Main Article Content
Abstract
This paper presents a review on algorithm development and crop water modelling with a focus on optimizing significant parameters related to crop factors, soil factors, and weather factors. The accurate representation and optimization of these parameters are crucial for reliable predictions and effective decision-making in agricultural practices. The objective of this review is to analyse the existing literature on algorithm development, parameter optimization techniques, and their application in crop water modelling, specifically emphasizing the importance of crop factors, soil factors, and weather factors. The review concludes with a discussion on the key findings and future directions in algorithm development and optimization for crop water modelling. It highlights potential research gaps and challenges that need to be addressed to improve the accuracy and efficiency of crop water modelling. The impact of optimized modelling approaches on sustainable agricultural practices and water management is also discussed. Overall, this comprehensive review provides valuable insights into the importance of algorithm development, optimization, and parameter selection in crop water modelling, specifically focusing on crop factors, soil factors, and weather factors.
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Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
Abdel-Magid, Y. L., & Mazumder, S. K. (2013). Optimizing crop water allocation using particle swarm optimization. Journal of Irrigation and Drainage Engineering, 139(4), 281-293. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000426.
Al-Amin, R., Hossain, M. B., & Yunus, A. (2022). Estimation of crop water requirement and irrigation scheduling of rice in southeastern region of Bangladesh using FAO-CROPWAT 8.0. Pp. 437-449. In Arthur, S., Saitoh, M., & Pal, S.K. (Editors). Advances in Advances in Civil Engineering. Lecture Notes in Civil Engineering, vol 184. https://doi.org/10.1007/978-981-16-5547-0_40.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements (FAO Irrigation and Drainage Paper 56). FAO, Water Resources, Development and Management Service.
Ashrostaghi, T., Aliniaeifard, S., Shomali, A., Azizinia, S., Abbasi Koohpalekani, J., Moosavi-Nezhad, M., & Gruda, N. S. (2022). Light intensity: The role player in cucumber response to cold stress. Agronomy, 12(1), 201. https://doi.org/10.3390/agronomy12010201
Bernal-Vicente, A., Cantabella, D., Petri, C., Hernández, J. A., & Diaz-Vivancos, P. (2018). The salt-stress response of the transgenic plum line J8-1 and its interaction with the salicylic acid biosynthetic pathway from mandelonitrile. International Journal of Molecular Sciences, 19(11), 3519. https://doi.org/10.3390/ijms19113519.
Bitew, Y., & Workie, M. (2017). Impact of crop production inputs on soil health: A review. Asian Journal of Plant Sciences, 16(3), 109–131. https://doi.org/10.3923/ajps.2017.109.131
Bouman, B., & Tuong, T. P. (2001). Field water management to save water and increase productivity in irrigated lowland rice. Agricultural Water Management, 49(1), 11-30. https://doi.org/10.1016/S0378-3774(00)00128-1
De la Rosa, J. M., Conesa, M. R., Domingo, R., Aguayo, E., Falagán, N., & Pérez-Pastor, A. (2016). Combined effects of deficit irrigation and crop level on early nectarine trees. Agricultural Water Management, 170, 120-132. https://doi.org/10.1016/j.agwat.2016.01.012
Esmaili, M., Aliniaeifard, S., Mashal, M., Asefpour Vakilian, K., Ghorbanzadeh, P., Azadegan, B., Seif, M., & Didaran, F. (2021). Assessment of adaptive neuro-fuzzy inference system (ANFIS) to predict production and water productivity of lettuce in response to different light intensities and CO2 concentrations. Agricultural Water Management, 258, 107201. https://doi.org/10.1016/j.agwat.2021.107201
