Search Results (69)

Breeding programs often overlook the use of root traits. Therefore, we investigated the relevance of sorghum root traits in explaining its adaptation to combined drought and heat stress (CDHS). Six (i.e., three pre-release lines + three checks) sorghum genotypes were established at two low-altitude (i.e., <600 masl) locations with a long-term history of averagely very high temperatures in the beginning of the summer season, under two management (i.e., CDHS and well-watered (WW)) regimes. At each location, the genotypes were laid out in the field using a randomized complete block design (RCBD) replicated two times. Root trait data, namely root diameter (RD), number of roots (NR), number of root tips (NRT), total root length (TRL), root depth (RDP), root width (RW), width–depth ratio (WDR), root network area (RNA), root solidity (RS), lower root area (LRA), root perimeter (RP), root volume (RV), surface area (SA), root holes (RH) and root angle (RA) were gathered using the RhizoVision Explorer software during the pre- and post-flowering stage of growth. RSA traits differentially showed significant (p < 0.05) correlations with grain yield (GY) at pre- and post-flowering growth stages and under CDHS and WW conditions also revealing genotypic variation estimates exceeding 50% for all the traits. Regression models varied between pre-flowering (p = 0.013, R² = 47.15%, R² Predicted = 29.32%) and post-flowering (p = 0.000, R² = 85.64%, R² Predicted = 73.30%) growth stages, indicating post-flowering as the optimal stage to relate root traits to yield performance. RD contributed most to the regression model at post-flowering, explaining 51.79% of the 85.64% total variation. The Smith–Hazel index identified ICSV111IN and ASAREACA12-3-1 as superior pre-release lines, suitable for commercialization as new varieties. The study demonstrated that root traits (in particular, RD, RW, and RP) are linked to crop performance under CDHS conditions and should be incorporated in breeding programs. This approach may accelerate genetic gains not only in sorghum breeding programs, but for other crops, while offering a nature-based breeding strategy for stress adaptation in crops. Full article

Most vegetable crop production in Mississippi (MS) occurs during the summer, characterized by high temperature and relative humidity. Lettuce yield and harvest quality are significantly affected by heat stress. To avoid the heat stress of the summer months, lettuce production in MS is either produced in controlled environments or during the winter months with cooler temperatures. This period, however, coincides with months with low solar radiation and shorter day length, resulting in a longer growing season and poor harvest quality. Therefore, this study was conducted to determine the optimum duration of light supplement on the growth, yield, and water use of greenhouse (GH) lettuce during the winter season in north Mississippi. In this study, three daily supplemental light duration regimes, 0 h, 4 h, and 8 h, starting at sunset, were evaluated across two lettuce cultivars, Green Forest (GF) and Ruby (RB). The study indicated that supplemental lighting significantly increased lettuce growth, yield, and water use. Although day length extension from 4 to 8 h of supplemental light had no yield benefits on the RB cultivar, extending day length from 4 to 8 h increased GF yield by 42%. It was also observed that the effects of light supplementation during low natural light quality at early or later growth stages differ between cultivars. Based on the results obtained from this study, a 4 h and 8 h post-sunset light supplementation is considered optimum for RB and GF lettuce cultivars, respectively, during the winter growing season in MS. Full article

This study evaluated the effects of photovoltaic (PV) arrays on critical surface parameters through analysis of observational data collected from a utility-scale PV power station located in Wujiaqu City, Xinjiang, in 2021. The results reveal that: (1) Installation of PV panels reduces surface albedo, which is significantly altered by dust storm conditions; (2) the installation of PV arrays increases the aerodynamic and thermal roughness length by increasing the frictional velocity across the mixed underlying surface; (3) the overall transport coefficients within the PV plant are higher than that of the reference site, with greater diurnal variation than nocturnal variation. The overall transport coefficient is highest in the unstable stratification conditions and lowest under stable stratification conditions; and (4) soil thermal property parameters exhibit seasonal variations. Significant changes in thermal conductivity and specific heat capacity were observed during spring thaw, high and fluctuating diffusivity in summer, and low and stable values in winter. The findings demonstrate that installing PV arrays in arid regions modifies surface energy balance and heat transfer characteristics. This provides a basis for optimizing PV station layouts and conducting climate impact assessments. Full article

