DEVELOPMENT OF A PREDICTIVE DECISION SUPPORT SYSTEM FOR CROP GROWTH AND MANAGEMENT OF SELECTED CROPS IN THE TROPICS

Authors

  • T. D. Akpenpuun Department of Agricultural and Biosystems Engineering, University of Ilorin, Ilorin, Nigeria https://orcid.org/0000-0002-3211-3005
  • R. G. Azuatalam Department of Agricultural and Biosystems Engineering, University of Ilorin, Ilorin, Nigeria https://orcid.org/0009-0005-2141-7678
  • A. A. Olaifa Department of Agricultural and Biosystems Engineering, University of Ilorin, Ilorin, Nigeria
  • Q. A. Akolade Department of Agricultural and Biosystems Engineering, University of Ilorin, Ilorin, Nigeria
  • A. Usman Department of Agricultural and Biosystems Engineering, Joseph Sarwuan Tarka University, Makurdi, Nigeria https://orcid.org/0009-0002-4061-427X
  • H. O. Sanusi Department of Agricultural and Biosystems Engineering, University of Ilorin, Ilorin, Nigeria https://orcid.org/0009-0004-7200-434X

DOI:

https://doi.org/10.63747/jeis.v1i1.18

Keywords:

Crop Decision Support System, Growing Degree Days, Predictive Modelling, Precision Agriculture, Remote Sensing, Phenological Stages

Abstract

Management of crops is critical in maximising agricultural production, particularly in the face of climate variability. In this research, a Crop Decision Support System (CDSS) is presented to utilize the concept of Growing Degree Days (GDD) to forecast crop stages and plan resource allocations. This CDSS features a user-friendly interface for calculating GDD, water requirements, and predictive models for various crops, including maize, tomatoes, rice, beans, and millet. Real-time and historical weather data were obtained using the Open-Meteo API and the Nigerian Meteorological Agency (NiMet). GDD and crop development were estimated by using ridge regression. The backend was developed using Python (Flask), and the frontend was built using Next.js. This system was tested by comparing the predicted phenological stages against literature-reported observations. Data reliability was supported by the fact that the results of statistical analysis (ANOVA) revealed no significant differences between NiMet and API temperature datasets. This CDSS generated accurate phenological forecasts and provided adequate decision support for irrigation and pest control. The work is a contribution to the development of scalable, data-driven crop management, with a future focus on the application of machine learning and remote sensing technologies.

References

Alagesan, A., Vaishnavi, P., & Karthikeyan, R. (2020). Development of computational decision-making tool for predicting the growth and development of rice crop using location-specific diurnal air temperature data. In S.M. Ramasamy, A.K. Soman, & R.V. Thampi (Eds.), Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies (pp. 439–444). Springer. https://doi.org/10.1007/978-981-15-3746-2_41

Alipour, H., Abdi, H., & Hamta, M. (2020). Assessment of growing degree-days values of phenological stages in Iranian bread wheat cultivars and landraces. Journal of Computational Biology, 12, 71–82. https://doi.org/10.29252/JCB.12.34.71

Anju, V.S., Kaur, S., & Dhaliwal, S.S. (2018). Agro-meteorological indices and phenology of rice cultivars under varied nitrogen and planting dates in Odisha. Pharma Innovation Journal, 11(10S), 373–548.

Angel, J.R., Widhalm, M., Todey, D., Massey, R., & Biehl, L. (2017). The U2U Corn Growing Degree Day tool: Tracking corn growth across the US Corn Belt. Climate Risk Management, 15, 73–81. https://doi.org/10.1016/j.crm.2016.10.002

Aslam, M.A., Ahmed, M., Stöckle, C.O., Higgins, S.S., Hassan, F.U., & Hayat, R. (2017). Can growing degree days and photoperiod predict spring wheat phenology? Frontiers in Environmental Science, 5, Article 57. https://doi.org/10.3389/fenvs.2017.00057

Bonea, D., & Dunăreanu, I.C. (2021). Impact of thermal requirement on growth and grain yield of maize hybrids under rain-fed conditions. Scientific Papers. Series A. Agronomy, LXIV(1), 131–142.

Bonhomme, R. (2000). Bases and limits to using ‘degree-day’ units. European Journal of Agronomy, 13(1), 1–10. https://doi.org/10.1016/S1161-0301(00)00058-7

Bullock, D.S., Boerngen, M., Tao, H., Maxwell, B., Luck, J.D., Shiratsuchi, L., Puntel, L., & Martin, N.F. (2019). The data-intensive farm management project: Changing agronomic research through on-farm precision experimentation. Agronomy Journal, 111(6), 2736–2746. https://doi.org/10.2134/agronj2019.03.0165

Cao, W., & Tibbitts, T. (1995). Leaf emergence on potato stems in relation to thermal time. Agronomy Journal, 87(3), 474–477. https://doi.org/10.2134/agronj1995.00021962008700030013x

Charalampopoulos, I., Polychroni, I., Droulia, F., & Nastos, P.T. (2024). The spatiotemporal evolution of the growing degree days agroclimatic index for viticulture over the northern Mediterranean basin. Atmosphere, 15(4), Article 485. https://doi.org/10.3390/atmos15040485

Dong, X., Ling, G., Zhang, P., et al. (2021). Responses of maize with different growth periods to heat stress around flowering and early grain filling. Agricultural and Forest Meteorology, 298, 108378. https://doi.org/10.1016/j.agrformet.2020.108378

