dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment The authors are indebted to  Frédéric COPPIN ,  Loïc CARASCO ,  Sarah ZAMANE  from the Laboratoire de recherche sur les transferts des radionucléides au sein des écosystèmes terrestres (IRSN/ PSE-ENV/SRTE ) for ICPMS measurements on some of our samples to confirm the reliability of the spectrophotometry method used to determine iodine concentration.
Crop diversification is considered as an important linchpin of the agroecological transition, whereas current dominant cropping systems are known to rely only on a few crops species -like cereals in the European Union (EU). To assess the benefits of crop diversification at large scale, an accurate description of current crop sequences is required as a baseline. However, such a description is lacking at the scale of the EU. Here, we developed the first map of dominant crop sequences in the EU for the period 2012-2018. We used the LUCAS dataset that provides temporally-incomplete land cover information from a stable grid of points covering the whole EU. Eight crop sequence types were identified using hierarchical clustering implemented on LUCAS data, and mapped over EU. We show, in France, that the relative importance of these eight crop sequence types (as estimated from LUCAS data) was highly consistent with those derived from an almost spatially-exhaustive temporally-complete national dataset (the French Land Parcel Identification System) for the same period, thus validating the method and the typology for this country.
, 2017 (Barbieri et al. , , 2019 (Barbieri et al. , , 2021)). A robust assessment of benefits expected from crop diversification at large scale requires an accurate description of current crop sequences as a baseline. Indeed several ecosystem services provided by diversified farming systems depend on the precrop effect, thus needing to know precisely the nature of the crops successively grown on a same field  (Bennett et al. , 2012). However, such a description is still lacking for Europe, for at least two reasons: (i) existing datasets at the European scale, e. g. the LUCAS dataset, provide information about land use categories and crop species cultivated on agricultural land, but no information about crop sequences because data are not available every year (e. g. the LUCAS dataset provides information every 3 years), (ii) even if national and sub-national datasets describing crop sequences are available within some EU-state members, e.
g. the Land Parcel Identification Systems in France  (Levavasseur et al. , 2016) , a lack of harmonization (e. g. spatial and temporal resolution) between these datasets makes them difficult to use in a cross-analysis. This situation has resulted in the production of regional to national studies of crop sequences  (Levavasseur et al. , 2016; Peltonen-Sainio and Jauhiainen, 2019; Stein and Steinmann, 2018; Xiao et al. , 2014)  but has hampered any analysis at the scale of the European Union (EU). Such an analysis would be useful, especially in the context of the EU's Farm to Fork strategy, in which the adoption of more diverse crop rotations is encouraged (EU, 2020). To overcome this problem, we developed an original method that combines European-level and national-level datasets to create a map of current dominant crop sequences at the European level.
Method overview The purpose of this work is to map current dominant crop sequences from the European Land Use Cover Area frame statistical Survey (LUCAS, d  'Andrimont et al. 2020). As this dataset is temporally incomplete (observations of land use on fixed points, only every three years), we proceeded in three steps (Figure  1 ) to assess how this incomplete information could be used to describe the diversity of crop sequences across the EU. First, we preprocessed the LUCAS data, by selecting points under nonperennial agricultural land cover. For each of these points, we calculated frequencies of eight crops or groups of crops across 2012, 2015 and 2018 (the three last years of observation). Second, based on this set of eight variables, LUCAS points were classified into eight groups (hereafter referred to as crop sequence types), combining a principle component analysis (PCA) with hierarchical clustering.
The LUCAS dataset The Land Use Cover Area frame Survey (LUCAS) is an in-situ land-cover (i. e. physical cover observed at the earth's surface, e. g. cereals, root crops, fodder crops) and land-use (i. e. socio-economic function of the observed earth's surface, e. g. agriculture, industry, residential) observation, carried out on approximately 300 000 points sampled from a stable grid of around 1 100 000 points across EU-28 from 2006, every three years. Last observation campaign was realized in 2018. The work presented here is based on the harmonized LUCAS database developed by d  'Andrimont et al. (2020) , which gathered, in a unique dataset, all the information collected from the beginning of this survey.
