Current United States Department of Agriculture-Agricultural Research Service research on understanding agrochemical fate and transport to prevent and mitigate adverse environmental impacts

Environmentally and economically viable agriculture requires a variety of cultivation practices and pest management options as no one system will be appropriate for every situation. Agrochemicals are some of the many pest control tools used in an integrated approach to pest management. They are applied with the intent of maximizing efficacy while minimizing off-site movement; however, their judicious use demands a practical knowledge of their fate and effects in agricultural and natural ecosystems. Agrochemical distribution into environmental compartments is influenced by the physical and chemical properties of the agrochemical and environmental conditions, ie soil type and structure, and meteorological conditions. Agricultural Research Service (ARS) researchers working in the area of agrochemical fate have focused on accurately describing those processes that govern the transport, degradation and bioavailability of these chemicals under conditions reflecting actual agronomic practices. Results from ARS research concerning the environmental fate and effects of agrochemicals have led to the development of science-based management practices that will protect vulnerable areas of the ecosystem. The new challenge is to identify these vulnerable areas and the temporal and spatial variations prior to use of the chemical by predicting how it will behave in environmental matrices, and using that information, predict its transport and transformation within an air- or watershed. With the development of better predictive tools and GIS (Geographic Information System)-based modeling, the risks of agricultural management systems can be assessed at the watershed and basin levels, and management strategies can be identified that minimize negative environmental impacts.     

Application of bioassay techniques to herbicide investigations

Bioassay techniques used in herbicide studies are based on the response of chosen organisms, superior plants or microorganisms, to the chemical. Various means of assessment are used: germination, weight or size of plant parts, modifications in physiological activities such as photosynthesis and transpiration, and typical symptoms. Several special bioassays are described. Dose-response relations are affected by the age of the indicator plant and environmental conditions of growth. Results can be estimated visually or by objective measurements; for correct interpretation appropriate controls and standards must be included in each experiment. Examples are given of bioassay procedures developed to investigate various aspects of herbicide behaviour: soil effects, dissipation from the soil surface, movement in soil, degradation and persistence.     

Comparison of statistical models in a meta-analysis of fungicide treatments for the control of citrus black spot caused by Phyllosticta citricarpa

Meta-analysis has been recognised as a powerful method to synthetize existing published data from different studies through a formal statistical analysis. Several statistical models have been proposed to evaluate the effectiveness of treatments against plant diseases using meta-analysis, but the sensitivity of the estimated treatment effects to the model chosen has not been investigated in detail in the context of plant pathology. In this paper, four different statistical models were defined to analyse fungicide control trials with binary outcomes. These models were used to conduct a meta-analysis on the effectiveness of fungicide treatments against citrus black spot, a fungal disease caused by the quarantine pathogen Phyllosticta citricarpa. The models differed in the assumption made on the variability of the treatment effect (constant or variable between experimental plots) and in the method used for parameter estimation (classical or Bayesian). Odds ratios were estimated for two groups of fungicides, copper compounds and dithiocarbamates, widely applied for CBS control using each model in turn. Classical and Bayesian statistical models led to similar results, but the estimated treatment effectiveness and their associated levels of uncertainty were sensitive to the assumption made about the variability of the treatment effect. Estimated odds ratios were different depending on whether the treatment effect was assumed to be constant or variable between experimental plots. The size of the confidence intervals was underestimated when the treatment effect was assumed constant while it was variable in reality. Because of the strong between-plot variability, the 90 % percentiles of the odds ratios were much higher than the point estimates, and this result revealed that, in some plots, treatment effectiveness could be much lower than expected. Based on our results, we conclude that it is not sufficient to calculate point estimates of odds ratio when the between-plot variability of the treatment effect is strong and that, in such case, it is recommended to compute the predictive distributions of the odds ratio.     

