dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter was attributed to countries, in which maize yields stagnated or Biogeosciences Discuss. , doi:10. 5194/bg-2016-527, 2016 Manuscript under review for journal Biogeosciences Published: 20 December 2016 c Author(s) 2016. CC-BY 3. 0 License. decreased within the past decades according to  Ray et al. (2012) , the first to all other. The same two maize cultivars were distributed in the GEPIC and PEPIC MFWs (Figure  1c ) based on the HDI. The high-yielding variety is planted in all countries with HDI≥80, which corresponds to high development. EPIC-BOKU used the 270 high-yielding variety in all grid cells (Figure  1d ).
Reported yield data and evaluation 285 Unless otherwise specified, all results were evaluated for the period 1980-2009, which corresponds to the span of the climate data across growing seasons in both hemispheres. Annual national average crop yields from the FAOSTAT database  (FAO, 2014)  were used for assessing model performance. Reported yields were de-trended using a 5-year moving mean average in order to remove trends in yields due to changes in technology and management. Model performance, which served here solely for comparing differences in model skills in relation 290 to differences in setups, was evaluated using time series correlation (tscorr) between national average simulated and reported yields using Pearson's product moment correlation coefficient r. A tscorr of r>0. 5 (corresponding to more than 50% of variance explained by the model) was selected as threshold for good model performance.
g. Brazil, Canada, Mexico, or Zambia). In turn, an optimal setup mostly based on GEPIC combined with management operations or soil data of EPIC-IIASA can be found e. g. in Greece, Italy or Japan. In most countries, the highest performance is achieved with a 435 rather even mix of setup options from both MFWs. Thereby, the setup components soil data, soil handling, and coefficients of EPIC-IIASA have a slightly higher frequency of 53-55% and the management a slightly lower with 49% in the optimally performing setups (Figure  S3 -3). The cultivar distribution of EPIC-IIASA in contrast shows the best performance in 72% of all countries. However, as the cultivar distributions of both MFWs are identical in various countries, also the GEPIC cultivar distribution shows the best performance in 60% of all 440 countries.
The HDI used in GEPIC and PEPIC (Figure  1c ) reflects socio-economic means for investing in improved inputs and access to knowledge. Finally, planting the same cultivar globally as done in EPIC-BOKU (Figure  1d ) ensures high spatial comparability of plant growth processes in the model. However, none of the 465 approaches can be considered to fully reflect the actual distribution of maize cultivars, including the challenging problem of sub-grid heterogeneity. Already the fact that there are strongly differing domestic agricultural systems in large transition countries like Brazil, China and India implies the requirement for a more nuanced approach of distributing cultivars. As neither the national, nor the subnational, socio-economic situation can fully serve as a proxy for agricultural management intensities, a global characterization of agricultural 470 production systems will be required to develop a valid dataset for current maize cultivar distributions.
This causes on average about 10% higher rainfed yield estimates in EPIC-IIASA (Figure  5 , column one), an effect that is largely cancelled if 610 sufficient irrigation water is supplied (Figure  S2 -3 and Figure  S2 -5, column one), and emphasizes the importance of high resolution and quality soil data, as well as also raising questions about how to handle subgrid heterogeneity in such parameters (e. g. Folberth et al. , 2016). The cultivar distribution predominantly scales absolute yield levels. Based on HImax of the cultivars and their coverage within the two MFWs, the genetic yield potential is 19% lower in the GEPIC dataset than in EPIC-615 IIASA. This corresponds closely to the difference observed among the corresponding yield estimates (Figure  5 , rows one and three vs. two and four).
Model framework performances in estimating reported crop yields 625 As the variability in observed national yield time series is often also driven by non-weather related variability of unknown importance, GGCMs cannot be expected to explain yield variability in all countries. Müller et al. (2016)  established a first benchmark for GGCM performance where the EPIC-MFWs show similar performance to the other GGCMs in that study. Here, a good agreement between estimated and reported yields was found for various major producing countries, where fertilizer inputs are usually moderate to high and stable harvest area 630 distributions within countries (as assumed in the constant global harvested areas dataset used for evaluations) can be expected In low-input regions in contrast, there is commonly little agreement with reported yields.
