared to their counterparts (35.8%) who were not members of government-assisted farmers’ associations. Average yield (2,370.15 kg/ha) and farm revenue (₦514,600.00) were higher among cassava farmers that were members of government-assisted farmers’ associations and significantly different from those that were nonmembers. Results further showed that members of government-assisted farmers’ association were more efficient (72.4%) than farmers that were non-members in the associations in the study area. On average, the profitability ratio (Return on Investment-ROI) for members of government-assisted farmers’ association was ₦2.32 per naira invested and ₦1.16 per naira invested for farmers who were not members. Bassey, Akpaeti and Umoh, (2014) in their study of the determinants of cassava output among small scale farmers in Akwa Ibom State, Nigeria, employed primary data collected through a multistage sampling technique from 90 cassava farmers to examine the determinants of cassava output in Akwa Ibom State, Nigeria. Data were analyzed using gross margin analysis, simple descriptive statistics, as well as Ordinary Least Square (OLS) regression technique. Findings indicated that educated (75.6%), female (68.9%) farmers, majority who were within the age bracket of 31- 40 years, , with an average household size and farming experience of 6 persons and 10 years dominated cassava production. The average Gross Margin and net income of N 154,840 and N 125,590 per hectare showed that cassava production was profitable. The study further showed that educational level, farm size, household size, farming experience, labour and extension visit, significantly influenced cassava output in the study area. Also, high cost of cuttings and other inputs,

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uneconomical size holdings, inadequate finance and storage facilities constituted the main cassava production problems in the study area Certain factors are responsible for the decline in production of cassava, despite the potential of cassava in addressing the increasing food demand of the growing population in Nigeria as well as its diverse uses, studies (IITA, 2011; Ogunleye et. al., 2014) have shown that the yield from and profit accruing to cassava farming among the smallholder farmers in Nigeria remained abysmally low. It is against this background that this research work seeks to critically examine the determinants or factors that influence cassava production among farming households in Oruk Anam LGA. Akwa Ibom State. The objective of this work are to: examine the factors that influence cassava production among food crop farming households and determine the productivity levels of cassava production among food crop farming household in the study area. Methodology This study was carried out in Oruk Anam Local Government Area of Akwa Ibom State. The Local Government Area is located in the Southern part of Akwa Ibom State, Nigeria. It lies between latitude 4o 401N and 5o N, and longitude 70o 301E and 70o 501E. It has a land mass of 511.73km sq and is characterized by a typically humid tropic/climate with a distinct dry and wet season.. The mean annual rainfall is high and ranging from2000mm-4000mm with a temperature range of 260C – 280C. Inhabitants are mostly farmers, who cultivate food and cash crops, craft men and civil servants. Oruk Anam local government has a population of about 172,654 persons comprising 86,415 females and 86,239 males (National Population Commision 2006). Questionnaires were used to elicit information from respondents. Random sampling method was used to select respondents in the study area, which consists of 9 clans. The first stage was a random selection of 5 clans out of the 9 clans in Oruk Anam. In second stage, two villages were randomly selected from each clan. In the the third stage, a random selection of 7 cassava farmers was made from each of the selected

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villages, making a total of 70 respondents selected for the study. To analyze the socio- economic and demographic characteristics of respondents, descriptive statistics were used (means, percentages and frequencies). Multiple linear regression analysis was used to examine the factors that influence cassava production among farming household in the study area. The formula is presented in its implicit form below: Q = f ( X1, , X2,. . . .Xn ) + e Q = output of cassava in kg E= error term where Q = dependent variable The explicit form of the model is given as with reference to the signs of the coefficients; Q = bo + b1X1 + b2X2 + b3X2 + b4X4 + … b9X9 + ei………(2) X1 = Labour cost in naira (Man days) X2 = farm rental or purchase value (naira) X3 = Expenditure on fertilizer (naira) X4= Expenditure on seed/ planting material X5 = Access to credit (proxied by loan amount accessed in the last one year) X6= Household size (number of persons) X7 = Educational attainment (number of years of schooling) X8 = Age of farmer (years) X9 = Farm size (hectares) Assessment of the productivity levels of cassava production among farming household in the study area, was analyzed using the APP, MPP and production elasticity generated from a linear production function. Results The result of the socioeconomic characteristics of the respondents showed that; majority of the respondents (71.4%) involved in cassava production in the study area were males and only 28.6% were females. The age distribution of respondents shows that, 18.6% falls in the age range of 30 to 39 years, about 58.6% falls in the age range of 40 to 49 years and 8.6% of the farmers fell within 50 t0 59 years and 14.3% were within age range of 60 to 69 years. The

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mean age among them stood at 48 years. This shows that the farmers are in economically active age, this is consistent with the findings of Rathnen et.al. (2002) The study showed that 1.4% of the respondents were divorced, 15.7% single, 14.3% were widower/widow and 68.6% are married. The result also indicated that the proportion of farmers with farming experience in the range of 10 to 20 years were 64.3%, 21 to 30 years were 18.6%, 31-40 years were about 17.1%. The mean farming experience was about 23 years,this implies that the farmers are highly experienced and this has a positive relationship on cassava production in the study area as, experience is synonymous with increase in knowledge which ultimately enhances the use of improved technology, Bassey and Okon, 2008 share similar views. The household size distribution of cassava farmer the study area showed that majority 65.7% had 6 to 10 members’ household size; while 11.4% of the total respondents had household size range of 1-5. An average household size of 9 persons was obtained for all respondents in the study area. The household size is large and can be a source of farming labor at least or no cost since agricultural production activities are labor intensive (Ofemade etal., 2008). The study revealed that majority of the respondents 44.3% had income range of 101,000 to 200,000; followed by those with range of 401,000 to 500,000 with 21.4%. 18.6% fell within the range of 201,000 to 300,000. The mean income stood at about 257,320. This means that majority of farmers in the study area produced cassava for commercial purpose. The educational qualification of the respondent showed that majority of them (41.4%) at most had primary school; while 14.3% of the respondents had attended higher education. This implies that the farmers may find it difficult to adopt technological innovations on methods of production as, education predisposes farmers to be innovative and puts them in a better position to cope with the challenges of new factors and products that the adoption of new technologies introduces to them (Adewuyi etal., 2013).

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Offor, O., and Ukpong, C.

Table 1: Socioeconomic characteristics of respondents Variables (n=70) Frequencies Percentages

Means

Age 30-39 years 40-49 years 50-59 years 60-69 years

13 41 6 10

18.6 58.6 8.6 14.3

Gender Male Female

50 20

71.4 28.6

11 48 10 1

15.7 68.6 14.3 1.4

4 29 27 10

5.7 41.4 38.6 14.3

8 46 7 9

11.4 65.7 10.0 12.9

10 60

14.3 85.7

Farming Experience 10-20 20-30 30-40

45 13 12

64.3 18.6 17.1

23

Income 1000-100000 101000-200000 201000-300000 301000-400000 401000-500000

7 31 13 2 15

10.0 44.3 18.6 2.9 21.4

257320

Farm Size Above a football field Football field size Half a football field

19 40 11

27.1 57.1 15.7

Marital Status Single Married Widow/Widower Divorced Educational Attainment No formal Education Primary School Secondary School Tertiary Education Household Size 1-5 6-10 11-15 16-20 Major Occupation Farming Others

Source: Field Survey data, 2017

48

9

30


Estimates of the factors affecting cassava production in the study area This objective was achieved by the use of Multiple linear regression analysis.Table 2 presents the estimates of the production function in different forms namely: linear, exponential, semi logarithm and double logarithm forms.The semi log form function was chosen as the lead equation because the R2 of 81.1 was the highest among other forms. The lead production function form (Linear form) also contains more significant coefficient of explanatory variables than other forms. The F- ratio (F-cal. = 32.8) of the lead equation was significant at 1% level probability level which implies that, the R2 was significant and the overall equation has goodness of fit. Table 2 indicates that all the independent variables had a positive relationship with cassava output except age, fertilizer and planting material which had negative relationship with the output. This is consistent with the findings of Akpan et al., (2017). This research shows that education is positive and significantly related to cassava output, when a farmer is educated he can better understand and assimilate farming information than illiterate counterparts. They are also high risk takers and they are more efficient in the use of productive resources to maximize output, presumably, due to their enhanced ability to acquire technical knowledge (Gbigbi, Bassey, Okon, 2010). The coefficient for labor was positive and significant at the 5 percent level, implying that the more the availability of labor, the more the

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increase in cassava output in the study area. Cassava production is highly labor intensive and farmers use manual labor for their farming Operations. Bassey et al., (2012) and Arikpo et al. (2009) shares similar views. Planting materials and fertilizer have negative coefficients and are significant at 5 percent levels of significance. This implies that these factors have a negative relationship with cassava output. As these farm factors increase, the quantity of cassava produced declines. Adewuyi et al., (2013) supports this finding. Age was also negative and significant at 7 percent level. This shows an inverse relationship between age and cassava production, the more the age of the farmer, the less the level of production. This is in line with apriori expectation as productivity is expected to decrease with increase in age. The coefficient of credit was positive and significant at 10 percent significance level. This means that access to credit increased production. Credit can significantly increase the ability of households with no or few savings to meet their financial needs for agricultural inputs; especially those that are highly necessary for weed, pest, and disease control and productive investments Nweke et al., (2002). The quantity of manure used was positive and statistically significant to cassava production, this is in line with apriori expectation as, manure use is expected to positively influence cassava production.

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Offor, O., and Ukpong, C.

Table 2:

Multiple regression estimates of the cassava production functions

variables

linear

t-value

semi log*

t-value

double log

t-value

exponential

t-value

Constant

8769.107

0.259

1.36E+06

8.842

71.902

11.701***

9.459

9.428

Age Education (years) household size (persons) Experience (years) income (Naira)

-734.901 8818.13

-1.371 1.269

-133720.557 54213.473

-7.119*** 8.125***

-5.741 1.921

-7.64*** 7.196***

-0.005 0.306

-0.338 1.483

48.035

0.02**

206046.86

0.0832

8.957

11.77***

0.079

1.088

687.828

0.855

190584.31

0.00318

-8.104

-12.03***

-0.018

-0.753

0.028

0.854

-51056.215

-11.123

-2.24

-12.199*

-1.94E-07

-0.197

farm size (ha)

446.725

0.096*

16892.772

0.0653

0.226

1.219

-0.173

-1.26

labour (mandays)

70.392

0.116

165138.416

12.088***

5.572

10.194***

0

0.007

fertilizer (kg)

-0.55

-0.154

-46644.756

-12.572***

-1.761

-11.867**

0

-1.081

manure (kg)

-6.107

-1.896

30462.906

13.891***

1.015

11.564

0

planting material (bundles) credit (naira)

109.69

0.856

-14533.619

-5.861***

-0.745

-7.505***

0.004

2.801*** 1.098

0

0.05*

44063.591

0.0967*

-1.705

-10.61**

-1.02E-08

-0.05

R-Squared

0.21

F-cal 2.6 *Lead equation Source: Field Survey data, 2017

0.81

0.77

0.22

32.8***

25.7***

2.76***

Level of productivity among cassava farmers in the study area This objective was analyzed by using production parameters such as APP, MPP and production elasticity generated from the linear form of production function. Table 3 shows the production parameters derived from the estimated production function presented in Table 2. The production parameters of interest were: production elasticity with respect to each farm factor, average productivity and marginal productivity of farm factors. The result showed that, the production elasticity with respect to farm land was inelastic, while its average productivity stood at 11832.5 and its marginal productivity was 2674.1 this implies that, land utilization by cassava farmers in the study area is in stage II in the classical production surface. Labour and quantity of manure used are in stage I as, their MPP is greater than their APP. Their levels of utilization depict stage I in the classical production surface because their average productivity is less than their respective

marginal productivity. Hence, given these production parameters, it implies that labours, quantity of manure are irrationally used by farmers in the production of cassava in the study area, as such their level of utilization should be increased. The implication here is that a unit increase in farm labour, quantity of manure used will lead to significant percentage decrease in cassava output. This result corresponds to the work of Ezedinma, (2000). The quantity of fertilizer used and planting material had a negative inelastic relationship with the output of cassava produced.. This means that, increase in fertilizer and planting material will create negative outcome on output of cassava produced by farmers in the study area. This implies that,, the level of utilization of these inputs shows that they are in stage III in the classical production surface, The values of the inputs elasticities and corresponding marginal productivity are negative, and thus their level of use should be reduced. Akpan et al.,(2017) and Ohen, Ene and Umeze (2014) share similar views. This could be attributed to

32


reasons such as; high cost of fertilizer, insufficient household labour and overutilization of farm land, this reduces the purpose of fertilizer application and use of planting materials by respondents in the study area. This result corresponds to the work of Ezedinma, (2000).

The result also showed the scale of return of 4.31 and is more than unity, hence depicting increasing return to scale. This implies that, increase in the use of farm inputs increases the level of cassava output produced by farmers in the study area.

Table 3: Production parameters for cassava production in Oruk Anam LGA Variables

MPP

APP

Elasticity

farm size (ha) labour (mandays) fertilizer (kg) manure (kg) planting material (bundles) Scale of Production

2674.1 3295.4 -28.4 19.7 -415.3

11832.1 591.4 16.2 19.4 557.5

0.226 5.572 -1.761 1.015 -0.745

Input Stage II I III I III

Utilization

4.31

Source: Field Survey data, 2017

Summary This research analyzed the factors that affect cassava production among food crop farmers in Oruk Anam Local Government Area. The result showed that majority of the respondents (71.4%) involved in cassava production in the study area were males and only 28.6% were females. The age distribution of respondents showed that, 18.6% fell in the age range of 30 to 39 years, about 58.6% were in the age range of 40 to 49 years and 8.6% of the farmers fell within 50 t0 59 years and 14.3% were within the age range of 60 to 69 years. The mean age among them stood at 48 years. The study showed that 1.4% of the respondents were divorced, 15.7% single, 14.3% were widower/widow and 68.6% are married. The result also indicated that the proportion of farmers with farming experience in the range of 10 to 20 years were 64.3%, 21 to 30 years were 18.6%, 31-40 years were about 17.1%. The mean farming experience was about 23 years. The household size distribution of cassava farmers in the study area showed that majority 65.7% had 6 to 10 members’ household size; while 11.4% of the total respondents had household size range of 1-5. An average household size of 9 persons was obtained for all respondents in the study area. AKSUJAEERD

The study revealed that majority of the respondents 44.3% had income range of N101,000 to N 200,000; followed by those with range of N 401,000 to N 500,000 with 21.4%. 18.6% fell within the range of N201,000 to N 300,000. The mean income stood at about N 257,320. This means that majority of farmers in the study area produced cassava for commercial purpose. The educational qualification of the respondent showed that majority of them (41.4%) went through primary school; 38.6% had their secondary education while 14.3% of the respondents had attended higher education. The semi log form function was chosen as the lead equation because the R2 of 81.1 was the highest among other forms. The F- ratio (F-cal. = 32.8) of the lead equation was significant at 1% level probability level which implies that, the R2 was significant and the overall equation has goodness of fit. All the independent variables had a positive relationship with cassava output except age, fertilizer and planting material which had negative relationship with the output. Age had a negative and insignificant coefficient of 133720.5. Educational background of the farmers was also insignificant but with a positive coefficient of 54212.4; Household size was significant at 5% and had a positive relationship Page 32

Offor, O., and Ukpong, C. of 206046.8; this indicates that, a unit increase in the number of persons in household will increase cassava output by 206046.8kg. Farm size was significant at 10% with a positive relationship of 16892.7; this shows that a unit increase in the size of land used by cassava farmers in the study area will increase output by 16892.7kg. Farming experience also showed a significant and positive relationship with cassava output (190584.2). This implies that 190584.2kg of cassava would be added to the total output if farmers’ years of experience increase by one percent. Access to credit was also significant at 10% and had a positive relationship with coefficient of 44063.6; which implies that a unit increase in the amount of loan accessed by farmers in the study area would increase cassava output by 44063.6kg. Conclusion The study focused on “factors that influence cassava production by farmers in Oruk Anam Area, Akwa Ibom State. Findings showed that, cassava farmers are not at the optimal level of productivity; this is because some factors of production are irrationally used. Given this scenario, subsidized farm input should be given to farmers to encourage and increase cassava productivity in the region. This information should be a guide to policy makers in the state, as this will help improve the level of cassava productivity in the state. References Abang, S. O. and Agom, D. I. (2004). Resource Use Efficiency of small-holder farmers: The case of cassava producers in Cross River State, Nigeria. Journal of Food, Agriculture and Environment, 2 (3&4): 87-97. Abang, S. O, Asadu, P.O (2008). A test of relative economic efficiency and application to Indian agriculture: Some further results. American Economic Review, 63: 214–223. Abang, S.O; Ekpe, E. and Usani, W.W. (2000).“Technical and Allocative Efficiency of yam production in Akampka. Cross River State. Quarterly Journal of International Agriculture(2): 65-69. Adelekan, B.A. (2010), “Investigation of ethanol productivity of cassava crop as a sustainable source of biofuel in tropical countries.African Journal of Biotechnology.9(35), 5 643-5 650.

