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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]

• Biological control (BC) technologies are necessary to address the significant constraint that Fall armyworm (FAW) poses in cereal production in Africa. • The study employs a step-by-step modeling approach to map suitable sites of BC technologies, specifically parasitoids (Cotesia icipe). • Pest infestation levels are estimated using an evolutionary adaptive Neuro-Fuzzy inference system with a high coefficient of determination (R2) > 0.89. • We utilize fuzzy inference theory to accurately map the suitability of different regions in Kenya for the adoption of augmentative biocontrol using the parasitoids C. icipe in maize farms. • Artificial intelligence-based methods provide an effective advisory tool for guiding the deployment of biological control agents, such as parasitoids, for sustainable FAW management. Fall armyworm (FAW) Spodoptera frugiperda (J. E. Smith) is a major pest affecting cereal production in Africa. Biological control (BC) technologies are being promoted as a sustainable alternative to chemical control, which can lead to health risks and environmental hazards. However, effective deployment of these technologies requires site-specific recommendations. In this study, we use a step-by-step modelling approach to map suitable sites for BC technologies, focusing on the parasitoid Cotesia icipe using the FAW level of infestation, the ecological niche of the parasitoid, and the FAW host crop. The level of pest infestation was estimated using an evolutionary adaptive Neuro-Fuzzy inference system (R2 > 0.89) while the pest ecological niche was obtained using the maximum entropy algorithm (area under curve, AUC > 0.9). A fuzzy operator was used to combine all fuzzified variables into a single layer that represents the landscape's overall suitability for C. icipe in maize farms. Our computational findings indicate that C. icipe holds substantial promise as a BC agent in maize farms, with suitability levels consistently surpassing 90% throughout maize cropping seasons. The findings demonstrate that the utilization of artificial intelligence, combined with data science and knowledge representation, serves as an effective advisory tool for guiding the deployment of BC agents, such as parasitoids, for the sustainable management of FAW. This approach enables informed decision-making and enhances the efficacy of FAW management strategies by providing valuable insights and recommendations based on data-driven and computer intelligence analyses. [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]

The main purpose of this study was to understand the spatio-temporal spread of the maize stem borer Busseola fusca (Fuller) (Lepidoptera: Noctuidae) in smallholder maize farms. The analysis carried out allowed the establishment of complementary sampling scheme and analysis that can be applied to investigate the propagation of stem borer damages and extended to other insect pests. This approach requires consideration of all plants point locations, the knowledge on the level of damage and its characterization. Results showed that there was a two-week interval between occurrence of the peaks of leaf damage and male adult moth abundance. The prior role of leaf damages in the farm infestation by B. fusca is revealed, and an estimate of the mean transition time between different damage types is provided. Furthermore, damaged plants exhibited a local spatial autocorrelation within a range of dependence of 0-10 meters; and the spatio-temporal pattern of B. fusca damage spread evolves as a spiral around an initial patch of damaged plants. By assuming a neighbor configuration of distribution of damaged plants nearby non-damaged, we showed that the inner plants are likely to become damaged within a time period of a week; thus, B. fusca infests farms in a systematic fashion. Overall, these results have useful implications for improving and optimizing existing field sampling methods for insect pest damages. The approaches used in carrying out the analysis further provided a deep understanding helpful to improve integrated pest management (IPM) strategies against stem borers, and offer IPM practitioners’ the opportunity to design, develop, and implement optimum control methods against B. fusca , an important pest of maize in Africa. [ABSTRACT FROM AUTHOR]

