Abstract: This study was designed to identify obstacles to the adoption of soil survey information in Abia and Imo States of Southeastern Nigeria. A structured interview schedule was used in obtaining information from 450 respondents. Data were analysed using percentages, multiple regression and factor analysis. Results showed that age (t = 2.21), education (t = 2.00) and years of farming experience (t = 2.06) were significant in influencing the adoption of soil survey information. The major factors affecting adoption of soil survey information were management and economic and technical and structural.
INTRODUCTION
Soil resource surveys provide factual information on the kinds of soil which cover the surface of the earth. Such resources surveys show spatial distribution of different kinds of soil on a map, their field characteristics, the physico-chemical properties, correlations and interpretations with many land uses. The quality of soil is fundamental in the determination of what crops and livestock products can be and therefore may be produced in a given region and on a given farm (Nerlove et al., 1996). Consideration of soil quality and information is vital to sustainable use in soil for arable farming (Holdren et al., 1995; Seybold et al., 2004). Non-use of soil survey information has resulted in soil and soil-related environmental problems (Onweremadu, 2006) and these problems are worsened by socio-economic pressures on soils caused by population increase (Ruecker et al., 2003).
In most developing countries of the humid tropics, including Nigeria, soil data are scarce available soil data are not usable (Lal and Ragland, 1993) and this is worsened by other constraining factors in natural resources management which include poverty, conflicting and uncoordinated policies, poor stakeholder involvement and shortage of technical manpower (Okedi, 2000). Where soil experts are available, language of delivery of soil survey information is complex (Akamigbo, 2002) and these attributes interact to reduce usage of soil data (Smith et al., 2004). Information services aim to build an offer in response to identified needs hence the need for information offer and not of document collection and management (Gachie and Ruault, 2006).
Non-usage of soil survey information has resulted in plant nutrient depletion, nutrient toxicity, heaving of architectural structures, collapse of engineering structures, compaction, flooding, poor yield and general food insecurity. Marginal and derelict lands are erroneously converted to agricultural farmlands and pastures. Consequently, there is increased soil degradation, especially by soil erosion in the study area. In the light of the above, Wilson (2001) suggested the application of scientific information in solving sub-Saharan African food needs so long as such information are presented in customized forms (Kufoniyi, 2000) possibly using geographic information systems. Few studies have been conducted on the applicability of soil survey information by land users in the study area (Akamigbo, 2000; Onweremadu et al., 2007). This study was therefore designed to find out the major obstacles to the adoption of soil survey information by owner-managed arable farmers of central southeastern Nigeria. Adoption of soil information is demanding in this area since it is characterized by soil originating from different parent materials and under varying land uses amidst a teeming population.
MATERIALS AND METHODS
The study area is Abia and Imo State with an area of 13, 032 km2 and lying between latitudes 4°40 and 8°15 N and longitudes 6°40 and 8° 15 E. Field surveys were conducted in 2005 and 2006. Six agricultural zones were identified in the two states and each agricultural zone consists of several local government areas. In all, 20 communities were purposively selected based on the large number of big farmers and accessibility. The communities and their geographical coordinates are shown in Table 1. A big farmer for the purpose of this study represented one registered and recognized by the agricultural unit of the local government area most of whom have sizeable farms. In each of the 20 communities, 30 big farmers were selected by simple random sampling, giving a total of 600 respondents in a target population of about 50,000 farmers.
A structured interview schedule was used in obtaining relevant information from the farmers (respondents). Interviewers were drawn from the localities for easy communication with respondents. The structured interview schedule was simple clear, logical and less ambiguous.
Validation of interview schedule was done, using content validity method, which is a way of determining the relevance and suitability of items included in the study (Chuta, 1992) following the jury method as used by Ajayi (1996).
| Table 1: | Selected study sites |
Items contained in the draft interview schedule for the research were subjected to thorough examination and criticism by three lecturers in the Department of Agricultural Extension, Federal University of Technology, Owerri, Nigeria. The relevance and suitability of items determined by lecturer experts formed the basis for the development of final interview which was used to collect data for the study.
The variables considered under biodata of respondents included: age, gender, educational status, years of arable farming, farm size, household size and membership of social organizations. A 3-point Likert type scale was developed and used to determine the extent in which the constraining factors listed posed an obstacle to the adoption of soil survey information. The response options and values assigned were as follows:
| Not a Problem (NP) | = | 0 |
| Little Problem (LP) | = | 1 |
| Much Problem (MP) | = | 2 |
Six major constraint variables, namely language of delivery shortage of trained personnel, lack of usable information, scantiness, of soil data, poverty and land tenure system were identified in the adoption of soil survey information. Specific issues bordering on each major constraint item were assessed and their grand mean was used to represent the major item. A varimax notated factor matrix was used to identify the most constraining obstacle to the adoption of soil survey information technology. Adoption scores were computed by using 7 stages in adoption, which were rated as follows:
Unaware = 0; aware = 1; interested = 2;
Evaluation = 3; trial = 4; adoption = 5 and discontinuance = 0
Data Analyses
Socioeconomic data were analyzed using percentages while factor analysis
was used in measuring major obstacles hampering adoption of soil survey information.
Adoption of soil survey information (dependent variable) was regressed to socio-economic
characteristics (independent variables).
