Impact of Environmental Degradation and POVERTY on Food Security
Impact of Environmental Degradation and POVERTY on Food Security: "Case study of White Nile State, Sudan" By Adil Yousif Yagoub Eisa B.Sc. (Honors) - 1997 University of Kordofan Supervisor Dr. Kamil Ibrahim Hassan A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Agricultural Economics Department of Agricultural Economics Faculty of Agriculture University of Khartoum DEDICATION To my dear Father Yousif Mother, Sisters, Brothers and All the family, I dedicate this work with my love Adil Acknowledgement Firstly, I am most grateful to Allah for assistance, health and patience he has given me to complete this work. I wish to express my special appreciation to my supervisor Dr. Kamil Ibrahim Hassan, Department of Agricultural Economics, Faculty of Agriculture, University of Khartoum for his help guidance, suggestions, advices and persistent encouragement to carry out this study. I am gratefully, indebted to my friend Adam Alajab, who helped me during the data collection and Abdelhameed for his invaluable help. Thanks are also extending to my uncles' families and to my friendly Uncle Khalid Dawelbait for their invaluable assistance and encouragement. Special thank to my sister Sayda for her finance and encouragement. Finally, thanks are also extended to my family for their encouragement during the course of this study. ABSTRACT The study was conducted in the White Nile State. It aimed to assess the environmental situation and poverty in the area and its impact on food security. This study aimed to investigate the socio-economic characteristics of the population, production cost, net returns and the effect of some socio-economic characteristics of the population and agronomic factors on crop yield in the area. The primary data were collected by a multi stage stratified random sample of 160 respondents (80 farmers and 80 non- farmers) in the White Nile State in the year 2002 using a questionnaire. In addition to the secondary data from the relevant sources. The descriptive statistics were used to investigate the socioeconomic characteristics of the respondent, environmental situation, food status and poverty distribution. Crop budget analysis was conducted to estimate the production cost and profitability of the crops under study. An econometric model of food crops (dura and wheat) constructed with yield being the dependant variable and the different agricultural inputs as independent variables. Cobb-Douglas production function was fitted to the data to investigate the effect of the main socio-economic and agronomic factors. The descriptive statistics analysis revealed that 91.2% of nonfarmer respondents were within the productive age group (20-60) years, while 33.8% of farmers were older (over 60) years. Although 47.5% and 70% from farmers and non farmers, respectively received formal education, which is reflected in the deteriorated situation of the agricultural labors that lead to the weak awareness of farmers on environmental issues, which resulted on deforestation of the region this deprived the local population from the benefits of trees. Most of the respondents said that the main factors curtailing agricultural productivity were shortage of irrigation water, lack of finance and inputs. The environmental impact clearly present on the cost structure that the cost of land preparation, irrigation and fertilizer represent a high percentile ratio comparable to the other items of cost that it present 53, 62, 68.4, 68.2 and 45% of the total cost for dura wheat, tomato, okra and onion, respectively. The studies revealed that vegetable were more profitable than food crops, but in spite of that farmer preferred to cultivate food crops rather than vegetables for the purpose of self satisfaction. Regression analysis for food crops showed that the environmental factors have significant effect on productivity and that the northern degraded area (stratification) has negative impact while irrigation has positive significant impact that mean the utilization of low level of irrigation input mainly due to soil erosion that logged the irrigation canals. This calls for the maintenance and rehabilitation of the irrigation system by mean of increasing the investment on the irrigation infrastructure, which appear on the positive significant effect of the production cost that the increase of the cost increased the productivity. In addition to that, some of the elasticities of the individual variable indicated the existence of increasing returns to scale for all crops. Time series analysis revealed that the trend of food crop productivity are declining, the productivity of dura and wheat will reach zero level after 55 and 18 years respectively. For food security and poverty alleviation the study recommended the following: • Putting the White Nile Scheme under governmental administration. • Provision of inputs at proper time and for reasonable prices. • Improvement of extension services and marketing systems. • Agricultural diversification and increase the area under the vegetables. ﺑﺴﻢ اﷲ اﻟﺮﺣﻤﻦ اﻟﺮﺣﻴﻢ ﻤﻠﺨﺹ ﺍﻷﻁﺭﻭﺤﺔ ﺃﺠﺭﻴﺕ ﻫﺫﻩ ﺍﻟﺩﺭﺍﺴﺔ ﺒﻭﻻﻴﺔ ﺍﻟﻨﻴل ﺍﻷﺒﻴﺽ .ﻭﻫﺩﻓﺕ ﺍﻟﺩﺭﺍﺴﺔ ﻟﺘﻘﻴﻴﻡ ﺍﻟﻭﻀﻊ ﺍﻟﺒﻴﺌﻲ ﻭﺤﺎﻟﺔ ﺍﻟﻔﻘﺭ ﺒﺎﻟﻤﻨﻁﻘﺔ ﻭﺃﺜﺭﻩ ﻋﻠﻲ ﺍﻷﻤﻥ ﺍﻟﻐﺫﺍﺌﻲ. ﺃﺭﺍﺩﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺍﻟﺘﻌﺭﻑ ﻋﻠﻰ ﺍﻟﺨﺼﺎﺌﺹ ﺍﻹﺠﺘﻤﺎﻋﻴﺔ ﻭﺍﻹﻗﺘﺼﺎﺩﻴﺔ ﻟﻠﻤﺴﺘﻬﺩﻓﻴﻥ ﻭﺘﺤﺩﻴـﺩ ﺘﻜﺎﻟﻴﻑ ﺍﻹﻨﺘﺎﺝ ﻭﺼﺎﻓﻰ ﺍﻟﻌﺎﺌﺩ ﻟﻜل ﻤﺤﺼﻭل ﻋﻠﻲ ﺤﺩﺓ ﻭ ﺃﺜـﺭ ﺒﻌـﺽ ﺍﻟﻌﻭﺍﻤـل ﺍﻹﺠﺘﻤﺎﻋﻴـﺔ ﻭﺍﻹﻗﺘﺼﺎﺩﻴﺔ ﻭﺍﻟﺤﻘﻠﻴﺔ ﻋﻠﻲ ﺇﻨﺘﺎﺝ ﺍﻟﻤﺤﺎﺼﻴل ﺒﺎﻟﻤﻨﻁﻘﺔ ﻭﺘﺄﺜﻴﺭ ﺫﻟﻙ ﻋﻠﻲ ﺩﺨل ﺍﻟﻤﺯﺍﺭﻋﻴﻥ. ﺠﻤﻌﺕ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻷﻭﻟﻴﺔ ﻓﻲ ﻋﺎﻡ 2002ﻡ ﻤﻥ ﻋﻴﻨﺔ ﻋﺸﻭﺍﺌﻴﺔ ﻁﺒﻘﻴﺔ ﻤﺘﻌﺩﺩﺓ ﺍﻟﻤﺭﺍﺤـل ﻤﻥ 160ﻤﺴﺘﻬﺩﻑ ) 80ﻤﺯﺍﺭﻋﻴﻥ ﻭ 80ﻏﻴﺭ ﻤﺯﺍﺭﻋﻴﻥ( ﻤﻥ ﻭﻻﻴﺔ ﺍﻟﻨﻴل ﺍﻷﺒﻴﺽ ﻋﻥ ﻁﺭﻴـﻕ ﺍﻹﺴﺘﺒﻴﺎﻥ ،ﺇﻀﺎﻓﺔ ﺇﻟﻲ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻟﺜﺎﻨﻭﻴﺔ ﻤﻥ ﺍﻟﻤﺼﺎﺩﺭ ﺫﺍﺕ ﺍﻟﺼﻠﺔ .ﺃﺨﻀﻌﺕ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺇﻟـﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻭﺼﻔﻲ ﺍﻹﺤﺼﺎﺌﻲ ﻟﻠﺨﺼﺎﺌﺹ ﺍﻹﺠﺘﻤﺎﻋﻴﺔ ﻭﺍﻹﻗﺘﺼﺎﺩﻴﺔ ﻟﻠﻤﺴﺘﻬﺩﻓﻴﻥ ﻭﺍﻷﻭﻀﺎﻉ ﺍﻟﺒﻴﺌﻴﺔ ﻭﺍﻟﻭﻀﻊ ﺍﻟﻐﺫﺍﺌﻲ ﻭﺍﻟﺘﻭﺯﻴﻊ ﺤﺴﺏ ﻤﻌﺎﻴﻴﺭ ﺍﻟﻔﻘﺭ .ﻭﺘﻡ ﻓﺤﺹ ﺍﻟﻤﻴﺯﺍﻨﻴﺔ ﺍﻟﻤﺯﺭﻋﻴﺔ ﻟﺘﺤﺩﻴﺩ ﻋﻨﺎﺼﺭ ﺍﻟﺘﻜﺎﻟﻴﻑ ﺍﻷﺴﺎﺴﻴﺔ ﻭﺭﺒﺤﻴﺔ ﺍﻟﻤﺤﺎﺼﻴل ﺘﺤﺕ ﻗﻴﺩ ﺍﻟﺩﺭﺍﺴﺔ .ﻜﻤﺎ ﺘﻡ ﺘـﺼﻤﻴﻡ ﺃﻨﻤـﻭﺫﺝ ﺇﻗﺘـﺼﺎﺩﻱ ﻗﻴﺎﺴﻲ ﻟﻤﺤﺎﺼﻴل ﺍﻟﺫﺭﺓ ﻭﺍﻟﻘﻤﺢ ﻭﺫﻟﻙ ﺒﻭﻀﻊ ﺍﻹﻨﺘﺎﺠﻴﺔ ﻜﻌﺎﻤل ﺘﺎﺒﻊ ﻭﻤﺨﺘﻠﻑ ﺍﻟﻤﺩﺨﻼﺕ ﻜﻌﻭﺍﻤـل ﻤﻔﺴﺭﺓ ،ﺃﺴﺘﺨﺩﻤﺕ ﺩﺍﻟﺔ ﺍﻹﻨﺘﺎﺝ )ﻜﻭﺏ -ﺩﻭﻗﻼﺱ( ﻟﻠﺘﻌﺭﻑ ﻋﻠﻲ ﺃﻫـﻡ ﺍﻟﻌﻭﺍﻤـل ﺍﻹﺠﺘﻤﺎﻋﻴـﺔ ﻭﺍﻹﻗﺘﺼﺎﺩﻴﺔ ﻭﺍﻟﺤﻘﻠﻴﺔ ﺍﻟﻤﺅﺜﺭﺓ ﻋﻠﻲ ﺇﻨﺘﺎﺠﻴﺔ ﻫﺫﻩ ﺍﻟﻤﺤﺎﺼﻴل. ﺃﻭﻀﺢ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ﺍﻟﻭﺼﻔﻲ ﺃﻥ %91.2ﻤﻥ ﻏﻴﺭ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻴﻘﻌﻭﻥ ﻀﻤﻥ ﻓﺌﺔ ﺍﻷﻋﻤﺎﺭ ﺍﻟﻤﻨﺘﺠﺔ) (60-20ﺴﻨﺔ ﺒﻴﻨﻤﺎ %33.8ﻤﻥ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻤﺴﻨﻴﻥ)ﻓﻭﻕ ﺍل (60ﺴﻨﺔ .ﻜـﺫﻟﻙ %47.5ﻭ %70ﻨﺎﻟﻭﺍ ﺘﻌﻠﻴﻤﺂ ﺭﺴﻤﻴﺂ ﻤﻥ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻭﻏﻴﺭ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻋﻠﻰ ﺍﻟﺘﻭﺍﻟﻰ ﻤﻤﺎ ﻴﻌﻨـﻰ ﺃﻥ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﺃﻗل ﺘﻌﻠﻴﻤﺂ ﻤﻤﺎ ﻗﺎﺩ ﺍﻟﻰ ﻀﻌﻑ ﺍﻟﻭﻋﻰ ﺒﺎﻟﻤﻭﻀﻭﻋﺎﺕ ﺍﻟﺒﻴﺌﻴﺔ ﺍﻟﺫﻯ ﺇﻨﻌﻜـﺱ ﻓـﻰ ﻏﻴﺎﺏ ﺍﻟﻐﻁﺎﺀ ﺍﻟﺸﺠﺭﻯ ﻭﺍﻟﻭﻋﻰ ﺒﺄﻫﻤﻴﺔ ﺍﻟﻨﺴﺒﺔ ﺍﻟﻤﺌﻭﻴﺔ ﻤﻥ ﺍﻷﺤﺯﻤﺔ ﺍﻟﺸﺠﺭﻴﺔ ﺍﻟﻭﺍﻗﻴـﺔ ﺇﻀـﺎﻓﺔ ﻹﻋﺘﻤﺎﺩ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻋﻠﻰ ﺍﻟﻤﻨﺘﺠﺎﺕ ﺍﻟﻐﺎﺒﻴﺔ ﻜﻤﺼﺩﺭ ﻟﻁﺎﻗﺔ ﺍﻟﻁﻬﻰ ﻤﻤﺎ ﻴﺅﺜﺭ ﺴﻠﺒﺂ ﻋﻠﻰ ﺍﻹﻨﺘﺎﺠﻴـﺔ ﺍﻟﺯﺭﺍﻋﻴﺔ ﻭﻗﺩ ﺫﻜﺭ ﺍﻟﻤﺴﺘﻬﺩﻓﻴﻥ ﺃﻥ ﺃﻫﻡ ﺍﻟﻌﻭﺍﻤل ﺍﻟﺘﻰ ﺘﻌﻭﻕ ﺍﻹﻨﺘﺎﺝ ﺍﻟﺯﺭﺍﻋﻰ ﻫﻰ ﻨﻘـﺹ ﺍﻟـﺭﻯ ،ﺍﻟﺘﻤﻭﻴل ﻭﺍﻟﻤﺩﺨﻼﺕ .ﺍﻷﺜﺭ ﺍﻟﺒﻴﺌﻰ ﻴﻅﻬﺭ ﺒﻭﻀﻭﺡ ﻓﻰ ﺍﻟﺘﻜـﺎﻟﻴﻑ ﺤﻴـﺙ ﺃﻥ ﺘﻜﻠﻔـﺔ ﺘﺤـﻀﻴﺭ ﺍﻷﺭﺍﻀﻰ ،ﺍﻟﺭﻯ ﻭﺍﻷﺴﻤﺩﺓ ﺘﻤﺜل ﻨﺴﺒﺔ ﻋﺎﻟﻴﺔ ﻤﻘﺎﺭﻨﺔ ﺒﺒﻘﻴﺔ ﺒﻨﻭﺩ ﺍﻟﺘﻜﺎﻟﻴﻑ ﺤﻴﺙ ﺘﻤﺜـل ،62 ،53 68.2 ،68.4ﻭ %45ﻤﻥ ﺠﻤﻠﺔ ﺍﻟﺘﻜﻠﻔﺔ ﻟﻜل ﻤﻥ ﺍﻟﺫﺭﺓ ،ﺍﻟﻘﻤﺢ ،ﺍﻟﻁﻤﺎﻁﻡ ،ﺍﻟﺒﺎﻤﻴﺔ ﻭﺍﻟﺒﺼل ﻋﻠﻰ ﺍﻟﺘﻭﺍﻟﻰ .ﻜﻤﺎ ﺃﻭﻀﺢ ﺘﺤﻠﻴل ﺍﻟﻤﻴﺯﺍﻨﻴﺔ ﺍﻟﻤﺯﺭﻋﻴﺔ ﺃﻥ ﺍﻟﻤﺤﺎﺼﻴل ﺘﺤـﺕ ﻗﻴـﺩ ﺍﻟﺩﺭﺍﺴـﺔ ﻤﺭﺒﺤـﺔ ﻟﻠﻤﺯﺍﺭﻋﻴﻥ ﻭﺃﻥ ﺃﻜﺜﺭ ﺍﻟﻤﺤﺎﺼﻴل ﺭﺒﺤﻴﺔ ﻫﻲ ﺍﻟﻁﻤﺎﻁﻡ ﺘﻠﻴﻬﺎ ﺍﻟﺒﺎﻤﻴﺔ ﺜﻡ ﺍﻟﺒﺼل ﻭﺍﻟـﺫﺭﺓ ﻭﺃﺨﻴـﺭﹰﺍ ﺍﻟﻘﻤﺢ ،ﻤﻤﺎ ﻴﺩل ﻋﻠﻰ ﺃﻥ ﻤﺤﺎﺼﻴل ﺍﻟﺨﻀﺭ ﺃﻜﺜﺭ ﺭﺒﺤﻴﺔ ﻤﻥ ﺍﻟﻤﺤﺎﺼﻴل ﺍﻟﻐﺫﺍﺌﻴﺔ ﻭﻟﻜﻥ ﺒﺎﻟﺭﻏﻡ ﻤﻥ ﺫﻟﻙ ﻨﺠﺩ ﺃﻥ ﺍﻟﻤﺯﺍﺭﻋﻴﻥ ﻴﺯﺭﻋﻭﻥ ﺍﻟﻤﺤﺎﺼﻴل ﺍﻟﻐﺫﺍﺌﻴﺔ ﻓﻀﻼ ﻋﻥ ﺍﻟﺨﻀﺭﻭﺍﺕ ﺒﻐﺭﺽ ﺍﻹﻜﺘﻔـﺎﺀ ﺍﻟﺫﺍﺘﻰ. ﺃﻅﻬﺭﺕ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻻﻗﺘﺼﺎﺩﻱ ﺍﻟﻘﻴﺎﺴﻲ ﻟﻠﻤﺤﺎﺼﻴل ﺍﻟﻐﺫﺍﺌﻴﺔ ﺃﻥ ﺍﻟﻌﻭﺍﻤل ﺍﻟﺒﻴﺌﻴﺔ ﻟﻬﺎ ﺃﺜﺭ ﻤﻌﻨﻭﻯ ﻓﻰ ﺍﻹﻨﺘﺎﺠﻴﺔ ﺤﻴﺙ ﺃﻥ ﺍﻟﻤﻨﺎﻁﻕ ﺍﻟﺸﻤﺎﻟﻴﺔ )ﺍﻟﻘﻁﺎﻋﺎﺕ( ﻟﻬﺎ ﺃﺜﺭ ﺴﺎﻟﺏ ﻋﻠﻰ ﺍﻹﻨﺘﺎﺠﻴﺔ ﺒﻴﻨﻤﺎ ﻟﻠﺭﻱ ﺃﺜﺭ ﻤﻌﻨﻭﻯ ﻤﻭﺠﺏ ﻭﺍﻟﺫﻯ ﻴﻌﻭﺩ ﻟﻠﻤﺴﺘﻭﻯ ﺍﻷﺩﻨﻰ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻤﻥ ﻫﺫﺍ ﺍﻟﻌﺎﻤل ﻨﻅﺭﺁ ﻟﻌﺎﻤـل ﺍﻟﺘﻌﺭﻴﺔ ﻭﺯﺤﻑ ﺍﻟﺭﻤﺎل ﺍﻟﺫﻯ ﻴﺅﺩﻯ ﺍﻟﻰ ﻏﻠﻕ ﻗﻨﻭﺍﺕ ﺍﻟﺭﻯ ،ﻤﻤﺎ ﻴﻌﻨﻰ ﺍﻟﺤﺎﺠـﺔ ﺍﻟـﻰ ﺇﺼـﻼﺡ ﻭﺇﻋﺎﺩﺓ ﺘﺄﻫﻴل ﺸﺒﻜﺔ ﺍﻟﺭﻯ ﺒﺯﻴﺎﺩﺓ ﺍﻹﺴﺘﺜﻤﺎﺭ ﻓﻰ ﻫﺫﺍ ﺍﻹﻁﺎﺭ ﻭﺍﻟﺫﻯ ﻴﻅﻬﺭ ﻓﻰ ﺍﻷﺜـﺭ ﺍﻟﻤﻌﻨـﻭﻯ ﺍﻟﻤﻭﺠﺏ ﻟﻠﺘﻜﺎﻟﻴﻑ ﺍﻟﺫﻯ ﻴﻌﻨﻰ ﺒﺯﻴﺎﺩﺓ ﺍﻟﺘﻜﺎﻟﻴﻑ ﺴﺘﺯﻴﺩ ﺍﻹﻨﺘﺎﺠﻴﺔ .ﻜﻤـﺎ ﺃﻭﻀـﺤﺕ ﺍﻟﺩﺭﺍﺴـﺔ ﺃﻥ ﻤﺠﻤﻭﻉ ﻤﺭﻭﻨﺎﺕ ﺍﻟﻌﻭﺍﻤل ﺍﻟﻔﺭﺩﻴﺔ ﺍﻟﻤﺘﻐﻴﺭﺓ ﺘﺩل ﻋﻠﻰ ﻭﺠﻭﺩ ﺘﺯﺍﻴﺩ ﻓـﻲ ﻋﺎﺌـﺩﺍﺕ ﺍﻟـﺴﻌﺔ ﻟﻜـل ﺍﻟﻤﺤﺎﺼﻴل. ﺃﻅﻬﺭ ﺘﺤﻠﻴل ﺍﻟﺴﻼﺴل ﺍﻟﺯﻤﻨﻴﺔ ﺃﻥ ﺇﻨﺘﺎﺠﻴﺔ ﺍﻟﻤﺤﺎﺼﻴل ﺍﻟﻐﺫﺍﺌﻴﺔ ﻤﺘﻨﺎﻗـﺼﺔ ،ﻭﺃﻥ ﺇﻨﺘﺎﺠﻴـﺔ ﺍﻟﺫﺭﺓ ﻭﺍﻟﻘﻤﺢ ﺴﺘﺼل ﺇﻟﻲ ﺍﻟﺼﻔﺭ ﺒﻌﺩ 55ﻭ 18ﺴﻨﺔ ﻋﻠﻲ ﺍﻟﺘﻭﺍﻟﻲ. ﺃﻭﺼﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺒﺈﻋﺎﺩﺓ ﺍﻟﻤﺸﺎﺭﻴﻊ ﺇﻟﻰ ﺍﻹﺩﺍﺭﺓ ﺍﻟﺤﻜﻭﻤﻴﺔ ﻭﺇﻋﺎﺩﺓ ﺘﺄﻫﻴﻠﻬـﺎ ﻤـﻥ ﺨـﻼل ﺇﺼﻼﺡ ﻨﻅﻡ ﺍﻟﺭﻱ ﻭﺘﻭﻓﻴﺭ ﻤﺩﺨﻼﺕ ﺍﻹﻨﺘﺎﺝ ﻓﻲ ﺍﻟﻭﻗﺕ ﺍﻟﻤﻨﺎﺴـﺏ ﻭﺒﺄﺴـﻌﺎﺭ ﻤﻌﻘﻭﻟـﺔ ،ﺘﻘﻭﻴـﺔ ﺍﻟﻭﺤﺩﺍﺕ ﺍﻹﺭﺸﺎﺩﻴﺔ ﻭﻨﻅﺎﻡ ﺍﻟﺘﺴﻭﻴﻕ ،ﺘﻭﻓﻴﺭ ﺍﻟﺘﻤﻭﻴل ﺍﻟﺯﺭﺍﻋﻲ ﻭﺘﻨﻭﻴﻊ ﺍﻟﺯﺭﺍﻋﺔ ﻭﺇﺴﺘﻘﻁﺎﻉ ﻤﺴﺎﺤﻪ ﺃﻜﺒﺭ ﻟﻤﺤﺎﺼﻴل ﺍﻟﺨﻀﺭ. LIST OF CONTENTS Page Dedication ................................................................................................... I Acknowledgement ...................................................................................... Ii Abstract ...................................................................................................... Iii Arabic Abstract ............................................................................................ v List of Contents ........................................................................................... vii List of Tables .............................................................................................. xi List of Appendices........................................................................................ xii List of Figures ............................................................................................. xiii CHAPTER ONE: INTRODUCTION ................................................... 1 1.1 General.............................................................................................. 1 1.2. Poverty ………………………………………………………………. 1 1.3. Food security....................................................................................... 3 1.4. Background to the problem................................................................. 4 1.5. Problem statement............................................................................... 6 1.6. Justification........................................................................................... 7 1.7. Objective of the study............................................................................ 9 1.8 Hypotheses formulation......................................................................... 9 1.9. Research Methodology.......................................................................... 9 1.9.1 Methods of data collection.................................................................. 9 1.9.2 Methods of analysis............................................................................ 12 1.10 Organization of the study..................................................................... 12 CHAPTER TWO: LITERATURE REVIEW ........................................ 14 2.1 Environmental Degradation................................................................... 14 2.1.1 Environment as a global crisis............................................................. 14 2.1.2 Meta problem...................................................................................... 21 2.1.3 Environment and poverty.................................................................... 22 2.1.4. Energy and environment.................................................................... 23 2.1.5 Environmental degradation in developing countries........................... 24 2.1.5 Environmental Degradation in Sudan................................................. 26 2.1.5.1 Over cultivation................................................................................ 27 2.1.5.2 Overgrazing...................................................................................... 27 2.1.5.3 Deforestation.................................................................................... 28 2.1.5.4 Desertification.................................................................................. 29 2.2. Food security......................................................................................... 30 2.2.1. Food Shortages and Emergencies....................................................... 32 2.3. Poverty........................................................... ...................................... 33 2.2.1. The role of agriculture to alleviate poverty........................................ 34 CHAPTER THREE: RESULTS AND DISCUSIONS............................ 36 3.1. Social characteristics of respondent families........................................ 36 3.2. Respondent level of Education.............................................................. 37 3.3. Institutional services.............................................................................. 41 3.4. Environmental services......................................................................... 41 3.5. Economic activities............................................................................... 43 3.6. Nutritional Status................................................................................... 45 CHAPTER FOUR:RESULTS OF THE CROP BUDGET ANALYSIS................................................................ 47 4.1. Production costs.................................................................................... 47 4.1.1. Land preparation cost......................................................................... 47 4.1.2. Seeds cost........................................................................................... 48 4.1.3. Irrigation cost........... ......................................................................... 48 4.1.4. Fertilizer cost........................ ............................................................. 49 4.1.5. Harvesting and threshing cost............................................................ 52 4.1.6. Sacks and Strings cost........................................................................ 52 4.1.7. Zakat.................................... .............................................................. 52 4.1.8. Other cost............................. ............................................................. 53 4.1.9. Total variable cost of production........................................................ 53 4.2. Analysis of crop returns............................ .......................................... 55 4.2.1. Crop yield............................................ .............................................. 55 4.2.2. Farm gate prices............................... ................................................ 55 4.2.3. The break even yield.......................................................................... 56 4.2.4. Gross returns (Ls/feddan) .................................................................. 58 4.2.5. Gross margins (Ls/feddan)................................................................. 58 4.2.6. The coefficient of private profitability (CCP)................................... 60 4.3. Respondent income and Poverty indicators.......................................... 63 CHAPTER FIVE: RESULTS AND DISCUSSION OF REGRESSION EQUATIONS.... .............................................................. 65 5.1. Theoratical Frame work.... ................................................................... 65 5.1.1. The production function.... ................................................................ 65 5.1.2. Some forms of production function.... .............................................. 66 5.1.2.1. Linear production function.... ....................................................... 66 5.1.2.2. The quadratic form.... .................................................................... 