Years Of Historical Weather Data Research Articles

Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data

Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture.

Analyses of Climate Variability and Trends in the Oil Palm Belt of Ghana

The current production of oil palm in Africa has been severely limited by long periods of drought and perennial floods due to the increased rates of climate change. The study was aimed at assessing the climatic variability trends in 7 major Oil Palm growing areas (Benso, Mpohor, Bogoso, Twifo Praso, Kade, Juabeng and Brewaniese) located in the oil palm belt in Ghana spanning the semideciduous and the wet evergreen rainforest zones of Ghana using 30 years of historical weather data (1993-2022). The data was analysed to evaluate the extent of seasonal changes in weather over the 30-year period using the Statistical Package for Social Sciences (SPSS 20.0 version), using descriptive statistics and time series analysis. Trend analysis was conducted using the non-parametric Mann-Kendall (MK) and Sen’s slope estimator tests. A seasonal trend in rainfall was observed at all sites with maximum levels observed between May and July as well as a minor rainy season in September to Mid-November. The Mann-Kendall trend analysis showed a generally increasing trend at most stations in rainfall, temperature and solar radiation. A generally increasing trend in annual rainfall was observed from 1993-2022 with significant trends at Mpohor and Juabeng. The trend was higher in the moist evergreen rainforest zone than in the semi-deciduous rainforest zone. An average increase of 6.7 mm/yr in rainfall trend was observed for the period. An increasing trend in monthly maximum, minimum and mean temperatures were also observed. Significant increases in mean temperatures were observed at Bogoso (0.022°C/yr), Benso (0.018°C/yr), Juabeng (0.011°C/yr) and Mpohor (0.017°C/yr). The trend in mean annual solar radiation was generally not significant except in Benso where the mean solar radiation increased at the rate of 0.044 MJ/day. Higher drought levels in the first half year combined with very heavy rains later in the year are features of the ongoing climate change menace.

Powering In-Field Continuous Robotic Systems Using Solar Energy Systems

Highlights Because of variable weather conditions, battery capacity must be at least 2.5 times larger than daily energy demands for continuous off-grid power. At a minimum, the nominal solar panel array size must be large enough to provide the daily energy demand in 3 hours. Because of a trade-off relationship, minimal battery capacity or solar panel size requires more than the minimum for the other component. Limiting operation to March through October reduced required sizes. Adding a backup generator with minimal usage of 50 kg of propane per year also reduced sizes. Abstract. Continuous in-field robotic and automated systems are challenged by the need for a continuous supply of power. This off-grid power supply can be diesel, gasoline, propane, or alternative energy sources like solar or wind. This study investigates the required solar panel arrays and energy storage capacities for an off-grid solar-battery power supply system. This study used 22 years of historical weather data from Lexington, Kentucky, and processed it with the National Renewable Energy Laboratory’s System Advisor Model to model hourly energy output from solar panel arrays of 2 kW, 3 kW, 4 kW, 5 kW, 10 kW, 15 kW, and 20 kW. This was combined with an energy storage model (simulated battery capacities of 5 kWh, 10 kWh, 15 kWh, 20 kWh, 30 kWh, 40 kWh, 50 kWh, and 60 kWh) and an energy use model (simulated daily energy demands of 3.6 kWh, 6 kWh, 12 kWh, 18 kWh, and 24 kWh) to determine which systems could operate over the entire 22 years without reaching critical minimum levels. For a 3.6 kWh daily energy demand, 32 combinations of solar panel arrays and battery capacities remained above critical levels, while this was only 12 combinations for a 6 kWh daily energy demand and no combination could support larger energy demands. An example a feasible system to support a 6 kWh energy demand is one that uses a 15 kW panel array (42 standard panels with an approximate system hardware cost of $22,650) and 30 kWh of energy storage capacity (which with lead acid batteries would cost $11,661 and require a volume of 300 L). There is a tradeoff between solar panel array size and battery capacity so other feasible systems can be realized by increasing one to make up for reductions in the other variable. Additionally, limiting system operation to March through October can reduce the size of the required panel array (to between 33% to 67% of the original size) or energy storage capacity (to between 40% to 67% of the original capacity) for a given daily energy load. The size of the solar battery system could be further decreased by adding an emergency generator that uses no more than 50 kg of propane annually. Battery capacities could be reduced by 17% to 38% compared to the original system, and the solar panel arrays could be reduced by 13% to 15% compared to the original. Specific reduction amounts depended on the specific configuration of the original system and the daily load level that had to be supported. A system that only operated from March through October with a backup generator could also support the 12 and 18 kWh daily energy demands, which could not be supported by the original system. Even with limiting operation to March through October and using an emergency generator, the minimal battery capacity was at least 2.5 times larger than the daily energy demand, and the solar panel array had to be large enough that the nominal output would provide the daily energy demand in 3 hours of full sun. However, because of the trade-off relationship, these minimums cannot be attained together. While continuous in-field robotic deployments show promise for improving agriculture, it will be critical to design with energy efficiency in mind and the off-grid power supply for wide-spread use across agricultural lands. Keywords: Agriculture robot, Autonomous, Batteries, Computer modeling, Energy, Power, Simulation, Sizing, Solar, System.

