PROFITABILITY ANALYSIS OF SNAIL FARMING IN OWERRI AGRICULTURAL ZONE IMO STATE 1Munonye, J. O and 2Moses, P 1AE- Federal University Ndufu Alike 2Imo State University Owerri Abstract The study examined snail production in Imo State, Nigeria using cross-sectional data. It was guided by five specific objectives: describe snail production system in the study area; determine costs and return of snail production; determine the factors that affect snail production in the study area. Questionnaire was the instrument of data collection. Purposive sampling technique was used to sample fifty snail farmers who constituted the respondents for the study. Data were analyzed using descriptive, inferential statistics and costs and return analysis. Results showed that the business of snail farming required low capital investment and was highly profitable with gross margin and net profit of N612, 130.00 and N609, 630.00 per snail per one cycle respectively. The return on investment was N3.04. This showed that snail production is profitable and would boost the farmers’ income and thereby improve their livelihood. Quantity of snails and gender positively and significantly influenced the profit realized. Inadequate funds and religious bias were the major constraints to increase production. Keywords: Profitability, Snail, Farming Contributed Paper prepared for presentation at the 93rd Annual Conference of the Agricultural Economics Society, University of Warwick, England. 15 – 17 April 2019 1 1.0: Introduction The tropical land snail belongs to a group referred to as Phylum Mollusca. Over 80% of Nigerian populace are poor to whom protein products such as, meat are a rare luxury. To avert danger of malnutrition, especially among children, the giant African land snail is a good substitute of source of protein (Bayode, 2009).The advantage of snail farming (Heliciculture) over most other livestock includes low capital requirement, for its establishment and operation, less demand of professional knowledge, very high fecundity and low mortality, less labour requirement and availability of ready domestic and international markets among others (Akinbile, 2000). In spite of the potentials and advantages of snail farming, widespread participation in its production by farmers has not been achieved in Nigeria (Baba and Adeleke, 2006). Snail population is affected by hunting, bush burning, and use of agro-chemicals, deforestation and change in weather. According to (Agbogidi, and Okonta, 2011) the potential for snails’ domestication and commercialization in Nigeria are yet to be fully exploited. There is an urgent need for shift from the dependence on wild sources of snail to domesticated ones. In recent times, rearing of micro livestock by rural household is becoming popular due to the fact that the households have realized the need to diversify their source of income. There is need to look inward and integrate into our farming system some non-conventional meat sources (Ebenebe, 2000). Objectives are to: determine the costs and return of snail farming and determine the factors that affect snail production. There are two main seasons in the zone: Dry and rainy seasons. The annual rainfall is between 2000mm and 2500mm, while the mean annual temperature is 2 between 26 -28 degree Celsius, with a relative humidity of 98% during the wet season. The zone is richly endowed with fertile land suitable for growth of arable crops like yam, cassava, cocoyam, melon, maize etc. The zone is in the tropical rainforest zone of Nigeria, which makes her vegetation habitable for many forest and livestock species including snail. Purposive sampling technique was used for the study. Five Local Government Areas were selected from the nine Local Government Areas that made up the Owerri Agricultural Zone. Five communities were purposely chosen from the five Local Government Areas due to the availability of snail farmers. Ten snail farmers were selected from each of the five local government areas, making a total of fifty snail farmers. Data for the study was collected from primary sources. The primary data were collected using a well- structured questionnaire. Descriptive and inferential statistics were used for the analysis. Costs and return analysis: TC= TFC + TVC; Net Farm Income (NFI) = TR – TC; Return on Investment = ⁄ Ordinary Least Square Regression was used to determine the effects of socio- economic factors on the profitability of Snail farming. Where Y= Returns of production, X1= Age of farmers, X2= Educational level, X3=quantity of snail, X5= Household size, X6= Cost of production, e = error term. 3 2.0: Results and Discussion 2.1: Description of respondents’ production system The production of farmers is presented in Table 1. It indicates that 92% of the respondents are practicing intensive system of production while 8% are practicing semi-intensive system of snail production. Snails are best reared using the intensive system of snail farming. Table 1: Distribution of Production System according to respondent System of Production Frequency Percentages Intensive 46 92.0 Semi-intensive 4 8.0 TOTAL 50 100 Source: Field Survey data, 2017 2.2: Different snail species produced by the respondent The species produced by the respondents are presented in Table 2. Table 2 shows the frequency distribution of the various species of snail. 