Abstract
Increasing natural vegetation in agricultural landscapes can create habitat for beneficial organisms such as pollinators and the natural enemies of crop pests. Adding perennial vegetation can also support biodiversity conservation and climate change mitigation objectives. However, implementing such changes to agricultural land use across large geographic areas will require a strategic approach. This study examined the amount and distribution of uncultivated areas in Canadian prairie croplands, focusing on Alberta’s agricultural zone (226,543 km2). The aim was to identify locations in this region that have potential for increasing non-crop land cover within fields. This assessment was based on a multi-scale model of landscape complexity that described the distribution of land cover as a function of the distance from field centres. It is based on the assumption that the land cover in the field neighbourhood is an informative index of how much non-crop area might realistically be maintained or restored in the field itself; i.e., because neighbouring lands will reflect the local environmental conditions that support the growth and establishment of non-crop vegetation as well as the likelihood that crop growers will remove areas from production. The model identified variation across the region in land cover distribution, with regions at latitudes between 52oN and 55oN demonstrating the greatest contrasts in the amount of non-crop land between the field and the field neighbourhood scale. These findings suggest that there remains capacity for land use decision-makers to optimize the distribution of non-crop land covers in ways the reduce the differences between these scales (i.e., to increase non-crop covers within fields to better represent the neighbourhood proportions). Modelling also revealed scale-dependent patterns, such as field margins without crops (400 – 500 m from field centres) broadly distributed across the region, and evidence that gradients in moisture and temperature have interacted with land use decisions to shape the proximity of non-crop area to fields.
Highlights
Increasing agricultural production to meet a growing global demand will require expansion and intensification of croplands (Godfray et al, 2010; Laurance et al, 2014; Egli et al, 2018), but this presents challenges for mitigating climate change and conserving biodiversity (Kleijn et al, 2009; Bustamante et al, 2014; Dalu et al, 2017; Karp et al, 2018)
The spatial term summarizing the effect of unmodelled geographic variables had an effect on the landscape complexity curve (Northing × Easting; Table 2b)
This study demonstrated that there is considerable geographic variation in the proximity of non-crop areas to fields across Alberta’s agricultural zone, and there remains capacity for growers and other land use decision makers to optimize this distribution
Summary
Increasing agricultural production to meet a growing global demand will require expansion and intensification of croplands (Godfray et al, 2010; Laurance et al, 2014; Egli et al, 2018), but this presents challenges for mitigating climate change and conserving biodiversity (Kleijn et al, 2009; Bustamante et al, 2014; Dalu et al, 2017; Karp et al, 2018). Land conversion decisions may face resistance because natural vegetation is slow or costly to establish in certain regions, or because the land is highly-productive and value is placed on maximizing the area in production Finding such areas is done by building a spatial model of the distribution of non-crop land cover and how it varies with proximity to field centers. The study region encompasses several environmental gradients that influence both the dominant type of vegetation and primary productivity (e.g., temperature, moisture, latitude, and elevation; Ecological Stratification Working Group, 1995) These gradients have the potential to affect the frequency of clearing and the rate of natural regrowth of vegetation, and may interact with the behavior of land use decision-makers to shape the observed landscape complexity. The potential for increasing landscape complexity was assessed by finding the difference in the mean non-crop area prediction between neighborhood and within-field scales for the centroid of each survey township
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