Desert Locust Plague Research Articles

Synergistic olfactory processing for social plasticity in desert locusts

Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic (solitarious) to swarming (gregarious). Here, we elucidate the implications of this transition on foraging decisions and corresponding neural circuits. We use behavioral experiments and Bayesian modeling to decompose the multi-modal facets of foraging, revealing olfactory social cues as critical. To this end, we investigate how corresponding odors are encoded in the locust olfactory system using in-vivo calcium imaging. We discover crowding-dependent synergistic interactions between food-related and social odors distributed across stable combinatorial response maps. The observed synergy was specific to the gregarious phase and manifested in distinct odor response motifs. Our results suggest a crowding-induced modulation of the locust olfactory system that enhances food detection in swarms. Overall, we demonstrate how linking sensory adaptations to behaviorally relevant tasks can improve our understanding of social modulation in non-model organisms.

Effects of Desert Locust Plague on Disaster Management in the Subsistence Agriculture of Ethiopia: Do Ex Ante or Ex Post Policy Measures Are More Appropriate in Agricultural Risk Management? An Analytical Review

Effects of Desert Locust Plague on Disaster Management in the Subsistence Agriculture of Ethiopia: Do Ex Ante or Ex Post Policy Measures Are More Appropriate in Agricultural Risk Management? An Analytical Review

Towards early response to desert locust swarming in eastern Africa by estimating timing of hatching

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.

Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

The potential habitat of desert locusts is contracting: predictions under climate change scenarios.

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 106 km2 and 1.55 × 106 km2, respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

Dryland Food Security in Ethiopia: Current Status, Opportunities, and a Roadmap for the Future

Given the impact of COVID-19 and the desert locust plague, the Ethiopian food security issue has once again received widespread attention. Its food crisis requires comprehensive and systematic research to achieve the United Nations Sustainable Development Goal of zero hunger. This review discusses the current situation and the causes of food security in Ethiopia. We focus on the challenges in the food security assessment field. The article lists seven typical causes of food insecurity and three roots of food security in Ethiopia. Long-term food security assessment and a comprehensive understanding and manageability for food security causes are considered as the main existing research challenges. Climate-resilient management, water management, and long-term ecosystem network monitoring and data mining are suggested as potential roadmap for future research.

Crucial Role of Juvenile Hormone Receptor Components Methoprene-Tolerant and Taiman in Sexual Maturation of Adult Male Desert Locusts.

Currently (2020), Africa and Asia are experiencing the worst desert locust (Schistocerca gregaria) plague in decades. Exceptionally high rainfall in different regions caused favorable environmental conditions for very successful reproduction and population growth. To better understand the molecular mechanisms responsible for this remarkable reproductive capacity, as well as to fill existing knowledge gaps regarding the regulation of male reproductive physiology, we investigated the role of methoprene-tolerant (Scg-Met) and Taiman (Scg-Tai), responsible for transducing the juvenile hormone (JH) signal, in adult male locusts. We demonstrated that knockdown of these components by RNA interference strongly inhibits male sexual maturation, severely disrupting reproduction. This was evidenced by the inability to show mating behavior, the absence of a yellow-colored cuticle, the reduction of relative testes weight, and the drastically reduced phenylacetonitrile (PAN) pheromone levels of the treated males. We also observed a reduced relative weight, as well as relative protein content, of the male accessory glands in Scg-Met knockdown locusts. Interestingly, in these animals the size of the corpora allata (CA), the endocrine glands where JH is synthesized, was significantly increased, as well as the transcript level of JH acid methyltransferase (JHAMT), a rate-limiting enzyme in the JH biosynthesis pathway. Moreover, other endocrine pathways appeared to be affected by the knockdown, as evidenced by changes in the expression levels of the insulin-related peptide and two neuroparsins in the fat body. Our results demonstrate that JH signaling pathway components play a crucial role in male reproductive physiology, illustrating their potential as molecular targets for pest control.

