There is tremendous uncertainty and risk associated with prediction of the EONR in corn–based systems, both at the field and sub-field scale ( Paz et al., 1999 Scharf et al., 2005 Tremblay et al., 2012).
![nitrogen model agriculture apsim nitrogen model agriculture apsim](https://d32ogoqmya1dw8.cloudfront.net/images/integrate/teaching_materials/food_supply/student_materials/schematic_eutrophication_744.png)
Nitrogen losses by leaching are proportional to the N rate applied and tend to increase rapidly at rates greater than optimal for crop use ( Haghiri et al., 1978 Cooper and Cooke, 1984 Andraski et al., 2000 Randall et al., 2000). Optimal N input needs to be considered when making N recommendations since it has the potential to improve N use efficiency, crop yield, and profitability as well as to reduce environmental impacts ( Wang et al., 2003 Lawlor et al., 2008 Kyveryga et al., 2009 Basso et al., 2016). The economic optimum nitrogen (N) rate (EONR) is the fertilizer rate at which crop yield increase is not large enough to pay for additional N application, and therefore more N would only result in unnecessary costs ( Sawyer et al., 2006). The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. However, for accurate year-by-year simulation of EONR the calibrated version should be used.
![nitrogen model agriculture apsim nitrogen model agriculture apsim](https://ars.els-cdn.com/content/image/1-s2.0-S0167880914000413-gr1.jpg)
For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha -1). The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps (b) compare crop model-based techniques in estimating optimal N rate for corn and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N.
#NITROGEN MODEL AGRICULTURE APSIM SIMULATOR#
We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean ( Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha -1) applied to corn. Improved prediction of optimal N fertilizer rates for corn ( Zea mays L.) can reduce N losses and increase profits.
#NITROGEN MODEL AGRICULTURE APSIM SOFTWARE#
Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. (2003) described many of the fundamental attributes of APSIM in detail.
![nitrogen model agriculture apsim nitrogen model agriculture apsim](https://cdn.britannica.com/s:700x500/77/54477-050-B52A4BA9/nitrogen-cycle.jpg)
From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains.