Emperical Evidence Orange Farmers in Muheza District, Tanga Region

Full Length Research Paper

Determinants of famers’ income in Tanzania: emperical evidence orange farmers in Muheza district, Tanga region

Makorere Robert* and Mbiha Emmanuel

Department of Agricultural Economics, Sokoine University of Agriculture, Tanzania.

Accepted 25 November, 2012

This paper assesses the critical factors that determine farmers’ income in Muheza district, Tanzania. A total of 152 respondents were selected through a simple random sampling technique and interviewed through administered questionnaire. Results show that at average every smallholder orange farmer statistically found to earn net farm income of TZS 1,464,940 generated out of orange farming enterprise. Empirically, the findings also show that through the use of stepwise multiple regressions, four critical factors were found to be the main determinant factors influencing net cash income out of the twelve examined. These are X4 (farm size), X9 (Number of orange trees at the farm produces oranges), X10 (Market prices earned), and X11 (Farm output/yield). In all twelve cases examined four variables together account for about 52% of the total variance in income of farmers within a given year. Appropriate policy recommendations are provided to improve farmers’ income in the study area and nationwide as well.

Key words: Orange, smallholder farmers, sources of cash income generation, net cash income.

INTRODUCTION

Smallholder farmers generate their income mainly by the health services and that limed income was allocated to sale of agricultural products (especially food crops) farm inputs. livestock and livestock by-products, non-agricultural The capitalization of farmers’ income in Tanzania has activities (off-farm employment, hand-craft items, local always been problematic. This is because most of the brew, charcoal and petty trading and remittance and gifts rural farmers do not keep records and most of them are from their relatives and friends (Collier et al., 1996; FAO/ not literate. Meanwhile, the Tanzanian government has Kilimo, 1995, Hella and Yona, 1999). A study conducted been trying to alleviate farmers’ problems through various by Oberoi and Singh (1980) in state of Punjab indicated programmes. Despite all these development efforts, the that the remittance raised the average income of the rural farmer is still regarded as poor. The basic questions households of out-migrants by 31% and the relative effect still remain: “what is the average income of the rural of remittances proved to be much greater on the poor farmer especially orange producers? What is the households than better-off households. On the other production level of the rural farmers in the study area? hand migration of people from rural to urban areas affects Are there some notable factors that can be isolated as the income of rural areas. However, various studies have critical determining the rural farmers’ income?” These established that large proportion of income generated is and other questions would be the bases of this study. allocated to family expenses such as food, education and The aim of this study therefore is to assess critical determinant factors affecting orange farmers’ income and to examine the varied factors that determine income differentiation among the rural orange farmers in the *Corresponding author: E-mail: [email protected]. study area. The agricultural sector is the largest sector of Tel: +255 (0) 232604380/ +255784 235089. Fax: +255 (0) 23 the district’s economy, employing over 70% of the adult 2604382 Robert and Emmanuel 7

labour force. The sector impacts on many aspects of A pilot survey to pre-test data collection instruments and to gain development in the district. Apart from striving to meet familiarization with the study areas was conducted in three villages namely Songa, Misozwe and Mamboleo. Using a closed and open- the food needs of the citizen, the agricultural sector ended administered questionnaire, data was then collected on demo- impacts strongly on the needs of the people and the graphic and socio-economic characteristics; number and names of overall quality of life of the people. At the same time, oranges varieties produced, farmers’ preferences for certain varieties, agricultural production and productivity depend largely on main reasons for selected orange varieties), production practice, orange output, volume of orange sold, production cost per output and average the quality of land and sustainable practices. selling price per orange produced in the agricultural season of 2010. Consequently, there is a need to make agriculture Questionnaires were administered by two trained enumerators together economically viable by seeking a balance between with the researcher from May, 2010 to December, 2010 as part of the research for PhD study. efficient and productive agricultural enterprise and environmental protection and sustainability (Olawepo, Analytical framework 2010). The study area is also characterized with some relative problems which are typical of a Tanzanian rural The data collected were analyzed through the use of descriptive statistics using percentages and cross tabulation as means of setting. The farmers here are saddled with problems explaining the outcomes of findings. The stepwise multiple regression associated with income generation and their access to analysis was also adopted in the analysis of data collected to measure fund, fruit processing police issues, transportation and identify the strength of the factors that strongly influence farmers’ problems and a host of others. The farmers’ income in income among smallholder farmers (Olawepo, 2010). The stepwise multiple regression model affords a well structured linear combination of this study is used as a major tool for the isolation of basic the various factors affecting income gaps among orange farmers factors that ought to be accorded priority in subsequent (Ugwamba, 2010, 2011).These variables were chosen based on past development policy. This will also help in the realization studies in similar interactions relating to farmers’ income. In this wise, of government efforts to improve orange production as the dependent variable is the farmers’ net income (income from orange farming activity) while twelve variables were selected as the well as orange farm income generation in the district in independent variables. Our regression equation would thus be: general. Y = b0 + b1 x1 + ... bn xn + e

