International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375

Determinants of Ginger Supply: The Case of and Kindo Koisha Woredas of Wolaita Zone, SNNPR,

Alemayehu Asale and Taye Buke Ashango* Wolaita University, College Of Agriculture.

Date of publication (dd/mm/yyyy): 15/03/2017

Abstract – This study tries to analyze the determinants of for instance at altitudes up to 2000 m, with rainfall often ginger (Zingiber Officinale Rosc) in Boloso Bombe and less than 1500 mm per year and at lower temperature Kindo Koisha woredas of Wolaita zone in SNNPR Hence, the (Anteneh et al., 2008). Nowadays, it is an important cash Multiple Linear Regression Model was employed to see crop in the South, Southwest and Northwestern parts of factors that determine the supply of ginger. The study has the country (Girma and Digafie, 2004). In the southern evaluated the main factors affecting the supply of ginger based on the Multiple Linear Regression Model. Thus, the parts of the country especially in Wolaita Zone (Kindo econometric model has identified the amount of introduced Koisha and Boloso Bombe districts and the areas nearby), seed made available for farmers to be the most important the crop is widely cultivated by farmers. The current variable affecting (positively) the supply of ginger. As a development policy and strategy has also targeted the crop result, the findings of this work have suggested Research for poverty reduction and food security goals for resource Institutions, NGOs and other stockholders can play a vital poor farmers at household levels. It seems to encourage role in addressing the farmers’ question of introduced seed. market oriented production via accessing appropriate Further, the number of livestock owned significantly and markets for farmers produce and thereby increasing positively affected the volume of ginger supplied. The results marketable surplus. Marketing activities, therefore, have of this manuscript also suggested building household assets (i.e. livestock) through household asset building programs an intrinsic productive value, in that it adds time, place will remedy the case. But income from other sources (exclude and possession utilities to products and commodities. ginger) adversely affected the amount of ginger supplied. Through the technical functions of storage, processing, Thus, the findings pointed out that farmers should compare and transportation and through exchange, marketing and contrast between the two and decide accordingly. Hence, increases consumer satisfaction from any given quantity of extension workers and other concerned bodies were expected output. An efficient agricultural marketing is crucial for to play valuable roles in teaching and directing the farmers effective agricultural and rural development, particularly so as they opt effectively and efficiently. with regard to sustained increase in agricultural Keywords – Ginger, Determinants, Supply and Farmers. production, farmer’s income and improvement of the food security capabilities (Arrora, 1997) I. Introduction Moreover, in the realm of economic growth, markets may provide the incentives for-profit-maximizing 1.1. Background participants to develop new technologies, products, and Ethiopia is one of the major centers of origin/diversity sources of supply, new markets and new methods of for several horticultural crops, including leafy vegetables, exploiting them. Markets can also provide a mechanism of root and tuber crops, spices, coffee and ornamental surplus extraction and inter-sect oral resource transfers, flowers (Jansen, 1981). Out of about 70 species cultivated most commonly from agricultural to non-agricultural in the world about 29 wild and semi-domesticated species sectors. Further, the development and expansion of are found in Ethiopia (Kochhar, 1998). One of the oldest markets can create increased demand through various known spice, ginger (Zingiber officinale Rosc.), has been means. For example, markets provide a source of used by man since several centuries not only as a spice but productive employment and income generation also as medicine. Ginger is indigenous to tropical India (Scarborough and Kydd, 1992). and South East Asia, Australia and Japan, with the main 1.2. Statement of the Problem center of diversity in Indo-Malaysia (Purseglove, 1972). In Ethiopia is an agricultural country where about 86 % of India, ginger in its fresh and/or dried form has its population lives in rural areas. Agriculture constituted innumerable uses in culinary and medicinal preparations. about 43 % of the gross domestic product in 1999/2000 India and China are the world’s largest producers and (Tassew,2008).Thus, agricultural products or commodities exporters of ginger. Other important producers are dominate the export market. According to the Ministry of Jamaica, Nigeria, Sierra Leone, Thailand, and Australia Agriculture, the crop sector still leads in foreign exchange (Jansen, 1981; Yiljep et al., 2005). contribution, among which coffee takes a lion share. The The literatures also argue that ginger is known to be Ministry of Agriculture also acknowledges that the role of introduced to Ethiopia as early as the 13th century and spices is not insignificant in the foreign exchange perhaps its cultivation has also been started since then. In earnings. One of the popular spices is ginger whose Ethiopia, it is often cultivated under suboptimal conditions demand has significantly been increasing over time (MOA, 2003). Copyright © 2017 IJRIES, All right reserved 27 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375

