DOI: 10.5061/dryad.9kd51c5gg
title: Bioecology of fall armyworm Spodoptera frugiperda (J. E. Smith), its management and potential patterns of seasonal spread in Africa

description: Fall armyworm, Spodoptera frugiperda (J. E. Smith) has rapidly spread in
 sub-Saharan Africa (SSA) and has emerged as a major pest of maize and
 sorghum in the continent. For effective monitoring and a better
 understanding of the bioecology and management of this pest, a
 Community-based Fall Armyworm Monitoring, Forecasting, Early Warning and
 Management (CBFAMFEW) initiative was implemented in six eastern African
 countries (Ethiopia, Kenya, Tanzania, Uganda, Rwanda and Burundi). Over
 650 Community Focal Persons (CFPs) who received training through the
 project were involved in data collection on adult moths, crop phenology,
 cropping systems, FAW management practices and other variables. Data
 collection was performed using Fall Armyworm Monitoring and Early Warning
 System (FAMEWS), a mobile application developed by the Food and
 Agricultural Organization (FAO) of the United Nations. Data collected from
 the CBFAMFEW initiative in East Africa and other FAW monitoring efforts in
 Africa were merged and analysed to determine the factors that are related
 to FAW population dynamics. We used the negative binomial models to test
 for effect of main crops type, cropping systems and crop phenology on
 abundance of FAW. We also analysed the effect of rainfall and the spatial
 and temporal distribution of FAW populations. The study showed variability
 across the region in terms of the proportion of main crops, cropping
 systems, diversity of crops used in rotation, and control methods that
 impact on trap and larval counts. Intercropping and crop rotation had
 incident rate 2-times and 3-times higher relative to seasonal cropping,
 respectively. The abundance of FAW adult and larval infestation
 significantly varied with crop phenology, with infestation being high at
 the vegetative and reproductive stages of the crop, and low at maturity
 stage. This study provides an understanding on FAW bioecology, which could
 be vital in guiding the deployment of FAW-IPM tools in specific locations
 and at a specific crop developmental stage. The outcomes demonstrate the
 relevance of community-based crop pest monitoring for awareness creation
 among smallholder farmers in SSA.
description: ​​​​​​Community-Based Fall Armyworm
 Monitoring, Forecasting, Early Warning and Management System
 (CBFAMFEW) The CBFAMFEW project established
 a network that allowed each selected village to have a trained CFP with a
 smartphone and a pheromone trap to collect FAW data. The CFPs were trained
 in the use of the FAO-developed FAMEWS mobile application (app) for data
 input on FAW incidence. CFPs were also trained on how to interpret data
 and provide timely advice to villagers and early warning of FAW attacks.
 Further training on regular field scouting and detecting the pest at
 various stages was also provided to the CFPs. To coordinate data
 collection and transmission, mobile phones were loaded with the FAW
 monitoring application app, FAMEWS. The FAMEWS app is available in Google
 Play in 13 languages, and was downloaded by farmers and extension workers.
 In total, 56 mobile phones were provided per country (1 mobile phone per
 village x 50 villages + 1 mobile per district officer x 5 officers + 1
 mobile for the National Coordinator). Hence a total of 336 mobile phones
 were deployed in the six countries. Parallel to FAW population monitoring
 activities in East Africa, similar initiatives were conducted in Ghana,
 Liberia, Zambia, Mozambique and South Sudan using similar approach. Data
 collected from these countries using the FAMEWS app were also included
 with data obtained from the six eastern African countries to map the FAW
 density across a wider area of SSA. Pheromone traps and field
 scouting For monitoring adults, two
 universal bucket traps (Unitrap) were installed in each village. Traps
 baited with FAW pheromone were deployed just after planting and monitoring
 started after the emergence of plant seedlings to detect the first arrival
 of moth pests. Traps and lure (lure blend: Z9-14Ac (81.7%); z11-16Ac
 (17.54%); Z7-12Ac (0.5%) and z9-12Ac (0.25%)) were supplied by Russell IPM
 Ltd, Unit 45, First Avenue, Deeside Industrial Park Deeside, Flintshire,
 CH5 2NU United Kingdom. The FAW monitoring kit also contained a toxicant
 which immobilized the moths once attracted to the device. One trap was
 placed inside the maize field and a second one outside the maize fields.
