dataesr/scientific-paragraphs-categorization huggingface dataset:
The development of national pest risk assessment and early warning systems can be complex, though. It requires the combining of expertise of different actors, well beyond those in pest modeling and pest management alone  (Magarey and Sutton 2007; FAO 2007). Many advances have been made in pest modeling, and several types of models are now available for pest risk-mapping and early warning  (Orlandini et al. 2017; Tonnang et al. 2017 ). However, equally important is the availability of and access to suitable input data sources (e. g. , pest data, weather data) to build or drive such systems, a deep understanding of farmer decision-making, and efficient communication means to deliver risk information to end users; for the last, in the case of farmers, this involves large numbers of people spread over vast areas.
As a result, pest risk systems have mostly been developed in high income countries and only applied in low-income countries for a handful of significant pests (e. g. , transboundary pests in Africa, see Box 1) and for import and export market access, but this situation is now changing. Recent innovations and advances in data availability (e. g. , earth observation (EO) data, meteorological data), data architectures, data management workflows, computing power and communications technology has allowed for increasingly sophisticated risk assessment and decision support systems to be developed and extended to end users. In particular, there has been a developing interest in the use of weather and environmental data derived from EO sources, as such data are available for large areas (Marques da  Silva et al.
2015). EO data have already proved to be useful in broad scale alert systems such as Global Forest Watch (GFW), the Famine Early Warning Systems Network (FEWS NET) and the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM). These advances in data availability and data management may now be combined with advances made in the field of extension and have the opportunity to make significant improvements in the field of pest prediction and subsequent extension of messages. Increasing access to mobile phone technology (World Bank 2019), along with the emergence of ICT-based advisory extension services, has allowed the extension sector to disseminate advice through multiple complementary communication channels, such as Short Message Services (SMS) and Unstructured Supplementary Service Data (USDD), on broader scales than previously possible  (Thakur et al.
Technology Developments and Their Application to Pest Risk Several pest risk prediction systems are now in place or in development for low-income countries that forewarn of within season pest and disease incursions. These systems provide alerts about near-future potential geographic hotspots of transboundary pests or build-up of local pests that can be used at any scale (national, regional and local) for warning of potential pest outbreaks. The development of these systems with a wide outreach has been possible thanks to the onset of increasingly accessible high-quality data with high spatial resolution derived from EO and meteorological sources used to drive the models, and the collation and generation of field and laboratory data to build, train and test the models.
Access to Datasets and Data Management Through numerous projects, an immense number of datasets on occurrence, abundance, and prevalence of pests has been collected across many countries. However, these data remain scattered, are not widely accessed and used, and no mechanisms exist for bringing these datasets together, enabling sharing for multiple uses. Data are heterogeneous, owing to the diversity of their sources, differences in objectives for collection, and multiple storage and retrieval formats. However, recently, with the advancement in data collection and collation instruments like crowdsourcing, EO and geospatial tools, and cross-cutting analytics like artificial intelligence (AI) and the internet of things (IoT), the development of cloud-based platforms (e. g. , 'data hubs') and mobile apps for real-time pest detection and risk profiling is highly possible.
This enables the integration of data on historical and ongoing collections of pests and associated natural enemies from disparate sources as its centerpiece and may act as repositories that can be utilized to build and validate pest risk prediction systems. With the availability of such diverse data sources, several initiatives have been underway to combine and utilize these data for the development of pest risk or other applications. For example, icipe through the data management, modeling, and geo-information (DMMG) unit is establishing a state-of-art data management workflow (DMWf) and advancing the use of 'big data' and cloud-based crosscutting processing technologies that allow for harmonized storage and analysis of petabytes of various data types. This includes observational, experimental, simulation and derived datasets.
The observational data are commonly collected through open-ended survey, observation and the use of equipment and devices to monitor and record information. Experimental data are obtained through functional involvement by the data collector that creates and gauges the change to establish causal relationships. Simulation data are obtained through mimicking known processes and applying computer-based methods for reproduction, while derived data are the result of the application of formulae used to transform the information. The DMWf provides a collaborative framework with cooperation between data scientists and information communication technology (ICT) experts. With relatively more complex datasets, the opportunity for more sophisticated data handling methods has emerged. The AI allows for the exploration and utilization of large datasets and predictors and the expansion of assessments beyond binary outcomes, and considers the costs of different types of forecasting errors to generate improved and accurate knowledge for decision-making with feedback and accountability in the context of IPM.