Monitoring and Predicting Desert Locust Plague Severity in Asia–Africa Using Multisource Remote Sensing Time-Series Data

The outbreak of large-scale desert locust plague in 2020 has attracted wide attention in the world and caused serious damage to food security and livelihood of African and Asian people. Remote sensing techniques can provide indirect feedback on locust plagues, facilitating quick, and real-time monitoring of the occurrence and development of locusts, which is of great significance for ensuring national and regional food security and stability. The hidden Markov model (HMM) is a classic machine learning model that has been widely applied in the fields of time-series data mining. In this study, we aim to predict the severity of locust plague in croplands using the time-series dynamic change features extracted from remote sensing data via HMM. In addition, we assess the damages on the croplands using change detection methods by comparing the crop spectrum before and after the locust plague from two-phase (Feburary 23 and March 7, 2020) hyperspectral images covering substudy area (northern Narok, Kenya). Evaluated by the ground truth data, the OA of predicted results of the plague severity in April, May, June, and July are 0.78, 0.71, 0.74, and 0.72, respectively. The land cover classification OA of the substudy area of the two-phase images are 97.45 and 96.14. Our study demonstrates the validity of the HMM-based method using the remote sensing time-series data to predict locust plague and evaluate its damage. The results of the cropland change detection suggest that the damage of locusts can be quantitatively evaluated using hyperspectral images.

Geographic Distribution of Desert Locusts in Africa, Asia and Europe Using Multiple Sources of Remote-Sensing Data

In history, every occurrence of a desert locust plague has brought a devastating blow to local agriculture. Analyses of the potential geographic distribution and migration paths of desert locusts can be used to better monitor and provide early warnings about desert locust outbreaks. By using environmental data from multiple remote-sensing data sources, we simulate the potential habitats of desert locusts in Africa, Asia and Europe in this study using a logistic regression model that was developed based on desert locust monitoring records. The logistic regression model showed high accuracy, with an average training area under the curve (AUC) value of 0.84 and a kappa coefficient of 0.75. Our analysis indicated that the temperature and leaf area index (LAI) play important roles in shaping the spatial distribution of desert locusts. A model analysis based on data for six environmental variables over the past 15 years predicted that the potential habitats of desert locust present a periodic movement pattern between 40°N and 30°S latitude. The area of the potential desert locust habitat reached a maximum in July, with a suitable area exceeding 2.77 × 107 km2 and located entirely between 0°N and 40°N in Asia-Europe and Africa. In December, the potential distribution of desert locusts reached its minimum area at 0.68 × 107 km2 and was located between 30°N and 30°S in Asia and Africa. According to the model estimates, desert locust-prone areas are distributed in northern Ethiopia, South Sudan, northwestern Kenya, the southern Arabian Peninsula, the border area between India and Pakistan, and the southern Indian Peninsula. In addition, desert locusts were predicted to migrate from east to west between these areas and in Africa between 10°N and 17°N. Countries in these areas should closely monitor desert locust populations and respond rapidly.

Crowdsourcing-Enabled Crisis Collaborative Decision Making

Crisis events put crisis response organizations in a unique and complex situation that requires critical real-time distributed decision-making so that lives and properties are saved and protected. With the growing development of collaborative technologies, citizen participation to the crisis management process has shifted from the passive one-way contribution of social networking data to a more active participation by performing specific tasks related to crisis data processing. This article presents a comprehensive approach for integrating the crowdsourcing process to the collaborative decisional process in crisis situations. Within the approach three aspects are highlighted: the coordination work that exists between the relevant stakeholders in making collective decisions, the modeling of case dependent activities within the decisional process, and the detailed modeling of decision-related tasks. The authors investigate the applicability of the proposal with a real-world case study of the Desert Locust Plague carried out in the Algerian National Institute of Plant Protection.

Remote Sensing Products and Services in Support of Agricultural Public Policies in Africa: Overview and Challenges

In the last decade, governments, international institutions, donors, the private sector, etc. have shown a renewed interest in agricultural issues in Sub-Saharan Africa (SSA). This interest came with a strong need for information in countries where the lack of reliable and timely basic information can be a problem. Thanks to its capacity to observe the Earth at local, regional, and global scales and from various vantage points, satellite remote sensing is a powerful tool to streamline the monitoring and improvement of the existing systems, and thus to support decision-making. However, the path from satellite images to public policy decisions is not straightforward, and today, only few operational information services are available in SSA countries (e.g., early warning systems for food security and desert locust plagues prevention, rangeland production forecasting). This paper aims to analyze the gap between the technical aspects of the remote sensing sciences and the pragmatic need for relevant data to address agricultural policies in Africa and produce operational recommendations. To achieve this goal, the authors (1) determine the information, and in particular the geoinformation, needed to develop, implement and evaluate agricultural public policies (2) illustrate the role of remote sensing as a public policy tool in SSA through an overview of the current off-the-shelf products and services derived from Earth Observation systems, and (3) propose an analysis of the existing gap between the remote sensing research community and the policy makers. Based on this review, the authors conclude that to benefit from this technological advancement and bridge the gap between technical analysts and policy makers, some key points are fundamental: capacity building, political will and institutional commitment, public-private partnership, and proofs of concept.

