Fall armyworm from a maize multi-peril pest risk perspective

We assembled 3,175 geo-tagged occurrences of fall armyworm worldwide and used that data in conjunction with information about the physiological requirements of the pest to spatially assess its global climate suitability. Our analysis indicates that almost the entire African maize crop is grown in areas with climates that support seasonal infestations of the insect, while almost 92% of the maize area supports year-round growth of the pest. In contrast, rich-country maize production largely occurs in temperate areas where only 2.3% of the area may allow the pest to survive year-round, although still subject to worrisome seasonal risks. This means the African maize crop is especially susceptible to damaging infestation from fall armyworm, on par with the risk exposure to this pest faced by maize producers throughout Latin America. We show that the maize grown in Africa is also especially vulnerable to infestations from a host of other crop pests. Our multi-peril pest risk study reveals that over 95% of the African maize area deemed climate suitable for fall armyworm, can also support year-round survival of at least three or more pests. The spatial concurrence of climatically suitable locations for these pests raises the production risk for farmers well above the risks posed from fall armyworm alone. Starkly, over half (52.5%) of the African maize area deemed suitable for fall armyworm is also at risk from a further nine pests, while over a third (38.1%) of the area is susceptible to an additional 10 pests. This constitutes an exceptionally risky production environment for African maize producers, with substantive and complex implications for developing and implementing crop breeding, biological, chemical and other crop management strategies to help mitigate these multi-peril risks.

Supplementary Figure 1.The two variables that contributed most to the first and second principal components used to map S. frugiperda environmental niches.(A) Minimum temperature of coldest week (°C), and (B) Lowest weekly radiation (Wm -2 ) Supplementary Table 1.Pool of bioclimatic variables available for fall armyworm environmental niche modeling prior to variable selection.
Variable  (2018), although they predict a generally expanded footprint for FAW across all the spatial extents tabulated in that figure .Our own estimates expand further on those areas with the climate potential to persistently support FAW populations for reasons we describe in the main body of the paper.
The evidence reported in Supplementary Figure 4, Panel b, reinforces the FAW suitability findings across each of the studies, this time for selected countries or U.S. states where FAW is reported to be expanding its footprint.
More specifically, across all the reported regions and countries there is an increase in the predicted climate-suitable areas for FAW persistence when moving from the earlier 2018 (du Plessis et al.) study, to the 2022 (Timilsena et al.) work, and then to the current study.In the case of our study, this reflects the reported (Wu et al. 2022) rapid elastic adaptation of FAW in the cooler end of its habitat.In modeling terms, this is reflected, in part, by the DV0 (lower temperature threshold) value of 8.7 o C that we chose, relative to the 12.0 o C value used by du Plessis et al. ( 2018) and Timilsena et al. (2022).This lowered DV0 value, along with other differences in the parametrization of our model (see Supplementary Table 4), meant our model aligns with both the lab findings of Valdez-Torres et al. ( 2012) regarding the minimum thermal threshold for FAW on maize and the expanded set of reported occurrence data we used to calibrate our model.
Finally, Supplementary Figure 4c shows the differences among each of the studies in terms of their FAW persistence predictions (i.e., EI > 0) within the geographical extent of maize production reported by You et al. (2014).Ramirez-Cabral et al. (2017) predicts that 70% of the global maize extent is likely to support year-round FAW presence.These global shares are substantially higher than those of du Plessis et al. ( 2018) who predicted a 51% share, Timilsena et al. (2022) a 54% share and this study 59%.Looking closer at the predicted persistence of FAW in Australia and Zimbabwe we see that Ramirez-Cabral et al. ( 2017) also predicts much larger FAW persistence shares than the other three studies.Notably, Zimbabwe's predicted persistence share within the area planted to maize is substantial for all four studies.For Australia, du Plessis et al. ( 2018) and Timilsena et al. (2022) predict persistence shares for maize of 24.1% and 32.4% respectively.Consistent with recent reports, 12 and as indicated by Maino et al. (2021) in their Australia specific model, our climatic suitability prediction for FAW in Australia encompasses the locations where FAW is present.The overall share of maize growing pixels that are climatically suitable for FAW is larger, 79%, but well below the 99% predicted by Ramirez-Cabral et al. (2017).Moreover, when cross referenced with the (total land mass) results presented in Supplementary Figure 4b, a sizable share of the FAW persistence we predict for Australia lies outside the geographic extent of maize production.

Table 2 .
Geographical and other spatial attributes of observed occurrence data.

Table 3 :
Timilsena et al. (2022)2017), du Plessis et al. (2018)Comparison ofRamirez-Cabral et al. (2017), du Plessis et al. (2018)andTimilsena et al. (2022)with the updated results from the present study.Here we empirically assess differences between the climatic suitability predictions reported byRamirez-Cabral et al. (2017), du Plessis et al. (2018),Timilsena et al. (2022), and the present study.To facilitate that, we took the CLIMEX parameters found inRamirez-Cabral et al. (2017, Table  1), du Plessis et al. (2018, Table1),Timilsena et al. (2022,Table 1), and Supplementary Table 3(in this document) and re-ran each model using the spatialized climate and crop geography data used for the present study.The resulting global geographic climate suitability maps from these four studies are given in Supplementary Figure2.From an initial visual inspection, the overall geographic suitability areas for du Plessis et al. (2018) andTimilsena et al. (2022)are in line with those that we predicted here.However, on closer inspection there are important differences among each of the predictions.Although, Ramirez-Cabral et al. (2017), like du Plessis et al. (2018) and the present study, identify high suitability climes for FAW in the tropical regions of the world, their model overpredicts suitability in temperate areas relative to du Plessis et al. (2018) and the present study.Notably, this overprediction is most evident in areas within European countries, New Zealand, and Southern Australia where Ramirez-Cabral et al. (2017) predicted year-round persistence of FAW in areas that lie outside the reported physiological requirements of fall armyworm.As can be seen in Supplementary Table 4, while Ramirez-Cabral et al. (2017) settled on a lower TTCS (cold stress temperature threshold) relative to du Plessis et al. (2018) and this current study, that enabled them to capture sub-tropical areas into which FAW is expanding, the expanded prediction well into temperate areas could possibly be a reflection of their use of seasonal (transient) populations of fall armyworm in North America to calibrate their model especially in relation with parameters that define optimal FAW thermal requirement.As a result, their predicted EI > 0 areasrepresenting locations with year-round climate suitability-extended into areas with climates that a) lay well outside the known physiological ranges of FAW, and b) beyond areas with reported sustained presence of the pest.For this reason, we opted to focus the remainder of our comparative assessment on the more comparable EI > 0 results reported by duPlessis et al. (2018)andTimilsena et al. (2022).continentlevelland masses with predicted values of EI > 0. The data in this figure confirms thatTimilsena et al. (2022)'s results are generally in line with those of du Plessis et al.
(Timilsena et al. 2022(2017), (du Plessis et al. 2018) reported by duPlesis et al. (2018)but were updated to reflect lab evidence on the minimum thermal threshold for FAW coupled with the expanded set of reported occurrence observations used to calibrate our model.The SM (Soil Moisture) index is one of the parameters used to assess pest population growth in CLIMEX.It ranges between 0 and 1. SM=0 indicates no growth, and at SM1 population growth is maximized.SM is a dimensionless index provided to indicate soil moisture content.Supplementary Section 1:Supplementary Figure4, Panel a provides a comparison amongRamirez-Cabral et al. (2017), (du Plessis et al. 2018),(Timilsena et al. 2022) and our model in terms of the percentage of the worldwide and