Short-Term Field Performance of Four Planting Strategies for Enhancing Tuber magnatum Mycelial Development in Former Arable Lands

Global climate change is significantly altering the large scale distributions of plants and animals. The Earth has warmed by 0.7°C during the last century. The consequences are already apparent in forest ecosystems as species are responding to the changing climate with shifts in their phenology and geographic distributions. The potential for large increases in global mean temperatures (e.g., 4.3 ± 0.7°C) by 2100 has significant implications for forest species and ecosystems. Under these varying climatic conditions, some species may go extinct either locally or regionally, with climate change acting synergistically with other extinction drivers. Tropical Asian forests contain several biodiversity hotspots and species-rich ecoregions. Our understanding of species’ and forest ecosystems’ vulnerability to global climate change in this region is limited. Addressing this problem is a critical task for current tropical Asian ecological research.The overall aim of this PhD thesis is to investigate the current and potential effects of climate change on the geographic distribution and composition of selected plant and mammal species in tropical Asian forests. The selected plants include Sal (Shorea robusta), Garjan (Dipterocarpus turbinatus) and Teak (Tectona grandis). These all are ecologically and economically important timber trees and are distributed widely across South and Southeast Asia. The selected mammals include Asiatic black bear (Ursus thibetanus), Asian elephant (Elephas maximus), Western hoolock gibbon (Hoolock hoolock) and Bengal tiger (Panthera tigris tigris). These threatened large mammals are of high conservation concern and are typically targeted by international conventions. I present a comprehensive review of the previous literature and new predictive models of species distributions that quantify potential climate change impacts on tropical forests. My results show that projected changes in temperature and rainfall extremes are potential threats to the diverse and species-rich forest ecoregions of tropical Asia.I used bio-climatic models and two scenarios of climate change (a moderate and an extreme Representative Concentration Pathway (RCP) scenario) to assess climate change impacts on the continental scale distributions of two threatened Dipterocarp trees Sal and Garjan, and the valuable timber species, Teak. Annual precipitation was the key bioclimatic variable for explaining the current and future distributions of Sal and Garjan. Suitable habitat conditions for Sal will decline by 24% and 34% by 2070 under the RCP4.5 and RCP8.5 scenarios, respectively. In contrast, the consequences of climate change appear less severe for Garjan, with a decline of 17% and 27% under RCP4.5 and RCP8.5, respectively. Changes in annual precipitation, precipitation seasonality and annual mean actual evapotranspiration may result in shifts in the distributions of Teak across tropical Asia. These findings can contribute to conservation planning for the species and their management under future climates.I developed habitat suitability models for the four large threatened mammals (Asiatic black bear, Asian elephant, Western hoolock gibbon and Bengal tiger), across their entire distributions in Asia. The results suggest that changes in annual precipitation, annual mean temperature, precipitation and temperature seasonality, and land use/land cover change could reduce suitable habitat for these large mammals and therefore increase their extinction risks. It can be concluded that increasing climate stress on tropical forests could lead to greater extinction risks of these threatened large mammals.The findings of this thesis provide a fundamental basis for further studies of climate change impacts on species distribution in tropical Asia, and highlight the conservation importance of the plant and animal species in the region. The modelling outputs can be used to categorize the natural habitats of Sal, Garjan and Teak as low to high risk under changing climates to inform conservation planning and forest management. Given the conservation importance of the threatened large mammals for maintaining a healthy forest ecosystem, the findings of the models can be used to categorize the likely suitable habitats under changing climates and preparing proper guidelines to reduce their extinction risks. To ensure wider applicability to conservation planning for species vulnerable to global climate change, the methods and analyses presented here for tropical Asia could be applied to other tropical regions (i.e., in Africa and the Americas), using different species groups and forest types.

