Shifting abundances of communities associated with nitrogen cycling in soils promoted by sugarcane harvest systems

Sugarcane cultivation supports Brazil as one of the largest world sugar and ethanol producer. In order to understand the impact of changing sugarcane harvest from manual to mechanized harvest, we studied the effect of machinery traffic on soil and consequently soil compaction upon soil microbial communities involved in nitrogen cycling. The impact of sugarcane harvest was dependent on soil depth and texture. At deeper soil layers, mechanized harvesting increases the abundance of nitrogen fixers and denitrifying communities (specifically nosZ clade I and II) while manual harvesting increases the abundance of ammonia oxidizers (specifically AOA) and increases denitrifying communities (nosZ clade I and II) on top and at intermediate depth. The effect of change on the harvest system is more evident on sandy soil than on clay soil, where soil indicators of compaction (bulk density and penetration resistance) were negatively correlated with soil microorganisms associated with the nitrogen cycle. Our results point to connections between soil compaction and N transformations in sugarcane fields, besides naming biological variables to be used as proxies for alterations in soil structure.     

Effects of nitrogen limitation on water relations of jack pine (Pinus banksiana Lamb.) seedlings

The water relations of shoots of young jack pine (Pinus banksiana Lamb.) seedlings were examined 6 and 15 weeks after the initiation of four different dynamic nitrogen (N) treatments using a pressure-volume analysis. The N treatments produced a wide range of needle N concentrations from 12 to 32 mg g^?1 dry mass and a 10-fold difference in total dry mass at 15 weeks. Osmotic potential at full turgor did not change over the range of needle N concentrations observed. Osmotic potential at turgor-loss point, however, declined as N concentrations decreased, indicating an increased ability of N-deficient jack pine plants to maintain turgor. The increase could be attributed largely to an increase in cell wall elasticity, suggesting that elasticity changes may be a common, significant adaptation of plants to environmental stresses. Dry mass per unit saturated water almost doubled as needle N level dropped from 32 to 12 mg g^?1 and was inversely correlated to the bulk modulus of elasticity. This suggests that cell wall elasticity is determined more by the nature of its cross-linking matrix than by the total amount of cell wall material present. Developmental change was evident in the response of some water relation variables to N limitation.     

Evaluation of automated analysis of 15N and total N in plant material and soil

Simultaneous determination of ¹⁵N and total N using an automated nitrogen analyser interfaced to a continuous-flow isotope ratio mass spectrometer (ANA-MS method) was evaluated. The coefficient of variation (CV) of repeated analyses of homogeneous standards and samples at natural abundance was lower than 0.1%. The CV of repeated analyses of ¹⁵N-labelled plant material and soil samples varied between 0.3% and 1.1%. The reproductibility of repeated total N analyses using the automated method was comparable to results obtained with a semi-micro Kjeldahl procedure. However, the automated method gave results which were 3% to 5% higher than those obtained with the Kjeldahl procedure. Since only small samples can be analysed, careful sample homogenization and fine grinding are very important. Evaluation of a diffusion method for preparing nitrate and ammonium in solution for automated ¹⁵N analysis showed that the recovery of inorganic N in the NH₃ trap was lower when the N was diffused from water than from 2 M KCl. The results also indicated that different proportions of the NO₃ ^- and the NH₄ ⁺ in aqueous solution were recovered in the trap after combined diffusion. The method is most suited for diffusing either NO₃ ^- or NH₄ ⁺ alone, but can be used for combined diffusion of the two ions.     

Effects of global environmental change on carbon partitioning in vegetative plants of Triticum aestivum and closely related Aegilops species

The use of fossil fuel is predicted to cause an increase of the atmospheric CO₂ concentration, which will affect the global pattern of temperature and precipitation. It is therefore essential to incorporate effects of temperature and water supply on carbon partitioning of plants to predict effects of elevated [CO₂] on growth and yield of Triticum aestivum. Although earlier papers have emphasized that elevated [CO₂] favours investment of biomass in roots relative to that in leaves, it has now become clear that these are indirect effects, due to the more rapid depletion of nutrients in the root environment as a consequence of enhanced growth. Broadly generalized, the effect of temperature on biomass allocation in the vegetative stage is that the relative investment of biomass in roots is lowest at a certain optimum temperature and increases at both higher and lower temperatures. This is found not only when the temperature of the entire plant is varied, but also when only root temperature is changed whilst shoot temperature is kept constant. Effects of temperature on the allocation pattern can be explained largely by the effect of root temperature on the roots' capacity to transport water. Effects of a shortage in water supply on carbon partitioning are unambiguous: roots receive relatively more carbon. The pattern of biomass allocation in the vegetative stage and variation in water-use efficiency are prime factors determining a plant's potential for early growth and yield in different environments. In a comparison of a range of T. aestivum cultivars, a high water-use efficiency at the plant level correlates positively with a large investment in both leaf and root biomass, a low stomatal conductance and a large investment in photosynthetic capacity. We also present evidence that a lower investment of biomass in roots is not only associated with lower respiratory costs for root growth, but also with lower specific costs for ion uptake. We suggest the combination of a number of traits in future wheat cultivars, i.e. a high investment of biomass in leaves, which have a low stomatal conductance and a high photosynthetic capacity, and a low investment of biomass in roots, which have low respiratory costs. Such cultivars are considered highly appropriate in a future world, especially in the dryer regions. Although variation for the desired traits already exists among wheat cultivars, it is much larger among wild Aegilops species, which can readily be crossed with T. aestivum. Such wild relatives may be exploited to develop new wheat cultivars well-adapted to changed climatic conditions.     

Dinitrogen fixation and infection of grass leaves byPseudomonas rubrisubalbicans andHerbaspirillum seropedicae

Bacteria causing mottled stripe disease in sugar cane, known asPseudomonas rubrisubalbicans, were shown to be able to fix molecular N₂ and to grow on it. The root associated diazotroph known asHerbaspirillum seropedicae, after artificial inoculation caused mottled stripe disease symptoms on sorghum and Napier grass but not on sugar cane. Both bacteria could be reisolated from leaves even 60 days after. Sugar cane leaves contained large numbers of these bacteria even in the uninoculated controls. Additional physiological characteristics of six strains ofP. rubrisubalbicans were compared with those of twoH. seropedicae strains and were shown to be very similar.     

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

Artificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.