Permanent Grassland Research Articles

The contribution of alternative habitats for conservation of plant species associated with threatened semi‐natural grasslands

Abstract Conservation of species associated with semi‐natural grasslands, a threatened habitat, is dependent on their ability to disperse between the few and fragmented patches remaining in the landscape. To maintain metapopulations dynamics and reduce the risk of regional extinction, it is essential to know whether other, more widespread, habitats can act as alternative habitat for the biodiversity associated with threatened habitats. Here, we study how four widespread habitat types in boreal landscapes—forest, permanent grassland, abandoned grassland and road verge—can contribute to the conservation of plant species found in semi‐natural grasslands which is a species‐rich ecosystem important for plant and pollinator diversity that has experienced extensive reduction, fragmentation and isolation. We compare richness of all vascular plants, insect‐pollinated plants and semi‐natural grassland specialists among habitat types in two regions is Norway where semi‐natural grasslands are few and fragmented. Based on overlap in community composition and local species richness, road verges were the most promising alternative habitat for both insect‐pollinated plants and semi‐natural grasslands specialists. Several habitat specialist species were, however, only found in semi‐natural grasslands and, for these species, no other habitat can be considered suitable as alternative habitat. Our results highlight that a holistic management perspective is needed to maintain biodiversity associated with semi‐natural grasslands. Thus, both the protection of remaining patches of the primary, threatened habitats as well as management of widespread, alternative habitats in the landscape should be prioritized.

Environmental Effectiveness of Greening Measures under the Common Agricultural Policy of European Union

The objective of this study is to evaluate the effectiveness of greening measures in the European Union during the period of 2014-2020. Studies carried out in various EU Member States by the European Court of Auditors and other independent authors estimated changes in farming practices as a result of greening over the period of 2014-2020 to be between 2% and5% of the EU’s agricultural area. The widest audit of greening performed by the European Court of Auditors in 2017 shows that the greening process induced changes of approximate 5% were divided as follows: 1.8% for crop diversification, 2.4% for ecological area and 1.5% for permanent grassland. Contrary to the low efficiency of greening, the payments made were on an average €80 per hectare, while the costs for its implementation are on an average €25-30 per hectare. Although the payments far exceed the costs incurred, little benefits were achieved on improved soil quality, biodiversity conservation and a reduction in Greenhouse Gases (GHG) by 2% only at EU level. Had the European Commission’s original and more ambitious proposal been adopted, GHG reductions would have reached 5% threshold level. Conclusively, greening is a complex measure having significantly low effectiveness in context of the degree of environmental protection offered by it.

Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI)

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France), which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May–October period, which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016–2020) was used. The detection mowing number was conducted at the pixel level, and then the results were aggregated at the plot level. An evaluation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94 and 0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

Will Changes in the Common Agricultural Policy Bring a Respectful Approach to Environment in EU Countries?

Abstract Under the 2014–2020 Common Agricultural Policy (CAP), the existence of two pillars is maintained and the link between them is strengthened. Regulation (EU) No. 1307/2013 provides a new system of direct payments. The system consisting of the decoupling of agricultural aid from production (partial decoupling) and the provision of general income support, which came into force in 2003, is being transformed into a system in which single farm payments are replaced by a seven-component system of multi-purpose payments: basic payment per hectare, greening component, additional payment to young farmers, redistributive payment, additional income support in areas with natural handicaps, coupled support, voluntary simplified scheme for small farmers. There are three measures related to greening payments: crop diversification: the farmer must grow at least two different crops; preservation of existing permanent grasslands; the creation of an area of ecological interest corresponding to at least 5% of the arable land of a holding over 15 hectares (excluding permanent grassland and perennial crops). The new CAP aims to increase agriculture‘s contribution to the EU‘s environmental and climate objectives, provide more focused support for smaller farmers and allow Member States greater flexibility to adapt measures to local conditions.

Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning.

