Book chapter · 2021
Contemporary Usage of Farm Management Information Systems in Nigeria
In Recent Developments in Individual and Organizational Adoption of ICTs, pp. 82–95, IGI Global · Published
Summary
Vajjhala et al. (2021) surveyed 105 farm owners, managers and labourers on farms using farm management information systems (FMIS) in the Jos Plateau, Nigeria, to test which demographic factors predict FMIS e-adoption. Only marital status (Rho = −.225, p = .026), gender and language were correlated with FMIS e-adoption, whereas age, education, land size and software experience were not. A discriminant model using these three factors was significant (Wilks’ Lambda = 0.892, p = .014), with marital status the most important predictor, and the authors suggest replication with larger sample sizes.
Practical implication. Policymakers framing policies to improve adoption of farm management information systems (FMIS) in Nigeria can draw on the finding that only marital status, gender and language were correlated with e-adoption.
- The Problem
- Farm management information systems could raise productivity in Nigeria, but the demographic factors that predict farmers’ adoption were unknown.
- The Methodology
- Positivist predictive correlational survey of 105 farm owners, managers and labourers on FMIS-using farms in the Jos Plateau, Nigeria (stratified random sampling), analysed with Pearson and Spearman correlation and discriminant analysis in SPSS 25.
- The Core Finding
- In a survey of 105 farm owners, managers and labourers on the Jos Plateau, only marital status, gender and language correlated with FMIS e-adoption; age, education, land size and software experience did not, and a discriminant model using the three significant factors was statistically significant (p = .014).
- The Citation
- Vajjhala, N. R., Strang, K. D., & Bitrus, N. S. (2021). Contemporary Usage of Farm Management Information Systems in Nigeria. In O. Yildiz (Ed.), Recent Developments in Individual and Organizational Adoption of ICTs (pp. 82–95). IGI Global. https://doi.org/10.4018/978-1-7998-3045-0.ch005
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the chapter, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The study surveyed 105 farm owners, farm managers and laborers using farm management information systems (FMIS) on farms of 0.5–40 hectares in the Jos Plateau region of Plateau State, central Nigeria, in March 2018.
- Finding 2. The Nigerian FMIS sample had a mean age of 31.5 years (SD = 7.9), was 51% female and 49% male, 56% married, and farmed an average of 3.4 hectares with an average of 2 years of FMIS software experience.
- Finding 3. The FMIS e-adoption construct in the Nigeria study showed high internal consistency, with a Cronbach’s alpha of 0.955, and a mean adoption intention of 4.5 (SD = 0.82) on a 1–5 scale.
- Finding 4. Of seven demographic factors tested among Nigerian farmers, only marital status (Rho = −.225, p = .026), gender (Rho = +0.151, p = .051) and language (p = .052) were correlated with FMIS e-adoption; age, education, land size and software experience were not.
- Finding 5. A discriminant analysis using marital status, gender and language to predict FMIS e-adoption among Nigerian farmers was significant (eigenvalue = 0.121, canonical correlation = +0.328, Wilks’ Lambda = 0.892, χ²(3) = 10.669, p = .014) and was reported to classify 100% of participants correctly.
- Finding 6. Marital status was the most important predictor of FMIS e-adoption among the Nigerian farmers (standardized coefficient +0.838, structure loading +0.810), followed by gender (+0.368) and language (−0.426).
- Finding 7. The authors interpret the Nigerian results as indicating that males were more likely to adopt FMIS software, and that widowed women left to run farms after their husbands were killed by Boko Haram were less likely to adopt FMIS.
Source: Vajjhala et al. (2021), In Recent Developments in Individual and Organizational Adoption of ICTs, pp. 82–95, IGI Global. DOI: 10.4018/978-1-7998-3045-0.ch005
The chapter in detail
What question does this chapter answer?
Which demographic factors—such as age, gender, marital status, education, language, land size and software experience—predict farmers’ e-adoption of farm management information systems (FMIS) in central Nigeria?
What did the chapter find, in detail?
In a survey of 105 FMIS-using farm owners, managers and laborers in the Jos Plateau region of central Nigeria, only marital status (Rho = −.225, p = .026), gender and language were correlated with FMIS e-adoption. A discriminant model using these three factors was significant (Wilks’ Lambda = 0.892, χ²(3) = 10.669, p = .014) and was reported to classify 100% of participants, with marital status the most important predictor.
Why does it matter?