• This paper proposes and describes an interesting and potentially powerful means to validate large-scale biocontrol agent impacts during a Classical biological control (CBC) programme. • The methodology derives the beneficiaries' farmers from the dispersal of parasitoids at a landscape level. • The methodology was applied to the endoparasitoid Fopius arisanus release to control fruit fly Bactrocera dorsalis in Africa. • The study's outcome is appropriate for agriculture stakeholders and development partners to evaluate the benefits of CBC programme. Following the invasion of Africa by the oriental fruit fly, Bactrocera dorsalis , Classical biological control (CBC) has been exploited as a safer alternative for its suppression by the introduction and release of the koinobiont endoparasitoid, Fopius arisanus. Although the parasitoids have been released in several African countries, the extent of their dispersal and the resulting extent in benefits to fruit growers have not yet been elucidated. This paper proposes an innovative, multi-level CBC impact analysis that combines cellular automata (CA) and ecological niche models to estimate parasitoid dispersal ranges and the range of farmers who benefit from it. Firstly, we provided a generic, systematic methodological approach using CA rules incorporated into species distribution. Secondly, the model was used to estimate the dispersal range of the parasitoid, based on the life history and bioecology of the host insect (fruit fly) and the parasitoid. Finally, the parasitoid dispersal coverage was mapped across fruit crops attacked by the target fruit fly, and the number of households that benefited from the parasitoids release programme was extracted from the area of the dispersal. The data was projected across all countries where the parasitoid has been released and validated. In Kenya, the model showed that F. arisanus had covered a total area of 50.34 km2 from the initial point of open field release; and at the continental scale, the model predicted that the parasitoid had covered a total area of 229.97 km2. The model estimated that 351,855 and 3,731,330 households had directly benefited from the release of F. arisanus between 2013 and 2018 in Kenya and at the continental level, respectively. The study's outcome is appropriate for providing feedback information on the impact of CBC to government and development partners to assist in making informed decisions on technological interventions. [ABSTRACT FROM AUTHOR]

The performance of discrete mathematical models to describe the population dynamics of diamondback moth (DBM) (Plutella xylostella L.) and its parasitoid Diadegma semiclausum was investigated. The parameter values for several well-known models (Nicholson¿Bailey, Hassell and Varley, Beddington, Free and Lawton, May, Holling type 2, 3 and Getz and Mills functional responses) were estimated. The models were tested on 20 consecutive sets of time series data collected at 14 days interval for pest and parasitoid populations obtained from a highland cabbage growing area in eastern Kenya. Model parameters were estimated from minimized squared difference between the numerical solution of the model equations and the empirical data using Powell's method. Maximum calculated DBM growth rates varied between 0.02 and 0.07. The carrying capacity determined at 16.5 DBM/plant by the Beddington et al. model was within the range of field data. However, all the estimated parameter values relating to the parasitoid, including the instantaneous searching rate (0.07¿0.28), per capita searching efficiency (0.20¿0.27), search time (5.20¿5.33), handling time (0.77¿0.90), and parasitism aggregation index (0.33), were well outside the range encountered empirically. All models evaluated for DBM under Durbin¿Watson criteria, except the May model, were not autocorrelated with respect to residuals. In contrast, the criteria applied to the parasitoid residuals showed strong autocorrelations. Thus, these models failed to estimate parasitoid dynamics. We conclude that the interactions of the DBM with its parasitoid cannot be explained by any of the models tested. Two factors may be associated with this failure. First, the parasitoid in this integrated biological control system may not be playing a major role in regulating DBM population. Second, and perhaps more likely, poor correlations reflect gross inadequacies in the theoretical assumptions that underlie the existing models. [Copyright &y& Elsevier]

Abstract: The Lotka–Volterra model was applied to the population densities of diamondback moth (DBM), Plutella xylostella (L.) and its exotic larval parasitoid Diadegma semiclausum (Hellen) data that was collected earlier by icipe’s DBM biological control team. The collections were done for 15 months before the release and 36 months after release of the parasitoid in two areas; in Werugha, Coast Province of Kenya and Tharuni, Central Province of Kenya, respectively. For each area in pre- and post-release periods, we estimated Lotka–Volterra model parameters from the minimization of the loss function between the theoretical and experimental time-series datasets following the Nelder-Mead multidimensional method. The model estimated a reduction in the value of the steady state of DBM population from 4.86 to 2.17 in Werugha and from 6.11 to 3.76 and 3.45 (with and without exclusion of the time before D. semiclausum recovery) in Tharuni when transiting from the pre- and post-release periods, respectively. This change was a consequence of the newly introduced parasitoid, in the areas. The study presented a successful and detailed technique for non-linear model parameters restoration which was demonstrated by the correct mimicking of empirical datasets from the classical biological control with D. semiclausum, in different areas of Kenya. The applied model has measured the parasitoids impact on the DBM biological control through a quantitative estimate of the effectiveness of the newly introduced species D. semiclausum. These equations may therefore be used as tool for decision making in the implementation for such pests’ management system strategy. [Copyright &y& Elsevier]

