Constraints and Diagnostic study to main crop production in Zalingei Locality, Central Darfur State

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1 Scientia Agriculturae E-ISSN: X / P-ISSN: DOI: /PSCP.SA Sci. Agri. 14 (2), 2016: PSCI Publications Constraints and Diagnostic study to main crop production in Zalingei Locality, Central Darfur State Elkhalil E. Breima 1, Abdelmniem T. Ahmed 2,Fathi H.,Abdelaziz A. Maruod E. Maruod 3 1. Department of Agricultural Economics, Agricultural Research Corporation, Zalingei research Station, Zalingei- Sudan 2. Agricultural Economics and Policy Research Centre, Agricultural Research Corporation, Shambat, Sudan 3. Department of Agricultural Economics and Rural Development, Faculty of Natural Resources and Environmental Studies, University of Kordofan, Elobeid, Sudan Paper Information A B S T R A C T This paper reports on the results of a diagnostic study conducted to assess Received: 16 January, 2016 the constraints of crop production of farmers in Zalingei locality during 2015/2016 cropping season. Agriculture in central Darfur state are kept in Accepted: 27 March, 2016 Published: 30 April, 2016 a mixed crop-livestock production system. agriculture have a multi function helping as source of draft power income generation. The objective of the study was to identify the opportunities and constraints of crop production and to use the information as a base line data for future Citation intervention. Multi-stage random sampling method was applied. 50 households were randomly selected via Structured questionnaires, focus Breima EE, Ahmed AT, Fathi H, Maruod AAME Constraints and Diagnostic study to main crop production in Zalingei Locality, Central Darfur State. Scientia group discussion and secondary data were collected from different sources. Statistical package for several sciences, Excel and multiple regression were used to analyze data. Results of frequency distribution of farmers Agriculturae, 14 (2), Retrieved from indicated that the majority of the sampled households had some sort of (DOI: education (60 %), while the rest were illiterate (40 %). Extension provision /PSCP.SA ) results showed that only 4 % of households benefited while 96 % said they have no extension services. Results pertaining climate change impact showed that 95.8 % of farmers crop production affected, on the other hand 4.2 of farmers were not affected. Regression results reported that, high costs of seeds, weeding costs, harvesting costs and threshing costs were significant at five percent from zero level except threshing costs have was significant at ten percent from zero level. Adjusted R2 was Input constraints was significant at ten percent from zero level identified, while biological and environmental constraints were significant at five percent from zero level and accordingly adjusted R 2 was estimated by 44.5%. Results of millet crop indicated that marketing constraints were affect production and they were significant at five percent from zero level. Adjusted R 2 negatively impact millet production by 31 %. Groundnut seeds costs was significant at five percent from zero level with adjusted R 2 29 %. sesame results showed that biological constraints was significant at five percent from zero level and the adjusted R 2 was found to be 15 %. Analysis of onion crop founded that seeds costs and weeding costs were highly significant impact at five and ten percent from zero level, respectively and the model fit regarding adjusted R 2 was an average costs constraints of 60.5 %. The potato results revealed that costs of insecticides, costs of fertilizers, harvesting costs and transportation costs were significant at five percent from zero level with adjusted R 2 of 66.9%. While marketing constraints and environmental constraints were significant at five percent and the explanatory variables affected production by 24.7 %. Analysis of sweet potato founded that weeding costs and production constraints were significant at five percent from zero level. Adjusted R 2 is greatly affected sweet potato production by 52 and 21 % respectively PSCI Publisher All rights reserved. Key words: Diagnostic, Constraints, Frequency distribution, Multiple regression Introduction Sudan is one of the largest countries in Africa, and has a population of 39 million according to the recent census in 2008, of which 79% are rural Agriculture is the largest production sector, which provides livelihood for 75% of the population and contributes around 90% of export earnings. This sector has two main sub-sectors, irrigated sub-sector in some areas of the central clay plain and rain-fed one in many areas of the western and central parts of the country. Climatic zones are classified into desert in the northern part of the country to semi desert, arid and tropical in the southern part of the country. The desert, which is not suitable for cultivation, occupies 30% of the total area, while the arable land is

