Search Results (127)

We present a model that integrates the mapping of the phenology and climatic suitability for the spotted lanternfly (SLF), Lycorma delicatula (White, 1845) (Hemiptera: Fulgoridae), to provide guidance on when and where to conduct surveillance and management of this highly invasive pest. The model was designed for use in the Degree-Day, Establishment Risk, and Phenological Event Maps (DDRP) platform, which is an open-source decision support tool to help to detect, monitor, and manage invasive threats. We validated the model using presence records and phenological observations derived from monitoring studies and the iNaturalist database. The model performed well, with more than >99.9% of the presence records included in the potential distribution for North America, a large proportion of the iNaturalist observations correctly predicted, and a low error rate for dates of the first appearance of adults. Cold and heat stresses were insufficient to exclude the SLF from most areas of the conterminous United States (CONUS), but an inability for the pest to complete its life cycle in cold areas may hinder establishment. The appearance of adults occurred several months earlier in warmer regions of North America and Europe, which suggests that host plants in these areas may experience stronger feeding pressure. The near-real-time forecasts produced by the model are available at USPest.org and the USA National Phenology Network to support decision making for the CONUS. Forecasts of egg hatch and the appearance of adults are particularly relevant for surveillance to prevent new establishments and for managing existing populations. Full article

Spodoptera eridania (Stoll, 1781) (Lepidoptera: Noctuidae) is an important pest with a broad host range and growing relevance due to its high dispersal capacity, recent invasions into Africa and Asia, and documented resistance to biological insecticides. Here, we assessed S. eridania flight phenology and seasonal dynamics in the Florida Panhandle, using pheromone trapping data to evaluate population trends and environmental drivers. Moths were collected year-round, showing consistent patterns across six consecutive years, including two distinct annual flight peaks: an early crop season flight around March, and a more prominent flight peak during September–October. Moth abundance followed a negative quadratic relationship with temperature, with peak activity occurring between 15 °C and 26 °C. No significant relationship was found with precipitation or wind. These results underscore the strong influence of abiotic factors, particularly temperature, on seasonal abundance patterns of this species. Our findings offer key insights by identifying predictable periods of high pest pressure and the environmental conditions that drive population increases. Understanding the flight phenology and behavior of this species provides an ultimate contribution to the development of effective IPM and insect resistance management (IRM) programs, promoting the development of forecasting tools for more effective, timely pest management interventions. Full article

Outbreaks of locust plagues are becoming increasingly frequent against the backdrop of climate change. Locust outbreaks in the Caucasus and Central Asia, especially in Kazakhstan, pose continuous threats to neighboring countries, including China, Kyrgyzstan, and more. However, locust outbreak forecasts and migration movement are yet to be studied in this area. In our study, we collected water level data in major lakes and water bodies, as well as annual average precipitation in the past 15 years in Kazakhstan, to analyze their contributions to locust outbreaks. Multiple linear regression analysis revealed a significant negative correlation between overall lake water level and the following year’s locust outbreak area in Kazakhstan. Considering that the overall lake water levels in 2023 and 2024 reached a quite low level historically, we predicted heavy locust outbreaks in 2025. Furthermore, through wind field analysis and wind-born trajectory modeling, we identified two migration routes of locusts from Kazakhstan into Xinjiang, China, riding the northwest wind, with lakes near the Sino-Kazakhstan border as the main sources. Overall, our study identified high locust outbreak challenges in Kazakhstan in recent years and determined two wind-supported migration routes of locusts invading China, which are significant for guiding monitoring and prevention efforts in the Sino-Kazakhstan border area. Full article

