Water effects on optical canopy sensing for late-season site-specific nitrogen management of maize

Lo, Tsz Him; Rudnick, Daran R.; Krienke, Brian; Heeren, Derek M.; Ge, Yufeng; Shaver, Timothy M.

doi:10.1016/j.compag.2019.04.006

Cited by 12 publications

(13 citation statements)

References 34 publications

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“…This supports the need for having an N recommendation tool that combines both temporal and spatial variability to better predict the EONR [5,78,79]. For ZP17, imagery was able to capture the response to the base N application, prior to in-season N application [23,25,53,59,80]. Imagery obtained after in-season N application (14 July and 4 September; Figure 4) indicated that the in-season application adequately addressed crop N needs as treatment differences were no longer present [24,25,80].…”

Section: Uav Sensor-based N Recommendationsmentioning

confidence: 69%

“…Use of the VR method may reduce the risk of yield losses caused by under applying N, however, environmental consequences to applying excess N must be considered [7][8][9]. Both VR methods were consistently below the recommended 0.95 sufficiency threshold [23,65] even after in-season N application occurred ( Figure 6). Conversely, using the 0.95 sufficiency threshold, there was no date or site that would have recommended additional in-season N prior to tasseling using the HN method ( Figure 6).…”

Section: Impact Of Sensor Normalization Approach On N Recomendationsmentioning

confidence: 99%

“…The original protocol identified 0.95 SI as the threshold to trigger in-season N application [23,25,56,65,72]. The SI threshold allows N application to be dictated by crop N need as expressed by the plant.…”

Section: Timing Of In-season N Recommendationmentioning

confidence: 99%

“…This greater percentage of the total N applied as a base rate potentially reduced the ability to capture within field variability with crop sensing. A higher N base rate also increases the risk that crop sensing will not detect N differences until after the target N application window (VT to R2 corn stages) [23,80,87]. Significant soil N mineralization could also occur, resulting in base N rates which exceed the EONR [24,88].…”

Section: Uav Sensor-based N Recommendationsmentioning

confidence: 99%

“…Water stress increases leaf reflectance at the visible and near infrared wavelengths [92,93], underestimating plant chlorophyll content [93] and decreasing the crop vigor and greenness [94]. Thus, we expect that incorporating other vegetative indices which are more responsive under water stress conditions (e.g., DATT) [95] or methods which incorporate canopy temperature would help isolate variable N sufficiency from optical canopy-sensing data in the presence of variable water sufficiency [23]. Further research on this approach is needed in light of recent commercial passive sensors offering both multispectral and thermal data simultaneously using a UAV (e.g., MicaSense ® Altum™).…”

Section: Impact Of Sensor Normalization Approach On N Recomendationsmentioning

confidence: 99%

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Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Thompson

Puntel

2020

Remote Sensing

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Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.

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Section: Uav Sensor-based N Recommendationsmentioning

confidence: 69%

Section: Impact Of Sensor Normalization Approach On N Recomendationsmentioning

confidence: 99%

Section: Timing Of In-season N Recommendationmentioning

confidence: 99%

Section: Uav Sensor-based N Recommendationsmentioning

confidence: 99%

Section: Impact Of Sensor Normalization Approach On N Recomendationsmentioning

confidence: 99%

See 3 more Smart Citations

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Thompson

Puntel

2020

Remote Sensing

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Nitrogen Management for Improving Water, Energy, and Food Security

Krienke

Rudnick

et al. 2022

Food, Energy, and Water Nexus

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Differences in soil water changes and canopy temperature under varying water × nitrogen sufficiency for maize

Rudnick

DeJonge

et al. 2020

Irrig Sci

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Crop nitrogen (N) status is known to affect crop water status and crop water use. To investigate further the N effects on soil water changes and on canopy temperature, three water levels × four N levels were imposed on two growing seasons of maize in west central Nebraska, USA. Soil water changes were measured using a neutron probe, whereas canopy temperature was measured using infrared thermometers on a ground-based mobile platform. At all water levels, soil water losses over monthlong intervals were generally greater as N levels increased. Given equal water levels, early afternoon canopy temperatures were usually lower with higher N levels, but no trend or even the opposite trend was occasionally observed. Jointly considering canopy reflectance and soil water depletion shows potential to explain much of the variation in estimated instantaneous water use among plots. However, determining the relative contributions of the canopy and soil factors on a particular day may require season-to-date knowledge of the crop. Further research on assimilating such sensor data for a combined stress coefficient would improve crop modeling and irrigation scheduling when variable water sufficiency and variable N sufficiency are simultaneously significant.

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Water effects on optical canopy sensing for late-season site-specific nitrogen management of maize

Cited by 12 publications

References 34 publications

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Nitrogen Management for Improving Water, Energy, and Food Security

Differences in soil water changes and canopy temperature under varying water × nitrogen sufficiency for maize

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