Control Strategies to Cope with Late Wilt of Maize

Control of maize late wilt disease (LWD) has been at the forefront of research efforts since the discovery of the disease in the 1960s. The disease has become a major economic restraint in highly affected areas such as Egypt and Israel, and is of constant concern in other counties. LWD causes dehydration and collapsing at a late stage of maize cultivation, starting from the male flowering phase. The disease causal agent, Magnaporthiopsis maydis, is a seed- and soil-borne phytoparasitic fungus, penetrating the roots at sprouting, colonizing the vascular system without external symptoms, and spreading upwards in the xylem, eventually blocking the water supply to the plant’s upperparts. Nowadays, the disease’s control relies mostly on identifying and developing resistant maize cultivars. Still, host resistance can be limited because M. maydis undergoes pathogenic variations, and virulent strains can eventually overcome the host immunity. This alarming status is driving researchers to continue to seek other control methods. The current review will summarize the various strategies tested over the years to minimize the disease damage. These options include agricultural (crop rotation, cover crop, no-till, flooding the land before sowing, and balanced soil fertility), physical (solar heating), allelochemical, biological, and chemical interventions. Some of these methods have shown promising success, while others have contributed to our understanding of the disease development and the environmental and host-related factors that have shaped its outcome. The most updated global knowledge about LWD control will be presented, and knowledge gaps and future aims will be discussed.

A participatory method for prioritizing invasive species: Ranking threats to Minnesota's terrestrial ecosystems

Terrestrial invasive species threaten the integrity of diverse and highly-valued ecosystems. The Minnesota Invasive Terrestrial Plants and Pests Center (MITPPC) was established by the state of Minnesota to fund research projects aimed at minimizing harms posed by the most threatening terrestrial invasive species to the state's prairies, wetlands, forests, and agriculture. MITPPC used the Analytic Hierarchy Process (AHP) to identify and prioritize diverse invasive species threats. We describe how MITPPC tailored AHP to establish its research priorities and highlight major outcomes and challenges with our approach. We found that subject matter experts considered factors associated with the severity of impact from invasion, rather than the potential for invasion, to be the greatest contributors in identifying the most threatening species. Specifically, out of the 17 total criteria identified by the experts to rank species, negative environmental impact was the most influential threat criterion. Currently, narrowleaf cattail, mountain pine beetle, and the causative agent of Dutch elm disease are top threats to Minnesota terrestrial ecosystems. AHP does not handle data-poor situations well; however, it allows for easy incorporation of new information over time for a species without undoing the original framework. The MITPPC prioritization has encouraged interdisciplinary, cross-project synergy among its research projects. Such outcomes, coupled with the transparent and evidence-based decision structure, strengthen the credibility of MITPPC activities with many stakeholders.

Optimising risk-based surveillance for early detection of invasive plant pathogens

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. By linking a mathematical model of pest spread with a computational optimisation routine, this study identifies where to look for invasive pests if we wish to detect them at an early stage; this method improves upon conventional methods of risk-based surveillance and is robust to model misspecification.

Modeling Commodity Flow in the Context of Invasive Species Spread: Study of Tuta absoluta in Nepal

Trade and transport of goods is widely accepted as a primary pathway for the introduction and dispersal of invasive species. However, understanding commodity flows remains a challenge owing to its complex nature, unavailability of quality data, and lack of systematic modeling methods. A robust network-based approach is proposed to model seasonal flow of agricultural produce and examine its role in pest spread. It is applied to study the spread of Tuta absoluta, a devastating pest of tomato in Nepal. Further, the long-term establishment potential of the pest and its economic impact on the country are assessed. Our analysis indicates that regional trade plays an important role in the spread of T. absoluta. The economic impact of this invasion could range from USD 17-25 million. The proposed approach is generic and particularly suited for data-poor scenarios.

Automatic Localization and Count of Agricultural Crop Pests Based on an Improved Deep Learning Pipeline

Insect pests are known to be a major cause of damage to agricultural crops. This paper proposed a deep learning-based pipeline for localization and counting of agricultural pests in images by self-learning saliency feature maps. Our method integrates a convolutional neural network (CNN) of ZF (Zeiler and Fergus model) and a region proposal network (RPN) with Non-Maximum Suppression (NMS) to remove overlapping detections. First, the convolutional layers in ZF Net, without average pooling layer and fc layers, were used to compute feature maps of images, which can better retain the original pixel information through smaller convolution kernels. Then, several critical parameters of the method were optimized, including the output size, score threshold, NMS threshold, and so on. To demonstrate the practical applications of our method, different feature extraction networks were explored, including AlexNet, ResNet and ZF Net. Finally, the model trained on smaller multi-scale images was tested on original large images. Experimental results showed that our method achieved a precision of 0.93 with a miss rate of 0.10. Moreover, our model achieved a mean Accuracy Precision (mAP) of 0.885.

