Crop Biotechnology for the Environment?

In public debates, agricultural biotechnology is almost invariably discussed as a potential threat to the environment and to human health. Without downplaying the risks associated with this technology we emphasize that if properly regulated, it can be a forceful tool to solve environmental problems and promote human health. Agricultural biotechnology can reduce environmental problems in at least three ways: it can diminish the need for environmentally damaging agricultural practices such as pesticides, fertilizers, tillage, and irrigation. It can reduce the land area needed for agriculture, thus reducing the CO₂ effect of agriculture and improving biodiversity. It can produce energy in a CO₂-neutral way (especially if new technologies involving the cultivation of microalgae become successful). Furthermore, agricultural biotechnology can have positive effects on human health by decreasing occupational and dietary exposure to pesticides, improving the nutritional value of food, and producing pharmaceuticals more efficiently. We argue that those who wish to give high priority to environmental goals cannot afford any longer to be mere onlookers while others decide the future directions of agricultural biotechnology.     

Carbon turnover and ammonia emissions during composting of biowaste at different temperatures

The effects of different process temperatures (40, 55, and 67 degrees C) during composting of source-separated household waste were studied in a 200 L compost reactor at an oxygen concentration of 16%. The overall decomposition measured as carbon mineralization, decomposition of different carbon constituents, and the dynamics of nitrogen mineralization and the microbial community, are reported. Ammonia emissions at 67 degrees C were more than double those at lower temperatures, and they were lowest at 40 degrees C. The decomposition rate, measured as CO2 emission, was highest at 55 degrees C. Decomposition of crude fat was slower at 40 degrees C than at 55 and 67 degrees C. The peak in microbial biomass was largest in the run at 40 degrees C, where substantial differences were seen in the microbial community structure and succession compared to thermophilic temperatures. Biowaste composting can be optimized to obtain both a high decomposition rate and low ammonia emissions by controlling the process at about 55 degrees C in the initial, high-rate stage. To reduce ammonia emissions it seems worthwhile to reduce the temperature after an initial high-temperature stage.     

Analysis of brood sex ratios: implications of offspring clustering

Generalized linear models (GLMs) are increasingly used in modern statistical analyses of sex ratio variation because they are able to determine variable design effects on binary response data. However, in applying GLMs, authors frequently neglect the hierarchical structure of sex ratio data, thereby increasing the likelihood of committing 'type I' error. Here, we argue that whenever clustered (e.g., brood) sex ratios represent the desired level of statistical inference, the clustered data structure ought to be taken into account to avoid invalid conclusions. Neglecting the between-cluster variation and the finite number of clusters in determining test statistics, as implied by using likelihood ratio-based L²-statistics in conventional GLM, results in biased (usually overestimated) test statistics and pseudoreplication of the sample. Random variation in the sex ratio between clusters (broods) can often be accommodated by scaling residual binomial (error) variance for overdispersion, and using F-tests instead of L²-tests. More complex situations, however, require the use of generalized linear mixed models (GLMMs). By introducing higher-level random effects in addition to the residual error term, GLMMs allow an estimation of fixed effect and interaction parameters while accounting for random effects at different levels of the data. GLMMs are first required in sex ratio analyses whenever there are covariates at the offspring level of the data, but inferences are to be drawn at the brood level. Second, when interactions of effects at different levels of the data are to be estimated, random fluctuation of parameters can be taken into account only in GLMMs. Data structures requiring the use of GLMMs to avoid erroneous inferences are often encountered in ecological sex ratio studies.     

State-of-the-art of ecological modelling with emphasis on development of structural dynamic models

The paper deals with two major problems in ecological modelling today, namely how to get reliable parameters? and how to build ecosystem properties into our models? The use of new mathematical tools to answer these questions is mentioned briefly, but the main focus of the paper is on development of structural dynamic models which are models using goal functions to reflect a current change of the properties of the biological components in the models. These changes of the properties are due to the enormous adaptability of the biological components to the prevailing conditions. All species in an ecosystem attempt to obtain most biomass, i.e. to move as far away as possible from thermodynamic equilibrium which can be measured by the thermodynamic concept exergy. Consequently, exergy has been proposed as a goal function in ecological models with dynamic structure, meaning currently changed properties of the biological components and in model language currently changed parameters. An equation to compute an exergy index of a model is presented. The theoretical considerations leading to this equation are not presented here but references to literature where the basis theory can be found are given. Two case studies of structural dynamic modelling are presented: a shallow lake where the structural dynamic changes have been determined before the model was developed, and the application of biomanipulation in lake management, where the structural dynamic changes are generally known. Moreover. it is also discussed how the same idea of using exergy as a goal function in ecological modelling may be applied to facilitate the estimation of parameters.     