Modelling the response of tomato on deficit irrigation under greenhouse conditions

To optimize crops irrigation strategy is crucial to improve the production sustainability in a climate change scenario characterized by an ever-increasing water shortage. Crops simulation models, combined with experimental data, can be useful tools. AquaCrop, a crop water productivity model, has been widely used to reach this aim in open field condition but it has been limited adopted under greenhouse conditions. This study aims to calibrate and validate AquaCrop model through a greenhouse tomato cultivation using a split plot experimental design. Crop irrigation management was the main treatment [full irrigation (FI) at 100 % crop evapotranspiration (ETc) vs. deficit irrigation (DI) at 75 % of FI] and fertilization [no fertilization, mineral fertilization, organic fertilization with compost, and organic fertilization with sieved (< 2 mm) compost] the subplots. Fresh yield, above-ground biomass, water productivity, and net irrigation requirements were simulated. The validated model also permitted to evaluate the impacts of changing temperature outside the greenhouse on fruits yield, biomass, and water productivity using 30 years of historical weather data. The results showed that the model accurately estimated crop parameters, although it tended to overestimate soil water content. On average, DI reduced fruit yield by 14.1 % compared to FI. Over the last 30 years, the validated model permitted to calculate an average fruits yield reduction due to DI of 12.6 %. Our findings suggest that models like AquaCrop can assist in optimizing greenhouse agriculture by predicting crop performance under different conditions. Our study also highlights that external temperature and AquaCrop can be used to estimate tomato yields in the greenhouse by providing decision support tools for end-users (farmers, farmer associations, and policymakers) seeking sustainable and efficient greenhouse farming practices in a changing climate.

Rainfall Prediction using Extreme Gradient Boosting

Abstract: Rainfall greatly affects human life in various sectors including agriculture, transportation, etc. and also can affect natural disasters such as drought, floods, and landslides. This situation prompts us to build an accurate rainfall prediction model so that prescriptive measures can be made. Previous research on rainfall prediction uses models that have their limitations and thus produce poor performance. This study aims to build a multivariate rainfall prediction model using the best performing technique to date namely the Extreme Gradient Boosting. This model is built based on 7 years of historical weather data collected by the weather station.

Maize planting date and maturity in the US central Great Plains: Exploring windows for maximizing yields

Region-specific guidelines for maize in the US Great Plains are needed to account for variability in farmer practices and inherent soil and climate differences. Therefore, it becomes critical to define environment tailored management strategies for maize crop to improve and sustain yields over time. The aims of this study were to: i) define homogenous maize yield environments classified as related to the maximum yield and its stability over time in the US central Great Plains region; ii) explore the combinations of planting date and hybrid maturity that lead to optimal yield for each of the previously defined homogenous maize yield environments; and iii) identify environments presenting different windows for maximizing yields (combinations of planting date by hybrid maturity). Hybrid maturity (growth cycle) by planting date combinations were explored at 70 sites within central and eastern Kansas and northern Oklahoma (herein defined as the US central Great Plains region) using 30 years of historical weather data. In addition to the analysis of an extensive maize field dataset, a crop growth model, Agricultural Production Systems Simulator (APSIM), was utilized to evaluate the crop yield and phenology of all hybrid maturity by planting date combinations to define environment tailored management strategies. The simulated yields were spatially clustered based on attainable yield and stability over time to delimit more homogeneous productivity environments. Overall, greater yields were attained with long-maturing hybrids (comparative relative maturity, CRM, 101 and 115). Delaying the planting date from April to mid-May decreased yield in eastern environments. Moreover, late planting maintained yields and increased stability in central environments. For central Kansas and Oklahoma, anticipation of physiological maturity date (3–16 days) with reduced yield penalties (<5%) were possible by using short-season hybrid maturities (CRM 85). Future scenarios for intensification could be explored in those environments presenting more windows for maximizing yields, either with early planting and harvest dates or via late planting providing time for a short crop before maize. Adoption of more region-specific guidelines for maize management could help to optimize yield and its stability over time and seek further opportunities to diversify risk and intensify our current maize-based farming systems in the US central Great Plains.

