Evidence overview · Agriculture, food security and farm information systems

What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys

Across six studies by Strang, Che, Bitrus and Vajjhala, the constraints on Nigerian agriculture reported by agricultural extension workers are mainly failures of government support: input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research). A multiple correspondence analysis of their ideas ranked affordable seeds and fertilizer, training, market information, roads, credit and mentors as strategically urgent (dimensions explaining 19% and 17% of inertia) (Strang et al., 2019, Outlook on Agriculture). On the technology side, surveys of farm management software users on the Jos Plateau found very high intention to keep using agricultural information systems (mean 4.49 on a 1–5 scale) that no tested belief factor predicted, and FMIS e-adoption linked only to marital status, gender and language (Strang et al., 2019, IJSAMI) (Vajjhala et al., 2021, Recent Developments in…). The evidence is exploratory and comes from small, regional samples.

A summary of 6 peer-reviewed studies by Narasimha Rao Vajjhala and co-authors, set against the literature those studies cite. Each study links to its own page with the full abstract and DOI.

Key takeaways

  1. Extension workers in North-East Nigeria identified six core gaps in government support for rural farmers: input quality and dissemination, fair subsidization, training, market facilitation, corruption and insecurity (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research).
  2. In a correspondence analysis of extension-worker ideas on the Nigerian food security crisis, affordable seeds and fertilizer, training, market information, roads, credit and mentors were rated strategically urgent (Strang et al., 2019, Outlook on Agriculture).
  3. Nigerian farmers using agricultural information systems reported very high intention to continue (median 5, mean 4.49), yet satisfaction and perceived usefulness did not significantly predict it (Strang et al., 2019, IJSAMI).
  4. Among FMIS users on the Jos Plateau, only marital status, gender and language were associated with e-adoption, while age, education, land size and software experience were not (Vajjhala et al., 2021, Recent Developments in…).
  5. A review of 2015–2020 agriculture studies found attitude change (22.58%) and training (16.94%) were the most studied subjects, with only weak evidence of information system use in West African agriculture (Strang et al., 2021, Opportunities and Strategic Use…).

Context: the agriculture crisis and food security in Nigeria

The papers describe Nigeria's agriculture crisis as long-running and driven by Boko Haram insurgency, corruption, climate change, weak infrastructure and low productivity (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research). They draw on literature reporting that Nigeria imports more wheat, rice and sugar than its farmers grow[5], that the bulk wheat import-to-export ratio is growing at an unsustainable 11% a year[4], and that food insecurity affected over 90 million citizens in 2017–2018[3] (Strang et al., 2019, Outlook on Agriculture) (Strang et al., 2021, Opportunities and Strategic Use…). Cited development reports describe dilapidated rural roads, failed Vision 20:2020 infrastructure projects, weak agricultural policy and poor coordination between levels of government[2] (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research).

The authors argue that rural farmers' own views were missing from this literature because of language, literacy, religious and scheduling barriers. To work around this, three studies consulted agricultural extension workers, experienced and mostly educated farmers who act as government-funded mentors, as proxies for the farmers they serve (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research) (Strang et al., 2019, Outlook on Agriculture). The technology studies ask a separate question: whether Nigerian farmers adopt and keep using agricultural information systems (AIS) and farm management information systems (FMIS) (Strang et al., 2019, IJSAMI) (Vajjhala et al., 2021, Recent Developments in…).

What extension workers reported about farm inputs, markets and insecurity

The focus group included 16 extension workers from Mubi South, Mubi North and Maiha local government areas of Adamawa State. Their farmer networks averaged 169 farmers each (SD = 230, median 67), and 10 of the 16 had more than 15 years of experience (Che et al., 2020, IJDI). The three extension-worker papers report a focus group of the same size and makeup and analyse it with different methods: consensual qualitative research, grounded theory in NVivo, and multiple correspondence analysis (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research) (Strang et al., 2019, Outlook on Agriculture). They are best read as complementary analyses of closely related data rather than independent replications.

