Computational models to improve surveillance for cassava brown streak disease and minimize yield loss

Computational models for improving surveillance for the early detection of direct introduction of cassava brown streak disease in Nigeria

Cassava is a key source of calories for smallholder farmers in sub-Saharan Africa but its role as a food security crop is threatened by the cross-continental spread of cassava brown streak disease (CBSD) that causes high yield losses. In order to mitigate the impact of CBSD, it is important to minimise the delay in first detection of CBSD after introduction to a new country or state so that interventions can be deployed more effectively. Using a computational model that combines simulations of CBSD spread at both the landscape and field scales, we model the effectiveness of different country level survey strategies in Nigeria when CBSD is directly introduced. We find that the main limitation to the rapid CBSD detection in Nigeria, using the current survey strategy, is that an insufficient number of fields are surveyed in newly infected Nigerian states, not the total number of fields surveyed across the country, nor the limitation of only surveying fields near a road. We explored different strategies for geographically selecting fields to survey and found that early and consistent CBSD detection will involve confining candidate survey fields to states where CBSD has not yet been detected and where survey locations are allocated in proportion to the density of cassava crops, detects CBSD sooner, more consistently, and when the epidemic is smaller compared with distributing surveys uniformly across Nigeria.

The role of orphan crops in the transition to nutritional quality-oriented crop improvement

Micronutrient malnutrition is a persisting problem threatening global human health. Biofortification via metabolic engineering has been proposed as a cost-effective and short-term means to alleviate this burden. There has been a recent rise in the recognition of potential that underutilized, orphan crops can hold in decreasing malnutrition concerns. Here, we illustrate how orphan crops can serve as a medium to provide micronutrients to populations in need, whilst promoting and maintaining dietary diversity. We provide a roadmap, illustrating which aspects to be taken into consideration when evaluating orphan crops. Recent developments have shown successful biofortification via metabolic engineering in staple crops. This review provides guidance in the implementation of these successes to relevant orphan crop species, with a specific focus on the relevant micronutrients iron, zinc, provitamin A and folates.

Emerging Themes and Approaches in Plant Virus Epidemiology

Plant diseases caused by viruses share many common features with those caused by other pathogen taxa in terms of the host-pathogen interaction, but there are also distinctive features in epidemiology, most apparent where transmission is by vectors. Consequently, the host-virus-vector-environment interaction presents a continuing challenge in attempts to understand and predict the course of plant virus epidemics. Theoretical concepts, based on the underlying biology, can be expressed in mathematical models and tested through quantitative assessments of epidemics in the field; this remains a goal in understanding why plant virus epidemics occur and how they can be controlled. To this end, this review identifies recent emerging themes and approaches to fill in knowledge gaps in plant virus epidemiology. We review quantitative work on the impact of climatic fluctuations and change on plants, viruses, and vectors under different scenarios where impacts on the individual components of the plant-virus-vector interaction may vary disproportionately; there is a continuing, sometimes discordant, debate on host resistance and tolerance as plant defense mechanisms, including aspects of farmer behavior and attitudes toward disease management that may affect deployment in crops; disentangling host-virus-vector-environment interactions, as these contribute to temporal and spatial disease progress in field populations; computational techniques for estimating epidemiological parameters from field observations; and the use of optimal control analysis to assess disease control options. We end by proposing new challenges and questions in plant virus epidemiology.

Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa

The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world's highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world's largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale.

Deep Transfer Learning for Cross-Species Plant Disease Diagnosis Adapting Mixed Subdomains

A deep transfer learning framework adapting mixed subdomains is proposed for cross-species plant disease diagnosis. Most existing deep transfer learning studies focus on knowledge transfer between highly correlated domains. These methods may fail to deal with domains that are poorly correlated. In this study, mixed domain images were generated from source and target image groups for improving the correlation between the mixed domain (training dataset) and the target domain (testing dataset). A subdomain alignment mechanism is employed to transfer knowledge from the mixed domain to the target domain. The proposed framework captures the fine-grained information more effectively. Extensive experiments were conducted and prove that the proposed method produces a more effective result compared with existing deep transfer learning technologies for poorly related subdomains.

Translating virome analyses to support biosecurity, on-farm management, and crop breeding

Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

Viruses of Yams (Dioscorea spp.): Current Gaps in Knowledge and Future Research Directions to Improve Disease Management

Viruses are a major constraint for yam production worldwide. They hamper the conservation, movement, and exchange of yam germplasm and are a threat to food security in tropical and subtropical areas of Africa and the Pacific where yam is a staple food and a source of income. However, the biology and impact of yam viruses remains largely unknown. This review summarizes current knowledge on yam viruses and emphasizes gaps that exist in the knowledge of the biology of these viruses, their diagnosis, and their impact on production. It provides essential information to inform the implementation of more effective virus control strategies.