Short-Term Weather Forecast Skill of Artificial Neural Networks

Abstract We evaluate the short-term weather forecast performance of three flavors of artificial neural networks (NNs): feed forward back propagation, radial basis function, and generalized regression. To prepare the application of the NNs to an operational setting, we tune NN hyperparameters using over two years of historical data. Five objective guidance products serve as predictors to the NNs: North American Mesoscale and Global Forecast System model output statistics (MOS) forecasts, the High-Resolution Rapid Refresh (HRRR) model, National Weather Service forecasts, and the National Blend of Models product. We independently test NN performance using 96 real-time forecasts of temperature, wind, and precipitation across 11 U.S. cities made during the WxChallenge, a weather forecasting competition. We demonstrate that all NNs significantly improve short-range weather forecasts relative to the traditional objective guidance aids used to train the networks. For example, 1-day maximum and minimum temperature forecast error is 20%–30% lower than MOS. However, NN improvement over multiple linear regression for short-term forecasts is not significant. We suggest this may be attributed to the small number of training samples, the operational nature of the experiment, and the short forecast lead times. Regardless, our results are consistent with previous work suggesting that applying NNs to model forecasts can have a positive impact on operational forecast skill and will become valuable tools when integrated into the forecast enterprise. Significance Statement We used approximately two years of historical weather data and objective forecasts for a number of cities to tune a series of artificial neural networks (NNs) to forecast 1-day values of maximum and minimum temperature, maximum sustained wind speed, and quantitative precipitation. We compare forecast error against common objective guidance and multiple linear regression. We found that the NNs exhibit about 25% lower error than common objective guidance for temperature forecasting and 50% lower error for wind speed. Our results suggest that NNs will be a valuable contributor to improving weather forecast skill when adopted into the existing forecast enterprise.

Research on design day generation method for air-conditioning system design considering the coincidence of hourly variation coefficient

The design day is the basis for calculating the cooling load, and a reasonable design cooling load is essential for balancing economics and reliability. The coincidence among weather parameters of design day is a key means to ensure the accuracy, while the coincident design-day hourly variation coefficients are less considered in existing studies. For this reason, this paper proposes a method to establish the coincident hourly variation coefficient for design days. The method first extracts the normalized hourly variation coefficients from 30 years of historical weather data. Based on this, a three-parameter coupled coefficient joint matrix is established and K-Means clustering is used to select representative coincident coefficients. Finally, a “typical line” method based on the weighting idea is proposed, and the final coincident design-day hourly variation coefficients are selected from the actual weather. To verify the rationality of the new method, different load scenarios were constructed with three design days and five typical buildings, and the rationality index was established by the ratio of indoor to outdoor risk levels. The results show that the rationality of the new method reaches 0.81, while the original method (Chinese and ASHRAE standard) is only 0.03 and 0.25 in Tianjin; further study shows that the new method is applicable in different climate zones and between different typical buildings, which provides a basis for the improvement of the existing design day generation method.

Rainfall Prediction System Using Machine Learning Fusion for Smart Cities.

Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.

Peach [Prunus persica (L.) Batsch] Cultivars Differ in Apparent Base Temperature and Growing Degree Hour Requirement for Floral Bud Break.

