Evaluation of five simulation models for predicting aldcarb and bromide behavior under field conditions

1990 ◽  
Vol 26 (11) ◽  
pp. 2679-2693 ◽  
Author(s):  
K. D. Pennell
Keyword(s):  
Simulation Models ◽  
Field Conditions

Microscale Variability of Atrazine and Chloride Leaching Under Field Conditions

1997 ◽  
Vol 11 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Guy A. Chammas ◽  
John L. Hutson ◽  
Jonathan J. Hart ◽  
Joseph M. DiTomaso
Keyword(s):  
Center Of Mass ◽  
Three Dimensional ◽  
Simulation Models ◽  
Field Conditions ◽  
Nonuniform Flow ◽  
High Background ◽  
Sampling Schemes ◽  
Atrazine Concentration ◽  
Herbicide Transport ◽  
Chloride Leaching

Pesticide leaching experiments using widely spaced sampling sites may not adequately characterize chemical leaching behavior such as nonuniform flow between sampling points. We conducted this study to determine the three-dimensional variability of atrazine and chloride movement within a small volume of soil (2,700 cm1) under field conditions. A 1-m2area of Williamson silt loam (coarse-silty, mixed, mesic, Typic Fragiochrept) was sprayed uniformly with atrazine (1.1 kg ai/ha) and chloride (80 kg/ha). We used the Leaching Estimation and Chemistry Model (LEACHM) to simulate chemical movement. After 6.5 cm of rainfall during a 29-d period, we sampled 36 squares (5 by 5 cm) in the central 30- by 30-cm portion of the treated area at six depth increments (0 to 2, 2 to 5, 5 to 10, 10 to 15, 15 to 21, and 21 to 30 cm) and determined atrazine and Cl−concentrations. We recovered 26% of the applied atrazine and 138% of the applied chloride. Low atrazine recovery may have been due to leaching beyond 30 cm and/or degradation while excess chloride recovery is attributed to high background concentrations. Coefficients of variation (CVs) for atrazine significantly increased with depth and ranged from 26 to 353%, while CVs for Cl−were independent of depth and ranged from 32 to 66%. Derived atrazine concentration isograms illustrated highly nonuniform herbicide transport. Although LEACHM overestimated atrazine movement in the upper 15 cm, it was fairly accurate in the lower 15 cm. The overall trend in Cl−flow was adequately predicted, even though the predicted Cl−concentrations were underestimated. LEACHM could not accurately predict nonuniform flow or the variability in solute concentrations between points. However, its prediction of the atrazine center of mass (about 4.7 cm) agreed well with the derived isograms. These findings demonstrate that localized nonideal solute transport may be missed in larger sampling schemes and in simulation models.

Evaluation of Five Simulation Models for Predicting Aldicarb and Bromide Behavior Under Field Conditions

1990 ◽  
Vol 26 (11) ◽  
pp. 2679-2693 ◽  
Author(s):  
K. D. Pennell ◽  
A. G. Hornsby ◽  
R. E. Jessup ◽  
P. S. C. Rao
Keyword(s):  
Simulation Models ◽  
Field Conditions

scholarly journals Dicamba emissions under field conditions as affected by surface condition

2020 ◽  
pp. 1-8
Author(s):  
Thomas C. Mueller ◽  
Lawrence E. Steckel
Keyword(s):  
Plant Material ◽  
Surface Condition ◽  
Simulation Models ◽  
Field Studies ◽  
Green Plant ◽  
Field Conditions ◽  
Bare Ground ◽  
Weed Species ◽  
Target Movement ◽  
Green Plants