67 4.1.2.3. Cubic or New-classical production function................................... 68 5.1.2.4. Cobb-Douglas production function................................................ 69 5.1.2. Model Specification.... ...................................................................... 71 5.1.3. The econometric model estimation method....................................... 71 5.1.4. The R-squared.... ............................................................................... 72 5.1.5 The test of hypothesis.... ..................................................................... 73 5.1.5.1. The T-test.... ................................................................................... 73 5.1.5.2. The F-test.... .................................................................................... 73 5.1.6. The selected production model........................................................... 74 5.2. Regression analysis results.... ............................................................... 75 5.2.1. Dura regression result.... .................................................................... 75 5.2.2. Wheat regression result...................................................................... 77 5.3. Discussion of the two crops regression equations....................................... 79 5.3.1. The irrigation variable........................................................................ 79 5.3.2. The farm income variable .................................................................. 80 5.3.3. Credit variable ................................................................................... 80 5.3.4. Extension services variable................................................................ 81 5.3.5. The cost of production variable......................................................... 81 5.3.6. The stratification variable…………………………………………... 81 5.4. The nature of returns to scale................................................................ 81 5.5. Forecasting food crop productivity ...................................................... 81 CAPTER SIX : CONCLUSION AND RECOMMENDATION 87 6.1. Summary.................................................. ............................................ 87 6.2. Conclusions........................................................................................... 88 6.3. Recommendations................................................................................. 90 REFERENCES .......................................................................................... 92 APPENDICES............................................................................................ 96 LIST OF TABLES Table Title No. 1.1 Five years average area and productivity (1975-1993) for cotton and Dura in White Nile schemes................................................................. 8 2.1 Consumption of selected items, North / South .................................... 19 2.2 Ratio of income of richest 20% to poorest 20% of world population 20 2.3 Absolute poverty, 1993....................................................................... 35 3.1 Respondent family characteristic........................................................ 38 3.2 Age distribution of respondent............................................................ 38 3.3 Respondent marital status.................................................................... 38 3.4 Respondent educational level.............................................................. 40 3.5 Agricultural credit and extension services.......................................... 40 3.6 Project management............................................................................. 40 3.7 Reason of non-agroforestry application.............................................. 42 3.8 Source of cooking power..................................................................... 42 3.9 Economic Activity............................................................................... 42 3.10 Livestock Ownership............................................................................ 44 3.11 Kind of Livestock owned..................................................................... 44 3.12 Impact of livestock in income............................................................. 44 3.13 Production constraints.......................................................................... 46 3.14 Number of meals per day.................................................................... 46 3.15 Meals components................................................................................ 46 4.1 The average production cost Ls/feddan............................................... 50 4.2 The percentage share of each item in the total cost of production....... 51 4.3 The average yield of crops per feddan season 2001-2002................... 57 4.4 The average farm gate price................................................................ 57 4.5 The break even yield of crops per feddan........................................... 59 4.6 The average gross returns Ls/feddan................................................... 59 4.7 The average gross margins Ls/feddan.................................................. 62 4.8 The coefficient of private profitability................................................. 62 4.9 The respondent average income …………………………………….. 64 4.10 The poverty indicator .......................................................................... 64 5.1 Dura regression equation......................................................................... 76 5.2 Wheat regression equation....................................................................... 78 LIST OF FIGURES Fig. No. Title Page No. 2.1 Rates of change on the planet................................................... 16 2.2 The distribution of world income.............................................. 18 5.1 Trend for dura productivity (sacks/feddan) ............................. 85 5.2 Trend for wheat productivity (sacks/feddan) ........................... 86 LIST OF APPENDICES App No. 1 Title No. Area and average production of cotton and Dura in White Nile Scheme from season 1973/74 to 1997/98..................................... 96 CHAPTER ONE INTRODUCTION 1. General Introduction: In recent years, environmental degradation has become a matter of great concern worldwide. Environmental degradation, ranging from soil erosion, desertification and air pollution to shrinking of ozone layer, pollution of the world oceans, global warming and deforestation is increasingly drawing the attention of the international community. In large parts this has resulted from the growing awareness of the linkage between the nature of economic activities and the environment on one hand and the desire to ensure that the design of economic policies takes due account of the environment conservation on the other hand. Most of developing countries depend mainly on their resource endowments for production and exports. Thus, excessive degradation of natural resources will limit their chances for sustainable development. In Sudan since independence there were major developmental plans adopted by the various political systems. In those plans, there was virtual neglect to their environmental consequences. The result is environmental degradation in its different forms. Accordingly, the impact of environmental degradation constituted a threat to the economic development and growth as well as the quality of life of people in the Sudan. Therefore, environmental conservation is necessary for maintaining the natural resources for economic development as well as for human existence. 1.1 Poverty: Poverty is becoming a stunning phenomenon and the denial of opportunities is presenting an obstacle and a challenge to the process of human development and to environmental conservation. As a phenomenon, poverty stems from a complexity of economic, physical, social and political factors and has adverse impacts on both the physical and social environments. Its results are more degradation to the physical and social environments and lower population quality. Lower population quality, in turns, implies vulnerability to death at all ages, more ignorance and inability to execute development (El Nayal, 2002). According to its human poverty index (HPI), and out of the 174 Countries listed. The Sudan ranks No.143 (UNDP, 1998), and local studies claim that at present, 93% of the Sudanese population live below poverty line (El Nayal, 2002). Poverty can be defined as poverty is a multi-dimensional phenomenon. It is not only "the lack of opportunities and choices most basic to human development, to lead a long, healthy and creative life, to enjoy dignity, self-esteem, the respect of others, and things that people value in life (UNDP, 1998). Poverty inflicts a negative impact on both the environment and the process of human development. Due to the diverse physical and cultural setups of the Sudan, the impacts of poverty on Sudanese population vary from one region to another. The main characteristic situation of the rural poor in the Sudan is one of high level of illiteracy rates, high disease incidence, high infant mortality rates, short life expectancy, lack of access to basic services and low per capita income, and conflicts over natural resources (water and grazing land) (El Nayal, 2002). 1.2 Food security: Food security defined as: assuring to human beings the physical and economic access to the basic food they need, is a broad, crosscutting issue, which has implications for a number of different sectors in the economy. Food Security is often associated with food self-sufficiency and the need to grow more food. However, in reality it has much stronger links with issues of poverty, employment and income generation. For low-income economies, where a large percentage of the population lives in the rural areas and depends on agriculture for its income, increasing food production may be an important element in increasing food security (FAO, 1997). This definition implies three different aspects: availability, stability and access to the basic food needed. This definition is clearly stated in terms of food security for each individual, and it can be argued that this is the most indeed some would say the only, meaningful definition of food security. The definition of household food security accepted by the committee on world food security refines this definition also follows: "Physical and economic access to adequate food for all households members, without undue risk of losing such access" (FAO, 1997). This introduces the concept of vulnerability (Ibid, 1997). Food availability is determined by the level of food supplies, composed of subsistence production and market supplies stemming from domestic production, foods stocks and food imports. Access to food is the result of the ability to express food needs (beyond subsistence production) as effective demand. Stability refers to variations and the risk of shortfalls in food production, supplies and/or demand over time. Food security is defined as a situation where both food supply and demand are sufficient to cover food requirements on a continuous and stable basis. This general definition of food security applies, in principle, to individual household as well as to aggregate national food security. Food insecurity prevails if, at any time (occasionally, repeatedly, or permanently), either the volume of food supply, or food demand, or both fall short of requirements (Ibid, 1997). 1.4. Background of the problem: Salih, et al. (1997) reported that the instability of management, lack of finance, reduction of cultivated area and decrease of productivity are among the main problems facing the White Nile schemes. The establishment of private pumps schemes dates back to the period (1929 – 1931) after the construction of Sennar and Gebel Awlia dams. To compensate the citizens for their flooded land due to the construction of Gebel Awlia dam, the government established in 1931 the White Nile Pump Schemes with a total area of 27.000 feddans around ElDuiem and to the South of the dam. In 1943, what was known as the White Nile Pump Schemes Board (WNPSB) was formed to run them. In June 1968, the Agricultural Reform Corporation (ARC) was created to act as an independent managing body for the schemes taken over by the government. Gradually, by 1970, all the private schemes with pumps above six inches came under the management of the agricultural Reform Corporation. In 1975 the rules of the Public Corporation for Agricultural Production (PCAP), were amended to add to its responsibilities, the management of the (ARC) schemes and those, which were run by the White Nile Pump Scheme Board (WNPSB). In 1978/79 a trend of decentralization, aiming at giving the Agricultural Corporations more autonomy in decision making and loosening the grip of the headquarter on them, led to the creation of White Nile Agricultural Corporation (WNAC) with its headquarters in Kosti and regional offices in El-Duiem and El-Renk. In 1986, a ministerial resolution was issued and the name has been changed from White Nile Agricultural Corporation (WNAC) to the White Nile Pump Scheme Administration (WNPSA), which in 1994/95 has been divided into two administrations, the Northern one in El-Duiem and the southern in Kosti. In 1996 until now the administrations charged to be many companies they are: ● The North White Nile Pump Schemes Company (El-Duiem). ● The South White Nile Pump Schemes Company (Kosti). ● The White Nile Agricultural services Company (IFAD). ● The White Nile Company (El-Gaabidda). ● Acala Company, and other companies and co-operative associations. The traditional rotation on the scheme is the three courses of cotton – 1/2 Dura, 1/2 follow – follow. Recently, wheat replaced dura or cotton in case water pumping is commenced late, particularly in some schemes in El-Duiem region. For a long period, the crop structure predominant in the scheme is the following: Dura, Cotton and Wheat. Production relations within the White Nile Pump Scheme were originally based on the joint account system (J.A). It started with 60% as the share of the scheme owner and 40% for the tenant. In 1968 the share of the tenants reached 50% of cotton net proceeds. In 1981/82, the (J.A) was abandoned to be replaced by what is known as the individual account (I.A). According to this new system, the White Nile Pump Scheme Administration provides irrigation water and other inputs, prepares the land and avails loans for the different agricultural operations. The cost of inputs and the rent of land and all other activities performed by the administration on behalf of the tenants would then be deducted from the gross proceeds of cotton and the balance would be given to the tenants. Food and Agriculture Organization (FAO) (1986) reported that during the last few years moving sands have reached White Nile Pumps schemes covered villages, clogged canals and made irrigated agriculture difficult or impossible in some areas. The causes of desertification in the White Nile State also include severe felling of natural forests. The over cutting helped by expansion in rainfed agriculture resulted into wind erosion and moving sands. Desertification in White Nile state has led to sedimentation of canals and coverage of fields with sand, reduction in the cropped area and low productivity, which reduced tenant income and hence resulted in food deficiency and non-sustainable development in the area. 1.5. Problem statement: In recent years, the efficiency of the major irrigated projects witnessed a considerable deterioration in productivity and thus decrease in tenants' income has been noticed. Environmental degradation in the White Nile state influenced cropped area, productivity and income of people in the affected areas. We notice the decline and fluctuation of cotton and dura cultivated area and average production Table (1) and appendix (1) show the average area and average productivity during seasons (1973-1998). This study intends to assess the impact of environmental degradation and poverty on the state of food and agricultural production. In this study crop, profitability will be investigated to assess the attitude of farmers' whether they seek for profitability or self-sufficiency. It is also the aim of the study to determine the factors affecting productivity. 