The Determinants of Agricultural Production and the
Optimum Cropping Pattern in the Northern State, Sudan
By
TARIG BASHIR ABDALLA
B.Sc. (Agric.) Honours
University of Khartoum
November 1989
M.Sc. (Agric. Economics)
University of Khartoum
April 1998
A thesis Submitted in Fulfillment for the Requirements of
the Degree of Ph.D. (Agric.)
Supervisor : Prof. Babiker Idris Babiker
Department of Agric. Economics,
Faculty of Agriculture,
University of Khartoum
March, 2005
21
‫‪Π‬‬
‫ت‬
‫ﺷﺎ ٍ‬
‫ﻏ ْﻴ َﺮ َﻣ ْﻌﺮُو َ‬
‫ت َو َ‬
‫ﺷﺎ ٍ‬
‫ت ﱠﻣ ْﻌﺮُو َ‬
‫ﺟ ﱠﻨ ﺎ ٍ‬
‫ﺸَﺄ َ‬
‫) َو ُه َﻮ اﱠﻟﺬِي أَﻧ َ‬
‫ن ُﻣ َﺘ ﺸَﺎ ِﺑﻬًﺎ‬
‫ن وَاﻟ ﱡﺮ ﱠﻣ ﺎ َ‬
‫ﺨ َﺘِﻠ ًﻔ ﺎ ُأ ُآُﻠ ُﻪ وَاﻟ ﱠﺰ ْﻳ ُﺘ ﻮ َ‬
‫ع ُﻣ ْ‬
‫ﻞ وَاﻟ ﱠﺰ ْر َ‬
‫ﺨ َ‬
‫وَاﻟ ﱠﻨ ْ‬
‫ﺣ ﺼَﺎ ِد ِﻩ‬
‫ﺣﻘﱠ ُﻪ َﻳ ْﻮ َم َ‬
‫ﻏ ْﻴ َﺮ ُﻣ َﺘﺸَﺎ ِﺑ ٍﻪ ُآﻠُﻮ ْا ﻣِﻦ َﺛ َﻤ ِﺮ ِﻩ ِإذَا َأ ْﺛ َﻤ َﺮ وَﺁﺗُﻮ ْا َ‬
‫َو َ‬
‫ﻦ(‬
‫ﺴ ِﺮﻓِﻴ َ‬
‫ﺤﺐﱡ ا ْﻟ ُﻤ ْ‬
‫ﻻ ُﻳ ِ‬
‫ﺴ ِﺮﻓُﻮ ْا ِإﻧﱠ ُﻪ َ‬
‫ﻻ ُﺗ ْ‬
‫َو َ‬
‫ﺻﺪق اﷲ اﻟﻌﻈﻴﻢ‬
‫)ﺳﻮرة‪ :‬اﻷﻧﻌﺎم ‪ -‬اﻵﻳﺔ‪:‬‬
‫‪(141‬‬
‫‪22‬‬
To my mother, father,
sisters and brothers.
To my wife Hagir, my
daughter Mawadda, my sons
Bashir and Mohamed and my
child Baraah
To my relatives and
friends
With ever lasting love
23
Acknowledgments
My special praise and thanks to almighty Alla, God of the World,
the most gracious, most merciful for uncountable bounties.
I would like to express my deep thanks, sincere gratitude and
indebtedness, to my supervisor Professor Babiker Idris Babiker for his
guidance, advice, patience and encouragement throughout the study.
Thanks are due to Omdurman Islamic University for the provision
of the financial support.
My thanks are also extended to all those who helped or encouraged
me in one way or another during my course of study.
Finally my deepest thanks, appreciation and gratitude to my family
for their patience, encouragement and unlimited support throughout my
study.
24
ABSTRACT
The study was conducted in the Northern State, where the farmers
live on both sides of the river banks. The environmental conditions are
suitable for growing horticultural and field crops. Most of the country's
demand for broad beans, spices and almost all the date palms production
is met by this state. In addition, the state has a comparative advantage in
wheat production.
Agricultural production in the Northern State faces problems of
high costs of production, low crop yields, low prices and accordingly low
farmer's income. The overall objective of this study is to evaluate the
farming system in the Northern State. Within this context the study
investigated the socio-economic characteristics of farmers, efficiency of
resource use, identified the constraints facing agricultural production in
the state and examined the effect of certain scenarios on farmers income,
resource use and crops mix.
The study depended mainly on primary data for the 2002/2003
agricultural season which was collected by direct interviewing of
respondents through a multistage-stratified random sampling technique,
using a structured questionnaire. The study also used secondary data
which was collected from the relevant institutional sources.
The data was subjected to both descriptive and statistical analysis.
Gross margin analysis, regression analysis using Cobb-Douglas
production function and the linear programming techniques were used.
The study showed that the largest area in Merowe locality was
cultivated by broad beans followed by wheat crop, while in Dongola
locality the two crops occupied approximately the same largest area.
Broad beans in Merowe and wheat in Dongola scored low yields and in
25
general the marginal value productivities of some resources were low
relative to their costs, which means the inefficient use of those resources.
Linear programming models showed a land use that is very
different from the current land use. The results allocated 4.0 feddans of
the available land of Merowe locality to broad beans beside the areas
restricted for tomato and onion crops. Wheat crop entered the plan at a
level just satisfying the consumption requirements. In Dongola locality
broad beans occupied its restricted area and the rest was allocated to
garlic and fennel crops. The wheat crop did not enter the plan. Land,
labour and capital are the constraining factors in Dongola locality, while
the constraining factors in Merowe locality were land and labour. The net
farm income was higher than that currently obtained by 184% and 116%
in the two localities respectively.
Many scenarios were made by developing the parameters of the
basic linear programming models to reflect a range of production options.
The scenarios reflect the effect of cost of production, prices,
productivities and adoption of improved technologies. According to the
results of these scenarios wheat could be produced on commercial basis
in Merowe locality when its present cost is reduced by 25%, when its
productivity is increased by 25% or when its present price is increased by
20%. While it could be produced on commercial basis in Dongola locality
when the present productivity is increased by 30% (i.e 11 sack/feddan)
together with 20% increase in its price or when the productivity obtained
by application of technical packages (18 sacks/feddan) is achieved.
The study recommended the use of comparative and absolute
production advantages and hence wheat is not to be produced in the state
under the present level of productivity and prices in order to make use of
tomato, onion and broad beans in Merowe locality and broad beans,
26
garlic and fennel in Dongola locality. For the production of wheat crop on
commercial basis the study recommended the improvement of its
productivity and prices. The study advised farmers to follow cropping
rotation and the lands cultivated in the summer season should not be
cultivated in the winter season of the same year and to improve the
fertility of marginal and less productive lands. The study also
recommended the improvement of extension services, credit facilities and
availability of agricultural inputs.
27
‫ﺧﻼﺻــــﺔ اﻷﻃﺮوﺣــﺔ‬
‫ﺃﺠﺭﻴﺕ ﺍﻟﺩﺭﺍﺴﺔ ﻓﻲ ﺍﻟﻭﻻﻴﺔ ﺍﻟﺸﻤﺎﻟﻴﺔ ﺤﻴﺙ ﻴﻘﻁﻥ ﺍﻟﺴﻜﺎﻥ ﻋﻠﻰ ﻀﻔﺘﻲ ﻨﻬﺭ ﺍﻟﻨﻴل‪.‬‬
‫ﺍﻟﻅﺭﻭﻑ ﺍﻟﻤﻨﺎﺨﻴﺔ ﻤﻼﺌﻤﺔ ﻟﺯﺭﺍﻋﺔ ﺍﻟﻤﺤﺎﺼﻴل ﺍﻟﺒﺴﺘﺎﻨﻴﺔ ﻭﺍﻟﺤﻘﻠﻴﺔ ﺤﻴﺙ ﺘﻠﺒﻲ ﻫﺫﻩ ﺍﻟﻭﻻﻴـﺔ‬
‫ﻤﻌﻅﻡ ﺍﺤﺘﻴﺎﺠﺎﺕ ﺍﻟﻘﻁﺭ ﻤﻥ ﻤﺤﺼﻭل ﺍﻟﻔﻭل ﺍﻟﻤﺼﺭﻱ ﻭﺍﻟﺘﻭﺍﺒل ﻭﻜـل ﺇﻨﺘـﺎﺝ ﺍﻟﺘﻤـﻭﺭ ‪،‬‬
‫ﺒﺎﻹﻀﺎﻓﺔ ﺍﻟﻰ ﺃﻥ ﺍﻟﻭﻻﻴﺔ ﻟﻬﺎ ﻤﻴﺯﺓ ﻨﺴﺒﻴﺔ ﻓﻲ ﺇﻨﺘﺎﺝ ﺍﻟﻘﻤﺢ ‪ .‬ﺘﻭﺍﺠﻪ ﺍﻹﻨﺘﺎﺝ ﺍﻟﺯﺭﺍﻋﻲ ﻓـﻲ‬
‫ﺍﻟﻭﻻﻴﺔ ﺍﻟﺸﻤﺎﻟﻴﺔ ﻤﺸﺎﻜل ﺍﺭﺘﻔﺎﻉ ﺘﻜﺎﻟﻴﻑ ﺍﻹﻨﺘﺎﺝ ‪ ،‬ﺘﺩﻨﻲ ﺍﻹﻨﺘﺎﺠﻴﺔ ‪ ،‬ﺘﺩﻨﻲ ﺍﻷﺴﻌﺎﺭ ﻭﺒﻨـﺎﺀﹰﺍ‬
‫ﻋﻠﻴﻪ ﺍﻨﺨﻔﺎﺽ ﺩﺨل ﺍﻟﻤﺯﺍﺭﻉ ‪ .‬ﺍﻟﻬﺩﻑ ﺍﻟﺭﺌﻴﺴﻲ ﻟﻬـﺫﻩ ﺍﻟﺩﺭﺍﺴـﺔ ﻫـﻭ ﺘﻘﻴـﻴﻡ ﺍﻟﺘﺭﻜﻴﺒـﺔ‬
‫ﺍﻟﻤﺤﺼﻭﻟﻴﺔ ﻟﻤﺯﺍﺭﻋﻲ ﺍﻟﻭﻻﻴﺔ‪ .‬ﻭﻀﻤﻥ ﻫﺫﺍ ﻤﻌﺭﻓﺔ ﺍﻟﺨﺼﺎﺌﺹ ﺍﻻﺠﺘﻤﺎﻋﻴﺔ ﻭﺍﻻﻗﺘـﺼﺎﺩﻴﺔ‬
‫ﻟﻠﻤﺯﺍﺭﻋﻴﻥ ‪ ،‬ﻤﻘﺎﺭﻨﺔ ﻜﻔﺎﺀﺓ ﺘﻭﺯﻴﻊ ﺍﻟﻤﻭﺍﺭﺩ ‪ ،‬ﺘﺤﺩﻴﺩ ﺍﻟﺘﺭﻜﻴﺒﺔ ﺍﻟﻤﺤﺼﻭﻟﻴﺔ ﺍﻟﻤﺜﻠﻰ ﻓﻲ ﻅـل‬
‫ﻅﺭﻭﻑ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﺍﻟﺭﺍﻫﻨﺔ ‪ ،‬ﺘﺤﺩﻴﺩ ﻤﻌﻭﻗﺎﺕ ﺍﻹﻨﺘﺎﺝ ﺍﻟﺯﺭﺍﻋﻲ ﺒﺎﻟﻭﻻﻴﺔ ‪ ،‬ﺇﺨﺘﺒـﺎﺭ ﺃﺜـﺭ‬
‫ﺒﻌﺽ ﺍﻟﺴﻴﺎﺴﺎﺕ ﻋﻠﻰ ﺩﺨﻭل ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ‪ ،‬ﺍﺴﺘﺨﺩﺍﻡ ﺍﻟﻤﻭﺍﺭﺩ ﻭﺍﻟﺘﺭﻜﻴﺒﺔ ﺍﻟﻤﺤﺼﻭﻟﻴﺔ ‪.‬‬
‫ﺍﻋﺘﻤﺩﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺒﺼﻭﺭﺓ ﺃﺴﺎﺴﻴﺔ ﻋﻠﻰ ﻤﻌﻠﻭﻤـﺎﺕ ﺃﻭﻟﻴـﺔ ﻟﻠﻤﻭﺴـﻡ ﺍﻟﺯﺭﺍﻋـﻲ‬
‫‪2003-2002‬ﻡ ﻭﺍﻟﺘﻲ ﺠﻤﻌﺕ ﻋﻥ ﻁﺭﻴﻕ ﺍﻻﺴﺘﺒﻴﺎﻥ ﺒﺎﻟﻤﻘﺎﺒﻠـﺔ ﺍﻟﺸﺨـﺼﻴﺔ ﻟﻠﻤـﺴﺘﻬﺩﻓﻴﻥ‬
‫ﺒﺈﺨﺘﻴﺎﺭ ﻋﻴﻨﺔ ﻋﺸﻭﺍﺌﻴﺔ ﻁﺒﻘﻴﺔ ﻤﺘﻌﺩﺩﺓ ‪ ،‬ﻜﻤﺎ ﺍﺴﺘﺨﺩﻤﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺃﻴﻀﺎ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻟﺜﺎﻨﻭﻴـﺔ‬
‫ﻭﺍﻟﺘﻲ ﺠﻤﻌﺕ ﻤﻥ ﻤﺼﺎﺩﺭ ﺫﺍﺕ ﺼﻠﺔ ‪.‬‬
‫ﺃﺨﻀﻌﺕ ﺍﻟﺩﺭﺍﺴﺔ ﻟﻠﺘﺤﻠﻴل ﺍﻟﻭﺼﻔﻲ ﻭﺍﻹﺤﺼﺎﺌﻲ ﺤﻴﺙ ﺘﻡ ﺍﺴﺘﺨﺩﺍﻡ ﺘﺤﻠﻴل ﺍﻟﻤﻴﺯﺍﻨﻴﺔ‬
‫ﺍﻟﻤﺯﺭﻋﻴـــﺔ ‪،‬ﺍﻟﺘﺤﻠﻴـــل ﺍﻻﺭﺘـــﺩﺍﺩﻱ ﺒﺎﺨﺘﻴـــﺎﺭ ﺩﺍﻟـــﺔ ﻜـــﻭﺏ ﺩﻭﻗـــﻼﺱ‬
‫)‪ (Cobb - Douglas‬ﻭﺃﺴﻠﻭﺏ ﺍﻟﺒﺭﻤﺠﺔ ﺍﻟﺨﻁﻴﺔ )‪.(Linear programming‬‬
‫ﺃﻭﻀﺤﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺃﻥ ﺃﻜﺒﺭ ﺍﻟﻤﺴﺎﺤﺎﺕ ﺍﻟﻤﺯﺭﻭﻋﺔ ﻓﻲ ﻤﺤﻠﻴـﺔ ﻤـﺭﻭﻱ ﺯﺭﻋـﺕ‬
‫ﺒﺎﻟﻔﻭل ﺍﻟﻤﺼﺭﻱ ﻴﻠﻴﻪ ﻤﺤﺼﻭل ﺍﻟﻘﻤﺢ ﻭﺃﻥ ﺃﻜﺒﺭ ﺍﻟﻤﺴﺎﺤﺎﺕ ﻓﻲ ﻤﺤﻠﻴﺔ ﺩﻨﻘﻼ ﺯﺭﻋﺕ ﺒﺎﻟﻘﻤﺢ‬
‫ﻭﺍﻟﻔﻭل ﺍﻟﻤﺼﺭﻱ ‪ .‬ﺍﻟﻔﻭل ﺍﻟﻤﺼﺭﻱ ﺤﻘﻕ ﺇﻨﺘﺎﺠﻴﺔ ﻤﺘﺩﻨﻴﺔ ﻓﻲ ﻤﺤﻠﻴﺔ ﻤﺭﻭﻱ ﻜﻤﺎ ﺃﻥ ﺍﻟﻘﻤـﺢ‬
‫ﻗﺩ ﺤﻘﻕ ﺇﻨﺘﺎﺠﻴﺔ ﻤﺘﺩﻨﻴﺔ ﻓﻲ ﻤﺤﻠﻴﺔ ﺩﻨﻘﻼ ‪ ،‬ﻭﻟﺫﻟﻙ ﻓﺈﻥ ﻗﻴﻤﺔ ﺍﻹﻨﺘﺎﺠﻴـﺔ ﺍﻟﺤﺩﻴـﺔ ﻟـﺒﻌﺽ‬
‫ﺍﻟﻤﻭﺍﺭﺩ ﺘﻌﺘﺒﺭ ﻤﺘﺩﻨﻴﺔ ﺒﺎﻟﻨﺴﺒﺔ ﻟﻠﺘﻜﻠﻔﺔ ﺍﻟﺤﺩﻴﺔ ﻤﻤﺎ ﻴﻌﻨﻲ ﻗﻠﺔ ﻜﻔﺎﺀﺓ ﺍﺴﺘﺨﺩﺍﻡ ﻫﺫﻩ ﺍﻟﻤﻭﺍﺭﺩ ‪.‬‬
‫ﻨﺘﺎﺌﺞ ﻨﻤﺎﺫﺝ ﺍﻟﺒﺭﻤﺠﺔ ﺍﻟﺨﻁﻴﺔ ﺍﻷﺴﺎﺴﻴﺔ ﺃﻭﻀﺤﺕ ﺍﻟﻔﺠﻭﺓ ﺍﻟﻜﺒﻴﺭﺓ ﺒـﻴﻥ ﺍﺴـﺘﺨﺩﺍﻡ‬
‫ﺍﻷﺭﺽ ﺍﻟﺫﻱ ﻴﻤﺎﺭﺴﻪ ﺍﻟﻤﺯﺍﺭﻋﻭﻥ ﻭﺍﻻﺴﺘﺨﺩﺍﻡ ﺍﻷﻤﺜل ‪.‬ﻨﺘﺎﺌﺞ ﺍﻟﻨﻤﻭﺫﺝ ﺃﻭﻀﺤﺕ ﺃﻨﻪ ﻴﺠﺏ‬
‫ﺯﺭﺍﻋﺔ ‪ 4.0‬ﻓﺩﺍﻥ ﻤﻥ ﺍﻟﻤﺴﺎﺤﺔ ﺍﻟﻤﺘﺎﺤﺔ ﻓﻲ ﻤﺤﻠﻴﺔ ﻤﺭﻭﻱ ﺒﻤﺤـﺼﻭل ﺍﻟﻔـﻭل ﺍﻟﻤـﺼﺭﻱ‬
‫ﺒﺠﺎﻨﺏ ﺍﻟﻤﺴﺎﺤﺎﺕ ﺍﻟﺘﻲ ﺨﺼﺼﺕ ﻟﻤﺤﺼﻭل ﺍﻟﻁﻤﺎﻁﻡ ﻭﺍﻟﺒﺼل ‪ .‬ﺸﻤﻠﺕ ﺍﻟﺨﻁﺔ ﻤﺤﺼﻭل‬
‫‪28‬‬
‫ﺍﻟﻘﻤﺢ ﺒﻤﺴﺘﻭﻯ ﻴﻜﻔﻲ ﻓﻘﻁ ﺍﺤﺘﻴﺎﺠﺎﺕ ﺍﻻﺴﺘﻬﻼﻙ‪ .‬ﻓﻲ ﻤﺤﻠﻴﺔ ﺩﻨﻘﻼ ﺍﺤﺘل ﺍﻟﻔﻭل ﺍﻟﻤـﺼﺭﻱ‬
‫ﺍﻟﻤﺴﺎﺤﺔ ﺍﻟﺘﻲ ﻗﻴﺩﺕ ﻟﻪ ﺒﺠﺎﻨﺏ ﻤﺤﺼﻭل ﺍﻟﺘﻭﻡ ﻭﺍﻟﺸﻤﺎﺭ ‪ ،‬ﺒﻴﻨﻤﺎ ﺘﻡ ﺍﺴﺘﺒﻌﺎﺩ ﻤﺤﺼﻭل ﺍﻟﻘﻤﺢ‪.‬‬
‫ﺍﻷﺭﺽ ‪ ،‬ﺍﻟﻌﻤﺎﻟﺔ ﻭﺭﺃﺱ ﺍﻟﻤﺎل ﻫﻲ ﻤﺤﺩﺩﺍﺕ ﺍﻹﻨﺘﺎﺝ ﻓﻲ ﻤﺤﻠﻴﺔ ﺩﻨﻘـﻼ ﺒﻴﻨﻤـﺎ ﻤﺤـﺩﺩﺍﺕ‬
‫ﺍﻹﻨﺘﺎﺝ ﻓﻲ ﻤﺤﻠﻴﺔ ﻤﺭﻭﻱ ﻫﻲ ﺍﻷﺭﺽ ﻭﺍﻟﻌﻤل ‪ .‬ﺼﺎﻓﻲ ﺩﺨل ﺍﻟﻤﺯﺍﺭﻉ ﺃﻋﻠﻰ ﻤـﻥ ﺍﻟـﺫﻱ‬
‫ﺤﻘﻕ ﻋﻤﻠﻴﹶًﺎ ﺒﻨﺴﺒﺔ ‪ 180%‬ﻭ ‪ 116%‬ﻓﻲ ﺍﻟﻤﺤﻠﻴﺘﻴﻥ ﻋﻠﻰ ﺍﻟﺘﻭﺍﻟﻲ‪.‬‬
‫ﺘﻡ ﻋﻤل ﺒﻌﺽ ﺍﻟﺴﻴﺎﺴﺎﺕ ﺒﺘﻁﻭﻴﺭ ﻤﻌﺎﻤﻼﺕ ﻨﻤﺎﺫﺝ ﺍﻟﺒﺭﻤﺠﺔ ﺍﻟﺨﻁﻴـﺔ ﺍﻷﺴﺎﺴـﻴﺔ‬
‫ﻟﻌﻜﺱ ﺨﻴﺎﺭﺍﺕ ﺍﻹﻨﺘﺎﺝ ‪ .‬ﻋﻜﺴﺕ ﺍﻟﺴﻴﺎﺴﺎﺕ ﺘﺄﺜﻴﺭ ﺘﻜﺎﻟﻴﻑ ﺍﻹﻨﺘﺎﺝ ‪ ،‬ﺍﻷﺴﻌﺎﺭ ‪ ،‬ﺍﻹﻨﺘﺎﺠﻴـﺔ‬
‫ﻭﺘﻁﺒﻴﻕ ﺍﻟﺤﺯﻡ ﺍﻟﺘﻘﻨﻴﺔ ﻋﻠﻰ ﺍﻹﻨﺘﺎﺝ ﺍﻟﻤﺤﻠﻲ ‪ .‬ﺒﻨﺎﺀﹰﺍ ﻋﻠﻰ ﻫﺫﻩ ﺍﻟﺴﻴﺎﺴﺎﺕ ﻴﻤﻜـﻥ ﺯﺭﺍﻋـﺔ‬
‫ﻤﺤﺼﻭل ﺍﻟﻘﻤﺢ ﺒﻤﺴﺘﻭﻯ ﺘﺠﺎﺭﻱ ﻓﻲ ﻤﺤﻠﻴﺔ ﻤﺭﻭﻱ ﻤﺘﻰ ﻤﺎ ﺘﻡ ﺘﺨﻔﻴﺽ ﺘﻜـﺎﻟﻴﻑ ﺍﻹﻨﺘـﺎﺝ‬
‫ﺍﻟﺤﺎﻟﻴﺔ ﺒﻨﺴﺒﺔ ‪ ، 25%‬ﻤﺘﻰ ﻤﺎ ﺯﺍﺩﺕ ﺇﻨﺘﺎﺠﻴﺘﻪ ﺍﻟﺤﺎﻟﺔ ﺒﻨـﺴﺒﺔ ‪ ، 25%‬ﻤﺘـﻰ ﻤـﺎ ﺯﺍﺩﺕ‬
‫ﺃﺴﻌﺎﺭﻩ ﺍﻟﺤﺎﻟﻴﺔ ﺒﻨﺴﺒﺔ ‪ 20%‬ﺃﻭ ﻤﺘﻰ ﻤﺎ ﺍﻨﺨﻔﻀﺕ ﺃﺴﻌﺎﺭ ﻤﺤﺼﻭل ﺍﻟﻁﻤﺎﻁﻡ ﺒﻨﺴﺒﺔ ‪.25%‬‬
‫ﺒﻴﻨﻤﺎ ﻴﻤﻜﻥ ﺯﺭﺍﻋﺘﻪ ﻓﻲ ﻤﺤﻠﻴﺔ ﺩﻨﻘﻼ ﺒﻤﺴﺘﻭﻯ ﺘﺠﺎﺭﻱ ﻤﺘﻰ ﻤﺎ ﺯﺍﺩﺕ ﺇﻨﺘﺎﺠﻴﺘﻪ ﺍﻟﺤﺎﻟﻴﺔ ﺒﻨﺴﺒﺔ‬
‫‪ 11) 30%‬ﺠﻭﺍل ‪/‬ﻓﺩﺍﻥ( ﻤﻊ ﺯﻴﺎﺩﺓ ﺴﻌﺭﻩ ﺍﻟﺤﺎﻟﻲ ﺒﻨﺴﺒﺔ ‪ 20%‬ﺃﻭ ﻤﺘﻰ ﺘﺤﻘﻘﺕ ﺇﻨﺘﺎﺠﻴـﺔ‬
‫ﺘﻁﺒﻴﻕ ﺍﻟﺤﺯﻡ ﺍﻟﺘﻘﻨﻴﺔ )‪ 18‬ﺠﻭﺍل ‪ /‬ﻓﺩﺍﻥ( ‪.‬‬
‫ﺃﻭﺼﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺍﻻﺴﺘﻔﺎﺩﺓ ﻤﻥ ﺍﻟﻤﻴﺯﺓ ﺍﻟﻨﺴﺒﻴﺔ ﻭﺍﻟﻤﻁﻠﻘﺔ ﻭﺫﻟﻙ ﺒﺯﺭﺍﻋﺔ ﻤﺤـﺼﻭل‬
‫ﺍﻟﻁﻤﺎﻁﻡ ‪ ،‬ﺍﻟﺒﺼل ‪ ،‬ﻭﺍﻟﻔﻭل ﺍﻟﻤﺼﺭﻱ ﻓﻲ ﻤﺤﻠﻴﺔ ﻤﺭﻭﻱ ﻭﺯﺭﺍﻋﺔ ﺍﻟﻔﻭل ﺍﻟﻤﺼﺭﻱ ‪ ،‬ﺍﻟﺘﻭﻡ‬
‫ﻭﺍﻟﺸﻤﺎﺭ ﻓﻲ ﻤﺤﻠﻴﺔ ﺩﻨﻘﻼ ‪ .‬ﻟﺯﺭﺍﻋﺔ ﻤﺤﺼﻭل ﺍﻟﻘﻤﺢ ﺒﻤﺴﺘﻭﻯ ﺘﺠﺎﺭﻱ ﺃﻭﺼـﺕ ﺍﻟﺩﺭﺍﺴـﺔ‬
‫ﺒﺘﺤﺴﻴﻥ ﺍﻹﻨﺘﺎﺠﻴﺔ ﻭﺍﻷﺴﻌﺎﺭ ‪ .‬ﻨﺼﺤﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﺒﺈﺘﺒﺎﻉ ﻨﻅﺎﻡ ﺍﻟﺩﻭﺭﺓ ﺍﻟﺯﺭﺍﻋﻴﺔ‪،‬‬
‫ﻋﺩﻡ ﺯﺭﺍﻋﺔ ﺍﻟﻤﺴﺎﺤﺎﺕ ﺍﻟﺘﻲ ﺘﺯﺭﻉ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺼﻴﻔﻲ ﻓﻲ ﻤﻭﺴﻡ ﺍﻟﺸﺘﺎﺀ ﺘﺤﺴﻴﻥ ﺨﺼﻭﺒﺔ‬
‫ﺍﻷﺭﺍﻀﻲ ﺍﻟﻬﺎﻤﺸﻴﺔ ﻭﺍﻷﺭﺍﻀﻲ ﻗﻠﻴﻠﺔ ﺍﻹﻨﺘﺎﺠﻴﺔ ‪ .‬ﺃﻭﺼﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺃﻴﻀﹰﺎ ﺒﺘﺤﺴﻴﻥ ﺨـﺩﻤﺎﺕ‬
‫ﺍﻹﺭﺸﺎﺩ ‪ ،‬ﺘﻭﻓﻴﺭ ﺍﻟﺘﻤﻭﻴل ﻟﻠﻤﺯﺍﺭﻋﻴﻥ ﻭ ﺘﻭﻓﻴﺭ ﻤﺩﺨﻼﺕ ﺍﻹﻨﺘﺎﺝ‪.‬‬
‫‪29‬‬
CONTENTS
Page No.
Chapter I: Introduction
1.1 The concept of macro and microeconomics
1.2 Contribution of agriculture to Sudan economy
1.3 Results of previous studies
1
2
6
1.4
1.5
1.6
1.7
1.8
Problem statement and justification
Objectives of the study
The hypotheses of the study
Research methodology
Organization of the thesis
Chapter II: background information
on the Northern State
Location and administrative structure
Human resources
Climate and vegetation
Types of agricultural lands
Sources of irrigation water
Agricultural schemes
Land utilization
Agricultural seasons
Fruit trees
Field crops
Animal wealth
Agricultural inputs
Seeds
Fertilizers
Pesticides
Agricultural credit
Irrigation inputs
Marketing
Storage and processing
Chapter III: The socio-economic characteristics and
farmer's income in the Northern State.
10
12
13
13
19
The general characteristics of the household head
Farmer's age
Marital status
Educational levels
Family
42
42
43
43
44
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.7.1
2.7.2
2.7.3
2.7.4
2.8
2.8.1
2.8.2
2.8.3
2.8.4
2.8.5
2.9
2.10
3.1
3.1.1
3.1.2
3.1.3
3.2
30
21
21
21
23
25
25
29
29
29
31
35
35
35
36
36
36
37
37
41
3.3
3.4
3.5
3.6
3.7
3.7.1
3.7.2
3.7.3
3.7.4
3.7.4.1
3.7.4.2
3.7.4.3
3.7.4.4
3.7.4.5
3.7.5
3.7.6
3.7.6.1
3.7.6.2
3.7.6.3
3.7.6.4
3.8
Source of irrigation
Characteristics of the private schemes
Agricultural holdings
Land tenure
Land utilization
Cropping intensity
Crops grown
Animal ownership
Agricultural operations
Land preparation
Sowing date
Weeding
Harvesting
Labour resource management
Irrigation
Input-output relationships
Seed rate
Fertilizer
The actual and the expected output
Seed varieties
Gross margin analysis
Chapter IV: Allocation efficiency analysis.
45
46
46
47
49
49
50
51
51
51
53
54
55
55
60
61
61
63
64
66
67
4.1
4.1.1
4.1.2
4.1.2.1
4.1.2.2
4.1.3
4.2
4.3
4.3.1
4.3.2
4.3.3
Theoretical framework
Introduction
Production function
General overview
Forms of the production function
Efficiency indices and optimization
The results of the production function
Allocation efficiency analysis
Land resource
Labour resource
capital resource
Chapter V: A linear programming model of a
representative farm in the Northern State.
