Phenological and physiological evaluation of first and second cropping periods of sorghum and maize crops

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.     

Distribution and diversity of leaf-cutting ants in Northeastern Argentina: species most associated with forest plantations

Abstract

Leaf-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.     

P0 protein of cotton leafroll dwarf virus-atypical isolate is a weak RNA silencing suppressor and the avirulence determinant that breaks the cotton Cbd gene-based resistance

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 (P0^CL-at) and evaluated its role in Cbd-resistance break in cotton plants. It was demonstrated that P0^CL-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 P0^CL. P0^CL and P0^CL-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, P0^CL-at was shown to be a weaker pathogenicity factor than P0^CL in N. benthamiana. Interestingly, trans-expressed P0^CL-at enabled CLRDV to systemically infect CBD-resistant plants, and a chimeric CLRDV-P0^CL-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 P0^CL-at is the avirulence (Avr) determinant involved in breaking cotton Cbd gene-based resistance.     

Climate change will affect the ability of forest management to reduce gaps between current and presettlement forest composition in southeastern Canada

Landscape Ecology - Forest landscapes at the boreal–temperate ecotone have been extensively altered. Reducing the gap between current and presettlement forest conditions through...     

Assessing seed desiccation responses of native trees in the Caribbean

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...     

Prediction of Genetic Gain in Sweet Corn using Selection Indexes

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...     

Proteomic profiles during adventitious rooting of Eucalyptus species relevant to the cellulose industry

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...     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

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.     

Strategy to identify resistance to white mould associated with high yield for irrigated common bean in Brazil

Development of common bean cultivars with partial white mould resistance through breeding techniques has been a challenge in Brazil. As yet, lines/cultivars from breeding programmes have not been investigated for resistance; therefore, this study screened 107 lines/cultivars for their reactions to white mould in 14 preliminary trials conducted under irrigation. Thirteen resistant lines/cultivars (three of Andean origin) and six Mesoamerican cultivars (three intermediately resistant and three susceptible) were selected for further investigation. These lines/cultivars and the resistant control A195 were evaluated in six advanced trials and two straw tests to assess the effectiveness of the screening procedure. In 11 preliminary trials, screenings were performed under moderate/high or higher disease pressure. These pressures occurred in two advanced trials in which, when yields were averaged across moderate/high and high pressures, 10 Mesoamerican lines/cultivars selected for resistance yielded 14%, 23%, and 38% more than intermediately resistant cultivars, A195, and susceptible cultivars, with median disease ratings (1–9 scale) of 4.5, 5.7, 5.7, and 6.7, respectively. In the straw test, three Andean lines/cultivars (A195 included) and two susceptible cultivars in the field were among those with the highest levels of physiological resistance. Thus, field rating under high disease pressure and greenhouse rating did not correlate significantly, suggesting that field trials are critical to evaluating resistance and to identifying high-yielding beans. Therefore, lines/cultivars from breeding programmes assessed in field trials may provide a low cost and fast way to identify high-yielding bean cultivars with partial resistance to white mould in the subtropical southern hemisphere.