The French LPIS dataset The French Land Parcel Identification System (LPIS) is based on yearly declarations made by farmers in compliance with Common Agricultural Policy subsidies. From 2008, it provides an annual almost spatially-exhaustive information of land use for agricultural land, detailing 28 crop categories until 2014, and approximately 300 from 2015. For our study, we used the "RPG Explorer Crop successions France version 2. 0" dataset developed by  Martin et al. (2021). This dataset compiles all annual LPIS data for France into a unique dataset of crop sequences for more than 18 million fields over the period 2012-2018, and associated areas. This dataset represents the most exhaustive dataset of crop sequences in France, in terms of both spatial and temporal resolution.
Preprocessing of LUCAS data From the harmonized LUCAS database  (d'Andrimont et al. , 2020) , we selected points with observations for the three most recent campaigns  (i. e. 2012, 2015, and 2018). We assumed the seven-year time period long enough to encompass the duration of main crop rotations, and thus we do not consider older campaigns  (i. e. 2006 and 2009)   2018 period was considered, only 9 094 points would have remained with information about crop cultivated in  2006, 2009, 2021, 2015 and 2018. This choice would also have limited the analysis to 11 among the 27 EU countries (i. e. Belgium, Czechia, Germany, Spain, France, Hungary, Italy, Luxembourg, The Netherlands, Poland and Slovakia). This is another reason why we considered only the three most recent campaigns. Depending on the purpose of analysis, crop sequences can be described in many different ways, including length and flexibility of rotation  (Castellazzi et al.
, 2008) , nature and function of crops or crop groups  (Barbieri et al. , 2017; Videla-Mensegue et al. , 2022) , diversity of crops or crop groups  (Beillouin et al. , 2021b; Tamburini et al. , 2020) , diversity of sowing dates  (Weisberger et al. , 2019) , order of succession of crops within the sequence  (Peltonen-Sainio et al. , 2019) , and temporal frequency or return time of crops within the sequence  (Nowak et al. , 2022). Given the characteristics of the LUCAS dataset, the exact order of crop succession within the sequences could not be described, nor could be the variety of sowing dates (e. g. the LUCAS dataset provides no information about whether the crop is sown in autumn or spring). Therefore, we chose to describe current dominant crop sequences by the temporal frequencies, over three years  (i.
e. 2012, 2015 and 2018) , of eight crops or groups of crops described below. This choice is a compromise between addressing common issues related to crop rotations (e. g. nitrogen management and the role of legumes) and avoiding unnecessary complexity. These eight groups were defined based on two criteria: (i) their agronomic relevance, and (ii) their importance in terms of cultivated area in Europe. Groups considered were (i) cereals (i. e. wheat, barley, oat, triticale, rye, corresponding to LC B11 to B19 in the LUCAS dataset nomenclature), (ii) corn (LC B16), (iii) rapeseed (LC B32), (iv) sunflower (LC B31), (v) pulses (i. e. dry pulses and soybean, corresponding to LC B33 and B41), (vi) root crops (i. e. beets and potatoes, corresponding to LC B21 to B23), (vii) forage legumes (i. e. alfalfa and clover, corresponding to LC B51 and B52) and (viii) temporary grassland (LC B53, B55, and E20).
Identification of crop sequence types with hierarchical clustering A principle component analysis (PCA) was performed with the PCA() function of the R package FactoMineR v2. 1  (Lê et al. , 2008)  on all the eight variables describing temporal frequencies of cereals, rapeseed, sunflower, pulses, corn, root crops, forage legumes, and temporary grassland. Seven components displayed eigenvalues higher than or close to 1, cumulating more than 99% of variance (Figure  2a ). These seven components were used as inputs for a hierarchical clustering, performed with the HCPC() function from the same R package. Inertia gain displayed a clear break from eight to nine (Figure  2b ), thus eight groups of crop sequences (crop sequence types) were considered. To analyze the geographical distribution of dominant crop sequences, observed locations of these eight crop sequence types were mapped across the whole EU.