2 estimated as 200 million feddans (Suliman, 2005). The vast area of the country, excluding the desert is suitable for livestock grazing. The country is endowed with huge natural resources including land and water. However, poor management of these resources in the contingency of environmental stress and natural calamities mainly drought have rendered the country not being able to persistently maintain its food security. Agriculture in central Darfur state are kept in a mixed crop-livestock production system. Agriculture have a multi function helping as source of draft power income generation. The state was endowed with different climatic zones and several cropping systems. In spite of these resources, The targeted area was characterized by socio-economic constraints (education, occupation, marital status, age, family size and experience), production constraints (low productivity, yield fluctuation, low quality of produce and floods problems), resources constraints (lack of credit, lack of labor, scarcity and lack of agricultural lands, compact and hard span soil,low soil fertility lack of irrigation water and storage facilities), input constraints (lack of improved seeds, lack of plows, lack of fertilizers and lack and miss use of pesticides, management constraints (low sowing date, shortage of rain fall, low yield of local seeds, low crop growth), marketing constraints (far markets distance, low product prices, higher seed costs, high transportation costs, taxes and duties, prices fluctuation, higher prices of improved sees, higher prices of fertilizers and pesticides, higher prices of fuel and higher prices of working labor, biological constraints (pests, diseases, Striga, animals and conflicts between farmers and animal owners, environmental constraints (erratic and low rain fall, higher temperature and whether condition) and policy constraints. There is no or little access to agricultural credit service. Therefore, addressing these constraints is very essential to develop a successful intervention program in this area. It is concluded that, future studies should focus on multidimensional improvement for solving problems and constraints in crop production and productivity. Zalingei locality lies between latitudes 14o- 12o 30o N and longitudes 23o - 22o 30o E. The climate of the area is generally typical to poor and rich savanna that is locally modified by the effect of the position of Jebel Marra massif (3040 asl.). Three seasons characterize much of the area, summer is generally dry, hot and short, extending from March to May. The rainy season (June October) is worm to moderately cool while winter is relatively cool and extending from November to February. Rain fall ranges from less than 450mm in the northern parts and mm in the southern parts. Materials and methods The study was conducted in Zalingei locality during 2014/2015 cropping season. Questionnaire household survey and focus group discussion regarding crop activities were conducted to collect primary data. In addition secondary information used from review of available documents of project activities and related fields. The study used multi-stage random sampling procedures. A total of three administrative unites were selected randomly. With respect twenty one (21) villages were randomly selected and farmers were randomly selected to represent villagers. According to higher homogeneity in the study area 50 households were randomly selected. statistical package for several sciences (SPSS), and Excel were applied. multiple regression model was used to analyze data. Discriptive statistics Statistics is a set of procedures for gathering, measuring, classifying, computing, describing, synthesizing, analyzing and interpreting systematically acquired quantitative data (Jaggi, cited 2015). It deals with the presentation of numerical facts, or data, in either tables or graphs form, and with the methodology of analyzing the data. Descriptive statistics is used to describe the characteristics of the sample in an accurate and unambiguous fashion in much way that information will be easily communicated to others, Shayib, Multiple regression Multiple regression is an econometric model that used to analyze the effect of production input on crop output. Production of all crops was explicitly expressed as a function of the inputs. The econometric model is explicitly specified as Yi = B0 + B1xi +Ei I = 1, 2, n Where Yi represent the i th response value, B0 is intercept (the mean value of y at x = 0) B1 (slope, regression coefficient) tell us that, on average, as x increase by 1 so y increase by B1, and Ei is the error term households Production constraints X1 = Bush cleaning costs SDG x2 =Seeds costs SDG x3 = Plowing costs x4 = Sowing costs x5 = Weeding costs X6 = Harvesting costs X7 = Threshing costs X8= transportation costs X9= Fertilizer costs X10 = Pesticides costs X11= Production constraints 256

3 X12= Input constraints X13= Resources constraints X14= management constraints X 15 = Extension constraints X16= Environmental constraints X17= Marketing constraints X18= biological constraints Results and discussions Distributions of households according to education level Exposure to education should increase a farmer s ability to obtain, process, and use information relevant to the crop production. The majority of the sampled households in Zalingei locality had some sort of education (60 %), while the rest were illiterate (40 %). this observation highlights that education is an important variable to take into account during the project roll-out. Where there are more illiterate segments, the project needs to enhance integration of extension and bottom up approaches within research project design and implementations. Similarly, in segments where most households are literate, the learning uptake would easily be achieved, this result was agreed with what had been said by Breima etal, 2012, Table 1. Distribution of households according to extension services It is hypothesized that contact with extension workers will increase a farmer s awareness and it has a positive impact on crop production. Results founded that only, 4.0% of the households benefited from extension services, while 96.0% had no extension services. This results implies that farmers decision negatively affect adoption of new crop technologies and intervention through extension activities is vital and necessary especially in the study area, Table 2. Distribution of households according to climate change perceptions Results pertaining climate change impact showed that 95.8% of farmers crop production affected by climate change, on the other hand 4.2 % of farmers were not affected. This results entail that more studies should be made to try to capture the hazard of climate change in Central Darfur state, Table 3. Multiple regression The study reported that, high costs of seeds, weeding costs, harvesting costs and threshing costs were significant at five percent from zero level except threshing have significant at ten percent from zero level. Adjusted R2 was this results negatively impact Dura production and 68 % of decreasing of Dura production and profitability is attributed to higher costs of explanatory and cultural practice costs. Other highly significant at ten percent from zero level identified by input constraints, while biological and environmental constraints were shown significant at five percent from zero level and thus adjusted R2 was estimated by 44.5 %. This results entailed that farmers incentives were reduce according to higher costs and by the other way this results will promote farmers and decision makers to intervene and introduce new technologies in order to minimize production costs. This results was coincide with what had been said by Olawepo, 2010, problem of high costs of production constraints receding increase food production and productivity in the study area. Results of millet crop showed that production and marketing constraints were affect production and they were significant at five percent from zero level. Adjusted R2 negatively impact millet production by 31 %. This results of higher production costs is might be due to insecurity situation in the study area and conflict between farmers and livestock owners which makes farmers not to grow and expand the area of production. This results also goes with Gebremedhin etal,2007 the high unit cost of production and marketing constraints is due to the small scale areas and low yield. Results pertaining groundnut seeds costs observed significant at five percent from zero level with adjusted R2 29 % this implies that 29 % of change in groundnut production is constrained by seeds costs. As already studies noted that production of sesame is highly sensitive to pests and diseases as well as weeds due to results showed biological constraints revealed significant at five percent from zero level and the adjusted R2 was found to be 15 %. Analysis of onion crop founded that seeds costs and weeding costs was significant at five percent and highly significant at ten percent from zero level, respectively and the model fit regarding adjusted R2 was an average costs constraints of 60.5 %. This results was agreed with Abdelaziz, 2008 that some cultural practices costs were negatively impact onion production. The potato production results revealed that costs of insecticides, costs of fertilizers, harvesting costs and transportation costs were significant at five percent from zero level with adjusted R2 of 66.9%. While marketing constraints and environmental constraints were significant at five percent and the explanatory variables affected production by 24.7 %. Analysis of sweet potato founded that weeding costs and production constraints were significant at five percent from zero level and adjusted R2 is greatly affected sweet potato production by 52 and 21 % respectively, Table