Agriculture contributes over 24% of Nigeria’s GDP and employs 68% of the labor force, yet farmers are not adopting FMIS to improve productivity. The authors state that the results should generalize to other rural farm decision makers in Nigeria and suggest males were more influential in FMIS use on farms, while widowed women left to run farms after Boko Haram violence were less likely to adopt FMIS.
This study is part of the evidence overview What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys
Methods and data
| Research question | What demographic factors predict e-adoption of farm management information systems by farmers in central Nigeria? |
|---|---|
| Design | Positivist, predictive correlational survey design using structured, interviewer-assisted questionnaires. |
| Sample | 105 farm owners, farm managers and laborers on FMIS-using farms (0.5–40 hectares) in the Jos Plateau, Plateau State, selected by stratified random sampling in March 2018. |
| Methods | Cronbach’s alpha, Pearson and Spearman correlation, and discriminant analysis in SPSS 25; survey offered in English, Hausa or Berom. |
| Main result | Marital status, gender and language predicted FMIS e-adoption (Wilks’ Lambda = 0.892, p = .014); marital status was the strongest predictor (standardized coefficient +0.838). |
| Implication | Demographic factors, especially marital status and gender, shape FMIS adoption among rural Nigerian farmers; replication with larger samples in other regions is recommended. |
| Citation | Vajjhala et al. (2021) · DOI 10.4018/978-1-7998-3045-0.ch005 |
Definitions and key terms
- Farm management information systems (FMIS)
- A planned system of collecting, processing, storing, and disseminating data in a form required to carry out farm-related operational functions (the chapter’s definition, after Sørensen et al., 2010). — as used in Vajjhala et al. (2021)
- E-adoption
- The acceptance and use of electronic means for accomplishing one or more tasks (the chapter’s definition). — as used in Vajjhala et al. (2021)
- Discriminant analysis
- A statistical technique that uses several independent factors to predict membership in the categories of a single categorical outcome, here adopting versus not adopting FMIS. — as used in Vajjhala et al. (2021)
Abstract
Agriculture is a critical sector in the Nigerian economy, contributing significantly to GDP as well as employment generation. Agricultural technology has evolved substantially over the last decade with significant advancements in farm management information systems (FMIS) as well as agricultural information systems (AIS). FMIS have evolved from addressing simple production tasks to handling complex across multifunctional sectors in farming enterprises. However, the adoption rates of FMIS have been low in Nigeria. In this chapter, the contemporary usage of farm management information systems in central Nigeria is examined, and the various constraints leading to the low adoption rates are explored. In this study, the factors impacting the FMIS adoption by rural farmers in central Nigeria were examined. The findings of this study indicated that some of the demographic factors were influencing the FMIS adoption by rural farmers in central Nigeria. The results of this study in this chapter should help policymakers in framing policies intended to improve FMIS adoption rates in Nigeria.
Abstract as published in Recent Developments in Individual and Organizational Adoption of ICTs.
Limitations and open questions
- The authors suggest the study be replicated with larger sample sizes using rural farmers from other regions in Nigeria and extended to other developing countries.
Open questions
- How do marital status and gender, including the situation of women widowed by insurgency violence, shape FMIS adoption in larger and more diverse samples?
Open questions from the evidence overview Agriculture, food security and farm information systems.
Practical implications
- Policymakers framing adoption policies can treat marital status as the most important predictor of farm management information systems (FMIS) e-adoption among the Nigerian farmers surveyed (standardized coefficient +0.838), followed by gender and language.
- Policymakers may note the authors’ interpretation that gender shaped adoption of farm management information systems (FMIS), and that widowed women left to run farms after Boko Haram violence were less likely to adopt FMIS.
- For policymakers, age, education, land size and software experience did not correlate with farmers’ e-adoption of farm management information systems (FMIS) in this survey.
- Researchers are advised by the authors to replicate the study with larger sample sizes using rural farmers from other regions in Nigeria, and to extend it to other developing countries.
Drawn from Vajjhala et al. (2021), In Recent Developments in Individual and Organizational Adoption of ICTs, pp. 82–95, IGI Global.
When this research may be relevant
This chapter may be relevant to literature on farm management information systems adoption in sub-Saharan Africa, demographic predictors of agricultural technology use, digital agriculture among smallholders in Nigeria, and discriminant analysis of adoption decisions.
