Steven J. Thomson

Agronomic and environmental implications of enhanced s-triazine degradation.

Novel catabolic pathways enabling rapid detoxification of s-triazine herbicides have been elucidated and detected at a growing number of locations. The genes responsible for s-triazine mineralization, i.e. atzABCDEF and trzNDF, occur in at least four bacterial phyla and are implicated in the development of enhanced degradation in agricultural soils from all continents except Antarctica. Enhanced degradation occurs in at least nine crops and six crop rotation systems that rely on s-triazine herbicides for weed control, and, with the exception of acidic soil conditions and s-triazine application frequency, adaptation of the microbial population is independent of soil physiochemical properties and cultural management practices. From an agronomic perspective, residual weed control could be reduced tenfold in s-triazine-adapted relative to non-adapted soils. From an environmental standpoint, the off-site loss of total s-triazine residues could be overestimated 13-fold in adapted soils if altered persistence estimates and metabolic pathways are not reflected in fate and transport models. Empirical models requiring soil pH and s-triazine use history as input parameters predict atrazine persistence more accurately than historical estimates, thereby allowing practitioners to adjust weed control strategies and model input values when warranted.

Adoption of an Unmanned Helicopter for Low-Altitude Remote Sensing to Estimate Yield and Total Biomass of a Rice Crop

A radio-controlled unmanned helicopter-based low-altitude remote sensing (LARS) platform was used to acquire quality images of high spatial and temporal resolution in order to estimate yield and total biomass of a rice crop (Oriza sativa L.). Fifteen rice field plots with five N treatments (0, 33, 66, 99, and 132 kg ha-1) having three replicates each were arranged in a randomized complete block design for estimating yield and biomass as a function of applied N. Images were obtained by image acquisition sensors mounted on the LARS platform operating at the height of 20 m over experimental plots. The rice yield and total biomass for the five N treatments were found to be significantly different at the 0.05 and 0.1 levels of significance, respectively, and normalized difference vegetation index (NDVI) values at panicle initiation stage were highly correlated with yield and total biomass with regression coefficients (r2) of 0.728 (RMSE = 0.458 ton ha-1) and 0.760 (RMSE = 0.598 ton ha-1), respectively. The study demonstrated the suitability of using LARS images as a substitute for satellite images for estimating leaf chlorophyll content in terms of NDVI values (r2 = 0.897, RMSE = 0.012). The LARS system described has potential to evaluate areas that require additional nutrients at critical growth stages to improve final yield in rice cropping.

Biological responses to glyphosate drift from aerial application in non-glyphosate- resistant corn

BACKGROUND Glyphosate drift from aerial application onto susceptible crops is inevitable, yet the biological responses to glyphosate drift in crops are not well characterized. The objectives of this research were to determine the effects of glyphosate drift from a single aerial application (18.3 m swath, 866 g AE ha(-1)) on corn injury, chlorophyll content, shikimate level, plant height and shoot dry weight in non-glyphosate-resistant (non-GR) corn. RESULTS One week after application (WAA), corn was killed at 3 m from the edge of the spray swath, with injury decreasing to 18% at 35.4 m downwind. Chlorophyll content decreased from 78% at 6 m to 22% at 15.8 m, and it was unaffected beyond 25.6 m at 1 WAA. Shikimate accumulation in corn decreased from 349% at 0 m to 93% at 15.8 m, and shikimate levels were unaffected beyond 25.6 m downwind. Plant height and shoot dry weight decreased gradually with increasing distance. At a distance of 35.4 m, corn height was reduced by 14% and shoot dry weight by 10% at 3 WAA. CONCLUSIONS Corn injury and other biological responses point to the same conclusion, that is, injury from glyphosate aerial drift is highest at the edge of the spray swath and decreases gradually with distance. The LD(50) (the lethal distance that drift must travel to cause a 50% reduction in biological response) ranged from 12 to 26 m among the biological parameters when wind speed was 11.2 km h(-1) and using a complement of CP-09 spray nozzles on spray aircraft.

Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features.

