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.
2022
Authors
Palingamoorthy Gnanamoorthy Qinghai Song Junbin Zhao Yiping Zhang Jing Zhang Youxing Lin Liguo Zhou Sadia Bibi Chenna Sun Hui Yu Wenjun Zhou Liqing Sha Shusen Wang S. Chakraborty Pramit Kumar Deb BurmanAbstract
The rapid conversion of tropical rainforests into monoculture plantations of rubber (Hevea brasiliensis) in Southeast Asia (SEA) necessitates understanding of rubber tree physiology under local climatic conditions. Frequent fog immersion in the montane regions of SEA may affect the water and carbon budgets of the rubber trees and the plantation ecosystems. We studied the effect of fog on various plant physiological parameters in a mature rubber plantation in southwest China over 3 years. During the study period, an average of 141 fog events occurred every year, and the majority occurred during the dry season, when the temperature was relatively low. In addition to the low temperature, fog events were also associated with low vapor pressure deficit, atmospheric water potential, relative humidity and frequent wet-canopy conditions. We divided the dry season into cool dry (November-February) and hot dry (March-April) seasons and classified days into foggy (FG) and non-foggy (non-FG) days. During the FG days of the cool dry season, the physiological activities of the rubber trees were suppressed where carbon assimilation and evapotranspiration showed reductions of 4% and 15%, respectively, compared to the cool dry non-FG days. Importantly, the unequal declines in carbon assimilation and evapotranspiration led to enhanced crop water productivity (WPc) on cool dry FG days but insignificant WPc values were found between FG and non-FG days of the hot dry season. Our results suggest that, by regulating plant physiology, fog events during the cool dry season significantly reduce water demand and alleviate water stress for the trees through improved WPc.
Authors
Petter Öhrn Mats Berlin Jan-Olov Weslien Malin Elfstrand Audrius Menkis Paal Krokene Anna Maria JönssonAbstract
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Jeannette K. Winsou Ghislain T. Tepa-Yotto Karl Thunes Richard Meadow Manuele Tamò May-Guri SæthreAbstract
Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith, 1797) (Lepidoptera: Noctuidae) was recorded for the first time in 2016 attacking maize fields in central and west Africa. Soon after, several other regions and countries have reported the pest in almost the entire sub-Saharan Africa. In the present study, we assumed that (i) a variety of alternative plant species host FAW, especially during maize off-season, (ii) a wide range of local parasitoids have adapted to FAW and (iii) parasitoid species composition and abundance vary across seasons. During a two-year survey (from June 2018 to January 2020), parasitoids and alternative host plants were identified from maize and vegetable production sites, along streams and lowlands, on garbage dumps and old maize fields in southern and partly in the central part of Benin during both maize growing- and off-season. A total of eleven new host plant species were reported for the first time, including Cymbopogon citratus (de Candolle) Stapf (cultivated lemon grass), Bulbostylis coleotricha (A. Richard) Clarke and Pennisetum macrourum von Trinius (wild). The survey revealed seven parasitoid species belonging to four families, namely Platygastridae, Braconidae, Ichneumonidae, and Tachinidae associated with FAW on maize and alternative host plants. The most abundant parasitoid species across seasons was the egg parasitoid Telenomus remus (Nixon) (Hymenoptera: Platygastridae). These findings demonstrate FAW capability to be active during the maize off-season in the selected agro-ecologies and provide baseline information for classical and augmentative biocontrol efforts.
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Authors
YeonKyeong Lee Payel Bhattacharjee Marcos Viejo Gareth Benjamin Gillard Simen Rød Sandve Torgeir Rhoden Hvidsten Ole Christian Lind Brit Salbu Dag Anders Brede Jorunn Elisabeth OlsenAbstract
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Precision weeding or site-specific weed management (SSWM) take into account the spatial distribution of weeds within fields to avoid unnecessary herbicide use or intensive soil disturbance (and hence energy consumption). The objective of this study was to evaluate a novel machine vision algorithm, called the ‘AI algorithm’ (referring to Artificial Intelligence), intended for post-emergence SSWM in cereals. Our conclusion is that the AI algorithm should be suitable for patch spraying with selective herbicides in small-grain cereals at early growth stages (about two leaves to early tillering). If the intended use is precision weed harrowing, in which also post-harrow images can be used to control the weed harrow intensity, the AI algorithm should be improved by including such images in the training data. Another future goal is to make the algorithm able to distinguish weed species of special interest, for example cleavers (Galium aparine L.).