Saliu Olushola
Inaugural Lecture that started in The University of Oxford in England was established as a way for a newly appointed Professor to publicly present his past academic stewardship, current research and future plans to the university community and the general public. It began with the announcement of the first professorial chair in 1862 which made Professor Montagu to be the first occupant. Here in Nigeria the first Inaugural Lecture was presented by Professor Tekena N. Tamuno at the University of Ibadan in October 1973.
The practice has since become a global University tradition which has given me the privilege to present my research findings today titled: Breaking the Jinx of Agricultural Technology Adoption: the Three-Legged Approach.To me, the most important aspect of an inaugural lecture is for the recommendations to be considered and used by policy makers such that it will not be counted as a mere academic exercise or a singular aim of joining the prestigious league of inaugural lecturers. I therefore call on policy authorities to check a compendium of inaugural lectures in the Universities for policy adoption.
Alhamdulillah for sound health and for putting me in a position to present this lecture today. I pray that it will be useful and adopted by agricultural technology users across the globe.
Agricultural Extension and Technology Adoption.
Adedoyin (1995) defines Agricultural Extension as a comprehensive programme of services deliberately put in place for expanding, strengthening, and empowering the capacity of the present and prospective farmers, farm families, other rural economic operators (processors, marketers, rural agro-industrialists, farm managers, farm labourers, farmers associations and communities, entrepreneurial), managerial and communication skills that they need to succeed in farming and farm-related occupations.
As such a pool of agricultural and other related knowledge from different sectors (e.g. agro-industry, research organisations, universities) will be needed to effect the capacity building of the farmers. Knowledge creates initiatives and innovation that may snowball to inventions and agricultural technology development.
Agricultural technology is the application of scientific knowledge to upgrade tools and scale up solution to problem of service delivery for effective and efficient farm massive output and profitable economy. Agricultural technology promotes productivity, greater efficiency, sustainability, automation, pest control and cost savings. If the adoption of agricultural technology is properly planned, coupled with good national policy, it will help the society to overcome challenges such as complexity, high cost, lack of standardization, education, resistance, environmental issues, infrastructure, legal, ethical and social barriers.
For a successful agricultural technology adoption, certain competencies must be acquired. Economic competence demands the need to have good knowledge of economics otherwise known as Extension Economics, planning competence (Extension Planning and evaluation) administrative competence (extension Administration and organization), competence in farmers community network (extension and rural sociology), communication competence (Extension communication). All these competencies ensure acquisition of enough knowledge to promote Agricultural technology adoption. If whatever agricultural technology generated in Soil Science, Crop Science, Food Science, Animal Production, Forestry, Fisheries, Agricultural Engineering is not adopted, output will remain very low. For massive output of food through technology adoption therefore, agricultural extension becomes a pivot course to pursue. Consequently, I wish to advise, Vice Chancellor, Sir, that an Agricultural Extension and Technology Advancement Studies be established as a Department in the University. I also wish that every faculty of agriculture in all universities across the globe embrace the initiative.
If there is any need to embark on large scale output of food in quantity and quality, the time is now. Presently, about 800 million people in the developing world do not have enough food to eat with about 180 million of the population in Africa of which about 33 million are undernourished Nigerians (United Nation World Food Programme (UNWFP, 2025).
Challenges such as food insecurity, food safety, feeding the teeming population, increased labour shortage, environmental degradation can be overcome by using innovative technologies that will move food production from traditional approach to an exciting high-tech industry.
Agricultural extension relies on the potent agricultural technology adoption and efficient participatory approach using different stakeholders such as researchers, policy makers, extensionists, educators, agro-investors and farmers themselves to improve agricultural production and farm investment. With effective communication, collaboration and cooperation of all stakeholders, increase in farm output enough to feed the nation and profitable economy to sustain the nation is realizable.
Jinx of Agricultural Technology Adoption
The Vice Chancellor, Sir, as a researcher, I have sufficient understanding, that Nigeria is blessed with the most elaborate research and extension institutions in sub-Saharan Africa with over estimated population of 200 million people, 71 million hectares of arable land, and 17 commodity-based research institutes, three International Research Centers, over 66 faculties of agriculture in Nigerian Universities with extension mandates, and a special extension institute (Agricultural Extension Research Liaison Services [AERLS]) (Saliu et al., 2009). It will not be wrong to share the aprori expectations that if agricultural technologies churned out by the above-named institutes are appropriately adopted by the huge farming population, Nigeria should be self-sufficient in food production and even feed other nations, hence the need to discuss the “Whys and Wherefores”.
