Calibration and validation of APSIM-Wheat Model in Mediterranean areas
DOI:
https://doi.org/10.25081/jp.2024.v16.8810Keywords:
APSIM-Wheat, Calibration, Data input, Statistical assessment, Crop simulationAbstract
The Agricultural Production Systems sIMulator-Wheat (APSIM-Wheat) model is one of the most widely used agricultural models. It is a powerful simulator that has been successfully calibrated and tested for many locations in the world, especially in Western Australia (WA). However, there is a noticeable lack of a standard guide for realizing the calibration validation of APSIM-Wheat that could be applied in areas with a Mediterranean climate similar to that of WA. Therefore, this study aims to examine crop simulations reported in published articles and to provide a detailed description of input data and statistical assessment, which represent the two main components of the calibration-validation protocols. The PRISMA (PREFERRED Reporting Items for Systematic Reviews and Meta-Analyses) method was used to identify and select relevant papers for this review. Following the analysis of 31 calibration protocols extracted from selected eligible articles, it was found that regardless of the objective of using APSIM-Wheat, the same category of data is required for calibration. As far as meteorological data is concerned, the information essential to this study was daily maximum and minimum air temperatures, rainfall (mm), and solar radiation. In the case of soil data, information about the texture and hydraulic characteristics, especially PAWC, DUL and LL was required. Regarding agricultural management data, this pertains to cultivated crops, Nitrogen fertilization (rate and time of application) and sowing (date and density). For the statistical evaluation, it was observed that 90 percent of studies analyzed in this review revealed the use of RMSE.
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References
Aggarwal, P. K. (1995). Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications. Agricultural Systems, 48(3), 361-384. https://doi.org/10.1016/0308-521X(94)00018-M
Ahmed, M., Akram, M. N., Asim, M., Aslam, M., Hassan, F., Higgins, S., Stöckle, C. O., & Hoogenboom, G. (2016). Calibration and validation of APSIM-Wheat and CERES-Wheat for spring wheat under rainfed conditions: Models evaluation and application. Computers and Electronics in Agriculture, 123, 384-401. https://doi.org/10.1016/j.compag.2016.03.015
Ammar, K. A., Kheir, A. M. S., & Manikas, I. (2022). Agricultural big data and methods and models for food security analysis—a mini-review. PeerJ, 10, e13674. https://doi.org/10.7717/peerj.13674
Anwar, M. R., Liu, D. L., Farquharson, R., Macadam, I., Abadi, A., Finlayson, J., Wang, B., & Ramilan, T. (2015). Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia. Agricultural Systems, 132, 133-144. https://doi.org/10.1016/j.agsy.2014.09.010
Archontoulis, S. V., Miguez, F. E., & Moore, K. J. (2014). A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean. Environmental Modelling & Software, 62, 465-477. https://doi.org/10.1016/j.envsoft.2014.04.009
Asseng, S., Bar-Tal, A., Bowden, J. W., Keating, B. A., Van Herwaarden, A., Palta, J. A., Huth, N. I., & Probert, M. E. (2002). Simulation of grain protein content with APSIM-N wheat. European Journal of Agronomy, 16(1), 25-42. https://doi.org/10.1016/S1161-0301(01)00116-2
Asseng, S., Jamieson, P. D., Kimball, B., Pinter, P., Sayre, K., Bowden, J. W., & Howden, S. M. (2004). Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO2. Field Crops Research, 85(2-3), 85-102. https://doi.org/10.1016/S0378-4290(03)00154-0
Asseng, S., Keating, B. A., Fillery, I. R. P., Gregory, P. J., Bowden, J. W., Turner, N. C., Palta, J. A., & Abrecht, D. G. (1998). Performance of the APSIM-wheat model in Western Australia. Field Crops Research, 57(2), 163-179. https://doi.org/10.1016/S0378-4290(97)00117-2