Ferrarezi, R. S., Lin, X., Gonzalez Neira, A. C., Tabay Zambon, F., Hu, H., Wang, X., Huang, J. H., & Fan, G. (2022). Substrate pH influences the nutrient absorption and rhizosphere microbiome of Huanglongbing-affected grapefruit plants. Frontiers in Plant Science, 13, 856937. https://doi.org/10.3389/fpls.2022.856937
Girsang, C. (2023). The role of information technology in improving resource management efficiency in sustainable agriculture. Jurnal Minfo Polgan, 12(2), 1698-1712. https://doi.org/10.33395/jmp.v12i2.12959
Ul Haq, Z., & Anwar, A. A. (2010). Irrigation scheduling with genetic algorithms. Journal of Irrigation and Drainage Engineering, 136(10). https://doi.org/10.1061/(ASCE)IR.1943-4774.0000238
Hatfield, J. L. (2015). Temperature extremes: Effect on plant growth and development. Weather and Climate Extremes, 10, 4-10. https://doi.org/10.1016/j.wace.2015.08.001
Hsiao, T. C., Heng, L. K., Steduto, P., Rojas-Lara, B., & Fereres, E. (2009). AquaCrop—The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal, 101(3), 448-459. https://doi.org/10.2134/agronj2008.0218s
Huang, F., Mo, X., Hu, S., & Li, L. (2020). Agricultural water optimization coupling with a distributed ecohydrological model in a mountain-plain basin. Journal of Hydrology, 590, 125336. https://doi.org/10.1016/j.jhydrol.2020.125336
Huang, J., Subasinghe, R., Malik, R. S., & Triantafilis, J. (2015). Salinity hazard and risk mapping of point source salinisation using proximally sensed electromagnetic instruments. Computers and Electronics in Agriculture, 113, 213-224. https://doi.org/10.1016/j.compag.2015.02.013
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using AI. Artificial Intelligence in Agriculture, 2, 1-12. https://doi.org/10.1016/j.aiia.2019.05.004
Kanda, E. K., Senzanje, A., & Mabhaudhi, T. (2021). Calibration and validation of the AquaCrop model for full and deficit irrigated cowpea (Vigna unguiculata (L.) Walp). Physics and Chemistry of the Earth, Parts A/B/C, 124(1), 102941. https://doi.org/10.1016/j.pce.2020.102941
Kopittke, P. M., Menzies, N. W., Wang, P., McKenna, B. A., & Lombi, E. (2019). Soil and the intensification of agriculture for global food security. Environment International, 132, 105078. https://doi.org/10.1016/j.envint.2019.105078
Lai, J., Liu, T., & Luo, Y. (2022). Evapotranspiration partitioning for winter wheat with shallow groundwater in the lower reach of the Yellow River Basin. Agricultural Water Management, 266, 107561. https://doi.org/10.1016/j.agwat.2022.107561
Li, Y., Ye, W., Wang, M., & Yan, X. (2009). Climate change and drought: A risk assessment of crop-yield impacts. Climate Research, 39(1), 31-46. https://doi.org/10.3354/cr00797
Li, M., Du, Y., Zhang, F., Fan, J., Ning, Y., Cheng, H., & Xiao, C. (2020). Modification of CSM-CROPGRO-Cotton model for simulating cotton growth and yield under various deficit irrigation strategies. Computers and Electronics in Agriculture, 179, 105843. https://doi.org/10.1016/j.compag.2020.105843
Liu, Y. J., Zhang, W., Wang, Z. B., Ma, L., Guo, Y. P., Ren, X. L., & Mei, L. X. (2019). Influence of shading on photosynthesis and antioxidative activities of enzymes in apple trees. Photosynthetica, 57(3), 857-865. https://doi.org/10.32615/ps.2019.081
Lobell, D. B., & Gourdji, S. M. (2012). The influence of climate change on global crop productivity. Plant Physiology, 160(4), 1686-1697. https://doi.org/10.1104/pp.112.208298
Mazzoncini, M., Sapkota, T., Bàrberi, P., Antichi, D., & Risaliti, R. (2011). Long-term effect of tillage, nitrogen fertilization, and cover crops on soil organic carbon and total nitrogen content. Soil & Tillage Research, 114, 165–174. https://doi.org/10.1016/j.still.2011.05.001