Bud break timing in peach [Prunus persica (L.) Batsch] is determined by the sequential fulfillment of a chilling requirement (CR) and a heat requirement (HR) for development. Genotypic variation in CR has been well characterized in peach. Adapting peaches to low chilling environment through reduced CR can make them susceptible to crop destroying spring frosts, if bloom occurs too early. Potential variation in HR between accessions has received less attention due to the methodological difficulty in assessing HR independently of CR. HR could vary in the magnitude of growing degree hours (GDHs) and/or the base temperature at which GDH accumulation begins. Characterizing HR traits in peach accessions could allow improved bloom time modeling and selection of phenotypes with improved spring frost avoidance through delayed bloom. We estimated GDH and apparent base temperature for floral bud break by observing time to floral bud break at several constant forcing temperatures. We evaluated 54 peach accessions (representing a range of CR) in which chilling had been saturated after >1,700 h at 3°C. Accessions differed widely in both the GDH requirement (2,015 to 11,191°C⋅h) and apparent base temperature (−1.85 to 8.69°C) for GDH accumulation. GDH and apparent base temperature were negatively correlated. A simulation exercise was performed to assess relative importance of varying base temperature vs. GDH for delaying bloom at different chilling accumulations at three locations in the southeastern United States using 30 years of historical weather data. The aim of this study was to determine whether there may be unrecognized diversity in peach germplasm for two HR traits (base temperature and thermal time) to enable breeding efforts to delay floral bud break and reduce the frost exposure risk of developing flowers and fruits. Our results suggest that selecting cultivars for increased GDH would be a safer, more reliable strategy for delaying bloom than increasing base temperature for GDH accumulation.

Impact of crop management and environment on the spatio-temporal variance of potato yield at regional scale

• We determine and quantify the main drivers of potato yield variability under irrigated conditions. • We used correlation analysis, decomposition of variability sources and maps to assess potato yield variability. • Using APSIM, three planting dates × four irrigation strategies were simulated. • Crop management factors explained the greatest potato yield variability when compared to environmental factors. • We identified a need to discriminate the drivers of potato yield variability regionally. The uncertainties associated with crop model inputs can affect the spatio-temporal variance of simulated yields, particularly under suboptimal irrigation. The aim of this study was to determine and quantify the main drivers of irrigated potato yield variance; as influenced by crop management practices as well as climate and soil factors. Using a locally calibrated crop model (APSIM), three planting dates × three irrigation strategies (plus a non-water limited treatment) were simulated using 30 years of historical weather data across potato production areas in Tasmania, Australia. We used (i) correlation analysis, (ii) variance decomposition and (iii) maps to visualise the spatial decomposition of variance of potato yield. Our results showed that the implementation of potential irrigation compensated for the impact of planting date on climate drivers of simulated yield and changed the most important yield driving factors from irrigation and planting date to global solar radiation (r = 0.69−0.81). Under early-planting, we found positive correlations of simulated yield vs. global solar radiation (r up to 0.75 under high and medium irrigation) and between the simulated yield and rainfall (r up to 0.55 under low irrigation). In general, a mix of negative and positive correlations were found for minimum and maximum temperature depending on soil type. Using variance decomposition analysis, we found that crop management factors explained the greatest yield variance depending on soil type related to plant available water capacity (PAWC). For soils with high PAWC (>200 mm), most variance was explained by global solar radiation (56–62 %) followed by planting date (43–47 %). However, when PAWC values decreased from 242 mm to 94 mm, the contribution of global solar radiation and planting date were reduced from 62 % to 5.8 % and from 47 % to 4.4 %, respectively, and the contribution of irrigation strategy increased from 0.4%–32.5%. We identified a need to quantify and differentiate the variance contribution of environmental and crop management factors on crop yield and within these factors, discriminate the key drivers of yield variance at regional scale.

Identifying Suitable Genotypes for Different Cassava Production Environments—A Modeling Approach

Crop simulation models can be used to identify appropriate genotypes and growing environments for improving cassava yield. The aim of this study was to determine the best genotypes for different cassava production environments using the cropping system model (CSM)–MANIHOT–Cassava. Data from cassava experiments that were conducted from 2009–2011 and 2014–2015 at Khon Kaen, Thailand, were used to evaluate the model. Simulations were then conducted for different scenarios using four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11, and CMR38–125–77), twelve planting dates (at monthly intervals starting in January and ending in December), and ten locations in Thailand under fully irrigated and rainfed conditions using 30 years of historical weather data. Model evaluation with the experimental data for total biomass and storage root yield indicated that the model classified well for relative productivity among different planting dates. The model indicated that growing cassava under irrigated conditions generally produced higher biomass and storage root yield than under rainfed conditions. The cassava genotype CMR38–125–77 was identified for high biomass, while the genotype Rayong 9 was identified as a good genetic resource for high yield. The December planting date resulted in the highest biomass for all locations, while the February planting date produced the highest storage root yield for almost all locations. The results from this study suggest that the CSM–MANIHOT–Cassava model can assist in determining suitable genotypes for different cassava production environments for Thailand, and that this approach could be applicable to other cassava growing areas.