Participants said corrupt politicians intercept government fertilizer and seed, mix it with fake products and sell it, and that the system that replaced the Growth Enhancement Scheme is severely broken (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research). Some farmers now make do with fewer than 3 bags of fertilizer where they once used 10 (Strang et al., 2019, Outlook on Agriculture). Other problems raised were middlemen with buying monopolies who manipulate weights, and government bans on motorbikes in Mubi North and Mubi South after the insurgency, which stop farmers reaching markets and extension workers reaching farms. Participants also cited incomes as low as $1 a day, fertilizer that costs more than a bag of corn, and too few extension workers, whose allowances have stopped (Che et al., 2020, IJDI) (Strang et al., 2019, Outlook on Agriculture) (Strang et al., 2021, Agricultural Research).

In the correspondence model, the first dimension captured constraint severity and the second captured productivity importance. The model's r² was 0.05 (p < 0.05), which the authors describe as a small but significant effect (Strang et al., 2019, Outlook on Agriculture). Ideas about storage, processing, safe agrochemical handling, retraining at research centres and farmers' cooperative unions fell into the resilience-building and operational quadrants (Strang et al., 2019, Outlook on Agriculture).

Farm management information systems adoption and AIS continuance

A survey on the Jos Plateau tested an expectation confirmation model. It drew on 105 farm owners, managers and labourers, of whom 97 gave valid responses (Strang et al., 2019, IJSAMI). Confirmation experience significantly influenced satisfaction (path = +0.524) and perceived usefulness (+0.383). Only two of five hypotheses were supported. Satisfaction (+0.132, p = .203) and perceived usefulness (+0.230, p = .886) did not predict continuance intention, and model fit was acceptable but not ideal (CFI = 0.959, RMSEA = 0.083) (Strang et al., 2019, IJSAMI). The authors suggest high power distance, limited awareness of alternative software, or AIS simply outperforming manual methods as possible explanations. They did not test these (Strang et al., 2019, IJSAMI).

A chapter using the same Jos Plateau sampling frame tested seven demographic predictors of FMIS e-adoption (Vajjhala et al., 2021, Recent Developments in…). Only marital status (Rho = −.225, p = .026), gender (Rho = +0.151, p = .051) and language (p = .052) were associated with it. A discriminant model built on these three was significant (Wilks' Lambda = 0.892, χ²(3) = 10.669, p = .014) and was reported to classify 100% of participants, but the canonical correlation was modest (+0.328) (Vajjhala et al., 2021, Recent Developments in…). The chapter's interpretation of marital status is not fully consistent. It reads the coefficient as favouring married and widowed farmers, but also speculates that women widowed by Boko Haram violence were less likely to adopt FMIS (Vajjhala et al., 2021, Recent Developments in…).

How the findings fit the wider literature on extension, credit and technology adoption

The extension-worker findings mostly agree with the literature the papers cite. Earlier work reported that the Growth Enhancement Scheme delivered inputs through the extension system, often free or heavily subsidized[6] (Strang et al., 2021, Opportunities and Strategic Use…). It linked credit shortages to lack of collateral, high interest rates, low financial literacy and complex banking procedures[7] (Strang et al., 2019, Outlook on Agriculture). It also found that a stronger extension-worker presence was associated with higher farmer productivity[8] (Strang et al., 2019, Outlook on Agriculture). The cited work also warns that farmers view training positively but often find its content irrelevant[9], and that even well-intended regulation struggles to reach rural Nigerians[10] (Strang et al., 2019, Outlook on Agriculture). Conflict research is cited for the effect of kidnapping and insecurity on farmer behaviour[1], and rural transport research for farmers' reliance on motorcycles and bicycles over 5–10 km a day[11] (Strang et al., 2021, Opportunities and Strategic Use…) (Strang et al., 2019, Outlook on Agriculture).

