dataesr/scientific-paragraphs-categorization huggingface dataset:
7 and 50. 6%. Manure VS values range between 2 and 197 kg VS t -1 FW and organic C content between 0. 18 and 233 kg C t -1 fresh weight (FW). Manure N concentration ranges from 0. 04 to 35. 8 kg of N t -1 FW, and TAN concentration ranges from 0 to 26. 2 kg of N t -1 FW. Manure pH varies between 4. 5 and 10. 7 and C/N ratio between 4 and 71, with most values >30 sourced from four studies  (Andersson, 1996; Chen et al. , 2018 ; Kierończyk,   High manure temperatures (between 40 and 67. 5 ˚C) relating to a study on solid manure are likely to be due to microbial activity in the manure heaps  (Miyatake & Iwabuchi et al. , 2006). Although the database has undergone a data entry quality control process, it is important to note that there are uncertainties in the measurements that can result in outliers in EFs and some variables.
IMPLICATIONS OF THE DATABASE This database was created with the aim of providing a central data repository for refining the EFs for the different animal categories according to the farming conditions. These EFs can then be used by different stakeholders to improve the accuracy of national inventories, perform environmental assessments, identify and quantify mitigation techniques, and prioritize influencing parameters. Through the collation of data from studies across the globe, this project has also highlighted the lack of studies in some important livestock production areas and the research needed to explore the standardization of future study approaches and harmonization of data from existing studies. For both housing and storage databases, the number of EFs from tropical climatic conditions is too small to reliably refine the current IPCC Tier 1 default EFs (IPCC, 2019) for tropical climates.
Moreover, for Africa, there are no EFs in the housing database and only six N 2 O EFs and six CH 4 EFs (all from Kenya) in the storage database. Reasons for this imbalance in the number of EFs between the different continents may include the availability of adapted measurement methods and variability in livestock systems. The low number of EFs for South America, which is a region with large numbers of highproducing livestock systems, may be due to the primary focus on assessment and mitigation of enteric CH 4 and that most of the cattle production system are grass based. During the construction of the database templates, many variables were identified for storage and for housing. Some of them are necessary for the characterization of the livestock systems, others for the conversion of the results into reference units such as kg CH 4 kg -1 VS stored or % of N excreted, and the majority for the interpretation of EFs.
There were no studies that included all variables listed in the database; this is not surprising given that each study had different objectives and therefore had different data requirements. However, to ensure that published gaseous emission measurements can be readily converted to EFs, we encourage researchers to include the reporting of crucial manure and animal variables, as listed by  Webb et al. (2021). Some variables, such as N and VS excreted, are also very rarely reported because their determination is often not considered for experiments that focus on emissions from housing-based manure systems. These variables require the implementation of specific protocols that are more likely to be included in experiments dealing with animal nutrition. Concerning CH 4 EFs for housing, most of the collated studies reported CH 4 concentrations within the housing and therefore may have included enteric CH 4 from ruminant livestock (and potentially pigs).
Because we were unable to distinguish the source of the CH 4 in these studies, we recommend caution in the application of these data as manure EFs. This is the first iteration of these two databases, which can be found at www. dataman. co. nz (Version 1. 1); however, it is planned to expand these datasets over time, based on the ongoing projects "Mitigating greenhouse gas Emissions from Livestock Systems" (MELS, EraNet Joint Call 2018), "Back to the Future: Reintegrating Land and Livestock for Greenhouse Gas Mitigation and Circularity" (Relive, www. reliveera. net), and "Multi-criteria assessment, decision support and management tools for sustainable circular mixed farming systems for dairy production" (Dairy Mix, www. suscrop. eu/ funded-projects/3rd-call/dairymix). The latter two projects were recently selected for funding as part of the EraNet Joint co-fund Call 2021.
CONCLUSION The Both databases revealed disparities in the number of available EFs between climatic zones (many more EFs developed for temperate zones than for tropical zones) but also between continents. Although there is a need for continued measurements in all regions of the world with adapted measurement protocols, this need is greatest in poorly represented regions such as Africa, Asia, and South America, where livestock production plays an important role but the number of available EFs is low. During the MELS project, data will be analyzed to develop revised EFs and generate functional relationships between emissions and activity/ancillary variables, enabling a refinement of national inventories and better assessment of the cost-effectiveness of a range of mitigation measures. The databases will be expanded over time by including new studies on GHG and NH 3 emissions across the world, allowing further refinement and disaggregation of EFs and improving our knowledge of key drivers along the manure management chain.