New applications of statistical tools in plant pathology

ABSTRACT The series of papers introduced by this one address a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. An analysis of variance based on the assumption of normally distributed responses with equal variances has been a standard approach in biology for decades. Advances in statistical theory and computation now make it convenient to appropriately deal with discrete responses using generalized linear models, with adjustments for overdispersion as needed. New nonparametric approaches are available for analysis of ordinal data such as disease ratings. Many experiments require the use of models with fixed and random effects for data analysis. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of normally distributed data using linear mixed models, and discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Model selection can be performed using Akaike's information criterion. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but new technologies such as microarrays supply estimates of gene expression for thousands of genes simultaneously and present challenges for statistical analysis. Applications to the study of the landscape of the field and of the genome share the risk of pseudoreplication, the problem of determining the appropriate scale of the experimental unit and of obtaining sufficient replication at that scale.     

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.     

Measurement and modelling of glyphosate fate compared with that of herbicides replaced as a result of the introduction of glyphosate-resistant oilseed rape

BACKGROUND: Crops resistant to glyphosate may mitigate the increasing contamination of the environment by herbicides, since their weeding requires smaller amounts of herbicides and fewer active ingredients. However, there are few published data comparing the fate of glyphosate with that of substitute herbicides under similar soil and climatic conditions. The objectives of the work reported here were (i) to evaluate and compare the fate in soil in field conditions of glyphosate, as used on glyphosate-resistant oilseed rape, with that of two herbicides frequently used for weed control on the same crop, albeit non-resistant: trifluralin and metazachlor, and (ii) to compare field results with predictions of the pesticide root zone model (PRZM), parameterized with laboratory data. Dissipation and vertical distribution in the soil profile of glyphosate, trifluralin and metazachlor were monitored in an experimental site located in Eastern France for 1 year. RESULTS: Herbicide persistence in the field increased as follows: metazachlor < glyphosate < trifluralin, contrary to laboratory results showing glyphosate to be least persistent. The main metabolite of glyphosate-aminomethylphosphonic acid (AMPA)-was more persistent than glyphosate. AMPA and trifluralin had the largest vertical mobility, followed by metazachlor and glyphosate. PRZM underestimated the dissipation rate of glyphosate in the field and the formation of AMPA, but its predictions for trifluralin and metazachlor were correct. The simulation of herbicides and AMPA distribution in the soil profile was satisfactory, but the mobility of trifluralin and metazachlor was slightly underestimated, probably because PRZM ignores preferential flow. In general, data from the laboratory allowed an acceptable parameterization of the model, as indicated by goodness-of-fit indices. CONCLUSION: Because of the detection of AMPA in the deep soil layer, the replacement of both trifluralin and metazachlor with glyphosate might not contribute to decreasing environmental contamination by herbicides. PRZM may be used to evaluate and to compare other weed control strategies for herbicide-resistant as well as non-resistant crops.     

Qualification of a Plant Disease Simulation Model: Performance of the LATEBLIGHT Model Across a Broad Range of Environments

ABSTRACT The concept of model qualification, i.e., discovering the domain over which a validated model may be properly used, was illustrated with LATEBLIGHT, a mathematical model that simulates the effect of weather, host growth and resistance, and fungicide use on asexual development and growth of Phytophthora infestans on potato foliage. Late blight epidemics from Ecuador, Mexico, Israel, and the United States involving 13 potato cultivars (32 epidemics in total) were compared with model predictions using graphical and statistical tests. Fungicides were not applied in any of the epidemics. For the simulations, a host resistance level was assigned to each cultivar based on general categories reported by local investigators. For eight cultivars, the model predictions fit the observed data. For four cultivars, the model predictions overestimated disease, likely due to inaccurate estimates of host resistance. Model predictions were inconsistent for one cultivar and for one location. It was concluded that the domain of applicability of LATEBLIGHT can be extended from the range of conditions in Peru for which it has been previously validated to those observed in this study. A sensitivity analysis showed that, within the range of values observed empirically, LATEBLIGHT is more sensitive to changes in variables related to initial inoculum and to weather than to changes in variables relating to host resistance.     