Theoretical framework
Economic choice concepts
Linear programming model
General overview
Assumptions of linear programming
The objective function
Specification of the model structure
71
71
71
71
72
75
76
81
81
83
85
5.1
5.1.1
5.1.2
5.1.2.1
5.1.2.2
5.1.2.3
5.2
31
89
89
90
90
92
93
94
5.3
5.3.1
5.3.2
5.3.3
Empirical specification of the model
Introduction
The activity set
Constraints
Chapter VI: Results and discussion of the linear
programming model.
97
97
98
111
6.1
6.2
6.2.1
6.2.2
6.3
6.3.1
6.3.2
6.3.3
6.3.4
6.5.5
Introduction
The basic solution
The free model run
The restricted model run
Policy analysis scenarios
The effect of the cost of production
The effect of crops prices
The effect of crops productivity
The effect of crops productivity and prices
The effect of technical packages
Chapter VII: Summary, conclusions, recommendations
115
115
115
119
127
127
129
130
131
132
and policy implications
7.1
7.2
7.3
7.4
Summary
Conclusions
Recommendations
Policy implications
136
143
145
146
7.5
Limitations of the study and suggestion for further
147
research
Bibliography
148
32
LIST OF TABLES
Table
1.1 Average area and production of the major crops grown
in Sudan for the period 1999/2000 – 2001/2002
1.2 The exploited agricultural areas (fed.) and the number
of farmers in the Northern State by locality
2.1 Names and areas (fed.) of reserved forestries in the
Northern State
2.2 Names and areas (fed.) of forestries to be reserved in
the Northern State
2.3 Names and areas (fed.) of the important irrigated
basins in the Northern State
2.4 Numbers and areas (fed.) of the private schemes in the
Northern State by locality
2.5 Numbers and areas (fed.) of the companies schemes in
the Northern State by locality
2.6 Dates of construction and areas (fed.) of the
government transformed schemes in the Northern State
2.7 Numbers and areas (fed.) of the cooperative schemes in
the Northern State
2.8 Estimated number of date palms by locality
2.9 Areas (fed.) of the major field crops grown in the
Northern State during the period 1990/91-2001/02
2.10 Average productivity (sack/fed.) of the main field
crops grown in the Northern State for the period 19902002
2.11 Types and number (head) of the animals in the
Northern State by locality
2.12 Types and amount of loans ( thousand SD) provided by
the ABS to the agricultural sector in the Northern State
for the seasons 2002 and 2003
2.13 The movement of crops prices (SD) in the Northern
State during the year 2000
3.1 The general characteristics of the household heads
3.2 The general characteristics of the family in the
Northern State by locality
3.3 Source of irrigation
3.4 Characteristics of private pump schemes
3.5 The average farm size (fed.) according to land tenure in
33
Page
No.
5
14
22
23
24
26
27
27
28
30
33
34
35
36
39
43
44
45
46
47
the Northern State by locality
3.6 Cropping intensity and cropped area in the different
seasons in the Northern State by locality and scheme
type
3.7 Types of crops grown and area (percent of the total
cropped area) in the Northern State for the 2003 season
by locality and scheme type
3.8 Animal ownership
3.9a Average monthly mandays per feddan and percentage
of family and hired labour in Merowe locality
3.9b Average monthly mandays per feddan and percentage
of family and hired labour in Dongola locality
3.10a Average labour requirements (mandays per feddan) of
the different crops grown in Merowe locality by
operation
3.10b Average labour requirements (mandays per feddan) of
the different crops grown in Dongola locality by
operation
3.11 Recommended and actual number of irrigations given
to major winter crops in the Northern State (season
2002/03)
3.12 Recommended and actual number of irrigations given
to wheat and broad beans crops in companies schemes
in the Northern State (season 2002/03)
3.13 Wheat input-output per feddan actual and
recommended for the private farms in the Northern
State (season 2002/03)
3.14 Broad beans input-output per feddan actual and
recommended for the private farms in the Northern
State (season 2002/03)
3.15 The actual number of irrigations and output (sacks per
feddan) for wheat and broad beans crops in the
Northern State according to scheme type
3.16 Fennel input-output per feddan actual and
recommended in the Northern State (season 2002/03)
3.17 Onion input-output per feddan actual and
recommended in Merowe locality (season 2002/03)
3.18 Garlic input-output per feddan actual and
recommended in Dongola locality (season 2002/03)
4.1 The regression equation for the whole sample in
Merowe and Dongola localities
34
49
50
51
57
58
59
59
61
61
62
63
64
65
65
66
78
4.2 The regression equation for private and companies
schemes in Merowe locality
4.3 The regression equation for private and companies
schemes in Dongola locality
4.4 Marginal value productivities and efficiency indices of
land for the whole sample of Merowe and Dongola
localities
4.5 Marginal value productivities and efficiency indices of
land in the Northern State by locality and scheme type
4.6 Marginal value productivities and efficiency indices of
labour for the whole sample of Merowe and Dongola
localities
4.7 Marginal value productivities and efficiency indices of
labour for Merowe and Dongola localities by scheme
type
4.8 Marginal value productivities and efficiency indices of
capital for the whole sample of Merowe and Dongola
localities
4.9 Marginal value productivities and efficiency indices of
capital in the Northern State by locality and scheme
type
5.1 An LP tableu of representive farm in the Northern
State
5.2 Crop production activities in Merowe locality
5.3 Crop production activities in Dongola locality
5.4 Hiring labour activities in Merowe locality
5.5 Hiring labour activities in Dongola locality
5.6 Crop selling, consumption and buying activities in
Merowe locality
5.7 Crop selling, consumption and buying activities in
Dongola locality
5.8 Borrowing capital activities in Merowe locality
5.9 Borrowing capital activities in Dongola locality
5.10 Capital transfer activities in Merowe locality
5.11 Capital transfer activities in Dongola locality
5.12 Average number of irrigations available per month in
private schemes of the Northern State by locality
5.13 Average number of irrigations given to winter crops
per month in Merowe locality (season 2002/03)
5.14 Average number of irrigations given to winter crops
per month in Dongola locality (season 2002/03)
35
79
80
81
82
83
84
85
86
96
99
100
102
103
105
106
108
109
110
110
112
113
113
6.1 Basic solutions of the free cropping LP models in
comparison to reality
6.2 Basic solutions of the restricted cropping LP models in
comparison to reality
6. 3 Number of mandays required by month in the basic
solutions in comparison to reality
6.4 The monthly average number of irrigations required in
the basic solutions in comparison to reality
6.5 The surplus irrigation water in the basic solution of the
LP models
6.6 Marginal value productivities of credit (SD/unit) in the
basic solutions of the LP models
6.7 Net farm income (SD) and crop mix (fed.) resulting
from lowering wheat cost by 50%
6.8 The net farm income (SD) and crop mix (fed.) of the
improved technologies in comparison to basic models
116
121
123
125
125
126
128
135
ABBREVIATIONS
SD
Sudanese dinar (1 US$ = about 260 SD).
Feddan (fed.) Land area unit, equivalent to 4200 meters (One feddan =
0.42 ha).
NSMAAI
Northern State Ministry of Agriculture, Animal Wealth
and Irrigation.
AOAD
Arab Organization for Agricultural Development.
IFAD
International Fund for Agricultural Development.
ABS
Agricultural Bank of Sudan.
ASF
Agricultural Supply Fund
36
HRS
Hudeiba Research Station
37
CHAPTER I
INTRODUCTION
1.1
The concept of macro and microeconomics:
Macroeconomics focuses on the behaviour of an entire economy. It
worries about national goals as full employment, control of inflation and
economic growth, without worrying about the wellbeing or behaviour of
specific individuals or groups. The essential concern of macroeconomics
is to understand and improve the performance of the economy as a whole.
Microeconomics is concerned with the details of this big picture. It
focuses on the individual firms and government agencies that actually
comprise the large economy. The interest in microeconomics is in the
behaviour of individual economic actors, what their goals are, how can
they best achieve these goals with their limited resources and how they
respond to various incentives and opportunities.
A primary concern of macroeconomics, for example is to
determine the impact of aggregate consumer spending on total output,
employment and prices. Very little attention is devoted to the actual
content of consumer spending or its determinants. Microeconomics, on
the other hand, focuses on the specific forces (tastes, prices, incomes) that
influence those decisions.
The distinction between macro and microeconomics is also
reflected in discussions of business investment. In macroeconomics we
want to know what determines the aggregate rate of business investment
and how those expenditures influence the nations total output,
employment and prices. In microeconomics we focus on decisions of
individual businesses regarding the rate of production, the choice of
factors of production and the pricing of specific goods (Schiller, 1991).
38
The
present
study
incorporates
both
the
micro
and
macroeconomics theories. The nature of farming activities prevailing in
the region and the policies that impact them dictate this approach.
1.2 Contribution of agriculture to Sudan economy
The agricultural sector is considered one of the most important
sectors in Sudan economy. This is due to its considerable share in the
country's gross domestic product (GDP).
As a result most of the
Sudanese population depend on it for livelihood and employment. It also
provides most of the raw materials for the local industry and a large share
of exports (Bank of Sudan, 2001).
Growth rate in this sector increased from 0.8% in 2000 to 4.7% in
2001. In spite of this its share in GDP decreased from 46.4% in 2000 to
45.6% in 2001. This was due to an increase in the share of the industrial
sector in GDP from 15% to 16.6%. In 2002 the contribution of
agricultural sector to GDP increased to 46.2% and its growth rate reached
8%. This was due to increase in growth rate of the mechanized rainfed
sub sector from 5.4% to 27.1% and the increase in growth rate of the
traditional rainfed sub sector from -12.6% to 38.2%. However, the growth
rate of the irrigated agriculture decreased from 10.2% to in 2001 to 2.2%
in 2002, the growth of forestry sub sector decreased from 5% to 4% and
the growth rate of animal wealth sub sector decreased from 6% to 2.5%
(Bank of Sudan, 2001 and 2002).
Agricultural production is divided into crop production and animal
production. The main agricultural crops include cotton, gum arabic, dura,
millet, wheat, groundnuts, sesame, and sunflower.
Cotton was the main cash crop of the country for decades. But its
importance started to decline. Since the 1970s average production
dropped from 930 thousand bales to 831 thousand bales and to 432
39
thousand bales during the 1980s and 1990s respectively. It dropped
further than this at the beginning of this century. All this was due to
shrinking in areas brought under cotton cultivation, a decline in
productivity and deterioration in the infrastructure. Cotton production
increased from 275 thousand bales during the season 1999/2000 to 396
thousand bales during the season 2000/2001 (44% increase) and
decreased by 4.5% to 378 thousand bales during the season 2001/2002.
The total sales reached 372 thousand bales during 2001 of which 352.2
thousand bales were exported. The value of cotton exports contributed
about 3% of the country's exports during 2001.
Gum arabic's importance declined as a major cash crop during the
last years of the last decade. After it registered the highest production
during 1994/1995 season, about 48.1 thousand tons, production began to
drop in the following successive years until it reached 8 thousand tons in
1999/2000 season, then it increased to 15.7 thousand M.T during
2000/2001 season and to 15.9 thousand M.T during 2001/2002 (1%
increase). The exported quantities of gum arabic dropped from 24
thousand M.T. in the year 2000 to 22 thousand M.T. in 2001. The value
of exported quantities contributed about 1% of the value of country's
exports in 2001.
Dura production increased in 1999/2000 season from 2347
thousand tons to 2488 thousand tons in 2000/2001 season, i.e. an increase
of 6%, despite a reduction in the total area harvested from 10780
thousand feddans to 9988 thousand feddans, i.e. a decrease of 7.3%. This
was due to the rehabilitation of the rain-fed agricultural sector and to
partial provision of financing, in addition to the fact that the mechanized
farming areas were less affected by pests. In 2001/2002 season dura
production increased from 2488 to 4394 thousand tons (79.7% increase).
40
Millet production decreased from 499 thousand tons in1999/2000
season to 481 thousand tons in 2000/2001 season i.e. a decrease of 3.6%,
due to a reduction in the area harvested. In 2001/2002 millet production
increased from 481 thousand tons to 578 thousand tons (20% increase).
Wheat production increased from 214 thousand tons in 1999/2000
to 303 thousand tons in 2000/2001season, i.e. an increase of 41.6%. That
was due to an increase in the area harvested from 219 thousand feddans
to 286 thousand feddans, i.e. an increase of 30.6%, in addition to an
increase in productivity from 977 kg/feddan to 1094 kg/feddan. In
2001/2002 wheat production decreased from 303 thousand tons in
2000/2001 to 274 thousand tons in 2001/2002 season (18.5% decrease).
This was due to reduction in area harvested from 286 thousand feddans to
275 thousand feddans (3.8% decrease) and the reduction in productivity
from 1,094 kg/feddan in 2000/20001 to 8998 kg/feddan in 2001/2002.
Sunflower production dropped from 8 thousand tons to 4 thousand
tons in 2000/2001 season, i.e. 50% decline. That was due to a reduction
in areas harvested from 49 thousand feddans to 13 thousand feddans, a
71.9% decline.
There have been a significant development in animal production
during the last few years. Average animal production growth rate
increased from 5.7% in 2000 to 6% in the year 2001. Its share in the GDP
was 21.7%. Total estimates of animal population increased from 124
million head in the year 2000 to 128 million head in the year 2001 (Bank
of Sudan, 2001).
Livestock resource were estimated in 2002 at 39,479 thousand
head of cattle, 48,136 thousand head of sheep, 41,485 thousand head of
goats, 3,342 thousand head of camels, 606 thousand head of horses and
6,720 thousand head of donkeys and mules (AOAD, 2003).
41
The average annual area and production of the major crops grown
in Sudan for the period 1999/2000 – 2001/2002 are shown in table 1.1.
Table 1.1 Average area (thousand feddans) and production (thousand
tons) of the major crops grown in Sudan for the period 1999/2000 –
2001/2002.
Crop
Area
Production
355
350*
-
13.4
Dura
11496
3076.3
Millet
5845.3
519.3
Wheat
260
255
Groundnuts
357.8
995
Sesames
4478
295
29
5.3
Cotton
Gum Arabic
Sunflower
• Cotton production in thousand bales.
Sources: Bank of Sudan Reports, 2001 and 2002.
42
1.3 Results of previous studies in the Northern State:
The Northern State is one of the most important agricultural areas
in the Sudan, where farmers live on both sides of the river bank.
Agriculture is the main economic activity in the state. It is facilitated by
the good experience of farmers who are familiar with agricultural
practices. The climatic conditions are favourable for the growth of many
field and horticultural crops. There are three distinct agricultural seasons
in the state, the winter season, the summer season and the flood season.
In the winter season, which is the main agricultural season, wheat, broad
beans, fennel, garlic, fenugreek, onion, tomatoes and other vegetables are
grown. Agricultural production in the state is carried out by private,
companies, cooperative and expansion schemes, where production is
primarily based on irrigation by pumping from the Nile and underground
water.
Saeid (1988) in his study of Silame basin, Ibrahim (1993) in his
study of Karima Abu-Hamad area and El-awad (1994) in his study of
Rubatab area showed that wheat has a negative gross margin while broad
beans has a positive gross margin. Elfeil (1993) in his study of the
Northern State found that the horticultural crops, except mangoes,
generate very high returns relative to the field crops. However, the field
crops, wheat, broad beans and sorghum all have positive gross margins.
Osman (1996) reported that in the River Nile State broad beans is most
profitable and wheat is grown for food security purposes. Mohamed
(1996) in his study of Dongola locality concluded that all crops grown
including wheat and broad beans are profitable. Abayazeed (1999) in his
study of Dongola locality concluded that wheat is profitable to farmers.
The Nile schemes farmers were the most profitable in wheat production
than matarat farmers. Mohamed (2000) in his study of matarat farming
system in Dongola locality stated that the gross margins results revealed
43
that all crops are profitable. Garlic is the most profitable followed by
fennel, broad beans and finally wheat.
Mohamed (1996) concluded that the highest cost items for wheat
were: irrigation, land rent, cost of fertilizer and harvesting cost. While for
broad beans the highest cost items were land rent, irrigation and seed
cost. Abayazeed (1999) stated that the cost analysis showed that irrigation
cost was the main cost for wheat crop followed by harvesting cost.
Mohamed (2000) found that irrigation had the highest share in the total
cost of production of winter crops.
Elfeil (1993) using regression analysis found that land, labour and
farm income are the main factors, which significantly affect the yield of
wheat, broad beans and dates, while off-farm income affect only broad
beans. Variables like cropping period, mechanical land preparation affect
only wheat yield, and variables like the number of trees per feddan, age
of the tree, fertilizer amount and number of waterings affect only dates
yield. Ibrahim (1993) in his regression analysis found that factors which
significantly affect the yield of wheat were: the cost of irrigation, cost of
seeds, cost of fertilizer and the cost of labour. El-awad (1994) found that
wheat has a negative gross margin and the number of waterings, seed
rate, farmer age and farmers education are the main factors which
significantly affected the production of wheat. Mohamed (1996) using
regression analysis found that the factors which significantly affected the
production of wheat and broad beans were: the cost of land preparation,
number of waterings, seed rate and fertilizer rate. Hwait-Alla (1998)
stated that for wheat yield only the cost of land preparation was
statistically significant variable, while the cost of irrigation and farm
income were statistically significant variables on broad beans yield.
Mohamed (2000) using regression analysis concluded that the
number of waterings, fertilizer amount, seed rate, method of land
44
preparation, weeding number, farm income, off-farm income and
educational level were the most important factors that significantly
affected the winter crops (wheat, broad beans, fennel and garlic). The
sum of elasticities of the individual variables indicated the existence of
increasing returns to scale for all crops.
Abd Elaziz (1999) concluded that land, labour and capital are used
inefficiently at varying degrees among farming districts in the River Nile
State. Mohamed (2000) results also implied some sort of inefficiency in
resource allocation. He attributed that to the inelastic supply of inputs, the
risk-averse nature of farmers, lack of credit to purchase such inputs and
absence of extension services. As a result there is lack of knowledge
about the importance of some agricultural inputs and the recommended
rates of application required. Elfiel et al (2001) reported that although
resources appeared to be allocated efficiently there were severe
constraints on the supply of certain inputs and credit to purchase inputs
was virtually unavailable. Given such a state of inelastic supply of some
inputs, farmers preferred to minimize risk rather than maximize profit.
Abd Elaziz (1999) revealed the following resource constraints to the
agricultural production in the River Nile State:
a-The shortage of irrigation water was observed during December,
January and February. This is the period of high irrigation water
consumption because it coincides with stage of flowering and grain filling
of dura, wheat, broad beans, chick pea and potato. The main reasons of
shortage of irrigation water are:
i- limited pump size
ii- High cost and unavailability of fuel and spare parts
iii- Lack of credit
b- Credit is a constraint especially during December, January and
February. These are the months of high water consumption and as a result
45
credit is needed to purchase fuel, oil and spare parts and agricultural
input.
c- Labour is a constraint during January, March and May. These are the
months during which planting, weeding and harvesting of winter crops
are performed.
d- land is a limiting factor in Abu Hamad district and there is no problem
of land in Shendi and Eddamer districts.
Abd Elaziz (1999) using linear programming model also stated that
the comparison of current cropping patterns with optimal ones revealed
that comparative and absolute production advantages are not made use of.
A free run was examined and the results showed that wheat is not to be
produced in the three farming districts in the River Nile State due to its
low yield and the crops to be grown are: onion, broad beans, chick pea
and dura.
Faki et al (1995) employed a linear programming model to analyze
various policy options for exposing the potential of Sudan's irrigated
sector in enhancing grain production for improved food security,
following a precious contribution of rainfed production over the past
decade, stated that, the scale of wheat production is an important issue,
rather than the decision to grow it. Wheat forms the major winter activity
in irrigation schemes, in the absence of which land remains idle. There
are limits to the expansion of cotton at the expense of wheat, imposed by
many factors such as its competition with sorghum and groundnut in the
summer months and the capacities of its available markets potential.
There will be a need to utilize idle land and water resources in winter
when Summer crops other than cotton are harvested, and wheat forms the
most economic alternative. Optimal area combination of cotton and wheat
for maximum real net returns to land and water in the Gezira scheme, as
delineated with variations in the areas of summer crops using a linear
46
programming model and analyzed by a quadratic regression model,
expose complementarities between the two crops up to a maximum cotton
area of 431000 feddans. Such a situation reveals the importance of
growing wheat up to levels of about 512000 feddans without competition
with cotton.
From the stated results of previous studies the following
conclusions emerge :
i- Horticultural crops are more profitable to farmers in the Northern State
than the field crops. Within the field crops, the broad beans and the minor
crops are profitable, while the wheat crop comes at the tail if it scored
positive gross margin.
ii- The highest cost items for the production of field crops are, cost of
irrigation, land rent, cost of fertilizer, cost of seeds and harvesting cost.
iii- The resources that are constraints to the agricultural production are,
land, labour, capital and irrigation water.
iv- Land, labour and capital are used inefficiently and this is due to the
inelastic supply of inputs, lack of credit to purchase such inputs, riskaverse nature of farmers and absence of extension services.
v- Comparative and absolute production advantages are not made use of.
1.4 Problem statement and justification
The Northern State is expected to have great contribution to the
agricultural production of the country due to the following advantages:
i- The favourable climatic conditions which are suitable for the
production of most important crops. Most of the country's demand for
broad beans, spices and almost all the date palms production is met by
this state. The total wheat area cultivated in the Northern State for the
year 2001/2002 was 71160 feddans compared to 311000 feddans
cultivated in the country (i.e. 23%) and the total wheat production in the
47
state was 71160 tons compared to 363000 tons produced in the country
(20%). About 62370 feddans of the broad beans crop were cultivated in
the Northern state compared to 138095 feddans cultivated in the country
(45%) and about 53326 tons produced by the state compared to 146000
tons produced in the country (37%).
ii- Availability of irrigation water from the River Nile, in addition to the
ground water.
iii- The soil in the Northern State is reported to be highly fertile
patricianly at the banks of the River Nile and irrigation water is of
excellent quality.
iv- The farmers of the Northern State are experienced and skilled farmers.
v- Availability of improved technical recommendations for most of the
winter crops.
In addition to these advantages the construction of Merowe dam
which was already started is expected to enhance the agricultural
production in the state.
Despite the opportunities mentioned above, the Northern State
faces manifold agricultural problems which, include:
i- High cost of production which can be attributed to:
a- High cost of inputs that arises from the fact that irrigated agriculture in
the state employs imported inputs such as pumps, spare parts, lubrication
oil, fertilizers and chemicals.
b-Diseconomies of scale:
This results from the small size of holdings and the size tend to
decrease overtime due to erosion and inheritance practice.
ii- low crop yields:
Low crop yields are attributed to low levels of inputs used resulting
from high cost of these inputs and sometimes unavailability.
iii- Low incomes from agricultural production.
48
This mainly results from:
a- low crop prices:
Farmers usually sell crops at very low prices after harvest due to their
high need for cash and lack of credit, marketing and storage facilities in
the state.
b- Government policies especially taxation on inputs and outputs.
The previous studies pertaining to the economics of agricultural
production in the Northern State investigated farmer’s income using gross
margin analysis, identified the individual factors contributing to crop
yields using regression analysis and few of them tackled the problems of
efficiency of resource use. No study tried to address the questions of the
optimum cropping pattern in the state. Abdelaziz (1999) using the linear
programming techniques addressed the question of optimization for the
River Nile State, but the farming system in the two states are different.
The land resource is considered more scare in the Northern State relative
to that in the River Nile State and there is wide variation in the soil
fertility. The soils in the River Nile State on the other hand are relatively
more homogenous, in additions to the fact that agricultural activities in
the two states are different. There is thus a need for a study that would
advise farmers on the best way of allocating their resources to maximize
profit.
1.5 Objectives of the study
The overall objective of the study is to evaluate the farming system
in the state, and to formulate an optimum resource allocation cropping
system.
The specific objectives are:
1- To identify the constraints facing the agricultural production in the
state.
49
2- To compare economic efficiency of resources use between different
localities and schemes in the state.
3- To determine the optimum cropping pattern and resource allocation for
the farmers in the state through linear programming model.
4- To test the impact of certain policies on resource allocation and farm’s
income.
5- To draw some policy implications which will, hopefully, help in
improving productivity, production and farmer's income in the state.
1.6 The hypotheses
The hypotheses to be tested include:
1- Factors of production, namely, land, labour and capital are allocated
efficiently.
2- Farmers are risk takers. This influences most of their production
decisions. They, for instance, try to maximize profit rather than minimize
the risk of producing certain crops.
3- Irrigation water, credit, labour and land are not constraining the
agricultural production in the state.
4- Farmers make use of the comparative and absolute production
advantages.
5- The number of irrigations and other inputs are applied according the
amount recommended by research authorities.
6- Land preparation is adequate and the productivity of crops is high in
the state.
1.7 Research methodology
1.7.1 The survey area
According to the existing administrative structure the Northern
State is divided into 4 localities: Wadi- Halfa, Dongola, El-dabba and
50
Merowe. Each locality is divided into a number of administrative units
and each of which consists of a number of villages.
According to the population census of 1993, the population of the
state is 510569. There is big variation in the population size in the
different localities. The population of Dongola locality is about 238780
(46.7%), followed by Merowe locality where the population is about
127043 (24.9%), while the population of El-dabba and Halfa localities are
about 81986 and 62654 (16.1% and 12.3% respectively) (NSMAAI,
1999).
The total exploited land in the state is about 387805 feddans, out of
which 56% in Dongola, 19% in Merowe, and 13.76% and 11% are in Eldabba and Halfa respectively.
The total estimated number of farmers in the state is about 267191
(about 52% of the total population in the state). About 58% of them is in
Dongola, 18% in Merowe, while 12% and 11.84% are in El-dabba and
Halfa localities respectively (Table, 1.2).
Table 1.2. The exploited agricultural areas (fed.) and the number of
farmers in the state by locality
Locality
Exploited land
%
Number of
%
farmers
Dongola
217012
55.96
155477
58.19
Merowe
73547
18.96
47997
17.97
El-dabbba
53335
13.76
32077
12.01
Halfa
43911
11.33
31640
11.84
Total
387805
100
267191
100
Source: NSMAAI, 1999
51
1.7.2 Sources of data
The study depended mainly on primary data, which were collected
by direct interviewing of sampled farmers using a structured
questionnaire, conducted in 2003 season. The study also used secondary
data collected from the relevant institutional sources. The available data
and cited literature were used to describe the existing farming system in
the state.
1.7.3 Sample design and sample size
Sampling is the selection of part of an aggregate material to
represent the whole aggregate. The level of precision in sampling result
can be improved either by increasing the sample size or by stratification
of the mother population into strata to create more homogenous subsamples. Increasing the sample size is usually restricted by limited level
of resources, where stratification reduces the overall population variance
to within strata variance since each will properly be represented in the
sample. Hence, stratification is very frequently employed in sample
design because it increases homogeneity within the strata and allows the
researcher to take a small representative sample to study the population.
The sample size to be chosen, however, is a trade- off between
level of precision and resources available in terms of cost, time and
facilities.
A multistage stratified random sampling technique was used. The
area of the Northern State is very large and there are wide distances
between the different localities. Hence only two localities were selected
to represent the Northern State. Dongola locality represents the northern
part of the state, while Merowe locality represent the southern part of the
state. These two localities together contribute about 74.92% of the
exploited lands and about 76.16% of the number of farmers in the state.
52
Dongola locality consists of a number of administrative units.
These are, Karma, Argo, Elhafeer, Sarg Elneil, Rural Dongola, Dongola
city, Elgolid, and Dongola Elajoze. The administrative units of Merowe
locality on the other hand, are Merowe, Merowe city, Rural Elgureir,
Rural Nuri, Rural Karima, Rurl Elzuma, Rural Elarak and Karima city.
All these administrative units, except Karima city, were
represented in the sample. Since each administrative unit consists of a
number of villages, a random number of villages from each
administrative unit was chosen and from these villages a proportional
number of random farmers were selected. The schemes owners and
farmers were interviewed using two different questionnaires. The actual
number of farmers interviewed was 140. Dongola locality represented by
80 farmers, while the remaining 60 farmers represented Merowe locality.
The schemes owners interviewed were 46 in both localities using a
separate questionnaire.
The information included in the farmer's questionnaire included
data relating to inputs and outputs of agricultural activities. More
specifically it related to use and prices of inputs, such as land, labour,
capital, fertilizers, irrigation, chemicals ..etc. Information related to
different agricultural operations such as land preparation, planting,
weeding, irrigation and harvesting is also included. The questionnaire
also include a part concerning yields, prices, and marketing.
The scheme owners questionnaire included information about
irrigation facilities such as pump size, life span of the pump, cost of
maintenance and cost of fuel. The questionnaire also included
information on number of farmers in the scheme, areas irrigated and
working hours. (Appendices 3 and 4).
53
1.7.4 Analytical techniques
In order to achieve the stated objectives the survey data collected
were subjected to both descriptive and statistical analysis. Gross margin
analysis, Cobb-Douglas production function and the linear programming
(LP) techniques were applied. The intended LP- matrix comprised
cultivation activities of winter crops, sales activities and hiring labour
activities. The constraints on the matrix included land, irrigation water,
labour, capital and subsistence requirements. The Lp matrix was
employed to test different scenarios (sensitivity analysis). This was done
through changing the parameters of the basic run. The scenarios included
changes of cost of production, changes of products prices, changes of
crops productivities and improved production technologies. The results of
the various runs were used for testing the stated hypotheses and as
options for planners and policy makers to increase productivity,
production, and farmers income in the state. Heady and Candler (1973)
reported that, linear programming can serve as an important management
aid to individual farms or marketing firms. The method also promises to
be useful in aggregative analysis relating to regional production patterns,
interregional
competition
and
many
policy
problems.
Linear
programming has been used as a research tool by agricultural economists,
to specify profit mixes of commodities produced by marketing firms, to
specify cost minimizing methods of processing products such as fertilizer
or mixed feeds, to specify special equilibrium interregional patterns of
resource use and product specialization in agriculture, and to solve related
types of problems. It can be used in examining the effect of institutional
restrictions on optimum production patterns and resource use. Ordinarily,
the objective will be one of maximizing or minimizing quantities. The
quantity to be maximized can be profit of a farm, the quantity to be
minimized may be the cost of production. In the context of farming,
54
programming and linear programming in particular, achieve the objective
through allocation of limited resources to the best alternative course of
action to generate the maximum benefit while meeting all physical and
logical constraints imposed on the farm. The advantage of linear
programming is that it allows solving complex problems and on top
provides the value for the objective function (optimal solution).
Furthermore, the by-products of the solution provide rich information on
economic issues such as prices and average productivities.