MIReAD, a minimum information standard for reporting arthropod abundance data

Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible. In particular, with the ever-present and growing threat of arthropod pests and vectors of infectious diseases, there are numerous historical and ongoing surveillance efforts, but the data are not reported in consistent formats and typically lack sufficient metadata to make reuse and re-analysis possible. Here, we present the first-ever minimum information standard for arthropod abundance, Minimum Information for Reusable Arthropod Abundance Data (MIReAD). Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re)usability from the "FAIR," (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests.

Pest categorisation of Xanthomonas oryzae pathovars oryzae and oryzicola

The EFSA Panel on Plant Health performed a pest categorisation for Xanthomonas oryzae pathovars oryzae (Xoo) and oryzicola (Xoc), the causal agents of the bacterial blight and the bacterial leaf streak of rice, respectively. These pathovars are widely distributed in Asia, Africa and Australia. Xoo is also reported in some states of the USA and in some other countries of America. The identity of both pathovars is well established and efficient identification methods are available. The major host is cultivated rice (Oryza sativa), but different Oryza spp. as well as Poaceae weeds are reported as alternative hosts, with some uncertainty concerning the actual host range. Both pathovars are seed associated, despite the fact that seed transmission is still controversial for Xoo. Both pathovars are already regulated in Directives 2000/29/EC, on harmful organisms for plants, and 66/402/EEC, on the marketing of cereal seeds. The main pathway for entry is seed. Should these pathovars enter into EU, they may establish and spread, and they may have an impact on the rice crops, with uncertainties. The knowledge gaps identified are (1) the quantity of EU importation of rice seeds, (2) the risk of introduction through unprocessed rice for consumption, (3) the suitability of the EU growing climate conditions for the bacteria to establish and spread, (4) role of seed transmission (Xoo), (5) the role of weeds in the epidemiology and especially in seed transmission and dispersal, (6) host range of weeds. As none of the pathovars is known to occur in the EU, they do not meet one of the criteria for being considered as Union regulated non-quarantine pests. Nevertheless, both pathovars meet the criteria assessed by EFSA for consideration as Union quarantine pest.

Modelling the impacts of pests and diseases on agricultural systems

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Climate Suitability for Magnaporthe oryzae Triticum Pathotype in the United States

Wheat blast, caused by the Triticum pathotype of Magnaporthe oryzae, is an emerging disease considered to be a limiting factor to wheat production in various countries. Given the importance of wheat blast as a high-consequence plant disease, weather-based infection models were used to estimate the probabilities of M. oryzae Triticum establishment and wheat blast outbreaks in the United States. The models identified significant disease risk in some areas. With the threshold levels used, the models predicted that the climate was adequate for maintaining M. oryzae Triticum populations in 40% of winter wheat production areas of the United States. Disease outbreak threshold levels were only reached in 25% of the country. In Louisiana, Mississippi, and Florida, the probability of years suitable for outbreaks was greater than 70%. The models generated in this study should provide the foundation for more advanced models in the future, and the results reported could be used to prioritize research efforts regarding the biology of M. oryzae Triticum and the epidemiology of the wheat blast disease.

Developmental database for phenology models: related insect and mite species have similar thermal requirements

Two values of thermal requirements, the lower developmental threshold (LDT), that is, the temperature at which development ceases, and the sum of effective temperatures, that is, day degrees above the LDT control the development of ectotherms and are used in phenology models to predict time at which the development of individual stages of a species will be completed. To assist in the rapid development of phenology models, we merged a previously published database of thermal requirements for insects, gathered by online search in CAB Abstracts, with independently collected data for insects and mites from original studies. The merged database comprises developmental times at various constant temperatures on 1,054 insect and mite species, many of them in several populations, mostly pests and their natural enemies, from all over the world. We show that closely related species share similar thermal requirements and therefore, for a species with unknown thermal requirements, the value of LDT and sum of effective temperatures of its most related species from the database can be used.