Abstract
AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management factors. In this research, to solve this problem and develop a model with fewer input data, artificial neural network (ANN), support vector regression (SVR), and combined support vector regression with firefly algorithm (SVR-FFA) were used. For this purpose, 440 scenarios were created in 2 farms located in Iran, and the values of yield and biomass obtained by the AquaCrop model were compared with intelligent models. Also, consumable seed and irrigation were considered as inputs of intelligent models. The 99WestW2 farm in Miandoab had a seed yield of 6.588 t/ha, and the WestW10 farm in Mahabad had a seed yield of 5.05 t/ha. The results of this research showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model was able to have the lowest amount of error so that for the amount of grain yield, the error values for the farms were 0.033 and 0.069 t/ha, respectively. The error value of biomass was obtained for farms as 0.057 and 0.066 t/ha respectively. After SVR-FFA model, SVR and ANN models also showed good performance due to proper accuracy and saving time. Finally, SVR-FFA, SVR, and ANN models were able to predict yield and biomass values in the shortest time and with the highest accuracy with only two inputs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The datasets generated during the current study are available from the corresponding author on resonable request.
References
Abdelaziz E, Saidur R, Mekhilef S (2011) A review on energy saving strategies in industrial sector. Renew Sustain Energy Rev 15(1):150–168
Ahmed AM, Sharma E, Jui SJJ, Deo RC, Nguyen-Huy T, Ali M (2022) Kernel ridge regression hybrid method for wheat yield prediction with satellite-derived predictors. Remote Sensing 14(5):1136
Araya A, Vara Prasad P, Ciampitti IA, Rice CW, & Gowda PH (2022) Using crop simulation models as tools to quantify effects of crop management practices and climate change scenarios on wheat yields in northern Ethiopia. Enhancing agricultural research and precision management for subsistence farming by integrating system models with experiments 29–47
Awad M, Khanna R, Awad M, Khanna R (2015) Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers 67–80
Aydin I, Karakose M, Akin E (2011) A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl Soft Comput 11(1):120–129
Benimam H, Si-Moussa C, Laidi M, Hanini S (2020) Modeling the activity coefficient at infinite dilution of water in ionic liquids using artificial neural networks and support vector machines. Neural Comput Appl 32(12):8635–8653
Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15(3):208–216
Doorenbos J, Kassam A (1979) Yield response to water. Irrig Drain Paper 33:257
Fan L, Zhang L (2022) Multi-system fusion based on deep neural network and cloud edge computing and its application in intelligent manufacturing. Neural Comput Appl 34(5):3411–3420
Feng P, Wang B, Li Liu D, Waters C, Xiao D, Shi L, Yu Q (2020) Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agric for Meteorol 285:107922
Garcia-Vila M, Morillo-Velarde R, Fereres E (2019) Modeling sugar beet responses to irrigation with AquaCrop for optimizing water allocation. Water 11(9):1918
Ghorbani MA, Shamshirband S, Haghi DZ, Azani A, Bonakdari H, Ebtehaj I (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil and Tillage Res 172:32–38
Guarin JR, Asseng S (2022) Improving wheat production and breeding strategies using crop models. In: Wheat Improvement: Food Security in a Changing Climate. Springer International Publishing, Cham, pp 573-591
Gupta S (2021) Artificial neural network modeling and exposure assessments: a new scaling approach. Hum Ecol Risk Assess Int J 27(1):30–49
Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5(6):989–993
Hamel L (2011) Knowledge discovery with support vector machines. Wiley, Hoboken
Hayati D, Yazdanpanah M, Karbalaee F (2010) Coping with drought: the case of poor farmers of south Iran. Psychol Dev Soc 22(2):361–383
