Variable Climatic Conditions Research Articles

Efficiency of the main application of fertilizers and top dressing of eggplant under drip irrigation on ordinary chernozems of the Rostov region

Relevance. Fertigation is the application of fertilizers, in which nutrients are dissolved in water for irrigation. This method is relevant when growing eggplant in the Rostov region, allowing effective use of fertilizers, since nutrients enter directly into the roots of plants, which eliminates their loss to evaporation or weathering. During fertigation, there is no need to carry out separate procedures for fertilization and irrigation, operations are combined into one, which reduces time and costs. The Rostov region is characterized by variable climatic conditions, including drought and strong winds. Fertigation ensures uniform and constant nutrition of plants, allowing them to better adapt to weather conditions, and can help increase yields by optimizing plant growth and development conditions.Material and methodology. In the conditions of the Rostov region, on ordinary heavy loamy chernozems, studies were conducted on the cultivation of eggplant on different backgrounds of the main application of mineral fertilizers in calculated doses before planting and using root fertilizing with complex fertilizers with trace elements with irrigation (fertigation), as well as leaf fertilizing with organomineral fertilizer Arksoil during the growing season.Results. It was revealed that mineral fertilizers and top dressing accelerated the growth and development of plants, and in the best case, the increase in indicators was 2 times higher compared with the control, while the height of the main stem, the number of lateral shoots and the number of ovaries on the plant, as well as the average weight of the fruit, significantly increased, which ensured an increase in plant productivity. The combined use of basic fertilizer and complex soluble fertilizers in the drip irrigation system has significantly increased plant productivity and increased yields to 77.82 t/ha. The application of high doses of fertilizers in combination with fertigation with water-soluble fertilizers did not actually affect the quality of fruits. The content of dry matter and nitrates remained at an optimal level.

Financially focused 3E optimization of innovative solar-powered dual-temperature refrigeration systems: Balancing cost-effectiveness with environmental sustainability

This research delves into the cutting-edge realm of solar-powered dual-temperature refrigeration, adhering to the 3E model emphasizing Energy, Economic, and Environmental considerations with a key focus on financial optimization. The study investigates the implementation of advanced refrigeration technologies, notably the integration of supercritical CO2 and ammonia cycles, tailored for dual-temperature applications. A distinctive aspect of this research is the strategic use of excess thermal energy, enhancing the efficiency of the refrigeration systems under varied climatic conditions. Central to the study is a thorough economic analysis aimed at reducing the Levelized Cost of Cooling (LCOC), while simultaneously enhancing the overall efficiency of the system. With the aid of the Engineering Equation Solver (EES) software, the system is modeled and the preliminary results indicate a promising decrease in cooling expenses (reaching to 90.25 $/GJ) alongside an increase in system efficiency (an improved efficiency of 33.17 %), with the total Coefficient of Performance (COP) demonstrating a significant advancement over traditional refrigeration models. This research underscores the effectiveness of harnessing solar energy in refrigeration technologies, contributing to financially viable and environmentally responsible cooling solutions. It provides valuable insights for the future development of refrigeration systems, which are both economically and environmentally sustainable.

Diversity and Ecological Assessment of Grasslands Habitat Types: A Case Study in the Calabria Region (Southern Italy)

