COMPARATIVE ANALYSIS OF YIELD AND PROFITABILITY OF MAIZE (Zea mays L..) PRODUCTION USING PRECISION AGRICULTURE IN THE SEMI-ARID REGION OF NIGERIA

Authors

  • Muntaka Mamman
  • H.Y. IBRAHIM
  • Aderemi G. Adesoji Faculty of Agriculture, Federal University Dutsin-Ma, Katsina State, Dutsin-Ma
  • 1Abdulhadi Muhammad Faculty of Agriculture, Federal University Dutsin-Ma, Katsina State, Dutsin-Ma
  • Musa Muhammad Faculty of Agriculture, Federal University Dutsin-Ma, Katsina State, Dutsin-Ma
  • Ahmed A. Abdullahi Faculty of Agriculture, Federal University Dutsin-Ma, Katsina State, Dutsin-Ma
  • Aliyu Abdulkadir Faculty of Agriculture, Federal University Dutsin-Ma, Katsina State, Dutsin-Ma
  • Hussaina Babba Usman Faculty of Agriculture, Federal University Dutsin-Ma, Katsina State, Dutsin-Ma
  • O. M. Olarewaju Faculty of Computing, Federal University Dutsin-Ma, Katsina State, Dutsin-

DOI:

https://doi.org/10.33003/jaat.2025.1102.007

Abstract

Maize (Zea mays L.) is a crucial staple crop in Nigeria, particularly in the semi-arid region where productivity is constrained by erratic rainfall, low soil fertility, and inefficient use of inputs. Precision Agriculture (PA), particularly Internet of Things (IoT) enabled nutrient management, offers a potential solution to enhance maize yield and profitability. However, empirical evidence on its effectiveness remains limited in Nigeria. This study conducted a dual-locational field trial at Dutsin-Ma and Malumfashi in Katsina State during the 2023/2024 rainy season to compare maize yield and profitability across four fertilization strategies (IoT-assisted nutrient application, recommended agronomic practice, farmers’ conventional methods, and a control) laid out in a Randomized Complete Block Design (RCBD) and replicated four (4) times. Results showed that the recommended agronomic practice consistently produced the highest yields at both study sites (3458 kg/ha in Dutsin-Ma and 4963 kg/ha in Malumfashi), followed by IoT-based application, which outperformed farmers’ conventional methods in Malumfashi but not in Dutsin-Ma. Partial budgeting analysis revealed that while the recommended practice had the highest net returns, IoT-based fertilization showed promising economic benefits, particularly in more favourable agro-ecological conditions. The findings highlight the potential of PA in optimizing input use and improving maize productivity, but also underscore the need for site-specific calibration and farmer adaptation strategies. Scaling up digital agriculture will require targeted investments in infrastructure, training, and policy support to enhance adoption among smallholder farmers in Nigeria’s dry lands.

References

Adediran, J. A., Adekiya, A. O., & Ajayi, A. O. (2020). Site-specific nutrient management for sustainable maize production in Nigeria. Journal of Plant Nutrition, 43(14), 2200–2212.

AGRA (2022). Africa Agriculture Status Report 2022: Accelerating African Food Systems Transformation. Nairobi, Kenya. https://agra.org

CENTRO INTERNACIONAL DE MEJORAMIENTO DE MAÍZ Y TRIGO (CIMMYT). (1988). La Formulación de Recomendaciones a partir de Datos Agronómicos: Un manual 25 metodológico de evaluación económica. Edición Completamente Revisada. México: CIMMYT.

Duncan, D.B.(1955). Multiple Range and Multiple F-test. Biometrics 11:1-42.

FAO (2021). The State of the World’s Land and Water Resources for Food and Agriculture – Systems at Breaking Point. Food and Agriculture Organization of the United Nations. https://www.fao.org

FAOSTAT (2023). Food and Agriculture Data. Food and Agriculture Organization of the United Nations. Retrieved from https://www.fao.org/faostat/

Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science, 327(5967), 828–831.

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research. 2nd Edition, John Willey and Sons, New York, 680pp

Nnadi, F. N., Okoroafor, A. C., & Ukwuaba, S. I. (2022). Adoption and constraints of precision agriculture technologies among farmers in Nigeria. Nigerian Journal of Agricultural Extension, 23(1), 45–56.

Parameswaran, L., Selvan, G., & Rajendran, P. (2020). IoT and Smart Agriculture: A Review. Materials Today: Proceedings, 33, 1121–1125.

Raza, M., Tripathi, N. K., & Rehman, A. (2019). Integration of Remote Sensing and IoT for Precision Agriculture: A Review. Remote Sensing Applications: Society and Environment, 15, 100249.

Reyes-Hernández, M. (2001) Economic analysis of agricultural experiments with partial budgets: Re-teaching the use of this approach.

Vanlauwe, B., Wendt, J., & Giller, K. E. (2015). A fourth principle is required to define Conservation Agriculture in sub-Saharan Africa: The appropriate use of fertilizer to enhance crop productivity. Field Crops Research, 155, 10–13.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big Data in Smart Farming. A Review. Agricultural Systems, 153, 69–80.

Zhang, C., Walter, M. T., & Niyogi, D. (2021). Smart farming and the future of agriculture: The role of IoT and AI. Computers and Electronics in Agriculture, 189, 106408.

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Published

2025-08-04

Issue

Vol. 11 No. 2 (2025): FUDMA Journal of Agriculture and Agricultural Technology (Special Issue)

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Articles

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