Pesticide exposure assessment in rice paddies in Europe: a comparative study of existing mathematical models

A comparative test was undertaken in order to identify the potential of existing mathematical models, including the rice water quality (RICEWQ) 1.6.4v model, the pesticide concentration in paddy field (PCPF-1) model and the surface water and groundwater (SWAGW) model, for calculating pesticide dissipation and exposure in rice paddies in Europe. Previous versions of RICEWQ and PCPF-1 models had been validated under European and Japanese conditions respectively, unlike the SWAGW model which was only recently developed as a tier-2 modelling tool. Two datasets, derived from field dissipation studies undertaken in northern Italy with the herbicides cinosulfuron and pretilachlor, were used for the modelling exercise. All models were parameterized according to field experimentations, as far as possible, considering their individual deficiencies. Models were not calibrated against field data in order to remove bias in the comparison of the results. RICEWQ 1.6.4v provided the highest agreement between measured and predicted pesticide concentrations in both paddy water and paddy soil, with modelling efficiency (EF) values ranging from 0.78 to 0.93. PCPF-1 simulated well the dissipation of herbicides in paddy water, but significantly underestimated the concentrations of pretilachlor, a chemical with high affinity for soil sorption, in paddy soil. SWAGW simulated relatively well the dissipation of both herbicides in paddy water, and especially pretilachlor, but failed to predict closely the pesticide dissipation in paddy soil. Both RICEWQ and SWAGW provided low groundwater (GW) predicted environmental concentrations (PECs), suggesting a low risk of GW contamination for the two herbicides. Overall, this modelling exercise suggested that RICEWQ 1.6.4v is currently the most reliable model for higher-tier exposure assessment in rice paddies in Europe. PCPF-1 and SWAGW showed promising results, but further adjustments are required before these models can be considered as strong candidates for inclusion in the higher-tier pesticide regulatory scheme.     

Integrated modeling environment for statewide assessment of groundwater vulnerability from pesticide use in agriculture

Atrazine, a herbicide widely used for corn production in the Midwest, has been detected in groundwater of several states, and has been identified as a possible human carcinogen. With the widespread use of pesticides in crop production, and the frequent detection of these chemicals in groundwater, large-scale risk assessments would help water resource managers to identify areas that are more susceptible to contamination and implement practices to ameliorate the problem. This paper presents an integrated, visual and interactive system for predicting potential environmental risks associated with pesticide contamination at spatial scales ranging from fields to landscapes and regions. The interactive system extends the predictive ability of the Pesticide Root Zone Model Release 2.0 (PRZM-2) to a landscape and statewide scale through integration with a geographic information system (GIS), graphical user interface and environmental databases. Predictions of statewide (Iowa) vulnerability of groundwater from atrazine leaching below the unsaturated zone were made to demonstrate the utility of the system, and the results were used in risk assessment. In the example application, atrazine fate and transport were evaluated using long-term climatic data (1980--1989) in combination with several environmental databases (eg STATSGO soils database) and exposure risks were expressed in terms of the probability of the predicted pesticide concentrations exceeding the maximum contaminant level (MCL) for drinking water. The results indicate that the predicted pesticide concentrations were significantly lower than the EPA-established MCL. In addition to providing an interactive environment for landscape-level assessment of potential risks from pesticide leaching, the system significantly reduces the time and resources needed to organize and manipulate data for use with PRZM-2, and provides an analytical framework for evaluating groundwater-leaching impacts of pesticide management practices.     

