dataesr/scientific-paragraphs-categorization huggingface dataset:
A herd was considered to be closed when, over the entire study period, animals only entered the herd via birth and exited the herd via death or slaughter. Because these herds do not trade with other herds, they do not contribute to Map spread and were excluded from the dataset. Only female animals were modelled, with the exception of the purchase of some male animals for breeding (see below). Hence, herds that did not exchange female animals with other herds were also excluded. Finally, herds were selected on the basis of size: herds with 15 animals or less, or five adults or less, were considered to be non-commercial dairy herds and were excluded. A total of 13 353 herds, with 4 494 768 dairy female animals, were included in the metapopulation. The retained herds belonged to one of six possible herd types (defined in Table  1 ), as described in Brock et al.
[28]. Figure  1  presents the distribution of herds included in the metapopulation per herd type. Some farms within the metapopulation conducted both dairy and fattening activities, however, for the purpose of modelling, it was assumed that the fattening activities took place on a different part of the farm with negligible contact between the dairy and fattening herds, and thus the fattening activities were not represented in the model. In Ireland, the use of natural service bulls following rounds of artificial insemination is common practice  [31]. To account for the risk of introducing Map into a herd via the purchase of bulls for breeding purposes, some males were also included in the dataset. Including bulls was only considered for herds that purchased dairy or beef bulls (83% of the herds).
The other half of the cows will be artificially inseminated. The number of cows present in a herd was determined on the first of May of a given year. Based on this calculation, if more bulls were needed than present in the trade data, dairy bulls that were born into a herd and kept until breeding age were selected. A total of 72 991 bulls were selected for 11 121 herds of the metapopulation and added to the dataset.
Between-herd Map transmission model The between-herd model connects all the within-herd dynamics through animal movements. Movements from and to a herd in the metapopulation are modelled as observed in the data. When an animal was moved via a market, the movement was represented as if it occurred directly from the source herd to the destination herd. The movement dataset comprises 2 304 149 animal movements of which 17% were between herds in the metapopulation, 20% were from a herd outside of the metapopulation to a herd in the metapopulation, and 63% were from a herd in the metapopulation to a herd outside of the metapopulation. The movement data define the date that a movement occurs, the age of the animal moved, and the source and destination herd. The animal to be moved is randomly selected from the relevant age group in the source herd, and thus can be of any health state.
Herd characteristics Seven herd characteristics are calculated from the movement data for every year: in-degree, out-degree, instrength, out-strength, polarity, herd size, and number of male animals introduced. The in-degree measures the number of herds that the herd of interest receives animals from. The out-degree measures the number of herds that the herd of interest sends animals to. The instrength measures the number of animals purchased by the herd of interest (incoming movements). The outstrength measures the number of animals sold by the herd of interest (outgoing movements). Polarity is defined as Polarity = #incoming animals-#outgoing animals #incoming animals+#outgoing animals. Polarity takes values between -1 and 1 and represents the trading behaviour of a herd, where herds with a polarity between -1 and -0.
Simulation settings Which herds were chosen to be infected at the start of the simulations had an effect on model predictions (Additional file 5). Therefore, we first assessed which herds were the most likely to be infected 10 years after Map introduction in the metapopulation when they were not the ones initially infected. On this basis, for 1000 replicates, 25% of the herds were randomly chosen to be initially infected irrespective of their herd characteristics. The within-herd prevalence is low in the majority of herds based on earlier analyses of field data  [15, 35]. Therefore, the within-herd prevalence in the herds that are initially infected was drawn from a Gaussian distribution N(-0. 42,0. 12), keeping only values below 0. 7 because higher values are rarely observed in the field (Additional file 6).
Herd characteristics considered: average in-degree (A), average out-degree (B), average in-strength (C), average out-strength (D), average herd size (E), and average number of males purchased (F). For the definition of the probability of a herd escaping infection or becoming infected see text. 13 353 herds) than in France (919 304 for 12 857 herds). It would, therefore, be interesting to investigate whether combining risk-based trading with intervention strategies would lead to similar results to those for France. We included breeding bulls in the dataset because we wanted to account for the associated risk of Map introduction. After their introduction in the herd, bulls were assumed to behave like, and reside in the same location  as, female animals of their age group. Transmission via semen  [46] [47] [48] , whether through natural service or artificial insemination, was not modelled as such.
