The NDICEA model, a tool to improve nitrogen use efficiency in cropping systems
Autor: | W.A.H. Rossing, G.J.H.M. van der Burgt, G.J.M. Oomen, A. S. J. Habets |
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Jazyk: | angličtina |
Rok vydání: | 2006 |
Předmět: |
Mean squared error
Soil organic matter Simulation modeling Soil Science Agricultural engineering PE&RC soil Agronomy Biologische bedrijfssystemen Organic farming farming systems mineralization Performance indicator Cropping system Agronomy and Crop Science Cropping Nitrogen cycle organic-matter Biological Farming Systems Mathematics |
Zdroj: | Nutrient Cycling in Agroecosystems, 74(3), 275-294 Nutrient Cycling in Agroecosystems 74 (2006) 3 |
ISSN: | 1385-1314 |
DOI: | 10.1007/s10705-006-9004-3 |
Popis: | The effective management of nitrogen dynamics is essential for cropping systems which have the double objective of achieving acceptable yields and minimizing environmental impact. The decisions to be made are both particularly complex and of great urgency to farmers, including all organic farmers, who rely on organic sources of nitrogen. Models can be useful means of providing a better understanding of the nitrogen dynamics and of supporting decision-making at tactical and strategic levels. This paper presents a model that aims at providing support in the decision-making process based on a target-oriented description of nitrogen dynamics in a cropping system. The NDICEA model describes soil water dynamics, nitrogen mineralization and inorganic nitrogen dynamics in relation to weather and crop demand. Crop yields are put in to the model, resulting in a target-oriented modelling approach which is distinctive from most other models. Parameter calibration is an inherent component of the modelling philosophy and is geared to establishing plot-specific factors. Using both quantitative and visual performance indicators, and different ratios of calibration to validation data, we evaluate the performance of NDICEA based on three treatments obtained from the Müncheberg dataset. Based on a maximum of 3 years of data for calibration, the root mean square error (RMSE) was found to vary between 14 kg N ha¿1 and 37 kg N ha¿1, and in the majority of cases absolute prediction error was less than 20 kg N ha¿1. We introduce a user-friendly version of the model that is aimed at farmers and extension workers. |
Databáze: | OpenAIRE |
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