Life cycle assessment of sunflower and rapeseed as energy crops under Chilean conditions

An option for the agriculture and energy sectors in Chile is the cultivation of energy crops, but environmental studies are first needed in the framework of a sustainable national energy policy.In this study, we used a cradle-to-farm gate Life Cycle Assessment (LCA) to compare environmental impacts and energy and water demand of rapeseed (Brassica napus L.) and sunflower (Helianthus annuus L.) in Chile, as potential oleaginous crops for first-generation biodiesel production. National agricultural data are used for the LCA inventory and process data of international databases are adapted to local conditions. The effect of field N₂O emissions and land use change is evaluated. The results indicate that, compared to sunflower, rapeseed production has a better environmental performance in 9 out of the 11 impact categories evaluated, and lower water consumption. The energy demand of rapeseed is 4.9 GJ/t seed, 30% less than that of sunflower. Mineral fertilizers cause the highest environmental impact in both crops. The analysis of the life cycle of fertilizers indicates that extraction of raw materials and its production are key stages. Attempts to reduce the environmental impact and energy requirement of both crops should be mainly associated with the evaluation of other types of fertilization. In addition, particularly for sunflower, low impact herbicides should be evaluated, seed yield improved and cultivation practices optimized. If the crops are produced on degraded grasslands, the greenhouse gas emissions may be reduced.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Identifying fecal sources in a selected catchment reach using multiple source-tracking tools

Given known limitations of current microbial source-tracking (MST) tools, emphasis on small, simple study areas may enhance interpretations of fecal contamination sources in streams. In this study, three MST tools-Escherichia coli repetitive element polymerase chain reaction (rep-PCR), coliphage typing, and Bacteroidales 16S rDNA host-associated markers-were evaluated in a selected reach of Plum Creek in south-central Nebraska. Water-quality samples were collected from six sites. One reach was selected for MST evaluation based on observed patterns of E. coli contamination. Despite high E. coli concentrations, coliphages were detected only once among water samples, precluding their use as a MST tool in this setting. Rep-PCR classification of E. coli isolates from both water and sediment samples supported the hypothesis that cattle and wildlife were dominant sources of fecal contamination, with minor contributions by horses and humans. Conversely, neither ruminant nor human sources were detected by Bacteroidales markers in most water samples. In bed sediment, ruminant- and human-associated Bacteroidales markers were detected throughout the interval from 0 to 0.3 m, with detections independent of E. coli concentrations in the sediment. Although results by E. coli-based and Bacteroidales-based MST methods led to similar interpretations, detection of Bacteroidales markers in sediment more commonly than in water indicates that different tools to track fecal contamination (in this case, tools based on Bacteroidales DNA and E. coli isolates) may have varying relevance to the more specific goal of tracking the sources of E. coli in watersheds. This is the first report of simultaneous, toolbox approach application of a library-based and marker-based MST analyses to flowing surface water.