dataesr/scientific-paragraphs-categorization huggingface dataset:
At a national scale, ground campaigns, such as those carried out as part of the Sen2Agri project in South Africa and Mali, collected data on the main crop types  (Defourny et al. , 2019). But these data are generally not available to validate global maps or train new classification algorithms, as they are often the responsibility of national sovereignty. At a local scale, datasets on crop types have been acquired, and are still acquired, across multiple world regions within the context of the JECAM (Joint Experiment for Crop Assessment and Monitoring; Available online: http://www. jecam. org/; accessed on 10 February 2020) international network. The JECAM initiative was first developed under the GEO (Group of Earth Observations) umbrella and then became the research and development component of GEOGLAM (GEO Global Agricultural Monitoring), to enable the global agricultural monitoring community to carry out cross-sites experiments and compare results based on disparate sources of data, using various methods, over a variety of local or regional cropping systems.
Data are acquired following a given protocol and nomenclature (see  Defourny et al. (2014) ). The experiment has been operating since 2013, and some in situ datasets produced at field scale have been used in different benchmarking mapping studies  (Waldner et al. , 2016; Inglada et al. , 2015). The aim of this data paper is to share with the community, harmonized in situ agricultural land use datasets mostly acquired within the JECAM initiative, at local scale, and focusing on emerging/developing countries on the tropics. These datasets include data collected on nine sites in seven countries of the tropical belt (Figure  1 ) with various farming systems (Figure  2 ; Table  1 ). The acquisition protocol has been adapted from  Defourny et al. (2014)  to take in account the characteristics of tropical agriculture (e.
Study sites Except for Cambodia, the study sites belong to the JECAM network (http://www. jecam. org/), and cover several hundred squared kilometers each. The table 1 provides a synthesis of the database by site. The JECAM Burkina Faso study site is a 60 x 60 km² area located around the commune of Koumbia, Tuy province, in the South-West of the country. The climate is tropical. The absence of significant relief and the relatively good conditions in terms of soil and climate favoured the densification of cropped surfaces, which span the majority of the area: arable lands cover more than 60% of the site, the remaining surface being either unsuitable for cultivation (e. g. rocky) or protected areas for nature conservation. The landscape is characterized by an alternation of large cropland areas made up of a patchwork of diversified small cropped fields (about 1 ha) and areas covered by natural vegetation.
Data collection The acquisition protocol is based on the JECAM guidelines  (Defourny et al. , 2014)  with adaptations to consider some characteristics of tropical agriculture (mainly small field size and accessibility). Field surveys were conducted yearly in each study zone, either around the growing peak of the cropping season, for the sites with a main growing season linked to the rainy season such as Burkina Faso, or seasonally, for the sites with multiple cropping (e. g. São Paulo site). Except for Senegal where a stratified sampling plan for field surveys was used  (Ndao et al. , 2021) , the GPS waypoints were gathered following an opportunistic sampling approach (called the "windshield survey") along the roads or tracks according to their accessibility (that can be difficult during the rainy season, leading to less surveys in secondary roads or tracks in some study areas), while ensuring the best representativity of the existing cropping systems in place  (Defourny et al.
, 2014; Waldner et al. , 2019). GPS waypoints were also recorded on different types of non-crop classes (e. g. natural vegetation, settlement areas, water bodies) to allow differentiating crop and non-crop classes. Waypoints were only recorded for homogenous fields/entities of at least 20 x 20 m² (against a minimum of 0. 25 ha with a minimum width of 30 m in JECAM guidelines). To facilitate the location of sampling areas and the remote acquisition of waypoints, field operators were equipped with GPS tablets (Trimble -Yuma2 or Handheld -Algiz 10X) providing access to a QGIS project with Very High Spatial Resolution (VHSR) images (orthorectified Pleiades or SPOT 6/7 images ordered just before the surveys, or PlanetScope images). This equipment allowed in situ recording of attributes relative to each waypoint on data entry forms (with automatic filing of IDs or dates and scrollable lists for other attributes to avoid data entry errors).