Approaches such as machine learning (ML) and deep learning (DL) enable the characterization, discrimination, classification, prediction, forecast and utilization of existing knowledge in pest management for appropriate interventions. Increased collaboration between EO and biological pest risk modeling experts and cutting-edge actors in extension of information have allowed these data sources to be utilized at a broad spatial scale to benefit those in receipt of early warning information. Data derived from EO sources can provide a consistent stream of measurements at regular time intervals with global coverage. These data can include various vegetation indices, which may be related to plant biomass or vigor (i. e. , Normalized Difference Vegetation Index: NDVI), or used within reanalysis datasets to give a broad range of atmospheric, land and oceanic climate variables (i.
e. , ERA5 ECMWF dataset https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era5). The quality, accuracy and availability of these data are increasing with each new space program (ESA 2020). Well-established vegetation proxies, such as the NDVI, have been used effectively by the FAO since 2010 to measure the amount of 'green area' so as to monitor potential locust habitat recession and growth (see  Box 1). These data have helped direct local teams on the ground to survey localities at higher risk of locust population build-up, and thus help direct monitoring and control resources  (Renier et al. 2015). Recently, data from the European Space Agency (ESA) have been used to classify different tree species and crop types  (Persson et al. 2018; Van Tricht et al. 2018) , and now such data are being used to monitor agricultural weed problems such as Striga or 'witchweed' in Kenya  (Mudereri et al.
444 B. Taylor et al. High-quality data feeds of meteorological observations are essential, as broadscale modeling approaches such as those used in pest risk prediction systems rely on an accurate estimation of localized conditions like temperature, humidity and rainfall  (Magarey et al. 2005). Mechanistic or deductive models use detailed knowledge of the pest/disease biology to predict the response of the organism to a specific climatic driver  (Venette 2010; Donatelli et al. 2017) , therefore access to accurate, high spatial and temporal resolution datasets is essential for the correct estimation of insect and disease outbreaks. In the recent past, weather data feeds for early warning systems have used observations from meteorological stations set up as either regional networks or farmer-owned stations  (Gleason et al.
2008; Magarey et al. 2001; Cressman 2016 ). However, networks require funds for their upkeep and coverage can be either geographically unrepresentative of the needs of a study or altogether limited  (Colston et al. 2018). Climate data products derived from EO sources and reanalysis datasets have the potential to overcome these issues by providing complete coverage at good spatial and temporal resolutions and can offer a wider range of variables that may be applicable to modeling needs  (Colston et al. 2018). Improved access to sophisticated weather models, such as the Unified Model (a numerical weather prediction model) available from the UK Met office, have also contributed to the development of disease early warning systems. Recent advances in the availability and access to these data have advanced the capabilities of models to deliver near real-time information.
This increasing amount and accessibility of data from varied sources offers great opportunities to inform agricultural stakeholders so that they can make better decisions when it comes to plant health challenges, and thus move towards reducing crop losses as outlined in SDG2. Recent projects such as the PRISE (Pest Risk Information Service) project funded by the UK Space Agency (UKSA), and a near real-time early warning system to predict future potential hotspots of two wheat diseases in Ethiopia  (Allen-Sader et al. 2019) , have utilized access to these improved data sources for the purpose of pest and disease early warning systems. Both systems have extended messages to relevant stakeholders (governments, farmers, extension workers) in order to inform better management decisions with the ultimate aim of reducing crop losses.
Validation of EO Data and Models Pest and disease risk prediction models driven using EO data inputs require field data for testing and validating species' presence, incidence and development. Historically, data collection in pest early warning systems has been limited by ground surveys, which may fail as a result of political unrest, border disputes, and inaccessible terrain, or can be limited by funds to generate these data. However, although detailed controlled studies remain vital for testing EO and pest models, there are now opportunities to collect supporting data from a much larger source. Increase in access to digital communication technology (GSMA 2020) enables data to be collected directly from farmers and to enrich early warning systems. This citizen science approach is adapting to new technologies that smartphones provide (GPS, digital cameras, internet connectivity).
Many efforts are also ongoing to build AI-based tools (applications and sensors) for pest and disease detection and identification through image processing (www. plantvillage. psu. edu; https://www. inaturalist. org/ home), which may be used for in-field diagnostics of pests and diseases or to assess local pest/disease pressure. The collation of accurate, or, in the term of iNaturalist, "research grade" datasets (Ueda 2021) relating to pest presence may contribute to the building, calibration and validation of early warning models. There is a growing societal acceptance of mass participation projects, and advances in statistical approaches allow these data to be analyzed in a less structured way  (Pocock et al. 2017). In order to be sustainable, these systems need to consider the incentives and motivations for users to contribute data.
