A Mathematical Model for the Effects of Fertilization on Nitrogen Concentrations in Unsaturated Soil on Blueberry Farms in Southern New Jersey

Nitrogen is commonly known as a food source for crops. However, the nitrogen compounds used in crop fertilizers, most commonly nitrate (NO₃) and ammonium (NH₄), are not widely understood. Blueberry plants do not take up these compounds as efficiently as organic nitrogen so varying amounts of leaching into the soil and groundwater will occur. A biogeochemical model consisting of ordinary and partial differential equations is implemented to computationally predict the concentrations of nitrate and ammonium in unsaturated soil of blueberry plants, specifically in the southern region of New Jersey. The model takes into account the type of soil of the region, the nitrate uptake of the plant, the water content in the roots region, the pressure heads in the soil pores, and the application rates of fertilizers containing nitrate, ammonium, and organic nitrogen. Computational simulations demonstrate that the model accounts for natural processes and, in addition, show that commonly used fertilizer application rates cause unnecessarily high concentrations of both nitrate and ammonium in the unsaturated soil level. Further, simulations show that decreasing nitrate fertilizer applications by 85.7% in annual and 91.8% in bi-annual schedules provides an optimal system for safe reapplication.     

GPU Accelerated Lattice Boltzmann Simulation of a Cyanide-Release Accident in the Danjiangkou Reservoir

A graphics processing unit (GPU) accelerated two-dimensional contamination transport model is developed using the lattice Boltzmann method (LBM). The intrinsic parallel features of LBM make it particularly suitable for implementation on GPUs. After validation with two benchmarks, the model was applied to a cyanide-release accident in the Danjiangkou Reservoir, using a numerical combination of hydrodynamics and solute transport. The results show good agreement with an Environmental Fluid Dynamics Code (EFDC) model. A single GPU workstation speedup reaches 1.58 times that of a central processing unit (CPU) calculation alone. This speedup provides the opportunity for applying the model as a pollution accident information and decision support system for the Middle Route of the South to North Water Diversion Project in China.     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.     

The Impact of Succession Stage on Share and Traits of Clonal Plants in Abandoned <Emphasis Type="Italic">Molinion caeruleae</Emphasis> Meadows

The impact of succession stage on the share of clonal plants in the plant cover, seed rain and seedling pool has been studied in the years 2014 and 2015. The investigations were carried out in abandoned Molinion caeruleae meadows dominated by small-statured species (Patch I), tall growing large-tussock grasses (Patch II) and shrub-willows (Patch III). Although the clonal taxa dominated in the plant cover, seed rain and seedling pool, their share in subsequent study sites was quite different. The spectra of life-history traits relevant to clonality in the plant cover and seed rain were similar and did not vary significantly among subsequent locations, while they mostly differed as observed in the seedling pool. The various spectra observed in seedling pool in subsequent study sites might be due to fact that the seed germination and recruitment of offspring are strongly influenced by habitat conditions which varied remarkably in the successive Patches.     

Periphyton Developed on Artificial Substrates: Effect of Substrate Type and Incubation Depth

The aim of this study was to assess the effect of substrate type and incubation depth on periphyton that had developed on artificial substrates. Uniform rectangular tiles made out of artificial substrates: glass, ceramic, willow tree and yew tree, were fixed on a floating buoy and deployed at three different depths in a photic zone of the Sava Lake (Belgrade, Serbia). Non-taxonomic attributes in the developed biofilm were estimated week-by-week from the start of the experiment in July, until its end in September 2014. Through assessment of substrate type and depth of incubation effect we concluded that these parameters for the fact influence periphyton development and composition. Glass was preferred by autotrophic component over ceramic and wooden substrates. In general, substrate type effect was diminished by increasing incubation depth. When non-taxonomic parameters are to be used in biomonitoring studies, our results suggest that glass substrate and shallow layer of water column (up to 50 cm) for incubation should be preferred.     

Investigating the oil curse in OECD and Non-OECD oil-exporting economies using green measures of income

Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations may not portray the true incomes of resource-intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without adjusting for the value of depleted natural resource stock. This treatment often leads to a positive bias in the national income computations of resource-rich economies. Unlike previous studies, we test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. Also, the paper deviates from most empirical studies in the literature by using the Arellano–Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil-exporting economies. Additionally, we employ two alternative measures of resource intensity in our explorations; the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income.     

Sustainability indicators in the swine industry of the Brazilian State of Santa Catarina

In recent decades, there has been a growing concern about measuring and evaluating the results of intensive production practices, such as the mode of integration practiced on a large scale by agro-industries. The present study discusses the application and the results of a Sustainability Indicators System in the context of the swine industry in the Brazilian State of Santa Catarina. These indicators, focused on the level of sustainability maturity, can be used as a way to diagnose and compare the efficiency of the industry from a broader perspective. The research findings indicate that most of the pig farms that have been studied can be classified as “in search of sustainability or sustainable.” Nevertheless, there are differences among the dimensions and between the most ranked farms and the others which must be considered for a better design and application of public policies and company strategies toward an effective and balanced social, economic and environmental sustainability.     

Discerning the factors explaining the change in energy efficiency

Due to the high rates of energy consumption and its impact on environment over the last decades, policy decision-makers are increasingly recognising the need to take actions that allow to address problems associated with the deployment of non-renewable resources and climate changes. One field of action has been the promotion of measures that contribute to improve energy efficiency of countries. The purpose of this study is to identify the main factors explaining changes in energy efficiency applying the multiplicative Log Mean Divisia Index decomposition method for a set of countries (Portugal, UK, Brazil and China) with different socio-economic background and energy mix. The results show that overall energy efficiency trends display different patterns between countries and the same happens within each country from a sectoral perspective. Major drivers of improvements of overall energy efficiency were the intensity effect and the affluence effect, whereas the driver that hampered those improvements was the energy consumption per capita. Some policy implications derived from the results achieved are: policy decision-makers should support measures that promote the adoption of energy-saving technologies resulting from new technological developments; policy measures should be directed to raise awareness of end-users regarding energy efficiency and energy conservation efforts; policy measures promoting economic growth through the development (or expansion) of sectors of activity that consume less energy can also be implemented; finally, policy instruments may also be used to reduce the costs of implementing energy efficiency and energy-saving measures to households and firms.     

Discrete-choice experiments valuing local environmental impacts of renewables: two approaches to a case study in Portugal

Despite the often mentioned environmental benefits associated with transition from fossil fuels to renewable energy sources, their use for electricity production has non-negligible negative environmental impacts. The most commonly mentioned in surveys concern different types of landscape impacts, impacts on the fauna and flora, and noise. These impacts differ by size and location of plants, and by source of energy, rendering the policy decision complex. In addition, there are other welfare issues to take into consideration, as positive and negative environmental impacts are not evenly distributed among population groups. This paper proposes to compare the welfare impacts of renewable energy sources controlling for the type of renewable as well as the specific environmental impact by source. To this end, two discrete-choice experiments are designed and applied to a national sample of the Portuguese population. In one case, only individual negative impacts of renewables are used, and in another case, the negative impacts interact with a specific source. Results show the robustness of discrete-choice experiments as a method to estimate the welfare change induced by the impacts of renewable energy sources. Overall, respondents are willing to pay to reduce the environmental impacts, thus making compensation for local impacts feasible. Moreover, the estimations reveal that respondents are significantly sensitive to the detrimental environmental effects of specific renewable energy sources, being willing to pay more to use these sources of energy relative to others.