Dent, Harison and Woodford (1986) reported that, linear
programming is one of a class of options research methods referred to as
mathematical programming. It was developed in the 1940's for use in
military options, but was subsequently found well suited to solving a
range of business and commercial planning problems. Today, it is one of
the most widely used operations research techniques for planning
purposes. The linear programming technique is a general methodology
that can be applied to a wide range of problems with the following
characteristics:
1- A range of activities are possible and the manager (farmer) can
exercise a choice in the selection of activities that he wishes to put into
operation.
2- Various constraints prevent free selection from the range of activities
and
3- A rational choice of a combination of activity levels is related to some
measures of manager's utility (for example, profit) associated with each
of the activities, that is an objective which can be quantified.
Hazel and Norton (1986) reported that, linear programming has
proved a very flexible tool for modeling complexities. However, the full
power of the approach depends on knowledge of a range of modeling
techniques.
55
The Cobb-Douglas production function was specified as a suitable
functional form for estimating parameters to be used in allocative
efficiency. Upton (1976) summarized the advantages of this function in
the following points:
i- It is easy to estimate.
ii- It resembles reality better than the linear form.
iii- Its theoretical fitness to agriculture.
iv- It is easy to understand and easy to interpret.
v- The regression coefficients immediately give the elasticities of the
product with respect to the factors of production.
vi- It permits the phenomenon of diminishing marginal returns without
using degrees of freedom.
Heady and Dillon (1961) remarked that the power function seems
to be the most appropriate, partly because the small number of degrees of
freedom involved in the estimation of multiplicative model seems
logically appropriate.
1.8 Organization of the thesis
The thesis consists of seven chapters. Chapter one: Introduction.
Chapter two gives a background information on the Northern State.
Chapter three describes the socio-economic characteristics and farmer’s
income in the Northern State. Chapter four includes a thorough
explanation of the theoretical framework, discusses the results of used
production function and analyses allocation efficiency. Chapter five
includes a thorough explanation of the theoretical framework,
specification of the model structure and empirical specification of the
linear programming model. Chapter six presents and discusses the results
56
of the basic linear programming model and discusses the results of the
different scenarios. Chapter seven presents the summary, conclusions and
the recommendations of the study.
57
CHAPTER II
BACK GROUND INFOFMATION
ON THE NORTHERN STATE
2.1 location and administrative structure
The Northern State which is lies between latitude 16 and 22°N and
between longitude 20 and 32°E is administratively divided into 4
localities, Wadi Halfa, Dongola, El-dabba and Merowe. The state
bordered by the Rebublic of Egypt to the North, the River Nile State to
the east, North Darfur State and Libia to the West and Khartoum State to
the South.
2.2 Human resources
According to the census of 1993, the population of the Northern
State is 510569. This is a relatively low population density (1.47
person/km2) compared to other parts of the country. The low population
density, which is due to the migration to other parts of the Sudan and
abroad is reflected on the unavailability of agricultural labour and
increase in the use of agricultural machinery for many agricultural
operations. Most of the population in the state live in rural areas, where
about 446,445 of the population (85.5%) live in villages. The urban
population is concentrated in Dongola, Karima, Merowe, El-dabba and
Halfa cities. The main tribes in the state are, Donogla, Shaigia, Bedairea,
Sukkoat, Mahas, Kababeesh, Hawaweer and Hassania (NSMAAI,1999).
2.3 Climate and vegetation
The Northern State lies in the arid and semi- arid climate zone,
where average annual rain-fall is less than 100mm (NSMAAI, 1996).
This climate is characterized by distinct seasonal months, where the
summer season extends from mid April till the end of September. The
58
average highest temperature during this season is 45° and the average
lowest temperature is 30°. The winter season extends from October till
the mid April and is characterized by cold to very cold climate without
rainfall.
The natural vegetation is mainly of a desert and semi- desert type.
The following types of trees, shrubs and grasses exist along the Nile
banks, seasonal valleys (wadies) and high terrace soils: Acacia
ehrenbergina, Cappris deciduas, Panicum tygidum, Tamarix aphylla,
Acacia nilotica, A. albida, A. seyal, A. tortilis, Salvadora persica,
Calatropis procera, I. Nilotica, Fageria critica, Hyphane thebaica,
Artislid sp, Capparis deciduas, Macrua crassifolia, Cyndom dactylon,
Indigofera spp., Cyperus rotudus (IFAD, 1995).
There are 14 forestry nurseries distributed in the state. Tables 2.1
and 2.2 show the names and areas of reserved forestries and forestries to
be reserved in the Northern State.
Table 2.1. Names and areas (fed.) of reserved forestries in the Northern
State.
Forestry name
Locality
Area (fed.)
Kadraka
`Dongola
150
Barakat Elmiloak
Dongola
285
Koadi
Dongola
35
Total
1785
Source: NSMAAI, 2002
59
Table 2.2. Names and areas (fed.) of the forestries to be reserved in the
Northern State
Forestry name
Locality
Area (fed.)
Eltarfa
Dongola
500
Argi
Dongola
225
Shibaika
Dongola
1100
Agabt Elhattani
500
Total
2325
Source: NSMAAI, 2002
2.4 Types of agricultural lands
The total area of the Northern State is about 347 thousand km2
(85.5 million feddans) out of which about 5% is suitable for agricultural
production. However this agricultural area is not fully utilized (AOAD,
1995).
Depending on the soil type, cultivated lands is divided into:
a- Islands and gerif lands:
These are highly fertile soils since their fertility is renewed
annually by silt deposits due to river flood. The productivity of these soils
is estimated to be about 20-25 sacks/fed, for most of the winter and
summer crops (NSMAAI, 2002).
b- River banks lands:
These are the narrow strip lands along the river banks and they are
characterized by highly fertile soils.
c- High terrace lands:
These soils are located away from the Nile. They are alkaline and
less fertile than those close to the river. These lands include most of the
expansion schemes which aim to rehabilitate wheat production in the
Northern State.
60
d- Irrigated basins:
These basins are covered annually due to river flood. The names
and areas of the important basins are shown in Table 2.3.
Table 2.3. Names and areas of the important irrigated basins in the
Northern State
Name
Area (fed.)
Elsilame basin
55000
Latti basin
6000
Argi basin
3000
Elaffad basin
3000
Elbakri basin
-
Source: NSMAAI, 2002
e- Valleys and low lands
These include wadi Elmugadum, wadi Elmalik, wadi Elkhuai, wadi
Arroup and Elgaoup low land.
Depending on the type of the ownership, lands can be divided into
three types: The first type is land registered as free holdings known as
"milik" land. The area of milik land is finite, susceptible to loss by river
washing “Hadam” and subjected to excessive fragmentation. The milik
land owner can sell it but this rarely happens because land has a very high
prestige value. The second type is "Miryi" lands adjacent to their milik
lands. This principle is known as the right of frontage. Miryi lands
include all lands which are not registered in the year 1905 and are
government lands leased under certain regulations. The third type of
lands is the hired government lands which are marginal lands of low
quality than the Miryi lands (Mohamed, 1996).
61
The average size of holding in the upper terrace lands is larger than
the lower terrace soils lands. The government lands were leased to the
farmers on the average farm size of 10 feddans.
2.5 Sources of irrigation water
The River Nile is the main source of irrigation water supply. The
Nile flow is more than adequate to supply needed quantities of irrigation
water. The River Nile water is of good quality because of the silt deposits
it brings to the field in the flood season. Beside this the research and
scientific analysis proved that there are abundant amounts of underground
water suitable for agricultural and domestic purposes. This is due to the
fact that large areas of the state lie within the Nubian sand stone aquifer
(NSMAAI, 1997). The cost of lifting underground water is extremely
high relative to the cost of lifting the surface water. The underground
water quality is sometimes doubtful and is by no way comparable to the
Nile water (Elfeil, 1993).
2.6 Agricultural schemes
Agricultural production in the state is carried out by agricultural
schemes of which the following four types are found:
i- Private pump schemes.
These schemes are characterized by the presence of pumps (3-4
inches diameter) for lifting water for irrigation purposes. The land here is
either owned or leased from the government. These schemes produce
both field and horticultural crops. The total number of the private
schemes in the state is 21700, out of which 6697 schemes (30.9%) use
underground water. The total area of these private schemes is estimated at
217932 feddans out of which 83685 feddans (38.4%) are irrigated from
62
the Nile and 134247 feddans (61.6%) are irrigated by underground water,
Table, 2.4 illustrates:
Table 2.4. Numbers and areas (fed.) of private schemes in the Northern
State by locality
Locality
Nile
Underground
irrigation
irrigation
Total
Number
Area
Number
Area
Number
Area
Halfa
1817
22350
154
760
2021
39482
Dongola
3173
23846
11412
114120
14670
331752
El-dabba
337
9560
1657
9476
2027
43809
Merowe
1370
28229
1780
9891
3193
62686
Total
6697
83685
15003
134247
21911
477729
Source: NSMAAI, 2002
The private schemes can be of joint-ownership for pumps where
household members or blood related groups who own or leased land get
entitled of small irrigated pump. The cost of the pump is either through
sharing by some or all economically active household members or by
relative migrants as a financial support for their families. The cost of
irrigation is paid by the share holders (Mohamed, 1996).
ii- Agricultural companies pump schemes
Land lords who are either traders or local leaders, not directly
involved in agricultural production share or individually own large sized
irrigation pumps. The total number of companies schemes in the state are
66, with the area of 178572 feddans as shown by Table 2.5.
63
Table 2.5. Numbers and areas (fed.) of the companies schemes in the
Northern State by locality
Locality
Number
Area
Halfa
1
1900
Dongola
39
153700
El-dabba
11
8612
Merowe
15
14360
Total
66
178572
Source: NSMAAI, 2002
These companies schemes include the government pump schemes
which are in the past publicly administered and run by government staff.
They have been transformed to agricultural companies since May 1996.
However, a decision was made in the year 2001 to put these schemes
under the direct supervision of the national government. Table 2.6 shows
dates of construction and areas of these schemes.
Table 2.6. Dates of construction and areas (fed.) of the transformed
schemes in the Northern State
Scheme
Date of construction
Area
Nuri
1917
3832.5
Elgureir
1917
2464.5
Elkulud
1927
1518.5
Ganati
1927
1800
Elgaba
1917
2674.29
Elburgage
1943
7000
Total
19289.79
Source: NSMAAI, 2003
64
The crops grown in companies schemes are, wheat, broad beans,
sorghum, and horticultural crops.
iii- Cooperative schemes
In this type of schemes a group of farmers or individuals purchase
a large irrigation pump (12-36 inches diameter). These schemes are
managed by boards of directors usually composed of local traders,
government, employees, land owners and farmers. The board is either
rewarded in cash in monthly payment or in kind at the end of the season.
The scheme is responsible for provision of irrigation water. The
main irrigation canals are shared by farmers but each farmer is
responsible for his miner canals. Farmers either cultivate their own land
or land of others on rent basis.
Cooperative schemes face more problems than do other types of
schemes because of managerial weaknesses in scheme's boards of
directors and financial problems. During the last years the government
encouraged the pump owners and farmers to carry out operations jointly
and round up their small holders to decrease the cost of production
(Mohamed, 1996).
The total number of cooperative schemes in the state is 145 with
the total area of 69925 feddans as shown by Table 2.7.
Table 2.7. Number and areas (fed.) of the cooperative schemes in the
Northern State by locality
Locality
Number
Area
Halfa
49
13472
Dongola
46
30086
El-dabba
22
16161
Merowe
28
10206
Total
145
69925
Source: NSMAAI, 2002
65
iv- Expansion schemes
These are schemes where the government has contributed in their
construction together with the beneficiaries efforts to ensure the increase
of cultivated area or to group the small farmers in order to benefit from
economies of large scale production. This type of schemes is known as
expansion schemes. They have large areas and are under the direct
supervision of the state ministry of agriculture.
2.7 Land utilization
2.7.1 Agricultural seasons
Agriculture is the main economic activity in the Northern State.
Agricultural production in the state is facilitated by the good experience
of farmers who are familiar with agricultural practices. The climatic
conditions are favourable for the growth of many field and permanent
crops. There are three distinct agricultural seasons in the state. These are,
the winter season "shitwi", the summer season "seifi" and the flood
season "damira". In the winter season, which is the main agricultural
season, wheat, broad beans, fennel, garlic, fenugreek, onion, tomatoes
and other vegetables are grown. In this season the climatic conditions are
favourable for crop growth, irrigation requirements are least. On the other
hand, summer season conditions are unfavourable for most of the crops,
water levels are low and irrigation costs are high. Consequently, only
small areas are cultivated. Sorghum is the main crop grown in the
summer beside legumes. During the flood season farmers cultivate
sorghum, maize, lubia and vegetables.
2.7.2 Fruit trees
The Northern State is specialized in production of fruits which
include dates, mangoes and citrus. Abd Alla (1998) stated that, farmers in
66
the Northern State focused on the growing of date palms and there is a
continuous expansion by growing of new off-shoots since date is a cash
crop and is assumed to be the most profitable as some cultural practices
such as land preparation, weeding ..etc. are not needed seasonally. Elfeil
(1993) reported that it is very clear that the horticultural crops (except
mangoes) are generating very high returns relative to the field crops.
These very high returns justify the noticeable movement towards more
production of the horticultural crops at the expense of the field crops,
which to day are confined to the areas where the production of
horticultural crops is not suitable, such as in areas susceptible to the river
flood. Table 2.8 shows the number of date palms in the Northern State.
Table 2.8. Estimated number of date palms in the Northern State by
locality
Locality
Estimated number
%
of date palms
Halfa
796391
18.1
Dongola
1545840
35.2
El-dabba
632582
14.4
Merowe
1416313
32.3
Total
4391126
100
Source: NSMAAI, 1996
Barakawi is the major variety grown in the state. The results of the
study carried by Abd Alla (1998) in Merowe area showed that Barakawi
variety contributed about 78.36% among the population of date palms,
followed by Jaw trees (11.82%) and Mishrig Wad Khatib variety
(3.35%). Gondeila and Medina varieties contributed 2.52% and 0.86%
respectively, while Bentamoda and Mishrig Wad Lagai which are high
quality varieties contributed only about 0.66% and o.67% respectively.
The important mango cultivars grown in the state are: Abu samaka,
Baladi, Hindi, Bashaier, Mabrouka, Singrest, Dibsha, Maygoma, Sinnari,
67
Shendi and Galb altour. However, farmers today focus on growing Abu
samaka cultivar due to the high demand for export.
The most important types of citrus trees grown in the state are
oranges and grapefruit.
2.7.3 Field crops
Wheat is the major food crop in the state. During the last years,
wheat area continuously increased through the wheat rehabilitation
programme which aimed at increasing the wheat cultivated in the state as
well as improving the crop yield. The increase in the area of wheat is
mainly due to the support given by the government for promotion of
wheat expansion. The support is in terms of improved seeds, fuel and
credit. Abayazeed (1999) listed the advantages which are reported by
NSMAAI that forced the country to consider the state and relocate the
wheat on its land in the following points:
i- The appropriate climate for the production of wheat. The temperature is
always between 6-30°c in winter and wheat requires 10-25°c in its
different stages of growth.
ii- Availability of fertile land along the banks of the Nile in the lower
terrace lands and also ability of further expansion in upper terraces and
valleys that run across the desert.
iii- Availability of water resources from the River Nile and underground
water.
iv- The very long experience of the farmers of the state.
v- Very high productivity, the average ranged between 13-15
sacks/feddan with ability to obtain 20 sacks/feddan if the technical
packages have been applied. Faki et al, (1994) reported that wheat
competitiveness has remained controversial. It is affected by many
factors, the most important of which are the yield-related technology
68
levels, weather variability, the presence of multiple exchange rate in the
economy as a whole, as well as differential levels for the valuation of
inputs and output. Ramram (2002) concluded that wheat has more
competitiveness with adoption of new technology. The competitiveness
of wheat was highest in the Northern Region compared to the Gezira
scheme.
The areas cultivated by different field crops for the last twelve
years are shown in Table 2.9.
It is clear from Table 2.9 that the wheat crop occupies the largest
cultivated area in the Northern State, followed by broad beans. However,
a sharp decline of wheat area in the year 2001/02 is recognized. The
yields of wheat and broad beans have improved, but they are still low
compared to those, which have been obtained at research stations. They
could increase if traditional methods are gradually replaced by improved
practices and if timely and adequate supplies of inputs are available.
Estimates of the yields of the important field crops cultivated in the state
for the period 1990-2002 are given in Table 2.10.
Table 2.10 shows that the productivity of wheat for the seasons
90/91-2001/02 was ranged between 6 and 15 sacks/feddan, with an
average being 10.5 sacks/feddan and the productivity of broad beans
ranged between 4 and 12 sacks/feddan, with an average of 8
sacks/feddan. The table also shows that the average productivity of
sorghum and zea maize for this period was 5.7 and 5.6 sacks/feddan
respectively, and the average productivity of fennel, garlic and fenugreek
for the same period was 7.8, 33.8 and 4.2 kantar/feddan respectively.
69
Table 2.9. Areas (fed.) of major field crops grown in the Northern State during the period 1990/91-2001/02
Crop
90/91
91/92
92/93
93/94
94/95
95/96
96/97
97/98
98/99
99/00
Wheat
55000 40000 25361 60000 100000 129000 151844 159150 102290 112248
2000/01 2001/02
120603
71160
Broad beans 35000 35000 24864 30000
51200
56569
61569
65470
60886
59170
74727
62370
Sorghum
10000 12000 13000 16000
20000
20000
29000
33000
21925
31157
25316
-
Zea maize
10000
8000
10000
5000
8000
10000
19258
28223
11904
25856
14225
-
Fennel
750
8000
900
1000
1000
2000
2500
4209
3177
7865
5389
9488.5
Garlic
5000
5500
6300
5000
65oo
10000
1000
1414
3177
2227
1866
2104.5
Fenugreek
400
450
600
700
900
1500
900
1005
923
2245
1778
1648.25
Source: NSMAAI, 2003
21
Table 2.10. Average productivity per feddan of the main field crops grown in the Northern State for the period 1990-2002
Crop
Unit
Wheat
Sack*
90/91 91/92 92/93 93/94 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02
6
7
7
9
10
13
14
15
9
12.5
13
10
Broad beans
"
5
5
6
7
7
9
10
12
4
10
12
9
Sorghum
"
4
6
5
7
5
5
8
3.5
5
6
8
-
Zea maize
"
4
4
6
5
7
5
5
8
3.5
6
8
-
Fennel
Kantar**
6
6
7
9
8
7
10
7
8
8
10
8
Garlic
"
20
20
25
30
25
30
40
40
40
45
45
45
Fenugreek
"
3
3
4
5
4
4
5
5
6
-
-
2.5
* One sack is approximately 95kg
** One kantar = 100 Ibs
Source: NSMAAI, 2003
22
2.7.4 Animal wealth
The Northern State is free from animal diseases and is
characterized by raising good local breeds of animals. Table 2.11 shows
the types and number of animals in the state.
Table 2.11. Types and number (head) of animals in the Northern State by
locality
Locality Cows
Sheep
Goats Camels Donkeys
Total
/Horses
Halfa
5633
137779 117562
1626
26782
289382
Dongola
125518
783030
284658
11221
114076
1318503
El-dabba
16868
101008
100007
5707
28609
252199
Merowe
19001
333933
248437
22888
75327
699586
Total
167020 1355750 750664
41442
244794
2559670
Source: NSMAAI, 2002
It is clear from the table that most of the animal wealth is
concentrated in Dongola locality (about 42.8%), followed by Merowe
locality (32.2%). The variation in number of animals from locality to
another affected directly distribution of farmers incomes in the state.
2.8 Agricultural inputs
2.8.1 Seeds
Farmers in the Northern State usually use traditional varieties
which are obtained from the previous harvest as seeds. In addition the
NSMAAI and the Agricultural Bank of Sudan (ABS) provides seeds to
the farmers in the state especially wheat seeds. Some farmers obtained
seeds from the local markets (Mohamed, 2000).
21
2.8.2 Fertilizers
The main sources of fertilizers in the state are ABS, Farmer's Bank,
Agricultural Supply Fund (ASF) and the local markets. The main
problem facing the farmers with respect of this input is its higher cost.
2.8.3 Pesticides
Plant protection department and local markets are the main sources
of pesticides in the state. The operation of pests and diseases control is
not applied by all farmers due to scarcity and high cost of pesticides and
the poor knowledge about the importance of pesticides.
2.8.5 Agricultural credit
Credit in the Northern State is provided by the branches of ABS
which are scattered in the state. The loans provided by ABS for the
Northern State in the year 2002 and 2003 contributed about 17% and 10%
respectively of the total loans provided by the bank for the country (ABS
records). The ABS provides short and intermediate loans. Table 2.12
shows the amount of loans provided by the ABS to the farmers in the
state for the years 2002 and 2003.
Table 2.12. Types and amount of loans (thousand SD) provided by the
ABS to the agricultural sector in the Northern State for the year 2002 and
2003
Type of loan
2002
2003
Seasonal
1,119,436,68
1,504,280,04
Intermediate
Total
207,150,00
130,575,60
1,326,586,68
1,634,855,64
Source: ABS, records
22
Other banks, such as Farmer's Bank also provide loans to the
farmers. The banks lending terms is depend on "Morabaha" system,
where Morabaha margin for the season 2002/03 was 15% per annum.
Another source of credit is the ASF which extends fund loans for
rehabilitation and establishment of new projects. The loans provided by
the ASF are lending as "Gard Hassan" i.e a free loan where there is no
payment of interest or cost of lending.
An informal type of credit is provided by land lords, merchants and
scheme owners to the farmers as "Sheil"1. Another informal type of
finance is the assistance and remittances from relatives but its
contribution to agriculture is limited.
Mohamed (2000) reported that the poor financial resources of the
farmers and the high cost of agricultural operations and inputs made the
availability of credit very important in the state. However, the low prices
of crops especially for the wheat crop, hinder the farmers ability to repay
the loans.
2.8.5 Irrigation inputs
Farmers in the Northern State use pumps for lifting irrigation
water. The high cost of petroleum products and spare parts increase the
cost of irrigation. Another problem facing farmers in this respect is the
weak maintenance facilities for the pump and lack of trained technicians.
2.9 Marketing
The Northern State is separated from Khartoum, the main
commercial center of the country, by 300 to 500 km, which in the past is
connected with a rough unpaved road. Today most of this road which is
1
Sheil is an informal credit, which is repaid in kind from the newly harvested crop in question based on
pre-season agreed upon-price.
23
known as "Shirian Elshamal" was paved. A railway line connects
Khartoum via Eldamer and Atbara along the Nile with Karim at the
eastern edge of the state. A railway connection also exists between Port
sudan and Karima via Atbara.
Elfeil (1993) reported that, the marketing of the crops in the state is
characterized by being free of government intervention. Cash crops are
usually sold immediately after harvest at very low farm gate price
because of the need for cash and because the individual farmers produces
is too small an amount to be moved to urban centers.
Dates, beans and most of the tradable agricultural products are
normally sold to local traders who usually lend money, in kind or in cash,
to the farmers in terms of allowing them to borrow the commodities they
want from their shops and pay them later after harvest. The farmers are
then obliged to sell their products to the traders since they need to come
to borrow again. The selling process usually take place right after harvest
when the prices normally are at their lowest levels. The traders can then
store the product till the prices are improved. Table 2.13 shows the
pattern of whole sale prices of different crops in the Northern State during
the year 2000.
The marketing channel in the state is illustrated in Figure 2.1.
24
Table 2.13. pattern of whole sale prices (SD) of different crops in the Northern State, during the year 2000
Crop
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Wheat
8250
8200
7900
5900
6500
6600
6500
6600
7243
7243
7433
Broad beans
18500
22500
13800
12700
16170
15000
16000
17000
17642
17642
12880
Fennel
12000
15000
12000
10000
9900
9000
9000
9600
12628
12628
14206
Fenugreek
16900
16000
9900
10500
10000
11200
12000
13000
12714
12714
1316
Sorghum
2500
4000
5500
3600
3800
3900
4500
4700
4986
4986
3500
Barakawi dates
4500
3500
3800
4000
5000
4500
6000
5500
4165
4165
3892
Jaw dates
2250
1700
1800
1900
1800
18000
2000
2600
1947
1947
1796
Source: NSMAAI records
21
Figure 2.1. The marketing outlet for field and horticultural crops in the
Northern State.
Producer
Whole sealer
Middlemen
Factory
Export
Local trader
Consumer
Within The Northern State
Whole sealer
Retailer
Export
Consumer
Within Khartoum State
1
2.10 Storage and processing
Proper storage facilities are unavailable in the state. Broad beans
and dates are stored in sacks and subjected to sunlight to prevent insects
infestation. The traditional stores constructed from local materials are
used for storage of some crops especially onion.
Karima date factory is the only date factory in Sudan. It was
established in 1959 for the purpose of fumigation, cleaning, grading,
processing and packing of dates. By-product such as syrup, vinegar and
alcohol are produced on exploratory scale. The maximum capacity of the
factory is 500 tons per year, but the factory is working below capacity
since commenced production. Another factory in the state is kareima
canning factory, which is produces mainly on tomato paste, in addition to
processing of other products.
2
CHAPTER III
THE SOCIO-ECONOMIC CHARACTERISTICS
AND FARMER’S INCOME IN THE NORTHERN STATE
This chapter describes the socio-economic characteristics of
farmers and the existing farming system in the Northern State. It includes
information on the general characteristics of the household heads, the
family and the utilization of resources as indicated by the results of the
survey conducted by the author in season 2003.
The chapter also
includes estimates of the farmer’s income using gross margin analysis.
3.1 The general characteristics of the household heads
3.1.1 Farmer's age
Age has an influence on the quality of farmer's decisions and
attitudes towards accepting new ideas and in turn has an important effect
on output of an individual. Elhadari (1968) stated that at age more than 50
years the propensity to manual effort can be expected to be decreased
with advanced age. Upton (1979) stated that, the farmer's age has an
influence on management performance although the overall direction of
this influence is not clear. On the one hand as man ages, he gains
experience and one would expect his decision making ability to improve.
On the other hand it has been found that goals change, with increasing
age, usually towards leisure and reducing work.
The results of the survey showed that the average age of the farmer
was 45.1 and 48.3 years in Merowe and Dongola localities respectively.
This indicated that most of the farmers are within the active age group
(15-64 years) (Table 3.1).
3
3.1.2 Marital status
All the farms in the sample are managed by males farmers. Table
3.1 shows that about 18.3% and 11.3% of the respondent farmers in
Merowe and Dongola localities respectively were not married. This
indicated that many farms are headed by young farmers. Ali (1976) found
that young farmers adopt new ideas more readily than older farmers. Elkhidir (1988) found that the young educated farmers scored high yields.
Table 3.1 The general characteristics of the household heads
Item
Merowe Dongola
Average age of the farmer
45.1
48.3
(13.3)*
(11.3)*
Percent of farmers having no education
10%
18.8%
Percent of farmers having khalwa education
5%
5%
Percent of farmers having primary education
23.3%
21.3%
Percent of farmers having intermediate education
15%
15%
Percent of farmers having secondary education
40%
36.3%
Percent of farmers having higher education
6.7%
3.8%
Percent of not married farmers
18.3%
11.3%
*Figures between brackets are standard deviations
Source: Author's survey, 2003
3.1.3 Educational levels
Education is considered an investment in human resources, and in
general it can be defined as the accumulation of experience to prepare an
individual for life. Education can change and improve farmers
knowledge, attitudes and skills. It helps farmers to grasp changes and
adjust more quickly and accurately to them (Omer, 1994). Ban and
4
Hawkins (1988) reported that, better educated farmers are more capable
of making their own decisions.
The results of the survey showed that, about 18.8% of the farmers
in Dongola locality were illiterate, having had no sort of education at all,
while in Merowe locality the percentage of illiterate farmers was 10%.
The percentages of farmers who attended khalwa and intermediate
education were the same in the two localities. The percentages of farmers
who attended the primary, secondary and higher education were slightly
higher in Merowe than Dongola locality (Table, 3.1). The presence of
some farmers who have higher education indicated that farmers in the
Northern State are relatively better educated. Wilson (1996) stated that
literacy in the Northern State is higher than other parts of the country.
3.2 Family
The average family size was 7.4 and 7.6 persons in Merowe and
Dongola localities respectively. Accordingly the monthly average man
days labour supply in Dongola was greater than Merowe locality (Table
3.2).
Table 3.2. The general characteristics of the family in the Northern State
by locality
Item
Merowe
Dongola
7.4
7.6
(2.9)*
(2.6)*
Percent of males
49.3%
49.4%
Percent of females
50.7%
50.6%
42
45
Average family size
Monthly average man days labour supply
*Figures between brackets are standard deviations.
Source: Author's survey, 2003
Table 3.2 also shows that about 49.4% of family members in Merowe
and Dongola localities respectively were males.
5
3.3. Source of irrigation
The results of the survey showed that most of farms in Merowe
locality (81.6%) were irrigated from the Nile, while only 36.3% of farms
in Dongola locality were irrigated from the Nile (Table, 3.3).
Table 3.3. Source of irrigation
Source
Merowe
Dongola
Percent of farms irrigated from the Nile
81.6%
36.3%
Percent of farms irrigated from underground
11.7%
55%
6.7%
8.7%
water
Percent of mixed irrigation from the Nile and
underground water
Source: Author's survey, 2003
The reason is that the underground irrigation in Merowe locality is
confined mostly to the high-terrace lands, where soils are sandy, alkaline
and accordingly not suitable for cultivation of seasonal crops and mostly
cultivated by date palms. In Dongola locality, more than 50% of the
farms were irrigated from underground water since there are abundant
soils at the lower-terrace lands which can not be irrigated from the Nile.
The results of the survey are consistent with the information
mentioned in chapter two by NSMAAI which shows that the area of the
private schemes irrigated from the underground water in Dongola locality
is 114120 feddans, while only 23846 feddans is irrigated from the
underground water in Merowe locality. The area irrigated from the Nile
in Merowe locality is 28229 feddans compared to 23846 feddans in
Dongola locality (Table 2.4). Mohamed (2000) reported that in the 1998
season about 47.5% of the wheat area and 50.4% of the broad beans area
in Dongola locality were irrigated by underground water.
6
3.4 Characteristics of the private pump schemes
Table 3.4. Characteristics of private pump schemes
Item
Merowe
Percent of 3// pumps
78.3%
Dongola
73.9%
Percent of 4// pumps
21.7%
26.1
Average number of farmers in the scheme
3.13
4.52
Average area of the scheme (fed.)
9.8
13.5
Average number of feddans that can be
1.6
1.3
irrigated daily
Source: Author's survey, 2003
Table (3.4) shows that most of small schemes in the Northern State
employ three inches pumps for irrigation. From the table, it is obvious
that the scheme area in Dongola locality is larger than in Merowe locality
and has more number of farms. The average number of feddans irrigated
daily is greater in Merowe due to the quality of soils since most of the
schemes in Merowe locality are irrigated from the Nile.
3.5 Agricultural holdings
The Northern State is characterized by a narrow strip of fertile soils
along the river banks resulting in small holdings and therefore the
agricultural production extended to the upper terrace lands (matarat).