The statistical information from the Ministry of 1.6. Scope and Limitations of the Study Agriculture and Rural Development indicated that 99 % of The study focuses on analysis of factors affecting ginger crop (ginger) production was from the Southern Nations, supply around Boloso Bombe and Kindo Koisha districts Nationalities and Peoples Regional State (SNNPRS) and of Wolaita zone in the SNNP region. Emphasis was given about 1 % was from the Oromia National Regional State. to those issues which place a remarkable influence up on The productivity of fresh rhizome ginger is about 11,522 ginger supplied to the market. Thus, the producers' side kg/ha in the SNNPRS, 2615 kg/ha in the Oromia National problems have got a special attention. Nevertheless, the Regional State and 7050 kg/ha nationally (MOA, 2003). study did not address the demand side problems in depth. Despite, the increasing demand for ginger the current Hence, the marketing aspects of ginger should further be income generating capacity of ginger producers when studied since they are equally important. compared to its immense potential of the areas is not as such encouraging. The primary reason among others II. METHODOLOGIES seems to be the farmers’ inability to access markets, which in turn reduces the incentive to participate in the economic 2.1. Description of the Study Areas transactions. Kindo Koisha is one of the twelve Woredas of Wolaita Furthermore, in the absence of an efficient, integrated zone. It constitutes about 23 Rural kebeles and two town and responsive market mechanism that is marked with administrative kebeles. The total population of the Woreda good performance, the possible increment in output, rural is estimated to be about 118,953 of which 58,287 male and income and foreign exchange resulting from the 60,665 female (CSA, 2007). Agro-ecological classification introduction of improved production technology could not of the woreda shows that the area is mainly kola (54%) be effective (Solomon, 2004). Earlier a study (Abrham, followed by woyna-dega and dega , 39% and 7% 2008) concentrated only on the genetic aspects of the crop respectively. The woreda is, thus, characterized as i.e. evaluation of ginger accessions for yield and oleoresin climatically hot area in the zone with temperature varying was undertaken particularly in ‘Parawocha’ and ‘Matala from 250c to 400c. The mean annual rain fall is 900mm Hembecho kebeles1 of Boloso bombe and with the max and min of 1400mm and 400mm woredas respectively .Otherwise, the supply side issues of respectively. Most PAs of the woreda are situated in the ginger particularly with respect to market is left unstudied low land as the altitude ranges from 700m to 2280m above till this point. This study, therefore, gives a due emphasis sea level. Moreover, the pedological back ground of the for determinants of ginger supply in Boloso Bombe and woreda shows that 35%, 20%, and 25% of the soil is Kindo Koisha woredas of wolaita zone. nitho-sole, clay soil, and sandy soil respectively. 1.4. Objectives of the Study Cereal crops like maize, teff , wheat, barely, and 1.4.1. Major objective sorghum are the major crops grown in the area followed The overall objective of this study is to analyze factors by root-crops such as sweet potato, cassava, potato, taro, affecting ginger supply in Boloso Bombe and Kindo yam , enset , etc. Leguminous plants like haricot bean, Koisha districts. bean, pea etc are also commonly grown in the area. Ginger 1.4.2. Specific objectives is the most important cash crop grown potentially in some To investigate the determinants of ginger supply, PAs like Kindo-Angela, Oidu-Chama, Tulicha, Sorto, and To identify the constraints and prospects of ginger Borkosh. Thus, crop production mixed with livestock production. rearing is the major economic activity of the woreda. 1.5. Significance of the Study The findings of this study would enable to see the behavior, structure, conduct, and performance of ginger marketing chain and channel in Boloso Bombe and Kindo Koisha woredas. Examining the structure and return within the chain help to pinpoint the input, which affects ginger production, marketing management, ginger marketing chain, efficiency and effectiveness. The direction of the flow of the produce through the marketing channel as it passes from producer to different marketing actors was identified. Examining different ginger market Arial map of Ethiopia chain would help to point out the weakness and strength in the chain of ginger marketing and provide guidelines as to which part of the chain deserves special attention for marketing improvement. The potential beneficiaries of the results of this study will be producers, traders, government and non-government organizations who want to intervene in ginger marketing, and researchers who want to investigate further. Therefore this work may help policy makers in designing appropriate policies to improve ginger Arial map of Wolaita Zone production and marketing in the country. Figure 1. Study areas Copyright © 2017 IJRIES, All right reserved 28 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375

