Ibrahim Elnour Ibrahim1,Mohammed Oman Hassan2

Economic analysis of sorghum production and productivity in Habila Mechanized Rain-fed Sector, South Kordofan State- Sudan

Ibrahim Elnour Ibrahim1,Mohammed Oman Hassan2

1, University of Kordofan, Faculty of Natural Resources and Environmental Studies, Department of Agricultural Economics and Agribusiness, Elobeid-Sudan.

2, University of Kordofan, Institute of Gum Arabic Research and Desertification Studies.Elobeid-Sudan.

*Corresponding author: Ibrahim Elnour Ibrahim

Email:

Abstract

The study examined the economic analysis of sorghum production and productivity in Hapial mechanized rainfed sector, South Kordofan State, Sudan. The specificobjective of this study was to evaluate the economics of sorghum production productivity, identify the socio-economic characteristics of the respondent,estimate the cost of production, gross margin, revenue, and input-output relationship. Primary data were obtained by aquestionnaire schedule. A total of 200farmers engaged in sorghum production were selected randomly. Datacollated were analyzed using suitable statistical tools such as descriptive statistics, partial budget analysis and multiple regression analysis.

Results from the analyses revealed that majority of the farmers, married, male, andhave the ability to read and write.The gross margin analysis showed that on average, the farm had attained a profit from sorghum production and productivity. The regression analysis showed that variable inputs such as educational level, family size, cultivated area, clearance process, land preparation, and harvesting cost, labor, weeding cost, sowing cost significantly affect sorghum production in the study area while, age of farmers, gender, and seeds quantity show not significant relationship to sorghum output.

Keyword: Sorghum, gross margin, regression, Habila scheme

1. Introduction:

Sorghum originated in northern Africa and has spread to many tropical and subtropical regions of the world. It can tolerate poor soils and thanks to some unique features of its anatomy, resist drought. Sorghum plants have a very large root-to-leaf surface area. The leaves have a waxy cuticle for protection and under water stress the leaf margins roll up to reduce transpiration. Plants will go into dormancy if the stress is too great. This makes sorghum a very important crop for millions of poor farmers around the world. Sorghum is the 5th most widely grown crop in the world. The largest area of sorghum production is in India, followed by Nigeria, Sudan and Niger. Fifty three percent of the world’s production area is located in sub-Saharan Africa. In sub-Saharan Africa sorghum covers the 2nd largest area after maize. Behind the USA, Nigeria is the second largest producer; Sudan and Ethiopia are 5th and 8th, respectively. In Western and Central Africa the area devoted to sorghum has more than doubled since the 1970s, but yields have not grown at the same rate. In the same period, the production area in Eastern and Southern Africa expanded by about 40%, although yields have also not followed at the same rate in these regions (FAOSTAT, 2010).

Sorghum is the fourth most important world cereals crop following wheat, rice, and maize. It is a staple food in the drier part of Africa, china and India ( Zalkuwi et al , 2013) and Robert et al , 2013). Sorghum is a cereal grain crop mostly grown in Africa, Asia and Central America, primarily to ease food insecurity. It is the world's fifth largest grain crop and Africa's second most important in terms of tonnage.

Sorghum is mostly grown in semi-arid or subtropical regions of the world due to its resistance to harsh droughts and long dry spells during the rainy season are a common)] It is reported that the effect of drought is more pronounced in the Semi-Arid Tropics (SAT), where rainfall is generally low, erratic and poorly distributed,[13]Sorghum has a short duration (typically three to four months from planting to harvest), and can be grown in a wide range of soil types feature ( Dennes, 1990).

Sorghum is the staple diet for most Sudanese. It is the staple in the rural areas in the central zone, being only challenged for supremacy on the sandy qoz lands of western Sudan by dukhn, and among urban areas it is challenged in some of the large towns where wheat bread is consumed. Furthermore, dura production takes up more land than any other crop.The importance of dura in Sudan can hardly be overstressed. Ever since the establishment of the Anglo - Egyptian Condominium in 1898 it has been considered a strategic crop by all governments, and expansion of production and the maintenance of a sufficient supply to meet the needs of a rising population has been a primary consideration in all government policy and action in the agricultural sector. The most important single development, however, took place after the Second World War when the mechanical crop production schemes (MCPSs) were started in Gadaref (Ibrahim, 2007).