The antestia bug Antestiopsis thunbergii (Hemiptera: Pentatomidae) is a major pest of Arabica coffee in African tropical highlands. It feeds on coffee plant vegetative parts and berries leading to a direct reduction in coffee yield and quality. This study aimed to determine A. thunbergii thermal requirements, and to obtain new information on the pest demography as influenced by temperature. Temperature-dependent models were developed using the Insect Life Cycle Modelling software (ILCYM) through a complete life table study at seven constant temperatures in the range 18–32 °C. Non-linear functions were fitted to A. thunbergii development, mortality, fecundity and senescence. Model parameters and demographic variables obtained from the models were given for each temperature and development stage. Life table parameters were estimated for nine constant temperatures, from 18 °C to 26 °C, using stochastic simulations. The minimum temperature threshold ( T min ) and the thermal constant ( k ) for the development from egg to adult were estimated from a linear function at 12.1 °C and 666.67° days, respectively. The maximum temperature threshold ( T max ) was estimated at 33.9 °C from a Logan model. The optimum temperature for immature stages’ survival was estimated to be between 22.4 and 24.7 °C. The maximum fecundity was 147.7 eggs female −1 at 21.2 °C. Simulated A. thunbergii life table parameters were affected by temperature, and the maximum value of intrinsic rate of increase ( r m ) was 0.029 at 22 °C and 23 °C. In general, the life cycle data, models and demographic parameters we obtained were in line with previous reports for antestia bugs or other stink bug species. The relationships between the pest thermal requirements and ecological preferences in highland coffee were discussed. Our results will contribute to risk prediction under climate change for this important coffee pest. [ABSTRACT FROM AUTHOR]

• A temperature-based model for predicting the efficacy of Metarhizium anisopliae against the brown ear tick. • Spatial projection of potential areas of efficacy of M. anisopliae isolate ICIPE7 Sensu stricto against R. appendiculatus in selected countries in East Africa. • A modelling framework for improving the integrated vector management (IVM) of cattle disease in East Africa. Cattle production is constantly threatened by diseases like East Coast fever, also known as theileriosis, caused by the protozoan parasite Theileria parva which is transmitted by ticks such as the brown ear tick, Rhipicephalus appendiculatus. To reduce the extensive use of chemical acaricides, fungal-based microbial control agents such as Metarhizium anisopliae have been tested and show promising results against R. appendiculatus both in field and in semi-field experiments in Africa. However, no known endeavors to link the spatial distribution of R. appendiculatus to climatic variables important for the successful application of M. anisopliae in selected East African countries exists. This work therefore aims to improve the successful application of M. anisopliae against R. appendiculatus by designing a temperature-dependent model for the efficacy of M. anisopliae against three developmental stages (larvae, nymphs, adults) of R. appendiculatus. Afterward a spatial prediction of potential areas where this entomopathogenic fungus might cause a significant epizootic in R. appendiculatus population in three selected countries (Kenya, Tanzania, Uganda) in Eastern Africa were generated. This can help to determine whether the temperature and rainfall at a local or regional scale might give good conditions for application of M. anisopliae and successful microbial control of R. appendiculatus. [ABSTRACT FROM AUTHOR]

• India is a hotspot for biological control agents of Rastrococcus iceryoides. • Eleven old and new R. iceryoides (host)-parasitoid associations were described. • Praleurocerus viridis and Anagyrus chryos were the most promising parasitoids recorded. • We predicted optimal suitability for parasitoid introduction into Africa and Southeast Asia. • Predictive modelling are novel tools to support future classical biological control program. Rastrococcus iceryoides (Green) (Homoptera: Pseudococcidae) is a major invasive pest of several horticultural crops [in Africa and Asia, outside its native range in India], with damage levels ranging from 30% to complete crop failure. Due to lack of effective co-evolved parasitoids in the invaded regions, maximum entropy (MaxEnt) and genetic algorithm for ruleset production (GARP) were used to identify climatically suitable areas in India for foreign exploration. Based on the outcome of the predictive models, an extensive survey was conducted in 15 major mango growing regions in the state of Tamil Nadu, India. Thereafter, both models were used to identify climatic compatibility habitats in the invaded regions of R. iceryoides. Our results revealed ten host plants belonging to eight families with considerably low levels of infestation. The percentage parasitism established using mummified R. iceryoides was relatively high ranging between 16.7 ± 1.4 to 91.3 ± 3.7%. Both old and new host-parasitoid associations were recorded with eleven parasitoid species described. Eight of the parasitoids recorded were new records of R. iceryoides. Among these parasitoids, Praleurocerus viridis Agarwal, Anagyrus chryos Noyes & Hayat and Neoplatycerus tachikawai Subba Rao were the most dominant and widespread parasitoid species, highly specific to R. iceryoides with percent parasitism of 53.2 ± 5.4, 31.3 ± 2.7 and 8.8 ± 2.9%, respectively. Using the occurrence data of the parasitoids, both models successfully identified optimal suitable habitats in Africa and Asia. Both models showed optimal performances with the value of the average area under the curve (AUC) of 0.98 for MaxEnt and 0.95 for GARP. However, the percentage contribution of the predictor variables that influenced the current and future predictions in the native and invaded range varied considerably. These findings demonstrate the importance of predictive modelling as novel tools to support future classical biological control program targeting R. iceryoides in the invaded regions. Our results provide important information to guide strategic planning for future classical biological control programmes. [ABSTRACT FROM AUTHOR]

