Soil Chemical Properties and Fire Severity Assessment Using VNIR Proximal Spectroscopy in Fire-Affected Abandoned Orchard of Mediterranean Croatia

This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO3, plant-available phosphorus (P2O5) and potassium (K2O), soil organic carbon (SOC), exchangeable calcium (exCa), magnesium (exMg), potassium (exK), sodium (exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P2O5, exCa, exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.

Floristic mosaics of the threatened Brazilian campo rupestre

The increase in rates of habitat loss requires an understanding of how biodiversity is distributed. Campo rupestre is an old, climatically buffered, and infertile landscape located in Brazil. Considered a biodiversity hotspot, the campo rupestre is mainly threatened by mining activity that requires a large operating area. Campo rupestre is known for its restricted distribution area and high abiotic heterogeneity, which modulates species coexistence and richness. To recognise the association between habitat type and plant communities, we propose to describe the floristic composition of herbaceous and shrub components in four habitats of the campo rupestre comprising quartzite and ferruginous substrate. We classified habitat types by the main surface soil features. In each habitat, we sampled ten 100-m2 plots to access information on the shrub and ten 1-m2 plots for the herbaceous component. Altogether we sampled 153 species, belonging to 38 families. The cluster analysis ordered by Sorensen metric indicates a clear distinction of species composition in the shrub component in the four habitats. However, the floristic composition of the herbaceous component was similar between the four habitats but showed a distinction when contrasting with the substrate type. Our results highlight the local taxonomic distinction between habitat types and substrates, indicating that the ecological distinction among substrate types of the campo rupestre cannot be overlooked in conservation and restoration actions.

Plant disease prediction using convolutional neural network

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity

Soil Communities: Who Responds and How Quickly to a Change in Agricultural System?

The use of conservation and sustainable practices could restore the abundance and richness of soil organisms in agroecosystems. Fitting in this context, this study aimed to highlight whether and how different soil living communities reacted to the conversion from an integrated to an organic orchard. The metataxonomic approach for fungi and bacteria and the determination of biological forms of diatoms and microarthropods were applied. Soil analyses were carried out in order to evaluate the effect of soil chemical features on four major soil living communities. Our results showed that the different taxa reacted with different speeds to the management changes. Fungi responded quickly to the changes, suggesting that modification in agricultural practices had a greater impact on fungal communities. Bacteria and microarthropods were more affected by abiotic parameters and less by the management. The diatom composition seemed to be affected by seasonality but the highest H’ (Shannon index) value was measured in the organic system. Fungi, but also diatoms, seemed to be promising for monitoring changes in the soil since they were sensitive to both the soil features and the anthropic impact. Our study showed that soil biodiversity could be affected by the conversion to sustainable management practices from the early years of an orchard onwards. Therefore, better ecological orchard management may strengthen soil sustainability and resilience in historically agricultural regions.

Assessment of microbial biomass pool in chronosequence coal mine spoil used as biomarker of mine spoil genesis

Extensive coal mining activities result mine spoil generation dumped in form of overburdens altering biogeochemical cycles and land degradation. Mine spoil generated after post-mining activities associated with heavy metal toxicity inhibit microbial growth. Being deficient in available nutrients due to lack of biologically rich topsoil, mine spoil represents a disequilibrated geomorphic system and poses problems for revegetation and restoration of coal mine spoil. Mine spoil genesis influencing ecosystem functionality demands physicochemical characterization and spatial distribution of microbial biomass pool in chronosequence coal mine spoil. Progressive improvement in clay, hydrological regimes, OC, TN and EP microbial biomass pool and BSR over time was evident from the study. Time dependent increase in integrating quotients was used to monitor the progress of mine spoil genesis. Decline in microbial metabolic quotient over time revealed the progress of mine spoil genesis. Stepwise multiple regression analysis revealed the contribution of physicochemical attributes influencing variability in microbial biomass. The shift in physicochemical properties and microbial biomass correlated well with the extent of land degradation, which can be used as effective biomarkers for monitoring the pace and progress of mine spoil genesis. The fresh coal mine spoil to attain soil features of nearby forest soil through mine spoil genesis shall take approximately 29.15 years. The study paves the way of greater understanding not only in the direction to design appropriate management strategies for ecosystem restoration but also to improve soil quality for sustainable development.

Are Cover Crops Affecting the Quality and Sustainability of Fruit Production?

The study of the interaction between fruit trees and cover crops has been addressed in numerous works over the last 50 years or more, evidencing the need to evolve from a productive orchard to an orchard that plays different ecosystem roles in terms of environmental sustainability rather than just productivity. This review, through an analysis of the scientific literature since the 1950s, highlights the development of sustainable soil management models in fruit tree orchards, mostly considering the relationship with fruit quality traits and with the ecosystem services that result from the adoption of cover crops, aiming at identifying and formulating technical recommendations in perennial orchards. Cover crop management surely improves soil features and positively influences fruit quality in perennial woody species, but also helps to develop a better habitat for beneficial insects, thus influencing pollination. A large number of scientific approaches highlight the beneficial use of a mixture of seeds in order to enhance biodiversity, aiming at improving ecosystem services for a transition towards more sustainable systems based on agroecological management.

Influence of Baltic Agro-Environmental Conditions on Yield and Quality of Fava Bean Crops in Conventional Systems

Fava bean (Vicia faba L.) yields are featured by high variability, influenced by the agro-environmental conditions during the growing seasons. These legume crops are sensitive to hydric and heat stresses. The adaptation depends on the efficiency of specific cultivars to use the available resources to produce biomass. This capacity is determined by the genotype and agronomical management practices. The present work aimed to uncover the influence of Baltic agro-environmental conditions (fava bean cultivar, plant density, climate, and soil features) on yield and protein content. For this, field trials were set under Baltic agro-climatic conditions, in Latvia and Estonia with five commercially available fava bean cultivars, representing broad genetic variation (‘Gloria’, ‘Julia’, ‘Jogeva’, ‘Lielplatones’, and ‘Bauska’). The results evidenced ‘Bauska’, ‘Julia’, and ‘Lielplatones’, as the most productive cultivars in terms of seed yield (4.5, 3.7, and 4.6 t ha−1, respectively) and protein yield (1.39, 1.22, and 1.36 t ha−1, respectively) under Estonian and Latvian agro-climatic conditions. Sowing these specific cultivars at densities of 30–40 seeds m−2 constitutes sustainable management for fava bean production in conventional cropping systems in the Baltic region.

Refinement Proposals for Geodiversity Assessment—A Case Study in the Bakony–Balaton UNESCO Global Geopark, Hungary

Geodiversity is the variety of natural elements that are excluded from biodiversity, such as: geological, geomorphological, and soil features including their properties, systems, and relationships. Geodiversity assessment measures these features, emphasising the characteristics and physical fragility of the examined areas. In this study, a quantitative methodology has been applied in Bakony–Balaton UGGp, Hungary. The Geopark’s area was divided into 2 × 2 km cells in which geodiversity indices were calculated using various data: maps, spatial databases, and elevation models. However, data sources differ significantly in each country: thematic information may not be entirely public or does not have the appropriate scale and complexity. We proposed to use universal data—geomorphons and a watercourse network—derived from Digital Elevation Models (DEMs) to calculate geomorphological diversity. Making a balance between the base materials was also an aim of this research. As sources with different data densities are used, some abiotic elements may be overrepresented, while others seem to have less significance. The normalisation of thematic layers solves this problem: it gives a proportion to each sub-element and creates a balanced index. By applying worldwide accessible digital base data and statistical standardization methods, abiotic nature quantification may open new perspectives in geoconservation.