Comparison of Field-scale Herbicide Runoff and Volatilization Losses: An Eight-Year Field Investigation
ABSTRACT An 8-yr study was conducted to better understand factors influencing year-to-year variability in field-scale herbicide volatilization and surface runoff losses. The 21-ha research site is located at the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD. Site location, herbicide formulations, and agricultural management practices remained unchanged throughout the duration of the study. Metolachlor [2-chloro--(2-ethyl-6-methylphenyl)--(2-methoxy-1-methylethyl) acetamide] and atrazine [6-chloro--ethyl--(1-methylethyl)-1,3,5-triazine-2,4-diamine] were coapplied as a surface broadcast spray. Herbicide runoff was monitored from a month before application through harvest. A flux gradient technique was used to compute volatilization fluxes for the first 5 d after application using herbicide concentration profiles and turbulent fluxes of heat and water vapor as determined from eddy covariance measurements. Results demonstrated that volatilization losses for these two herbicides were significantly greater than runoff losses ( < 0.007), even though both have relatively low vapor pressures. The largest annual runoff loss for metolachlor never exceeded 2.5%, whereas atrazine runoff never exceeded 3% of that applied. On the other hand, herbicide cumulative volatilization losses after 5 d ranged from about 5 to 63% of that applied for metolachlor and about 2 to 12% of that applied for atrazine. Additionally, daytime herbicide volatilization losses were significantly greater than nighttime vapor losses ( < 0.05). This research confirmed that vapor losses for some commonly used herbicides frequently exceeds runoff losses and herbicide vapor losses on the same site and with the same management practices can vary significantly year to year depending on local environmental conditions.
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ABSTRACT: Meeting the competing demands for freshwater of the urban, industrial, and agricultural communities is increasingly challenging as the global population continues to grow and the need for potable water, food, fiber, and fuel grows with it. One of the keys to meeting these demands is maximizing the efficiency of water use in agricultural applications. Toward this end, a thorough understanding of the factors driving evapotranspiration and their response to spatiotemporal variations in local environmental conditions is needed for the development and validation of numerical and remote sensing-based models. Moreover, because these exchange processes are strongly nonlinear, scaling measurements collected at one scale to another remains a nontrivial task. In an effort to identify the key environmental drivers controlling the latent heat flux (λE) from agro-ecosystems and their potential impacts on upscaling in-situ flux measurements, eddy covariance and micrometeorological data collected over maize and soy at three distinct sites located in Maryland, Iowa, and Minnesota, respectively were evaluated for the years between 2007 and 2011. The magnitudes of the evaporative fluxes were comparable for measurements collected during clear-sky days with similar environmental conditions; on average, the measurements of λE agreed to within 50 W m-2, or approximately 10%. When considered in terms of evaporative fraction (fe), however, there were marked differences among the sites. For example, while the magnitude and diurnal pattern of fe for mature maize at the Minnesota site was nearly constant (fe = 0.66) during the day, fe at both the Maryland and Iowa site increased steadily during the day from a minimum value near 0.68 at midmorning to peak value of 0.87 in the afternoon. These differences appear to be primarily linked to differences in soil moisture and vegetation density at the various sites. As such, this research underscores the impact of local environmental conditions in controlling land-atmosphere exchange processes. It also underscores the importance accurately describing local environmental conditions when modeling surface fluxes.01/2013; 19:239–245. DOI:10.1016/j.proenv.2013.06.027
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ABSTRACT: The impact of agriculture on regional air quality creates significant challenges to sustainability of food supplies and to the quality of national resources. Agricultural emissions to the atmosphere can lead to many nuisances, such as smog, haze, or offensive odors. They can also create more serious effects on human or environmental health, such as those posed by pesticides and other toxic industrial pollutants. It is recognized that deterioration of the atmosphere is undesirable, but the short- and long-term impacts of specific agricultural activities on air quality are not well known or understood. These concerns led to the organization of the 2009 American Chemical Society Symposium titled . An outcome of this symposium is this special collection of 14 research papers focusing on various issues associated with production agriculture and its effect on air quality. Topics included emissions from animal feeding operations, odors, volatile organic compounds, pesticides, mitigation, modeling, and risk assessment. These papers provide new research insights, identify gaps in current knowledge, and recommend important future research directions. As the scientific community gains a better understanding of the relationships between anthropogenic activities and their effects on environmental systems, technological advances should enable a reduction in adverse consequences on the environment.Journal of Environmental Quality 09/2011; 40(5):1347-58. DOI:10.2134/jeq2011.0142 · 2.35 Impact Factor
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ABSTRACT: Accurate prediction of soil moisture spatial-temporal variations remains critical in agronomic, hydrological, pedological, and environmental studies. Traditional approaches of soil moisture monitoring and prediction have limitations of being time-consuming, labour-intensive, and costly for direct field observation; and having low spatial resolution for remote sensing, and inconsistent accuracy and reliability for landscape feature (e.g. topography, land use, vegetation) modelling. Innovative and effective approaches for accurate soil moisture simulation are needed. Pedological properties, including soil structure, particle size distribution, porosity, horizon, redox feature, and organic matter content, have been accepted as important factors controlling soil moisture and can be potentially used in soil moisture prediction. However, pedological properties mostly lack quantification (e. g. redox feature, horizon, soil structure), and soil sampling and analysis are time-consuming and costly, especially at large spatial scale. These limitations have restricted the utilisation of pedological information to predict soil moisture spatial-temporal variations at different spatial scales. To overcome these difficulties, new tools including geophysical tools and computed tomography, and new methods including mining soil survey information and integrating pedological information with landscape features and modelling, are proposed in this paper.Soil Research 01/2012; 50(8):625. DOI:10.1071/SR12228 · 1.24 Impact Factor