ABSTRACT

This study examined crop residue usage and determined factors influencing the decision to allocate as well as the intensity of crop residue in Kano state, Nigeria. A multi-stage sampling technique was employed to select 160 farming households in three local government areas of the state. Data collected were analyzed with descriptive statistics and double hurdle Tobit model. The results of the study showed that on a general note, crop residues allocated for own animal feeding had the major share. Farmers preferred using crop residue for feeding than mulching. The allocation of the legume residues for feed purposes was about 64 percent; the share for cereal residues of animal feed was 26 percent. Other important competing uses of crop residue of legumes and cereals were also different. These included stall feeding, burning, house construction and fuel. About 17 percent of legumes residues are sold either on field or offsite. Legume residues were major sources for redistributing nutrient within the farm and between farm units (within the systems). More of legume crop residue (CR) was used within the farm/community (88.9 percent) while only 11 percent was exported. The decision to adopt cereal crop residue as livestock feeds was positively and significantly influenced by age, education, access to credit facilities and quantity of cereal crop residue available to the farmers. On the other hand, decision to use legume crop residue was positively and statistically influenced by farm size and access to extension facilities. However, the intensity of use of both categories of residues was mostly determined by age, education and access to credit. Furthermore, results indicated that where both residues were available, farmers complemented the use of one with another. Concerted efforts should therefore be made at increasing awareness and education on the use of crop residues in the crop-livestock system. Similarly, facilitation of extension services in crop residue training and increased access to credit will reduce the degree of residue export from the system.

Key words: Double hurdle Tobit model, multi-stage sampling technique, legumes, cereal, crop residue.

Study area

 

The study area was Kano State. Kano State is a state located in North-Western Nigeria. Kano state borders Katsina State to the North-west, Jigawa State to the North-east, Bauchi State to the South-east and Kaduna State to the South-west. Kano State has been a commercial and agricultural state, which is known for the production of groundnuts as well as for its solid mineral deposits. The state has more than 18,684 km² of cultivable land and is the most extensively irrigated state in the country. The primary occupation of most of the inhabitants is agriculture in the form crop farming and animal husbandry. The cultivation of food and cash crops remain common engagements of the people (Olofin, 1987). The mean annual rainfall is about 850 mm. The rainfall is highest in August (single maximal) with a sharp decline in September and an abrupt end in October (Olofin, 1980).

 

Sampling technique and data analysis

 

A multi-stage sampling technique was used for this study. The first stage involved a purposive selection of Kano South senatorial zone comprising of 16 LGAs. The second stage was also a purposive selection of three LGAs namely, Albasu, Wudil and Garko based on the intensity of crop-livestock production management system, marketing and utilization prevailing in the area. The third stage involved proportionate sampling of eight (8) villages based on their population. The villages chosen were Fadisonka, Indabo, Lajawa, Lamire, Utai and Kausani in Wudil LGA. Kafin Malama was chosen in Garko LGA while Saya Saya was selected Albasu LGAs. At the household level, farmers were stratified into very poor, poor middle and wealthy based on the degree of ownership of livestock and landed properties. In each category, 5 households were selected to make 20 households per village. On the overall 160 households were interviewed using a structured questionnaire. The sample size could have presented a limitation on the ability of the study to capture effects adequately at household level. But in view of the concentration of households that use crop residue in the study area and fewness of villages in the Sahel region that practiced crop-livestock integration, the sample can to a large extent describe the scenario of crop residues usage. Data collected were analyzed using descriptive statistics and double hurdle model.

 

Econometric specification

 

Double hurdle model

 

This study used the double hurdle model which was originally proposed by Cragg (1971). This has been recently used in the study of agricultural technology adoption (Gebremedhin and Swinton, 2003; Simtowe and Zeller, 2006; Langyintuo and Mungoma, 2008; Asfaw et al., 2010). This model assumes that households must cross two hurdles or make two decisions in order to adopt a given technology, e.g., crop residue (CR). The first decision is to decide whether to adopt or not (probability of adoption). The second decision is about the share of land that the household will allocate for its cultivation (intensity of adoption) which is conditional on the first decision. The model allows for the possibility that the probability and intensity of adoption have different explanatory variables and even variables appearing in both may have different effects (Asfaw et al., 2010; Teklewold et al., 2006; Simtowe and Zeller, 2006; Langyintuo and Mungoma, 2008. The first hurdle, decision to adopt CR (d) is expressed as:

 

 

The second hurdle, intensity of adoption (y) is expressed as

 

Where d_i* is a latent variable that describes household decision to adopt CR, y_i* is a latent variable describing the intensity of adoption and y_i is the observed response on intensity of CR. z and x are vectors of variables explaining the decision to adopt and intensity of use of CR respectively. α and β are vectors of the parameters. ε_i is an error term with mean 0 and variance 1. μ_i is also an error term with mean 0 and variance σ². The two error terms are assumed to be independent. They are based on the assumption that the double hurdle model is equivalent to a combination of a probit model and a truncated regression. The two hurdles are normally estimated with the maximum likelihood method of probit regression for probability of adoption using all observations. They also employ truncated regression using the non-zero observations (Gebremedhin and Swinton, 2003; Teklewold et al., 2006).

 

 

Empirical model

 

The empirical model employed for each of the two stages of the double hurdle model is as stated below:

 

 

The dependent variable is the proportion of cereal or legume crop residue used for feed. The explanatory / independent variables included farmer, farm and institutional factors postulated to influence adoption of technologies. These variables include age of the household head in years (HHHHAGE), education of the household head (HHEDU) measured in years, number of people in the household (HHSIZE), livestock ownership (TLU) measured in Tropical Livestock Units, access to credit (CREDITAC), farm size of the respondents (PLOTSIZE) and extension services (EXTENSION) proxied with possession of radio or mobile phones via with the information relating to crop residue uses are disseminated Also included were the value for which land is leased for agricultural purposes (RENT) in Naira, quantity of legume crop residue (QTLEGUMECR) or/and cereal crop residue (QTCEREALCR) available (Table 1).

 

 

The rationale for inclusion of these factors was based on previous agricultural technology adoption literature and the analysis of these systems. The effect of age (AGE) on the use of crop residue could be negative or positive irrespective of intensification gradients and .manners of redistribution. Previous studies show that the age of individuals affect their mental attitude to new ideas and influences adoption in several ways (Feder et al., 1985; Nkonya et al., 1997; Oluoch-Kosura et al., 2001; Bekele and Drake, 2003). Younger farmers have been found to be more knowledge-able about new practices and may be more willing to bear risk and adopt innovation because of their longer planning horizons. The older the farmers, the less likely they are to adopt new practices as he gains confidence in his old ways and methods. On the other hand, older farmers may have more experience, resources, or authority that may give them more possibilities for trying a new technology. Thus for these study, there is no agreement on the sign of this variable as the direction of the effect is location- or technology-specific (Feder et al. 1985; Nkonya et al. 1997; Oluoch-Kosura et al. 2001; Bekele and Drake 2003).

 

Education (HHEDU) was hypothesized to positively influence the decision and proportion of residue that would be redistributed in the farm and in the system and negatively related to the export of nutrients from the systems. Education increases the ability of farmers to use their resources efficiently and the allocative effect of education enhances the farmer’s ability to obtain, analyze and interpret information (Feder et al., 1985; Alene et al., 2000; Nkonya et al., 1997; Oluoch-Kosura et al., 2001).

 

Household size (HHSIZE) has been identified to have either posi-tive or negative influence on adoption (Manyong and Houndekon, 1997, Zeller et al., 1998; Oluoch-Kosura et al., 2001; Bamire et al., 2002; Bekele and Drake, 2003). However, larger family size could be associated with a greater labor force being available to the household for the timely operation of farm activities including crop residue use. More labor hours will be spent on transporting crop residue away from the farm. The study hypothesize that increased household size could favour export of crop residue away from the farm.

