**apsimx: R package for APSIM-X (NextGen) and APSIM Classic (7.x)**

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This package allows for interaction with APSIM-X ("Next Generation")
and/or APSIM 'Classic' (7.10). It can inspect, edit, run and read
APSIM files in both platforms. The format is JSON for APSIM-X and XML
for 'Classic'.

**Package requirements**

* Imported R packages: DBI, jsonlite, knitr, RSQLite, tools, utils, xml2

* Suggested R packages: BayesianTools, chirps, datasets, daymetr, future, ggplot2,
  GSODR, listviewer, maps, metrica, mvtnorm, nasapower, nloptr, parallely, reactR, rmarkdown, sensitivity,
  soilDB, sp, spData, sf, ucminf

* APSIMX:

* and/or APSIM (7.x) 'Classic'

NOTE ON REQUIREMENTS:

* Current versions of APSIM Next Gen do not require additional software
to run on Mac or Linux. The dotnet runtime environment is shipped with the image/package.

* For current versions, on Windows, the 'Microsoft Windows Desktop Runtime' 3.1.14 is required. The installer also updated GTK3 to 3.24.20 during the installation.

* For older versions of APSIM Next Gen (before Sept 2021) the mono framework was required to run on Mac and Linux (Debian). Mono should be installed first (in Mac and Linux). 

**If you are running the latest version of APSIM Next Gen, you do 
not need to install the Mono Framework**.

**Mono framework download**:
https://www.mono-project.com/download/stable/

**.Net Core download for Mac**:
https://dotnet.microsoft.com/download

**APSIMX download**:
https://www.apsim.info/download-apsim/

Since APSIM Next Gen 2021.04.01 (at least) .NET framework 4.6 or higher is 
required for Windows and I have had to update the Mono framework to 
6.12 on Mac (again this applies to pre Sep 2021).

If you want to install this package from github try in R:

> library(devtools) \
> devtools::install_github("femiguez/apsimx") \
> library(apsimx)

or the lightweight 'remotes' package

> library(remotes) \
> remotes::install_github("femiguez/apsimx") \
> library(apsimx)

It is **not** necessary to build the vignettes as they are also at:
https://femiguez.github.io/apsimx-docs/

*Note*: Building the vignettes does not require the presence of APSIM-X as I
have recently eliminated the APSIM-X dependency.

Still, if you want to build the vignettes, then use this instead
of the second line above:

> devtools::install_github("femiguez/apsimx", build_vignettes = TRUE,
> build_opts = c("--no-resave-data", "--no-manual")) 

If you do build the vignettes, there is an introduction to the package

> vignette("apsimx")

and a document which might help you build your own scripts either in R
or other languages

> vignette("apsimx-scripts")

If you have any questions contact Fernando E. Miguez (femiguez *at* iastate.edu)

Some papers that cite this package (updated Jan 15 2026):

41\. Zhang, Y.; Ai, P.; Ma, Y.; Fu, Q.; Ma, X. Global Sensitivity 
Analyses of the APSIM-Wheat Model at Different Soil Moisture Levels. 
Plants 2025, 14, 2608. https://doi.org/10.3390/plants14172608

40\. Catherine Gilbert, German Mandrini, Elhan Ersoz, Nicolas Martin,
The seasonal characterization engine, an application for describing 
environment from the perspective of crop development SoftwareX, Volume 33,
2026, https://doi.org/10.1016/j.softx.2025.102477.

39\. Li, Y.; Yao, Y.; Du, M.; Dong, L.; Yuan, J.; Li, G. APSIM NG Model Simulation of Soil 
N2O Emission from the Dry-Crop Wheat Field and Its Parameter Sensitivity Analysis. 
Agronomy 2025, 15, 834. https://doi.org/10.3390/agronomy15040834

38\. Maas, E. D. v. L., & Sihi, D. (2025). Management alternatives for climate-smart agriculture 
at two long-term agricultural research sites in the United States: 
A model ensemble case study. Agronomy Journal, 117, e70146.
https://doi.org/10.1002/agj2.70146

37\. Marziotte, L., Carcedo, A. J. P., Mayor, L., Prasad, P. V. V., Peraza, J. A., 
& Ciampitti, I. A. (2025). An in-silico approach exploring sorghum source:sink 
balance across sorghum hybrids: How many leaves are enough?. 
Crop Science, 65, e21449. https://doi.org/10.1002/csc2.21449

36\. Hosseinpour, S., Pirdashti, H., Kaveh, M. et al. 
Determination of a Sustainable Management for Rice Production Through a Modeling Approach. 
Int. J. Plant Prod. 19, 47–64 (2025). https://doi.org/10.1007/s42106-024-00319-x

35\. Panelo, J. S., Miguez, F. E., Schnable, P. S., & Salas-Fernandez, M. G. (2025). 
Crop growth model-enabled genetic mapping of biomass accumulation dynamics in 
photoperiod-sensitive sorghum. The Plant Genome, 18, e70111. 
https://doi.org/10.1002/tpg2.70111