The monitoring of soil moisture content (SMC) at very high spatial resolution (<10m) using unmanned aerial systems (UAS) is of high interest for precision agriculture and the validation of large scale SMC products. Data-driven approaches are the most common method to retrieve SMC with UAS-borne data at water limited sites over non-disturbed agricultural crops. A major disadvantage of data-driven algorithms is the limited transferability in space and time and the need of a high number of ground reference samples. Physically-based approaches are less dependent on the amount of samples and are transferable in space and time. This study explores the potential of (1) a hybrid method targeting the soil brightness factor of the PROSAIL model using a variational heteroscedastic Gaussian Processes regression (VHGPR) algorithm, and (2) a data-driven method employing VHGPR for the retrieval of SMC over three grassland sites based on UAS-borne VIS-NIR (399-1001 nm) hyperspectral data. The sites were managed by mowing (Fendt), grazing (Grosses Bruch) and irrigation (Marquardt). With these distinct local pre-conditions we aimed to identify factors that favor and limit the retrieval of SMC. The hybrid approach presented encouraging results in Marquardt (RMSE = 1.5 Vol_%, R2 = 0.2). At the permanent grassland sites (Fendt, Grosses Bruch) the thatch layer jeopardized the application of the hybrid model. We identified the complex canopy structure of grassland as the main factor impacting the hybrid SMC retrieval. The data-driven approach showed high accuracy for Fendt (R2 = 0.84, RMSE = 8.66) and Marquardt (R2 = 0.4, RMSE = 10.52). All data-driven models build on the LAI-SMC relationship. However, this relationship was hampered by mowing (Fendt), leading to a lack of transferability in time. The alteration of plant traits by grazing prevents finding a relationship with SMC in Grosses Bruch. In Marquardt, we identified the timelag between changes in SMC and plant response as the main reason of decrease in model accuracy. Yet, the model performance is accurate in undisturbed and water-limited areas (Marquardt). The analysis points to challenges that need to be tackled in future research and opens the discussion for the development of robust models to retrieve high resolution SMC from UAS-borne remote sensing observations.

Estimation of Nutritive Value and Protein Degradability of Trifolium Repens and Poa Pratensis as the Dominant Pasture Species, Under Simulated Rotational Grazing

Summary A cutting trial was conducted to determine the nutritional value, crude protein (CP) fractions distribution and CP ruminal degradability of white clover (Trifolium repens L.) and Kentucky bluegrass (Poa pratensis L.) as the dominant species of permanent grassland. The legume and grass herbage was obtained in the field experiment carried out on pasture throughout a spring growth season where rotational grazing was simulated using three consecutive cuts, which were compared. The fractionation of CP, according to Cornell Net Carbohydrate and Protein System (CNCPS v6.5), was used to predict protein degradability of the legume and grass forage. A significant (p &lt;0.05) lowering of the CP content in the legume and grass herbage was found, while NDF (neutral detergent fiber) and ADF (acid detergent fiber) significantly increased (p &lt;0.05) as the spring growth season progressed. A significant decrease (p &lt;0.05) of fractions A1 and A2 was noted in the legume and grass herbage, with a significant (p &lt;0.05) increase of CP fractions B2 and C as the vegetation season progressed. A higher rumen degradable protein (RDP) content (p &lt;0.05) of white clover and Kentucky bluegrass herbage was obtained in the first harvest, compared with the second and third one. Significantly larger values (p &lt;0.05) of net energy were observed in the white clover and Kentucky bluegrass forage obtained from the first cut, compared with later regrowths, and white clover was characterized by a higher energy content of 12.4–33.1% (dry matter basis), relative to Kentucky bluegrass.

