Ontwikkeling van plantenextracten ter bestrijding van droge mollen tot semi-commerciële producten

Droge mollen, veroorzaakt door Verticillium fungicola), vormen een belangrijk probleem in de champignonteelt. Besmetting van een teelt kan een aanzienlijke opbrengstderving tot gevolg hebben en de ziekteverwekker kan door champignonvliegen snel verspreid worden in een kwekerij. Droge mollen kunnen op dit moment nog worden bestreden door het gewasbeschermingsmiddel Sporgon. De verwachting is echter dat Sporgon binnen een termijn van een paar jaar niet meer beschikbaar zal zijn. Om die reden is onderzocht of het mogelijk is om alternatieven voor de bestrijding van droge mollen te ontwikkelen op basis van plantenextracten

Can volatiles emitted by compost during spawn run be used to detect green mould infection early?

In recent years green mould (Trichoderma aggressivum) has presented big problems to the Dutch mushroom industry. T. aggressivum infects compost at a very early stage and in the Dutch situation infection most likely takes place at the compost yard. Even though compost producers in the Netherlands are very keen to prevent green mould problems, occasionally still a number of crops get infected. Therefore there is a need for a reliable method that allows early detection of Trichoderma green mould. Although qPCR methods have been developed for quantitation of T. aggressivum, these cannot be used for detection in compost. In the Netherlands spawn run is performed in bulk at the compost yards and is referred to as phase 3 composting. During this process, spawned compost is incubated in tunnels and ventilated with large volumes of air to control compost temperature. During this process the compost is inaccessible for sampling. Literature data showed that Agaricus bisporus and T. aggressivum use volatiles to affect each other’s growth rate. We tested the possibility to detect Trichoderma green mould using the volatiles that are emitted during spawn run. This eventually could lead to a sophisticated non-invasive detection method of T. aggressivum in the process air of the tunnels, without the need to sample inside the tunnel during spawn run. For this we compared volatiles that are produced in non-infected compost with volatiles that are produced in infected compost. In our experimental model, 300 g of phase 2 compost, is spawned and inserted in aerated glass vessels. Compost is colonised at an air temperature of 24°C. After 7, 10 and 14 days of spawn run, process air is sampled both in infected and non-infected cultures and analysed by coupled gas chromatography mass spectrometry (GC-MS). During this 14-day period white mushroom mycelium develops in the non-infected compost. In the infected compost the compost turns black with occasional tufts of white mycelium and green spores. Volatile blends that are produced during normal compost colonisation (when Agaricus bisporus interacts with Scytalidium thermophilum and other micro flora present in compost) differ from those produced during colonisation of T. aggressivum infected compost. Some of the volatiles appear to be specific for T. aggressivum infected compost. Next to this also consistent differences in the overall pattern of volatile production are seen. Infections with T. harzianum, T. atroviride, an Aspergillus species, or Smokey mould (Penicillium citreonigrum) produce different volatile patterns. Significant differences between the volatile blends of infected and non-infected compost are visible after 7 days of compost colonisation. In commercial practice of phase 3 composting, tunnels are likely to be partially infected. On-going research is directed at studying larger amounts of compost that is only partially infected

Onderzoek naar de toepassing van plantenextracten terbestrijding van groene schimmel en spinnenwebschimmel

Behalve droge mollen (Verticillium fungicola var. fungicola kunnen ook groene schimmel (Trichoderma) en spinnewebschimmel (Cladobotryum dendroïdes) een aanzienlijke schade veroorzaken. De schade wordt voor groene schimmel resp. spinnewebschimmel geschat op 2 tot 5 resp. 1 tot 2 miljoen euro per jaar. In dit project is onderzocht of plantenextracten preventief of curatief gebruikt kunnen worden ter bestrijding van deze ziekten

