Search Results (91)

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

To address the scientific challenges of low water–fertilizer use efficiency and the difficulty in achieving the synergistic improvement of the yield and quality in solar greenhouse cucumber production on the North China Plain, this study investigated the effects of varying water and nitrogen supplies on cucumber growth, yields, water–nitrogen use efficiency, and quality. The aim was to establish optimized water and nitrogen management strategies for high-yield, high-quality, and resource-efficient cultivation. A two-factor completely randomized design was implemented, with three irrigation levels (W1: $1.0E_{p-20}$, W2: $0.75E_{p-20}$, and W3: $0.5E_{p-20}$) based on cumulative pan evaporation and four nitrogen application amounts (N1: 432 kg·ha⁻¹, N2: 360 kg·ha⁻¹, N3: 288 kg·ha⁻¹, N4: 216 kg·ha⁻¹). Cucumber growth indicators were observed during the growing season, and the water and nitrogen application rates were scientifically optimized. The results showed that a full water and nitrogen supply enhanced the leaf area index, dry weight accumulation, and yield. Moderate water and nitrogen savings had a minimal impact on plant growth and production while significantly improving the water and fertilizer use efficiency. Using principal component analysis to comprehensively evaluate the cucumber quality, it was found that the irrigation amount had a significant impact on quality, with the quality improving as the irrigation amount decreased. By employing a regression formula and spatial analysis methods, this study optimized the water and nitrogen application rates with the goals of maximizing the cucumber yield, water–nitrogen efficiency, and quality. For spring cucumbers, the recommended combination is an irrigation amount of 225~240 mm and a nitrogen application amount of 350~380 kg·ha⁻¹. For autumn cucumbers, the recommended combination is an irrigation amount of 105~120 mm and a nitrogen application amount of 375~400 kg·ha⁻¹. This research provides theoretical and technical support for high-yield, high-quality, and efficient irrigation and nitrogen management in solar greenhouses in the North China Plain. Full article

An automatic, weight-based, small 20 cm diameter pan was used for real-time calculations of evaporation and precipitation in a semiarid environment. The water evaporated from the evaporimeter (E_P) was found to be a significant predictor of evapotranspiration (ET_O; r² = 0.84), which was calculated with the Penman–Monteith (P-M) equation by retrieving climatic data from a weather station. The results revealed seasonal variations of the pan coefficient (K_P; dimensionless), with a mean value estimated at 0.84 (±0.16). Validation of ET_O measurements using a calibrated regression model (ET_O = 0.831*E_P + 0.025), against the P-M equation indicated a high correlation coefficient (r² = 0.99, slope of the regression line of 0.9). The present paper evaluates and discusses the potential of using a reduced-size pan for real-time monitoring of water evaporation and precipitation, proposing an open-source irrigation decision support system. Full article

Doce de leite is a caramel-like confection, mainly produced in several Latin American countries, with increasing popularity worldwide. This overview outlines nine distinct industrial technologies for the production of doce de leite: (1) total batch manufacturing process; (2) batch manufacturing system with fractionated mix addition; (3) manufacturing with pre-concentration in a vacuum evaporator and finishing in an open pan; (4) manufacturing with pre-concentration in a vacuum evaporator, finishing in an open pan, and lactose micro-crystallization; (5) continuous flow manufacturing with total concentration in a vacuum evaporator and a viscosity control holding tank (hot well); (6) manufacturing with total concentration in a vacuum evaporator and sterilization in an autoclave system; (7) manufacturing with sucrose pre-caramelization and a total batch system; (8) manufacturing in colloidal mill without an evaporation process; and (9) manufacturing based of doce de leite bars with a sucrose crystallization stage. We conducted a literature review to gather data on the discussed processes and their principal characteristics, which may be pertinent to doce de leite manufacturers. The choice of a specific process will depend on the desired doce de leite characteristics, the type of doce de leite to be produced, and the manufacturing company’s requirements. When properly integrated, these technologies contribute to efficient and profitable production, yielding high-quality products with appropriate chemical, microbiological, and sensory characteristics at an industrial scale. Full article