 The trap was hung from a suspended pole about 1.5 m above the ground, and
 one trap was used for every 0.5–2 ha. The pheromone lure was replaced
 every 3–6 weeks. The traps were checked and emptied weekly, and trapped
 moths were then sorted to identify FAW. Field scouting was conducted at
 least twice a week, from the seedling and early whorl stages of the maize
 crop. This was also the time that farmers and extension workers sampled
 for egg masses, larvae, damage symptoms and the presence of other pests
 such as the African Armyworm (AAW) <i>Spodoptera exempta</i>
 (Walker) and stemborers. The maize fields were scouted using a “W” pattern
 approach, which involved sampling 10 consecutive plants at five different
 spots along the “W” transect. FAW and non-target moth counts from
 pheromone traps, and field scouting data were recorded and entered into
 the FAMEWS app. The mean percentage of plants infested with FAW, AAW, and
 stemborers, was automatically tabulated. Data were also collected on the
 condition of traps and whether the lures (pheromones) had been replaced.
 Additional data collected with the FAMEWS app included dates, country,
 geolocation, crop information (variety, planting date, irrigated or
 rain-fed, fertilizers used, and crop growth stage), general health of the
 crop, the cropping system (e.g. mono/intercropping, seasonal, rotation and
 push-pull), pest management practice adopted (chemical pesticides or
 biopesticides), and rainfall. Push-pull encompasses intercropping maize
 with the legume <i>Desmodium</i> spp. (<i>Desmodium
 intortum</i> for climate-smart push-pull or <i>Desmodium
 uncinatum </i>for conventional push-pull<i> </i>) and a
 border row of Napier grass <i>Pennisetum purpureum</i> or
 <i>Brachiaria</i> <i>brizantha</i> cv Mulato II
 (for climate-smart push-pull) around the plot; both
 <i>Desmodium</i> sp. and Napier grass are perennial fodder
 plants (18). Seasonal cropping is a farming practice in which the same
 crop is grown in the same area for a number of growing seasons. In the
 case of maize, it is mainly rain fed, and the land remain fallow between
 seasons. While crop rotation is the practice of growing a series of
 different types of crops in the same area across seasons. FAW monitoring data
 validation and processing A national coordinator in each
 country was appointed to validate the data collected by the CFPs through
 FAMEWS, and who uploaded to the global platform maintained by FAO
 (http://www.fao.org/fall-armyworm/monitoring-tools/famews-global-platform/en/) through the global coordinator. The data presented in this study were downloaded from the FAO global platform, which has the entire database collected for the FAMEWS app between January 2018 to June 2019. The various data entries were officially requested from the FAO, cleaned and analyzed in Microsoft Excel and R software 3.6.1.
description: <strong>GENERAL INFORMATION</strong>
 1. Title of Dataset: Bioecology of fall armyworm Spodoptera
 frugiperda (J. E. Smith), its management and potential patterns of
 seasonal spread in Africa CBFAMFEW I 2. Author
 Information<br>     A. Principal Investigator Contact
 Information<br>         Name: Saliou Niassy<br>       
  Institution:ICIPE<br>         Address: DUDUVILLE KASARANI<br>
         Email: sniassy@icipe.org     B. Associate or
 Co-investigator Contact Information<br>         Name: Sevgan
 Subramanian<br>         Institution: ICIPE<br>       
  Address: Duduville, icipe<br>         Email:
 ssubramania@icipe.org     C. Alternate Contact
 Information<br>         Name: <br>       
  Institution: <br>         Address: <br>       
  Email:  3. Date of data collection (single date,
 range, approximate date):  4. Geographic location of
 data collection:  5. Information about funding sources
 that supported the collection of the data: We thank FAO, which made the
 data used in this paper freely available. The authors express their
 gratitude to farmers, extension agents and all technical staff members
 involved in the data collection in the different countries. USAID/OFDA
 contributed to the national capacity that enabled the data to be
 generated.  <strong>SHARING/ACCESS
 INFORMATION</strong> 1. Licenses/restrictions
 placed on the data: No, data is open access 2. Links to
 publications that cite or use the data: None 3. Links
 to other publicly accessible locations of the data: None
 4. Links/relationships to ancillary data sets: Partnership with
 FAO 5. Was data derived from another source?
