Cover Image
QR Code

Mapping Tillage Practices Using Spatial Information Techniques.

Monitoring tillage practices is important for explaining soil quality and yield trends, and their impact on environmental quality. However, a common problem in sustainable residue management is scarcity of accurate residue maps. Because predictive insights on soil quality dynamics across a spatial d...

Full description

Published in:Environmental Management Vol. 66; no. 4; pp. 722 - 732
Main Authors: Obade, Vincent de Paul, Gaya, Charles
Format: Journal Article
Published: Springer Nature Oct2020
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146122682&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 146122682
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        0364152X
        O5H
      jtl: Environmental Management
      issn: 0364152X
      maglogo: N
    pubinfo:
      dt: Oct2020
      vid: 66
      iid: 4
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        146122682
        144662366
        10.1007/s00267-020-01335-z
        146122682
      ppf: 722
      ppct: 10
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Mapping Tillage Practices Using Spatial Information Techniques.
      aug:
        au:
          Obade, Vincent de Paul
          Gaya, Charles
        affil: BioResource and Agricultural Engineering Department, Cal Poly San Luis Obispo, 1 Grand Avenue, San Diego, CA, USA
      sug:
      ab: Monitoring tillage practices is important for explaining soil quality and yield trends, and their impact on environmental quality. However, a common problem in sustainable residue management is scarcity of accurate residue maps. Because predictive insights on soil quality dynamics across a spatial domain are vital, this entry explicates on a new remote sensing-based technique for assessing surface residue cover. Here, an empirical model for mapping surface residue cover was created by integrating line-transect % residue cover field measurements with information gleaned from ground spectroradiometers and Advanced Wide-Field Sensor (AWiFS) satellite imagery. This map was validated using non-photosynthetic vegetation (NPV) fractional component extracted by spectral mixture analysis (SMA). SMA extracts fractional components of sensed signals in imagery, which within agricultural fields are NPV, green vegetation, bare soil, and shade. A stepwise linear regression between residue estimates by line transect and map generated using satellite imagery had R2 = 87%. Upon map categorization according to surface residue for a single AWiFS imagery encompassing an area of 836,868 ha, but focused on corn (Zea mays) fields within South Dakota, revealed that <4% of these corn fields had >15% surface residue cover left in the field by November 2009. Findings such as these may guide policy on soil quality, which is directly correlated with residue management. In the future, the spatial distribution of surface residues remaining after harvest in field planted with other crops and other seasons will be mapped. Besides, the efficacy of integrating hyperspectral sensor data to enhance accuracy will be investigated.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N