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Recommendations Based on the finding of the study, the following recommendations may be appropriate for increasing the farmer’s productivity.  State Government should subsidize the price of fertilizer for cassava farmers in the region and, ensure that it is easily accessible to farmers. This will help to increase the efficiency of farm resource use among cassava farmers in the region.  An increase in education impact positively on the production of cassava. Government should intensify effort to extend adult education to rural area as this will help farmers to acquire and process relevant information more effectively and also, equip the farmers with better managerial skills, which will eventually lead to an improvement in total factor productivity of cassava.  Farmers should be encouraged to produce more, by making available to them micro loans with single digit interest rates.  Input acquisition centres should be located close to the farmers where they can easily access planting materials and other agro inputs for their farms.

Adepoju, A.A. (2012). Investigating endogeneity effects of social capital on household welfare in Nigeria: A control function approach.Quarterly Journal of International Agriculture.Assessment of profitability and efficiency of cassava production 51 (1): 7396. Arikpo, J. D., Adinya, I. B., Angba., A. O., Agbogo, A. E., Ahong., E. A., Nyienakuna., M. G., Eze, A. N., Anyanorah, N. C & Ogar, E. N. (2009). Econometric Analysis of Variables affecting platation mono-cropping system in central Cross River State, Nigeria.Journal of Agriculture, Forestry and Social Sciences, 7(2):75-87. Ayoola, G. B. (2009) Prodigies of Agricultural Economy and Policy.An inaugural lecture, University of Agriculture, Makurdi. Inaugural Lecture Series No. 9

34 Bassey,

Bassey,

Factors influencing cassava production among farm households N. E., Akpaeti, A.J.and Umoh,I.U. (2014).Determinants of cassava output among small scale farmers in Nigeria: A survey of Akwa Ibom State farmers. Journal of Agricultural Extension, Economics & Sociology 3(4):319-330 N. E., Akpaeti, A.J.and Udo,U.J. (2012).Labour choice decisions among cassava crop farmers in Akwa Ibom State, Nigeria. International Journal of Food and Agricultural Economics 12(3):145-156

Bassey, N.E and Okon, U. E (2008).Socio-economic Constraints to the Adoption of Improved cassava production and Processing Technologies in Mbo Local Government Area of Akwa Ibom State, Nigeria.Nigerian Southeast Journal of Agricultural Economics & Extension 1(2),9-17. Central Bank of Nigeria (CBN) (2003).Central Bank of Nigeria Annual Reports and Statements of Accounts. Eboh, E.C., Larsen, B., Oji, K.O., Achike, A.I., Ujah, O.C., Odu, M., Uzochukwu, S.A. and NZE C.C.P.(2006) “Renewable natural resources, sustainable economic growth and poverty reduction in Nigeria” AIAE Research Paper1. Enugu. African Institute of Applied Economics Efficiencies of Small scale Cassava Growers in Five Selected Local Government Area of Cross River State”. Global Journal of Pure and Applied Science 7, (1). Egesi, C., Mbanaso, E., Ogbe, F., Okogbenin, E. and Fregene, M. (2006). “Development of cassava varieties with high value root quality through induced mutations and marker-aided breeding”. NRCRI, Umudike Annual Report 2006.:2-6 El-Sharkawy, M.A. (2004), “Cassava biology and physiology.Plant Molecular Biology. 56( 4): 481-501, http://dx.doi.org/10.1007/s11103005-2270-7

Area of Anambra State, Nigeria.Agricultural System.17 :197-210. Fakayode, S.B, Babatunde, R.O and Ajao, R (2008): “Productivity Analysis of Cassava-Based Production Systems in the Guinea Savannah: Case Study of Kwara State” American-Eurasian Journal of Scientific Research 3(1):33-39. Published by International Digital Organization for Scientific Information (IDOSI) FAO Food and Agricultural Organization(2012). www.fao.org/DOCREP/005/Y4636E/y4636 e05.htm FAOSTAT (2009).Online Statistical Rome, Italy.www.fao.org.

Database.

FAOSTAT (2013) Food and agriculture data.Available at http://faostat.fao.org/ faostat/collections? Feder Henri-Ukoha A.& Orebiyi J. S., (1990). Determinants of loan acquisition from the financial institutions by small-scale farmers in Ohafia Agricultural Zone of Abia State, South-east Nigeria.Journal of Development and Agricultural Economics. 3(2):67-74. Federal

Ministry of Agriculture and Rural Development FMARD (2001) Nigeria Rural Development Sector Strategic Main Report, Abuja, Nigeria.

Food and Agricultural Organization (FAO) (2005). International Trade in cassava products: an African Perspective Government Areas of Niger State, Nigeria; M.Sc. Thesis, RSUST, PH, Nigeria. Gbigbi, M.T., Bassey, N.E. and Okon, U.E (2010).Analysis of technical efficiency in cassava production in DeltaState.Nigerian Southeast Journal of Agricultural Economics and Extension 9(1&2):115-123

Ezedinma, C. I. (2000). Farm Resource Allocation and Profitability of Arable Crop Enterprises in the Humid Forest Inland valley Ecosystem.A case study of Ozu Abam in Southern Nigeria.UNISWA Journal of Agriculture. 9: 48-56.

Hillocks, R.J. (2002), “Cassava in Africa”, Chapter 3, in: Hillocks, R.J., J.M. Thresh and A.C. Bellotti (eds.), Cassava: Biology, Production and Utilization, CABI, Wallingford, United Kingdom, pp. 41-54,http://ciatlibrary.ciat.cgiar.org/articulos_ciat/cabi_06c h3.pdf.

Fakayode, M and Dowese, J. K (2008) Bases for Farm Resource Allocation in Traditional Farming Systems: A Comparative Study of Productivity of Farm Resource in Abakaliki

International Cooperative Alliance (ICA).(2010). Annual congress report. 15 Route des Morillons 1218 Grand Saconnex Geneva Switzerland. Available at http//:www.ica.coop

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Offor, O., and Ukpong, C. International Institute of Tropical Agriculture (IITA) 2011. Integrated cassava project in conjunction with Presidential initiative on cassava: A study on the impact of IITA’s processing research on Nigeria’s staple food system. [Online] Available from http:// www.iita.org/2011-iita-in-thenews;jsessionid.[Accessed Nov. 18, 2015]. International Cooperative Alliance Kolawole O. and Ojo S. O. (2007). Economic efficiency of small scale food crop production. Kormawa, P. and Akoroda, M. O. (2003). Cassava Supply Chain Arrangements for Industrial Utilization in Nigeria, IITA, Ibadan Kuye, O.O (2015) ‘’Comparative analysis of performance of Bank of Agriculture and selected commercial banks (FBN and UB) in enhancing cassava production by farmers in South-south Nigeria (2009-2013)’’ Unpublished PhD Thesis submitted to the Department of Agricultural Economics, Management and Extension, Ebonyi State University, Abakaliki. Pp138-139. National

Population Comission (NPC) (2006). National Population and Housing Census Statistics in Nigeria.

Nweke FI, Spencer DSC, Lynam JK (2002). TheCassava Transformation: Africa’s BestKept Secret. Michigan State University Press, East Lansing. Nweke, F. (2004): “New challenges in the cassava transformation in Nigeria and Ghana. Environment and Production Technology Division”. International Food Policy Research Institute (IFPRI). Washington, USA. Ogunleye, A. S., Adeyemo, R., Bamire, A. S. and Binuomote, S. O. (2014).Cassava production and technical efficiency in Ayedaade Local Government Area of Osun State, Nigeria.Elixir International Journal of Agriculture 64: 24465-24468. .

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Ogunleye, A. S., Adeyemo, R., Bamire, A. S. and Kehinde, A. D. (2017). Assessment of profitability and efficiency of cassava production among government and nongovernment assisted farmers association in Osun State, Nigeria. African Journal of Rural Development 2(2):225-233. Okoli, P.I. and Umebali, E.E. (2008), “Revitalizing the Nigerian agriculture to meet the challenges of the 21st century” in proceedings of the 10th annual national conference of the Nigerian Association of Agricultural Economics held at the University of Abuja, Abuja p432-438. Okorji, E. C. (1983). Consequences on Agricultural Productivity of Crop Stereotyping along Sex-lines.A case study of four villages in Abakiliki Area of Anambra State, Nigeria.Unpublished M. Sc Thesis, University of Nigeria, Nsukka, Abia State. Okoro E, Lemchi J, Ezedinma C, Dixon A, Akoroda M, Sanni L, Okechukwu R, Marco P, Nkumbira J, Ogbe F, Illona P, Tarawali G (2005). Technological Challenges of Cassava Commercialization and Industrialization in Nigeria. Guest Paper Presented at the 2005 African Technology Day Organized by the African Institute of Applied Economics (AIAE) and the African Technology Policy Studies Network (ATPS). Onwueme, I.C & Sinha, T.D. (1991). Field Crop Production in Tropical Africa, Technical Centre for Agriculture and Rural Cooperation ( CTA), Ede, The Netherlands. Prochnik, S. et al. (2012), “The cassava genome: Current progress, future directions”, Tropical Plant Biology. 5,(1): 88-94, http://dx.doi.org/10.1007/s12042-011-9088z Salick, J., N. Cellinese and S. Knapp (1997), Indigenous diversity of cassava: Generation, maintenance, use and loss among the Amuesha, Peruvian Upper Amazon.Economic Botany,51(1): 6-19


ESTIMATION OF PRICE VARIABILITY OF SELECTED AGRICULTURAL PRODUCTS IN AKWA IBOM STATE, NIGERIA: 2000-2015 Akpan, O. D1* and Udoh, E. J1 1

Department of Agricultural Economics & ExtensionUniversity of Uyo, Akwa Ibom State, Nigeria

Correponding author: [email protected], mobile: +2348032717955 Abstract The study estimated price variability of selected agricultural products in Akwa Ibom State between 2000to 2015. Objectively, it assess trend in prices of agricultural products, percentage changes in prices and its effect on output and the determinants of price variability of agricultural products in the study area. Secondary were obtained from Ministry of Economic Development, Akwa Ibom State, Akwa Ibom Agricultural Development Program (AKADEP), National Bureau of Statistics (NBS) and Central Bank of Nigeria (CBN) publications. Simple percentages and multiple regression models were used to analyse data obtained and results are presented in graphs and tables. Findings show trending agricultural product prices and also a fluctuating percentage change in the prices of agricultural products. Agricultural products price variability show a positive relationship to agricultural output. A variation in prices of the products results in a corresponding change on output. Also, pump price, exchange rate, average rainfall, aggregate input price and inflation rate were the determinants of variability of prices of agricultural products in the study area. Study suggests the implementation of policies which will buffer the agricultural sector from the effect of variability of agricultural product prices. Also, policies which reduce the rate of inflation will minimize price variability among agricultural commodities and consequently reduce inefficiency, distortions and misallocation of resources in agriculture that might be caused by inflation. Keywords: Price variability, Trending, Agricultural products, Nigeria Introduction Instability in commodity prices among markets is highly detrimental to the marketing system and the economy as a whole, resulting in inefficiency in the allocation of resources depending on the source of variability. Comparing the role of agriculture in Nigeria economic growth and development, with any other major sector in the economy, it is highly competitive and most of the output are perishable, agriculture is the least able to pass input and output cost increases (Ukoha, 2008). Price variability of agricultural products constitutes one of the most threatening problems facing the agricultural sector worldwide today. This is mainly due to inelastic nature of aggregate demand and supply for the products, associated with their characteristics. Some studies identified widespread inter-regional and inter-seasonal variation in prices as factors that affect adequate planning of farming, industrial operations and output expansion. The effect of AKSUJAEERD

this is felt as the fluctuating prices of agricultural products take many unaware, causing an increased concern over the ability of the world food economy to adequately feed billions of people now and in future. The international Monetary Fund (2008) reported that most of the fuel occurred in developing The International Monetary Fund (2008) reported that most of the food crises are triggered by the surging prices of food and fuel occurred in developing countries. Many of these developing countries are food importers and so far have seen their annual food import bills double since 2000. Central Bank (2016), reported that about $11bn (N3.1tn) was spent to import four (4) consumable commodities -rice, wheat, fish, and sugar- annually. Food import is seen to be growing at a rate of 11 per cent per annum (CBN, 2016), and this is worrisome! Agricultural product prices have been fluctuating widely over the last few years, Page 36

Akpan, O. D. and Udoh, E. J. affecting not only consumers but producers too, and the negative food security impact of higher food prices is greatest on poor consumers in the developing countries’ In Akwa Ibom State, study carried out by Etuk and Mfonobong, (2016) shows that retail market prices of selected food commodities are increasing overtime. It show no uniformity in terms of the periods during which these price increase occurred for the selected agricultural commodities during the period (2000-2015). The degree of variability in commodity prices is traditionally believed to depend heavily on output levels and on the nature and frequency of unexpected shifts in demand and supply. Thus, essentially all market forces affecting commodity price formation could potentially come into play in determining price variability of commodities such as climatic conditions, total acreage for cultivation, government policies, and international trade factors such as import, export and exchange rate. Price variability is not only harmful to consumers but also affect producers both in business and decision making. On the positive side, higher product prices enable producers to invest in promising productivity and production. Although high prices can technically be good news for farmers, price fluctuation is extremely dangerous, as farmers and other agents in the food chain risk losing their investments if prices fall intermittently. Most rural farmers do not have enough investment capital as well as insurance option to absorb such unpredictable shocks. Randy (2015) submitted that extreme variability in commodity prices, particularly of food commodities, affects farmers adversely because their incomes are uncertain. Famers have variable income through sales of their products and this may be caused by factors such as varying prices of inputs, weather, change in acreage, lack of storage facilities, change in technology etc. such farmers cannot be sure of what income they will receive from year to year to enable them plan and allocate their resources efficiently. Agricultural products price variability has greater impact on the developing world. Garri, plantain, palm oil, chicken, okra, pepper and maize are the classified among the most staple food in the study area. Unstable prices for

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important food stables listed can cause undesired economic, social and political consequences. Highly unstable prices can lead to inefficient agricultural production decisions, especially when markets for credit and risk are poorly developed. The human costs of food price shocks can be disastrous for the poor, because food staples often constitute a large share of poor farmers’ incomes and poor consumers’ expenditures (Gilbert &Morgan 2010). Price variability of agricultural products leads also to increased volatility of farming household’s income which may increase their rate of diversification from farming to non-farm activities, thereby reducing productivity in agriculture (Akpan and Edet, 2016). When prices of commodities within agriculture vary due to different factors, such movement may decrease economic welfare for society as a whole and the agricultural sector in particular (Ukoha, 2008), resource allocation efficiency is decreased because decision makers have less useful information on prices to guide them in decision making. When prices fluctuate, even if they are tolerable on average, the short-term shock make both smallholder farmers and poor consumers vulnerable to long-term poverty situations, forcing them to take up temporary coping mechanisms such as distress asset sales or reduced intake of nutritious foods, and making them food insecure. The risk associated with choosing which commodity to produce increases with inflation (Ukoha, 2008). Hence this study has the following objectives: to assess the trend of price of selected agricultural products in the study area, to estimate the annual percentage variation in prices of selected agricultural products in the study area from 2000 – 2015, to assess the effect of agricultural products price variability on selected agricultural output in the study area over the years and estimate the determinants of agricultural price variability in the study area. Theoretical framework Cobweb Theory This study is based on cobweb theory propounded by Nicholas Kaldor in 1934. It asserts that supply adjusts itself to changing conditions of demand which are manifested through price changes not instantaneously but

38

Estimation of price variability of selected agricultural products

after certain period. The cobweb theory is an economic model that explains why prices might be subject to periodic fluctuations in certain types of market. It describes cyclical supply and demand in a market where the amount produced must be chosen before prices are observed; it is based on the time lag between supply and demand decisions. The cobweb theory suggests

that prices can become stuck in a cycle of everincreasing volatility. Example, if price fall, many farmers will go out of business, the next year supply will fall. This causes price to increase. However, this higher price acts as incentive for greater supply. Therefore, next year supply increases and prices fall again.