 Yes.<br>     A. If yes, list source(s): FAO, FAMEWS
 6. Recommended citation for this dataset: Community based fall
 armyworm monitoring, forecasting and early warning (CBFAMFEW)
 <strong>DATA & FILE
 OVERVIEW</strong> 1. File
 List: Niassy_et_al_FAMEWS_Plos.xlsx 2. Relationship
 between files, if important: All collected using FAMEWS App
 3. Additional related data collected that was not included in the
 current data package: Rainfall data 4. Are there
 multiple versions of the dataset? no<br>     A. If yes, name of
 file(s) that was updated: <br>         i. Why was the file
 updated? <br>         ii. When was the file updated? 
 <strong>METHODOLOGICAL INFORMATION</strong>
 1. Description of methods used for collection/generation of
 data: <br> <br> 2.1.    Survey sites <br> The CBFAMFEW
 project was implemented in six eastern African countries (Burundi,
 Ethiopia, Kenya, Rwanda, Tanzania and Uganda) between August 2017 and
 August 2019. In each country, five maize-growing districts were selected,
 and in each district, 10 villages were selected for data collection, with
 two Community Focal Persons (CFPs) per village. The selected districts in
 each country for the study are presented in Figure 1, and the locations
 for larval scouting and trapping of FAW males in the six countries are
 presented in Figure 2. 2.2.    Community-Based Fall
 Armyworm Monitoring, Forecasting, Early Warning and Management System
 (CBFAMFEW) The CBFAMFEW project established a network
 that allowed each selected village to have a trained CFP with a smartphone
 and a pheromone trap to collect FAW data. The CFPs were trained in the use
 of the FAO-developed FAMEWS mobile application (app) for data input on FAW
 incidence. CFPs were also trained on how to interpret data and provide
 timely advice to villagers and early warning of FAW attacks. Further
 training on regular field scouting and detecting the pest at various
 stages was also provided to the CFPs. To coordinate data collection and
 transmission, mobile phones were loaded with the FAW monitoring
 application app, FAMEWS. The FAMEWS app is available in Google Play in 13
 languages, and was downloaded by farmers and extension workers. In total,
 56 mobile phones were provided per country (1 mobile phone per village x
 50 villages + 1 mobile per district officer x 5 officers + 1 mobile for
 the National Coordinator). Hence a total of 336 mobile phones were
 deployed in the six countries. Parallel to FAW population monitoring
 activities in East Africa, similar initiatives were conducted in Ghana,
 Liberia, Zambia, Mozambique and South Sudan using similar approach. Data
 collected from these countries using the FAMEWS app were also included
 with data obtained from the six eastern African countries to map the FAW
 density across a wider area of SSA.  2.3.    Pheromone
 traps and field scouting  For monitoring adults, two
 universal bucket traps (Unitrap) were installed in each village. Traps
 baited with FAW pheromone were deployed just after planting and monitoring
 started after the emergence of plant seedlings to detect the first arrival
 of moth pests. Traps and lure (lure blend: Z9-14Ac (81.7%); z11-16Ac
 (17.54%); Z7-12Ac (0.5%) and z9-12Ac (0.25%)) were supplied by Russell IPM
 Ltd, Unit 45, First Avenue, Deeside Industrial Park Deeside, Flintshire,
 CH5 2NU United Kingdom. The FAW monitoring kit also contained a toxicant
 which immobilized the moths once attracted to the device. One trap was
 placed inside the maize field and a second one outside the maize fields.
