MARKETING ANALYSIS OF CHILLI PEPPER IN KANO STATE MARKETS, NIGERIA

Authors

  • A. T. Lawal
  • A. Y. Gaya
  • A. Alhassan
  • I. Sulei
  • F. S. Abdulwahab
  • H. Umar

DOI:

https://doi.org/10.33003/fjs-2021-0504-778

Keywords:

Analysis, Chilli, Marketing, pepper, and Kano State

Abstract

The study analyzed the marketing of chilli pepper in selected markets in Sumaila local government Kano State, Nigeria. The study focused on the socioeconomic characteristics of the chilli pepper marketers, profitability and marketing efficiency of chilli pepper as well as constraints of chilli pepper marketing. The result of socioeconomic characteristics showed that majority of the chilli pepper marketers (96.2%) were male and 46.2% were between the ages of 30-39years, and the majority of the marketers had attended various level of education in the study area which means they can read and write. The results showed that 69.8% of the sampled marketers have less than 10 people in their household. The result further revealed that 49.1% of the marketer’s haves less 10 years’ experience in the marketing of chilli pepper in the study area. Chilli pepper marketing was profitable venture with a net margin of 2,520.74 per 30kg of bag. Marketing of chilli pepper was found to be efficient in view of the high marketing efficiency obtained. The result revealed that the constraints faced by chilli pepper marketers were lack of uniform measure, poor credit facilities, transportation, inadequate storage facilities and poor market infrastructures. It can be concluded that pineapple marketing was profitable and efficient in the study area despite the constraints faced by the marketers. And therefore can be recommended that Construction of good road network to reduce damage of pineapple during transportation and plastics trays should be provided

References

Ali, A.M., Abdulla, H., and Snášel, V. (2011). Overview and Comparison of Plagiarism Detection Tools. DATESO. 161–172.

Blackboard. Blackboard safeassign: A plagiarism prevention tool. https://www.blackboard.com/teaching-learning/learning-management/safe-assign. Last accessed: 26 March 2021.

Britannica. Definition of plagiarism. https://www.britannica.com/topic/plagiarism. Last accessed: 20 March 2021.

Checkforplagiarism. Checkforplagiarism.net. www.checkforplagiarism.net. Last accessed: 26 March 2021.

Clough, P. and Stevenson, M. (2011). Developing a corpus of plagiarised short answers. Language Resources and Evaluation, 45(1):5–24.

DeLong, D. (2012). Unintentional plagiarism. Global Journal of Engineering Education, 4(1):137–155.

Dr Dataman. Looking into natural language processing (NLP). https://dataman- ai.medium.com/, 2018. Last accessed: 26 March 2021.

Dreher, H. and Williams, R. (2006). Assisted Query Formulation Using Normalised Word Vector and Dynamic Ontological Filtering. FQAS. Lecture Notes in Artificial Intelligence, 282–294.

Foltýnek, T., Dlabolová, D., Anohina-Naumeca, A. et al. (2020). Testing of support tools for plagiarism detection. International Journal of Educational Technology in Higher Education, 17(1):46. https://doi.org/10.1186/s41239-020-00192-4.

Heinrich, E. and Maurer, H. (2000). Active documents: Concept, implementation and applications. Journal of Universal Computer Science, 6(12):1197–1202.

Hoad, T. and Zobel, J. (2003). Methods for identifying versioned and plagiarised documents. Journal of the American Society for Information Science and Technology, 54(3):203–215.

ICAI. International center for academic integrity. http://www.academicintegrity.org. Last accessed: 22 March 2021.

Ison, D. (2017). Academic Misconduct and the Internet. Handbook of Research on Academic Misconduct in Higher Education.

KIT. Jplag – detecting software plagiarism. https://jplag.ipd.kit.edu. Last accessed: 26 March 2021.

Kumar, A. (2021). The role of AI in plagiarized text. Learning Hub. https://learn.g2.com/ai-for- plagiarism, 2020. Last accessed: 26 March 2021.

Marcos. (2019). A beginner’s guide to ruby on rails mvc (model view controller) pat- tern. https://hackernoon.com/beginners-guide-to-ruby-on-rails-mvc-model-view-controller- pattern-4z19196a, 2019.

McCabe, D. L. (2005). Cheating among college and university students: A north american perspective. International Journal for Educational Integrity, 1(1):1-11.

Meyer zu Eissen, S. and Stein, B. (2006). “Intrinsic Plagiarism Detectionâ€, in Proceedings of the 28th European Conference on IR Research (ECIR), Lecture Notes in Computer Science (LNCS), 3936: 565–569, doi: 10.1007/11735106_66.

Niezgoda, S. and Way, T. (2006). Snitch: A software tool for detecting cut and paste plagiarism. ACM SIGCSE Bulletin, 38(1):51–55.

Oladeji, F., Ajayi, O., Koleoso, R., Uwadia, C. (2018). Third eye – a plagiarism checker for academic theses. National Conference on Digital Inclusion: Opportunities, Challenges and Strategies, 27(1):225–236.

Pennycook, A. (1996). Borrowing others’ words: Text, ownership, memory and plagiarism. TESOL Quarterly, 30(2):201–230.

Pertile, S., Moreira, V. P., & Rosso, P. (2016). Comparing and combining content- and citation-based approaches for plagiarism detection. Journal of the Association for Information Science and Technology, 67(10), 2511–2526. https://doi.org/10.1002/asi.23593.

PlagAware. Plagaware. www.plagaware.com. Last accessed: 26 March 2021.

Python. Python 3.0 release. https://www.python.org/download/releases/3.0, 2008. Last accessed: 14 March 2021.

Rezaeian, N. and Novikova, G. (2017). Detecting near-duplicates in Russian documents through using fingerprint algorithm simhash. Procedia Computer Science, 103(1):421–425.

RHIG (2021). Random House Compact Unabridged Dictionary. Random House Information Group. Last accessed: 26 July 2021

Shivakumar, N. and Garcia-Molina, H. (1999). Finding near-replicas of documents on the web. Lecture Notes in Computer Science, 1590(1):204–212.

Sulaiman, R. (2018). Types and Factors Causing Plagiarism in Papers of English Education Students. Journal of English Education, 3(1):17–22.

Szuchman, L. (2010) Writing with Style: APA Style Made Easy. Cengage Learning.

Turnitin. Turnitin. www.turnitin.com. Last accessed: 26 March 2021.

Vani, K. and Gupta, D. (2016). Study on extrinsic text plagiarism detection techniques and tools. Journal of Engineering Science and Technology, 9(4): 2511-2526.

Published

2022-01-17

How to Cite

Lawal, A. T., Gaya, A. Y., Alhassan, A., Sulei, I., Abdulwahab, F. S., & Umar, H. (2022). MARKETING ANALYSIS OF CHILLI PEPPER IN KANO STATE MARKETS, NIGERIA. FUDMA JOURNAL OF SCIENCES, 5(4), 38 - 43. https://doi.org/10.33003/fjs-2021-0504-778

Most read articles by the same author(s)