Abstract The evolution and widespread distribution of glyphosate-resistant broadleaf weed species catalyzed the introduction of dicamba-resistant crops that allow this herbicide to be applied POST to soybean and cotton. Applications of dicamba that are most cited for off-target movement have occurred in June and July in many states when weeds are often in high densities and at least 10 cm or taller at the time of application. For registration purposes, most field studies examining pesticide emissions are conducted using bare ground or very small plants. Research was conducted in Knoxville, TN, in the summer of 2017, 2018, and 2019 to examine the effect of application surface (tilled soil, dead plants, green plants) on dicamba emissions under field conditions. Dicamba emissions after application were affected by the treated surface in all years, with the order from least to most emissions being dead plants < tilled soil < green plant material. In fact, dicamba emissions were >300% when applied to green plants compared to other surfaces. These findings suggest that dicamba applications made to bare ground will likely underestimate what may occur under normal field use conditions when POST applications are made and the crop canopy or weed groundcover is nearly 100% green material. A potential change to enhance the accuracy of current environmental simulation models would be to increase the theoretical findings to allow for the effect of green plant material on dicamba emissions under field conditions.

ALACHLOR PERSISTENCE UNDER SPANISH FIELD CONDITIONS AND APPLICABILITY OF SIMULATION MODELS

Soil Science ◽  
1994 ◽  
Vol 158 (5) ◽  
pp. 337-344 ◽  
Author(s):  
L. MARTÍNEZ ◽  
Y. LECHÓN ◽  
C. SANCHEZ-BRUNETE ◽  
J. L. TADEO
Keyword(s):  
Simulation Models ◽  
Field Conditions

scholarly journals Seeding date, photoperiod and nitrogen effects on specific leaf area of field-grown wheat

1998 ◽  
Vol 78 (1) ◽  
pp. 51-61 ◽  
Author(s):  
G. K. Hotsonyame ◽  
L. A. Hunt
Keyword(s):  
Leaf Area ◽  
Specific Leaf Area ◽  
Simulation Models ◽  
Sowing Date ◽  
Field Conditions ◽  
Leaf Position ◽  
Leaf Production ◽  
Individual Leaf ◽  
Sowing Dates ◽  
The Impact

Specific leaf area (SLA), the ratio of leaf area to leaf weight, is an important plant characteristic that affects the rate of dry matter production of crop canopies. It is affected by the conditions of growth of both isolated plants and crop communities, but the extent to which various environmental factors impact on SLA under field conditions is not clearly understood. This study was conducted to study the variability in SLA of leaves on the main culm, and in the leaf canopy as an entity, under different conditions of photoperiod, nitrogen and temperature for a number of wheat genotypes grown under field conditions. Five plantings at approximately bimonthly intervals on 12 May, 5 July and 22 September 1993; and on 9 June and 10 August 1994 were made under both natural photoperiod and an extended photoperiod of 20 h. A split plot design with two levels of nitrogen (0 kg N ha−1 and 150 kg N ha−1) as main plots and four genotypes of wheat comprising two spring types (Norseman and Roblin) and two winter types (Ruby and Harus) as subplots was used.SLA of individual leaves varied among leaf positions, but the pattern of variation was dependent on sowing date and genotype. For May and June sowing dates, SLA increased with leaf number up to leaf 5 and then declined with subsequent leaf numbers for the spring genotypes, but increased to leaf 5 and changed little thereafter for the winter types. For July or August sowing and for both spring and winter genotypes, the change in SLA with leaf position was less clear. The results further showed that some of this variability in SLA with leaf position could be accounted for by the mean air temperatures over which the leaves developed. As temperatures increased from 8 °C to 26 °C, SLA increased to a maximum value achieved at 18–20 °C and then declined. However, there was a large scatter of SLA values around 18–20 °C, due partly to some lower SLA values for the July and August sowing dates. This suggests the impact of other factors such as radiation and the degree of mutual shading within the canopy on SLA. Mean canopy SLA reflected the individual leaf values during the period of leaf production, and varied with sowing date and genotype, although the pattern of genotypic variability was inconsistent over sowing dates; it decreased rapidly after spike emergence presumably reflecting leaf aging. Neither nitrogen nor photoperiod had significant effects on SLA on both individual leaf and canopy bases.The results suggest that temperature is one factor affecting SLA under field conditions, but that further work to identify other factors impacting on SLA in the field will be necessary. For application of simulation models to situations in which temperatures are likely to vary, an accounting for the impact of temperature on the SLA of individual leaves would be desirable. Key words: Specific leaf area (SLA), sowing date, temperature, simulation, wheat

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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