Fertilization Rapidly Alters the Feeding Activity of Grassland Soil Mesofauna Independent of Management History

Nitrogen fertilization of permanent grasslands affects soil fauna communities by modifying their taxonomic composition, population dynamics and feeding activity. However, it is not well understood if the edaphic fauna adapts to these external inputs so that the immediate response to fertilizer application depends on the long-term nutrient management strategy. We performed a field experiment in permanent grasslands under agricultural management in three regions across Germany. We used experimental fertilization with an organic plant-sourced fertilizer along a long-term nutrient management gradient to study the immediate and long-term effects of fertilization and their interdependence on the taxonomic composition and feeding activity of the soil mesofauna (Nematoda, Oribatida, and Collembola). Sampling season, soil properties, vegetation structure, and geographic location were considered as additional predictor variables to reflect heterogeneity in environmental conditions. The taxonomic composition, richness and total abundance of soil mesofauna communities were significantly affected by long-term nutrient management, but not by experimental fertilization. However, N pulses rapidly (within days) reduced the feeding activity estimated with bait-lamina strips independent of long-term nutrient management strategies. Experimental addition of organic plant-sourced fertilizer may have led to a rapid build-up of microbial biomass, providing alternative food sources for the soil mesofauna and causing a shift away from the bait-lamina substrate. Our study indicates that community changes associated with the long-term nutrient management regime in permanent grasslands do not alter the strong functional response of the soil mesofauna to N pulses. There is an urgent need to develop nutrient management strategies for permanent grasslands that take into account both the conservation of the edaphic faunal community and changes of ecosystem functions caused by rapid responses of the soil mesofauna to fertilizer inputs.

The Impact of the Renovation of Grassland on the Development of Segetal Weeds in Organic Farming

The intensive use or discontinuation of the use of swards can compromise biodiversity, yields, and feed quality; thus, leading to the degradation of permanent grasslands. Various methods of renovation are employed to restore the usability of degraded swards. In the years 2013–2016, a monofactorial field experiment was carried out on the Experimental Farm in Grabów (province of Mazowieckie, Poland). The experiment involved swards being reseeded after ploughing (P) and after disking with a compact harrow (H), with a non-renovated sward as the control treatment. The plots under renovation were reseeded with a lucerne–grass mixture. Both seedbed preparation methods for the renewed sward, ploughing (P) or shallow disking to a depth of 5 cm (H), were found to be effective for increasing sward yields, restricting weed growth, and reducing the number of weeds in the sward. The ploughing-based renovation method (P) had a strongly restrictive effect on biodiversity, as expressed by the H’ function value vis-à-vis the harrow method (H) and the non-renewed control (NR). Dicotyledonous species accounted for 92.3% of the weed population in the studied treatments. Of these, Taraxacum officinale, Achillea milefolium, Capsella bursa-pastoris, and Plantago maior occurred in the highest numbers.

Responses of selected beetle families (Carabidae, Chrysomelidae, Curculionidae) to non-crop habitats in an agricultural landscape

Agricultural intensification has caused a simplification of agricultural landscapes, accompanied by increasing field sizes and a reduction of non-crop habitats. To mitigate negative impacts of intensification, it is necessary to understand to what extent different non-crop habitats contribute to the maintenance of biodiversity in agroecosystems. Here, we compared the taxonomic diversity of three beetle families among four habitat types—wheat fields, grassy field margins, wildflower-sown areas under power poles, and permanent grassland fallows, in an agricultural landscape in western Germany. Carabidae were caught by pitfall trapping, Chrysomelidae and Curculionidae by suction sampling. We found surprisingly little variation among habitat types, though the rarefied species number tended to be higher in grassland fallows and field margins than under power poles and in wheat fields. Nevertheless, species assemblages differed substantially among habitat types. In Carabidae, grassland fallows were dominated by hygrophilous species with poor dispersal ability as opposed to all other habitat types being dominated by open landscape species with high dispersal ability. In Chrysomelidae and Curculionidae, power pole islands differed from the other habitat types with predominantly open landscape species, whereas wheat fields and grassland fallows were clearly dominated by eurytopic species. Our results thus highlight the need for a combination of different conservation measures for enhancing the functional diversity of beetle assemblages.