Modeling soil texture and residue management effects on conservation agriculture productivity in Nepal

Conservation agriculture (CA) is characterized by zero or minimum tillage, permanent land cover with crop residue or plant growth, and crop rotation. Only a few studies have addressed the impact of soil texture on relative CA:CP productivity, and none have simultaneously evaluated soil texture interactions with optimal residue retention rate. We simulated the interaction effects of soil texture and residue retention on crop yield for CA and conventional practices (CP) for a monsoon-season maize [Zea mays L.] and dry-season wheat [Triticum aestivum L.] double-cropping system in the midhills of Nepal. The simulation used 35 years of historical weather data, fifteen soil profile descriptions representing a range of soil texture classes, and variable rates of residue retention. Crop models were calibrated with field data and simulation results evaluated against measured field data from other published studies. Optimal maize residue retention rate was 60 % and optimal wheat residue retention rate was 10 %, regardless of soil texture. At optimal residue retention, CA maize yielded 0.37 Mg ha−1 less than CP on clayey soils and 0.25 Mg ha−1 less than CP on loam soils, equivalent to 5 % and 4 % yield loss, respectively. However, mean wheat yield was greater with CA than CP for all soil textures, with an average yield increase of 0.17 Mg ha−1, or 18 % increase in wheat yield. The time required for CA to produce equivalent maize yields to CP increased by approximately 1.23 yr per % of soil clay. Possible non-linearities exist in the relationship between soil clay and time-to-equivalent maize yield. Wheat yield was more stable, but maize yield less stable, with CA compared with CP for all soil texture categories.

Rainfall prediction using Extreme Gradient Boosting

Rainfall greatly affects human life in various sectors including agriculture, transportation, etc. and also can affect natural disasters such as drought, floods, and landslides. This situation prompts us to build an accurate rainfall prediction model so that prescriptive measures can be made. Previous research on rainfall prediction uses models that have their limitations and thus produce poor performance. This study aims to build a multivariate rainfall prediction model using the best performing technique to date namely the Extreme Gradient Boosting. This model is built based on 7 years of historical weather data collected by the weather station. The result had demonstrated that the model is capable of producing accurate predictions for daily rainfall estimates with training RMSE of 2.7 mm and the testing MAE of 8.8 mm.

Power fluctuations in high-installation- density offshore wind fleets

Abstract. Detailed simulation of wind generation as driven by weather patterns is required to quantify the impact on the electrical grid of the power fluctuations in offshore wind power fleets. This paper focuses on studying the power fluctuations of high-installation-density offshore fleets since they present a growing challenge to the operation and planning of power systems in Europe. The Belgian offshore fleet is studied because it has the highest density of installation in Europe by 2020, and a new extension is expected to be fully operational by 2028. Different stages of the future installed capacity, turbine technology, and turbine storm shutdown technologies are examined and compared. This paper analyzes the distribution of power fluctuations both overall and during high wind speeds. The simulations presented in this paper use a new Student t-distributed wind speed fluctuation model that captures the missing spectra from the weather reanalysis simulations. An updated plant storm shutdown model captures the plant behavior of modern high-wind-speed turbine operation. Detailed wake modeling is carried out using a calibrated engineering wake model to capture the Belgium offshore fleet and its tight farm-to-farm spacing. Long generation time series based on 37 years of historical weather data in 5 min resolution are simulated to quantify the extreme fleet-level power fluctuations. The model validation with respect to the operational data of the 2018 fleet shows that the methodology presented in this paper can capture the distribution of wind power and its spatiotemporal characteristics. The results show that the standardized generation ramps are expected to be reduced towards the 4.4 GW of installations due to the larger distances between plants. The most extreme power fluctuations occur during high wind speeds, with large ramp-downs occurring in extreme storm events. Extreme ramp-downs are mitigated using modern turbine storm shutdown technologies, while extreme ramp-ups can be mitigated by the system operator. Extreme ramping events also occur at below-rated wind speeds, but mitigation of such ramping events remains a challenge for transmission system operators.

