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Data and scripts belonging to:

Insight in molecular degradation patterns and co-metabolism during rose waste co-composting

Reference: de Nijs, E. A., Jansen, B., Absalah, S., Bol, R., & Tietema, A. (2023). Insight in molecular degradation patterns and co-metabolism during rose waste co-composting. Biogeochemistry, 166(2), 55-66.https://doi.org/10.1007/s10533-023-01092-1
e.a.denijs@uva.nl


INTRODUCTION
This dataset and these scripts supports the article 'Insight in molecular degradation patterns and co-metabolism during rose waste co-composting' as published in Biogeochemistry. 

Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we aimed to gain insight in the decomposition patterns underlying rose waste composting and to identify co-metabolisms of ligneous materials. Samples were taken during a six-month experiment and analyzed by pyrolysis-GC/MS (see manuscript for analytical details).


EXPERIMENT DETAILS 
The experiment was conducted at the laboratory facilitates of the University of Amsterdam in 2021. 

Five treatments were tested in triplicate, resulting in a total of 15 mixtures which were composted for 181 days. Samples were taken at an increasing interval on day 0, 7, 15, 23, 35, 58, 90 and 181. These samples were measured by pyrolysis-GC/MS. 

Approximately 6 kg was composted in 30L buckets with aeration holes, the treatments were as follows (mix ratios on fresh weight basis): 
• R - Roses: 20 % flower, 50 % stem, 30 % leaves
• T - Roses + Tomato: 16 % flower, 40 % stem, 24 % leaves, 20 % tomato
• K - Roses + Kalanchoe: 16 % flower, 40 % stem, 24 % leaves, 20 % kalanchoe
• C - Roses + Compost:16 % flower, 40 % stem, 24 % leaves, 20 % compost
• CL - Roses, more leaves + Compost: 16 % flower, 24 % stem, 40 % leaves, 20 % compost

Further experimental details are described in detail in de Nijs, Maas, Bol & Tietema, 2023. 


DATA & SCRIPTS 

• PREPARE DATA
script: lab_compost_pygcms_prepare_data.R
function script: load & organize the data exported from Chromeleon. Each sequence is exported separately. data is organized and exported as .CSV files. 
input: files from: 20210413_data_txt, 20210416_data_txt, 20210421_data_txt, 20210923_data_txt
output: 
- data_oak_all_R.CSV > pyrolysis results on the oak samples used as control
- data_py_all_R.CSV > pyrolysis results of all compost samples
- data_RW_all_R.CSV > pyrolysis results of all raw wastes measured

all three CSV files have 11 columns 
- sequence: date when sequences was ran on the pyrolysis GCMS
- ID: identification of mixture with replicate number and sampling moment
- PeakName: component name of the identified peak
- mainGroup: the main group to which the identified peak belongs
	Other, B = benzenes, Bf = benzofurans, PAH = Polyaromatic hydrocarbons, Ph = phenols, Te = terpenes, 
	Ps = polysaccharides, N = N-containing compounds, Al = aliphatics, Lg = lignin phenols
- subgroup: specification of compound group if applicable (for alkane/alkene and subdivision of lignin) 
- RetTime: retention time, duration that a compound spends in the chromatographic column before being detected
- Area: area under the integrated peak 
- RelArea: relative area in proportion
- samplingPoint: sampling moment at which the samples were taken, ranging from 0 (day zero) to 7 (day 181)
- day: day of sampling since start of experiment
- mixType: type of mixture (R, T, K, C, CL - see above for mixture compositions)


• DATA PROCESSING
script:lab_compost_pygcms_data_processing.R
function script: process pyrolysis data & visualisations
input: data_oak_all_R.CSV, data_py_all_R.CSV, data_RW_all_R.CSV (data as described above), 
lab_compost_exp_all_timepoints_R.CSV, as described below
120 rows, 31 columns as follows (absent or missing values are left blank)
- ID: identification if mixture and replicate number
- Mix: mixtype
- nr: replicate number
- bucket: bucket number (ranging from 1 to 15)
- timepoint: sampling point (ranging from T0 to T7)
- sample_date: date of sampling
- day: day of sampling since start of experiment
rest of the columns are parameters measured, see de Nijs et al., 2023 for analysis details: 
mc_perc = moisture content %, om_perc =Organic matter %, om_perc_no_outliers = OM_perc without outliers, air_dry_mc = air dry moisture content (dried at 40 degrees celcius), C_perc = carbon %, N_perc = nitrogen %, 
CN_ratio = carbon/ nitrogen ratio, pH = pH, EC_mS = electrical conductivity in mS, TOC_mgC = total dissolved organic carbon in mg carbon per g, tN_mgN_g = dissolved nitrogen in mg nitrogen per g, phenol_mg_EGA_kg = phenol in mg EGA equivalent per kg, NO3_mg_g = nitrate in mg per g, NO2_mg_g = nitrite in mg per g, NH4_mg_g = ammonia in mg per g, N_NO3_mg_g = Nitrogen-nitrate in mg per g, N_NH4_mg_g = Nitrogen-ammonia in mg per g, nitrificatie = nitrification, mineralizatie = mineralization, CEC_cmol_kg = cation exchange capacity in cmol per kg,
resp_mg_co2_kg_h = respiration in mg CO2 per kg per h



REFERENCE
De Nijs, E. A., Maas, L. M. E., Bol, R., & Tietema, A. (2023). Assessing the potential of co-composting rose waste as a sustainable waste management strategy: Nutrient availability and disease control. Journal of Cleaner Production, 399, 136685. https://doi.org/10.1016/j.jclepro.2023.136685


LICENSE
This work is licensed under CC BY 4.0. https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1
