Journal Article
Evaluation of crop model-based simplified marginal net return maximising nitrogen application rates on site-specific level in maize
E. Memic; J. Trenz; S. Heshmati; S. Graeff
Precision Agriculture · Vol. 25, Issue 6 · pp. 2721-2739 · 2024
Abstract
Crop growth models such as DSSAT-CERES-Maize have proven to be useful for analysing plant growth and yield within homogenous land units. The paper presents results of newly developed model-based site-specific Soil Profile Optimisation (SPO) tools in combination with an updated version of an already published Nitrogen Prescription Model (NPM). Site-specific soil profiles were generated through an inverse modelling approach based on measured site-specific yield (point-based) and tops weight (above-ground biomass time-series) and evaluated. Site-specific soil profiles generated based only on measured yield variability were able to explain 72% (R 2 0.72) of yield variability (dependent variable) based on selected soil profile input parameters (independent variable). Site-specific soil profiles generated based on measured yield and tops variability simultaneously (multiple target variable) explained 68% of yield variability (R 2 0.68). The NPM uses the SPO generated site-specific soil profiles for economic evaluation of site-specific N application rates. NPM simulated N application rates, aiming at the maximisation of marginal net return (MNR) were 25% lower compared to the uniform N application rates with an assumed grain and N price of 0.17 and 0.3 Euro kg −1 respectively, under rainfed conditions over three years based on soil profiles generated via an inverse modelling approach only from measured yield variability (one target variable). N application rates were 28% lower when based on soil profiles generated from simultaneously included grain and tops variability in the inverse modelling approach. The results highlight the importance of site-specific fertilizer management when maximising MNR.
Bibliographic Information
| Journal | Precision Agriculture |
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| Publisher | Springer |
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| Publication Date | 2024-12-01 |
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| Publication Year | 2024 |
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| Volume | 25 |
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| Issue | 6 |
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| Pages | 2721-2739 |
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| Document Type | Journal Article |
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| Print ISSN | 1385-2256 |
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| eISSN | 1573-1618 |
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| DOI | 10.1007/s11119-024-10126-z |
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