NAF
International Working Paper Series
Year 2014
paper n. 14/5
Description and optimization of sedentary
production system (Semi-mechanize) in
Nuba Mountains, Western Sudan
Ahmed M. Murakah , Khalid Hamdan and Fadelmola M. Elnour
Agricultural Research Corporation (ARC)
Sudan, Elobied Research Station
The online version of this article can be found at:
http://economia.unipv.it/naf/
Scientific Board
Maria Sassi (Editor) - University of Pavia
Johann Kirsten (Co-editor)- University of Pretoria
Gero Carletto - The World Bank
Piero Conforti - Food and Agriculture Organization of the United Nations
Marco Cavalcante - United Nations World Food Programme
Luc de Haese - Gent University
Stefano Farolfi - Cirad - Joint Research Unit G-Eau University of Pretoria
Ilaria Firmian -IFAD
Mohamed Babekir Elgali – University of Gezira
Luca Mantovan – Dire Dawa University
Firmino G. Mucavele - Universidade Eduardo Mondlane
Michele Nardella - International Cocoa Organization
Nick Vink - University of Stellenbosch
Alessandro Zanotta - Delegation of the European Commission to Zambia
Copyright @ Sassi Maria ed.
Pavia -IT
[email protected]
ISBN 978-88-96189-21-4
Description and optimization of sedentary production system (Semi-mechanize) in Nuba
Mountains, Western Sudan
Ahmed M. Murakah , Khalid Hamdan and Fadelmola M. Elnour
Agricultural Research Corporation (ARC). Sudan, Elobied Research Station
Corresponding Author’s Email: [email protected] Tel: +249912688261
ABSTRACT
This study was carried out in Nuba Mountains seasons (2008-2009). The main objectives
of the study are, to describe, analyze and identify possible optimization for the sedentary
production systems (Semi-mechanize). And to identify the socio-economic factors that
affecting the level of production. Primary data were collected using structured
questionnaire. A representative sample size of 50 farmers was selected using multi-stage
clustered design. Group discussions using guidelines questions were also used to augment
information collection. Secondary data were collected from the relevant institutional
sources. Descriptive statistics, linear regression analysis (Cob-Douglas function) and
linear programming (LP) were applied to analyze the data. From the linear regression the
results showed that (land, labor, capital) have positive effectiveness with different
significant. The linear programming results also revealed that Dura and Sesame entered
the optimal solution.
Key words: sedentary production system, Semi-mechanize, socio-economic factors,
linear regression analysis and linear programming.
Introduction
The Nuba mountains area is inhabited by more than sixty different tribes of different
origins and accordingly different livelihood sources and strategies exist. Agriculture in
Nuba Mountains constitutes the main source of livelihood for the majority and is the
base for any current and future economic growth in the region. There are several
patterns of agricultural production in the Nuba Mountains and even different from
other part of the country i.e. the home garden) jubraka ,(which are principally used for
growing early maturing field crops to meet household consumption needs before the
normal crop maturation time. The other two sub-systems are the traditional production
and the semi-mechanized one .The semi-mechanized farming system is distinguished by
its small or middle area ;in addition to farmers in this sector often use the traditional
tools and use of mechanization, at least in land preparation and threshing of produce .
Also grow definite crops including (Dura, cowpea, sesame ,Cotton ,groundnuts ,(Annual
report, 2007) .
Material and methods
This study which was carried out in Nuba Mountains area covers most of Southern
Kordofan state, and lies between longitudes 29 – 31 ° and latitudes 10 - 12 ° north. Clay
soils dominate in the Nuba Mountains and rocky lands as well. There is also gardud soil
in the flat plains of the area as well as the black cotton soils.
In this study primary data were collected using structured questionnaire. A
representative sample size of 50 farmers was selected using multi-stage clustered
design, A Group discussions using guideline or check list were also used to augment
information collection. Multiple visits were organized to collect information in short
duration, for both the administration of the questionnaire and filling gaps for group
discussions. The primary data covered resource utilization and production
characterization, mainly basic information about the socio-economic characterization of
Nuba farmers. And the secondary data were collected from the relevant institutional
sources. In order to achieving the research objectives the study used descriptive
statistics measures along with Statistical test regression analysis and linear
programming models.
