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The genetic progress achieved by animal breeding programs may be affected by genotype-by-climate interactions (GxC). This systematic literature review assesses the scientific evidence for GxC on multiple traits of cattle, pigs, and poultry. Two search engines (Scopus, Web of Science) were queried for original peer-reviewed scientific (English full-text) studies. We included (1) observational studies and designed experiments considering dairy or beef cattle, swine, chicken or turkeys, where (2) at least one production, fertility, or health trait was tested for GxC, (3) the existence of GxC was tested directly based on temperature, relative humidity, or climatic indices for heat or cold stress, and (4) genetic effects within a breed or line were investigated. The search resulted in 46 eligible studies, with a (low) risk that the requirement of full-text English studies may have resulted in some work not included in this review. Our review shows an increase in GxC studies from 2015 onwards. Cattle was the most studied species (n = 36; 78.3%), whereas eligible studies on turkeys were lacking. Climatic parameters used in the studies ranged from well-known parameters (e.g., temperature, temperature-humidity index) to more complex indices combining temperature, relative humidity, wind speed and solar radiation. All observational studies (n = 40; 87.0%) used weather station data. In total, 75 traits were studied, which were predominantly production traits regardless of species. Studies on fertility and health traits have been emerging from 2010 onwards, but their numbers still lag far behind those for production traits. Genotype-by-climate interaction was confirmed in 54.0% of the study outcomes. This systematic review shows that little is known about the role of GxC in health and fertility traits in cattle, and for all traits in pigs and poultry in general. As current evidence shows that genotype-by-climate interaction is common across species and traits, we suggest to collect detailed climatic data and use them to assess the presence of GxC in indoor and outdoor production systems, as well. [ABSTRACT FROM AUTHOR]

The paper considers a problem of modeling the impact of the future climate on the yield of crops (in the example of spring wheat). The authors have analyzed the sources of climatic data and values of agrometeorological indicators for past and future periods. Using agroclimatic parameters, they have developed a mathematical model of the dependence of the median of the distribution function of spring wheat yield on climatic changes. The authors performed the calculations using this model and analyzed the obtained results. The analysis indicates a significant increase in the yield level of spring wheat in 2030–2040 and an increase in the risk of losses of its yield compared to historical data. [ABSTRACT FROM AUTHOR]

Outdoor thermal comfort is an essential aspect of sustainable architecture, and it is key to maintaining a safe outdoor environment. Several programs have been developed to predict thermal comfort based on climate parameters, as well as the built environment, and to forecast comfort within the urban context. Solar radiation and wind speed can be manipulated by the constructed environment. This research uses Jabal Al Natheef as a case study. An attempt is made to evaluate the outdoor thermal comfort of the urban environment and to identify the contributing factors that impede or facilitate outdoor thermal comfort in Amman. The goal of this research is to investigate behavioral factors, including perception, in addition to environmental parameters, such as thermal adaptation and solar radiation intensity, as well as the green infrastructure implementation. A comprehensive literature analysis of outdoor thermal comfort over the last decade is conducted in this paper, which included adaptive and rational thermal comfort approaches, from the standpoint of contextualizing the behavioral perspective that is connected to the use of urban space. This research employed a scenario-based approach, enabling site assessment and community participation. The possibility of enhancing the outdoor thermal comfort in Jabal Al Natheef is discussed. Furthermore, we identify the effect of design measures that influence outdoor thermal comfort. Recommendations for improving outdoor thermal conditions in the region to improve urban life and stimulate outdoor activities are provided. The results indicate that vegetation has a significant effect on reducing air temperature by providing shade. Urban areas with more vegetation have a mean radiant temperature that is significantly reduced, by almost 7 °C, especially during the summer. [ABSTRACT FROM AUTHOR]

In the context of climate change, the paper presents an analysis of the evolution of grapevine phenophases, in the period 1991-2021, for four grapevine cultivars Selena, Blasius, Rubin and Radames, and two clones Sauvignon blanc 9 Bl, Feteasca alba 29 Bl homologated at SCDVV Blaj. The study is based on the climatic and phenological data from SCDVV Blaj. The climatic data have been processed into fourteen parameters and for the phenological periods the starting date was analyzed. Regarding the average annual temperature, during 2010-2021 there is an increase of 1.1 ºC compared to the multiannual average reference temperature (calculated as the annual average of the years 1975-2010), which means that in the studied area, during the period 2010-2021 there was a warming compared to reference years. The average duration of the vegetation period from 2010-2021 is shorter than the reference period by 3.2 days. In 2011 the annual aridity index denotes a semi-arid dry climate, and in 2012, 2015, 2017 and 2019 a moderately humid climate. The hydrothermal coefficient was higher in the studied period compared to the reference period, registering values between 1.0 (2011) and 2.8 (2016), which has a positive influence on grape production. The Huglin index for the studied period is between 2237.6 in 2021 and 2928.5 in 2012, with an average of 2542.6 which places Tarnave vineyard area in a warm climate zone. Concerning the precipitation, a slight increase of 34.7 mm/m2 compared to the multiannual reference amount was monitored. In these conditions the warming of the climate induced an earlier budburst with an average of 8 days, correlated with an earlier maturation with 9-11 days in average. So far for the blooming and veraison phenophases starting date, no differences of the outrunning were clearly observed. [ABSTRACT FROM AUTHOR]