This has various reasons such as (a) management may be very particular, e. g. slash and burn agriculture or crop rotations that are commonly not reflected in the GGCMs, (b) yields depend strongly on agro-environmental processes such as nutrient cycling, (c) plant protection measures are often low, rendering the impact of pests and 635 diseases larger than in high-input regions, (d) yields reported in global databases have often been estimated, and (e) cultivars may be highly adapted to local environmental conditions and consumer preferences. In addition, the fact that EPIC takes only the major stress (out of nutrient deficit, water deficit, adverse temperature, and limited root aeration) on each day of the growing season into account to limit biomass production implies interactions between nutrient input levels and the model's sensitivity to weather-induced stresses.
CC-BY 3. 0 License. parameterization of GEPIC in combination with GEPIC's soil handling that allow for r>0. 5. Vice versa, the parameterization of EPIC-IIASA causes a worse performance in Brazil if combined with the soil data of EPIC-IIASA, but a high tscorr if combined with the cultivar and soil data of GEPIC. The most dramatic contrast in setups and their performance is found in Indonesia and Ukraine, where those resulting in higher performance in 650 the first cause lower performance in the latter, albeit none of the scenarios yields r>0. 5. Identifying country-or region-specific optimal setups will hence require an even wider range of setup permutations, ideally based on more detailed datasets or the inclusion of findings from local and field-scale model studies that allow for reducing the dimensions of such an exercise.
However, prioritizing and 670 selecting input data for further harmonization of the whole ensemble is not a straightforward process. None of the MFWs evaluated herein exhibited an outstanding performance in reproducing reported yields that would allow for justifying the selection of its maize cultivar or wheat type distribution. In order to overcome issues related to the wheat type distribution, following phases of the GGCMI project will collect winter and spring wheat simulations separately for regions in which both may be grown in order to allow for ex-post distributions 675 according to wheat type masks. A harmonization of major maize cultivars appears less feasible in the short run due to differing scopes among research groups. Although the cultivar distribution of EPIC-IIASA performs overall better than that of GEPIC, it is as well not optimal in all regions.
Plant residue management can differ substantially from farm to farm as it is subject to farm type and socio-economic drivers and may hence rather be addressed by scenario analyses in future GGCM studies. Further harmonization of GGCMs in terms of subroutines and parameterization, on the other hand, can be expected to become a long-term process and care must be taken to ensure that the harmonized variables are a 690 genuinely improved representation of reality, and not just a more limited sampling of the uncertainty space. Single parameters and subroutines such as PET estimation methods or microbial turnover processes are often representative for specific climatic and agro-ecologic regions. However, up to now they cannot be attributed spatially based on any available covariates in global datasets.
Hence, running a range of MFWs based on the same field-scale model in different parameterizations allows meanwhile to bracket likely yield estimates by 695 covering gaps in the understanding of agro-environmental processes. Further regional and field assessments, in combination with climate and pedologic zoning, will be required to derive reasonable spatially explicit datasets on the optimal PET estimation method, parameterization of microbial activity, and common SOM turn-over rates among others. 700 0. 9 0. 99 0. 8 0. 9 0. 9 Coefficient allocating root growth 0. 5 0. 5 0. 7 0. 5 0. 5 N volatilization coefficient 0. 005 0. 7 0. 03 0. 005 0. 3 1) Annual renewal of soil profile if a static soil profile is used and wind/water erosion is turned on 2) Describes the dependence of curve number (CN) estimation on soil moisture, which can be based on five methods, among them soil moisture gradient with profile depth or calculation of a daily soil moisture index (SMI) 3) Field capacity (FC) and wilting point (WP) can be estimated by 10 different methods -among them Rawl's method used by most MFWs -.
at a spatial resolution of 0. 5°x0. 5° were provided by the ISI-MIP and GGCMI projects. The climate data are based on temperature and Biogeosciences Discuss. , doi:10. 5194/bg-2016-527, 2016 Manuscript under review for journal Biogeosciences Published: 20 December 2016 c Author(s) 2016. CC-BY 3. 0 License.