With the growing demand for global energy transition, integrated energy systems (IESs) have emerged as a key pathway for sustainable development due to their deep coupling of multi-energy flows. Accurate load forecasting is crucial for IES optimization and scheduling, yet conventional methods struggle with complex spatio-temporal correlations and long-term dependencies. This study proposes ST-ScaleFusion, a multi-scale time–frequency complementary hybrid model to enhance comprehensive energy load forecasting accuracy. The model features three core modules: a multi-scale decomposition hybrid module for fine-grained extraction of multi-time-scale features via hierarchical down-sampling and seasonal-trend decoupling; a frequency domain interpolation forecasting (FI) module using complex linear projection for amplitude-phase joint modeling to capture long-term patterns and suppress noise; and an FI sub-module extending series length via frequency domain interpolation to adapt to non-stationary loads. Experiments on 2021–2023 multi-energy load and meteorological data from the Arizona State University Tempe campus show that ST-ScaleFusion achieves 24 h forecasting MAE values of 667.67 kW for electric load, 1073.93 kW/h for cooling load, and 85.73 kW for heating load, outperforming models like TimesNet and TSMixer. Robust in long-step (96 h) forecasting, it reduces MAE by 30% compared to conventional methods, offering an efficient tool for real-time IES scheduling and risk decision-making. Full article

Energy transition is a challenge for remote northern communities mainly relying on diesel for electricity generation and space heating. Solar-assisted ground-coupled heat pump (SAGCHP) systems represent an alternative that was investigated in this study for the Kuujjuaq Forum, a multi-activity facility in Nunavik, Canada. The energy requirements of community buildings facing a subarctic climate are poorly known. Based on energy bills, technical documents, and site visits, this study provided an opportunity to better document the energy consumption of such building, especially considering the recent solar photovoltaic (PV) system installed on part of the roof. A comprehensive model was developed to analyze the building’s heating demand and simulate the performance of a ground-source heat pump (GSHP) coupled with PV panels. The air preheating load, accounting for 268,200 kWh and 47% of the total heating demand, was identified as an interesting and realistic load that could be met by SAGCHP. The GSHP system would require a total length of at least 8000 m, with boreholes at depths between 170 and 200 m to meet this demand. Additional PV panels covering the entire roof could supply 30% of the heat pump’s annual energy demand on average, with seasonal variations from 22% in winter to 53% in spring. Economic and environmental analysis suggest potential annual savings of CAD 164,960 and 176.7 tCO₂eq emissions reduction, including benefits from exporting solar energy surplus to the local grid. This study provides valuable insights on non-residential building energy consumption in subarctic conditions and demonstrates the technical viability of SAGCHP systems for large-scale applications in remote communities. Full article

Quantifying extreme weather events (EWEs) and understanding their impacts on vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs—extreme heat, extreme cold, extreme wetness (surplus precipitation), and extreme drought (lack of precipitation). The EWE extremity thresholds were found statistically using detrended long time series (2000–2022) ERA5 meteorological data through z-score transformation. The analysis was based on a grassland ecosystem in the Mongolian Plateau (MP) from 2000 to 2022. Using solar-induced chlorophyll fluorescence data and event coincidence analysis, we evaluated the probability of GL anomalies coinciding with EWEs and assessed the vegetation sensitivity to climate variability. The analysis showed that 83.7% of negative and 87.4% of positive GL anomalies were associated with one or more EWEs, with extreme wetness (27.0%) and extreme heat (25.4%) contributing the most. These findings highlight the dominant role of EWEs in shaping phenological shifts. Negative GL anomalies were more strongly linked to EWEs, particularly in arid and cold regions where extreme drought and cold shortened the growing season. Conversely, extreme heat and wetness had a greater influence in warmer and wetter areas, driving both the lengthening and shortening of GL. Furthermore, background hydrothermal conditions modulated the vegetation sensitivity, with warmer regions being more susceptible to heat stress and drier regions more vulnerable to drought. These findings emphasize the importance of regional weather variability and climate characteristics in shaping vegetation phenology and provide new insights into how weather extremes impact ecosystem stability in semi-arid and arid regions. Future research should explore extreme weather events and the role of human activities to enhance predictions of vegetation–climate interactions in grassland ecosystems of the MP. Full article

