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In this paper, we examine the diversity of risks that affect farming in the Northern Guinea Savanna of Nigeria. We also investigate the perspectives of these risks in relation to their economic implications on the farming enterprises. We also show that through reorganization of these risks, some derived factors have the ability to present themselves whether as corresponding to existing categorization of the variables or not and also to enable us know which of the factors is more important than the other. Gross margin and factor analytical methods were used in computing the estimated results on a cross sectional sample of 348 farming households. Results show that farmers who were grouped under natural risk incurred the least mean production cost of N11, 115.61, while the highest mean production cost of N 15,998.18 was incurred by farmers grouped under production risks. The highest mean revenue of N18, 998.16 was recorded by farmers under production risk which translated into a mean gross margin of N65, 999.85. Verifying whether some derived factors would correspond to the existing categorization of 14 risk types (from 5 sources) which the farmers faced, results from the factor analysis and the consequent F-tests from ANOVA show no marked or significant differences among the identified factors and the existing risk sources. Consequently, the individual effect or importance of the original 14 risk types that the sampled farmers considered important can be dully represented and effectively regrouped into five sources (factors) as natural, technical, social, ecosocial and biochemical. *Corresponding author. Present Address: Forum for Agricultural Research in Africa, c/o Institute for Agricultural Research, Sabo Bakin Zuwo Road, P.O.Box 1062 Kano, Nigeria. E-mails: lolarinde@fara-africa.org; lolarinde@yahoo.com 1.0 INTRODUCTION Risk is uncertainty that affects an individual’s welfare, and is often associated with adversity and loss (Bodie and Merton, 1998). Risk is also uncertainty that “matters”, and may involve the probability of losing money, possible harm to human health, repercussions that affect resources (irrigation, credit), and other types of events that affect a person’s welfare. Farming is a financially risky occupation. On a daily basis, farmers are confronted with an ever-changing landscape of possible price, yield, and other outcomes that affect their financial returns and overall welfare (Harwood et. al. 1999). The consequences of decisions or events are often not known with certainty until long after those decisions or events occur, so outcomes may be better or worse than expected. Oftentimes, surveys have asked farmers about the most important types of risk that they confront in their farming operations (Harwood et. al. 1999). These types of questions are typically part of a larger survey that inquires about producers’ risk management strategies, and offers respondents a list of concerns that they can score in terms of importance. Scores generally are not ranked relative to one another, meaning that producers independently analyzed each concern on the list. Sources of risk in farming include among others production or yield risk, price or market risk, institutional risk, human or personal risks and financial risks. Production or yield risk occurs because agriculture is affected by many uncontrollable events that are often related to weather, including excessive or insufficient rainfall, extreme temperatures, hail, insects, and diseases. Technology plays a key role in production risk in farming. Price or market risk reflects risks associated with changes in the price of output that may occur after the commitment to production has begun. In agriculture, production generally is a lengthy process. Livestock production for example, typically requires ongoing investments in feed and equipment that may not produce returns for several months or years. Institutional risk results from changes in policies and regulations that affect agriculture. This type of risk is generally manifested as unanticipated production constraints or price changes for input or for output. Farmers are also subject to human and personal risks that are common to all business operators. Disruptive changes may result from such events as death, divorce, injury, or the poor health of a principal in the farm firm. In addition, the changing objectives of individuals involved in the farming enterprise may have significant effects on the longrun performance of the operation. Financial risk results from the way the firm’s capital is obtained and financed. A farmer may be subject to fluctuations in interest rates on borrowed capital, or face cash flow difficulties if there are insufficient funds to repay creditors. The above risks constitute major agricultural constraints which farmers always have to contend with. In the Northern Guinea Savanna of Nigeria, risks associated with farming can be categorized into the following (Olarinde, 2005): Natural risks (drought, flood, wind