Rice serves as the primary food source for the majority of the world’s population. In terms of irrigation water, the highest volume of irrigation water is utilized in paddy irrigation. Excessive water use causes both waste of limited water resources and various environmental problems. The drip irrigation method with high water use efficiency will reduce both the need for irrigation water and the environmental footprint of paddy production. This study was conducted to investigate the effects of two different irrigation intervals (2 and 4 days) and four irrigation levels (150%, 125%, 100%, and 75% of evaporation from a Class-A pan) on the nutritional traits of three different paddy cultivars (Ronaldo, Baldo, and Osmancık). Increasing irrigation intervals and decreasing irrigation levels reduced the nutritional properties (protein, oil, starch) of the rice grains. In addition, increasing irrigation levels also increased the phytic acid and dietary fiber contents. The highest protein (7.14%) and total starch (87.10%) contents were obtained from the 150% irrigation treatments. The highest amylose content (20.74%) was obtained from the 75% irrigation treatment. In general, it was found that irrigation levels should be applied at 125% and 150% to increase the mineral content of rice grains. Although water deficits decreased the nutritional properties of the paddy cultivars, drip irrigation at an appropriate level did not have any negative effects on nutritional traits. Full article

Despite the continuous development of greenhouse cultivation technology, the influence mechanism of covering conditions on the root distribution of greenhouse crops remains unclear, which is emerging as a significant research topic at present. The interaction between mulching and irrigation plays a key role in the root growth of greenhouse tomatoes, but its specific impact awaits in-depth exploration. To explore the response patterns of greenhouse crop root distribution to the drip irrigation water amount under mulching conditions, the tomato was chosen as the research object. Three experimental treatments were set up: mulched high water (Y0.9), non-mulched high water (N0.9), and mulched low water (Y0.5) (where 0.9 and 0.5 represent the cumulative evaporation from a 20 cm standard evaporation pan). We analyzed the water and thermal zone of tomato roots as well as the root distribution. Based on this, a root distribution model was constructed by introducing a mulching factor (f_m) and a water stress factor (K_s). After carrying out two years of experimental research, the following results were drawn: (1) The average soil water content in the 0–60 cm soil layer was Y0.9 > N0.9 > Y0.5, and the average soil temperature in the 0–30 cm soil layer was Y0.5 > Y0.9 > N0.9. (2) The interaction between mulching and irrigation had a significant impact on the distribution of tomato roots. In the absence of mulch, the root surface area, average root diameter, root volume, and root length density initially increased and then decreased with depth, with the maximum root distribution concentrated around the 20 cm soil layer. Under mulched conditions, roots were predominantly located in the top layer (0–20 cm). Under the film mulching condition, the distribution range of root length density of low water (Y0.5) was wider than that of high water (Y0.9). (3) Root length density exhibited a significant cubic polynomial relationship with both the soil water content and soil temperature. In the N0.9 treatment, root length density had a bivariate cubic polynomial relationship with soil water and temperature, with a coefficient of determination (R²) of 0.97 and a normalized root mean square error (NRMSE) of 20%. (4) When introducing the film mulching factor (f_m) and water stress factor (K_s) into the root distribution model to simulate the root length density distribution of Y0.9 and Y0.5, it was found that the NRMSE was 22% and R² was 0.90 under the Y0.9 treatment, and the NRMSE was 24% and R² was 0.98 under the Y0.5 treatment. This study provides theoretical support for the formulation of scientifically sound irrigation and mulching management plans for greenhouse tomatoes. Full article

The modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity (RH) and minimum temperature (t_min) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity. Full article

Water scarcity and increasing salinity stress are significant challenges in the farming sector as they often exacerbate each other, as limited water availability can concentrate salts in the soil, further hindering plant growth. Lettuce, a crucial leafy vegetable with high nutritional value, is susceptible to water availability and quality. This study investigates the growth and development of lettuce plants under water scarcity and varying levels of salinity stress to identify effective strategies for reducing water consumption while maintaining or improving plant productivity. Field experiments were designed to simulate three drought levels (50, 75, and 100% of class A pan evaporation) and three salinity stress levels (control, 1500, and 3000 ppm NaCl), assessing their impact on lettuce’s morphological and biochemical parameters. The combination of reduced water supply and high salinity significantly hindered growth, underscoring the detrimental effects of simultaneous water deficit and salinity stress on plant development. Non-stressed treatment enhanced nitrogen, phosphorus, and potassium contents and progressively decreased with the reduction in water supply from 100% to 50%. Interestingly, higher salinity levels increased total phenolic, flavonoid, and antioxidant activity, suggesting an adaptive stress response. Moreover, antioxidant activity, evaluated through DPPH and ABTS assays, peaked in plants irrigated with 75% ETo, whether under control or 1500 ppm salinity conditions. The Yield Stability Index was highest at 75% ETo (0.95), indicating robust stability under stress. The results indicated that lettuce could be cultivated with up to 75% of the water requirement without significantly impacting plant development or quality. Furthermore, the investigation demonstrated that lettuce could thrive when irrigated with water of moderate salinity (1500 ppm). These findings highlight the potential for reducing water quantities and saline water in lettuce production, offering practical solutions for sustainable farming in water-scarce regions. Full article

