The impacts of agricultural payments on groundwater quality: Spatial analysis on the case of Slovenia

The European Common Agricultural Policy still follows its primary goals, i.e. quality food at affordable prices and a decent standard of living for farmers, fifty years after its adoption. Moreover, this policy adapts to the changing needs of society and the new challenges, mostly preservation of the environment, nature and biodiversity in rural areas. Although the Common Agricultural Policy receives the largest share of European budget, the funds are decreasing over time, especially direct payments, which aim to provide basic income support to farmers in the European Union. On the other hand, agri-environmental payments are gaining importance. Policy decision-makers should be interested in the question of impacts of growing eco-conditionality of agricultural spending. New insights would help them to be successful in achieving the goals of sustainable agriculture. The purpose of this paper is to estimate the impacts of production support payments and rural development payments on the quality of groundwater. We use the small EU country Slovenia as an example. The baseline indicators are the level of nitrates and pesticides in groundwater, while the impacts were estimated using spatial error model. The results show that direct payments, coupled subsidies and investment grants raise the level of pesticides in groundwater, but do not have any statistically significant impact on the level of nitrates in groundwater. Furthermore, we did not find any statistically significant effects of agri-environmental payments on decrease of groundwater pollution with nitrates. However, our findings revealed that agri-environmental payments are effective in reducing pesticides in groundwater, although only to a limited extent. These results imply a problem of insufficient targeting of agri-environmental measures on the one hand, and suggest that greening of direct payments is necessary and entirely justified.     

Analyses of landuse change impacts on catchment runoff using different time indicators based on SWAT model

Previous researches mainly focused on the runoff responses to landuse change based on annual, seasonal or monthly time scales, there are few studies based on daily scale. We conducted a comprehensive investigation into runoff responses on the daily scale as well as annual and monthly time scales using SWAT, and compared the impacts of time scales with different time indicators quantitatively. Jinjiang, a coastal catchment of southeast China with a humid sub-tropical climate, was used for simulations. A calibrated SWAT model produced satisfactory reproduction of annual, monthly and daily runoff processes over a nine-year (2002–2010) period at three gauging stations. Runoff was then simulated and compared using the same meteorological input but two different landuse scenarios (1985 and 2006, with reduced forest and increased cropland and urbanized area). The results showed varying change in runoff among three time scales and three catchments. The annual runoff had the smallest increase between two scenarios, monthly runoffs had medium rates (increasing in all months except October–November), and daily runoff had the largest rates with the increase in flood peaks but decrease in drought flows, because of the variable influence on interception/evapotranspiration loss, percolation and antecedent soil water storage. Indicators of different time scales (annual runoff, monthly runoff, maximum 1-day and 5-day flood runoff, minimum 1-day and 7-day runoff) proved appropriate for analysing landuse change impacts.     

Both facilitation and limiting similarity shape the species coexistence in dry alkali grasslands

Facilitation is an important driver of community assembly, and often overwhelms the effect of competition in stressed habitats. Thus, net effect of biotic interactions is often positive in stressed grasslands, where dominant species and litter can protect the subordinate species. Besides facilitation, niche partitioning can also support species coexistence leading to limiting similarity between subordinate species. Our aim was to provide a detailed analysis of fine-scale biotic interactions in stressed alkali grasslands. We supposed, that there are positive relationships between the main biomass fractions and species richness. We expected the expansion of trait ranges and the increase of trait dissimilarity with increasing biomass scores (total litter, green biomass of dominant species) and species richness. We studied the relationships between main biomass fractions, species richness, functional diversity and functional trait indices (ranges, weighted means and Rao indices). We used fine-scale biomass sampling in nine stands of dry alkali grasslands dominated by Festuca pseudovina. The detected relationships were always positive between the main biomass fractions (green biomass of dominant species, total litter and green biomass of subordinate species) and species richness. We found that the green biomass of dominant species and total litter increased ranges and dissimilarity of functional traits. Our results suggest that in dry alkali grasslands facilitation is crucial in shaping vegetation composition. The green biomass of dominant species and total litter increased the biomass production of subordinate species leading to overyielding. We found that mechanisms of facilitation and limiting similarity were jointly shaping the species coexistence in stressed grasslands, such as alkali grasslands.     