The results of the survey showed that the size of the agricultural
holdings in Merowe locality ranged between 0.3 and 50 feddans, with the
average being 10.4 feddans, while in Dongola locality it ranged between
1.5 and 50 feddans with the average being 12 feddans.
7
According to the type of scheme, the average agricultural holding
in the private schemes was 11.2 and 13.7 feddans in Merowe and
Dongola localities respectively, while in the companies schemes it was
6.9 and 10.5 feddans in Merowe and Dongola localities respectively.
3.6 Land tenure
Ibrahim (1993) stated that, registration of land ownership has
started at the beginning of the 19th century. Most of the river banks are
privately owned. The land owners of those plots have the first right to
cultivate land adjacent to their own lands, which after a certain period of
time can be registered under their names.
The results of the survey showed that, farmers in the Northern State
cultivated their own lands (milik), governmental lands which includes
(meryi), rented lands or cultivated lands for others on share cropping
basis. The size of milik lands in Dongola locality was 50% of the total
farm size, while it was only 26.92% in Merowe locality. The
governmental and meryi lands contributed about 42.5% and 30.77% of
the farm size in the two localities respectively. The remaining 42.3% in
Merowe locality and 7.5% in Dongola locality were shared cropped lands
(Table, 3.5).
Table 3.5. The average farm size (fed.) according to land tenure in the
Northern State by locality
Type of land
Merowe
Dongola
Size
%
Size
%
Milik
2.8 (5.15)* 26.9
6 (8.2)
50
Meryi and governmental land
3.2 (7.6)
30.8 5.1 (6.74) 42.5
4.4 (6.65)
42.3 0.9 (2.97)
7.5
Shared cropped land
Total
10.4
100
12
100
*Figures between brackets are standard deviations
Source: Author's survey, 2003
8
Farmed land in the Northern State is either privately owned or
leased by government to farmers. On the governmental schemes, the
remaining 30% belong to the government but leased to farmers. In
cooperative pump schemes, 64% of the land is privately owned and 36%
is owned by the government. Most of the land under cultivation are
relatively small holdings. The structure of the family farm suggests that
land is farmed by extended family. A farm unit is frequently made up of
more than one farm holding. It may include owned land, share cropped
land with other family members or relatives as owners, and leased land
from government.
Siddig, (1999) stated that there are different crop sharing
arrangements in the state but the 50-50 crop sharing arrangement with
little variation in the cost sharing structure is dominants. The main
reasons behind this crop sharing are: the small sizes of farms, limited
land ownership and the high cost of production in addition to other miner
reasons. Outdated registration of land is one of the main constraints
facing farmers and their accessibility to formal credit. Where credit is
accessible it has its own constraints of high cost (interest rate charged),
complicated loan insuring procedure and the time of repayment. Another
form of share cropping arrangements is the one-third-two-third share
cropping, where the tenant paid about 35% of the total variable cost of
production and received about 33% of the total return. In this
arrangement both the tenant and land owner earned profit that is nearly in
proportion to their contributions.
The results of the survey conducted by the author showed that
private lands can be rented on cash basis or cultivated as rented lands
without sharing in the cost of production. It was found that the rental rate
ranged between 1/12 up to 1/6 of the produce, depending on the fertility
of soils and types of crops grown.
9
3.7 Land utilization
3.7.1 Cropping intensity
The cropping intensity, which is the percentage of cropped area to
the total cultivated area, in the private schemes approached 100% in
Merowe locality and exceeded 100% in Dongola locality, while it
approached 100% in the companies schemes of Dongola locality and
exceeded 100% in companies schemes of Merowe locality (Table, 3.6).
The high cropping intensity is dictated by the fact that agricultural land in
the state is limited and the good fertile soils is a narrow strip along the
river banks where most of the population live. In addition, there is
continuous expansion in growing permanent crops mainly date palms.
Table 3.6 Cropping intensity and cropped area in the different seasons in
the Northern State by locality and scheme type
Item
Mreowe
Dongola
Private
Companies
Private
Companies
Cropping intensity
94.3%
121.22%
106.4%
81.3%
Cropped area in the winter
55.03%
29.38%
61.39%
73.4%
Cropped area in the summer
16.22%
23.86%
27.23%
16.34%
Area of the permanent crops
28.75%
46.76%
11.38%
10.26%
Source: Author's survey, 2003
Table 3.6 also shows that about 28.8% and 11.4% of the cultivated
private schemes area is under permanent crops in Merowe and Dongola
localities respectively, while for the companies schemes the area under
permanent crops contributed about 46.8% and 10.3% in the two localities
respectively.
10
The cropping intensity over 100% means that part of the land is
cultivated more than once during the year i.e some summer cropped areas
cultivated also in the winter season. In addition, farmers in the state do
not follow any cropping rotation the thing which lowers the soil fertility
and accordingly decline the productivity of the winter cops.
3.7.2 Crops grown
Table 3.7. Types of crops grown and areas (percent of the total cropped
area) in the Northern State for the 2003 year by locality and scheme type.
Merowe
Dongola
Crop
Private
Companies
Private
Companies
Wheat
12.33
24.58
27.37
46.67
Broad beans
26.09
4.68
26.64
26.25
Fennel
5.6
-
2.92
-
Onion
3.32
0.12
-
-
Garlic
-
-
2.41
0.47
Zea maize
-
-
0.36
-
Tomato
6.64
-
-
-
Vegetables
1.04
-
1.68
-
Lucern
2.47
10.67
3.8
1.52
Fruits
0.38
0.36
-
-
Dates
25.9
35.73
7.59
8.75
Source: Author's survey, 2003
Table 3.7 shows the types of crops grown and areas as percent of
the total cropped area in the state. In Merowe private schemes broad
beans occupied the largest area (26.09%), followed by date palms
(25.9%), while wheat crop came fourth and occupied 12.33%. In Dongola
11
private schemes wheat occupied 27.37%, followed by seifi crops
(27.23%), wheat and broad beans occupied 26.64%. In Merowe
companies schemes 35.7% of the cultivated land is under date palms,
followed by wheat crop (24.6%) and seifi crops (23.9%), while broad
beans occupied only 4.7%. In Dongola companies schemes the largest
area was cultivated by wheat and broad beans which occupied 46.7%
and26.3% respectively.
3.7.3 Animal ownership
Livestock production plays an important role in the agricultural
production in the Northern State. They are considered a cash as well as a
food source for the family. They are usually maintained on crop residues
and fodders. The animals raised are sheep, goats, cows, donkeys and
poultry. Table (3.17) shows the average animal ownership per family.
Table 3. 8. The farmer's average animal ownership in the Northern State
by locality
Sheep
Goats
Cows
Donkeys
Camels
Poultry
Source: Author's survey, 2003
Merowe
10.2
3.3
1
2
0.2
3.3
Dongola
8
4.3
2
1.6
2.1
2.1
3.7.4 Agricultural operations
The agricultural operations include land preparation, sowing,
weeding and harvesting.
3.7.4.1 Land preparation
The land and seed-bed preparations are usually done by oxen
plough and hired tractor. Disc ploughing by hired tractor is usually done
12
once every two to three years and sometimes after longer intervals (Elfeil,
1993). Wilson (1996) stated that, in the Northern State of Sudan, land
preparation starts as annual floods begin to recede during early September
or early October. Abd-Elaziz (1999) reported that land preparation for the
winter crops start early in the season to avoid the problems of
unavailability and high cost of hiring tractors and traditional ploughs late
in the season.
The results of the survey conducted by the author for the season
2003 revealed that: in Merowe locality, all the sampled farmers (100%)
carried first ploughing, only one farmer carried second ploughing and
68% carried leveling. In Dongola locality, 95% of the sampled farmers
carried first ploughing, 34% carried second ploughing and 89% carried
leveling.
The land preparation is usually done by oxen plough and hired
tractor. Tractor land preparation is assumed to be very beneficial to the
crops since it is a heavy deep kind of ploughing. Mohamed (2000) stated
that the tractor type of land preparation is done every two to three years,
or even at longer intervals due to its high cost.
The results of the survey also showed that, in Merowe locality,
more than half of the sampled farmers (54%) used traditional plough and
the rest (46%) used tractor plough. While in Dongola locality, most of the
sampled farmers (69%) used tractor plough and about 31% of them used
traditional plough.
The results of the survey also revealed that, most of the respondent
farmers in Merowe locality applied land cleaning, ploughing and leveling
for the cultivation of wheat crop in October and applied the operation of
raising guduals and tagnads in November. While in Dongola, most of the
sampled farmers prepared land for wheat crop in November.
13
The land preparation for broad beans in the two localities was
applied in October by most of the farmers.
3.7.4.2 Sowing date
The sowing date of the crops are very important factors affecting
the yield of the crops. Wilson (1996) stated that, there is a severe
reduction of yields of wheat for late sowing, and the potential yield loss is
as high as 60% if sowing is delayed until December. Farmers are not
convinced that yield is a function of date and late sowing have negative
impact on wheat productivity. Labuda, cited by Hwait Alla (1998)
concluded that, the late sowing reduced the yields of seeds of broad beans
at the milky- stage by about 50% compared with early sowing.
The results of different experiments conducted at Hudeiba
Research Station, show that the optimum sowing dates for the majority of
winter crops range between the first half of October and the second week
of November (Abd Elaziz, 1999).
The results of the survey conducted by the author for the season
2003, revealed that, about 55% and 54% of the wheat growers in Merowe
and Dongola localities respectively delayed the planting of the crop till
December, while about 38% and 39% of them in the two localities
respectively planted the crop in November. About 59% and 55.6% of the
broad beans growers in Merowe and Dongola localities respectively
planted the crop in November, while about 30.7% and 37.8% of them in
the two localities respectively planted it in October. About 7.7% and
4.4% of them in the two localities respectively delayed the planting of the
crop till December and about 2.6% and 2.2% of them in Merowe and
Dongola localities respectively delayed the planting till January. The
cultivation of fennel crop in Merowe locality started in November and
December, while in Dongola locality it started in October and November.
14
The planting of onion crop in Merowe locality started in September and
extended till February, while the planting of garlic cop in Dongola
locality was applied in October and November.
Elfeil (1993) attributed the late sowing to the unavailability of
agricultural inputs such as credit, seeds, fuel and spare parts. He stated
that, the late sowing, due to the late arrival of inputs and the late harvest
due to the farmers busy schedules at harvest times, are common practices
in the Northern State. Another reason for late sowing date is that farmers
sow sorghum as damira crop in addition to other seifi crops which results
in late sowing of wheat and other winter crops which follow these crops
on the same land.
3.7.4.3 Weeding
ِ
Annual weeds (grasses) which are encouraged by watering are
some of the problems facing agricultural production in the Northern
State. Ahmed et al (1994) reported that Dongola area is characterized by
intensive weed infestation levels. Ibrahim (1987) reported that the weeds
problem is more serious in the Northern State than the River Nile State.
Proper one weeding for wheat crop and proper two weedings were
recommended for broad beans crop (Abd Elaziz, 1999).
The results of the survey conducted by the author for the season
2003, showed that most of wheat and broad beans growers in Merowe
locality applied one weeding during the season, while those in Dongola
locality applied three weedings during the season.
Weeding method is dominated by using sickle, hand pulling and
hoe "toria". The operation does not add any cost for the farmer since it is
done voluntarily by women and children who weed the fodder for their
animals. Herbicides are rarely applied for weed control since farmers are
not familiar with them.
15
3.7.4.4 Harvesting
The cutting and collection of wheat and broad beans is performed
manually by family, hired and Nafir labour, while the threshing is done
by the threshers. The cutting, collection and threshing of fennel and zea
maize crops is done manually. Onion and garlic crops harvesting is
carried by labour and traditional plough.
The optimum time of harvesting varies from one location to
another. The recommended harvesting period for wheat is 120 days after
sowing and for broad beans is 100 days after sowing (NSAAI, 2002).
The results of the survey conducted by the author for the season
2003 shows that, most of wheat growers in Merowe locality harvested the
crop in March, while most of those in Dongola locality harvested it in
April. Broad beans crop was harvested in March by most of farmers in
the two localities respectively. Fennel crop was harvested in February and
March, onion was harvested in June, garlic was harvested in April and
zea maize was harvested in February.
Elamin (1996) stated that the late harvesting is usually an outcome
of shortage of labour and machinery. Harvesting dates were however
positively correlated with sowing date . AOAD (1990) reported that the
delay of wheat harvesting is a common practice and is known to cause a
loss of 28% of the total wheat output.
3.7.4.5 Labour resource management
Agriculture in the Northern State is labour intensive, most of the
agricultural operations such as sowing, weeding, cutting and collection of
the crops are carried out manually. All farmers depend on their family
labour which includes the farmer, his sons, daughters and wife. Also
farmers get the help of their relatives and neighbors which is locally
16
known as "Nafir". Labour shortage is supplemented by hired labour.
IFAD (1986) reported that, most of the labour used for the different
operations is family labour with additional labour supplied through
mutual assistance and hired labour. Tables (3.9 a and 3.9 b show the
average monthly man days per feddan and the percentage of family and
hired labour in Merowe and Dongola localities respectively.
It is clear from the tables that there was high demand for labour in
October, November and December since the operations of land
preparation, planting and weeding were applied during these months with
variation in the time of planting for the different crops and between the
two localities. Also there was high demand for labour in March and
17
Table 3.9a. Average monthly mandays per feddan and percentage of family and hired labour in Merowe locality
Mon
Wheat
Broad beans
Fennel
Onion
Tomato
F
%
H
%
F
%
H
%-
F
%
H
%
F
%
H
%
F
%
H
%
Sept.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
14.73
100
-
-
Oct.
3.11
92.01
0.27
7.99
5.24
73.29
1.91
26.71
6.81
77.92
1.93
22.08
6.17
83.27
1.24
16.73
8.38
100
-
-
Nov.
3.39
67
1.67
33
6.47
79.98
1.62
20.02
12.6
94.17
0.78
5.83
6.01
80.67
1.44
19.33
1.14
91.64
0.56
8.36
Dec
7.2
84.11
1.36
15.89
7.96
94.54
0.46
5.46
4.66
100
-
-
8.07
89.67
0.93
10.33
4.9
74.81
1.65
25.19
Jan.
5.2
100
-
-
2.97
95.81
013
4.19
3
100
-
-
8.18
75.74
2.62
24.26
10
55.1
8.15
44.9
Feb.
3.24
90.25
0.35
9.75
2.28
73.31
0.83
26.69
15
100
-
-
4.95
88.39
0.65
11.61
9.45
50.4
9.3
49.6
Mar
6.24
60.23
4.12
39.67
4.42
57.4
3.28
42.6
19.65
99.29
0.14
0.71
3.64
90.1
0.4
9.9
6.6
42.58
8.9
57.42
Apr
-
-
-
-
-
-
-
-
-
-
-
-
2.05
69.02
0.92
30.98
6.4
51.61
6
48.39
May
-
-
-
-
-
-
-
-
-
-
-
-
4.23
50.24
4.19
49.76
3.15
46.6
3.61
53.4
June
-
-
-
-
-
-
-
-
-
-
-
-
11.39
52.88
10.15
47.12
-
-
-
-
Source: Author's survey, 2003
1
Table 3.9b. Average monthly mandays per feddan and percentage of family and hired labour in Dongola locality
Wheat
Month
Source:
Broad beans
Fennel
Garlic
Zea maize
F
%
H
%
F
%
H
%
F
%
H
%
F
%
H
%
F
%
H
%
Oct.
3.49
70.36
1.47
29.64
5.18
100
-
-
6.44
6.39
4.05
38.61
6.5
63.11
3.8
36.89
9.77
95.13
0.5
4.87
Nov.
4.03
100
-
-
7.31
92.53
0.59
7.47
6.88
69.78
2.98
30.22
7.4
92.85
0.57
7.15
11.23
100
-
-
Dec.
7.46
92.1
0.64
7.9
7.9
93.6
0.54
6.4
8.18
58.22
5.87
41.78
9.2
100
-
-
9.07
100
-
-
Jan.
7.34
95.45
0.35
4.55
5.01
97.85
0.11
2.15
4.71
85.02
0.83
14.98
5.7
100
-
-
21
100
-
-
Feb.
2.85
100
-
-
2.04
89.47
0.24
10.53
11.39
100
-
-
3.5
100
-
-
13
100
-
-
March
1.21
100
-
-
9.2
94.1
0.58
5.9
9.8
95.15
0.5
4.85
2
100
-
-
-
-
-
-
April
10.43
88.54
1.35
11.46
0.85
68
0.4
32
-
-
-
-
18.8
90.39
2
9.61
-
-
-
-
Author's
survey,
2
20
April months when the major winter crops i.e wheat and broad beans are
harvested.
The demands for labour for the different agricultural operations of
the different winter crops in Merowe and Dongola localities are shown in
Tables 3.10a and 3.10b respectively.
Table 3.10a. Average labour requirements (man days per feddan) of the
main crops in Merowe locality (winter season 2002/2003) by locality
Operation
Wheat
Broad
Fennel
Onion
Tomato
beans
Land cleaning
2.69
2
1.22
3.43
3.58
Raising field
4.98
5.59
4.95
5.06
5.18
Planting
1
3.12
2.46
10.94
11.71
Weeding
1.65
9.69
11.26
13.34
7
Irrigation
5.64
9.47
12.03
15.5
20.8
Harvesting
10.83
7.7
32.65
28.96
59.65
Total
36.2
37.57
65.57
77.23
107.92
canals and ridges
Source: Author's survey, 2003
Table 3.10b. Average labour requirements (man days per feddan) of the
different crops in Dongola locality (winter season 2002/2003) by locality
Operation
Wheat
Land cleaning
3.14
Raising fields
3.17
canals and ridges
Planting
3.02
Weeding
10.04
Irrigation
9.47
Harvesting
11.78
Total
40.62
Source: Author's survey, 2003
Broad
beans
2.83
3
Fennel
Garlic
Zea maize
3.57
4.88
4.5
3.17
5.67
7.6
3.26
11.23
8.6
11.03
39.95
4.82
14.52
12.15
21.69
61.63
6.4
10.3
14.3
20.8
59.47
4.67
9.3
12
25.33
64.57
3.7.5 Irrigation
Irrigation is the most important factor to agricultural production in
the Northern State. The other agricultural operations like sowing,
fertilizer application and harvesting depend on the availability and timing
of irrigation water.
Elfeil (1993) stated that, due to irrigation problems, the applied
number of waterings are significantly below the recommended ones for
all the crops produced in the Northern State. They irrigate 3 to 5 times
with irrigation intervals that may exceeds weeks for wheat crop. He
concluded that all of the crops are close to 40% short of the recommended
number of waterings. Abd Elaziz (1999) in his study in the River Nile
State found the actual number of waterings for the winter crops is less
than the recommended number of waterings and referred the deviation
from the optimum number of waterings to the limited pump size,
unavailability and high cost of gasoline, high cost of spare parts and lack
of credit. He also stated that, farmers extended the irrigation interval up to
21 days where as the recommended interval for winter crops range
between 7 and 10 days.
The results of the survey conducted by the author for the season
2003, showed an obvious improvement in irrigation operation in the
Northern State, where the number of waterings applied for the wheat crop
in Merowe locality exceeded the recommended number of waterings by
22% in the private farms, while the shortage of 33% was recognized in
the companies schemes for the wheat crop. The number of waterings
applied for broad beans in this locality was 4.3% and 33% short of the
recommended number of irrigation in the private and companies schemes
respectively. In Dongola locality the shortage of 15.9% of the
recommended number of irrigation for wheat crop was recognized in both
private and companies schemes. While the numbers of waterings applied
for broad beans crop were 22% and 9% short of the recommended
number of irrigation in the private and companies schemes respectively.
(Tables 3.11, 3.12).
Table 3.11. Recommended and actual number of waterings given to
major winter crops in the private farms in the Northern State (Season
2002/2003).
Crop
R
Merowe
A
%gap
Dongola
A
%gap
Wheat
9
11
+22%
7.57
15.9%
Broad beans
Fennel
Onion
9
15
16
8.6
10.5
14.1
4.4%
11.87%
7
7.3
-
22.2%
-
Garlic
Zea maize
Tomato
15
-
21.7
-
7.5
8
-
-
Source: Author's survey, 2003, HRS and NSMAAI reports
Table 3.12. Recommended and actual numbers of waterings given to
wheat and broad beans crops in companies schemes in the Northern State
(season 2002/2003)
Crop
R
Merowe
A
%gap
Dongola
A
%gap
Wheat
9
6
33.3%
7.6
15.6%
Broad beans
9
6
33.3%
8.2
8.9%
Source: Author's survey, 2003
3.7.6 Input-output relationships
3.7.6.1 Seed rate
The optimum amount of seeds per unit area is assumed to increase
the output of the crops. The seed rate below the optimum decrease the
output, while the seed rate beyond the optimum increases the cost of
production and decreases the quantity and quality of the output. Elfeil
(1993) stated that, the quality of seeds and the amount of seeds applied
per unit area depends on the farmer's knowledge and expertise, which are,
of course, a function of farmer's education and age as well as a function
of the extension services which are lacking in the Northern Sudan. Osman
(1996) found that, the seed rate explains the variation in wheat and broad
beans yield in the River Nile State. According to (HRS) the
recommended seed rate for wheat and broad beans crop is 50-55 kg per
feddan.
The results of the author's survey for the season (2003) showed that
the quantity of wheat seeds applied was beyond the recommended seed
rate by 21% and 33% in Merowe and Dongola localities respectively. The
quantity of broad beans seeds exceeded the recommended seed rate by
5% and 50% in Merowe and Dongola localities respectively (Tables, 3.13
and 3.14).
Table 3.13. Wheat input-output per feddan actual and recommended for
the private farms in the Northern State (2002/2003 season)
Variable
R
A
50-55
66.3
Fertilizer rate (kg)
80
(38.32)*
65
(49.82)*
Period of cropping
(days)
120
Output (sacks)**
18
Seed rate (kg)
Merowe
%gap
A
+20.5%
-18.8%
73.1
-31.67%
*Figures between brackets are standard deviations
**One sack is approximately 100 kg.
Source: Author's survey, 2003 and HRS reports
+32.9%
(36.42)*
71.4
-10.75%
(31.13)*
113
-14.17%
123
(14.18)*
(21.32)*
12.3
(5.1)*
Dongola
%gap
8
(3.58)*
+2.5%
-55.5%
Table 3.14. Broad beans input- output per feddan actual and
recommended for the private farms in the Northern State (2002/2003
season)
Variable
Seed rate (kg)
Merowe
Dongola
R
A
%gap
A
%gap
50-55
57.6
+4.7%
82.5
+50%
(29.2)*
Fertilizer rate
(28.43)*
None
7.5
-
3.6
-
100
123.6
+23.6%
130.7
+30.7%
(kg)
Period of
cropping (days)
Output
(sacks)**
(17.52)*
14
6
(19.94)*
57.14%
(4.31)*
7.7
-45%
(3.65)*
*Figures between brackets are standard deviations
**One sack is approximately 95 kg.
Source Author's survey, 2003, HRS and NSMAAI reports
3.7.6.2 Fertilizer
Osman (1996) found that the amount of fertilizer applied per
feddan explains the variation in wheat yield. Hudeiba Research Station
recommended for wheat crop 80 kg urea per feddan, while no fertilizer is
recommended for broad beans since it is a leguminous crop.
The results of the survey conducted by the author in the season
2003 showed that, the average fertilizer rate applied for wheat crop was
less than the recommended one by 18.8% and 10.8% in Merowe and
Dongola localities respectively, while only few farmers applied fertilizer
for broad beans (Tables 3.13, 3.14).
3.7.6.3 The actual and the expected output
The actual yields levels of all crops grown were significantly below
the expected ones. Tables 3.13 and 3.14 show that, the yields of wheat
crop was 31.7% and 55.5% less than the targeted yields in the private
farms in Merowe and Dongola localities respectively. The yields of broad
beans were 57% and 45% less than the targeted yield in the private farms
in the two localities respectively.
The results of the survey also showed that, wheat yields in
companies schemes were 7.7 and 6.7 sacks per feddan in Merowe and
Dongola localities respectively, and the yields of broad beans were 4.2
and 8.8 sacks per feddan in the two localities respectively (Table 3.15).
Table 3.15.
The actual number of waterings and output (sacks per
feddan) for wheat and broad beans crops in the Northern State according
to scheme type
Merowe
Private Companies
Dongola
Private
Companies
Wheat
Number of waterings
11
6
7.5
7.5
12.3
7.7
8.1
6.66
Number of waterings
8.6
6
7
8.2
Output (sacks/fed.)
6.6
4.2
7.6
8.8
Output (sacks/fed.)
Broad beans
Source: Author's survey, 2003.
The actual outputs and recommended inputs for fennel, onion and
garlic crops are shown in tables (3.16), (3.17) and (3.18).
Table 3.16. Fennel input-output per feddan actual and recommended in
the Northern State (2002/03 season)
Merowe
Seed rate (Ibs)
Dongola
R
A
%gap
A
%gap
20
14.9
25.5%
21.8
+9%
(6.39)*
Fertilizer rate
90
(kg)
55.8
(24.1)
34.2%
57.5
(35.96)*
Period of
120
cropping (days)
(45.54)*
-
125.8
(21.38)*
-
Output
120
(kantar)**
5.2
36.11%
+4.83%
(23.75)*
-
7.6
(3.45)*
(7.61)*
*Figures between brackets are standard deviations.
** One kantar = 100 Ibs.
Source: Author's survey, 2003 and NSMAAI reports
Table 3.17. Onion input-output per feddan actual and recommended in
Merowe locality
Variable
Seed rate (Ibs)
R
A
%gap
7-8
6.7
4.2%
(4.05)*
Fertilizer rate (kg)
80
98
+22.5%
(65.71)*
Period of cropping (days)
-
158
-
(40.52)*
Output (sacks)**
-
91.7
(76.43)*
*Figures between brackets are standard deviations.
** One sack is approximately 95 kg.
Source: Author's survey, 2003 and HRS reports
-
Table 3.18. Garlic input-output per feddan actual and recommended in
Dongola locality (2002/2003 season)
Variable
Seed rate (Ibs)
R
A
%gap
200
330
+65%
(2.97)*
Fertilizer rate (kg)
90
110
+22.22%
(93.7)*
Period of cropping (days)
Output (kantar)
180
-
-
-
36.8
-
(14.2)*
*Figures between brackets are standard deviations.
Source: Author's survey, 2003 and NSMAAI reports
3.7.6.4 Seed varieties
In general farmers in the Northern State used traditional varieties
which they obtain from the previous harvest and very rarely used
improved varieties because they are risk averse in addition, to the fact that
it is very difficult to obtain reliable supplies of improved varieties. The
lack of use of improved varieties are assumed at least to cause low yields
obtained in the state (Elfeil, 1993). El-amin (1996) stated that the wheat
seeds varieties recommended by researchers for the Northern State, are,
wadi Elneil, Condor and the newly released variety Elneilin. Wadi Elneil
variety is more preferred to Condor due to its similarity in characteristics
to the local varieties. Condor is an early maturing variety and is suited for
late cultivation. Another variety grown in the Northern State is variety
GZZa 155 which is tolerant to lodging, birds attack and shattering.
Hussain (1987) stated that wadi Elneil out yield the variety GZZa 155
with an average yield advantage of 20-22%.
The results of the survey indicated that most of farmers in the state
adopted the improved seeds varieties, where only about 30% and 39.6%
of wheat growers in Merowe and Dongola localities respectively
cultivated Baladi varieties. In Merowe locality, about 40% of wheat
growers cultivated Kandor variety, 22% cultivated Wadi ElNeil variety
and only 8% cultivated GZZa variety. In Dongola locality, about 34% of
wheat growers cultivated Wadi ElNeil variety, 9.4% cultivated GZZa
variety, 7.5% cultivated Kandor variety, 5.7% cultivated Depera variety
and 3.8% cultivated ElNelein variety.
With regard to the broad beans crop, most of farmers in Merowe
locality adopted improved seeds and only about 37.5% of them cultivated
"baladi" varieties, while most of farmers in Dongola locality cultivated
baladi varieties and about 48% of them adopted the improved seeds.
3.8 Gross Margin analysis
3.8.1 The total cost of production
The results of the gross margin analysis ( Appendices 1 and 2)
indicated that average total variable costs of production for the private
schemes of Merowe locality were SD 62918.8, SD 57799.4, SD 45280.4,
SD 109060 and SD 101935.9 for wheat , broad beans, fennel, onion and
tomato crops respectively. In Dongola locality the average total variable
costs of production for wheat, broad beans, fennel, garlic and zea maize
were respectively SD 47310.3, SD 56374.3, SD 48213.4, SD 79935 and
SD 45243.3.
In Merowe locality onion and tomato crops recorded the highest
costs of production since they are labour intensive crops and required
additional cultural practices like transplanting and more number of
waterings. The total variable costs of wheat were higher than those of
broad beans, since farmers applied more number of waterings for wheat
and the yield of wheat was better than that of broad beans.
In Dongola locality, garlic recorded the highest cost of production
since it is a labour intensive crop. Broad beans costs was higher than that
of wheat cost because the yield of broad beans was better than the wheat
yield and the prices were twice as that of wheat.
The cost of wheat in Merowe was higher than that recorded in
Dongola since farmers in Merowe locality applied more waterings and
inputs and obtained higher yields. The total variable costs of production
of broad beans were approximately the same in the two localities.
3.8.2 Crops returns
3.8.2.1 Yields:
The average yields of crops in Merowe locality were 1.23, 0.627,
0.231, 8.7, 5.53 ton per feddan for wheat, broad beans, fennel, onion and
tomato primary product respectively. In addition culled tomatoes, which
are usually dried and sold at the end of the season gave an additional
yield of 0.24 tons per feddan.
In Dongola locality the average yields for wheat, broad beans,
fennel, garlic ad zea maize were respectively 0.850, 0.731, 0.338, 1.636
and 1.5 ton per feddan.
The average yield of wheat in Merowe was higher than that of
Dongola, while farmers of Dongola locality obtained the yield of broad
beans and fennel which were higher than those obtained in Merowe
locality .
3.8.2.2 Gross returns
The yields per feddan and prices were used to calculate the returns
per feddan. On average the gross returns for the crops grown in Merowe
locality were SD 75645, SD 77315.7, SD 55380, SD 178815 and SD
332200 per feddan for wheat, broad beans, fennel, onion and tomato
crops respectively, while for those grown in Dongola locality, they were
SD 51850, SD 91040, SD 93860, SD 126960 and SD 60000 for wheat ,
broad beans, fennel , garlic and zea maize respectively.
In Merowe locality the tomato crop scored higher gross returns per
feddan due to the relatively higher yield and prices, followed by onion
and then broad beans. The gross returns of wheat were slightly lower than
the gross returns of broad beans due to low prices of wheat crop. Fennel
crop came at the tail due to its low yield.
In Dongola locality, garlic crop scored the higher gross returns due
to its relatively high yield, followed by fennel and then broad beans,
while wheat and zea maize scored the lowest gross returns due to low
yields and prices for wheat and low prices for zea maize.
