dataesr/scientific-paragraphs-categorization huggingface dataset:
Data assimilation experiments In this section we present several experiments that have been realized using the SECHIBA-YAO system. They were designed to control the 11 most influential internal parameters of SECHIBA when we assimilate the land surface temperature (LST). In order to deal with non-dimensional control parameters with the same order of magnitude, preprocessing has been applied. The control parameters were first divided into two groups. The first group includes physical parameters, which have a physical dimension. In the present work, these parameters were normalized by dividing them by their prior values in order to control non-dimensional parameters. In such a way, given that the prior value is the true value (in the case of twin experiments), a value of 1 for these parameters indicates that the control parameter has been correctly reconstructed.
Since the control parameters are normalized, we apply a perturbation which is of the form of a random noise limited up to 50 % of the true parameter whose value is 1, so the perturbed value belongs to [0. 5, 1. 5]. If the control parameter values posterior to the assimilation process are close to 1, it means that the assimilation was successfully achieved. Differences between the values retrieved and the prior values represent relative errors in the parameter estimation posterior to assimilation. In order to show the benefit of data assimilation in SECHIBA, we conducted several experiments using SECHIBA-YAO. Prior to the assimilation process, different scenarios were defined for the tests (Table  3 ). A scenario makes reference to the experimental conditions. It includes the definition of the vegetation functioning type (PFT), the type of observation to be assimilated, the observation sampling, the time sampling, the atmospheric forcing file, the subset of control parameters, the assimilation window size and the time of the year to start the assimilation.
For bare soil, by contrast, the dominant processes are those related to the soil thermodynamics, explaining why k capa , k cond and k emis are the most sensitive parameters. In general, sensitivities are higher in bare soil conditions for the control parameters, except for min drain and mx eau. Since min drain is not sensitive to the land surface temperature, this parameter is no longer controlled. Only the 10 most influential parameters are used in the following sections. The next section presents the different assimilation experiments that we have performed using the SECHIBA-YAO software.
Discussion and conclusion In this study the adjoint of SECHIBA was implemented using adjoint semi-generator software denoted YAO. The land surface temperature gradients with respect to each control parameter were computed by SECHIBA-YAO, which permitted us to carry out a sensitivity analysis of the parameter influence on the synthetic LST estimation on the one hand and to conduct several assimilation experiments on the other hand. The first contribution of this paper was the sensitivity analysis results. They showed exactly which parameters of the model are the most sensitive and have to be controlled during the assimilation process. However, it is important to mention that sensitivity analysis depends on the region, the forcing, the PFT, and the time period (hour and day), among other factors.
Code and data availability The distributed version of SECHIBA-YAO provides an opportunity for scientists to perform their own assimilation. The distributed version allows the control of the five most influent internal parameters of SECHIBA, depending on the vegetation type. In addition, LST or satellite brightness temperature can be used as observations. The distributed version of SECHIBA-YAO is available in a GitHub repository (https://github. com/brajard/sechibavar/ archive/v1. 0. zip); the user can download the software, save it in a local repertory and run the makefile in order to build a local executable. Documentation and two instruction files are available in order to guide the user towards their own implementation. Users can modify the forcing file, the initial date to the assimilation, the parameter value and their perturbation if needed.
Acknowledgements. This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (U. S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program and AfriFlux)  and the global  FLUXNET  project (http://daac. ornl. gov/FLUXNET/fluxnet. html). A special thanks to  M. Crepon  from  LOCEAN  for his active participation in the revision of this article. P. Peylin  and  F. Chevalier  are acknowledged for fruitful discussions. We thank also  F. Maignan  for her continuous support in the use of the ORCHIDEE model, and  M. Berrondo  for the assistance in writing this article. Edited by:  J. Kala  Reviewed by:  B. Coudert ,  R. Mechri , and  A. Kallel.
assess whether or not organic farming expansion would result in more diversified and multifunctional landscapes than conventional farming. Better understanding of organic crop rotations and land-use composition is also a key -and currently lacking -component to assess the capacity of organic farming to feed the planet  20, 24, 25. Data on crop rotations are scarce, highly dispersed and poorly unified, mostly due to the lack of global datasets. Knowledge gaps are especially large when addressing developing countries and organic systems  26. Crop rotation data are most commonly collected by farm surveys, experimental plots  27  and field maps  28  , and are therefore difficult to retrieve at large spatial scales. Remote sensing has been attempted to collect land-use intensity, i. e. , cropping frequency and short crop rotation, but only at the regional scale  [29] [30] [31].