All these attributes are described in Table  2. In the specific case of Burkina Faso, Senegal-Niakhar and Brazil-São Paulo sites, the same fields were revisited each year, in order to study crop rotations and fallow practices in the region. For the South Africa site, some points were collected by helicopter using the Producer Independent Crop Estimates System (PICES  (Fourie, 2009) ) method developed by the National Crop Statistics Consortium. Flights were performed at an average altitude of 500 feet and a low flying speed allowing to record GPS points and to determine land use using a GPS tablet associated with a GIS interface and a recent VHRS image. Only clearly identifiable land covers have been kept in the database.
Post-processing Once the waypoints were acquired, the boundaries of each field or non-crop entity were digitized on the VHSR images in the QGIS software, and the class labels (and other attributes, see Table  2 ) were attached to the polygon database. Additional noncrop polygons were added by CAPI (Computer Assisted Photo Interpretation) of the VHSR images for the built-up areas, water bodies, wetlands, mineral surfaces, and natural forest classes (land covers clearly identifiable on images). To avoid digitizing errors, this step was performed by the same operator as the one who did the field surveys. Despite this, if there was doubt on the delineation of a given entity (e. g. fuzzy boundaries, high heterogeneity), the given entity was removed from the database. Finally, the topology of each entity was controlled externally.
Quality Checking Due to the nature of the dataset (in situ observation), validation is not possible. However, quality control was performed all along the data chain, from the acquisition to the post-processing, to ensure the quality of the datasets and their homogeneity throughout sampled years and locations. First, the acquisition protocol was described in a technical guide provided to the field teams so that nothing was forgotten during the campaigns. The dropdown list in the data entry form reduced input and post-processing errors. The surveys were carried out by agronomists with geoprocessing skills, accompanied by a national researcher or technician with expertise in the local farming systems. Second, during the post-processing step, the orthorectification of the VHSR images used to digitize the fields was checked from one year to the next, for multi-year sites, and corrected if necessary by taking homologous points.
Representativeness of data sets Because of their small size, these sites cannot be considered representative of the entire country in which they are located; however, they are claimed to be representative of an area that encompasses more than the JECAM site. In order to specify the extent of this representative area, we referred to existing zoning maps. We used the two reference maps available for Southern countries: the FEWS-NET livelihood zones map (https://fews. net/fews-data/335) and the FAO farming systems map (http://www. fao. org/farmingsystems/mapstheme_01_en. htm). The livelihood zones are produced at national scale and are available for 38 developing countries. The zones are defined as geographical areas within which people share broadly the same patterns of livelihood (i. e. , broadly the same production system, the same income earning opportunities and patterns of trade) (see  Grillo and Holt (2009)  for more details).
Farming system maps are available for the Global South (covering 130 countries). The classes are defined as a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods and constraints  (Dixon et al. , 2001; Auricht et al. , 2014). Although these two maps were not produced for the same purposes, they are derived using similar criteria (agro-climatology, elevation, landscape, dominant pattern of farm activities, etc. ) that are closely related to the agricultural land use as recorded in the database. In Table  3 , are given the type and extent of the zones where are located our JECAM study sites, for both maps. Unfortunately, the livelihood maps are available only for four of the JECAM countries presented here. With a mean size of the zone around 20 000 km² (Table  3 ), we are pretty confident that our JECAM sites are representative of 275 the livelihood zone they belong to.
The datasets presented here can thus be used to train or validate land cover maps of the corresponding zones. The farming system zones are much larger (between 300 000 km² and 2 Mkm²) and include a larger diversity of environmental and farming conditions; in these conditions it is not possible to argue that the JECAM sites are representative of such large areas; thus, the JECAM datasets need to be completed with other datasets belonging to the same https://doi. org/10. 5194/essd-2021-125 It is also important to mention that other agro-ecological zoning (AEZ) can be used (even if only few are directly related to the agricultural land use) or that each user can produce their own AEZ and use it to delineate the area in which the JECAM dataset can be used to train classification algorithms. Open Access Earth System Science Data Discussions Preprint.
Validation through study cases In addition, the in situ JECAM dataset and its derived land use/land cover products have been used in a wide spectrum of studies covering several aspects linked to agricultural monitoring attesting the good quality of the dataset and good spatial representativeness of tropical countries farming systems. First specific site studies have been conducted to test several methodological aspects. For instance, land use maps combining a supervised object-based approach with multi sources high spatial resolution time series were developed in Madagascar  (Lebourgeois et al. , 2017) and in Brazil (de Oliveira Santos et al. , 2019). The brazilian site (São Paulo) was also included in a broader study presenting an inter-comparison of several cropland mapping methodologies over 5 contrasting JECAM sites (Brasil, Ukraine, Russia, Argentina and China) in terms of growing conditions, characteristics and cropping practices  (Waldner et al.