Livestock manure management systems are one of the main sources of greenhouse gas and ammonia emissions worldwide. • We built a database of CH 4 , NH 3 , and N 2 O emissions from livestock housing and outdoor manure storage. • The housing database contains 392, 1,281, and 351 emission factors for CH 4 , NH 3 , and N 2 O, respectively. • The storage database contains 243, 654, and 421 emission factors for CH 4 , NH 3 , and N 2 O, respectively. • Due to the lack of studies, not all regions of the world are in the database. F I G U R E 1 Summary of process for collating data of DATAMAN database and the focus of the current paper. ELFE, ELevage et Facteurs d'Emission (Vigan et al. , 2019) was sourced from published peer-reviewed research, theses (undergraduate and postgraduate), and conference papers.
Number of observations for a selection of quantitative variables relative to the total CH 4 , NH 3, and N 2 O emission factors for housing Number of values (in % of the total number of values for each gas, dry matter; FW, fresh weight. a Total number of emission factors. F I G U R E 3 Histogram of emission factors, manure and climate variables for all manure type included in the N 2 O, NH 3 , and CH 4 housing datasets. The y axis shows frequency (count). Manure DM, manure dry matter; R. H. , relative humidity; T, temperature broilers, 20 for layers), and 47 are for beef and dairy cattle (with 21 values for solid manure).
Histogram of emission factors (EFs), manure, and climate variables for all manure types included in the N 2 O, NH 3 , and CH 4 storage datasets. The y axis shows frequency (count). For the CH 4 EF a single high value of 92% is not represented on the histogram to make it more legible. For C/N ratio, nine values >100 (three of each of ratios 162, 483, and 607) are not represented on the histogram to make it more legible. Refer to Table6for descriptions of abbreviations. Manure C, manure organic C; T, temperature 2014; van der Weerden et al. , 2014). Average air temperature measured during the studies ranges from -8 to 30 ˚C. Twentyeight EFs (1 for N 2 O, 3 for CH 4 , and 24 for NH 3 ) correspond to studies with negative average air temperature and 196 with average air temperatures above 20 ˚C, mainly in temperate wet climatic zones.
• Livestock manure management systems are one of the main sources of greenhouse gas and ammonia emissions worldwide. • We built a database of CH 4 , NH 3 , and N 2 O emissions from livestock housing and outdoor manure storage. • The housing database contains 392, 1,281, and 351 emission factors for CH 4 , NH 3 , and N 2 O, respectively. • The storage database contains 243, 654, and 421 emission factors for CH 4 , NH 3 , and N 2 O, respectively. • Due to the lack of studies, not all regions of the world are in the database. F I G U R E 1 Summary of process for collating data of DATAMAN database and the focus of the current paper. ELFE, ELevage et Facteurs d'Emission (Vigan et al. , 2019) was sourced from published peer-reviewed research, theses (undergraduate and postgraduate), and conference papers.
F I G U R E 5 5 Histogram of emission factors (EFs), manure, and climate variables for all manure types included in the N 2 O, NH 3 , and CH 4 storage datasets. The y axis shows frequency (count). For the CH 4 EF a single high value of 92% is not represented on the histogram to make it more legible. For C/N ratio, nine values >100 (three of each of ratios 162, 483, and 607) are not represented on the histogram to make it more legible. Refer to Table6for descriptions of abbreviations. Manure C, manure organic C; T, temperature 2014; van der Weerden et al. , 2014). Average air temperature measured during the studies ranges from -8 to 30 ˚C. Twentyeight EFs (1 for N 2 O, 3 for CH 4 , and 24 for NH 3 ) correspond to studies with negative average air temperature and 196 with average air temperatures above 20 ˚C, mainly in temperate wet climatic zones.