Boloso Bombe, on the other hand, is a woreda in the 2.3. Data type and Methods of Data Collection zone with a total population of about 117,330(57,131 male The data for the study was collected from primary and and 60,199 female). The 2007 census also shows the secondary sources. The primary sources include key population constitutes about 14,039 and 3,620 male and informant survey, personal observation, and producers’ female household head respectively. As far as the agro- surveys. The producers survey was conducted to collect ecological back ground of the woreda is concerned, the data on quality and quantity of ginger, variety area seems to be mainly ‘kola’(75%) followed by ‘woyna- identification, input utilization, price, buyers’ purpose, and dega’(20%) and dega (5%) with the altitude ranging from market location. This was done through visual 1375m to 2277m above sea level. The total area of the observation, and by interviewing buyers and sellers woreda is about 21,859 ha out of which 13,592 ha (62%) (producers) at the time of ginger transaction. A pre-tested is cultivable land. About 1560 ha is grazing land, and the and semi-structured schedule or questionnaire was area of 3207 ha is covered by tree. The remaining 3500 ha designed to collect data on marketing channels, costs, land is found to be uncultivable. Agriculture especially price of ginger, and constraints or problems of ginger crop production is the dominant form of economic activity marketing etc. where ginger production takes a lion share followed by Six enumerators with education level of diploma (2) and cereals like maize, teff, etc. and root crops such as enset, degree (4) were recruited and trained mainly concerning sweet-potato, potato, taro, yam etc. technique of interviewing. 2.2. Sampling Design and Sample Size Key informants survey was made to identify the Five kebeles (one from Kindo Koisha and four from prospects and constraints on ginger production, the Boloso Bombe) were purposively selected out of the total effectiveness of production, marketing constraints, credit 43 kebeles in the two woredas based mainly on their facilities, availability of extension services, access to and maximum area of land allocated for ginger. Then, a availability of market information and marketing cost. complete and separate list of ginger producers in each Moreover, secondary data related to market fees, facilities kebeles was prepared. Finally, the total of 120 producers and services were collected from the Woredas' Office of was selected based on proportional probability sampling Agriculture and other relevant sources. i.e. according to the number of ginger producers in each 2.4. Methods of Data Analysis kebele. Econometric method of data analysis was employed so as to meet the information requirement of the study. Table 1. Sample Size. 2.4.1. Supply Function Name of PA Total No. of producers Sample size A number of studies investigated about factors that (Farmers) mainly affect marketable supply of agricultural Ajora 1,457 36 commodities. Among others, Wolday (1994) pointed out Bedaye 1,024 25 the major factors that influence the marketable supply of Mole 1,000 24 teff, maize, and wheat at Alaba Siraro district using cross- Ose 943 23 sectional data and he investigated the relationship of farm level marketable supply of cereals to capture the effect of Kindo Angela 494 12 explanatory variables on the marketable supply of food Total 4,918 120 grain, he adopted multiple regression analysis with Source:”Own survey, 2016/17” dummy and continuous variables as explanatory variables. In this study, he found out that among the independent As can be seen from the (Appendix Table 1), the total of variables, access to market, size of output and family size about 6,992 ha land is allocated for ginger in the two had affected the marketable supply of the food grains. districts. Boloso Bombe seems to be relatively potential Another study by Wolelaw (2005) find out the major Woreda of ginger than Kindo Koisha since the total of factors that affect the marketable supply of rice at Fogera 5,500 (78.7%) ha land is allocated for ginger where as it is district using multiple linear regression models. He 1,492.25 (21.3%) ha in Kindo Koisha. Thus, four potential investigated the relationship between the determinant kebeles namely Ajora, Bedaye, Mole, and Ose were factors of supply and the marketable supply of rice and his selected from Boloso Bombe. The kebele named Bombe study revealed that the current price, lagged price amount Gebere Mehaber was not selected because its number of of rice production at farm level and consumption at ginger producers is relatively fewer than that of Mole and household level had influenced marketable supply of rice Ose though the three kebeles were found to allocate equal at the district. area of land for the crop. On the other hand, the kebele Again a study that was undertaken by Kindei (2007) named Kindo Angela was selected from Kindo Koisha using cross-sectional data, found out the major factors that woreda based on the sample criteria mentioned so far. affect marketable supply of sesame in Metema district. In The total of 120 producers i.e. 36 from Ajora, 25 from the study, multiple linear regression was implemented to Bedaye, 24 from Mole, 23 from Ose, and 12 from Kindo identify the marketable supply of sesame and the Angela were proportionally selected based on the number explanatory variables which were hypothesized in the of producers in each sample kebeles. In other words, model. And, his study acknowledged that amount of proportional random sampling procedure was employed to sesame productivity, use of modern inputs, number of select the 120 producers from the five kebeles. language spoken by the household head, number of oxen

