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Maize dry matter production and macronutrient extraction model as a new approach for fertilizer rate estimation

Overview of attention for article published in Anais da Academia Brasileira de Ciências, May 2017
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Title
Maize dry matter production and macronutrient extraction model as a new approach for fertilizer rate estimation
Published in
Anais da Academia Brasileira de Ciências, May 2017
DOI 10.1590/0001-3765201720160525
Pubmed ID
Authors

KARLA V. MARTINS, DURVAL DOURADO-NETO, KLAUS REICHARDT, JOSÉ L. FAVARIN, FELIPE F. SARTORI, GUILHERME FELISBERTO, SIMONE C. MELLO

Abstract

Decision support for nutrient application remains an enigma if based on soil nutrient analysis. If the crop could be used as an auxiliary indicator, the plant nutrient status during different growth stages could complement the soil test, improving the fertilizer recommendation. Nutrient absorption and partitioning in the plant are here studied and described with mathematical models. The objective of this study considers the temporal variation of the nutrient uptake rate, which should define crop needs as compared to the critical content in soil solution. A uniform maize crop was grown to observe dry matter accumulation and nutrient content in the plant. The dry matter accumulation followed a sigmoidal model and the macronutrient content a power model. The maximum nutrient absorption occurred at the R4 growth stage, for which the sap concentration was successfully calculated. It is hoped that this new approach of evaluating nutrient sap concentration will help to develop more rational ways to estimate crop fertilizer needs. This new approach has great potential for on-the-go crop sensor-based nutrient application methods and its sensitivity to soil tillage and management systems need to be examined in following studies. If mathematical model reflects management impact adequately, resources for experiments can be saved.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 21%
Student > Ph. D. Student 4 14%
Student > Doctoral Student 2 7%
Other 2 7%
Professor 2 7%
Other 5 18%
Unknown 7 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 36%
Engineering 4 14%
Computer Science 1 4%
Business, Management and Accounting 1 4%
Psychology 1 4%
Other 2 7%
Unknown 9 32%