Effect of calcareous and siliceous amendments on N2O emissions of a grassland soil

Liming of acidic agricultural soils has been proposed as a strategy to mitigate nitrous oxide (N₂O) emissions, as increased soil pH reduces the N₂O/N₂ product ratio of denitrification. The capacity of different calcareous (calcite and dolomite) and siliceous minerals to increase soil pH and reduce N₂O emissions was assessed in a 2-year grassland field experiment. An associated pot experiment was conducted using homogenized field soils for controlling spatial soil variability. Nitrous oxide emissions were highly episodic with emission peaks in response to freezing–thawing and application of NPK fertilizer. Liming with dolomite caused a pH increase from 5.1 to 6.2 and reduced N₂O emissions by 30% and 60% after application of NPK fertilizer and freezing–thawing events, respectively. Over the course of the 2-year field trial, N₂O emissions were significantly lower in dolomite-limed than non-limed soil (p < .05), although this effect was variable over time. Unexpectedly, no significant reduction of N₂O emission was found in the calcite treatment, despite the largest pH increase in all tested minerals. We tentatively attribute this to increased N₂O production by overall increase in nitrogen turnover rates (both nitrification and denitrification) following rapid pH increase in the first year after liming. Siliceous materials showed little pH effect and had no significant effect on N₂O emissions probably because of their lower buffering capacity and lower cation content. In the pot experiment using soils taken from the field plots 3 years after liming and exposing them to natural freezing–thawing, both calcite (p < .01) and dolomite (p < .05) significantly reduced cumulative N₂O emission by 50% and 30%, respectively, relative to the non-limed control. These results demonstrate that the overall effect of liming is to reduce N₂O emission, although high lime doses may lead to a transiently enhanced emission.     

A minimum data set for evaluating the ecological soil functions in remediation projects

Purpose

Soil functioning becomes a matter of growing concern in soil remediation projects as, apart from preparing contaminated land for construction purposes, some parts of the sites are usually transformed into green spaces for recreation and inspiration. The objective of this paper is to develop and apply a minimum data set (MDS) for evaluating the ecological soil functions for green areas in remediation projects.

Materials and methods

The MDS was chosen from the previous applications in literature. Using a nonlinear scoring algorithm to transform observed data into sub-scores for evaluating ecological soil functions, the MDS was applied on the Kvillebäcken site in Sweden. The mean sub-scores of the individual soil quality indicators (SQIs) were integrated into a soil quality index to classify the soil into one of the five soil classes. Monte Carlo simulations were used to treat the uncertainties in the predicted soil class resulting from spatial heterogeneity of SQIs, a limited sampling size, and analytical errors.

Results and discussion

The suggested MDS consists of soil texture, content of coarse material, available water capacity, organic matter content, potentially mineralizable nitrogen, pH, and available phosphorus. The high mean sub-score for organic matter at Kvillebäcken indicated that the soil was rich on organic matter thus having a good water storage and nutrient cycling potential. However, the low mean sub-score for potentially mineralizable nitrogen indicated limited biological activity in the soil. The low mean sub-score for the content of coarse fragments indicated plant rooting limitations. Further, the soil quality index (that integrates the sub-scores for SQIs) corresponded to soil class 3 and a medium soil performance with a high certainty.

Conclusions

The suggested MDS can provide practitioners with relevant basic information on soil’s ability to carry out its ecological functions. The suggested scoring method helps to interpret and integrate information from different SQIs into a decision-making process in remediation projects.     

Importance of growth characteristics for yield of barley in different growing systems: will growth characteristics describe yield differently in different growing systems?

The interest in organic grown cereals has increased the need for variety tests under organic growing systems and/or the knowledge on whether growth characteristics describe yield differently under conventional and organic conditions. This paper is a contribution to that question by examining the relationships between some important growth characteristics in barley trials in both systems in Northern Sweden and in Denmark. Mixed model analyses were used for regressions of growth characteristics (or transformations of those) on yield (and log-transformed yield), allowing the slope to depend on the growing system. The analyses showed that diseases seemed to have a less negative effect on yield in the organic growing system than in the conventional growing system if pesticides were not applied. For other characteristics the effect depended on the country. This was the case for grain characteristics where the effect of volume weight in the Swedish trials was much larger in the conventional growing system than in the organic growing system, while a non-significant difference in the opposite direction was found for the trails from Denmark. For the trials from Denmark the effect of grain weight was much larger in the organic growing system than in the conventional growing system, but there was only a small and non-significant difference in the Swedish trials. In both countries there was a significant interaction between the two grain characteristics.     