2. Methodology

2.1 The Study Area

The pen plain blocks are rectangular (Habila), isometric east of (Delami), or elongated side by side in the manner of piano keys (north and east of Dilling, south of Jebel Umm Heitan). In 1968 the government, with the objective of increasing crop production and putting an end to the periodic famines resulting from shortages of dura in western and southern Sudan, started a mechanized crop-production programme in the Nuba Mountains region similar in many respects to that already applied in Blue Nile and Kassala provinces. The programme started in the Habila area with schemes covering some 200,000 feddans under the Mechanized Farming Corporation (MFC). At the same time a number of unofficial schemes were developed in the same area. The MFC provides these and its own schemes with improved seeds, machinery, and maintenance services in exchange for payment. Today there are some 375,000 feddans under official planned schemes. Of these, privately operated schemes under the MFC occupy some 200,000 feddans (53 per cent of the total area) and are the most active. The main criterion for allotment of a scheme is proof of the acquisition of enough capital to purchase the necessary farming equipment and to cover the running costs of the agricultural operations. The result has been the concentration of these schemes in the hands of a small minority of traders and wealthy people, usually from outside the region. Most of these schemes are run through hired resident agents (or wakils), (Thimm, 1979).

"Directed" schemes started in 1973 with credit financed by the World Bank. The loans from the Bank are provided to the MFC, which then distributes them to farmers. Beside loans for clearing the land, the corporation provides each farmer with a tractor, a disc, and a trailer if available. The repayment period for loans extends over five years, while land clearance loan repayment extends over ten years. Schemes are allotted 1,500 feddans and are to be cropped on the basis that one third will be left fallow.The co-operative sector is similar to the directed sector from the point of view of services. The only difference is that in the assignment of lands, priority is always given to co-operative schemes.The general services that are provided for these different schemes include hafir (water reservoir) excavation, machinery maintenance, harvesters, seeds, general necessary information about varieties of seeds, sowing dates and harvesting times, and also help in resolving conflicts between individual farmers and between the farmers and nomads.

The objective of Habila scheme:

(a)For settlement of nomads in western Sudan.

(b)Generate significant private employment opportunities.

(c) Successful in involving local people in the development process.

2.2 Data Collection and Sampling Techniques

In this study primary and secondary data were used. The primary data are collected from a field survey by using structural questionnaire for 200 respondents distributed randomly due to socio-economic characteristic and homogeneity of Habila scheme population, in addition to secondary data were collected from relevant sources.

2.3 Data Analysis

2.3.1 Descriptive Statistics

The descriptive statistics employed includes frequencies and percentages ratios. This was used to analyze the socio-economic characteristics of the farmer as well as the constraints associated with sorghum production. This tool was used to achieve objective number.

2.3.2 Gross margin analysis

Gross margin analysis is by definition the different between the gross farm income and total variable cost (Olukosi and Erhabor, 1988). Normally, gross margin analysis is used to test the effects of changes that do not alter the fixed cost of production, especially the cost of land and other durable factors. It is used to determine the potential profitability and effect on farmer’s farm income. It has the advantage of being simple as well as useful in the analysis of the profitability of small farms that have small fixed costs (Samm, 2009).

The gross margin analysis was estimated from costs and returns in sorghum production. The tool was used to achieve objective (ii).

Gross margin model is expressed as follows:

GM = TR – TVC (1)

Where:

GM = Gross margin (SDG)

TR = Total revenue or total value of output from the sorghum enterprise (SD).

TVC = Total variable cost or the costs that are specific in producing (sorghum) output (SDG).

2.3.3 Regression analysis:

A general linear model involving p independent variables is

y = β0+ β1X1 + β2X2 +……+ βPXP+ ε (2)

Where:

Y = total output (dependant variable)

X1,X2... and Xp, is the independent variables for which data have been collected).

β0, = intercept (a constant of regression model

β1, β2, … and βp= Parameters should be estimated

ε = Disturbance random error

Regression Analysis Model Building

Y = β0+ β1X1+ β2X2+ β3X3+ … +β15X15+ ε (3)

Where:

Y = sorghum total output (sack)

X1 = Age; X2= Gender;X3= Educational level; X4 = Family size

X5 = Cultivated area in feddan; X6= First cleaning processing;X7 = Land preparation

X8 = Sowing cost; X9 = Weeding cost; X10= Labor; X11 = Seeds andX12 = Harvesting cost

3. Results& Discussion:

3.1 Socio – economic characteristics of the farmers Household members:

As shown in table(1), the socio-economic characteristics of the respondents revealed that majority of household heads (97%) were males of total respondent’s distribution in the Habila scheme. Sorghum production seems to be a male dominated activity in the study area. The male domination of sorghum farming may in turn be due to highdemands of time and efforts required to work in such enterprise. This agrees with the study of Baiyegunhi and Fraser, (2009).