Similar results were found for transmission within French dairy herds, when transmission rate parameters were varied with 50% compared to the reference scenario  [9]. For the between-herd part of the model, the only assumption that was made is that the herd and within-herd prevalence of herds outside of the metapopulation is the same as the herd and within-herd prevalence of herds within the metapopulation. Data to validate this assumption are lacking, and therefore it was simplest to assume that the average risk of introducing an infected animal from within and from outside of the metapopulation was the same. Brock et al. [28]  identified 17 different herd types in Ireland based on nine variables. Our focus was on dairy herds, and therefore, only herds belonging to one of the following six dairy herd types were included in the model.
options  (Tall et al. , 2014). To that end, they need to be usercentered and site-specific (i. e. , customized), integrative of other aspects besides weather/climate information (e. g. , technical itinerary), affordable, accessible, and usable by all end-users. This study investigates the effects of customized climate information services (CCS) on land and labor productivity in Ghana and Burkina Faso. A delivery process is a form of an agroclimatic information package called "AgInfo"  (Saley et al. , 2020). AgInfo, a newly developed tool, is a short, weekly message addressing farmers whose fields are geo-referenced and the physical features (e. g. , surface area, soil type, slopes) are known beforehand. The message contains a seasonal forecast for the next three months, a 24-hour forecast, a deterministic 7-day forecast, and practical instructions (i.
e. , Technical itineraries). Predictions, based on the analyses of pluviometric extremes, are generated in a single day and are valid for a week. The agricultural practice instructions and technical itineraries consider the type of crop (e. g. , maize, millet, sorghum, cowpea), the stage of development, and the predicted weather/climatic events. The AgInfo package also contains verification scores of the forecasts focused on « false starts of cropping season », « dry spells greater than or equal to 10 days,» and heavy rain events considering the three heavy rain categories defined by  Salack et al. (2018). Categories 1 and 2 occur most often between weeks 27 and 35 (1 July to 31 August). Category 1 is characterized by a cumulative daily amount varying between 37 and 65 mm, while category 2 has a daily amount above 65 mm/day but below 85 mm/day.
Category 3 is challenging to predict and occurs between the 28th and 38th week of the year (10 July to 22 September). It is the most damaging category of heavy rainfall and is identified by rainfall rates exceeding 85 mm/day. The AgInfo package is transmitted by voicemail to users through their mobile phones with language options for Mooré, Dagara, Fara-Fara, French, and English. After it is transmitted to the farmers, the AgInfo package is monitored and evaluated through an Agro-climatic field school (AFS) animation. The AFS is a personalized interview session (via telephone or face-to-face) with the user on the content of AgInfo (see Table  of  an example of AgInfo bulletin), and other aspects not found in the package, including trend and variability (past/future) of the locality, downscaled climate information, crop pests, and diseases, etc.
They are organized one month before the planting, two months after sowing, and one month after harvest. In addition, the farmer participates in verifying the quality of the AgInfo package immediately (after its reception) and during the AFS sessions. This permanent interaction between the production team of the AgInfo service and the beneficiaries makes it possible to verify and improve the cases of failure and document the instances of success. Farm operations cost statements were used to collect in situ data on crop yields, input prices, and best practices in Bolgatanga (Ghana),  Dano and Ouahigouya (Burkina Faso). Using the added value approach, land and labor productivity were computed. To better differentiate between the effects of CCS and those of a good cropping season  (Vaughan et al.
, 2019; Tall et al. , 2018) , historical data from normal cropping seasons were used to build a baseline scenario. The experimented plots' land and labor productivity in each study area were compared to that of the baseline scenario, and changes were summarized. Our findings demonstrated that land and labor productivity were higher in Dano and Bolgatanga than in Ouahigouya. In Dano, beneficiaries have successfully followed the guidelines and the recommendations provided by CCS. The rigorous application of CCS, particularly AgInfo, has led to significant yield gains. Therefore, the success of CCS in the Sahel will likely require more commitment.
We used operating accounts to collect in situ data related to farming operations, including the quantification of farm labor. As the use of climate services is much linked to decision-making and the management of farm costs, we choose to make calculations based on added value involving gross margins and intermediate consumption, which is more plausible than the use of econometric models  (Vaughan et al. , 2019). An in-depth description of the study area, experiments, and methods used to quantify land and labor productivity are detailed in Section 2. The results are discussed in Section 3. The last section concludes by discussing the study's implications for economic policy.
Sampling, data Collection, Co-production, and delivery of CCS. Household, farming systems, and market data from various sources were acquired in different steps. First, we used expert interviews and investigations from the national institutions of statistics in Burkina Faso and Ghana (e. g. , INSD), the Ministry of Agriculture (Burkina Faso), and the Ministry of Food and Agriculture (Ghana) to collect information and secondary panel datasets on production factors (land, labor, and capital), yields, and markets (access and prices). Additionally, nine focus group discussions -FDGs-(3 in each demonstration site) of 120 farmers were used to identify communities vulnerable to heavy rain events, soil erosion, long dry spells, and floods. The choice of FGDs participants was based on three criteria: i) the participating farmer has a next of kin enrolled in at least high school who can assist in the verification of the use, the co-monitoring and co-evaluation of the CCS; ii) the participating farmer has owned farmland for at least five years (e.