Identification of Sources of Resistance to Phoma medicaginis Isolates in Medicago truncatula SARDI Core Collection Accessions, and Multigene Differentiation of Isolates

ABSTRACT Phoma medicaginis is a necrotrophic fungal pathogen, commonly found infecting the annual medic Medicago truncatula. To differentiate eight P. medicaginis isolates, five gene regions were examined: actin, beta-tubulin, calmodulin, translation elongation factor 1-alpha (EF-1alpha), and the internal transcribed spacer ribosomal DNA. Sequence comparisons showed that specimens isolated from M. truncatula in Western Australia formed a group that was consistently different from, but allied to, a P. medicaginis var. medicaginis type specimen. EF-1alpha contained a hyper-variable 55-bp repeat unit, which forms the basis of a rapid polymerase chain reaction-based method of reliably distinguishing isolates. Characterization of three isolates showed that all exhibited a narrow host range, causing disease only in M. sativa and M. truncatula among eight commonly cultivated legume species sampled. Infection of 86 M. truncatula single-seeded accessions showed a continuous distribution in disease phenotypes, with the majority of accessions susceptible. On a 1-to-5 disease reaction scale increasing in severity, individual fungal isolates showed means of 2.6 to 3.2, and scores ranged from 1 to 4.8 among accessions. The results presented here suggest that M. truncatula harbors specific and diverse sources of resistance to individual P. medicaginis genotypes.     

The effects of aggregating sub-grid land surface variation on large-scale estimates of net primary production

The use of large grid cell databases (1/2° to 5°) to drive nonlinear ecosystem process models may create an incompatibility of scales which can often lead to biased outputs. Global simulations of net primary production (NPP) often assume that bias due to averaging of sub-grid variations in climate, topography, soils, and vegetation is minimal, yet the magnitude and behavior of this bias on estimates of NPP are largely unknown. The effects of averaging sub-grid land surface variations on NPP estimates were evaluated by simulating a 1° × 1° land surface area as represented by four successive levels of landscape complexity, ranging from a single computation to 8,456 computations of NPP for the study area. Averaging sub-grid cell landscape variations typical of the northern US Rocky Mountains can result in overestimates of NPP as large as 30 %. Aggregating climate within the 1° cell contributed up to 50 % of the bias to NPP estimates, while aggregating topography, soils, and vegetation was of secondary importance. Careful partitioning of complex landscapes can efficiently reduce the magnitude of this overestimation.     

Butterflies in Swedish grasslands benefit from forest and respond to landscape composition at different spatial scales

Context

Loss and fragmentation of semi-natural grasslands has critically affected many butterfly species in Europe. Habitat area and isolation can have strong effects on the local biodiversity but species may also be strongly affected by the surrounding matrix.

Objectives

We explored how different land cover types in the landscape explained the occurrence of butterfly species in semi-natural grasslands.

Methods

Using data from 476 semi-natural grasslands in Sweden, we analysed the effect of matrix composition on species richness and occurrence. Additionally, we analysed at which spatial scales butterflies responded to matrix types (forests, semi-natural grasslands, arable land and water).

Results

Forest cover showed the strongest positive effect on species richness, followed by semi-natural grasslands. Forest also had a positive effect on red-listed species at local scales. Responses to matrix composition were highly species-specific. The majority of the 30 most common species showed strong positive responses to the amount of forest cover within 200–500 m. There was a smaller group of species showing a positive response to arable land cover within 500–2000 m. Thirteen species showed positive responses to the amount of semi-natural grasslands, generally at larger scales (10–30 km).

