Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII

Wilson, Grace L.; Mulla, D. J.; Galzki, J.; Laacouri, Aicam; Vetsch, Jeff; Sands, Gary R.

doi:10.1007/s11119-019-09668-4

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…The experimental boundaries for both the small plot and whole field experiments near Waseca, Minnesota are illustrated in Figure 1 [17]. The previous crop (i.e., in 2018) was soybeans, and the Nicollet clay loam (fine-loamy, mixed, superactive, mesic Aquic Hapludolls) and Webster clay loam (fine-loamy, mixed, superactive, mesic Typic Endoaquolls) soils were the two predominant soil series at the site.…”

Section: Waseca Experimentsmentioning

confidence: 99%

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

et al. 2020

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The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.

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Section: Waseca Experimentsmentioning

confidence: 99%

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

et al. 2020

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“…The rate of 150 kg N ha −1 is the typical average rate used by farmers in the region (Bierman et al., 2012). The application rate of 100 kg N ha −1 corresponded to a lower rate used on fields receiving an N rate based on in‐season crop N requirement monitoring (Wilson et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Minnesota fields managed using VRN compared with a single spring application showed no differences in crop yield between uniform and VRN fertilizer management but received 22–33% less N on average (Wilson et al., 2020). The measured NO 3 –N concentrations from subsurface drainage effluent were less for fields managed with VRN fertilizer than fields receiving a single spring fertilizer application.…”

Section: Introductionmentioning

confidence: 99%

Predicting nitrate‐nitrogen loads in subsurface drainage as a function of fertilizer application rate and timing in southern Minnesota

et al. 2020

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Fertilizer management practices that focus on applying N fertilizer at the right rate and time have been proposed as a practical option to reduce NO 3-N losses from subsurface drained agricultural fields. In this study, regression equations were developed to predict NO 3-N losses for a corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] rotation in southern Minnesota, using fertilizer application timing and rate and growing season precipitation as inputs. The equations were developed using the results of the field-scale hydrologic and N simulation model DRAINMOD-NII, first calibrated and validated for three sites in southern Minnesota, and then run with different combinations of N fertilizer application rates and timings. Fertilizer timing treatments included a single application in the fall or spring and a split-spring application (half applied preplant and the remaining applied as sidedress). The predictive regression equations showed that the split fertilizer application timing could reduce regional N loads by 28% compared with spring or fall applications. Greater reductions were predicted when the split timing was combined with lower N fertilizer rates. Utilizing the split application timing and reducing the fertilizer rate by 10 and 30% showed 33 and 41% reductions in N loads, respectively, compared with current fertilizer management practices. Such reductions in fertilizer application rates could be achieved through the use of variable-rate nitrogen (VRN) fertilizer technologies. Results of this modeling study indicate that synchronizing fertilizer application with crop requirements and utilizing VRN technologies could significantly reduce N loads to surface waters in southern Minnesota.

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“…For example, [20] reported that the median economically optimal N fertilizer rate (EONR) for corn ranged from 63 to 208 kg N ha −1 y −1 for three major soil areas in the US. However, improper N management during corn-soybean rotations in the Midwest US has been reported to contaminate groundwater with NO 3 -N [21][22][23]. About 98% of the corn-producing areas in Illinois receive N fertilizers and in 2018, Illinois farmers used about 20 kg ha −1 higher N fertilizers for corn productions than the average N applied for other corn-producing states in the US [24].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Nitrogen Budget for a Long-Term Row Crop Production System in the Midwest USA

et al. 2020

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In the Midwestern United States, subsurface drainage (commonly known as tile drains) systems have been extensively used for sustaining agricultural production. However, the tile drains have raised concerns of facilitating the transport of agricultural chemicals from the fields to receiving waters. Data from a long-term field experiment in the Little Vermilion River (LVR) watershed of east-central Illinois, USA, shows that the tile drain systems have contributed to increased nitrate N (NO3-N) to the receiving water body, Georgetown Lake Reservoir, over time. We conducted more than 10 years of research on fate and transport of NO3-N in tile drain water, surface runoff and soil N. Corn (Zea mays L.) and soybean (Glycine max L.) were planted in rotation for this watershed. We evaluated N balance (inputs and outputs) and transfer (runoff and leaching) components from three sites with both surface and subsurface flow stations within this watershed, and N budgets for individual sites were developed. Nitrogen fertilizer application (average 192 kg ha−1 y−1) and soil N mineralization (average 88 kg ha−1 y−1) were the major N inputs for corn and soybean, respectively in this watershed. Plant N uptake was the major N output for both crops during this entire study period. Annual N uptake for the LVR watershed ranged from +39 to +148 (average +93) kg ha−1 and −63 to +5 (average −32) kg ha−1, respectively, for corn and soybeans. This data indicates that most of the soil mineralized N was used during soybean production years, while corn production years added extra N in the soil. Surface runoff from the watershed was negligible, however, subsurface leaching through tile drains removed about 18% of the total rainfall. Average NO3-N concentrations of leaching water at sites A (15 mg L−1) and B (16.5 mg L−1) exceeded maximum contaminant level (MCL; 10 mg L−1) throughout the experiment. However, NO3-N concentrations from site E (6.9 mg L−1) never exceeded MCL possibly because 15–22% lower N was received at this site. We estimated that the average corn grain yield would need to be 28% higher to remove the additional N from this watershed. Our study suggests that N application schemes of the LVR watershed need to be reevaluated for better N management, optimum crop production, and overall environmental sustainability.

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Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII

Cited by 7 publications

References 26 publications

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

Predicting nitrate‐nitrogen loads in subsurface drainage as a function of fertilizer application rate and timing in southern Minnesota

Agricultural Nitrogen Budget for a Long-Term Row Crop Production System in the Midwest USA

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