Sources of Spatial Soil Variability and Weed Seedbank Data for Variable-Rate Applications of Residual Herbicides

Rose V. Vagedes, Purdue University

Abstract

Soil residual herbicides are a vital component of the best management practices (BMPs), to provide early season weed control in most cropping systems. The availability of a biologically effective dose of a soil residual herbicide in the soil solution is dependent on several soil parameters including soil texture, organic matter (OM), and pH. Soil residual herbicides are currently applied as a uniform application rate over an individual field; yet soil properties can vary spatially within agricultural fields. Therefore, areas of the field are being over- and under-applied when using a uniform application rate. By integrating variable-rate (VR) technology with soil residual herbicides, the correct rate could be applied based on the intra-field soil variability. However, the extent of spatial soil variability within a field and the impact on herbicide application rates has not been well-characterized to inform whether soil residual herbicide applications should move towards variable rate applications. Therefore, the objectives of this research were to 1) determine the extent of intra-field variability of soil texture and organic matter in ten commercial Indiana fields, 2) quantify the reliability of five different combinations of spatial soil data sources, 3) determine the impact of soil sample intensity on map development and the classification accuracy for VR applications of soil residual herbicides, 4) quantify the impact of VR herbicide application on the total amount and spatial accuracy of herbicide applied according to product labels, and 5) determine if the intensive spatial characterization of soil properties is related to weed seedbank abundance and species richness to improve predictive weed management using soil residual herbicides. Commercial soil data was generated by intensively collecting 60 soil samples in a stratified random sampling pattern in 10 agricultural fields across Indiana. Analysis of this data from commercial fields confirmed inherent field variability that would benefit from multiple management zones according to the labeled rate structures of pendimethalin, s-metolachlor, and metribuzin. Therefore, further research was conducted to determine an accurate and reliable method to delineate the fields into management zones for variable-rate residual herbicide applications based on the spatial soil variability and herbicide labels. A modified Monte Carlo cross validation method was used to determine the best source of spatial soil data and sampling intensity for delineating management zones for variable-rate applications of pendimethalin, s- metolachlor, and metribuzin. These sources of spatial soil data included: Soil Survey Geographic database (SSURGO) data, intensive soil samples, electrical resistivity sensors, and implement mounted optical reflectance sensors using VNIR reflectance spectroscopy. The mean management zone classification accuracy for maps developed from soil samples with and without electrical conductivity were similar for 75% of all maps developed across each field, herbicide, and sampling intensity. The method of using soil sampling data combined with electrical conductivity (SSEC) maps was most frequently the top performing source of spatial soil data. The most reliable sampling intensity was one sample per hectare which resulted in lower root mean squared error (RMSE) OM values, higher management zone classification accuracy, and more reliable predictions for the number of management zones within each field. Using VR maps developed from SSEC with one sample per hectare sampling intensity, additional research was conducted to compare the amount of herbicide and field area that was over-or under-applied with a uniform application rate compared to a VR application for 10 corn and soybean residual herbicides. Although research from our previous study documented that spatial soil variability was extensive enough to require two or more management zones for all fields, the same labeled herbicide dose defined for multiple soil condition led to 20% of all maps not requiring a variable rate application (VRA). Additionally, no difference was shown in total amount applied of herbicide in an individual field between a variable and uniform application rate for all herbicides. Nonetheless, nearly half of all VR maps had 10% or more of the field area misapplied with a uniform application rate and justifies further research to determine if the proper placement of residual herbicide adds value through increased weed control in the field areas being under-applied. Similar to soil residual herbicides, weed seedbank abundance and species richness were impacted by the variable soil conditions present within the field area. The seedbanks favor establishment in areas of the field that promote vigorous germination, growth, and reproduction next to the competing crop. Therefore, soil sampling and weed seedbank greenhouse grow-outs were conducted in four fields to gain a better understanding in the relationship between the spatial soil and weed seedbank variability. All weed seedbank characteristics were shown to be spatially aggregated. Even though no individual or combination of soil parameters consistently explained the variability of weed seedbank abundance, species richness, or individual weed species across all four fields. However, clay content was the most persistent soil parameter to negatively impact (lower seedbank values) the soil weed seedbank.

Degree

M.Sc.

Advisors

Young, Purdue University.

Subject Area

Agricultural chemistry|Agriculture|Agronomy|Chemistry|Organic chemistry|Soil sciences

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