The age of the farmers ranged between 22 and 67 years and the mean age was 44.08 years. This implies that majority of the farmers were in the active age that can make positive contribution to agricultural production. Age structure is one of the factors that are used to distinguish the farming systems. Siddig (1999) cited that a farmer’s age is one of his demographic characteristic which influences the quality of his decision and his attitude toward accepting new ideas.

Most respondents (90%) were married. This contributed widely to the use of family labor by the households as the wives and children constituted the labor force.

The survey showed that the majority of the farmers 31% (Table 1) have attained secondary education; this level of education indicates that the farmers level of awareness and their abilities to take decisions on how and what to produce, and adopting new agricultural technologies, and manage inputs.

The farmers' household size ranged between 1 and 31 members with mean of 10 members. A large household size also means more mouth to feed, such that for a given farm size large households could produce a smaller market surplus (Minot et al, 2006). However, in traditional agriculture, the larger the household size the more labor force is available for farm activities.

As shown in the (table 2), the farm size of sorghum was ranged between 7 and 3660 feddan with mean farm size of 218.44 feddan and standard deviation of 410 feddan. The average size of cultivated area and non-productive area is (211.22 and 7.2) feddan respectively. The larger the farm size, the higher the tendency of diversification of crop production thus leading to production for home consumption and for sale (Minot, 1999). In addition to that the average production and productivity of sorghum in Habila is (417 and 2) sacks respectively.

Table (1): Descriptive statistics of farmer's Socio-economic characteristics in Habila scheme

Number / %
Gender
Male / 194 / 97.0
Female / 6 / 3.0
Total / 200 / 100.0
Educational level
Illiterate / 16 / 8.0
Khalwa / 56 / 28.0
Primary / 54 / 27.0
Secondary / 62 / 31.0
University / 12 / 6.0
Total / 200 / 100.0
Marital status
Married / 180 / 90.0
Single / 18 / 9.0
Divorce / 2 / 1.0
Total / 200 / 100.0
Unit / Minimum / Maximum / Mean / Std. Deviation
Age / Year / 22.0 / 67.0 / 42.02 / 10.876
Family size / Person / 1.0 / 31.0 / 10 / 7
Number of male / Person / 1.0 / 20.0 / 4.98 / 4.01
Number of female / Person / .00 / 17.0 / 4.91 / 3.429

Sources (field survey by author)

Table (2): Sorghum statistics: (Production and Productivity)/Sacks, (Cultivated area and Non-productive area)/feddan in Habila Scheme.

Unit / Minimum / Maximum / Mean / Std. Deviation
Area / Fed / 7.0 / 3660.0 / 218.44 / 410.97
Cultivated area / Fed / 3.5 / 3660.0 / 211.22 / 410.52
Non-productive area / Fed / 0.0 / 262.5 / 7.198 / 29.72
Production / Sack / 5.0 / 4000.0 / 416.91 / 710.25
Productivity / Sack / 0.23 / 5.71 / 1.95 / 0.95

Sources (field survey by author)

3.2 Gross margin analysis results

The partial budget used to decide the most profitable production methodsout of several alternatives. When we constructup a budget, the expected feddan under crops are then evaluated and theproductivity and production are estimated. Then we calculated revenueand costs and finally the gross margin is estimated by subtracting the totalvariables costs from revenue. If the budget is estimated for generalsystem of farming, then we predict which system is more profitable(Hala, 2003).

As shown in table 3, the different variable cost were calculated per feddan basis for the sorghum, the partial budget analysis result revealed that the total variable cost, total revenue and gross income for sorghum productivity (706.69, 895.54 and 188.84) SDG respectively. While on average the total variable cost, total revenue and gross income for sorghum production (149268.6, 189155.2 and 39886.57) SDG, respectively.

