dataesr/scientific-paragraphs-categorization huggingface dataset:
In phase I, the 2012-2014 Andhra Pradesh Children and Parents' Study census was used to randomly recruit eligible household members from 2 villages, Patelguda and Thumaloor, to understand the general food environment. The 2 villages were selected based on their levels of urbanicity. For phase I, data collection occurred between June and August 2017 using semistructured in-depth interviews (n ¼ 18). From each household, 1 man and 1 woman aged between 18 and 65 y were recruited using convenience sampling. Trained field workers from the Indian Institute of Public Health, Hyderabad, India, conducted the interviews. Phase II of the study collected data through focus group discussions to understand fruit and vege-   [27]. In Kenya, Bukachi et al. [10]  conducted a study in low-income informal settlements in 6 wards in the North and South Dagoretti The current study used the same data set  [10].
To ensure confidentiality and anonymity, all potential identifiers were replaced with pseudonyms. The current study used the same data set  [28]. Two low-income urban districts in Hanoi were selected as the study areas in Vietnam  [29] [30] [31]. Geographic areas within the 2 districts were stratified based on the availability and accessibility of supermarkets and formal wet markets within walking distance of participants' households. Quantitative and qualitative data collection occurred from November 2017 to October 2018  [30, 31]. Experienced data collectors with rigorous training in data collection methods conducted the qualitative interviews. Households were randomly sampled in each stratum using a door-to-door sampling strategy. The qualitative study was conducted on a subsample of households; of the 35 households selected, participants from 14 households were included.
In-depth interviews were conducted with the primary respondents identified from the larger sample (women of childbearing age or born after 1966, primarily responsible for household food acquisition) and their mothers (-in-laws) living in the same household. The interviews explored patterns of everyday food consumption practices over time and across generations  [30]. Researchers obtained full informed verbal consent from all participants. The study received ethical approval from the Hanoi Medical University, Vietnam, in June 2017 (IRB00003121). The data used in the current study came from in-depth qualitative interviews  [29]. The 6 project study sites differed by urbanicity, that is, 2 urban [Ghana  [25]  and Vietnam  [29] ], 3 peri-urban [India  [27] , Kenya  [6] , and Tanzania  [28] ], and 1 rural [Guinea  [26] ] (Table  1 ).
Codes listed under each theme were categorized to form subthemes. Pattern coding identified subthemes from the transcript data  [35]. The subthemes organized similarly coded data and described the conditions and characteristics of each theme. Transcripts were coded in Microsoft Word using the comments feature. Then, the extracted codes and corresponding text segments were converted from a Word document to an Excel document using a program written in Python 3. 10. 1. Matrices were developed for each project to tabulate the following for extracted codes: 1) double coded (yes/no); 2) coder and double coder, if applicable; 3) transcript document label; 4) code; 5) text segment; and 6) interviewee demographic information including age, sex, occupation, socioeconomic status, and education. Available demographic information varied with each project.
Food-choice processes derive from the consumer's experiences and are specific to their context  [51, 52]. An individual's dynamic nature is reflected in their decision-making processes, with shifts occurring throughout their life course  [53, 54]. This study highlights the complex nature of individual decision making in the context of food safety. Given what is known about the multiple levels of influence of food choice  [45] , we expect consumers' perspectives on food safety to form through similar interactions across the personal, social, and environmental levels of influence. This analysis used data from projects implemented in parts of Africa and Asia that provided information about food safety. Although the samples used in this study were not representative of all LMIC populations, the projects that contributed data were diverse regarding the sample demographics, urbanicity, and geographic location (Table  1 ).
Published scientific literature and global nutrition reports We searched for evidence at the level of SSA, Africa, low-and middle-income countries (LMICs) and then globally for the 13 policy actions, until we found evidence. The need to incorporate evidence from outside of Africa was useful in informing the potential impact of the different policy actions, where data were lacking from within Africa. However, including insights from further afield is a drawback due to differing contexts. We searched for recent systematic reviews in MEDLINE and Google Scholar as a starting point. We then undertook hand-searches of reference lists of these reviews.