Copyright © 2017 IJRIES, All right reserved 29 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375 owned, sesame area and time of selling of sesame affected comparable across different ginger markets (local, regional the marketable supply of sesame positively. Similar study and national level). by Rehima (2006) identified that the main factors affecting 2.5.1. Dependant Variable marketable supply of red pepper at Alaba and Siltie Quantity of ginger supplied (GMQi): It is continuous districts of the SNNPR, using cross-sectional data with variable that represents volume of ginger supplied to both dummy and continuous explanatory variables. In her market. It is a dependent variable and other explanatory work, she employed Tobit model and found out that variables may have a negative or positive relation to the distance to the markets, frequency of contacts with the variable (Gujarati, 2003). extension agents, quantity of pepper produced and access 2.5.2. Independent (Explanatory) Variables (Xi) to market information influenced marketable supply of These are variables that are assumed to influence pepper positively in the districts. Similarly a study was quantity supply of ginger. Selection of these variables undertaken by Assefa (2009) to identify the determinants needs to consider that the omission of one or more of marketable supply of honey with both dummy and relevant variables or inclusion of one or more irrelevant continuous explanatory variables in Atsbi Wemberta variables may result in specification error which may district of eastern zone of Tigray national state. In his reduce the capability of the model in exploring the study, he used multiple regression models and found out economic phenomena empirically. that education level of beekeeping households, quantity of Land size (SA): This is the total land area that the honey produced and the lagged price of honey influenced farmer actually owns. It is a continuous variable measured the marketable supply of honey. Thus, recent studies are in hectares. They can allocate this for different crops like normally using regression models to estimate the supply maize, haricot bean, sweat potato, etc. However, it is function. As well, in this study, Linear Multiple obvious that the size of acreage that can be allocated for Regression Model will be fitted to analyze the supply of ginger increases with larger land size (Rehima, 2008). ginger in Boloso Bombe and Kindo Koisha woredas of Hence, it is assumed that this variable is positively related Wolaita zone. to volume of market supply of ginger. 2.4.2. Econometric Model Specification Amount of improved seed available (AIS): This is a According to Greene (2003), the multiple linear continuous variable measured in quintal. Improved seed is regression model is specified as Y= f(price, ginger output, obviously more productive than local variety access to extension service, education level, access to (Woldemicheal, 2008). Thus, this variable is hypothesized market information, experience in ginger production, sex to have a positive effect up on the market participation and of house hold head, access to credit, age, etc…). The the volume of market supply. econometric model specification of supply function in a Experience in ginger production (EGP): This is the matrix notation is estimated by number of years in producing or cropping ginger; it is a Yi=β0+βiXi+Ui (7) continuous variable and a ssumed to have a positive effect Where Yi= amount of ginger supplied to the market up on the volume of market supply. β0= the constant intercept Age of the household head (AgHH): It is a continuous βi= a vector of estimated coefficient of the explanatory variable measured in years. As to Rehima (2006), age is a variables proxy measure of farming experience of households. Aged Xi=a vector of explanatory variables household are believed to be wise in resource use, and it is Ui=disturbance term expected to have a positive effect on marketable supply. 2.4.3. Determinants of Marketable Supply of Ginger in Family Size (FS): It is a continuous variable measured Boloso Bombe and Kindo Koisha in adult equivalent (Storck et al., 1991) i.e. the availability As to Tomek and Robinson (1985) careful definitions of of active labor force in the household, which affects terms are essential. Total supply in specific period may farmers’ decision to market participation. Since production depend not only on current production but also on is the function of labor, availability of labor is assumed to carryover stocks and imports. It is not possible to include a have positive relation with volume of supply. However, complete set of variables that could affect the household family size might have positive or negative effect on level of supply of ginger. However, in this work, an market participation and marketable surplus of food crops. attempt was made to estimate the determinants of the A study conducted by Wolday (1994) identified that supply of ginger in Boloso Bombe and Kindo Koisha family size has a significant positive effect on quantity of districts. The main activity was which factor affects and teff marketed and negative effect on quantity of maize how? Thus, the possible variables which are hypothetical marketed. to influence the quantity of ginger supply need to be Education of the household head (EdHH): Intellectual explained. Thus, the main variables expected to have capital or education, measured in terms of formal effect on quantity supply of ginger are explained in the schooling the household head is a continuous variable and following section. assumed to have a positive effect on sales decision. 2.5. Hypothesis, Variable construction and Definition Sometimes, however, because of cultural and socio The data covers information necessary to make farm economic characteristics, education has opportunity costs level indices of social, economic, demographic and in alternative enterprises (Lapar et. al., 2002). So it is environmental outcomes and efficiency indicators impossible to have a definite expectation of the effect of education on market participation and sales volume. Copyright © 2017 IJRIES, All right reserved 30 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375