Asseng, S., Turner, N. C., & Keating, B. (2001). Analysis of water- and nitrogen-use efficiency of wheat in a Mediterranean climate. Plant and Soil, 233, 127-143. https://doi.org/10.1023/A:1010381602223
Bahri, H., Annabi, M., M’Hamed, H. C., & Frija, A. (2019). Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context. Science of The Total Environment, 692, 1223-1233. https://doi.org/10.1016/j.scitotenv.2019.07.307
Bai, H., Xiao, D., Wang, B., Liu, D. L., & Tang, J. (2022). Simulation of Wheat Response to Future Climate Change Based on Coupled Model Inter-Comparison Project Phase 6 Multi-Model Ensemble Projections in the North China Plain. Frontiers in Plant Science, 13, 829580. https://doi.org/10.3389/fpls.2022.829580
Bell, L. W., Hargreaves, J. N. G., Lawes, R. A., & Robertson, M. J. (2009). Sacrificial grazing of wheat crops: Identifying tactics and opportunities in Western Australia’s grainbelt using simulation approaches. Animal Production Science, 49(10), 797-806. https://doi.org/10.1071/AN09014
Bellocchi, G., Rivington, M., Donatelli, M., & Matthews, K. (2011). Validation of Biophysical Models: Issues and Methodologies. In E. Lichtfouse, M. Hamelin, M. Navarrete & P. Debaeke (Eds.), Sustainable Agriculture (Vol. 2, pp. 577-603) Dordrecht, Netherlands: Springer. https://doi.org/10.1007/978-94-007-0394-0_26
Beltran-Peña, A., Rosa, L., & D’Odorico, P. (2020). Global food self-sufficiency in the 21st century under sustainable intensification of agriculture. Environmental Research Letters, 15(9), 095004. https://doi.org/10.1088/1748-9326/ab9388
Boote, K. J., Jones, J. W., Hoogenboom, G., & White, J. W. (2010). The Role of Crop Systems Simulation in Agriculture and Environment. International Journal of Agricultural and Environmental Information Systems, 1(1), 41-54. https://doi.org/10.4018/jaeis.2010101303
Briak, H., & Kebede, F. (2021). Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model. Scientific Reports, 11, 23173. https://doi.org/10.1038/s41598-021-02668-3
Brown, H., Huth, N., & Holzworth, D. (2018). Crop model improvement in APSIM: Using wheat as a case study. European Journal of Agronomy, 100, 141-150. https://doi.org/10.1016/j.eja.2018.02.002
Bryan, B. A., King, D., & Zhao, G. (2014). Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps. Environmental Research Letters, 9(4), 044005. https://doi.org/10.1088/1748-9326/9/4/044005
Bustos-Korts, D., Boer, M. P., Malosetti, M., Chapman, S., Chenu, K., Zheng, B., & van Eeuwijk, F. A. (2019). Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies. Frontiers in Plant Science, 10, 1491. https://doi.org/10.3389/fpls.2019.01491
Carcedo, A. J. P., Junior, N. V., Marziotte, L., Correndo, A. A., Araya, A., Prasad, P. V. V., Min, D., Stewart, Z. P., Faye, A., & Ciampitti, I. A. (2023). The urgency for investment on local data for advancing food assessments in Africa: A review case study for APSIM crop modeling. Environmental Modelling & Software, 161, 105633. https://doi.org/10.1016/j.envsoft.2023.105633
Chaki, A. K., Gaydon, D. S., Dalal, R. C., Bellotti, W. D., Gathala, M. K., Hossain, A., & Menzies, N. W. (2022). How we used APSIM to simulate conservation agriculture practices in the rice-wheat system of the Eastern Gangetic Plains. Frontiers in Plant Science, 275, 108344. https://doi.org/10.1016/j.fcr.2021.108344
Chen, C., Fletcher, A., Ota, N., Oliver, Y., & Lawes, R. (2023). Integrating long fallow into wheat-based cropping systems in Western Australia: Spatial pattern of yield and economic responses. Agricultural Systems, 204, 103561. https://doi.org/10.1016/j.agsy.2022.103561
Chen, C., Wang, B., Feng, P., Xing, H., Fletcher, A. L., & Lawes, R. A. (2020). The shifting influence of future water and temperature stress on the optimal flowering period for wheat in Western Australia. Science of The Total Environment, 737, 139707. https://doi.org/10.1016/j.scitotenv.2020.139707