1.6. Justification: Environmental degradation in many forms is a serious problem that threats economic development. Due to the lack of reliable data on its impact, it is difficult to quantify the adverse effect and the exact cost on the economy. In White Nile State, there are many phenomena that need to be investigated: The major policy change shifting from cotton producing before independence to major food production in the recent drought years. Besides wind erosion, desertification and poverty necessitate intervention. We believe it is high time to evaluate the effect of these changes on development in form of resource use, crop yields and returns. This call for an overall study to assess farmers attitude towards cropping patterns e.g. assess the profitability of crops in order to achieve a healthy environment and higher income to keep up the White Nile state in sustainable manner as a food supplier. Even a surplus could be achieved for export to earn foreign currency. Table (1.1) Five years average area and productivity (1975-1993) for cotton and Dura in White Nile schemes. Cotton The period Dura Average Average area Average area Average 1000 fed productivity/kg productivity/ 1000 fed kantar/ feddan 1973/74-77/78 125.8 3.02 70.2 241.2 1978/79-82/83 85.4 2.42 31.6 423.6 1983/84-87/88 50.2 3.04 38.6 397.2 1988/89-92/93* 23.2 3.27 47.6 462 1992/93-97/98 36.4 3.55 49.2 446 * in 1991/92 cotton was not cultivated Source: statistics and information management, Ministry of Agriculture and Forestry. 1.7. Objective of the study: The main objective of this study is to seek ways for conserving and improving natural resources existing, and trying to evaluate the expected impact on: ? Food security; ? Poverty alleviation, and ? Improving living condition for the future generations in a sustainable manner. 1.8. Hypotheses formulation: Hypotheses are based on the objectives of the study. The following hypotheses will be tested: ● Environmental factors (rainfall, irrigation and population density), has strong effect on crop yield. ● Cost of production, credit, irrigation availability and rainfall are the main constrains of agricultural production in WNS. ● The people in the White Nile State live under poverty line. 1.9 Research Methodology: 1.9.1 Methods of data collection: 1.9.1.1 Data source: Both primary and secondary data will be used. Secondary data will be obtained from textbooks, published papers, reports, Ph.D., M.Sc. Theses and from governmental administrations, research centres, the White Nile Scheme Companies and Ministry of Agriculture and Forests … etc. For primary data, two types of questionnaires will be designed. Type one is a farmer's questionnaire for the owners and rentals in the scheme. The second being a household questionnaire for the targeted population (people inhabiting the study area). This is mainly to collect cross-sectional data for the season 2002 – 2003. 1.9.1.2 Sampling technique: Historically, the northern (Ed-Duiem) region had been dominated by schemes established by the government, while the majority of the schemes in (Kosti) region were established by the private sector. Although there is no clear-cut line of demarcation between the regions, it is hypothesized that, tenants in the different regions, have different attitudes towards agricultural work. The justification for this hypothesis stems from the assumption that, the regions are of different ethnic groups and accordingly different population characteristics. For improving the precision of estimates and to avoid bias in selection, a multi–stage stratified random sample, in which the population of the White Nile farmers is divided into two strata, was used. This resulted in Ed-Duiem and Kosti stratum. The use of stratification is justified by the existence of the above-mentioned differences between the strata and the belief that, each stratum is relatively more or less homogenous. At later stage of stratification, from Ed-Duiem stratum, which consists of two regions namely Um Gar and Abgar, three schemes are selected randomly; Wakara, Um Gar and El-Ain have been selected. In addition, from Kosti stratum, which consists of four regions namely Am jalala, Algaiger, Rabak and Um Hany, three schemes are selected randomly, Khour Agwal, Gezira Abba and Um Hany. At a further stage of stratification and for the purpose of the study, the simple random sampling is used for the selection of respondents from each scheme since lists of tenants are available in the headquarters of the concerned schemes. The selection was then carried out with the help of random numbers. 1.9.1.3 Sample size: The sample size to be chosen is a trade-off between the level of precision aimed at and the resources available in terms of time, cost and other facilities. The sample size determined according to the formula: n= KV D Where: K = Z value (the normal deviation of 0.9 probability). V = the estimated standard deviation of out put in the area of the study, which assume to be 2.39 according to Abdel-Rahim (1989). D = the magnitude of the difference to be detected "significant level" (0.05). So according to the formula above the sample size was: n= 1.654 (2.39) 0.05 = 78.631 = 80 respondents. 1.9.2 Methods of analysis: The analytical techniques to test the above hypotheses include: 1. Descriptive statistics to present the bio-data of the respondents include: mean, standard deviation, percentage, frequency distribution and tabular. 2. Private profitability of crops for main crops cultivated in the area and comparing different crops to achieve the best ones. The coefficient of private profitability (CPP) shows the extent to which the production of a crop is profitable or not profitable for the producer. Coefficient of private profitability is of primary interest to the individual producers. CPP = Total revenue per feddan at farm-gate Total production cost at farm-gate A coefficient of less than one (CPP < 1.00) indicates that it is not profitable to the producer to produce that product as the particular productivity level. 3. Multiple regressions to investigate the main constrains of agricultural production in WNS. 1.10 Organization of the study: The study will consist of five chapters: Chapter one: An introductory chapter includes general information, background; statement of the problem summarizes the problems facing the study area, justification to explore the reasons behind the choice of the research topic, objectives of the study, hypotheses, methodologies and organization of the study. Chapter two: Deals with literature review and background, it concentrates on various aspect of environmental degradation and includes the causes of environmental degradation in Sudan, food security, poverty and the role of agriculture to alleviate poverty. Chapter three: descriptive chapter deals with descriptive procedures to assess the main characteristics of parameter and presents causes and consequences of environmental degradation and non-sustainable development in the area. Chapter four: Results analysis and discussion of crop profitability. Chapter five: Impact of environmental factors and poverty on food crops. Chapter six: Conclusions and recommendations. CHAPTER TWO LITERATURE REVIEW 2.1 Environmental Degradation: 2.1.1 Environment as a global crisis: In recent years it is common to talk about global problems, although environmental concern–or more properly concern about problems caused by development–first arose on a local scale in the expanding industrial economics of the North, where such problems are as love canal and, later, seveso were seen as local, despite there seriousness (David, 1996). In the mid – 1990's, some 30 years after evidence of trans-border pollution began to accumulate, it does indeed seem appropriate to talk in terms of a global crisis. A considerable amount of evidence is now available to prove that if unsustainable development continues, it will be at the cost of even greater human suffering world wide, and will create even more serious and pervasive ecological damage to the biosphere. Such evidence is documented in great detail in a range of publications–U.N. Agency reports "UNEP, UNDP and FAO", World Bank reports …etc.–from the mass of statistics three issues standout: ● The increase in human related activities and its impacts on the resources: Since 1900 the world's population has tripled and global industrial production has increased 50 times. Some 80 % of the increase in industrial production has occurred since 1950 (Mac Neill, 1989 cited in Reid, 1996). Throughout much of the century, there have been accelerating increases in the consumption of both renewable and nonrenewable resources. Agricultural production has risen dramatically (but so have desertification, soil erosion and the salinization of productive lands). It has been estimated that humanity now consumes about 40% of total terrestrial photosynthetic production (Reid, 1996). Over recent decades, these increases have been accompanied by accelerating rates of deforestation, declined in fish stocks, loss of agricultural land, loss of soil, depletion of fresh water, loss of habitats, loss of species and loss of biodiversity. These losses in natural resources are paralleled by a loss of human diversity in many skills and in many areas (e.g. cultural diversity). While many of the impacts of these changes appear to be most graphically illustrated in the poorer countries, there are by no means confined to tropical regions. These unsustainable trends can be represented by the annotated sketch graphs in figure (2.1). These accelerating trends have been punctuated by dramatic events, the natural disasters (flood, drought, disturbance of the weather patterns and famine) which seem to follow each other with increasing frequency. During the 1970's the death toll from such events increased six fold over the preceding decade (WCED, 1987). Even more serious is the evidence that the combined effects of resource depletion on a massive scale and the global diffusion of waste emissions (particularly greenhouse gases and chlorofluorocarbon) represent another order of ecological damage. The accumulated evidence makes it increasingly plain that environmental problems have been problems of development all along. The sheer weight of the evidence of ecological damage leads more and more people to doubt seriously weather the end justifies the means. ● Growing Inequity Between Rich and Poor: The global situation is represented by the graph in figure (2.2) which shows the distribution of world income at the beginning of the 1990's. The disparities in income are matched by many other inequities, including the inequitable consumption of resources, as shown in table (2.1). These inequities mean that, for the poorest life is a daily confrontation with a reality which is hardly imaginable to most northerners but which exists on a massive scale. It is estimated that over a billion people (approximately 20 % of the world's population) exists in conditions of extreme poverty, lacking adequate nutrition, access to safe drinking water, sanitation, health care and housing. Children are at particular risk from these deprivatation. A child born in the South is on average about 15 – 20 times less likely to survive beyond the age of five than a child in the North. About 4 million of the children who die before the age of five in the South each year die of diseases caused by lack of safe water and sanitation (Elhot, 1994). More Table (2.1): Consumption of selected items, North / South Item Share percent ADR EDR North South North / South North / South Meat 64 36 6 52 Cereals 48 52 3 6 Round wood 46 54 1 6 Paper, etc. 81 19 14 115 Fertilizer 60 40 5 6 Iron and steel 80 20 13 22 Cars 92 8 24 320 Electricity 81 19 13 46 CO2 70 30 8 27 Notes: The average disparity ratio (ADR) is the ration of per capita consumption levels in North and South. For example, on average a person in the North eats six times as much meat as a person in the South. The extreme disparity ratio (EDR) is the ratio between the riches and poorest countries, using the USA and Indian. Thus on average and American eats 52 times as much meat as an Indian, the Eastern European countries are included in the North, and the newly industrialized countries of Asia are included in the South. The ADR is therefore smaller than the real disparities between many European countries and the low income countries of Asia and Africa. Source: Parikh, et al. (1991). Quoted in Reid, 1996. and more people try to grow staple food on unsustainable soil and have to travel further and further for fuel wood and water. Declining yields, exhausted soils and the increasing marginalization of the landless poor mean increasing rates of migration to settlements around cities. Even in the remotest areas, indigenous groups find their livelihoods threatened. Moreover, in some countries, repressive regimes deny their citizens human rights or deprive them of civil liberties. This is not, however, a static situation, the plight of the poorest, relative to the richest, has been worsening over recent decades. The gap continues to widen as table (2.2) indicates (David, 1996). Table (2.2): Ratio of income of richest 20% to poorest 20% of world population Years Ratio 1960 30 : 1 1970 32 : 1 1980 45 : 1 1989 59 : 1 1991 61 : 1 Source: UNDP, 1991 and UNDP, 1994. ● Population Growth: The world population of almost six billion is now rising by over 90 million a year. It has more than doubled since 1950 and may double again. It is estimated that, because of the phenomenon known as "demographic momentum" world population will not stabilize till the year 2040 at a figure of some where between 8 billion (the UN's low projection) and 14 billion (the UN's high projection) (WECD, 1987). Despite significant falls in the rate of increase in some countries, "a head lie four decades of the fastest growth in human numbers in all history" (Reid, 1996). It is almost impossible to contemplate such figures without relating them to the question of food supplies. While it is accepted that at present the world produces enough food to feed the present population (despite and estimated one billion suffering from chronic malnutrition), there will have to be at least a doubling of the food production in 1993 if the population of 2040 is to be adequately fed. Yet per capita food production has declined in many countries since 1980; world per capita grain production continued to increase till 1990, since when it has dropped (Brown, et al. 1994); and it seems doubtful if increased applications of nitrogenous fertilizers will increase yields, by the amount require (although other developments, e.g. in biotechnology and genetic engineering, may have this potential). Such considerations have let Paul & Ann Ehrlich, authors of the population explosion, to assert, "arresting global population growth should be second only in importance to avoiding nuclear war in humanity's agenda (Ehrlich & Ehrlich, 1990). 