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The main data in this dataset are observed rainfall runoff events. During this events runoff, erosion and related pesticide concentrations in the particulate and dissolved phase are measured. For each runoff event timeseries are available of measured discharge (1 minute interval), suspended sediment (5 minute interval) and pesticide concentrations in both phases. In addition, we collected data from field observations and interviews with land managers, data is available on pesticide applications and concentrations of pesticides in soil.

Data and metadata of crop yield and quality from different cases studies and long terms from WP3 "Crop production and quality", derived from H2020 Diverfarming project. This workpackage provides robust and sound data about how diversified cropping systems with low-input practices and efficient use of resources have positive effects on crop production and quality, and so, farm yields and economic revenues for farmers is increased. This can increase trading productivity with high quality outputs, so improving the competitiveness of European agriculture in the global market. http://www.diverfarming.eu.

R scripts and data for analyzing the results of the microcosm experiment "Stressors in a bottle: a microcosm study on phytoplankton community response to coinciding climatic stressors"

Measuring the characteristics of raindrops is essential for different processes studies. There have been many methods used throughout history to measure raindrops. In recent years, automatic image recognition and processing systems have been used with high-speed cameras to characterize rainfall by obtaining the spectrum of droplet sizes and their speeds and thus being able to use this technology to calibrate rainfall simulators. In this work, two phases were carried out: in the first one, individual drops with terminal speeds of different sizes were measured and processed both in speed and in shape with a high-speed camera; and in the second phase, a calibration procedure was designed but in multidrop images, determining the characteristics of the drops produced by a rain simulator. According to results, the real shape of each drop depending on the size was determined, from round to ovaloid shapes, and the terminal velocity of water drops with different sizes was measured. Based on the rain images used to calibrate a rainfall simulator, it was observed that, with a higher intensity of rain, the drops produced were smaller, which contrasts with real rain, in which just the opposite happens. This calibration evaluates their resemblance to reality, calculates the real kinetic energy of the rain they produce and see if they can be used to model events in nature.

Measuring the characteristics of raindrops is essential for different processes studies. There have been many methods used throughout history to measure raindrops. In recent years, automatic image recognition and processing systems have been used with high-speed cameras to characterize rainfall by obtaining the spectrum of droplet sizes and their speeds and thus being able to use this technology to calibrate rainfall simulators. In this work, two phases were carried out: in the first one, individual drops with terminal speeds of different sizes were measured and processed both in speed and in shape with a high-speed camera; and in the second phase, a calibration procedure was designed but in multidrop images, determining the characteristics of the drops produced by a rain simulator. According to results, the real shape of each drop depending on the size was determined, from round to ovaloid shapes, and the terminal velocity of water drops with different sizes was measured. Based on the rain images used to calibrate a rainfall simulator, it was observed that, with a higher intensity of rain, the drops produced were smaller, which contrasts with real rain, in which just the opposite happens. This calibration evaluates their resemblance to reality, calculates the real kinetic energy of the rain they produce and see if they can be used to model events in nature.

Polylactic acid (PLA) bioplastic was introduced to the market as an environmentally friendly potential solution for plastic pollution. However, the effects of bioplastic debris mixed with composts on soil macroinvertebrates, plant growth and soil conditions are still unknown. Soil macroinvertebrates are soil health indicators. A reduction in their abundance is a sign of soil degradation. The objectives of this study were (i) to assess PLA debris in greenhouse composts, and (ii) to test the ecotoxicological effects of PLA debris mixed with compost on Lumbricus terrestris, a soil organism model, and on Triticum aestevium, a plant growth model. The study was comprised of three stages: (1) determine the PLA debris size distribution in composts; (2) assess the ecotoxicological effects of real-world concentrations (0% to 5%) of PLA mixed with compost on earthworm mortality and reproduction; and (3) assess the influence of compost mixed with real-world PLA concentrations on plant growth and physicochemical soil conditions. One percent of PLA debris was found in green composts, 40% of composted PLA debris measured between 1–10 mm, with a concentration of 82.8 ± 17.4 microplastics.gram−1 compost. A concentration of 1% PLA in composts resulted in significant mortality in earthworms. No significant effects of PLA mixed with composts were observed on plant growth or soil physicochemical conditions. Further studies are required in order to test the effect of this biopolymer on different earthworm and plant’ species.

Spatio - temporal soil moisture measurements at 40 cm depth in an agricultural field in Haps, N. Brabant, the Netherlands. A total of 25 stations are distributed in a cultivates field that covers approximately 2.3 ha. of land area. The datasets were collected over three seasons (summer 2017, winter 2017, summer 2018) using 5TM soil moisture sensors. The measurements were logged every 15 minutes. In 2017, the crops in the field were potato and corn. After the harvest, a portion of the field was converted to grass for grazing. This was not desired because the sensors could be damaged by cattle. Twelve stations were relocated to another part of the field to avoid the grass field. In 2018, the only corn was planted where the stations were installed. Sandy soils were encountered at the study site.

This article introduces a simple and cost-saving method developed to extract, distinguish and quantify light density microplastics of polyethylene (PE) and polypropylene (PP) in soil. A floatation method using distilled water was used to extract the light density microplastics from soil samples. Microplastics and impurities were identified using a heating method (3-5s at 130°C). The number and size of particles were determined using a camera (Leica DFC 425) connected to a microscope (Leica wild M3C, Type S, simple light, 6.4×). Quantification of the microplastics was conducted using a developed model. Results showed that the floatation method was effective in extracting microplastics from soils, with recovery rates of approximately 90%. After being exposed to heat, the microplastics in the soil samples melted and were transformed into circular transparent particles while other impurities, such as organic matter and silicates were not changed by the heat. Regression analysis of microplastics weight and particle volume (a calculation based on image J software analysis) after heating showed the best fit (y=1.14x+0.46, R2=99%, p<0.001). Recovery rates based on the empirical model method were >80%. Results from field samples collected from North-western China prove that our method of repetitive floatation and heating can be used to extract, distinguish and quantify light density polyethylene microplastics in soils. Microplastics mass can be evaluated using the empirical model.