Conceptual framework Population Growth

Agricultural output

Importatio n Poverty (64%)

Input prices Government Policies

Exchang Inflation e

Hunger (70%) Agricultural Product Price Variability

Agricultural Products Supply And Demand

Investment

(60%) Low productivity Destabilized budget

Climate Variability

Technologica l change

Low

Increased

Government expenditure

Figure 1: Determinants of Price Variability Prices of agricultural products exhibit variability for many reasons. Some price changes may be largely predictable, such as those caused by a factor as seasonality of the product, whereby price is lowest during and soon after harvest and highest immediately before harvest. On the other hand, some factors such as weather shock and may be typically unpredictable and may lead to unpredictable changes in prices, especially if stock is low. It is seen that multiple factors contribute to price variability of agricultural products. In short term, the benefits of high prices go primarily to farmers with a large marketed surplus, and these farmers are not the poorest. People usually buy more food than they sell. Thus, high food prices tend to worsen AKSUJAEERD

poverty, food insecurity and malnutrition among other negative effects. However, high prices represent an opportunity to spur long-term investment in agriculture, which may contribute to sustainable food security in the longer run. Figure 1 describes the nature of price variability of agricultural products, showing the various determinants of price variability and its effect on consumers and investors. Review of related literature Tothora and Monika (2011) discussed the main challenges of price volatility in agricultural commodity markets and concluded that though volatility has always been a feature of agricultural commodity market, the evidence suggests that volatility has increased at least in Page 38

Akpan, O. D. and Udoh, E. J. some commodity market and there seems to be an overlap between periods of high prices and increased volatility. It was identified that volatility peaks also seem to coexist with decreased stocks. The study abstained from considering the development of fundamentals and macroeconomic factors, such as exchange rate developments. Etuk and MfonObong (2016) estimated the percentage changes in prices of agricultural products. Data obtained was analysed using Coefficient of variance (CV) and result revealed that mean annual percentage changes in the retail prices of selected products showed a fluctuating trend. Huka et al., (2014) studied how price variability affect small scale farmers’ development, and found several contributions of good price to farmers’ livelihood as well as enhancing the use of better farming technology. Etuk and Mfon-Obong (2016) analysed the market prices of selected agricultural commodities in Akwa Ibom State. Data obtained were analysed using regression model and findings showed that retail market prices of selected food commodities such as rice, garri, yam, maize, palm oil, egg, beans and plantain in Akwa Ibom State all showed an increasing trend in prices overtime. Edet et al., (2014) assessed price transmission and market integration of paw-paw and leafy Telfairia in Akwa Ibom State, Nigeria. The trend analysis showed that the products in the rural and urban markets have positive significant relationships with time and positive exponential growth rate. Akpan et al., (2009) in their study examined the price transmission and market integration of maize and bean in the rural and urban market of Akwa Ibom State Nigeria. Using a time series data, the trend analysis showed that prices of maize and beans in the rural and urban markets had exponential growth rates were less than one. Also, Pearson Correlation Coefficient (PCC) for the pairs revealed significant and linear symmetric relationships. This implies the existence of symmetric market information flows between the rural and urban markets for maize and beans in the study area. Akanni and Okeowo (2011) analysed the aggregate output supply response of selected food grains in Nigeria. The main objective was

39

to examine the various determinants of the quantities of cereals, considering prices changes as one of the determinants. Findings indicated that Producer price of rice was positive and statistically significant at 1% level. There is the tendency for the price of agricultural products to drop, which may consequently reduce the level of domestic production and thus discourage commercial production. Obayelu and Salau (2010) examined the response of aggregate agricultural output to exchange rate and price movements of food and export crops in Nigeria using available time series data. Augmented Dickey Fuller (ADF) and unit root test were used for the analysis and results showed that total agricultural output respond positively to increases in exchange rate and negatively to increases in food prices both in the short and long run. The significance of food crop prices and exchange rate at 5% and 1% respectively both in the short and long run suggest that changes in these variables are passed immediately to agricultural output. Mustapha and Culas (2013) assessed the impact of increased agricultural commodity price variability. Cross sectional data collected was analysed using Pearson Correlation and regression analysis. The study found that food prices in Africa is influenced by events such as population growth, oil price fluctuations, implication policies, water availability and political instability. The effect of food price variability was also identified as one of the causes of nourishment rate and rising death rate in sub-Saharan Africa, including Nigeria. Etuk and Mfonobong (2016) in their study found variations in prices of agricultural products are due to their bulkiness, the transportation and handling costs, their perishable nature and the seasonal pattern of production which is influenced by weather condition. Akpan and Udoh (2009) estimated the functional relationship between relative price variability of grains and inflation rate in various agricultural policy periods in Nigeria, result revealed that inflation has a positive significant effect on relative price variability of grains. The result further showed that the Structural Adjustment Programme (SAP) and civilian post Structural Adjustment Programme agricultural policy

40


regimes brought about a positive significant shift in the coefficient of inflation which implies an increase in the price variability of grains.

Annual percentage change in prices collected for the selected products was estimated using percentage change, given as:

Methodology Study Area The study was conducted in Akwa Ibom State, located in the coastal South-South region of Nigeria. The region is popularly called the Niger Delta region or the oil rich region of Nigeria. The state is located between latitudes 4°32’N and 5°33’N and longitudes 7°25’E and 8°25’E. It has a total land area of areas of 7,246km2. Akwa Ibom State has a tropical climate marked by two distinct season, dry (November – March) and wet (April – October). The mean annual rainfall ranges from 2,000mm to 3,000mm, depending on the area. Naturally, maximum humidity is recorded in July while the minimum occurs in January. The mean annual temperature of the state lies between 26°C and 29°C and average sunshine of about 1,450 hours per year. The state is bordered on the east by Cross River State, on the west by Rivers State and Abia State, and on the South by the Atlantic Ocean. Akwa Ibom State has a population of about 3,902,051. The state is basically an agrarian society where crops like maize, okra, cassava, yam and rice are cultivated in large quantities. Politically and for ease of administration, the State is divided into 31 Local Government Councils or Areas

%∆=

Sources of data Secondary data obtained from Ministry of Economic Development, Akwa Ibom State, Akwa Ibom Development Programme (AKADEP) office, Central Bank Nigeria (CBN) publications, and National Bureau of Statistics (NBS). Average annual prices per kilogram and the output (tonnes) of selected agricultural products (garri, plantain, palm oil, chicken, okra, pepper and maize) between the periods of 2000 to 2015 were collected. Methods of data analysis The trend of price of each selected agricultural products were evaluated using descriptive statistics where graphs are used to show the trend (pattern) in prices of selected products in Akwa Ibom State. AKSUJAEERD

×

----------------- (1)

Where; %∆ = percentage change in period y of a product = price of product in current year = price of a product in preceding year Descriptive statistics was used to show the effect of price variability of each agricultural product on the output in the state. Multiple Regression analysis model was used to analyse the determinants of price variability on agricultural products. The regression model is specified as follows: = + + + + + + + + ---------- (2) = 1,2,3, … ,16 Where: = agricultural product price variability, given by price variability index = pump pricein year t-1(N) = population in year t-1(no.) = exchange rate in year t-1(N) = average rainfall in year t-1 (mm) = input price in year t-1 (N) = government expenditure in year t-1 (N) = inflation in year t-1(CPI) = period (year) = Error Term Is the intercept , , , , , , , are the regressions co-efficients. Results and discussion This chapter presents results and discussion of the findings of the study. The analyses in this study were based on seven agricultural commodities: garri, palm oil, plantain, chicken, okra, pepper, maize. These produce are among the agricultural commodities produced in Akwa Ibom State.

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Akpan, O. D. and Udoh, E. J.

41

Amount N

Trend in price variability of selected agricultural products in Akwa Ibom State from 2000 – 2014. 800 700 600 500 400 300 200 100 0 1995

GARRI /7cm CUP PLANTAIN / Kg PALM OIL CHICKEN/ kg OKRA /kg PEPPER (FRESH)/kg 2000

2005

2010

2015

2020

MAIZE /7cm CUP

Year

Figure 2. Trend in price variability of selected agricultural products in Akwa Ibom State Garri had the lowest average annual price range within the period relative to the other crops selected for the study. Garri price has an undulating trend during the period and increased from N6.75 in 2000 to N22.2 in 2015. The least price for the period was recorded in 2000 with N6.75/kg. The first national policy on agriculture was adopted in 1988 and was expected to remain valid for about thirteen years, from 1988 to 2000. The policy was aimed at the attainment of selfsustaining growth in all the sub sectors of agriculture and the structural transformation necessary for the overall socio-economic development of the country as well as the improvement in the quality of life while the highest price was observed in 2013 with N32.29.This result is in line with the findings of Etuk and Mfonobong (2016) in their study on the analysis of market prices of selected agricultural commodities in Akwa Ibom State. The trend showed a persistent increase as the price increased from N6.75 in 2000 to N29.56 in 2005 at percentage change of 71.6% in 2001, 31.3% in 2002, 19.6% in 2003, 26.3% in 2004 and 28.6% in 2005. The price fell in 2006 and 2007 at -2.4% and -53.1% respectively. The percentage increase in prices between 2008 and 2009 was constant (34.7% and 34.7% respectively) and in 2011, there was a fall in the price of garri from N27.93 in 2010 to N19.29 in 2011(-30.9%). This may be linked to provision made by the state in partnership with the

Federal Government and World Bank providing grants to cooperatives under FADAMA III programme. This caused an increase in cassava production, thereby causing garri price to fall. It increased afterwards but fell later in 2014 and 2015 at -22.8% and -11.0% from N32.29 in 2013 to N22.2 in 2015. The annual prices of plantain fluctuated between N57.5 to N165.5 with its peak periods in 2001, 2007, and 2013. These peak periods were observed to come after about 5 years interval, reasons for this result may be attributed to weather, demand for plantain, productivity, cost of transportation, inflation etc. The result also showed that plantain had the highest percentage variation (156.5%) in price in 2001. The price fell the next two years (2002 and 2003) at a percentage of -21.2% and 7.1%, increased the next two years (2004 and 2005) by 5.8% and 13.6%. In 2006, the price fell again by -13.6% and increased in 2007 by 72.0%. the price of plantain fell again in the next two years (2008 and 2009) at -9.5% and -23.4% respectively and rose the next two years (2010 and 2011 at 13.5% and 6.9%. the price fell then from year 2012 to 2015 except in 2013 where it increased at 46.1% change from the previous year. The result showed that the average yearly prices of plantain rose and fell mostly at two years interval. This may be said to be due to climatic variation. This result is consistent with that of Udoka, Akpan

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and Patrick (2015), in their analysis on price transmission of unripe plantain in Akwa Ibom State, that the price trend show undulating fluctuation between 2006 and 2013 but galloped towards the peak in 2010. The price of palm oil had fluctuated from 2000 to 2007 and then rose almost steadily thereafter. Palm oil prices as any other agricultural product is responding to seasonal variation as price increased throughout the period. This shows an inverse relationship between palm oil prices and the demand for palm oil in the study area. This observation is in line with that made by Ntuokwa and Uduak (2012) in analysing palm oil prices in Ini local government area of Akwa Ibom state. The implication therefore is that government policies and programmes have been unable to affect palm oil production since it is a perennial crop. Chicken had the highest yearly prices relative to the other products in this study from 2000 -2015. Its price fluctuated with peak in 2006, 2008 and 2012, went down from 2012 – 2015. The result shows an undulating trend throughout the period, with the price of chicken increasing in 2000, 2001, 2002, 2003, 2004, 2005 and 2006 with percentage changes of 8.4%, 17.9%, and 9.2%, 7.5%, 9.9% and 13.3% respectively. Between 2006 and 2007, the price of chicken was almost constant with a percentage change of 1.0%. The price peaked in 2008 at 22.9% from ₦406.25 to ₦499.28 and further increased and had its highest price (₦747.24) in 2012. Chicken price reduces again in 2013 to 2015 at percentage changes of -8.6%, 3.5% and -1.4%. The results obtained may be due to the high cost of production in the livestock subsection as compared to crops. Assessing the variation in prices of okra, okra prices increased by an average of 10.6% in 2001, 15.4% in 2002 and 41.7% in 2003.Okra recorded a positive growth in price between 2006 and 2007 of a percentage change of 74.4% and gave okra the highest price of ₦226.18 while its lowest price for the period was recorded in year 2000. The prices of okra fluctuated tremendously at almost the same rate as plantain throughout, having the highest prices in 2004, 2007, 2009, 2012 and 2017. This variation may be associated with cost of production and storage of the product and the demand on the product in relation to other products.

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The trend analysis as in figure 1 shows that pepper has average price over the study period (20002015). The growth in prices of pepper ranged between ₦47.0 and ₦226.18 from 2000 to 2007 with an average percentage change of between 5.4% and 45.4%. The highest percentage change was observed in 2009 to be 108.7% and the lowest (-25.6%) in 2010. The price per kg of pepper fluctuated slightly from 2001 -2008, peaked in 2009 and reduced in 2010 due to financial contribution made by the Federal government under Commercial Agricultural Credit Scheme (CACS) to boost agricultural production, the price fluctuates slightly till 2015. From the graph, it shows that there is no significant change in price of pepper in the study area. Price instability in pepper is associated with its perishable nature. The implication therefore is that business speculators who buys and store to take advantage of time are most unlikely to make any substantial profit. It is advisable for producers to sell immediately as they produce pepper. Maize and garri shows almost same pattern in price variation. The price of maize fluctuated slightly throughout until 2014 and increased significantly in 2015. The trend pattern of maize prices within the period of 2000 to 2015 shows asymptotic fluctuations in the prices of this product from one year to the other. The prices increased throughout from 2000 to 2015, except in 2003 where it fell from ₦15.98 in 2002, to ₦14.8% in 2003 at -6.9%. In 2013, it fell from from₦39.81 in 2012 to ₦37.91 in 2013 at -4.8% and in 2014, where it fell from ₦37.91 in 2013 to ₦31.42 in 2014 at -17.1%.The prices of maize is slightly higher than that of garri until 2003 to 2006 where it reduced slightly and rose again above that of garri from 2007. Possible contributions to the variability of this product may be associated to cost of storage. Price instability in maize is also associated with its perishable nature, pest and disease infestation and the seasonal pattern of production which is influenced by weather condition. These results indicate that the trend in prices of agricultural products in Akwa Ibom State generally increases as the years go by irrespective of the numerous agricultural policies and programs made by the different administration.