Similarly, ¼ hectare of the total farmland of each control farmer was used as a control plot. The vast majority of these experimental plots were easy to access, close to homes, marketplaces, roadsides, village playgrounds, schools, and churches for showcasing and disclosure to the public. In the second step, additional household surveys were conducted. Primary data and information were collected on farmers' perception of pluviometric extremes, production factors (land, labor, and capital), yields, markets (access and prices), income off-farm activities, and consumption. These household datasets helped build the baseline scenario. The third step concerned the operationalization of the CCS in the form of the agro-climatic information package (AgInfo) applied on the demonstration plots. AgInfo is a package of climate information (e.
g. , 3-monthly seasonal forecast, 24-hour and 7-day forecasts) embedded with technical itineraries and adapted advisory (e. g. , "start sowing on 12 June"; "sow corn and sorghum at a row spacing of 80 cm × 40 cm"; "construct drainage system in washed hydromorphic soils", etc. ) to guide the implementations on the individual experimental plots (See Table  1 ). The package is disseminated by voicemail via farmers' mobile phones in five languages (i. e. Mooree, Dagaree, Frafra, English and French). With their mobile phones and own rain gauges, farmers and extension agents provided on-the-fly feedback to enable verification of the weather information embedded into the AgInfo package. In support of the practical implementation of the package, small on-farm soil water conservation techniques and anti-erosion structures (e.
The third AFS session occurs after harvest and before the onset of the next cropping season. Technicians, scientists and farmers meet and fam results are presented. The results of the previous season are assessed, yields are measured and practices are evaluated. Difficulties and failures are also assessed, lesons learnt and successes encouraged. The AgInfo package was continuously delivered to the treatment farmers, and agricultural extension agents ensured that the package was properly applied on the experimental plots by the farmers. For instance, during the 2017 cropping season, some advice was given to treatment farmers whose farmland experienced waterlogging problems. According to the participatory diagnosis with the farmer, confirmed by the first rains, the site was experiencing a temporary waterlogging issue linked to capillary rises.
Finally, over the two-year testing of the CCS, data, and information were collected on agricultural outputs (crop yields, land, labor, fertilizers, seeds, input and output prices, and income) through direct measurement and observations, as well as individual farm operations cost statements for all the selected farmers. Yields were measured by a yield square device (two square meters) repeated twice on the same plot. These squares were arranged randomly. The grains are then collected, dried, and weighed. The average is then estimated and extrapolated to the hectare. These supplementary yield data were compared against the baseline scenario data to assess the control farmers' agricultural performance and treatment farmers.
A season that records greater than 150% of the 1981-2010 average rainfall is a "highly above normal". Using the World Meteorological Organization (WMO) climatological classification method, analysis of rainfall data collected from meteorological services helped identify normal cropping seasons over the previous 30 years. We built a new corresponding dataset on crop yields and prices of agricultural inputs and outputs of the identified normal cropping seasons. Data analysis helped us calculate land and labor productivity for the baseline scenario (i. e. average over 30 years of normal rainy seasons) for each study site in order to consolidate the choice of treatment and control farmers. The prices used to measure output are local market prices. For each farm, the required number of workers per day to grow one hectare of crops was assessed.
Impacts of the CCS The CCS delivered during this investigation was the distribution of the AgInfo package and its operationalisation in experimental plots of the treatment farmers. The CCS had a strong positive impact on land and labor productivity of corn, cowpea and sorghum. Compared to the baseline scenario, the 2017 and 2018 cropping seasons were classified as "bad rainy seasons" according to the farmers and agricultural extension agents due to the weather characteristics, and were confirmed by the weather data analysis (Fig. 6 ). Indeed, data analysis showed that the 2017 and 2018 rainy seasons recorded more rainfall extremes than the cropping seasons of the baseline scenario and the normal average season over 1981-2010. The seasonal total amount of rainfall and the number of rainy days were lower with post-onset and post-floral long dry spells and heavy rain events (Fig.
3, 4, and 6). With the proper application of the AgInfo package on the experimental plots of the treatment farmers, the negative effect of rainfall extremes was much less significant on the grain yield of corn, sorghum, and cowpea, as well as on land and labor productivity  (Figs. 3, 4 and 6). Some treatment farmers had abandoned their plots in the previous cropping seasons due to heavy rain events and waterlogging. Following the years 2017 and 2018, with the climate information and application of the AgInfo package, farmers had better yields, leading to better land and labor productivity. In Bolgatanga, corn and cowpea yields increased significantly. Compared to the baseline scenario, corn and cowpea results of the treatment farmers increased by 99% and 53%, respectively (Fig. 6 ). Land and labor productivity of both corn and cowpea also increased by more than 100%.