Context Habitat loss is a global problem and in Fiji >50% of the land area once covered by forests has been converted to grasslands and agricultural land. About 99% of Fiji’s endemic biodiversity and 80% of the land bird species have been identified as forest species. Aims In this study, we compare forest and grassland sites and test for consistency in avian diversity, abundance, foraging guild, and distribution status (endemic, native, introduced to Fiji) over a 5-year period (2016–2020). Methods We surveyed bird communities using the point count method with a 100 m radius and 7-min observation period per site. Key results A one-way analysis of similarities (ANOSIM) analysis showed significant differences in species composition and bird abundance between the forested habitats and grassland habitats. A general linear model test showed significant differences in foraging guild composition and distribution status between forested and grassland habitats. There were no significant differences between the three forested sites (primary montane forest, secondary old-growth forest, old-growth mahogany plantations with regenerating native species), while grassland sites had stronger annual change in species composition. Implications Forest cover, irrespective of whether these forests are of primary or secondary nature, therefore plays an important role in maintaining the native and endemic land bird species and other biodiversity in oceanic island ecosystems such as Viti Levu Island, Fiji.

The low quality of Entisol is caused by low availability of nitrogen, phosphorus, and potassium as well as low soil pH, so the addition of vermicompost is necessary. The purpose of this study was to determine the effect of vermicompost application on soil quality and to obtain the highest vermicompost dosage on the availability of nutrients such as N, P, K, and the yield of corn. The field experiment used a Randomized Completely Block Design (RCBD) with 7 dosages of vermicompost and three replications. The treatments were V0 = 0; V1 = 5; V2 = 10; V3 = 15; V4 = 20; V5 = 25; V6 = 30 Mg ha-1. Variables observed consisted of 1) initial soil and vermicompost characterization analysis, 2) soil analysis and N, P, and K content in leaves, and yield of corn. The results showed that Entisol was classified as low quality; however, vermicompost quality complied with the requirements of quality organic fertilizer. The experiment also indicated that the highest pH of Entisol (5.31) and K content in corn leaves (2.12%) were obtained at a dose of vermicompost 25 Mg ha-1. Meanwhile, the highest weight of husked corn cobs (415.55 g plant-1) and weight of unhusked corn cob (351.11 g plant-1) were obtained at a vermicompost dose of 15 Mg ha-1.

Truffles represent one of the most expensive edible fungi. About two-thirds of all known truffle records originate from Europe. Historically, the Mediterranean region in Southern Europe is associated with truffle cultivation and it is widely regarded as a center of truffle distribution in Europe. On the contrary, little is known about truffles in the central and northern regions of Europe. Here, native truffle species in Poland, their symbiotic ectomycorrhizal (ECM) tree partners, and their continental-scale distribution were studied. Altogether, 16 truffle species were identified based on the sequences of the Internal Transcribed Spacer (ITS) rDNA, a barcode region for fungi. Truffle species were associated with deciduous tree species of Fagales (Fagaceae, Betulaceae, Juglandaceae), Malvales (Malvaceae), Malpighiales (Salicaceae), and coniferous trees of Pinales (Pinaceae). Fagales trees constituted 70% of ECM associations formed by truffle species. Three genera of the order Fagales, Quercus, Fagus, and Corylus formed 50% of all ECM associations. Coniferous trees of Pinales formed 16% of associations. Two other orders of deciduous trees, Malvales and Malpighiales, together formed 14% of ECM associations. All but three identified truffle species exhibited low specificity to their ECM tree partners. Two-thirds of truffle species formed ECM symbiosis with both coniferous and deciduous tree species, but the share of coniferous tree partners was considerably lower than that of deciduous trees, reaching up to 30% for T. anniae, T. puberulum, and T. borchii (clade/puberulum). All the identified truffles were noted in both Central Europe and the Mediterranean region. Among them, about 80% of truffle species were widely distributed across the continent and represented by similar or higher numbers of records in Northern Europe, i.e., above the 48th parallel north, than in Southern Europe. This study showed higher taxa richness of native truffle species in Poland, but low specificity to their ECM tree partners. However, further studies on the regional-scale distribution of truffles in Poland are needed to improve the knowledge of the patterns of truffle distribution in forest ecosystems and the potential productivity of edible truffle species with high economic value.