To overcome these difficulties, we developed a global database using a meta-analysis approach by collecting data on the composition of crop rotations (i. e. regardless of the temporal sequence of crops within rotations) from the scientific literature about organic vs. conventional farming performances. Our database is composed of data from 77 publications with information about 238 unique rotations and covering 26 countries worldwide (Supplementary Fig. S1 ). We supplemented this analysis by constructing a database on organic and conventional global land-use using data from FAOSTAT and FiBL (see Methods section). This second database provided information about organic vs. conventional crop areas for a series of six annual crop categories at the national scale for 50 countries on five continents.
Even if the direct comparison of the two datasets has some limitations -because the rotation dataset assesses temporal crop diversity at the field scale, whereas the land-use dataset assesses spatial diversity at the national scale-pairing these two data sources helps to estimate how results from local-scale studies translate into large scale census. By analyzing this rotational database, complemented by the land-use information, we aimed to (i) estimate to what extent rotations differ between organic and conventional farming; (ii) investigate whether such differences vary in different global regions; and (iii) verify whether global land-use data were consistent with the rotation results. This study focuses on temporary arable crops (excluding perennial and permanent crops and fodders) that together provide the bulk of calories and proteins to humans and livestock animals and that cover 70 and 92% of the global cropland area in organic and conventional farming, respectively.
1 ), even if differences between production systems are not significant. In contrast, conventional rotations have a lower diversity, especially in the global region "Others", i. e. , in tropical and subtropical countries. However, the land-use dataset did not confirm the higher diversity of organic systems. In fact, land-use tends to be slightly less diverse in organic systems than in their conventional counterparts, in particular for the global tropical and sub-tropical 'Others' region. We found similar results for the equitability of crop categories, although most differences were not significant (Fig. 1 ). This result might be because the land-use dataset does not contain information on some crop categories, i. e. , fodders, catch crops, etc. , that contribute to the higher diversity in the rotation dataset.
Additionally, especially in the tropics, organic farming is strongly focused on a few export commodities such as vegetables, permanent crops, spices and fruits  32. Such specialization on a small set of permanent crops might explain the discrepancy between the two datasets when focusing on arable farming systems only. Organic and conventional rotations have different crop compositions. We found that the composition of rotations significantly differed between farming systems (Table  S1 ). Organic rotations are composed of primary cereals (i. e. wheat, maize and rice; 29 ± 2% of the rotation length), secondary cereals (i. e. spelt, barley, rye, triticale, oat, sorghum, millet and pseudocereals; 17 ± 2%), pulses (15 ± 2%) and temporary fodders (24 ± 2%), whereas the remaining 15% is shared among oilseeds, root crops, industrial crops and vegetables (Fig.
S2 ). Our results also showed that catch crops and undersown cover crops are 2. 4 and 8. 7 times more frequent in organic systems compared to conventional systems, respectively, even though their total number in rotations remains low. These rotation characteristics based on our meta-analysis dataset were in good agreement with the land-use data. The latter confirmed that cereals (primary and secondary) compose the greatest fraction of organic cropland use (up to 61 ± 4%) and showed that the share of grain pulses was similar in the two datasets, even though the land-use share of oil crops and vegetables was higher than the rotation dataset (Fig. S2 ). At the global scale, organic rotations have fewer cereals and more temporary fodders. Our analysis showed that organic rotations have a 10% lower abundance of cereals compared to their conventional counterparts at the global scale (Fig.
These results represent critical information about organic systems since most land-use datasets about croplands critically lack data on temporary fodders and non-harvested crops such as cover or catch crops. We also found that, at the global scale, grain pulses (e. g. , soybean, beans and peas) are slightly more abundant in organic rotations although the difference was not statistically significant (Table  S1 ). Finally, we found that organic rotations include slightly less oilseed and root crops (Fig. 2 ). These results from the meta-analysis of the scientific literature were confirmed by the global land-use data, which showed 16% lower frequency of cereals in organic compared to conventional systems at the global scale (Fig. 3 ) (although additional details about primary vs. secondary cereals and intercropping were not available in the land-use datasets).
The land-use dataset also confirmed that grain pulses are slightly more abundant, while oilseed and root crops are slightly less abundant in organic farming compared to conventional farming (Fig. 3 ). Organic rotations have more nitrogen-fixing crops. Although organic rotations do not significantly exhibit a higher share of grain pulses at the global scale (Fig. 2 ), our results showed that nitrogen-fixing crops are more abundant in organic farming than in conventional farming. This is due to temporary fodder compositions (Fig. 4 ) that include more legumes than their conventional counterparts. It is also due to catch and undersown cover crops that are both more frequent and are more often composed of nitrogen-fixing species than in conventional systems (Fig. 4 ), as well as to the higher frequency of cereal intercropping with legume crops.
