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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]

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.