Grasslands differ in many types depending on the diversity of climatic conditions and substrates. Due to the great wealth of species found in semi-natural grasslands, they conserve an essential part of the biodiversity of the European Union (EEC 43/92), defined as habitats of community interest. Using the region of Calabria (southern Italy) as a case study, this study aims to evaluate how species assemblages and a set of indicators applied to them can be used to analyse and characterize the diversity, ecological features and conservation status of grassland habitats. Vegetation analysis was carried out using the phytosociological method, and habitat ecological characteristics were defined by Ellenberg’s ecological indicator adapted to the Italian flora. Multivariate analysis of the surveys by means of cluster analysis and Principal Components Analysis (PCA) made it possible to define eight habitat groups according to EEC Directive 43/92: 6110 Rocky or basophilous calcareous grasslands of the Alysso-Sedion albi, 6170 Alpine and subalpine calcareous grasslands, 6220*a Pseudo-steppe with grasses and annuals of the Thero-Brachypodietea dominated by a Lygeum spartum, 6220*b Pseudo-steppe with grasses and annuals of the Thero-Brachypodietea dominated by Hyparrhenia hirta, 6210* Semi-natural dry grasslands and scrub facies on calcareous substrates (Festuco-Bromometalia) (*important orchid sites), 6230* Species-rich Nardus grasslands, on siliceous substrates in mountain areas (and submountain areas, in Continental Europe), 6410 Molinia meadows on calcareous, peaty or clayey-siltladen soils (Molinion caeruleae), 6420 Mediterranean tall humid herb grasslands of the Molinio-Holoschoenion, 6430 Hydrophilous tall herb fringe communities of plains and of the montane to alpine levels. Temperature (T) and oisture (U) are the most statistically significant ecological factors in differentiating different habitat types. Analysis of diversity, assessed through Shannon and Evenness indices, showed that it is strongly correlated with habitat diversity as soil moisture increases. The study of ecological characteristics, correlated with biodiversity indices, helps to characterize grassland habitats, providing guidance for the implementation of active conservation policies for these habitats.

Assessing Solar Radiation Forecasting: a WRF-Solar Model Evaluation in Kupang, Indonesia

In response to the challenges faced by solar power plants in Kupang, Indonesia – particularly during the peak of the rainy season – this study aims to enhance the WRF-Solar (Weather Research and Forecasting–Solar) numerical weather prediction model. Accurate short-term solar radiation forecasting is crucial for managing electricity supply disruptions and controlling operational costs in the City of Kupang and its surroundings. The unpredictable weather patterns – characterized by intense cloud activity during these seasons – pose significant challenges for reliable solar energy generation, thereby making precise weather modeling an essential tool for effective power plant management. Spanning 2020–2022, the research focused on the peak rainy seasons, utilizing a specifically configured WRF-Solar model to simulate solar radiation. Validation of the WRF-Solar model using observational data from the Automatic Solar Radiation Stations at the Kupang Climatological Station showed correlation values consistently above 0.50. However, the model exhibited a tendency to overestimate radiation intensity for GHI and DNI parameters (correlation 0.78 and 0.54) and underestimate for DHI (correlation 0.80), with RMSE values ranging from 47–320 W/m² and MBE values from –41.84 to 116.34 W/m². The model's accuracy was notably higher under clear conditions, with lower RMSE and MBE values but decreased significantly under overcast conditions, hence indicating sensitivity to cloud cover. This study highlights the WRF-Solar model's capability in capturing the diurnal pattern and hourly fluctuations of solar radiation while also underscoring the need for improved cloud representation to enhance accuracy under diverse climatic conditions.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

A Look at Genomic Selection Techniques for Climate Change Adaptation and Production in Livestock

Livestock production profoundly intersects with global climate dynamics, contributing to greenhouse gas emissions and confronting vulnerability to climate impacts. In addressing these challenges, imperative adjustments are requisite to fortify the climate robustness of livestock systems. Notably, the prevalent reliance on commercial breeds with limited genetic diversity exposes production strategies to disruption, especially if these breeds are confined to environments that may lose economic viability under future climate scenarios. Consequently, understanding the adaptability of animal populations to forthcoming environmental conditions is paramount for sustaining livestock production. Assessing the genetic underpinnings of climate adaptation necessitates the exploration of tailored genomic selection techniques encompassing both production traits, presumed to have moderate heritability, and adaptation traits, presumed to have low heritability. Through a nuanced examination of genomic selection dynamics, insights into the genetic mechanisms fostering resilience in livestock populations amidst shifting environmental contexts are garnered. Employ genomic analysis to pinpoint genetic markers associated with traits like heat tolerance, disease resistance, and feed efficiency in livestock. Collaborate across disciplines to develop tailored breeding programs integrating these markers, and validate their effectiveness through rigorous field trials and ongoing monitoring to enhance livestock resilience and productivity in varied climatic conditions. Elucidating these mechanisms and their application in breeding programs offers a comprehensive understanding of how genetic advancements can enhance both production efficiency and climate resilience in livestock. This discourse aims to bridge the chasm between scientific inquiry and pragmatic implementation, thereby facilitating informed decision-making in livestock breeding strategies tailored to mitigate the ramifications of climate change.

