Improving Pesticide Uptake Modeling into Potatoes: Considering Tuber Growth Dynamics

Gong, Yishu; Li, Zijian; Fantke, Peter

doi:10.1021/acs.jafc.1c00151

Cited by 37 publications

(41 citation statements)

References 40 publications

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“…The reason for relatively wide measurement ranges could be due to different compositions (e.g. lipid and water contents) of the periderm for different types of potatoes 38 . As this study aimed to introduce a periderm‐based modeling framework for the plant uptake of pesticides, some input variables such as periderm and medulla compositions were taken from current literature as default values, 42,43 which should be adjusted according to different types or varieties of potatoes when comparing model results to specific potato varieties.…”

Section: Resultsmentioning

confidence: 99%

“…

(days −1 ) is the growth rate of the potato medulla and

(days −1 ) is the degradation rate of the pesticide in the potato medulla, which is approximated using

. 38 By replacing

according to Eqn ( 1 ), Eqn ( 2 ) can be solved with the initial condition of

0

as:

where

is the bioconcentration factor of the pesticide in potato medulla (

) for the nonperiderm model, which is defined as the ratio of

at a given time step. We note that the term BCF describes the bioaccumulation of pesticide in organisms (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…radius, mass, and surface area) does not change over time in the model, even though the growth dilution of pesticides in potatoes is included in the model. 10 , 12 , 14 We tested the dynamic solution against more simplified solutions assuming constant coefficients based on fixed tuber characteristics and found that pesticide dynamics are not very sensitive to differences in tuber characteristics over time, 38 particularly for the periderm compartment (i.e. the thin layer) at harvest.…”

Section: Methodsmentioning

confidence: 99%

“…

K_{SW}

(L kg −1 or L L −1 ) and

K_{MW}

(L kg −1 ) are the bulk soil‐water and medulla‐water partition coefficients, respectively.

k_{g}

(days −1 ) is the growth rate of the potato medulla and

k_{m}

(days −1 ) is the degradation rate of the pesticide in the potato medulla, which is approximated using

k_{d}

38 . By replacing

C_{S} ((), t)

C_{S} ((), 0) \exp ((), - k_{d} t)

according to Eqn (), Eqn () can be solved with the initial condition of

C_{M}^{NP} ((), t) = 0

as:

C_{M}^{NP} ((), t) = C_{S} ((), 0) ((), \frac{k_{M}^{+ normalS}}{k_{normalM}^{- S} + k_{normalg} + k_{normalm} - k_{normald}}) ([], \exp ((), - k_{d} t) - \exp ((), - ((), k_{M}^{- normalS} + k_{g} + k_{m}) t))

{BCF}_{M}^{NP} ((), t) = \frac{C_{M}^{NP} ((), t)}{C_{S} ((), t)} = ((), \frac{k_{M}^{+ normalS}}{k_{normalM}^{- S} + k_{normalg} + k_{normalm} - k_{normald}}) ([], 1 - \exp (()))

…”

Section: Methodsmentioning

confidence: 99%

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Improved plant bioconcentration modeling of pesticides: The role of periderm dynamics

Fantke

2021

Pest Management Science

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BACKGROUND There is a continuous need to advance pesticide plant uptake models in support of improving pest control and reducing human exposure to pesticide residues. The periderm of harvested root and tuber crops may affect pesticide uptake, but is usually not considered in plant uptake models. To quantify the influence of the periderm on pesticide uptake from soil into potatoes, we propose a model that includes an explicit periderm compartment in the soil–plant mass balance for pesticides. RESULTS Our model shows that the potato periderm acts as an active barrier to the uptake of lipophilic pesticides with high KOW, while it lets more lipophobic pesticides accumulate in the medulla (pulp). We estimated bioconcentration factors (BCFs) for over 700 pesticides and proposed parameterizations for including the effects of the periderm into a full plant uptake modeling framework. A sensitivity analysis shows that both the degradation half‐life inside the tuber and the lipophilicity drive the contributions of other aspects to the variability of BCFs, while highlighting distinct dynamics in the periderm and medulla compartments. Finally, we compare model estimates with measured data, showing that predictions agree with field observations for current‐use pesticides and some legacy pesticides frequently found in potatoes. CONCLUSION Considering the periderm improves the accuracy of quantifying pesticide uptake and bioconcentration in potatoes as input for optimizing pest control and minimizing human exposure to pesticide residues in edible crops.

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Section: Resultsmentioning

confidence: 99%

“…

(days −1 ) is the growth rate of the potato medulla and

(days −1 ) is the degradation rate of the pesticide in the potato medulla, which is approximated using

. 38 By replacing

according to Eqn ( 1 ), Eqn ( 2 ) can be solved with the initial condition of

0

as:

where

is the bioconcentration factor of the pesticide in potato medulla (

) for the nonperiderm model, which is defined as the ratio of

at a given time step. We note that the term BCF describes the bioaccumulation of pesticide in organisms (e.g.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…

K_{SW}

(L kg −1 or L L −1 ) and

K_{MW}

(L kg −1 ) are the bulk soil‐water and medulla‐water partition coefficients, respectively.

k_{g}

(days −1 ) is the growth rate of the potato medulla and

k_{m}

(days −1 ) is the degradation rate of the pesticide in the potato medulla, which is approximated using

k_{d}

38 . By replacing

C_{S} ((), t)