Simulating maize/bean polycultures using functional-structural plant modelling

Climate change, a growing global population and soil degradation put significant stress on food production and threaten food security, both on a global scale and in individual agricultural communities. This necessitates studies that explore sustainable agricultural intensification. Traditional farming systems have received increased attention, as aspects of these systems (such as niche complementarity) might provide sustainable solutions. This work centers around the three sisters, a polyculture of maize (Zea mays), bean (Phaseolus vulgaris) and squash (Cucurbita spp.), and the milpa, a complex Maya polyculture centered around maize and bean. Building on an existing functional-structural plant (FSP) model for maize, a novel FSP model for common bean is developed (in the XL language, on the GroIMP platform), encompassing twining behaviour and physical plant-plant interactions. This allows us to simulate maize/bean polycultures, where common bean climbs upwards around the maize stalk. As the model contains many input parameters, of which some are difficult or costly to parameterise, a global sensitivity analysis (GSA) is paramount for identifying (un)important parameters in the model. This decreases dimensionality of the large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. GSA is therefore performed on monocultures of maize and common bean (growing on poles). To this end, the popular Elementary Effects GSA method is adapted to make it suitable for models with dimensional inputs, inputs taking values on arbitrary intervals or discrete inputs. Our results show the benefit of performing GSA on plant models: for both maize and bean, less than 30% of input parameters where classified as important for most model outputs. In addition, performing GSA on plant models leads to new insights about both the model and the plant developmental processes it describes. The hope is that this work will inspire more plant modellers to routinely incorporate sensitivity analysis in their research. Subsequently, the model for maize and bean is used to assess architectural facilitation in light capture in maize/bean polycultures. Simulation results agree with experimental observations in the literature of overyielding in polycultures including maize and climbing bean. This indicates that aboveground processes (also) play an important role in the phenomenon of overperforming. In addition, it confirms that such agricultural systems may play a role in sustainable agricultural intensification. The maize/bean model presented in this work is one of the first examples of an aboveground FSP model of a polyculture with complex physical plant-plant interaction. Our results suggest that FSP modelling could be a valuable tool to investigate such agricultural systems. In this work, we have shown that it is possible to model maize/bean crop mixtures, making an aboveground model of the three sisters only a small step away

Psychological Distance to Science as a Predictor of Science Skepticism Across Domains

This article presents and tests psychological distance to science (PSYDISC) as a domain-general predictor of science skepticism. Drawing on the concept of psychological distance, PSYDISC reflects the extent to which individuals perceive science as a tangible undertaking conducted by people similar to oneself (social), with effects in the here (spatial) and now (temporal), and as useful and applicable in the real world (hypothetical distance). In six studies (two preregistered; total N = 1,630) and two countries, we developed and established the factor structure and validity of a scale measuring PSYDISC. Crucially, higher PSYDISC predicted skepticism beyond established predictors, across science domains. A final study showed that PSYDISC shapes real-world behavior (COVID-19 vaccination uptake). This work thus provides a novel tool to predict science skepticism, as well as a construct that can help to further develop a unifying framework to understand science skepticism across domains.</p

Simulating maize/bean polycultures using functional-structural plant modelling

Climate change, a growing global population and soil degradation put significant stress on food production and threaten food security, both on a global scale and in individual agricultural communities. This necessitates studies that explore sustainable agricultural intensification. Traditional farming systems have received increased attention, as aspects of these systems (such as niche complementarity) might provide sustainable solutions. This work centers around the three sisters, a polyculture of maize (Zea mays), bean (Phaseolus vulgaris) and squash (Cucurbita spp.), and the milpa, a complex Maya polyculture centered around maize and bean. Building on an existing functional-structural plant (FSP) model for maize, a novel FSP model for common bean is developed (in the XL language, on the GroIMP platform), encompassing twining behaviour and physical plant-plant interactions. This allows us to simulate maize/bean polycultures, where common bean climbs upwards around the maize stalk. As the model contains many input parameters, of which some are difficult or costly to parameterise, a global sensitivity analysis (GSA) is paramount for identifying (un)important parameters in the model. This decreases dimensionality of the large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. GSA is therefore performed on monocultures of maize and common bean (growing on poles). To this end, the popular Elementary Effects GSA method is adapted to make it suitable for models with dimensional inputs, inputs taking values on arbitrary intervals or discrete inputs. Our results show the benefit of performing GSA on plant models: for both maize and bean, less than 30% of input parameters where classified as important for most model outputs. In addition, performing GSA on plant models leads to new insights about both the model and the plant developmental processes it describes. The hope is that this work will inspire more plant modellers to routinely incorporate sensitivity analysis in their research. Subsequently, the model for maize and bean is used to assess architectural facilitation in light capture in maize/bean polycultures. Simulation results agree with experimental observations in the literature of overyielding in polycultures including maize and climbing bean. This indicates that aboveground processes (also) play an important role in the phenomenon of overperforming. In addition, it confirms that such agricultural systems may play a role in sustainable agricultural intensification. The maize/bean model presented in this work is one of the first examples of an aboveground FSP model of a polyculture with complex physical plant-plant interaction. Our results suggest that FSP modelling could be a valuable tool to investigate such agricultural systems. In this work, we have shown that it is possible to model maize/bean crop mixtures, making an aboveground model of the three sisters only a small step away