MATERIALS AND METHODS Where Y is farmers’ income, X 1 to X 10 are the determinant factors that influence net cash income, whilst “b 0” refers to slope of equation and “e” The study was carried out in Muheza District, one of the districts in refers to stochastic error tem respectively; And b 1 to b11 refer to Tanga region, in Tanzania Mainland. It is the largest orange producer in determinant of coefficients. These twelve independent variables are: X 1- Tanga region (Makange, 2009; Mwanakatwe, 2006; Erick, 2008), that is Age of farmer, X 2 – Experience of farmer, X 3- Gender of farmer, X 4- why the area was chosen for the study. Muheza District lies south and Farm size, X 5- Farm distance (Distance to market), X 6-education of west of Tanga district and is bordered by Mkinga to the north, Pangani farmer, X 7- Cash credit earned, X 8- Cost of agricultural inputs and in the south and Korogwe district in the west. Muheza District has a total implements, X9- Number of orange trees produces oranges, X 10 - Market 2 2 area of 1,974 km and arable land covers 1,145 km (Muheza District price earned, X 11 - Farm outputs/yield. Report, 2009). The multivariate analysis of the multiple regression version was used The dominant climate in Tanga Region is warm and wet. It is found to predict the factors that influence smallholder farmers’ net income in along the coast and in the inland. Generally, the region experiences two the study area. However, it is hypothesized that smallholder farmers’ major rainfall seasons, with long rains between March and May and net income of the surveyed farmers was influenced by farm size, farm short rains between October and December. experience, farm yield, market prices received, farm family labor size, number of orange trees in the farm produce oranges, farm distance, credit received, farmers’ education, market information and age of a Sampling and data collection farmer. These variables were analyzed using the multiple regression analytical tool in determine factors influence orange farmers’ net income Data collected was purposively selected from 13 villages based on the in the study area.A lot of problems were uncounted during data volume of orange production namely Kwa-bada, Mtindiro, Mkuzi, Mindu, collection and visitation to the study area. Some of these problems Ngomeni, Bwembela, Songa, Kwa-Mhamba, Kivindo and Kwa-Lubuji, serve as limitation to the validity of these discussions. However, efforts Misozwe, Kicheba and Mamboleo villages. In those villages, were made to make the data more reliable and free of biases. The respondents were randomly selected from farmers’ meeting called by human nature of the data required constituted major problems, some ward extension officers (WEOs) because in some villages there was no respondents saw some enquiries about their income and social village extension officer. The WEOs were informed at least a day prior conditions as invasions of privacy. Most of rural residents were to the visit, and they were requested to call for smallholder orange suspicious when it comes to asking questions concern farmer’s income farmers’ meeting on the day of the visit. In total, 152 farmers were and social life. Secondly, lack of physical record keeping. The included in this study. However, the proportion of women who showed respondents were subjected to responses to questions on the subject up to the meetings was relatively small. This phenomenon is not matter to memory recall. The implication of this is that the findings of uncommon for it has been well documented that the gender division of this study depend largely on the accuracy of the data used for the study. labour which allocates all childcare, household activities and water and However, we (researcher and enumerators) secured the co-operation of wood carrying to women, constraints the capacity of women to the community leaders and district horticulturalist in various studied participate in market based production irrespective of opportunities area and this helped us to generate very reliable data at the long run. (Kaaria et al., 2007; World Bank, 2009). Secondly, purposively sampling was used to select key informants of the study. Key informants were selected basing on their positions in the district, wards and villages. The RESULTS AND DISCUSSIONS key informants were District Agriculture and Livestock Development Officers (DALDO), Extension officers, Village Chairperson and Ward Orange sub-sector in Muheza district Executive Officer). Purposive sampling is recommended when sample elements and locations are chosen to fulfill criteria or characteristics or have attributes relevant to the study. In Muheza, orange trees were first planted in Muheza 8 Sky. J. Agric. Res.