Enhanced awareness and knowledge among arable (field crop) farmers and agronomists are pivotal for making informed integrated pest management (IPM) decisions. In this study, we had 3 primary objectives: (i) identifying the key factors influencing the adoption of IPM, (ii) gathering insights into the currently perceived pest threats, and (iii) identifying any necessary intervention tools or methods for modifying the flow of information. In 2021, a telephone survey involving 267 arable and arable/livestock farmers and 26 agronomists was conducted in Scotland. The survey was designed to gather information on the perceived invertebrate pest, disease, and weed threats, the extent of IPM implementation, and the sources of information consulted. Several factors influenced the uptake of IPM, including farm type, location, agronomist engagement, farmer’s age, education, and membership in schemes promoting IPM. Notably, there were disparities in perceptions of the most significant pest threats between arable farmers and agronomists. This points to a division of decision-making responsibilities and, consequently, varying information needs. Agronomists typically require technical information related to pesticide effectiveness and disease management, whereas arable farmers seek guidance on cultural approaches for managing weeds and invertebrate pests. Furthermore, preferences for information sources also differed, with agronomists showing a preference for social media as a source of IPM information, while farmers favored the farming press. Ultimately, a deeper understanding of IPM practices equips farmers to actively participate in IPM discussions with their agronomists. This enables the collaborative development of an effective IPM strategy and, in turn, increases the adoption of IPM practices.

Among the ascomyces truffles, there are several genera with an excellent record as edible fungi, and two of these are of considerable economic importance: Terfezia and Tuber. Of these two genera, only Tuber had been cultivated commercially (for decades), until now. More recently, biotechnological methods on fungal inoculum and mycorrhizal plant production, as well as plantation management, have been developed to cultivate, for the first time, some species of the Terfezia genus (Honrubia et al. 2001, 2005; Morte et al. 2004, 2006). These procedures are presented in this chapter. Here, we attempt to evaluate conclusions on the basis of recent truffle production data from the first field plantations. The genus Terfezia belongs to the so-called “desert truffles” which are a complex family of mycorrhizal hypogeous fungi, mainly containing species of the genera Terfezia, Picoa, Tirmania and Tuber. Their geographical distribution is limited to arid and semiarid lands, mostly in countries around the Mediterranean basin, such as: southern Spain, Portugal, Italy, France, Hungary, Turkey, from Morocco to Egypt, Israel, the Arabian Peninsula, Iran, Iraq, Libya, Syria, and Kuwait. In addition, some desert truffle species have been found in South Africa (Botswana) (Marasas and Trappe 1973), in North America and Japan (Trappe and Sundberg 1977), and China (Wang, unpublished data). Generally, the regions where desert truffles grow have an annual rainfall which ranges from 50 to 380 mm. The truffle season produces good yields if the rainfall ranges from 70 to 120 mm in North African countries and from 100 to 350 mm in countries of southern Europe. The rainfall distribution is very important as far as both quantity and the time of the rainfall are concerned; that is, no later than the beginning of December in North African and Middle Eastern countries and no later than the beginning of October in countries of southern Europe.

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha-1, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies.

Food vs Biofuel

Extent of Arable Crop Farmers’ Involvement in Non-Farm Activities in Ondo State

Aluminum-Dependent Root-Growth Inhibition in Arabidopsis Results from AtATR-Regulated Cell-Cycle Arrest

Invertebrate Diversity and the Ecological Role of Decomposer Assemblages in Natural and Plantation Forests in Southern Benin

Ecological focus area - EFA: the biological value of olive groves. A case study in Sardinia (Italy)

