Maize is a major staple food crop grown across agro-ecological zones and production systems in SSA. About 95% of maize is consumed in different food options across many communities of various sociocultural backgrounds. Maize is the major cereal consumed by both livestock and human beings and makes out a major share of everyday food. Maize is also the major cereal marketed after sorghum and millet in Nigeria. Food security and cereal production are interrelated in a country like Nigeria with a vast rural and farming population. Consequently, factors that affect cereal production have a direct impact on food security as the majority of poor population depends on cereals as they are comparatively cheaper than any other form of diet.
However, despite the importance of maize and its potential to increase productivity, maize production is constrained by many challenges, comprising low and erratic rainfall, long dry seasons, poor soil fertility and Striga. For the past number of decades, IITA, in collaboration with NARIs, developed some promising early-maturing maize and STR varieties which met the requirements of small-scale farmers and which was disseminated in northern Nigeria and West Africa savannah at large.
Maize is highly susceptible to Striga parasite and causes huge damage to the host plant across the main maize producing areas of the continent (AATF, 2006). A collection of available data suggests that Striga had invaded 2.4 million ha of land under maize, inflicting yield losses of about 1.6 million tons per annum, which amounts to a total annual value of about US$383 million (AATF, 2006). In Nigeria, Striga is most severe in the northern region where it infests 835,000 ha, causing annual maize losses of an estimated 505,308 tons, valued at US$205.66 million per year (AATF, 2006).
Striga depresses maize yield by 20% to 100%, often leaving farmers with little or no food grain at harvest. Losses from Striga alone account for about 100% of Nigeria’s deficit in maize. However, the greatest losses are suffered by millions of small-scale farmers, who see their crops destroyed
annually, unable to produce enough food to nourish their families or make some obvious improvements in their livelihoods. Too many farmers have developed the attitude toward Striga, that they were born and expected to die with Striga in their fields, since it follows them everywhere they go.
Small-scale farmers suffer more from parasitic weeds because they do not have the resources to buy inputs and are rigid in their cropping systems. With the growing population pressure in Nigeria and increase in cropping intensities, Striga is becoming a serious problem, mostly in areas with poor soil fertility, sandy soils and low rainfall where host plants are too weak to compete for nutrients, water and light (Singh & Emechebe, 1997a). Striga plants are hard to manage because their seeds are produced in enormous amounts and their dormancy or mechanisms for adaptation allow the seeds to stay alive in the soil for several years. It is believed that the Striga problem cannot be suppressed and solved through a single approach but rather through an integrated approach (Oswald & Ransom, 2004). Therefore, adoption of crop resistance, chemical control, crop rotation, seed treatment, biological control, and other phytosanitary practices were deployed in the Bauchi and Kano States of Nigeria in order to achieve satisfactory and sustainable control through the ISM programme (Singh & Emechebe, 1997a; Singh & Emechebe, 1997b).
IITA, in collaboration with NARIs and some universities, have developed varieties of improved maize that have a high grain yield cum Striga tolerant and resistant. However, considering the economic importance of cereal production, maize in particular, in Nigeria and the suitability and volume of investment made towards improving its productivity, there is a need to understand why many farmers are not adopting ISM technologies despite its suitability and ease of application.
There has also been no study so far that explored the prospects of the use of ISM technology adoption and its economic benefits to farmers in northern Nigeria. This study was, therefore, an attempt to explore those knowledge gaps. It also provided an opportunity to arrive at relevant policy and management implications to inform future strategies in the agricultural sector, particularly in maize production. The specific objectives of the study were to:
(i) identify the socioeconomic characteristics of the maize-producing household and their perceptions of ISM technology attributes in the study area;
(ii) determine factors influencing potential adoption and intensity of adoption of ISM technologies by farming households in the study area;
(iii) estimate the potential impact of ISM technology adoption on livelihood improvement, income and food security of maize-farming households in the study area; and
(iv) assess the financial and economic profitability of, and identify the constraints to, adoption of ISM technologies at smallholder farm level in the study area. These specific objectives were addressed by using various conceptual and empirical models.