Perspectives of spatial scale in a wildland forest epidemic

The challenge of observing interactions between plant pathogens, their hosts, and environmental heterogeneity across multiple spatial scales commonly limits our ability to understand and manage wildland forest epidemics. Using the forest pathogen Phytopthora ramorum as a case study, we established 20 multiscale field sites to analyze how host-pathogen-environment relationships vary across spatial scales of observation in a wildland pathosystem. We developed statistical models of disease intensity across five nested levels of spatial aggregation, from an individual host through four broader spatial extents of observation. Analyses were conducted from two spatial perspectives: a focal view, where disease intensity at one scale was examined as a function of broader-scale landscape conditions, and an aggregate view, where disease intensity and landscape conditions was observed at the same scale of spatial aggregation. For each perspective, separate models were developed to compare direct field measurements of host density versus less expensive remotely sensed estimates of host habitat as predictors of disease in landscape-scale studies. From both perspectives, models using direct measurements of host density performed better than models using remotely sensed estimates of host habitat across all four spatial extents. We found no significant difference in model performance at the individual level. From the focal view, the performance of host density models declined with increasing spatial extent, whereas the performance of host habitat models improved with spatial extent. These results illustrate how the scale of observation – both spatial extent and measurement detail – can influence conclusions drawn from epidemiological models of wildland pathosystems.     

Assessing disease severity: accuracy and reliability of rater estimates in relation to number of diagrams in a standard area diagram set

Error in estimates of plant disease severity occur and standard area diagrams (SADs) help improve accuracy and reliability. The effects of diagram number in SADs are unknown. The objective of this study was to compare estimates of pecan scab severity made without SADs, and using three‐, five‐, seven‐ or 10‐diagram SADs. Disease severity was estimated to the nearest percent (NPE), or classified to the closest reference diagram value using a scale. Twelve raters assessed 20 images of scabbed pecan valves with and without the SADs using NPEs and the scale method (values were converted to midpoints prior to analysis). Increases in diagram number using NPEs did not necessarily result in more accurate or reliable estimates. Inter‐rater reliability was positively correlated with number of diagrams using NPEs (r = 0·3288 (P < 0·0001)) or a scale (r = 0·2803 (P < 0·0001)). The least accurate estimates improved the most using SADs with NPEs, but the gain did not relate to number of diagrams; as few as three diagrams reduced error of inaccurate estimates as much as five, seven or 10 diagrams. When used as a scale, only estimates made with 10‐diagram SADs had similar accuracy and inter‐rater reliability to estimates made using SADs with NPEs. Maximum disease severity and the disease severity range for the pathosystem and the SADs are important factors and will probably influence resulting accuracy and reliability. The ramifications of diagram numbers in SADs and assessment methods on accuracy and reliability of disease estimates are discussed.     

Mobility and persistence of four herbicides in soil of a South Australian vineyard

The use of persistent herbicides has increased the potential for contamination of soil, soil water and groundwater. The mobility, dissipation and fate of four herbicides, norflurazon, oxadiazon, oxyfluorfen and trifluralin, used in South Australian viticulture, have been studied in a typical sand‐over‐clay vineyard soil. Following herbicide application at field rates to plots up‐slope of miniature lysimeters, surface soil and soil water were sampled regularly over the period of annual rainfall. The concentration of each herbicide in the soil cores, surface soil and soil water was determined by GLC‐NPD following solid‐phase concentration procedures where necessary. Oxadiazon dissipated more quickly than the other three herbicides in the soil. Norflurazon was the most mobile of these herbicides in this soil. However all four herbicides were found in the soil water within the first year, though only norflurazon was found in the soil water in the subsequent year. Norflurazon moved laterally to a greater extent than the other herbicides. © 2000 Society of Chemical Industry     

Assessment of effects on non-target plants from sulfonylurea herbicides using field approaches

Since their introduction in the early 1980s, there have been a number of field studies conducted to assess the effects of sulfonylurea herbicides on non-target plants (i.e. plants not labeled for use). In these studies a wide variety of plant response assessment techniques have been used to measure effects on non-target plants. This paper examines the relationship of short-term plant response measurements to plant productivity measurements such as yield or quality. Whether short-term plant response measurements have a practical degree of accuracy and precision appropriate for hazard assessment on non-target plants from sulfonylureas is discussed. A comprehensive review of published literature and unpublished field studies of the effects of sulfonylureas on the yield and quality of non-target plant species is reported. When this information is coupled with exposure factors and environmental fate characteristics, the risks to non-target plants from sulfonylureas are similar to those from other herbicides used at higher application rates. © 1998 SCI.     