The gross returns of wheat in Merowe was higher than that of
Dongola, while those of broad beans and fennel were higher in Dongola
locality due also to the variation in the yields obtained.
3.8.2.3 Gross margins
On average gross margins per feddan for wheat, broad beans,
fennel, onion and tomato crops in Merowe locality were SD 12726.2,
SD19516.3, SD 10099.6, SD 69755 and SD 230264.1 respectively, while
in Dongola locality the gross margins were SD 4539.7, SD 34666.7, SD
45646.6, SD 47025 and SD 14756.7 for wheat, broad beans, fennel, garlic
and zea maize respectively.
In Merowe locality tomato crop recorded the highest gross margin
followed by onion. These two crops are the high value crops grown in
Merowe locality. The tomato crop scored the highest gross margin due to
the high yield obtained and relatively good prices, while onion crop came
second although its price was relatively low due to the high yield
obtained. The gross margin of broad beans was higher than that of wheat
crop due to the high prices of broad beans which was twice as that of
wheat in season 2002/2003. Fennel crop came at the tail due to its low
yield obtained.
In Dongola locality the most profitable crops were garlic and
fennel, followed by broad beans, while zea maize crop came at the tail.
CHAPTER IV
ALLOCATION EFFICIENCY ANALYSIS
This chapter provides, the theoretical framework of the production
function, discusses the results of the production function used in the study
and analyses allocation efficiency.
4.1 Theoretical framework
4.1.1 Introduction
One of the objectives of this study is to compare the economic
efficiency of the resources used in the different farming areas in the
Northern State. To meet this objective, the Cobb-Douglas production
function was specified as a suitable functional form for estimating the
parameters to be used in allocative efficiency.
4.1.2 Production function
4.1.2.1 General overview
Production functions are normally estimated to investigate the
impact of some selected variables on yield. A production function is
defined as the relationship between quantities of various inputs used per
unit time and the maximum quantity of the output produced at that
particular time (Mansifield, 1985). Schiller (1991) reported that the
production function represents maximum technical efficiency that is, the
most output attainable from any given level of factor input.
Historically, the refinements in the concepts relating to the
production functions grew out of economics, probably because of the
following reasons: (1) The nature of the production function is important
in economic development and in determining the extent to which national
products can be increased from a given resource stock. (2) The
magnitudes of the production function coefficients serve as the base for
determining optimum patterns of international or interregional trade. (3)
The conditions under which a total output can be imputed to the factors
from which it is produced with the product just exhausted depends on the
nature of the production function. (4) The production function provides
half or one of two general categories of the data needed in determining or
specifying the use of resource and the pattern of outputs which maximize
firm profits. (5) The algebraic nature of the supply function rests in large
part, upon the nature of the production function (Heady and Dillon,
1961).
Upton (1987) stated that the transformation of inputs into outputs is
made up of a number of determinants, which are technical, behavioural,
organizational and to some extent legal in nature. In 1979 he reported that
the production function in theory should include inputs of all resources,
such as, variable soil nutrients, climate, pests and diseases that might
influence yields. Because of the impossibility of specifying all of these
variables separately, some may be lumped together into a broad category
such as land or labour, and the other variables which are considered to be
unimportant can be ignored.
4.1.2.2 Forms of the production function
Numerous algebraic equations form can be used in deriving
production functions. No single form can be used to characterize
agricultural production under all environmental conditions. The algebraic
form of the function and the magnitudes of its coefficients will vary with
soil, climate, type and variety of crop or livestock, resources being varied,
state of mechanization and the magnitude of other inputs. Hence, a
problem in each study is the selection of an algebraic form of function
which appear or is known to be consistent with the phenomena under
investigation. Guides on appropriate algebraic forms may come from
previous investigations and the theories of the sciences involved.
Selection of any specific type of equation to express production
phenomena automatically imposes certain restraints or assumptions in
respect to the relationship involved and the optimum resource quantities
which will be specified. However, some equations are more flexible than
others (Heady and Dillon, 1961).
a- Linear production functions
The linear production function can be written as follows:
Y= a + b1x1 +b2x2 +...+bnxn
Linear production functions assume a linear relationship between
inputs used and output produced (i.e straight line relationship). Hence,
marginal product is constant and there are no diminishing marginal
returns. All inputs substitute each other, with a constant rate of technical
substitution. Therefore, a linear function is not satisfactory on theoretical
grounds. The justification for use of this form is based on the ease of its
estimation by the ordinary least squared method.
b- Quadratic production function
The quadratic production function in case of two variable inputs is
Y = a + b1x1+ b2x2 - b3x21 -b4x22 -b5x1x2
Where, a: is a constant
bi: is a coefficient
Y: is the level of out put
x1, x2: are the levels of each of the two variable inputs
The advantages of this function is that it is easy to estimate.
Therefore, there is a technical optimum beyond which the total product
falls. The possible disadvantage of this function is that it can not show
both increasing marginal product at low levels of inputs and decreasing
marginal products at high levels of inputs in the same equation.
c- Cubic or Neo-classical production function
The equation of this function with one exogenous variable is:
Y = a +bx +cx2 -dx3
Where,
Y = The output
a, b, c, d = Coefficients
x = input
This type of production function shows the stages of production.
The first stage is "stage I" where output increases at increasing rate as
input is increased, the second stage "stage II" where output increases at
decreasing rate as more input is used and the third "stage III" in which
output decreases as more input is used. The function shows the law of
diminishing marginal returns.
d- Cobb-Douglas production function
The simple form of this function can be written as follows:
ui
Y = ax1b1x b2 2 ....x bn
n e
Where,
Y: is the dependent variable (output).
a: is the constant
xi: is the independent (explanatory variable).
i = 1, 2, ...n
bi: is the regression coefficient to be estimated, which is the partial
elasticity of production with respect to the individual resource. The sum
of these elasticities determine the nature of return to scale which could
indicate the percent by which the output will change if all factors are
changed by a given percent.
If the sum of the elasticitties is equal to one, this means that a one
percent increase in all inputs will result in a one percent increase in the
output, i.e constant returns to scale. While if the sum of elasticities is
greater or less than unity, output will be increased by a greater or smaller
percent than input i.e increasing or decreasing returns to scale.
The Cobb-Douglas function is known as logarithmic function or
even more precisely a double logarithmic function, to distinguish it from
other functions where only one side of the equation is transformed into
logarithms. The logarithmic form of this function is represented by the
following equation:
Log y = log a + b1 logx1 + b2 logx2 + ...bn logxn + e.
4.1.3 Efficiency indices and optimization
Allocation efficiency is defined as maximizing profit by using
least-cost combination of inputs. On the other hand, technical efficiency
refers to increasing output without varying the level of inputs, whereas
economic efficiency combines both allocative and technical efficiencies
(Eisa and Elfiel, 2001).
Efficiency has usually been judged by comparing estimated
marginal value products (MVPs) with the relevant marginal factor cost
(MFC) for factors of production employed. Mathematically, the
expression below gives the efficiency index (IExi) of a particular
resource.
IExi = MVPxi
MFCxi
for resource x i
The marginal value product (MVP) of a factor of production can be
directly computed from its input elasticity of production when output is
measured in value terms (Heady and Dillon, 1961). Mathematically, the
MVP of a resource is calculated from the equation:
MVPxi = ei Ŷ
xi
Where,
MVPxi = the marginal value product of resource xi
ei
= input elasticity of production for xi
Ŷ
= the geometric mean of gross value of farm production
xi
= the geometric mean of resource xi
Marginal cost is the increase in total cost associated with a one-unit
increase in production (Schiller, 1991). Elfeil et al (2001) reported that
the marginal costs are estimated as the costs of purchasing an additional
unit of input at market price.
Efficiency resource use is reached when the expected value of IExi
is positive and approaches unity. In other words, a resource can be
expanded until its MVP is exactly equal to its MFC. A comparison will
give an insight into the differences in efficiency of resource use between
the different agricultural areas and agricultural schemes in the Northern
State.
4.2 The results of the production function
A Cobb-Douglas production function of log-log form was fitted to
the data which were collected during 2002/03 season in the Northern
State, to relate the value of whole farm output to the different inputs, in
order to test for the resource allocative efficiency. The definition and
composition of each of the variables included in the equation are briefly
given below:
a- The intercept:
This may represent some variables that are easy to incorporate in the
equation such as the management factor, the weather conditions, land and
labour qualities, etc. In applied research, no major significance is
normally attached to this term because there are relatively few instances
where the intercept has an obvious economic interpretation (Elfeil et al,
2001).
b- Land:
The land variable was the total land resource actually cultivated by the
farmer. It was measured in feddans. Elfeil et al, (2001) reported that
irrigated land in the Northern State is one of the most scarce resources.
c- Labour:
The labour variable represented the total labour employed by each farm
during the winter season 2003. It was measured in mandays.
d- Capital expenses:
The capital expenses variable was the cash expenditure reported by the
farmer for purchased inputs such as seeds, fertilizers, chemicals, sacks,
cost of land preparation, threshing and irrigation.
The dependent variable was the value of the whole farm output for
the season 2003 expressed in Sudanese dinars.
The results of the production function for the whole sample in
Merowe and Dongola localities and within each locality for private and
companies schemes are shown in Tables 4.1, 4.2 and 4.3.
Table 4.1. The regression equation for the whole sample in Merowe and
Dongola localities
Dependent variable : Whole farm output (SD)
Merowe
Dongola
Variable
Coefficient
T- value
Coefficient
T- value
Constant
1.565
1.913
0.948
1.451
(0.818)1
Farm size (fed.)
-0.081
(0.653)
-1.116
(0.073)
Total labour
0.696***
(mandays)
Operating
(SD)
R- squared
F- ratio
1
0217*
-1.998
(0.068)
5.306
(0.174)
capital
-0.157**
0.380***
2.646
(0.178)
1.706
0.522***
(0.219)
(0.193)
0.744
0.652
54.236***
47.461***
Values in brackets are standard errors
***Statistically significant at 0.01 level
**Statistically significant at 0.05 level
*Statistically significant at 0.10 level
Source: Computed, using data from the survey of 2003
3.622
Table 4.2. The regression equation for private and companies schemes in
Merowe locality
Dependent variable : Whole farm output (SD)
Private
Companies
Varible
Coefficient
T- value
Coefficient
T- value
Constant
1.701**
1.825
-4.052**
-4.104
(0.932)1
Farm
size
(fed.)
-0.099
-1.131
(0.080)
Total labour 0.616***
(mandays)
(0.987)
-0.178*
(0.079)
3.511
(0.237)
0.264***
0.251
capital (SD)
(0.266)
R- squared
0.677
0.950
32.182***
37.983***
1
2.309
(0.189)
Operating
F- ratio
-1.860
1.460
0.831***
0.238
Values in brackets are standard errors
***Statistically significant at 0.01 level
**Statistically significant at 0.05 level
*Statistically significant at 0.10 level
Source: Computed, using data from the survey of 2003
7.160
Table 4.3. The regression equation for private and companies schemes in
Dongola locality
Dependent variable : Whole farm output (SD)
Private
Companies
Vaeiable
Coefficient
T- value
Coefficient
T- value
Constant
1.307*
1.692
-0.508
-0.392
(0.773)1
Farm
size
-0.132
(fed.)
(0.080)
Total labour
0.429***
(mandays)
(0.216)
Operating
0.448***
(1.296)
-1.348
-0.243*
(0.142)
2.478
0.126
2.601
0.390***
(0.228)
(0.390)
R- squared
0.617
0.779
30.063***
18.815***
1
0.449
(0.356)
capital (SD)
F- ratio
-1.899
Values in brackets are standard errors
***Statistically significant at 0.01 level
**Statistically significant at 0.05 level
*Statistically significant at 0.10 level
Source: Computed, using data from the survey of 2003
2.931
4.3 Allocation efficiency analysis
4.3.1 The land resource
Efficiency resource is reached when the expected value of the
resource efficiency index (IExi) is positive and approaches unity. The
land variable regression coefficient had a priori negative sign for each of
the regression equations applied for the whole sample of Merowe and
Dongola localities and within each locality for private and companies
schemes. This indicated that additional units of land resource would
reduce the value of output. However, the reduction of output is significant
at 5% level of significance for Dongola whole sample and it was
significant at 10% level of significance for companies schemes of the two
localities (Tables 4.1, 4.2 and 4.3). Accordingly the marginal value
productivity of land and land efficiency index carry negative signs
(Tables 4.4 and 4.5).
Table 4.4. Marginal value productivities and efficiency indices of
land for the whole sample of Merowe and Dongola localities
Item
Merowe
Dongola
217512.15
126420.29
5.09
7.47
Average productivity of land (SD)
42733.23
16923.73
Marginal value productivity of land
-3461.39
-2657.03
9338.77
9063.76
-0.37
-0.29
Mean value of output (SD)
Mean land input (fed.)
(SD)
Marginal cost of land (SD)
Land
efficiency
(MVPland/MFCland)
Source: Calculated
index
Table 4.5. Marginal value productivities and efficiency indices of land in
the Northern State by locality and scheme type
Merowe
Item
Private
Mean value of output 250379.33
Dongola
Companies
Private
Companies
53176.25
130735.73
113474
(SD)
Mean land input (fed.)
5.61
2.47
7.79
6.52
Average productivity
44630.9
21528.85
16548.83
17403.99
-4418.46
-3832.14
-2184.45
-4229.17
9338.77
9338.77
9063.76
9063.76
-0.47
-0.41
-0.24
-0.47
of land (SD)
Marginal
value
productivity of land
(SD)
Marginal cost of land
(SD)
Land efficiency index
(MVPland/MFCland)
Source: Calculated
The negative land variable regression coefficient may be due to
variation in soil fertility and the limited quantities of inputs and
management efforts that large holdings received. As discussed in chapter
two, the lands of the Northern State can be classified according to soil
type into islands and gerif lands, river banks lands and high terrace lands.
There is large variation in soil fertility between these classes. The islands
and gerif lands are more productive than the river banks lands and the
latter are more productive than high terrace lands. Elfiel et al, (2001)
reported that, although land quality is variable, it is difficult to capture the
quality directly as the necessary data are not available. Siddig, (1999)
stated that an inverse relationship between farm size and the total variable
cost of production of wheat was detected. A similar relation was also
observed between farm size and productivity of wheat where the small
farmers were found to use more inputs (seeds and fertilizers) per unit area
relative to large farmers. Abd Alla, (1998) found an inverse relationship
between farm size and date yield. He attributed this inverse relationship
to the following:
i- As the area cultivated by date palms increased it extends away from the
river banks and hence the soil fertility decreases.
ii- As the area of date palms is increased, the number of trees per unit
area decreases because the off-shoots of date palms are very expensive
and most of farmers are unable to purchase a large number of seedlings
for the extended areas.
iii- Small areas receive more management efforts than the extended areas.
4.3.2 Labour resource
The information used for comparing the MVP and MFC of labour
is presented in Tables 4.6 and 4.7.
Table 4.6. Marginal value productivities and efficiency indices of labour
for the whole sample of Merowe and Dongola localities
Item
Merowe
Dongola
217512.15
126420.29
Mean labour input (mandays)
105.26
75.75
Average productivity of labour (SD)
2066.43
1668.92
Marginal value productivity of labour (SD)
1438.24
634.19
Marginal cost of labour (SD)
700
700
Labour efficiency index (MVPlab/MFClab)
2.05
0.9
Mean value of output (SD)
Source: Calculated
Table 4.7. Marginal value productivities and efficiency indices of labour
for Merowe and Dongola localities by scheme type
Merowe
Item
Mean
Private
value
Dongola
Companies
Private
Companies
53176.25
130735
113474
119.36
34.72
79.46
64.61
2097.68
1531.57
1645.29
1756.29
1292.17
404.33
705.83
221.29
700
700
700
700
1.85
0.57
1.01
0.32
of 250379.33
ouput (SD)
Mean
labour
input (mandays)
Average
productivity
of
labour (SD)
Marginal
value
productivity
of
labour (SD)
Marginal cost of
labour (SD)
Labour efficiency
index
(MVPlab/MFClab
Source: Calculated
From table (4.6), it is obvious that the marginal value productivity
of labour for the whole sample of Merowe locality exceeded the marginal
factor cost of labour, while the opposite occurred in the whole sample of
Dongola locality. The labour efficiency ratio for Merowe locality was
found to be 2.05, while it was 0.9 in Dongola locality. This implies that
labour has been in short supply for Merowe locality and its use should be
extended if efficiency is to be achieved. The divergence of this ratio from
unity in Dongola locality reveals that Dongola farmers could be described
as efficient in allocating the labour resource.
Within the two localities the labour efficiency ratio was 1.85 and
0.57 in private and companies schemes of Merowe locality respectively,
while it was 1.01 in Dongola private schemes and 0.32 in Dongola
companies schemes (Table, 4.7). These results indicate that labour was
underutilized in Merowe private schemes, efficiently utilized in Dongola
private schemes, while the resource was utilized beyond the optimum
levels in companies schemes of the two localities. The labour resource
seems to be under utilized in Merowe private schemes since farmers grew
some labour intensive crops, namely onion and tomato. These crops are
profitable crops, but farmers cultivated small areas when compared to
areas cultivated by wheat and broad beans and hence the expansion in
growing of these crops increase farmers incomes.
4.3.3 Capital resource
The information used for comparing the MVP and MFC of capital
are presented in Tables 4.8 and 4.9.
Table 4.8. Marginal value productivities and efficiency indices of capital
for the whole sample in Merowe and Dongola localities
Item
Merowe
Dongola
Mean value of ouput (SD)
217512.15
126420.29
Mean capital input (SD)
92450.53
63124.91
Average productivity of capital (SD)
2.353
2.003
Marginal value productivity of capital (SD)
0.511
1.0456
Marginal cost of capital (SD)
1.15
1.15
Capital efficiency index (MVPcap/MFCcap)
0.44
0.91
Source: Calculated
Table 4.9. Marginal value productivities and efficiency indices of capital
in the Northern State by locality and scheme type
Merowe
Item
Dongola
Private
Companies
Private
Companies
Mean value of output (SD)
250379.33
53176.2
130735.73
113474
Mean capital input (SD)
99460.33
57401.25
64662.72
58511.5
2.52
0.93
2.02
1.94
0.6325
0.7728
0.905
0.958
1.15
1.15
1.15
1.15
0.55
0.67
0.79
0.83
Average
productivity
of
capital (SD)
Marginal value productivity
of capital (SD)
Marginal cost of capital
(SD)
Capital
efficiency
index
(MVPcap/MFCcap)
Source: Calculated
Table, (4.8) shows that the capital efficiency ratio was 0.44 and
0.97 for the whole sample of Merowe and Dongola localities respectively
which indicates that capital was utilized beyond the optimum level in
Merowe locality and it could be described as efficiently utilized in
Dongola locality. Within the two localities the capital efficiency ratio was
0.55 and 0.67 in private and companies schemes of Merowe locality
respectively, while in private and companies schemes of Dongola locality
it was respectively 0.79 and 0.83 (Table, 4.9). These results reveal that
the capital resource was used beyond the optimum level in private and
companies schemes of the two localities. However, the divergence from
unity indicates that the capital resource was optimally used by companies
schemes when compared to private schemes. The over utilization of the
capital resource is consistent with the results discussed in chapter three
which showed that an improvement in agricultural inputs supply and
application is observed. The applied quantities of seeds and fertilizers for
wheat and broad beans crops exceeded the recommended rates. Also the
number of irrigations applied exceeded the recommended one for wheat
in private schemes of Merowe locality and it was slightly lower than the
recommended quantities in Dongola locality. For broad beans Merowe
private schemes farmers applied the required number of irrigations and it
was lower by 22% in Dongola locality. Although farmers spend more on
purchasing agricultural inputs, they obtained low yields specially for the
broad beans. This was mainly due to expansion of cultivation into
marginal lands. Farmers in companies schemes seem to be more efficient
when compare to private schemes farmers. Although they spend less on
purchasing agricultural inputs, they obtained better yields. When the uses
of different resources were compared the labour and capital efficiency
ratio were 2.05 and 0.44 in Merowe whole sample which means that
labour was under utilized, while capital was over utilized. In Dongola
whole sample labour and capital efficiency ratios have the same value
which was 0.9. This indicates that labour and capital resources were
efficiently utilized in Dongola locality. In Merowe private schemes,
labour efficiency ratio was 1.85, while the capital efficiency ratio was
0.55. This indicates that labour was under utilized while capital was over
utilized. In Merowe companies schemes the labour and capital efficiency
ratios were 0.57 and 0.67 respectively which means that both resources
were used beyond the optimum level. In Dongola private schemes the
labour efficiency ratio was 1.01, while the capital efficiency ratio was
0.79. This indicates that labour was utilized efficiently while capital was
over utilized. In Dongola companies schemes the labour efficiency ratio
was 0.32, while the capital efficiency ratio was 0.83 which means that
capital was utilized optimally when compared to labour resource.
Thus, it could be concluded that the capital resource was used
beyond the optimum level in private and companies schemes of the two
localities due to low yields obtained, while the labour resource was under
utilized in Merowe private schemes, efficiently utilized in Dongola
private schemes and used beyond the optimum levels in companies
schemes of the two localities. Labour resource seems to be under utilized
in Merowe private schemes since farmers grew tomato and onion crops
which are labour intensive and profitable crops and there fore efficiency
is to be achieved if the areas of these crops are expanded.
CHAPTER V
A LINEAR PROGRAMMING MODEL OF A REPRESENTIVE
FARM IN THE NORTHERN STATE
This chapter discusses the linear programming model development
and its specifications.
5.1 Theoretical framework
5.1.1 Economic choice concepts
Factors of production are the inputs: land, labour and capital. The
quantities of available resources are limited. We can not produce every
thing we want in the quantities we desire. Resources are scarce relative to
our desire, because our resources are services. Opportunity costs exist in
all situations where available resources are not abundant enough to satisfy
all our desire. In all such situation, we must make hard decisions about
how to allocate our scarce resources among competing uses. Our ability
to alter the mix of output depends in part on the capability of factors of
production to move from one industry to another. Two issues arises, first,
can the resources be moved, second, how efficient will the resources be in
a new line of production (Schiller, 1991). The opportunity cost is defined
as, the most desirable goods or services that are forgone in order to obtain
something else. The opportunity cost of a product is measured by the
most desirable goods and services that could have been produced with the
same resources.
Dent, Harrison and Woodford (1986) defined the shadow price of
the activity as a measure of its true profitability after taking account of
both cash returns and opportunity cost. The terms shadow price,
opportunity
cost
and
marginal
value
product
are
often
used
synonymously when referring to resources. Beneke and Winterboer
(1973) reported that shadow prices for production activities indicate how
the value if the program would change (how much income would be
penalized) if an additional unit of the activity were forced into the final
plan. They will be refered to frequently as income penalties. Shadow
prices for the disposable activities provide information concerning the
productivity of added resources (relation of restraints).
5.1.2 Linear programming model
5.1.2.1 General overview
Programming is one of the techniques, that are used in allocation
problems. Mathematically, linear programming is defined as the analysis
of problems in which a linear function of a number of variables is to be
maximized (or minimized) when those variables are subjected to a
number of restraints in the form of linear equalities (Dorfman, Samuelson
and Solow (1954). Linear programming generally refers to the
computational method used in prescribing production patterns which
maximize profit of firms, minimizing cost of producing a specific
commodity or related types of aggregative analysis. The term linear refers
to the fact that straight line relationships are employed in linear
programming. A more precise mathematical definition of linear
programming is that, linear programming techniques involve the
maximization of a linear function, subject to linear inequalities (Heady
and Candler, 1973).
Dent, Harrison and Woodford (1986) reported that, the simplex
method is based on the economic principle of opportunity cost. It is
defined as a mathematical procedure that checks the corner points of the
production possibility boundary so as to locate the most profitable plan.
Beneke and Winterboer (1973) defined it as a mathematical procedure
(algorithm) that use addition, subtraction, multiplication and division in a
particular sequential way to solve problems.
In 1949 George B.Dentzig published the simplex method for
solving linear programs. Since that time a number of individuals have
contributed to the field of linear programming in many different ways
includes theoretical development, computational aspects, and exploration
of new application of the subject. The simplex method of linear
programming enjoys wide acceptance because of (1) its ability to model
important and complex management decision problems and (2) its
capability for producing solutions in a reasonable amount of time
(Bazaraa and Jarvis, 1977).
Hazel and Norton (1986) reported that, early application of linear
programming in farm planning assumed profit maximizing behavior.
Heady and Candler (1973) reported that, a linear programming problem
has three quantitative components, an objective, alternative methods or
processes for attaining the objective and resources or other restrictions. A
problem which has these three components can always be expressed as a
linear programming problem. For the typical farm management or
working efficiency problem the objective will be maximum income or
minimum cost. There is, however no reason why the objective should be
so restricted. If the manager wishes to make certain other specifications
as to his objective these can easily be included. Given the objective, it is
obvious that, unless it can be attained in more than one way, there is no
problem to be analyzed. A linear programming problem does not exist
unless resources are restricted or limited.
Several individuals have contributed to the development of linear
programming, among them Van Neumann, Leontief, Laplace, Weyl,
Stigeler, Cornfield, Koopmans and Dantzig (Abd Elaziz, 1999). Many
researchers in the world applied linear programming in the last years,
from those Majumder (1998), Darwish et al (1999), Neto et al (1997),
Salinas et al (1999), Pennel (1999), Frizzone et al (1997), Kassie et al
(1998), Goswami (1997) and Zahoor (1997).
The individuals who contributed to the application of linear
programming in African agriculture are, Heyer (1996), Delgado (1997),
Schultz (1964) and Metson (1978).
Abd Elaziz (1999) applied linear programming for the analysis of
small private farms in the River Nile State. Linear programming was also
applied in the modern and traditional agriculture of the Sudan. In the
traditional farming of Northern Sudan, linear programming was used by
Ahmed (1988) for evaluating new faba bean techniques in Northern
Sudan, and Ahmed and Faki (1992 and 1994) for investigating the
prospects of technology adoption in small pump schemes in Wad Hamid
and Rubatab areas in the River Nile State.
5.1.2.2 Assumptions of linear programming
Several assumptions are used in linear programming. If these
assumptions do not apply to the problem under consideration, linear
programming may not provide a sufficient precise solution. These
assumptions are explained below:
- Additivity and linearity:
The activities must be additive in the sense that when two or more
are used, their total product must be the sum of their individual products.
An equivalent statement is , the total amount of resources used by several
enterprises must be equal to the sum of the resources used by each
individual enterprise. Thus no interaction is possible in the amount of
resources required per unit of output regardless of whether activities are
produced alone or in various proportions.
- Divisibility:
It is assumed that factors can be used to produce commodities that
can be produced in quantities which are fractional units. That is, resources
and products are considered to be continuous to be infinitely divisible.
- Finiteness:
It is assumed that there is a limit to the number of alternative
activities and to the resource restrictions which need to be considered.
- Single-value expectations:
In general, the linear programming method used widely to date
employs the standard linear programming assumption that resources
supplies, input-output coefficients, and prices are known with certainty.
(Heady and Candler, 1973).
Other assumptions summarized by Hazel and Norton ( 1986) are:
- Optimization:
It is assumed that an appropriate objective function is either
maximized or minimized.
- Fixedness:
At least one constraint has a non-zero right hand side coefficient.
- Homogeneity:
It is assumed that all units of the same resource or activity are
identical.
- Proportionality:
The gross margin and resource requirements per unit of activity are
assumed to be constant regardless of the level of the activity used.
5.1.2.3 The objective function
The objectives of traditional farming have been a source of legacy
in agricultural economics literature. Three schools of thought emerged
from the discussion: The first one is represented by the neoclassical
theory which advocates a profit maximization aim, the second school
which is the extreme of the first one, argues that the fulfillment of the
family grain requirement is the sole objective of peasant farmers, while
the third school represents a middle ground between the other two
theories. That is , peasant farmers have a combination of objectives which
may be listed in the following:
i- Securing an adequate and assumed food supply.
ii- Ensuring a cash income to meet other material needs.
iii- Avoidance of risk, or simply, survival in an uncertain environment.
iv- Other leisure and socially related objectives (Malik, 1994).
Hazel and Norton (1986) reported that for small-scale farmers in
developing countries, a more primary objective is often to provide their
families with adequate food. Such farmers face rural markets that are
often incomplete, exploitative or unreliable and trading cash crops for
food is a rarely sound strategy for ensuring survival. Under these
circumstances, it is not surprising that farmers first allocate resources to
assure necessary food supplies, and only then are remaining resources
used to generate cash income. Another important consideration for smallscale farmers is often a desire to enjoy more leisure. As incomes rise
above subsistence level and the required drudgery to eke out a living is
reduced.
5.2 Specification of the model structure
The objective function of the representative farm model maximizes
net farm income after satisfying family requirements of the main food
crop which is wheat. The mathematical form of the model used is as
follows:
Max z = ∑
t=1
n
CjXj
Subject to ∑ aijxj
n
t =1
<= bi
And
Xj >= 0, all j = 1 to n
Where:
Z = objective function value
Cj= gross margin per feddan of the jth farm activity i.e input/output
coefficients.
Xj = the level of the jth farm activity
aij = the quantity of the ith resource required to produce one unit of the
jth activity i.e input/output coefficients
bi = vector of resource availability .
The above structure was then formulated into a matrix that gives
the model's technical input-output coefficients and resource endowments
as shown in Table (5.1).
The first row of the matrix represents the procedure's objective
function. The objective function is maximization of gross farm income
after satisfying consumption requirements from wheat. The positive signs
in the objective function are the prices per unit of output (Q1...Q5). The
negative signs in the objective function include the cost of production of
crops (CRP1...CRP5), cost of labour hiring (HL1...HL10), morabaha
margin of capital borrowed (BC1...BC10) and prices per unit of wheat
brought from the market. The zero value in the objective function is given
to wheat produced for consumption since it does not involve cash
transactions. On the other hand, capital transfers are assigned a zero value
since they do not involve any actual expenditure of funds but are rather a
capital transfer from one period to another.
In the constraint row, demand and supply of resource are
represented by negative and positive coefficients. The positive
coefficients in the left hand side indicate the resource requirement per
unit of activity. The level of activity times the coefficient gives the total
nst.
ld
1 ld
2 ld
3 ld
4 ld
5 ld
1
10
1
10
T Rep.
d CPT
t1
t9
ht cons.
1 prod.
2 prod.
3 prod.
4 prod.
5 prod.
Unit
Fed.
"
"
"
"
"
m.d.
"
"
SD
"
"
"
"
Nu.
"
"
sack
"
"
"
"
"
-SD
-SD
-SD
1
1
1
1
1
-SD
SD
SD
SD
SD
SD
0
SD
-SD
Hired
labour
Capital
transfer
Borrowed
credit
CT1..CT9
BC1..BC10
..
_SD
0
..
0
-SD
..
-SD
End capt.
-SD
HL1.. HL10
Capt. Rep.
jec. Fun.
