Calibration and performance evaluation of the APSIM-Wheat model in foot hills of Western Himalayas
APSIM-Wheat model calibration in Western Himalayas
Keywords:
APSIM-Wheat, Calibration, Validation, Wheat, Sowing date & SimulationAbstract
The APSIM-Wheat model was calibrated and validated for the Western Himalayan foothills using field data from wheat varieties HD-2967 and PBW-502 under varied sowing dates (15th November, 25th November, and 5th December) and irrigation levels (three, four, and five irrigations). Calibration involved adjusting phenological, genetic, radiation, and water-use parameters to align simulated outputs with observed data from 2017-18. Key phenological stages like emergence, anthesis, and maturity showed close agreement, with RMSE values ranging from 2.02 to 9.03 days and percentage RMSE from 2.99% to 31.97%. For biological and grain yields, the model slightly overestimated values with RMSEs of 3.50 to 5.34 q/ha. Validation using 2018-19 data confirmed the model's reliability, as it accurately simulated crop performance with acceptable RMSEs. These results demonstrate the model's potential for simulating wheat growth and yield under different management practices.
References
Ahmed, M. and Hassan, F. 2011. APSIM and DSSAT models as decision support tools. In 19th International Congress on Modelling and Simulation (2011) at Perth, Australia, December 12–16:1174-1180.
Ahmed, M., Akram, M.N., Asim, M., Aslam, M., Hassan, F., Higgins, S., Stokle, C.O., and Hoogenboom, G. 2016. Calibration and validation of APSIM-Wheat and CERES-Wheat for springwheat under rainfed conditions: Models evaluation and application. Computers and Electronics in Agriculture, 123(4):384-401.
Asseng, S., Zhu, Y., Basso, B., Wilson, T., and Cammarano, D. 2014. Simulation modeling: applications in cropping systems. Encyclopedia of Agriculture and Food System,102–112.
Bassu, S., Asseng, S., Motzo, R. and Giunta, F. 2009. Optimizing sowing date of durum wheat in a variable Mediterranean environment. Field Crop Res., 111 (2):109–118.
Carberry, P. S., Hochman, Z., Hunt, J. R., Dalgliesh, N. P., McCown, R. L., Whish, J. P.M. and Rees, H. 2009. Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops. Crop and Pasture Science, 60(11): 1044-1056.
Chen, C., Wang, E., Yu, Q. 2010. Modelling the effects of climate variability andwater management on crop water productivity and water balance in the North China Plain. Agric. Water Manage,. 97 (8):1175–1184.
Deshpande, S. B., Fehrenbacher, J. B. and Beavers, A. H. 1971. Mollisols of Tarai region of Uttar Pradesh, northern India. Morphology and mineralogy.Geoderma, 6(3): 179- 193.
Eitzinger, J., Trnka, M., Hosch, J., Zalud, Z., and Dubrovsky, M. 2004. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecol. Model., 171 (3):223–246.
Donatelli, M., Van Ittersum, M. K., Bindi, M. and Porter, J.R. 2002. Modelling cropping systems-highlights of the symposium and preface to the special issues. Eur. J. Agron., 18 (2):1–11.
Godwin, D.C. and Singh, U. 1998. Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In: Tsuji, G., Hoogenboom, G., Thornton, P. (Eds.), Understanding Options for Agricultural Production. Springer, Netherlands, 40(1):55–77.
Graves, A. R., Hess, T., Matthews, R. B., Stephens, W., and Middleton, T. 2002. Crop simulation models as tools in computer laboratory and classroom-based education. Journal of Natural Resources and Life Sciences Education, 31(1):48-58.
Holzwortha, D.P., Hutha, N. I. and dVoil P.G. 2011. Simple software processes and tests improve the reliability and usefulness of a model Environment, Modelling & Software. Ecol. Model., 26 (4):510-516.
Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth,D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg,K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S.,Chapman, S., McCown, R.L., Freebairn, D.M. and Smith, C.J. 2003. An overview ofAPSIM, a model designed for farming systems simulation. Eur. J. Agron., 18 (4):267–288.
Kumar, N., Nain, A. S., Roy, S. and Suman, K. 2014. Coalesced impact assessment of climate change through simulation modelling. “International Symposium on “New Dimensions in Agrometeorology for Sustainable Agriculture” jointly being organised by G. B. Pant University of Agriculture & Technology, Pantnagar and Association of Agrometeorologists, Anand, 24.
Ma, Y., Feng, S. and Song, X. 2015. Evaluation of optimal irrigation scheduling and groundwater recharge at representative sites in the North China Plain withSWAP model and field experiments. Comput. Electr. Agric., 116:125–136.
Martina, M.M.S., Olesen, J. E. and Porterac, J.R. 2014. A genotype, environment and management (GxExM) analysis of adaptation in winter wheat to climate change in Denmark. Agricultural and Forest Meteorology, 187: 1-13.
McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P. and Freebairn, D.M. 1996. APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 50(30):255-271.
Monsi, M. and Saeki, T. 2005. On the factor light in plant communities and itsimportance for matter production. Ann. Bot-London, 95 (3): 549–567.
Mohanty, M., Probertb, M. E., K Reddy, L.S., Dalalad, R.C., Mishra, A.K., Subba Rao, A. Singh, M. and Menziesa, N.W. 2012. Simulating soybean–wheat cropping system: APSIM model parameterization and validation Simulating soybean–wheat cropping system: APSIM model parameterization and validation. Agric. Ecosyst. Environ., 152:68-78.
Nain, A.S., Dadhwal, V. K. and Singh, T. P. 2004. Use of CERES-Wheat model for wheat yield forecast in central Indo-Gangetic Plains of India. Journal of Agricultural Science, 142: 59–70.
Ritchie, J.T., Singh, U., Godwin, D.C. and Bowen, W.T. 1998. Cereal growth, development and yield. In: Tsuji, G., Hoogenboom, G., Thornton, P. (Eds.), Understanding Options for Agricultural Production. Springer, Netherlands, 13(2):79–98.
Rosenzweig, C. and Parry, M.L. 1994. Potential impact of climate change on world food supply. Nature,367 (6459): 133–138.
Sayre, K.D., Rajaram, S. and Fischer, R.A. 1997. Yield potential progress in short breadwheats in Northwest Mexico. Crop Sci., 37 (1): 36–42.
Timsina, J., Godwin, D., Humphreys, E., Yadvinder, S., Bijay, S., Kukal, S.S., Smith, D. 2008. Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model. Agric. Water Manage., 95 (9): 1099–1110.
Wang, E. and Engel, T., 2002. Simulation of growth, water and nitrogen uptake of wheat crop using the SPASS model. Environ. Modell.Softw. 17 (4): 387–402.
Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R.,O’Donnell, J. and Rowe, C.M. 1985. Statistics for the evaluation and comparison of models. J. Geophys. Res.: Oceans 90 (C5): 8995–9005.
Zhao, G., Bryan, A. and Song X. 2014. Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental and management parameters. Ecological Modellin, 45: 89-90.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Neha Pareek, Dr. A. S. Nain Nain, Dr. Anil Kumar Singh
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Publisher and Authors