Overview of a Passive Tracking Index for Monitoring Wild Canids and Associated Species

Population density estimates for many animal species are often difficult or expensive to obtain, and they rely on assumptions that, if violated, result in unmeasurable estimation errors. Density estimates also may be unnecessary for research or management purposes, because an index that tracks changes in a population within appropriate time and geographic constraints could provide the information necessary to make management decisions or to evaluate the impact of a control program. We review research on a passive tracking index where observations are made on a series of tracking plots placed on lightly used dirt roads. The number of sets of tracks (individual intrusions) are recorded for each species of interest on each plot on consecutive days. The mean number of intrusions over the plots is calculated for each day for each species. The index is the mean of the daily means. These design and measurement methods present valuable advantages over most traditional tracking plot methods. Because no scents or baits are used as attractants, no conditioning of animals to the plots biases the results. This also permits multiple species, predator and prey alike, to be simultaneously monitored. Using the number of animal intrusions as observations produces results that are far more sensitive to change than tracking surveys where only presence or absence of spoor are recorded for each plot. Of particular importance, the statistical properties inherent to this data structure permit calculation of standard errors, confidence intervals and statistical tests, without subjectively subdividing the data.     

Tebuconazole dissipation and metabolism in Tifton loamy sand during laboratory incubationt

The fungicide tebuconazole is widely used to control soil-borne and foliar diseases in peanuts and other crops. No published data are currently available on the extent and rate at which this compound degrades in soil. Unpublished data summarized in registration documents suggest that the compound is persistent, with 300-600 days half-life. We conducted a 63-day laboratory incubation to evaluate tebuconazole's dissipation kinetics and impact on soil microbial activity in Tifton loamy sand. Tifton soils support extensive peanut production in the Atlantic Coastal Plain region of Georgia and Alabama. Products containing tebuconazole are applied to an estimated 50% of the peanut acreage in the region. At the end of the incubation, 43 (+/-42)% of the parent compound was recovered in soil extracts. The first-order kinetic model, which gave a good fit to the dissipation data (r2 = 0.857), yielded a soil half-life (t1/2) of 49 days. This is 6-12 times more rapid than t1/2 values described in unpublished tebuconazole registration documents. Four degradates were identified. Tentative structural assignments indicated that degradates were derived from hydroxylation of the parent compound and/or chlorophenyl ring cleavage. Cleavage products showed a steady increase during the incubation, and on a molar basis were equal to 63% of the time zero tebuconazole concentration. No significant effect on soil microbial biomass was observed, indicating that when the compound is applied at normal agronomic rate it does not impact soil metabolic activity. Use of the soil-half life data derived in this study should improve the accuracy oftebuconazole fate assessments for Coastal Plain peanut production. The study also indicated that environmental assessment of selected degradates may be needed to fully evaluate risks of tebuconazole use.     

鲁西北黄泛区农田土壤稳定入渗率空间变异性分析

以中科院禹城综合试验站102 m×52 m空间的农田表层土壤入渗试验为基础,通过对实测数据的统计得到该地块的表层土壤稳定入渗率fc具有较大的空间变异性,并服从对数正态分布.应用变差函数对fc的空间结构进行分析,发现fc的空间结构可用指数变差函数模型进行描述,拟合模型所得到的方差值与试验结果的统计值基本相近,其相关距离为23.98 m;最后利用了Kriging最优内插估值法,绘制了fc的空间等值分布图.     

A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed, China

This study presented a simulation-based two-stage interval-stochastic programming(STIP) model to support water resources management in the Kaidu-Konqi watershed in Northwest China.The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing(ITSP).The distributed hydrological model was used for establishing a rainfall-runoff forecast system,while random parameters were provided by the statistical analysis of simulation outcomes.The developed STIP model was applied to a real case of water resources management planning in Kaidu-Konqi watershed,where three scenarios with different water resources management policies were analyzed.The results indicated that water shortage mainly occurred in agriculture,ecology and forestry sectors.In comparison,the water demand from municipality,industry and stockbreeding sectors can be satisfied due to their lower consumptions and higher economic values.Different policies for ecological water allocation can result in varied system benefits,and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.     