Assessment of Passive Solar Heating Systems’ Energy-Saving Potential across Varied Climatic Conditions: The Development of the Passive Solar Heating Indicator (PSHI)

This study aims to evaluate the energy-saving potential of passive solar heating systems in diverse global climates and introduce a new indicator, the passive solar heating indicator (PSHI), to enhance the efficiency of building designs. By collecting climate data from 600 cities worldwide through a simulation model, the present study employs polynomial regression to analyze the impact of outdoor temperature and solar radiation intensity on building energy savings. It also uses K-means cluster analysis to scientifically categorize cities based on their energy-saving potential. The findings underscore the benefits of both direct and indirect solar heating strategies in different climates. Significantly, the PSHI shows superior predictive accuracy and applicability over traditional indices, such as the irradiation temperature difference ratio (ITR) and the irradiation degree hour ratio (C-IDHR), especially when outdoor temperatures are close to indoor design temperatures. Moreover, the application of a cluster analysis provides hierarchical guidance on passive heating designs globally, paving the way for more accurate and customized energy-efficient building strategies.

Transcriptome-wide association mapping provides insights into the genetic basis and candidate genes governing flowering, maturity and seed weight in rice bean (Vigna umbellata)

BackgroundRice bean (Vigna umbellata), an underrated legume, adapts to diverse climatic conditions with the potential to support food and nutritional security worldwide. It is used as a vegetable, minor food crop and a fodder crop, being a rich source of proteins, minerals, and essential fatty acids. However, little effort has been made to decipher the genetic and molecular basis of various useful traits in this crop. Therefore, we considered three economically important traits i.e., flowering, maturity and seed weight of rice bean and identified the associated candidate genes employing an associative transcriptomics approach on 100 diverse genotypes out of 1800 evaluated rice bean accessions from the Indian National Genebank.ResultsThe transcriptomics-based genotyping of one-hundred diverse rice bean cultivars followed by pre-processing of genotypic data resulted in 49,271 filtered markers. The STRUCTURE, PCA and Neighbor-Joining clustering of 100 genotypes revealed three putative sub-populations. The marker-trait association analysis involving various genome-wide association study (GWAS) models revealed significant association of 82 markers on 48 transcripts for flowering, 26 markers on 22 transcripts for maturity and 22 markers on 21 transcripts for seed weight. The transcript annotation provided information on the putative candidate genes for the considered traits. The candidate genes identified for flowering include HSC80, P-II PsbX, phospholipid-transporting-ATPase-9, pectin-acetylesterase-8 and E3-ubiquitin-protein-ligase-RHG1A. Further, the WRKY1 and DEAD-box-RH27 were found to be associated with seed weight. Furthermore, the associations of PIF3 and pentatricopeptide-repeat-containing-gene with maturity and seed weight, and aldo–keto-reductase with flowering and maturity were revealed.ConclusionThis study offers insights into the genetic basis of key agronomic traits in rice bean, including flowering, maturity, and seed weight. The identified markers and associated candidate genes provide valuable resources for future exploration and targeted breeding, aiming to enhance the agronomic performance of rice bean cultivars. Notably, this research represents the first transcriptome-wide association study in pulse crop, uncovering the candidate genes for agronomically useful traits.

Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Wetlands are of global importance in providing essential ecosystem services but are also sensitive to climate change and human activities. Monitoring and assessing wetland vulnerability are crucial for ecological conservation and management strategies. However, the framework of wetland vulnerability assessment and the underlying mechanisms have not been well studied. In this study, the spatiotemporal variations in wetland vulnerability on the Qinghai‒Tibet Plateau (QTP) between 1990 and 2020 were investigated based on the ecosystem pattern-process-function framework. The key driving factors were identified by partial least squares structural equation modelling (PLS-SEM) and multiscale geographically weighted regression (MGWR) models. Our results showed that the wetland ecosystem pattern index (EPI), ecosystem process index (EPOI), ecosystem function index (EFI), and wetland vulnerability index (WVI) all demonstrated an increasing pattern from northwest to southeast. Between 1990 and 2020, the mean WVI values gradually decreased from 0.616 to 0.588, indicating a steady improvement in the wetland ecosystem on the QTP. Rapid urbanization increased the EPOI, while rugged topography increased both the EPI and EPOI, and the increase in hydrological abundance enhanced the EFI, which in turn contributed to an increase in the WVI. Conversely, climatic conditions led to a reduction in the EPI, which in turn resulted in a significant decrease in the WVI. Therefore, although urbanization and topographical and hydrological factors have somewhat exacerbated the WVI on the QTP, variable climatic conditions have driven the decline in wetland vulnerability in the last three decades. Furthermore, our results indicated that the proposed framework could provide a comprehensive approach for wetland vulnerability assessment and useful implications for wetland conservation and management.

Archaeobotanical data from the Sabor Valley reveal shifting moments in landscape and agriculture in NW Iberia during the Holocene

NW Iberia is dominated by Atlantic climate areas that favour pollen preservation, useful for palaeoecological studies. However, the region also includes Mediterranean sectors in which preservation of such palaeoenvironmental evidence is more difficult. To overcome these constraints, archaeological plant macroremains can be used to help characterize flora and vegetation dynamics at a local and regional level. To fill the gap in knowledge in an understudied Mediterranean region, a large archaeobotanical study was conducted at the river Sabor valley, NE Portugal. With 13 archaeological sites sampled for charcoal, fruits and seeds, it allowed the study of vegetation throughout the Holocene, starting in the Mesolithic up to Modern times, with some chronological gaps. Tree taxa dominates the older period and an expansion of shrubby taxa since Bronze age was observed. Diversification of plants used during the Iron Age and Roman period suggests an intensification of resources exploitation and deforestation. These trends seem to be related with changes in human settlements and productive strategies. During Prehistory, agricultural fields were established in flat and wide areas, and, during Iron Age, fortified granaries were used to store large amounts of grains, particularly free-threshing wheat. Drastic changes in settlement during Roman times and the establishment of small farms producing wine and/or olive oil were testified by the occurrence of Olea and Vitis in both anthracological and carpological datasets. Data from more recent periods is scarce. Results highlight that the combination of several proxies and integration of archaeological evidence helps to understand ecological dynamics in areas without pollen data and contributes to the characterization of heterogeneous areas under diverse climatic conditions and with a variety of social trends.

Environmental impacts on intraspecific variation in Ambrosia artemisiifolia genome size in Slovakia, Central Europe

The quantity of DNA in angiosperms exhibits variation attributed to many external influences, such as environmental factors, geographical features, or stress factors, which exert constant selection pressure on organisms. Since invasive species possess adaptive capabilities to acclimate to novel environmental conditions, ragweed (Ambrosia artemisiifolia L.) was chosen as a subject for investigating their influence on genome size variation. Slovakia has diverse climatic conditions, suitable for testing the hypothesis that air temperature and precipitation, the main limiting factors of ragweed occurrence, would also have an impact on its genome size. Our results using flow cytometry confirmed this hypothesis and also found a significant association with geographical features such as latitude, altitude, and longitude. We can conclude that plants growing in colder environments farther from oceanic influences exhibit smaller DNA amounts, while optimal growth conditions result in a greater variability in genome size, reflecting the diminished effect of selection pressure.

Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers

Improving streamflow prediction plays a significant role in flood warning, mitigation and development purposes. Therefore, this paper aims to compare the prediction capability of a useful conceptual hydrologic model hydrologiska byråns vattenbalansavdelning (HBV) enhanced and equipped with an automatic calibration procedure (Genetic Algorithm) along with two well-known deep learning (DL) algorithms, including Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This study was concentrated on the daily streamflow of two river basins with diverse climatic and hydrological conditions, namely the Halilrood River with fragmented and intermittent flows (located in Iran) and the Tweed River with a permanent stream flow (Located across the border of Scotland and northern England). These were particularly selected to address both seasonal and perennial riverine cases. For reproducing the streamflow, 10-year records of the daily discharge, precipitation, and temperature (from satellite and ground resources), and evapotranspiration were used as the main input data. The accuracy of the implemented models was assessed using several indices. The findings show that 1) the LSTM and GRU show better accuracy than the employed hydrologic model for estimating daily discharges (especially in the seasonal river basin); 2) the DL approaches need only meteorological data inputs (precipitation and temperature) and are independent of involved uncertainties in hydrological modeling such as physical characteristics of basins; and 3) best streamflow simulations were achieved by the LSTM (best agreement with the observations). In general, LSTM has the highest priority to be connected with online hydrological rainfall-runoff simulations and dynamical forecast models.

Carbon offset potential of rooftop photovoltaic systems in China

As a promising tool for mitigating carbon emission, rooftop photovoltaics have attracted worldwide attention. This study examines the carbon offset potential of rooftop photovoltaics in 31 provinces in China. The future trends of carbon offset potentials over the lifetime of rooftop photovoltaic systems are investigated. The findings reveal that the carbon offset potentials vary significantly across different regions in China due to the combined effects of variations in climate conditions and the energy mix for electricity generation. The northern regions possess significantly higher carbon offset potentials than the southwestern areas. In 2021, the annual total carbon offset potential ranged from the lowest at −2.0 kg CO2/m2 in Tibet to the highest at 169.3 kg CO2/m2 in Beijing. The projected future trends indicate that Beijing and Tibet will respectively maintain their highest and lowest carbon offset potential rankings. Most provinces are projected to witness decreasing carbon offset potentials. In terms of the carbon offset potential over the lifetime of rooftop photovoltaics, the northern regions − Beijing, Tianjin and Inner Mongolia − rank the top three. Not only can these findings serve as reference for developing targeted policies for carbon mitigation in China, the methodology of this study can also be applied in other places to determine their carbon offset potentials of rooftop photovoltaics.

Plant selection for rain gardens in temperate climates: The case of Izmir, Turkey

Rain gardens have gained importance in recent years as a green infrastructure strategy. These gardens, created to capture, filter, and absorb runoff from impervious surfaces, offer a sustainable method for addressing water-related challenges in urban areas. Incorporating rain gardens into urban areas not only addresses the challenges of heavy rainfall and flooding but also brings about ecological advantages by encouraging biodiversity, improving water quality, increasing resilience, and enhancing the aesthetic appeal of urban settings. Plant selection in rain gardens plays a crucial role in their effectiveness and sustainability. The research focused on exploring the importance of carefully choosing plants for rain gardens, aiming to help in selecting the most suitable flora and creating visually appealing, resilient, and ecologically important landscapes. For this purpose, Izmir Katip Celebi University was selected as a study area. The first step was to locate an appropriate space for a rain garden and evaluate its potential for a rain garden implementation. Next, plants suitable for a rain garden in a temperate climate were listed. Among these plants, those that can be found in Izmir were selected after contacting nurseries. Only fourteen of them were available. Utilizing the plants listed that align with the project requirements and the plant design criteria such as diverse color, high density, and proportion outlined in the literature review, a proposal for a rain garden design was recommended. Since the rain garden consists of three different zones (dry, wet, and moderate), the plants were arranged accordingly. This design was tailored to suit existing conditions, such as a temperate climate and proximity to the building. Factors like varying climate conditions or alternative rain garden placements were not accounted for in this design. Given the necessity for diverse plant selections in varying climates, research carried out across different regions holds significant value. This study, conducted in Izmir province, will enrich existing literature and provide municipalities with crucial guidance in plant selection in a rain garden project, offering valuable insights.

Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling

This study explores the critical issue of understanding the potential impacts of climate change on the habitat suitability of the highly endangered forest-dwelling Mangshan pit viper (Protobothrops mangshanensis) in China. Through the application of the MaxEnt model, high-resolution bioclimatic datasets, and species occurrence data, the research aims to elucidate the spatial and temporal dynamics of P. mangshanensis distribution from the present to the years 2050 and 2070. Through the integration of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and exploring different shared socioeconomic pathway (SSP) scenarios (SSP126, SSP370, and SSP585), the study seeks to provide comprehensive insights into the potential variations in habitat suitability under diverse future climate conditions. The methodology employed involves the construction of the MaxEnt model utilizing the BioClim dataset and 83 species occurrence points. The SSP scenarios mentioned above represent future climate change scenarios, and the accuracy of the model is evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Key findings reveal that the MaxEnt model exhibits high accuracy (AUC = 0.998), pinpointing the current suitable habitat for P. mangshanensis to be confined to the Mangshan area within the Nanling Mountains, covering an approximate area of 1023.12 km2. However, projections based on future climate scenarios suggest notable shifts in habitat suitability dynamics. While potential suitable habitats may emerge in the northwest of the current range, the existing suitable habitats are anticipated to undergo significant reduction or even complete disappearance. Notably, precipitation during the driest month emerges as a critical determinant influencing the distribution of the species. In conclusion, the study underscores the exacerbating impact of climate change on habitat deterioration and survival risks for P. mangshanensis, emphasizing the urgent need for conservation measures to safeguard the remaining suitable habitats for this endangered species. The implications of these findings are far-reaching, with the anticipated contraction of the snake’s range potentially leading to its disappearance and increased habitat fragmentation. By shedding light on the potential distributional changes of P. mangshanensis in Mangshan, the research provides valuable insights for informing targeted conservation strategies and policy interventions aimed at mitigating the adverse effects of climate change on endangered species.

Chorematic modeling to represent dynamics in the quinoa agroecosystems in Peru.

Our research occurred in the Andean region, one of the eight global centers of domestication of plant species grown for agriculture. The shores of Lake Titicaca (located between Peru and Bolivia), at 3800 meters above sea level, are recognized as the center of origin of quinoa (Chenopodium quinoa Willd.). In this region, complex societies have emerged, thanks to the development of water and soil management technologies. They have managed to overcome high mountain territories' extreme and variable climatic conditions. These societies have traditionally protected and preserved quinoa as food for present and future generations through their long-standing knowledge and cultivation practices. The fieldwork occurred in the context of Andean family farming, and our study analyzes nature-society dynamics with a chorematic approach and interviews with local communities. The interest of this work is the transformation of the landscape at the scale of the mountain agroecosystem to understand better the impacts of rural development policies. Chorematic modeling was applied to two periods, before and after 1970, a pivotal year in Peru for agriculture, to show how socio-spatial dynamics in the Andean environment are changing, particularly concerning the evolution of quinoa cultivation. The results show that wild quinoa relatives' distribution is strongly linked to the socio-spatial organization of the agroecosystem. Different species of wild quinoa relatives are maintained by villagers for their multiple foods, medicinal and cultural uses in natural areas, grazed areas, on edge, and also within cultivated fields. However, this management is changing under the pressure of global issues related to the international quinoa market, whose requirements imply reducing the presence of wild relatives in cultivated fields.

The Economic and Environmental Aspects of Miscanthus × giganteus Phytomanagement Applied to Non-Agricultural Land

Miscanthus × giganteus (M × g) is a promising energy crop in phytotechnology with biomass production. Despite considerable vegetation and harvest under varying climate conditions and across different soils, field-scale studies on utilising M × g remain scarce. Analysing the literature and our own findings, this study intends to highlight the potential of M × g phytotechnology for revitalising non-agricultural lands (NAL), including brownfields, and illustrate the expediency of applying biochar to enhance biomass yield, energy efficiency, and economic feasibility. To validate the feasibility of M × g production on brownfields, two scenarios within the value chain “biomass–biogas–electricity” for green harvest were examined. The assumptions were as follows: (1) a methane yield of 5134 m3 ha−1 y−1, and (2) substrate-specific methane yields of 247 and 283 mL (g oDM)−1 for the first and subsequent years, respectively. The findings suggest that Scenario 2 is better suited for cultivating M × g on brownfields/NAL, being more sensitive and eliminating inaccuracies and the generalisations of results. From the third year onward, the revenue of M × g production on biochar-amended brownfields showed greater potential for future profitability. Future research should confirm the positive trend in the energy efficiency ratio of M × g phytotechnology on a larger scale, particularly in real brownfield applications.