The first objective was achieved by using descriptive statistics to analyse the data by means of t- tests and chi-square for continuous and categorical variables. In Chapter 3, farmers’ socio- economic characteristics between the ISMA project and NPIAs, between ISM technology adopters and non-adopters, and their general perceptions of the technologies were analysed. Adoption rates and indices were employed to measure the penetration and performance indices, which are indicators used to evaluate the acceptability or success levels of the deployed technologies to farmers in the study areas. The performance index indicates the real number of farmers reached from the targeted number of sampled households that should have been reached. The penetration index shows the number of households from the actual number reached, who actually adopted ISM technologies. The data used for this study were collected by using a multi-stage sampling procedure from a cross-section of 643 respondents, selected from 80 communities (353 adopters and 290 non-adopters from both PIAs and NPIAs. The results revealed a significant overall adoption rate of 55% of all the technologies of the targeted population across the two states (multiple responses were considered). In the Bauchi State, a 52% adoption rate was achieved for at least one technology, while the adoption rate reported for the Kano State for at least one technology was 48%. The performance difference in the ISM technology adoption is 11% between project intervention and NPIAs.
Factors influencing adoption and intensity of adoption among smallholder farmers was analysed in Chapter 4. The econometric analysis employed the DH approach, which involved a probit model as the first hurdle and a truncated regression as the second hurdle, to identifying the factors influencing adoption and the intensity of adoption of ISMA technologies among the sample of smallholder farmers. This model assumes that farm households must cross two hurdles in order to adopt the technology. The first hurdle is the decision to adopt or not (probability of adoption) while
the second hurdle is sharing the land that allocated for the technology (intensity of adoption), which is conditional on the first decision. The model allows for the probability of adoption and the intensity of adoption (with various explanatory variables). Even variables appearing in both hurdles may have different effects. The results suggest that farmers who are better off in terms of exogenous income and living further away from extension offices are less likely to adopt ISM technologies, while farmers with higher farm income, awareness of the technology, participation in on-farm trials, access to cash remittances and contact with extension agents are more likely to adopt these technologies (p<0.01). Marital status, household size, farm size and access to cash remittances are the most significant variables influencing the intensity of adoption. In the study area, maize farmers who adopted ISM technologies were found to attain higher maize yield when compared to non-adopters. This may lead to a positive increase in total farm income.
By controlling the confounding factors such as farm level and farmer characteristics, resource endowments and other factors that are exogenous in nature, the difference in the impact of ISM technology adoption on farm incomes using an ESR model were examined in Chapter 6. The ESR accounts for possible sample selectivity bias as well as for endogeneity. The study compared the expected farm income under real adoption with the counterfactual situations that the household adopted ISM technology or not, and applied this procedure to the household survey data collected in 2014. The findings indicate that ISM technologies have a positive effect on farm income, as measured by farm income levels PAE. Findings further indicate that ISM adoption increased farm income PAE unit by 66%. However, the impact of technology on farm income is smaller for farmers who did adopt the technology than for farmers who did not adopt in the counterfactual situation that they adopted the technology. The FGT approach was employed in Chapter 6 and used to provide a sign of the poverty incidence, poverty depth and severity of poverty in the study area. The treatment procedure determines the impact of ISM technology adoption on farm productivity. The findings in this chapter are critical to both public and private bodies that targeted intervention to reduce poverty and food insecurity in accordance with the proof provided by the ISMA project intervention.
Economic indicator methods were used in Chapter 7 to determine the viability and profitability of ISMA projects by using several financial ratios, including GM analysis, break-even analysis, payback period analysis, NPV, the BCR and IRR.
Finally, Chapter 8 assessed and analysed the economic benefit of some selected ISM technologies from among farmers who participated in the on-farm trials and demonstrations across various communities in the study areas. Estimates of the maize-legume rotations and IRM were compared with estimated GM estimates of the farmer or local practices. Primary data was collected from 148 farmers who tried maize-legume rotations for two years and 50 farmers who tried IRM technologies. These farmers’ plots were monitored over the entire production cycle and input- output data was captured. The data was used to estimate income, average cost of production, and other financial analyses.
The remainder of this chapter presents the conclusions (section 10.2), followed by key policy recommendations (section 10.3), the implementation of which could promote ISM technology adoption and intensity of adoption towards eradication of Striga on farmers’ fields, and enhance household food security in northern Nigeria. Section 10.4 describes the limitations of the study and, finally, section 10.5 concludes the chapter with suggestions for further research.