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Percentage change in prices of selected agricultural products in Akwa Ibom State from 2000-2015 Table 1. Percentage changes in prices of selected agricultural products from 2000-2015 Years Crops Garri Plantain P. oil Chicken Okrs Pepper Maize

2000 -

2001

2002

2003

2004

2005

2006

2007

2008

71.6 156.5 17.1 8.4 10.6 45.4 27.3

31.3 -21.2 3.2 17.9 15.4 18.2 36.0

19.6 -7.1 22.5 9.2 41.7 -22.8 -6.9

26.3 5.8 -16.1 7.5 23.5 5.4 31.1

28.6 13.7 -1.8 9.9 -14.3 20.5 0.4

-2.4 -13.6 48.2 13.3 74.4 19.2 29.9

-53.1 72.0 -2.2 1.0 44.1 35.1 -17.1

34.7 -9.5 12.5 22.9 -14.5 -13.0 18.0

2009 34.8 -23.4 8.5 4.3 9.4 108.7 6.1

2010

2011

13.7 13.5 3.2 8.0 -25.9 -25.6 9.6

-30.9 6.9 3.5 12.7 9.7 -13.0 23.3

2012 24.5 -1.5 2.1 18.0 55.3 3.8 11.6

2013 34.5 46.1 2.0 -8.6 -0.5 39.1 -4.8

2014 -22.8 -23.8 3.1 -3.5 -15.6 6.2 -17.1

2015 -11.0 -7.1 6.4 -1.4 13.9 -6.5 333.1

Source: Computed by the researcher, 2018

The highest average annual percentage change in price for the period was observed for maize (32.03%). Handling cost and cost of storage of this commodity contributed to this observed high variability in price. Price instability in maize is also associated with its perishable nature, pest and disease infestation and the seasonal pattern of production which is influenced by weather condition. Pepper, okra had an average percentage change in retail prices of 14.71%, 15.15%, respectively. Plantain and garri had and average percentage change of 13.82% and 13.29% respectively for the period,

2000-2015. This observed moderate percentage in retail prices of these products may have resulted due to weather conditions, export of the products and government policies which encouraged investment in agriculture. Chicken and palm oil had the least average annual percentage change of 7.97% and 7.48%. This may be attributed to involvement of farmers in poultry production, making the price of poultry product less volatile. Palm oil is also produced in large quantities in the state and is not a seasonal product as compared to other crops studied.

Effect of Price Variability on Output 80.0

200

60.0 150

40.0 20.0 0.0 1990 -20.0

pv gar CASSAVA 2000

2010

2020

-40.0 -60.0

Figure 3.Effect of Price Variability of Garri on Output of Cassava

100

pv pln PLANTAIN

50 0 1995 2000 2005 2010 2015 2020 -50

figure 4. Effect of price variability of plantain on output of plantain

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800

4000

600

3000

400

pv po

2000

pv ckn

200

PALM OIL

1000

CHICKEN

0 1990 -200

2000

2010

0 1990 -1000

2020

Figure 5. Effect of price variability of palm oil on Output of palm oil on output of chicken

2000

2010

2020

figure 6. Effect of price variability of chicken

150

80 60

100

40 pv okr

20

OKRA (MT)

0 1990 -20

2000

2010

pv fpp

50

PEPPER

0 1995

2020

-40

2000

2005

2010

2015

2020

-50

Figure 7. Effect of price variability of okra on output

Figure 8. Effect of price variability of peper on output

400 300 200

pv mz

100

MAIZE

0 1995 2000 2005 2010 2015 2020 -100

Figure 9. Effect of price variability of maize on output of maize Figure 3 above shows the trend of price variability of garri (%) and the output of cassava (metric tonnes). Price variability fluctuates across the years within the period (2000 – 2015) with the highest percentage (71.6%) change observed in 2001. The output of cassava increased from 0.85 metric ton in 2000 to 2.8 metric tons in 2015.

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Figure 4 shows the result of variability in price of plantain (%) and the output of plantain (metric tons) obtained between the periods of 2000 and 2015. Price of plantain fluctuated highly within the period, being highest in 2001 with 156.5%. There was a steady increase in the output of plantain from 5.1 metric tons in 2000 to 12.0 metric tons in

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Akpan, O. D. and Udoh, E. J. 2014. Output of plantain reduced remarkably in 2015 to 1.2 metric tons in 2015. The result of price variability of palm oil (%) and output (metric tons) for each year between 2000 and 2015 is shown in figure 5. Result shows that price fluctuated throughout the period with the highest price variability of 48.2% obtained in 2006. Output of palm oil increased throughout the period from 350.51 metric tons to in 2000 to 725.65 metric tons in 2015 except in 2003 where there was a drastic reduction in output of 38.0 metric tons. The least output (38.0 metric tons) was observed in 2003 and highest (725.65 metric tons) observed in 2015. Figure 6 shows the trend in price variability of chicken (%) and the output of chicken in metric tonnes. Between periods 2000 to 2015, the output of chicken increased throughout with the fluctuating price variability (%) except in fear 2012 which the output fell from 3585 metric tons in 2011 to 3052.25 metric tons in 2012. Output also reduced again in 2014 and 2015 from 3750.5 metric tons in 2013 to 3500.25 metric tons and 3550.1 metric tons in 2014 and 2015 respectively. The highest output (3750 metric tons) was obtained in 2013 at price variability of (-8.6%) and the least output (1012.5 metric tons) obtained in year 2000. In figure 7, the result shows that the price variability (%) of okra fluctuates within the period of 2000 – 2015. The output of okra increased at a

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low rate within the period with a difference of not more than 1 metric ton between the years. Okra output was highest in 2015 at 3.5 metric ton and least in 2000 with output of 0.27 metric ton. The result of price variability of pepper (%) and the output of pepper for each year between 2000 and 2015 is shown in figure 8. Result showed a fluctuating trend pattern of price variability of pepper from 0% to 108.7%. The highest price variability of pepper was 108.7% and was observed in 2009. The output of pepper within the period increased at lower rate. Between 2000 and 2003, pepper maintained a constant output of about 0.1 metric tons, from 2004 to 2006, output increased to about 0.2 metric tons, it increased in 2007 and 2008 to 0.3 metric tons. In 2009 and 2010, output increased to 0.44 metric tons and 0.77 metric tons respectively. It increased further to about 1 metric ton in 2011 and 2012 and to about 2 metric tons in 2013 to 2015. The graph in figure 9 shows that percentage change in price of maize fluctuated and is highest in 2015. This necessarily does not have any noticeable effect on the output as the output increased within the period (2000-2015). The output kept increasing between 2000 and 2005 from 26.1 metric tons to 57.5 metric tons and became stable in 2006 and 2007 with an output of about 62 metric tons. It further increased from 65.1 metric tons in 2008 to 105.5 metric tons in 2015, which it showed the highest output and the least (26.1 metric tons) observed in 2000.

Table 1: Estimation of the determinants of price variability Coefficient Std. Error t-ratio Const 433.357 226.036 1.9172 PMP__ 8.0101 1.99674 4.0116 POP_ -0.00012883 8.91689e-05 -1.4448 EXC__ 4.45442 1.70099 2.6187 AVR_ -0.640412 0.219121 -2.9226 AIP__ -0.250094 0.0926897 -2.6982 GVE_ 3.01307e-08 4.17418e-08 0.7218 INF__ 0.0341163 0.00717504 4.7549 Mean dependent var 97.20000 S.D. dependent var Sum squared resid 23336.17 S.E. of regression R-squared 0.873930 Adjusted R-squared F(7, 8) 7.537576 P-value(F) Rho -0.352878 Durbin-Watson

p-value 0.09151 * 0.00389 *** 0.18652 0.03071 ** 0.01921 ** 0.02715 ** 0.49095 0.00144 *** 109.8205 54.00945 0.763618 0.005343 2.379173

Source: computed from field survey using Gretl.* , ** ‘*** Significant at 10%, 5%, and 1% respectively

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The multiple regression analysis result above shows the determinants of price variability of agricultural products in Akwa Ibom state. The result shows that pump price, exchange rate, average rainfall, aggregate input prices and inflation were best determinants of price variability of agricultural products in the state and were significant at 5% and 1%. The coefficient of multiple determination (R2) was 0.874. it shows that 87.4 percent of the observed variations in prices of agricultural products was accounted for and explained by the independent variables. The adjusted R2 was 0.764 (76.4%). Pump price (PMP) was statistically significant at 1% level and had a positive coefficient of 8.0101, showing a positive relationship to price variability. This is in conformity to the a priori expectation because an increase in pump price leads to an increase in price variability of agricultural products, due to the changes in the cost of production, processing and transportation. Most agricultural machinery and technology depends on petroleum and any increase or decrease in oil prices in the world market could lead to increase in cost of production of agricultural products. The result also shows that exchange rate (EXC) had a positive significance of 4.454 at 5% level of significance on price variability of agricultural products. The higher the exchange rate, the higher the price variability of agricultural products from period to period. Average rainfall (AVR) was found to be negatively related to price variability at 5% level of significance. This implies that a decrease in rainfall by0.640 leads to a significant increase in price variability of agricultural products, this is because low rainfall leads to low productivity and where rainfall is to be substituted for by irrigation, additional cost is incurred. This finding was a deviation from the a piori expectation and consistent to the result obtained by Houghton(2001), which shows that precipitation variability results to varying moisture content of the soil that would not be optimal for the crops that are being cultivated. Aggregate input price (AIP) negatively correlated to price variability of agricultural products at 5% level of significance. This signifies that 0.25 reduction in aggregate input prices will lead to an increase in price variability AKSUJAEERD

of agricultural products. This also is in contrast with the a piori expectation and in line with the findings of Thomton et al. (2010) which indicated that the availability of farm inputs like fertilizers, finance, machinery, seeds and chemicals has been limited, causing an increase in the cost of production. Inflation (INF) was statistically significant at level of 1% and had a positive coefficient of 0.034, showing a positive relationship to price variability of agricultural products. This is in conformity with a priori expectation because increase in inflation increases cost of production thereby causing an increase in price variability of the products. The result is consistent with that of Akpan and Udoh (2009), which showed a positive significant shift in the coefficient of inflation which implies an increase in the price variability of grains. Also, Ukoha (2008) established quantitative relationship among relative price volatility of agricultural commodities and inflation in Nigeria, results showed that there was a significant positive impact of inflation on price variability, both in short run and long run. Conclusion and recommendations From the result obtained, it is evident that food price instability is a frequent occurrence, causing macroeconomic shocks and political turmoil, which discourage long-run investment and curtail growth. Also, price and quantity of output has a positive effect on each other, which corresponds to the theory of supply. Thus variability of products prices cause shocks which affects output in the next season, in line with Cobweb theory. No rational policy should aim at totally eradicating price variability of agricultural products, but effort could be put to suppress the effect of the factors which determine price variability of agricultural products. The following recommendations are made based on the findings and observation of the study; 1) Alternative private arrangement that do not require government to bear the risk of price change in commodity markets should be considered, such as commodity indexed bonds or insurance schemes of various types, to substitutes Page 46


47

for direct government intervention in price formation to reduce risk. 2) Policies which will buffer the agricultural sector from the effect of variability of agricultural product prices and policies which reduce the rate of inflation will minimize price variability among agricultural commodities and consequently reduce inefficiency, distortions and misallocation of resources in agriculture should be implemented. 3) Management agencies should be established to provide necessary tools

and information for farm operators and other participants in agricultural markets to better understand and manage risk associated with producing and selling agricultural commodities. 4) Since increase in price brings about an increase in production, In line with the theory of Supply which states that the higher the price, the higher the quantity supplied: government should implement policies that will subsidize the price of agricultural products to consumers and increase the level of food security in the country.

References Akanni, K. A., Okeowo, T. A (2011)’ Analysis of Aggregate Output Supply Response of Selected Food Grains in Nigeria’. Journal of Stored Products and Post-harvest Research Vol. 2(14), pp. 266 – 278, ©2011 Academic Journals Full Length Research Paper. Akpan, O. D. and Edet, J. U. (2016) “Comparative Measure of Income Volatility Of Farm Households in Uyo, Akwa Ibom State, Nigeria: GARCH – CV Approach” American Journal of Research Communication, 2016, 4(5):96-112 www.usa.journals.comISSN :2325-4076.

in Nigeria” Journal of Economics and Sustainable Development, Vol.5, No.5, pp. 47-57.

Akpan, S. B and E. J. Udoh (2009) “Estimating Grain Relative Price Variability and Inflation Rate Movement in Different Agricultural Policy Regimes in Nigeria. Humanity and Social Science ‘Journal 4 (20): 107- 113, ISSN 1818-4960. Akpan, S. B., Udoh, E. J. (2009) “Relative Price Variability of grains and Inflation Rate Movement in Nigeria”.Global Journal of Agricultural Sciences, Vol. 8(2) 147-151. Ayinde, O.E., Bessler D. A. and Oni, F E. (2014) ‘Analysis of Supply Response and Price Risk on Rice Production in Nigeria’ pp 1-20. Central Bank of Nigeria (CBN) 2016: Statistical Bulletin. Central Bank of Nigeria (CBN), (2011). “Animal Report for the Year Ended 31st December, 2011. Ebi, B. O., A. S. Ape (2014)“Supply Response of Selected Agricultural Export Commodities

Edet, G. E., S. B. Akpan and I. V. Patrick (2014) “Assessment of Price Transmission and Market Integration of Pawpaw and Leafy Telferia in Akwa Ibom State, Nigeria”. American Journal of Experimental Agriculture, 14(11): 1367-1384 pp. Etuk, E. A and M. O. Effiong (2016) “Analysis of Market Prices of Selected Agricultural Commodities in Akwa Ibom State (19962014). Journal of Marketing Consumer Research.Vol. 26. pp. 1-6. Gilbert C. L. & Morgan G. W. (2010) ‘Has Food Price Volatility Risen?’ Revised Version, 8th April, 2010.Workshop on Methods to Analyze Price Volatility. Seville, Spain. January, 2010. Huka, H., C. Ruoja and A. Mchopa (2014) “Price Fluctuation of Agricultural Products and its Impact on Small Scale Farmers Development: Case Analysis from Kilimanjaro Tanzania”. European Journal of Business and Management, 6(36) pp. 155160. International Monetary Fund (IMF) (2000). World Economic Outlook, Washington DC: International Monetary Fund. Ministry of Finance and Economic Development, (2004) ‘Retail Market Price Publication of Selected Commodities in Urban Markets in Akwa Ibom State’, Research and Statistics Department Uyo, Akwa Ibom State.

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Mustapha, U. M. and Culas, R. J. (2013) “Causes, Magnitude and Consequences of Price Variability in Agricultural Commodity Market: An African Perspective” pp 1-26 Obayelu , A. E. and A. S. Salau (2010) ‘Agricultural Response to Prices and Exchange Rate in Nigeria: Application of Co-integration and Vector Error Correction Model (VECM)’ © Kamla-Raj 2010, Agricultural Science Journal, 1(2) pp 73-81. Randy S. (2015) “Assessing Agricultural Commodity Price Variability” Agricultural Outlook Economic Rsearch Service/ USDA (202) 694-5293 Thornton, Philip K., Jones, Peter G., Alagarswamy, Gopal, Andresen, Jeff, Herrero, Mario (2010): Adapting to climate change: Agricultural system and household impacts in East Africa, Agricultural Systems, Volume 103, Issue 2, Pages 73-82. 45. Timmer, P. (2011). Managing price volatility: Approaches at the global, national, and

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household levels. Paper presented during fourth presentation (May 26, 2011) in a Symposium Series on Global Food Policy and Food Security hosted by the Center on Food Security and Environment at Stanford University. Tothova M. (2011) “Main Challenges of Price Volatility in Agricultural Commodity Markets” I. Piot- Lepetit, R. M’Barek (eds)., Methods to Analyze Agricultural Commodity Price Volatility, LLC 2011 pp. 13-29. Udoka, S. J., Akpan, S. B. and Patrick, I. V. (2015) “Analysis of Price Transmission of Unripe Plantain in Akwa Ibom State, Nigeria”. Nigerian Journal of Agriculture, Food and Environment.11(2):170 -177 Published Ukoha, O. O. (2008) “Relative Price Variability and Inflation: Evidence from Agricultural Sector in Nigeria” Pakistan Journal of Social Science 5(1): 1-9, 2008.