Quality assessment The crop sequence types derived from incomplete temporal sequences (LUCAS dataset) were compared to crop sequence types derived from complete temporal sequences (French LPIS dataset). To this aim, a three-step procedure was followed. First, a random forest (RF) model was trained on LUCAS data to predict crop sequence type from crop (or crop group) frequencies (i. e. a total of eight predictors). The RF model was fitted using the randomForest() function of the R package randomForest v4. 6. 14  (Liaw and Wiener, 2002) , with default settings. The RF model showed good performances, as indicated by an out-of-bag (OOB) error rate of only 0. 12% (Table  3 ). Second, the RF model was applied on French LPIS data to classify observed crop sequences into the eight crop sequence types.
Third, the crop sequence type distributions derived from the LUCAS and from French LPIS datasets were compared at the national and regional levels in France. This allowed to check if the lower temporal and spatial resolutions of the LUCAS dataset led to some bias in the identification of crop sequence types and their geographical distribution. To ensure the map of crop sequence types presented in this paper could be used as a baseline in studies assessing scenarios for the future of agriculture in the EU, it appears essential to assess its consistency with observed land use. To this aim, average crop or crop group harvested areas between 2012 and 2018 were calculated from (i) their relative frequencies within the crop sequence types derived from the LUCAS dataset, (ii) relative importance of crop sequence types, and (iii) total arable land area.
Eight types of crop sequences identified based on the LUCAS dataset Eight different types of crop sequences were identified based on the LUCAS dataset (Table  2 ). Two crop sequence types largely dominate in terms of relative importance within the EU: (i) the first one accounts for 40% of LUCAS points, and is dominated by temporary grasslands (90%), followed by cereals (4%) and corn (2%); (ii) the second one accounts for 21% of LUCAS points, and is dominated by cereals (87%), followed by corn (6%) and grasslands (4%). Together, these two crop sequence types represent 61% of LUCAS points. Then comes a group of three crop sequence types of moderate relative importance, each one representing 7% to 9% of LUCAS points. This group includes a crop sequence type dominated by rapeseed (43%) and cereals (43%), a second one dominated by corn (81%) and cereals (15%), and a third one dominated by root crops (43%) and cereals (35%).
3. 2. 1 Incomplete temporal crop sequences are a good proxy of complete temporal crop sequences at national and regional scale in France On average at the national scale in France, the relative importance of crop sequence types estimated from the LUCAS dataset is in good agreement (R²=0. 93, RMSE=0. 04) with estimates based on the French LPIS dataset, with no systematic bias (Figure  3 , Figure  S1 ). However, estimates based on the LUCAS dataset overestimate the proportion of the "grasslands" crop sequence type, and slightly underestimate the proportion of the "rapeseed and cereals", "root crops and cereals", and "forage legumes and cereals" ones. At the regional scale, the agreement between the relative importance of crop sequence types derived from the LUCAS or French LPIS datasets remains good, but varies by crop group (Figure  4 ).
The crop sequence types "cereals", "corn and cereals", "forage legumes and cereals" and "sunflower and cereals" show the best agreement, with high R² (higher than 0. 8), low RMSE (lower than 0. 05), and no systematic bias, whereas the crop sequence type "pulses and cereals" shows the lowest agreement (R 2 =0. 26, RMSE=0. 02). Overestimation of the crop sequence type "grasslands" for almost all regions, and underestimation of the crop sequence types "rapeseed and cereals" and "root crops and cereals for the regional highest values are confirmed. Overall, regional differences in crop sequence types are well captured with the LUCAS dataset, as shown by two conclusions. First, any given crop sequence type may have a low relative importance in one region and a high one in another. This range of relative importance across regions is well captured by estimates derived from the LUCAS dataset for all crop sequence types (Figure  4 , Figure  S2 ).
