Econometric analysis of resource-use efficiency and profitability in cowpea-based farming systems of Ondo State, Nigeria

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Econometric analysis of resource-use efficiency and profitability in cowpea-based farming systems of Ondo State, Nigeria

Temitope Olanrewaju Bello

Article ID: 3550
Vol 6, Issue 2, 2025

DOI: https://doi.org/10.54517/ama3550

Received: 3 April 2025; Accepted: 6 May 2025; Available online: 19 May 2025; Issue release: 30 June 2025

VIEWS - 51 (Abstract)

Download PDF

Abstract

This study assessed the efficiency of resource use and profitability in cowpea-based farming systems among smallholder farmers in Ondo State, Nigeria. Data were collected from 160 respondents using a multistage sampling technique and a structured questionnaire. Analytical tools included descriptive statistics, gross margin analysis, and a Cobb-Douglas production function estimated via Ordinary Least Squares (OLS). Results indicated that cowpea production is highly profitable, yielding a return on investment (ROI) of 4.26, meaning that every ₦1 invested generates ₦4.26 in return. The gross margin and net profit per hectare were ₦932,881.08 and ₦885,100.39, respectively. The Cobb-Douglas model showed that farm size, fertilizer, cowpea seed, and agrochemicals significantly and positively influenced output, whereas labor had a significant negative effect. The estimated returns to scale (0.43) suggested decreasing returns to scale. Further analysis of Marginal Value Product (MVP) and Marginal Factor Cost (MFC) revealed underutilization of land and agrochemicals, and overapplication of labor. Major constraints identified included high transportation costs, price instability, environmental hazards, and limited access to capital and market information. The study concludes that while cowpea farming remains economically viable, addressing technical inefficiencies and systemic barriers is essential for enhancing productivity and profitability.

Keywords

cowpea production; input; productivity; profitability; resource-use efficiency; Nigeria

References

1. Oluwole IO, Attama PI, Onuigbo FN, Atabo I. Agriculture: a panacea to economic growth and development in Nigeria. Journal of Economics and Allied Research. 2021; 6(2): 134-146.

2. Osai-Azubuike V, Nwogwugwu U, Nwokoye E. The Role of Agriculture in Economic Development: in Nigeria. Journal of International Economic Relations and Development Economics. 2023; 3(2): 103-111.

3. Panzeri D. A Bioprospecting Multidisciplinary Approach to Valorise Biodiversity: The Case of Bowman-Birk Protease Inhibitors in Vigna unguiculata (L.) Walp [PhD thesis]. University of Milan-Bicocca; 2023.

4. Alabi TT. Variation in Response of Cowpea (Vigna unguiculata L. Walp.) Genotypes to Inoculant and Phosphorus Applications for Seed Yield Improvement [Master’s thesis]. Kwara State University; 2024.

5. Ajeigbe HA, Singh BB, Ezeaku IE, et al. On-farm evaluation of improved cowpea-cereals cropping systems for crop-livestock farmers: Cereals-cowpea systems in Sudan savanna zone of Nigeria. African Journal of Agricultural Research. 2010; 5(17): 2297-2304.

6. Issoufa BB, Ibrahim A, Abaidoo RC. Agronomic and economic benefits of integrated nutrient management options for cowpea production. Experimental Agriculture. 2020; 56(3): 440-452. doi: 10.1017/s0014479720000071

7. Bello TO, Oguntade AE, Afolayan TT. Profitability and Efficiency of Cassava Production in Ekiti State, Nigeria. Agriculture Archives. 2025; 4(1): 10-19. doi: 10.51470/agri.2025.4.1.10

8. Ijigbade JO, Olutumise AI, Toluwase SOW, et al. Assessing the efficiency and profitability potentials of honey input supply: The case of South West Nigeria. Tropical Agriculture. 2023; 100(4): 351-364.