On technology, the AIS model builds on expectation confirmation theory[13] and the perceived-usefulness construct[14], which were developed outside African agriculture (Strang et al., 2019, IJSAMI). The cited literature attributes part of Africa's lag in food production to low AIS adoption[15] and describes FMIS moving from record-keeping to complex systems[16] (Strang et al., 2019, IJSAMI) (Vajjhala et al., 2021, Recent Developments in…). It lists obstacles such as non-standard data, connectivity, cost and usability[17], and notes that adoption studies from Europe and the USA assume complete information and unconstrained access, which smallholders in Sub-Saharan Africa lack[18] (Vajjhala et al., 2021, Recent Developments in…). The Nigerian results partly conflict with prior expectations. Cited EU work found younger and more educated farmers more likely to adopt[12] (Che et al., 2020, IJDI), but age and education were not significant in the FMIS sample (Vajjhala et al., 2021, Recent Developments in…). The marital-status evidence drew mainly on US consumer research[20]. Continuity of use matters because efficiency gains from FMIS may not last without it[19] (Vajjhala et al., 2021, Recent Developments in…). The review chapter concludes that American adoption models transfer poorly to rural Nigeria (Strang et al., 2021, Opportunities and Strategic Use…).

Methods, limitations and practical implications

The review of 2015–2020 empirical studies found that 50.81% of studies spanned multiple levels of analysis. Attitude change (22.58%) and training (16.94%) were the most frequent subjects, and the review found only weak evidence of significant information system use in West African agriculture (Strang et al., 2021, Opportunities and Strategic Use…). The authors caution that topic frequency reflects research quantity, not problem severity (Strang et al., 2021, Opportunities and Strategic Use…). The empirical studies share clear limits. They rely on 16 extension workers as proxies for about 900 farmers in one state, on quadrant labels chosen by the authors, and on a single survey of about 100 FMIS users in one region (Strang et al., 2021, Agricultural Research) (Strang et al., 2019, Outlook on Agriculture) (Strang et al., 2019, IJSAMI) (Vajjhala et al., 2021, Recent Developments in…).

The practical recommendations are consistent across the papers. Government should repair input dissemination, restore extension-worker allowances and demonstration plots, improve security and roads, and support farmers' cooperatives and produce associations (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research) (Strang et al., 2019, Outlook on Agriculture). Policymakers funding agricultural software should also educate farmers about alternative AIS products (Strang et al., 2019, IJSAMI).

Evidence table

StudySetting / dataMethodKey result
Che et al. (2020)
International Journal of Development Issues
16 extension workers, 3 LGAs of Adamawa State, North-East Nigeria (2019)Consensual qualitative research focus group; NVivo discourse analysisSix core government-support gaps: input quality/dissemination, subsidization, training, markets, corruption, insecurity
Strang et al. (2021)
Agricultural Research
16 extension workers representing about 900 farmers, Northeast NigeriaGrounded theory focus group; NVivo thematic concept maps; member checkingSix core areas incl. social responsibility to reduce corruption and local security; too few extension workers
Strang et al. (2019)
Outlook on Agriculture
16 extension workers, Mubi South, Maiha, Mubi North; 6-hour focus groupCQR with nominal brainstorming; multiple correspondence analysis (SPSS 25)Two dimensions (19% and 17% of inertia; r² = 0.05, p < 0.05); 20 ideas in four priority quadrants
Strang et al. (2019)
International Journal of Sustainable Agricultural Management and Informatics
97 valid responses from farm owners, managers, labourers, Jos Plateau (March 2018)Expectation confirmation model; CFA and structural equation modelingConfirmation → satisfaction +0.524, → PU +0.383; no predictor of continuance (mean 4.49)
Vajjhala et al. (2021)
Recent Developments in Individual and Organizational Adoption of ICTs
105 FMIS-using farmers on 0.5–40 ha farms, Jos Plateau (March 2018)Spearman correlation; discriminant analysisMarital status (Rho = −.225), gender, language predict e-adoption; Wilks' Lambda = 0.892, p = .014
Strang et al. (2021)
Opportunities and Strategic Use of Agribusiness Information Systems
Empirical agriculture literature 2015–2020, weighted toward NigeriaCritical review; Pareto keyword and contingency analysis50.81% multi-level studies; attitude change 22.58%, training 16.94%; weak evidence of IS use