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat.

Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

Impacts of Inter-annual Wind and Solar Variations on the European Power System

SummaryWeather-dependent renewable energy resources are playing a key role in decarbonizing electricity. There is a growing body of analysis on the impacts of wind and solar variability on power system operation. Existing studies tend to use a single or typical year of generation data, which overlooks the substantial year-to-year fluctuation in weather, or to only consider variation in the meteorological inputs, which overlooks the complex response of an interconnected power system. Here, we address these gaps by combining detailed continent-wide modeling of Europe's future power system with 30 years of historical weather data. The most representative single years are 1989 and 2012, but using multiple years reveals a 5-fold increase in Europe's inter-annual variability of CO2 emissions and total generation costs from 2015 to 2030. We also find that several metrics generalize to linear functions of variable renewable penetration: CO2 emissions, curtailment of renewables, wholesale prices, and total system costs.

Evaluation of Nitrogen Fertilization Patterns Using DSSAT for Enhancing Grain Yield and Nitrogen Use Efficiency in Rice

ABSTRACTTemporal variation of rice growth and nitrogen (N) uptake generally follow a sigmoid curve and may respond positively to the N-fertilizer application at critical growth stages. In this study, it was hypothesized that the amount of N-fertilizer applied at critical growth stages possibly follows a geometric pattern such as line, parabola, and sinusoidal to attain maximum yield and nitrogen use efficiency. To test and identify the best pattern, short-term modeling-field testing-long-term modeling strategy was followed. The patterns with the highest simulated yield and nitrogen use efficiency from short-term modeling were tested in the field. Finally, long-term evaluation of N-fertilization patterns was performed using 25 years of historical weather data, resulting in the line pattern with 14% more yield and 25% less NO3− leaching in comparison to the conventional N-Fertilization pattern. Therefore, line pattern may be adopted to enhance the yield and nitrogen use efficiency in rice.

Simulating weather effects on potato yield, nitrate leaching, and profit margin in the US Pacific Northwest

The US Pacific Northwest is one of the most productive potato regions in the world. However, due to the high inputs, nitrate contamination of groundwater is frequently documented, and maximizing crop productivity while minimizing nitrate leaching is still challenging. The goal of this study was to assess how irrigation level, soil type, and weather condition during various phenological phases would affect tuber yield and the associated nitrate leaching and profit margin. The Cropping System Model (CSM)-SUBSTOR-Potato was used to simulate the response variables for various scenarios that comprised two soil types, five irrigation levels, five phenological phases, five weather conditions, and 75 years of historical weather data for 3 locations in this region. The simulation results showed that nitrate leaching was higher with a higher amount of irrigation and for a lighter soil. Tuber yield and profit margins were lowest for a lighter soil and highest for 300 mm of irrigation for an extremely-drained soil and 400 mm of irrigation for a well-drained soil. The increase in profit margins with an increase in total irrigation up to 400 mm was highest for a well-drained soil, whereas the decrease in profit margins with an increase in irrigation beyond a total amount of 300 mm was larger for an extremely-drained soil. For the different types of weather scenarios that were studied, only severe hot weather had an impact on tuber yield and profit margins. The reduction was highest at tuber bulking and significant when hot weather continued from sprout development through tuber bulking or from plant establishment through tuber maturation. However, any change in weather condition from the long-term average for any growth phase did not affect leaching. These findings might be helpful to potato growers in this region to protect their potatoes from adverse weather conditions through appropriate mitigation strategies.

The smoothing effect for renewable resources in an Afro-Eurasian power grid

Abstract. Renewable power systems have to cope with highly variable generation. Increasing the spatial extent of an interconnected power transmission grid smooths the feed-in by exchange of excess energy over long distances and therefore supports renewable power integration. In this work, we investigate and quantify the balancing potential of a supergrid covering Europe, Africa and Asia. We use ten years of historical weather data to model the interplay of renewable generation and consumption and show that a pan-continental Afro-Eurasian supergrid can smooth renewable generation to a large extent and reduce the need for backup energy by around 50 %. In addition, we show that results for different weather years vary by up to approximately 50 %.