Integrated Pest Management (IPM) technologies offer effective solutions to reduce the negative effects of crop pests while considering human and environmental health. However, disseminating these technologies faces several barriers, with one of the most significant being the lack of farmer awareness regarding their availability, deployment, and uptake. Digital tools are perceived as a new form of leverage for overcoming these barriers. This study analyzes current IPM digital tools and their potential to boost farmers' awareness of the deployment and adoption of IPM technologies. From a software engineering perspective, this study aims to emphasize the critical functionalities and limitations of various IPM dissemination tools. It provides valuable insights to improve the adoption process and streamline the dissemination of IPM technologies. Through a systematic search in Google, Scopus and Web of Science for journal articles, over 32 dissemination tools were identified. The study thoroughly assesses these tools and identifies 5 main limitations hindering their regular use, especially in developing countries. Among the most significant limitations are the inadequate representation of tools developed in developing countries, lack of agroecological customization, and insufficient offline functionalities. Building on these findings, a user-centered design is employed to propose a software architecture for a novel Decision Support System (DSS) tailored to farmers and experts. The architecture comprises a local database for offline access, a mapping engine for data visualization, a conversation module with a triangulation engine for knowledge sharing, and an agroecology engine for technology recommendation based on an agroecological classification of the user's landscape. Drawing from the review, identified limitations, and the proposed architecture, we illustrate how the resulting novel DSS is anticipated to improve the dissemination of IPM technologies. • First Literature Review that explicitly focuses on web-based Decision Support Systems (DSS) for Integrated Pest Management (IPM) since 2015. • In this literature review, 340 published papers up to April 2023 were retrieved, of which 32 primary studies were selected for a detailed analysis. • The main functionalities and modules of current web-based IPM DSS are discussed. • Five critical limitations are identified, providing insight for future DSS development and paving the way for further research in the field. • A novel three-tier software architecture is proposed as a road map for the development of innovative tools to enhance the dissemination rate of IPM technologies. [ABSTRACT FROM AUTHOR]

• New information on locust hatching timing and location is produced. • The information can be used to mitigate severe impact of locust plagues in eastern Africa. • A fuzzy logic modeling framework is developed suitable for ecological modeling in data scarce regions. Desert locust (Schistocerca gregaria) plagues threaten agricultural production, food security and the environment across Africa, the Middle East, and Southwest Asia. Control methods targeting adult desert locusts present significant challenges and financial costs. Recognizing this, we developed a ground-breaking fuzzy set Mamdani type inference model that provides an innovative solution for early warning alerts. The model aids in predicting the juvenile stages of locust development, thereby preventing wide-scale locust swarming and mitigating its extensive damages and socioeconomic costs. The novelty of our approach lies in our unique application of environmental variables relevant for locust breeding to estimate the timing and location of desert locust hatching. Additionally, we improved the algorithmic handling of these variables, with localized desert locust bands data used as a proxy for hatching timing with a temporal offset of 35 days. The model's boundary conditions were determined using a training area in Sudan, where comprehensive ground data was available. This rule set was subsequently applied to Turkana County in Kenya, a data-scarce region, demonstrating the model's applicability and success in different contexts. The model's accuracy, assessed by data from the Sudan training site, demonstrated a remarkable score of 82% for true predictions. Furthermore, the model correctly identified the months of highest hatching probabilities in Turkana during 2020, demonstrating its real-world effectiveness and practical value. A correlation analysis affirmed that hatching was associated with increases in chlorophyll levels and precipitation accumulations. Our study marks a significant advancement in predicting the timing of hatching using fuzzy logic in data-scarce environments. By operationalizing more targeted early responses to desert locust infestations, our model facilitates more effective locust control. This study stands as an important contribution to locust management strategies, with substantial implications for agricultural production and food security in affected regions. [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]