Questions researchers ask

Why did these studies interview agricultural extension workers instead of farmers?
The authors argue that language, literacy and cultural barriers, together with insecurity and busy cropping calendars, had kept rural North-East Nigerian farmers out of research. Extension workers are experienced, mostly educated farmers who mentor large networks, so they were used as proxies (Che et al., 2020, IJDI) (Strang et al., 2021, Agricultural Research). The authors acknowledge this limits generalization and call for direct farmer surveys (Che et al., 2020, IJDI).
Does satisfaction predict continued use of farm software in Nigeria?
Not in this sample. In a structural equation model of 97 Jos Plateau farmers, satisfaction (+0.132, p = .203) and perceived usefulness (+0.230, p = .886) did not significantly influence continuance intention, although confirmation experience did influence satisfaction (+0.524) (Strang et al., 2019, IJSAMI). The authors call the result controversial and recommend replication (Strang et al., 2019, IJSAMI).
What role does corruption play in Nigeria's farm input system?
Extension workers reported that government inputs are diverted by local politicians and middlemen, mixed with fake products and resold, so farmers cannot tell genuine seed and fertilizer from fake (Strang et al., 2021, Agricultural Research) (Che et al., 2020, IJDI). They described the process that replaced the Growth Enhancement Scheme as severely broken (Che et al., 2020, IJDI) (Strang et al., 2019, Outlook on Agriculture), a view consistent with earlier work on that scheme[6] (Strang et al., 2021, Opportunities and Strategic Use…).
How strong is the statistical evidence in these studies?
It is modest. The correspondence model had r² = 0.05 (Strang et al., 2019, Outlook on Agriculture), and the FMIS discriminant function had a canonical correlation of +0.328 (Vajjhala et al., 2021, Recent Developments in…). The qualitative studies rest on 16 participants from one state (Strang et al., 2021, Agricultural Research).