Table 1. Categorization of orange farmers.

Categorization Farm scale Frequency Percentage Small scale farm 0.4 - 2 ha 111 73 Medium scale farm 2.2 ha – 6ha 35 23 Large scale farm 6.2 ha and above 6 4

Source: Surveyed Data (2010).

Table 2. Average statistics of the farmers (n = 152).

Variable Mean Value Farmers’ age 48.07 Years Farmers’ gender (116 Males and 36 Females) 0.76 Male and 0.24 Female Farmers’ education level 7.27 Years Farming experience 9.81 Years Farm size (area cultivate and produces oranges) 4 (Acres) Farm output/yield (Quantity of oranges harvested) 79,964 Quantities Farmers’ household size 6 Numbers Farm cost of inputs 136,960.59 TZS Farm family labor 5 Man-days Non-farm family labor (Hired labor) 1 Man-days Net farmers’ income(Income from orange being sold): (Table 4.27: ANP:18.32 1,464,940.5/= TZS per Year 2010 TZS per quantity)

Source: Surveyed Data (2010).

district in the early 1900s by Anglican Missionaries at Smallholder farmers Magila mission and then spread in the neighborhood with rapid expansion to other village such us Potwe (Potwe In Muheza district, orange farmers are divided into three ward), Semungano (Kilulu ward) and Tanga town. The main orange farm scale such the small scale farm (0.4 - propagation of oranges was mainly achieved during the 2ha); medium size farm (2 – 6 ha); and large scale farmer period 1930 to 1940 due to the presence of a nursery run with a farm size scale of more than 6 ha cropping with by Mlingano Sisal Research Station near Muheza. This is orange fruits trees. Table 1 shows categories of orange accompanied by plants of different varieties and other farmers in Tanzania. citrus species produced by the station by vegetative Explicit the statistic result shows that most of the orange propagation were distributed to farmers free of charge. farmers in the surveyed were owned small farm scale of The general opinion among scholars is that orange an average 5 acres (2 ha) followed by medium scale 35 production in the district grew to be of major economical (23%) farmers and 6 (4%) farmers hold large scale farm. importance during the late 1970’s (Kikuu, 2002). In Despite there are great number of small scale farmers in addition, it’s important to note that the orange industry the surveyed area, there is currently a development of within Muheza is still experiencing tremendous growth to medium and large scale units orange farms, which is date. considered an indicator of the positive trend in In Muheza district, normally, oranges are produced development of orange plantations in the surveyed area. throughout the year. Every four months oranges flower and two months later the fruits ripen which gives two seasons per year. The production season is between Social- economic statistic of the respondents May to November with peak season between May and August. The period from September to November is low A summary of the socio-economic statistics of the season, while December to March is regarded as a respondents is shown in Table 2. period of orange scarcity. Several orange varieties grow The result indicated that, on the average, a typical orange in Muheza district. They are Valencia early and Valencia farmer was 48.07 adult years, followed by 0.76 males late (Msasa), Mediterranean sweet (Nairobi), Jaffa, and 0.24 females found in orange farming activity, Washington and Matombo sweet. attained 7.27 years of education (i.e., primary education) Robert and Emmanuel 9