Sex of the household Head (SxHH): It is a dummy existence of multicollinearity. They are Variance Inflation variable of the form 1=male; 0=female. Obviously both Factor (VIF) for continuous variables association and men and women take part in crop production in mixed Contingency Coefficient (CC) for dummy variable farming. Generally, men contribute more labor in association. cultivating, weeding, etc. Women also participate in In order to detect the multicollinearity problem for weeding, fertilizer application like compost etc. However, continuous variables, the Variance Inflation factor (VIF) it is male who participate dominantly in such activities. 1 = 2, for each coefficient in a regression as a diagnostic Therefore, it is assumed that male headed household 1−푅푗 participates more in ginger market and vice versa for statistic is used, where Rj represents a coefficient for females. determining the subsidiary or auxiliary regression of each Access to credit (ACr): It is a binary or dummy variable independent continuous variable X. As a rule of thumb, if which may take the form 1=access for information; 0=no VIF value of a variable exceeds 10, which will happen if 2 access to information. As to Rehima (2006), this can be Rj exceeds 0.9, there exists high degree of viewed in two ways: First, increased debt in other multicollinearity (Gujarati, 2003). Hence, in this study, activities may lead lack of free collateral in order to secure Variance Inflation Factor (VIF) was employed to estimate loans for market selling activities. In this case it is the degree of multicollinearity among the explanatory assumed to relate negatively to market participation and continuous variables of the supply function. The same the volume of supply. In the second case, the existing loan way, Contingency Coefficient (CC) was employed for the may be the result of previous borrowing that has occurred dummy variables. On other hand, test for for the production and selling decision ad may therefore heteroscedasticity will be undertaken in the study. Among signal greater propensity to sell. In this case, the amount of the many test statistics for heteroscedasticity, the Breusch loan received is assumed to have positive effect on market and Pagan test of hetroscedasticity was used especially for participation decision and sales volume of farm its simplicity. Thus, Breusch and Pagan devised a households. Therefore, it is not possible to predict the Lagrange Multiplier test of the hypothesis that: exact impact of credit access. 2 2 ′ Number of extension visits (ExVis): The number of 휎푖 = 휎 푓(훼0 +훼 푍푖) (8) extension visit made by the extension agent measures the variable. Extension visits improves the households’ Where Zi is a vector of independent variables. The intellectual capital, which improves ginger production and model is homoscedastic if α = 0. The test was carried out divert production resources to markets. Therefore, number with a simple regression: of extension visits has a direct effect on market 1 2 LM= 푒푥푝푙푎𝑖푛푒푑 푠푢푚 표푓 푠푞푢푎푟푒푠 𝑖푛 푡ℎ푒 푟푒푔푟푒푠푠𝑖표푛 표푓 푒푖 / participation decision and sales volume. 2 (푒′푒 ⁄ 푛) 표푛 푍 (9) Access to market information (MI): This is also a 푖 The null hypothesis can be tested by the usual t-test or dummy variable which takes the form 1=yes; 0=no. F-test. Farmers marketing decisions are based on market price information, and poorly integrated markets may convey inaccurate price information, leading to inefficient product III. RESULTS AND DISCUSSIONS movement. Therefore, it is expected that market information is positively related market participation and 3.1. Constraints and Prospects of Ginger Production marketable surplus. Study conducted by Gotez (1992) on Market oriented production is the driving force of food marketing behavior identified better information increased productivity. Given the current level of significantly raises the probability of market participation production of ginger, there might be numerous constraints for potential selling households. that impede productivity. Thus, it should be seen in both When the assumptions of the Classical Linear production and marketing sides of the commodity. The Regression model (CLR) are dishonored, the parameter production side constraints may refer, in broad sense, to estimates of the OLS model may not be Best Linear farmers’ production constraints where as the marketing Unbiased Estimator (BLUE). For this reason, it is essential side constraints to traders’ marketing constraints. to see the case of multicollinearity and hetroscedasticity However, there might again be future circumstances that between the variables that affect the supply of ginger in better help the agents (producers, traders and consumers) the study area. Hence, before fitting significant variables cop up with the existing situation and thereby improve into the model, it is necessary to check these problems productivity. Thus, the prospects can possibly be of policy, among the continuous variables and see the association institutional, or technological. between the discrete variables, which seriously affect the 3.1.1. Farmers’ Production and Marketing parameter estimates. According to Gujarati (2003), Constraints multicollinearity refers to a situation where it becomes The focus group discussion has identified that erratic difficult to identify the separate effect of explanatory nature of rainfall, lack of training, unavailability of inputs, variables on the dependent variable as there exists strong small land holding, soil infertility, lack of oxen, lack of relationship among them. Alternatively, multicollinearity credit, seasonality of roads, theft, demand problem, is a situation where the explanatory variables are highly inadequate market information, cheating of scale, and poor correlated. There are two measures which suggest the productivity itself to be the major constraints that hamper

Copyright © 2017 IJRIES, All right reserved 31 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375 production and productivity of ginger in the study area. irrigation. The case of demand is directly or indirectly Most farmers responded that cheating of scale (78.3 %), related to price and quality. The farmers disclosed that seasonality of roads (74.2 %), and erratic nature of rainfall there were times, in the near past; no one bought their (71.7 %), demand (71.7 %), inadequate market produce (i.e. ginger) as a result of which farmers information (71.7 %), and lack of credit (70.8 %) to be the commonly threw it away in the markets whereas others first top six constraints (Table 2). totally ceased its production. The possible reason for this, As far as the case of scale is concerned, the sample as to the exporters, was that countries in demand for farmers especially in Ajora, Ose, and Kindo Angela have ginger did not want to buy the Ethiopian ginger as it does complained that traders there at local market obviously not satisfy their quality criteria. In addition, the steal weights (2-5 kg out of 100 kg). Similarly, all farmers dissemination of price information was not even as to the complain that what traders consider as a ‘Fersula’ was 20 farmers though most of them responded that they have kg when they purchase but 17 kg when they sell. Thus, access to market information. traders here also take 3 kg away from farmers. As to the Farmers also pointed out that credit was not available. farmers, traders begin to renegotiate up on weights in For instance, farmers in Ose (73.9 %), Mole (79.2 %), and cases when they (farmers) become conscious of this. Kindo Angala (83.3 %) complain the unavailability of Moreover, all farmers in Mole, Ose, and Kindo Angela credit more than any other kebeles. And, the farmers also criticize that roads were not suitable for them to move need credit in the form inputs like ginger seed. The survey their produce to regional markets. Personal observations of result also shows that input was not available for farmers. these kebeles also show that the roads there were full of As presented in (Table 2), ginger seed (introduced variety) risks and bad topography. However, the Administration was really unavailable as 53.3 % of the total sample Office of both woredas responded that roads to these farmers complained it. The X2-test also suggests that the kebeles in all directions were planned and be implemented relation to be real across the kebeles as .07 is significant at in near future. As shown in the table, the X2-test also 10.0 % significance level. The possible reason is that confirms that the case is real across the kebeles as 0.0001 farmers want to curtail using local variety seed and is significant at 1.0 % significance level. increase introduced variety in some kebeles (e.g. Ajora As to the case of erratic rain fall, farmers in all kebeles and Bedaye ) whereas others (e.g. Kindo Angela) were reacted that the rain to be inconsistent and they could not obviously in a great need and want to increase its run activities as to their plans. This shows the inflexibility utilization. of rain fed production where farmers do not practice