Data availability The dataset is ready for use on any GIS software, and can be filtered by region, year or key words. It is distributed with a CC-BY licence. The database, as well as the Kmz file locating the study areas, are available online on the CIRAD DataVerse in Jolivot, Audrey et al. , 2021, "Harmonized in situ JECAM datasets for agricultural land use mapping and monitoring in tropical countries", doi:10. 18167/DVN1/P7OLAP, CIRAD Dataverse, V2 (https://doi. org/10. 18167/DVN1/P7OLAP).
Conclusion Accurate mapping of cropland and associated cropping practices in smallholder farming systems of tropical countries is crucial for the improvement of agricultural monitoring systems at local and or global scales. The essential prerequisite to reach such objectives is to have available in situ datasets representative of the diverse agricultural practices in tropical countries. This paper presented an harmonized in situ crop type dataset acquired between 2013 and 2020 over nine sites spread over seven tropical countries. This dataset collected in the framework of the JEAM initiative is unique and very valuable because it is produced at the field scale, based on in situ observation, quality-controlled, and standardized observation for various tropical cropping systems, including small-holder farming systems.
These characteristics allow this dataset to be used as a benchmark to assess the performances and the robustness of newly developed classification algorithms for cropland and crop type/practices mapping in diverse and documented agricultural conditions. In addition, this dataset can also be used to validate the cropland class of existing global or national LULC products, in particular those recently produced with Sentinel/Landsat image time series, and some crop type and practices (fallow, double cropping) classes. In the end it should be part of publicly online datasets and algorithm sharing platforms as promoted by the JECAM network and  Long et al. (2020)  who encourage the sharing of datasets for remote sensing applications, and more broadly to the scientific community, land use planners and agricultural monitoring agencies.
Ground data collection and pre-processing : BN and MD ( Senegal Niakhar ), IT ( Senegal Nioro ), AC, ER, MA, SaD, StD, VA and VL (Madagascar), AB, AJ, CJ, DL, LL, MC, RG and StD (Burkina), GL (Brazil -Sao Paulo), AB and BB (Brazil -Tocantins), CL and MM (Kenya), AJ and MG (Cambodia), AJ and AT (South Africa) Acknowledgements This research was mainly supported by  CIRAD  (scientists funding), and by various projects ( CNES   APR TOSCA  projects,  SIGMA FP7 n°603719 ;  ESA   Sen2Agri  n°  ESRIN 400109979/14/I-AM ;  CNPq n°454292/2014-7  and  n°307560/2016-3 ;  FAPESP - Microsoft Research  n°2014/50715-9 , the  SERENA  project funded by the  Cirad-INRA metaprogramme GloFoodS). The SPOT and Pleiades images were acquired through the  GEOSUD Program  EQPX-20 funded by  French National Research Agency.
Total number of polygons (percentage of crop polygons in the dataset) Mean polygon size (ha)* Percentage of polygons obtained from ground survey Nb. of crop type classes Open Access Earth System Science Data Brazil, Double 2 533 (56%) 150 67% 7 Tocantins cropping (2015 and (625) 2016) Senegal, Single 2 1 403 (74%) 0. 54 83% 5 Niakhar cropping (2018 and (400) 2019) Senegal, Single 1 457 (46%) 1. 17 48% 6 Nioro cropping (2018) (400) Kenya, Multiple 1 1 647 (77%) 0. 14 100% 26 Muranga cropping (2015) (250) Cambodge, Double 1 529 (25%) *** 28% 5 Kandal cropping (2014 / Small (2 826) 2015) fields South Africa, Single 1 1 741 (59%) *** 38% 10 Mpumalanga cropping (2017) Small (3 600) fields * Areas calculated on cropland polygons ** 16 field campaigns in 4 years *** Burkina Faso, Single 7 6 264 (79%) 0.
