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1.
Maria Antonia Machado Barbosa Kacilda Naomi Kuki Pedro Santos Peno Bengala Emilly dos Santos Pereira Angélica Fátima de Barros Sebastián Giraldo Montoya Leonardo Duarte Pimentel 《Journal of Agronomy and Crop Science》2020,206(2):263-276
Environmental conditions influence phenology and physiological processes of plants. It is common for maize and sorghum to be sown at two different periods: the first cropping (spring/summer) and the second cropping (autumn/winter). The phenological cycle of these crops varies greatly according to the planting season, and it is necessary to characterize the growth and development to facilitate the selection of the species best adapted to the environment. The aim of this study was to characterize phenological phases and physiological parameters in sorghum and maize plants as a function of environmental conditions from the first cropping and second cropping periods. Two parallel experiments were conducted with both crops. The phenological characterization was based on growth analyses (plant height, leaf area and photoassimilate partitioning) and gas exchange evaluations (net assimilation rate, stomatal conductance, transpiration and water-use efficiency). It was found that the vegetative stage (VS) for sorghum and maize plants was 7 and 21 days, respectively, longer when cultivated during the second cropping. In the first cropping, the plants were taller than in the second cropping, regardless of the crop. The stomatal conductance of sorghum plants fluctuated in the second cropping during the development period, while maize plants showed decreasing linear behaviour. Water-use efficiency in sorghum plants was higher during the second cropping compared with the first cropping. In maize plants, in the second cropping, the water-use efficiency showed a slight variation in relation to the first cropping. It was concluded that the environmental conditions as degree-days, temperature, photoperiod and pluvial precipitation influence the phenology and physiology of both crops during the first and the second cropping periods, specifically cycle duration, plant height, leaf area, net assimilation rate, stomatal conductance and water-use efficiency, indicating that both crops respond differentially to environmental changes during the growing season. 相似文献
2.
Andrés F. Sánchez-Restrepo Nadia L. Jiménez Viviana A. Confalonieri Luis A. Calcaterra 《国际虫害防治杂志》2019,65(3):244-257
AbstractLeaf-cutting ants (LCA) are considered one of the main herbivores and one of the most destructive pest insects of the Neotropics. Northeastern Argentina harbors the greatest species richness of these ants and in turn comprises the highest surface with forest plantations. Our aim was to establish which species of leaf-cutting ants are most commonly associated with forest plantations by analyzing their geographic distribution using published and unpublished species occurrence data. Also, estimate their potential areas of distribution along a latitudinal gradient that entirely encompasses northeastern Argentina using Ecological Niche Modeling. Only seven of the 20 species recorded were strongly associated with productive systems along the gradient, but only 2–3 species in each region could be considered high-risk species for forest plantations. High-risk species composition shows a turnover between regions. Our models show the potential distribution areas where LCA could become more abundant and dominant, and possibly causing a detrimental effect on the forest plantations in the studied region. We find that ecological niche models are useful tools to assess the environmental suitability of important LCA. 相似文献
3.
Y. C. Agrofoglio V. C. Delfosse M. F. Casse H. E. Hopp I. Bonacic Kresic V. Ziegler-Graff A. J. Distéfano 《Plant pathology》2019,68(6):1059-1071
Cotton blue disease (CBD) is the most important disease present in cotton crops in South America and cotton leafroll dwarf virus (CLRDV) is the causal agent. The disease has been controlled by sowing cotton varieties resistant to CLRDV. However, in the 2009/10 growing season, an outbreak due to an atypical CLRDV isolate (CLRDV-at) occurred in northwest Argentina. Although CLRDV and CLRDV-at genomes are very closely related, the symptoms they produce in cotton plants are quite different. P0 is the most divergent protein between the isolates and in CLRDV is a silencing suppressor protein. This work characterized the silencing suppressor activity of the P0 protein encoded by CLRDV-at (P0CL-at) and evaluated its role in Cbd-resistance break in cotton plants. It was demonstrated that P0CL-at, despite having a mutation in the consensus of the F-box-like motif, was able to suppress local RNA silencing, but displayed lower activity than P0CL. P0CL and P0CL-at showed no differences in the interaction with Gossypium hirsutum SKP1 orthologue (GSK1) and Nicotiana benthamiana SKP1 and both P0 proteins triggered destabilization of ARGONAUTE1. However, when the ability to enhance PVX symptoms was evaluated, P0CL-at was shown to be a weaker pathogenicity factor than P0CL in N. benthamiana. Interestingly, trans-expressed P0CL-at enabled CLRDV to systemically infect CBD-resistant plants, and a chimeric CLRDV-P0CL-at infectious clone succeeded in establishing infection in CBD-resistant cotton varieties with symptoms resembling those produced by CLRDV-at. These results strongly suggest that P0CL-at is the avirulence (Avr) determinant involved in breaking cotton Cbd gene-based resistance. 相似文献
4.
Boulanger Yan Arseneault Dominique Boucher Yan Gauthier Sylvie Cyr Dominic Taylor Anthony R. Price David T. Dupuis Sébastien 《Landscape Ecology》2019,34(1):159-174
Landscape Ecology - Forest landscapes at the boreal–temperate ecotone have been extensively altered. Reducing the gap between current and presettlement forest conditions through... 相似文献
5.
Mattana Efisio Peguero Brígido Di Sacco Alice Agramonte Wilvin Encarnación Castillo Wilkin Rafael Jiménez Francisco Clase Teodoro Pritchard Hugh W. Gómez-Barreiro Pablo Castillo-Lorenzo Elena Terrero Encarnación Marianny Way Michael J. García Ricardo Ulian Tiziana 《New Forests》2020,51(4):705-721
New Forests - Native trees from the Caribbean were tested for seed desiccation responses, by adapting the “100-seed test” protocol. Ninety-seven seed lots of 91 species were collected... 相似文献
6.
da Silva Isadora Gonçalves Castoldi Renata de Oliveira Charlo Hamilton César de Souza Miranda Mateus Nunes Thaíssa Dias Cardoso Costa Luciene Lacerda Lemes Ernane Miranda 《Journal of Crop Science and Biotechnology》2020,23(2):191-196
Journal of Crop Science and Biotechnology - The cultivation of sweet corn is expanding in Brazil, but there are serious constraints about the availability of commercial cultivars. The selection of... 相似文献
7.
de Almeida Márcia Rodrigues Schwambach Joséli Silveira Vanildo Heringer Angelo Schuabb Fett Janette Palma Fett-Neto Arthur G. 《New Forests》2020,51(2):213-241
New Forests - Adventitious rooting (AR) is an obligatory step for vegetative propagation of commercial woody species. Paper industries have interest in Eucalyptus globulus Labill and its hybrids... 相似文献
8.
9.
Ernesto San-Blas Gabriel Paba Néstor Cubillán Edgar Portillo Ana M. Casassa-Padrón César González-González Mayamarú Guerra 《Plant pathology》2020,69(8):1589-1600
Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling. 相似文献
10.
Baudron Frédéric Schultner Jannik Duriaux Jean-Yves Gergel Sarah E. Sunderland Terry 《Landscape Ecology》2019,34(2):341-356
Landscape Ecology - It remains unclear how agricultural landscapes can best serve multiple purposes such as simultaneously maintaining agricultural productivity and conserving biodiversity. Our... 相似文献