Result and discussion:
Socio-economic characteristics
According to the World Bank 2001 the gender structure of farm households as referred
to socially constructed roles, learned behaviors and expectations associated with
females and males. It includes the ways in which those differences, whether real or
perceived have been valued, used and relied upon to classify women and men and to
assign roles and expectations to them. The survey results table (1) showed that the
majority of farmers (78%) in Nuba Mountains were males, reflecting that women
farmers were involved in nearby farm (Jubraka) because of their other reproductive
engagement.
Siddig, (1999) stated that farmers’ age is one of the demographic characteristic which
influences the quality of decision and his attitude toward accepting new ideas. Table (1)
showed that the highest percentage of the farmers in the survey was found within the
productive age of 40-65 year.
On the other hand farmer educa tion in general can be defined as accumulation of
knowledge and experience to prepare an individual farm (Ahmed, 1996; Siddig, 1999).
From the survey two- third of farmers has attended primary and secondary school
education (table 1). This will enable them to make the better choice for their resources
allocation, understand the technical packages and addressing the farm problems and
ultimately reduce the expected risk and increase farm output and income.
According to (Richard, et al., 2002) the Raining season lasts four months from June to
September with two intermediate month of uncertain rainfall, May and October. From
table (2) the land preparations for agriculture in Nuba Mountains, in semi-mechanize
were performed in June (38%) while cultivation date started in July (64%). The results
reveal that 60.7 % of the farmers use local tools for lands preparation, while 22% and
1.3 % use tractor and animal drawn implements respectively. In addition to that most
(77.9%) of the farmers use traditional way for seeding, weed control and threshing. Also
70.7 % of farmers use local seed table (3).
Land tenure
The “tenure” is used to signify the relationship between tenant and land or as common
law systems, to the legal regime in which land is owned by an individual. This respect all
private owner are either its tenants or sub-tenants (Suleman, 1998). From the survey
most (70%) of Nuba Mountain farmers table (4) owns farm land, while the remaining
(30) are either heritage, renting, gift and share land.
Production problems
The survey results revealed that 41.2% of problem came from pests and 23% from
challenge between cattle tenders and farmers while 18% from diseases and only 17.8%
from
scarcity
of
rainfall
as
depicted
in
table
(5),
Also table (6) revealed that 87.8% of farmers have no extension service In spite of
technical information and new innovation on the field of agriculture. Also the study
indicated that 66% of farmers have no available seeds.
Crops productivity
Table (7) showed that the productivity of Dura, sesame, groundnut, cowpea and cotton
were 319.5, 103.7, 283.5, 147, 178 kg/feddan respectively.
Table (1): Socio-economic characteristics of farmers’
Percentage (%)
Items
1. Gender
78
Male
22
Female
2. Age (years)
5.5
Less than 15
31
15-40
45.5
40-65
18
65 and above
3. Educational level
22
Illiterate
17.3
Khalwa
30
Primary
22.7
Secondary
8
University
Source: field survey 2008-2009
Table (2): Land preparation and sowing date
Sowing
August
July
June
May
16%
64%
18%
2%
Source: field survey2008-2009
July
10%
June
38%
land preparation
May
April march
36%
14%
2%
Month
Table (3): Technical packages, seed types and sources, weeding and harvest
land preparation
Local +tractor disc
Animal tract
Tractor disc
Local tools
16%
1.3%
22%
60.7%
Way of cultivation seeds
Hand + tractor disc
By tractor disc
By hand
7.3%
14.7%
78%
Varity of seed
Improved + local
Local
Improved
8%
70.7%
21.3%
Source of seeds
organization
Market
Farm
8%
27%
65%
Weeding method
Tractor disc
By herbicides
By traditional tools
8%
1.3%
90.7%
Way of threshing
Hand+ harvester
By harvester
By hand
15%
20%
65%
Source: field survey 2008-2009
Table (4): Distribution (%) of land tenure system and agricultural type soil to all sub
systems
Land tenure
participation
Rent
Gift
Heritage
Own
1.3%
8.7%
7.3%
12.7%
70%
Table (5): farmer's perception to production problems
%
Items
41.2%
Pests
18%
Diseases
23%
Conflicts over resources
17.8%
Low rainfall
Source: field survey 2008-2009
Table (6): farmer's access to extension and seed services
%
Items
Extension service:
11.2%
Access
87.8%
Not access
Available of seed varieties:
44%
Available
66%
Not available
Source: field survey 2008-2009
Table (7): Average cultivated areas (feddan), production (kg), productivity (kg/feddan),
value of production (SDG), labour (man days) and total cost of production:
Cotton
Cowpea
groundnut
Sesame
dura
Crop
7
4.5
2.6
13.8
22.7
Area
1246
662.25
742.5
1423.5
7254
Production
178
147
283.5
103.66
319.5
Productivity
Mean
production
(SDG)
1220
625.5
728.7
2128.9
4272.9
31.67
9.33
26.8
15.1
39.26
Labor man
days
732
386
244.5
977.45
1880.63
Production
Cost(SDG)
388
239.5
484.2
1131.5
2392.2
Source: field survey 2007
Where, GM = Gross margin, SDG= Sudanese Genih.