Acknowledgements We acknowledge the support and data provision by the  Agricultural Intercomparison  and  Improvement  Project  705  (AgMIP). AA and TAMP were funded by the  European Commission 's  7th Framework Programme , under Grant Agreement number  603542  ( LUC4C ). CF, JB, NK, and RS were supported by the  European Research Council  Synergy Grant number  ERC-2013-SynG-610028  ( IMBALANCE-P ).
QTL detection in RIL Quantitative trait loci detection was performed by simple interval mapping  [36]  using the expectation maximization (EM) algorithm method implemented in R/QTL package  [37]. A log 10 transformation was applied to FW, FIR and COB as trait distributions deviated from normality. A 1000-permutation test was performed to estimate significant threshold. The LOD threshold was 2. 76, corresponding to a genome-wise significance level of ˛ = 0. 10. For each detected QTL, position, LOD score, confidence interval (CI -for a decrease in the LOD score of one unit), average phenotypic values of the two parental alleles and percentage of phenotypic variation explained (PVE) were displayed. The genetic-CIs were translated into physical intervals (Physical-CI) onto the tomato genome (assembly 2.
GWAs panel The tomato diversity panel consisted of 163 accessions composed of 28 S. lycopersicum (S. L. ), 119 S. lycopersicum cv. cerasiforme (S. C. ) and 16 S. pimpinellifolium (S. P. ) as described in  [32] , and  [39]. Plants (four replicates) were grown in plastic tunnel in Avignon, France during summers 2007 and 2008. At least 10 fruits per plot were measured for the same traits, as in the RIL and MAGIC populations, as described in  [34, 39] , except for flowering time and plant height. Phenotypic data collected in 2007 and 2008 were averaged over the two years and log 10 transformed when the Shapiro-Wilk test evidenced a non-normal distribution.
Data availability Input RIL data (genotypes and phenotypes) are provided as Supplemental data S1. MAGIC map details and genotype data are available in  [16]. MAGIC phenotypes are provided in Supplemental data S2. MAGIC SNPs, polymorphisms and QTLs are deposited on the GNPis repository hosted at https://urgi. versailles. inra. fr/gnpis [45]. Genome-wide association input data (K and Q matrices as well as genotypes and phenotypic data) and results are deposited on the GNPis repository hosted at https://urgi. versailles. inra. fr/ association.
Thus, managing all the phenotype data in a common database is highly important today  [74]. In tomato, the Sol Genomics Network  [75]  (http://solgenomics. net/) concentrates Solanaceae genome sequences, polymorphisms as well as a few QTLs and phenotypic data. Genome annotation provides gene catalog under the QTLs. Thus high quality gene annotation is also strongly needed, as a large percentage of genes are still with unknown function. Finally transcriptome (RNAseq) data on several organs, developmental stages and genotypes  [76]  also provide cues for the identification of candidate genes. The identification of candidate genes underlying a QTL relies on a set of arguments related to gene location, function, expression and polymorphism. When a candidate gene has expression variation linked to the phenotype in parental lines, eQTLs can be mapped and the colocation of a trait QTL, a related gene and its eQTL may confirm that a polymorphism close to the gene is responsible for its variation and putatively responsible for trait variation  [77].
Conclusion Genome sequences and NGS technologies provided flood of genomic information such as genetic variants responsible of quantitative traits that we have to manage. But the remaining limitation is no longer genotyping or sequencing but is to properly phenotype in a high throughput and reproducible way. Relevant populations are now of high importance together with the phenotype precision. It is urgent to gather all QTL data in databases in order to be able to perform meta-analyses to decipher the genetic determinants of agronomic traits. Thus, we clearly demonstrated that the combination of QTL analysis (in RIL and MAGIC populations) and GWAs precisely mapped and identified the QTLs and avoided false positives. Combined to data of polymorphisms in large populations and expression profiles we should quickly identify new causal variants responsible for the variation of important traits.
