Understanding growers' decisions to manage invasive pathogens at the farm level

Globalization causes plant production systems to be increasingly threatened by invasive pests and pathogens. Much research is devoted to support management of these risks. Yet, the role of growers' perceptions and behavior in risk management has remained insufficiently analyzed. This article aims to fill this gap by addressing risk management of invasive pathogens from a sociopsychological perspective. An analytical framework based on the Theory of Planned Behavior was used to explain growers' decisions on voluntary risk management measures. Survey information from 303 Dutch horticultural growers was statistically analyzed, including regression and cluster analysis. It appeared that growers were generally willing to apply risk management measures, and that poor risk management was mainly due to perceived barriers, such as high costs and doubts regarding efficacy of management measures. The management measures applied varied considerably among growers, depending on production sector and farm-specific circumstances. Growers' risk perception was found to play a role in their risk management, although the causal relation remained unclear. These results underscore the need to apply a holistic perspective to farm level management of invasive pathogen risk, considering the entire package of management measures and accounting for sector- and farm-specific circumstances. Moreover, they demonstrate that invasive pathogen risk management can benefit from a multidisciplinary approach that incorporates growers' perceptions and behavior.     

Time-dependent infectivity and flexible latent and infectious periods in compartmental models of plant disease

Compartmental models have become the dominant theoretical paradigm in mechanistic modeling of plant disease and offer well-known advantages in terms of analytic tractability, ease of simulation, and extensibility. However, underlying assumptions of constant rates of infection and of exponentially distributed latent and infectious periods are difficult to justify. Although alternative approaches, including van der Plank's seminal discrete time model and models based on the integro-differential formulation of Kermack and McKendrick's model, have been suggested for plant disease and relax these unrealistic assumptions, they are challenging to implement and to analyze. Here, we propose an extension to the susceptible, exposed, infected, and removed (SEIR) compartmental model, splitting the latent and infection compartments and thereby allowing time-varying infection rates and more realistic distributions of latent and infectious periods to be represented. Although the model is, in fact, more general, we specifically target plant disease by demonstrating how it can represent both the van der Plank model and the most commonly used variant of the Kermack and McKendrick (K & M) model (in which the infectivity response is delay Gamma distributed). We show how our reformulation retains the numeric and analytic tractability of SEIR models, and how it can be used to replicate earlier analyses of the van der Plank and K & M models. Our reformulation has the advantage of using elementary mathematical techniques, making implementation easier for the nonspecialist. We show a practical implication of these results for disease control. By taking advantage of the easy extensibility characteristic of compartmental models, we also investigate the effects of including additional biological realism. As an example, we show how the more realistic infection responses we consider interact with host demography and lead to divergent invasion thresholds when compared with the "standard" SEIR model. An ever-increasing number of analyses purportedly extract more biologically realistic invasion thresholds by adding additional biological detail to the SEIR model framework; we contend that our results demonstrate that extending a model that has such a simplistic representation of the infection dynamics may not, in fact, lead to more accurate results. Therefore, we suggest that modelers should carefully consider the underlying assumptions of the simplest compartmental models in their future work.     

Postharvest water relations in cut rose cultivars with contrasting sensitivity to high relative air humidity during growth

A constant high relative air humidity (RH) during cultivation can strongly reduce the vase life in some cut rose cultivars. We studied three contrasting cultivars in their tolerance to high RH in order to analyse in detail the water relations during postharvest and better understand this genotypic variation. Plants were grown at moderate (60%) and high (95%) RH, and cut flowers were placed in water immediately after cutting. Flowers of cv. Pink Prophyta grown at high RH did not open throughout vase life, while flower opening of cvs. Frisco and Dream was not affected by preharvest RH. Cultivation at high RH resulted in about 80% shorter vase life in Pink Prophyta, whereas in Dream and Frisco the negative effect was considerably smaller (15 and 9% shorter vase life, respectively). The shorter vase life and reduced flower opening of cut roses grown at high RH was due to a higher rate of transpiration both in the light and dark periods. It was found that the leaves of Pink Prophyta grown at high RH could partly close their stomata upon lowering of the water potential or when flower stalks were fed with abscisic acid, but stomata remained far more open than in leaves grown at moderate RH. The RH during cultivation did not affect stem hydraulic conductivity and its recovery after air emboli induction. Preventing vascular occlusion largely alleviated the high-cultivation-RH effect on vase life and flower opening, showing that the effect of high-cultivation-RH becomes only important if water uptake is limited.     

Two-mode clustering of genotype by trait and genotype by environment data

In this paper, we demonstrate the use of two-mode clustering for genotype by trait and genotype by environment data. In contrast to two separate (one mode) clusterings on genotypes or traits/environments, two-mode clustering simultaneously produces homogeneous groups of genotypes and traits/environments. For two-mode clustering, we first scan all two-mode cluster solutions with all possible numbers of clusters using k-means. After deciding on the final numbers of clusters, we continue with a two-mode clustering algorithm based on a genetic algorithm. This ensures optimal solutions even for large data sets. We discuss the application of two-mode clustering to multiple trait data stemming from genomic research on tomatoes as well as an application to multi-environment data on barley.     

Highly conserved protective epitopes on influenza B viruses

Identification of broadly neutralizing antibodies against influenza A viruses has raised hopes for the development of monoclonal antibody-based immunotherapy and "universal" vaccines for influenza. However, a substantial part of the annual flu burden is caused by two cocirculating, antigenically distinct lineages of influenza B viruses. Here, we report human monoclonal antibodies, CR8033, CR8071, and CR9114, that protect mice against lethal challenge from both lineages. Antibodies CR8033 and CR8071 recognize distinct conserved epitopes in the head region of the influenza B hemagglutinin (HA), whereas CR9114 binds a conserved epitope in the HA stem and protects against lethal challenge with influenza A and B viruses. These antibodies may inform on development of monoclonal antibody-based treatments and a universal flu vaccine for all influenza A and B viruses.     

Diversity in starch, protein and mineral composition of sorghum landrace accessions from Ethiopia

Sorghum (Sorghum bicolor (L.) Moench) is an important food crop in arid and semi-arid tropics where it is grown mainly due to its drought tolerance. In Ethiopia, sorghum is grown in different agro-ecological zones primarily for food. Landraces are widely cultivated in the region due to their adaptation to the growing conditions and other characteristics such as food quality, quantity, and quality of by-products. The objective of this research was to determine the genetic variation in protein, mineral composition, total starch and its components in sorghum landraces collected from the western regions of Ethiopia. Sorghum whole grains were analyzed for crude protein, total starch and its components, and mineral profile (calcium, magnesium, potassium, phosphorus, iron, manganese, zinc, and sodium). The analysis of variance showed significant differences among the sorghum landraces in nutritional composition. There was highly significant correlation between phosphorus and magnesium, and between zinc and phosphorus and protein which indicated that some interaction existed in absorption and translocation between minerals and protein content. The genetic variation found among the sorghum landrace accessions in chemical composition can be exploited in the breeding program.