 The trap was hung from a suspended pole about 1.5 m above the ground, and
 one trap was used for every 0.5–2 ha. The pheromone lure was replaced
 every 3–6 weeks. The traps were checked and emptied weekly, and trapped
 moths were then sorted to identify FAW. Field scouting
 was conducted at least twice a week, from the seedling and early whorl
 stages of the maize crop. This was also the time that farmers and
 extension workers sampled for egg masses, larvae, damage symptoms and the
 presence of other pests such as the African Armyworm (AAW) Spodoptera
 exempta (Walker) and stemborers. The maize fields were scouted using a “W”
 pattern approach, which involved sampling 10 consecutive plants at five
 different spots along the “W” transect (20). FAW and non-target moth
 counts from pheromone traps, and field scouting data were recorded and
 entered into the FAMEWS app. The mean percentage of plants infested with
 FAW, AAW, and stemborers, was automatically tabulated. Data were also
 collected on the condition of traps and whether the lures (pheromones) had
 been replaced. Additional data collected with the FAMEWS app included
 dates, country, geolocation, crop information (variety, planting date,
 irrigated or rain-fed, fertilizers used, and crop growth stage), general
 health of the crop, the cropping system (e.g. mono/intercropping,
 seasonal, rotation and push-pull), pest management practice adopted
 (chemical pesticides or biopesticides), and rainfall. Push-pull
 encompasses intercropping maize with the legume Desmodium spp. (Desmodium
 intortum for climate-smart push-pull or Desmodium uncinatum for
 conventional push-pull) and a border row of Napier grass Pennisetum
 purpureum or Brachiaria brizantha cv Mulato II (for climate-smart
 push-pull) around the plot; both Desmodium sp. and Napier grass are
 perennial fodder plants [18]. Seasonal cropping is a farming practice in
 which the same crop is grown in the same area for a number of growing
 seasons. In the case of maize, it is mainly rain fed, and the land remain
 fallow between seasons. While crop rotation is the practice of growing a
 series of different types of crops in the same area across
 seasons. 2.4.    FAW monitoring data validation and
 processing  A national coordinator in each country was
 appointed to validate the data collected by the CFPs through FAMEWS, and
 who uploaded to the global platform maintained by FAO
 (http://www.fao.org/fall-armyworm/monitoring-tools/famews-global-platform/en/) through the global coordinator. The data presented in this study were downloaded from the FAO global platform, which has the entire database collected for the FAMEWS app between January 2018 to June 2019. The various data entries were officially requested from the FAO, cleaned and analyzed in Microsoft Excel and R software 3.6.1 [27]. 2.5.    Weather data and mapping methodology Monthly rainfall data for 2018 and 2019 were sourced from Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). CHIRPS incorporates satellite imagery with in-situ station data to create 1 km resolution gridded rainfall time series data (www.chc.ucsb.edu/data/chirps/). The geo-referenced points were used to extract monthly rainfall records (proxy data) in millimeters of the FAW infested areas in the six countries using the point sampling tool in QGIS software (http://qgis.osgeo.org). The FAW density was plotted against monthly rainfall data to establish whether there was a relationship between rainfall patterns and the abundance of FAW in the six countries. We interpreted effects of rainfall from a pest management perspective, whereby downpours, for instance could influence the pest flight and neonate movement and feeding on maize plants [28, 29]. Rainfall can both negatively and positively affect the general plant health, particularly in rain-fed cropping systems.  Spatial distribution of FAW density (number of insects per 1 km2) in target countries, based on both trap and scouting data, were represented using heat maps. The FAW traps and scouting georeferenced data dating from January 2018 to June 2019 were organized on a quarterly basis based on the collection date. The points were clustered quarterly for the year 2018 to the 2nd quarter of 2019. The heat maps were developed using the kernel density tool in QGIS software (http://qgis.osgeo.org). Kernel density estimation is a powerful non-parametric technique for estimating probability density function of variables. The tool calculates the density of point features around each output raster cell. The density was calculated based on an accumulation of the number of FAW geo-referenced records in a sampled location, with a higher number of FAW records  resulting in a higher value in the heat map. The heat maps were developed with