I studied technology adoption on several crops, livestock and plausible enabling environment germane for agricultural technology adoption to thrive and enhance farm output, income and sustainable growth of agro-processing industry. Oil palm technology adoption attracted my attention in the year 2005.
In 1830, when the Landers came down to the River Niger, they found Oil Palm as the most widely grown cash crop in Kogi East. By 1857 an oil mill was established at Gbobe close to the rich oil palm farm land. The oil mill flourished but today it is history. Investigation revealed that an improved variety (Tenera) introduced to the farmers was only adopted and sustained by 29%, 53% did not adopt at all while 18% adopted (figure 1) and later discontinued due to unavailability of back up support by extension workers. Pest attack by termite at young age (56%) (figure 3) was responsible for poor oil palm seedlings survival. Poor access to extension services was identified as the most serious constraints to the adoption of improved oil palm technology (figure 2). However, those who adopted the improved variety of oil palm had higher yield (Saliu et al., 2006).
Figure 1: Degree of Adoption
Source: Saliu et al. (2006)
Figure 2: Constraints to Adoption
Source: Saliu et al. (2006)
Figure 3: Pest Attack on Oil Palm Seedlings
Source: Saliu et al. (2006)
The use of inorganic fertilizer as a technology to improve the fertility of the soil for better output of maize was also studied. Maize is a staple food as well as industrial crop for flour mills, breakfast cereals, and baby food in Nigeria. We still import from Lebanon, Italy, Belgium and India. The inability to meet up with the local demand for maize could be partly traced to the poor nature of Nigerian soil. Federal Ministry of Agriculture and Rural Development FMARD (2002), revealed that 99.5 percent of the farmers in the savanna ecological zone of Nigeria depend on the use of inorganic fertilizer. The general rating of soil fertility in the savanna zone revealed that any soil in the zone should be considered “rich” for maize production if it contains greater than 0.2 percent nitrogen, greater than 20 mg/kg phosphorus and greater than 0.40 mg/kg of potassium
However, Saliu and Obasi (2011), posited that, only 0.7 percent of the farmers tested their soil before fertilizer application (table 1) which implies that more than 99.2% of the farmers applied fertilizer based on blanket recommendation without considering the specific soil nutrient deficiency.
Nevertheless, in a country like India, fertilizer companies and State governments engaged in soil testing of farming community free of charge before introducing fertilizer to them. Soil characteristics vary widely even within a small plot of land, let alone a whole ecological zone. The practice of applying fertilizer based on recommendation for wide ecological zone is therefore not correct and will not give optimum yield of maize. (Saliu and Obasi, 2011).
Table 1: Pattern of Fertilizer Use by Small Scale Maize Farmers (N-302)
Type of Knowledge Frequency Percentage
Number of times fertilizer applied
Non 55 18.2
Once 229 75.8
Twice 18 6.0
Total 302 100.00
Quantity of fertilizer per maize stand
No Measurement 164 54.30
1 – 5g 24 8.00
6 – 10g 22 7.28
11 – 15g 81 26.82
16 – 20g 10 3.30
21 – 25g 1 0.30
Total 302 100.00
Place of fertilizer storage
Ventilated store 27 9.0
Living room 158 52.3
Hut 100 33.1
No provision for such 17 5.6
Total 302 100.00
Means of fertilizer application
Bare hand 243 80.5
Hand glove 43 14.2
Precision machine 1 0.3
Spoon 15 5.0
Total 302 100.00
Method of determining inorganic fertilizer use
Soil testing 2 0.7*
No specific method 2 0.6
Observation of plant growing on farmland 298 98.7
Total 302 100.00
Type of inorganic fertilizer use
No fertilizer use 32 14.6
NPK 241 79.8
Urea 15 5.0
CAN 2 0.6
Total 302 100.00
Source: Saliu and Obasi, 2011
The poor soil situation has further been found to degenerate in Nigeria due to population explosion, indiscriminate felling of trees for timber, over exploitation of fire wood for fuel and charcoal, clearing of land for agricultural expansion, and industrial development. These activities have exposed the land to rapid soil organic matter decomposition, environmental degredation and accelerated wind and water erosion which has left large vast of Nigerian soil to be indeed less supportive for sustainable agricultural production. A renowned agroforestry expert (Ikojo, 2008), made us to understand that about 65 million hectares of intact forest cover of Nigeria in 1897 has reduced to 4 million hectares.
