Spatiotemporal dynamics and modelling support the case for area-wide management of citrus greasy spot in a Brazilian smallholder farming region

Citrus greasy spot (CGS), caused by Zasmidium citri, induces premature defoliation and yield loss in Citrus spp. The epidemiology of CGS is well understood in high humidity areas, but remains unaddressed in Brazil, despite differing climatic conditions and disease management practices. The spatiotemporal dynamics of CGS was characterized in the Recôncavo of Bahia (Brazil) at four hierarchical levels (quadrant, plant, grove and region). A survey conducted in 19 municipalities found the disease to be present throughout the region with an incidence of 100% in groves and plants, and higher than 70% on leaves. Index of dispersion (D) values suggest the spatial pattern of symptomatic units lies between random and regular. This was confirmed by the parameters of the binary power law for plants and their quadrants (log(A)<0 and b<1). No consistent differences were observed in the disease incidence at different plant heights. We introduce a compartmental model synthesizing CGS epidemiology. The collected data allow such a model to be parameterised, albeit with some ambiguity over the proportion of new infections that result from inoculum produced within the grove vs. external sources of infection. By extending the model to include two populations of growers – those who control and those who do not – coupled by the airborne inoculum, we investigate likely performance of cultural controls accessible to citrus growers in Northeastern Brazil. The results show that control via removal of fallen leaves can be very effective. However, successful control is likely to require area-wide strategies, in which a large proportion of growers actively manage disease

Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030)

10.1890/ES10-00192.1Ecosphere22

The persistent threat of emerging plant disease pandemics to global food security

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics

Correction for Ristaino et al., The persistent threat of emerging plant disease pandemics to global food security

Correction for “The persistent threat of emerging plant disease pandemics to global food security,” by Jean B. Ristaino, Pamela K. Anderson, Daniel P. Bebber, Kate A. Brauman, Nik J. Cunniffe, Nina V. Fedoroff, Cambria Finegold, Karen A. Garrett, Christopher A. Gilligan, Christopher M. Jones, Michael D. Martin, Graham K. MacDonald, Patricia Neenan, Angela Records, David G. Schmale, Laura Tateosian, and Qingshan Wei, which published May 21, 2021; 10.1073/pnas.2022239118 (Proc. Natl. Acad. Sci. U.S.A. 118, e2022239118). The authors note that Table 1 appeared incorrectly. In the third column, first row, “Panama disease F. odoratissimum (TR4)” should instead appear as “F. oxysporum f. sp. cubense tropical race 4 (TR4).” The corrected table appears below. The online version has been corrected

Agricultural landscape structure and invasive species : The cost-effective level of crop field clustering

Invasive pests in agricultural settings may have severe consequences for agricultural production, reducing yields and the value of crops. Once an invader population has established, controlling it tends to be very expensive. Therefore, when the potential impacts on production may be great, protection against initial establishment is often perceived to be the most cost-effective measure. Increasing attention in the ecological literature is being given to the possibility of curbing invasion processes by manipulating the field and cropping patterns in agricultural landscapes, so that they are less conducive to the spread of pests. However, the economic implications of such interventions have received far less attention. This paper uses a stochastic spatial model to identify the key processes that influence the vulnerability of a fragmented agricultural landscape to pests. We explore the interaction between the divergent forces of ecological invasion pressure and economic returns to scale, in relation to the level of clustering of crop fields. Results show that the most cost-effective distances between crop fields in terms of reducing food production impacts from an invasive pest are determined by a delicate balance of these two forces and depend on the values of the ecological and economic parameters involved. If agricultural productivity declines slowly with increasing distance between fields and the dispersal range of the potential invader is high, manipulation of cropping structure has the potential to protect against invasion outbreaks and the farmer can gain benefit overall from maintaining greater distances between fields of similar crops