C_{S} ((), 0) \exp ((), - k_{d} t)

according to Eqn (), Eqn () can be solved with the initial condition of

C_{M}^{NP} ((), t) = 0

as:

C_{M}^{NP} ((), t) = C_{S} ((), 0) ((), \frac{k_{M}^{+ normalS}}{k_{normalM}^{- S} + k_{normalg} + k_{normalm} - k_{normald}}) ([], \exp ((), - k_{d} t) - \exp ((), - ((), k_{M}^{- normalS} + k_{g} + k_{m}) t))

{BCF}_{M}^{NP} ((), t) = \frac{C_{M}^{NP} ((), t)}{C_{S} ((), t)} = ((), \frac{k_{M}^{+ normalS}}{k_{normalM}^{- S} + k_{normalg} + k_{normalm} - k_{normald}}) ([], 1 - \exp (()))

…”

Section: Methodsmentioning

confidence: 99%

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Improved plant bioconcentration modeling of pesticides: The role of periderm dynamics

Fantke

2021

Pest Management Science

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“…Pesticides play important roles in controlling plant diseases, weeds and insects to ensure crop productivity and promote the sustainable development of agriculture [ 1 , 2 , 3 ]. It has been reported that nearly 3.5 million tons of synthetic pesticides are applied every year to control pests worldwide [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Nanopesticide Formulation from Pyraclostrobin and Graphene Oxide as a Nanocarrier and Application in Controlling Plant Fungal Pathogens

et al. 2022

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Efficient and environment-friendly nanopesticide delivery systems are critical for the sustainable development of agriculture. In this study, a graphene oxide nanocomposite was developed for pesticide delivery and plant protection with pyraclostrobin as the model pesticide. First, graphene oxide–pyraclostrobin nanocomposite was prepared through fast adsorption of pyraclostrobin onto graphene oxide with a maximum loading of 87.04%. The as-prepared graphene oxide–pyraclostrobin nanocomposite exhibited high stability during two years of storage, suggesting its high potential in practical application. The graphene oxide–pyraclostrobin nanocomposite could achieve temperature (25 °C, 30 °C and 35 °C) and pH (5, 7 and 9) slow-release behavior, which overcomes the burst release of conventional pyraclostrobin formulation. Furthermore, graphene oxide–pyraclostrobin nanocomposite exhibited considerable antifungal activities against Fusarium graminearum and Sclerotinia sclerotiorum both in vitro and in vivo. The cotoxicity factor assay revealed that there was a synergistic interaction when graphene oxide and pyraclostrobin were combined at the ratio of 1:1 against the mycelial growth of Fusarium graminearum and Sclerotinia sclerotiorum with co-toxicity coefficient values exceeding 100 in vitro. The control efficacy of graphene oxide–pyraclostrobin nanocomposite was 71.35% and 62.32% against Fusarium graminearum and Sclerotinia sclerotiorum in greenhouse, respectively, which was higher than that of single graphene oxide and pyraclostrobin. In general, the present study provides a candidate nanoformulation for pathogenic fungal control in plants, and may also expand the application of graphene oxide materials in controlling plant fungal pathogens and sustainable agriculture.

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Modeling pesticide residue uptake by leguminous plants: a geocarpic fruit model for peanuts

2022

Pest Management Science

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BACKGROUND: Pesticide residues are frequently found in leguminous plants; however, no modeling approaches predict residue concentrations in edible legume seeds. In this study, a geocarpic fruit model, simplified for neutral organic compounds, was proposed for high-throughput simulations (over 700 pesticides) of the residue uptake by peanut plants, which characterized three scenarios, namely (i) pesticide foliar application during the pre-seed development stage, (ii) foliar application during the seed development stage, and (iii) soil contamination before plant germination.RESULTS: In the foliar application scenario, in general, lipophilic pesticides have high simulated residue unit doses (RUDs, residue concentrationsinplantsper 1.0kg ha −1 of pesticide application) in peanutleavesowing tointensifieduptake via surface deposition,whereas hydrophilic pesticides have high simulated RUDs in peanuts because the uptake of residues via diffusion is enhanced. For the soilcontamination scenario, organic compounds with moderate lipophilicity have a high bioconcentration potential (i.e. the soil-plant system) in leaves and peanuts, due to large transpiration stream concentration factors (TSCFs) that boost the uptake via transpiration. CONCLUSIONS:The simulation results have some degrees of agreement with field measurements, indicating that the proposed model can be used as a screening tool for dietary risk assessment of pesticides in peanuts. In future research, pH-dependent physicochemical properties (e.g. soil-water partition coefficient and TSCF) and degradation rate constants of chemicals need to be refined to improve the simulation analysis.

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Improving Pesticide Uptake Modeling into Potatoes: Considering Tuber Growth Dynamics

Cited by 37 publications

References 40 publications

Improved plant bioconcentration modeling of pesticides: The role of periderm dynamics

Improved plant bioconcentration modeling of pesticides: The role of periderm dynamics

Nanopesticide Formulation from Pyraclostrobin and Graphene Oxide as a Nanocarrier and Application in Controlling Plant Fungal Pathogens

Modeling pesticide residue uptake by leguminous plants: a geocarpic fruit model for peanuts

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