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| Rapid determination modeling of slurry nitrogen and phosphorus from dairy farm based on samples compounding |
| Received:January 31, 2023 |
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| KeyWord:sample representative;near infrared spectroscopy;dairy farm slurry;nitrogen and phosphorus content;samples compounding;rapid measurement model |
| Author Name | Affiliation | E-mail | | LIU Shengbo | College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191 China | | | SUN Di | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191 China | | | LI Mengting | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191 China | | | ZHAO Run | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191 China | zhaorun01@caas.cn | | ZHANG Keqiang | College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
1. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China | kqzhang68@126.com |
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| Abstract: |
| In order to improve the representativeness of samples, an ideal correction model of near infrared spectroscopy (NIRS) was constructed to realize the rapid measurement of nitrogen and phosphorus content in slurry of dairy farm. The study was counted on the original samples that gathering from representative uncovered points throughout slurry movement process in dairy farm. Compound samples were made in terms of different proportions to fill in the "black box" samples which were not easy to be collected. Applying the partial least square, original models, compound models and fusion models were respectively built up relying on the optimal spectral preprocessing. The results showed that the coefficients of variation (CV) of total nitrogen (TN) and total phosphorus (TP) of the original and compound samples were reduced by 0.103 and 0.107, respectively, compared to the original samples. Both the homogeneity of concentration distribution and richness of spectral information were improved. Compared to the original model, the coefficient of determination (R2pred) of TN and TP was promoted by 0.049 and 0.061, respectively. The residual predictive deviation (RPD) was improved by 1.547 and 0.176, respectively. Compared to the compound model, R2pred of TN and TP was promoted by 0.026 and 0.022, respectively. RPD was improved by 0.470 and 0.052, respectively. The validation results showed that the R2pred of TN model and TP model were 0.903 and 0.878 while RPD were 2.916 and 2.508, respectively. Researches indicate that not only the sample representativeness from calibration sets but also the prediction performance of models are availably improved by means of samples compounding, supporting the fast and accurate quantification of nutrients before the land application. |
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