Table 2. Farmers’ production and marketing problems (% of farmers) N=36 N=25 N=24 N=23 N=12 N=120 Ajora Bedaye Mole Ose Angela Total x2 Poor productivity (of land or seed) 5.6 24.0 12.5 17.4 33.3 15.8 0.13 Erratic nature of rainfall 72.2 68.0 62.5 78.3 83.3 71.7 0.7 Lack of training 61.1 76.0 66.7 73.9 66.7 68.3 0.8 Unavailability of inputs (e.g. seed) 58.3 64.0 33.3 43.5 75.0 53.3 0.07* Deficiency of cultivable land 25.0 44.0 50.0 47.8 25.0 38.3 0.2 Soil fertility problem 36.1 24.0 12.5 30.4 8.3 25.0 0.2 Lack of oxen 33.3 52.0 37.5 26.1 25.0 35.6 0.3 Lack of credit 63.9 64.0 79.2 73.9 83.3 70.8 0.5 Seasonality of roads 25.0 84.0 100.0 100.0 100.0 74.2 .0001*** Theft problem 44.4 32.0 37.5 30.4 8.3 34.2 0.2 Demand problem 66.7 68.0 79.2 82.6 58.3 71.7 0.5 Inadequate market information 58.3 84.0 83.3 69.6 66.7 71.7 0.2 Cheating of scale 88.9 60.0 70.8 87.0 83.3 78.3 0.05* Note: *= significant at 10.0 % significance level, ***= significant at 1.0 % significance level. Source: “Own survey, 2016/17”.

3.2. Prospects of Ginger Production increased production and the resulting benefit. As a result, Ginger is grown as cash crop in Ethiopia i.e. it is farmers (58.3%) also believe that more market exported to outside markets. The suitable soil type, opportunities will be emerging in the future and guarantee temperature or agro climatic condition gives the highest the comparative advantage of producing ginger (Table, 3). advantage for the study area to grow ginger. In addition, The Table also depicts that farmers (72.5%) expect the the emergence of new market opportunities (e.g. Middle current infrastructure development mainly that of road will East countries like Yemen, Dubai, etc.) will encourage give them advantage to safely move their produce to Copyright © 2017 IJRIES, All right reserved 32 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375 markets. Angela were even planning to practice a precipitation form The existence of a cooperative Union named as Damota of irrigation. Thus, 66.7 % of the sample farmers in the Wolayta Farmers’ Cooperative Union will also help them kebele responded that expanding irrigation practice will take higher advantage in accessing better markets. really give them high production advantage. The X2-test Accordingly, sample farmers (48.3 %) are hopeful of also tells the relation to be real as .04 is significant at 5.0 strengthening such cooperative institutions in that it will % significance level. Given the current demand and the make them gain opportunity more than ever (Table, 3). resulting gain from ginger production and marketing, the Thus, the cooperatives will take fresh ginger at agreed government is advised to formulate effective and workable price from farmers then, further process it (i.e. machine policy (e.g. of license and taxes) that govern the drying) and sell quality ginger at better prices. Further, commodity. Consequently, the efficiency of ginger farmers also seem that they have arranged themselves to production and trade will be improved thereby increasing expand irrigation practices as 41.7 % responded it to be an the comparative advantage of the commodity for the inspiring issue in the area. For instance, personal producers as well as traders. communication has shown that some farmers in Kindo

Table 3. Prospects of Ginger production (% of farmers) N=36 N=25 N=24 N=23 N=12 N=120 Ajora Bedaye Mole Ose Angela Total x2 Government policy emphasis 52.8 72.0 54.2 56.5 41.7 56.7 .4 Infrastructure development 77.8 68.0 62.5 78.3 75.0 72.5 .7 Expansion of irrigation 41.7 20.0 37.5 56.5 66.7 41.7 .04** New market opportunities 61.1 64.0 62.5 34.8 75.0 58.3 .12 Strengthening saving institutions 61.1 52.0 41.7 34.8 41.7 48.3 .31 (e.g. Cooperatives) Note: **= significant at 5.0 % significance level. Source:”Own survey, 2016/17”

3.3. Factors Affecting the Supply of Ginger (SxHH), formal education of household head (EdHH), The determinants of the marketable surplus of ginger access to cedit (ACr), and access to market information hypothesized were summarized in (Table 4). Thus, out of (MI) are dummy variables where as the rest 11 variables the 14 independent variables which affect the supply of as shown in the table are continuous variables. ginger, 4 were dummy variables: sex of household head