Risk assessment of mold growth across the US due to weather variations

Mold growth in residential and commercial buildings poses a serious problem to various communities. There are several facets of the problem, the most prominent of which are chronic public health risks, business losses, and monetary and labor resources needed for mold containment and mitigation. In addition, there may be severe renovation work needed if the problem is left unattended. This paper describes a probabilistic framework for mold growth risk assessment on various construction materials and shows its application across a variety of climate conditions. Mold growth is contingent upon three ingredients: temperature, moisture, and nutrients. A specific construction material of interest represents the latter, while the other two are modeled through Monte-Carlo simulations. Mold growth is characterized by the mold growth index, which is tracked over ten years, with hourly time resolution. Analysis of the temperature and relative humidity empirical data revealed that these quantities are statistically dependent. Accounting for these interdependencies is crucial for realistic simulations of temperature and humidity and were numerically reproduced in the developed method. The developed framework allowed for realistic assessment of mold growth risk. It was applied across 60 US metropolitan areas in different climatological zones to quantify mold growth risk. The proposed framework allows for quantitative risk assessment of mold growth in different climatological conditions. Such a capability is needed for decision makers for timely mold mitigation, preventing health hazards, and avoiding large financial losses for emergency mold remediation and building repair.

Continuous In-woods Production of Biochar Using a Trailer-mounted Air Curtain Burner.

Fuel treatments and other forest restoration thinning practices aim to reduce wildfire risk while building forest resilience to drought, insects, and diseases and increasing aboveground carbon (C) sequestration. However, fuel treatments generate large amounts of unmerchantable woody biomass residues that are often burned in open piles, releasing significant quantities of greenhouse gases and particulates, and potentially damaging the soil beneath the pile. Air curtain burners offer a solution to mitigate these issues, helping to reduce smoke and particulates from burning operations, more fully burn biomass residues compared to pile burning, and eliminate the direct and intense fire contact that can harm soil beneath the slash pile. In an air curtain burner, burning takes place in a controlled environment. Smoke is contained and recirculated by the air curtain, and therefore burning can be conducted under a variety of climatic conditions (e.g.,wind, rain, snow), lengthening the burning season for disposal of slash material. The mobile pyrolysis unit that continuously creates biochar was specifically designed to dispose of residual woody biomass at log landings, green wood at landfills, or salvaged logged materials and create biochar in the process. This high-carbon biochar output can be used to enhance soil resilience by improving its chemical, physical, and biological properties and has potential applications in remediating contaminated soils, including those at abandoned mine sites. Here, we describe the general use of this equipment, appropriate siting, loading methods, quenching requirements, and lessons learned about operating this new technology.

Impact of cultivation conditions on physicochemical characteristics of Miscanthus × giganteus biomass

AbstractMiscanthus × giganteus has emerged as a successful energy crop for remediating marginal land, offering potential as an alternative energy source for climate change mitigation. This study aimed to assess the physicochemical properties for gasification of Miscanthus × giganteus biomass from plantations with diverse soil origins, characteristics, contamination levels, age, and climate conditions, aiming to determine their influence on the energy properties. Fuel parameters, i.e.: ash content, volatile matter, calorific value, ultimate analysis, and ash melting behavior were measured. Analysis of biomass samples revealed significant variations in the ash content and volatile matter with the average values of 4.5 and 78.8%, respectively, depending of the produced biomass. The calorific value remained consistently high, ranging from 18 to 19 MJ kg−1, even when biomass was derived from the contaminated soil. Additionally, biomass ash exhibited complete melting at temperatures exceeding 1300 °C, signifying its suitability for gasification without encountering technical obstacles. Employing PCA analysis, the significance of plantation age, location, and soil characteristics in shaping the physicochemical properties of Miscanthus × giganteus biomass for gasification was highlighted. The results illustrated that soil contamination by trace elements had a minimal impact on the physicochemical properties, whereas plantation age and climate conditions significantly influenced to the fuel properties of biomass.