Data analysis All statistics were performed with Statgraphics Centurion XV (version 15. 2. 06, StatPoint, Inc. , Herndon, Virginia, USA). Analyse of variance was performed for each treatment and site for PNI, CP, (Qw + Pr60min) and Olsen P. Statistical significance (P<0. 05) was tested using a Fisher's exact test. The response to P fertilization was characterized by calculating the relative DM yield for each combination of site and year  (Colwell, 1963) , as follows: Relative DM yield = (DM yield in control plots / maximum DM yield) × 100 (8) The maximum DM yield was the yield of P-fertilized plots at sites with only one P rate (FR-ERC, FR-GRA, and RO-DAM) or the yield of plots that received the highest P rate at sites with several P rates (CA-LEV and CH-LES). To describe the relationship between the relative DM yield and PNI, a linear-plateau model was used, as follows: Relative DM yield = a + b × PNI if PNI < critical PNI (9) Relative DM yield = plateau of relative DM yield if PNI > critical PNI  (10)  where a and b are fitted parameters.
6 mg kg -1 at FR-GRA for fertilized treatments (Table  4 ). Data obtained from sorption-desorption and isotope dilution procedures showed that, for each site, the total amount of diffusive oPion in soils (Pr) increased as CP and time increased (Fig. 3 ); the magnitude of the response differed among sites. For similar CP ranges of less than 1 mg P L -1 , Pr measured in the FR-ERC soil was twice that measured in the CA-LEV soil, regardless of the time (t) (Fig. 3 ). The entire dataset served to estimate the parameters of Eq. (  7 ) (Table  3 ). For each site, except RO-DÂM, the entire CP dataset (Table  2 ; Table  4 ) and the parameter estimates of the Freundlich kinetic equation (Table  3 ) were used to calculate (Qw + Pr) values for increasingly long periods of diffusion. For each value of t, a general relationship was built between (Qw + Pr) and PNI (data not shown); the best fit was obtained for a diffusion time t of 60 minutes (Pr60min) (Fig.
The overall critical PNI value, corresponding to the inflection point of the linear-plateau curve, was 92%. This value was close to PNI = 100% which theoretically represents the boundary between P-limited and non-P-limited growth conditions according to  Thélier-Huché et al. (1999). The difference between this threshold (92%) and the statistical value (100%) given by  Duru and Thélier-Huché (1997)  and  Thélier-Huché et al. (1999)  is likely due to the smallest dataset that they used to determine the critical P-dilution curve or to establish the relationship between relative yield and PNI. This study provides the first evidence that the relative DM yield of a P-limited sward, , is a direct function of its P status assessed with PNI, within different site-treatments. These results validate the PNI approach as an adequate tool for a posteriori assessment of grassland P nutrition.
Since interactions between N and P nutrition control forage response to fertilization  (Duru and Thélier 1997) , we tested to what extend NNI and KNI, for CH-LES site, could explain relative DM yield variability between years. For each CA-LEV and FR-GRA treatments, there was no significant relationship between NNI and PNI, between relative DM and NNI; including NNI as a co variable did not improve none of the model obtained with PNI (Fig. 1b ). No significant relationship was obtained either when testing the relationships for the three CA-LEV data sets pooled together. At site level, these results confirmed that for both sites and treatment optimal N and P nutrition allowed optimal biomass production. On the other hand, negative relationships between relative DMY and PNI at CA-LEV P0 and CA-LEV P1 (Fig.
1b ) could result from P luxury consumption and accumulation which increased as relative DM decreased. For CH-LES, there was no significant relationship between NNI and PNI, relative DMY and NNI and between relative DMY and KNI for any of the treatments. Including NNI or KNI as co variable did not improve the model between relative DM and PNI for CH-LES P0 (Fig. 1b ). Finally, when pooling the three CH-LES data sets, we got a significant relationship between relative DMY and NNI (R 2 =0. 44; p=0. 018); best fit was obtained with PNI as a single variable (R 2 =0. 65; p=0. 0016). This result demonstrated that, under P and N limited conditions, inter annual relative DM variability was better explained by PNI than NNI. For FR-ERC, there was a significant relation between NNI and PNI (R 2 =0. 49; p=0.