The above was expressed using a multiple regression model as follows:
| (1) |
| Where, Y | = | Adoption of soil information |
| A | = | Intercept |
| b1-b6 | = | Regression coefficients |
| X1 | = | Age |
| X2 | = | Education status |
| X3 | = | Years of farming experience |
| X4 | = | Farm size |
| X5 | = | Household size |
| X6 | = | Membership of social organization |
| e | = | Error term |
RESULTS
Only 75% (that is, 450) of the 600 questionnaire forms were returned. A greater number of the respondents were males (Table 2). Majority of the farmer respondents were aged 41 to 50 years and did not complete secondary school education (54.2%).
| Table 2: | Percentage distribution of respondents according to their socio-economic characteristics (N = 450) |
| Table 3: | Multiple regression analysis on the relationship between socio-economic variables and adopted on of soil survey information (n = 450) |
| SE: Standard error; *Significant at p<0.05; NS: not significant | |
Yet, 97.2% the respondents had formal education with many (66.3%) having 11-20 years of experience. The study further showed that a good number of the farmers (65%) were cultivating 1.1 -2.0 hectares of arable land while 65% of them had a household size of 5-9, with 59.3% belonging to one to two social organizations.
The results (Table 3) indicated that three independent variables, namely age (t = - 2.21), education (t = 2.00) and years of experience (t = 2.06) were significant in explaining 25% of variation in adoption. The estimated value of adoption (Y) is shown as follows.
Y = 3.95-0.03 X1 + 0.01X2 +
0.07 X3-0.03 X4 + 0.03 X5 + 0.29 X6 |
Where these terms are already defined in Eq. 1.
Based on the item loadings, factor I was named management and economic constraints while factor 2 was referred to a technical and structural constraints (Table 4). Thus these two factor classifications represent the main obstacles to adoption of soil survey information.
| Table 4: | Main obstacles to the adoption of soil survey information (N = 450) |
Specific items which worsen management and economic factor were scanty soil data (0.88), land tenure (0.81), language of delivery (0.78) and poverty (0.68) while Factor 2 was influenced by shortage of trained personnel (0.86), lack of useable soil information (0.69) and scanty soil data (0.52).
DISCUSSION
Many of the respondent-farmers did not complete secondary education, implying difficulty in understanding scantly available complex soil data. This stresses the relevance of education in increasing adoption of modern agricultural technologies (Madukwe, 1995), who reported that level of education is one of the variables affecting adoption of improved farm practices. A majority of the farmers had more than 11 years of experience (97.1%), suggesting that they have been cultivating arable crops for a reasonable number of years without much information. This implies that farmers still hold tenaciously to traditional farming practices, which is consistent with the findings of Onweremadu (1994). This is possibly aggravated by poverty due to large household size of 5-9 direct dependents and many un-recorded dependents since the culture promotes extended family system. Yet, majority of farmers have farms less than 2.0 hectares (85.1%) indicating that arable farming is still at subsistence level. Again, majority (59.3%) belonged to one to two social organizations, which according to Onu (1991) serve as a forum through which farmers could exchange ideas about new farm practices.
Age related greatly with adoption of soil survey information in this study, in consonance with findings of Ajala (1992), who reported a strong relationship between age and adoption of technologies. Educational status was another variable that influenced adoption of soil survey information and this is consistent with previous studies (Onu, 1991; Ajala, 1992) which established a good relationship between education and adoption. Education informs and leads to understanding of complex soil data and innovations. In the same manner, years of experience had a positive relationship with adoption of soil survey information, implying that farmers are likely to adopt more modern technologies especially when subjected too many crop failures.
Among the technical and structural constraints (Factor 2), shortage of personnel related significantly and negatively (-0.86) with adoption of soil survey information while lack of usable soil information had a good significant positive association (0.69) with adoption of soil survey information. Most graduates of agricultural extension study agriculture which may not give them enough preparations to solve soil based technical constraints and this reduces their efficacy in convincing farmers to adopt soil survey information. A well trained extension worker in soil survey information can transform such complex soil data to customized or user-friendly forms (Kufoniyi, 2000; Okedi, 2000). Farmers do not agree with complex scientific explanations (Barr and Cary, 1992) but act quite rationally by preferring to adopt simple innovations. Land tenure (0.66) is another technical constraint, which is in line with the findings of Onweremadu (2006) that land tenure system had a relatively high rating (39%) on the applicability of soil information in two lowland states or Southeastern Nigeria. Tenants in Africa refuse to undertake improvements on land that does not belong to them (Spore, 1994). These farmlands they believe are temporarily owned and communal decision may change with time. Yet, these decisions are not easily influenced by women who feel greater pains from lower soil productivity but have low voice (21.8%). In most areas of the study site, women are culturally inhibited to answer questions on some socio-economic issues and this could by why a greater number of the respondents were males (78.2%). This result is consistent with the findings of similar studies in the area (Angba, 2003; Oladele and Adu, 2003).
CONCLUSIONS
The study examined obstacles to adoption of soil survey information in southeastern Nigeria of the humid tropics. The study revealed that majority of the respondents were males and fall between the ages of 41-50 years. Results showed that some socio-economic characteristics, namely age, education and years of experience had a significant relationship with the adoption of soil survey information. It further revealed that management and economic constraints as well as technical and structural constraints immensely influenced adoption of soil survey information.
ACKNOWLEDGMENT
We are grateful to the Academic staff, Department of Agricultural Extension, Federal University of Technology Owerri Nigeria for guiding this research.