2.1.2 Meta problem: Some commentators use the term Meta – problem to refer to a cluster of very closely interconnected problems. e.g., in some tropical countries soil erosion, decline in soil fertility and productivity, lack of labour, the consequent failure to maintain, say, soil terraces or irrigation channels, poor management of water sources and encroaching desertification are all really part of one larger problem of inappropriate – or unsustainable – rural development. Each aspect of the problem is so closely related to the others that any attempt to solve it is unlikely to be successful unless the others are treated simultaneously. The term Meta problem is also a useful way of referring to very large underlying problems. These usually represent powerful forces, which generate a number of problems on different scales and indifferent guises. It is possible to identify a number of meta problems e.g., rate of population growth in the South, over population in the North, poverty, unequal access to resources, systems of land tenure and ownership, lack of democratic rights and the western model of development. 2.1.3 Environment and poverty: Rural poverty is generally seen as both a cause and consequence of environmental degradation in most developing countries. The relationship is, however, complex. Although there were many instance in recent years where the poor over exploited and irreversibly degraded their natural resource base, where was also evidence of environmentally sound and often innovative ways in which the poor secured their livelihood. (FAO, 1993). In many parts in the world, it is the poor who suffer most from environmental degradation, from climate change, from deforestation, from drought, disease and from wide spread desertification. These environmental conditions can, at worst threaten their very survival. Environment is inevitably global, and what happen in one part of the glop has inescapable effects elsewhere, and on our common future. No society can afford environmental degradation (Reed, 2001). In 1987, WCED published the report: "our common future" it is goes on to trace the relationship between environment and development: "many forms of development erode the environmental resources on which they must be based, and environmental degradation can undermine economic development". There are also reciprocal links between poverty and the environment, poverty being recognized "as a major cause and effect of global environmental problems" (WCED, 1987). It is the high level of consumption of resources, largely in the North but also among the elites in the South, that accounts for development's impact on both planet and people, and that forces the poor in the South exhaust resources and destroys ecosystems in their attempts to maintain their existence. 2.1.4. Energy and environment: There are multiple links between energy and the environment; between wood use and deforestation and desertification, between air pollution and the choice of fuel technology for domestic use, industry, transport...etc. Climatic change is in timely related to energy supply and use (Mann, 2001). It has been estimated that almost one-third of world's populationaround 2 billion people- does not has access to adequate energy services; many of these rely on fuel wood and animal dung for the majority of their energy needs. With population increases, the number without access to adequate energy services will increase. Yet energy resources are plentiful, special when one considers renewable energies such as those from the sun, wind, water and the earth (ibid, 2001). Energy is essential aspect of human life. Food must be grown and cooked, water moved from its source, goods carried to and from the market. For the poor many of the basic energy inputs are currently human, such as those for water and wood gathering requiring precious time and effort, particularly for women and children. Alternatives to human power can free time and radically improve basic livelihood, helping to alleviate poverty. Improved energy services also lay the foundations for the development of new enterprises and improved opportunities for access to health services, education and communication. One might say that improve energy services brings choice (ibid, 2001). 2.1.5 Environmental degradation in developing countries: Recently environmental degradation has become a matter of central concern for both industrialized as well as developing countries. Environmental conservation, far from being a luxury is an essential ingredient for maintaining the national resource based upon which most nations depend for their continued economic development. (Ward Ford and Partow, 1989). Developing countries are facing degradation not only in their physical environment, but also in their social environment. Degradation physical environment results into misuse of natural resources, which cause reduction in social environment quality. This in turn results in hunger, poverty, high mortality and morbidity, sanitation and migration. Moreover, in those countries environmental problems and poverty are separable. Most of the developing countries are similar in social and political structure, lack of institutional capacity and political structure and absence of public investment. In 1987, many of the developing countries particularly in Africa were facing devastating drought and prolonged famine and declining stable food production. These carried the phenomenon of environmental refugees and provoked rural–urban migration. In effect, natural resources essential for human survival and sustainable development are increasingly being destroyed and / or being depleted. At the same time, human demand for those resources is growing faster than the rate of their replenishment it was reported that if the current rate of land degradation continues, close to one third of the world arable land will be destroyed in the next thirty years. (I.U.C.N., 1986). In Africa, north of the equator, 35 percent of arable land is believed to be affected by winds or water erosion or silinization. Furthermore, in the developing countries productive land become scarce since the loss of forests together with rising population pressure on agriculture, which forced farmers to shorten their fallow period, ultimately led to degrading the productive capacity of the land (WRI, 1985). 2.1.5 Environmental Degradation in Sudan: Sudan like other developing countries has been seriously affected by environmental degradation during the last decades, mainly in the form of deforestation, drought and desert creep. In fact the Sudan was once a home of great coniferous forests in the remote geological past – forests, specially in the northern parts of the Sudan are constantly changing from the coniferous forest and moist tropical forests to the present denuded sandy undulating plains. The different national governments in Sudan exhibited enormous concern and awareness with the environmental issues and the importance of environmental conservation measures. The major environmental problems in Sudan stem from the virtual neglect of environmental consideration in policies, strategies and plans for the development of the country. In these, there is no adequate consideration for the simplest environmental principles pertaining to the proper utilization of available resources for development purposes. As a result, the country has undergone tremendous environmental degradation during the last four decades. The major environmental problems of the Sudan are depletion and misuse of natural resources through over cultivation, overgrazing, and deforestation, desertification and drought, environmental refugees and displacement, misuse of agricultural chemicals and wild life destruction. 2.1.5.1 Over cultivation: Over-cultivation refers to the expansion in cropping both under rain-fed and/or irrigated agriculture and shortening of follow periods. Grainger (1990) reported that over-cultivation occurs when farmers try to use land more intensively regardless of the natural fertility of the soil, damages its structure and exposes it to erosion. In dry land areas overcultivation is caused by either expanding cultivation of cash crops or irrigated food crops as a response to outside pressures or because these areas are unable to produce enough food to sustain the high and increasing population demand. 2.1.5.2 Overgrazing: Overgrazing occurs where the number of livestock is increased i.e. too many animals are grazed on the same area of rangeland leading to the destruction of vegetation and the compaction and erosion of soil (Grainger, 1990). Causes of overgrazing in Sudan include the enormous increase in livestock particularly sheep and cattle, deterioration of rangeland, settlement of nomads in order to provide services; and the provision of waterholes (Boon, 1990). Desert Encroachment Control and Rehabilitation Program (D.E.C.A.R.P) (1976) indicated that the livestock number in the Sudan increased nearly four times in the period (19571966). This increase was only due to development of more water points and improvement in veterinary surveys. Grainger (1990), reported that livestock density can rise in four main ways which include reduction of grazing area for cropping, nomads settlement scheme, the breakdown of traditional controls on the grazing of range land and herds increment. 2.1.5.3 Deforestation: Deforestation means the loss of trees with their many functions (Boon, 1990). Deforestation is attributed partly to clearing of land for cultivation and partly to fuel wood consumption (Noordwijk, 1984). Boon (1990) cited : "the major causes of destruction of trees include the following : drought, clearing for agriculture, charcoal making for urban area, fuel wood collection for rural areas, construction of housing, grazing pressure of livestock, excessive cutting of branches for fodder, uncontrolled fire e.g. improving grazing, clearing lands for crops and honey collection". According to the FAO between 1990 and 2000 (13.7) million hectares of forest per year disappeared from developing countries (Ngangue, 2001). Energy in Sudan is mainly obtained by felling trees 70 percent for firewood, charcoal production, mechanized farming expansion resulted in removal of large area of forest. Misuse of land and mismanagement of forest decreased forestland. In all Sudan that practice common, removal of forest exposed the soil erosion and is the main reason for desertification (Boon, 1990). 2.1.5.4 Desertification: Recently the issue of desertification in both developed and developing countries has received much concern worldwide is considered as one of the major environmental problem facing the world. The term desertification has been given different definitions among them "desertification is a process of environment degradation" (Bayoumi, 1984). Another definition of desertification is " a process that lead to a reduction of plant biomass and carrying capacity to support crops and livestock" (U.N. 1977), also UNCED defined it as "desertification is land degradation in arid, semi-arid, and dry sub-humid areas resulting from various factor including climatic variations and human activities. Desertification affects one sixth of the world's population, 70% of all dry land, amounting to 3.6 billion hectares, and one quarter of the total land area of the world. The most obvious impact of desertification, in addition to widespread poverty, is the degradation of 3.3 billion hectares of the total area of rangeland, constituting 73% of the rangeland with a low potential for human and animal carrying capacity ; decline in soil fertility and soil structure on about 47% of the dry land areas constituting marginal rainfed cropland; and the degradation of irrigated cropland, amounting to 30% of the dry land areas with a high population density and agricultural potential. In combating desertification on rangeland, rain-fed cropland and irrigated land, preventative measures should be launched in areas which are not yet affected or are only slightly affected by desertification; corrective measures should be implemented to sustain the productivity of moderately desertified land; and rehabilitative measures should be taken to recover severely or very severely desertified dry lands The priority in combating desertification should be the implementation of preventive measures for land that are not yet degraded, or which are only slightly degraded. However, the severely degraded areas should not be neglected. In combating desertification and drought, the participation of local communities, rural organization, national governments, non-governmental organization and international and regional organizations is essential. 2.2. Food security: World Bank defines Food security as the access by all people at all times to enough food for an active, healthy life (Abdul-Hai, 1996). Its essential elements are the availability of food and the ability to acquire it. Food insecurity, in turn, is the lack of access to enough food. There are two kinds of insecurity: chronic and transitory. Chronic food insecurity is a continuously inadequate diet caused by the inability to acquire food. It affects household that persistently lack the ability either to buy enough food or to produce their own. Transitory food insecurity is temporary decline in household access to enough food. It results from instability in food prices, food production, or household income-and, in its worst form it produces famine (Maxwell, 1991). Maxwell reported that a country and people are food secure when their food system operates efficiently in such away as to remove the fear that there will not be enough to eat. He argues that food security will be achieved when equitable growth ensures that (Vulnerable) groups have sustainable livelihood...in addition... food security requires the efficient and equitable operation of the food system(Maxwell, 1991). In particular, food security will be achieved when the poor and vulnerable, particularly, women, children and those living in marginal areas, have secure access to the food and want (Abdul-Hai, 1996). A food system can be defined as Maxwell pointed: a food system is the combination of agro-ecological and socio-economic processes, which determines the production, marketing and consumption of food (Maxwell, 1991). A food system should in principle be efficient and equitable. "Efficient" means that all stages in the food chain, from production to final consumption, should be efficient in a social-welfare sense. Production policies should take account of dynamic comparative advantage; marketing margins should provide no more than normal profits in the long term; and consumer prices should reflect real scarcity values. "Equitable" means that the benefits of production should be distributed equally and that food should be available to all (ibid, 1991). As with a farming system, a food system is the product of determinants, which may be exogenous or endogenous. Endogenous factors will include the characteristics of the natural resource base and the society, which draws upon it, as well as the range of government policies, which modify the interaction between environment, economy and society; exogenous factors will include the operation of world markets for agricultural inputs, food and alternative agricultural product (Ibid, 1991). 2.2.1. Food Shortages and Emergencies: Food shortage caused by natural and human caused disasters continues to affect many countries in all regions of the world. As of early 2001, there were 33 countries and more than 60 million people facing food emergencies of varying intensity (FAO, 2001). In eastern Africa, some 13 million people still rely on food assistance because of the lingering effects of last year's drought, coupled with conflict in some parts. The situation is particularly severe in Eritrea, Ethiopia, Kenya and Sudan, where recent drought have sharply reduced food production and killed large numbers of livestock. However, recent rains, and the near-normal rainfall forecast for most eastern Africa during the March to May 2001 cropping season, have improved the food outlook for the sub-region. In the Kenya, the severe drought in 1999/2000 seriously undermined the food security of nearly 4.4 million people. In Eritrea, more than 1.8 million people are in need of urgent assistance owing to displacement by the war with neighbouring Ethiopia and to drought. The outlook for the 2001 agricultural season remain bleak, with farmer so far unable to return to their farms and large tracts of land still inaccessible because of the risk of landmines. In the Sudan, serious food shortages have emerged because of drought. The continuing civil conflict is aggravating the situation by impeding rural households' cultivation activities (FAO, 2001). 