DOI: 10.5061/dryad.fn2z34v3q
title: Agricultural plastic pollution reduces soil function even under best management practices

description: Soil plastic contamination is considered a threat to environmental health
 and food security. Plastic films which are widely used as soil mulches are
 the largest single source of agricultural plastic pollution. Growing
 evidence indicates that high concentrations of plastic negatively affect
 critical soil functions. However, the relationships between agricultural
 plastic accumulation and its biogeochemical consequences in regions with
 relatively low levels of soil plastic pollution remain poorly
 characterized. We sampled farms across the California Central Coast (a
 region of global agricultural importance with extensive plastic
 mulch-based production) to assess the degree and biogeochemical
 consequences of plastic pollution in fields subject to ‘best practice’
 plastic mulching application and removal practices over multiple years.
 All farms exhibited surface soil plastic contamination, macroplastic
 positively correlated with microplastic contamination levels, and
 macroplastic accumulation was negatively correlated with soil moisture,
 microbial activity, available phosphate, and soil carbon pool size. These
 effects occurred at less than 10% of the contamination levels reported to
 degrade field soils, but were relatively subtle, with no detectable
 relationship to microplastic concentration. Identifying declines in soil
 quality with low levels of macroplastic fragment accumulation suggests
 that we must improve best management plasticulture practices to limit the
 threat to soil health and agricultural productivity of unabated plastic
 accumulation.
description: Macroplastics and surface soil samples have been collected from
 12 fields belongs to 5 different farms. Macro and micro plastics
 contamination was measured in form of number and mass concentration per
 ha, surface area per ha, mass per surface area etc., only number
 concentration (coung per kg soil) was estimated for microplastics from
 collected soil samples. Different Biogeochemical
 properties such as gravimetric moisture (soil_moist), total inorganic
 nitrogen (TiN), Olsen-P, particulate organic matter (POM), minera
 associated organic matter (MAOM), soil respiration and microbial biomass
 was also estimated. The relationship between
 macroplastic and microplastic accumulation in soil was tested using a
 linear mixed effect model, with ‘field’ nested within ‘farm’ treated as a
 random effect. The influence of macroplastic (count per ha, surface area
 per ha, mass per ha, mass per surface area) and microplastic (count
 kg<sup>-1</sup> soil) on soil properties was also tested with
 ‘field’ nested within ‘farm’ treated as a random effect. Because clay
 content strongly influences soil biogeochemical properties and can
 increase the retention of microplastics in the soil even after typical
 mulch removal practices , average clay content at the field level was
 included as a covariate. Response variables were log transformed when
 assumptions of normality were not met and the transformation improved the
 residuals (gravimetric soil moisture, P content, POM and MAOM). Response
 variables that had values which fell below detection (e.g., ‘0’) were left
 untransformed (observed for TIN, soil respiration, microbial biomass, and
 microplastic count kg<sup>-1</sup> soil). Predictor variables
 (macroplastic count per ha, and macroplastic mass/surface area) were
 rescaled by log transformation to improve model fit. The “ggeffect”
 function was used to predict the response variable and associated 95%
 confidence interval based on the “lmer” model to fit the obtained data
 points. Data analysis was completed using R (version 4.3.1) and RStudio
 (version 2023.09.0-463) using the packages ‘lme4’ and ‘lmerTest’. All
 plots were made using the ggplot2 package . Field and farm identity is
 anonymized for data presentation.
description: # Agricultural plastic pollution reduces soil function even under best
 management practices
 [https://doi.org/10.5061/dryad.fn2z34v3q](https://doi.org/10.5061/dryad.fn2z34v3q) ## README: Description of Data and File Structure ## Overview This dataset includes experimental parameters, analysis scripts, images, and processed data files for studying the relationship between macroplastic and microplastic accumulation in soil, along with their effects on soil properties. Below is a detailed description of the files and their contents. ### 1. **CDFA\_Metadata\_all.xlsx** * This Excel file contains all the experimental parameters, including response and predictor variables from 12 fields nested in 5 farms. For data presentation, the identity of each field and farm has been anonymized. ### 2. **CDFA\_final\_script.Rmd** * This R Markdown file includes all the R code used for data analysis and generating the plots that represent the final findings of the study. ### 3. **Microplastics\_images.zip** * This zipped folder contains a directory named `MP_images`, which includes extracted microplastic images from all fields. ### 4. **ENVI\_DAT\_File1.zip** * This zipped folder contains `.ENVI` and `.dat` files exported from micro-FTIR, used to process the IR spectra for scanned filters from fields a to d. ### 5. **ENVI\_DAT\_File2.zip** * This zipped folder contains `.ENVI` and `.dat` files exported from micro-FTIR, used to process the IR spectra for scanned filters from fields e and f. ### 6. **ENVI\_DAT\_File3.zip** * This zipped folder contains `.ENVI` and `.dat` files exported from micro-FTIR, used to process the IR spectra for scanned filters from fields g to i. ### 7. **ENVI\_DAT\_File4.zip** * This zipped folder contains `.ENVI` and `.dat` files exported from micro-FTIR, used to process the IR spectra for scanned filters from fields j to l. ### 8. **Polymer\_characterization\_script.Rmd** * This R Markdown file contains the code used to process the spectra from the `.ENVI` and `.dat` files, identifying microplastic polymer types using the OpenSpecy library. **Parameters used:** * Signal to noise threshold (`sn_threshold`): Set as the lowest intensity from particles compared to the background. * Correlation threshold (`cor_threshold`): Particles with a correlation &lt; 0.7 to reference spectra were excluded. * Area threshold (`area_threshold`): Artifacts from the background were removed. The output `.csv` file, named `particle_details_all`, provides information such as `max_cor_val`- maximum correlation value, `polymer_class` - polymer type, `area` - particle area, `perimeter` - particle perimeter etc. This file was used to extract polymer identity for visually counted particles. Polymers identified (excluding organic matter and minerals) were normalized to the number of visually counted particles on the filters to represent the polymeric composition of the extracted microplastic particles. Any particle with an area ≤ 3750 µm² was excluded to avoid particles smaller than 150 µm. --- ## Code/Software * Data analysis was performed using **R** (version 4.3.1) and **RStudio** (version 2023.09.0-463) with the following packages: * `lme4` and `lmerTest` for statistical modeling * `ggplot2` for generating plots ### Statistical Modeling * The relationship between macroplastic and microplastic accumulation in soil was tested using a linear mixed-effect model, with ‘field’ nested within ‘farm’ treated as a random effect using R script named as `CDFA_final_script.Rmd` .  * Macroplastic (count per ha - plastic particle number count per hectare of field, surface area per ha - surface area of plastic per hectare field, mass per ha - mass of plastic per hectare field, and mass per surface area - mass per unit surface area of field) and microplastic (count per kg soil - microplastic particle number count per kg of soil) influence on soil properties were tested with similar random effects. * Clay content was included as a covariate due to its strong influence on soil biogeochemical properties and retention of microplastics. * Log transformation was applied to response variables when assumptions of normality were not met (e.g., gravimetric soil moisture, P content - Phosphorous content in mg per kg of soil, POM -  particulate organic matter, and MAOM - mineral associated organic matter). Untransformed response variables included TIN - total inorganic nitrogen, soil respiration, microbial biomass, and microplastic count per kg soil, particularly when values fell below detection. * Predictor variables such as macroplastic count per ha and macroplastic mass/surface area were log-transformed to improve model fit. * The `ggeffect` function was used to predict response variables with a 95% confidence interval based on the linear mixed-effect model.