Development of a nitrogen recommendation tool for corn considering static and dynamic variables

Puntel, Laila A.; Pagani, Agustín; Archontoulis, Sotirios

doi:10.1016/j.eja.2019.01.003

Cited by 39 publications

(33 citation statements)

References 73 publications

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“…The ML algorithms predicted end-of season yield with a RRMSE of 13%-14%, which is comparable to the fit of the simulation model to the field data (figure S3) or even better considering that only information up to planting time was considered in this study (Martinez-Feria et al 2018, Puntel et al 2019. On the other hand, the RRMSE of cumulative annual N loss (harvest to harvest) was about 4 times higher than for yield and much greater than the error of the simulation model itself, indicating that annual N loss cannot be reliably predicted with information up to planting time.…”

Section: Discussionsupporting

confidence: 71%

“…For example, Ramanantenasoa et al (2019) evaluated the performance of various ML based meta-models to emulate the complex process-based models in predicting ammonia emissions produced by agricultural activities and demonstrated the superiority of random forests compared to LASSO regression. Lawes et al (2019) used ML and APSIM modeling to predict optimum N rates for wheat, Puntel et al (2019) and Qin et al (2018) used ML and experimental data to predict optimum N rates to maize, while others are exploring coupling ML and simulation models to develop faster and more flexible tools for impact regional assessments (Fienen et al 2015) and simulation model parameterization (Gladish et al 2019).…”

Section: Discussionmentioning

confidence: 99%

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Maize yield and nitrate loss prediction with machine learning algorithms

Shahhosseini

Martinez-Feria

et al. 2019

Environ. Res. Lett.

142

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Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked:(1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%-40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Maize yield and nitrate loss prediction with machine learning algorithms

Shahhosseini

Martinez-Feria

et al. 2019

Environ. Res. Lett.

142

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“…A number of approaches have been used to predict economically optimal N-fertilizer application rates (EONR) including yield goal assessments, pre-plant and pre-sidedress soil NO 3 − tests, crop canopy sensing, and maximum return to N calculators based on regionally specific empirical Nfertilizer rate trials (Sawyer and Nafziger, 2005;Puntel et al, 2016Puntel et al, , 2019; for a review see Morris et al, 2018). Additionally, studies have also attempted to quantify optimum site-specific seed densities (Licht et al, 2017), which may represent a more economically impactful management change in many cropping systems compared to changes in nutrient applications.…”

Section: Introductionmentioning

confidence: 99%

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

McNunn

Heaton

Archontoulis

et al. 2019

Front. Sustain. Food Syst.

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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.

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“…temperature and precipitation) explained approximately 8–20% of predicted yields, whereas 48% was accounted for other soil parameters (e.g. soil depth, initial soil water and SMN levels, and C/N ratio) (Archontoulis et al., 2020; Puntel, Pagani, & Archontoulis, 2019).…”

Section: Resultsmentioning

confidence: 99%

Relationship of in‐season soil nitrogen concentration with corn yield and potential nitrogen losses

Preza‐Fontes

Nafziger

Christianson

et al. 2020

Soil Science Soc of Amer J

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New process‐based tools for predicting in‐season soil nitrogen (N) levels has the potential to provide timely information for N management decisions for corn (Zea mays L.) production systems in the U.S. There is, however, little published data supporting the assumption that soil mineral N (SMN, NH4‐N + NO3‐N at 0–60 cm) is correlated with yield response at different vegetative growth stages. Moreover, the degree to which changes in SMN influence the risk of N losses is uncertain. Data from 32 site‐years of field experiments in Illinois (2015–2018)—that included 12 combinations of N fertilizer rate, timing, and source—were used to evaluate the relationship between SMN concentration and grain yields across vegetative growth stages and estimate the exceedance probability of N losses. Overall, SMN across vegetative growth explained 46–61% of the variation in grain yield. Critical level of SMN that optimized yield decreased from 23.4 mg kg−1 at V5‐V7 to 9.1 mg kg−1 at VT‐R1 growth stage, but it was consistent, ranging from 14.7 to 16.3 mg kg−1, among sampling periods between V8 and V16 stages. While increasing SMN from deficiency (below critical levels) to sufficiency (at critical levels) increased yields by 22% (11.8 vs. 14.4 Mg ha−1), it also increased the probability of environmental N losses throughout vegetative growth, indicating a clear tradeoff between production and sustainability goals. These results help guide the development of sustainable in‐season N management strategies by illustrating the importance of incorporating risks of environmental N losses when trying to reach optimum grain yield levels.

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Development of a nitrogen recommendation tool for corn considering static and dynamic variables

Cited by 39 publications

References 73 publications

Maize yield and nitrate loss prediction with machine learning algorithms

Maize yield and nitrate loss prediction with machine learning algorithms

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

Relationship of in‐season soil nitrogen concentration with corn yield and potential nitrogen losses

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