Biological Response of Soybean and Cotton to Aerial Glyphosate Drift

When glyphosate is applied to glyphosate-resistant (GR) crops, drift on to off-target sensitive crops may cause injury and mortality. An aerial application drift study was conducted in 2009 to determine biological effects of glyphosate on non-glyphosate-resistant (non-GR) cotton (Gossypium hirsutum L.) and non-GR soybean [Glycine max (L.) Merr.]. Glyphosate at 866 g ae/ha was applied using an Air Tractor 402B agricultural aircraft in an 18.3 m spray swath to crops at the two- to three-leaf stage. Visual plant injury, chlorophyll, shikimate, plant height, and shoot dry weight were determined at one, two, and three weeks after application (WAA) of glyphosate. Biological responses differed between crops as a function of downwind drift distance. For example, at 3 WAA soybean was dead 6 m downwind from the spray swath, whereas cotton sustained 85% visual injury. Plant injury was not observed beyond 25.6 m downwind in soybean and 35.4 m downwind in cotton at 3 WAA. Chlorophyll reduction was higher (80%) in soybean compared with cotton (43%) at 0 m from the edge of the spray at 1 WAA. Shikimate levels 1 WAA decreased from 1518% at 0 m to 209% at 35.4 m downwind in soybean; at the same sampling time shikimate levels in cotton decreased from 464% at 0 m to 0% at 35.4 m. At 35.4 m downwind, shoot dry weight (5–13%) and plant height (6–8%) were reduced in both crops at 3 WAA. The biological response of soybean and cotton to glyphosate drift decreased with increased distance from the edge of spray swath. These biological data suggested that soybean was more susceptible to glyphosate drift than cotton and elevated shikimate level could be used as a sensitive indicator to confirm plant exposure to glyphosate drift.

Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification

BACKGROUND Palmer amaranth (Amaranthus palmeri S. Wats.) is a troublesome agronomic weed in the southern United States, and several populations have evolved resistance to glyphosate. This paper reports on spectral signatures of glyphosate-resistant (GR) and glyphosate-sensitive (GS) plants, and explores the potential of using hyperspectral sensors to distinguish GR from GS plants. RESULTS GS plants have higher light reflectance in the visible region and lower light reflectance in the infrared region of the spectrum compared with GR plants. The normalized reflectance spectrum of the GR and GS plants had best separability in the 400-500 nm, 650-690 nm, 730-740 nm and 800-900 nm spectral regions. Fourteen wavebands from within or near these four spectral regions provided a classification of unknown set of GR and GS plants, with a validation accuracy of 94% for greenhouse-grown plants and 96% for field-grown plants. CONCLUSIONS GR and GS Palmer amaranth plants have unique hyperspectral reflectance properties, and there are four distinct regions of the spectrum that can separate the GR from GS plants. These results demonstrate that hyperspectral imaging has potential application to distinguish GR from GS Palmer amaranth plants (without a glyphosate treatment), with future implications for glyphosate resistance management. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Assessment of soybean injury from glyphosate using airborne multispectral remote sensing

BACKGROUND Glyphosate drift onto off-target sensitive crops can reduce growth and yield and is of great concern to growers and pesticide applicators. Detection of herbicide injury using biological responses is tedious, so more convenient and rapid detection methods are needed. The objective of this research was to determine the effects of glyphosate on biological responses of non-glyphosate-resistant (non-GR) soybean and to correlate vegetation indices (VIs) derived from aerial multispectral imagery. RESULTS Plant height, shoot dry weight and chlorophyll (CHL) content decreased gradually with increasing glyphosate rate, regardless of weeks after application (WAA). Accordingly, soybean yield decreased by 25% with increased rate from 0 to 0.866 kg AI ha(-1) . Similarly to biological responses, the VIs derived from aerial imagery - normalized difference vegetation index, soil adjusted vegetation index, ratio vegetation index and green NDVI - also decreased gradually with increasing glyphosate rate, regardless of WAA. CONCLUSION The VIs were highly correlated with plant height and yield but poorly correlated with CHL, regardless of WAA. This indicated that indices could be used to determine soybean injury from glyphosate, as indicated by the difference in plant height, and to predict the yield reduction due to crop injury from glyphosate.