Study selection A systematic review on the Mediterranean agroforestry crop yield was carried out in September 2022 using the Web of Science Core Collection (WoS, Clarivate) and SCOPUS (Elsevier B. V. ) databases. The search terms used in both databases are listed in the Table  1. Primary literature was identified by the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses" method  (Page et al. 2021). The search produced 1203 records, 503 from WoS and 700 from SCOPUS (Fig. 2 ). The search was further refined for article type (original research), language (English), and subject category (agricultural science, environmental science, plant science, soil science, dairy animal science, food science, and agricultural engineering), yielding 498 and 501 documents from WoS and SCOPUS, respectively.
To produce a final number of primary studies from the two databases, duplicates were removed (n = 372) through the tool check for duplicates in Mendeley (Mendeley Ltd, Elsevier B. V. ), resulting in 627 documents (Table  SM-1). Subsequently, a two-tier screening was conducted, looking for publications presenting replicated crop yield data from field trials in both agroforestry and control (sole crop) carried out within the geographic boundaries of this study, namely Bsk, Csa, and Csb of the Köppen-Geiger climate classification  (Peel et al. 2007 ). In the first-tier screening, title, keywords, and abstract were assessed, and 251 reports were excluded due to lack of representativeness [out of the study area (n = 12), not original research (n = 12), livestock management (n = 39), greenhouse, pots, and lab trials (n = 5), ecosystem service studies not including yield (n = 62), economic, environmental, and social science studies not including yield (n = 52), and modeling studies not including empirical methods (n = 69)].
Full texts (n = 376) were then assessed for eligibility in the second-tier screening, and 354 reports were removed since met the exclusion criteria, namely (i) lack of yield data per unit area of annual crop in the sole cropping system (control); (ii) lack of yield data per unit area of annual crop in the agroforestry system; (iii) absence of information about the experiment design and/or data replication; (iv) lack of information about data variance; (v) absence of information about geographic area or experiments outside the Mediterranean climatic zones; (vi) different agronomic practices between agroforestry and sole cropping systems; (vii) experiments from greenhouses, pots, and lab or controlled environments; and (viii) studies only presenting modeled results, farmer interviews, and systematic reviews.
If precipitation was not available in a study throughout the experimental growing season(s), the geographic coordinates of the site (or otherwise the nearest location) were used to gather information using the Climate Information tool of AQUASTAT (FAO's Global Information System on Water and Agriculture n. d. ). When the crop growing cycle was not reported in a study, the growing season length was assumed to be the same for the crop group (grain cereal, grain legume, sown forage grass, sown forage legume, sown forage mixture, and natural vegetation) tested in similar environmental conditions. The final number of studies selected for data collection had 161 pair comparisons (agroforestry vs sole crop) from diverse agroforestry systems (Table  2, Table SM-2).
SD = SE √ n obtained by dividing the length of the confidence interval by 3. 92 (α = 0. 05), and then multiplying by the square root of the sample size. In studies without data variance (n = 4), the SDs were assumed as 1/10 of the means  (Shi et al. 2017). The means and 95% CIs of the Wlog(RR) were calculated by a bootstrap resampling procedure. For studies with a sample size lower than three, the Wlog(RR) means were reported without estimated CIs. A treatment effect was considered significant if the 95% CIs for means did not overlap with zero. In addition, the effect of the explanatory variables on the Wlog(RR) was analyzed by eight different linear mixed models using the SAS PROC MIXED procedure (Statistical Software ver. 9. 4, SAS Institute Inc. , USA, Cary, NC). The restricted maximum likelihood (REML) was used to estimate the heterogeneity variance, as this method is known to be unbiased  (Langan et al.
When fixed factors resulted significant in a model, the jackknife analysis was carried out to assess the sensitivity of estimates of Wlog(RR) to each study of the dataset. One study from the dataset was removed each time, and models were run with the remaining data. The Akaike information criterion (AIC) was used as estimator of the quality of the statistical models. A random-effect model was employed for the analysis of study heterogeneity. The effect size index was the standardized difference in means. The test of the null hypothesis that all studies in the analysis share a common effect size was analyzed by the Q test for heterogeneity, while the proportion of the variance in observed effects over the true effects was addressed by the I 2 statistic  (Higgins et al. 2002). The absolute amount of dispersion of the effect size was assessed by prediction intervals  (Borenstein 2022).
