Site‐specific nitrogen balances based on spatially variable soil and plant properties

Martin Mittermayer, August Gilg, Franz Xaver Maidl, Ludwig Nätscher, Kurt Jürgen Hülsbergen

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

In this study, site-specific N balances were calculated for a 13.1 ha heterogeneous field. Yields and N uptake as input data for N balances were determined with data from a combine harvester, reflectance measurements from satellites and tractor-mounted sensors. The correlations between the measured grain yields and yields determined by digital methods were moderate. The calculated values for the N surpluses had a wide range within the field. Nitrogen surpluses were calculated from − 76.4 to 91.3 kg ha−1, with a mean of 24.0 kg ha−1. The use of different data sources and data collection methods had an impact on the results of N balancing. The results show the need for further optimization and improvement in the accuracy of digital methods. The factors influencing N uptake and N surplus were determined by analysing soil properties of georeferenced soil samples. Soil properties showed considerable spatial variation within the field. Soil organic carbon correlated very strongly with total nitrogen content (r = 0.97), moderately with N uptake (sensor, r = 0.60) and negatively with N surplus (satellite, r = − 0.46; sensor, r = − 0.56; harvester, r = − 0.60). Nitrate content was analysed in soil cores (0 to 9 m) taken in different yield zones, and compared with the calculated N surplus; there was a strong correlation between the measured nitrate content and calculated N surplus (r = 0.82). Site-specific N balancing can contribute to a more precise identification of the risk of nitrate losses and the development of targeted nitrate reduction strategies.

Original languageEnglish
Pages (from-to)1416-1436
Number of pages21
JournalPrecision Agriculture
Volume22
Issue number5
DOIs
StatePublished - Oct 2021

Keywords

  • N balance
  • Nitrogen surplus
  • Sensor data
  • Site-specific farming
  • Soil heterogeneity
  • Spatial variation

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