Acknowledgments We thank  Danielle Mongrain  (Canada),  Luc Stévenin  (Switzerland),  Éric Leroux  and  Denis Vialan  (France), for their technical assistance. We also express our gratitude to  Bernard Jeangros (Agroscope, Switzerland ) and  Vasile Cardașol  ( Research and Development Institute for Meadows, Romania ) who initiated long-term field experiments, and to  Marcel Fort  for providing access to the long-term experiment in Ercé, France. The authors thank  Sandra Plancade  for help in data analysis. Long-term field experiments were supported by  Agriculture and Agri-Food Canada, Agroscope (Switzerland) ,  Research and Development Institute for Meadows, Romania , and the « Flexibilité stoechiométrique dans les agroécosystèmes » project of the  French National Institute for Agriculture, Food and Environment (INRAE).
Funding This work was supported by the  Center for Engineering Mechanobiology , a  National Science Foundation Science and Technology Center , under grant agreement CMMI: 15-48571 Funding This work was supported as part of The  Center for Lignocellulose Structure and Formation , an  Energy Frontier Research Center  funded by the  US Department of Energy, Office of Science, Basic Energy Sciences  under Award #  DE-SC0001090. Funding Research in the Faulkner lab is supported by the  Biotechnology and Biological Research Council Grant  ( BB/ L000466/1 ,  BBS/E/J/000PR9796 ) and the  European Research Council  ( 725459 , "INTERCELLAR"). Funding Research in the author's lab is supported by the  National Science Foundation  ( NSF IOS-2139351 ) and the  Office of Science (BER) ,  US Department of Energy  ( DOE-SC0014395 ).
Funding Research in my laboratory is funded by the  European Research Council Advanced Grant  (Contract  787613 ) and the  Austrian Academy of Sciences. Funding This work was supported by the  Simons Foundation-Life Science Research Foundation  [Meyer  LSRF18/Carnegie Fund 10861 ] and the  Carnegie Institution for Science. Funding Research in the Roeder lab  is supported by the  National Institute of General Medical Sciences  of the  National Institutes of Health  under Award Number  R01GM134037  to AHKR and the  National Science Foundation (NSF)   IOS 1553030  to AHKR.
Each observation day started between 8:00 and 9:30 in the morning and ended by 17:00. Behavioral data were collected using the continuous sampling method, and each focal animal was observed for 30 s, four times a day. As days shortened over time, observations within the same day were spaced from 3 h (period 1) to 1 h and a half (periods 2 and 3). The behaviors were divided into states and events. States were all behaviors lasting more than 1 s, and events those lasting less than 1 s. The events were recorded in 'all occurrences' , meaning that every event behavior was counted within the 30 s of each focal animal. The observer recorded the duration of the state behaviors using a stopwatch and the number of occurrences of the event behaviors. The full ethogram is described in Table  1. Individuals had their behavior recorded over 16 days (6 days in period 1 and 5 days for period 2 and period 3), resulting in 32 min of observation per chicken.
The potassium ferricyanide reducing antioxidant power test (PFRAP) The PFRAP of OS samples was assayed with the outlined procedure of Oyaizu  [23]. To 1 mL of sample extracts, 2. 5 mL phosphate buffer (0. 2 M, pH 6. 6) and 2. 5 mL potassium ferricyanide (1%, w/v) were added. After incubation at 50 °C for 20 min, 2. 5 mL trichloroacetic acid (10%, w/v) was added. Two and one half mL of this mixture were then mixed with 2. 5 mL water and 0. 5 mL (1%, w/v) FeCl 3. After leaving the mix for 30 min, the absorbance at 700 nm was registered. Data are presented as mg equivalent of the ascorbic acid standard per g of extract (mg AAE/g DW). All OS extracts were assessed three times.
Metal chelating activity (MCA) This test was applied on OS extracts by the previous modified method of Dinis and Madeira  [24]. 60 μL of FeSO 4 (0. 2 mM) was mixed with 40 μL of diluted samples. Then 80 μL of 2 mM solution of ferrozine were added. The decrease of purple ferrozine-Fe 2+ complex was assessed by determining absorbance at 550 nm (Fluostar Optima plate reader) after 10 min at room temperature. Using a calibration curve (y = -0. 0161x + 0. 9112) with EDTA standard at the concentrations 10-75 μg/mL, we calculated data as mg EDTA equivalent/g of dry mass (mg EDTA/g). All OS samples were analyzed in triplicate.