Application of Artificial Neural Networks Sensitivity Analysis for the Pre-Identification of Highly Significant Factors Influencing the Yield and Digestibility of Grassland Sward in the Climatic Conditions of Central Poland

Progressive climate changes are the most important challenges for modern agriculture. Permanent grassland represents around 70% of all agricultural land. In comparison with other agroecosystems, grasslands are more sensitive to climate change. The aim of this study was to create deterministic models based on artificial neural networks to identify highly significant factors influencing the yield and digestibility of grassland sward in the climatic conditions of central Poland. The models were based on data from a grassland experiment conducted between 2014 and 2016. Phytophenological data (harvest date and botanical composition of sward) and meteorological data (average temperatures, total rainfall, and total effective temperatures) were used as independent variables, whereas qualitative and quantitative parameters of the feed made from the grassland sward (dry matter digestibility, dry matter yield, and protein yield) were used as dependent variables. Nine deterministic models were proposed Y_G, DIG_G, P_G, Y_GB, DIG_GB, P_GB, Y_GC, DIG_GC, and P_GC, which differed in the input variable and the main factor from the grassland experiment. The analysis of the sensitivity of the neural networks in the models enabled the identification of the independent variables with the greatest influence on the yield of dry matter and protein as well as the digestibility of the dry matter of the first regrowth of grassland sward, taking its diverse botanical composition into account. The results showed that the following factors were the most significant (rank 1): the average daily air temperature, total rainfall, and the percentage of legume plants. This research will be continued on a larger group of factors influencing the output variables and it will involve an attempt to optimise these factors.

Species-Enriched Grass-Clover Mixtures Can Promote Bumblebee Abundance Compared with Intensively Managed Conventional Pastures

(1) Land use intensification has led to serious declines in biodiversity, including in forage production systems for dairy cows. Agri-environmental schemes, such as enriching grasslands in floral species, were shown to be an effective tool to promote biodiversity in higher trophic levels. Here, we studied an innovative pasture-based dairy production system with floral-species-enhanced temporary grasslands, with respect to the effect on wild bee abundance and species richness. (2) We studied three grass-clover mixtures with perennial ryegrass and clover species with different levels of plant diversity for flower cover and wild bees. The grass-clover pastures were rotationally stocked with cattle and parts of the pastures were excluded from grazing. Intensively managed conventional permanent grasslands were studied as the common land use type. Wild bees were caught by sweep netting. Wild bee diversity was analysed with a general linear mixed model. For species richness, an incidence-based coverage estimator was calculated. (3) In total, 541 wild bees from 10 species were found. No wild bees were caught on the conventional grasslands. Wild bee abundance and species richness did not differ among the three grass-clover mixtures, but with increasing flower cover of white clover (Trifolium repens), wild bee abundance increased. Except for one solitary wild bee individual, the recorded community exclusively consisted of bumblebees. While generalist species that are commonly found on farmland dominated, rare long-tongued bumblebees made up 10% on the grazed sites of the multispecies mixture and made up 20% on the ungrazed strips of the binary mixture and multispecies mixture. (4) We conclude that multispecies mixtures can provide resources for generalist bumblebee species, especially when compared with conventional grasslands that offer no resources. Considering that the multispecies mixture has been also shown to reduce greenhouse gas emissions and nitrate leaching, while maintaining high forage yields, our findings add to the potential to promote a wide range of ecosystem services. Yet, should their full potential be enfolded, grazing should partially be excluded, and the mixture should be extended by plant species with more open flowers, suitable for solitary wild bees.