The existence of publication bias was assessed by funnel plots of the standard error by standardized difference in means, and of the precision (1/SE) by standardized difference in means. Furthermore, the Duval and Tweedie's Trim and Fill method and the Egger's test of the intercept procedure were used. Computations were carried out using the software Comprehensive Meta-Analysis Version 4 (Biostat Inc. , Englewood, NJ, USA).
We ranked tree density on low, medium, and high cover, and found an inverse gradient in the response variable with tree cover, which agrees with other meta-analysis carried out in agroforestry and hedgerow systems in more temperate environments  (Van Vooren et al. 2016; Ivezić et al. 2021). Hedgerows, windbreaks, and field shelterbelts might deserve attention, as it has been demonstrated that these systems can increase crop yield and quality worldwide  (Kort 1988; Baldwin 1988 ); however, these agroforestry systems were not available in our dataset. In Germany, for instance, Kanzler et al. (  2019 ) observed a 16% grain yield increase of winter wheat in a short-rotation poplar hedgerow (48-m alley), which was related to the significantly lower atmospheric evaporative demand and wind speed in agroforestry than open field.
Nineteen new studies were retrieved from other sources, but none of these was eligible. Although there is a the limited number of observations in our dataset (n = 161), we included also 1-year studies, which is usually a shortcoming for agronomic experiments; nonetheless, it was possible to assess the variance of the dataset, and the sensitivity analysis did not detect influence from any single study (except for the study #427 in model 3), thus confirming the reliability of the selected approach. This paper focused on the crop yield of herbaceous species in agroforestry as compared with the sole crop. Although results suggest a negative effect of tree on crop yield, it must be pointed out that the alternative products provided by trees and livestock were not included, as well as the ecosystem services (i.
It revealed an intermediate inheritance with disomic tendency, allowing intergeneric recombination and opening the opportunity for phenotype-genetic association studies. In the present work, we used a GBS approach anchoring on the P. trifoliata reference genome (https://phytozome. jgi. doe. gov/; accessed on 3 Marsh 2022) for simultaneous SNP identification and high-throughput genotyping of the tetraploid population. Diagnostic SNPs for the four ancestral species constitutive of the parents (C. reticulata, C. maxima, C. medica, and P. trifoliata) were identified, and the TraceAncestor software  [5]  was applied to analyze the phylogenomic structure of each hybrid. This analysis permitted the identifications and locations of the interspecific (C. reticulata/C. medica) and intergeneric (Citrus/Poncirus) recombination points for the Swingle citrumelo and Volkamer lemon, respectively.
Genetic Linkage Maps of Tetraploid Swingle Citrumelo and Volkamer Lemon GBS data from tetraploid rootstock hybrids were filtered with less than 20% missing data, and VCFHunter software identified 212,072 polymorphisms. For genetic mapping, data were filtered with less than 15% missing data for genomic windows (of 20 DSNPs) and less than 20% for individuals. The method we used to estimate the doses is based on assuming the physical ordering is correct. To generate the genetic map, we used a matrix of 1318 genomics windows and 95 hybrids for the tetraploid Swingle citrumelo and 1016 genomics windows and 96 individuals for Volkamer lemon. Marker-pairwise recombination frequencies were calculated, and nine linkage groups were identified during the linkage grouping constitution. Without preconceptions, we found that all the windows of the same chromosome were localized in the same linkage group.
SNP Calling Raw sequencing data were demultiplexed using GBSX v1. 1. 2 [89]  for paired-end reads and using Sabre (https://github. com/najoshi/sabre; accessed on 3 May 2022) for single-end reads. Cutadapt v1. 8  [90]  was used to remove the adapter sequences and lowquality bases (-q 20,20). Processed reads shorter than 30 were discarded. SNP calling and filtering were performed with the VcfHunter package described in [91] (https://github. com/SouthGreenPlatform/VcfHunter/; accessed on 3 May 2022) using VcfPreFilter. 1. 0. py and vcfFilter. 1. 0. py utilities. This procedure included mapping reads against the P. trifoliata reference genome (v1. 3. 1, https://phytozome. jgi. doe. gov/; accessed on 3 May 2022) using the BWA software  [92]. For SNP calling, a minimal coverage by accession to keep genotype calling was set to 10, and a Minimal Allele Frequency (MAF) to keep genotype calling was set to 0.