GM(SDG)
Regression models
In this study, to address the nature of relationship between dependent and
independent variables and level of influences, the Cobb-Douglas production function
was chosen and has been transformed from non-linear to linear after taking the natural
logrithium of both sides.
The general form of the equation is written as:
Y = AXa1 Xb2 Xc3 eu
Where y = output (dependent variable) of all activities of dura, sesame, cowpea, maize
and okra in monetary term, A = intercept, X1 = land in fedaan, X2 = labour in man-days,
X3 = capital in Sudanese genih SDG (independent variables) and u is the error term, a, b,
c are regression parameters. Then the transformed form is:
LNY = alnX1+blnX2 + clnX3+ u.
Regression results
Table (8) showed that the explanatory powers (R square) were high, 73% these
coefficients mean that around 73% of the explained variations in the output of all semimechanize activities are explained by variable included in the equations. Moreover, the
F-test of each equation indicates its significance so, the estimated equations can be
written as:
Y = 3.684 X10.567 X20.236 X30.358u
Or Ln y = 3.684+0.567lnX1 +0.236lnX2 + 0.358lnX3 +u
Land (feddan) influences the output by 56.7 with significantly at 0.038. This result
indicated that, when land increase by 1% output increased by 56.7% in case consistently
the others factors.
Labour (man days) influences the output by 23.6 with significantly at 0.063. This result
revealed that: when increasing the labor input by 1% increases output by 23.6% in case
consistently the others factors.
Capital (SDG) influences the output by 35.8 with significantly at 0.193. This result
revealed that: when increasing the capital input by 1% increases output by 35.8% in case
consistently the others factors.
Solving linear programming
The objective function: maximize Z
Z = ax1 + bx2 + cx3 +dx4 + ex5
Where a, b, c, d, e are coefficients of objective function
The general formula of the inequalities:
Ax1 + Bx2 + Cx3 + Dx4 + Ex5 ............≤ H
Where A, B, C, D, E are the coefficients of constraints inequalities
H is the right hand side (RHS)
Model for Semi-mechanize
By letting X1 = Dura, X2 = Sesame, X3 = Groundnut, X4 = Cowpea and X5 = Cotton, then
algebraic version of the model became:
Max Z = 2392.2X1 + 1131.5X2 + 484.2X3 + 239.5X4 + 388X5
Such that:
22.7X1 + 13.8X2 + 2.6X3 + 4.5X4 + 7X5 ≤ 50.6
39.26X1 + 15.1X2 + 26.8X3 + 9.33X4 + 31.67X5 ≤ 122.16
1880.63X1 + 977.45X2 + 244.5X3 + 386X4 + 732X5 ≤ 4220.58
And:
X1, X2, X3, X4, X5 ≥ 0
Table (9) Revealed that by the results of linear programming of semi-mechanize system
when the area that form the structure of the crops which achieve an attractive return is
23 feddan dura, 5 feddan cowpea and the rest of the space for other crops of the total
area of 50 feddan ideal. From that table five crops can be grown in semi-mechanize
system: Dura, sesame, groundnut, Cowpea and Cotton each of which has specified per
feddan requirement for land, labour and capital. Production of (22.7) Dura requires
39.26 days and 1880.63(SDG) capital cost, production of (13.8) sesame requires 15.1
days and 977.45(SDG) capital cost, production of (2.6) Groundnut requires 26.8 days and
244.5(SDG) capital cost while production of (4.5) Cowpea requires 9.33 days and
386(SDG) capital cost and production of (7) Cotton requires 31.67 days and 732 (SDG)
capital cost. A total of 50.6(feddan), 122.16(days) and 4220.58`` (SDG) are potentially
available, being the amount providing by land, labour and capital respectively.