Glyphosate, AMPA and glufosinate in soils and earthworms in a French arable landscape

Although Glyphosate-based herbicides are often marketed as environmentally friendly and easily biodegradable, its bioavailability and risks to wildlife raise significant concerns. Among non-target organisms, earthworms which live in close contact with the soil can be directly exposed to pesticides and harmed. We investigated soil contamination and the exposure of earthworms to glyphosate, its metabolite AMPA, and glufosinate in an arable landscape in France, both in treated (i.e. temporary grasslands and cereal fields under conventional farming), and nontreated habitats (i.e. hedgerows, permanent grasslands and cereal fields under organic farming) (n = 120 sampling sites in total). Glyphosate, AMPA and glufosinate were detected in 88%, 58% and 35% of the soil samples, and in 74%, 38% and 12% of the earthworm samples, respectively. For both glyphosate and AMPA, concentrations in soils were at least 10 times lower than predicted environmental concentrations. However, the maximum glyphosate soil concentration measured (i.e., 0.598 mg kg−1) was only 2 to 3 times lower than the concentrations revealed to affect earthworms (survival and avoidance) in the literature. These compounds were found both in conventional and organic farming fields, thus supporting a recent study, and for the first time they were detected in hedgerows and grasslands. However, glyphosate and AMPA were more frequently detected in soils from cereal fields and hedgerows than in grasslands, and median concentrations measured in soils from cereal fields were significantly higher than in the two other habitats. Bioaccumulation of glyphosate and AMPA in earthworms was higher than expected according to the properties of the molecules. Our findings raised issues about the high occurrence of glyphosate and AMPA in soils from cropped and more natural areas in arable landscapes. They also highlight the potential for transfer of these molecules in terrestrial food webs as earthworms are prey for numerous animals.

Hyperspectral-Based Classification of Managed Permanent Grassland with Multilayer Perceptrons: Influence of Spectral Band Count and Spectral Regions on Model Performance

Detailed knowledge of botanical composition is a key factor for the sustainable and site-specific management of permanent grassland and facilitates an improvement in the performance and efficiency of livestock feeding. Spectral-based data acquisition combined with machine learning has the potential to classify species groups and plant parts in permanent grassland with high accuracy. However, a disadvantage of this method is the fact that hyperspectral sensors with a wide spectral range and fine spectral and high spatial resolution are costly and create large amounts of data. Therefore, the question arises as to whether these parameters are necessary for accurate grassland classification. Thus, the use of sensors with lower spectral and spatial resolution and correspondingly lower data processing requirements could be a conceivable approach. Therefore, we investigated the classification performance with reduced predictor sets formed by different approaches in permanent grassland. For pixel-based classification, a cross-validated mean accuracy of 86.1% was reached using a multilayer perceptron (MLP) including all 191 available predictors, i.e., spectral bands. Using only 48 high-performing predictors, an accuracy of 80% could still be achieved. In particular, the spectral regions of 954 nm to 956 nm, 684 nm to 744 nm and 442 nm to 444 nm contributed most to the classification performance. These results provide a promising basis for future data acquisition and the analysis of grassland vegetation.

Improving insect conservation values of agri-environment schemes through diversified seed mixtures

To counteract ongoing biodiversity declines, many European countries have implemented agri-environment schemes (AES) such as ecological focus areas (EFAs), the efficiency of which is often questioned due to low levels of habitat quality. We established new grasslands (NG) using a more diverse seed mixture specifically adapted to mimic low-intensity meadows occurring in our study region in Lower Austria. We compared abundance, species richness, species assemblages and β diversity of solitary bees, bumblebees, syrphids, butterflies, orthopterans, true bugs and cicadas in currently subsidized legume-grass mixtures targeted at supporting pollinators (SG), old grasslands (OG) and NG over a two-year period. Bees, bumblebees and syrphids clearly profited from NG in terms of abundance and species richness, while SG were significantly less attractive. Butterflies were most abundant in OG and significantly less attracted to both SG and NG. True bugs were particularly attracted to NG and SG. However, we did not find any preference in cicadas. Orthopteran species richness was similar in all three types, but individual numbers were significantly lower in NG than in OG. OG and NG supported highly distinctive species assemblages. For solitary bees, syrphids, true bugs and cicadas distinctiveness of sites (β diversity) was more decisive for overall diversity than local diversity. We urge policy makers to put more effort into the establishment of EFAs by using high quality seed mixtures of regionally adapted species and increased plant diversity. Furthermore, preserving permanent grasslands, which support different sets of insect assemblages, should be focused as well to maximize biological conservation value.