9. Olutumise AI, Bankole AS, Olutumise BO, et al. Gender differential in allocative efficiency of oil palm processors in Southwest, Nigeria. Kasetsart Journal of Social Sciences. 2023; 44(2). doi: 10.34044/j.kjss.2023.44.2.02

10. Ogundari K. Determinants of profit efficiency among small-scale rice farmers in Nigeria: A profit function approach. Research Journal of Applied Sciences, Engineering and Technology. 2013; 5(3): 262-267.

11. Olutumise AI, Ekundayo BP, Omonijo AG, et al. Unlocking sustainable agriculture: climate adaptation, opportunity costs, and net revenue for Nigeria cassava farmers. Discover Sustainability. 2024; 5(1). doi: 10.1007/s43621-024-00249-8

12. Singh BB, Ajeigbe HA. Improved cowpea-cereals-based cropping systems for household food security and poverty reduction in West Africa. Journal of Sustainable Agriculture. 2007; 19(1-2): 25-41. doi: 10.1300/J411v19n01_08

13. Ilesanmi JO, Bello TO, Oladoyin OP, et al. Evaluating the Effect of Microcredit on Rural Livelihoods: A Case Study of Farming Households in Southwest Nigeria. International Journal of Economic, Finance and Business Statistics. 2024; 2(5): 289-302. doi: 10.59890/ijefbs.v2i5.2619

14. Ajeigbe HA, Kamara AY, Chikoye D. Improved Legume-cereal based cropping systems for improved productivity and natural resource management by resource-poor crop-livestock farmers in West Africa. In: Proceedings of the International Research on Food Security, Natural Resource Management and Rural Development; 2008.

15. Beye A, Diakhate PB, Diouf O, et al. Socio-Economic Constraints of Adopting New Cowpea Varieties in Three Agro-Ecological Zones in the Senegalese Peanut Basin. Sustainability. 2022; 14(21): 14550. doi: 10.3390/su142114550

16. Nwagboso C, Andam KS, Amare M, et al. The economic importance of cowpea in Nigeria trends and Implications for achieving agri-food system transformation. Intl Food Policy Research Institute; 2024.

17. Anthony L, Ebukiba ES, Alabuja FO. Differentials in profitability and efficiency of cowpea marketing in rural, semi-urban and urban markets in Nasarawa State, Nigeria. American International Journal of Agricultural Studies. 2023. 7(1): 20-31. doi: 10.46545/aijas.v7i1.287

18. Oladoyin OP, Bello TO, Akinrotimi AF, et al. Assessing the profitability and choice of feed-type determinants in egg production: Evidence from Ondo State, Nigeria. Innovare Journal of Agricultural Science. 2025; 13(2).

19. Saka JO, Agbeleye OA, Ayoola OT, et al. Assessment of varietal diversity and production systems of cowpea (Vigna unguiculata (L.) Walp.) in Southwest Nigeria. Journal of Agriculture and Rural Development in the Tropics and Subtropics. 2018; 119(2): 1–14. doi: 10.17170/kobra-2018121864

20. Afe AI, Nuruden K, Yaman U. Evaluation of growth and yield of cowpea (Vigna unguiculata L.Walp) cultivars and sorghum (Sorghum bicolor L. Moench) with their competitive behavior in cowpea-sorghum intercropping. Badeggi journal of agricultural research and environment. 2023; 5(1): 35-48. doi: 10.35849/bjare202301/86/004

21. Krejcie RV, Morgan DW. Determining Sample Size for Research Activities. Educational and Psychological Measurement. 1970; 30(3): 607-610. doi: 10.1177/001316447003000308

22. Kamara AY, Ekeleme F, Omoigui LO. Influence of planting date on some agronomic traits of cowpea genotypes in the Sudan savanna of Nigeria. African Journal of Agricultural Research. 2009; 4(6): 571-577.

23. Ayanwale AB, Ariyo OC, Ogunlade I. Socio-economic determinants of cowpea production among small-scale farmers in Oyo State, Nigeria. Journal of Agricultural Extension. 2016; 20(2): 104–113.

