Abstract
A key goal of precision agriculture is to achieve the maximum crop yield while minimizing inputs and loses from cropping systems. The challenge of precision agriculture is these factors interact with one another on a subfield scale. The seeding density and the nitrogen (N) fertilizer application rate are two of the most important inputs influencing agronomic, economic and environmental outcomes including yield, return on investment (ROI) and nitrate (NO3-) leaching. A cropping system simulation framework is used to predict site-specific subfield optimum seeding density and (N) fertilizer application rates for the economic optimum (maximum ROI) versus agronomic optimum (maximum yield). 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 performance was evaluated using multiple years of precision yield monitor data obtained from a continuous maize (Zea mays L.) cropping system field experiment with varying N-fertilizer rates. Subfield model estimates of crop yield were sensitive to initial conditions related to historical management of the field and had an r2 = 0.65 and a root mean square error of 2356.0 kg ha-1. A site-specific application of the framework comparing economic optimum seeding density and N-fertilizer rates with agronomic optimum values estimated the average ROI benefit to be up to 12.1% with a NO3- leaching reduction of up to 15.2 kg ha-1 at the economic optimum. However, in a minority of cases NO3- leaching was greater at the economic optimum, indicating that managing to maximize ROI rather than yield may not always reduce environmental impacts. Our results suggest that managing cropping systems for the economic optimum is plausible using publicly available data with our framework and may likely lead to improved environmental outcomes.
Highlights
Optimizing the use of input resources in agricultural land management is critical to maintaining sustainable and profitable cropping systems
Regional MRTN tools that incorporate N-fertilizer prices and use empirical data to predict yield response to variable N-fertilizer rates provide field-scale approximations of optimums, but do not provide site-specific subfield recommendations or adjust for year-to-year variability (Sawyer et al, 2006). Cropping system models such as Agricultural Production Systems sIMulator (APSIM) are capable of predicting such site-specific subfield yield responses, determining how these models can best be applied to provide land managers with actionable information to use within their existing management operations is difficult
Similar geographical focus was given in Jin et al (2019), which used an APSIM framework to estimate regional economic optimum nitrogen fertilizer rates based on subfield management zone simulations across many Midwest fields
Summary
Optimizing the use of input resources in agricultural land management is critical to maintaining sustainable and profitable cropping systems. 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. 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. 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). Modest increases in ROI are reported from the use of VRT and adoption has remained relatively limited (28% of US maize hectares) compared to other precision agriculture technologies such as yield monitors and GPS guidance systems (70 and 54% of US maize hectares, respectively, Schimmelpfennig, 2016)
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