Land Suitability and Crop Pattern Model using Integrated Pollination Intelligence Algorithm and Remote Sensing
A mathematical model for crop pattern coupled with economic and environmental factors of agricultural production constructed with remote sensing and metaheuristic based algorithm is considered in this work. The model is expected to serve as a support system for farm managers' decision making process. Geographic data showing soil properties of major cities in Benue State, Nigeria using remote sensing, was integrated to the model to obtain analyzed suitability information for selected crops. A class of modern optimization algorithms was thereafter used to find optimum cropland pattern. A net production value of $1,592,107,000.00$ was obtained by using the model compared to an initial production value of $1,364,460,000.00$ recorded in the study area. The study suggests that soil properties must be considered along side with economic factors before choosing the types of crop to be planted on a piece of land. This study has shown the efficacy of optimization tools which should be dully employed by farmers in decision making process. The data used to support the findings of this study are included within the article.
H. Aimin, Uncertainty risk aversion and risk management in agriculture, Agriculture and Agricultural Science Procedia 1 (2010), 152-156. https://doi.org/10.1016/j.aaspro.2010.09.018
J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
S. Kirkpatrick, J. Gelatt and M. Vecchi, Optimization by simulated annealing, Journal of Science 220(4598) (1983), 671-680. https://doi.org/10.1126/science.220.4598.671
R. Storn and K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11 (1997), 341-359. https://doi.org/10.1023/A:1008202821328
X. S. Yang, Nature Inspired Metaheuristic Algorithm, Luniver Press, Cambridge, United Kingdom, 2008.
X. S. Yang, Flower pollination algorithm for global optimization, in: Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, vol. 7445, pp. 240-249, Springer, Berlin, Heidelberg, 2012. https://doi.org/10.1007/978-3-642-32894-7_27
Raj Krishna, The optimality of land allocation: a case study of Punjab, Indian Journal of Agricultural Economics 18(1) (1963), 63-73.
J. C. Campbell, J. Radke, J. T. Gless and R. M. Wirtshafter, An application of linear programming and geographic information system: cropland allocation in Antigua, Environment and Planning A 24 (1992), 535-549. https://doi.org/10.1068/a240535
M. O. Wankhade and H. S. Lunge, Allocation of agricultural land to the major crops of saline track by linear programming approach: a case study, International Journal of Scientific and Technology Research 1(9) (2012), 21-25.
A. M. Alabdulkader, A. I. Al-Amoud and F. S. Awad, Optimization of the cropping pattern in Saudi Arabia using a mathematical programming sector model, Agric. Econ. – Czech 58(2) (2012), 56-60. https://doi.org/10.17221/8/2011-AGRICECON
F. Majeke, J. Majeke, N. Chabuka, J. Mufandaedza, M. Shoko, J. Chirima, T. Makori and C. Matete, A farm resource allocation problem. A case study of model A2 resettled farmers in Bindura, Zimbabwe, International Journal of Economics and Management Science 2(7) (2013), 1-4.
A. F. Angelo, Hybrid metaheuristics for crop rotation, Anais do Congresso de Matematica (CMAC) (2013), 53-58.
R. Ashutosh and S. Prakash, Optimal allocation of agricultural land for crop planning in Hirakud canal command area using swarm intelligence techniques, ISH Journal of Hydraulic Engineering (2018), 1-13. https://doi.org/10.1080/09715010.2018.1508375
C. N. Ejieji and A. E. Akinsunmade, Agricultural model for allocation of crops using pollination intelligence method, Applied Computational Intelligence and Soft Computing 2020, Article ID 4830359, 6 pp. https://doi.org/10.1155/2020/4830359
FAO, A Framework for Land Evaluation, FAO Soils Bulletin No. 32, Rome, Italy, 1976.
C. Sys, E. van Ranst, J. Debaveye and F. Beernaert, Land Evaluation, Part 3: Crop Requirements, General Administration for Development Cooperation, Brussels, Belgium, 1991, 199 pp.
Z. Mosleha, H. Salehia, A. Fasakhodib, A. Jafaric, A. Mehnatkeshd and I. Borujenie, Sustainable allocation of agricultural lands and water resources using suitability analysis and mathematical multi-objective programming, J. GEODERMA 303 (2017), 52-59. https://doi.org/10.1016/j.geoderma.2017.05.015
D. Djaenudin, R. Oktaviain, S. Hartoyo and H. Dwijarabowo, Modelling of land allocation behaviour in Indonesia, Procedia Environmental Sciences 33 (2016), 78-86. https://doi.org/10.1016/j.proenv.2016.03.059
F. Karms, N. Amacha, W. Katerji, W. Wu, A. Domiquez and S. Baydoun, Using remote sensing to improve crop water allocation in scarce water resources environment, International Journal of Science and Research (IJSR) 5(1) (2016), 1481-1495. https://doi.org/10.21275/v5i1.NOV152595
R. C. Abah and B. M. Petja, Crop suitability mapping for rice, cassava, and yam in North Central Nigeria, Journal of Agricultural Science 9(1) (2017), 96-108. https://doi.org/10.5539/jas.v9n1p96
F. Terdoo, T. Gyang and T. R. Iorlamen, Annual cropped area expansion and agricultural production: implications for environmental management in Benue State, Nigeria, Ethiopian Journal of Environmental Studies and Management 9(4) (2016), 430-442. https://doi.org/10.4314/ejesm.v9i4.4
FAO, Nigeria at a Glance, 2016. Retrieved from http://www.fao.org/nigeria/fao-in-nigeria/nigeria-at-aglance/en/
This work is licensed under a Creative Commons Attribution 4.0 International License.