2.3. Poverty: Poverty refers to the inability to attain a minimal standard of living. Criteria for assessing minimum nutritional needs and other necessities vary from country to country. Since they reflect country-specific conditions, national priorities and concept of welfare and rights, the minimum acceptable level of consumption. The poverty thresholdgenerally rises as national income increases. Despite difficulties in selecting a single poverty threshold, such a threshold is necessary in order to make cross-country comparisons (FAO, 1993). Poverty can be said to exist in a given society when at least one person does not have a level of well-being deemed to constitute a reasonable minimum by the standards of that society. A poverty line is the starting point for poverty analysis, the yardstick used in assessing well-being and determining who is poor and who is not. People are counted as poor when their measured standard of living (generally in either income or consumption) is below the minimum acceptable level. The poverty line is essentially defined as the value of income or consumption necessary for the minimum standard of nutrition and other necessities. Poverty lines can be defined in absolute or relative terms. Absolute poverty refers to the position of an individual or household in relation to a poverty line whose real value is fixed overtime. Relative poverty refers to their position vis-à-vis the average income in the country. For poverty assessments, the concept of absolute poverty is preferred because it facilitates comparative analysis. However, at the same time, it is unclear whether "a minimum acceptable level" can be given absolute meaning. 2.3.1. The role of agriculture to alleviate poverty: The extend and depth of poverty in the developing world at the close of the 20th century is astonishing. About 1.3 billion people – 30 percent of the population – live in absolute poverty, with only a dollar a day or less per person to meet food, shelter and other needs. Not surprisingly, hunger, malnutrition and associated diseases are widespread: more than 800 million people do not have access to sufficient food to lead healthy, productive lives; millions more lives on the edge of hunger; and more than 180 million preschool children are significantly under-weight for their age, sowing the seeds for future food insecurity (Neumann et al. 1999). Absolute poverty is deeply entrenched in South Asia and SubSaharan Africa where two-fifths of the populations are absolutely poor (Table 2.3). Poverty is a rural phenomenon in most of the developing world, specially the low-income countries. The rural poor makeup more than 75 percent of the poor in many Sub Saharan Africa and Asian countries (ibid, 1999). Table (2.3): Absolute poverty, 1993 Region People living below US$1 per day Number (million) Percent South Asia 514.7 43.1 East Asia and the Pacific 445.8 26.0 Sub-Saharan Africa 218.6 39.1 Latin America and the Caribbean 109.6 23.5 Eastern Europe and Central Asia 14.5 3.5 Middle East and North Africa 10.7 4.1 1.313.9 29.4 Total Source : World Bank, (1996). Poverty reduction and the World Bank CHAPTER THREE Socio-economics Characteristics of respondent This chapter presents and discusses the empirical results of the study. It presents the socio-economic characteristics of respondents, the institutional services, environmental situation, economic activity and nutritional status in the area. 3.1. Social characteristics of respondent families: Production, consumption pattern and decision-making are partially affected by size, age and sex distribution of producer household members (Elgozouli, 1998). Table (3.1) shows the respondents family characteristics. The family structure in this area is regarded as important factor that influence the respondents' contribution to economic activities, with regard to family characteristic in this study the average family size is 6.7 & 5.65 persons for farmers and non-farmers respectively. Yassin (1996) cited that tenants' age is assumed to be one of the factors that determine productivity; also Atta (1990) reported that the distribution of the farmers' population in term of age helps in the determination of their potential labor force qualitatively and quantitatively. Considering respondent age table (3.1) shows the average respondent age is 54 & 42 years for farmers and non-farmers respectively. With regard to respondent age distribution table (3.2) shows that most of respondents were within the productive age 15- 60 year, 66.3% &91.3% for farmers and non-farmers respectively and only 33.7% and 8.7% were above 60 years old; this result show that there is a considerable number (one third) of the farmers were above the productive age which will affect the productivity of the farmers, also this result revealed that agriculture was been un-incentive job so the majority of young's producers works in other fields rather than agriculture. Table (3.3) shows that about 82.5% of respondent were married; and few of them 21.2% have one wife. With regard to classification of respondent 92.5% &72.5% of farmers and non-farmers respectively were married which reflect that married farmers were more than non-farmers one's, this result imply that farmers were better than non-farmers by means of income opportunity and settlement. 3.2. Respondent level of Education: Education is considered as a human investment. It helps the tenants to improve their cultural, techniques and adopt new idea (Atta, 1990). The level of farmer education and expertise are assumed to have significant effects on the output of the agricultural crops (El feil, 1993). Through Education farmer's knowledge and skills can be improved and many researcher have investigated the relationship between farmers' education level and their willingness to adopt new methods and techniques seeking for higher rate of output, and they have found the educational level were positively associated with increased efficiency (Elshafie, 1992). Table (3.1): Respondent family characteristic Average Characteristic Farmers Non farmers Respondent Age 54.1 42.1 Family size 6.7 5.65 Years of Education 4.7 6.6 Source : Field survey, 2002 Table (3.2): Age distribution of respondent Age interval Farmers Non farmers Freq. % Freq. % 22-34 3 3.8 27 33.8 35-47 23 28.8 28 35 48-60 27 33.8 18 22.5 61-73 23 28.8 7 8.8 74-86 4 5 0 0 Source : Field survey, 2002 Table (3.3): Respondent marital status: Marital status Farmers Non farmers Freq. % Freq. % Bachelor 6 7.5 22 27.5 Married with one wife 55 68.8 49 61.3 Married with two wife 19 23.8 6 7.5 Married with three wife 0 0 3 3.8 Source : Field survey, 2002 Table (3.4) shows that 70% & 85% of farmers and non-farmers respectively have some sort of education, among those 67.9% & 82.4% of farmers and non-farmers were received formal education and 32.1% &17.6% with informal education (Khalwa) and 20% of farmer with higher and university education, and 33.8% of non-farmer with the same levels of education. However, only 30% & 15 % of farmers and nonfarmers respectively were illiterate. This result show that the non-farmers engage in education more than farmers as a result of farmers demand for family labor to participate in the agricultural activities which reduces the chance for farmer family to continuous their education or in sometime to engage in the schools. Also the average years of education of non-farmer are more than farmer one's, table (3.1) show that farmers average years of education is 4.6 years, although the non-farmer one is 6.6 years, the difference may attributed to farmers need for more labor for different agricultural activities and livestock care, which may force or not allow them to attend schools. This results raising the probability that nonfarmer in this area have higher capability than farmer enabling them to accept, adopt and participate in the environmental programme; this result reflect the need for agricultural extension services and it is more comparable with what was mentioned by Ruttan (1987) when he said "in fact one of the major production constraints in the developing countries was found to be the lack of knowledge about the importance of certain modern inputs which was assumed to be due to lack of extension services and farmer education programme as general". Table (3.4): Respondent educational level: Education level Farmers Non farmers Freq. % Freq. % Illiterate 24 30 12 15 Khalwa 18 22.5 12 15 Elementary 22 27.5 29 36.3 Higher secondary 12 15 21 26.3 University 4 5 6 7.5 Source : Field survey, 2002 Table (3.5): Agricultural credit and extension services: Type of service Freq. % Received Credit services 21 26.3 Did not received Credit 59 73.7 Received Extension services 19 23.8 Did not received Extension services 61 76.2 Source : Field survey, 2002 Table (3.6): Project management: Project management Freq. % Good 52 65 Moderate 18 22.5 Bad 10 12.5 Source : Field survey, 2002 3.3. Institutional services: Considering extension services table (3.5) show that 67.3% of farmers interviewed farmers reported no existence of any sort of extension services, and only 23.8% of them received extension services; this result is comparable with what was mentioned by Ruttan (1987) above. In addition, farmers reported that 73.8% of them did not receive any credit, but only 26.2% have received some sort of credit. These results reflect the inefficient role of the institution to providing agricultural services, and this similar to what was reported by Elfeil (1993) that he said, "the inefficient agricultural finance is one of the main factors curtailing the agricultural production in north Sudan". Table (3.6) show that the majority of farmers 65% reported that their project management were good, and only 22.5% reported that their project management were bad and 12.5% said their management were moderate. 3.4. Environmental services: All respondent in the area reported that there was no any forest cultivation or trees planting in the recent years in the area. With regard to farmers table (3.7) show that the main reasons for non-agroforestry application were the shortage of irrigation 74% of farmers - 50% of them report only the shortage of irrigation is the main causes of nonagroforestry and 24% refers the reasons to shortage of irrigation and they afraid from been birds aviary, also 21% said that it did not include in the rotation so they did not practice it and few farmers 5% reported that was due to unprofitability and it was constraining cultivation. Table (3.7): Reason of non-agroforestry application: Reason Freq. % Shortage of Irrigation 40 50 Not include in rotation 17 21.25 It constraint cultivation 2 2.5 Shortage of Irrigation and afraid from been Birds Nest 19 23.75 Not Profitable 2 2.5 Source : Field survey, 2002 Table (3.8): Source of cooking power: Source Freq. % Charcoal and Firewood 96 60 Gas 56 35 Animal dung 8 5 Source: Field survey, 2002 Table (3.9): Economic Activity: Fundamental Job Farmers Non farmers Freq. % Freq. % Merchant 6 7.5 3 3.75 Free work 2 2.5 8 10 Laborer 3 3.75 60 75 Governmental Officer 8 10 9 11.25 Farmer 61 76.25 0 0 Source : Field survey, 2002 Considering non-agroforestry practice with the result of table (3.8) which shows that 60% of the respondents use forest products as cooking power, which seem to be the main causes of disappeared trees cover in the area and escape to have strong impact on desertification and decreased productivity; 35% of respondent used gas and 5% used animal dung as cooking power. This result is comparable with what Mann 2001 reported" it has been estimated that almost one- third of the world's population does not have access to adequate energy services; many of these rely on fuel wood and animal dung for the majority of their energy need" and also he said that there are multiple links between energy and the environment; between wood use and deforestation and desertification; hence deforestation, soil erosion, desertification and pollution destroy the means of subsistence in the study area and exacerbate the precarious nature of existence. 3.5. Economic activities: Table (3.9) shows that majority of farmers 76% the farm activity is the main job for them and approximately 24% of them practice the farming as secondary job. Nevertheless, the non-farmers most of them 75% were laborer in many field among them agriculture. This result indicates that the agriculture has considerable role in the economic activity in this area. Considering the livestock ownership table (3.10) show that the farmers owned livestock more than non-farmers 65% and 31.3%, respectively. Table (3.10): Livestock Ownership: Farmers Marital status Non farmers Freq. % Freq. % Owned Livestock 52 65 25 31.25 Did not owned Livestock 28 35 55 68.75 Source : Field survey, 2002 Table (3.11): Kind of Livestock owned: Kind of livestock Farmers Non farmers Freq. % Freq. % Cow 23 44.23 6 24 Sheep 21 40.39 7 28 Goat 34 65.38 18 72 Donkey 32 61.52 6 24 Source : Field survey, 2002 Table (3.12): Impact of livestock on income: Livestock affect Freq. % Increase the income 71 92.2 Did not affect the income 6 7.8 Source : Field survey, 2002 Those who owned Cow were 44.23% and 24% for farmers and non-farmers respectively and who owned Sheep were 40.39% and 28% respectively and who owned Goats were 65.38% and 72% respectively table (3.11) which indicate that the farmers were better than non-farmers in diversifying their income sources, most of respondents who owned livestock reported that the livestock has increased their income 92.2% and only 7.8% reported that livestock did not affect their income table (3.12). Table (3.13) shows the production constraints, about 89% reported that irrigation was among production constraints and 92.5% reported that lack of finance constraining the agricultural production, 90% reported that the lack of inputs, 11% reported extension services and few reported that marketing and management among the factor that constraining agricultural practices. 3.6. Nutritional Status: Considering the numbers of meal per a day table (3.14) shows that the majority of respondent in the area 77.5% had three meals per a day and only 22.5% of them had two meals per a day. With regard to meal component table (3.15) shows that there were only 38% with daily meat in their meal, and 50% of them sometimes eat meat and 11.25% without any meat in their meals. The result reflect only 27.5% of respondents have meal with complete component, this small percentage indicate that the people in this area were insufficient to make self satisfaction for their basic nutritional needs by means of food affordability or\and accessibility. Table (3.13) Production constraints Production Constraints Freq % Shortage of irrigation 71 88.75 Lack of finance 74 92.5 Lack of inputs 72 90 Bad management 5 6.25 Extension services 9 11.25 Weak marketing 6 7.5 Freq % Two meals 36 22.5 Three meals 124 77.5 Freq % Meat, starch, vegetables, and sugar 44 27.5 Meat, starch and vegetables 18 11.25 Starch 12 7.5 Starch and vegetables 6 3.75 Starch and sometimes meats and vegetables 66 41.25 Sugar, starch and sometimes meats and vegetables 14 8.75 Source : Field survey, 2002 Table (3.14) Number of meals per day Number of meals per day Source : Field survey, 2002 Table (3.15) Meals components Meals components Source : Field survey, 2002 CHAPTER FOUR RESULTS OF THE CROP PROFITABILITY This chapter discusses the production costs, yields, farm gate prices, estimation of the gross returns and gross margins and coefficient of private profitability of the main field crops produced namely, dura, wheat, tomato, okra and onion to assess the farmer attitude towards farming practice and their rationality. In this chapter, average costs, yields and prices were those of 2001-2002 season. 