Chisanga, C. B., Phiri, E., & Chinene, V. R. N. (2017). Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review. Scientific Research and Essays, 12(18), 167-187. https://doi.org/10.5897/SRE2017.6521
Cichota, R., Vogeler, I., Sharp, J., Verburg, K., Huth, N., Holzworth, D., Dalgliesh, N., & Snow, V. (2021). A protocol to build soil descriptions for APSIM simulations. MethodsX, 8, 101566. https://doi.org/10.1016/j.mex.2021.101566
Dolling, P. J., Fillery, I. R. P., Ward, P. R., Asseng, S., & Robertson, M. J. (2006). Consequences of rainfall during summer–autumn fallow on available soil water and subsequent drainage in annual-based cropping systems. Australian Journal of Agricultural Research, 57(3), 281-296. https://doi.org/10.1071/AR04103
Gaydon, D. S., Balwinder-Singh, Wang, E., Poulton, P. L., Ahmad, B., Ahmed, F., Akhter, S., Ali, I., Amarasingha, R., Chaki, A. K., Chen, C., Choudhury, B. U., Darai, R., Das, A., Hochman, Z., Horan, H., Hosang, E. Y., Kumar, P. V., Khan, A. S. M. M. R., ... Roth, C. H. (2017). Evaluation of the APSIM model in cropping systems of Asia. Field Crops Research, 204, 52-75. https://doi.org/10.1016/j.fcr.2016.12.015
Gaydon, D. S., Probert, M. E., Buresh, R. J., Meinke, H., Suriadi, A., Dobermann, A., Bouman, B., & Timsina, J. (2012). Rice in cropping systems—Modelling transitions between flooded and non-flooded soil environments. European Journal of Agronomy, 39, 9-24. https://doi.org/10.1016/j.eja.2012.01.003
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food Security: The Challenge of Feeding 9 Billion People. Science, 327(5967), 812-818. https://doi.org/10.1126/science.1185383
Hammer, G. L., van Oosterom, E., McLean, G., Chapman, S. C., Broad, I., Harland, P., & Muchow, R. C. (2010). Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. Journal of Experimental Botany, 61(8), 2185-2202. https://doi.org/10.1093/jxb/erq095
Hao, S., Ryu, D., Western, A., Perry, E., Bogena, H., & Franssen, H. J. H. (2021). Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis. Agricultural Systems, 194, 103278. https://doi.org/10.1016/j.agsy.2021.103278
Hoffmann, H., Zhao, G., van Bussel, L., Enders, A., Specka, X., Sosa, C., Yeluripati, J., Tao, F., Constantin, J., Raynal, H., Teixeira, E., Grosz, B., Doro, L., Zhao, Z., Wang, E., Nendel, C., Kersebaum, K. C., Haas, E., Kiese, R., ... Ewert, F. (2015). Variability of effects of spatial climate data aggregation on regional yield simulation by crop models. Climate Research, 65, 53-69. https://doi.org/10.3354/cr01326
Holt-Giménez, E., Shattuck, A., Altieri, M., Herren, H., & Gliessman, S. (2012). We Already Grow Enough Food for 10 Billion People … and Still Can’t End Hunger. Journal of Sustainable Agriculture, 36, 595-598. https://doi.org/10.1080/10440046.2012.695331
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., Moore, A. D., Brown, H., Whish, J. P. M., Verrall, S., Fainges, J., Bell, L. W., Peake, A. S., Poulton, P. L., Hochman, Z., & Keating, B. A. (2014). APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327-350. https://doi.org/10.1016/j.envsoft.2014.07.009
Holzworth, D., Huth, N. I., Fainges, J., Brown, H., Zurcher, E., Cichota, R., Verrall, S., Herrmann, N. I., Zheng, B., & Snow, V. (2018). APSIM Next Generation: Overcoming challenges in modernising a farming systems model. Environmental Modelling & Software, 103, 43-51. https://doi.org/10.1016/j.envsoft.2018.02.002
Kamali, B., Lorite, I. J., Webber, H. A., Rezaei, E. E., Gabaldon-Leal, C., Nendel, C., Siebert, S., Ramirez-Cuesta, J. M., Ewert, F., & Ojeda, J. J. (2022). Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain. Scientific Reports, 12, 4049. https://doi.org/10.1038/s41598-022-08056-9