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan. [ABSTRACT FROM AUTHOR]

This work investigates the impact of environmental changes on the efficiency of the photovoltaic panel system. To analyze the impact, we first study the effect of the current climatic parameters on the evolution of the output energy of two solar photovoltaic modules, such as irradiance, temperature and wind speed. Meteorological data was acquired using specific sensors for temperature, irradiance, and wind speed; these data have been initially conditioned by using of 34972A LXI data acquisition and thereafter, interfaced to a PC using IEEE 488.2 general purpose interface bus (GPIB). We use the LabVIEW real-time interface to display and store the collected data in the PC. Finally, from the data stored, we have carried out the discussions on the experimental results-based data driven analysis using clustered heatmap which supports the approval of this impact using climate forecasts and strategy to adopt for an efficient and economical used and consequently a solar photovoltaic energy under climatic constraints is proposed. [ABSTRACT FROM AUTHOR]

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This paper presents the application of regression trees as a versatile alternative to other machine learning and statistical modelling techniques to forecast the power generation at five renewable power plants: one large hydropower plant, two mini hydropower plants, and two wind farms in Sri Lanka. The prediction models for each power station were developed by varying the depth of the regression tree. The regression tree model with the lowest depth that forecasts the output (power) in terms of all the predictor variables was selected for each power station and the accuracy of the models was evaluated by means of the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). According to the degree of the above performance indicators, i.e. very low values of MAE, MAPE, and RMSE supplemented by R2 of 0.95 or more, the regression tree method proved to be a convenient forecasting technique to predict the power generation at both hydro and wind power plants. Further, it could be found that a good correlation between the input and output variables paves the way for a smaller depth in the regression tree. Moreover, regression trees presented here could accurately identify the relationship between the power generated and the most influential weather factors, without being affected by potential outliers or missing values while managing collinearity too. Extension of this study would enable to generalize the prediction of renewable power generation based on the regression tree method, leading towards minimizing the use of fossil fuel.

The mountain forests of the Northwestern Caucasus represent unique refugia for the native biodiversity of flora and fauna. Endemic species are also preserved among soil invertebrates, including the group of earthworms, which are important ecosystem engineers. This study assesses the spatial distribution of the endemic anecic species of earthworms Dendrobaena nassonovi nassonovi Kulagin, 1889 in deciduous, coniferous–deciduous, small-leaved, and conifer forests of the Northwestern Caucasus (a total of 1028 geographical points were surveyed, of which the species was found in 185 points) based on our own field data by modeling the current potential areas using the Maxent software. The D. n. nassonovi potential area maps show a high probability of the species inhabiting mid- and high-mountain forests of the Northwestern Caucasus and being confined to mixed coniferous–deciduous and beech forests. The optimum soil and climatic parameters, as well as a lack of large-scale business operations in the mid- and high-mountain forests, make it possible for these ecosystems to remain suitable refugia, in particular for the endemic anecic species of Caucasus earthworms D. n. nassonovi. [ABSTRACT FROM AUTHOR]

The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place addressing the need of predictions and forecasting of climatic behavior, the uncertainty of atmospheric and geoprocesses poses a severe challenge to the effectivity of efforts in dealing with disasters. It has become very crucial to understand the future atmospheric uncertainty process and further predict and forecast for analysis of different activities based on geographical locations. Climate parameters are mandatorily required to improve system activities and output analysis of acquired data. For a sustainable adaptation, this is very important on how the uncertainty of variations in climatic parameters is dealt and the decisions based upon appropriate forecasting model are taken. The work has primarily focused on the development of an appropriate model, training the model with massive monthly rainfall and temperature data which can be used to analyze any climatic predictions and provide support to deal with adverse future situations. This model determines the appropriate amount of rainfall and temperature varied during the dry season and helps set a relationship with other input variables like humidity and maximum and minimum temperature. The model has been trained and tested using the obtained data and performed well with better accuracy. The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis. Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction. The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size. [ABSTRACT FROM AUTHOR]