Table 4. Description of dependent and explanatory variables used in the Multiple Linear Regression Model Variable Description Type Value GMQ Quantity of ginger supplied to market Continuous Amount of ginger sold (qt) EGP Experience of ginger production Continuous Number of years SxHH Sex of household head Dummy 1= male, 0= female AgHH Age of household head Continuous Number of years EdHH Formal education of household head Dummy 1= yes, 0= otherwise FS Family size of household Continuous Adult equivalent LvStck Number of livestock Continuous TLU (exclude oxen) Ox Number of ox owned Continuous Number of ox SA Total land cultivated Continuous Hectares of land AIS Amount of introduced variety seed Continuous Quintals of seed LAb Labor involved in ginger production Continuous Number of labor ACr Access to credit Dummy 1= yes, 0= otherwise ExVis Extension visit Continuous Number of extension visit MI Access to market information Dummy 1= yes, 0= otherwise Incothr Any other income Continuous Income in thousands (ETB) exclude that of ginger Source:”Own survey, 2016/17”

3.3.1. Results of the Multiple Linear Regression found to be significant (Table 5). Before fitting the Model (MLRM) significant variables in to the model multicollinearity After running the multiple linear regressions, three among the explanatory variables and hetroscedasticity variables, namely number of livestock (LvStck), amount were checked. Thus, variance inflation factor (VIF) was of improved seed (AIS), and other income (Incothr) were used to check the multicollinearity among continuous Copyright © 2017 IJRIES, All right reserved 33 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375 explanatory variables whereas the contingency coefficient by 11.9 qt. The rationale is that if farmers spend the was used to see the dummy variables association. And, the borrowed money in ginger production activities like land result shows that there was no severe multicollinearity preparation, planting, weeding, harvesting, the production problem as VIF was below 10 for all variables. The process goes faster. This in turn ends in improved contingency coefficient also shows there was no production and increased marketable surplus. significant association between the dummy explanatory Number of extension visits (ExVis): As expected, variables. Similarly, the Breuch-Pagan/Cook-Weisbrg test extension visit positively affected the amount of ginger of hetroscedasticity suggested that the problem was supplied to market. A marginal increase in number of insignificant (Appendix 6). extension visit increased the amount of ginger supplied to Land Size (SA): This was the cultivated land allocated market by 0.5 qt. This implies that extension visit for ginger (ha) in the season (in 2010). As hypothesized so improves the households’ intellectual capital, which also far, it has positively affected the amount of ginger improves ginger production and diverts it to market. supplied to market. Thus, a unit increase (i.e. one ha Education of the household head (EdHH): Formal increase) in land allocated for ginger, would give rise to education of the household head positively affected the 11.1 qt increase in the amount of ginger supplied to dependent variable as hypothesized before. Thus, formal market. This shows that if farmers allocate more area of education increased the amount of ginger supplied to land for ginger, the amount produced of ginger will likely market by 0.7 qt. This implies that formal education increase as a result of which the marketable amount also usefully develops the household awareness level as a increases. result of which they increase production and supply more Experience in Ginger production (EGP): As to market. hypothesized earlier, the variable is positively related to Number of livestock owned (LvStck): Livestock owned amount of ginger supplied to market. A marginal increase (exclude ox) also positively and significantly (at 5.0 % in years of ginger production, would led a 1.1 qt increase significance level) affected the dependent variable. Thus, a in the amount ginger supplied to market. The implication marginal increase in number of livestock (TLU) increased is that experienced households are more productive than the amount of ginger sold by 7.9 qt. The rationale is that those non experienced households as a result of which an increased livestock obviously increases soil fertility by they contributed more quintals of ginger to market. contributing manures. Improved fertility of land in turn Amount of Introduced Seed utilized (AIS): As results in increased production of ginger thereby increased hypothesized before, introduced variety seed utilized marketable surplus. Moreover, households owning dairy positively and significantly (at 1.0 % significance level) cows might sell some dairy products (e.g. butter) and buy affected the amount of ginger supplied to market. Thus, a inputs like ginger seed there by increase ginger production one quintal increase of introduced variety seed increased and its marketable surplus. the amount of ginger supplied to market by 2.2 qt. The Family size (FS): Family size has negatively affected inference is that the introduced variety seed is by far the dependent variable. As shown in (Table 32), a productive than the local variety seed as consequence of marginal increase in family member (adult equivalent), which farmers who utilized more of it increased the reduced the volume of ginger sold by 3.7 qt. The logic is marketable surplus accordingly. that the households’ food requirement increases with the Labor involved ( LAb): Labor involved in ginger increasing family member. Hence, satisfying the family production is positively related to the amount of ginger food consumption requirement becomes the first choice supplied to market. Thus, a one or marginal increase in among other decisions. Therefore, the family gives a due labor itself resulted in 7.9 qt increase of ginger supplied to emphasis for production of food crops (e.g. maize, sweet market. The implication is that increased number of labor potato, wheat, etc.) as a result of which they allocate more in ginger production increases its production. If the area of land for these crops and less for ginger. number of labor involved in ginger production increased, Age of household head (AgHH): As hypothesized, age ginger production activities (e.g. land preparation, of household positively affected the amount of ginger sold. weeding and harvesting, etc) would be accelerated. This Thus, a year increase in the age gives rise to 1.1 qt might end in improved production and the resulting increase of ginger sold. The principle is that aged increased marketable surplus of ginger. households have wider experience which helps them Sex of household head (SxHH): As hypothesized earlier, produce large and sell more. it is male headed family which supply more ginger to Access to Market information (MI): Access to market market than female headed family. Accordingly, being information has positively affected the amount of ginger male headed family gives rise to 6.5 qt increase in the supplied to market. Thus, making market information amount of ginger supplied to market. The justification is accessible for farmers on average increased the volume of that male headed family would contributed more labor ginger sold by 4.5 qt. This implies that if farmers get thereby increased production and marketable surplus of adequate, consistent, and timely price information they ginger. will adjust their production accordingly and supply Access to credit (ACr): As was expected, the credit sufficient amount of ginger to market. access is related to positively to the amount of ginger Other income (Incothr): Other income, in this sense, is supplied to market. The regression result shows that access income obtained from other agricultural and non to credit increases the amount of ginger supplied to market agricultural activities. Other agricultural activities refer to