the assumption that the sampling protocol for scouting and installation of traps was spatially unbiased. This analysis helps in identifying the hot-spot areas with high infestation rates. On the other hand, we utilized a maize cropping calendar which was freely available from FAO Global Information and Early Warning System (GIEWS: http://www.fao.org/giews/countrybrief/index.jsp) to explore the influence of maize sowing, growing, harvesting and fallow periods on FAW adults density during the main and second seasons. For a regional overview of FAW population dynamics, we also utilized FAW occurrence and density data from 11 African countries (six in East Africa and five in other regions) that are available in the FAO global platform. Further, data collected from pheromone traps (universal bucket traps) in the eastern and other African countries were quarterly mapped at the regional level. Specifically, we overlaid the quarter FAW density heat maps in 2018 and 2019 to show the change in FAW density over time in each location, and differences in FAW density between locations at a point in time. Further, we mapped the all sites in the region where FAW was consistently observed across all the quarters in the year (i.e. year-round distribution).  2. Methods for processing the data: <br> <br> 2.6 Statistical analysis Trap counts were modelled using the Negative Binomial Model (NBM) which accommodates overdispersion of integer counts data. The NBM was used to evaluate the effect of main crop types, rotation crop and cropping systems on FAW trap counts. Incident rate ratios (IRR) were estimated for the different levels of each factor relative to a chosen reference level of a factor in question. IRR is a relative measure of incidence rate, such that IRR = 1 means no effect of the exposure, IRR &gt; 1 means positive effect of exposure and IRR &lt; 1 means negative effect of exposure. Similary, the NBM was also used to study the effect of crop phenology on trap counts. For main crop factor, maize was used as a reference category while for cropping systems, we used seasonal cropping as a reference level. “Beans” was used a reference group for the crop rotation factor. The effect of maize crop stage on FAW trap counts and larval counts (as proportion of infested plants) was analysed for each country separately in view of high variability between countries. While the FAW trap counts were analysed using the NBM, proportion of larval infested plants were modeled using quasi-binomial model. All tests were performed at the 5% significance level. 3. Instrument- or software-specific information needed to interpret the data: <br> Analyses were performed using R version 3.6.1 [27]. 4. Standards and calibration information, if appropriate:  5. Environmental/experimental conditions: N/A 6. Describe any quality-assurance procedures performed on the data: FAMEWS APP 7. People involved with sample collection, processing, analysis and/or submission:  The CBFAMFEW project established a network that allowed each selected village to have a trained CFP with a smartphone and a pheromone trap to collect FAW data. The CFPs were trained in the use of the FAO-developed FAMEWS mobile application (app) for data input on FAW incidence. CFPs were also trained on how to interpret data and provide timely advice to villagers and early warning of FAW attacks. Further training on regular field scouting and detecting the pest at various stages was also provided to the CFPs. To coordinate data collection and transmission, mobile phones were loaded with the FAW monitoring application app, FAMEWS. The FAMEWS app is available in Google Play in 13 languages, and was downloaded by farmers and extension workers. <strong>DATA-SPECIFIC INFORMATION: </strong> 1. Number of variables: 48 2. Number of cases/rows: 109712 3. Variable List: <br> <br> ID    date    date by month    cropFieldSizeUnit    scouting    traps    locationName    cropVariety    cropPlantingDate    rainLastDate    country    userCountry    region    latitude    longitude    cropMain    cropFertilizer    cropIrrigation    cropStage    cropHealth    cropSystem    cropFieldSize    rainAmount    trapsCount    trapsConfirmedFAW    trapsSuspectedFAW    trapsOtherSpecies    cropRotation    scoutingPlantsChecked    scoutingPlantsFAW    scoutingPercentageFAW    fawCropDamage    fawCobDamage    fawNaturalEnemies    fawDeadLarvae    fawControlUndertaken    fawControlChemicalPesticideName    fawControlChemicalPesticideLitres    scoutingStageFAW    fawCurrentDamage    fawPreviousDamage    fawControlLocalTypes    fawControlBiopesticideName    fawControlBiopesticideLitres    fawLarvaeKilledByNaturalEnemies    rotationIntercroppingCrop 4. Missing data codes: N/A 5. Specialized formats or other abbreviations used: Excel