Analysis of Agro Alternatives to Boost Cameroon’s Socio-Environmental Resilience, Sustainable Development, and Conservation of Native Forests

Located in Central Africa, Cameroon is a country with strong social inequalities and fragile governance and institutions. This has a direct impact on the sustainable development of its territory, communities, and native forest, which are subject to constant socio-environmental and economic pressures due to overexploitation. This research has three purposes: (1) to conduct a comparative theoretical/empirical diagnosis on the quality of Cameroon’s institutional framework, governance, and public policies related to territorial sustainability; (2) to assess the impact of the three clusters identified among the 44 stakeholders interviewed (forestry companies/certifiers; NGOs/communities; and banks/public institutions) on each other; and (3) to analyze the contribution of the use of cassava (Manihot esculenta) as an agro alternative to Cameroon’s socio-ecological resilience, sustainable development, and conservation of native forests. The research found: (1) the need for mixed governance with joint accountability to find equitable and lasting sustainable solutions for the parties involved, making communities/ethnic groups visible in the decision-making process; and (2) the agro use of cassava has a positive impact on socio-ecological resilience by contributing to employment, the protection of devastated soils, and the provision of quality food, and by reducing pollution from the cement industry through using cassava waste as an input.

Horizon scanning for prioritising invasive alien species with potential to threaten agriculture and biodiversity in Ghana

Invasive alien species (IAS) continue to shape the global landscape through their effects on biological diversity and agricultural productivity. The effects are particularly pronounced in Sub-Saharan Africa, which has seen the arrival of many IAS in recent years. This has been attributed to porous borders, weak cross border biosecurity, and inadequate capacity to limit or stop invasions. Prediction and early detection of IAS, as well as mechanisms of containment and eradication, are needed in the fight against this global threat. Horizon scanning is an approach that enables gathering of information on risk and impact that can support IAS management. A study was conducted in Ghana to establish two ranked lists of potential invasive alien plant pest species that could be harmful to agriculture, forestry, and the environment, and to rank them according to their potential threat. The ultimate objective was to enable prioritization of actions including pest risk analysis, prevention, surveillance and contingency plans. Prioritisation was carried out using an adapted version of horizon scanning and consensus methods developed for ranking IAS worldwide. Following a horizon scan of invasive alien species not yet officially present in Ghana, a total of 110 arthropod and 64 pathogenic species were assessed through a simplified pest risk assessment. Sixteen species, of which 14 were arthropods and two pathogens, had not been recorded on the African continent at the time of assessment. The species recorded in Africa included 19 arthropod and 46 pathogenic species which were already recorded in the neighbouring countries of Burkina Faso, Côte d’Ivoire, and Togo. The majority of arthropod species were likely to arrive as contaminants on commodities, followed by a sizable number which were likely to arrive as stowaways, while some species were capable of long distance dispersal unaided. The main actions suggested for species that scored highly included full pest risk analyses and, for species recorded in neighbouring countries, surveys to determine their presence in Ghana were recommended.

Optimal Control of Plant Disease Epidemics with Clean Seed Usage

The distribution and use of pathogen-free planting material ("clean seeds") is a promising method to control plant diseases in developing countries. We address the question of minimizing disease prevalence in plants through the optimal usage of clean seeds. We consider the simplest possible S-I model together with a simple economic criterion to be maximized. The static optimization problem shows a diversity of possible outcomes depending on economical and epidemiological parameters. We derive a simple condition showing to what extent subsidizing clean seeds relative to the epidemiological features of the disease may help eradicate or control the disease. Then we consider dynamic optimal control and Pontryagin's maximum principle to study the optimal usage of clean seeds to control the disease. The dynamical results are comparable to the static ones and are even simpler in some sense. In particular, the condition on the critical subsidy rate that makes clean seed usage economically viable is unchanged from the static optimization case. We discuss how these results may apply to the control of maize lethal necrosis in East-Africa.

Institutional approaches for plant health provision as a collective action problem

The provision of plant health has public good attributes when nobody can be excluded from enjoying its benefits and individual benefits do not reduce the ability of others to also benefit. These attributes increase risk of free-riding on plant health services provided by others, giving rise to a collective action problem when trying to ensure plant health in a region threatened by an emerging plant disease. This problem has traditionally been addressed by government intervention, but top-down approaches to plant health are often insufficient and are increasingly combined with bottom-up approaches that promote self-organization by affected individuals. The challenge is how to design plant health institutions that effectively deal with the spatial and temporal dynamics of plant diseases, while staying aligned with the preferences, values and needs of affected societies. Here, we illustrate how Ostrom’s design principles for collective action can be used to guide the incorporation of bottom-up approaches to plant health governance in order to improve institutional fit. Using the ongoing epidemic of huanglongbing (HLB) as a case study, we examine existing institutions designed to ensure citrus health under HLB in Brazil, Mexico, the United States and Argentina, and discuss potential implications of Ostrom’s design principles for the collective provision of plant health under HLB and other plant diseases that are threatening food security worldwide. The discussion leads to an outline for the interdisciplinary research agenda that would be needed to establish the link between institutional approaches and plant health outcomes in the context of global food security.