University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 51 8 2020 10 22 Estimation of In-season Basil Nitrogen Requirement Using the Nitrogen Nutrition Index Estimation of In-season Basil Nitrogen Requirement Using the Nitrogen Nutrition Index 2039 2049 76841 10.22059/ijswr.2020.303135.668624 FA Hadisseh Rahimikhoob Department of Irrigation and Reclamation Engineering Faculty of Agriculture Engineering and Technology College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. 0000-0002-3848-7470 Teymour Sohrabi Department of Irrigation and Reclamation Engineering Faculty of Agriculture Engineering and Technology College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. 0000-0001-7571-4604 Mojtaba Delshad Horticultural Sciences Department Faculty of Agriculture Engineering and Technology College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. 0000-0003-2685-0348 Journal Article 2020 05 26 Nitrogen is one of the most widely used nutrients for crop development. Application of nitrogen fertilizers plays a crucial role in increasing agricultural yield and production. In order to achieve maximum crop yield and reduce the negative environmental effects, application of nitrogen fertilizers should be based on the dynamics between nitrogen supply and crop demand. In order to manage the consumption of nitrogen fertilizers, it is necessary to accurately predict the crop nitrogen requirements at different growth stages. In present study, the concept of critical nitrogen concentration (N_c) has been used as an effective approach to determine nitrogen status and estimate nitrogen requirement of basil. For this purpose, basil was cultivated in the research greenhouse of the College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Different nitrogen fertilizer treatments were applied at seven levels with three replications. Crop dry matter and nitrogen concentrations were measured seven times during the growth period. Then nitrogen nutrition index (NNI) and basil nitrogen requirement (NR) were calculated for the first experiment. Results showed that, there was a significant regression relationship between basil NNI and NR (R²> 0.96). Accordingly, the NR prediction model was developed using NNI values and day after planting (DAP). Then, accuracy and performance of the NR-NNI relationship were verified by the second experiment data. The values ​​obtained for RMSE, NMB and NRMSE statistical indicators were less than 1 kg / ha, 0.10% and 3.10%, respectively, which indicates high accuracy of the model for prediction of crop nitrogen requirement. In general, results showed that estimation of crop NR based on the concept of N_c could be used as a scientific and suitable approach for managing nitrogen application in agricultural production. Nitrogen is one of the most widely used nutrients for crop development. Application of nitrogen fertilizers plays a crucial role in increasing agricultural yield and production. In order to achieve maximum crop yield and reduce the negative environmental effects, application of nitrogen fertilizers should be based on the dynamics between nitrogen supply and crop demand. In order to manage the consumption of nitrogen fertilizers, it is necessary to accurately predict the crop nitrogen requirements at different growth stages. In present study, the concept of critical nitrogen concentration (N_c) has been used as an effective approach to determine nitrogen status and estimate nitrogen requirement of basil. For this purpose, basil was cultivated in the research greenhouse of the College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Different nitrogen fertilizer treatments were applied at seven levels with three replications. Crop dry matter and nitrogen concentrations were measured seven times during the growth period. Then nitrogen nutrition index (NNI) and basil nitrogen requirement (NR) were calculated for the first experiment. Results showed that, there was a significant regression relationship between basil NNI and NR (R²> 0.96). Accordingly, the NR prediction model was developed using NNI values and day after planting (DAP). Then, accuracy and performance of the NR-NNI relationship were verified by the second experiment data. The values ​​obtained for RMSE, NMB and NRMSE statistical indicators were less than 1 kg / ha, 0.10% and 3.10%, respectively, which indicates high accuracy of the model for prediction of crop nitrogen requirement. In general, results showed that estimation of crop NR based on the concept of N_c could be used as a scientific and suitable approach for managing nitrogen application in agricultural production. https://ijswr.ut.ac.ir/article_76841_7b524e4b54bd6ef14e5aa264ce0e7b3b.pdf