FAO (2025). Crop Water Requirement Tool (CROPWAT-online). Aquastat Database. Rome: Food and Agriculture Organization. Retrieved July 2025, from https://www.fao.org/aquastat/cropwat

Food and Agriculture Organization (2020). Manual on Integrated Fall Armyworm Management. Rome: FAO. Retrieved July 2025, from https://www.fao.org/publications/card/en/c/CA7729EN/

Fraisse, C., Bellow, J., & Brown, C. (2007). Degree days: Heating, cooling, and growing. EDIS, 2007(1). https://doi.org/10.32473/edis-ae428-2007

Gu, S. (2016). Growing degree hours – A simple, accurate, and precise protocol to approximate growing heat summation for grapevines. International Journal of Biometeorology, 60(7), 1123–1134. https://doi.org/10.1007/s00484-015-1105-8

Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., … & Ritchie, J.T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7

Kalaisekar, A. (2016). Insect Pests of Millets: Systematics, Bionomics, and Management. Academic Press. ISBN 9780128042434

Keramitsoglou, I., Sismanidis, P., Sykioti, O., Pisinaras, V., Tsakmakis, I., Panagopoulos, A., Argyriou, A., & Kiranoudis, C.T. (2023). SENSE-GDD: A satellite-derived temperature monitoring service to provide growing degree days. Agriculture, 13(5), 1108. https://doi.org/10.3390/agriculture13051108

Liu, L.-W., Lu, C.-T., Wang, Y.-M., Lin, K.-H., Ma, X., & Lin, W.-S. (2022). Rice (Oryza sativa L.) growth modeling based on growth degree day (GDD) and artificial intelligence algorithms. Agriculture, 12(1), 59. https://doi.org/10.3390/agriculture12010059

Mathews, J., Kruger, L., & Wentink, G. (2018). Climate-smart agriculture for sustainable agricultural sectors: The case of Mooifontein. Jàmbá: Journal of Disaster Risk Studies, 10(1), a492. https://doi.org/10.4102/jamba.v10i1.492

McMaster, G.S., & Wilhelm, W.W. (1997). Growing degree-days: One equation, two interpretations. Agricultural and Forest Meteorology, 87(4), 291–300. https://doi.org/10.1016/S0168-1923(97)00027-0

Oliveira, M. (1998). Calculation of budbreak and flowering base temperatures for Vitis vinifera cv. Touriga Francesa in the Douro region of Portugal. American Journal of Enology and Viticulture, 49(1), 74–78. https://doi.org/10.5344/ajev.1998.49.1.74

Pacheco, R., Álvarez, E., Ferrer, J., Bonilla, H., Gómez-Correa, J., Hernández, M., & Artiles, L.R. (2019). Accumulation of degree days and their effect on the potential yield of 15 eggplant (Solanum melongena L.) accessions in the Colombian Caribbean. Revista Facultad Nacional de Agronomía Medellín, 72(3), 8895–8902. https://doi.org/10.15446/rfnam.v72n3.77112

Parthasarathi, T., Velu, G., & Jeyakumar, P. (2013). Impact of crop heat units on growth and developmental physiology of future crop production: A review. Journal of Crop Science and Technology, 2(1), 2319-3395.

Russell, M.P., Wilhelm, W.W., Olson, R.A., & Power, J.F. (1984). Growth analysis based on degree days. Crop Science, 24(1), 28–32. https://doi.org/10.2135/cropsci1984.0011183x002400010007x

Sanon, M., Hoogenboom, G., Traoré, S., Sarr, B., García, A., Some, L., … & Roncoli, C. (2014). Photoperiod sensitivity of local millet and sorghum varieties in West Africa. NJAS - Wageningen Journal of Life Sciences, 68, 29–39. https://doi.org/10.1016/j.njas.2013.11.004

Sha, Z., & Zhang, M. (2007). Development of web-based decision support system for field-based crop management. In Proceedings of the 1st International Conference on Digital Ecosystems and Technologies (pp. 52–57). IEEE. https://doi.org/10.1145/1341012.1341076

Steduto, P., Hsiao, T.C., Raes, D., & Fereres, E. (2009). AquaCrop — The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101(3), 426–437. https://doi.org/10.2134/agronj2008.0139s

Undersander, D.J., & Christiansen, S. (1986). Interactions of water variables and growing degree days on heading phase of winter wheat. Agricultural and Forest Meteorology, 38(3–4), 169–176. https://doi.org/10.1016/0168-1923(86)90056-0

Villordon, A., Clark, C.A., Ferrin, D.M., & LaBonte, D.R. (2009). Using growing degree days, agrometeorological variables, linear regression, and data mining methods to help improve prediction of sweetpotato harvest date in Louisiana. HortTechnology, 19(1), 133–144. https://doi.org/10.21273/HORTSCI.19.1.133

Zhou, G., & Wang, Q. (2018). A new nonlinear method for calculating growing degree days. Scientific Reports, 8, Article 10149. https://doi.org/10.1038/s41598-018-28392-z

Downloads

Published

2025-07-28

Issue

Vol. 1 No. 1 (2025): Volume 1, Issue 1, July 2025

Section

Articles

License

Copyright (c) 2025 Journal of Engineering Innovation and Sustainability

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Journal of Engineering Innovation and Sustainability (JEIS), in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Journal of Engineering Innovation and Sustainability." A copyright transfer form can be downloaded from the JEIS Website (https://jeis.com.ng/index.php/jeis). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.