Table (3):Sorghum Gross Margin (for feddan and harvested area) in SDG

Productivity / Production
Items / Mean / Std. Deviation / Mean / Std. Deviation
Land preparation / 60.71 / 113.01 / 12823.65 / 23869.99
Sowing / 60.72 / 113.01 / 12825.42 / 3869.01
Rre-sowing / 9.71 / 28.07 / 2050.43 / 5928.28
First weeding / 53.74 / 124.51 / 11350.81 / 26299.83
Second weeding / 15.39 / 62.42 / 3251.97 / 13183.52
Labor / 29.13 / 41.54 / 6153.72 / 8774.05
Pesticides / 4.20 / 14.96 / 887.95 / 3159.923
Seeds / 24.76 / 47.69 / 5229.62 / 10072.045
Harvesting / 404.91 / 643.48 / 85525.87 / 135916.809
Taxes / 22.17 / 98.41 / 4681.80 / 20787.2003
Zakat / 21.25 / 54.38 / 4487.40 / 11486.97
Total variable cost/SDG / 706.69 / 1341.48 / 149268.6 / 283347.6
Productio/Sacks / 1.97 / 3.36 / 416.91 / 710.25
Farm Gets Price/Sacks / 450
Revenue/SDG / 895.54 / 1527.99 / 189155.2 / 322743.14
Gross Income / 188.84 / 186.52 / 39886.57 / 39395.5

3.3 Estimation of Sorghum production function

The results of regression analysis in table 4 showed that the f-value for model for this analysis was 217.56 and was significant at the 1% level of significant; this indicates that the model explanatory variables have good fitness for the underlying data for sorghum output and supporting the hypothesis that at least one of the coefficients is not different from zero and could not be rejected at the 1 % level of significance compared to the tabulated F-value.

The estimated R2 value of the model was 0.975 and adjusted R2 value was 0.97. this result indicates that about 97 percent of the variation in farm level of sorghum production was attributed to the hypothesized variables X1, X2, X3, … and X12.

However, from hypothesized variables, the majority of variable was significant except three variables (age, gender and seeds quantity).

The estimated coefficient of educational level and family size is positive and significant at (1% and 5%) level of significant, respectively, which implies that sorghum production increase with the increase in education and family member of farm operators.The implication is that farmers with more education schooling tend to be more efficient in crop production, because education enhance the ability of farmers to make good use of information about production inputs and acquire technical knowledge,this finding agrees with comparable findings (Himayatullah, 2011)

The cultivated area has positive and significant at 1 percent which implies that an increase in cultivated area increase sorghum production, on other an increase in cultivated area by one feddan lead to increase in total production by 1.26 sacks . this result support the finding of Kindie (2007) and finding of Ahmed (2004).

The estimated coefficient factor (cleaning process and land preparation cost) showed positive and significant at 1 percent.

The sowing and weeding cost variable showed negative and significant at 1% level of significant, negatively significant it means that any increase in sowing and weeding cost which decrease sorghum output. This negative significant of sowing and weeding parameters may reflect that the farmers of scheme may face shortage in labors, insufficient credit, high cost of sowing and weeding.The coefficient of harvesting is positive significant at 1% level of significant

Table (4): Estimation of Sorghum production function

Variables / Coefficient / Standard Error / t-values
(Constant) / -174.31 / 148.24 / -1.18
Age / -0.569- / 1.391 / -0.41
Gender / 18.68 / 73.069 / 0.256
Educational level / 38.28*** / 12.412 / 3.084
Family size / 5.76** / 2.580 / 2.232
Cultivated area / 1.26*** / 0.491 / 2.572
First cleaning processing / 0.001*** / 0.000 / 3.624
Land preparation / 0.001*** / 0.000 / 3.358
Sowing / -0.001*** / 0.000 / -3.29
Weeding / -0.002*** / 0.000 / -7.519
Labor / -0.001** / 0.000 / -1.77
Seeds / -29.66 / 31.826 / -0.932
Harvesting cost / 0.000*** / 0.000 / 7.424
R2 / 0.975
Adjusted R2 / 0..970
f-value / 217.556
N = 100
*** significant at 1 per cent level
** significant at 5 per cent level

4. Conclusion

Sorghum has become the rural population's main cereal food. It is estimated that at least half of the grain consumed by farmers in central Kordofan is sorghum imported from mechanized rainfed enterprise in South Kordofan state.

The study assesses and evaluates the economic analysis of sorghum production and productivity in Habila scheme and specifically measures the profitability of sorghum and the relationships between different variable inputs and sorghum output. Sorghum production was found to be viable and profitable and the majority of variable inputs significant.