and storm, disease and pest); social risk (theft of produce, bush fire, invasion of farms by cows); economic risk (producer price fluctuation, insufficient and untimely supply of fertilizer, insufficient credit and insufficient supply of seeds); technical risk (poor soil, insufficient access to chemicals, scarce labour and insufficient processing facilities). Studying the diversity of the risks facing farmers in the Northern Guinea Savanna of Nigeria, and their economic implications particularly on crop farmers is an important milestone in the numerous efforts to provide solutions to the various agricultural constraints and challenges which resource poor farmers in sub Saharan Africa have been battling with. After the introduction, section 2 which is on materials and methods describes the data and study area and the method of data analysis. Section 3 is on results and discussion, which interprets and discusses the estimated results. Section 4 concludes by giving a summary of findings and the implications of results. 2.0 MATERIALS AND METHODS 2.1 Data and Study Area The data used for this study are from a cross-sectional sample of smallholder crop farmers in the Northern Guinea savanna agro ecological belt of Nigeria. The data were obtained in respect of the 2004/05 agricultural year in a survey (see Olarinde, 2005). The sample comprised 348 households selected from the 23 local government areas (LGAs) and from the four Agricultural Development Programme (ADP) zones of Kaduna State. Structured questionnaire and checklists were used to obtain the data through personal interviews and focus group discussions. Detailed information on various aspects of the farm–household under various farming risk conditions and for different crop activities was collected. This included the household’s demographic characteristics, farm size, cropping patterns, crop output in actual and value terms, labour and non–labour inputs in actual and value terms. The risk sources and types which affected the majority of the sampled farmers were identified as (i) natural risks, e.g. drought, flood, wind and storm, diseases and pests: (ii) social risks, e.g. theft of produce, bush fire, invasion of farm by cows; (iii) economic risks, e.g. producer price fluctuation, insufficient supply of seeds, (iv) production risks, e.g. poor soil, lack of spraying equipment, lack of chemical, (v) technical risks, e.g. scarce labor, insufficient credit facilities. Technical risks are those that are related to production. It is worth mentioning that identification of the risk sources and types and their nomenclature is necessarily the outcome of a focus group discussion between the researchers and the village extension practitioners who live and are conversant with the farming activities and needs of the sampled households. 2.2 methods of data analysis The results of the focus group discussion were employed to classify the sampled farmers based mainly on the characteristic features of the risks they faced during the agricultural year of survey. This was done for each of the four ADP zones and made to coincide with the major cropping patterns in the study area. The cropping pattern is typically a multiple crop system which consists of maize plus one or two of other cereals or grain legumes in the northern, drier axis and maize plus one or two grain legumes and root crops in the southern humid axis (Tables 1 and 2). Table 1: Lay-out of Sample and Survey Design by Risk Type and by ADP Zone Risk Types Natural Social Economic Production Technical Total (nn) (ns) (ne) (np) (nt) (nz) ADP Zones Birmin Gwari 21 16 24 10 09 80 Lere 13 11 16 06 06 52 Maigana 31 25 36 15 13 120 Samaru 25 20 29 12 10 96 Total 90 72 105 43 36 348 Source: Survey Data n= sample size: nn= natural risk; ns = social risk; ne = economic risk; np = production risk; nt = technical risk; nz = zonal total Table 2: Cropping Patterns of Respondents by Risk Type and by ADP Zone Natural Social Economic Production Technical ADP Zones Birmin Gwari mz/gc/rc mz/mil/sb mz/cp/gn mz/gc/mit mz/mlt/gn Lere mz/gc/sb mz/gc/cp mz/rc/sb mz/gc/gn mz/mlt/sb Maigana mz/gc/mlt mz/gc/gn mz/gc/sb mz/gc/rc mz/cp/gn Samaru mz/gn/ym mz/gc/cs mz/cp/cy mz/rc/pt mz/gc/cs Source: Survey Data mz = maize; gc = guinea corn; cp = cowpea; rc = rice; mlt = millet;gn = groundnut; sb = soybean; cy = cocoyam; ym = yam; pt = potatoes; cs = cassava Descriptive statistical tools which include scattered diagrams were used to compare the means and standard deviations of the farmers’ characteristics, which included the age, years of formal schooling, cropped area, household size and farming experience. Bar charts were used to depict and compare the gross margin components of the farmers’ cropping activities, e.g. cost, revenues and gross margins. Analysis of variance (ANOVA) was used to compare the cost, revenues and gross margins for the four ADP zones and under the five identified risk situations. At a

Economic Perspectives of the Diversity of Risks among Crop Farmers in the Northern Guinea Savanna of Nigeria