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EXTENT OF ADOPTION OF INDIGENOUS METHODS FOR THE TREATMENT OF MALARIA AMONG CASSAVA FARMERS IN AKWA IBOM STATE, NIGERIA *Udousung, I. J1, Udoumoh I. D2 and Effiong, U. U3 1

Department of Agricultural Economics and Extension, Akwa Ibom State University, AKS. 2 Department of Soil Science, Akwa Ibom State University. 3 Centre for General Studies, Akwa Ibom State University. *Corresponding author: [email protected] , 07067428381,08025295899 Abstract The study assessed the extent of adoption of indigenous methods of treating malaria among cassava farmers in Akwa Ibom State. Primary data were collected with the aid of structured questionnaire and responses recorded during focus group discussion. Sample sizes of 120 cassava farmers were randomly selected for the study. Data were analysed using descriptive statistics and Logit Regression model. Findings revealed that 56.7% of the respondents were male while 43.3% were female. A large proportion (50.8%) of the respondents was within the age range of 31- 40 years. A total of fourteen (14) methods of indigenous malaria therapies were known to cassava farmers in AKS,only six (6) variables showed high adoption. These were; mixture of lemon grass + mango( bark)+lime + dogongaro + palmwine (x̄= 3.71), unripe pawpaw +unripe pineapple +lime +lemon grass + H20 (x̄ 3.33 ), mixture of garlic +onion +H20 (x̄= 3.22) vinegar (x̄= 3.54), grape H20 (x̄=2.87) and moringa latifolia + H20 (x̄=2.80). With the grand mean pooled data ( x̄ =2.58), hence farmers in the state were conversance of a good number of therapies for the treatment of malaria in the area. Logit regression analysis revealed that age, gender, educational attainment and years of farming experience were the major determinant of the use of IM. The study recommends enlightenment campaigns to be carry out through extension agent to sensitize farmers on the potentials and benefit of using indigenous therapies to improve their health needs in order complement the high cost of orthodox medicine. Keywords: Indigenous knowledge, Adoption, Treatment, Malaria Introduction The need to boost cassava production as a means of increasing food supply and reducing rural poverty have continuously been advocated (Adeyemo, et. al., 2010),especially in subSaharan Africa where a significant proportion of the rural population is food insecure and malnourished (Matata, et al.,2008), where the attainment of food security is intrinsically linked with reversing agricultural stagnation and safeguarding the National resource base (Cleaver,et. al.,1994) Cassava is one of the important staples food crop that is grown throughout the tropics and consumed by almost every household and is often intercropped with other crops. Its superiority over other staples arises from its ability to thrive well and yield exceedingly under average soil conditions and its high tolerance to adverse environmental conditions such as droughts and highly acidic soils.

At the microeconomic level ill-health reduces individuals labour productivity (amount of output) per one unit of input produced) and labour supply (Suhreke et al.,2008). It reduces physical strength and work days/hours available for farm work. It is more probable that health shocks affects worker productivity (Asenso Okyere et al., 2010). Health status even emerges as the main determinant of labour supply by older workers in several studies (Suhreke et. al., 2008). Malaria, caused by parasites of the genus Plasmodium, is one of the leading infectious diseases in many tropical regions, including Nigeria, a West African country where transmission occurs all year round. Many of the inhabitants use plants as remedies against fever and other symptoms of acute malaria, as reported herein (Alonso, P. L et al.,2005). Some of these plants and plants product have their

50

Extent of adoption of indigenous methods for the treatment of malaria among farmers

antimalarial efficacies scientifically demonstrated and the active compounds isolated with their probable mechanisms of action studied. Medicinal plants are used to treat diseases also where the biodiversity of plants occur in parallel with endemic transmission of malaria. The search for new drugs based on plants is important due to the emergence and widespread of chloroquine-resistant and multiple drug-resistant malaria parasites, which require the development of new antimalarials. An acquaintance with antimalarial plants may be a springboard for new phytotherapies that could be affordable to treat malaria, especially among the less privileged native people living in endemic areas of the tropics, mostly at risk of this devastating disease. In Africa, up to 80% of the population use traditional medicine for primary healthcare. 71% in Chile,65% in India,48% in Australia, 70% in Canada, 49% in France and 42% in United States. Nigeria is not left out of this embrace. Traditional medicine is said to be popular among 70% of the population( Batta, 2012). In Nigeria, Malaria is a major public health problem, with an estimated 100 million malaria cases and over 300,000 deaths per year in Nigeria (Nigeria Malaria Fact Sheet, 2011). Monetary loss due to malaria in Nigeria is estimated to be about 132 billion naira in terms of treatment cost, prevention and loss of man-hours (FMOH, 2007). Malaria constitutes a major public health problem globally (WHO, 2000) about 93% of the 550 million people living in Africa are at risk of malaria infection (WHO,1995) The disease represents one of the major causes of morbidity and mortality throughout Nigeria where it is holoendemic in status (Salako, 1986;EMOH,1989). The World Health Organization estimates that over 300 million new cases of malaria arise yearly with approximately two to three millions deaths resulting from contraction. Malaria is endemic in tropical Africa, with an estimated 90% of the total malaria incidence and death occurring there, particularly amongst pregnant women and children. In Akwa Ibom State, malaria is responsible for one in four child deaths and poses a great economic burden on household and AKSUJAEERD

government (AKMOH,2000; Opara et al., 2004). It is also a major cause of maternal death, abortion, stillbirth, premature delivery and anaemia (FMOH, 1989; 2004). Considering the fact that over 80% of the populace lives in the rural area where there is no access to healthcare delivery centers and a greater percentage of the people depends solely on traditional methods of treating malaria. It is important to look at health problems like malaria that grossly affect the morbidity and mortality rates, as well as the economy of a developing country, such as Nigeria. A large percentage of its population lives in extreme poverty in rural areas, without access to potable water and adequate healthcare. Many people in our society prefer health care seeking with the traditional medicine practitioners because it is affordable and familiar with the culture, beliefs and practices of their culture (WHO, 2008). Hence the objectives of this paper are to: i. Identify the socio- economic characteristics of cassava farmers in the study area ii. Identify the prevalence of malaria therapy in the study area iii. Assess the extent of adoption of indigenous treatment of malaria among cassava farmers. Methodology The study was conducted in Akwa Ibom State, The state lies between latitude 4 o 31’’ and 5 o 31’’ North and longitudes 7o 35’’ and 8o 35’’East; occupies a total land area of 7,245,935km2; and has an estimated population of 3,920,208 (NPC, 2006). Data used for the study were mainly primary data which were obtained from cassava farmers from three (Abak, Etinan and Uyo) out of six Agricultural Development Program (ADP) Zones in the state. Frequencies and percentages were used to analyze the socio-economic characteristics of the respondents. Collated data were analyzed with the aid of descriptive and inferential statistical tools. Sampling technique/ analytical technique A multi-stage sampling technique was adopted in the study. At the first stage, a simple random sampling technique was used to select three (3) Page 50

Udousong, et al. agricultural zones from Akwa Ibom State out of her six (6) ADP zones; at the second stage simple random sampling technique was used to select 12 extension blocks from each of the zones, the third stage was a purposive selection of two (2) cells from each of the selected 12 blocks giving a total of 24 cells. Finally, five (5) cassava farmers were randomly selected from each of the cells, giving a total of 120 cassava farmers as the respondents which served as the sample size. In order to measure respondents’ extent of adoption, fourteen (14) variables were presented. In the analysis, using a four point likert - type scale of - always utilized (4), mostly utilized (3) occasionally utilized (2) and not utilized (1) variables with mean scores of 2.5 and above were adjudged utilized while mean score below 2.5 were adjudged not utilized. Each of the frequency was multiplied with the number of categorization code. X = Ʃ fn /nr Where: Ʃ = Summation f = Frequency of each of the response made n = Likert value nr = number of respondents (x̄) = mean = ( F x 1) + ( F x 2) + ( F x3) + ( F x 4) = T Results and disscussion Socio-economic characteristics of cassava farmers in Akwa Ibom State Table 1 reveals that 56.7% of the respondents were male while 43.3% were female. A large proportion (54.2%) of the respondents were within the age ranged of 31 - 40. With regard to educational status, 25% of the respondents had

51

acquired a primary/ vocational level of education. In essence, a good number 50% of the respondents had little or no formal education. While 48% of the respondents were on full-time, others combined farming with other form of economic pursuits. Table 1 also shows that 71.6% respondents had acquired more than 10 years of farming experience. The implication of this is that experienced farmers appreciate the negative effect of this disease. It is more probable that health shocks affects worker productivity ( Asenso Okyere et al., 2010). Health status even emerges as the main determinant of labour supply by older workers in several studies (Suhrekeet al.,2008). A good number (66.7%) of the respondents eared between ₦10,000 – ₦20,000 per month. A large proportion of the respondents (52.5%) received information of cassava farming practices from their fellow farmers while 40.8% of the respondents received information from radio/ television, a negligible number 4.17% respondent’s received information from extension agents. According to Ali et al., (2008), agricultural extension primarily deals with human resources development and the transfer of technology and knowledge for agricultural research centres to farmers. Extension workers are professionals in the extension system responsible for developing individual farmers in the community. Since they are constantly and consistently in torch with the farmers as well as the rural dwellers, it becomes necessary that their campaign should include proper education of their clienteles on the enormous plant and plants product to improve the health condition of farmers.

52


Table 1: Socio-economic characteristics of cassava farmers in Akwa Ibom State, Nigeria (n = 120 ) Total S/N 1.

Variables Sex Male Female 2. Age Distribution (years) 31 - 40 40 – 50 51 - above 3. Educational Status Primary /vocational Secondary Tertiary None of above 4. Primary Occupation Farming Farming/ Trading Farming/ Civil Service 5. Farming Experience Less than 10 years More than 10 years 6. Farm Income Less than (₦) 10,000 ₦ 10 – ₦20,000 More than ₦20,000 7. Source of awareness Radio/TV Newspaper Extension agents Fellow farmers Source: field survey, 2018.

Frequency

(%)

68 52

56.7 43.3

65 30 26

54. 2 25.0 28.0

30 20 10 60

25.0 16.7 8.3 50.0

58 53 9

48.33 44.16 7.5

34 86

28.33 71.63

30 80 10

25.0 66.7 8.3

49 3 5 63

Table 2 showed the distribution of respondents on the preference of indigenous practices for the treatment of malaria in the study area, the preference here was measured in terms of accessibility and patronage. A large proportion (33.3%) of the respondents acknowledged preference use of lemon grass + mango( bark) +lime + dogongaro + palmwine for the treatment of malaria, while 19.2% of the respondents admitted preference of unripe pawpaw +unripe

AKSUJAEERD

40.8 2.5 4.17 52.5 pineapple +lime +lemon grass + H20, only a negligible number ( 3.3%) preferred Costus afer (Mbritem) + ginger + onion + garlic for the treatment of malaria. The study corroborates with Chan, 2008 that in Asian and African countries ,80% of the population depends on traditional medicine for primary healthcare and about 60% of the young children in some African countries suffering from fever, presumably caused by malaria are treated at home with herbal remedies.

Page 52

53

Udousong, et al.

Table 2: Prevalence of indigenous therapy for treatment of malaria in AKS, Ngeria S/N 1 2 3 4 5 6 7 8 9 10

Variables Mixture of lemon grass + mango( bark)+lime + dogongaro + palmwine Unripe pawpaw +unripe pineapple +lime +lemon grass + H20 Aloe vera + pure honey Mixture of garlic +onion +H20 Gangronema latifolium ( Utasi) + H20 Bitter leave Moringa latifolia + H20 Fern (Iyamma) + lipton + H20 Mbritem (Costus afer) + ginger + onion + garlic + H20 Lipton +tomtom + soda H20

Frequency

%

Rank

40

33.3

1st

23

19.2

2nd

12 11 9 8 7 5

10 9.2 7.5 6.7 5.8 4.2

3rd 4th 5th 6th 7th 8th

4

3.3

9th

1 120

0.8 100

10th

Source : Field survey, 2018. 4.3 Extent of adoption of indigenous methods for malaria treatment in AKS, Nigeria A total of fourteen (14) indigenous malaria methods were known to cassava farmers in Akwa Ibom State, only six (6) variables had highly adopted which include mixture of lemon grass + mango(bark)+lime + dogongaro + palmwine (x̄= 3.71), unripe pawpaw +unripe pineapple +lime +lemon grass + H20 (x̄ 3.33 ), mixture of garlic + onion + ginger + H20 (x̄= 3.22) vinegar (x̄= 3.54), grape H20 (x̄=2.87) and moringa latifolia + H20 (x̄=2.80). With the grand mean of the pooled data ( x̄ =2.88), which is above the midpoint of 2.50. This implies that cassava farmers in Akwa Ibom State were conversance of a good number of therapies for the treatment of malaria in the state. This could be attributed to the fact that chloroquine the cheapest drug for malaria treatment has lost its potency, thus implying more cost for malaria

treatment and reduction of income among rural famers (Oladepe et. al., 2010).This study corroborates with Oreagba, et. al., (2011), which said that 66.8% of the respondents use traditional medicine to treat various ailments including malaria. Also, during the focus group discussion (FGD) it was revealed that a large proportion of the famers utilized indigenous therapies for the treatment of human diseases such as malaria, typhoid, diabetes, diarrhea etc. The rural dwellers rely almost exclusively on indigenous medicine (IM) for their healthcare needs in order to remain economically active. Were it not for indigenous medicine therefore, it is probable that the food problem in Nigeria would have been more acute than it presently is (Mafimisebi and Oguntade, 2010).

54


Table 3: Distribution of the respondents according to the extent of adoption of malaria treatment in AKS, Nigeria

Variables

S/N

1

Extent of Adoption of Malaria Therapies in AKS Always Mostly Occasionall Not Utilize Utilized y Utilized utilize d d 95(4) 18(3) 5(2) 2(1)

Total

Mean Score

Rank

Remark

446

3.71

1st

Highly Utilized

58

47

12

3

400

3.33

2nd

Highly Utilized

62

32

17

9

387

3.22

3rd

54

23

26

17

354

2.95

4th

Highly Utilized Highly utilized Highly Highly utilized Highly utilized Low utilized

4

Mixture of lemon grass + mango( bark)+lime + dogongaro + palmwine Unripe pawpaw +unripe pineapple +lime +lemon grass + H20 Mixture of garlic +onion + ginger +H20 Bitter leave

5 6

Grape H20 Moringa latifolia + H20

49 49

23 23

32 32

16 16

345 345

2.87 2.87

5th 6th

7

47

23

29

21

336

2.80

7th

20

22

32

46

256

2.13

8tth

9

Fern (Iyamma) + lipton + H20 Mbritem Costus afer( Mbritem) + ginger + onion + garlic Lipton +tomtom + soda H20

24

18

24

54

252

2.10

9th

10

Cashew

14

18

51

37

249

2.07

10th

11

Vinegar

22

22

18

58

248

2.06

11th

12

21

11

42

46

247

2.05

12th

13

Gangronema latifolium ( Utasi) + H20 Moringa luciada

11

23

45

41

244

2.03

13th

14

Aloe vera + pure honey

14

20

38

48

240

2.00

14th

2

3

8

Low utilized Low utilized Low utilized Low utilized Low utilized Low utilized

Source: Field survey, 2018. Conclusion/recommendations A total of fourteen (14) indigenous methods of malaria treatment were known to cassava farmers in Akwa Ibom State, only six (6) variables were highly adopted which include mixture of lemon grass + mango( bark)+lime + dogongaro + palmwine (x̄= 3.71), unripe pawpaw +unripe pineapple +lime +lemon grass + H20 (x̄ 3.33 ), mixture of garlic +onion +ginger +H20 (x̄= 3.22) vinegar (x̄= 3.54), grape H20 (x̄=2.87) and moringa latifolia + H20 AKSUJAEERD

(x̄=2.80). With the grand mean of pooled data ( x̄ =2.58), which is above the midpoint of 2.50; This imply that cassava farmers in Akwa Ibom State were conversance of a good number of therapies for the treatment of malaria in the state. Based on the findings of this study, it was recommended.  Indigenous medicine practitioners should be encouraged to improve hygienic conditions in the preparation, Page 54