Research topics addressed: farm management information systems; FMIS; technology adoption; e-adoption; agriculture; Nigeria; Jos Plateau; rural farmers; smallholder farmers; demographic factors; marital status; gender; discriminant analysis; Spearman correlation
How to cite
- APA 7
- Vajjhala, N. R., Strang, K. D., & Bitrus, N. S. (2021). Contemporary Usage of Farm Management Information Systems in Nigeria. In O. Yildiz (Ed.), Recent Developments in Individual and Organizational Adoption of ICTs (pp. 82–95). IGI Global. https://doi.org/10.4018/978-1-7998-3045-0.ch005
- MLA 9
- Vajjhala, Narasimha Rao, et al. “Contemporary Usage of Farm Management Information Systems in Nigeria.” Recent Developments in Individual and Organizational Adoption of ICTs, edited by O. Yildiz, IGI Global, 2021, pp. 82–95. https://doi.org/10.4018/978-1-7998-3045-0.ch005.
- IEEE
- N. R. Vajjhala, K. D. Strang, and N. S. Bitrus, “Contemporary Usage of Farm Management Information Systems in Nigeria,” in Recent Developments in Individual and Organizational Adoption of ICTs, O. Yildiz, Ed. IGI Global, 2021, pp. 82–95, doi: 10.4018/978-1-7998-3045-0.ch005.
BibTeX
@incollection{vajjhala2021farm,
title = {Contemporary Usage of Farm Management Information Systems in Nigeria},
author = {Vajjhala, Narasimha Rao and Strang, Kenneth David and Bitrus, Nankyer Sarah},
booktitle = {Recent Developments in Individual and Organizational Adoption of ICTs},
editor = {Yildiz, O.},
pages = {82--95},
year = {2021},
publisher = {IGI Global},
doi = {10.4018/978-1-7998-3045-0.ch005},
url = {https://doi.org/10.4018/978-1-7998-3045-0.ch005}
}RIS
TY - CHAP TI - Contemporary Usage of Farm Management Information Systems in Nigeria AU - Vajjhala, Narasimha Rao AU - Strang, Kenneth David AU - Bitrus, Nankyer Sarah ED - Yildiz, O. T2 - Recent Developments in Individual and Organizational Adoption of ICTs PY - 2021 DA - 2021 SP - 82 EP - 95 PB - IGI Global SN - 9781799830450 DO - 10.4018/978-1-7998-3045-0.ch005 UR - https://www.narasimharao.net/research/farm-management-information-systems-adoption-nigeria/ LA - en ER -
Links and files
Related work
Related work: the literature this chapter cites
The cited literature attributes part of Africa’s lag in food production to low AIS adoption[1] and describes FMIS moving from record-keeping to complex systems[2]. It lists obstacles such as non-standard data, connectivity, cost and usability[3], and notes that adoption studies from Europe and the USA assume complete information and unconstrained access, which smallholders in Sub-Saharan Africa lack[4]. The marital-status evidence drew mainly on US consumer research[5]. Continuity of use matters because efficiency gains from FMIS may not last without it[6]. The cited work warns that farmers view training positively but often find its content irrelevant[7].
- [1] 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. https://doi.org/10.1111/j.1574-0862.2011.00545.x
- [2] 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. https://doi.org/10.1016/j.compag.2015.05.011
- [3] 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. https://doi.org/10.1016/j.compag.2018.12.044
- [4] 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. https://doi.org/10.1111/agec.12175
- [5] 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. https://doi.org/10.1111/fcsr.12230
- [6] 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. https://doi.org/10.1016/j.compag.2015.10.013
- [7] 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. https://doi.org/10.2298/JAS1704423K
From the evidence overview What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Vajjhala et al. (2021).
Related research
Related publications on agriculture, food security and farm information systems
Structural equation modelling of 97 Jos Plateau farm owners, managers and labourers showed that confirmation experience significantly drove satisfaction (path = 0.524) and perceived usefulness (path = 0.383), yet no factor significantly predicted continuance intention, which was uniformly very high.
Extension workers representing about 900 rural farmers in Northeast Nigeria identified six core failures of government support, among them input quality and dissemination, fair subsidization, training, market facilitation and corruption.
A critical review of 2015–2020 empirical studies identifies lack of modern-method training, corrupt input dissemination, poor infrastructure, terrorism-driven insecurity, lack of credit and weak strategic planning as the key problems, and finds only weak evidence of significant information-system use in agriculture.
Extension workers from three local government areas of Adamawa State confirmed themes from the literature and added new ones, showing that rural farmers face significant problems with government support in six areas: farm input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity.
A focus group of extension workers representing about 900 farmers produced a significant two-dimension model of solutions, ranking affordable seeds and fertilizer, modern farming training, market information, roads and transport, affordable credit and agricultural mentors as the most strategically urgent.