Wht buy.
Q1 .. Q5
Wht cons.
Crop selling
CRP1 .. CRP5
Activities
Crop prod.
Table 5.1 An LP tableu of representative farm in the Northern State
0
0
1
1
1
c
…
c
c
…
c
…
c
c
…
C
…
C
C
…
C
…
C
C
…
1
c
…
c
c
…
c
c
C
C
c
-1
-1
SD
SD
-SD
-SD
1
-1 +1
-SD
-SD
SD
-1
1
SD
SD
-1
1
c
..
c
c
…
c
C
…
C
C
…
C
c
…
c
1
1
-c
-1
-c
-c
-c
-c
C = Coefficient neither equal zero, nor equal one or minus one, SD = Monetary coefficients in Sudanese Dinars
Bi = Level of resource in the model
Source: Constructed from survey data.
Dir.
<=
>=
>=
>=
>=
>=
<=
"
"
<=
"
RHS
Bi
0
0
0
0
0
Bi
Bi
Bi
Bi
Bi
"
<=
>=
<=
"
"
=
<=
"
"
"
"
Bi
Bi
Bi
Bi
Bi
Bi
Bi
o
0
0
0
0
derived demand by that activity. The sum of total derived demands for all
crops should not exceed the right hand side of constraints inequalities.
The first constraint row (land) represents land availability and
states that the total land allocating to cropping activities is bounded by
farm size (total land). A set of constraints indicate the availability of
labour by month (lab1, lab2, ...etc). These constraints insure that labour
demand by crop production is bounded by supply each period. Labour is
supplied by family labour and by labour hiring. A set of water constraints
(wat1, wat2 ...etc) demanded by crop production in each month should
not exceed the water supply by the pumping activities. A set of operating
capital constraints (OC1, OC2, ...etc) insures that cash demanded by crop
production in each month should not exceed the available capital. Cash is
available from the farmer's own funds and the amount borrowed from the
banks. The credit limit constraint states that the money borrowed should
not exceed the amount provided by the banks. A crop balance insures that
what is produced is what is sold plus domestic consumption.
A consumption constraint insures that the household consumption
of wheat is being met by either farm production and/or market purchase.
5.3 Empirical specification of the model
5.3.1 Introduction
As discussed in chapter three the data was collected by a post
harvest field survey in the winter season 2003. Two localities were
selected from the four localities in the Northern State according to the
existing administrative structure. These two localities are Merowe and
Dongola. They reflect different resource endowments, different resource
use and different production relations. Representative farm models were
built for each farming system in the two localities.
The linear programming model was applied to farmers of private
schemes since they were represented by the majority of the sampled
farmers.
5.3.2 The activity set
The activity set in the model includes the following:
i- Crop production activities.
ii- Hiring labour activities.
iii- Crop selling activities.
iv- Wheat consumption and buying activities.
v- Borrowing activities.
vi- Transfer activities.
1- Crop production activities
Table 5.2 and 5.3 show a portion of the LP matrix representing the
crop production activities in Merowe and Dongola localities. The crop
production activities in Merowe locality are wheat, broad beans, fennel,
onion and tomato. While in Dongola locality the crop production
activities were, wheat, broad beans, fennel and garlic.
The objective function coefficients for the production activities
represent the total cost of production per feddan excluding the cost of
hired labour and capital. The costs carry negative signs since they draw
from the value of the objective function.
Average from the selected sample were used to estimate the labour
coefficients. The labour input-output coefficients were calculated on per
feddan basis which is the unit of the producing activities. The producing
activities are linked with production balance equations and their
respective yield per feddan is shown as a negative figure in these
equations, meaning supplying with that level per feddan.
Table 5.2. Crop production activities in Merowe locality
Activities
Wht
Objective function
Constraints
Total land
Wht land
Bean land
Fen. Land
Onion land
Tomato land
TL Sept.
TL Oct.
TL Nov.
TL Dec.
TL Jan.
TL Feb.
TL March
TL April
TL May
TL June
OC Sept.
OC Oct.
OC Nov.
OC Dec.
OC Jan.
OC Feb.
OC March
OC April
OC May
OC June
CPTL Rep.
End CPTL
Wht cons.
Wht prod.
Bean prod.
Fen. Prod.
Bean
Fen.
Toma.
Onion
54948.9 48552.6 38580.4 88152.8 89943.5
Unit
Fed.
1
"
1
"
0
"
0
"
0
"
0
m.d
0
"
3.38
"
5.06
"
8.56
"
5.2
"
3.59
"
10.36
"
0
"
0
"
0
SD
0
"
7153
"
0
"
15629
"
4064
"
4064
"
10376.4
"
0
"
0
"
0
"
0
"
0
Sack
0
"
-12.3
"
0
kantar
0
1
0
1
0
0
0
0
7.15
8.09
8.42
3.1
3.11
7.7
0
0
0
0
7306
11622
4902
2869.5
2869.5
7225.4
0
0
0
0
0
0
0
-6.6
0
Onion prod.
Sack
0
0
Toma. Prod.
tin
0
0
Source: Constructed from survey data.
1
0
0
1
0
0
0
8.74
13.38
4.66
3
15
19.79
0
0
0
0
3781
1143.7
4449.6
3337.2
2224.8
2978
0
0
0
0
0
0
0
0
-5.2
0
0
1
1
0
0
0
0
0
0
1
0
0
1
0
14.73
7.41
8.38
7.45
6.7
9
6.55
10.8
18.15
5.6
18.75
4.04
15.5
2.97
17.6
8.42
0
21.54
0
0
12810
0
5174.5
4402
2332
17452.6 2332
7013.4
2332
4528.8 14057
4528.8 25005
3019.2 12502.3
1509.6
0
20157.5
0
0
0
0
0
0
0
0
0
0
0
0
0
-91.7
0
0
-615
Dir.
RHS
<=
>=
"
"
"
"
<=
"
"
"
"
"
"
"
"
"
<=
"
"
"
"
"
"
"
"
"
"
>=
=
<=
"
"
8.15
0
0
0
0
0
20
36
36
36
36
36
36
36
36
36
362425
0
48973
0
0
0
0
0
0
0
48973
4111398
8.5
0
0
0
"
"
0
0
Table 5.3. Crop production activities in Dongola locality
Activities
Wht
Bean
Fen.
Gar.
Objective function -42055.4 -479826 -38555.1 -73396.5
Constraints Unit
Dir. RHS
Total land Fed.
1
1
1
1
<= 11.32
Wht land
"
1
0
0
0
>=
0
Bean land "
0
1
0
0
"
0
Fen. Land "
0
0
1
0
"
0
Garlic land "
0
0
0
1
"
0
TL Oct.
"
4.96
5.18
10.49
10.3
<=
20
TL Nov.
"
4.03
7.9
9.86
7.97
"
33
TL Dec.
"
8.1
8.44
14.05
9.2
"
33
TL Jan.
"
7.69
5.12
5.54
5.7
"
33
TL Feb.
"
2.85
2.28
11.39
3.5
"
33
TL March "
1.21
9.78
10.3
2
"
33
TL April
"
11.78
1.25
0
20.8
"
33
OC Oct.
"
0
5767.4
15555 35312.9
" 152693
OC Nov.
"
5963.6 13700.6 2579.6
1731
"
99844
OC Dec.
"
14835
2742.8 2579.6
3462
"
0
OC Jan.
"
2489.5
2601
1289.8
3462
"
0
OC Feb.
"
2489.5
2601
1289.8
1731
"
0
OC March "
1244.8
4189
2746.4
1731
"
0
OC April
"
5380.8
0
0
7455
"
0
CPTL Rep. "
0
0
0
0
"
99844
End CPTL "
0
0
0
0
>= 252537
Wht cons. Sack
0
0
0
0
=
9.5
Wht prod. "
-8.5
0
0
0
<=
0
Bean prod. "
0
-7.7
0
0
"
0
Fen. Prod. kantar
0
0
-7.6
0
"
0
Gar. prod. Sack
0
0
0
-36.8
"
0
Source: Constructed from survey data.
2- Hiring labour activities
Tables 5.4 and 5.5 show the portion of the matrix representing HL
activities in Merowe and Dongola localities respectively. The labour
hiring activities were introduced in the models to supplement the family
labour on a monthly basis. The unit of the activity is one manday. A
standard manday was taken as the effort exerted by a healthy adult in the
age of 15-64 years in working day. A one day labour input was assumed
to be 0.75 standard manday for women and 0.5 for children and old
person (Malik 1994 and Abd Elaziz 1999).
The objective function coefficient of each of the hired labour activities
represents average monthly wage rates estimated from the field survey.
The coefficients carry negative signs since they draw from the value of
the objective function.
Objec. Funcion
Constraints Unit
HL June
HL May
HL Apr.
HL Mar.
HL Feb.
HL Jan.
HL Dec.
HL Nov.
HL Oct.
HL Sep.
Table 5.4. Hiring labour activities in Merowe locality
Activities
700 700 700 700 700 700 700 700 700 700
Dir.
-1
<=
TL Oct.
"
-1
"
TL Nov.
"
-1
"
TL Dec.
"
-1
"
TL Jan.
"
-1
"
TL Feb.
"
-1
"
TL Mar.
"
-1
"
TL Apr.
"
-1
"
TL May
"
-1
"
TL June
"
-1 "
OC Sept.
SD 700
<=
OC Oct.
"
700
"
OC Nov.
"
700
"
OC Dec.
"
700
"
OC Jan.
"
500
"
OC Feb.
"
500
"
OC Mar.
"
700
"
OC Apr.
"
700
"
OC May
"
500
"
OC June
"
500 "
Source: Constructed from survey data.
TL Sept.
m.d
RHS
10
36
36
36
36
36
36
36
36
36
362425
0
48973
0
0
0
0
0
0
0
Objec. function
HL Apr.
HL Mar.
HL Feb.
HL Jan.
HL Dec.
HL Nov.
HL Oct.
Table 5.5. Hiring labour activities in Dongola locality
Activities
-700 700 700
500 500 700 700
Constraints Unit
TL Oct.
m.d. -1
TL Nov.
"
-1
TL Dec.
"
-1
TL Jan.
"
-1
TL Feb.
"
-1
TL Mar.
"
-1
TL Apr.
"
-1
OC Oct.
SD 700
OC Nov.
"
700
OC Dec.
"
700
OC Jan.
"
500
OC Feb.
"
500
OC Mar.
"
700
OC Apr.
"
700
Source: Constructed from survey data.
Dir.
<=
"
"
"
"
"
"
<=
"
"
"
"
"
"
RHS
20
33
33
33
33
33
33
152693
99844
0
0
0
0
0
3- Selling activities
These are activities which dispose of produced crops (through their link
to the production balance equations) by selling. Five and four selling
activities corresponding to each of the five and four crops grown in
Merowe and Dongola localities respectively. Objective function
coefficients for the selling activities represent average price per unit of
sale. This sale value includes the value of crop plus that of crop
byproduct for tomato crop in Merowe locality. Objective function
coefficients of selling activities also appear as supplying the operating
capital stream in the months where selling takes place (Tables 5.6 and
5.7).
54
0
Wht buying
195
0
Wht cons.
Toma. selling
1171 1065
5
0
Onion selling
615
0
Fen. selling
Bean selling
Objec. function
Wht selling
Table 5.6. Crop selling, consumption and buying activities in Merowe
Locality
Activities
750
0
0
Constrain Uni
ts
t
OC Sept. SD
OC Oct.
OC Nov.
OC Dec.
OC Jan.
OC Feb.
OC Mar.
"
"
"
"
"
"
OC Apr.
OC May
OC June
"
"
"
Wht cons. sac
k
Wht prod. "
Bean
prod.
Fen.
Prod.
Oni.
Prod.
Toma
prod.
"
"
615
0
1171 1065
5
0
Dir RHS
.
<= 36242
5
"
0
" 48973
"
0
"
0
"
0
"
0
54
0
195
0
1
12.3
1
-6.6
-5.2
"
"
Source: Constructed from survey data
91.7
61
5
-1
"
"
"
0
0
0
=
8.5
<=
0
"
0
"
0
"
0
"
0
Gar. selling
Wht cons.
610
0
1182
4
1235
0
345
0
750
0
Wht
b i
Fen.
Selling
Objec. function
Bean
selling
Activities
Wht selling
Table 5.7. Crop selling, consumption and buying activities in Dongola
locality
0
Constraint
s
OC Oct.
Uni
t
"
Dir
.
"
OC Nov.
OC Dec.
OC Jan.
OC Feb.
OC Mar.
"
"
"
"
"
"
"
"
"
"
15269
3
99844
0
0
0
0
OC Apr.
"
"
0
Wht cons.
sac
k
"
"
"
"
Wht prod.
Bean prod.
Fen. Prod.
Gar. Prod.
1182
4
1235
0
610
0
345
0
1
-8.5
1
-7.7
-7.6
Source: Constructed from survey data.
36.8
=
-1
<=
"
"
"
RHS
4- Wheat consumption and buying activities
Wheat consumption and buying activities include meeting consumption
requirements of wheat through production and or buying. Wheat
consumption activity is included in the model with a zero objective
function coefficient (no cost) and is linked to wheat consumption
constraint and wheat production balance equation. The buying activity is
permitted to allow households to satisfy wheat consumption constraint in
case model production could not satisfy this constraint. The objective
function value for this buying activity represents the average price
households pay for purchased wheat (Table 5.6 and 5.7).
5- Borrowing activities
Borrowing activities are used to supplement the amount of cash owned
by the farmers. The main formal source of agricultural credit in the
Northern State is the ABS. The seasonal loans provided by the ABS are
in the form of agricultural inputs such as fuel, fertilizers and seeds. The
bank lending term depend on Morabaha system, where the Morabaha
margin determined by the Bank of Sudan for the year 2003 was 15%.
This rate appear as a negative coefficient in the objective function (Tables
5.8 and 5.9).
6- Transfer activities
Tables 5.10 and 5.11 show the portion of the matrix representing transfer
activities in Merowe and Dongola models respectively. These activities
allow the passing of unused capital in a specific month to the next one.
The coefficients of capital transfer in the objective function carry zero
values since it does not involve money transactions.
Table 5.8. Borrowing capital activities in Merowe locality
OC Sept.
Un
it
SD
OC Oct.
OC Nov.
OC Dec.
OC Jan.
OC Feb.
OC Mar.
OC Apr.
OC May
OC June
CPT Rep.
End CAPT
"
"
"
"
"
"
"
"
"
"
"
BC Oct.
BC Nov.
BC Dec.
BC Jan.
BC Feb.
BC Mar.
BC Apr.
BC May
BC June
CPT.Rep.
End CPT
Objec. Function
Constraints
BC Sept..
Activities
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
0
0
Dir
.
<=
-1
-1
-1
-1
-1
-1
-1
-1
-1
1.15
1.15
1.15
Source: Constructed from survey data.
1.15
1.15
1.15
1.15
1.15
1.15
-1
1.15
1
-1
1
"
"
"
""
"
"
"
"
"
<=
>=
RHS
36242
5
0
48973
0
0
0
0
0
0
0
48973
41139
8
Table 5.9. Borrowing capital activities in Dongola locality
Source:
Unit
"
"
"
"
"
"
"
"
"
End CPT
BC Apr.
BC Mar.
BC Feb.
-0.15 -0.15 -0.15 -0.15 -0.15 -0.15 0
BC Jan.
BC Nov.
-0.15
BC Dec.
BC Oct.
Objec. Function
Constraints
OC Oct.
OC Nov.
OC Dec.
OC Jan.
OC Feb.
OC Mar.
OC Apr.
CPT Rep.
End CAPT
CPT. Rep
Constraints
0
Dir.
<=
"
"
"
"
"
"
<=
>=
-1
-1
-1
-1
-1
-1
1.15
1.15
1.15
1.15
1.15
1.15
-1
1.15
1
-1
1
Constructed
from
survey
RHS
152693
99844
0
0
0
0
0
99844
252537
data.
TC Mar./Apr.
TC Apr./May
TC May/June
Objec. function
0 0 0 0 0
Constraints unit
OC Sep.
SD 1
OC Oct.
"
-1 1
OC Nov.
"
-1 1
OC Dec.
"
-1 1
OC Jan.
"
-1 1
OC Feb.
"
-1
OC Mar.
"
OC Apr.
"
OC May
"
OC June
"
Source: Constructed from survey data
TC Feb./Mar.
TC Jan./Feb.
TC Dec./Jan.
TC Nov./Dec.
TC Sep./Oct.
S
TC Oct./Nov.
Table 5.10 . Capital transfer activities in Merowe locality
Activities
0
0
0
0
1
-1
1
-1
1
-1
1
-1
Dir.
<=
"
"
"
"
"
"
"
"
"
RHS
362425
0
48973
0
0
0
0
0
0
0
TC Feb./Mar.
TC Mar./Apr.
Objec. Function
0 0 0
Constraints
Unit
OC Oct.
SD 1
OC Nov.
"
-1 1
OC Dec.
"
-1 1
OC Jan.
"
-1
OC Feb.
"
OC Mar.
"
OC Apr.
"
Source: Constructed from survey data.
TC Jan./Feb.
TC Dec./Jan.
TC Nov./ Dec.
TC Oct/Nov
Table 5.11. Capital transfer activities in Dongola locality
Activities
0
0
0
1
-1
1
-1
1
-1
Dire.
<=
"
"
"
"
"
"
RHS
152693
99844
0
0
0
0
0
5.3.3 Constraints
i- Land
The amount of the available land per farmer was calculated and the
averages obtained were 8.15 and 11.32 feddans in Merowe and Dongola
localities respectively. These lands do not include the areas cultivated by
permanent crops (dates, mangoes, citrus and lucern). The values of these
lands represent the right hand side of the corresponding equation as
shown in tables 5.2 and 5.3.
ii- Labour
Labour is a classical input assumed to affect output of the different
crops. Elfeil (1993) stated that although the labour is assumed to be
abundant and sometimes of negative marginal value in the developing
countries, but still there are times when labour may become a
constraining factor of production. This happens during the time of
weeding, harvesting and such operations that demand large amounts of
labour in just limited time. So, it is actually the limited time of the
operations rather than any thing else.
The amount of labour available in the two localities was calculated
as follows:
1- The unit of analysis is the household head.
2- Family labour supply was measured using standard mandays.
3- The labour requirements of the perennials was subtracted from the
available family labour available in the winter season. The average of the
available family labour supply in each month plus the possible hired and
nafir labours in the respective month of the model represents the right
hand side of the equation as shown in Tables 5.4 and 5.5.
iii- Irrigation
Irrigation in the Northern State depend mainly on lift pumps. Elfeil
(1993) stated that the shortages of fuel and spare parts for these pumps
and for the other agricultural machinery have resulted in contraction of
the cropped areas to a large extent as well as in very big reductions in the
output per unit of areas cropped.
Irrigation is expressed in number of waterings
available on a
monthly basis. The water requirements of the perennials crops were
subtracted from the available water supply. These number of irrigations
represent the right hand side of irrigation constraint. Table 5.12 shows the
average number of waterings available in small private schemes in
Merowe and Dongola localities. While the average number of waterings
given to the winter crops per month in Merowe and Dongola localities are
shown in Tables 5.13 and 5.14 respectively.
Table 5.12. Average number of waterings available per month in private
schemes of the Northern State by locality
Month
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
March
April
May
Source: Author's survey, 2003
Merowe
48.7
48.7
48.7
48.7
48.7
48.7
48.7
48.7
48.7
Dongola
34.3
34.3
34.3
34.3
34.3
34.3
34.3
34.3
34.3
Table 5.13. Average number of waterings given to winter crops per
month in Merowe locality (season 2002/2003).
Month
Wheat
Brod
beans
Sept.
0
0
Oct.
0
0
Nov.
0
2
Dec.
4
3
Jan.
3
2
Feb.
3
2
March
1
0
April
0
0
May
0
0
Total
11
9
Source: Author's survey, 2003
Fennel
Onion
Tomato
Total
0
0
2
4
3
2
0
0
0
11
0
0
0
1
4
3
3
2
1
14
3
4
3
3
3
3
2
1
0
22
3
4
9
13
15
13
6
3
1
67
Table 5.14. Average number of waterings given to winter crops per
month in Dongola locality (season 2002/2003)
Month
Wheat
Broad
beans
Oct.
0
0
Nov.
0
2
Dec.
3
2
Jan.
2
2
Feb.
2
1
March
1
0
Total
8
7
Source: Author's survey, 2003
Fennel
Garlic
Total
1
2
2
1
1
0
7
1
1
2
2
1
1
8
2
5
9
7
5
2
30
iv- Operating capital and credit constraints
The available working capital required to finance purchases of
agricultural inputs can be an important constraint on farm plan. Some
working capital may be available from the farm family's own savings, but
this can often be supplemented by borrowing (Hazel and Norton, 1986).
The available capital to finance the agricultural production in the
Northern State may be available from farmer's own funds and are often
supplemented by borrowing. The available cash at the beginning of the
season represents the farmer's own funds. It includes sales of date crop
which is harvested in September and savings from farm, off-farm
activities and assistance from sons, daughters and relatives. The average
amount of cash available at the beginning of the season was found to be
SD 362425 and SD 152692 in Merowe and Dongola localities
respectively. Acredit limit is imposed reflecting the amount of credit
provided by the ABS to each farmer per year. The average limits of credit
obtained from the results of the survey were SD 48973 and SD 99843.8 in
the two localities respectively.
A cash balance row has been defined for each month. The cash
balance row requires that the initial amount of funds available plus any
credit borrowed in a specific month, must be at least as large as the
working capital requirements for crop production during the month less
any receipts from crop sales. However, if there is a surplus cash balance
at the end of month, this surplus can be transferred to the next month
through transfer activity. An end cash balance must be adequate to repay,
with Morabaha margin, all the credit during the year.
v- Consumption
A consumption constraint of wheat is imposed. The survey data
showed that the average annual consumption of the family was 8.5 and
9.5 sacks in Merowe and Dongola localities respectively.
vi- The crop balance constraint
The crop balance constraints indicated that the crop produced is
completely used through sales and/or consumption.
CHAPTER VI
RESULTS AND DISCUSION OF THE
LINEAR PROGRAMMING MODEL
6.1. Introduction
This chapter presents and discusses the results of the basic models
and present the different scenarios by changing their parameters. The
information drawn from the LP solution includes the value of the
objective function, the optimum enterprise combination, the level of
resources used and their representive marginal productivities. The results
of the basic models were validated by comparing the results to the real
situation in the state as shown by the results of the field survey conducted
in the year 2003. The results in this chapter were obtained by two runs.
The first run was free run, where the areas of different cultivated crops
were not restricted (i.e assigned >= 0 in the basic models). The
application of this run depend on the fact that farmers in the Northern
State do not follow any cropping rotation plus the assumption that
farmers are profit maximizers and all crops require fertile soils. The
second run is a restricted run, in which the cultivated crops were
restricted to their maximum areas (i.e assigned <= Bi in the basic
models). This run was applied to satisfy the need of diversification and
aversion of natural and market related risks, in addition to the fact that
some high value crops require highly fertile soils.
6.2. The basic solution
6.2.1. The free model run
The basic solution of the free run LP model is given in Table 6.1.
Further calculations were made on the results for estimating resource
utilization.
Table 6.1. Basic solutions of the free cropping LP models in comparison
to the observed
Item
Cropping pattern
Wheat
Broad beans
Fennel
Onion
Garlic
Tomato
Resource use
-Total land
-Family labour
-% (utilized FL/total
FL*100)
-Hired and nafir labour
% (utilized HL and
NL/total
utilized
labour*100)
-Total labour
-%(utilized TL/TL*100)
-Total
number
of
irrigations
Returns
-Net farm income
-Net
farm
income
(wheat
consumption
requirement is satisfied)
Ratio of real income to
model
Unit
Merowe
Model
Actual
Dongola
Model
Actual
Fed.
Fed.
Fed.
Fed.
Fed.
Fed.
0.00
0.00
0.00
0.00
8.15
1.3
2.75
0.6
0.35
0.7
0.00
7.74
0.37
3.19
-
3.75
3.65
0.4
0.33
-
Fed.
MD
8.15
252
84%
5.7
247.4
76.4%
11.3
231
100%
8.13
222.7
96.2%
MD
169.4
37%
43.1
15%
226
49.34%
120
35%
MD
435.4
80.4%
179.3
290.5
54%
66.2
457
98%
82.2
342.3
73%
61.2
SD
SD
1,701,614
1,637,864
319,086.4
266,811.4
457,843.4
386,593.4
233,704.7
175,754.7
SD
16.3%
45.46%
A- Cropping pattern
From Table (6.1) we can see that there is a big difference between
the actual land used allocated between the different crops and the
optimum land allocated as the model answer for the two localities. The
model answer confined all the available land in Merowe locality to
tomato crop. While actually farmers allocated most of the cultivated land
to broad beans, followed by wheat and then tomato, fennel and onion
crops.
The LP results allocated most of the available land in Dongola
locality to broad beans, followed by garlic and fennel crops. In practice,
wheat and broad beans crops occupied approximately the same largest
areas, while small areas were cultivated by fennel and garlic crops.
Wheat crop under the free cropping LP model did not enter the
plan in the two localities, which was attributed, in addition to its low
prices in the state, to its low productivity in Dongola locality. As
discussed in chapter two the productivity of wheat for the 90/912001/2002 seasons ranged between 6 and 15 sacks per feddan, with an
average being 10.5 sacks. The average productivity of wheat in Merowe
locality for the season 2002/2003 was 12.3 sacks per feddan which was
higher than the average of the past twelve years, while the average
productivity for the season 2002/2003 in Dongola locality was 8 sacks
per feddan which was lower than the average of the last twelve years.
The tomato crop in Merowe locality occupied all the area in the LP
results, since it is a profitable crop and farmers are familiar with its
production. The production of this crop was encouraged in the past by the
establishment of Kareima canning factory which was operated mainly for
tomato processing. However, tomato cultivated areas contracted in the
last years due to the deterioration of the canning factory, the infestation of
the crop by hallouk parasite (Orobanche remosa) and the spread of leaf
curl disease which is transmitted by the white fly insect.
B- Resource use
i- Labour use in the basic free cropping model in comparison to reality.
From Table 6.1 the total labour required in the solution of Merowe
locality LP model was 436 mandays compared to 290.5 mandays actually
utilized during the winter season 2002/2003. In Dongola locality the total
labour required in the model answer were 456.9 mandays compared to
342.2 mandays actually utilized. In Merowe locality about 84% of the
total family labour was utilized in the optimum solution, while the
percentage actually utilized was 76.4%. In Dongola locality all family
labour was required in the optimum solution compared to 96.2% utilized
in real situation. The percentages of the hired and nafir labour required in
the optimum solution was 37.6% and 49.34% in Merowe and Dongola
localities respectively. While the percentages of hired and nafir labour
actually utilized were 14.83% and 35% in the two localities respectively.
ii- Irrigation water required in the basic models in comparison to reality
From Table 6.1 the total number of irrigations required in the
optimum solution of Merowe locality was 179.3, compared to 66
waterings actually utilized in the season 2002/2003. The number of
waterings required in the optimum solution of Dongola locality was 82.2
waterings compared to 61.2 irrigations actually applied.
C- Net farm income
Table 6.1 includes two figures for net farm income, in one the
value of wheat consumption is included and in the other wheat
consumption requirement is satisfied. It is observed from the table that
the values of the net farm income from the basic free cropping models
were greater than the values actually obtained in the two localities. The
value of the objective function in Merowe locality was SD 1637864
compared to SD 266811.4 actually obtained (i.e the difference of 514%).
In Dongola locality the value of the objective function was SD 386593.4
compared to SD 175754.7 actually obtained (the difference of 120%).
The big differences between the models values and the real ones were
because in the optimum solution all land in Merowe locality was confined
to tomato crop, which is a high value crop and most of land in Dongola
locality was confined to broad beans which had relatively good prices.
While in real situation most of Merowe locality lands was cultivated by
wheat and broad beans crops, and in Dongola locality the largest portion
of lands was cultivated by wheat crop. Wheat had had low prices in
addition to its low productivity in Dongola locality. Broad beans had had
relatively good prices, but its productivity was low in Merowe locality.
This is consistent with the results of the gross margin analysis, where the
crops which occupied small cultivated areas in practice scored high gross
margins and those occupied large areas recorded low gross margins. The
linear programming also consistent with the regression analysis which
showed that the marginal value productivities of some resources were low
relative to their marginal costs.
6.2.2 The restricted model run
A- Cropping pattern
The free cropping models option does not reflect the existing
situation in the two localities. Some crops are usually grown to limited
areas because of the need for diversification and aversion of natural and
market related risks. The maximum areas usually allocated to these crops
are limits used in the LP models as constraints.
In Merowe locality model the cropping restriction was made by
two steps. In the first step only tomato crop which occupied all the land in
the free cropping run results was restricted to its maximum area. The
optimum solution in this step allocated 5.6 feddan to onion crop beside
2.6 feddans confined to tomato crop. The net farm income decreased from
SD 1637864 to SD 954924.2 (42% reduction). In the second step the area
of onion crop was also restricted to its maximum area usually cultivated.
In Dongola locality only the area of broad beans crop was restricted to its
maximum area usually cultivated. The optimum solutions of the basic
restricted cropping models are shown in Table 6.2.
From Table (6.2), broad beans crop occupied the largest area as the
answer of the basic restricted cropping LP models of Merowe locality,
beside the areas restricted for tomato and onion crop. Fennel crop did not
enter the plan due to its low productivity. The productivity of fennel crop
for the season 1990/91-2001/02 ranged between 6 and 10 kantar per
feddan, with an average of 7.8 kantar. The average productivity obtained
for the season 2002/03 was 5.2 kantar, which is lower than the average
for the past twelve years (see table 2.10). The wheat crop entered the plan
at a level just satisfying the consumption requirements.
Table 6.2.
Basic solutions of the restricted cropping LP models in
comparison to the observed
Merowe
Item
Cropping pattern
Wheat
Broad beans
Fennel
Onion
Garlic
Tomato
Resource use
-Total land
-Family labour
-% (utilized FL/total
FL*100)
-Hired and nafir labour
-% (utilized HL and
NL/total
utilized
labour*100)
-Total labour
-%
(utilized
TL/TL*100)
-Total number of
irrigations
Returns
-Net farm income
(including value of
wheat consumption)
-Net farm income
(wheat consumption
requirement
is
satisfied)
Ratio of actual income
to model
Dongola
Unit
Model
Actual
Model
Actual
Fed.
Fed.
Fed.
Fed.
Fed.
Fed.
0.69
3.96
0.00
0.9
2.6
1.3
2.75
0.6
0.35
0.7
0.00
4.6
3.32
3.38
-
3.75
3.65
0.4
0.33
-
Fed.