Climate change poses a significant threat to Ethiopian agriculture, impacting both cereal and livestock production through rising temperatures, erratic rainfall, prolonged droughts, and increased pest and disease outbreaks. These challenges intensify food insecurity, particularly for smallholder farmers and pastoralists who rely on climate-sensitive agricultural systems. This systematic review aims to synthesize the impacts of climate change on Ethiopian agriculture, with a specific focus on cereal production and livestock feed quality, while exploring effective adaptation strategies that can support resilience in the sector. The review synthesizes 50 peer-reviewed publications (2020–2024) from the Climate Change Effects on Food Security project, which supports young African academics and Higher Education Institutions (HEIs) in addressing Sustainable Development Goals (SDGs). Using PRISMA guidelines, the review assesses climate change impacts on major cereal crops and livestock feed in Ethiopia and explores adaptation strategies. Over the past 30 years, Ethiopia has experienced rising temperatures (0.3–0.66 °C), with future projections indicating increases of 0.6–0.8 °C per decade resulting in more frequent and severe droughts, floods, and landslides. These shifts have led to declining yields of wheat, maize, and barley, shrinking arable land, and deteriorating feed quality and water availability, severely affecting livestock health and productivity. The study identifies key on-the-ground adaptation strategies, including adjusted planting dates, crop diversification, drought-tolerant varieties, soil and water conservation, agroforestry, supplemental irrigation, and integrated fertilizer use. Livestock adaptations include improved breeding practices, fodder enhancement using legumes and local browse species, and seasonal climate forecasting. These results have significant practical implications: they offer a robust evidence base for policymakers, extension agents, and development practitioners to design and implement targeted, context-specific adaptation strategies. Moreover, the findings support the integration of climate resilience into national agricultural policies and food security planning. The Climate Change Effects on Food Security project’s role in generating scientific knowledge and fostering interdisciplinary collaboration is vital for building institutional and human capacity to confront climate challenges. Ultimately, this review contributes actionable insights for promoting sustainable, climate-resilient agriculture across Ethiopia. Full article

The issue of pesticide and chemical residue in food has drawn increasing public attention, making effective control of plant pests and diseases a critical research focus in agriculture. Monitoring of pest populations is a key factor constraining the precision of pest management strategies. Low-cost and high-efficiency monitoring devices are highly desirable. To address these challenges, we focus on Cacopsylla chinensis and design a portable, AI-based detection device, along with an integrated online monitoring and forecasting system. First, to enhance the model’s capability for detecting small targets, we developed a backbone network based on the RepVit block and its variants. Additionally, we introduced a Dynamic Position Encoder module to improve feature position encoding. To further enhance detection performance, we adopt a Context Guide Fusion Module, which enables context-driven information guidance and adaptive feature adjustment. Second, a framework facilitates the development of an online monitoring system centered on Cacopsylla chinensis detection. The system incorporates a hybrid neural network model to establish the relationship between multiple environmental parameters and the Cacopsylla chinensis population, enabling trend prediction. We conduct feasibility validation experiments by comparing detection results with a manual survey. The experimental results show that the detection model achieves an accuracy of 87.4% for both test samples and edge devices. Furthermore, the population dynamics model yields a mean absolute error of 1.94% for the test dataset. These performance indicators fully meet the requirements of practical agricultural applications. Full article

Maize is one of the three staple grains, and its global demand has risen sharply in recent decades. However, insect pests are causing significant production losses. Despite this, few studies have yet investigated future range shifts in major insect pests affecting maize. Here, we used a unified framework to build 24 multi-algorithm models to forecast their future range shifts under future climate change scenarios (SSP126 and SSP585, representing optimistic and pessimistic scenarios, respectively). Habitat suitability was projected to increase in most regions. Significant range expansions were identified for all of them, with future climate changes being the primary driver for most. High-range overlaps were predominantly observed in the USA, Mexico, and other regions. We also identified species showing the largest ranges and range shifts, suggesting the priority species in our strategies against their impacts on maize. The relative roles of climate and crop availability in the range dynamics of major insect pests affecting maize could be, to a certain extent, determined by whether they are monophagous on crop hosts or not. High-range overlap in key maize-producing regions highlights the substantial threat they pose to global maize production. Therefore, mitigating future climate changes could be a crucial strategy to reduce their impacts on future maize production. Full article

This study employs an accounting approach to quantitatively assess Chengdu’s ecological value, focusing on agriculture, forestry, animal husbandry, fisheries, climate regulation, water conservation, water quality purification, and air quality improvement. The value of each indicator is calculated and visualized using ArcGIS 10.8, with predictions made for four future time intervals. The analysis reveals the spatial distribution patterns of ecological value across Chengdu. The results indicate the following: (1) From 2015 to 2019, Chengdu’s ecological value indicators demonstrated a positive growth trend, with notable increases in recreation services (CNY 178.5 billion), agriculture, forestry, animal husbandry, and fisheries (CNY 32.88 billion), and water conservation (CNY 9.26 billion). Values exhibited a general decrease from the city center outward. (2) Water quality purification, air quality improvement, and pest control values exhibited slight declines in 2015, 2017, and 2019 compared to 2015. (3) Ecological values demonstrate spatial diversity, with lower values in central areas for categories such as soil conservation and a “high-low-high” pattern for water conservation. Recreation services exhibit a “high in the center, low around the edges” pattern. (4) The gray prediction model forecasts that by 2027, the values for agriculture, forestry, animal husbandry and fisheries, water conservation, and soil conservation will double relative to 2015. Climate regulation and air quality improvement values are predicted to triple, while water quality purification exhibits minimal change. Pest control is expected to decline to 67% of its 2015 value, while the value of recreation services will increase to 12 times its 2015 value. The results of this study reveal the evolutionary characteristics of the ecological value volume index in Chengdu, fill a gap in the field of ecological value volume measurement and prediction in the region, and provide scientific support for understanding the evolutionary trajectory of Chengdu’s ecological environment. Full article

Fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith, 1797) (Lepidoptera: Noctuidae), is a major pest in the U.S. and has spread globally, causing severe agricultural losses in different countries. Due to its high mobility and potential for long-distance dispersal, understanding FAW migration is a key tool for forecasting outbreaks and implementing timely management measures. Recent studies using stable hydrogen isotopes indicated reverse (southward) migration of Helicoverpa zea Boddie (Lepidoptera: Noctuidae). Here, we tested the reverse migration hypothesis for FAW in North America. Estimation of the hydrogen isotopic ratio on 324 samples collected in Florida, an intermixing zone at the edge of the continental U.S., indicated evidence of reverse migration in samples of FAW moths. They showed a high probability of origin from the U.S. Corn Belt, with a greater probability of origin in Nebraska, South Dakota, Minnesota, Kansas and Wisconsin. This southward movement provides new insights into the risk of spreading pesticide resistance alleles in this species to southern regions and contributes to the improvement of integrated pest management and insect resistance management programs. Full article

Aphids are the main agricultural pests that affect the quality and yield of peppers in the greenhouse. Efficient early prediction of aphid occurrence is of great significance for the development of digitization and information technology in intelligent agriculture. Forecasting accuracy could be improved by the incorporation of feature interactions into pest forecasting. This study integrates multiple environmental factors to efficiently predict the number of aphids and the aphid strain rate in the greenhouse. We propose a two-level distributed multi-source information fusion approach, which integrates a one-dimensional convolutional neural network (1D CNN) and Long Short-Term Memory (LSTM). To enhance the accuracy of regional environmental parameters, a weighted average algorithm employs environmental sensor data in the first level of fusion. In the second fusion level, a heterogeneous sensor fusion algorithm allows for the integration of multi-source data to model the connection between environmental factors and aphid dynamics. Finally, the improved 1D CNN-LSTM fusion model and other models were tested to verify the effectiveness and robustness of the proposed model. The experimental results show that the total root mean square error of the proposed model is 1.503, which is obviously better than the other networks. In the test set, the total root mean square error of the model for predicting the aphid number and strain rate is 1.378 and 0.337, respectively, compared with existing network models such as 1D CNN, LSTM, and back propagation (BP). The experimental results show that the proposed model has obvious advantages for predicting the aphid number and strain rate. It provides a promising step forward in pest management, offering precise, environmentally friendly solutions that enhance crop yield and quality. Full article

Modern agriculture is increasingly challenged by the need for scalable, sustainable, and connectivity-resilient digital solutions. While existing smart farming platforms offer valuable insights, they often rely heavily on centralized cloud infrastructure, which can be impractical in rural or remote settings. To address this gap, this paper presents AGRARIAN, a hybrid AI-driven architecture that combines IoT sensor networks, UAV-based monitoring, satellite connectivity, and edge-cloud computing to deliver real-time, adaptive agricultural intelligence. AGRARIAN supports a modular and interoperable architecture structured across four layers—Sensor, Network, Data Processing, and Application—enabling flexible deployment in diverse use cases such as precision irrigation, livestock monitoring, and pest forecasting. A key innovation lies in its localized edge processing and federated AI models, which reduce reliance on continuous cloud access while maintaining analytical performance. Pilot scenarios demonstrate the system’s ability to provide timely, context-aware decision support, enhancing both operational efficiency and digital inclusion for farmers. AGRARIAN offers a robust and scalable pathway for advancing autonomous, sustainable, and connected farming systems. Full article