Temperature variations have a significant impact on the performance and durability of rigid (concrete) pavement. As concrete is subjected to daily and seasonal temperature changes, it experiences thermal expansion and contraction. These movements, if not properly managed, can lead to cracking, joint deterioration, and loss of structural integrity. The pavement system is adversely affected by intense heat and significant flooding. This study aims to analyze the impact of several parameters on the performance of rigid pavement under typical, thermal, and flooding situations. This study investigates the properties of concrete and the dimensional design of rigid pavement with FEACONS IV software to assess their impact on the performance of concrete pavement during thermal and flooding conditions. The main conclusions of this study derived from the FEACONS IV analysis are as follows. Rigid pavement can enhance load-carrying capacity due to a lower elastic modulus, adequate flexural strength, and aggregates with a lower coefficient of thermal expansion. Increased thickness of concrete slabs and shorter slab lengths assist in minimizing load- and temperature-induced stresses. The increase in the subgrade modulus reaction value during flooding conditions improves pavement strength. However, in higher thermal conditions, a higher subgrade reaction modulus can increase the stress induced by temperature and load. Rigid pavement using porous limestone aggregate exhibits a reduced elastic modulus and coefficient of thermal expansion, suggesting higher resilience compared to rigid pavement composed of river gravel or granite. The findings suggest that higher thermal conditions will cause pavement damage. Agencies need to account for higher temperatures while designing and maintaining pavement. Flooding saturates the concrete pavement and subgrade layer, adversely affecting its performance over time. Full article

The recent success of emission reduction policies in China has significantly lowered sulfur dioxide (SO₂) levels. However, accurately forecasting these concentrations remains challenging due to their inherent non-stationary tendency. This study introduces an innovative hybrid deep learning model, RF-VMD-Seq2Seq, combining the Random Forest (RF) algorithm, Variational Mode Decomposition (VMD), and the Sequence-to-Sequence (Seq2Seq) framework to improve SO₂ concentration forecasting in five coastal cities of northern China. Our results show that the predicted SO₂ concentrations closely align with observed values, effectively capturing fluctuations, outliers, and extreme events—such as sharp declines the Novel Coronavirus Pneumonia (COVID-19) pandemic in 2020—along with the upper 5% of SO₂ levels. The model achieved high coefficients of determination (>0.91) and Pearson’s correlation (>0.96), with low prediction errors (RMSE < 1.35 μg/m³, MAE < 0.94 μg/m³, MAPE < 15%). The low-frequency band decomposing from VMD showed a notable long-term decrease in SO₂ concentrations from 2013 to 2020, with a sharp decline since 2018 during heating seasons, probably due to the ‘Coal-to-Natural Gas’ policy in northern China. The input sequence length of seven steps was recommended for the prediction model, based on high-frequency periodicities extracted through VMD, which significantly improved our model performance. This highlights the critical role of weekly-cycle variations in SO₂ levels, driven by anthropogenic activities, in enhancing the accuracy of one-day-ahead SO₂ predictions across northern China’s coastal regions. The results of the RF model further reveal that CO and NO₂, sharing common anthropogenic sources with SO₂, contribute over 50% to predicting SO₂ concentrations, while meteorological factors—relative humidity (RH) and air temperature—contribute less than 20%. Additionally, the integration of VMD outperformed both the standard Seq2Seq and Ensemble Empirical Mode Decomposition (EEMD)-enhanced Seq2Seq models, showcasing the advantages of VMD in predicting SO₂ decline. This research highlights the potential of the RF-VMD-Seq2Seq model for non-stationary SO₂ prediction and its relevance for environmental protection and public health management. Full article