Reforestation through small scale village-based farmers’ participation now forms one of the strategies embarked upon by several agencies in Nigeria. Kogi Aforestation Project was therefore investigated. Government reforms designed to reduce widespread deforestation have to be complemented not only by large scale reforestation programs but by small scale village-based tree growing exercise through farmers’ participation to strengthen forest law enforcement.
Pretty (1994) asserts that participation can be differentiated into seven types, that is participation could be passive, information giving, consultative, material incentives, functional interactive and self-mobilization. Kogi Afforestation Project which received financial support from World Bank between 1978 and 1984 was put in place to advance sustainable afforestation in the State. The project established government afforestation plots, prepared nurseries for improved tree seedlings and sold to participating farmers.
Only 23.64 percent of the participating farmers planted between 201 to 400 trees while 54.54 percent planted less than 200 trees. Passive, consultative and material incentives were the forms of participation used in the Kogi Afforestation project and a major constraint was the farmers lack of knowledge. This resulted in low adoption.
The highest percentage of the respondents who adopted the improved tree seedling was 53.64 percent. About 46.26 percent adopted non woody forestry technology, while 41.82 percent adopted Processing and Harvesting technologies.
Afforestation technologies such as exotic trees, improved seedlings, agroforestry, fruit collection, pure stand species and indigenous trees received high adoption scores of 4.90, 4.74, 4.38, 4.47, 4.44 and 4.38 respectively (table 2) while technology such as clipping, seed scarification and pulping had low adoption scores (2.60). However, a X2 value of 23.31 than tabulated of 9.49 at 0.05 significant level at 4 degrees of freedom gave a significant difference between technology adopted and income generated (table 3) (Saliu et al., 2010).
Adoption of selective exploitation and mixed stand were not significantly adopted. Mixed stand with crop will help to promote afforestation business adoption.
Table 2: Adoption scores for afforestation technologies.
Technology adopted Frequency Percentage Adoption
n=110 score
Improved seedling 59 53.6 4.74**
Direct seeding 37 33.6 4.08
Seeds scarification 14 12.7 2.94*
Selective extraction 28 25.5 3.72
Total felling 37 33.6 4.08
Clipping 10 9.09 2.61*
Prune collection 35 31.8 4.00
Leaves collection 38 34.5 4.12
Fruit collection 49 44.5 4.47**
Root and bark 23 20.9 3.48*
Saw milling 50 45.5 4.50
Pulping 28 16.4 3.22
Electric poles 45 40.1 4.35
Indigenous trees 46 41.8 4.38**
Exotic trees 64 58.2 4.90**
Pure stand species 48 43.6 4.44**
Mixed stand species 35 31.8 4.00
Regeneration 27 24.5 3.67
Agro forestry 46 41.8 4.38**
Footnote:
** high adoption score
*Low adoption score
Source: Saliu et al. (2010)
Table 3: Chi-Sqaure on cost and income generation from technologies adopted
S/N Technology adopted Category of income generated from Total
Technology adopted in Naira frequency
- Fruit trees 13 16 5 34
- Timber 10 18 30 58
- Multipurpose 11 5 2 18
Total 34 39 37 110
Degree of freedom = R – 1(3 – 1) x (C – 1) (3 – 1) = 2 x 2 = 4
Source: Saliu et al. (2020)
Agroforestry system is a technology of global significance. It is a system where arable crops are planted alongside tree crops to complement soil nutrient supply and protect the environment and also act as a source of food, income and fuel for many rural farmers. The introduction of agroforestry technology in Nigeria has received a lot of enthusiasm and hope across many countries.
A Study of Agroforestry System adoption was carried out in the north central zone of Nigeria to ascertain the adoption level of agroforestry technology in the said zone. North Central of Nigeria has the largest land mass for the growth of both forest and cereal crops with a popular vegetation cover otherwise known as Guinea Savanna (Saliu et al., 2016).
A performance measuring scale revealed Mean scores of 4.22, 4.24, 4.39 confirmed the perception of farmers along the research statement: agroforestry systems protect our environment against wind and water erosion, planting drought resistant plants can serve as food for ruminant during drought, while indigenous knowledge about type of trees to adopt will help to make the right choice of trees respectively (table 4).