Open questions

References

Studies summarised

Other literature cited

  1. [1] Adelaja, A., & George, J. (2019). Effects of conflict on agriculture: Evidence from the Boko Haram insurgency. World Development, 117, 184–195. doi:10.1016/j.worlddev.2019.01.010 https://doi.org/10.1016/j.worlddev.2019.01.010
  2. [2] AfDB (2013), Federal Republic of Nigeria Country Strategy Paper 2013-2017, Africa Development Bank/Africa Development Fund (AfDB), Nairobi.
  3. [3] Olomola AS and Nwafor M (2018) Nigeria Agriculture Sector Performance Review. Lagos, Nigeria: Nigerian Institute of Social and Economic Research (NISER): International Institute of Tropical Agriculture (IITA).
  4. [4] Omeje, E.E., Arene, C.J. and Okpukpara, C.B. (2019), “Impact of agricultural protection on agricultural growth in Nigeria: political economy perspective (1980-2016)”, Review of Agricultural and Applied Economics, Vol. 22 No. 1, p. 41.
  5. [5] Fawole, W.O. and Ozkan, B. (2018), “Food insecurity risks perception and management strategies among households: implications for zero hunger target in Nigeria”, New Medit, Vol. 17 No. 2, pp. 29-42, doi: 10.30682/nm1802c. https://doi.org/10.30682/nm1802c
  6. [6] Omorinre, A. K., Osanyinlusi, O. I., Amoke, A. B., Olusola, O. A., & Isaiah, O. A. (2018). The growth enhancement support scheme: Productivity and shortcomings among rice farmers in Ekiti State, Nigeria. Acta Universitatis Danubius: Oeconomica, 14(3), 200.
  7. [7] Michael, A., Giroh, Y. D., Polycarp, M., & Ashindo, Z. E. (2018). Analysis of rural farm households’ access to formal agricultural credit in Yola south local government area, Adamawa state, Nigeria. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(4), 947–954. doi:10.11118/actaun201866040947 https://doi.org/10.11118/actaun201866040947
  8. [8] Adetimehin OD, Okunlola JO and Owolabi KE (2018) Utilization of agricultural information and knowledge for improved production by rice farmers in Ondo State, Nigeria. Journal of Rural Social Sciences 33(1): 76.
  9. [9] Kazeem, A. A., Dare, A., Olalekan, O., Abiodun, S. E., & Komolafe, T. L. (2017). Attitudes of farmers to extension trainings in Nigeria: Implications for adoption of improved agricultural technologies in Ogun state southwest region. Journal of Agricultural Sciences, 62(4), 423–443. doi:10.2298/JAS1704423K https://doi.org/10.2298/JAS1704423K
  10. [10] Clark LF and Hobbs JE (2018) Informational barriers, quality assurance and the scaling up of complementary food supply chains in Sub-Saharan Africa. Outlook on Agriculture 47(1): 11–18.
  11. [11] Idowu EO, Ayanwale AB, Rabirou K, et al. (2012) Effect of rural transportation system on agricultural productivity in Oyo State, Nigeria. Journal of Agriculture and Rural Development in the Tropics and Subtropics 113(1): 13.
  12. [12] Barnes AP et al (2018) Exploring the adoption of precision agricultural technologies: a cross regional study of EU farmers. Land Use Policy J 80(1):163–174. https://doi.org/10.1016/j.landusepol.2018.10.004
  13. [13] Bhattacherjee, A. (2001) ‘Understanding information systems continuance: an expectation-confirmation model’, MIS Quarterly, Vol. 25, No. 3, pp.351–370.
  14. [14] Davis, F.D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’, MISQ, Vol. 13, No. 3, pp.319–340, DOI: 10.2307/249008. https://doi.org/10.2307/249008
  15. [15] Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. doi:10.1111/j.1574-0862.2011.00545.x https://doi.org/10.1111/j.1574-0862.2011.00545.x
  16. [16] Fountas, S., Carli, G., Sørensen, C., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., & Tisseyre, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 11(5), 25–42. doi:10.1016/j.compag.2015.05.011 https://doi.org/10.1016/j.compag.2015.05.011
  17. [17] Tummers, J., Kassahun, A., & Tekinerdogan, B. (2019). Obstacles and features of Farm Management Information Systems: A systematic literature review. Computers and Electronics in Agriculture, 157(1), 189–204. doi:10.1016/j.compag.2018.12.044 https://doi.org/10.1016/j.compag.2018.12.044
  18. [18] Shiferaw, B., Kebede, T., Kassie, M., & Fisher, M. (2015). Market imperfections, access to information and technology adoption in Uganda: Challenges of overcoming multiple constraints. Agricultural Economics, 46(1), 475–488. doi:10.1111/agec.12175 https://doi.org/10.1111/agec.12175
  19. [19] Carrer, M. J., Filho, H. M., Batalha, M. O., & Rossi, F. R. (2015). Farm management information systems (FMIS) and technical efficiency: An analysis of citrus farms in Brazil. Computers and Electronics in Agriculture, 119(1), 105–111. doi:10.1016/j.compag.2015.10.013 https://doi.org/10.1016/j.compag.2015.10.013
  20. [20] Koo, S. H. (2017). Consumer differences in the United States and India on wearable trackers. Family and Consumer Sciences Research Journal, 46(1), 40–56. doi:10.1111/fcsr.12230 https://doi.org/10.1111/fcsr.12230