Analysis of Impact of Various Factors on Downwind Deposition Using a Simulation Method

The drift of aerially applied crop protection and production materials is studied using a novel simulation-based design of experiments approach. Many factors that can potentially contribute to downwind deposition from aerial spray application are considered. This new approach can provide valuable information about the significant level of the impact from all factors and interactions among them that affect drift using simulation software such as AGDISP. The application efficiency, the total downwind drift, the cumulative downwind deposition between 30.48 m (100 ft) and 45.72 m (150 ft), and the deposition at 30.48 m (100 ft), 76.2 m (250 ft), and 152.4 m (500 ft) are established as the performance metrics. The most significant factors will be identified using statistical analysis based on simulation results, and suggestions for improvement will be made. Through preliminary study, the new simulation-based method has shown the potential for statistic analysis without conducting time-consuming field experiments. The new method can be used to search for the optimal spray conditions, which could be used to generate guidelines for applicators to achieve an optimal spray result. The effective use of simulation tool through the identification of significant factors can greatly simplify the field study.

Evaluation of Application Accuracy and Performance of a Hydraulically Operated Variable-Rate Aerial Application System

An aerial variable-rate application system consisting of DGPS (Differential Global Positioning System)-based guidance, an automatic flow controller, and a hydraulically controlled pump was evaluated for response to rapidly changing flow requirements and accuracy of application. Spray deposition position error was evaluated by direct field observation of water-sensitive paper (WSP) cards while traveling east to west and north to south across rate change boundaries. Data from the flow controller and a custom-built flowmeter monitor were used to evaluate flow controller error and variable-rate system error while making applications to a series of four management zones (28, 47, 56, and 37 L ha-1; each 81 m long). Observations of WSP showed that average spray deposition position error magnitude was 5.0 m when traveling east to west and 5.2 m when traveling north to south. Statistical analysis indicated that direction of travel had a non-significant effect on the magnitude of spray deposition position error. Flow controller error and variable-rate system error were evaluated from data collected while making applications to a series of four management zones (each zone required approximately 1.2 s) with application rates of 28, 47, 56, and 37 L ha-1. Areas under time plots of required and actual flow rates were compared and indicated flow controller error ranging from -1.0% to 2.1%. Variable-rate system error due to rate change timing was evaluated by comparing required rates from the system to required rates from the prescription. Area under time plots of these variables showed that average rate timing error for six application passes ranged from -9.1% to 1.4% with an average of -3.04%. Considering the speed at which changes have to be made for aircraft typically flying at 65 m s-1, the hydraulically operated variable-rate system performed well for location accuracy of deposition, response to changing flow rates, and accuracy of application amounts for the prescription.

Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis

This paper studies the effect of storage techniques for transporting collected plant leaves from the field to the laboratory for hyperspectral analysis. The strategy of collecting leaf samples in the field for laboratory analysis is typically used when ground truthing is needed in remote sensing studies. Results indicate that the accuracy of hyperspectral measurements depends on a combination of storage technique (in a cooler or outside a cooler), time elapsed between collecting leaf samples in the field and measuring in the laboratory, and the plant species. A nonlinear model fitting method is proposed to estimate the spectrum of decaying plant leaves. This revealed that the reflectance of soybean leaves remained within the normal range for 45 min when the leaves were stored in a cooler, while soybean leaves stored outside a cooler remained within the normal range for 30 min. However, cotton leaves stored in a cooler decayed faster initially. Regardless of storage technique, results indicate that up to a maximum of 30 min can elapse between plant leaf sampling in the field and hyperspectral measurements in the laboratory. This study focused on cotton and soybean leaves, but the implication that time elapsing between sampling leaves and measuring their spectrum should be limited as much as possible can be applied to any study on other crop leaves. Results of the study also provide a guideline for crop storage limits when analyzing by laboratory hyperspectral sensing setting to improve the quality and reliability of data for precision agriculture.