Desert locust detection using Earth observation satellite data in Mauritania

Desert locust plagues have threatened food security in northern African countries for centuries. To prevent their effects, current early warning systems in arid environments need to be improved using the latest and most advanced modelling techniques and Earth observation datasets. Previous studies have analysed certain environmental predictors such as NDVI or soil moisture individually in an effort to detect suitable areas. However, we introduce new variables (Surface Temperature, LAI and Soil Moisture Root Zone) from the SMAP satellite and apply different machine learning methods in our species distribution model in order to identify desert locust presence. We obtain highly satisfactory model results (KAPPA & TSS = 0.901 and ROC = 0.986) to detect the probability of presence and, hence, likely breeding areas based on environmental factors. The most relevant variables were surface temperature, NDVI and soil moisture at root zone under different time scenarios. This study also confirms the potential of the SMAP satellite to retrieve critical temperatures due to its time pass, in addition to reinforcing the NDVI product from MODIS as a reliable environmental predictor. These results demonstrate the validity of this new approach based on machine learning methods to identify favourable breeding areas in Mauritania.

Desert locust plague monitoring using time series satellite data

Abstract: Desert locust has caused great losses to food security in East Africa andSouthwest Asia since its outbreak in 2019. This study selected six locust damaged countries (India,Pakistan,Ethiopia,Kenya,Somalia, andYemen) as the research object. The vegetation coverage curves in these six countries from February 2000 to June 2020 were obtained based on the remote sensing data. Then, the desert locust damage area is monitored by determining determine the locust damage threshold of different vegetation cover types (cropland, grassland and shrub) based on the change of vegetation coverages. The results showed that desert locust caused serious damage to vegetation. By the end of June 2020, Desert Locust harmed vegetation area of 1058.3 thousand hectares, 792.9 thousand hectares, 1137.5 thousand hectares, 936.8 thousand hectares, 780 thousand hectares and 763.5 thousand hectares inIndia,Pakistan,Ethiopia,Kenya,Somalia andYemen, respectively. The research results laid the foundation for real-time, rapid, and large-scale monitoring of locust plague dynamics, and provide a scientific basis for reasonable and economic prevention of locust plague. Keywords: Desert locust plague, vegetation cover, monitoring, time series DOI: 10.33440/j.ijpaa.20200304.111 Citation: Geng Y, Dong Y Y, Zhao L L, Huang W J, Ruan C, Zhang H S, Zhang B Y. Desert locust plague monitoring using time series satellite data. Int J Precis Agric Aviat, 2020; 3(4): 24–30.

Role of remote sensing in desert locust early warning

Desert locust (Schistocerca gregaria, Forskål) plagues have historically had devastating consequences on food security in Africa and Asia. The current strategy to reduce the frequency of plagues and manage desert locust infestations is early warning and preventive control. To achieve this, the Food and Agriculture Organization of the United Nations operates one of the oldest, largest, and best-known migratory pest monitoring systems in the world. Within this system, remote sensing plays an important role in detecting rainfall and green vegetation. Despite recent technological advances in data management and analysis, communications, and remote sensing, monitoring desert locusts and preventing plagues in the years ahead will continue to be a challenge from a geopolitical and financial standpoint for affected countries and the international donor community. We present an overview of the use of remote sensing in desert locust early warning.

Jeff Waage—building a coalition of disciplines for global health

Jeff Waage—building a coalition of disciplines for global health

A critique of “Preventive control and desert locust plagues”

Magor, J.I., Lecoq, M., Hunter, D.M. [2008. Preventive control and desert locust plagues. Crop Protection 27, 1527–1533, doi:10.1016/j.cropro.2008.08.006], claim that their version of a desert locust preventive control programme has caused plagues to be rarer, shorter and geographically more limited, but without producing any evidence of control campaign success. There are many reasons why success is unlikely, some of which they admit. Their “model” does not support the case; it has sundry flaws and is not consistent with actual events.