Conclusions

Our study showed that surrounding forest is beneficial for many grassland butterfly species and that forests might mitigate the negative effects of habitat loss caused by agricultural intensification. Also, semi-natural grasslands were an important factor for species richness at larger spatial scales, indicating that a landscape consisting mainly of supporting habitats (i.e. forests) are insufficient to sustain a rich butterfly fauna.

     

Ground cover increases spatial aggregation and association of insect herbivores and their predators in an agricultural landscape

Context

Enhancing ground cover vegetation is an important agricultural practice that regulates herbivore and predator insects in agricultural landscapes. However, the effects of ground cover on the spatial distributions of these organisms have scarcely been explored.

Objectives

Our goal was to measure the effects of ground cover on the spatial aggregation and association of insect herbivores and predators, which might contribute to the control of herbivorous pests.

Methods

We conducted our experiments in peach orchards at two sites in eastern China. The two sites have experimental units with ground cover treatments that created a heterogeneous landscape. We conducted a 2-year experiment to investigate the abundance and distribution of herbivores (leafhoppers) and predators (ladybirds), using geostatistics to analyze their spatial aggregation and association.

Results

The abundance of predators increased and that of herbivores decreased in ground cover orchards compared to control orchards without ground cover. The proportion of spatial structure was greater than 0.75 for both herbivores and predators in the control orchards, indicating a lack of spatial aggregation, and less than 0.75 in peach orchards with ground cover, indicating spatial aggregation. The correlation of spatial aggregation between herbivores and predators was significantly positive in the ground cover treatment, indicating association of the two insect guilds. In control orchards, on the other hand, this was not significant.

Conclusions

The presence of ground cover increased predator abundance, spatial aggregation of herbivores and predators as well as their spatial association, suggesting a mechanism for more efficient control of herbivorous pests in peach orchards.

     

A hematologic and blood chemical study of Swedish purchased calves.

The hemoglobin (Hb), packed cell volume (PCV), erythrocytes (R.G.), serum iron (SI) and unsaturated iron binding capacity (UIBG) were examined in a total of 386 purchased calves for the duration of 1 year. The calves were tested within 3 days of arrival at the buyer’s herd. The average age of the calves was 28 ± 10 days ().The results may be summarized as follows:

1. Approx. 35% of the calves had Hb values ≦ 10.0 g/100 ml.
2. Fifteen% of the calves had ≦ 6.0 × 10⁶ R.C. per µl.
3. Fifty-three calves or about 14% showed SI values ≦ 40 µg/100 ml and 131 calves or 34% ≦ 80 µg/100 ml.
4. Twenty-seven % of the calves had UIBG values > 501 µg/100 ml.
5. Almost half the calves (48%) had a saturation percentage of transferrin with iron below 20% and 138 calves (36%) below 15%.

These figures among others in the study indicate that 13–35% of the purchased calves suffered from iron deficiency anemia.     

The role of parameterization in comparing source attribution models based on microbial subtyping for salmonellosis

Source attribution methods attribute human cases of a zoonotic disease to a certain source putatively responsible for this disease. Identifying and quantifying the contribution of different sources to human infections is important for taking appropriate actions for reducing the exposure of the consumer to zoonotic pathogens. One widely used method is the microbial subtyping approach, whose principle is to compare the frequency of pathogen subtypes from different sources (e.g. animals or food) with the frequency of these subtypes in human cases. This paper studies the relationship between a Bayesian microbial subtyping approach described by Hald and coworkers subsequently modified by David and coworkers, here called the Hald model, and a frequentist approach known as the “Dutch model.” The comparison between the Bayesian and frequentist model is done for two data sets on salmonellosis in Germany from different time periods (year 2004–2007 and 2010–2011). The results of both approaches are in good agreement with each other for the used data. It is shown here mathematically that a certain parameterization can be used to transform the probabilistic Hald model into a deterministic form, which is equivalent to the Dutch model. That certain parameterization secures independence of the model outcomes from the choice of so‐called unique subtypes (which are unique in the sense that they are found exclusively in one of the sources). It is shown that deviating from that certain parameterization leads variations in the model outcome dependent on which unique subtypes are chosen in the process of modelling.