and gained about 9.81 years of experience in orange farm size are also the higher income earner especially in farming activity. The mean for farmers’ household size, Muheza district. This is because the higher the farm farm family labor and hired labor were 6 numbers, 5 Man- size/area the higher the income of farmers, all things days and 1 Man-days respectively. Furthermore, a typical being equal. X 11 (Farm output/yield) was appeared to be farmer had an average farm area of 4 acres, spent a positively important determinant factor influence farmers’ mean value of TZS. 136,960.59/= on farm inputs, in order income among farmers with a joint correlation of 0.615 to produce an average of 79.964 oranges and and co efficient determination of 0.378. This also means consequently earn mean net farmers’ income of TZS. that only 2.2% variation of income is explained by this 1,464,940.5/=. variable. It also means that about 37.8 of the joint variance in income determination is jointly explained by the two variables X 4 and X 11 . One can thus infer that the Critical determinants of farmers’ net cash income higher the quantity of production and higher scale of production, the less the net cost of produce, the higher Having discussed fully on the production of farmers within the variation in income. This is evident that farmers with a farming season and incomes accruing to the different high yield are also the higher income earners especially categories of the producers, efforts were made by the in the study area. This is because the higher the farm principal researcher to assess the critical factors that yields the higher the income of farmers, all things being determine farmers’ net cash income in the study area. equal. This may however be affected by other market Eleven variables were selected as determinants of conditions and environmental factors. Such market variation in income as earlier discussed. A stepwise factors may be the prevailing prices of commodities per multiple regression analysis was carried out. Through piece and the market schedule within a specified period. stepwise multiple regression analysis, four independent The market price earned (X 10 ) also appeared to be factors were found to be the main determinant factors important determinant factor of income generation among that influence farmers’ net cash income in the study area farmers with a joint correlation of 0.705 and co efficient out of twelve examined. These were: farm size (X 4), farm determination of 0.497 This also means that only 10.9% output/yield (X 11 ), market prices received (X 10), and variation of income is explained by this variable. It also number of orange trees in the farm produces oranges means that about 49.7% of the joint variance in income (X 9). A dependent variable was farmers’ income. determination is jointly explained by the two variables X 11 The dependent variable (Y) is the farmers’ income and X 10 . One can further infer that the higher the market accrued from orange trade while variables X 1 to X 12 are price earned, the higher the variation in income relatively. the independent variables. The multiple regressions on Likely, number of orange trees produce oranges at the Table 3 suggest several findings. In all 11 cases, 4 of the farm (X 11 ) has also related well to the variation in farm variables were found to be significant at the specified income with a joint correlation of 0.721 and a coefficient tolerant level of 0.50 entries into the model. These are determination of 0.520, and a joint contribution of 52% by farm size (X 4), farm output/yield (X 11 ), market prices the three variables. This indicates also that about 3.3% received (X 10 ) and number of orange trees in the farm additional variation in total farm income is explained by produces oranges (X 9). high number of orange trees produce oranges at the farm. It could be inferred that higher orange trees produce oranges improve income of the farmers. Impact of orange farming sub-sector on farmers’ net In line with, altogether four predictors examined cash income account for about 52% of the variation in farmers’ income; the remaining 48% would be accounted for by It is hypothesized that farm cash income of the survey those predictors not included in the model or error in farmers was influenced by certain variables including research. This is coefficient of determinant (R2) = 52%. farm area/ size (X 4), farm output per quantity(X 11 ), market The adjusted R2 = 72.1%, this value express that the price earned(X 10 ), and (X 9) number of orange trees model generalized results by 72.1%, this is only where produce oranges at the farm. These variables were the model was derived from the population rather than a analyzed using the stepwise multiple regression method sample. Furthermore, the study tested whether the to find their effects on farm net cash income. The result is assumption of independent error was tenable using shown in Table 3. Durbin- Watson statistic. In the linear equation result of From the findings, it is shown that X4 (farm size) in a Durbin-Watson (DW) statistic was 1.810. The Durbin particular year is probably the best predictor of farmers’ Watson statistical value is approximately equal to 2, income. The simple correlation co-efficient of this variable which signifying the absence of serious correlations is 0.507 and co-efficient determination of 35.6. This between independent errors in the model. However, the indicates that about 35.6% of the variance is associated explanatory liner regression model can thus be written as with variation in the total income of a farmer within a farming season. This is evident that farmers with high Y = - 2.454 + 1.980X 4 + 4.417X 11 + 1.163X 10 + 1849X 9 10 Sky. J. Agric. Res.

Table 3. Impact of socio-economic characteristics of respondents on net cash income.

Independent Variables Co efficient Standardize T-ratio Significant Deviation Constant -2.454 465070.496 -5.277 .009** Farm size (X4) 1.980 91157.231 2.172 .000** Farm output/yield (X11) 4.417 2.020 2.186 .031* Market prices received (X10) 1.163 18901.649 6.154 .030* Number of orange trees (X9) 1849.903 696.876 2.655 .000**

F-statistic 39.8% R 50.7% R2 52% Adjusted R2 72.1% Durbin-Watson statistic 1.810

Source: Surveyed Data (2011): * Significant at 5% degree. ** Significant at 1% degree R refers to joint coefficient; R2 refers to coefficient of determinant.