Extreme climate events have become more frequent under global warming, significantly affecting vegetation phenology and carbon cycles in Central Asia. However, the mediating effects of intensity of compound drought and heat events (CDHEs) and compound moisture and heat events (CMHEs) on grassland phenology and their trends in the relative contributions to grassland phenology over time have remained unclear. Based on the calculation results of grassland phenology and compound events (CEs), this study used trend analysis, partial least squares regression structural equation modeling (PLS-SEM), and ridge regression analysis to investigate the mediating effect and the temporal trend in relative contribution of CEs to grassland phenology in Central Asia, and the magnitude of sensitivity of grassland phenology to CEs. This study revealed that the start of season (SOS) was advanced by 0.4 d·a⁻¹, end of season (EOS) was delayed by 0.5 d·a⁻¹, and length of season (LOS) extended by 0.8 d·a⁻¹ in 1982–2022. The duration of the CDHEs (0−37 days) was greater than that of the CMHEs (0−9 days) in Central Asia. The direct effects of CDHEs and CMHEs on grassland phenology were generally negative, except for the direct positive effect of CDHEs on LOS. The indirect effects of temperature and precipitation on grassland phenology through CDHEs and CMHEs were greater than their direct effects on phenology. The relative contribution of CDHEs to grassland phenology was consistently greater than that of CMHEs, and both the relative contribution curves showed a significant upward trend. The sensitivity of grassland phenology to CDHEs was higher than its sensitivity to CMHEs at 0.79 (SOS), 1.18 (EOS), and 0.72 (LOS). Our results emphasize the mediating effects of CDHEs and CMHEs on grassland phenology. Under the influence of CDHEs and CMHEs, the LOS will further lengthen in the future. Full article

This study evaluated the productive performance and carcass traits of broiler chickens during the warm season in dry tropical conditions. Two hundred, 1-day-old chicks were used. Birds were fed on reduced (RED) and standard (STD) diets, and two essential oils (EOs) levels, 0 and 200 ppm. The RED diets were formulated with 10% less energy and 10% less protein than STD diets. In the starter phase, weight gain was greater (p < 0.01) in birds fed STD than birds fed RED. In the starter phase, productive variables were not affected (p ≥ 0.14) by EOs. In the finisher phase, greater feed intake (p = 0.02) and higher weight gain (p = 0.04) were observed in chickens on STD. Feed conversion ratio was similar (p = 0.97) for STD and RED. Throughout this study (1–42 d), greater feed intake (p = 0.02) and higher weight gain (p < 0.01) were found in chickens on STD. Feed conversion ratio was similar (p = 0.51) for STD and RED. Broiler chickens on EO-supplemented diets had better feed conversion (p ≥ 0.08). Hot carcass weight was greater (p < 0.01) in birds on STD. Greater yields of leg-thigh (p = 0.01), back (p = 0.01), and wings (p < 0.01) were observed in RED. Carcass yields, breast yield, pH, and meat temperature were not influenced (p ≥ 0.14) by nutrient concentrations in the diet. Carcass evaluations were not affected (p ≥ 0.11) by EOs. Body temperature was lower (p = 0.03) in birds receiving RED, however EOs had no effect (p ≥ 0.22) on body temperature. For the length of the study, at 20:00 h, broiler chickens fed on RED diets showed lower (p = 0.04) body temperatures. In conclusion, broiler chickens were under heat stress during the study, and body temperatures were reduced in chickens fed on RED diets or with EOs. Diet modification or EO-supplementation may improve feed conversion, though RED diets may have a negative effect on weight gain or carcass values. Full article

In the winter season, citrus nursery production faces challenges including shorter days, lower light levels, and lower temperatures that delay vegetative budbreak and scion shoot growth. With the goal of improving the production cycle in the citrus nursery, we investigated the effect of supplemental LED light on the production of bud-grafted citrus trees during short winter days. Three experiments were conducted under different temperature conditions. “Washington” Navel sweet orange (Citrus sinensis) was budded on Carrizo citrange (C. sinensis × Poncirus trifoliata) and Rubidoux trifoliate (P. trifoliata) rootstocks in early December. Light treatments included no supplemental light, day-length extension to 16 h with LED light, and night interruption (1 h of LED light at night). Day-length extension and night interruption were studied with and without preconditioning plants with the respective light treatments for 6 weeks before grafting. Day-length extension increased the scion growth, but only when supplemental heating was provided, implying that low temperatures are a limiting factor for plant growth. Growth effects were stronger when the Navel scion was grafted on Carrizo compared to Rubidoux, likely because of the higher dormancy of the latter rootstock. Night interruption did not affect scion budbreak or growth under any of the tested conditions. Preconditioning enhanced scion growth in some instances. The results suggest that the use of supplemental LED light to extend the day length may increase plant growth during the short winter days, but the effects are limited under low-temperature conditions. Full article