Number of emission factors Continent Country CH 4 NH 3 N 2 O Summary of the database for CH 4 , NH 3 , and N 2 O emission factors for animal housing Europe Austria 10 10 18 Belgium 77 107 77 Czech Republic 0 11 0 Denmark 33 51 29 France 43 212 87 Germany 22 27 1 Hungary 2 2 0 Ireland 0 37 0 Italy 25 39 14 Lithuania 0 6 0 The Netherlands 14 115 7 Poland 27 47 18 Portugal 9 18 15 Slovenia 0 3 0 Slovakia 4 18 4 Spain 6 19 6 Sweden 6 15 0 United Kingdom 0 138 0 Total 278 875 276 North America and Brazil 0 7 1 South America Canada 26 55 18 United States 82 306 47 Total 108 368 66 Oceania Australia 6 9 6 Total 6 9 6 Asia China 0 24 3 South Korea 0 5 0 Total 0 29 3 Grand total 392 1,281 351 populations (e. g. , Ireland, United Kingdom, and Brazil), there are no housing CH 4 EFs. T A B L E 1.
Number of emission factors Continent Country CH 4 NH 3 N 2 O Summary of the database for CH 4 , NH 3 , and N 2 O emission factors for outdoor manure storage Europe Austria 3 37 40 Belgium 0 0 2 Denmark 8 57 12 France 0 46 36 Germany 18 36 24 Italy 24 91 31 The Netherlands 0 10 0 P o r t u g a l 0 8 8 Spain 8 17 0 Sweden 11 6 12 Switzerland 0 6 12 United Kingdom 38 100 22 Total 110 414 199 North America and Brazil 1 0 0 South America Canada 35 51 36 United States 4 70 10 Total 40 121 46 Oceania New Zealand 0 28 28 Total 0 28 28 Africa Kenya 6 0 6 Total 6 0 6 Asia China 54 76 98 Japan 33 15 36 V i e t n a m 0 0 8 T otal 87 91 142 Grand total 243 654 421 T A B L E 4.
ACKNOWLEDGEMENTS Special thanks to all the staff and collaborators of the  Ministry of Higher Education and Scientific Research of Tunisia the support of MESRS (Tunisia)   PEJC competitive Program  project  20PEJC01-13 , of the  Institute of Materials Science of Mulhou-seFrance  ( IS2M ), specially for characterization realized on the technical ISO9001 platforms (Electron microscopy, XRD, FTIR) and of the Water Researches and  Technologies Center Borj Cedria-Tunisia.
Only when primary data is not available (i. e. , original data from scientific research, surveys, case studies, or monitoring with a low level of aggregation) does the World Food LCA database provide statistics-based data, i. e. , aggregated generic data  (Nemecek et al. , 2015). Consequently, data scarcity poses a major challenge regarding uncertainty, and researchers have searched for ways to reduce it. Lindeijer and Weidema (2000)  claimed that farm typologies in LCA could be used "to lower data variability, to allow a better selection of representative farms for detailed research, and to better determine the marginal effects of a studied change"; the better the typology, the lower the variability within each farm type, and the higher the variability between farm types  (Köbrich et al. , 2003).
ASD has already been used for modelling environmental impacts; primarily, for assessing specific impacts such as eutrophication  (Mabon et al. , 2009; Moreau et al. , 2012; Lacoste et al. , 2016). Avadí et al. (2016)  used it to build regional LCA, with farm-level data issued from a previous ASD  (Mabon, 2008 ) along with regional farm-level surveys and statistics (e. g. , main production outputs, land use), and scaled-up results by using proxy data (e. g. , glyphosate for pesticides). To go a step further, our aim is to build an innovative method based on ASD to secure data collection for Life-Cycle Inventory (LCI) at a regional scale, despite data scarcity, in highly diverse agrarian systems. ASD will be used to describe the farm typology in the area, and account for the diversity of farming and cropping systems.
g. : the type of fertiliser, the dose, the spreader use, etc). They have a significant influence on agricultural LCA results  (Cowie et al. , 2012; Modahl et al. , 2012). Related data must therefore be collected specifically for the system under study. Data necessary for a good modelling of foreground processes include not only "activity data", but also other "site-specific" data (e. g. , soil texture, crop variety) that are useful to correctly model emissions and consumptions  (Bellon-Maurel et al. , 2014; Röös et al. , 2010). However, LCA also considers emissions/ consumptions which are related to the resources used for the activities of the farm, e. g. , related to agricultural machinery building or to energy/ input production. Such "background processes" occur upstream or downstream (e.