Box 1: Rolling Out a Cost-Effective Surveillance and Early Warning System to Manage the Acute Desert Locust Crisis The desert locust, Schistocerca gregaria (Orthoptera:Acrididae) is an eruptive, transboundary pest, which affects Africa and parts of Asia. Under certain conditions, the locust forms large swarms, which affect large geographies and severely impact food production. Given the relationship between local environmental conditions, abundance of vegetation and locust biology, it is possible to use state-of-the-art approaches to collect data on locust presence, monitor movement, model the potential spatial extent of the locusts and assess crop damage to produce a dynamic and reactive response to locust outbreaks. In addition, schemes such as the FAO Desert Locust Information Service (DLIS) are able to forewarn of potential conditions, which may lead to the formation of swarms, thus preventing future swarms.
Activity Example Monitoring presence of populations Innovative digital tools like smart phone apps (e. g. , e-locust3M), as means of crowdsourcing, for real-time desert locust data collections, tracking and monitoring the spread of the pest. High-resolution remote sensing systems mounted on unmanned aerial vehicles (UAV), i. e. , drones, for timely desert locust surveillance and monitoring in remote and/ or inaccessible areas. The key aim of pest risk prediction systems should be to communicate risks and mitigation strategies to those who need the information most, with the aim of reducing potential losses, and allow time for sustainable interventions to be made. Such extension messaging should consider the technological capabilities of the end user. Rapid large-scale investment in telecommunication and the subsequent reduced cost of mobile phones and internet connectivity has resulted in the widespread accessibility of mobile phones across Africa and Asia, including their most rural areas (World Bank 2019), with an estimated 34% of the surveyed population owning a smartphone in Kenya, and 53% owning an older device without internet connectivity  (Krell et al.
Data should be published openly (when possible) following FAIR (findability, accessibility, interoperability, and reusability) principles, so that data are findable, accessible, interoperable and reusable. Openly accessible data can be shared through common interactive web platforms such as the Global Biodiversity Information Facility (GBIF) or institutional repositories such as those hosted by CABI, FAO or IITA. This will bridge the data gap in national, regional and local surveillance and improve data systems, linkage and the sharing of pest data. Overall, the modeling platforms themselves can serve as means of communication and networking. It is important to ensure that these early warning and monitoring systems are truly sustainable (self-managing and self-funding) in the long-term, and public-private partnerships will be key in ensuring this.
For instance, applying AI on a CDM extract could uncover hidden patterns, unknown correlations, trends, preferences, and other information that can help stakeholders make better and more informed decisions for the target insect pests and weeds. The AI may be utilized for the optimization of spatial positioning of pest traps that auto-disseminate sustainable interventions such as biopesticides  (Guimapi et al. 2019 ). Global environmental monitoring platforms provide portals for policy and national and regional decision-makers to view datasets and reports, however, there is now an opportunity to bring early warning to the farmer level. Advances in digital technology have demonstrated great opportunities for disseminating data to local scales and communicating this information so as to aid decisions made in the field.
To achieve greater impact, these large datasets must be turned into timely information that can support agricultural decision-making at a local scale, to avoid preventable losses. To be effective, pest early warning system outputs must reach the farmer in the form of actionable advice. In order to effectively manage pests and diseases, farmers need timely warnings on taking preventative actions, advice on when to prepare and stock plant protection products, and alerts on the optimum times to monitor their crops for particular problems in order to act. The combination of this improved extension with the availability of high-quality, high temporal and spatial resolution datasets that can drive models within pest risk prediction systems is opening up opportunities to extend the outputs of models to broader geographical audiences and reach those who need the information most.
Leveraging Data, Models & Farming Innovation to Prevent, Prepare for & Manage. 449 For the long-term sustainability of early warning systems, the technological infrastructure and capabilities that are available in western countries need to be made accessible to low-income countries. Capacity-building for key actors, organizations and services in the plant health system is an integral part of promoting the uptake and success of such innovations that incorporate EO data and the use of models. Sufficient training and support are required to promote the adoption of novel systems into national, regional and local early warning dissemination services. If digital-based technologies of any theme are to create sustainable lasting impacts on farmers and crop health systems, policymakers need to shift to a more inclusive digital understanding and acceptance  (Steinke et al.
45 ± 0. 21 Mg C for pairwise comparisons of organic compared to non-organic farming, based on datasets from 74 studies. Nitrous oxide mitigation evidence was modest for tropical agriculture overall, and data on methane mitigation was very limited. Evidence from the global North suggests that reliance on organic nutrient sources and organic farming would likely avoid increased nitrous oxide emissions compared to the use of synthetic nitrogen fertiliser (medium evidence, medium agreement). As the greenhouse gas (GHG) footprint of outcomes depends on where system boundaries are drawn, multi-scalar analysis is needed to capture flows of inputs and GHG impacts beyond the farm scale; for example, emissions associated with nutrient sources (e. g. , industrial fertiliser production), land-use change or feed production  (Connor 2018).
