Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients
Livestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, ra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/963 |
| _version_ | 1797476854869786624 |
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| author | Xiaoyu Feng Rebecca A. Larson Matthew F. Digman |
| author_facet | Xiaoyu Feng Rebecca A. Larson Matthew F. Digman |
| author_sort | Xiaoyu Feng |
| collection | DOAJ |
| description | Livestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, rapid, and cost-effective technique for inline analysis of animal manure. This study investigated a NIR sensing system with reflectance and transflectance modes to predict N speciation in dairy cow manure using a spiking method. In this study, 20 dairy cow manure samples were collected and spiked to achieve four levels of ammoniacal nitrogen (NH<sub>4</sub>-N) and organic nitrogen (Org-N) concentrations that resulted in 100 samples in each spiking group. All samples were scanned and analyzed using a NIR system with reflectance and transflectance sensor configurations. NIR calibration models were developed using partial least square regression analysis for NH<sub>4</sub>-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE) were selected to evaluate the models. A transflectance probe with a 1 mm path length had the best performance for analyzing manure constituents among three path lengths. Reflectance mode improved the calibration accuracy for NH<sub>4</sub>-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH<sub>4</sub>-N (R<sup>2</sup> = 0.83; RMSE = 0.65 mg mL<sup>−1</sup>) and approximate predictions for Org-N (R<sup>2</sup> = 0.66; RMSE = 1.18 mg mL<sup>−1</sup>). Transflectance was excellent for TS predictions (R<sup>2</sup> = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH<sub>4</sub>-N and Org-N calibration models and other manure parameters were not observed indicating the predictions of N contents were not affected by TS, ash, and PS. |
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| format | Article |
| id | doaj.art-f02ed45941f149c2bffbfd0ea6d5cdda |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:41Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-f02ed45941f149c2bffbfd0ea6d5cdda2023-11-23T21:54:56ZengMDPI AGRemote Sensing2072-42922022-02-0114496310.3390/rs14040963Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure NutrientsXiaoyu Feng0Rebecca A. Larson1Matthew F. Digman2Department of Biological Systems Engineering, University of Wisconsin-Madison, 201 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USADepartment of Biological Systems Engineering, University of Wisconsin-Madison, 201 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USADepartment of Biological Systems Engineering, University of Wisconsin-Madison, 201 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USALivestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, rapid, and cost-effective technique for inline analysis of animal manure. This study investigated a NIR sensing system with reflectance and transflectance modes to predict N speciation in dairy cow manure using a spiking method. In this study, 20 dairy cow manure samples were collected and spiked to achieve four levels of ammoniacal nitrogen (NH<sub>4</sub>-N) and organic nitrogen (Org-N) concentrations that resulted in 100 samples in each spiking group. All samples were scanned and analyzed using a NIR system with reflectance and transflectance sensor configurations. NIR calibration models were developed using partial least square regression analysis for NH<sub>4</sub>-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE) were selected to evaluate the models. A transflectance probe with a 1 mm path length had the best performance for analyzing manure constituents among three path lengths. Reflectance mode improved the calibration accuracy for NH<sub>4</sub>-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH<sub>4</sub>-N (R<sup>2</sup> = 0.83; RMSE = 0.65 mg mL<sup>−1</sup>) and approximate predictions for Org-N (R<sup>2</sup> = 0.66; RMSE = 1.18 mg mL<sup>−1</sup>). Transflectance was excellent for TS predictions (R<sup>2</sup> = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH<sub>4</sub>-N and Org-N calibration models and other manure parameters were not observed indicating the predictions of N contents were not affected by TS, ash, and PS.https://www.mdpi.com/2072-4292/14/4/963NIRmanure sensingnitrogenspikingdairy cow |
| spellingShingle | Xiaoyu Feng Rebecca A. Larson Matthew F. Digman Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients Remote Sensing NIR manure sensing nitrogen spiking dairy cow |
| title | Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients |
| title_full | Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients |
| title_fullStr | Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients |
| title_full_unstemmed | Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients |
| title_short | Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients |
| title_sort | evaluation of near infrared reflectance and transflectance sensing system for predicting manure nutrients |
| topic | NIR manure sensing nitrogen spiking dairy cow |
| url | https://www.mdpi.com/2072-4292/14/4/963 |
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