g. , recycling steps, after resource usage) and are not described with the same accuracy as foreground processes: background data are taken from database libraries incorporated in LCA software: in our case, EcoInvent 2. 2. The third step is Impact Assessment (LCIA), in which these inventory data are processed using an environmental impact assessment model to be converted into potential environmental impacts. For instance, the 2008 LCIA ReCiPe method  (Goedkoop et al. , 2012)  generates 18 "midpoint impacts" -including climate change, human toxicity, and water depletion -as well as three aggregated impact categories called "endpoint impacts" -i. e. , human health, ecosystems and resources. In ReCiPe, water depletion is routinely modelled only up to the midpoint level in volumetric units. However, in the present work, we will model it up to the endpoint level, due to the local high water stress  (Pfister et al.
• In Steps 1 to 3, farming and cropping systems are identified through ASD. Based on a stratified sampling of farms, activity data are collected at the: crop, field, farm and regional levels, and enriched with additional data specific to LCI requirements. • Data gaps are checked and filled in Step 4. • Then, LCI is built using a bottom-up approach (Steps 5 to 8). At each level, data are aggregated, and internal exchanges of material flows are accounted for. LCI are delivered for each cropping system (Step 6), farming system (Step 7), and for the whole farming region (Step 8). In Step 1, various sources of information are consulted to better understand local context dynamics and resulting influences on regional farming systems. Review of literature, along with field work, consisting in landscape analysis, and the historical reconstitution of local farming activities are used to sketch a preliminary typology of farming systems.
, 1996)  which has been parameterised using the soil and crop features of the case study. The last available information sources are expert knowledge and data from the literature. The methods most likely used to fill missing data gaps, i. e. , analogies, crop-models, expert knowledge and literature are summarised in Table  2. Such extrapolated data-whatever the method usedlead to higher epistemic uncertainty than that associated with data collected directly during interviews. Therefore, extrapolated data will be rated lower in terms of quality in the crop dataset (supplementary information, Section S. 2. 1, Table  S2 ) and in subsequent analysis regarding uncertainty propagation (Section S. 2. 2). Step 5, and the following steps encompass the Life Cycle Inventory and LCA. In Step 5, the LCI is built at the elementary level of the crop: the activity data related to the crops in each cropping systems are converted into LCI data or "inventory data".
Inventory data are made up of input and output flows of materials and energy involved in each elementary process. For example, "producing 1 kg of pepper" comprises material and energy flows involved in the manufacture and transportation of inputs, e. g. , fertilisers and pesticides (specific molecules, active ingredients and amounts are specified), along with the output flows of polluting emissions-notably field emissions -into the air, water and soil compartments of the environment (Fig. 2 ). Data collected at the farm level, such as machinery and water used for irrigation, are disaggregated respectively into the crop and cropping systems to which they contribute, according to the ratio of hours spent on each of them, versus total lifetime. In standard ASD, machinery data are collected to calculate economic depreciation to be deducted from the operator's turnover in order to calculate their income.
Conversely, the ASD-based LCI aims to characterise not only the total lifetime of a piece of machinery, but also its quantified use in relation to each crop in order to assign to each crop the share of the environmental impacts it has caused. Lastly, field emissions are assessed based on activity data collected by ASD-based LCI, and complemented with data related to soil / weather. They are computed with dedicated models and emission factors for nitrogen emissions, in accordance with local soil and climate conditions  (Brentrup et al. , 2000; Bouwman and Boumans, 2002a, 2002b; IPCC, 2006; European Environmental Agency and EMEP/EEA, 2009). Details are given in Supplementary information S. 1. 1. The EcoInvent 2. 2 LCI database was used for background processes. Steps 6, 7 and 8 consist in aggregating LCIs computed at crop levels.
In the present study, uncertainty was computed for each crop within a cropping system, and assessed regarding the quality of its "crop dataset", i. e. , the set of activity data related to the whole crop cycle until harvest, as in  Röös et al. (2010). The global uncertainty factor (i. e. , of the cropping system) is then computed as the linear combination of the squares of the geometric standard deviations of each crop included, weighted according to their share.
