Using a Crop Modeling Framework for Precision Cost-Benefit Analysis of Variable Seeding and Nitrogen Application Rates

Abstract

A key goal of precision agriculture is to achieve the maximum crop yield while minimizing inputs and loses from cropping systems. The challenge for precision agriculture is that these factors interact with one another on a subfield scale. 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. 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. The framework is used estimate differences in yield, ROI, NO 3 − leaching, and N 2 O emissions corresponding with economic optimum (maximum ROI) and agronomic optimum (maximum yield) inputs. 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. 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. 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 . 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 2 O emissions reductions of 2.5 and 7.6 kg ha −1 , respectively. 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. 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.

Bayesian approach for maize yield response to plant density from both agronomic and economic viewpoints in North America

“…A second lesson, consistent with a past study looking at yield-density relationship 39 , is that latitude and hybrid relative maturity (CRM) in combination with the attainable yields within this factor, play a critical role in identifying more regional specific yield-density models. Similar responses on yield to plant density were more recently documented in maize crop 17,25,30,[39][40][41][42] , studying individual factors, in public-and private-field research plots and on-farm experimentation. The overall optimal plant density values reported in the scientific literature presented similar variation to the uncertainty ranges documented in this study.…”

“…Lastly, targeting the right economic optimum plant density (EOPD) is a realistic production approach 30 . Maize is sold as a commodity, where farmers are price-takers.…”

A Research Road Map for Responsible Use of Agricultural Nitrogen

“…Precision agriculture is not exclusively focused upon N management, but improving NUE is a common goal given the potential variance of crop N demand across the landscape and with time. Site-specific management can help tailor N applications, improve NUE, increase profits, and/or minimize risk of N loss (Balafoutis et al, 2017;Cao et al, 2017;Muschietti-Piana et al, 2018;McNunn et al, 2019;Wang et al, 2020). NUE, as a performance outcome, can also be used to evaluate management decisions in fields characterized by high spatial and temporal variability in biophysical conditions (Li et al, 2019).…”

Simulation of management strategies to mitigate nitrogen losses from crop rotations in Southland, New Zealand

“…The model allows continuous simulation of changes in soil N and water status in response to weather, management and crop uptake 9 . APSIM has successfully been applied in the assessment of environmental impacts of agricultural activities, as well as to guide N‐fertiliser application based on plant N status and remote sensing 10–12 …”