4.1. Production costs: Production cost is the cost of producing a certain amount of product in a particular period. For purpose of calculating production costs, certain items are to be determined. These include land preparation, seeds, irrigation, fertilizer, harvesting and threshing, sacks and strings, Zakat and other costs (transportation and marketing). The average production cost for dura, wheat, tomato, okra and onion is shown in table (4.1). 4.1.1. Land preparation cost: The land preparation is done by tractor, animal traction and sometimes manually. The tendency is to use tractor for land preparation. On the average, the cost of land preparation for dura, wheat, tomato, okra and onion where found to be LS 35291/feddan, LS 23880/feddan, LS 47500/feddan, LS 47500/feddan and LS 52300/feddan, respectively. The cost of land preparation for vegetables (tomato and okra) and onion are higher than those of dura and wheat. This may be due to the fact that the vegetables and onion require a good seedbed preparation (field survey, 2002). The share of land preparation in the total cost of production was found to be 22.44%, 8.27%, 21.74%, 25.82% and 16.63% for dura, wheat, tomato, okra and onion, respectively table (4.2). 4.1.2. Seeds cost: Due to the shortage of improved seeds, some farmers retain their seeds from the previous harvest. Also in 2002 season, farmers obtain seeds from the local market and State Ministry of Agriculture (field survey, 2002). On average the cost of seeds was found to be Ls 5930/feddan, Ls 38150/feddan, Ls 54000/feddan, Ls 21500/feddan and Ls 25000/feddan for dura, wheat, tomato, okra and onion, respectively table (4-1). The highest seeds cost is that of tomato followed by that of wheat, onion, okra and finally dura. The tomato seeds cost was found to be the highest as its seeds become very expensive during sowing time. This is also true for wheat and onion, which have higher cost of seeds than okra and dura. Dura seeds cost was found to be the lowest because of the low price of its seeds than other crops. The percentage share of seeds cost was found to be 3.77%, 13.22%, 24.71%, 11.68% and 7.95% for dura, wheat, tomato, okra and onion, respectively table (4-2). 4.1.3. Irrigation cost: Irrigation cost is positively related to the number of irrigations. This cost includes cost of pump operation. The average total cost of irrigation for dura, wheat, tomato, okra and onion was found to be Ls 35640/feddan, Ls 113470/feddan, Ls 65000/feddan, Ls 52000/feddan and Ls 58000/feddan, respectively table (4.1). The highest average cost of irrigation is that of wheat followed by tomato, onion, okra and finally dura. The wheat seems to have the highest irrigation cost than other crops because wheat is highly sensitive to irrigation and need frequent irrigation. Hence, it receives more number of irrigations per season. The percentage share of irrigation cost in the total cost of production was found to be 22.67%, 39.30%, 29.75%, 28.26% and 18.45% for dura, wheat, tomato, okra and onion, respectively table (4.2). 4.1.4. Fertilizer cost: On average, the cost of fertilizer was found to be Ls 12330/feddan, Ls 41670/feddan, Ls37000/feddan, Ls26000/feddan and Ls31700/feddan for dura, wheat, tomato, okra and onion, respectively table (4.1). The percentage share of fertilizer cost in the total cost of production was found to be 7.84%, 14.43%, 16.93%, 14.13% and 10.08% for dura, wheat, tomato, okra and onion, respectively table (4.2). Table (4.1) the average production cost Ls/feddan dura Wheat tomato okra Onion Land preparation 35290 23880 47500 47500 52300 Seeds 5930 38150 54000 21500 25000 Irrigation 35640 113470 65000 52000 58000 Fertilizer 12330 41670 37000 26000 31700 Harvesting and threshing 36160 40180 10000 18000 40000 Sacks and strings 21250 17580 16000 85000 Zakat 2230 7410 Others 8400 6360 5000 Total 157230 288700 218500 Source : Field survey, 2002. 8450 3000 14000 184000 314450 Table (4.2): The percentage share of each item in the total cost of production: Dura Wheat Tomato Okra Onion (%) (%) (%) (%) (%) Land preparation 22.44 8.27 21.74 25.82 16.63 Seeds 3.77 13.22 24.71 11.68 7.95 Irrigation 22.67 39.30 29.75 28.26 18.45 Fertilizer 7.84 14.43 16.93 14.13 10.08 Harvesting and threshing 23.00 13.92 4.58 9.78 12.72 Sacks and strings 13.52 6.09 8.7 27.03 Zakat 1.42 2.57 Others 5.34 2.20 Source : Field survey, 2002. 2.69 2.29 1.63 4.45 4.1.5. Harvesting and threshing cost: Dura, tomato, okra and onion were manually harvesting (cutting and collection) and threshing but wheat was harvested and threshed mechanically. On average, the cost of harvesting and threshing was found to be Ls 36160/feddan, Ls40180/feddan, Ls10000/feddan, Ls18000/feddan and Ls40000/feddan for dura, wheat, tomato, okra and onion, respectively table (4.1). The highest cost is that of wheat because it done mechanically. The percentage share of harvesting and threshing were found to be 23%, 13.92%, 4.58%, 9.78% and 12.72% for dura, wheat, tomato, okra and onion, respectively table (4.2). 4.1.6. Sacks and Strings cost: The cost of sacks per feddan is positively related to the yield per feddan and the price of the sack. Accordingly the cost of sacks for dura, wheat and onion was found to be Ls21250/feddan, Ls17580/feddan and Ls85000/feddan, respectively table (4.1).The higher cost of sacks and strings is that of onion because of its higher output per feddan compared to other crops and the cost of sacks is positively related to the output as mentioned. The percentage share of sacks and strings cost was found to be 13.52%, 6.09%and 27.03% for dura, wheat and onion, respectively table (4.2). 4.1.7. Zakat: In the study area "Zakat" is usually not imposed on vegetables (field survey, 2002). For dura, wheat and onion it paid about one-forty of total production. Zakat cost varies according to yields and prices. On average, the cost of zakat was found to be Ls2230/feddan, Ls7410/feddan and Ls8450/feddan for dura, wheat, and onion, respectively table (4.1). In this study, the highest cost of onion is due to the yield but in case of wheat versus dura its attribute to the wheat prices. The percentage share of zakat cost was found to be 1.42%, 2.57% and 2.69% for dura, wheat and onion, respectively table (4.2). 4.1.8. Other cost: The other cost included transportation and marketing costs. On average, the other cost was found to be Ls8400/feddan, Ls6360/feddan, Ls5000/feddan, Ls3000/feddan and Ls14000/feddan for dura, wheat, tomato, okra and onion, respectively table (4.1). The percentage share of the other cost was found to be 5.34%, 2.20%, 2.29%, 1.63% and 4.45% for dura, wheat, tomato, okra and onion, respectively table (4.2). 4.1.9. Total variable cost of production: The average total variable cost of production was found to be Ls157230/feddan, Ls288700/feddan, Ls218500/feddan, Ls184000/feddan and Ls314450/feddan for dura, wheat, tomato, okra and onion, respectively. The higher total cost of production was that of onion because of high cost of sacks and irrigation. In addition, the higher cost of land preparation and harvesting can be added to the first ones. To identify the magnitude of each item in the total cost of production for each crop the percentage itemized per feddan cost of production was calculated table (4.2). The main cost item for all crops is the irrigation cost, which represented about 22.67%, 39.30%, 29.75%, 28.26% and 18.45% of the total cost of production per feddan for dura, wheat, tomato, okra and onion, respectively. The highest cost items for dura is harvesting and threshing 23% followed by irrigation 22.67%, land preparation 22.44%, sacks and strings 13.52%, fertilizer 7.84%, others 5.34%, seeds 3.77% and finally zakat (1.42%). The highest cost item for wheat is irrigation 39.30% followed by fertilizer 14.43%, harvesting 13.92%, seeds 13.22%, land preparation 8.27%, sacks 6.09%, zakat 2.57% and finally others (2.20%). The highest cost item for tomato is irrigation 29.75% followed by seeds 24.71%, land preparation 21.74%, fertilizer 16.93%, harvesting 4.58% and finally others (2.29%). The highest cost item for okra is irrigation 28.26% followed by land preparation 25.82%, fertilizer 14.13%, seeds 11.68%, harvesting 9.78% and finally other (1.63%) The highest cost item for onion is sacks 27.03% followed by irrigation 18.45%, land preparation 16.63%, harvesting 12.72%, fertilizer 10.08%, seeds 7.95% and finally others (4.45%). 4.2. Analysis of crop returns: The average yield and price for 2001-2002 season were used in this analysis. 4.2.1. Crop yield: The average yield of dura, wheat, tomato, okra and onion were found to be 6.39 sacks/feddan, 5.95sacks/feddan, 250baskets* /feddan, 32 bags**/feddan and 28.3sacks/feddan, respectively table (4.3). The average yield mentioned by El-jak (2002) was 15 sacks/feddan, 10sacks/feddan, 350baskets*/feddan, 45bags**/feddan and 60sacks/feddan for dura, wheat, tomato, okra and onion, respectively. Thus, the result showed that the yield obtained was very low. Solh (1996) reported that the average yield of wheat in Sudan is generally low as it affected by many environmental and cultural practices. The low yields could be attributed to low input use. 4.2.2. Farm gate prices: Prices used in this study were the farm gate prices. Farm gate prices are the price that the farmer receives for his crop when he sells his product at the boundary of his farm. These prices vary considerably from the market prices; the farmers always end up receiving price lower than the market prices of their products. The farm gate prices used in this study were collected during the field survey, 2002. The average farm gate price obtained for all crops were found to be Ls27800/sack, Ls49820/sack, Ls4000/baskets*, Ls25000/bags** and Ls12000/sack for dura, wheat, tomato, okra and onion, respectively table *: Guffa **:field Internal Kenana2002. sugar polyethylene bags. (4.4) survey, Intermediaries or local merchants usually buy the crops under the study. The differences in farm gate prices for each crop among the farmers was due to the time in which farmers sell their crops i.e. the farm gate prices of the crops is unstable through out the year, depending on the local supply and demand. The highest farm gate price is that of wheat followed by dura, okra, onion and tomato, respectively table (4.4). The tomato and onion crops are highly sensitive to storage and because of lack of storage and processing facilities farmers tend to buy these crops at low prices just when they harvest. 4.2.3. The break-even yield: The break-even yield is defined as the yield that just covers the cost of production. It is equal to the total cost of production per feddan divided by price per unit of yield. Break even point (yield) = Total cos t of production / feddan Pr ice / unit of yield Accordingly, the break-even yield of dura was 5.66 sacks/feddan. This means that the average yield of dura per feddan in the study area was more by 0.73 sacks/feddan. For wheat, the break-even yield was 5.79 sacks/feddan. This means that the average yield of wheat obtained by farmer was more by 0.16 sacks/feddan. Table (4.3): The average yield of crops per feddan season 2001-2002 Crop Yield Unit Dura 6.39 Sacks Wheat 5.95 Sacks Tomato 250 Baskets* Okra 32 Bags** Onion 28.3 Sacks Source : Field survey, 2002. Table (4.4): The average farm gate price Crop Farm gate price Ls/feddan Dura 27800 Wheat 49820 Tomato 4000 Okra 25000 Onion 12000 Source: Field survey, 2002. The break-even yield for onion was 26.2 sacks/feddan. Therefore, the average yield of onion per feddan in the study area was more by 2.1sacks/feddan table (4.5). For vegetables both okra and tomato the break-even yield is less than the average yield of them in the study area. From the above calculations, the break-even yield was less than the average yield per feddan for all crops and this result indicates that the output per feddan for all crops covers its actual cost of production in the White Nile State in 2001-2002 season for the crops under the study. 4.2.4. Gross returns (Ls/feddan): The yield and price analysis mentioned were used to calculate the returns per feddan for all crops. On average, the gross returns for dura, wheat, tomato, okra and onion were found to be Ls177642/feddan, Ls 292430 / feddan, Ls1000000 / feddan, Ls800000 / feddan and Ls 339600 /feddan, respectively table (4.6). The highest returns obtained in the study area were that of tomato followed by okra, onion, wheat and dura, respectively. On average, the gross returns are higher than the total cost of production for all crops under the study in 2001-2002 season. 4.2.5. Gross margins (Ls/feddan): Gross margins are the difference between gross returns and the total variable cost of production. In other words net income (profit) is obtained by deducting expenses from revenue. Table (4.5): The break-even yield of crops per feddan: Crop The break even yield Unit Dura 5.66 Sacks Wheat 5.79 Sacks Tomato 54.625 Baskets* Okra 7.36 Bags* Onion 26.2 Sacks Source: Field survey, 2002. Table (4.6) the average gross returns Ls/feddan Crop average gross returns Ls/feddan Dura 177642 Wheat 292430 Tomato 1000000 Okra 800000 Onion 339600 Source: Field survey, 2002. The gross margin for all the crops were found to be Ls20412/feddan, Ls3730/ feddan, Ls 781500/feddan, Ls 616000/feddan and Ls 25150 / feddan for dura, wheat, tomato, okra and onion, respectively table (4.7). The highest gross margins were that of the tomato followed by that of okra, onion, wheat and dura, which scored the least gross margins. However, wheat and dura have the least gross margins when compared to other crops, farmers have exclusively grown them for food security i.e. farmers grow food crops with less marginal returns as a measure against risk to avoid supply shortage and prices fluctuations of food crops. In general, the gross margins of vegetables (tomato and okra) were higher than that of onion, wheat and dura because of the very high yield of vegetables. 4.2.6. The coefficient of private profitability (CCP): The coefficient of private profitability is the extent in which the production of a crop is profitable or unprofitable for the producers i.e. it is a decision criterion for the producer whether to produce a certain crop or not. The coefficient of private profitability is equal to the total returns per feddan at farm gate price divided by the total cost per feddan. Coefficient of private profitability = Total returns / feddan at farm gate price Total cos t / feddan The private profitability coefficient is the same as the benefit/cost ratio, if CCP is less than unity (1) it is unprofitable to produce that product at present productivity level, and/or the present price level. Table (4.8) shows that this ratio (CCP ratio) is greater than one for all crops under the study. Hence, all crops are profitable at producer level. The crops under the study can be ranked according to their CCP; tomato comes first followed by okra, dura, onion and finally wheat, respectively. This results revealed that inspite of high profitability of vegetables the farmers cultivate food crops in their ways for seeking the food selfsufficiency which reflect the irrational attitudes of farmers; that call for intervention so as to incentive the farmers to cultivate the more profitable crops. Table (4.7): The average gross margins Ls/feddan: Crop average gross margins Ls/feddan Dura 20412 Wheat 3730 Tomato 781500 Okra 616000 Onion 25150 Source: Field survey, 2002. Table (4.8): The coefficient of private profitability. Crop CCP ratio Dura 1.13 Wheat 1.01 Tomato 4.58 Okra 4.32 Onion 1.08 Source: Field survey, 2002. 