Pesticide exposure assessment for surface waters in the EU. Part 1: Some comments on the current procedure

In 2001, the European Commission introduced a risk assessment project known as FOCUS (FOrum for the Coordination of pesticide fate models and their USe) for the surface water risk assessment of active substances in the European Union. Even for the national authorisation of plant protection products (PPPs), the vast majority of EU member states still refer to the four runoff and six drainage scenarios selected by the FOCUS Surface Water Workgroup. However, our study, as well as the European Food Safety Authority (EFSA), has stated the need for various improvements. Current developments in pesticide exposure assessment mainly relate to two processes. Firstly, predicted environmental concentrations (PECs) of pesticides are calculated by introducing model input variables such as weather conditions, soil properties and substance fate parameters that have a probabilistic nature. Secondly, spatially distributed PECs for soil–climate scenarios are derived on the basis of an analysis of geodata. Such approaches facilitate the calculation of a spatiotemporal cumulative distribution function (CDF) of PECs for a given area of interest and are subsequently used to determine an exposure concentration endpoint as a given percentile of the CDF. For national PPP authorisation, we propose that, in the future, exposure endpoints should be determined from the overall known statistical PEC population for an area of interest, and derived for soil and climate conditions specific to the particular member state. © 2016 Society of Chemical Industry     

土壤水分特征曲线模型参数识别的多邻域粒子群算法

Van Genuchten模型（简称VG模型）是目前运用最为广泛的土壤水分特征曲线模型,提出适宜的优化算法进行模型参数识别也是一个非常重要的研究方向。针对标准的粒子群算法易陷入局部最优的缺点,给出了一种多邻域粒子群算法,可以有效地克服粒子群算法易陷入局部最优的缺点,并利用该算法对VG模型参数进行识别,最后用所求解的参数对不同类型土壤持水性能进行了试验。数值实验结果表明,多邻域粒子群算法能够有效地应用于VG模型的参数识别,与其它算法相比在性能和精度上都有所提高,而且对参数的取值范围也可以较大地放宽。因此,多邻域粒子群算法可以作为VG模型参数识别的一种新方法。     

Adsorption,desorption and dissipation of metolachlor in surface and subsurface soils

BACKGROUND: Variations in soil properties with depth influence retention and degradation of pesticides. Understanding how soil properties within a profile affect pesticide retention and degradation will result in more accurate prediction by simulation models of pesticide fate and potential groundwater contamination. Metolachlor is more persistent than other acetanilide herbicides in the soil environment and has the potential to leach into groundwater. Reasonably, information is needed about the dissipation and eventual fate of metolachlor in subsoils. The objectives were to evaluate the adsorption and desorption characteristics and to determine the dissipation rates of metolachlor in both surface and subsurface soil samples. RESULTS: Adsorption of metolachlor was greater in the high‐organic‐matter surface soil than in subsoils. Lower adsorption distribution coefficient (K_ads) values with increasing depth indicated less adsorption at lower depths and greater leaching potential of metolachlor after passage through the surface horizon. Desorption of metolachlor showed hysteresis, indicated by the higher adsorption slope (1/n_ads) compared with the desorption slope (1/n_des). Soils that adsorbed more metolachlor also desorbed less metolachlor. Metolachlor dissipation rates generally decreased with increasing soil depth. The first‐order dissipation rate was highest at the 0–50 cm depth (0.140 week^?1) and lowest at the 350–425 cm depth (0.005 week^?1). Degradation of the herbicide was significantly correlated with microbial activity in soils. CONCLUSION: Metolachlor that has escaped degradation or binding to organic matter at the soil surface might leach into the subsurface soil where it will dissipate slowly and be subject to transport to groundwater. Copyright © 2009 Society of Chemical Industry