Numerical exploration of the parameter plane in a discrete predator–prey model

We propose a variant of the discrete Lotka–Volterra model for predator–prey interactions. A detailed stability and numerical analysis of the model are presented to explore the long time behaviour as each of the control parameter is varied independently. We show how the condition for survival of the predator depends on the natural death rate of predator and the efficiency of predation. The model is found to support different dynamical regimes asymptotically including predator extinction, stable fixed point and limit cycle attractors for co-existence of predator and prey and more complex dynamics involving chaotic attractors. We are able to locate exactly the domain of chaos in the parameter plane using a dimensional analysis.     

A real-time computational model for estimating kinematics of ankle ligaments

Background: An accurate assessment of ankle ligament kinematics is crucial in understanding the injury mechanisms and can help to improve the treatment of an injured ankle, especially when used in conjunction with robot-assisted therapy. A number of computational models have been developed and validated for assessing the kinematics of ankle ligaments. However, few of them can do real-time assessment to allow for an input into robotic rehabilitation programs. Method: An ankle computational model was proposed and validated to quantify the kinematics of ankle ligaments as the foot moves in real-time. This model consists of three bone segments with three rotational degrees of freedom (DOFs) and 12 ankle ligaments. This model uses inputs for three position variables that can be measured from sensors in many ankle robotic devices that detect postures within the foot–ankle environment and outputs the kinematics of ankle ligaments. Validation of this model in terms of ligament length and strain was conducted by comparing it with published data on cadaver anatomy and magnetic resonance imaging. Results: The model based on ligament lengths and strains is in concurrence with those from the published studies but is sensitive to ligament attachment positions. Conclusions: This ankle computational model has the potential to be used in robot-assisted therapy for real-time assessment of ligament kinematics. The results provide information regarding the quantification of kinematics associated with ankle ligaments related to the disability level and can be used for optimizing the robotic training trajectory.     

Prediction Intervals in Nonlinear Regression

By treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θ, we construct two-sided nominally-q-prediction intervals by applying the usual linear model theory. The derivation of the truncated series expansion of the expected coverage of the prediction intervals at a feasible value of the parameter vector is described. The quadratic approximation of the expected coverage is then obtained for a two-parameter nonlinear model. Finally we show how we may construct the prediction intervals when a certain type of nonlinear transformation of the parameter vector has been applied.     

Evolutionary relationships and sequence-structure determinants in human SARS coronavirus-2 spike proteins for host receptor recognition

Coronavirus disease 2019 (COVID-19) is a pandemic infectious disease caused by novel severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). The SARS CoV-2 is transmitted more rapidly and readily than SARS CoV. Both, SARS CoV and SARS CoV-2 via their glycosylated spike proteins recognize the human angiotensin converting enzyme-2 (ACE-2) receptor. We generated multiple sequence alignments and phylogenetic trees for representative spike proteins of SARS CoV and SARS CoV-2 from various host sources in order to analyze the specificity in SARS CoV-2 spike proteins required for causing infection in humans. Our results show that among the genomes analyzed, two sequence regions in the N-terminal domain “MESEFR” and “SYLTPG” are specific to human SARS CoV-2. In the receptor-binding domain, two sequence regions “VGGNY“ and ”EIYQAGSTPCNGV” and a disulfide bridge connecting 480C and 488C in the extended loop are structural determinants for the recognition of human ACE-2 receptor. The complete genome analysis of representative SARS CoVs from bat, civet, human host sources, and human SARS CoV-2 identified the bat genome (GenBank code: MN996532.1) as closest to the recent novel human SARS CoV-2 genomes. The bat SARS CoV genomes (GenBank codes: MG772933 and MG772934) are evolutionary intermediates in the mutagenesis progression toward becoming human SARS CoV-2.