The activity gross margins in the objective function are much profitable for each unit
(feddan) with a gross margin of 2392.2(SDG) and 1131.5(SDG) for Dura and Sesame
respectively, while Groundnut, Cotton, and Cowpea were 484.2, 388, 239.5 (SDG)
respectively.
The activity gross margins in the objective function are much profitable for each unit
(feddan) with a gross margin of 2392.2(SDG) and 1131.5(SDG) for Dura and Sesame
respectively, while Groundnut, Cotton, and Cowpea were 484.2, 388, 239.5 (SDG)
respectively.
The result of semi-mechanize revealed that the Cowpea was the most profitable one
while Dura was most profitable and the total of semi-mechanize farm it was 6415.9 SDG
table (10).
Table (8): regression results
Level of
Significance
T-value
0.000
5.068
0.038
2.203
0.063
1.962
standardized
coefficients
(Beta)
B
Explanatory
variables
3.684
Constant
0.567
0.236
Land (feddan)
Labour (man
days)
Capital (SDG)
0.193
(R2 = 73%) R square
1.343
F = 20.1
0.358
Sample size (50)
Table (9): linear programming results
Right hands
cotton
(feddan)
Cowpea
(feddan)
Groundn
ut
(feddan)
Sesame
(feddan)
Dura
(feddan)
Row name
Maximize
388
239.5
484.2
1131.5
2392.2
Objective
function
(SDG)
≤ 50.6
7
4.5
2.6
13.8
22.7
Land
(feddan)
≤ 122.16
31.67
9.33
26.8
15.1
39.26
Labour
(man days)
≤ 4220.58
732
386
244.5
977.45
1880.63
Capital
(SDG)
Sample size (50)
Table (10): optimal solution
cotton
cowpea
groundnut
sesame
Dura
Crop variety
0
3256.7
0
0
3159.2
Optimal solution
SDG = Sudanese Gen
Conclusions
The conclusions of this study were summarized in the following points:
1- The regression results showed that the explanatory power (R square) were high
coefficients and explained variations in the yield of all activities. The F-test indicates its
overall significance. From The analysis of linear programming revealed that among five
varieties three crops were entered as the optimal solution.
•
Pests and diseases highly affected production in Nuba Mountain.
3- The study found that extension services, protection services, conflict
between
farmers and cattle tenders. To solve these problems, government could provide
agricultural extension services to all farmer’s and make relationship and contact
between farmers and extension agent more cordial and suitable as a way of solving for
all this problems.
Reference
Annual report. (2007). Ministry of Agriculture and forests Southern Kordofan State
Ahmed, A. E .(1996) .Productivity and resources allocation efficiency of major field crops
in the Gezira scheme ,MSc thesis department of rural economy faculty of agriculture
university Khartoum ,sudan.
World Bank. (2001). Engendering Development Through Gender Equality in Rights,
Resources, and Voice, World Bank Policy Research Report, World Bank and Oxford
University.
Siddig, R. A. (1999). The Economics of Crop Production Under The Different Land
Tenure System in Merowe Province, M.Sc Thesis, Department of Agricultural Economic,
Faculty of agriculture, University of Khartoum, Sudan.
Suleman, M. (1998).The Nuba Mountains of Sudan, Resources, Access, Violent Conflict,
and
Identity,
Alternatives, London, United Kingdom.
Institute
for
African
Richard, k., Maturin, M., Aboubakar, N. (2002). Analysis of constraints to agricultural
.production in the Sudan-sahilian zone of Cameroon using a diagnostic survey