Udousong, et al. packaging and handling of the indigenous medicine, the dosage regimen of indigenous medicine in different dosage forms such as tablets, capsules, coloured and flavoured syrups, to encourage NAFDAC approval.  Broadcasting stations and media houses should intensify effort on the extent of advertisement of indigenous method, both the print and electronic media in order to create awareness just like the orthodox method.  Enlightenment campaigns to be carry out through extension agent to sensitize farmers on the potentials and benefit of using indigenous therapies to improve References Adeyemo R. Oke JT, Akinola O. Economic Efficiency of Small scale farmers in Ogun state, Nigeria. Tropicultura. 2010;28(2):84-88. Ali, Hassan, O. K. Maimunah, I., Turiman, S., Abu, D.S. (2008). Extension Workers as a Leader of Farmers: Influence of Extension Leadership Competence and Organizational Commitment on Extension Workers’ performance in Yemen. The Journal of International Social Research. Volume 115 Summer 2008. Alonso,P.L, J. Sacarlal, J.J. Aponte, A. Leach, E. Macete, P. Aide, B. Sigauque, J. Milman, I. Mandomando, Q. Bassat, C. Guinovart, M. Espasa, S. Corachan, M. Lievens, M.M. Navia, M.C. Dubois, C. Menendez, F. Dubovsky, J. Cohen, R. Thompson, W.R. BallouDuration of protection with RTS, S/AS02A malaria vaccine in prevention of Plasmodium falciparum disease in Mozambican children: single-blind extended follow-up of a randomised controlled trial. Asenso-Okyere, K.,Kwaw A., Aragon, C., Thangata, P., Mekonnen, D. A.(2010) HIV and AIDS and farm labour productivity : A review of recent evident in Africa. Joural of Development and Agricultural Economics Vol.2(12).,pp401-406 Available online at http//www.academicjourals.org/JDAE ISSN 2006- 9774@2010 Batta, H .E., (2012) Press Coverage of Traditional Medicine Practice in Nigeria. Journal of Communication, 3(2).:Pp. 75 -89

55

their health needs in order complement the high cost of orthodox medicine.  Governments should formulate appropriate policies to promote and encourage the indigenous medicine practices considering the inhaling effects of drugs to humanity.  Government should upgrade the activities of indigenous medicine healers by integrating them into orthodox medicine research, workshops and trainings in the university teaching hospitals. This will enable a larger percentage of the rural dwellers to have access to qualitative treatment. Chan, M (2008) Address at the WHO Congress on traditional medicine in Beijin, People Republic of China on 7th November 2008 Cleaver KM, Screiber S (1994). Spiral, Population, agriculture and environment of Saharan African, World Bank, Washington D.C. Department Of Agriculture.DA implementing 5 years Cassava self-sufficiency Plan.Press Release, Department of Agriculture Regional Field Unit; 2009. Federal Ministry of Agriculture and Rural Development .Crop Area Yield Survey.Abuja, Nigeria: Federal Ministry of Agriculture and Rural Development: Project Coordinating Unit; 2003. Federal Ministry of Health (2007).National Frame work for monitoring and Evaluation of Malaria Control in Nigeria. FMOH, Abuja. Lancet, Matata PZ, Ajayi OC, Oduol PA, Agumya(2005) A. Socioeconomic factors influencing adoption of improved fallow practices among small holders farm in Western Tanzania. Int NGO Journal. 2008;3(4):068-073. pp. 2012-2018 Mafimisebi, T. E ., and Oguntade, A. E. (2010) Preparation and use of plant medicines for farmers’ health Southwest Nigeria : sociocultural, magico - religious and economic aspects Journal of Ethnobiology and Ethnomedicine, 6:1 dio:10 1186/1746- 4269-6-1

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Nigeria Malaria Fact Sheet, (2011).http://photos.state.gov/libraries/nigeria/23 1771/Public/December-MalariaFactSheet2.pdf Oreagba,I.A., Oshikoya,K A and Amachru, M. (2011).Herbal medicine use among urban Residents in Lagos, Nigeria. Journal of BioMed Central Complement and Alternative Medicine. Doi 10 . 1186/ 1472 – 6882/117

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Suhreke, M.,Tansel. S.,McKee M. Rocco, L. (2008). Economic cost of ill –health in the European Region . Worth Health Organization ( WHO). European Ministerial Conference on Health System: ‘Health System Health and Wealth’ Estonia 25 – 27 June World Health Organization – Traditional Medicine. (2008).http://www.who.int/topics/traditional_me dicine/en/

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ISBN: 978-978-34560-5-7

AKSUJAEERD 1 (1): 57 – 66, 2018 AKSU Journal of Agricultural Economics, Extension and Rural Development. © Department of Agricultural Economics and Extension, Akwa Ibom State University, AKSU, Nigeria, December

MODELLING THE DETERMINANTS OF INCOME VOLATILITY OF FARM HOUSEHOLDS IN AKWA IBOM STATE, Udoh, E. J1and Akpan, O. D1* 1

Department of Agricultural Economics & ExtensionUniversity of Uyo, Akwa Ibom State, Nigeria *Corresponding author: [email protected], mobile: +2348032717955 Abstract The determinant of income volatility of farm households in Akwa Ibom State is the aim of this study. Primary data were collected from farm households for a period of one year (April 2015 to March, 2016), using structured questionnaire. Income volatility indices were generated and measured from each farming household using Generalized Autoregressive Conditional Heteroskedacity (GARCH) and Coefficient of Variation (CV). The determinants of income volatility were estimated using Ordinary Least Square (OLS). The OLS regression result for both GARCH and CV reveals that; household size, educational status, and farm size, number of income activities, normalized farm and non-farm income, cost of inputs, household expenditure on food and non-food items significantly influences income volatility at varied probabilities level. (1%, 5% and 10%) and affects income of farm households. It was recommended among others that, yearly measurement and monitoring of income volatility of farming households should be carried out by government and other development agencies in Akwa Ibom State. Keywords: Determinants, Income Volatility, GARCH, Coefficient of Variation and Farm Households Introduction Economic well-being of rural farmers is affected not only by the level of income but by fluctuations in price and output, (Poon and Weersink, 2011). In sub-Sahara Africa (SSA) especially Nigeria, volatility in households’ income is widespread among the rural poor and remain persistently high, (Hackerand Solon, 2003). Worldwide, the rural sector harbors vast majority of the poor accounting for more than 70% of total population estimated at about 6,602,224,175 people, (World Bank, 2007). Between 1980 and 2012 Nigeria’s rural poverty indices were higher than urban poverty and majority of the rural poor derived their livelihood from subsistence agriculture as rural income is equated with agricultural income, (NBS, 2004 and Fausat, 2012). However, the rural economy in sub-Saharan Africa (SSA) is strongly based on agriculture relative to other regions. In Nigeria about 70% of her over 180 million people depend directly on farm and farm related activities for survival or depend on these activities to complement other sources of livelihood, (Olawepo, 2010).

Globally, average income is about $2.5 or N390 per person (UNDP, 2012). In Nigeria and Akwa Ibom State in particular, more than 65% of the rural poor lives under $2.00 or N312.00 per day (threshold poverty) while two-third (2/3) lived under or around $1.25 or N195 a day (extreme poverty), UNDP (2012), Mankiw (2000) and Omoh (2012). Available statistics have shown that since independence, economic growth has by-passed the poor. Derek De Janvry and Elizabeth (2011) submitted that most countries in Asia and Latin America have experienced rapid reduction in rural poverty while a reverse is observed in sub-Saharan Africa where disparitybetween rural and urban incomes has tended to widen in Nigeria. Festus (2017) reported that, there is a grave gap in income inequalities and growing poverty in Nigeria are not moving in tandem with the nation's Gross Domestic Product. This has forced economists to conclude that the disparate trend typifies the imperfections in the country's macroeconomic structure. Review of the global welfare measures reveals that Nigeria is one of the most unequal nations in Africa in terms of income distribution and Akwa Ibom State is one

58

Udoh, E. J., and Akpan, O. D.

of the States with the highest income inequality and is prominent among the small scale holder farmers and other low income earners in the public service, (UNDP, 2012); CBN, (2011); IFAD, (2011); Livingstone, Sihonbeger and Deleway, (2011)). Currently, it is estimated that, Nigeria might have more poor people than India and China. Poverty profile in Nigeria shows that, Akwa Ibom State is the third state with the highest poverty rate of 27.1% in the south-south zone after Cross River State (31.2%) and Bayelsa state (32.5%), (UNDP, 2010). NBS (2004) report shows that, Akwa Ibom State has the second highest unemployment rate of over 25.8% after Delta state in the south-south zone, but leads other states in the zone in terms of income generating opportunities. Majority (69%) of the farm households in Akwa Ibom State are facing income uncertainties, both market related (price fluctuations) as well as non-market related (output variation). These uncertainties do not only induce substantial income risk, but are detrimental to the farming household’s income and welfare (Ayakale, 2008). Olatona (2007) submitted that, in developing economies over 84.08% of income variation is caused by market and non-market related factors. Kareen (2010) in a study on rising income volatility and its implications in United States of America (USA) reported that, many households suffer devastating income uncertainties which have led to the introduction of market oriented economic reforms thereby exposing farming households to global market conditions. The fundamental question arising from the above discourse is what are the determinants of income volatility among farm households in Akwa Ibom State? The study aimed to examine the determinants of income volatility among farm households in Akwa Ibom State, Nigeria. Review of empirical literature Newman et al., (2009) reported that program participation among households with volatile incomes may be affected by many factors. Umoh (1994) in a study on household expenditure pattern found that, an increase in income disparity among self-employed than wage earners and among households heads with AKSUJAEERD

or without secondary education concluded that there was disparity in income distribution among agrarian households in the study area while differences exists in consumption patterns of households which are linked to changes in disposable income between households. Orok (2005) reported that standard of living in rural households in Nigeria is linked to variation in income which is attributed to different individual capacity within the households and observed that an increase in level of education, age and socialization activities of an individual in the society influences the amount of disposable income of farm households. Lanjaour (1999) found that education was significant and positively correlated with offfarm income. Farm households with more education were involved in non-agricultural selfemployment such as handicrafts, commerce and machinery repairs and agro-processing. Babatunde (2008), in a study on the role of offfarm income diversification found that, education and age coefficients were significant at 5% level, that households head with older heads benefits from agricultural employment, while education was particularly important for households with income from non-agricultural employment and self-employed activities. Dong (2005) found that, an increase in off-farm income increases the overall volatility of income among farm households in Vietnam. Weersick (2011) reported that, the coefficient of variation (CV) in farm income was significantly greater than that of off-farm income but both measures were inversely related to household’s permanent income sources of the operation. Also, pensioners and livestock farmers were found to have a lower (CV) for both farm and off-farm income compared to business focused farmers.Hertz (2007) in a regression analysis of the determinants of income volatility at the state level revealed that, states with higher shares of employment in agriculture, wholesale and retail trade, and other services as compared to manufacturing and those with lower union coverage rates, experienced greater volatility (over 60%). Wicher et al., (2004) found that off-farm income contributes about 22% to total farm household income and associates with a wide variation of farm household income with an appreciable incidence of negative income Page 58

Modelling the determinants of income volatility of farm households because of the volatility in farm, business income from year-to-year. Awoyemi (2009) found that, seasonality is another price volatility factor and that may cause crops price to behave in a rather unpredictable manner. Ibekwe et al (2010)found that age was not statistically significant at 5% level but positively correlated with income. Similarly, the cause of household income volatility is subject to changes in family structure and joint decisions (household labour supply, joint job search, family formation and dissolution etc.) of household members. Ngwafon et. al., (1997) reported that, over 83.8% and 77.7% of expenditures by the poor was spent on food. Austin and Zimmerman (2008) in a study on measuring trends in income variability in USA found that, the volatility of family income has increased overtime (a trend that is robust to a large variety of modeling choices) but the trend in individual income volatility is less clear. Moreover, a clear pattern in volatility of individual earnings or income, but family earnings and income exhibit a pattern of increasing volatility over time. Also, family income exhibits an upward trend in measured volatility of 1.5% per year (making the level of volatility roughly half again as high after 30 years), with substantial crucial deviations from trend. Theoretical framework The classical theory of income This study leverages on the classical theory of income distribution and welfare. The classicalists emphasized on inherent ability of the economy to achieve and maintain full employment equilibrium. Proponents of the classical school upheld that, the economy is inherently stable, that deviations from full employment equilibrium are automatically corrected by adjustments in prices, wages (income), and interest rates. Therefore, the link between income volatility and farm household is found on the framework of Friedman Permanent Income Hypothesis. A Tubman type of model interprets the permanent income and absolute saving theories and defined normal income as a distributed lag income and used Koyck transformation to obtain an equation that yields separate estimates of the transitory and permanent (normal) marginal properties in a

59

linear form. The original formulation of Friedman’s theory is;

( )

p =by

+ CyT (2) Where = permanent income, YT = transitory income, = measured income, b = marginal propensity to save. C = consumption being a function of income. Similarly, permanent income denoted by yp, regarded as the annuity value of wealth: = (3) r = interest rate assumed to be fixed, w = annuity value of wealth. Friedman (1953) and Palley (2005) decompose measured total disposable income y, into permanent component (Yp), and transitory component (YT). The permanent income component is deemed systematic but unobservable, reflecting factors that determine the household’s wealth, while the transitory component reflects “chance” income fluctuations. = + + + … + anXTh - (4 ) Where; a1, a2 and an are coefficients of households permanent and transitory income, Yhis household income, XPh and XTh are variables representing permanent and transitory incomes respectively. Research methodology The study area, population and data collection sources The study was conducted in Akwa Ibom State, Nigeria. It is located in Nigeria’s oil-rich southsouth zone known as the Niger Delta region which occupies one of the largest wetland in West Africa. Geographically, it is located at latitude 4o321and 5o331 north, longitude 7o251 and 8o241 square meters. It has thirty one (31) local government areas and Uyo as its state capital. The state is bordered on east by Cross River State, on the west by Rivers and Abia State and on the south by the Atlantic Ocean. The state has an estimated population of over 3.93 million people, (NPC, 2006). The population drawn for this study comprised of farm households who predominantly grows vegetables and other root crops in the study area.

60


Panel data were collected for both for both for both dry and wet season’s started March to August and September to February, 2018 respectively. The instrument used for primary data collection was questionnaire whilesecondary data were sourced from official publications of National Bureau of Statistics (NBS), General household survey of 2016, Nigeria Statistical Factsheets on Economic and Social Development (various editions) and Central Bank of Nigeria Statistical Bulletin (various editions). Respondents were selected based on the Akwa Ibom State Agricultural Development Programme (AKADEP) structure. Sampling Procedures and Techniques The study used multi-stage sampling technique in the selection of respondents. Given the grouping of the State into six agricultural zones: Uyo, Etinan, Ikot Ekpene, Eket, Abak and Oron.The first stage involves purposive selection of three (3) out of six (6) agricultural zones; (Uyo, Abak and Oron). The reason for the purposive selections of these zones is to have a representative sample across the State (3) out of six (6) agricultural zones in the state: In each of the zones, nine (9) blocs were randomly selected. In the second stage, twenty seven (27) cells were randomly selected from nine (9) blocs. In each of the sub-cell, two (2) villages were randomly selected making up a total of fifty (54) four sub-cells (villages). The fourth stage involved simple random sampling (ballot) of ninety (90) respondents; ten (10) from each bloc and 30 from each zone). Out of ninety (90) sampled households, sixty (60) were selected from upland communities in Uyo and Abak zones while thirty (30) were selected from the wetland or riverine communities in Oron zone. The determinants of income volatility and estimation technique The determinants of income volatility were estimated using Ordinary Least Square method (OLS). The implicit form of the model is stated thus; = ( ) . . . . (5) Where ˭ Income volatility indices generated from Generalized Autoregressive Conditional Heteroskadascity (GARCH), while AKSUJAEERD

Xiareexplanatory variables as stated in equations 6, 7, 8 and 9 below. Income volatility indices generated through GARCH were used as dependent variable. The functional forms of the GARCH income equations are stated thus; Linear Function )+ ( ) = + ( )+ ( ) + ( )+ ( ) + ( )+ ( ) + ( ) + ( ( ) + + . . . .. . . . . . ( ) Exponential Function: )+ ( + ( )+ ( + ( )+ + ( )+ + ( ) + ( ( ) + + . . . .. . . . . . ( )

=

) ) ( (

) )

Double log Function: =

( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + + . . . .. . . . . . ( )

Semi-log Function: =

+

( ) ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + ( ) + + . . . .. . . . . . ( )

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61

Modelling the determinants of income volatility of farm households Where, 0 is constant, 1  10 are estimated parameters. = are income volatility indices generated from GARCH and CV respectively, HHS=household size, EDUSTAT =educational status, AGE = age of household heads, FMS = farm size, INCACT = number of income activity, NFMY = normalized farm income, NNFMY = normalized non-farm income, INPCO = input cost, HEXPF = household expenditure on food, HEXPNF = household expenditure on non-food, t = error term. The study used four functional forms

namely, Linear, Exponential, Double-log and Semi-log as stated in equations 6, 7, 8 and 9 were estimated to determine the factors that induced volatility on farm household’s income in Akwa Ibom State. Equations 6, 7, 8 and 9 were estimated using Ordinary Least Squares (OLS). Income volatility indices generated through Generalized Auto-regressive Conditional Heteroskadasticity (GARCH) and the Coefficient of Variation (CV) were used for the study.

