Computational biogeographic distribution of the fall armyworm (Spodoptera frugiperda J.E. Smith) moth in eastern Africa.

The fall armyworm (FAW), Spodoptera frugiperda J.E. Smith, has caused massive maize losses since its attack on the African continent in 2016, particularly in east Africa. In this study, we predicted the spatial distribution (established habitat) of FAW in five east African countries viz., Kenya, Tanzania, Rwanda, Uganda, and Ethiopia. We used FAW occurrence observations for three years i.e., 2018, 2019, and 2020, the maximum entropy (MaxEnt) model, and bioclimatic, land surface temperature (LST), solar radiation, wind speed, elevation, and landscape structure data (i.e., land use and land cover and maize harvested area) as explanatory variables. The explanatory variables were used as inputs into a variable selection experiment to select the least correlated ones that were then used to predict FAW establishment, i.e., suitability areas (very low suitability - very high suitability). The shared socio-economic pathways, SSP2-4.5 and SSP5-8.5 for the years 2030 and 2050 were used to predict the effect of future climate scenarios on FAW establishment. The results demonstrated that FAW establishment areas in eastern Africa were based on the model strength and true performance (area under the curve: AUC = 0.87), but not randomly. Moreover, ∼27% of eastern Africa is currently at risk of FAW establishment. Predicted FAW risk areas are expected to increase to ∼29% (using each of the SSP2-4.5 and SSP5-8.5 scenarios) in the year 2030, and to ∼38% (using SSP2-4.5) and ∼35% (using SSP5-8.5) in the year 2050 climate scenarios. The LULC, particularly croplands and maize harvested area, together with temperature and precipitation bioclimatic variables provided the highest permutation importance in determining the occurrence and establishment of the pest in eastern Africa. Specifically, the study revealed that FAW was sensitive to isothermality (Bio3) rather than being sensitive to a single temperature value in the year. FAW preference ranges of temperature, precipitation, elevation, and maize harvested area were observed, implying the establishment of a once exotic pest in critical maize production regions in eastern Africa. It is recommended that future studies should thus embed the present study's modeling results into a dynamic platform that provides near-real-time predictions of FAW spatial occurrence and risk at the farm scale.

The possibilities of explicit Striga (Striga asiatica) risk monitoring using phenometric, edaphic, and climatic variables, demonstrated for Malawi and Zambia.

Food insecurity continues to affect more than two-thirds of the population in sub-Saharan Africa (SSA), particularly those depending on rain-fed agriculture. Striga, a parasitic weed, has caused yield losses of cereal crops, immensely affecting smallholder farmers in SSA. Although earlier studies have established that Striga is a constraint to crop production, there is little information on the spatial extent of spread and infestation severity of the weed in some SSA countries like Malawi and Zambia. This study aimed to use remotely sensed vegetation phenological (n = 11), climatic (n = 3), and soil (n = 4) variables to develop a data-driven ecological niche model to estimate Striga (Striga asiatica) spatial distribution patterns over Malawi and Zambia, respectively. Vegetation phenological variables were calculated from 250-m enhanced vegetation index (EVI) timeline data, spanning 2013 to 2016. A multicollinearity test was performed on all 18 predictor variables using the variance inflation factor (VIF) and Pearson's  correlation approach. From the initial 18 variables, 12 non-correlated predictor variables were selected to predict Striga risk zones over the two focus countries. The variable "start of the season" (start of the rainy season) showed the highest model relevance, contributing 26.8% and 37.9% to Striga risk models for Malawi and Zambia, respectively. This indicates that the crop planting date influences the occurrence and the level of Striga infestation. The resultant occurrence maps revealed interesting spatial patterns; while a very high Striga occurrence was predicted for central Malawi and eastern Zambia (mono-cultural maize growing areas), lower occurrence rates were found in the northern regions. Our study shows the possibilities of integrating various ecological factors with a better spatial and temporal resolution for operational and explicit monitoring of Striga-affected areas in SSA. The explicit identification of Striga "hotspot" areas is crucial for effectively informing intervention activities on the ground.

Biogeography of cereal stemborers and their natural enemies: forecasting pest management efficacy under changing climate.

BACKGROUND: Climate warming presents physiological challenges to insects, manifesting as loss of key life-history fitness traits and survival. For interacting host-parasitoid species, physiological responses to heat stress may vary, thereby potentially uncoupling trophic ecological relationships. Here, we assessed heat tolerance traits and sensitivity to prevailing and future maximum temperatures for the cereal stemborer pests, Chilo partellus, Busseola fusca and Sesamia calamistis and their endo-parasitoids, Cotesia sesamiae and Cotesia flavipes. We further used the machine learning algorithm, Maximum Entropy (MaxEnt), to model current and potential distribution of these species. RESULTS: The mean critical thermal maxima (CTmax ) ranged from 39.5 ± 0.9°C to 44.6 ± 0.6°C and from 46.8 ± 0.7°C to 48.5 ± 0.9°C for parasitoids and stemborers, with C. sesamiae and Ch. partellus exhibiting the lowest and highest CTmax respectively. From the current climate to the 2050s scenario, parasitoids recorded a significant reduction in warming tolerance compared with their hosts. Habitat suitability for all stemborer-parasitoid species was spatially heterogeneous under current and future climatic scenarios. Cotesia sesamiae C. flavipes and B. fusca exhibited significant habitat loss, whereas Ch. partellus and S. calamistis showed a significant habitat gain under future 2050s predictions. Model metrics based on mean area under the curve ranged from 0.72 to 0.84 for all species, indicating a good predictive performance of the models. CONCLUSION: These results suggest C. sesamiae and C. flavipes may face survival constraints or extirpation compared with their pest hosts when environmental temperature reaches their upper thermal limits earlier, likely reducing pest regulation through density-mediated effects. The results demonstrate potential destabilization of stemborer-parasitoid trophic systems potentially compromising biocontrol efficacy under climate warming. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Climate change and specialty coffee potential in Ethiopia.

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.

A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data.

Information on weed occurrence within croplands is vital but is often unavailable to support weeding practices and improve cropland productivity assessments. To date, few studies have been conducted to estimate and map weed abundances within agroecological systems from spaceborne images over wide-area landscapes, particularly for the genus Striga. Therefore, this study attempts to increase the detection capacity of Striga at subpixel size using spaceborne high-resolution imagery. In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. Firstly, multidate and multiyear Sentinel-2 (S2) data (2017 to 2018) were utilized to map cropland and non-cropland areas using the random forest algorithm within the Google Earth Engine. The non-cropland class was thereafter masked out from a single date S2 image of the 13th of December 2017. The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ hyperspectral data. The gathered in-situ hyperspectral data were resampled to the spectral waveband configurations of S2 and three representative EMs were inferred, namely: (1) Striga, (2) crop and other weeds, and (3) soil. Overall classification accuracies of 88% and 78% for the pixel-based cropland mapping and subpixel Striga detection were achieved, respectively. Furthermore, an F-score (0.84) and a root mean square error (0.0075) showed that the MESMA subpixel algorithm provides plausible results for predicting the relative abundance of Striga within each S2 pixel at a landscape scale. The capability of MESMA together with a cropland classification hierarchical approach was thus proven to be suited for Striga detection in a heterogenous agroecological system. These results can be used to guide in the adaptation, mitigation, and remediation of already infested areas, thereby avoiding further Striga infestation of new croplands.