Data uncertainty with ASD-based LCI When ASD is completed (Step 3), we have 70 datasets describing the activity data (and additional data) for the 70 different cropping systems. Each dataset is given a mark (from excellent to poor) according to its completeness. When more than two-thirds of the dataset is completed with activity data, the dataset is scored "excellent". When between 1/3 and 2/3 data activities are known, the dataset is given an "intermediate" score. When less than one third of the data activity is collected, the dataset is scored "poor". There are 19 "excellent", 49 "intermediate" and 2 "poor" scores; for details, see supplementary information S. 2. 1. Intermediate-and poor-quality datasets are then extrapolated to fill the data gaps. Table  6  shows the pedigree matrix approach, with crop datasets obtained using different data collection methods.
For the sake of clarity, the single livestock system existing among all farming systems was not used for the uncertainty analysis. For each of the 6 categories encompassed by the pedigree approach, which can contribute to data quality, a score, named the "pedigree matrix factor" (PMF) is estimated by experts based on data quality. The better the data quality level, the lower the PMF score with 1 set as the lowest limit (smallest uncertainty level). The first column (0) shows ideal data and therefore receives a PMF score of 1. The second column (1) represents the PMF of the statistical data (FADN type). Columns 2 to 4 concern data collected with ASD-based LCI before extrapolation, and give PMF for "poor", "intermediate" and "excellent" datasets respectively. Columns 5 and 6 show improvements by extrapolation of datasets previously classified as "intermediate" and "poor".
In relation to the PMF mark assigned to each type of dataset, the pedigree matrix also provides the corresponding "uncertainty factor" (Ui), which are to be combined to compute the global uncertainty factor as the square geometric standard deviation  (Frischknecht et al. , 2007). Details on how the PMF scores were assigned to each data quality level for each of the three data collection methods can be found in Supplementary information S. 2. 2. The uncertainty factor (UF) for all the crop datasets (i. e. of the aggregation of the 70 datasets) was obtained by a linear combination of uncertainty factors obtained for each of the three quality levels in the dataset, weighted according to their share of the total: 19/70, 49/70 and 2/70 respectively, for good, intermediate and poor quality datasets.
In the ideal case, in which all PMF are optimal, the minimum UF is 1. 03. The UF obtained for datasets modelled using statistics is the worst, i. e. , 1. 37. It is 1. 20 and 1. 12 for data obtained with ASD-based LCI before and after extrapolation, respectively. As the uncertainty interval around the geometric mean (μg) containing 95% of values is given by: {μ g /UF g ; μ g *UF g } with UF g =σ 2 g , this means thatwith regard to statistics datathe uncertainty interval obtained with ASD-based LCI (by stratified sampling) is divided approximately by two and by four before and after extrapolation, respectively, In conclusion, the application of ASD-based LCI leads to a significant reduction in LCI data uncertainty, despite the very conservative and stringent assumptions made when assigning pedigree scores.
Crop rotations and intercropping cannot be adequately described with remote sensing systems, although they are key components of farming systems, and together represent a major lever that could be used to achieve sustainable agriculture  (Kassam et al. , 2009; Guillou et al. , 2013). The ASD method allowed us to capture data which are crucial to LCI. Activity data related to each crop include crop management practices and were collected during field visits and interviews with different local stakeholders, mostly farmers. The survey grid is usually built ad hoc in the ASD  (Jouve, 1986) ; this means that it can be specifically designed for the purposes of a LCA. For example, "ASD-based LCI" focuses on the chemical properties of every fertiliser and pesticide, which are major contributors to field emissions in agricultural systems  (Avraamides and Fatta, 2008) , beyond the standard technical and economic data (e.
Conclusion Our main objective was to build a robust method to carry out regional LCA of farming activities, despite the high diversity of farming systems and data scarcity, two factors which increase the uncertainty of input data (and therefore outputs) in LCA. To overcome this dual challenge, which is of critical importance at the regional scale, we propose an innovative method, which combines ASD and LCA to conduct the first and most difficult step, of LCA: Life Cycle Inventory. This method, which we have named "ASD-based LCI" was applied to a 6000 ha pilot area, characterized by intensive irrigated farming in the Kairouan plain, Tunisia, to build a Life Cycle Inventory that reflected the diversity of farming activities at the regional scale. ASD was used to characterise the farming systems and their inner functioning, a necessary step before a stratified sampling of farms that were chosen for each archetype to conduct data collection on farm activities.