Results and discussion Table 1: Descriptive Statistics of GARCH and CV Measure of Income Volatility Indices Volatility Measure Maximum Minimum Mean STD Skewness Kurtosis Gaech(On and Off season)

0.99

0.26

0.58

0.17

0.54

2.68

CV (On and Off season)

0.74

0.49

0.61

0.05

-0.25

2.51

Source: Author’s analyzed data, 2018. GARCH=Generalized autoregressive conditional heteroskadasticity, CV= coefficient of variation Table 1 above presents descriptive statistics of the variables used in the study. It reveals that, the maximum GARCH and CV values are 0.99 and 0.74 respectively for both on and off season periods. Minimum values for GARCH and CV are 0.26 and 0.49 while mean values are 0.58 and 0.61. Standard deviation for GARCH and CV values is 0.17 and 0.05 respectively. The

probability skew for GARCH is 0.54 showing positive but moderate skew while the probability for CV is -0.25 implying negative skewed of values. Similarly, result from table 1 further shows the kurtosis values (2.68 and 2.51) for both GARCH and CV of income volatility indices. The GARCH kurtosis value for is relatively higher than the CV kurtosis value.

62


Table 2: Regression Results of the Determinants of Income Volatility among Farming Households in Akwa Ibom State: GARCH and CV Approach GARCH - MULTIPLE REGRESSION RESULT LIN.(LE) EXP. D. LOG SEMILO G Constant

CV – MULTIPLE REGRESSION RESULT LINEAR EXP. D.LOG SEMILOG

0.328 (1.670)* 0.19 (1.916)** -0.004 (-4.001)*** -0.003 (-1.037) -0.017 (-7.001)*** 0.024 (1.681)*

-0.989 (-2.902)*** 0.028 (1.604) -0.006 (-1.017) -0.006 (-1.072) -0.073 (-0.756) 0.048 (1.936)**

-2.732 (-1.166) -2.34 (-0.860) -0.023 (-0.293) -0.065 (-0.276) -0.120 (-1.408) 0.330 (2.09)**

-0.589 (0.436) -0.124 (-0.790) -0.019 (-0.421) -0.036 (-0.265) -0.058 (-1.172) 0.171 (1.88)**

0.675 (10.91)*** 0.010 (-3.33)*** 0.000 (-0.661) 0.000 (-0.870) 0.120 (5.113)*** 0.003 (3.007)***

-0.390 (-3.779) -0.001 (-0.208) -0.001 (-0.575) -0.001 (-0.813) 0.003 (0.110) 0.004 (0.590)

-0.421 (0.618) -0.030 (-0.380) -0.011 (-0.483) 0.100 (1.462) 0.013 (0.506) 0.022 (0.472)

0.662 (1.625) -0.022 (-0.458) -0.008 (-0.568) 0.057 (0.405) 0.008 (0.517) 0.015 (0.548)

0.120 (3.009)*** 0.014 (4.607)***

0.005 (0.690) 0.006 (1.187)

0.099 (1.261) 0.074 (1.612)

0.059 (1.296) 0.042 (1.595)

-0.00 (0.349) 0.025 (-.029)***

0.001 (0.325) -3.896E-5 (-0.027)

0.008 (0.336) -0.015 (-1.115)

0.004 (0.296) -0.009 (-1.120)

INPCO

0.102 (11.34)***

1.541E-5 (0.463)

0.071 (0.890)

0.039 (0.856)

-1.020E-5 (-1.686)*

-1.649E-5 (-1.636)

-0.041 (1.772)*

-0.025 (-1.84)*

HEXPF

-0.032 (-2.909)*** 7.059E.6 (2.327)***

-3.170E-7 (-0.104) 1.100E-5 (2.108**

0.004 (0.031) 0.183 (2.13)**

-0.011 (-0.138) 0.113 (2.28)***

3.514E-7 (0.633) -0.134 (33.51)***

5.749E-7 (0.621) 2.278E-8 (-1.429)

0.029 (0.761) -0.037 (-1.477)

0.019 (0.825) -0.021 (-1.428)

Diagnostic Statistics 95.2 R2

14.5

17.2

16.1

69.8

8.3

16.3

16.6

Adj R2 LOG Likehood Fcal Normality Test

90.0 35.67 7.417*** 9.14***

3.7 -13.94 1.340 0.234

6.1 35.439 1.543 5.389

4.7 -1.364 1.416 0.0263

59.1 139.607 3.767*** 5.822

3.3 93.62 0.720 9.464

5.01 93.72 1.443 5.747

5.3 93.62 1.467 9.464

8.545*** -49.35 -21.85 -38.27

1.206 49.87 77.36 60.96

0.537 -48.87 -2.009 -38.07

0.0263 44.726 71.596 55.53

7.131 -257.2 -229.7 -246.13

1.848 -165.24 -137.74 -154.15

0.886 -65.4 -38.6 -54.6

1.845 -165.2 -137.7 -154.2

HHS EDUSTAT AGE FMS INCACT

NFMY NNFMY

HEXPNF

Reset Test AIC Schwarz Criteria Hannan Quin

Source: Extracted from Computer Print-out by the Researcher, 2018. ***, **,* represents10%, 5%and 1% probability levels.

The determinants of income volatility among farming households in Akwa Ibom State were examine and presented in Table 2 above. Multiple regressions for both GARCH and CV were specified and the relationship between income volatility indices and its correlates were estimated. Four functional forms namely, linear, exponential, double log and semi-log for both AKSUJAEERD

GARCH and CV were specified and estimated using the variables stated in equation (6). The parameter estimates of the diagnostic tests as well as the respective coefficients of the variables in each of the functional forms are presented in Table 4. The linear equation of the GARCH result was selected as the led equation considering the number of variables and the Page 62

Modelling the determinants of income volatility of farm households

63

values of diagnostic test parameters that are significant. For the led equation (Linear2 GARCH), the R2 is 0.952 and the R adjusted R 2 R 2 is 0.90. The value of the R2 (0.952) implies that, about 95.2% of variations in farm households income was captured and explained by the independent variables in the model. The Fcal of 7.41 was statistically significant at 1% level implying that, the estimated GARCH income volatility function was adequate for use in the analysis and it shows goodness of fit. The normality of 9.114 was significant at 1%. RESET-test was 8.546 and significant at 1% probability level implying that the selected equation is appropriately correct, not misspecified and the assumption of linearity among the variables is correct. The constant term of 1.670 was significant at 10% level. The coefficient of household size (HHS) was significant and positively related to income volatility of farming households in the study area, implying that an increase in number of household members by one person will lead to a 1.9% increase in volatility of household income. Income volatility among farming households had a significant positive relationship with educational status of the household head. The coefficient of this variable was significant at 1% level implying that an additional year of education reduces income volatility by 0.004%. Consistent with these results are Walker et al (2004) in Britain and Dong (2005) in Vietnam.The coefficient of farm size was significant at 1% and negatively correlated with income volatility of farming households. It reveals that, an additional cultivable land may lead to a 0.017% reduction in income volatility among farming households. The result further shows that, the coefficient of number of income generating activities (NIGA) was statistically significant at 10% level and positively correlates with income volatility of farming households in

the study area. This implies that additional source of income will reduce volatility among farm households by 2.4% in the study area. The coefficient of normalized farm income (NFMY), was statistically significant at 1 percent level and positively correlates with income volatility. This result implies that, a naira increase in income leads to about 0.12% increase in income volatility. This could be linked to the fact that, farm income contributes higher (as compared to nonfarm income) to the total household income. The coefficient of non-farm income (NNFMY) is 0.014 and is statistically significant at 1%. This implies that a naira increase in income from non-farm activity leads to a 0.014% increase in income volatility. The coefficient of input cost (INPCO) was also statistically significant at 1 percent level and positively correlated with income volatility. The result shows that a naira increase in cost of inputs will lead to similar increase in production cost. The coefficient of household expenditure on food (HHEXP) was statistically significant at 1 percent level and negatively correlated with income volatility of farming households in the study area. The result reveals that a naira increase in household expenditure on food may likely reduce income volatility by 0.032%. Similarly, the coefficient of household expenditure on non-food items such as education, clothing, communication etc., shows a positive relationship with income volatility and significant at 1% level. This relationship establishes the link between farm household expenditure on non-food items and income volatility in the study area. A naira increase in expenditure on non-food items increases income volatility by 0.007%. An increase in expenditure on non-food items may lead to a marginal increase in income volatility of farm households in the study area. This finding is consistent with Oscar et al., (2004) and Mishra et al., (1997).

Conclusion and recommendation An empirical investigation of the determinants of income volatility of farming households was carried out. The result shows the relationship between the determinants of income volatility and its effects on farm households’ wellbeing in Akwa Ibom State. Household size, educational

status, number of income activities, household experience in farming significantly influences farm households income. The study recommended that relevant government institutions –Akwa Ibom Agricultural Development Programme (AKADEP),

 

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International Institute for Tropical Agriculture (IITA) and the Integrated Farmers Scheme (IFS) among others, should support rural farmers in areas of creditand other relevant farming inputs References Adebayo, O. (2005). Sources and Measurement of Income Volatility among Some Rural and Urban Households in Ibadan Metropolis.BSc.Project.Department of Agricultural Economics, University of Ibadan, Nigeria. (Unpublished) pp. 23-27. Akpan, O. D.(2008). Measuring Income Volatility of Farming Households in Akwa Ibom State, Nigeria. A PhD Thesis, Department of Agricultural Economics and Extension. University of Uyo, Nigeria. Austin, N. and Zimmerman, S. (2008). Measuring Trend in Income Volatility. Urban Institute Research 7: 198-206. United States of America. Awoye mi, T. T. (2009) Explaining Rice Prices Shocks in Nigeria: Rice Policy and Food Security in Sub-Sahara Africa Implications for Policy intervention Dept. of Agricultural Economics University of Ibadan, Nigeria. Ayakale, A. S. (2008). Households Demand for Groundnut Cake in Ibadan North Local Government, Oyo State. M.Sc. Project. University of Ibadan.

including extension services to boost agricultural production which will in turn improves farmers income, rural savings and investment in Akwa Ibom State.

Elizabeth, E. (2010). Economic Analysis of Cassava Production in Akwa Ibom State.Journal of Agriculture and Biology, 1(4): 612-624. Escobal, J. (2011): The Determinants of Non-farm Income Diversification in Rural Peru. World Development, 29(30): 497-508. Fausat,

A. F. (2012). Income Diversification Determinants among Farming Households in Konduga, Borno State, Nigeria.AcademicResearch Bulletin (2): 1-7.

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John, N, Iheanacho, A. C. and Iretin D. (2011).Effect of Socio-Economic Characteristics of Food Crop Farmers on the Selection of Cropping Strategies against Drought in Borno State, Nigeria.Lincoln University Journal of Science, 2(1): 13-18. Karen, P. (2010). The Rising Income Volatility and its Implications: Income Rollerstar. Pathway String 2010. Lanjour

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(2006).National Population Commission.Published 2006 Population Census.

Nelson, D.B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59: pp. 347-370. Newman. C., and Jollife, D. (2009). Income Volatility is Rising with Mixed Effects on Nutrition Assistance Participation. www.ers.usda.gov.ii amber waves 4(4). pp. 1-4. Ngwafon J. and Thomas, S. (1997). Evolution of Poverty and Welfare in Nigeria.Policy Research Working Paper 45; pp4552.World Bank. Olatona, M. O. (2007). Agricultural Production and Farmers Income in Afon District, Unpublished BSc. Project: Department of Geography, University of Ilorin. Olawepo, R. A. (2010): Determining Rural Farmers Income: A Rural Nigeria Experience. Journal of African Studies and Development, 2(4): 99-108. Omoh, H. (2012). “Is Nigeria in a Poverty Trap and Population Explosion? Broken Links. Economic Resource Services (ERS). Economics Review, 45: pp. 1-5.

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Agriculture and Agro-Products Industry Network, Working Paper 2011-3. Ravallion, M. and Chen, S. (2007). “China’s Uneven Progress AgainstPoverty”. The World Bank Report.6:9. Sihonberger, S. and Delaway, S. (2011). Sub-Saharan Africa: The State of Smallholders in Agriculture. A Paper Presented at the (IFAD) International Food for Agricultural Development via Paolo Di Dono, 44, Rome 00142, Italy. Conference on New Directions for Smallholder Agriculture 24-25 January 2011. Staatz, J. and Dembele, N, Stephens, M. (2007): “Worker displacement and the Added Worker Effect “Journal of Labour Economics, 20: 504-537. Umoh, G. S. (1994). Household Income Distribution Pattern Under SAP: The Case Study of Uyo Metropolis. In: Ibom Journal of Social Sciences, 1(2): 104-112. UNDP

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UNDP(2012). United Nation Development Programme. African Human Development Report 2012: Towards a Food Secured Future. https://undp.un.org.www.undp.org/africa and www.afthdr.org. Walker. T. D. Tschirley, J. Low, M. Pequenino Tangue, D. Boughton. E. Payongayong and M. Weber (2004). Determinants of Rural Income, Poverty and Perceivers Well-being in Mozambique in 2001-2002. Research Paper No 57 E of Ministry of Agriculture and Rural Development. Economics Directorate Mozambique.

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Poon, K. and Weesink, A. (2011).Factors Affecting Availability in Farm and Off-farm Income.Structure and Performance of

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ISBN: 978-978-34560-5-7

AKSUJAEERD 1 (1): 67 – 76, 2018 AKSU Journal of Agricultural Economics, Extension and Rural Development. © Department of Agricultural Economics and Extension, Akwa Ibom State University, AKSU, Nigeria, December

ECONOMICS OF YAM (Dioscorea rotundata) PRODUCTION AMONG SMALL HOLDER FARMERS IN ABIA STATE, NIGERIA 1

Amusa, T. A., 2Isiwu, E. C and 3Oketoobo, E. A.

