Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2025
Abstract
Since the 1950s, the use of plastics in agriculture has helped solving many challenges related to food production, while its persistence and mismanagement has led to the plastic pollution we face today. A variety of biodegradable plastic products have thus been marketed, with the aim to solve plastic pollution through complete degradation after use. But the environmental conditions for rapid and complete degradation are not necessarily fulfilled, and the possibility that biodegradable plastics may also contribute to plastic pollution must be evaluated. A two-year field experiment with biodegradable mulches (BDMs) based on polybutylene adipate terephthalate (PBAT/starch and PBAT/polylactic acid) buried in several agricultural soils in mesh bags showed that also under colder climatic conditions does degradation occur, involving fragmentation after two months and depolymerization by hydrolysis, as shown by Fourier-transform infrared spectroscopy. The phytopathogenic fungus Rhizoctonia solani was found to be associated with BDM degradation, and the formation of biodegradable microplastics was observed throughout the experimental period. Between 52 and 93 % of the original BDM mass was recovered after two years, suggesting that accumulation is likely to happen in cold climatic regions when BDM is repeatedly used every year. Mass loss followed negative quadratic functions, implying increasing mass loss rates over time. Despite the range of climatic and edaphic factors, with various agricultural practices and vegetable productions at the study locations, the parameters that significantly favored in situ BDM degradation were higher soil organic matter content and temperatures.
Authors
Valentina Sierra-Jimenez Robert J. Macias Jonathan P. Mathews Vincent Carre Sébastien Leclerc Alice Budai Farid Chejne Jimena Castro-Gutiérrey Alain Celzard Vanessa Fierro Manuel Garcia-PerezAbstract
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Authors
Lucas K. Johnson Zhiqiang Yang Angela Erb Ryan Bright Grant M. Domke Tracey S. Frescino Crystal B. Schaaf Sean P. HealeyAbstract
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Authors
Andres Perea Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Mehmet Bakir Huping Cao Richard Estell Brandon Bestelmeyer Andres F. Cibils Santiago A. UtsumiAbstract
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Abstract
Chocolate spot (CS), caused by Botrytis fabae, is one of the most destructive fungaldiseases affecting faba bean (Vicia faba L.) globally. This study evaluated 33 fababean cultivars across two locations and over 2 years to assess genetic resistance andthe effect of fungicide application on CS progression. The utility of unmanned aerialvehicle–mounted multispectral camera for disease monitoring was examined. Signif-icant variability was observed in cultivar susceptibility, with Bolivia exhibiting thehighest level of resistance and Louhi, Sampo, Vire, Merlin, Mistral, and GL Sunriseproving highly susceptible. Fungicide application significantly reduced CS severityand improved yield. Analysis of canopy spectral signatures revealed the near-infraredand red edge bands, along with enhanced vegetation index (EVI) and soil adjustedvegetation index, as most sensitive to CS infection, and they had a strong negativecorrelation with CS severity ranging from −0.51 to −0.71. In addition, EVI enabledearly disease detection in the field. Support vector machine accurately classified CSseverity into four classes (resistant, moderately resistant, moderately susceptible, andsusceptible) based on spectral data with higher accuracy after the onset of diseasecompared to later in the season (accuracy 0.75–0.90). This research underscores thevalue of integrating resistant germplasm, sound agronomic practices, and spectralmonitoring for effectively identification and managing CS disease in faba bean
Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A GilliganAbstract
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Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A. GilliganAbstract
Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model’s predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.