Table 4: Perception of Farmers on Agroforestry Practices
Research Items RESPONSES Mean
SA A U D SD Score
Agroforestry systems protect my environment 457 81 81 92 11 4.22**
Against wind/water erosion
The non adoptions of the system do not have 118 230 81 268 25 3.21
Any negative effect on my crops
Reduction in rainfall and high heat do not have 91 136 82 351 39 2.76*
Anything to do with adoption of Agroforestry
The fetching of charcoal for firewood will 162 311 119 26 23 3.44
Reduce plant cover
I encourage my family to plant both crops and 135 421 59 86 17 3.77
And trees every year
Government did not give me any opportunity to 108 148 98 256 107 2.83*
Contribute to the solution on adoption of agroforestry
Preservation of the ecosystem services can 146 447 208 13 13 4.39
Reduce disasters
Reducing grazing pressure on land by animals 372 207 108 23 12 4.22
Can reduce desert formation
Planting drought resistant plants can serve as 380 201 87 41 13 4.24**
Emergency food for ruminant during drought
Indigenous knowledge about type of trees to 407 225 65 7 7 4.39**
Adopt will assist me to make the correct choice of trees
Footnote:
** high mean score
*low mean score
Source: Source: Saliu et al. (2016a)
However, the trend of agroforestry adoption between 2008 and 2013 revealed a downward trend in both Kogi and Benue States (figure 4) which implies that some farmers discontinued the adoption of agroforestry within the years under consideration. The team of researchers therefore sought to know why the farmers discontinued adoption.
Figure 4: Trend of Agroforestry Adoption between 2008 and 2013 in Kogi and Benue State
Source: Source: Saliu et al. (2016a)
Finding reveals that some farmers discontinued the adoption of agroforestry technology because of limited size of land, (3.79), inadequate management knowledge practised (3.71), old age with no younger ones to take care (3.59) and lack of market for agroforestry produce at the peak of the production season (3.55) (table 5). This act could lead to post harvest losses. Examples of such are mangoes, cashew fruits and oranges.
Table 5: Farmers’ Reasons for Discontinuing Adoption of Agroforestry Practices
| Research Item | Responses | Mean Score | ||||
| SA | A | U | D | SD | ||
| I discontinued the practice because I could not increase the planting on my limited individual land | 168 | 271 | 250 | 28 | 5 | 3.79** |
| I stopped the adoption because the extended family land where I planted could not guarantee the sustainability | 36 | 201 | 251 | 195 | 39 | 3.00 |
| I planted agroforestry trees as shared cropping with another farmer who could not continue with the terms of sharing our crops | 49 | 167 | 283 | 210 | 13 | 3.04 |
| Inadequate knowledge of agroforestry management discouraged to continue adoption | 160 | 249 | 264 | 40 | 9 | 3.71** |
| I am getting old and there are no young ones to take care, so I stopped adopting | 74 | 341 | 298 | 35 | 14 | 3.59** |
| The rural women are not encouraged to help the male farmers’ adoption and management of agroforestry | 58 | 224 | 338 | 82 | 19 | 3.30 |
| Unavailability of good varieties of seedlings made me to stop adopting | 50 | 93 | 431 | 93 | 53 | 2.98 |
| Fruits from fruits trees produced are wasting because of lack of storage or processing facilities | 70 | 190 | 420 | 27 | 15 | 3.38 |
| Lack of market for agroforestry produce discouraged me to continuing adoption | 169 | 147 | 332 | 64 | 13 | 3.55** |
Footnote:
** high mean score for reasons advanced for discontinuation of adoption
Source: Saliu et al. (2016a)
In essence, the majority of the farmers were pleasantly disposed to the adoption of agroforestry systems but inadequate knowledge, poor storage, unattractive market and old age discouraged them from sustaining the adoption. Value addition through agro-processing would have reduced the post-harvest losses while weed control through adoption of agroforestry will become a problem if such farmers discontinued.
This is because the tree component of agro-forestry practice acts as shade to reduce the impact of weed on the farm. The growth of weed is therefore a constraint to optimization of output by farmers, especially the arable crop farmers. The Vice Chancellor, Sir, herbicide use has been found to be a technology that can safely be adopted to keep down the weeds and optimize profit.
Spear grass (Imperata cylindrica) has been known to require extra effort to control as weeds in some arable farms. The use of herbicide such as glyphosate to eliminate or reduce the impact of weed such as spear grass has been found to improve the output of farm produce (Avav and Okereke, 1997).