32% of the farmers have Archatina marginata, 2% have Archatina fulica, and 26% have both Archatina achatina and Archatina marginata and 40% of the farmers have Archatina achatina, this implies that most farmers prefer archatina achatina because it lays more eggs and very affordable, although they are small in size. 4 Table 2: Distribution of respondents according to Specie Species Frequency Percentage Archatina achatina 20 40 Archachatina marginata 16 32 Archatina fulica 1 2 A.achatina & A.marginata 13 26 TOTAL 50 100 Source: Field Survey data, 2017 2.3: Determination of Costs and Return of the Snail Farming The costs and return analysis were used to determine the profitability of snail production enterprise in the study area. Table 3, shows that the total production cost per 600kg of Snail in the study area was N200, 370.00 while the total revenue per 600kg was N810, 000.00. The analysis showed that snail producers realized N609, 630.00 per 600kg as Net Revenue. The return per naira investment was N3.04. This is in line with the findings of Ahmadu and Ojogho (2012), who reported the returns per naira invested in snail to be N1.73 in Edo State of Nigeria. Snail production was economically viable because the return on investment (ROI) was 3.04 which imply that for every N1.00 invested, N3.04 was realized. This shows that snail production in the study area was profitable. Labour cost was the largest component of the total cost (N90, 000.00). This is at variance with the other types of livestock such as poultry where feed is the most expensive input (Ahmadu, Erhabor and Jimoh, 2010). The cost of these vegetative materials might be low but the labour involved in obtaining them might have contributed to the high labour cost. This finding confirmed the assertion that snail production requires high capital investment ( Cobbinah, Vink and Onwuka, 2008). 5 Table 3: Distribution according to Cost and Returns of Snail Production in the study Area Item Unit Quantity Cost(N) Amount(N) a)Revenue of snail Kg 600 1350 810,000 Variable Cost b)Breeding stock 600 120 72,000 c) Fumigation grams 300 300 d) Feed Kg 32,320 32,320 e) Labour Man- 15,000 90,000 days f) Transportation 2,500 2,500 g)Utilities (eg water, 750 sanitation etc.) h)Total Variable Cost 197,870 I)Gross margin 612,130 Fixed Cost j)Depreciation on fixed 2,500 2,500 input k) Total fixed cost 2,500 2,500 l) Total Cost 200,370 m) Net returns 609,630 Return on 3.04 investment(ROI) Source: Field survey data, 2017 6 2.4: Determination of the Factors that Affect Snail Production The result in Table 4 shows the estimated multiple regression analysis in four functional forms: Linear, Exponential, Semi-log, and Double log. The semi-log model was chosen as the lead equation because it provided the best fit and has the highest number of statistical significance variables. The F-statistic was significant at 1% level of probability indicating a good fit for the model used. The result of the semi-log shows that the coefficient of multiple determinations (R2) was 0.633 implying that the independent variables (X1-X5) jointly explained 63.3% of the variation in snail production. The remaining 37% were taken care of by variables not included in the model. Household size, cost of production were negatively related to the output of snail farmer. Increase in them reduced output. Number of snail and gender confirmed that increase in these input increase the level of returns generated. Household size and gender were statistically significant at 5% level while number of snail was positive and significant at 1% level of probability and cost of production was negatively significant at 1%. Being significant shows that the contributed to change in output. Age and education were not significant but were positively related to output. 7 Table 4: Multiple Regression Estimates of the Factors that Affect Snail Production Variables Semi-log Linear Exponential Double log Constant 7067725.864 631785.370 17.451 93.527 (3.512)*** (2.657)** (6.955)*** (4.287)*** Age 102306.349 2663.628 0.017 0.590 (1.645) (2.130)** (1.323) (0.575) Education 21780.343 23175.390 0.159 0.095 (0.581) (1.264) (0.823) (0.232) No of snail 24509.382 6.992 6.239e-005 0.176 (2.818)*** (2.206)** (1.865) (1.862)* Household -71838.943 -42087.311 -0.341 -0.693 Size (-2.082)** (-1.753)* (-1.346) (-1.719)* Sex 60589 13788.533 0.169 0.43 (2.304)** (2.131)** (2.475)** (2.609)** Cost of -747054.009 -27.477 0.000 -8.534 Production (-3.8464)*** (-3.279)*** (-3.452)*** (-4.073)*** R2 0.633 0.606 0.578 0.585 F-ratio 12.363*** 11.033*** 9.808*** 10.104*** N 50 50 50 50 8 Source: Field survey data, 2017, *significant at 10%, **significant at 5%, ***significant at 1%, Figures in parenthesis are t-values 3.0H: Constraints to snail farming in the study area Constraints to snail farming were shown in Table 5.