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., ... Smith, C. J. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18(3-4), 267-288. https://doi.org/10.1016/S1161-0301(02)00108-9
Kersebaum, K. C., Boote, K. J., Jorgenson, J. S., Nendel, C., Bindi, M., Frühauf, C., Gaiser, T., Hoogenboom, G., Kollas, C., Olesen, J. E., Rötter, R. P., Ruget, F., Thorburn, P. J., Trnka, M., & Wegehenkel, M. (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environmental Modelling & Software, 72, 402-417. https://doi.org/10.1016/j.envsoft.2015.05.009
Khaliq, T., Gaydon, D. S., Ahmad, M.-D., Cheema, M. J. M., & Gull, U. (2019). Analyzing crop yield gaps and their causes using cropping systems modelling–A case study of the Punjab rice-wheat system, Pakistan. Field Crops Research, 232, 119-130. https://doi.org/10.1016/j.fcr.2018.12.010
Kheir, A. M. S., Alkharabsheh, H. M., Seleiman, M. F., Al-Saif, A. M., Ammar, K. A., Attia, A., Zoghdan, M. G., Shabana, M. M. A., Aboelsoud, H., & Schillaci, C. (2021). Calibration and Validation of AQUACROP and APSIM Models to Optimize Wheat Yield and Water Saving in Arid Regions. Land, 10(12), 1375. https://doi.org/10.3390/land10121375
Kouadio, L., Newlands, N., Potgieter, A., McLean, G., & Hill, H. (2015). Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool. Agricultural Sciences, 6(7), 686-698. https://doi.org/10.4236/as.2015.67066
Lawes, R. A., Oliver, Y. M., & Robertson, M. J. (2009). Integrating the effects of climate and plant available soil water holding capacity on wheat yield. Field Crops Research, 113(3), 297-305. https://doi.org/10.1016/j.fcr.2009.06.008
Li, Y., Hou, R., Liu, X., Chen, Y., & Tao, F. (2022). Changes in wheat traits under future climate change and their contributions to yield changes in conventional vs. conservational tillage systems. Science of The Total Environment, 815, 152947. https://doi.org/10.1016/j.scitotenv.2022.152947
Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., & Schlenker, W. (2013). The critical role of extreme heat for maize production in the United States. Nature Climate Change, 3, 497-501. https://doi.org/10.1038/nclimate1832
Ludwig, F., & Asseng, S. (2006). Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Agricultural Systems, 90(1-3), 159-179. https://doi.org/10.1016/j.agsy.2005.12.002
Luo, Z., Wang, E., Sun, O. J., Smith, C. J., & Probert, M. E. (2011). Modeling long-term soil carbon dynamics and sequestration potential in semi-arid agro-ecosystems. Agricultural and Forest Meteorology, 151(12), 1529-1544. https://doi.org/10.1016/j.agrformet.2011.06.011
McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P., & Freebairn, D. M. (1996). APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 50(3), 255-271. https://doi.org/10.1016/0308-521X(94)00055-V
Meinke, H., Hammer, G. L., van Keulen, H., Rabbinge, R., & Keating, B. A. (1997). Improving wheat simulation capabilities in Australia from a cropping systems perspective: water and nitrogen effects on spring wheat in a semi-arid environment. Developments in Crop Science, 25, 99-112. https://doi.org/10.1016/S0378-519X(97)80012-8
Monjardino, M., McBeath, T., Ouzman, J., Llewellyn, R., & Jones, B. (2015). Farmer risk-aversion limits closure of yield and profit gaps: A study of nitrogen management in the southern Australian wheatbelt. Agricultural Systems, 137, 108-118. https://doi.org/10.1016/j.agsy.2015.04.006
Oliver, Y. M., & Robertson, M. J. (2013). Quantifying the spatial pattern of the yield gap within a farm in a low rainfall Mediterranean climate. Field Crops Research, 150, 29-41. https://doi.org/10.1016/j.fcr.2013.06.008
Oliver, Y., Wong, M., Robertson, M., & Wittwer, K. (2006). PAWC determines spatial variability in grain yield and nitrogen requirement by interacting with rainfall on northern WA sandplain.