2018 ), micro-level models such as life-cycle accounting (LCA)  (Hauschild et al. 2018 ) and social LCA  (Huertas-Valdivia et al. 2020) , or through hybrid approaches  (Nakamura and Nansai 2016). Consequently, these outputs have to be translated into impacts via impact pathways (Impact Institute 2019). For many environmental externalities, there are databases for such pathways, such as those based on Recipe  (Huijbregts et al. 2016) , although pathways for ecosystem and biodiversity are more complex  (TEEB 2018; Dasgupta 2021). Impact pathways for social and, in particular, health externalities are less mature. If the functional unit is a product, investment or organization, the final quantification step is the attribution of impact to the functional unit (Capitals Coalition 2021a; Impact Institute 2020; VBA 2021).
6) to a smaller proportion in Ethiopia (44. 3), Rwanda (34. 1), Tanzania (32. 7),  Uganda (31. 8)  and Burundi (9. 3), as reported in World Bank (2021). For each country, we compare the fuel costs for cooking to the cost of the food itself. In each case, the least costly pulse in the dataset is spotted beans, whose nationally-representative cost per 57 g serving in 2017 USD ranged from 8. 6 cents in Rwanda to 17. 2 cents in Ethiopia. We then compare that to the cost of each type of fuel. The results shown in Fig. 3  reveal no consistent ranking in fuel costs, but in two cases (Rwanda electricity and Uganda gas), the cost of fuel exceeds the cost of the beans themselves. Our analysis so far focuses only on meal costs at national average market prices for ingredients and fuel. How each food is actually cooked varies with household circumstances, including the availability of kitchen equipment, local variation in costs at each time and place, and cultural or demographic factors that influence each household's division of labor related to meal preparation.
oecd. org/switzerland/producerandconsumersupportestimatesdatabase. htm). Suggestions about reallocating or "repurposing" 600-700 billion USD in agricultural "subsidies" are based on those estimates. However, the OECD database shows that not all the transfers are agricultural "subsidies" that can be repurposed. by contributions from beneficiaries-known as "non-contributory programs"). Here, we focus on the social assistance programs using data from the World Bank's ASPIRE database. It is based on household surveys, and therefore it may not capture all governmental programs. Also, the database focuses on developing and emerging countries only. On the other hand, it provides a useful disaggregation of social protection programs and of the distribution of benefits across the population. 12 For the countries in the ASPIRE database, the median of social assistance expenditures is less than 1.
2% of their GDP. Another key characteristic to consider 666 E. Díaz-Bonilla Table  3  shows an estimate of the money allocated to those programs in current dollars for the period 2014-2018, using the categories in ASPIRE, except Cash Transfers and Social Pensions (CT+SP), which aggregates the three separate categories of conditional cash transfers, unconditional cash transfers, and social pensions. 13 12 These are part of the broader category of social protection, which includes programs financed by the beneficiaries ("contributory"). Developing countries spend about 1. 1 trillion dollars (916 billion without China) in social protection (annual average 2010-2017; based on IFPRI's SPEED database using data from the IMF), or about 3. 5-4. 0% of the GDP. 13 The ASPIRE database covers 125 countries, 43 from Africa, mostly from sub-Saharan Africa (AFR), 15 from East Asia and the Pacific (EAP) (including China), 29 from Europe and Central Asia (ECA) (including Russia, Hungary, Ukraine), 22 from Latin America and the Caribbean (LAC), 10 from the Middle East and North Africa (MENA), and 6 from South Asia (SAR) (including India).
These numbers suggest significant problems with the targeting of these programs that are intended to help the poor and hungry. 15 In particular, countries in Africa seem to suffer the dual problem of both lower levels of expenditure overall (a median of about 0. 9% of GDP) and ineffective targeting of the poorest groups. Financing SDG2 and Ending Hunger 667 Source: ASPIRE and WDI/WB 14 The estimates are from the author using all the annual household surveys for all the countries in the database (several countries have more than one household survey, and the years for each country vary; the average year of the surveys in the database is 2011). Benefit incidence is calculated as the percentage of benefits going to each quintile relative to the total benefits going to the population (Sum of all transfers received by all individuals in the quintile)/Sum of all transfers received by all individuals in the population).
Therefore, more significant investment is needed in more and better data, and inclusive annual national and subnational reporting mechanisms need to be developed and implemented to assess progress on commitments for food security and nutrition outcomes and actions in a timely way  (Hendriks et al. 2021). Collecting, managing and reporting data requires extensive information systems. "Big data" systems offer opportunities to analyze vast datasets that reveal patterns, trends and associations, especially in multi-sectoral applications such as those seen in the SGDs and national performance and monitoring situations related to food systems through innovative approaches and algorithms. Some applications include fraud and risk detection and logistic planning in programs and price comparisons, as well as predictive and proactive health disease and health management systems  (NASAC 2018).