MD
8.15
289
86.5%
5.7
247.4
76.4
11.3
231
100%
8.13
222.7
96%
MD
176.6
39%
43.1
15%
227.2
49.6
120
35%
MD
465.6
86%
290.5
54%
458.2
342.2
73.2%
113
66
82.4
61
SD
809654
319086.4
437284.5
233704.7
SD
757804
266811.4
378859.5
175754.7
35.2%
46.39%
In the results of the LP model for Dongola locality, broad beans occupied
its restricted area (4.6 feddans), followed by garlic and fennel crops. The
wheat crop did not enter the plan due to its low productivity and low
prices. The productivity of wheat for the season 2002/03 was 8.5 sacks
per feddan in Dongola locality which was lower than 10.5 sacks per
feddan, the average for the past twelve years. The productivity of fennel
crop in Dongola locality was better than that of Merowe locality. The
average obtained was 7.6 kantar per feddan compared to 7.8 kantar per
feddan, the average for the past twelve years. The garlic crop yield
obtained in the season 2002/03 was 36.8 kantar per feddan which was
higher than 33.8 kantar per feddan, the average for the past twelve years.
B- Resource use
i- Labour use in The basic restricted cropping model in comparison to the
actual use.
From Table 6.2 the total labour required for the optimum plan was
465.6 mandays compared to 290.5 mandays actually utilized. In Dongola
locality the total mandays required were 461.2 compared to 342.5
mandays actually utilized.
About 87% of the family labour was required for the optimum plan
of Merowe locality. Family labour are all required during the months
from September to April. In Dongola locality all family labour was
required during all months of the season (October to April). The
percentage of hired and nafir labour required for the optimum plan of
Merowe locality was 39% of the total required labour, while the required
percentage for Dongola plan was 49.6%. Table 6.3 shows the number of
mandays required for the optimum plan of the restricted cropping models.
Table 6.3.
Number of mandays required in the basic solutions in
comparison to the observed
Merowe
Month
Dongola
Model
Observed
Model
Observed
Sept.
24
10.3
-
-
Oct.
59
37.8
61
45
Nov.
59.6
44.2
69
50.5
Dec.
64.4
44.8
96
69.8
Jan.
54
33.6
61.2
51.6
Feb.
60
37.3
33
24.7
March
81.6
58.8
62
45
April
36
13.4
76
55.6
May
7.6
3
-
-
June
19.4
7.5
-
-
Total
465.6
290.5
458.2
342.2
Table 6.3 shows two peaks of labour utilization in the optimum
solution and real situation of Merowe locality, the first one was during
November and December and the second one was during March. The
table also shows two peaks of labour utilization in the optimum solution
and the real situation of Dongola locality, the first peak was during
November and December, while the other was during March and April.
ii- Irrigation water use in the basic models in comparison to reality
From Table 6.2 the total number of irrigations required for the
optimum solution of the restricted model of Merowe locality was 113
compared to 66.2 waterings actually utilized. In Dongola locality the
number of 82.4 waterings was required for the optimum solution
compared to 61 waterings actually utilized in the winter season
2002/2003. Table 6.4 shows the monthly average number of waterings
required in the optimum solution, while the surplus irrigation water per
month is shown in Table 6.5.
Table 6.4. The monthly average number of irrigations required in the
basic solutions in comparison to the observed
Merowe
Month
Dongola
Model
Observed
Model
Observed
Sept.
7.8
2.1
-
-
Oct.
10.4
2.8
6.7
0.7
Nov.
15.7
8.8
19.22
8.4
Dec.
23.33
18.3
22.6
20
Jan.
21.39
14.7
19.25
16
Feb.
20.49
13.8
11.3
12
March
8.59
3.8
3.33
4.1
April
4.4
1.4
-
-
May
0.9
0.4
-
-
Total
113
66.1
82.4
61.2
Table 6.5. The surplus irrigation water in the basic solutions of the LP
models
Month
Merowe
Dongola
Sept.
40.9
-
Oct.
38.3
27.6
Nov.
32.98
15.07
Dec.
25.35
11.7
Jan.
27.30
15.02
Feb.
28.20
23
March
40.1
30.92
April
44.3
-
May
47.8
-
Total
235
123.5
iii- Credit use in the basic models
The results showed no problem of credit in the model of Merowe
locality. While it showed a credit problem during October, November,
December, January and February in the restricted model of Dongola
locality.Table 6.6 shows the marginal value productivities of credit in the
basic models.
Table 6.6. Marginal value productivities of credit (SD/unit) in the basic
solutions of the LP models
Month
Merowe
Dongola
Sept.
0.00
-
Oct.
0.00
0.15
Nov.
0.00
0.15
Dec.
0.00
0.15
Jan.
0.00
0.15
Feb.
0.00
0.15
March
0.00
0.00
April
0.00
0.00
May
0.00
0.00
June
0.00
0.00
C- Net farm income
From Table (6.2), the net farm income in the optimum solution of
the LP model of Merowe locality was SD 757804 which was higher than
the actual achieved by 184%. In Dongola locality , the net farm income
was SD 378859.5 which was higher than the actual value obtained in the
season 2002/2003 by 116%.
6.3 Policy analysis scenarios (Sensitivity analysis)
In this section the basic linear programming models were
developed by changing their parameters to reflect a range of production
options found in the system. The scenarios reflect the effect of cost of
production, prices, productivities and adoption of improved technologies.
6.3.1 The effect of the cost of production
6.3.1.1 The impact of lowering the present cost by 25%
This scenario was developed to examine the impact of lowering the
present cost of all crops by 25%. The results increased the net farm
income in Merowe locality by 18% from SD 757804 to SD 893434.2 and
no change in the crop mix.
In Dongola locality the net farm income increased from
SD 378859.5 to SD 542399.3 (43% increase). The crop mix was changed
and the largest area (6.2 feddans.) was allocated to garlic, broad beans
crop occupied its restricted area (4.6 feddans), while fennel crop occupied
only 0.4 feddans.
6.3.1.2 The impact of lowering the present cost by 50%
The results of this scenario has no effect in the crop mix of
Merowe locality, however, the net farm income increased from SD
757804 to SD 1047426 (30% increase). In Dongola locality the net farm
income increased by 78% from SD 378859.5 to SD 675624.3. The crop
mix was slightly changed and allocated 6.7 feddans to garlic crop beside
the restricted area of the broad beans crop (4.6 feddan).
6.3.1.3. The impact of lowering wheat cost by 25% and 50%
The results of lowering wheat cost by 25% increased the net farm
income in the model of Merowe locality by 3% from SD 757804 to SD
782404.3. Wheat occupied the largest area (4.65 feddan) beside the
restricted areas of tomato and onion crops. The results of lowering wheat
cost by 50% increase the net farm income in the model of Merowe
locality from SD 757804 to SD 830313.3 (9.6% increase) and also wheat
crop occupied the same largest area. Broad beans and fennel crops did not
entered the plan due to their low productivities.
In Dongola locality the net farm income and the crop mix were not
changed by lowering wheat cost by 25%. The results of lowering wheat
cost by 50% increased the net farm income from SD 378859.5 to SD
382765.5 (1% increase) and wheat crop entered the plan at a level just
satisfying the consumption requirements (1.11 feddans) as shown by
Table 6.7.
Table 6.7. Net farm income (SD) and crop mix (fed.) resulting from
lowering wheat cost by 50%
Item
Merowe
Dongola
Net farm income
830313.3
382765.5
Wheat
4.6
1.11
Broad beans
0.00
4.6
Fennel
0.00
2.83
Onion
0.9
-
Garlic
-
2.74
2.6
-
Cropping pattern
Tomato
6.3.2 The effect of crops prices
6.3.2.1 The impact of increase in wheat price by 20%
The results was changed only the model of Merowe locality. The
net farm income increased from SD 757804 to SD 778421.1 (2.7%
increase). Wheat crop occupied the largest area (4.6 feddans), beside the
restricted areas of tomato and onion crops. Broad beans and fennel were
excluded due to their low productivities.
6.3.2.2 The impact of decrease in tomato price in Merowe locality by
25%
This run was made to averse marketing risk with respect to tomato
crop. The supply of tomato product is elastic since it is a perishable
vegetable and hence the expansion in its production expected to face low
prices.
The results of this scenario reduced the net farm income by 34%
from SD757804 to SD 502661.3. Wheat occupied the largest area (4.6
feddan), beside the restricted areas of tomato and onion crops. Broad
beans and fennel crops were also excluded.
6.3.2.3 The impact of decrease in tomato price by 25% and increase
in wheat price in Merowe locality by 20%
The results of this scenario decreased the net farm income by 26%
from SD 757804 to SD 562556.1. It allocated 4.6 feddan to wheat, beside
the restricted areas of tomato and onion crops.
6.3.2.4 The impact of decrease in tomato price by 50% and increase
in wheat price in Merowe locality together by 20%
The results of this scenario decreased the net farm income by 54%
from SD 757804 to SD 346691.1. The crop mix was the same as the
previous scenario.
6.3.3 The effect of crops productivity
6.3.3.1 The impact of increase in wheat productivity by 25%
In the model of Merowe locality the net farm income increased
from SD 757804 to SD 807178.5 (6.5% increase). Wheat crop occupied
the largest area (4.65 feddans) beside the areas restricted for tomato and
onion crops. In the model of Dongola the net farm income increased from
SD 378859.5 to SD 379617.3 (0.2% increase). Broad beans occupied its
restricted area (4.6 feddans), fennel and garlic occupied 2.93 and 2.86
feddans, respectively and only 0.89 feddans was confined to wheat crop
which is satisfied a part of the consumption requirements.
6.3.3.2 The impact of increase in broad beans productivity by 25%
In Merowe locality, the net farm income increased from SD
757804 to SD 842527.7 (11% increase). Broad beans occupied the largest
area (4.65 feddan), beside the restricted areas of tomato and onion crops.
In Dongola locality net farm income increased by 27% from SD 378859.5
to SD 482201.3. Broad beans occupied its restricted area (4.6 feddan),
fennel and garlic crops occupied 3.32 and 3.37 feddans respectively.
6.3.3.3 The impact of increase in productivity of wheat and broad
beans together by 25%
The results of this scenario increased the net farm income in
Merowe locality by 12% from SD 757804 to SD 849496.3. Wheat crop
occupied the largest area (4.65 feddans) beside, the areas restricted to
tomato and onion crops.
In Dongola locality the net farm income
increased by 27% from SD 378859.5 to SD 482959. Broad beans
occupied its restricted area (4.6 feddans), fennel occupied 2.93 feddans,
garlic occupied 2.86 feddans, while only 0.89 feddans were allocated to
wheat crop.
6.3.3.4 The impact of increase in productivity of wheat in Dongola
locality by 50%
The answer of this scenario increased the net farm income by 2%
from SD 378859.5 to SD 387318.1. Broad beans occupied its restricted
area (4.6 feddans), fennel occupied 3 feddans, garlic occupied 2.95
feddans and wheat also occupied only 0.74 feddans.
6.3.4 The effect of crops productivity and prices
6.3.4.1 The impact of increase in productivity of wheat in Dongola
locality by 25% and in its price together by 20%
In the results of this scenario the wheat crop occupied only 0.89
feddans which is satisfied only a part of the consumption requirements.
6.3.4.2 The impact of increase in productivity of wheat in Dongola by
30% and in its price together by 20%
The results of this scenario increased the net farm income by 3%
from SD 378859.5 to SD 389918.7. It allocated the largest area (5.46
feddans) to the wheat crop, while broad beans crop occupied its restricted
area (4.6 feddans) and only 0.95 and 0.28 feddans were allocated
respectively to fennel and garlic crops.
6.3.5 Impact of application of technological packages
Technological packages can be defined as the transfer of
technology and innovation generated by research scientists aims at
improving farm productivity to maximize farmer's returns. It is broadly
defined as a change in the total farm output resulting from given set of
production inputs. Bushara (1987) stated that the adoption of improved
production practices by farmers is not just a question of transfer of
technology. An essential prerequisite to this is securing resources and
inputs to the farmers.
The implementation of newly developed technologies is expected
to affect crop organization due to their impact on yields as well as cost of
production (Abd Elaziz, 1999). According to ICARDA, cited by
Mohamed (2000), the yield of broad beans crop in Sudan increasing
between 27% to 115% were obtained by farmers adopting improved
production package. Wilson (1996) stated that wheat yield at HRS is
almost four times that scored on small holder plots, the gap in yield gives
some indications of the potential for increasing wheat output. The
packages recommended by HRS for wheat and broad beans , the major
winter crops are summarized by Abd Elaziz (1999) as follows:
- Proper land preparation of applying first ploughing, second ploughing
and then leveling.
- The recommended sowing date of wheat and broad beans crops is in the
first week of November.
- Cultivation of improved seeds.
- Application of 9 number of irrigations for the two crops.
- Proper weed control by two hand weedings.
- Application of 80 kgs of fertilizer per feddan for wheat crop.
- The outcomes of the on farm demonstrations of the packages are 18
sacks and 14 sacks per feddan for wheat and broad beans respectively.
The farmer's practices against the technical packages were
discussed in details in chapter three. The deviations from the proper
practices can be summarized in the following points:
- Land preparation
i- About 54% and 31% of the sampled farmers in Merowe and Dongola
localities respectively employed traditional plough. Solh (1995) stated
that wheat production in the Sudan is fully mechanized. For farmers to
obtain high yields and make profits, machinery has to be efficiently used
and managed.
ii- Approximately all farmers in Merowe locality and about 66% of
farmers in Dongola locality did not applied second ploughing.
iii- About 32% and 11% of farmers in the two localities respectively did
not applied leveling.
- Sowing date:
About 55% and 54% of wheat growers and about 31% and 38% of
broad beans growers in Merowe and Dongola localities respectively
delayed the cultivation of wheat and broad beans crop till December.
- Improved seeds:
About 30% and 40% of wheat growers in Merowe and Dongola localities
respectively cultivated traditional seeds. While the percentages of farmers
cultivated traditional seeds of broad beans crop in the two localities were
38% and 52% respectively.
- Number of irrigations
The average number of irrigations given for wheat and broad beans in
Dongola locality were 7.6 and 7 irrigations for the two crops respectively.
- Fertilizer application
The average quantities of fertilizers applied for wheat crop were 65 and
71.4 kgs per feddan in the two localities respectively.
- Weed control
The number of weeding applied by most of wheat and broad beans
growers were one weeding in Merowe and three weedings in Dongola
locality.
- Yields
The average yields obtained by wheat growers in Merowe and Dongola
localities were 12.6 and 8 sacks/fed in the two localities respectively.
While the average of 6 and 7.7 sacks per feddan were obtained by broad
beans growers in the two localities respectively.
This scenario was made by changing the parameter of wheat and
broad beans activities in the basic models according to the improved
technological packages.
The results of this scenario showed that the adoption of improved
technology increased the net farm income in Merowe locality by 52%
from SD 757804 to 1149693. Broad beans occupied the largest area (5.55
feddans) beside the restricted area of tomato crop. Broad beans occupied
the largest area by adoption of improved technology since it is a high
value crop and the prices for the season 2002/2003 was twice as that of
wheat crop, and for this reason wheat crop did not entered the plan (Table
6.8). In Dongola locality the net farm income increased by 123% from
SD 378859.5 to SD 845399.The wheat crop occupied the largest area (6.3
feddans), broad beans occupied its restricted area (4.6 feddans) and only
0.4 feddans was occupied by fennel crop as shown by Table 6.8.
adoption of improved technology land , labour and capital
With
are the
constraining factors in Dongola locality, while, the constraining factors in
Merowe locality are the land and labour. The peak months of labour
utilization are November, December and March in Merowe locality and
November, December, January and April in the model of Dongola
locality.
Table 6.8. The net farm income (SD) and crop mix (feddan) of the
improved technologies in comparison to basic models
Item
Merowe
Dongola
-Low technology
757804
378859.5
-Improved technology
1149693
845399
-Low technology
0.69
0.00
-Improved technology
0.00
6.3
-Low technology
3.69
4.6
-Improved technology
5.55
4.6
Fennel
0.00
0.4
Onion
0.00
-
Garlic
-
0.00
2.6
-
Net farm income
Cropping pattern
Wheat
Broad beans
Tomato
CHAPTER VII
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
7.1. Summary
The Northern State has the potential to have great contribution to
the agricultural production of the country due to the advantages of:
favourable climatic conditions, availability and good quality of irrigation
water, highly fertile soils, availability of improved technical knowledge
recommended by researchers for most of the winter crops and well
experienced and skilled farmers. In addition, the construction of Merowe
dam is expected to enhance the agricultural production in the state
through electrifying the pumps .
Despite these advantages, the agricultural production in the state
faces problems of high costs of production, low crop yields, low prices
and accordingly low farmer's income.
The overall objective of this study is to evaluate the farming
system in the Northern State and identify the constraints that stand in the
way of its potential contribution to the Sudan's economy. Within this
context the study investigated the socio-economic characteristics of
farmers, comparison of economic efficiency of the use of resources, the
optimum cropping patterns under farmers conditions, identify the
constraints facing agricultural production in the state and examine the
effects of the introduction of certain scenarios on farm income, resource
use allocation and crop mixes.
The study depended mainly on primary data for the 2002/2003
agricultural season which was collected by direct interviewing of
respondents through a multistage-stratified random sampling technique
using structured questionnaires. The respondents were individual private
farmers and agricultural companies schemes owners. The study also used
secondary data which was collected from the relevant institutional
sources.
In order to achieve the stated objectives the survey data collected
was subjected to both descriptive and statistical analysis. Regression
analysis using Cobb-Douglas production function and the linear
programming techniques were applied.
The study showed that the average age of the farmer was 45 and
48.3 years in Merowe and Dongola respectively. The percentages of
illiterate farmers were 10% and 19% in the two localities respectively.
The average family size was found to be 7.4 and 7.6 persons in Merowe
and Dongola respectively.
The structure of the farm is either owned lands, shared cropped
lands or public lands. The farm size ranged between 0.3-50 feddans, with
an average of 10.4 feddans in Merowe, while it ranged between 1.5-50
feddans, with an average of 12 feddans in Dongola. According to the type
of the scheme, the average farm size in private schemes was 11.2 and
13.7 feddans in Merowe and Dongola respectively. While it was 6.9 and
10.5 feddans in the companies schemes of the two localities respectively.
Most of the sampled farms (81.6%) in Merowe locality were
irrigated from the Nile, while only 36.3% of those in Dongola were
irrigated from the Nile. Most of small schemes in the state employed
three inches pumps.
Cropping intensity in the private schemes was 94.3% and 106.4%
in Merowe and Dongola respectively. While in companies schemes it was
121% and 81.3% in the two localities respectively. The areas cultivated in
the winter season contributed about 55% and 61.4% of the total cultivated
areas in the private schemes of Merowe and Dongola respectively, while
it contributed about 29.4% and 73.4% in the companies schemes of the
two localities respectively.
In the private schemes of Merowe the broad beans crop occupied
the largest cultivated area (26%), followed by date palms (25.9%),while
wheat crop came fourth and occupied 12.3%. In the private schemes of
Dongola, wheat crop occupied the largest cultivated area (27.4%), and
broad beans crop occupied 26.6%.
In Merowe companies schemes, 35.7% of the cultivated land is
under date palms, 24.6% occupied by wheat crop, 23.9% occupied by
summer crops, while only 4.7% was cultivated by broad beans crop. In
Dongola companies schemes the largest area were cultivated by wheat
(46.7%) and broad beans (26.3%).
All the sampled farmers in Merowe locality applied the first
ploughing, only one farmer applied second ploughing and about 68%
applied the leveling practice. In Dongola locality, about 95% of the
sampled farmers applied the first ploughing, 34% applied the second
ploughing and 89% applied leveling. More than half (54%) of the
sampled farmers in Merowe employed traditional plough, while only 31%
of those in Dongola employed the traditional plough.
About 55% and 54% of the respondents wheat growers in Merowe
and Dongola respectively delayed the planting of the crop till December,
while about 38% and 39% of them in the two localities respectively
planted the crop in November. About 59% and 55.6% of the broad beans
growers in Merowe and Dongola respectively planted the crop in
November, about 30.7% and 37.8% in the two localities respectively
planted it in October, about 7.7% and 4.4% in Merowe and Dongola
respectively planted it in December and about 2.6% of them in the two
localities planted the broad beans crop in January.
The number of waterings applied for wheat crop exceeded the
recommended number of waterings by 22% in the private schemes of
Merowe, while those applied in companies schemes was lower than the
recommended one by 33%. The number of irrigations applied for broad
beans in this locality was 4.4% and 33% short of the recommended
number of irrigations in private and companies schemes respectively. In
Dongola locality the shortage of 15.9% of the recommended number of
irrigations for wheat crop observed in both private and companies
schemes. While the number applied for broad beans crop was 22% and
9% short of the recommended number of irrigations in private and
companies schemes respectively.
The quantity of wheat seeds applied was beyond the recommended
seed rate by 21% and 3% in Merowe and Dongola respectively, while the
quantity of broad beans seeds exceeded the recommended seed rate by
5% and 50% in the two localities respectively. The average fertilizer rate
applied for wheat crop was less than the recommended one by 18.8% and
10.8% in the two localities respectively.
Most of farmers in the state adopted improved seeds varieties,
where only about 30% and 39.6% of wheat growers in the two localities
respectively cultivated baladi (traditional) varieties.
The yields of wheat crop obtained in the private schemes were
31.7% and 55.5% less than the targeted yields in Merowe and Dongola
respectively. In companies schemes the yields obtained were 57% and
62% less than the targeted ones in the two localities respectively. The
yields of broad beans obtained in private schemes were 57% and 45%
less than the expected yields in Merowe and Dongola respectively, while
in companies schemes they were 70% and 37% less than the expected
yields in the two localities respectively.
The results showed that largest area in Merowe locality was
cultivated by broad beans, followed by wheat crop, while in Dongola
locality the two crops occupied approximately the same largest areas.
Broad beans in Merowe and wheat in Dongola scored relatively low
yields and in general the marginal value productivities of some resources
were low relative to their costs. The regression coefficient of the land
resource showed a negative relationship between the land variable and the
farm income in private and companies schemes of the two localities. The
utilization of land resource was more efficient in private schemes of
Merowe locality compared to companies schemes, while in Dongola
locality land utilization is more efficient in companies schemes compared
to private schemes. The labour resource was under utilized in Merowe
private schemes, efficiently utilized in Dongola private schemes. The
resource was utilized beyond the optimum level in companies schemes of
the two localities. The capital resource too was utilized beyond the
optimum level in private and companies schemes of the two localities.
The results of the basic solution of the linear programming model
showed a land use that is very different from the current land use. The
results allocated 3.96 feddans of the available land of Merowe locality to
broad beans beside the areas restricted for tomato (2.6 feddans) and onion
(0.9 feddans). The wheat crop entered the plan at a level just satisfying
the consumption requirements while fennel crop did not entered the plan.
In Dongola locality broad beans occupied its restricted area of 4.6
feddans, garlic occupied 3.38 feddans, while wheat crop did not entered
the plan.
Land is a constraining factor in the two localities, family labour
was fully utilized in the months of September to April in Merowe locality
and in the months of October to April in Dongola locality. Credit did not
come up as a problem in Merowe locality but it was a constraining factor
during the months of October to February in Dongola locality. There is
no problem of irrigation water in the two localities.
The net farm income in Merowe locality was SD 757804, which
was higher than the current level obtained by 184%. In Dongola locality
the net farm income was SD 378859.5, which was higher than the value
actually obtained in the 2002/2003 season by 116%.
The results of the scenario lowering the cost of production of all
crops by 25% increased the net farm income by 18% and 43% in the two
localities respectively. The crop mix in Merowe locality was not changed,
while in Dongola locality it was changed. The largest area, about 6.2
feddans went to garlic instead of fennel crop
Lowering the present cost of production of all crops by 50%
increased the net farm income by 30% and 78% in Merowe and Dongola
localities respectively and the crop mix was slightly changed in Dongola
locality.
Lowering only the wheat cost by 25% increased the net farm
income in Merowe locality by 3% and wheat crop occupied the largest
area. The scenario has no effect on the net farm income and the crop mix
in Dongola locality.
Lowering wheat cost by 50% increased the net farm income in its
restricted model by 9.6% and 0.7% in Merowe and Dongola respectively.
Wheat crop occupied the largest area in Merowe locality, while it entered
the plan at a
level just satisfying the consumption requirements in
Dongola locality.
The results of the scenario of increasing wheat price by 20%
changed only the model of Merowe locality, where the net farm income
was increased by 2.7% and the wheat crop occupied the largest area.
Lowering tomato price in Merowe locality by 25% reduced the net
farm income by 34% and wheat crop occupied the largest area.
Lowering tomato price in Merowe locality by 25% and increasing
wheat price by 20% decreased the net farm income by 26% and also the
largest area was allocated to wheat crop.
Lowering tomato price by 50% and increasing wheat price by 20%
in Merowe locality decreased the net farm income by 54%, wheat crop
occupied the largest area and the tomato crop still occupied its restricted
area.
The results of the scenario of increasing wheat productivity by 25%
increased the net farm income in Merowe locality by 6.5%, wheat crop
occupied the largest area, beside the areas restricted for tomato and onion
crops. In Dongola locality the net farm income increased only by 0.2%
and 0.89 feddans were allocated to wheat crop which meets only part of
the consumption requirements.
Increasing broad beans productivity by 25% increased the net farm
income by 11% and 27% in the two localities respectively and broad
beans occupied the largest areas.
Increasing wheat and broad beans productivities together by 25%
increased the net farm income by 12% and 27% in Merowe and Dongola
localities respectively. Wheat crop occupied the largest area in Merowe,
while the largest area in Dongola locality was allocated to broad beans
crop.
Increasing the productivity of wheat in Dongola by 50% increased
the net farm income by 2%, broad beans occupied the largest area, and
only 0.74 feddans were allocated to wheat crop.
In the solution of the scenario of increasing wheat productivity in
Dongola by 25% and its price together by 20%, wheat crop also occupied
only 0.89 feddans.
Increasing wheat productivity in Dongola locality by 30% and its
price by 20% increased the net farm income by 3% and the largest area
(5.46 feddans) was allocated to wheat crop.
The results of the scenario of applying technical packages
increased the net farm income by 52% and 123% in the two localities
respectively. Broad beans occupied the largest area in Merowe locality,
while in Dongola locality the largest area was allocated to wheat crop.
7.2 Conclusions
1- The farms in the Northern State are managed by active educated
males.
2- The high cropping intensity indicated that the lands in the Northern
State are exhausted.
3- Most of the farmers employed traditional plough and the practice
of the second ploughing is rarely applied due to small holdings.
4- Most of farmers delayed the sowing of wheat till December.
5- The number of waterings applied for wheat in the private schemes
of Merowe exceeded the recommended number, while it was less
than the recommended number of waterings in companies schemes
of Merowe and private and companies schemes of Dongola.
6- The number of applied waterings for broad beans was less than the
recommended one.
7- The quantity of the applied wheat and broad beans seeds exceeded
the recommended seed rate by varying degrees among the two
localities.
8- The quantities of the applied fertilzer for wheat crop was less than
the recommended one
9- Most of the wheat growers in the state adopted improved seed
varieties, while most of broad beans growers in Merowe locality
cultivated improved seeds and most of them in Dongola cultivated
traditional seeds.
10- The actual yields level of all crops grown were significantly
below the recommended ones.
11- Land and capital were inefficiently used in private and companies
schemes of the Northern State, while the labour resource was
efficiently used in the private schemes of Dongola locality.
12- Farmers of the Northern State could be described as profit
maximizers since they spend more in purchasing agricultural inputs
and at the same time they are risk averse and so they intend to
diversify cultivation of food and cash crops.
13- Land, labour and capital were the main factors constraining
agricultural production in the Northern State.
14- The basic solution of the LP model indicated that comparative
and absolute advantages are not made use of, wheat crop should
not be produced on commercial basis in the two localities. The
crops to be grown in Merowe locality are broad beans, tomato,
onion and wheat crop at a level just satisfying consumption
requirements. While those to be grown in Dongola are broad beans,
garlic and fennel. As a result the net farm income increased by
184% and 364% respectively.
15- According to the results of the basic model wheat could be
produced on commercial basis in Merowe locality when its present
costs are reduced by 25%, when its price is increased by 20%,
when its present productivity is increased by 25% (i.e 15.4
sacks/feddan) or when tomato price is decreased by 25%.While in
Dongola locality wheat could be produced on commercial basis
when its present productivity is increased by 30% (i.e 11
sacks/feddan) together with 20% increase in its price, or when the
productivity obtained by adoption of improved technology (18
sacks/feddan) is achieved.
16- The adoption of improved technology scenario indicated that the
crops to be grown in Merowe are broad beans, tomato and onion,
while those to be grown in Dongola are wheat, broad beans and
small areas of fennel crop.
7.3 Recommendations
Based on the findings of the study, the following are
recommended:
1- Comparative and absolute production advantages should be made use
of. Hence, wheat is not to be produced in the state under the present level
of productivity and prices, in order to make use of the state's comparative
advantages in tomato, onion and broad beans in Merowe and broad beans,
garlic and fennel in Dongola locality. As a result farm income would
increase by 184% and 364% in the two localities respectively and
consequently the efficiency of resource can be improved.
2- To produce the wheat crop in commercial levels in Merowe locality,
policy measures must be made to raise the present yield to 15
sacks/feddan, or its present price by 20%. While in Dongola locality, the
present yield must be increased to 18 sacks/feddan or to 11sacks/feddan
together with increase in its price by 20%.
3- Field crops should not be cultivated in non productive lands (i.e high
terrace, marginal or any less fertile lands). However, the less productive
lands can be cultivated by date palms.
4- Since the high cropping intensity means that lands in the Northern
State are exhausted, farmers should be advised to follow cropping
rotation and the lands cultivated in the summer season should not be
cultivated in the winter season of the same year.
5- The improvement of the extension services in the state is essential to
advise farmers to apply the technical packages, namely, the optimum
sowing date, appropriate seed rate, high yielding varieties, recommended
fertilizer rate and recommended number of irrigations. This will improve
farmers income and increase efficiency in resource use.
6- A more efficient credit system is required to enable farmers meet their
requirements.
7- Supply of agricultural inputs should be at the right time and reasonable
prices.
7.4 Some policy implications
The major cultivated crops in the Northern State, wheat and broad
beans, face production problems of low yields and inappropriate prices.
This leads to low farmer's income and encourages migration from the
state. The regression analysis applied in the study showed low marginal
value productivities of some resources relative to their costs, while the LP
analysis gave a land use that is very different from the current land use.
For the improvement of such a situation the following policies will be
appropriate:
i- Emphasis in production should be on high value crops such as
horticultural crops and some spices. This could be at the expense
of wheat since productive lands in the state are rather limited.
ii- The vertical expansion :
The increase in land productivity require the application of the
scientific research recommendations. The availability of facilities to
the extension department to be more efficient and effective in
imparting technical knowledge to farmers is imperative.
The adoption of improved production practices by farmers is not
just a question of transfer of technology, an essential prerequisite to
this is securing resources and inputs to the farmers
iii- In addition to impact of increase in productivity on reducing the
costs referred to above, facilitation access credit for tractors and
other implements will enhance competition and consequently
reduce costs of agricultural of operations.