Peake, A. S., Huth, N. I., Kelly, A. M., & Bell, K. L. (2013). Variation in water extraction with maize plant density and its impact on model application. Field Crops Research, 146, 31-37. https://doi.org/10.1016/j.fcr.2013.02.012
Probert, M. E., Dimes, J. P., Keating, B. A., Dalal, R. C., & Strong, W. M. (1998). APSIM’s water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural Systems, 56(1), 1-28. https://doi.org/10.1016/S0308-521X(97)00028-0
Robertson, M. J., Gaydon, D., Hall, D. J. M., Hills, A., & Penny, S. (2005). Production risks and water use benefits of summer crop production on the south coast of Western Australia. Australian Journal of Agricultural Research, 56(6), 597-612. https://doi.org/10.1071/AR04249
Seidel, S. J., Palosuo, T., Thorburn, P., & Wallach, D. (2018). Towards improved calibration of crop models – Where are we now and where should we go? European Journal of Agronomy, 94, 25-35. https://doi.org/10.1016/j.eja.2018.01.006
Tahir, N., Li, J., Ma, Y., Ullah, A., Zhu, P., Peng, C., Hussain, B., Danish, S. (2021). 20 Years nitrogen dynamics study by using APSIM nitrogen model simulation for sustainable management in Jilin China. Scientific Reports, 11, 17505. https://doi.org/10.1038/s41598-021-96386-5
Tang, C., Asseng, S., Diatloff, E., & Rengel, Z. (2003). Modelling yield losses of aluminium-resistant and aluminium-sensitive wheat due to subsurface soil acidity: effects of rainfall, liming and nitrogen application. Plant and Soil, 254, 349-360. https://doi.org/10.1023/A:1025597905001
Tyczewska, A., Twardowski, T., & Woźniak-Gientka, E. (2023). Agricultural biotechnology for sustainable food security. Trends in Biotechnology, 41(3), 331-341. https://doi.org/10.1016/j.tibtech.2022.12.013
Wallach, D., Palosuo, T., Thorburn, P., Hochman, Z., Gourdain, E., Andrianasolo, F., Asseng, S., Basso, B., Buis, S., Crout, N., Dibari, C., Dumont, B., Ferrise, R., Gaiser, T., Garcia, C., Gayler, S., Ghahramani, A., Hiremath, S., Hoek, S., ... Seidel, S. J. (2021). The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise. Environmental Modelling & Software, 145, 105206. https://doi.org/10.1016/j.envsoft.2021.105206
Wallach, D., Palosuo, T., Thorburn, P., Mielenz, H., Buis, S., Hochman, Z., Gourdain, E., Andrianasolo, F., Dumont, B., Ferrise, R., Gaiser, T., Garcia, C., Gayler, S., Hiremath, S., Horan, H., Hoogenboom, G., Jansson, P.-E., Jing, Q., Justes, E., ... Seidel, S. J. (2022). Calibration of crop phenology models: Going beyond recommendations. bioRxiv.
Wang, E., van Oosterom, E., Meinke, H., Asseng, S., Robertson, M., Huth, N., Keating, B., & Probert, M. (2003a). The new APSIM-Wheat Model: performance and future improvements. 11th Australian Society of Agronomy Conference.
Wang, K., Shi, L., Zheng, B., & He, Y. (2023b). Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions. Frontiers in Plant Science, 14, 1138966. https://doi.org/10.3389/fpls.2023.1138966
Wimalasiri, E. M., Jahanshiri, E., Chimonyo, V., Azam-Ali, S. N., & Gregory, P. J. (2021). Crop model ideotyping for agricultural diversification. MethodsX, 8, 101420. https://doi.org/10.1016/j.mex.2021.101420
Wolday, K., & Hruy, G. (2015). A Review on: Performance Evaluation of Crop Simulation Model (APSIM) in Prediction Crop Growth, Development and Yield in Semi Arid Tropics. Journal of Natural Sciences Research, 5(21), 34-39.
Wong, M. T. F., & Asseng, S. (2006). Determining the Causes of Spatial and Temporal Variability of Wheat Yields at Sub-field Scale Using a New Method of Upscaling a Crop Model. Plant and Soil, 283, 203-215. https://doi.org/10.1007/s11104-006-0012-5
Yang, J. M., Yang, J. Y., Liu, S., & Hoogenboom, G. (2014). An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems, 127, 81-89. https://doi.org/10.1016/j.agsy.2014.01.008
Yang, X., Zheng, L., Yang, Q., Wang, Z., Cui, S., & Shen, Y. (2018). Modelling the effects of conservation tillage on crop water productivity, soil water dynamics and evapotranspiration of a maize-winter wheat-soybean rotation system on the Loess Plateau of China using APSIM. Agricultural Systems, 166, 111-123. https://doi.org/10.1016/j.agsy.2018.08.005
Zaman, A., & Maitra, S. (2018). Crop modeling: a tool for agricultural research. MOJ Food Processing & Technology, 6(4), 350-353. https://doi.org/10.15406/mojfpt.2018.06.00186
Zeng, W., Wu, J., Hoffmann, M. P., Xu, C., Ma, T., & Huang, J. (2016). Testing the APSIM sunflower model on saline soils of Inner Mongolia, China. Field Crops Research, 192, 42-54. https://doi.org/10.1016/j.fcr.2016.04.013
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