Discussion The maps of crop sequence types derived from our study fill an important void in our knowledge of the spatial distribution of crop sequences in the EU. Despite they were based on uncomplete data, both in time and space (the LUCAS dataset), these maps have been shown to represent well the distribution of crop sequence types in France at both national and regional scales when compared to an almost spatially-exhaustive dataset of temporally complete crop sequences in France. This consistency shows that the temporal incomplete information from LUCAS (i. e. only three crops known -2012, 2015, and 2018 -from a seven-year crop sequence) allows to distinguish crop sequence types. While the quality assessment of this map of crop sequence types in the EU would benefit from comparisons with other datasets at national or subnational levels in other countries, we nevertheless highlight that all crop sequence types are present in France allowing for a complete quality assessment over the eight crop sequence types.
Yet, the comparison with the French LPIS dataset reveals an overestimation of grassland crop sequences, which leads to an underestimation of other crop sequence types. A possible explanation for this overestimation could be the inability of the three sample years available in the LUCAS dataset  (i. e. 2012, 2015, 2018)  to capture the full diversity of longer crop rotations, such as rotations including temporary grassland. For example, let's consider a cyclical crop rotation starting in 2012 with 3 years of consecutive grassland followed by wheat, maize and barley. Then, observation in 2012, 2015 and 2018 would be grassland, wheat, and grassland respectively, yielding a proportion of grassland of two-third instead of half. Another possible explanation could be that temporary grassland may sometimes be confused with permanent grassland, during LUCAS observations, as well as during farmers' declaration in the French LPIS.
23). On the other hand, Montenegro displays the highest sampling effort (1. 77), but also an important discrepancy between estimated and observed harvested areas. Indeed, this high rate hides a low number of observations (15) for a small arable land. Thus the map accuracy may be limited either by a low sampling effort or by a low number of observations in small-sized countries. We assume our maps of crop sequences to be useful for future studies dealing with agricultural issues that are sensitive to crop rotations, including nature-based pest control  (Lechenet et al. , 2016) , pesticide use intensity  (Jacquet et al. , 2011) , nitrogen management and cycling  (De Notaris et al. , 2018) , biodiversity  (Li et al. , 2021) , and crop yield  (Bennett et al. , 2012). We also highlight that the methodology presented in this paper that leverages temporally incomplete information about crop sequences based on the LUCAS dataset can be adapted to specific objectives of future studies.
e. winter wheat within a "rapeseed and cereals" crop sequence may be cultivated differently as within a "pulses and cereals" one. Thus, these crop sequence types may display contrasted environmental, economic and social performances. The relevance of this crop sequence typology to differentiate cropping systems with contrasted performances could be assessed by combining a dataset including crop sequence with a one gathering crop management information. We hope the methodology developed here will stimulate a more detailed description of crop sequences, both in time and space, and their effect on agricultural production and sustainability in future studies. Note that crop sequence types have been given short names that reflect dominant crops or crop groups within the sequence. For example, the crop sequence type "grasslands" also includes cereals and corn, but these crops are less frequent in the sequence.
Acknowledgements This work was supported by the  LegValue  project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  N°727672. Access to some confidential data, on which is based this work, has been made possible within a secure environment offered by  CASD -Centre d' accès sécurisé aux données  (Ref. 10. 34724/CASD).
Data Collection and Sample Characteristics A total of 100 students from two local universities in Guayaquil participated in the study through their involvement in classroom projects. The projects were informed to each classroom, and those students willing to participate registered. Two trainings were given to the registered students: one on the contents of the survey and another on how to approach people on the streets of the city. To obtain heterogeneous data, people from the city were approached in the streets surrounding transited public places, such as commercial centers, parks, markets, and bays, between May and June 2021. Printed questionnaires were distributed to willing participants, who completed them in 20 to 30 min. The respondent population was made.
