APSIM - Evolution towards a new generation of agricultural systems simulation

By D. Holzworth, N. Huth, P. Voil, E. Zurcher, N. Herrmann, Greg McLean, Karine Chenu, Erik J. van Oosterom, V. Snow, C. Murphy and 36 more. Environmental Modelling & Software, 2014

1,561 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/4af33b3419eea21f3c4716d1382d0b8f033b5e65

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Agronomy and Crop Science

1. J. Kirkegaard
2. G. Sileshi
3. F. Akinnifesi
4. Daniel Rodríguez
5. James R. Hunt
6. M. Unkovich
7. Enli Wang
8. B. Mudge

In Ecology, Evolution, Behavior and Systematics

1. J. Kirkegaard
2. James R. Hunt
3. Daniel Rodríguez
4. V. Sadras
5. Enli Wang
6. M. Unkovich
7. B. Mudge
8. M. Bielich

In Plant Science

1. V. Sadras
2. James W. Jones
3. Daniel Rodríguez
4. J. Kirkegaard
5. Kenneth J. Boote
6. G. Sileshi
7. James R. Hunt
8. Enli Wang

Citations in the corpus

• Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry (2017, methodology): “The Next Generation version of the APSIM modelling framework (Holzworth et al., 2014, 2015), which includes the maize model used here in its release version, was used to simulate the long and short term interactions amongst maize and gliricidia.”
• Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia (2017, methodology): “Alternatively, an analysis of historic climate records using a crop simulator such as 137 APSIM (Holzworth et al., 2014; Keating et al., 2003) allows one to identify 138 management strategies to achieve optimum sowing date and flowering period 139 (Asseng et al.”
• Machine learning for large-scale crop yield forecasting (2020, background): “Reusability has not been a design goal in agricultural system modeling; more emphasis has been placed on the underlying science (Holzworth et al., 2014).”
• Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020, methodology): “The APSIM software platform general description We used the Agricultural Production Systems sIMulator (APSIM; Holzworth et al., 2014) version 7.7 in the first year and version 7.8 in subsequent years.”
• Interactions between water and nitrogen in Australian cropping systems: physiological, agronomic, economic, breeding and modelling perspectives (2016, methodology): “Here, we outline aspects of the water and nitrogen economy of crops from a modelling perspective with a primary focus on APSIM (Probert et al. 1998; Holzworth et al. 2014).”