Acknowledgements We acknowledge the experimental team of  INRA GAFL  for their help in experimentation,  Yolande Carretero ,  Justine Gricourt ,  Esther Pelpoir  and  Renaud Duboscq  for their help in phenotyping. We thank the experimental and informatic INRA-EPGV team:  Aurélie Bérard ,  Auréle Chauveau ,  Rémi Bounon ,  Maria Tchumakov  and  Elodie Marquand. This work was supported by  CEA-IG/CNG , by performing the DNA QC and providing access to  INRA-EPGV  to their Illumina Sequencing Platform. We acknowledge groups of  Anne Boland  (DNA and  Cell Bank  service) and  Marie-Thérèse Bihoreau  ( Illumina HT Sequencing). The  ANR MAGIC-Tom   SNP  project  09-GENM-109G  and the European Solanaceae Integrated Project EUSOL ( Food-CT-2006-016214 ) supported this work. LP was supported by a postdoctoral  INRA  fellowship, EA by an  INRA   PhD fellowship  and JD by a grant from the  Embassy of France in Thailand in Junior Research  Fellowship Program 2014.
Statistical Analysis. All data were subjected to oneway analysis of variance (ANOVA) using SPSS 15. 0 for Windows (SPSS, Inc. , Chicago, IL). Mean values of treatment groups were compared using Duncan's multiple range tests. Anderson-Darling tests were applied to determine normality of blood profile data. Parameters following normal distribution were tested using ANOVA; post hoc comparisons were performed with the Tukey tests. Parameters not following normal distribution even after natural logarithm transformation were tested using Kruskal-Wallis tests; post hoc comparisons were performed using Bonferroni-corrected Mann-Whitney tests. Significant level was  < 0. 05 for all tests.
Clones were sequenced both strands on ABI 3100 using BigDye (PerkinElmer, Courtaboeuf, France) terminator chemistry following the manufacturer's protocol, and sequences were then identified back to each individual using their pairs of tags. A 2-time recapture effort was used in order to recover at least one allele for a maximum of individuals. In parallel, each individual was also directly sequenced with the standard primers and the second allele was deduced from the presence of double peaks (heterozygous sites) in the latter sequence when compared to the cloned one for all the recaptured individuals. Sequences of cox-1 were also obtained by direct sequencing and compared to the preexisting cox-1 sequences in GenBank. Sequences were finally aligned and corrected manually with the GEN-EIOUS software  (Drummond et al.
Genetic diversity, species divergence, and statistical tests For each gene, the number of segregating sites (S), the gene diversity (Hd), the theta of Watterson h w, and the nucleotide diversity p were estimated using DNAsp v5. 10 software  (Librado and Rozas 2009). The estimators h w and p (both estimating the population parameter 4N e. l) were then compared using the Tajima's D test, D being supposed to be close to zero when the locus is evolving neutrally (i. e. , no selection and no demographic changes). The divergence K J-C (Jukes-Cantor correction) was also calculated between the two species and between putative allelic lineages under strong linkage disequilibrium. Linkage disequilibrium among segregating sites and recombination among alleles were tested by calculating the Z n S statistics of  Kelly (1997)  and the minimum number of recombination events (R m ) of  Hudson and Kaplan (1985) , respectively.
Mismatch curves were also produced for all genes and each species using the same software to test whether the distribution of segregating sites follows a model of population expansion. Rogers and Harpending (1992)  indeed argued that qualitative and quantitative aspects of a population's genetic history should be uncovered by the analysis of frequency distributions of pairwise sequence mismatches under the hypothesis of no selection. Date at which populations started expansion was estimated with the package Fluctuate of the software LAMARC  (Kuhner 2006)  according to the exponential growth equation: h t = h 0 e Àgt using a rather high substitution rate of c. a. 2% per My for significant fits to the model of population under expansion. Phylogenetic trees were constructed from the aligned coding sequences of the alleles with the MEGA 5.
Maximum-likelihood Sweep_Bott test This maximum-likelihood program was used for each species to test the fit of our sequence datasets either to a selective sweep or a bottleneck using all loci including cox-1 with a MCMC approach of 100,000 initial iterations, and a second step of 1,000,000 iterations with 20 optimizations processes and a theta prior ranged from 0 to 30. Sweep_Bott computes coalescent trees and fits them against three distinct population models (the null constant size model M1, the bottleneck model M2, and the sweep model M3) using a coalescent-based likelihood method to determine whether the time and the strength of the diversity reduction is sufficiently similar between loci to suggest an historical bottleneck (M2) or more locus-dependent to detect a sweep (M3). Selective sweeps (M3) can be inferred by studying departure from the null model for each locus independently since, contrary to expectations in the case of a veritable bottleneck, departure from the neutral model under a selective sweep should affect only a few loci.