McCready, M. S., & Dukes, M. (2011). Landscape irrigation scheduling efficiency and adequacy by various control technologies. Agricultural Water Management, 98(5), 697-704. https://doi.org/10.1016/j.agwat.2010.11.007
Mohan Kumar, B., & Kunhamu, T. K. (2022). Nature-based solutions in agriculture: A review of the coconut (Cocos nucifera L.)-based farming systems in Kerala, “the Land of Coconut Trees.” Nature-Based Solutions, 2, 100012. https://doi.org/10.1016/j.nbsj.2022.100012
Moore, C. E., Meacham-Hensold, K., Lemonnier, P., Slattery, R. A., Benjamin, C., Bernacchi, C. J., Lawson, T., & Cavanagh, A. P. (2021). The affect of increasing temperature on crop photosynthesis: From enzymes to ecosystems. Journal of Experimental Botany, 72(8), 2822–2844. https://doi.org/10.1093/jxb/erab090
Nguyen, D. C. H., Ascough, J. C., Maier, H. R., Dandy, G. C., & Andales, A. A. (2017). Optimization of irrigation scheduling using ant colony algorithms and an advanced cropping system model. Environmental Modelling & Software, 97, 32-45. https://doi.org/10.1016/j.envsoft.2017.07.002
Nikolaou, G., Neocleous, D., Christou, A., Kitta, E., & Katsoulas, N. (2020). Implementing sustainable irrigation in water-scarce regions under the impact of climate change. Agronomy, 10(8), 1120. https://doi.org/10.3390/agronomy10081120
Nti, I. K., Zaman, A., Nyarko-Boateng, O., Adekoya, A. F., & Keyeremeh, F. (2023). A predictive analytics model for crop suitability and productivity with tree-based ensemble learning. Decision Analytics Journal, 8, 100311. https://doi.org/10.1016/j.dajour.2023.100311
O’Geen, A. T. (2013). Soil water dynamics. Nature Education Knowledge, 4(5), 9. Retrieved from https://www.nature.com/scitable/knowledge/library/soil-water-dynamics-103089121/
Oliver, D. P., Bramley, R. G. V., Riches, D., Porter, I., & Edwards, J. (2013). Soil physical and chemical properties as indicators of soil quality in Australian viticulture. Australian Journal of Grape and Wine Research, 19, 129-139. https://doi.org/10.1111/ajgw.12016
Peng, Y., Xiao, Y., Fu, Z., Dong, Y., Zheng, Y., Yan, H., & Li, X. (2019). Precision irrigation perspectives on the sustainable water-saving of field crop production in China: Water demand prediction and irrigation scheme.
Pinheiro, E. A. R., de Jong van Lier, Q., & Šimůnek, J. (2019). The role of soil hydraulic properties in crop water use efficiency: A process-based analysis for some Brazilian scenarios. Agricultural Systems, 173, 364-377. https://doi.org/10.1016/j.agsy.2019.03.019
Qi, X., Feng, K., Sun, L., Zhao, D., Huang, X., Zhang, D., Liu, Z., & Baiocchi, G. (2022). Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions. One Earth, 5(10), 1139-1152. https://doi.org/10.1016/j.oneear.2022.09.008
Ramanathan, K. C., Saravanan, S., Krishna, K. M., Srinivas, T., & Selokar, A. (2019). Reference evapotranspiration assessment techniques for estimating crop water requirement. International Journal of Engineering and Technology, 8(4), 1094-1100. https://doi.org/10.35940/ijrte.D6738.118419
Rastogi, M., Kolur, S. M., Burud, A., Sadineni, T., Sekhar, M., Kumar, R., & Rajput, A. (2024). Advancing water conservation techniques in agriculture for sustainable resource management: A review. Journal of Geography, Environment and Earth Science International, 28, 41-53. https://doi.org/10.9734/jgeesi/2024/v28i3755
Rohilla, K., Hari Prasad, K. S., & Ojha, C. S. P. (2016). Effect of infiltration on sediment transport in irrigated channels. Journal of Irrigation and Drainage Engineering, 142(7), 04016024. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001018
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