Plentiful and diverse Organic wastes such as green, food, pruning and landscaping waste necessitate upon effective and efficient recycling strategies such as composting. In this article, for the first time, machine learning (ML) models have been applied to model compost generation rate as a function of climatic parameters and organic waste content. The data size contained approximately 864 sample points records of the meteorological parameters (Modern-Era Retrospective Analysis), organic waste (Central Pollution Control Board) and compost yields (Open Government Data) data for 2010–2021. The modelling efforts involved the consideration of MLP and traditional ML algorithms namely k-nearest neighbour (kNN), gradient boosting (GB) and random forest (RF) for prediction and autoregressive integrated moving average (ARIMA) model supplemented ML models for long-term forecasting of the compost generation rate. Model validation resulted in an RMSE of 0.757 and R2 of 0.99 for GB model, and a correlation index of 0.68 between observed and predicted values to thereby outperform all other models. However, forecasted data for ten years after the predicted outcomes resulted in the best performance of ARIMA–MLP model with a standard error of 21.1152 and a CP yield of 74,958 kg. Thereby, the findings affirm upon the evidence for the limitations of the broader application of the empirical approaches and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific compost prediction and forecasting models to assist integrated circular agricultural system development for a sustainable global future. [ABSTRACT FROM AUTHOR]

Artemisia jacutica Drob. (Yakut wormwood) is a biennial plant endemic to Eastern Siberia. This species is distributed across the Republic of Sakha (Yakutia) and southern Central Siberia, colonizing salt meadows and grasslands, steppe areas, saline to sodic surface soil patches, abandoned agricultural lands, and ruderal habitats. The species is rather rare in Buryatia, due to the southeastern frontier of its area. The essential oil of the Yakut wormwood contains chamazulene as a principal compound component with an anti-inflammatory effect. The data generated within the survey and the published statistics indicate that the climatic conditions affect the formation of the essential oil component composition in plants, which is obviously appropriate to provide the best adaptation of the plants to their environmental growing conditions. Therefore, the effects of climatic conditions on the essential oil component composition in the Yakut wormwood plants result in the formation of two chemotypes known as Buryat and Yakut. It has been ascertained that the formation of these chemotypes occurs due to the differences between the rates of heat and moisture supply to the lands favorable for plant growth, expressed by the hydrothermal coefficient (HTC). The temperature–humidity extremeness parameters defining the coefficient based on annual data greatly affect the ratio of components in the essential oil. The climate-related factors (air temperature, precipitation, etc.) and their integrated potential can influence certain constituents and their proportions in the essential oil, affecting their variability. [ABSTRACT FROM AUTHOR]

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash–Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site. [ABSTRACT FROM AUTHOR]

Climate change threatening the word food security including fisheries production. The current study aimed to find the relationship between different climatic variables and fish production by generating spatial maps. The climatic parameters data viz., mean maximum temperature; mean minimum temperature; total rainfall, and mean wind speed were plotted in GIS (Geographical Information System) platform and thematic mapping of it's for all the districts of Maharashtra for the year 1975 and 2017 on quarter basis was done using QGIS 3.6.1. The thematic maps of the percentage change in parameters of all the quarters of the year 2017 and 1975 were done for all the zone of Maharashtra. In addition, the thematic maps of the percentage change in parameters along with fish production of all the quarters of the year 2017-18 for the year 2012 were done for all the zone. The sensitivity analysis of climatic parameters with inland fish production for Maharashtra state and all the seven zones were done using GAM (Generalized Additive Model). The mean maximum temperature (MMAX) and total monthly rainfall (TMRF) were found to the most significant factors in the prediction of inland fish production. The average increase in MMAX during the 2nd and 3rd quarters (April to September) was less than the average increase in MMAX during the 1st and 4th quarters, whereas, in case of rainfall an average decrease was estimated during the 2nd and 3rd quarter. The current study found that Nagpur and Pune were the two highest fish-producing regions, and Konkan and Aurangabad were the two least productive regions. [ABSTRACT FROM AUTHOR]

Motivation: Leaf traits represent an important component of plant functional strategies, and those related to carbon fixation and nutrient acquisition form the leaf economics spectrum. However, observations of functional leaf traits are underrepresented in tropical regions in comparison with those in temperate areas. Brazil, a country with continental scale and vast biodiversity is a timely example, where many biomes are impacted by human activities and climate change. However, leaf traits relevant to understand vegetation responses to these impacts remain poorly quantified for many species found in the country. We compiled an extensive data set of four functional leaf traits for native woody species occurring in the Brazilian territory. In addition to trait observations, sampling dates and geo‐references were compiled and climatic parameters and soil properties of each sampling site were extracted from several databases. Main types of variables contained: The LT‐Brazil data set contains 3,479, 1,216, 775 and 775 clean observations of leaf mass per area, leaf nitrogen (N) concentration per unit mass, leaf phosphorus (P) concentration per unit mass, and leaf N : P ratio, respectively, from native woody species, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties. Spatial location and grain: We compiled trait observations from 223 sites under native vegetation distributed in all main biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa and Pantanal) across the Brazilian territory. Time period and grain: The data represent information published and/or sampled during the last 25 years. Major taxa and level of measurement: Our compilation was focused on trait data observed for native woody species, excluding monocots, palm trees, herbs, and hemiparasitic plants. Thus, 108, 478 and 1,321 botanical families, genera and species were included, covering c. 9% of the woody angiosperm flora of Brazil. Software format: Data are provided as comma‐separated value (.csv) files. [ABSTRACT FROM AUTHOR]