24. Amaza PS, Bila Y, Iheanacho AC. Identification of factors that influence technical efficiency of food crop production in West Africa: Empirical evidence from Borno State, Nigeria. Journal of Agriculture and Rural Development in the Tropics and Subtropics. 2006; 107(2): 139-147.

25. Ajeigbe, Hakeem A, et al. Effect of different cowpea-cereal row to row planting systems and insecticide sprays of cowpea on the quality of the crop residues. 2007; 1057-1061.

26. Olubunmi-Ajayi TS, Akinrinola OO, Ibrahim A, et al. Assessing technical, economic, and allocative efficiencies of maize-rice-based farmers across scale economies in Southwest Nigeria. Agriculture Archives: An International Journal. 2025; 4(1): 01-09.

27. Moyofolorun Opeyemi Adeyeye, Olaniyi Oluwatosin Ojo, Tolulope Seun Olubunmi-Ajayi, et al. Optimizing resource-use efficiency of yam producers in Ondo state, Nigeria: a path to enhanced food production. International Journal of Advanced Economics. 2024; 6(4): 124-138. doi: 10.51594/ijae.v6i4.1074

28. Oladoyin OP, Olubunmi-Ajayi TS, Adeyeye MO, et al. Effect of the Anchor Borrowers Programme (ABP) on rice production in Ekiti State, Nigeria. Science. 2024; 7(3): 56–69.

29. Afolayan TT, Olutumise AI, Oguntade AE, et al. Herdsmen-farmer conflicts and their effects on agricultural productivity and rural livelihoods. International Journal of Agricultural Science, Research & Technology (IJASRT) in Extension & Education Systems. 2024; 14(4): 261-273.

30. Akinbola AE, Olubunmi-Ajayi TS, Ijigbade JO, et al. Market concentration and efficiency’s determinants of tomato production in Oyo State, Nigeria. International Journal of Applied Research in Social Sciences. 2023; 6(7): 1333-1345. doi: 10.51594/ijarss.v6i7.1278

31. Oparinde LO, Olutumise AI, Adegoroye A. Does Agroforestry Technology Adoption Affect Income Inequality among Arable Crop Farmers in Southwest, Nigeria? A Gender Perspective. Sarhad Journal of Agriculture. 2023; 39(4). doi: 10.17582/journal.sja/2023/39.4.848.860

32. Olubunmi-Ajayi TS, Amos TT, Borokini EA, et al. Profitability and technical efficiency of maize-based cropping system farmers in Ondo State, Nigeria. International Journal of Agricultural Science, Research & Technology (IJASRT) in Extension & Education Systems. 2023; 13(1): 11-22.

Refbacks

There are currently no refbacks.

This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

Indexing & Archiving

- - - - J-Gate

News & Announcements

2025-02-01

Highlight article: Innovations in Modern Agriculture: UAVs and CNNs Transform Cotton Water Stress Monitoring

In the realm of modern agriculture, the integration of cutting-edge technologies is revolutionizing the way we approach sustainable farming practices. A recent study published in Advances in Modern Agriculture titled "Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery" has garnered significant attention for its innovative approach to precision irrigation management. Conducted by researchers from Institute of Data Science and the AgriLife Research and Extension Center of Texas A&M University (authors's information is below). This study introduces a novel method for classifying cotton water stress using unmanned aerial vehicles (UAVs) and convolutional neural networks (CNNs), offering a powerful solution for optimizing water use in agriculture.

2024-09-11

Smart agriculture is becoming a reality!

Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.

2024-05-01

Notice on the prohibition of using AI techniques to generate papers!

This journal values human initiative and intelligence, and the employment of AI technologies to write papers that replace the human mind is expressly prohibited. When there is a suspicious submission that uses AI tools to quickly piece together and generate research results, the editorial board of the journal will reject the article, and all journals under the publisher's umbrella will prohibit all authors from submitting their articles.

Readers and authors are asked to exercise caution and strictly adhere to the journal's policy regarding the usage of Artificial Intelligence Generated Content (AIGC) tools.

More Announcements...

Journal Center

Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.

more

Article Tools

Indexing metadata

How to cite item

Review policy