4.3 Respondent income and Poverty indicators: Total income is the sum of farm income and non-farm income. Considering the total income for farmers and non-farmers table (4.9) represent the average farm and non-farm income of the respondents for the farmers and non-farmers, the average farm income was Ls 3030.1937 represent 81.91% of the total income and the average non-farm income was Ls 669.2563 and Ls 3077.375 representing 18.09% and 100% for farmers and non-farmers, respectively. Poverty indicators and distribution based on the poverty alleviation strategy, which determines the subsistence level by one dollar per person per day and the adequate level by 1.7 dollar per person per day (Poverty workshop, 2000). Accordingly, the population distributed to the following category: • Richest the lower limit of their income is above the adequate level. • Non-poor their income is higher than the adequate level and lower than the richest. • Poor their income is under the adequate level and above subsistence level. • Very poor their income is under the subsistence level. According to the indicators above, table (4.10) show that the most of the respondents were very poor 82.5 and 86.25 for farmers and non-farmers respectively, and the percentage of poor people under the poverty line were found to be 96.25%. Table (4.9) the respondent average income (per thousand Ls): Farmer Non-farmer Average % Average % Farm income 3030.1937 81.91 0 0 Non-farm income 669.2563 18.09 3077.375 100 Total income 3699.45 100 3077.375 100 Source: Field survey, 2002 Table (4.10) the poverty indicator: Farmer Non-farmer Freq % Freq % Richest 1 1.25 3 3.75 Non-poor 1 1.25 1 1.25 Poor 12 15.0 7 8.75 Very poor 66 82.5 69 86.25 Source: Field survey, 2002 CHAPTER FIVE RESULTS AND DISCUSION OF REGRESSION EQUATIONS This chapter presents the results, analysis and discussion of regression equations for wheat and dura to investigate the factors that curtailing agricultural production and calculate their return to scale. Also, include time series analysis for food crop productivity to investigate the trends of their productivity. 5.1. Theoretical Framework: 5.1.1. The production function: Production function is normally estimated to investigate the impact of some selected variables on yield. The 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 (Manfield, 1970). In addition, it can be defined as "a relationship or a mathematical relationship between an output (Y) and different inputs (Xs) used to produce it" (Ibrahim, 1993). Heady and Dillon (1961) concluded that numerous algebraic equations can be used in deriving production functions. No single form can be used to characterize agricultural production under all environments, the algebraic form of the function and the magnitude of its coefficient will vary with soil, climate type and variety of crop or livestock resources being varied, state of mechanization, magnitudes of others inputs in a fixed quantity for the firm. Upton (1976) reported that the production function in theory, would include inputs of resources such as available soil nutrient, pest and disease that might influence yield and because impossibility of specifying all of these variables separately, some may be lumped together into a board category, such as land and labor. Other variables, which are considered unimportant, can be ignored. Production function can be represented by tables' schedules, or mathematical equations, to determine maximum output that can be produced from specified combinations of inputs given the existing state of technology, the output will change when the quantity of inputs changed. Mathematically the production function can be represented as follow: Qi = f (X1, X2.....Xn) Where: Qi = output of the product j. Xi = inputs used. i = 1 to n. 5.1.2. Some forms of production function: 5.1.2.1. Linear production function: Is the straight-line relationship between certain levels of output produced and given amount of inputs used. It is a process by which one or more outputs are produced in a fixed proportion by the application of one or more inputs in a fixed proportion, the general formula of linear production function is: Y = a + b1 X1 + b2 X2 +.........+bn Xn Where: Y = Yield. a = constant term. b1, b2,......,bn = coefficients. X1, X2....Xn = explanatory variables. The first derivative, which is the marginal product, is constant and it does not show diminishing marginal returns. It represents only situation, where the change in the dependent variable for a unit change in the independents variables will be expected to be just the same regardless of the larger or how smaller the independent variables were i.e. constant slope. 5.1.2.2. The quadratic form: The quadratic function allow for both declining and negative marginal products. The elasticity of production is not constant as in the power function but varies with the magnitude of inputs as indicated by the elasticity equation for the function: Y = a + bX – cX2 Where: Y = Yield. a = constant term. b and c = coefficients. X = input. E (elasticity) = bX − 2cX 2 a + bX − cX 2 The marginal products (MPs) do not bear a fixed ratio to each other as in the case of Spillman function thus MP equation is: dy = b – 2cX dx Therefore, the marginal product curve is linear. The minus significant before (c) indicate diminishing marginal returns, while (a) represent the production forthcoming from the mix of fixed resources. The advantage of the quadratic function is that besides being easy to estimate it possesses a technical optimum beyond which the total product fall. The possible disadvantage of the function is that it cannot show both increasing marginal product at low levels of inputs and decreasing marginal product at higher levels of inputs in the same equation i.e. cannot show increasing and diminishing marginal returns in a single response curve. 4.1.2.3. Cubic or New-classical production function: The equation of this function with one exogenous variable is: Y = a + bX + cX2 – dX3 Where: Y = output. a, b, c& d = coefficients terms. X = input. This type of production function shows the stages of production. The first stage "stage 1" in which output increases at increasing rate as input increased, the second stage where output increases at a decreasing rate as more inputs is used and the third stage in which output decreased as more inputs is used. The function shows the low of diminishing marginal returns. 5.1.2.4. Cobb-Douglas production function: The general form of Cobb-Douglas function is: Y = a X1b1 X2 b2 ........Xnbn Where: Y = output (dependent variable). X1, X2........Xn = inputs (independent variables). a = constant term. b1, b2.........bn = regression coefficient to be estimated which is the partial elasticity of production with respect to the individual resources. The sum of these elasticities determine the nature of returns to scale which would indicate the percent by which the output will change if all factors are changed at a given percent. If the sum of the elasticities (∑bn) equals one, this means that one percent increase in all inputs will result in one percent increase in the output i.e. constant returns to scale. When the sum of the elasticities is less than one, one percent increase in all inputs will result in less than one percent increase in the output i.e. decreasing returns to scale, while with the a sum of elasticities greater than one, output will be increased by a greater percentage than of input increase i.e. increasing returns to scale. As ordinary least square (OLS) can not be applied to non-linear functions, Cobb-Douglas production function has to be transformed to log-form by converting all variables measured, both inputs and output into their logarithms, and then using OLS regression method to estimate the coefficients(Upton,1987). The transformed function can be written as: Log Y = log a + b1 logX1+ b2 log X2 +.........+ bn log Xn The Cobb-Douglas production function is sometimes known as logarithmic function. The advantages of the Cobb-Douglas function stated by Upton (1976) are: 1. It is easy to estimate so it can be extended to include more variables; it may show diminishing returns to scale. 2. Its resemblance to reality better than linear form. 3. It is easy to understand and easy to interpret. 4. Its theoretical fitness to agriculture. 5. The regression coefficient, immediately give the elasticities of the product with respect to the factor of production( also called flexibilities), that is we get answer to the question by how much percentage will product increase on average if the given factor increase by one percent. 6. This form of production function permits the phenomena of diminishing marginal returns, without using many degrees of freedom. Its possible disadvantage is that there is no technical optimum or maximum total product, since there are no negative marginal returns (no stage three). This may lead to overestimate the economic optimum of high levels of inputs. 5.1.2. Model Specification: The specification of the model depends on many consideration, some are related to selection of exogenous (independent) variables that are assumed to influence the variability of the dependent variable. In specifying the economic model, Heady and Dillon(1961) stated that, first a single equation or a system of equation is appropriate, secondly, relevant set of variables have to be chosen and thirdly, a set of hypothesis has to be made in appropriate algebraic forms of equations. They also stated that the ideal model has two adjustments, first, the number of variables has to be determined; secondly, the functional representation should be statistically manageable. The choice between different forms depends largely on subjective judgment, as does the choice of variable inputs to be included in the function. 5.1.3. The econometric model estimation method: The method used for the estimation of the econometric model is the OLS estimation method. The OLS is mathematical technique use for calculating the regression equation that summarizes and describes the functional relationship between a set of variables. The technique based on the principle of minimizing the sum of squared residuals has the desirable property of mathematical objectivity. The criterion of OLS provides estimates that possess many useful and desirable properties (El feil, 1993). The OLS is extremely popular. This popularity stems from the fact that the ordinary least estimator has a large number of desirable properties making it the overwhelming choice for the "optimal" estimator when the estimating problem is accurately characterized by the classical linear regression model (El feil, 1993). From these properties are: 1. The computation cost: because of the popularity of the OLS estimator, many packaged computer routines exist and the estimation of the OLS involves very little computation cost. 2. The least square: because the OLS estimator is designed to minimize the sum of the squared residuals, it is automatically "optimal" on this criterion. 3. The R-squared: Because the OLS estimator is "optimal" on the least square criterion, it will automatically be "optimal" on the R-squared criterion. 4. Unbiasness: among all the linear unbiased estimators, the OLS estimators have the smallest variance-covariance matrix. Thus, OLS are the best linear unbiased estimators (BLUE). 5.1.4. The R-squared: The R-square the coefficient of multiple regression is the percentage variation in the dependent variable explained by the regression portion of the equation. It expresses as how much of the variation in the dependent variable is explained by the independent variables in the regression equation. Computer packages calculate the Rsquared automatically. The R- square is the square of the correlation coefficient between Y and its OLS estimate Ŷ. The OLS maximize the Rsquared and because the R-squared is used as an index of how well an estimator fits the sample data, the OLS estimator is often called the best fitting estimate and high R-squared is called a good fit. Good parameter estimate are define in terms of R-squared and consequently the measure of R-squared is not of much importance and is meaningless in economics, unless the linear regression model classical assumptions are satisfied the R-square could be low because of high disturbance term(Ibid, 1993). 5.1.5 The test of hypothesis: For the validity of the estimated regression models and for the regression coefficients to make any sense at all, there are many hypotheses that have to be tested before any conclusions could be reached these are: 5.1.5.1. The T-test: The T-test is related to the individual coefficient in the regression model. They are used to test whether each individual coefficient is significantly different from zero or not i.e. whether if there is any relationship at all. The T-values are calculated by the division of regression coefficient of any variable by its standard error. 5.1.5.2. The F-test: The F-test is the same as the T-test, but rather than testing the individual coefficients, it test the whole regression model whether the equation hold or not. The null hypothesis here assumes that all regression coefficients are simultaneously equal to zero. F-values are calculated in the following way: F = (RSS/K)/ (ESS/ (n-(k+1)) Where: RSS = Regression sum of squares. ESS = Error sum of squares. K = numbers of independent variables. K-1 = numbers of independent coefficients. n = number of observations. 5.1.6. The selected production model: In deriving production functions, numerous algebraic equations and forms can be used. No single form can be used to characterize agricultural production under all environment, types and variety of crops. Hence, problem in each study is the selection of algebraic form functions, which appear or is known to be constant with the phenomena under investigation. However, some equations are more flexible than others (Heady & Dillon, 1961). In this study to show the degree of influence, level of significance and nature of relationship between the dependent and independent variables, both the linear and double logarithmic form of Cobb-Douglas function were proposed and tried in the first run of this analysis. Based on both economic logic and statistical considerations, the double logarithmic form of Cobb-Douglas function type was found to be the best form that describes the relationship between the dependent and independent variables. This type of function can be expressed as linear and easy to fit by the model of OLS. The general form of the transformed logarithmic Cobb-Douglas production function could be written as follows: Log Y = log a +b1 log X1 + b2 log X2 + ......... +bn log Xn Where: Y = the crop yield in sacks per feddan. X1, X2 ........ Xn = are exogenous variables. b1, b2.........bn = are the production elasticities with respect to individual resources. The above model is applied and estimated for the main food crops in the area, wheat and dura for the season 2001/2002. The dependent variable is the crop yield per feddan and the independent variables tried for both crops included: irrigation availability, farm income, non-farm income, cost of production, rainfall, population density, credit and extension services received. 5.2. Regression analysis results: The results of the regression equations for both dura and wheat are represented in this section; the output per feddan for each crop represents the dependent variable. 