R2= 52% carried out to assess factors that explain the variation of farmers’ income in a farming season. Result shows that This means when there are improvements in both the four factors were found to be the main determinants of farm area/ size (X 4), farm output per quantity(X 11 ), market farmer’s income improvement out of the twelve price earned(X 10 ), and number of orange trees produce examined. These are X4 (farm size/area), X 9 (Number of oranges at the farm(X 9), farmers well being and income trees produces oranges at the farm), X 10 (Market price will not only be stable but will increase significantly. earned), and X 11 (farm output/yield per quantity). In all twelve cases examined four variables together account for about 52% of the total variance in income of farmers Extra sources of net cash income among orange within a given year. farmers In order to improve net cash income of smallholder orange farmers, the following recommendations are Most farmers have different sources of income. For suggested. The government should encourage the instance, the overall mean from oranges is 1,986,899.67 development of local industries that will process oranges TZS, which accounts for 54.3% of the variation in total at the rural areas; this will reduce the spoilage of farm farmers’ income for the year. Evident shows orange produce. Similarly, government should have a sound farmers differ income level because they have differ also policy that will make capital and credit facilities more in sources of income such formal employment, business accessible to the local producers. The development and enterprise, sales of livestock, non-orange crops, cash formation of more cooperative societies among the credit received and, retirement benefits source of income. people will also enhance increase income among the It is observed the difference in income among orange farmers. smallholder orange farmers caused by those different sources of income opportunities. Specifically, the evident shows that orange farming ACKNOWLEDGEMENTS accounts for 54.3% variation in total farmers’ income for the year, followed by employment reward counts for The authors would like to thank the Mzumbe University 9.2%, retirement benefits counts for 7.8%, business for funding this study. We authors would like to thank Mr. counts for 6.7%, other crops account for 6.1%, cash James Titiba Wanjara, Ms. Nahida Mbwana and other credit received counts for 5.2% and livestock account for research enumerators for their assistance in the 4.1% variation in total farmers’ income. This implies that collection of data. farmers’ income differ from one to another because they don’t have the same sources of income. REFERENCES

Collier P, Radwan SW (1992). Labour and Poverty in Rural Tanzania”, CONCLUSIONS AND RECOMMENDATIONS Ujamaa and Rural Development, Tanzania. pp. 15-20 Erick I (2008). Jimbo la Muheza Linaongoza Kwa Kilimo cha Explicitly the study has examined the critical determinant Machungwa Nchini, Daily factors influence smallholder farmers’ net income in the Newspaper, 02 June, 2008, Tanzania. p.3 FAO/Kilimo (1995). Identification for social economic constrains for study area. A stepwise multiple regression analysis was Robert and Emmanuel 11

agriculture production, Sokoine University of Agriculture, Morogoro, State of Nigeria, American-Eurasian J. Agric. & Environ. Sci., 8(1): 1 - 6. Tanzania. pp. 18-21 Ugwumba COA (2011). Analysis of Catfish Farming System and its Hella JP, Yona G (1999). Cash Income Generation and Expenditure Impact on Net Farm Income in Anambra State of Nigeria, ARPN J. of Allocation Characteristics in Smallholder Farming System: Case Agric. and Biol. Sci., 6(2): 26 - 30. Study of Mufindi District”, Agricultura Productivity and Sustainability in World Bank (2009). Foreign Aid, Grassroots Participation and Poverty Developing Countries: Strategies, Achievements and Constraints, Alleviation in Tanzania”, A World Bank Country Study on Agriculture Agricultural Economics Society of Tanzania, Morogoro, Tanzania, 2: in Tanzania since 1968, Tanzania. 30 - 35 Makange A (2009). Muheza Walilia Kiwanda cha Matunda”, Tanga, Nipashe Newspaper, 4 June, 2009, Tanzania. p. 4 Mwanakatwe T (2006). Tatizo la Soko la Matunda Tanga, Lawadidimiza Wakulima, Tanzania, Nipashe Newspaper, p.3 Muheza District Council Profile (2008). Agricultural Sector Development Projects, Government Printer, Dar-Es-Salaam, Tanzania. pp. 15 - 20 Muheza District Report (2009), Taarifa ya Mipango na Mikakati ya Utekelezaji wa Zao la Michungwa katika Wilaya ya Muheza, Muheza, Tanga, p. 4 Oberoi H, Singh A (1980). Migration”, remittances and rural development, The Free Press, Punjab, India, pp. 20-25. Olawepo RA (2010), “Determining rural farmers’ income: A rural Nigeria experience”, Available at: http:// www.academicjournlas.org/JASD (Accessed 4 May 2011). Ugwumba COA (2010). Integrated Farming System and its Effect on Farm Cash Income in Awka South Agricultural Zone of Anambra