Logit regression and Z test were used to find out the adoption of glyphosate herbicide for the control of spear grass among yam farmers in the North Central Part of Nigeria (Ajanya et al., 2014). The appropriate knowledge of application of glyphosate by farmers and age were found (p<0.01 α p<0.05) to significantly influence the adoption of glyphosate by yam farmers (table 6), while Yam output by farmers who adopted glyphosate and those who did not adopt were not significantly different (table 7). Perhaps inadequate knowledge of the appropriate use of the herbicides could be responsible for the indifference in yam output.
Table 6: Result of the logit regression analysis of socio-economic factors influencing adoption of glyphosate herbicide
| Variables | Coefficients | Std error | Z | P>[z] |
| Age | -.094 | .0353428 | 2.66 | 0.008** |
| Education | .019 | .046789 | 0.41 | 0.684 |
| Household size | .092 | .0756999 | 1.21 | 0.206 |
| Farm size | .092 | .0756651 | 1.22 | 0.223 |
| Farm experience | .045 | .030775 | 0.24 | 0.08* |
| Income | 8.80 | 3.60e-06 | 0.24 | 0.80 |
| Extension visit | .138 | .082071 | 1.68 | 0.092* |
| Knowledge of application of glyphosate | 3.27 | .5606729 | 5.84 | 0.000** |
LR Chiz (8) = 68.65
Pr = 0.0000
NB: P>(z) values* and ** denote 5 and 1 percent level of significance respectively
Source: Ajanya et al. (2014)
Table 7: Comparison of the yam output valued in Naira of participating and non-participating farmers of Glyphosate herbicide
| Variables | Mean | Standard error of mean difference | Z-cal | Z-tal | |
| Contact (sample A) | 136628 | 20808.51 | 1.9348 | 1.960 | |
| Non-contact (sample B) | 96366.67 | 20808.51 |
Source: Ajanya et al. (2014)
Weeds have been found to harbor pest and diseases. As such a farm that is suffering from low weed control may be a host for a number of diseases such as downy mildew, on grains or cassava mosaic on cassava leaves. Using resistant maize to downy mildew will reduce the impact of the disease.
Downy mildew is a disease that affects maize (Zea mays L.) and other plant crops. It is a fungal infection that appears on leaves and flowers. The said disease is caused by Peronosclerospora sorghi which often results in yield losses.
Yield losses caused by downy mildew disease on maize in Nigeria are about 93% (Thakhur and Mathur 2002). The disease causes stunted growth and the crazy top. If susceptible maize varieties are planted, the infected crop may not produce cobs resulting in 100% single plant yield loss.
The Vice Chancellor, Sir, adoption of downy mildew resistant maize technology in the Guinea Savanna agricultural zone was examined to determine the factors influencing the adoption of the said technology (Saliu et al., 2016b).
Logit regression revealed that access to credit facility and farming experience significantly influenced the adoption of downy mildew resistant maize variety at 5% and 1% level respectively (table 8).
Table 8: Result of the Logit Regression on the influence of Socio-Economic Characteristics of farmers on the adoption of Downy Mildew Resistant Maize Variety
| Variables | Coefficients | Standard error | Significant level |
| Age | 0.13578857 | 0.0898779 | 0.31 |
| Education | .0.4668472 | 0.4861724 | 0.337 |
| Household size | -0.076786 | 0.309946 | 0.558 |
| Farm size | 0.3722537 | 0.2921179 | 0.203 |
| Farm experience | -0.1951315 | 0.0771128 | 0.011** |
| Access to credit | 1.37161 | 510847 | 0.007* |
| Extension contact | 0.4268377 | 0.419662 | 0.309 |
LR Chi2 (7) = 60.60
Prob > Chi2 = 0.0195
Pseudo R2 = 0.6616
** 5% significant level
* 1% significant level
Source: Saliu et al. (2016b)
Table 9: Constraints of farmers on the adoption of Downy mildew resistant maize variety.
| S/N | Constraint statement | VS (3) | S (2) | LS (1) | Total No. of Respondent | Total sum of Constraint score | Mean score |
| 1. | High cost of tractor hiring | 11 | 27 | 1 | 120 | 351 | 2.92** |
| 2. | Inaccessibility to tractor | 93 | 22 | 5 | 120 | 327 | 2.92** |
| 3. | High cost of fertilizer | 61 | 42 | 17 | 120 | 284 | 2.36 |
| 4. | Unavailability of Downy Mild Resistant Maize (DMRM) variety | 60 | 40 | 20 | 120 | 280 | 2.33 |
| 5. | Inadequate knowledge of DMR variety practices | 63 | 5 | 52 | 120 | 261 | 2.1 |
| 6. | High cost of DMRM herbicides | 56 | 13 | 51 | 120 | 245 | 2.04 |
| 7. | Inadequate extension contact | 55 | 14 | 51 | 120 | 244 | 2.03 |
| 8. | Inaccessibility to credit facility | 58 | 51 | 11 | 120 | 287 | 2.39 |
| 9. | High cost of purchasing DMRM | 0 | 27 | 93 | 120 | 147 | 1.22* |
| 10. | Low profitability | 2 | 9 | 109 | 120 | 133 | 1.10* |
Footnote:
** serious constraints
* weak constraints
Source: Saliu et al. (2016b)
The main constraints to the adoption of downy mildew resistant maize variety was basically the availability and cost of hiring tractor to expand the size of their farms having received high maize output and income from the adopted variety (table 9).