3  and 4 ). Results have shown an increase in land and labor productivity of corn of 298% and 247%, respectively. Land and labor productivity of cowpea increased by 168% and 149%, respectively. These exceptional results can be attributed to applying the AgInfo package on marginal lands that have previously recorded deficient land and labor productivity. For instance, some treatment farmers' plots were eroded or waterlogged with capillary rises. On average, corn and cowpea yields, including land and labor productivity on these plots before the intervention, were very low (Figs. 3, 4,  and 6 ). In Ouahigouya, sorghum and cowpea yields, which increased by 18% and 28%, respectively, were comparable to the baseline scenario (Fig. 6 ). However, land and labor productivity was negative (Figs. 3  and 4 ).
In General, although the results are mixed, the Dano site was most successful, specifically during the 2017 cropping season. During the 2017 cropping season, only one treatment plot showed bad yields with low land and labor productivity. There were several reasons: given the morphology and the topography of the targeted plot (down a slope with capillary rise of water from the water table), corn and cowpea were subjected to a waterlogging effect. Moreover, the farmer concerned did not believe the climate information and advice given by the agricultural extension agents. Consequently, he did not properly implement the technical itinerary as suggested by the AgInfo package (e. g. misuse of CCS, such as failure to take appropriate decisions in order to avoid the fertilisers being washed away by runoff, no respect for the timing of weeding that often led to weed outbreaks).
The CCS experiments with the treatment farmers revealed some drawbacks of the process. For example, the practical applications of the AgInfo package in the experimental plots required more time. The farmer allocated the family workforce to different crops and the labor constraint was of great importance  (Sanfo and Gérard, 2012). In Bolgatanga, farmers faced difficulties applying the practices suggested by the AgInfo package. They blamed a delayed onset of farm activities (Fig. 6 ) and still had to face challenges with regard to better understanding of the CCS system. In Ouahigouya, performance was very low, showing negative land and labor productivity, i. e. , production costs (seeds, fertiliser, labor, and construction of infrastructure) were greater than the gains generated by CCS (Fig.
7 ). Implementing the instructions contained in the AgInfo package required more training and other interactions, such as several meetings with agricultural extension agents and farmers  (Rola et al. , 2002). The results of this study did not isolate the effects of climate information but rather the effects of a technology package consisting of climate information and agricultural advice. Even if climate information guides decision-making in agricultural activities (timing of sowing, weeding, and applying fertilizer, type of soil fertility management to be used), the results revealed that the isolated climate information does not guarantee an increase in productivity. The significant reported increase in land and labor productivity was directly tied to the advice (proper use and application of the correct timing of fertilizers, soil restoration, and fertility management upon receiving climate information).
Acknowledgements This work was funded by the  UPSCALERS  project ( AURG II-1-074-2016 ), which is part of the  African Union Research Grants  financed through the Financing Agreement between the  European Commission  and the  African Union Commission  ( DCI-PANAF/2015/307-078 ). The content of this document is the sole responsibility of the authors and can under no circumstances be regarded as reflecting the positions of the  African Union Commission or European Union Commission. The  West African Science Service Centre on Climate Change and Adapted Land Use  (www. wascal. org) is sponsored by the  German Federal Ministry of Education and Research (BMBF).
The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.
Genotype data The 16 founder lines and the MAGIC DH lines were all genotyped with the Affymetrix 600K Axiom SNP array  (Unterseer et al. 2014) , resulting in genotype data for 517,769 SNPs. A total of 503,902 SNPs were used from the 600K after filtering out invariant sites and sites that were not located on autosomal chromosomes according to the B73 AGPv4 reference genome  (Jiao et al. 2017). Linkage disequilibrium was calculated as R 2 of pairwise SNPs using the software PLINK and the 600K SNP data (Supplementary Fig. 1 )  (Purcell et al. 2007).
Phenotype data The MAGIC F1 plants were phenotyped in 5 different field locations in 4 different years, resulting in 6 distinct environmentyears  (Hudson et al. in prep). The environments included  Blois, France (2014 and 2017 ), Graneros, Chile (2015) ,  Nerac, France (2016 ), St. Paul, France (2017 ), and Szeged, Hungary (2017). The environments represent a range of latitudes and water stress, from vegetative and flowering water deficit  (Nerac, 2016)  to optimum well-watered conditions  (Graneros, 2015). In each environment, we grew a minimum of 292 and a maximum of 309 of the DH lines. Each genotype was grown with 2 replicates in each environment. In all environments, 6 traits were measured: grain yield, PH, female flowering date (DTS), male flowering date [days to anthesis (DTA)], thousand kernel weight (TKW), and harvest grain moisture (HGM).