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Abstract
Weeds persist and cause yield losses in Zea mays L. even where highly effective herbicides are extensively used. Growers are faced with the need to use pest management practices more effectively while minimizing non-target effects. Site-specific weed management (SSWM) recognizes spatial and temporal diversity of sites (soils, climate, and pests) and conceptually offers ways to help balance crop production needs, economic sustainability, and environmental stewardship.
Using species-specific weed distributions in SSWM can more appropriately match the level of management efficacy with the pest problem. Compared to uniform postemergent (POST) herbicide applications, site-specific applications reduced herbicide load by 12 to 98% while maintaining weed control. Reduction in preemergent (PRE) herbicide use was similar, depending on the weed distribution and herbicide use algorithm. In most cases PRE and POST herbicide use was successfully reduced at some field locations. Survivors were most likely to occur in locations with fitness-enhancing site conditions.
Quantifying variability in site properties is important in devising SSWM strategies using soil-applied herbicides. Crop and weed fitness was highly correlated to soil properties (as high as 0.76), namely soil texture and organic carbon, when isoxaflutole was applied across a variable field. Isoxaflutole dose needed to achieve 80% biomass reduction was highly species-specific, ranging from <1 to 32 g ha−1</super> for Abutilon theophrasti and 1 to 162 g ha−1 for Sorghum bicolor. Spatial heterogeneity of soil affinity for herbicide results in differential weed fitness and contributes to weed ‘patchiness’. To maximize the potential benefit of SSWM, biological and spatial information of both the weed and soil should be considered.
In order to extend the relevance of research results, a method was developed that coupled unique efficacy trials of an experimental site with soil survey data to characterize countywide patterns in herbicide bioactivity. This is an initial step in defining the inference space of field plot research and provides a geographical framework for prioritizing the potential value of SSWM development and adoption.