One of the oldest oilseed crops is sesame, which is mainly cultivated due to its valuable oleic/linolenic fatty acid ratio. The application of precise irrigation and fertilisation is crucial to ensure the continuity and productivity of sesame production, especially in arid and semi-arid regions. This study aimed to determine the effect of drip irrigation and nitrogen levels on sesame’s oil and fatty acid composition. For this purpose, four nitrogen doses (N₀: 0 kg ha⁻¹, N₃₀: 30 kg ha⁻¹, N₆₀: 60 kg ha⁻¹ and N₉₀: 90 kg ha⁻¹) and three different irrigation water levels (I₅₀, I₇₅ and I₁₀₀, which correspond to 50, 75 and 100% evaporation levels from the evaporation of the Class A pan) were applied. The highest oleic acid content (43.06%) was obtained for the I₇₅N₉₀ treatment. In the case of linoleic fatty acid, the greatest value (43.66%) was for I₅₀N₀ treatment. The effects of irrigation and nitrogen doses on oleic acid and linoleic acid content were inverse of each other. An increase in applied irrigation water increased the linoleic acid content. However, it caused a decrease in oleic acid content. Increasing the nitrogen dose increased the oleic acid content and caused a decrease in linoleic acid content. Furthermore, this study showed that the I₅₀N₆₀ treatment (50% Epan and a rate of 60 kg N ha⁻¹) is the most effective for achieving high grain and oil yields in sesame cultivation. The results obtained provide practical guidance for farmers in sesame cultivation. Full article

Researchers are depending more than ever on remote sensing techniques to monitor and assess the agricultural water status, as well as to estimate crop water usage or crop actual evapotranspiration. In the current work, normal and stressed baselines for irrigated wheat genotypes were developed in an arid part of the Sohag governorate, Egypt, using infrared thermometry in conjunction with weather parameters. The experiment was carried out in a randomized complete block design in the normal and drought stress conditions based on three replicates using ten bread wheat genotypes (G1–G10), including five accessions, under drought stress. A standard Class-A-Pan in the experimental field provided the daily evaporation measurements (mm/day), which was multiplied by a pan factor of 0.8 and 0.4 for normal and stressed conditions, respectively. The relationship between the vapor pressure deficit (VPD) and canopy-air temperature differences (Tc − Ta) was plotted under upper (fully stressed) and lower baseline (normal) equations. Accordingly, the crop water stress indexes (CWSIs) for the stressed and normal baselines for wheat genotypes were developed. Additionally, the intercept (b) and the slope (a) of the lower baseline equation were computed for different genotypes. The results indicate that, before applying irrigation water, the CWSI values were high in both growing seasons and under all irrigation regimes. After that, the CWSI values declined. G10 underwent stress treatment, which produced the greatest CWSI (0.975). Conversely, the G6 condition that received well-watered irrigation yielded the lowest result (−0.007). When compared to a well-watered one, the CWSI values indicated a trend toward rising stress. There existed an inverse link between the CWSI and grain yield (GY); that is, a lower CWSI resulted in better plant water conditions and a higher GY. Under standard conditions, the wheat’s highest GY was recorded in G2, 8.36 Ton/ha and a WCSI of 0.481. In contrast, the CWSI result for the stress treatment was 0.883, indicating a minimum GY of 5.25 Ton/ha. The Water Use Efficiency (WUE) results demonstrated that the stress irrigation regime produced a greater WUE value than the usual one. This study makes a significant contribution by investigating the techniques that would allow CWSI to be used to estimate irrigation requirements, in addition to determining the irrigation time. Full article

The future of humanity depends on successfully adapting key cropping systems for food security, such as corn (Zea mays L.), to global climatic changes, including changing air temperatures. We monitored the effects of climate change on harvested yields using long-term research plots that were established in 2001 near Fort Collins, Colorado, and long-term average yields in the region (county). We found that the average temperature for the growing period of the irrigated corn (May to September) has increased at a rate of 0.023 °C yr⁻¹, going from 16.5 °C in 1900 to 19.2 °C in 2019 (p < 0.001), but precipitation did not change (p = 0.897). Average minimum (p < 0.001) temperatures were positive predictors of yields. This response to temperature depended on N fertilizer rates, with the greatest response at intermediate fertilizer rates. Maximum (p < 0.05) temperatures and growing degree days (GDD; p < 0.01) were also positive predictors of yields. We propose that the yield increases with higher temperatures observed here are likely only applicable to irrigated corn and that irrigation is a good climate change mitigation and adaptation practice. However, since pan evaporation significantly increased from 1949 to 2019 (p < 0.001), the region’s dryland corn yields are expected to decrease in the future from heat and water stress associated with increasing temperatures and no increases in precipitation. This study shows that increases in GDD and the minimum temperatures that are contributing to a changing climate in the area are important parameters that are contributing to higher yields in irrigated systems in this region. Full article