5.2.1. Dura regression result: Table (5.1) shows the dura regression equation, the dependent variable is the yield (sacks/feddan). The model includes two categories of independent variables i.e. continuous variables include: farm income, cultivated area, total cost of production and crop yield & dummy variables include credit, extension services and stratification. Table (5.1) Dura regression equation: Variable coefficient Std. error T-value Significant. Constant -1.987 1.652 -1.89 * Irrigation 0.543 0.917 9.521 *** Credit -0.009 0.249 0.837 Extension 0.381 1.392 3.912 ** Farm income 0.617 0.006 4.913 *** Dura cost of production 0.328 0.062 7.624 *** Stratification -0.635 0.876 5.497 *** R2 = 0.783 Adj. R2 = 0.728 Std. error = 2.3278 F = 18.271 Significant. = 0.000 The model specified gave R-square of 0.783 which mean that 78.3% of the observed variation in dura yield was explained by the variations in the independent variables. The F-statistic of 180271which is significant at all level of significance indicating that the model is highly significance in explaining the variation in the dura yield. The coefficient of independent variable in the model represents the elasticities, which indicate the change in yield relative to the change in the inputs. Among the variable included in the model variables are significantly different from zero at 99%, 99%, 99%, 99%& 95% level of significance for irrigation, farm income, dura cost, stratification and extension services, respectively. The credit variable included in the model was not significantly different from zero at any level of significance. 5.2.2. Wheat regression result: Table (5.2) show the wheat regression equation, with wheat yield as dependent variable; cost of production, area cultivated, time of planting, dura yield, irrigation and population density as continuous independent variables, while stratification and credit as dummy variables. The result gave R- square of 0.943, which indicate that 94.3%of the observed variation in the wheat yield was explained by the variation in the independent variables. The F-statistic of 127.913which is significant at all level of significance indicating that the model is highly significance in explaining the variation in the wheat yield. Table (5.2) Wheat regression equation: Variable coefficient Std. error T-value Significant Constant 3.271 0.736 4.512 ** Irrigation 0.517 0.213 -18.329 *** Credit 0.324 0.425 5.738 *** Extension 0.112 0.874 1.392 Farm income 0.269 0.293 6.128 *** 0.316 0.021 3.293 ** -0.295 0.009 7.115 *** Wheat cost of production Stratification R2 = 0.885 Adj. R2 = 0.877 Std. error = 0.8330 F = 113.685 Significant. = 0.000 The coefficients of independent variable in the model represent the elasticities, which indicate the change in yield relative to the change in the independent variables. Irrigation, farm income, credit, stratification and wheat cost of production variables among the model variables in the model are significantly different from zero at 99%, 99%, 99%, 99% & 95% level of significance, respectively. The extension variable included in the model was not significantly different from zero at any level of significance. 5.3. Discussion of the two crops regression equations: The effect of each independent variable in the yield of dura and wheat will be discussed separately these independent variables are: 5.3.1. The irrigation variable: The irrigation variable has coefficient (elasticities) of 0.543 and 0.517, which are significantly different from zero at 99% level of significance for both wheat and dura, table (5.1) and (5.2). These coefficients are the elasticities of yield with respect to irrigation variable or wheat and dura production. These elasticities revealed that with increasing the irrigation variable by a one percent will increase the productivity of wheat and dura by 5.43% and 5.17%, respectively. Many previous studies showed that irrigation shortage is considered to be the most agricultural constraints in the irrigated scheme (El feil, 1993). The irrigation shortage is due to inadequate supply of irrigation inputs (fuel, spare part.............etc.) and their unavailability in proper time and at reasonable price. However, these high elasticities are enough to explain how the problems associated with irrigation inputs are constraining the production. This may have led the company in the White Nile State to apply less number of irrigation than the recommended because of their poor financial resources and unavailability of formal credit on time and with adequate amount to purchase irrigation inputs even when these inputs are available in the market. 5.3.2. The farm income variable: The farm income is expected to have significant effect on the output of the different crops because of the role that it can play on the purchase of different agricultural inputs. The farm income variable has got co-efficient of 0.617 and 0.269 which are significantly different from zero at 99% level of significance for wheat and dura, respectively table (5.1) represent the elasticities which indicate the change in yield relative to the change in inputs, a one percent increase in the farm income will increase the yield of wheat and dura by 6.17% and 2.69%, respectively. 5.3.3. Credit variable: Credit has got a co-efficient of -0.009 &0.324 for dura and wheat which is significantly different from zero for wheat at 99% level of significance table (5.2). The significance may be due to the high variation in the credit received by the farmers. 5.3.4. Extension services variable: Extension services have got co-efficient of 0.381 & 0.112 this coefficient is significantly different from zero at 95% for dura and not significance for wheat. 5.3.5. The cost of production variable: The cost of production has got a significant co-efficient of 0.328 &0.316 for wheat and dura at 95%&99%, respectively. Which indicate that a one percent increase in cost of production will increase the yield of wheat and dura by 3.28% &3.16%, respectively. This attribute to the low level of inputs used. 5.3.6. The stratification variable: The stratification variable has got a significant co-efficient of -0.635 & -0.295 for dura and wheat at 99%&99%, respectively. Which indicate that the northern area of the study has a negative impact on the yield of the both crops by 6.35% & 2.95% for dura and wheat, respectively. 5.4. The nature of returns to scale: As mentioned above the sum of elasticities determine the nature of returns to scale, the sum of elasticities of individual variables is 1.101 and 1.369 for wheat and dura, respectively. This means that a one percent increase in the scale of inputs results in 11% &13.69% increase in wheat and dura yield per feddan, respectively. Consequently, an increasing return to scale existed for both crops since the summation of their partial elasticities is greater than one. 5.5. Forecasting food crops productivity: Time series data on productivity of food crops (wheat and dura) are analyzed to arrive at the trends of productivity for the period 1990/91 to 1999/2000. Table (5.3) shows the annual productivity of dura, it is clear that the productivity of dura shows considerable fluctuations from one season to another as can be seem from figure (5.1). The real trend for dura productivity is decreasing. The negative sign of the coefficient in the trend equation support this. The trend shows that by the year 2044/2045 the productivity of dura will reach zero sack per feddan. Table (5.4) also shows the productivity of wheat which is decreasing and has negative sign of the coefficient in the trend equation. The trend show that by the year 2007/2008 the productivity of wheat will reach zero sack per feddan The instability in the wheat productivity as measured by the coefficient of variation is quite high than dura. It is interesting to note that the instability associated with the food crops is lower in dura as compared to wheat. This mainly because dura crop received almost full supervision by farmers because it is his main food crop. The higher instability of wheat productivity attributed to that wheat crop is very sensitive to environmental factors i.e. drought condition, irrigation shortage as well as water logging. Table (5.3): Trend for dura productivity (sacks/feddan) Year Actual productivity Trend 1990/91 5 4.5825 1991/92 4 4.4975 1992/93 4 4.4125 1993/94 5 4.3275 1994/95 5 4.2425 1995/96 4 4.1575 1996/97 4 4.0725 1997/98 3 3.9875 1998/99 2 3.9025 1999/2000 6 3.8175 Trend equation Y = 4.6675 – 0.085x Table (5.4): Trend for wheat productivity (sacks/feddan) Year Actual productivity Trend 1990/91 2.8 4.175 1991/92 3.0 3.925 1992/93 4.8 3.675 1993/94 4.9 3.425 1994/95 4.5 3.175 1995/96 2.8 2.925 1996/97 2.0 2.675 1997/98 2.0 2.425 1998/99 2.0 2.175 1999/2000 1.7 1.925 Trend equation Y= 4.425 – 0.25x CHAPTER SIX SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 6.1. Summary: The study depended mainly on primary data, collected by means of questionnaire and direct interview of the respondent in the White Nile Scheme for the season 2001/2002. A multi-stage stratified random sample of 160 respondents was selected (80 farmers and 80 non-farmers). In addition, secondary data was used. Descriptive statistics have been used throughout the study , the statistical analysis of socio-economic characteristics of the respondent revealed that, most of the respondents are literate and have some sort of education which enable them to adopt the extension service and technical package; the majority of them were within the productive age group. The average family size was 6.7 and 5.65 persons for farmers and nonfarmers, respectively, and the average income was Ls 3699450 and Ls 3077375 for farmers and non-farmers, respectively. The analysis of the crop budget shows that the crops had positive marginal returns per feddan. 20412, 3730, 781500,616000 and 25150 for dura, wheat, tomato, okra and onion, respectively. The analysis of cost structure showed that the irrigation recorded the higher share relative to the total cost per feddan followed by land preparation, harvest and threshing, fertilizer and seeds respectively. Cobb-Douglass production function (log-form) has been estimated to test for crop yield and to see the effect of factors on the output of dura and wheat. The estimated production function for both crops show that the equation were estimated with good explanatory power the result indicated that irrigation, credit, cost of production, stratification and farm income explaining the variation in wheat yield for dura irrigation, extension, cost of production, stratification and farm income were significant variables. All variables have positive except credit and stratification for dura and extension and stratification for wheat. Generally, the interviewed respondents indicate that the major problem facing them was irrigation, extension, credit, marketing and inputs availability. 6.2. Conclusions: The study revealed the following conclusions: 1. All crops were found to be profitable since their average gross returns exceeds the average cost of production. With respect to profitability tomato, okra, onion, dura and wheat ranked from first to fifth, respectively. Vegetables are more profitable than food crops but inspite of that farmers were growing wheat and dura to secure food. This indicated by the large area cultivated by both crops in the season under study and the larger number of farmers growing food crops. 2. The cost structure analysis indicate that irrigation cost, land preparation, harvesting and threshing and sack respectively were the main item of cost for dura, for wheat irrigation, fertilizer, harvesting and threshing and seeds respectively were found to be the main cost items, whereas irrigation, seeds, land preparation and fertilizers respectively accounts for the highest share in the total cost of production for tomato, for okra irrigation, land preparation, fertilizer, and seed respectively were found to be the main cost items, on the other hand sacks, irrigation, land preparation and harvest and threshing respectively recorded the highest share in the total cost of production of onion. For all crops under the study, irrigation cost was found to be the main cost item. 3. The highest total variable cost of production was scored by onion followed by wheat, tomato, okra and dura. 4. According to the regression analysis it could be argued that irrigation, credit, cost of production, extension, stratification and lag farm income are the main factors affecting the production of food crops in White Nile Schemes. 5. The sum of elasticities of the significant independent variables determined the nature of returns to scale; wheat and dura are characterized by increasing returns to scale since their significant elasticities is more than one. 6. The higher cost of irrigation relative to the other inputs and the significance of irrigation for wheat and dura indicated that irrigation is found to be the most important factor constraining the agricultural production in the White Nile Schemes. 6.3. Recommendations: For the purpose of the study, the following recommendations are proposed: 1. Reform the scheme management by putting the scheme under governmental authority for period not less than five years during this period there must be major policies to be conduct: a. Since irrigation is the main constrains of crop production there is a need for rehabilitation of the irrigation system, cleaning the canals and maintaining the regulators. b. Facilitate the extension service through farmer education program, supply farmers with modern inputs and follow the recommended technical packages, such as improved seeds, recommended fertilizer rate, reasonable seed rate and enough irrigation to increase the productivity and profitability. c. Strengthening the farmer's financial abilities through an efficient credit system that aims to finance the small farmers at acceptable interest rate, easy terms of repayment, simple procedures in obtaining credit and encouragement of effective use of loans by farmers. Credit should be appropriate both in time and amount to perform agricultural operation at proper time. d. Incentives have to be provided to the farmers in terms of good and stable farm get prices through improvement of marketing system and storage facilities. e. Construct the agro-forestry program and keep attention to the green belt of the scheme which is removed now and there is a very need to establish this belt by conduct the legal percent of the shelter belt for the irrigated schemes. f. Increase the area under vegetables and providing technical package and finance them. 2. Provide finance, veterinary and medicine services to improve livestock wealth. 3. Provide productive loans to alleviate poverty and supporting the environment practice by avail alternative for forest product used as energy source. REFERENCES Abdel Rahim, B.E. (1989). Factors associated with participation of rural leadership in policies and programmes formulation of the White Nile Agricultural Production Corporation and Duiem extension unit. M.Sc. Thesis, Faculty of Agriculture, University of Khartoum. 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Page 52. اﻟﻤﺼﺎدر اﻟﻌﺮﺑﻴﺔ ﺻﺎﻟﺢ ،ﻋﻠﻰ ﻋﺒﺪ اﻟﻌﺰﻳ ﺰ ؛ ﻣﺤﻤ ﺪ ﻋﻠ ﻰ ﺳ ﻼﻣﺔ ؛ﻋﺒ ﺪ اﻟﻘ ﺎدر ﺗﺮآ ﺎوى وﻋﻠ ﻰ إﺑ ﺮاهﻴﻢ اﻟﺨﻠﻴ ﻞ ) (1997ﺗﻘ ﻮﻳﻢ اﻟﻌﻼﻗ ﺎت اﻻﻗﺘ ﺼﺎدﻳﺔ واﻹدارﻳ ﺔ وﻋﻼﻗ ﺎت اﻹﻧﺘ ﺎج ﺑ ﺸﺮآﺎت اﻟﻨﻴ ﻞ اﻷﺑ ﻴﺾ؛وزارة اﻟﺰراﻋ ﺔ واﻟﻐﺎﺑﺎت ،اﻟﺨﺮﻃﻮم ،اﻟﺴﻮدان . ورﺷ ﺔ ﻋﻤ ﻞ اﻟﺒﺮﻧ ﺎﻣﺞ اﻹﺳ ﺘﺮاﺗﻴﺠﻲ ﻟﻤﻜﺎﻓﺤ ﺔ اﻟﻔﻘ ﺮ ﻓ ﻲ اﻟ ﺴﻮدان ) (2000ﻣ ﺴﻮدة ﻋﻨﺎﺻ ﺮ إﺳ ﺘﺮاﺗﻴﺠﻴﺔ ﻣﻨﺎهﻀﺔ اﻟﻔﻘﺮ ﻓﻰ اﻟﺴﻮدان ) (2020 -2000ﻗﺎﻋﺔ اﻟﺼﺪاﻗﺔ ؛ اﻟﺨﺮﻃﻮم ؛ اﻟﺴﻮدان . Appendix (1) Area and average production of cotton and Dura in White Nile Scheme from season 1973/74 to 1997/98. Crop Season 1973/74 1974/75 1975/76 1976/77 1977/78 1978/79 1979/80 1980/81 1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 1987/88 1988/89 1989/90 1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/2001 Cotton Dura Productive area 1000 fed Average productivity/kantar/ fed Productive area 1000 fed Average productivity/kantar/ fed 145 151 123 104 106 88 98 61 95 85 62 65 52 55 73 40 32 17 4 14 24 44 78 22 11 9 - 3.4 3.4 2.5 2.7 3.1 2.7 1.3 2.6 2.5 3 3.4 4 2.5 2.8 2.5 2.8 2.8 3.5 5.2 4.6 4.6 3.5 1.5 0.9 1.8 - 93 74 67 85 32 39 31 29 35 24 57 30 65 22 19 20 29 80 69 40 40 55 50 67 34 46 52 62 215 194 194 194 188 438 461 355 379 485 386 400 450 300 450 320 450 500 500 540 520 650 500 560 450 620 570 Source : statistic and information management, Ministry of Agriculture and Forest.
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