Copyright © 2017 IJRIES, All right reserved 34 International Journal of Research and Innovations in Earth Science Volume 4, Issue 2, ISSN (Online) : 2394-1375 all agricultural activities excluding ginger production. 4.2. Policy Implications And, non agricultural activities include off farm activities, Based on the findings of this study, the following policy daily laborer, petty trade, etc. Moreover, income from inferences can be suggested as the production and supply some other sources like in the form of remittance is also of ginger to market should be promoted: included in this category. Other income has negatively and I. The results of regression model suggests that, significantly (at 1.0 % significance level) affected the building household assets, in general, and livestock amount of ginger sold. Thus, a 1,000 birr increase in other holding, in particular, will likely help increase income reduced the amount of ginger sold by 0.52. The productivity and production of ginger thereby boost rationale is that other income and income from ginger act its marketable surplus. Since livestock holding is as substitute to each other. In other words each 1,000 birr another important variable influenced (positively) fund from other sources lead farmers curtail ginger the volume of ginger supplied, a considerable production and reduce marketable surplus with the emphasis should be given to wards building the marginal rate of substitution to be 0.52. farmers’ ability of owning livestock (e.g. making credit access etc.). Table 5. Results of the Multiple Linear Regression Model II. Since income from other sources (exclude ginger) Variables Coefficient Std.Error T-value has negatively affected the quantity supplied of EGP 1.1 .8 1.3 ginger, farmers should make compare and contrast SxHH 6.5 8.1 0.8 between income from ginger sale and that from AgHH 1.1 1.0 1.2 other activities (exclude ginger). By doing so, they EdHH 0.7 1.2 0.6 can easily identify the best side and decide FS -3.7 3.5 -1.1 accordingly. Once again, the role of extension LvStck 7.9 3.3 2.4** agents and other concerned bodies in teaching and Ox 0.1 9.6 0.01 directing the farmers is vital. SA 11.1 12.2 0.9 AIS 2.2 0.6 3.5*** REFERENCE LAb 7.9 4.9 1.6 ACr 11.9 8.0 1.5 [1] Abbott, J.C, J.P Markeham,1981. Agricultural Economics and ExVis 0.5 0.7 0.8 Marketing Tropics. Intermediate Tropical Agricultural Series, MI 4.5 8.9 0.5 Longman, UK. 1:40. [2] Abrham Shumbulo, 2008. Evaluation of Ginger (Zingiber Incothr -0.52 0.3 1.73* Officinale Rosc) Accessions for yield And Oeleoresin in -cons -31.1 30.6 -1.1 Southern Ethiopia. MSc thesis presented to School of Graduate Note: *= significant at 1 %, **= at 5 %, and ***=10 % Studies of the Awassa University. 22p. significance level. [3] Amemiya, T., 1985. Advanced Econometrics. T.J. Press Padstow Ltd., Great Britain. 205p. 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AUTHOR'S PROFILE

Taye Buke Ashango, was born in Sorei Hombba kebele, Boloso sorie woreda of Wolayta Zone on February 23, 1975. He attended his primary, secondary and high school in Dubbo Catholic mission, Areka and Bodditi schools respectively until 1994. He joined Awassa College of Agriculture in 1995 and graduated with B.Sc. Degree in Plant Production and Dry Land Farming in 1999. He then served as senior expert in different positions for Office of Agriculture in Kacha Bira woreda of Kambata Tambaro zone for three years. And then he was appointed as head of woreda / district Agriculture and Rural Development Coordination office and worked there until he joined Hawassa University for his M.Sc study. He joined Hawassa Copyright © 2017 IJRIES, All right reserved 36