This paper aims to study the performance of solar collectors of various sizes under different weather conditions in different Japanese cities, i.e., Kofu City, Nagoya City and Yamagata City. The heat generated by the solar collector was used to conduct a biogas dry reforming reactor for producing H₂ to feed a solid oxide fuel cell (SOFC). This study revealed that the output temperature of a solar collector T_fb in April and July was higher than that in January and October irrespective of city. The optimum length of the absorber (dx) of the collector was 4 m irrespective of city. It was clarified that the T_fb in Yamagata City in January and October, i.e., winter and autumn, is lower than that in Kofu City and especially Nagoya City, which is strongly influenced by the tendency of solar intensity (I), not the velocity of the surrounding air (u_a). On the other hand, the T_fb is almost the same in April and July, i.e., spring and summer, irrespective of city. The amount of produced H₂ via the biogas dry reforming reactor and the power generated by the SOFC using H₂ in spring and summer were higher compared to the other seasons irrespective of city. This study revealed that the highest available household number per month was 4.7, according to the investigation in this study. Full article

High temperatures in late spring, summer, and autumn are one of the main elements limiting cucumber production. Heat stress in cucumber plants leads to significant changes in sex expression and negative effects at the physiological stage, including decreased absorption of water and nutrients, decreased photosynthetic functions, and increased respiration, which in turn affects growth and production. Therefore, it is very difficult for cucumber plants exposed to heat stress to improve their productivity, especially in arid and semi-arid regions. Cucumber plants bear a variety of flower forms, including staminate, pistillate, and hermaphrodites that occur in different arrangements. In addition, maleness is considered the main problem in cucumber production, as it significantly reduces fruit yield. Recently, the growth, flowering, and productivity of plants have been controlled with the help of growth-regulating substances, and one of these substances is naphthalene acetic acid (NAA), which is of great importance in modifying sex in cucumber and various cucurbit crops. Two experiments were conducted during 2022 and 2023 during the summer season to study the effect of foliar spraying of two levels of potassium nitrate, 500 mg/L and 1000 mg/L, and two levels of NAA, 50 mg/L and 100 mg/L, individually and together, and the comparison treatment in nine treatments on growth traits, sex ratio, fruit traits and yield of cucumber (Cucumis sativas L.) plants in three replicates in a completely randomized block design. The results indicated that applying the treatment containing 1000 mg/L KNO₃ + 100 mg/L NAA led to obtaining the highest value in most vegetable growth, flowering, and production parameters, with a significant increase compared to the control and all other treatments. Followed by treatments consisting of potassium nitrate and NAA, which gave high values in the traits of sex ratio %, fruit setting %, fruit yield, and most other traits. This study showed the importance of spraying with both NAA and potassium nitrate, individually or in combination, and the high combined effect of both in the treatments that contained each of them in the vegetative growth traits and floral traits such as the number of female flowers, the sexual ratio, fruit set percentage, weight fruit, length fruit, and the fruit yield in cucumber plants grown in the summer season with high temperatures Full article

Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments. Full article

The discharge of warm water from water source heat pump (WSHP) energy stations can cause local changes in the water temperature, leading to changes in the water quality around and downstream of the discharge outlet, resulting in a cumulative effect that alters the aquatic habitat elements and affects the ecosystem stability. This study took the demonstration project of the water source heat pump energy station in the Hankou Binjiang International Business District of Wuhan City as an example. Based on measured data, this study established the control equation for the warm water discharge of the project and used FLUENT 2022 R1 software to establish a numerical model of warm water diffusion. This study analyzed the changes in discharge water temperature during unfavorable conditions in both summer and winter during dry and wet seasons by using four parameters: equal area surface, volume, length, and width of the area of influence. The results suggest that the impact of the project’s warm water discharge on the temperature of the river body will quickly decay and its impact range will be limited. Hence, the project will not have adverse effects on the water and ecological environment in the mid–lower reaches of the Yangtze River. Full article