Impacts of the Psyllid Arytinnis hakani (Homoptera: Psyllidae) on Invasive French Broom in Relation to Plant Size and Psyllid Density

Legumes are grown as nurse crops in agriculture because they increase soil microbial life and productivity. Native legumes have potential in ecological restoration to mimic the benefits found in agriculture plus they enhance the restored ecosystem. This study was initiated to compare the growth rates, nodulation characteristics, and nitrogen fixation rates of a native versus a non-native legume. The two legumes were partridge pea (Cassia fasciculata); a native, wild, annual legume and soybean (Glycine max `Century Yellow); a domesticated, agricultural, annual legume native to Asia. Plants were grown for 11 weeks in pots containing silica sand and received a nitrogen-free Hoagland's nutrient solution. Beginning at week 12, plants were harvested weekly for four consecutive weeks. Nodulated root systems were exposed to acetylene gas and the resulting ethylene amounts were measured. The two legumes exhibited significant differences in nodule size and shape and plant growth rate. In soybean, nodules were large, spherical, and clustered around the taproot while in partridge pea, nodules were small, irregularly shaped, and spread throughout the fibrous root system. Soybean plants had a significantly faster growth rate at the onset of the experiment but partridge pea maintained a constant growth rate and eventually exceeded soybean plant size. In spite of these observed differences, partridge pea and soybean plants were equally efficient at reducing acetylene to ethylene. These results indicate partridge pea has the potential to produce as much nitrogen in the field as soybean. Native legumes such as partridge pea deserve further research to explore their use as nurse crops in agricultural or restoration regimes.

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VipariNama: RNA viral vectors to rapidly elucidate the relationship between gene expression and phenotype

Plant Physiology ◽

10.1093/plphys/kiab197 ◽

2021 ◽

Author(s):

Arjun Khakhar ◽

Cecily Wang ◽

Ryan Swanson ◽

Sydney Stokke ◽

Furva Rizvi ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Viral Vectors ◽

Viral Vector ◽

Tobacco Rattle Virus ◽

Plant Size ◽

Great Promise ◽

Attractive Alternative ◽

Gibberellin Biosynthesis ◽

In Planta

Abstract Synthetic transcription factors have great promise as tools to help elucidate relationships between gene expression and phenotype by allowing tunable alterations of gene expression without genomic alterations of the loci being studied. However, the years-long timescales, high cost, and technical skill associated with plant transformation have limited their use. In this work we developed a technology called VipariNama (ViN) in which vectors based on the Tobacco Rattle Virus (TRV) are used to rapidly deploy Cas9-based synthetic transcription factors and reprogram gene expression in planta. We demonstrate that ViN vectors can implement activation or repression of multiple genes systemically and persistently over several weeks in Nicotiana benthamiana, Arabidopsis (Arabidopsis thaliana), and tomato (Solanum lycopersicum). By exploring strategies including RNA scaffolding, viral vector ensembles, and viral engineering, we describe how the flexibility and efficacy of regulation can be improved. We also show how this transcriptional reprogramming can create predictable changes to metabolic phenotypes, such as gibberellin biosynthesis in N. benthamiana and anthocyanin accumulation in Arabidopsis, as well as developmental phenotypes, such as plant size in N. benthamiana, Arabidopsis, and tomato. These results demonstrate how ViN vector-based reprogramming of different aspects of gibberellin signaling can be used to engineer plant size in a range of plant species in a matter of weeks. In summary, VipariNama accelerates the timeline for generating phenotypes from over a year to just a few weeks, providing an attractive alternative to transgenesis for synthetic transcription factor-enabled hypothesis testing and crop engineering.

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Studies on Colchicine Induced Chromosome Doubling for Enhancement of Quality Traits in Ornamental Plants

Plants ◽

10.3390/plants8070194 ◽

2019 ◽

Vol 8 (7) ◽

pp. 194 ◽

Cited By ~ 11

Author(s):

Ayesha Manzoor ◽

Touqeer Ahmad ◽

Muhammad Bashir ◽

Ishfaq Hafiz ◽

Cristian Silvestri

Keyword(s):

Chromosome Doubling ◽

Ornamental Plants ◽

Plant Size ◽

Unreduced Gametes ◽

Chromosome Counting ◽

Morphological And Physiological Traits ◽

Routine Procedures ◽

Mitotic Inhibitor ◽

Intense Color

Polyploidy has the utmost importance in horticulture for the development of new ornamental varieties with desirable morphological traits referring to plant size and vigor, leaf thickness, larger flowers with thicker petals, intense color of leaves and flowers, long lasting flowers, compactness, dwarfness and restored fertility. Polyploidy may occur naturally due to the formation of unreduced gametes or can be artificially induced by doubling the number of chromosomes in somatic cells. Usually, natural polyploid plants are unavailable, so polyploidy is induced synthetically with the help of mitotic inhibitors. Colchicine is a widely used mitotic inhibitor for the induction of polyploidy in plants during their cell division by inhibiting the chromosome segregation. Different plant organs like seeds, apical meristems, flower buds, and roots can be used to induce polyploidy through many application methods such as dipping/soaking, dropping or cotton wool. Flow cytometry and chromosome counting, with an observation of morphological and physiological traits are routine procedures for the determination of ploidy level in plants.

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High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

Journal of Forestry Research ◽

10.1007/s11676-021-01360-6 ◽

2021 ◽

Author(s):

Marcus Vinicius Vieira Borges ◽

Janielle de Oliveira Garcia ◽

Tays Silva Batista ◽

Alexsandra Nogueira Martins Silva ◽

Fabio Henrique Rojo Baio ◽

...

Keyword(s):

Neural Networks ◽

High Throughput ◽

Mean Squared Error ◽

Vegetation Indices ◽

Plant Size ◽

Eucalyptus Species ◽

Forest Inventories ◽

Spectral Bands ◽

High Throughput Phenotyping ◽

Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

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Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

Plants ◽

10.3390/plants10010095 ◽

2021 ◽

Vol 10 (1) ◽

pp. 95

Author(s):

Heba Kurdi ◽

Amal Al-Aldawsari ◽

Isra Al-Turaiki ◽

Abdulrahman S. Aldawood

Keyword(s):

Data Mining ◽

Plant Size ◽

Support Vector ◽

Classification Algorithms ◽

Palm Tree ◽

Rhynchophorus Ferrugineus ◽

Red Palm Weevil ◽

Palm Weevil ◽

Using Data ◽

F Measure

In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.

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Optimal plant size with variable cost and revenue streams

European Journal of Operational Research ◽

10.1016/0377-2217(89)90411-6 ◽

1989 ◽

Vol 43 (1) ◽

pp. 78-87 ◽

Cited By ~ 1

Author(s):

Itzhak Krinsky ◽

Mahmut Parlar

Keyword(s):

Plant Size ◽

Variable Cost ◽

Revenue Streams

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