The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Evaluation of the APSIM model in cropping systems of Asia
Open accessAbstract
Resource shortages, driven by climatic, institutional and social changes in many regions of Asia, combined with growing imperatives to increase food production whilst ensuring environmental sustainability, are driving research into modified agricultural practices. Well-tested cropping systems models that capture interactions between soil water and nutrient dynamics, crop growth, climate and farmer management can assist in the evaluation of such new agricultural practices. One such cropping systems model is the Agricultural Production Systems Simulator (APSIM). We evaluated APSIM’s ability to simulate the performance of cropping systems in Asia from several perspectives: crop phenology, production, water use, soil dynamics (water and organic carbon) and crop CO2 response, as well as its ability to simulate cropping sequences without reset of soil variables. The evaluation was conducted over a diverse range of environments (12 countries, numerous soils), crops and management practices throughout the region. APSIM’s performance was statistically assessed against assembled replicated experimental datasets. Once properly parameterised, the model performed well in simulating the diversity of cropping systems to which it was applied with RMSEs generally less than observed experimental standard deviations (indicating robust model performance), and with particular strength in simulation of multi-crop sequences. Input parameter estimation challenges were encountered, and although ‘work-arounds’ were developed and described, in some cases these actually represent model deficiencies which need to be addressed. Desirable future improvements have been identified to better position APSIM as a useful tool for Asian cropping systems research into the future. These include aspects related to harsh environments (high temperatures, diffuse light conditions, salinity, and submergence), conservation agriculture, greenhouse gas emissions, as well as aspects more specific to Southern Asia and low input systems (such as deficiencies in soil micro-nutrients).
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Cited by
Coupling machine learning and crop modeling improves crop yield prediction in the US corn belt
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Metadata
- Authors
- 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., Laing, A. M., Liu, L., Malaviachichi, M. A. P. W. K., Mohapatra, K. P., Muttaleb, M. A., Power, B., Radanielson, A. M., Rai, G. S., Rashid, M. H., Rathanayake, W. M. U. K., Sarker, M. M. R., Sena, D. R., Shamim, M., Subash, N., Suriadi, A., Suriyagoda, L. D. B., Wang, G., Wang, J., Yadav, R. K., & Roth, C. H.
- Venue
- Field Crops Research
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 204
- Pages
- 52-75
- ISSN
- 0378-4290 (ISSN-L)
- OpenAlex
- W2576100979
- Cited by
- 267
- References
- 94
- FWCI
- 19.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- 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. https://doi.org/10.1016/j.fcr.2016.12.015
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