4 Conclusions Study concluded that majority of farmers had some source of education. Results indicated that farmers have no access to extension services and climate change negatively affected crop production. Results of multiple regression revealed that most production constraints were negatively affected farmers crop. The analysis of multiple regression regarding some production costs and constraints was negatively impact crop production and showed different ranges of probability levels which indicates the effect of explanatory variables accompanied with the adjusted coefficient of determination on crop production as well as farmers income. The study reached some recommendations There is a need to increase the availability of important agricultural inputs and mechanization services to farmers through encouraging active private sector participation in agricultural services delivery. The formation and strengthening of institutions that can help to reduce the transaction cost for input markets is also imperative. Table:1 shows education levels illiterate khalwa elimentry primary secondary university Post graduate study Total Table:2 shows Households access to extension services yes no Total Table:3 shows climate change across yield yes no Total Not cited 2 - Table: 4 shows production constraints indicators against crop production, Zalingei locality Crop Explanatory variable coefficients Standard error T. value Sig Adjusted R2 Dura seeds costs (SDG) * Weeding costs (SDG) * Harvesting costs * Threshing costs ** Inputs constraints *** Biological constraints * Environmental constraints * Millet Production constraints * Marketing constraints * Groundnut Seed costs (SDG) * 0.29 Sesame Biological constraints * 0.15 Onion Seeds costs * Weeding costs ** Potato insecticide costs * Fertilizer coats * Harvesting costs * Transportation costs * Marketing constraints * Environmental constraints * Sweet potato Weeding costs * 0.52 Production constraints *

5 Source: HHS survey 2014, *=sign. at ten percent level, **=sign. At five percent level, ***=sign. At one percent, production constraints =(low yield, low animal production, production fluctuation, floods, bad quality of product), Inputs constraints ( lack of improved seeds, lack of pesticides, lack of fertilizers, lack of plows, lack of credit ), Biological constraints=( pests, diseases, animals ), Environmental constraints=( rains, temperature, whether fluctuation), Marketing constraints= (Far market, higher seeds prices, low product prices, taxes and duties, Transportation costs, price fluctuation, Exploitation), management constraints=(late sowing, low yield of local varieties, weakness of crop establishment), Resource constraints=(lack of labors, lack of lands, lack of irrigation water, lack of storage), extension constraints includes (lack of extension, lack of training), Environmental constraints (Lack and shortages of rains, high temperature, weather fluctuation References Abdelaziz HH Economics of Onion Production in the Northern Part of Omdurman Province, Khartoum State, Department of Agricultural Economics, Faculty of Agricultural Studies. Sudan University of Science and Technology. Breima EE, Elnour FM Innovations for sustainable production and utilizations of Pearl millet in drought prone areas of North Kordofan State- Sudan. Gebremedhin H, Emana B Constraints and Opportunities of Horticulture Production and Marketing in Eastern Ethiopia, DCG Report No. 46. P.26 Jaggi S. cited Descriptive statistics and exploratory data analysis. Indian Agricultural Statistics Research Institute. Library Avenue, New Delhi Olawepo RA Constraints to Increased Food Productivity in Rural Areas: An Example from Afon District, Ilorin, Nigeria, Department of Geography, University of Ilorin Pm b 1515,Ilorin, Nigeria. Shayib MA Applied statistics. Suliman SA Some problems of agriculture in the Sudan, Amira pring press, Khartoum Bahari, Sudan 259