Hsiao TC, Heng L, Steduto P, Rojas-Lara B, Raes D, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy J 101(3):448–459
Hu D, Zhang C, Cao W, Lv X, Xie S (2021) Grain yield predict based on GRA-AdaBoost-SVR model. J Big Data 3(2):65–76
Jabal Z K, Khayyun T S, & Alwan IA (2022) Impact of climate change on crops productivity using MODIS-NDVI time series. Civil Engineering Journal 8(06)
Kareem FAA, Shariff AM, Ullah S, Keong LK, Mellon N (2018) Total and partial uptakes of multicomponent vapor-gas mixtures on 13X zeolite at 343 K: experimental and modeling study. Microporous Mesoporous Mater 258:95–113
Kargar K, Samadianfard S, Parsa J, Nabipour N, Shamshirband S, Mosavi A, Chau K-W (2020) Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Eng Appl Comput Fluid Mech 14(1):311–322
Khandelwal M, Kankar P (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4(3):427–433
Kibue GW, Liu X, Zheng J, Pan G, Li L, Han X (2016) Farmers’ perceptions of climate variability and factors influencing adaptation: evidence from Anhui and Jiangsu. China Environ Manag 57(5):976–986
Kim I-S, Son J-S, Park C-E, Kim I, Kim H (2005) An investigation into an intelligent system for predicting bead geometry in GMA welding process. J Mater Process Technol 159(1):113–118
Moazenzadeh R, Mohammadi B, Shamshirband S, Chau K-W (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900
Nguyen VD, Nguyen HT, Vranova V, Nguyen LT, Bui QM, Khieu TT (2021) Artificial neural network modeling for Congo red adsorption on microwave-synthesized akaganeite nanoparticles: optimization, kinetics, mechanism, and thermodynamics. Environ Sci Pollut Res 28(8):9133–9145
Niedbała G (2019) Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield. J Integr Agric 18(1):54–61
Porvazn E, Karkehabadi Z, Arghan A (2017) Ranking and analysis of factors affecting the sustainability of urban neighborhoods and in the city of Mahabad. IAU Int J Soc Sci 7(3):1–14
Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal 101(3):438–447
RezapourAndabili N, Safaripour M (2022) Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB. Environ Monit Assess 194(7):1–13
ShiftehSome’e B, Ezani A, Tabari H (2013) Spatiotemporal trends of aridity index in arid and semi-arid regions of Iran. Theoretical and applied climatology 111(1):149–160
Silvestro PC, Pignatti S, Pascucci S, Yang H, Li Z, Yang G, Huang W, Casa R (2017) Estimating wheat yield in China at the field and district scale from the assimilation of satellite data into the Aquacrop and simple algorithm for yield (SAFY) models. Remote Sensing 9(5):509
Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts Underlying Principles Agron J 101(3):426–437
Vaferi B, Eslamloueyan R, Ayatollahi S (2011) Automatic recognition of oil reservoir models from well testing data by using multi-layer perceptron networks. J Petrol Sci Eng 77(3–4):254–262
Verma A (2022) SVM, CNN and VGG16 Classifiers of Artificial Intelligence used for the detection of diseases of rice crop: A review. Sentim Anal Deep Learn Proc ICSADL 2021:917–931
Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver press
Yazdanpanah M, Hayati D, Zamani GH, Karbalaee F, Hochrainer-Stigler S (2013) Water management from tradition to second modernity: an analysis of the water crisis in Iran. Environ Dev Sustain 15(6):1605–1621
Zarghami M (2005) Uncertain criteria in ranking inter-basin water transfer projects in Iran. In: 73rd Annual Meeting of ICOLD. Civilica, Tehran
Zhang T, Su J, Liu C, Chen W-H (2019) Bayesian calibration of AquaCrop model for winter wheat by assimilating UAV multi-spectral images. Comput Electron Agric 167:105052
Zhang C, Xie Z, Wang Q, Tang M, Feng S, Cai H (2022) AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity. Agric Water Manag 266:107580
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sharafi, M., Behmanesh, J., Rezavardinejad, V. et al. Evaluation of AquaCrop and intelligent models in predicting yield and biomass values of wheat. Int J Biometeorol 67, 621–632 (2023). https://doi.org/10.1007/s00484-023-02440-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00484-023-02440-4