Green Corridors May Sustain Habitats for Earthworms in A Partially Converted Grassland

Permanent grasslands provide a healthy and undisturbed environment. We investigated how mulching altered the soil physicochemical parameters, earthworm abundance, biomass, species composition and vegetation cover compared to grassland and cultivated alfalfa field. Microplots (2 × 2 m) were mulched with either weed control fabric (WF) alone or WF combined with straw (WF + S) on a grassland to grow tomato in Ősagárd (Nógrád county, Hungary) between 2018 and 2021. We had two other microhabitats: a conventionally cultivated alfalfa field (CA) and grassland (GR). We measured soil parameters (physical: soil moisture content (SMC), soil penetration resistance (SPR); chemical: pH, soil organic matter; and biological: earthworm abundance, biomass, species composition and vegetation). SMC was significantly higher on covered plots (WF; WF + S) compared to CA and GR. SPR values were the highest in CA and GR at 20 cm depth. The abundance and biomass of earthworms were the highest in GR and lowest in CA in all seasons. Plant abundance was highly influenced by season and habitat. Despite the higher compaction and lower SMC figures, grass vegetation still provided a more suitable environment for earthworms than mulched plots (WF, WF + S). Therefore, where there is agricultural production on grassland, we suggest leaving uncultivated and uncovered patches as biodiversity corridors.

Intensity and Biodiversity: The ‘Green’ Potential of Agriculture and Rural Territories in Poland in the Context of Sustainable Development

In the paper we provide an analysis of the potential of agricultural holdings for biomass production, an assessment of the level of farm sustainability and a description of the farming conditions of agricultural holdings depending on the degree of natural valuation of a given area. The approach makes it possible to assess the level of sustainability of farms, also in the context of their potential and taking into account the quality of the production space and its importance for increasing biodiversity. We use public and unpublished data of Statistics Poland (2005, 2007, 2016) as well as data on farms keeping accounts for the Polish FADN in 2016–2018. The paper shows that although concentration processes are progressing, farm productivity is increasing, but this is linked to specialisation, leading to efficient management of labour and land resources. Nevertheless, the decreasing number of farms with proper winter vegetation cover and optimal balances was confirmed. The farming conditions of the farms should take into account the share and degree of natural value of the production space of the areas (municipalities) in which they are located. The study found that farms in municipalities with a high share of high natural value farmland (HNVf) areas, compared to the other municipalities, characterized by lower livestock density per ha UAA, which indicates their less intensive organisation of agricultural production. Moreover, in these municipalities, as their natural value increased, the share of forests and waters in the total area increased and the share of permanent grassland in the total area decreased slightly.

Socioeconomic effects of a bottom-up multifunctional land consolidation project

Taking agricultural land out of production or shifting from crop production to permanent grassland have recently been proposed in Denmark as general measures for contributing to greenhouse gas (GHG) reductions. One of the particular features of such measures is the creation of co-benefits, as taking agricultural land out of intensive production may affect a range of ecosystem services and economic goods, such as reduced nutrient loads and emissions of GHGs, improved biodiversity, and improved recreational opportunities. In this paper, we present results from an ex ante socioeconomic Cost-Benefit Analysis (CBA) with the purpose of assessing whether the expected benefits of a bottom-up local development plan for multifunctional land use in the catchment of Lake Glenstrup in Denmark will outweigh the expected costs. After quantifying the wide range of impacts, we apply benefit transfer to value them within the CBA framework. Among the primary effects are reduced emission of GHGs, reduced leaching of nutrients, increased recreational options, and opportunity costs from agriculture. The results indicate that the initiatives could lead to a net social gain of 1.4 mil. €. We perform a sensitivity analysis which shows that the choice of the spatial extent of the recreational effects has a large impact on the results, which range from 0.4 to 5.2 mil. €. Also, the shadow price of reduced GHG emissions play a major role. Finally, a distribution analysis shows that especially farmers bear the majority of costs, whereas other stakeholders enjoy most of the benefits.

