GIS-based spatial nitrogen management model for maize: short- and long-term marginal net return maximising nitrogen application rates View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

E. Memic, S. Graeff, W. Claupein, W. D. Batchelor

ABSTRACT

Crop growth models including CERES-Maize and CROPGRO-Soybean have been used in the past to evaluate causes of spatial yield variability and to evaluate economic consequences of variable rate prescriptions. In this work, a nitrogen prescription program has been developed that simulates the consequences of different nitrogen prescriptions using the DSSAT crop growth models. The objective of this paper is to describe a site-specific nitrogen prescription and economic optimizer program developed for computing spatially optimum N rates over long periods of weather and plant population for maize (Zea mays L.) using the CERES-Maize model. The application of the model was demonstrated on a field in Germany and another one in the USA to evaluate the concept across different environmental conditions. The user can determine the short- and the long-term optimal spatial nitrogen prescription based on crop price and nitrogen cost. The program simulated short-term optimum N applications that averaged 9% (McGarvey field, USA) and 48% (Riech field, Germany) lower than the uniform rates actually applied in the fields. The program indicated different site-specific N management options for low and high yielding fields under the assumed prices for maize and N. The implementation of a site-specific plant population management was investigated. A site-specific-optimization of plant population showed a higher profitability in the heterogeneous field in Germany. Hard pan depth, hard pan factor, root distribution factor and the percentage of available soil water across the heterogeneous field were useful indicators in predicting the magnitude of site-specific plant population benefits over uniform rates. More... »

PAGES

295-312

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11119-018-9603-4

DOI

http://dx.doi.org/10.1007/s11119-018-9603-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107038423


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