When combined with a simple estimation of the amount of nitrogen (N) fixed by these leguminous crops, we estimate that, overall, leguminous grain pulses, fodders, catch and undersown cover crops provide 2. 6 times more nitrogen to soils farmed organically than they do in conventional rotations. Unfortunately, these crop types have not been tracked in the land-use datasets, making it difficult to assess how representative the results from our meta-analysis are for the crops grown on actual organic vs. conventional farms.
Discussion Despite their key role in cropping system performances, crop rotations lack systematic analysis in the scientific literature. Our study made it possible to address part of this knowledge gap by comparing organic vs. conventional rotations. In particular, our meta-analysis approach allowed to retrieve systematic information on rotations from a large body of scientific papers and reports. In addition, the comparative approach adopted in this study, which also included an assessment of organic vs. conventional land-use in different crop types at the national scale, was essential to provide information on both organic and conventional production and to highlight system differences between organic and conventional farms. Importantly, our results emphasized the role of temporary fodders, catch and undersown cover crops in organic systems -crops that are typically not included in national land-use databases on organic or conventional agriculture  34, 35.
This specific information is of great importance since these non-harvested crops often play critical and multifunctional roles in both organic and conventional farming. However, our study has some limitations. Firstly, rotation data are difficult to identify based on abstract screening of publications since crop rotations are typically not the focus of a study and information about crop rotations is generally presented in the Materials and Methods section. Some data may therefore have been discarded during our literature search. Secondly, scientific papers mainly report information from experimental field trials, which are not necessarily representative of real farming rotations  36. In our dataset, 88% of rotations was derived from experimental data, whereas the remaining 12% was derived from on-farm data.
Our parallel analysis based on land-use data made it possible to at least partly address these problems since it allowed to include information on the crop types grown in the countries under-represented in the meta-analysis dataset. However, the comparison of the two datasets is not straightforward. Indeed, while most rotation data were extracted from agronomic papers aiming at comparing cropping systems that were designed based on sound agronomic knowledge and that were possibly designed to test new cropping systems, land-use developed by farmers may be driven by non-agronomic drivers, e. g. , economic factors. In addition, the rotation dataset provides temporal data from small-scale studies whereas the land-use dataset brings spatial results about the global crop area. Yet, making the parallel between the two datasets is unique to estimate how local results translate into global, spatial census.
Materials and Methods Rotation dataset. Literature search and publication screening. We collected the data on organic vs. conventional rotations through both an original literature search and the reuse of existing databases on similar topics. The original literature search was undertaken using the 'Web of Science' portal. We used a complex Boolean search containing (i) the term ecological, biological or organic next to (ii) the term farming, agriculture, cropping or production, in combination with (iii) the term rotation, comparison or conventional. The last search was conducted on October 28, 2016, turning up 431 papers. In addition to this literature search, we retrieved the databases referenced by Seufert et al. 46  , De Ponti et al. 52  , and Ponisio et al. 44  about organic vs. conventional crop yields.
These databases accounted for an additional 264 publications, leading to a total of 695 papers. The abstracts of these 695 initially retrieved papers were first screened to verify whether crop rotation data were actually present, resulting in the selection of 301 records. These 301 papers were further screened by checking if (i) they provided different organic and conventional treatments, i. e. if equal rotation were reported, the study was discarded, (ii) they reported complete rotation schemes, and (iii) the organic treatment was either certified organic or in line with the definition of organic agriculture given in the Basic Standards for Organic Production and Processing of the International Federation of Organic Agricultural Movement (IFOAM)  55. Papers' methods that provide equal rotations in both conventional and organic cropping systems may -in most cases-be interpreted as a choice to attenuate the difference between the two farming systems, since they might focus on different parameters but the rotation itself.
We also excluded multiple publications reporting on the same trials to avoid double counting. Publications reporting rotations in multiple countries were considered as different entries, using the country as the discriminating criterion. As suggested by De Ponti et al. 52  , data prior to 1985 were not included because they were considered outdated, with the exception of long-term trials. Following such criteria, the screening yielded only 77 publications for further analysis, including 238 unique rotations covering 26 countries worldwide (Fig. S3 ). The majority of data came from Europe (42%) and North America (49%). The complete list of studies is provided in the Supplementary Table  S3. Data extraction. Information on rotation length, number of crops, catch and undersown cover crops were recorded from each publication, regardless of their temporal sequence in the rotation.
