Improving and evaluating the soil cover indicator for agricultural land in Canada. (January 2015)

• Record Type: Journal Article
• Authors: Huffman, Ted; Liu, Jiangui; Green, Melodie; Coote, Dick; Li, Zhe; Liu, Huanjun; Liu, Tingting; Zhang, Xinle; Du, Yuneng
• Is Part Of: Ecological indicators. Volume 48(2015)
• Page Start: 272
• Page End: 281
• Publication Date: 2015-01
• Subjects: Soil cover -- Agri-environmental indicator -- Snow cover -- Plant cover fraction -- Residue cover fraction; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2014.07.008
• Languages: English
• ISSNs: 1470-160X

Abstract

Abstract: A soil cover days (SCD) model has been developed by Agriculture and Agri-Food Canada for use as an agri-environmental indicator to monitor the relationship between agricultural production activities and agri-environmental quality. The SCD indicator integrates information on crops, soils, climate, and field activities to estimate the total equivalent number of days that agricultural soils are covered by crop canopy, crop residue and snow in a given year. Daily cover fractions of plant and residue for a given crop in an ecoregion are simulated using typical crop calendar and field management practices, and the equivalent number of days that soil is covered by snow in winter is derived from long term climate normals. The equivalent SCD for a spatial unit is then derived as the area-weighted sum of different crops and different management practices within the unit. This paper presents the SCD framework, details an assessment of the accuracy of the model and outlines future improvements. Annual snow days derived from 30-year climate normals as used in the model was strongly correlated (excluding mountain areas) with that derived from satellite data ( R 2 = 0.45, n = 48), even though the remote sensing product showed significant temporal and spatial variability. Crop residue fraction estimated by the model was strongly correlated with field data collected over major crop areas and crop types ( R 2 = 0.74, n = 55), and modelled plant cover fraction was well correlated with that derived from remote sensing data ( R 2 = 0.57, n = 57). Large discrepancies were observed for some samples due to deviation of the actual crop calendar from that estimated using climate normals. National map showing the change in the indicator from 1981 to 2011 reveals changes in crop and residue management practices.