{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:31:36Z","timestamp":1775104296020,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dairy Innovation Hub","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 (NH4-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 NH4-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R2) 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 NH4-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH4-N (R2 = 0.83; RMSE = 0.65 mg mL\u22121) and approximate predictions for Org-N (R2 = 0.66; RMSE = 1.18 mg mL\u22121). Transflectance was excellent for TS predictions (R2 = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH4-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.<\/jats:p>","DOI":"10.3390\/rs14040963","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T21:36:24Z","timestamp":1645047384000},"page":"963","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-6738","authenticated-orcid":false,"given":"Xiaoyu","family":"Feng","sequence":"first","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, 201 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7198-2994","authenticated-orcid":false,"given":"Rebecca A.","family":"Larson","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, 201 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6069-011X","authenticated-orcid":false,"given":"Matthew F.","family":"Digman","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, 201 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"908","DOI":"10.13031\/aea.12796","article-title":"Nutrient variability following dairy manure storage agitation","volume":"34","author":"Sharara","year":"2018","journal-title":"Appl. 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