Finally, the location of each study was retrieved through the country in which the study took place. Countries were grouped according to three main global regions: Europe, North America and Others (Fig. S1 ). Countries other than European and North American were grouped into one single region due to the low number of data retrieved in such countries (n = 22, 9% of the dataset), in order to obtain balanced data groups for the statistical analysis. Overall, the number of organic rotations was slightly higher than the conventional one (53% and 47%, respectively). This is because some studies reported one conventional rotation compared to two, or more, organic rotations. We estimated the nitrogen fixed by pulses, temporary fodders, catch and undersown cover crops by assigning a leguminous species to each crop category (i.
e. , pea for pulses, alfalfa for fodders and vetch for catch and cover crops) and using the model of Høgh-Jensen 56. Calculations were computed considering a field size of 1 ha. Land-use dataset. We created an original database on organic vs. conventional land-use by collecting country-level statistical data from the Research Institute of Organic Agriculture (FiBL, Switzerland)  34  for organic agricultural land-use and from FAOSTAT  35  for conventional agricultural land-use, for the years 2010-2014. Since the original structure of the two databases differed, datasets were restructured in order to allow data comparability of arable crop categories. To do so, land-use data, i. e. , the harvested area for each crop category, were expressed according to the following crop categories: cereals (primary and secondary), pulses (including soybeans), oilseeds, root crops, industrial crops and vegetables.
No information on organic temporary fodders was available in either of the databases. Hence, we could not compare the two systems' land-use based on this specific crop category. Information at the crop species level in the FiBL database was not detailed enough to run an analysis at that level. The data about land-use under conventional agriculture were retrieved by subtracting the area under organic farming (provided by FiBL) from the data on arable land-use provided by FAOSTAT for each country. The across-years land-use average was calculated and used for further analysis. For each country and production system (organic and conventional), the land-use share of each crop category was calculated as the area under the specific crop category divided by the cropland area under the total number of crop categories considered.
The data were filtered by removing countries for which the share of organic area was lower than 0. 5% of the total agricultural area. Overall, land-use from 50 countries were compared. European and North America countries represent 62% of the dataset, followed by Asian (16%), Latin American (10%), African (10%) and Oceanian (2%) countries. Countries were grouped according to the same three global regions defined for the rotation dataset (i. e. , Europe, North America and Others) to facilitate comparisons of datasets as much as possible. Nevertheless, the region "Others" was not directly comparable between the two datasets since the composition of the countries was slightly different.
Statistical analysis. We examined richness and diversity of organic and conventional rotations and land-use by using Shannon's diversity and equitability indices. Shannon's diversity index (H, Eq. 1) helped to assess the relative abundance of crop categories, providing an indication about species diversity, while the Equitability index (E H , Eq. 2) helped to assess whether the different crop categories have an even share in both rotations and land-use. The two indices were calculated as follows: where p i represents the proportion of crop category i = = E H H H S ln( ) (2) H max where S is the total number of crop categories. The data expressed as counts (i. e. , rotation length, total number of crops and number of catch and undersown cover crops) were analyzed using a Generalized Mixed Model following a Poisson distribution.
The production system (organic vs. conventional), global region and their interaction were included as fixed factors. The 'study' was included as a random effect to account for possible "study effects" and data overdispersion. The data expressed as percentages (i. e. , share of the different crop categories in each rotation and land-use) were analyzed using a Permutational Analysis of Variance (non-parametric MANOVA) with distance matrices to test the null hypothesis of no difference between production systems, global regions and their interactions. This made it possible to partition distance matrices among sources of variation and to fit a linear model to the different matrices. The partial R-squared (r 2 ) obtained indicates the percentage of variance that is explained by the factors. The significance of each explanatory variable was computed from F-tests based on sequential sums of squares from permutations of the raw data  57.
The analysis was run using the Bray-Curtis dissimilarity index, and the number of permutations to compute the significance tests was set to 999. We tested the differences in the share of each crop category between production systems, global regions and their interactions using a non-parametric Kruskal-Wallis test, followed by a post-hock pairwise Dunn test. Differences between production systems in terms of Shannon diversity were tested by using a Linear Mixed Model (production system as the fixed factor; studies' number as a random effect to account for possible "study effects"), and a Linear Model (production system as the fixed factor), respectively, for the rotation and the land-use datasets, followed by a classical analysis of variance. Normality of data was verified through a Shapiro-Wilk test and residual check plots.