In maize ( Zea mays L.) cropping systems seed density and N-fertilizer are two of the most important decision criteria influencing yield, profitability, and nutrient losses to the environment ( Licht et al., 2017 Morris et al., 2018). Variable rate technology (VRT) provides a mechanism for varying the allocation of input resources. Areas where N-fertilizer is applied in excess of crop demand are often correlated with higher susceptibility to nitrate (NO 3 −) leaching, nitrous oxide (N 2O) emissions, and other environmental losses, while under-fertilized areas may result in limited crop productivity, lost opportunity for profit, and decreased economic return ( Link et al., 2006 Basso et al., 2016).
This spatial variability leads to over- and under-fertilization in different parts of the field when using uniform seeding densities and nitrogen (N) fertilizer rates. However, farm fields are characterized by subfield variability linked to soil properties, topography, competition with pests and weeds, as well as other factors that directly or indirectly influence plant health. Optimizing the use of input resources in agricultural land management is critical to maintaining sustainable and profitable cropping systems. Our results suggest that managing cropping systems for the economic optimum is plausible using publicly available data with our framework and will likely lead to improved environmental outcomes.
However, in a minority of cases NO 3 − leaching was greater at the economic optimum, indicating that managing to maximize ROI rather than yield may not always reduce environmental impacts. A site-specific application of the framework comparing economic optimum seeding density and N-fertilizer rates with agronomic optimum values estimated an average ROI benefit of 7.2% as well as an average NO 3 − leaching and N 2O emissions reductions of 2.5 and 7.6 kg ha −1, respectively. Subfield model estimates of crop yield were sensitive to initial conditions related to historical management of the field and had an r 2 = 0.65 and a root mean square error of 1645.0 kg ha −1. Framework performance was evaluated using multiple years of precision yield monitor data obtained from a conventionally managed continuous maize ( Zea mays L.) cropping system field located in north central Iowa on which varying N-fertilizer rates were applied. The framework couples the process-based APSIM cropping system model with the SSURGO soils database, Daymet weather data service, land grant university estimates of crop production costs and commodity price estimates, and the R statistics software. The framework is used estimate differences in yield, ROI, NO 3 − leaching, and N 2O emissions corresponding with economic optimum (maximum ROI) and agronomic optimum (maximum yield) inputs. Here a cropping system model framework is used to predict site-specific subfield optimum seeding density and (N) fertilizer application rates based on publicly available data sources. Seeding density and nitrogen (N) fertilizer application rates are two of the most important inputs influencing agronomic, economic and environmental outcomes in cropping systems including yield, return on investment (ROI), and nitrate (NO 3 −) leaching. The challenge for precision agriculture is that these factors interact with one another on a subfield scale.
Values of RUE are very close to the upper bound value. In this case, minimizations converge towards different values for the parameters (3 for ExtinctionCoeff and 2 for RUE), which indicates the presence of local minima. The number in white, 2 in this case, is the minimization that lead to the minimal value of the criterion among all repetitions. Numbers represent the repetition number of the minimization and the size of the bubbles the final value of the minimized criterion. Among them, the EstimatedVSinit.pdf file contains the following figures:įigure 1: plots of estimated vs initial values of parameters ExtinctionCoeff and RUE. Ĭomplementary graphs and data are stored in the folder which path is given in optim_options$path_results.
#Apsim generic crop template install
# Install and load the needed libraries if ( ! require ( "CroptimizR" ) ) # DEFINE THE PATH TO THE LOCALLY INSTALLED VERSION OF APSIM (should be something like C:/path/to/apsimx/bin/Models.exe on windows, and /usr/local/bin/Models on linux) apsimx_path <- "D:\\Home\\sbuis\\Documents\\OUTILS\\APSIM2021.\\Bin\\Models.exe"
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