 

Institutional factors of training on crop residue use (EXTENSION) as well as access to credit (CREDITAC) are hypothesized to positively influence the redistribution of nutrients in the farm and in the systems. The training variable incorporates the information that the farmers obtain on their production activities on the importance and application of innovations through counselling and demonstrations by extension agents on regular bases. The effect of this information on adoption varies depending on channel, source, content, motivation, and frequency. The present study hypothesized that the respondents who frequently receive training have higher probability of adoption than those that do not (Adesina and Zinnah, 1993; Shiferaw and Holden, 1998; Oluoch-Kosura, 2001; Bamire et al., 2002; Mazvimavi and Tmomlow, 2009). Access to electricity (ELECTRICITY) is generally perceived to reduce the use of biomass such as CR for household energy (like fueling, burning). It is employed through the use of electric boiler and cooker especially for    domestic purposes. However, such role  depends  on  its affordability by rural households. It is hypothesized that access to electricity will reduce export of crop residue from the farm and thereby aiding redistribution of nutrients in the system.

 

The variable, credit access (CREDITAC), takes cognizance of farmers’ access to sources of credit to finance the agricultural activities and thereby boosts farmers’ readiness to adopt technological innovations. It is hypothesized that the variable has a positive influence on the probability of the adoption and use of improved technologies (Zeller et al., 1998; Oluoch-Kosura et al., 2001; Bekele and Drake, 2003). It is measured as a dichotomous variable with access being one, and zero for no access. It is expected to boost redistribution of nutrients within the farm or systems.

 

The variables QTCEREALCR and QTLEGUMECR are hypothesized to positively influence redistribution of nutrients within the farm and outside the systems as they indicate the level crop residue production. But availability of one in a given system may reduce the quantity of another needed at any point in time. Measure of livestock holdings possessed by the households (TLU) could be positively or negatively related to redistribution of nutrients in the farm because it can serve as a source of manure for increased crop residue production. The livestock can also feed on crop residue thereby exporting it away from the system. Ownership of larger number of livestock is expected to increase the demand for crop residue as feed. Moreover, the demand for crop residue as feed potentially on livestock type households keep (Erenstein and Thorpe, 2010).

 

The value for which land is leased or rented (RENT) is expected to be negatively related to the quantity of crop residue produced. And the lower the production of crop residue, tieless will be the quantity available for redistribution in or out of the farming system.

Socioeconomics characteristics and asset ownership of farming households

 

According to Hassan and Babu (1991), the level of asset ownership in a household is an indication of its endowment. It provides a good measure of the state of households in times of food crisis, resulting from famine, crop failures, or natural disasters. In general, household capital assets or livelihood resources could be classified into five: Human assets (e.g. household labour capacity, family and non-family labour), natural assets (e.g. total and cultivated farm land), physical assets (e.g. ownership of cattle, bicycle, radio, television, etc,), financial assets (e.g. access to cash credit and remittances) and institutional/social capital assets (e.g. access to social networks and membership of associations) (Elis, 2000). Tables 2 and 3 show socioeconomics characteristics and asset ownership of farming households.

 

 

 

Age has been found to determine how active and productive the head of the household would be. Age has also been found to accelerate the rate of household adoption of innovation that in turn affects household productivity and livelihood improvement strategies (Derion and Kushmen, 1996). Average household head age was 45 years, which is still within economic active working life. The farming household size was relatively large   with   an   average  household  size  of  11.  Large household size could provide family labour for the household especially where hire labour is scarce. It could also place higher burden on the household in term of feeding and sustenance demands of its members. In traditional agriculture, household labour endowment which can be a proxy to family size is an important factor when new technologies are introduced into an area. Availability of labour will go a long way to determine the adoption of such technologies. In the absence of sufficient family labour, the cost of hiring labour or opportunity cost of labour can hinder the adoption or promotion of new agricultural technologies. However, a person equivalent labour force was 4.5, indicating that children and old aged people characterized the family size. This might imply that thus hiring of labour for farm work will be a major alternative to meet labour demand of the farming households.

 

Education of the household heads is another socioeconomic feature which also fall under human capital: it is expected that the higher the level/years of education, the higher the probability of taking the right decision, read simple instruction relating to farming and take necessary precautions where necessary.

 

The level of education determines the level of opportunities available to improve livelihood strategies. The average year of education was 9 years, meaning that at least average household head could read and write. Extension services would also play a major role in building the knowledge stock of farming communities. They help farmers to translate research results into improvement in crop and livestock production and thus livelihoods. Visits by extension agents to farmers and participation in field day/training are cost effective ways of reaching out with the new technologies to a larger number of farmers. More than 70% of households had contact with farmers on various issues relating to crop livestock production and its technologies.

 

With respect to natural assets, 96% of the respondent own personal land while the average farm size cultivated was 4.5 ha. Average TLU per household which was based on ownership of ruminants was 5. This may suggest availability animal dung for farm manuring and other purposes that will help ensure soil fertility maintenance and management. It is also availability of CR from household farms to serve as feeds to livestock.

 

Physical asset comprises the basic infrastructure required to support livelihoods in a given environment (rural or urban). These basic infrastructures include adequate water supply, sources of energy, secure shelter, and access to transportation and communication facilities. Table 3 indicates that majority of the household heads have basic assets and. On the average, 97% of the household heads owned houses. Seventy two percent have access to electricity power supply, 52% own mobile phone and 97% possessed radio. Majority of the respondents (97%) possessed radio. This implies that radio is the highest means of information/communication available to farmers.

 

Information on farming activities including crop residues (CR) management could be accessed through radio. Communication on marketing of CR can be done using mobile phones. Although, very few farming household own car/vehicle, but majority 50% of them have motor bike, which can facilitate transportation for effective CR management. Only 12% had access to power chopper in their CR management. Table 1a reveals that ‘well’ and ‘pipe born water’ were the highest sources of water in the study areas. All the households have access to drinking water. However, water access for irrigational practices is absent across the project villages but 91 percent have access to water for livestock production. Grass is predominantly used for roofing (75%). Iron and asbestos roofing is employed by 11% while only 6percent used crop residue for roofing. On the other hand, majority (76%) used mud for their wall material; 14% claimed that their wall material is dried brick; 8% used bamboo/wood and only 1% used concrete.

 

With respect to social capital, quite numbers of the household heads are member of different agricultural associations. About 43% are member of crop association. This could be an avenue for accessing credit facilities among the members. It could be a forum for productive ideas in the farming activities especially on CR management. In the study area, for financial capital, crop, livestock, labor and business were the major sources of income. Business/self employment had the major share A significant proportion of households in the study areas reported an access to credit (28%) with the majority from informal sector (54%). About 97.4% of the respondent in Kano has cultivated a good habit of saving through acquisition of livestock (72.3%). Saving in banks is low (9.2%).

 

Information on crop residue and its technologies

 

The result shows that 89% of the household sold crop residue for monetary gains. Different storage type existed for crop residue; namely - field heap, home heap, room and hanger type. About 18% of the households heaped crop residue on the field. 49% of households heaped crop residue at home at the backyard, 34% of household kept crop residue in a room and about 9% of the household used hanger in storing crop residue. However, home heap constitutes the highest storage type used (49%). About 97% of household stored all part of cereal plant as crop residue. Only 3% of the households stored leaves as crop residue. About 85% of the households stored all part of legume plant but only 10% stored leaves as crop residue.

 

 

Crop residues uses and its determinants

 

Crop residue uses

 

Table 4 summarizes crop residues uses  by  type  and purposes. Ten purposes of legume and cereals residues uses, with three major grouping, were distinguished: (i) those that redistribute nutrient within the farm (e.g. mulching, and stall feeding and grazing by own animal); (ii) those that redistribute nutrients within the system (e.g. grazed by other animals; sold to others on filed; sold later), and (iii) those that export nutrient out of the system (e.g. burning, household fuel, construction and used for other purposes).

 

 

On a general note, crop residues allocated for own animal feeding had the major share. Farmers preferred using CR for feeding than mulching. The allocation of the legume residues for feed purposes was 63.52% while for cereal residues the share of animal feed was 26.15%.

 

Other important competing uses of CR of legumes and cereals were also different. These included stall feeding, burning, house construction and fuel.

 

About 17% of legumes residues are sold either on field or offsite. Legume residues were major sources for redistributing nutrient within the farm and between farm units (within the systems). More of legume CR was used within the farm/community (88.9%) while only 11% was exported.