DSNPs Identification Ancestral dose analysis along the genome was performed based on the number of allelic reads of ancestral diagnostic SNPs (DSNPs). According to the known phylogenomic structures of the parents, four ancestral species were considered: P. trifoliata, C. medica, C. maxima, and C. reticulata. Diagnostic SNPs for the ancestral species were selected using GBS data from eight accessions representative of the considered taxa: three for P. trifoliata, one for C. maxima, two for C. medica, and two for C. reticulata. The search for diagnostic SNPs for the three Citrus ancestral species and P. trifoliata was based on the estimation of the inter-population differentiation parameter (GST) defined by  [94]. For each taxon, GST estimation was performed considering two sub-populations: (1) the taxon concerned (T i ) and (2) a theoretical population of the three other taxa (T -i ).
trifoliata/C. reticulata and P. trifoliata/C. maxima heterozygosity. Therefore, estimating the allelic doses of C. medica and P. trifoliata in each tetraploid hybrid allows us to infer without ambiguity the phylogenomic structure of the corresponding Volkamer lemon ovule and Swingle citrumelo pollen (Figure  7 ). The GBS data, the DSNP matrix, and TraceAncestor software were used to analyze the contribution of the four ancestral species along the genome of the intergeneric hybrids, following the method described by  [5]  using genomic windows of 20 DSNPs. We generated two matrixes from the hybrid mosaic structure: one for the Volkamer lemon parent with the dose estimation of C. medica for each window and another for the Swingle citrumelo parent with the dose estimation of P. trifoliata. Then, singletons were identified and replaced with missing data, as recommended by Van Os et al.
Recombination Rate Changes in the meiotic recombination rate along the genome are estimated with the online software « MareyMap Online » https://lbbe-shiny. univ-lyon1. fr/MareyMapOnline/ [101]  accessed on 9 June 2022, with the following parameters: Loess estimation method with Span = 0. 15. Recombination rates were estimated and visualized in Circos for the Swingle citrumelo and Volkamer lemon genetic map and the trifoliate orange genetic map generated by  Huang et al. (2018; [59] ).
Centromeric positions were estimated for areas with low recombination rates and gene density. For each locus, the estimation of PP was performed as follows: Considering the tetrasomic parameter (τ = 1 -PP) that defines the proportion of gametes resulting from random meiotic chromosomal pairing used in several previous studies of meiotic behavior of tetraploid citrus  [18, [55] [56] [57] 67, 102]  and according to [56], for centromeric loci, the probability of the three possible diploid gametes produced for an A1A1A2A2 duplex marker can be calculated using the following formulae: P(A 1 A 1 ) = τ/6 (3) P(A 2 A 2 ) = τ/6 (4) P(A 1 A 2 ) = 1 -τ/3 (5) The probability of obtaining the observed gamete population as a function of τ is: L(τ) = C × (P(A 1 A 1 ) x1 ) × (P(A 2 A 2 ) x2 ) × (P(A 1 A 2 ) x3 ) = C × (1/6τ) x1+x2 × (1 -1/3τ) x3 (6) where C is a combinatory coefficient constant for the observed data and x1, x2, and x3 are the number of observed A 1 A 1 , A 2 A 2 , and A 1 A 2 gametes.
Root System Architecture Phenotyping A phenotypic analysis of the root architecture of the hybrids was performed on fourmonth-old cuttings. The analyses were performed on 100 genotypes with 6 replicates per genotype. Photographs of the root system of each individual were taken and analyzed using ImageJ software  [105]  to determine the total root area, length of the longest root, number of secondary roots, and root diameter. Statistical analyses were then performed in R using the R Studio V3. 5. 3 environment (R Studio Team, 2016, R Core Team, 2018, Vienna, Austria).