Agriculture is a vital contributor to global food security but faces escalating threats from environmental fluctuations and pest incursions. Among the most prevalent and destructive pests, Jassid (Amrasca biguttula) and Thrips (Thrips tabaci) frequently afflict cotton, okra, and other major crops, resulting in substantial yield losses worldwide. This paper integrates five machine learning (ML) models to predict pest incidence based on key meteorological attributes, including temperature, relative humidity, wind speed, sunshine hours, and evaporation. Two ensemble strategies, soft voting and stacking, were evaluated to enhance predictive performance. Our findings indicate that a stacking ensemble yields superior results, achieving high multi-class AUC scores (0.985). To demystify the underlying mechanisms of the best-performing ensemble, this study employed SHapley Additive exPlanations (SHAP) to quantify the contributions of individual weather parameters. The SHAP analysis revealed that Standard Meteorological Week, evaporation, and relative humidity consistently exert the strongest influence on pest forecasts. These insights align with biological studies highlighting the role of seasonality and humid conditions in fostering Jassid and Thrips proliferation. Importantly, this explainable approach bolsters the practical utility of AI-based solutions for integrated pest management (IPM), enabling stakeholders—farmers, extension agents, and policymakers—to trust and effectively operationalize data-driven recommendations. Future research will focus on integrating real-time weather data and satellite imagery to further enhance prediction accuracy, as well as incorporating adaptive learning techniques to refine model performance under varying climatic conditions. Full article

The desert locust is documented as one of the most destructive polyphagous plant pests that require preventive or proactive management practices due to its phase polyphenism, rapid breeding, transnational migration, and heavy feeding behaviour. Desert locust situation analysis, forecasting and early warning are complex due to the systemic interaction of biological, meteorological, and geographical factors that play different roles in facilitating the survival, breeding and migration of the pest. This article seeks to elucidate the factors that affect desert locust distribution and review the application of earth observation (EO) data in explaining the pest’s infestations and impact. The review presents details concerning the application of EO data to understand factors that affect desert locust breeding and migration, elaborates on impact assessment through vegetation change detection and discusses modelling techniques that can support the effective management of the pest. The review reveals that the application of EO technology is inclined in favour of desert locust habitat suitability assessment with a limited financial quantification of losses. The review also finds a progressive advancement in the use of multi-modelling approaches to address identified gaps and reduce computational errors. Moreover, the review recognises great potential in applications of EO tools, products and services for anticipatory action against desert locusts to ensure resource use efficiency and environmental conservation. Full article

This study introduces a hybrid AutoRegressive Integrated Moving Average (ARIMA)—Long Short-Term Memory (LSTM) model for predicting and managing sugarcane pests and diseases, leveraging big data for enhanced accuracy. The ARIMA component efficiently captures linear patterns in time-series data, while the LSTM model identifies complex nonlinear dependencies. By integrating these two approaches, the hybrid model effectively handles both linear trends and nonlinear fluctuations, improving predictive performance over conventional models. The model was trained on 33 years of meteorological and pest occurrence data, and its effectiveness was evaluated using mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ARIMA-LSTM model achieves an MSE of 2.66, RMSE of 1.63, and MAE of 1.34, outperforming both the standalone ARIMA model (MSE = 4.97, RMSE = 2.29, MAE = 1.79) and LSTM model (MSE = 3.77, RMSE = 1.86, MAE = 1.45). This superior performance highlights its ability to effectively capture seasonal variations and complex nonlinear patterns in pest outbreaks. Beyond accurate forecasting, this model provides valuable decision-making support for agricultural management, aiding in early intervention strategies. Future enhancements, including the integration of additional variables and climate change factors, could further expand its applicability across diverse agricultural sectors, improving crop yield stability and pest control strategies in an increasingly unpredictable climate. Full article

With the intensification of global climate change, the discerning identification of carbon emission drivers and the accurate prediction of carbon emissions have emerged as critical components in addressing this urgent issue. This paper collected carbon emission data from Chinese provinces from 1997 to 2021. Machine learning algorithms were applied to identify province characteristics and determine the influence of provincial development types and their drivers. Analysis indicated that technology and energy consumption had the greatest impact on low-carbon potential provinces (LCPPs), economic growth hub provinces (EGHPs), sustainable growth provinces (SGPs), low-carbon technology-driven provinces (LCTDPs), and high-carbon-dependent provinces (HCDPs). Furthermore, a predictive framework incorporating a grey model (GM) alongside a tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) model was employed to forecast carbon emissions for the forthcoming decade. Findings demonstrated that this approach provided substantial improvements in prediction accuracy. Based on these studies, this paper utilized a combination of SHapley Additive exPlanation (SHAP) and political, economic, social, and technological analysis—strengths, weaknesses, opportunities, and threats (PEST-SWOTs) analysis methods to propose customized carbon emission reduction suggestions for the five types of provincial development, such as promoting low-carbon technology, promoting the transformation of the energy structure, and optimizing the industrial structure. Full article

Insecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP₅₀). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments. Full article