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Maize (Zea mays) is a critical staple crop in sub-Saharan Africa, where millions of people rely on for nutrition and livelihoods. However, the devastating impact of lepidopteran stemborer pests on maize production is a significant challenge. These pests cause yield losses ranging from 30 to 100%, with severe consequences for smallholder farmers. Additionally, climate change exacerbates the situation by altering pest behaviour and distribution. To address this complex issue, we investigated the influence of two climate-resilient maize varieties, CKH122114 and DH02, on maize-stemborer interactions under semi-field conditions. Our study integrated multiple parameters, including plant physiology, insect behaviour, and ecological relationships. We conducted controlled experiments in a screenhouse, infesting plants with Chilo partellus (Swinhoe) (Lepidoptera: Crambidae), a significant stemborer pest. The results demonstrated distinct responses between the two maize varieties to pest infestation. Variety DH02 exhibited longer stem tunnels, higher larval weight, and greater larval survival, translating into reduced stem biomass and grain yield. In contrast, variety CKH122114 displayed more resilience to pest damage. The study underscores the critical role of crop variety in shaping plant-pest interactions and emphasizes the significance of climate-resilient crop varieties in adapting to climate change and pest pressures. In conclusion, our investigation highlights the complex interplay between crop genetics, pest behaviour, and environmental factors in shaping the outcomes of pest infestations. By creating and advocating for crop varieties, that can withstand climate challenges, help farmers address pest issues and adapt to climate change. This approach offers a sustainable and forward-looking strategy to ensure the long-term viability of global food production and agriculture. [ABSTRACT FROM AUTHOR]

A wide range of insects affect crop production and cause considerable yield losses. Difficulties reside on the development and adaptation of adequate strategies to predict insect pests for their timely management to ensure enhanced agricultural production. Several conceptual modelling frameworks have been proposed, and the choice of an approach depends largely on the objective of the model and the availability of data. This paper presents a summary of decades of advances in insect population dynamics, phenology models, distribution and risk mapping. Existing challenges on the modelling of insects are listed; followed by innovations in the field. New approaches include artificial neural networks, cellular automata (CA) coupled with fuzzy logic (FL), fractal, multi-fractal, percolation, synchronization and individual/agent-based approaches. A concept for assessing climate change impacts and providing adaptation options for agricultural pest management independently of the United Nations Intergovernmental Panel on Climate Change (IPCC) emission scenarios is suggested. A framework for estimating losses and optimizing yields within crop production system is proposed and a summary on modelling the economic impact of pests control is presented. The assessment shows that the majority of known insect modelling approaches are not holistic; they only concentrate on a single component of the system, i.e. the pest, rather than the whole crop production system. We suggest system thinking as a possible approach for linking crop, pest, and environmental conditions to provide a more comprehensive assessment of agricultural crop production. [ABSTRACT FROM AUTHOR]

Monitoring key pollinator taxa such as the genus Ceratina requires precise near real-time predictions to facilitate better surveillance. The potential habitat suitability of Ceratina moerenhouti was predicted in the Eastern Afromontane biodiversity hotspot (EABH) in Kenya using presence-only data, to identify their potential distribution and vulnerability due to climate change. Bioclimatic, edaphic, terrain, land surface temperature, and land use and land cover (LULC) variables were used as predictors. Three machine learning techniques, together with their ensemble model, were evaluated on their suitability to predict current and future (the shared socioeconomic pathways (SSPs), i.e., SSP245 and SSP585) habitat suitability. Predictors were subjected to variable selection using the variance inflation factor resulting in a few (n = 9) optimum variables. The area under the curve (AUC) and true skill statistic (TSS) were used for the accuracy assessment of the modeling outputs. The results indicated that 30% and 10% of the EABH in Murang'a and Taita Taveta counties are currently suitable for C. moerenhouti occurrence, respectively. However, future projections show a ±5% decrease in C. moerenhouti habitats in the two counties. Further, the ensemble model harnessed the algorithm differences while the random forest had the highest individual predictive power (AUC = 0.97; TSS = 0.96). Clay content, LULC, and the slope were the most relevant variables together with temperature and precipitation. Integrating multi-source data in predicting suitable habitats improves model prediction capacity. This study can be used to support the maintenance of flowering plant communities around agricultural areas to improve pollination services. • Monitoring key pollinator taxa facilitates better surveillance and the development of empirical interventions. • The potential habitat suitability of Ceratina moerenhouti was established in the Eastern Afromontane biodiversity hotspot. • Three robust machine learning algorithms and their ensemble had a very high prediction accuracy. • Integrating multi-source data improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

The oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae) is a major pest of fruit and vegetable production systems on several continents. The pest has invaded many countries, causing considerable impact on fruit production systems and commercialization. In this study we determined the relationship between temperature and development, survival and reproductive parameters of B. dorsalis on an artificial diet under laboratory conditions under 7 constant temperatures (10, 15, 20, 25, 30, 33 and 35 °C) with 70 ± 10% relative humidity and a photoperiod of L12:D12. We validated the laboratory results with a full life table analysis under semi-natural conditions in a screenhouse. We used the Insect Life Cycle Modeling (ILCYM) software for all mathematical models and simulations applied to all life history parameters. Bactrocera dorsalis completed its development at temperatures ranging between 15 and 33 °C with the mean developmental time of egg, larva, and pupa ranging between 1.46 and 4.31 days, 7.14–25.67 days, and 7.18–31.50 respectively. The models predicted temperatures ranging between 20 and 30 °C as favorable for development and survival, and 20 to 25 °C for optimal fecundity of B. dorsalis. Life table parameters showed the highest gross reproductive rate (GRR), net reproductive rate (R o), intrinsic rate of increase (r m), and finite rate of increase (λ) between 25 and 31 ᵒC while generation time (T) and doubling time (D t) were low at this interval. The effects of future climate change on B. dorsalis life history parameters were further investigated and the outcome from this study will help in the management of B. dorsalis in different agroecologies in the context of ongoing climate change. • Temperature affects the development and survival of Bactrocera dorsalis. • Temperature ranging between 20 and 30 ᵒC are optimal for the development and reproduction of Bactrocera dorsalis. • Non-linear models are suitable to predict the development and reproduction of Bactrocera dorsalis within temperature gradient. [ABSTRACT FROM AUTHOR]