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.     

Exploiting chemical ecology to manage hyperparasitoids in biological control of arthropod pests

Insect hyperparasitoids are fourth trophic level organisms that commonly occur in terrestrial food webs, yet they are relatively understudied. These top‐carnivores can disrupt biological pest control by suppressing the populations of their parasitoid hosts, leading to pest outbreaks, especially in confined environments such as greenhouses where augmentative biological control is used. There is no effective eco‐friendly strategy that can be used to control hyperparasitoids. Recent advances in the chemical ecology of hyperparasitoid foraging behavior have opened opportunities for manipulating these top‐carnivores in such a way that biological pest control becomes more efficient. We propose various infochemical‐based strategies to manage hyperparasitoids. We suggest that a push‐pull strategy could be a promising approach to ‘push’ hyperparasitoids away from their parasitoid hosts and ‘pull’ them into traps. Additionally, we discuss how infochemicals can be used to develop innovative tools improving biological pest control (i) to restrict accessibility of resources (e.g. sugars and alternative hosts) to primary parasitoid only or (ii) to monitor hyperparasitoid presence in the crop for early detection. We also identify important missing information in order to control hyperparasitoids and outline what research is needed to reach this goal. Testing the efficacy of synthetic infochemicals in confined environments is a crucial step towards the implementation of chemical ecology‐based approaches targeting hyperparasitoids. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

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...     

Jack beans and vetiver grass growth on iron ore tailing sediments from the Doce River dam disaster in Brazil: plant growth regulator effects under different edaphic conditions

Journal of Soils and Sediments - After the greatest environmental disaster in the history of Brazil and the deposition of the iron ore tailings in alluvial regions, the process of revegetation for...     

Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors

Precision Agriculture - Crop monitoring through remote sensing techniques enable greater knowledge of average variability in crop growth. Canopy sensors help provide information on the variability...