 

Regression analysis

 

Adoption and intensity of use of cereal CR as livestock feeds

 

Factors determining farmers’ decision of crop residues use are numerous and complex (Harries, 1999). This is particularly true in the early stage of crop livestock intensification systems where locally available organic resources are under competitive uses. Factors influencing adoption and intensity of adoption of cereal CR as livestock feeds are shown in Table 5. The decision to adopt cereal CR as livestock feeds was positively and significantly influenced by age, education, access to credit facilities and quantity of cereal crop residue available to the farmers. An increase in age by one year led to 5% increase in the probability of using cereal CR as feeds for livestock. However, a one year increase in education of average household increased the probability of using cereal CR as feeds for livestock by about 15%. On the other hand, a one percent increase in access to credit resulted in about 150% increase in probability of adopting cereal CR as feed. This might not be unconnected with increased production as a result of better funding ability of farming households.

 

 

As regard intensity of use of cereal CR for livestock feed, increase in farming experience used as proxied for age indicated that 1 year increased the quantity of cereal CR used for livestock feed by about 3%. Experienced household heads preferred using their CR for feeds than selling it. Literate farmers know the importance of using CR for feeds than immediate gain of trading that may not be profitable in the long run. Access to information made available by increased education has positive and statistical significant influence on the quantity of CR used for the feeding.

 

 

A one year increase in education increased the quantity of CR used for feeding livestock by about 8percent. Similarly, a one unit increase in access to credit increased the quantity of CR residue used for livestock feeds by about 30%. Access to credit will provide alternative means of getting fund for energy, construction materials and other uses to which cereal CR were being used for. However, availability of alternate source of feed like legume CR has a negative influence on the quantity of cereal CR used for feeding. The use of legume CR as feeds complemented the probability of using cereal CR as feeds.

 

This may indicate that farmers know the importance of combining cereal and legume CRs to maximize livestock production. A one percent increase in the quantity of legume CR available led to about 1% reduction in the quantity of cereal CR used for livestock feeds. The effect of TLU was negative and not significant.

 

Adoption and intensity of use of legume CR as livestock feeds

 

Factors influencing the adoption and intensity of adoption of legume CR were investigated. Table 6 shows factors influencing the adoption and intensity of adoption of legume CR. Extension facilities made available through the use of mobile phones and radio was a significant variable positively influencing decision to use legume CR as livestock feed and not for sales. One percent increase in access to extension facilities increased the probability farmers deciding to use legume CR as livestock feed by about 124%. This is because mobile phones provide a medium for farmer-to-farmer interaction through which information is spread on technological adoption. The size of land used by the household also has positive and significant influence on the decision to use legume CR as feeds for livestock. An increase in farm size by 1 ha increased probability of using legume CR as livestock feeds by about 14%. On the other hand, access to credit discouraged the used of legume CR for livestock feed. This might be because access to credit might provide money for another means of feeding livestock one unit increase in access to credit decreased the probability of using legume CR as livestock feed by about 100%.

 

 

Intensity of use of legume CR as livestock was influenced by age, education, extension and the quantity of crop residue produced on the farm. Increase age by 1 year increased the quantity of legume CR used for livestock feed by about 5%. Literate farmers know the importance of using CR for feeds than immediate gain of trading that may not be profitable in the long run. Education has positive and statistical significant influence on the quantity of legume CR used for the feeding. The better educated a farmer is the less he will want to sell his legume CR. A one year increase in education increased the quantity of CR used for feeding livestock by about 11%. Extension facilities also played significant role in influencing the quantity of legume CR used for livestock feed. Increase in access to extension facilities by 1 units increased the quantity of legume CR used by about 70%. Similarly, a one unit increase in access to credit increased the quantity of legume CR used by about 54%. This implied that access to credit will provided alternative means of getting fund for energy and construction materials. The use of cereal CR as livestock feeds also complemented the use of legume CR. This implies that farmers know the importance of combining cereal and legume CRs to maximize livestock production. A one percent increase in the quantity of legume CR available led to about 0.4% reduction in the quantity of legume CR used for livestock feeds. Although, TLU was negative in the second hurdle, but it was not significant.

 REFERENCES