Preventive control and Desert Locust plagues

The original locust control strategy assumed that plagues arose when swarms escaped from outbreak areas and bred successfully in the surrounding invasion area. Thus, control within outbreak areas could prevent plagues. Plague prevention was achieved for species with small, relatively accessible outbreak areas providing resources allocated to preventive control allowed continuous monitoring and rapid treatment as outbreaks appeared. Plague prevention was less successful for species such as the Australian Plague Locust and the Desert Locust in which pre-plague bands and swarms develop and migrate within very large geographical areas. The success of preventive control can be demonstrated, however, by comparing population size and distribution before and after the strategy was implemented using effective control techniques. Some but not all recent plagues of the Australian Plague Locust have been prevented and, although the frequency of Desert Locust plagues has not declined in the last 40 years, their duration and extent has been sufficiently reduced to achieve the original objective of keeping major agricultural zones in the invasion area locust free. In Australia, substantial resources and the latest technology were available to implement early intervention and its success has been clearly demonstrated. We used a simple model to quantify the case for early intervention in Desert Locust campaigns and our simulations suggest that applying even a conservative rate of control from the beginning of an upsurge as part of early intervention would further reduce the size of upsurges and plagues and would contribute to a better managed Desert Locust population.

The Use of New Technologies in Desert Locust Early Warning

A number of new technologies have been introduced by the Food and Agriculture Organization (FAO) of the UnitedNationsandadoptedforusebycountriesaffected by the Desert Locust ( Schistocerca gregaria ). This paper describes how field teams, decision makers, and analysts use these technologies to record, transmit and manage surveyandcontroldata,tomonitorecologicalconditions and to forecast locust breeding and migration. The regular and rational use of such technologies has led to better early warning and prevention of Desert Locust plagues.

Linking Locust Gregarization to Local Resource Distribution Patterns Across a Large Spatial Scale

Abstract Spatial resource distribution patterns play an important role in mediating density-dependent phase change (gregarization) in locusts. The degree of contagion or aggregation of resources in a habitat can increase the probability of locust gregarization by increasing the frequency of contact among individual locusts. The spatial distribution patterns of two resources upon which gregarization can occur, the tussock grasses Aristida pungens and Panicum turgidum (Poaceae), were examined in two adjacent regions of the desert locust (Schistocerca gregaria Forskal) plague recession area in Mauritania that differ in their frequencies of locust gregarization. The hypothesis that the distribution of grass tufts should be more aggregated and thus more likely to promote locust phase change in the high frequency gregarization area was tested. Tufts were more abundant, and both species were larger in the high frequency gregarization area. The spatial distribution patterns of tufts in both areas were largely agg...

Acute Toxicity of Locust Insecticides to Two Indigenous Invertebrates from Sahelian Temporary Ponds

During desert locust plagues large amounts of insecticides are used for control operations. Drift from these treatments and accidental overspraying may contaminate small surface waters such as temporary ponds. The present study describes methods for static acute toxicity tests with two abundant organisms that occur in temporary ponds in the African Sahel region: the fairy shrimp Streptocephalus sudanicus Daday (Branchiopoda, Anostraca, Streptocephalidae) and the backswimmer Anisops sardeus Herrich-Schåffer (Hemiptera, Notonectidae). The organisms were captured in the field and 48-h static toxicity tests were conducted in the laboratory. The assays were used to screen the toxicity of 11 formulated synthetic insecticides used in desert locust control and of spores of the mycopesticide Metarhizium anisopliae var. acridum. Most of the synthetic insecticides tested were highly toxic to both organisms (LC50 or EC50<1 mg/L). Exceptions were the toxicity of diflubenzuron to A. sardeus (moderately toxic: 1<LC50<10 mg/L), and that of fenitrothion (moderately toxic) and malathion (slightly toxic: 10<EC50<100 mg/L) to S. sudanicus. M. anisopliae var. acridum was moderately toxic to S. sudanicus and only slightly toxic to A. sardeus. EC50 values of the insecticides for S. sudanicus were not significantly correlated with L(E)C50 values for Daphnia magna collected from the literature. For A. sardeus a significant correlation with D. magna was found, but even in this data set the two species had widely differing sensitivities to some insecticides.