7.5 Limitations of the study and suggestion for further research :
The present study formulated an optimum resource allocation
cropping system according to scheme type and not according to the type
of the agricultural land, although there is wide variation in soil fertility
even within the same scheme. Hence the regression analysis applied in
the present study gave a negative relationship between the size of the land
resource and the value of the farm output. Further research should aim at
determining the optimum cropping pattern and allocation of agricultural
resources according to soil types of the agricultural lands.
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Appendices
Appendix 1. Gross margin (SD per feddan) of the main crops grown in Merowe
locality (winter season 2003)
1.1 Wheat:
Average yield ton/fed.
Average price (SD/ton)
Value of production
Production costs:
Land preparations:
Land cleaning
Ploughing
Levelling
Raising field canals and ridges
= 1.23
= 61500
= 75645
= 947.6
= 4131.3
= 3021.7
= 3430.3
11530.9
Agricultural operations:
Planting
Irrigation
Harvesting
Threshing and backing
Transportation
= 983.3
= 14901.1
= 2653.7
= 4275.7
= 2029.8
24798.6
Inputs used
Seeds
Fertilizers
Sacks
= 4748.3
= 5462.2
= 2716.4
12926.9
Other costs
Zakat
Land rent
= 3228.8
= 10433.6
13662.4
Total
Gross margin (SD/fed.)
Source: Author's survey,2003
= 62918.8
= 12726.2
1.2. Broad beans
Average yield ton/fed.
Average price (SD/ton)
Value of production
Production costs:
Land preparations:
Land cleaning
Ploughing
Levelling
Raising field canals and ridges
= 0.627
= 123310.52
= 77315.7
= 1383.3
= 4753.3
= 2552.7
= 3134.2
11823.5
Agricultural operations:
Planting
Irrigation
Harvesting
Threshing and backing
Transportation
= 2259.4
= 12912.9
= 2469.9
= 4759.1
= 966.3
23367.6
Inputs used
Seeds
Sacks
Insecticides
= 8752.5
= 1500
= 597.5
10850
Other costs
Zakat
Land rent
= 3514.4
= 8243.9
11758.3
Total
Gross margin (SD/fed.)
Source: Author's survey, 2003
= 57799.4
= 19516.3
1.3. Fennel:
Average yield ton/fed.
= 0.231
Average price (SD/ton)
= 239740
Value of production
= 55380
Production costs:
Land preparations:
Land cleaning
= 800
Ploughing
= 2523.8
Levelling
= 1257.2
Raising field canals and ridges = 3000
7581
Agricultural operations:
Planting
= 1500
Irrigation
= 12236.3
Harvesting
= 1400
Transportation
= 1185
16321.3
Inputs used
Seeds
= 4516.7
Fertilizers
= 4693.3
Sacks
= 1792.9
11002.9
Other costs
Zakat
= 2769
Land rent
= 7606.2
10375.2
Total
= 45280.4
Gross margin (SD/fed.)
= 10099.6
Source: Author's survey, 2003
1.4. Onion
Average yield ton/fed.
Average price (SD/ton)
Value of production
Production costs:
Land preparations:
Land cleaning
Ploughing
Levelling
Raising field canals and ridges
=8.7
= 20553.45
= 178815
= 818
= 4402.1
= 3563.4
= 5113.3
13896.8
Agricultural operations:
Planting
Irrigation
Harvasting
Transportation
= 4543
= 21134.3
= 10232.9
= 8985
44895.2
Inputs used
Seeds
Fertilizers
Insecticides
Herbicides
Sacks
= 4900.2
= 7479.5
= 450
= 525
= 11172.5
24527.2
Other costs
Zakat
Land rent
= 8940.8
= 16800
25740.8
Total
Gross margin (SD/fed.)
Source: Author's survey, 2003
= 109060
= 69755
1.5. Tomato
Average yield (ton/fed.
= 5.35
Average price (SD/ton)
= 48933
Value of primary production
= 270600
Average quantity of byproduct (ton/fed.)
=0.244
Average price (SD/ton)
= 252459
Value of byproduct
= 61600
Total value of production
= 332200
Production costs:
Land preparations:
Land cleaning
= 2700
Ploughing
= 2412.5
Levelling
= 2421.6
Raising field canals and ridges
= 3253.4
10787.5
Agricultural operations:
Planting
= 6439
Irrigation
= 17100
Transportation
= 46900
Inputs used
Seeds
= 2825.6
Fertilizers
= 2818.4
Insecticides
= 2065.4
7709.4
Other costs
Land rent
= 13000
Total
= 101935.9
Gross margin (SD/fed.)
= 230264.1
Source: Author's survey, 2003
Appendix 2. Gross margin (SD/fed.) of the main crops grown in Dongola
locality (winter season 2003)
2.1. Wheat:
Average yield ton/fed.
Average price (SD/ton)
Value of production
Production costs:
Land preparations:
Land cleaning
Ploughing
Levelling
Raising field canals and ridges
= 0.850
= 61000
= 51850
= 557.3
= 3261.3
= 2702.2
= 1235.7
7756.5
Agricultural operations:
Planting
Irrigation
Harvesting
Threshing and backing
Transportation
= 510.8
= 9958.1
= 2951.1
= 2394.8
= 850
16664.8
Inputs used
Seeds
Fertilizers
Sacks
= 5234.3
= 5866.5
= 2136
13236.8
Other costs
Zakat
Land rent
= 2440
= 7212.2
9652.2
Total
Gross margin (SD/fed.)
Source: Author's survey, 2003
= 47310.3
= 4539.7
2.2. Broad beans
Average yield ton/fed.
Average price (SD/ton)
Value of production
Production costs:
Land preparations:
Land cleaning
Ploughing
Levelling
Raising field canals and ridges
= 0.731
= 124543
= 91041
= 2115.3
= 3238.8
= 2528.6
= 1571.9
9454.6
Agricultural operations:
Planting
Irrigation
Harvasting
Threshing and backing
Transportation
= 2122.2
= 9103.9
= 2582.3
= 3682.5
= 826.6
18317.5
Inputs used
Seeds
Fertilizers
Sacks
= 11099.5
= 141.7
= 1893.7
13134.9
Other costs
Zakat
Land rent
= 4552
= 10915.3
15467.3
Total
Gross margin (SD/fed.)
Source: Author's survey, 2003
= 56374.3
= 34666.7
2.3. Fennel
Average yield tom/fed.
= 0.338
Average price (SD/ton )
= 12350
Value of production
= 277692.3
Production costs:
Land preparations:
Land cleaning
= 2458.3
Ploughing
= 2450
Levelling
= 3616.7
Raising field canals and ridges = 1000
9525
Agricultural operations:
Planting
= 2000
Irrigation
= 9028.5
Harvesting
= 4200
Transportation
= 1163.3
16391.8
Inputs used
Seeds
= 3416.7
Fertilizers
= 4782.2
Sacks
= 1583
9781.9
Other costs
Zakat
= 4693
Land rent
= 7821.7
12514.7
Total
= 48213.4
Gross margin (SD/fed.)
= 45646.6
Source: Author's survey, 2003
2.4. Garlic
Average yield ton/fed.
= 1.636
Average price (SD/ton)
= 77603.91
Value of production
= 126960
Production costs:
Land preparations:
Land cleaning
= 1062.5
Ploughing
= 3325
Levelling
= 3612.5
Raising field canals and ridges = 1665
9665
Agricultural operations:
Planting
= 1000
Irrigation
= 13849
Harvesting
= 2811
Transportation
= 2400
20060
Inputs used
Seeds
= 17822.2
Fertilizers
= 8822
Sacks
= 5055
31699.2
Other costs
Zakat
= 6969
Land rent
= 11541.8
18510.8
Total
= 79935
Gross margin (SD/fed.)
= 47025
Source: Author's survey, 2003
2.5. Zea maize
Average yield ton/fed.
= 1.500
Average price (SD/ton)
= 40000
Value of production
= 60000
Production costs:
Land preparations:
Land cleaning
= 2000
Ploughing
= 4500
Levelling
= 3000
Raising field canals and ridges = 2000
11500
Agricultural operations:
Planting
= 1500
Irrigation
= 7440
Harvesting
= 2500
Threshing and backing
= 2000
Transportation
= 1500
14940
Inputs used
Seeds
= 4000
Sacks
= 3750
7750
Other costs
Zakat
= 3000
Land rent
= 8053.3
11053
Total
= 45243.3
Gross margin (SD/fed.)
= 14756.7
Source: Author's survey,2003
‫‪Π‬‬
‫ﻣﻠﺤﻖ رﻗﻢ ‪3:‬‬
‫اﺳﺘﻤﺎرة اﺳﺘﺒﻴﺎن اﻟﻤﺰارﻋﻴﻦ‬
‫ﺍﻟﻤﺤﻠﻴﺔ ‪.………………………………:‬ﺍﻟﻘﺭﻴﺔ ‪..…………………:‬‬
‫ﺍﻻﺴﻡ ‪ .…………………………………:‬ﺍﻟﻌﻤﺭ ‪..…………………:‬‬
‫ﻤﺴﺘﻭﻱ ﺍﻟﺘﻌﻠﻴﻡ ‪ .……… :‬ﺍﻟﺤﺎﻟﺔ ﺍﻻﺠﺘﻤﺎﻋﻴﺔ ……‪ ..‬ﻋﺩﺩ ﺍﻓﺭﺍﺩ ﺍﻻﺴﺭﺓ……‪..‬‬
‫ﺍﻓﺭﺍﺩ ﺍﻻﺴﺭﺓ ‪……………………………………………………… :‬‬
‫اﻟﻨﻮع‬
‫اﻻﺳﻢ‬
‫اﻟﻌﻤﺮ‬
‫ﺻﻠﺔ اﻟﻘﺮاﺑﺔ‬
‫ﺳﺎﻋﺎت اﻟﻌﻤﻞ اﻟﻤﺰرﻋﻲ‬
‫اﻟﻮﻇﻴﻔﺔ‬
‫ﺍﻟﺤﻴﺎﺯﺓ ﺍﻟﻜﻠﻴﺔ ……………‪ .‬ﻤﻠﻙ ﺤﺭ …………… ﻤﻴﺭﻱ ……………‪.‬‬
‫ﺍﺨﺭﻱ ) ﺤﺩﺩ(‬
‫ﺍﻟﻤﺴﺎﺤﺔ ﺍﻟﻜﻠﻴﺔ ﺍﻟﻤﺯﺭﻭﻋﺔ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺼﻴﻔﻲ ‪……………………………:‬‬
‫ﺍﻟﻤﺴﺎﺤﺔ ﺍﻟﻜﻠﻴﺔ ﺍﻟﻤﺯﺭﻭﻋﺔ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺸﺘﻭﻱ ‪……………………………:‬‬
‫ﻤﺴﺎﺤﺔ ﺍﻟﻤﺤﺎﺼﻴل ﺍﻟﻤﺴﺘﺩﻴﻤﺔ ) ﺍﻟﺠﻨﺎﻴﻥ ( ‪..………………………………:‬‬
‫ﻧﻈﺎم اﻟﻤﺸﺮوع ‪-:‬‬
‫ﺨﺎﺹ )‬
‫( ‪ ،‬ﺸﺭﻜﺔ )‬
‫( ‪ ،‬ﺘﻌﺎﻭﻨﻲ )‬
‫( ‪ ،‬ﺍﺨﺭﻱ ) ﺤﺩﺩ (‬
‫ﻤﻼﺤﻅﺎﺕ ‪..……………………………………………………… :‬‬
‫ﻣﺼﺪر اﻟﺮي ‪-:‬‬
‫ﺍﻟﻨﻴل )‬
‫( ‪ ،‬ﻤﻴﺎﻩ ﺠﻭﻓﻴﺔ)‬
‫( ‪ ،‬ﺍﻟﻨﻴل ‪ +‬ﻤﻴﺎﻩ ﺠﻭﻓﻴﺔ )‬
‫(‬
‫اﻟﻤﺤﺎﺻﻴﻞ اﻟﻤﺰروﻋﺔ ‪-:‬‬
‫اﻟﻤﻮﺳﻢ اﻟﺼﻴﻔﻲ ‪-:‬‬
‫‪.‬‬
‫اﻟﻤﺤﺼﻮل‬
‫اﻟﻤﺴﺎﺣﺔ‬
‫اﻻﻧﺘﺎج‬
‫اﻟﻜﻤﻴﺔ اﻟﻤﺒﺎﻋﺔ‬
‫اﻟﻤﺤﺼﻮل‬
‫اﻟﻤﺴﺎﺣﺔ‬
‫اﻹﻧﺘﺎج‬
‫اﻟﻜﻤﻴﺔ اﻟﻤﺒﺎﻋﺔ‬
‫اﻟﺘﺎرﻳﺦ‬
‫ﺳﻌﺮ اﻟﻮﺣﺪة‬
‫ﻣﺤﺎﺻﻴﻞ ﻣﺴﺘﺪﻳﻤﺔ ‪-:‬‬
‫اﻟﺘﺎرﻳﺦ‬
‫ﺳﻌﺮ اﻟﻮﺣﺪة‬
‫اﻟﺜﺮوة اﻟﺤﻴﻮاﻧﻴﺔ ‪-:‬‬
‫ﺍﻟﻌﺩﺩ‬
‫ﺍﻟﻨﻭﻉ‬
‫ﺍﻟﻌﺩﺩ ﺍﻟﻤﺒﺎﻉ‬
‫ﺘﺎﺭﻴﺦ ﺍﻟﺒﻴﻊ‬
‫ﺴﻌﺭ ﺍﻟﻭﺤﺩﺓ‬
‫ﺍﺒﻘﺎﺭ ‪.…………………………………………………………… :‬‬
‫ﺠﻤﺎل ‪.……………………………………………………………:‬‬
‫ﺤﻤﻴﺭ ‪.……………………………………………………………:‬‬
‫ﻀﺄﻥ ‪.……………………………………………………………:‬‬
‫ﻤﺎﻋﺯ‪...……………………………………………………………:‬‬
‫ﺩﻭﺍﺠﻥ ‪……………………………………………………………:‬‬
‫اﻵﻻت اﻟﺘﻲ ﻳﻤﺘﻠﻜﻬﺎ اﻟﻤﺰارع ‪-:‬‬
‫اﻟﻘﻴﻤــﺔ‬
‫اﻵﻟﺔ‬
‫ﺳﻌﺮ اﻟﺒﻴﻊ ) ﻓﻲ ﺣﺎﻟﺔ اﻟﺒﻴﻊ (‬
‫ﺍﻟﻤﻬﻥ ﻏﻴﺭ ﺍﻟﺯﺭﺍﻋﻴﺔ ………………‪ ..‬ﺍﻟﺩﺨل ﺍﻟﺸﻬﺭﻱ …………………‬
‫ﻤﻼﺤﻅﺎﺕ ‪…………………………………………………………:‬‬
‫اﻟﺘﺤﻮﻳﻼت ﻣﻦ اﻟﺨﺎرج ‪-:‬‬
‫اﻟﻤﺒﻠﻎ‬
‫اﻟﺘﺎرﻳﺦ‬
‫ﺍﻟﻤﺒـــﺎﻟﻎ ﻟـــﺩﻱ ﺍﻟﻤـــﺯﺍﺭﻉ ﻗﺒـــل ﺒﺩﺍﻴـــﺔ ﺍﻟﻤﻭﺴـــﻡ ﺍﻟـــﺸﺘﻭﻱ‬
‫‪.…………………………………:‬‬
‫اﻟﺴﻠﻔﻴﺎت ‪-:‬‬
‫اﻟﻤﺼﺪر‬
‫اﻟﻘﻴﻤﺔ‬
‫ﺗﺎرﻳﺦ اﺳﺘﻼم اﻟﺴﻠﻔﻴﺔ‬
‫ﺗﺎرﻳﺦ اﻟﺴﺪاد‬
‫ﺍﻨﻔﺎﻕ ﺍﻻﺴﺭﺓ ﺍﻟﺸﻬﺭﻱ ﻋﻠﻲ ﺍﻟﺴﻠﻊ ﺍﻻﺴﺘﻬﻼﻜﻴﺔ ‪……………………………:‬‬
‫ﻋﻼﻗﺎت اﻻﻧﺘﺎج ‪-:‬‬
‫ﻤﺴﺌﻭﻟﻴﺔ ﺼﺎﺤﺏ ﺍﻟﻤﺸﺭﻭﻉ ‪……………………………………………:‬‬
‫ﻤﺴﺌﻭﻟﻴﺔ ﺍﻟﻤﺯﺍﺭﻉ ‪……………………………………………………:‬‬
‫ﻨﺼﻴﺏ ﺼﺎﺤﺏ ﺍﻟﻤﺸﺭﻭﻉ …………‪ ..‬ﻨﺼﻴﻑ ﺍﻟﻤـﺯﺍﺭﻉ ………‪ ..‬ﻨـﺼﻴﺏ‬
‫ﺼﺎﺤﺏ ﺍﻻﺭﺽ …………………………‪..‬‬
‫ﺍﻟﻤﺸﺎﻜل ﺍﻟﺘﻲ ﺘﻭﺍﺠﻪ ﺍﻟﻤﺯﺍﺭﻉ ‪..……………………………………… :‬‬
‫…………………………………………………………………‪..‬‬
‫ﻤﻘﺘﺭﺤﺎﺕ ﺍﻟﺤﻠﻭل ‪……………………………………………………:‬‬
‫…………………………………………………………………‪..‬‬
‫ﺗﻜﺎﻟﻴﻒ اﻻﻧﺘﺎج ﻟﻠﻤﻮﺳﻢ اﻟﺸﺘﻮي ‪-:‬‬
‫ﺍﺴﻡ ﺍﻟﻤﺤﺼﻭل ‪…………………:‬ﺍﻟﻤﺴﺎﺤﺔ ﺍﻟﻤﺯﺭﻭﻋﺔ ‪..……………… :‬‬
‫ﺗﻜﺎﻟﻴﻒ ﺗﺤﻀﻴﺮ اﻻرض ‪-:‬‬
‫اﻟﺘﺎرﻳﺦ‬
‫اﻟﻌﻤﻠﻴﺎت‬
‫اﻟﺰﻣﻦ‬
‫اﻟﻄﺮﻳﻘﺔ‬
‫ﻋﻤﺎل ﻋﺎﺋﻠﺔ‬
‫اﻟﻤﺼﺪر‬
‫اﻟﺘﻜﻠﻔﺔ‬
‫ﻋﻤﺎل اﺟﺮة‬
‫ﻧﻈﺎﻓﺔ اﻻرض‬
‫ﺍﻟﺤﺭﺍﺜﺔ ﺍﻻﻭﻟﻲ‬
‫ﺍﻟﺤﺭﺍﺜﺔ ﺍﻟﺜﺎﻨﻴﺔ‬
‫ﺍﻟﺘﺴﻁﻴﺢ ﺍﻻﻭل‬
‫ﺍﻟﺘﺴﻁﻴﺢ ﺍﻟﺜﺎﻨﻲ‬
‫ﻋﻤل ﺍﻟﺠﺩﺍﻭل ﻭﺍﻟﺘﻜﺎﻨﺩ‬
‫ﻋﻤل ﺍﻟﺴﺭﺍﺒﺎﺕ‬
‫ﻣﻼﺣﻈﺎت ‪…………………………………………………………:‬‬
‫اﻟﻌﻤﻠﻴﺎت اﻟﺰراﻋﻴﺔ ‪-:‬‬
‫اﻟﻌﻤﻠﻴﺎت اﻟﺰراﻋﻴﺔ‬
‫اﻟﺘﺎرﻳﺦ‬
‫اﻟﻄﺮﻳﻘﺔ‬
‫اﻟﺰﻣﻦ‬
‫ﻋﻤﺎل ﻋﺎﺋﻠﺔ‬
‫اﻟﺘﻜﻠﻔﺔ‬
‫ﻋﻤﺎل اﺟﺮة‬
‫ﺍﻟﺯﺭﺍﻋﺔ‬
‫ﺍﻟﺭﻗﺎﻋﺔ‬
‫ﺍﺯﺍﻟﺔ ﺍﻟﺤﺸﺎﺌﺵ )‪(1‬‬
‫ﺍﺯﺍﻟﺔ ﺍﻟﺤﺸﺎﺌﺵ )‪(2‬‬
‫ﺍﺯﺍﻟﺔ ﺍﻟﺤﺸﺎﺌﺵ )‪(3‬‬
‫ﻤﻼﺤﻅﺎﺕ ‪..……………………………………………………… :‬‬
‫ﻣﺮات اﻟﺮي‬
‫ﺍﻻﻭﻟﻲ‬
‫ﺍﻟﺜﺎﻨﻴﺔ‬
‫ﺍﻟﺜﺎﻟﺜﺔ‬
‫ﺍﻟﺭﺍﺒﻌﺔ‬
‫ﺍﻟﺨﺎﻤﺴﺔ‬
‫ﺍﻟﺴﺎﺩﺴﺔ‬
‫ﺍﻟﺴﺎﺒﻌﺔ‬
‫ﺍﻟﺜﺎﻤﻨﺔ‬
‫اﻟﺘﺎرﻳﺦ‬
‫اﻟﺰﻣﻦ‬
‫ﻋﻤﺎل ﻋﺎﺋﻠﺔ‬
‫ﻋﻤﺎل اﺟﺮة‬
‫ﻤﻼﺤﻅﺎﺕ ‪……………………………………………………… :‬‬
‫اﻟﺘﻜﻠﻔﺔ‬
‫ﻣﺪﺧﻼت اﻻﻧﺘﺎج ‪-:‬‬
‫اﻟﻌﻨﺼﺮ‬
‫اﻟﻨﻮع‬
‫اﻟﻜﻤﻴﺔ‬
‫اﻟﻤﺼﺪر‬
‫اﻟﺘﻜﻠﻔﺔ‬
‫ﺍﻟﺒﺫﻭﺭ‬
‫ﺍﻟﺴﻤﺎﺩ‬
‫ﺍﻟﻤﺒﻴﺩﺍﺕ‬
‫ﺍﻟﺨﻴﺵ‬
‫ﻣﻼﺣﻈﺎت ‪…………………………………………………………:‬‬
‫اﻟﺤﺼﺎد ‪-:‬‬
‫ﻋﻤﻠﻴﺎت اﻟﺤﺼﺎد‬
‫اﻟﺘﺎرﻳﺦ‬
‫اﻟﺰﻣﻦ‬
‫اﻟﻄﺮﻳﻘﺔ‬
‫ﻋﻤﺎل ﻋﺎﺋﻠﺔ‬
‫ﻋﻤﺎل اﺟﺮة‬
‫اﻟﺘﻜﻠﻔﺔ‬
‫ﺍﻟﻘﻁﻊ‬
‫ﺍﻟﺠﻤﻊ‬
‫ﺍﻟﺩﻕ‬
‫ﺍﻟﺘﻀﺭﻴﺔ‬
‫ﺍﻟﺘﻌﺒﺌﺔ‬
‫ﺍﻟﺘﺭﺤﻴل‬
‫ﻤﻼﺤﻅﺎﺕ ‪…………………………………………………………:‬‬
‫ﺘﻜﺎﻟﻴﻑ ﺍﺨﺭﻱ ‪ :‬ﺍﻟﻀﺭﺍﺌﺏ …………………‪ ..‬ﺍﻟﺯﻜﺎﺓ ‪…………………:‬‬
‫ﺍﻻﻨﺘﺎﺝ ﺍﻟﻜﻠﻲ ﻤﻥ ﺍﻟﻤﺤﺼﻭل ………………‪ ..‬ﺍﺴﺘﻬﻼﻙ ﺍﻻﺴﺭﺓ …………‪..‬‬
‫ﺒﺫﻭﺭ ﻤﺤﻔﻭﻅﺔ ﻟﻠﻤﻭﺴﻡ ﺍﻟﻘﺎﺩﻡ ………………‪ ..‬ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﻭﺯﻋﺔ ﻟﻼﺨﺭﻴﻥ …‪..‬‬
‫ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺨﺯﻨﺔ …………………………‪ .‬ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺒﺎﻋﺔ ……………‪.‬‬
‫ﺴﻌﺭ ﺍﻟﻭﺤﺩﺓ …………………………‪ .‬ﻤﻜﺎﻥ ﺍﻟﺒﻴﻊ ………………‪.‬‬
‫ﻓﺘﺭﺓ ﺍﻟﺘﺨﺯﻴﻥ……………………………‪..‬ﺘﻜﻠﻔﺔ ﺍﻟﺘﺨﺯﻴﻥ ……………‪..‬‬
‫ﻤﻼﺤﻅﺎﺕ ‪…………………………………………………………:‬‬
‫ﻣﻠﺤﻖ رﻗﻢ ‪4 :‬‬
‫اﺳﺘﻤﺎرة اﺳﺘﺒﻴﺎن اﺻﺤﺎب اﻟﻤﺸﺎرﻳﻊ‬
‫ﺍﻟﻤﺤﻠﻴﺔ ‪.………………………………:‬ﺍﻟﻘﺭﻴﺔ ‪..…………………:‬‬
‫ﺍﻻﺴﻡ……………………‪ .‬ﺍﻟﻌﻤﺭ ‪.. …………:‬ﻤﺴﺘﻭﻱ ‪.……………:‬‬
‫ﻨﻅﺎﻡ ﺍﻟﻤﻠﻜﻴﺔ ‪ :‬ﻤﻠﻙ ﺤﺭ )‬
‫ﻨﻭﻉ ﺍﻟﻤﺸﺭﻭﻉ ‪ :‬ﺨﺎﺹ )‬
‫ﻤﺼﺩﺭ ﺍﻟﺭﻱ ‪-:‬‬
‫ﺍﻟﻨﻴل )‬
‫( ‪ ،‬ﻤﻴﺎﻩ ﺠﻭﻓﻴﺔ )‬
‫( ﻤﻴﺭﻱ )‬
‫( ﺘﻌﺎﻭﻨﻲ )‬
‫( ﺤﻜﻭﻤﻲ ) ( ﺍﺨﺭﻱ ) ﺤﺩﺩ (‬
‫( ﺸﺭﻜﺔ )‬
‫( ‪ ،‬ﺍﻟﻨﻴل ‪ +‬ﻤﻴﺎﻩ ﺠﻭﻓﻴﺔ )‬
‫(‬
‫(‬
‫ﻤﺴﺎﺤﺔ ﺍﻟﻤﺸﺭﻭﻉ …………… ﻋﺩﺩ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻓﻲ ﺍﻟﻤﺸﺭﻭﻉ ……………‪.‬‬
‫ﻤﻼﺤﻅﺎﺕ ‪…………………………………………………………:‬‬
‫ﻨﻭﻉ ﺍﻟﻁﻠﻤﺒﺔ ‪ ………………………:‬ﺤﺠﻡ ﺍﻟﻁﻠﻤﺒﺔ ‪.…………………:‬‬
‫ﺍﻟﻌﻤﺭ ﺍﻻﻓﺘﺭﺍﻀﻲ ﻟﻠﻁﻠﻤﺒﺔ ‪ .…………:‬ﺍﻟﻌﻤﺭ ﺍﻟﺤﺎﻟﻲ ﻟﻠﻁﻠﻤﺒﺔ ‪.……………:‬‬
‫ﺴﻌﺭ ﺸﺭﺍﺀ ﺍﻟﻁﻠﻤﺒﺔ ﻤﻥ ﺍﻟﺒﻨﻙ ……‪ ..……..‬ﻤﻥ ﺍﻟﺴﻭﻕ ‪…………………:‬‬
‫ﻋﺩﺩ ﺴﺎﻋﺎﺕ ﻋﻤل ﺍﻟﻁﻠﻤﺒﺔ ﻓﻲ ﺍﻟﻴﻭﻡ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺸﺘﻭﻱ ‪.‬‬
‫ﻋﺩﺩ ﺴﺎﻋﺎﺕ ﻋﻤل ﺍﻟﻁﻠﻤﺒﺔ ﻓﻲ ﺍﻟﻴﻭﻡ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺼﻴﻔﻲ‬
‫ﻋﺩﺩ ﺍﻟﺴﺎﻋﺎﺕ ﺍﻟﺘﻲ ﻴﻤﻜﻥ ﺘﺸﻐﻴﻠﻬﺎ ﺒﺎﻟﺠﺎﻟﻭﻥ ﺍﻟﻭﺍﺤﺩ ‪.‬‬
‫ﻋﺩﺩ ﺍﻟﺴﺎﻋﺎﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ﺘﺸﻐﻴﻠﻬﺎ ﻟﺭﻴﻪ ﻭﺍﺤﺩﺓ ﻟﻠﻔﺩﺍﻥ‬
‫ﻋﺩﺩ ﺍﻻﻓﺩﻨﺔ ﺍﻟﺘﻲ ﻴﻤﻜﻥ ﺭﻴﻬﺎ ﻓﻲ ﺍﻟﻴﻭﻡ ‪.‬‬
‫ﻜﻤﻴﺔ ﺍﻟﻭﻗﻭﺩ ﺍﻟﻤﺴﺘﻬﻠﻙ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺸﺘﻭﻱ‬
‫ﺍﻟﻤﺼﺩﺭ ……………………‪ ..‬ﺍﻟﺴﻌﺭ ……………………………‪.‬‬
‫ﻜﻤﻴﺔ ﺍﻟﺯﻴﺕ ﺍﻟﻤﺴﺘﻬﻠﻙ ﻓﻲ ﺍﻟﻤﻭﺴﻡ ﺍﻟﺸﺘﻭﻱ ‪.‬‬
‫ﺍﻟﻤﺼﺩﺭ ……………………‪ ..‬ﺍﻟﺴﻌﺭ ……………………………‪.‬‬
‫ﺘﻜﺎﻟﻴﻑ ﺍﻟﺼﻴﺎﻨﺔ ﻟﻠﻤﻭﺴﻡ ﺍﻟﺸﺘﻭﻱ ……… ﺘﻜﺎﻟﻴﻑ ﺘﺸﻐﻴل ﺍﻟﻁﻠﻤﺒﺔ …………‪.‬‬
‫ﻤﻼﺤﻅﺎﺕ ‪.……………………………………………………… :‬‬
‫ﻋﻼﻗﺎت اﻻﻧﺘﺎج ‪-:‬‬
‫ﻤﺴﺌﻭﻟﻴﺔ ﺼﺎﺤﺏ ﺍﻟﻤﺸﺭﻭﻉ‬
‫ﻤﺴﺌﻭﻟﻴﺔ ﺍﻟﻤﺯﺍﺭﻉ‬
‫ﻗﺴﻤﺔ ﺍﻻﻨﺘﺎﺝ ‪ :‬ﻨﺼﻴﺏ ﺼﺎﺤﺏ ﺍﻟﻤﺸﺭﻭﻉ …‪ .……..‬ﻨﺼﻴﻑ ﺍﻟﻤﺯﺍﺭﻉ ………‬
‫ﺍﻟﻤﺸﺎﻜل ﻭﺍﻟﻤﻘﺘﺭﺤﺎﺕ …………‪……………… ..………………… .‬‬