Maximum-likelihood HKA test A multilocus HKA test  (Hudson et al. 1987 ) was performed separately for both species as it compares both polymorphism and divergence between loci to infer whether some of them depart from neutral evolution. To perform such a test, the MLHKA software was chosen as it conducts maximum-likelihood analysis of multilocus polymorphism and divergence data for testing for the action of natural selection on candidate genes  (Wright and Charlesworth 2004). The likelihood of the null model (M 0 ) was evaluated assuming that all loci evolved neutrally (all selection coefficients set to 1) and compared with alternative selective models for which, at least, one locus was allowed to vary under selective constraints (selective coefficient different from 1). The statistical significance of the selective model was assessed by a likelihood ratio test against either the null model or, when more than one locus was under selection, against the one-locus models that corresponded to the tested loci.
To discriminate between these alternative hypotheses, a multilocus approach by maximum likelihood was used with the software Sweep_Bott  (Galtier et al. 2000)  to fit either a bottleneck or a selective sweep model using our 4 sampling sequence datasets for each species. Model parameters, likelihood and LRTs are summarized in Table  3. For both species, models M2 (population bottleneck) and M3 (sweep) both better explained the gene coalescences than the null model of constant size. However, based on the shape of the gene coalescences, the bottleneck model appeared to better fit for the present patterns of genetic diversity in E. crystallorophias whereas the sweep model was more appropriate in E. superba (see Table  3 ).
MacDonald-Kreitman test To detect whether diversifying selection may have strengthened species divergence or whether balancing selection occurs at a given locus, a MacDonald-Kreitman test was performed on the sequence datasets using nonsynonymous and synonymous mutations accumulated for both the polymorphism within and the divergence between species. Fisher tests had significant P-values for both hsp70 paralogs B (P-value = 0. 0002***, p a /p s = 1. 03, K a /K s = 0) and C (P-value = 0. 0280*, p a /p s = 0. 680, K a /K s = 0. 133) but not paralog A (P-value = 0. 700, p a /p s = 0. 200, K a /K s = 0. 330) indicating that selection may have played a role in shaping genetic diversities of heat-shock proteins by maintaining specific allelic lineages in both species. For paralogs B and C, the p a /p s ratios were indeed much higher than the K a /K s ratios allowing conclusions to be drawn about the accumulation of several nonsynonymous mutations at intermediate frequency in the polymorphism of at least one species.
Maximum-likelihood HKA test The occurrence of either a selective sweep or the maintenance of allelic lineages by balancing selection was tested using the MLHKA software: a multilocus maximum-likelihood method based on the Hudson-Kreitman-Aguad e test  (Wright and Charlesworth 2004). Although the number of loci was low (4), an alternative selective model with only one locus under selection was tested against the neutral model (Euc: LnL = À29. 45, time since speciation = 2. 88; Eus: LnL = À28. 88, time since speciation = 1. 32) for each locus and was powerful enough to detect positive selection (Table  4 ). This test showed that hsp70 paralogs A and C were not evolving neutrally in E. crystallorophias with selective coefficients of 0. 07 (sweep: LnL = À23. 97) and 4. 17 (balancing selection: LnL = À26.
30), respectively (Table  4 ). This finding was also supported by additional LRT tests comparing a model with two loci under selection with (1) the null model and (2) the retained models for which paralog A or paralog C were under selection. The sequence dataset better fitted a two-loci model PA&PB (LnL = À22. 00) than either the null model or the model with PA alone (LRT = 14. 9** and LRT = 3. 94*, respectively). Other tests with at least 2 loci under selection were all significantly better than the null model but not when tested against PA or PC alone. With regard to the E. superba Table  3. Likelihood ratio tests (LRT) performed by the Sweep_Bott software between coalescent models of bottleneck, selective sweeps, or constant population size for each species. 4p, 6p, and 12p represent the number of parameters within each model, namely the four monolocus theta values in M1, the theta values plus the bottleneck strength (S) and the time elapsed since the bottleneck (T) in M2, and the same theta values plus the sweep strength (S i ) and the time elapsed since the sweep (T i ) for each locus in M3.