34\. Intensifying cropping sequences in the US Central Great Plains: 
an in silico analysis of a sorghum–wheat sequence. Lucia Marziotte, 
Ana J. P. Carcedo, Daniel Rodriguez, Laura Mayor, V. Vara Prasad, 
Ignacio A. Ciampitti. Front. Plant Sci., 29 May 2025.
https://doi.org/10.3389/fpls.2025.1525128

33\. Hangxin Zhou, Yuchen Wei, Mingming Wang, Liujun Xiao, Zhongkui Luo,
Enhancing whole-profile soil organic carbon predictions in croplands through
a depth-resolved modelling approach. Soil & Environmental Health. 
Volume 3, Issue 3, 2025. https://doi.org/10.1016/j.seh.2025.100156.

32\. Raigne, J. G., Higgins, R. H., Elli, E. F., Archontoulis, S. V., Dutta, S., 
Miguez, F. E., & Singh, A. K. (2025). Genetic variability in biomass partitioning 
and surface residue carbon-nitrogen ratios in soybean. 
Crop Science, 65, e70155. https://doi.org/10.1002/csc2.70155

31\. Climate-adaptative management strategies for soybean production under ENSO 
scenarios in Southern Brazil: An in-silico analysis of crop failure risk. 
Gabriel Hintz, Ana Carcedo, Luiz Felipe Almeida, Geomar Corassa, Tiago Horbe, 
Luan Pott, Raí Schwalbert, Trevor Hefley, P.V. Vara Prasad, Ignacio Ciampitti.
Agricultural Systems, Volume 222, 2025, https://doi.org/10.1016/j.agsy.2024.104153.

30\. Assessing yield stability of pearl millet and rice cropping systems across West Africa 
using long-term experiments and a modeling approach. Louis Kouadio, Kristina Fraser, 
Ali Ibrahim, Kazuki Saito, Fatondji Dougbedji, Kalimuthu Senthilkumar.
Published: May 27, 2025. https://doi.org/10.1371/journal.pone.0317170

29\. A Modeling Approach for Quantifying the Ecosystem Services of Managed Prairie Systems
Osterloh, Marissa. Iowa State University ProQuest Dissertations & Theses, 2025. 31934073.

28\. Abebe, T.M.; Degefie, D.T.; Abera, W.; Liben, F.M.; Mkuhlani, S. (2025) 
Integrating legume-based systems into wheat monoculture: Agronomic and soil
fertility impacts in the southeastern highlands of Ethiopia. 
CGIAR Sustainable Farming Science Program. 28 p.

27\. Quantifying Ecosystem Service Trade-Offs to Advance Sustainable Intensification of 
U.S. Corn Belt Agriculture. Magala, Richard. 
Iowa State University ProQuest Dissertations & Theses, 2025. 31935615.

26\. Ecohydrological Modeling of the Novel Perennial Ground Cover (PGC) System
Olowoyeye, Oluwatuyi S. Iowa State University ProQuest Dissertations & Theses,
2025.32117403.

25\. Current and future adaptation potential of heat-tolerant maize in Cameroon:
a combined attribution and adaptation study.
Lennart Jansen, Sabine Undorf and Christoph Gornott. DOI: 10.1088/1748-9326/ada459

24\. Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions
van Versendaal Pirez, Emmanuela. Kansas State University 
ProQuest Dissertations & Theses, 2025. 31937010.

23\. Simulating within-field spatial and temporal corn 
yield response to nitrogen with APSIM model. Thompson et al. (2024).
https://doi.org/10.1007/s11119-024-10178-1

22\. APSIM NG Model Simulation of Soil N2O Emission from the Dry-Crop 
Wheat Field and Its Parameter Sensitivity Analysis. Li et al. (2025).
https://doi.org/10.3390/agronomy15040834

21\. An in-silico approach exploring sorghum source:sink balance across 
sorghum hybrids: How many leaves are enough? Marziotte et al. (2025).
https://doi.org/10.1002/csc2.21449

20\. Changes in the leaf area-seed yield relationship in soybean driven by genetic, 
management and environments: implications for high-throughput phenotyping.
Chiozza et al. (2024). https://doi.org/10.1093/insilicoplants/diae012

19\. Identifying environments for canola oil production under diverse seasonal crop water stress levels.
Correndo et al. (2024) https://doi.org/10.1016/j.agwat.2024.108996

18\. PACU: Precision agriculture computational utilities. dos Santos and Miguez.
https://doi.org/10.1016/j.softx.2024.101971

17\. Determination of a Sustainable Management for Rice Production Through a Modeling Approach.
Hosseinpour et al. (2025). https://doi.org/10.1007/s42106-024-00319-x

16\. weatherOz: An API Client for Australian Weather and Climate Data Resources in R. 
Pires et al. (2024). DOI: 10.21105/joss.06717

