Abstract
Abstract Traditional simulation models are often point based; thus, more research is needed to emphasize spatial simulation, providing decision‐makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine‐resolution data from coarse‐resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the artificial neural network, creating a hybrid modeling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modeling workflows with the spatial APSIM next‐generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg N ha −1 to the analogues' actual yield at the same grid points, which showed good agreement ( d = 0.89) between the spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement ( d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha −1 ) relative to the potential yield.
Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 3
Claim · model-written summary
Spatial APSIMx_R predicted wheat yield with agreement d = 0.89 against actual yield at 100 kg N ha −1.
- Taxon
- wheat
Triticum aestivumt0 - Treatment
- 100 kg N ha −1e0
- Outcome
- agreemente0
- Value
- d = 0.89e0
- Comparator
- actual yielde0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“wheat yield”
e0“The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg N ha −1 to the analogues' actual yield at the same grid points, which showed good agreement ( d = 0.89) between the spatially predicted and actual yield.”
Claim · model-written summary
The hybrid model raised agreement with actual wheat yield to d = 0.95.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- agreemente0
- Direction
- highere0
- Value
- d = 0.95e0
- Comparator
- actual yielde0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“wheat yield”
e0“The hybrid approach increased such precision, resulting in higher agreement ( d = 0.95) with actual yield.”
Claim · model-written summary
Sakha95 yield dropped by 65% under 0 kg N ha −1 relative to potential yield.
- Taxon
- wheatTriticum aestivumt0
- Material
- Sakha95e0
- Treatment
- 0 kg N ha −1e0
- Outcome
- yielde0
- Direction
- drope0
- Value
- 65%e0
- Comparator
- potential yielde0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“wheat yield”
e0“the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha −1 ) relative to the potential yield.”
The same questions in other papers
nitrogen · yield · Triticum aestivum (wheat)Positive and negative results
323 papers in the same direction as this paper · 1,737 in the opposite direction
- positive
- positive
- positive
2,057 papers more in this group
See all 2,061 papers →Claims are grouped by organism, treatment and what was measured or targeted. A paper counts as positive when any of its claims in the group is positive.