1

2&3

Department of Agricultural Economics, Michael Okpara University of Agriculture, Umudike Department of Agricultural/Home Economics Education, Michael Okpara University of Agriculture, Umudike. Corresponding author: [email protected]; +2348036185143

Abstract The study investigated economics of yam production among small holder farmers in Abia State, Nigeria. Primary data were collected from 180 randomly sampled yam farmers using close-ended structured questionnaire. The data collected were analysed using both descriptive and inferential statistics such as frequency, percentage, mean, gross margin analysis and multiple regression analysis. The results showed that about 40% of yam farmers in the area owned their farmlands for yam production, had mean age of 51 years and mean years of yam production experience of 24 years. The Total Revenues (TR) in production was ₦230,590.00 and Gross Margin (GM) of ₦88,390.00. The Net Return (NR) of the yam producers in the area was ₦86,585.00 with Profitable Index (PI) of 0.38. The result of the multiple regression analysis showed that double-log functional form had the best fit with R2 value of 0.71. Variables such as age, years of education, farming experience, farm size and labour were significant at 1% while gender and household size were significant at 5% and cost of yam seeds was significant at 10%. Major constraints facing yam farmers in the area are: high cost of planting materials, scarcity of labour, theft or insecurity, poor road network, and lack of storage facilities. The study recommends formulation of policies to guarantee increased access to land, provision of credit facilitiesin form of soft loans to enablethem procure necessary inputs for increased yam production. Keywords:Yam, Economics, Output, Small holder Farmers, Abia State. Introduction Nigeria as a developing nation is principally agrarian with greater percentage of her labour force engage in the agricultural sector of the economy. The agricultural sector plays an important role in Nigeria’s economy, contributing about 40% of the GDP (Olomola, 2006) and employing 65% of the adult labour force (Adedipe, Okuneye and Ayinde, 2004). Of great significance in agricultural sector in Nigerian economy is the production of food crops such as yam. Yam belongs to the genus “Dioscorea” and family “Dioscoreaceae”.Yam (Dioscorea spp) is among the most important food crops grown in Nigeria and across Africa. There are over 60 species of yam out of which six species are mostly grown in Nigeria. These include white yam (Dioscorea rotundata), yellow yam (Dioscorea cayenensis), water yam (Dioscorea

alata), trifoliate yam (Dioscorea dumentorum), arial yam (Dioscorea bulbifera) and Chinese yam (Dioscorea esculenta). Out of these six species produced by Nigerian farmers, white yam (Dioscorea rotundata) is the most popular, with high social and economic value; hence, the focus of this study. Many important cultural values are attached to white yam, especially during wedding, religious (as thanksgiving items in churches) and other socio-cultural ceremonies.Izekor and Olumese (2011) reported that due to the importance attached to yam, many communities celebrate the new yam festival annually in Nigeria. Traditionally, yam is a prestige crop that is viewed and received with high respect, prominently during special gatherings such as new yam festivals in rural communities of eastern, central and southwest Nigeria (Nahanga and Bečvařova, 2015). In affirmation, Sanusi and Salimonu (2006) submitted that in Nigeria, yam is part of the

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Amusa, et al.

religious heritage of several tribes and often plays a key role in festivals and ceremonies. White yam (Dioscorea rotundata) is one of the major staple food crop in Nigeria and has potential for livestock feed and industrial starch production (Ayanwuyi,et al, 2011). The demand for yam at households level in Nigeria is very high. It is eaten in different forms such as fufu (pounded yam or poundo yam and amala in Nigeria), boiled, fried and roasted (IITA, 2009). It is a major source of energy in diet of Nigeria people. It can also be processed into crude flour by drying thin slices in the sun and then pound or ground into flour. Yam can further be processed into instant flakes producing a food similar to instant potato and can also be made into fried chip. Most of starch industries also make use of yam as one of their important raw materials (Ibitoye and Onimisi, 2013). Yam is rich in carbohydrate (75.5 - 83.3%), amino acid and vitamins (Thiamine, Riboflavin and Ascorbic acid). Yam contains a high value of protein (2.4%) and substantial amount of vitamins and minerals than some other common tuber crops (Ekunwe, Orewa and Emokaro, 2008). White yam contribute about 200 dietary calories daily for more than 95 million people in Nigeria and as an important source of income and livelihood security to more than 55% of Nigerians who are involved in various stages of its production, transportation, marketing and processing. Therefore, the objective of Nigerian’s food security programme of increasing agriculturalproduction for food selfsufficiency can hardly be achieved without efforts towards increased production of important staple like yam. This is because, white yam is a principal tuber crop in Nigerian economy, in terms of land under cultivation and in the volume and value of production. Nigeria is a major producer of yam accounted for over 65% (38 million metrictons) of the world yam production. This is valued at $7.75billion and cultivated about 2.9 million hectares of land in 2012 (FAO, 2013). Udemezue and Nnabuife (2017) also confirmed that Nigeria contributes two-thirds of global yam production yearly. According to the report of IITA (2009), the cultivation of yam is a very AKSUJAEERD

profitable farm enterprise despite its high costs of production and price fluctuations in the markets. An average profit per yam tuber,after harvest and storage in most producing areas in Nigeria at the peak period was calculated at over US$13,000 per hectare harvested. According to Philip et al., (2006), the major yam producing areas in Nigeria includethe middle belt (Benue,Nasarawa, Kwara, Kogi and Niger), south-eastern and southwestern parts of Nigeria. Abia State is one of the major yam producing areas in southeastern Nigeria with about 85% of the farm households producing yam. In recent years with continuous increase in population, yam has become more expensive as its production is not keeping pace with the demand for the commodity.Udemezue and Nnabuife (2017) affirmed that irrespective of the growing attentions given to yam production in Nigeria, its production is still below average and this could be as a result of some limitations occasioned by the activities of yam production coupled with pests and diseases that could retard its growth.Hence, deliberate effort to increase production of yam in Nigeria needs to be urgently put in place if the challenges of food security must be put under control. The cost of producing yam tubers is observed to be high in the country. This is largely due to the high cost of seed yam and other inputs and that situation has caused yam cultivation to suffer a severe setback. In order to encourage increased production among small holders’ farmers to meet increasing demand and supply gap for yam, the economic assessment of the production of the commodity must be empirically carried out. The study therefore specifically estimated the gross margin of yam production, estimated determinants of output of the farmers and identified the major constraints to the production of yam in the study area. Materials and methods Area of Study The study was carried out in Abia State, southeastern part of Nigeria. The state is located within the tropical rainforest zone and lies between longitudes 70 101 and 80 East of the Greenwich meridian and latitudes 40401 and 60141 North of the equator. Abia State is made Page 68

Economics of yam (Dioscorea rotundata) production among small holder farmers up of seventeen (17) administrative local government areas broadly divided into three agricultural zones which include: Aba, Ohafia and Umuahia agricultural zones. The state lies south of Enugu and Ebonyi States as well as east of Anambra and Imo States. It is bounded in the east by Cross River and Akwa Ibom States and in the south by Rivers State. Abia State has a climate marked by two major seasons; the raining season which lasts between April to October and dry season lasting from November to March like other states in the rainforest zone. Abia State occupies a land area of about 5,243.775sq.km which is approximately 5.85% of the total land area of Nigeria. The estimated population of Abia State according to report of National Bureau of Statistics (2012) is3,256,642people. Agriculture is one of the main occupations of the people of Abia State, providing income and employment for more than 65% of the population. Food crops grown in the state include yam, cassava, maize, cocoyam and different types of vegetables while the main agricultural cash crops in the state are cashew, mango, citrus and oil palm among others. Sampling and Data Collection Multi stage sampling technique was used to select 180 yam producers (farmers) across the three agricultural zones in the state. The first stage involved selection of all the three agricultural zones (Aba, Ohafia and Umuahia) in the state due to the vast production of yam across the state. In second stage, two local government areas (LGAs) were randomly sampled from each of the three agricultural zones making six LGAs for the study. At the third stage, random sampling was used to select two communities from each of the six LGAs making 12 communities for the study. The fourth stage of the sampling involved random selection of 15 yam producers from each of the 12 selected communities making a total of 180 famers that constituted the sample for the study. With the assistance of agricultural extension agents and key informants in the selected communities, the lists of the small holder yam farmers were compiled from which sampling was drawn.

69

Data for this study were obtained from primary source through the use of structuredclose-ended questionnaire. The data for the study were collected in 2017 cropping season by the researchers and their assistants. Out of the 180 copies of the questionnaire administered to the farmers, 164 copies were retrieved and considered good for the study. Thus, data extracted from the 164 copies of the retrieved questionnaire were used for the study. Primary data gathered included socio-economic characteristics of the yam producers, the cost and returnson yam production, output of yam farmers in kg and major constraints to the production of yam in the study area. Data were analysed using means, gross margin and ordinary least square (OLS) multiple regression analysis. Gross Margin Analysis Gross margin analysis used for estimate cost and returns of small holders yam farmers in this study is expressed as: GM = TR − TVC … … … … … . (1) Where: GM = Gross Margin TR = Total Revenue TVC = Total Variable Cost Rate of Returns on Investment (RRI) 100 = … … . … … … . … … … (2) 1 Where: RRI = Rate of Returns on Investment NR = Net Returns TC = Total Cost Profitability Index (PI) PI =

… … . . … … … … … … … … . (3)

Where: NR = Net Returns TR = Total Revenue Operating Expense Ratio (OR) OR =

… … … … … … . … … . (4)

Where: TVC - Total Variable Cost TC = Total Revenue Multiple regression Ordinary Least Squire (OLS) multiple regression analysis was employed to estimate determinants of output of small holder yam farmers in kg. The following is the implicit form of the model:

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Amusa, et al.

Y = f (X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12 + ℓ) … … . (5) Where: Y = output (in kg) X1 = Gender (dummy = 1 if male; 0 if female) X2 = Age (in years) X3 = Household Size X4 = Years of Education X5 = Years of Farming Experience X6 = Number of Extension Contacts X7 = Farm Size (ha) X8 = Labour (mandays) X9 = Unit cost of yam seed(in ₦) X10 = Location (dummy = 1 if rural; 0 if urban) X11 = Land Ownership Status (dummy = 1 if owned; 0 if otherwise) Three functional forms: linear, semi-log and double-log were estimated using the Ordinary Least Square (OLS). This was considered necessary in order to select the functional form with the best fit. In the semi-log and double log forms, 0 values in the dummies were replaced with 0.0001. This is because, the number 0 is undefined for log.

Results and discussion Land ownership status Table 1 represents the frequency and percentage distribution of the yam producers by land ownership status. The Table shows that 40% of the farmers owned the land they use for yam production, about 27 % inherited the land they use from their ancestors, about 13% of the farmers bought the land they use, while 11% and 8% of the yam producers acquired farm lands through leased and rented respectively. This result implies that land acquisition in the study area is typically based on land tenure system and communal land is more available to farmers. This agrees with the findings of Manyong and Houndekon (2000) that divided inheritance is the dominant tenurial arrangement for accessing land West Africa. In addition, the result of this study corroborated that of Amusa and Simonyan (2018) who found that about 59% of the farmers in southwest Nigeria acquire their farm lands through inheritance or owned, followed by 18% of the farmers who acquired their lands through lease/rent.

Table 1: Frequency and percentage distribution of the respondents by land ownership Land Ownership Status Frequency Percentage (%) Owned 66 40.2 Leased 18 11.0 Purchased 22 13.4 Inherited 45 27.4 Rent 13 8.0 Total 164 100 Source: Field Survey, 2017 Table 2 presents the summary of the mean and standard deviations of the socio-economic variables of yam producers that were used in the study. The mean of output of the yam producers is 530kg, with mean age of 51 years, household size of 8 persons, average of 9 years of education and 24 years of yam production experience. The mean of extension contact in 2017 cropping is 2 contact, the mean farm size for yam cultivation is 0.63 ha, with mean labour manday of ₦1,340.00 mean unit cost of yam seed for planting of ₦130.00 and mean cost of harvested yam tuber of ₦490.00. The result of AKSUJAEERD

the study agreed with that of Ibitoye and Onimisi (2013) who found that majority of about 90% of yam producers in Kogi State are still within the productive age bracket of 21 to 60 years. This was also similar to the findings of Odinwa, et al. (2011) on the average age of yam farmers in Northern area of River State. The findings further corroborated that of Udemezue and Nnabuife (2017) who found mean household size of yam producers in Anambra state to be 6 persons and with mean farm size of 1.35 ha for the yam producers. This result also supported that of Oguntate, Thonmpson and Ige Page 70

Economics of yam (Dioscorea rotundata) production among small holder farmers

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(2010) who found mean household size of yam could increase family labour for yam production. farmers in Oyo State to be 8 persons which Table 2: Summary of the socio-economic characteristics of the farmers SN Variables Min Max Mean SD 1 Output 350 970 530 85.26 2 Age 30 72 51 11.56 3 Household Size 2 16 7 3.15 4 Years of Education 1 18 9 4.69 5 Years of farming Experience 3 50 24.10 13.47 6 Extension Contacts 0 6 2.3 1.08 7 Farm Size 0.05 1.7 0.63 0.32 8 Labour manday 150 3,000 1,340 100.75 9 Unit cost of yam seeds 100 185 130 23.84 10 Unit cost of yam tuber 200 1,250 490 62.30 Source: Field Survey, 2017 From the computation, the Profitable Index (PI) Costs and returns of yam producers in Abia of yam producers in the study area was 0.38. State Table 3 presents the cost and returns in yam This indicates that about 38% of the Total production in Abia State. The result shows that Revenue (TR) generated from yam production the estimated Total Revenue (TR) of yam constitutes the net income of the farmers. production was ₦230,590.00 and Gross Margin Hence, this indicates that yam production is (GM) of ₦88,390.00. The Net Return (NR) of highly profitable in Abia State. the yam producers in the area was ₦86,585.00. Table 3: Costs and return of yam farmers in the study area Items Amount (₦) Variable Cost (VC) Yam Seeds 25,650.00 Agrochemicals 5,970.00 Labour 41,400.00 Fertilizers/Manure 34,980.00 Transportation 13,700.00 Miscellaneous cost 20,500.00 Total Variable Cost (TVC) 142,200.000 Fixed Cost (FC) Depreciation on farm tools 920.00 Depreciation on storage facilities 885.00 Total Fixed Cost (TFC) 1,805.00 Total Cost (TC) 144,005.00 Total Revenue (TR) 230,590.00 Gross Margin (GM) 88,390.00 Net Return (NR) 86,585.00 Profitability Index (PI) 0.38 Rate of Return on Investment (RRI) 60% Rate of Return on Variable Cost (RRVC) 61% Operating Expenses Ratio (OER) 0.62 Source: Field Survey, 2017 The Rate of Return on Investment (RRI) of 60% indicates that an average yam producer in the

study area earns 60% profit on every one naira (₦1.00) invested in yam production. The Rate of

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Return on Variable Cost (RRVC) of yam produced was 61% which suggest that 61% profit was realized from every naira (₦1.00) incurred as variable cost in yam production. The Operating Expense Ratio (OR) of 0.62 indicates that the variable cost consumed 62% of expenditure. The findings of this study agrees with that of Mohammed, Apata, Peter and Fidelis (2010) on factors declining cassava production in Ogori-Magongo L.G.A. of Kogi State where the authors found an estimated profitability index (PI) of 0.37. Ibitoye and Onimisi (2013) on a study also found that yam production is profitable in Kogi State with a gross margin of ₦121,200 ha and return on investment (ROI) of 0.43. In agreement with the findings of this study, Asala and Ebukiba (2016) found that the production of yam on one hectare of farm field translated to a net profit of ₦450,000.00 in north-central Nigeria. Socio-economic characteristics of the farmers influencing yam output Table 4 shows the results of the regression analysis on factors influencing yam output. Three functional forms (linear, semi-log and double-log) were estimated, the double-log functional form had the best fit, based on the values of R2 (0.71), number and levels of significance of independent variables and their signs. For instance, the R2 value of 0.71 indicates that the significant variables are responsible for about 71% of the variation in the output of yam producers. The F-value of

(27.888) implies that the overall equation was significant at (p0.10) correlated with access to weather information. As anticipated, farmers who have access to weather information are better equipped to take on adaptation. However, majority (95%) of the rural farmers did not have access to weather information (Table 2), probably because, the facilities were not there or they were not educated enough to understand since most of them had low level of education. More so, as data for this study revealed that the farmers had limited access to extension service, which could have properly educated them of weather information. Other studies on access to weather information and climate change adaptation have reported significant positive relationships (ACCCA, 2010; Deressa et al., 2008; Otitoju, 2013).

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The result shows that there are positive relationships between household size and the probability of choosing moderate adaptation (p0.10), respectively. An increase in household size by one unit raises the chances of choosing moderate adaptation over low adaptation by 20.4 percent. This result is supported by the findings of Gbetibouo (2009) and Temesgen et al. (2014), but varies with that ofBirungi & Hassan (2010) and Otitoju (2013). Arguments in favour of this result indicated that large households have a high labour endowments compared to small ones, which could be deployed profitably in farming activities including the implementation of climate change adaptation practices (Croppenstedt, Demeke & Meschi, 2003). According to Gbetibouo (2009), large households are more predisposed towards choosing labour-intensive adaptation measures. The results showed that land tenure security had significant and positive effects on choice of climate change adaptation. The probability of choosing moderate adaptation increases by 3.12 percent (p