Agricultural production during the Classic Period (c.1,700 to 1050 BP) in the Central Maya Lowlands was comprised of a variety of techniques that were used to satisfy dietary needs and to stimulate its subsistence economy. The complexity of those methods was a consequence of a variable topography and previous forest management practices that likely resulted in wide-spread deforestation, and subsequently large-scale erosion which limited arable land. The Classic Maya solution to limitations in arable land, augmented by increased erosion seems to have come in the form of geotechnical constructions placed in a variety of positions along the contours of hillsides that could have mitigated soil loss and provide leveled platforms for the cultivation of consumable resources. While retaining wall viability can be measured based on their ability to withstand earth pressures, the effectiveness of the planting platforms would have relied heavily on the nutrient availability required for plant development. This research sought to investigate the geotechnical constructions, as well as the soil properties resulting from their implementation and use at the Central Lowland Maya site of Yax Ch’am. While the results of this investigation indicated comparable designs in two retaining wall structures at the site, those structures had varied responses to lateral earth pressures. Consequently, soil analysis indicated increased phosphorus availability along the northern reaches of the hillside and deficiencies across the westernmost terrace.

In Precision Agriculture one of the basic tasks is the classification of land zones in either arable or non-arable land. Several studies have been conducted using data obtained from soil analysis or local exploration of the parcels. However, sometimes only data from satellite images are available and then the problem not only becomes more challenging but also more interesting to solve because it is much more cost-effective. In this paper, we consider different spectral and thermal bands from the Landsat 8 satellite images corresponding to a vineyard located in Galicia, a region in Northeastern Spain, and apply a range of supervised Machine Learning methods to classify the different land zones. We conclude that an adequate choice of the algorithm parameters together with feature selection techniques can yield a classification that is both highly effective and efficient.

Soils contain about three times the amount of carbon globally available in vegetation, and about twice the amount in the atmosphere. However, soil organic carbon (SOC) has been reduced in many areas, while an increase in atmospheric CO2 has been detected. Recent research works have shown that it is likely that past changes in land use history and land management were the main reasons for the loss of carbon rather than higher temperatures and changes of precipitation resulting from climate change. The primary scope of this work was to estimate soil organic carbon stock (CS) variations in Italy during the last three decades and to relate them to land use changes. The study was also aimed at finding relationships between SOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, and land use, but also at verifying the possible bias on SOC estimation caused by the use of data coming from different sources and laboratories. The soil database of Italy was the main source of information in this study. In the national soil database is stored information for 20,702 georeferentiated and dated observations (soil profiles and minipits) analysed for routine soil parameters. Although the observations were collected from different sources, soil description and analysis were similar, because all the sources made reference to the Soil Taxonomy and WRB classification systems, and soil analyses followed the Italian official methods. Besides horizon description and analysis, soil observations had a set of site information including topography, lithology, and land use. The SOC and bulk density referred to the first 50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rock fragments volume, and bulk density. A set of geographic attributes were considered to spatialize point information, in particular, DEM (100 m) and derived SOTER morphological classification, soil regions (reference scale 1:5,000,000) and soil systems lithological groups (reference scale 1:500,000), soil moisture and temperature regimes (raster maps of 1 km pixel size), land cover (CORINE project, reference scale 1:100,000) at three reference dates: years 1990 and 2000, and an original update to 2008, obtained with field point observations. The interpolation methodology used a multiple linear regression (MLR). CS was the target variable, while predictive variables were the geographic attributes. Basic statistical analysis was performed first, to find the predictive variables statistically related to CS and to verify the bias caused by different laboratories and surveys. After excluding the biased datasets, the best predictors were selected using a step-wise regression method with Akaike Information Criterion (AIC) as selection and stop criterion. The obtained MLR model made use of the following categorical attributes: (i) decade, (ii) land use, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime, (vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soil aridity index (dry days per year), and, (x) elevation. The interaction between decade and land use variables was also considered in the model. Results indicated that CS was highly correlated with the kind of main type of land use (forest, meadow, arable land), soil moisture and temperature regimes, lithology, as well as morphological classes, and decreased notably in the second decade but slightly increased in the third one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias caused by the variables like “laboratory” and “survey source” could be as large as the 190%.