• Fruit fly infestation data was modelled using Artificial Neural Network. • Fuzzy logic was used to transform plant physiology stages to numeric variable. • Fuzzy Neural Network can be used as a predictive tool for fruit fly population. • Prediction of insect pest infestation can allow for timely control of the pests. Avocado (Persea americana) production is increasing in Kenya, with both small and largeholder farming for domestic and export markets. However, one of main challenges that limit production is infestation by insect pests, notably the oriential fruit fly Bactocera dorsalis and Ceratitis spp. fruit flies, which cause direct crop losses and are indirectly responsible for non-tariff trade barriers due to stringent export requirements. Data on weekly pest trap counts were collected between September 2017 and December 2020 within orchards in avocado plantations. Fuzzy neural network (FNN) were used to model the population dynamics of B. dorsalis and Ceratitis spp. Weekly pest counts, rainfall, average temperature, relative humidity and avocado plant physiological stages were used for predictive modeling in different orchards. The performance of the resulting models was evaluated using coefficient of determination (R2), mean absolute error (MAE), mean relative approximation error (MRAE) and root mean squared error (RMSE). FNN models achieved satisfactory results in predicting the dynamics of the pests in the orchards, with most of the models obtaining R2 > 0.85. We demonstrated how FNN models can be used as predictive tools for managing and controlling fruit fly pest populations in these plantations, and how they may be suitable to predict fruit fly or other pests in similar cropping systems. Once the input variables are known, they can be loaded into the FNN models to predict field pest populations, and based on threshold values, allow for implementation of timely and adequate control measures such as the use of biopesticides. [ABSTRACT FROM AUTHOR]

In the Eastern Africa highlands, the gradual transformation of natural ecosystems to smallholding coffee-based agrosystems has resulted in more fragmented landscapes. Major pests of coffee find appropriate living conditions leading to high infestation rates and the need for smallholder farmers to implement pest control measures. This study aims to understand the influence of landscapes on the ecology of three major coffee pests: the coffee berry borer (CBB), Hypothenemus hampei , and the Antestia bugs Antestiopsis thunbergii (ABT) and A. facetoides (ABF). The study was conducted on a typical smallholder coffee-based landscape in central Kenya. The pest abundance was assessed monthly for two years in a network of 30 coffee plots spread across the coffee agro-ecological subzones (AEsZ), namely upper midland UM1 and UM2, and the transition zones between UM1 and UM2 and between UM2 and UM3, herein referred to as TZ1 and TZ2, respectively. Landscape metrics, viz. patch density, Euclidean nearest neighbour distance, proximity index, contagion index, interspersion and juxtaposition index were derived from a spatially explicit land cover map, based on 10 m Sentinel 2 data for nine buffer zones of radius ranging from 50 m to 1000 m around each sampled plot. Redundancy analysis (RDA) was used to establish the relationships between the observed pest abundances and landscape metrics, elevation, and AEsZ. Landscape indicators achieved the highest correlation with the pest abundances within a 300 m radius (Adjusted R2 > 0.5). Whereas beyond 300 m landscape scale, the predictor variables resulted in weak relationships (Adjusted R2 < 0.5) between the pests abundance and landscape metrics. We noted a strong influence of elevation and adjacency to cropland on Antestia bug populations. Specifically, ABF populations were negatively correlated with low elevation, whereas ABT's were positively correlated with high elevation zone. On the other hand, CBB was strongly influenced by contiguous coffee patches, especially in UM1 and UM2. Therefore, we recommend reducing connectivity between coffee patches for the management of CBB, whereas further studies should be conducted to identify secondary hosts of Antestia bugs that should not be adjacent or within coffee stands. • Landscape indicators achieved the highest correlation with pest abundances within a 300 m landscape scale • Antestia bugs were strongly influenced by elevation gradient and adjacency to cropland. • Coffee berry borer was strongly influenced by contiguous coffee patches within a limited landscape scale of 100 m • Reducing the connectivity of coffee patches will be useful in managing coffee berry borer. • Further studies should be conducted to identify secondary hosts of Antestia bugs. [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]

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. Unlabelled Image • African cropping systems hinder the spaceborne detection of Striga weeds • Remotely sensed spectral data often shadows analytical spectral characteristics • A cropland mask was used as fraction constraint in MESMA to enhance Striga detection • This integration successfully reduced spectral confusion and computation time • The method is plausible for Striga detection in complex agroecological systems [ABSTRACT FROM AUTHOR]