Data Collection and Sample Characteristics A total of 100 students from two local universities in Guayaquil participated in the study through their involvement in classroom projects. The projects were informed to each classroom, and those students willing to participate registered. Two trainings were given to the registered students: one on the contents of the survey and another on how to approach people on the streets of the city. To obtain heterogeneous data, people from the city were approached in the streets surrounding transited public places, such as commercial centers, parks, markets, and bays, between May and June 2021. Printed questionnaires were distributed to willing participants, who completed them in 20 to 30 min. The respondent population was made aware of the purpose of the study and that their private information would be kept confidential before completing the surveys.
The methods are scientifically sound, and the MS is well written. As a consequence, I have a few minor remarks listed hereafter, but also I point out several drawbacks. The first one is about the recurrent use of the word "cocktail" even in cases where the related concepts and paradigms would justify using "mixture" instead. The second concern is about a validation of the approach, a way to corroborate the findings and interpretation, which is partly lacking. This may be achieved by considering further comparison with one of the references already cited, and by adding data and results from environmental surveys in France which report the mixtures actually found in environmental matrices (soil, air, water) and biota. The test of the temporal robustness should be refined by comparing the output from the "core" year 2017 to the output obtained from the dataset on the other years without including 2017.
These issues are detailed in specific comments. Thank you for the positive comments and for pointing out the issue regarding the use of "cocktail" and "mixture". We checked their use throughout the manuscript and replaced "cocktail" with "mixture" where appropriate. See below for a more detailed response. We agree that a comparison of the purchase data to environmental contamination data would strengthen our results. To test whether data on pesticide purchase can be representative of environmental contamination we used a dataset on water contamination in French rivers and water bodies (Naïades; http://www. naiades. eaufrance. fr/acces-donnee). We generally found positive, albeit not extremely strong, correlations between purchase and contamination data, suggesting that the pesticide purchase data we used are indeed related to environmental contamination.
Beronius, A. , Zilliacus, J. , Hanberg, A. , Luijten, M. , van der Voet, H. , van Klaveren, J. , 2020  We initially avoided the word "mixtures" to avoid confusion with the statistical tool "mixture model". However, the reviewer is right and we have modified the text throughout the manuscript to replace "cocktail" with "mixture" where appropriate. We have hence renamed "mixture models" to "model-based clustering" to avoid the confusion (p7, l. 179). Line 88. The reference of the database and/or URL must be added. Indeed the reference was missing, we added it (P4; l. Lines 103-112. To make it clear for the readers, it would be necessary to explain the administrative and spatial meaning of postcode in France. (what is "township" versus "district", the average size of township surfaces and their potential variability).
Organic farming also raises the issue of other substances without mandatory reporting that may be used in organically farmed areas. This is now explained P. 23, lines 676-679. Line 301-312. It is a very good point that authors checked the reliability of the method over time. However, the fact that the data from the year 2017 are present in the two dataset is a flaw, since this of course artificially increases the probability of correlation, creates an absence of statistical independence between the measurements. It may be recommended to perform the same approach but comparing the output from 2017 to the output from the dataset 2015-2018 without 2017. We agree that including year 2017 in the two analyses contributes to obtaining correlated results. However, the rationale behind the comparison of the 2017 outputs with the 2015-2018 outputs was to evaluate how sensitive the results were with respect to the choice of a specific year vs.
"substances are used with in the buying area", with? Corrected. "substances are used within the buying area" (P21 line 618) Line 531. "effects have already been studied. but mostly on pairs of substances". Please check, the dot likely not should be there. This has been changed in the main text (P21 line 638) Line 533. A few recent studies under controlled conditions addressed broader mixtures, for instance using mixtures of herbicide/insecticide/fungicide or even larger using soils sampled in natura. See for instance: We agree that a comparison of the purchase data to environmental contamination data would strengthen our results. To test whether data on pesticide purchase can be representative of environmental contamination we used a dataset on water contamination in French rivers and water bodies (Naïades; http://www.