STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.

The SUCEST-FUN regulatory network database: Designing an energy grass

Modern sugarcane cultivars are complex hybrids resulting from crosses among several Saccharum species. Traditional breeding methods have been employed extensively in different countries over the past decades to develop varieties with increased sucrose yield and resistance to pests and diseases. Conventional variety improvement, however, may be limited by the narrow pool of suitable genes. Thus, molecular genetics is seen as a promising tool to assist in the process of developing improved varieties. The SUCEST-FUN Project (http://sucest-fun.org) aims to associate function with sugarcane genes using a variety of tools, in particular those that enable the study of the sugarcane transcriptome. An extensive analysis has been conducted to characterise, phenotypically, sugarcane genotypes with regard to their sucrose content, biomass and drought responses. Through the analysis of different cultivars, genes associated with sucrose content, yield, lignin and drought have been identified. Currently, tools are being developed to determine signalling and regulatory networks in grasses, and to sequence the sugarcane genome, as well as to identify sugarcane promoters. This is being implemented through the SUCEST-FUN (http://sucest-fun.org) and GRASSIUS databases (http://grassius.org), the cloning of sugarcane promoters, the identification of cis-regulatory elements (CRE) using Chromatin Immunoprecipitation-sequencing (ChIP-Seq) and the generation of a comprehensive Signal Transduction and Transcription gene catalogue (SUCAST Catalogue).

Active crop sensor to detect variability of nitrogen supply and biomass on sugarcane fields

Nitrogen management has been intensively studied on several crops and recently associated with variable rate on-the-go application based on crop sensors. Such studies are scarce for sugarcane and as a biofuel crop the energy input matters, seeking high positive energy balance production and low carbon emission on the whole production system. This article presents the procedure and shows the first results obtained using a nitrogen and biomass sensor (N-Sensor (TM) ALS, Yara International ASA) to indicate the nitrogen application demands of commercial sugarcane fields. Eight commercial fields from one sugar mill in the state of Sao Paulo, Brazil, varying from 15 to 25 ha in size, were monitored. Conditions varied from sandy to heavy soils and the previous harvesting occurred in May and October 2009, including first, second, and third ratoon stages. Each field was scanned with the sensor three times during the season (at 0.2, 0.4, and 0.6 m stem height), followed by tissue sampling for biomass and nitrogen uptake at ten spots inside the area, guided by the different values shown by the sensor. The results showed a high correlation between sensor values and sugarcane biomass and nitrogen uptake, thereby supporting the potential use of this technology to develop algorithms to manage variable rate application of nitrogen for sugarcane.

Determination of the intrinsic kinetic parameters of sulfide-oxidizing autotrophic denitrification in differential reactors containing immobilized biomass

Nitrogen removal coupled with sulfide oxidation has potential for the treatment of effluents from anaerobic reactors because they contain sulfide, which can be used as an endogenous electron donor for denitrification. This work evaluated the intrinsic kinetics of sulfide-oxidizing autotrophic denitrification via nitrate and nitrite in systems containing attached cells. Differential reactors were fed with nitrified synthetic domestic sewage and different sulfide concentrations. The intrinsic kinetic parameters of nitrogen removal were determined when the mass transfer resistance was negligible. This bioprocess could be described by a half-order kinetic model for biofilms. The half-order kinetic coefficients ranged from 0.425 to 0.658 mg N-1/2 L-1/2 h(-1) for denitrification via nitrite and from 0.190 to 0.609 mg N-1/2 L-1/2 h(-1) for denitrification via nitrate. In this latter, the lower value was due to the use of electrons donated from intermediary sulfur compounds whose formation and subsequent consumption were detected. (C) 2011 Elsevier Ltd. All rights reserved.

Intracellular biosynthesis and removal of copper nanoparticles by dead biomass of yeast isolated from the wastewater of a mine in the brazilian Amazonia

In this study was developed a natural process using a biological system for the biosynthesis of nanoparticles (NPs) and possible removal of copper from wastewater by dead biomass of the yeast Rhodotorula mucilaginosa. Dead and live biomass of Rhodotorula mucilaginosa was used to analyze the equilibrium and kinetics of copper biosorption by the yeast in function of the initial metal concentration, contact time, pH, temperature, agitation and inoculum volume. Dead biomass exhibited the highest biosorption capacity of copper, 26.2 mg g(-1), which was achieved within 60 min of contact, at pH 5.0, temperature of 30°C, and agitation speed of 150 rpm. The equilibrium data were best described by the Langmuir isotherm and Kinetic analysis indicated a pseudo-second-order model. The average size, morphology and location of NPs biosynthesized by the yeast were determined by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and transmission electron microscopy (TEM). The shape of the intracellularly synthesized NPs was mainly spherical, with an average size of 10.5 nm. The X-ray photoelectron spectroscopy (XPS) analysis of the copper NPs confirmed the formation of metallic copper. The dead biomass of Rhodotorula mucilaginosa may be considered an efficiently bioprocess, being fast and low-cost to production of copper nanoparticles and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process

Biosynthesis and uptake of copper nanoparticles by dead biomass of Hypocrea lixii isolated from the metal mine in the Brazilian Amazon Region

A biological system for the biosynthesis of nanoparticles (NPs) and uptake of copper from wastewater, using dead biomass of Hypocrea lixii was analyzed and described for the first time. The equilibrium and kinetics investigation of the biosorption of copper onto dead, dried and live biomass of fungus were performed as a function of initial metal concentration, pH, temperature, agitation and inoculum volume. The high biosorption capacity was observed for dead biomass, completed within 60 min of contact, at pH 5.0, temperature of 40 °C and agitation speed of 150 rpm with a maximum copper biosorption of 19.0 mg g(-1). The equilibrium data were better described using the Langmuir isotherm and kinetic analysis indicated that copper biosorption follows a pseudo-second-order model. The average size, morphology and location of NPs biosynthesized by the fungus were determined by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and transmission electron microscopy (TEM). NPs were mainly spherical, with an average size of 24.5 nm, and were synthesized extracellularly. The X-ray diffraction (XRD) analysis confirms the presence of metallic copper particles. Infrared spectroscopy (FTIR) study revealed that the amide groups interact with the particles, which was accountable for the stability of NPs. This method further confirmed the presence of proteins as stabilizing and capping agents surrounding the copper NPs. These studies demonstrate that dead biomass of Hypocrea lixii provides an economic and technically feasible option for bioremediation of wastewater and is a potential candidate for industrial-scale production of copper NPs.