Table 10: Mean score of Adoption stages of other technologies adopted along with Downy Mildew Resistant
| Stages of Adoption | |||||||
| S/N | Technology | Awareness (1) | Interest (2) | Evaluation (3) | Trial (4) | Adoption (5) | Mean score |
| 1. | Fertilizer | 23 | 13 | 0 | 2 | 82 | 3.89* |
| 2. | Herbicides | 70 | 2 | 2 | 3 | 23 | 2.06 |
| 3. | Tractor hiring | 76 | 43 | 0 | 0 | 1 | 1.39** |
| 4. | Field pesticides | 22 | 14 | 0 | 1 | 83 | 3.9* |
| 5. | Post-field pesticides | 43 | 15 | 0 | 1 | 61 | 3.1 |
Source: Saliu et al. (2016b)
The stages of adoption of farmers in the process of embracing the downy mildew resistant variety also supported the fact that tractor hiring (table 10) remained the main obstacle to expansion of downy mildew resistant maize farm. Rice (Oryza sativa) which is a major staple food in Nigeria and therefore in high demand has also received much attention in the area of improved technologies for effective and efficient rice production geared toward meeting the domestic need of the populace. However, Nigeria incurs about one billion dollars’ worth of imported rice annually to complement local production. Several factors have been associated with adoption behavior among rice farmers in Nigeria. These include factors such as personal, institutional, environmental and socio-economic factors (Matata et al., 2001). Ordered Probit regression was therefore used to analyze the Socio-economic determinants of improved rice technologies adoption in Kogi State Nigeria. Membership of Cooperative (1.029277 at 1% significance), source of fund (0.0100499 at 1% significance) and Source of labour (0.2746477 at 5% significance) (table 11) determined the adoption of rice technologies, while Marginal effect on farm size, household size and contact with extension agents favored the adoption of all the eight most important technologies (table 12) which could be used as a measure towards pleasant disposition to commercial rice farming (Saliu et al., 2016c).
Table 11: Regression result of the influence of Socio-economic characteristics on the adoption of improved rice technologies.
NB: figures in parentheses are z-value* and ** denote 5% and 1% significance respectively.
Source: Saliu et al., 2016c).
Table 12: Marginal effects of Socio-economic determinants on adoption of improved rice technologies at different levels from adoption of between 1-8 number of technologies adopted.
Source: Saliu et al., 2016c).
If we deploy technologies effectively and efficiently to maximize output of arable and cash crops, how do we add value to preserve the product and/ or promote the market value of our produce and products? Among the crops that one may need to add value to are: cassava, maize, rice, and so on.
The International Fund for Agricultural Development (IFAD) has a value chain development programme designed to support the Government of Nigeria with the intention to enhance productivity, promote agro-processing and increase access to markets.
Benue and Kogi States cassava farmers benefited from the IFAD intervention. A research carried out by a team of researchers to analyse the economics of Cassava production by IFAD Participants and non-Participants in Benue and Kogi States revealed a benefit ratio of 1:6.64 for the participants and 1:5.83 for non-participants (table 13). This implies that for every N1.00 invested, in cassava production, the participant got N6.64 while the non-participants got N5.83k.
It also followed that the Cassava Processors who participated in IFAD processing method of enhancing value addition had a benefit-cost ratio of 1:3.22 while non-participants had 1:2.03, (table 14) which implies that those who participated in IFAD Programme had more profit than non-participants (Odekina et al.,2024).
Table 13: Estimate of Cost and Returns to Cassava Production by Participants and Non-participant Cassava Producers
| Participant Cassava Farmers | Non-Participant Cassava Farmers | |||
| Revenue and Cost Items | Value (N) | % Total Cost | Value (N) | % Total Cost | <