Growth and the Environment in Canada: An Empirical Analysis

Standard reduced form models are estimated for Canada to examine the relationships between real per capita GDP and four measures of environmental degradation. Of the four chosen measures of environmental degradation, only concentrations of carbon monoxide appear to decline in the long run with increases in real per capita income. The data used in the reduced form models are also tested for the presence of unit roots and for the existence of cointegration between each of the measures of environmental degradation and per capita income. Unit root tests indicate nonstationarity in logs of the measures of environmental degradation and per capita income. The Engle-Granger test and the maximum eigenvalue test suggest that per capita income and the measures of environmental degradation are not cointegrated, or that a long-term relationship between the variables does not exist. Causality tests also indicate a bi-directional causality, rather than a uni-directional causality, from income to the environment. The results suggest that Canada does not have the luxury of being able to grow out of its environmental problems. The implication is that to prevent further environmental degradation, Canada requires concerted policies and incentives to reduce pollution intensity per unit of output across sectors, to shift from more to less pollution-producing-outputs and to lower the environmental damage associated with aggregate consumption.environment, economic growth, Canada

Intellispread®: Precision aerial topdressing

Aerial topdressing – the aerial application of fertilisers over farmland using specialist agricultural aircraft – is an integral part of New Zealand’s agricultural heritage. The procedure was born and developed there, so it makes sense that New Zealand researchers are behind much of its development. Dr Miles Grafton and Matthew Irwin from Massey University on North Island, believe that increasing the efficacy of aerial topdressing is possible by reducing the role of a currently crucial part of the procedure: the pilot.London, U.K

USING MONTE CARLO SIMULATIONS TO ACHIEVE THE BEST RESPONSE FROM NITROGEN ON GRAZED PASTURE UNDER A LEGISLATED NITROGEN CAP IN NEW ZEALAND: A REVIEW

(c) The Author/sPastoral and crop farming systems have traditionally used the application of nitrogen (N) to achieve an optimal economic production response. This nitrogen response is estimated from an exponential function that approaches an as ymptote, which is typical of most fertilizer response curves. The optimal economic N response is often achieved when appli cation rates are greater than plant utilization rates, often resulting in leaching, nitrogen run-off, and volatilization of ni trogenous compounds. These losses can have an impact on freshwater quality and contribute to greenhouse gas (GHG) emissions. In New Zealand, urine from N-fertilized pasture grazed by dairy cattle has been shown to be the most problematic source of N losses. As part of New Zealand’s National Environmental Standards (NES), a synthetic N cap of 190 kgN ha-1yr-1 on grazed pasture and crops has been implemented to reduce nutrient enrichment of fresh water. This study reviewed the use of multiple split applications of N to improve N fertilizer use efficiency and pasture response and used Monte Carlo simulations to demonstrate improved response to split N applications rather than a single optimal application based on economic response. In addition, spreading accuracy also became less important as all the low-application variation occurred along the steepest part of the response curve where this variation results in added yield.fals

Robust Detection of Dynamic Community Structure in Networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (`optimization variance') and over randomizations of network structure (`randomization variance'). Because the modularity quality function typically has a large number of nearly-degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.Comment: 18 pages, 11 figure

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

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