Dynamic model of Lake Chozas (León, NW Spain)—Decrease in eco-exergy from clear to turbid phase due to introduction of exotic crayfish

We developed a dynamic model of the phosphorus cycle in Lake Chozas, a small shallow water body in León (NW Spain). The calibrated model simulated seasonal dynamics of phosphorus concentrations in major components of the lake's ecological network before and after 1997, the year when an invasive allochthonous crustacean, the Louisiana red swamp crayfish (Procambarus clarkii), was introduced into the lake. The shift from clean to turbid phase, due to grazing by crayfish on submerged vegetation, caused a gradual decrease in eco-exergy, reflecting an increase in entropy, related to breakdown of ecosystem internal equilibria. This case study verifies the hypothesis of Marchi et al. (2010) that, after an initial relatively stable state, the allochthonous species may cause an increase in entropy indicating perturbation of the ecosystem.     

Modelling spatial and temporal variability in a zero-inflated variable: The case of stone pine (Pinus pinea L.) cone production

Modelling masting habit, i.e. the spatial synchronized annual variability in fruit production, is a huge task due to two main circumstances: (1) the identification of main ecological factors controlling fruiting processes, and (2) the common departure of fruit data series from the main basic statistical assumptions of normality and independence. Stone pine (Pinus pinea L.) is one of the main species in the Mediterranean basin that is able to grow under hard limiting conditions (sandy soils and extreme continental climate), and typically defined as a masting species. Considering the high economical value associated with edible nut production, the masting habit of stone pine has been a main concern for the forest management of the species. In the present work we have used annual fruit data series from 740 stone pine trees measured during a 13 years period (1996-2008) in order: (a) to verify our main hypothesis pointing out to the existence of a weather control of the fruiting process in limiting environments, rather than resource depletion or endogenous inherent cycles; (b) to identify those site factors, stand attributes and climate events affecting specific traits involved in fruiting process; and (c) to construct a model for predicting spatial and temporal patterns of variability in stone pine cone production at different spatial extents as region, stand and tree. Given the nature of the data, the model has been formulated as zero-inflated log-normal, incorporating random components to carry out with the observed lack of independence. This model attains efficiencies close to 70-80% in predicting temporal and spatial variability at regional scale. Though efficiencies are reduced according to the spatial extent of the model, it leads to unbiased estimates and efficiencies over 35-50% when predicting annual yields at tree or stand scale, respectively. In this sense, the proposed model is a main tool for facilitating decision making in some management aspects such as the quantification of total amount of cones annually supplied to nut industry, design of cone harvest programs or the optimal application of seedling felling.     

Alterations in the breeding habitats for two endangered raptor species along the Sava River basin, Croatia

Changes in habitat suitable for breeding of two sympatric raptor species (Haliaeetus albicilla and Aquila pomarina) were analysed along one of the most important breeding sites in Croatia for both species. The habitat suitability modelling was used to assess the influence of forestry practice during 2000-2006 using the known data on nesting places along research area. The four most important variables for lesser spotted eagle were elevation, distance from the nearest pasture, vertical distance to the nearest channel network and broadleaved forest placement (second axis from the principal component analysis of the enhanced vegetation index (EVI) index of MODIS images; November-March). The variables where white-tailed eagles showed greatest shift from overall habitat characteristics in the research area were broadleaved forest (second axis from the principal component analysis of the EVI index), height above the sea level, distance from the small settlements, vertical distance to channel network - all with negative loadings. The results clearly reveal the disproportion of suitable forests for raptors that were cut down in comparison to maturation of suitable forests.     

Importance of climate uncertainty for projections of forest ecosystem services

Regional Environmental Change - Mountain forests provide a wide range of ecosystem services (ES, e.g., timber production, protection from natural hazards, maintaining biodiversity) and are...