Conclusions In this study, we analyzed GBS data of a tetraploid hybrid rootstock population of 103 progenies from the cross between Swingle citrumelo and Volkamer lemon. Estimation of allelic doses of C. medica and P. trifoliata ancestors in each tetraploid hybrid allowed the deduction of the phylogenomic structure of gametes of both Volkamer lemon and Swingle citrumelo, respectively. Two high-density recombination landscapes and a meiotic behavior analysis of tetraploid Swingle citrumelo and Volkamer lemon were established. Concerning the Poncirus diploid genetic map previously published, the recombination rate was lower compared to tetraploid Swingle citrumelo and Volkamer lemon. This can be mainly explained by the high preferential chromosome pairing detected. The measured meiotic parameter is defined primarily as an intermediate inheritance with a high disomic tendency for Swingle citrumelo.
Data sources The analysis was conducted using individual data of BW, BW gain, and DM intake (DMI) from 342 growing crossbred Holstein bulls which initially had an age of 197 ± 39. 5 days and a starting BW of 269 ± 70. 9 kg. The crossbreed Holstein bulls were born from Holstein Dams and beef cattle sires and were phenotypically identified as Holsteins. Data were collected during two experiments conducted in two periods (2021-2022) on a commercial farm (Agromont, Montgai, Lleida, Spain). After a four-week adaptation period, animals were fed two commercial and similar pelleted concentrates (Diet A and Diet B, as shown in Table  1 ) until the end of the fattening period. Individual data of DMI, BW, and average daily gain (ADG) data were recorded over 126 days of the fattening period. The animals were managed similarly as in  Llonch et al.
Diets and feed management Animals were fed two different common fattening diets used in intensive production conditions in Spain. These diets consisted of concentrate, and wheat straw with the following composition (on a DM basis): 59 g/kg of CP, 766 g/kg of neutral detergent fibre, and 67 g/kg of ash. Both diets were provided ad libitum and in separate feeders. The formulation of both concentrates was done using Brill Ò software, following INRA (  2018 ) guidelines (more details are presented in the dietary formulation section). The concentrates were distributed in the form of pellets using electronic feeders. Both concentrates had corn grain as the main ingredient, and they then differed regarding their protein source (soybean vs peas meal with distiller's dried grains with solubles (DDGs)).
Different structures of random effect were evaluated, from simple (with only the study effect) to nested factors (including the study, diet, and animal). The best random structure was identified from the lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), using the restricted maximum likelihood method in the ''nlme" library  (Pinheiro and Bates, 2006) , in R software. In addition, the proportion of the model's variance explained by random and fixed effects was extracted through the r. squar-edGLMM function from the ''MuMin" library. Y ij ¼ B 0 þ b 0;sðdðaÞÞ À Á þ B 1 þ b 1;sðdðaÞÞ À Á Â X ij þ e ij In addition to the linear mixed model, quadratic and exponential relationships were also explored, while maintaining the same random structure. The best-fitting model for our data was selected based on the smallest AIC, BIC, and RMSE values.
e. , ADG) while minimising costs. Once the optimal change points were found, and to simulate the effect of adjusting NE and MP concentrations, diets were reformulated at these points to precisely meet the estimated animal requirements. The reformulation was performed on Brill Ò software, using the same ingredients as in concentrate A and concentrate B. In this software, the process involves an optimisation of diet cost while meeting the nutritional requirements. Steps 6 and 7: Estimating animal performance, feeding costs, and environmental impact of multiphase vs. commercial diets The theoretical animal performance (ADG and feed efficiency) in response to the newly adjusted diet was estimated using INRA, 2018 guidelines (Section II),  Hoch et al. , (2004) , and the body gain composition model for Holstein bulls reported by  Diaz et al.
759 Â ((DMI Â 1 000) Â Dietary N concentration (%)/6. 25);  (Waldrip et al. , 2013). Although recent models have been developed to predict enteric methane emissions, the model from  Ellis et al. (2009)  was employed in this study due to its favourable balance between prediction accuracy and the availability of input data. For instance, in a comparative study conducted by  Benaouda et al. (2019) , the models proposed by  Escobar-Bahamondes et al. (2017)  and  Ramin and Huhtanen (2013)  outperformed other predictive models. However, it is important to note that these models require inputs that are not available in our database, such as fat or ether extract concentration in the diet.