15\. A cost-effective approach to estimate plant available water capacity. Gajurel et al. (2024).
https://doi.org/10.1016/j.geoderma.2024.116794

14\. van Versendaal et al.  Integrating Field Data and a Modeling Approach to Inform Optimum Planting Date × Maturity Group for Soybeans under Current and Future Weather Conditions in Kansas. Sustainability 2023, 15(2), 1081; https://doi.org/10.3390/su15021081

13\. R. H. K. Rathnappriya et al. Global Sensitivity Analysis of Key Parameters in the APSIMX-Sugarcane Model to Evaluate Nitrate Balance via Treed Gaussian Process. Agronomy 2022, 12(8), 1979; https://doi.org/10.3390/agronomy12081979

12\. Francisco Palmero, Ana J.P. Carcedo, Ricardo J. Haro, Ezequiel D. Bigatton, Fernando Salvagiotti, Ignacio A. Ciampitti. Modeling drought stress impacts under current and future climate for peanut in the semiarid pampas region of Argentina. Field Crops Research. 2022. https://doi.org/10.1016/j.fcr.2022.108615.

11\. Elsa Lagerquist, Iris Vogeler, Uttam Kumar, Göran Bergkvist, Marcos Lana, Christine A. Watson, David Parsons,
Assessing the effect of intercropped leguminous service crops on main crops and soil processes using APSIM NG.
Agricultural Systems. 2024. https://doi.org/10.1016/j.agsy.2024.103884.

10\. Daniel Pasquel, Davide Cammarano, Sébastien Roux, Annamaria Castrignanò, Bruno Tisseyre, Michele Rinaldi, Antonio Troccoli, James A. Taylor. Downscaling the APSIM crop model for simulation at the within-field scale,
Agricultural Systems. 2023. https://doi.org/10.1016/j.agsy.2023.103773.

9\. Tommaso Tadiello, Mara Gabbrielli, Marco Botta, Marco Acutis, Luca Bechini, Giorgio Ragaglini, Andrea Fiorini, Vincenzo Tabaglio, Alessia Perego. A new module to simulate surface crop residue decomposition: Description and sensitivity analysis. Ecological Modelling. Volume 480. 2023. https://doi.org/10.1016/j.ecolmodel.2023.110327.

8\. Lopez-Cruz, M., Aguate, F.M., Washburn, J.D. et al. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 14, 6904 (2023). https://doi.org/10.1038/s41467-023-42687-4

7\. Ignacio Massigoge, Ana Carcedo, Jane Lingenfelser, Trevor Hefley, P.V. Vara Prasad, Dan Berning, Sara Lira, Carlos D. Messina, Charles W. Rice, Ignacio Ciampitti. Maize planting date and maturity in the US central Great Plains: Exploring windows for maximizing yields. European Journal of Agronomy. Volume 149. 2023. 
https://doi.org/10.1016/j.eja.2023.126905.

6\. Yang, Xuening and Zhang, Xuanze and Zhao, Zhigan and Ma, Ning and Tian, Jing and Xu, Zhenwu and Zhang, Junmei and Zhang, Yongqiang, Rainfall and Maximum Temperature are Dominant Climatic Factors Influencing Apsim-Maize Cultivar Parameters Sensitivity in Semiarid Regions. Available at SSRN: https://ssrn.com/abstract=4693866 or http://dx.doi.org/10.2139/ssrn.4693866

5\. Determining site-specific corn nitrogen rate over time with APSIM model. L.J. Thompson, S. Archontoulis, and L.A. Puntel. https://doi.org/10.3920/978-90-8686-947-3_138

4\. Kheir, A. M. S., Mkuhlani, S., Mugo, J. W., Elnashar, A., Nangia, V., Devare, M., & Govind, A. (2023). Integrating APSIM model with machine learning to predict wheat yield spatial distribution. Agronomy Journal, 115, 3188–3196. https://doi.org/10.1002/agj2.21470

3\. Acceptability and Evaluation of APSIM-Qryza for Promoting Water and Nitrogen Productivity in Paddy Fields. 
Shayan Hosseinpour, Hemmatollah Pirdashti, Mohammad Kaveh, Hamze Dokoohaki. https://doi.org/10.21203/rs.3.rs-2677879/v1

2\. Augmenting agroecosystem models with remote sensing data and machine learning increases overall estimates of nitrate-nitrogen leaching. Matthew Nowatzke, Luis Damiano, Fernando E Miguez, Gabe S McNunn, Jarad Niemi, Lisa A Schulte, Emily A Heaton and Andy VanLoocke. 20 October 2022. Environmental Research Letters, Volume 17, Number 11
DOI: 10.1088/1748-9326/ac998b

1\. Laurent, A., Cleveringa, A., Fey, S. et al. Late-season corn stalk nitrate measurements across the US Midwest from 2006 to 2018. Sci Data 10, 192 (2023). https://doi.org/10.1038/s41597-023-02071-9