Carbon sequestration of hazelnut orchards in central Italy

The cultivation of European hazelnut (Corylus avellana L.) is showing a rapid increase due to the high global demand for hazelnuts from the confectionery industry also affecting Italy, which is the second largest hazelnut producing country after Turkey. Like most tree crops, hazelnut tree planting includes land preparation and frequent tillage operations during the first years of growing seasons to avoid weed competition, whereas after 3 years the soils are usually permanently grass covered. The aim of this work is to develop a model of carbon sequestration based on the age of hazelnut orchards, using permanent grasslands as reference for previous land use. The results demonstrate that soil carbon stock (sCS30) tends to decrease between 23% and 58% during the first 3–5 years after cultivation. This carbon depletion is only partially compensated by the carbon sequestration of the hazelnut trees, variable from about 0.6–3.3 Mg C ha−1. Adult hazelnut orchards showed high levels of sCS30, as well as higher carbon stock (CS) from tree biomass, resulting in similar, or even higher carbon sequestration potential of the grassland. In some cases, excessive nitrogen fertilization seems to decrease the carbon sequestration potential of the soil. In conclusion, the land use change from grassland to hazelnut cultivation has a short-term negative impact in terms of soil carbon sequestration. In orchards with optimal fertilization and management, sCS30 is recovered and exponentially increased, highlighting the high carbon sequestration potential offered by this kind of perennial nut crop.

Barium, Lithium and Titanium Content in Herbs of Mid-Field Wet Depressions in East-Central Poland

This paper presents the results of research on the Ba, Li and Ti content in six species of herbs sampled from mid-field wet depressions and from the soil. These temporary flooded depressions were surrounded by arable crops, permanent grassland and shrubby vegetation. The research area was located in the eastern part of the Mazovian Voivodeship, east-central Poland. The following plants were used in the experiment: corn mint (Mentha arvensis L.), purple marshlocks (Comarum palustre L.), silverweed (Potentilla anserina L.), yarrow (Achillea millefolium L.), yellow loosestrife (Lysimachia vulgaris L.) and gypsy-wort (Lycopus europaeus L.). The Li, Ba and Ti content of plants, bottom sediment and soil was determined by the ICP-AES method after previous dry mineralization. Of the six herb species, Mentha arvensis L. was with the greatest accumulation potential of the chemical elements. However, no excessive Ba, Li and Ti content was found in herbs growing at different distances from arable fields, permanent grassland and shrubby vegetation. The highest Ba content was found in periodically flooded soil (zone II), while the highest amounts of Li and Ti were recorded in non-flooded soil (zone III).

Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland

Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved site-specific management and livestock feeding. A systematic approach was developed to analyze vegetation of managed permanent grassland using hyperspectral imaging in a laboratory setting. In the first step, hyperspectral images of typical grassland plants were recorded, annotated, and classified according to species group and plant parts, that is, flowers, leaves, and stems. In the second step, three different machine learning model types—multilayer perceptron (MLP), random forest (RF), and partial least squares discriminant analysis (PLS-DA)—were trained with pixel-wise spectral information to discriminate different species groups and plant parts in individual models. The influence of radiometric data calibration and specific data preprocessing steps on the overall model performance was also investigated. While the influence of proper radiometric calibration was negligible in our setting, specific preprocessing variants, including smoothening and derivation of the spectrum, were found to be beneficial for classification accuracy. Compared to extensively preprocessed data, raw spectral data yielded no statistically decreased performance in most cases. Overall, the MLP models outperformed the PLS-DA and RF models and reached cross-validation accuracies of 96.8% for species group and 88.6% for plant part classification. The obtained insights provide an essential basis for future data acquisition and data analysis of grassland vegetation.