Water evaporation-driven energy harvesting is an emerging mechanism for contributing to green energy production with low cost. Herein, we developed polyacrylonitrile (PAN) nanofiber-based evaporation-driven electricity generators (PEEGs) to confirm the feasibility of utilizing electrospun PAN nanofiber mats in an evaporation-driven energy harvesting system. However, PAN nanofiber mats require a support substrate to enhance its durability and stability when it is applied to an evaporation-driven energy generator, which could have additional effects on generation performance. Accordingly, various support substrates, including fiberglass, copper, stainless mesh, and fabric screen, were applied to PEEGs and examined to understand their potential impacts on electrical generation outputs. As a result, the PAN nanofiber mats were successfully converted to a hydrophilic material for an evaporation-driven generator by dip-coating them in nanocarbon black (NCB) solution. Furthermore, specific electrokinetic performance trends were investigated and the peak electricity outputs of V_oc were recorded to be 150.8, 6.5, 2.4, and 215.9 mV, and I_sc outputs were recorded to be 143.8, 60.5, 103.8, and 121.4 μA, from PEEGs with fiberglass, copper, stainless mesh, and fabric screen substrates, respectively. Therefore, the implications of this study would provide further perspectives on the developing evaporation-induced electricity devices based on nanofiber materials. Full article

It is imperative to elucidate the process of evaporation in lakes, particularly those that are freshwater and are situated in middle and high latitudes. Based on one-year evaporation and high-frequency meteorological–water quality data of Lake Wuliangsuhai, this study analyzed the applicability and driving mechanism of the evaporation model. These dynamics are elucidated by the vorticity covariance method combined with the multivariate constrained evaporation Modelling method. The findings of this study revealed that (1) Lake evaporation (ET) is affected by multiple meteorological–water quality constraints, and the water quality indicators significantly related to ET are also affected by lake stratification. The coupled meteorological–water quality evaporation model can explain 93% of the evaporation change, which is 20% higher than the traditional meteorological Modelling evaporation model. (2) The nighttime ET is mainly affected by the thermal inertia lag, and the nighttime ET loss in Lake Wuliangsuhai accounts for 37.34% of the total evaporation, which cannot be ignored. (3) The actual water surface evaporation of the lake is much smaller than that measured by the pan conversion method and the regional empirical C formula method. The cumulative evaporation of Lake Wuliangsuhai from the non-freezing period to the early glacial period converted from meteorological station data is 1333.5 mm. The total evaporation in the non-freezing period is 2.77~3.68 × 10⁸ m^3, calculated by the lake area of 325 km², while the evaporation calculated by the eddy station is 1.91 × 10⁸ m³. In addition, the ET value measured by the cumulative C formula method was 424.2% higher than that of the model method and exceeded the storage capacity. Low-frequency and limited environmental index observations may lead to an overestimation of the real lake evaporation. Therefore, in situ, high-frequency meteorological–water quality monitoring and the eddy method deserve more consideration in future research on lake evaporation. Full article

The absence of accurate measurement or calculation techniques for crop water requirements in greenhouses frequently results in over- or under-irrigation. In order to find a better method, this study analyzed the accuracy, data consistency and practicability of the Penman–Monteith (PM), Hargreaves–Samani (HS), Pan Evaporation (PAN), and Artificial Neural Network (ANN) models. Model-calculated crop evapotranspiration (ET_C) was compared with lysimeter-measured crop evapotranspiration (ET_C) in the National Precision Agriculture Demonstration Station in Beijing, China. The results showed that the actual ET_C over the entire experimental period was 176.67 mm. The ET_C calculated with the PM, HS, PAN, and ANN model were 146.07 mm, 189.45 mm, 197.03 mm, and 174.7 mm, respectively, which were different from the actual value by −17.32%, 7.23%, 11.52%, and −1.12%, respectively. The order of the calculation accuracy for the four models is as follows: ANN model > PAN model > PM model > HS model. By comprehensively evaluating the statistical indicators of each model, the ANN model was found to have a significantly higher calculation accuracy compared to the other three models. Therefore, the ANN model is recommended for estimating ET_C under greenhouse conditions. The PM and PAN models can also be used after improvement. Full article

The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (P_Bias = −1.65%, MAE = 1.27 mm d⁻¹, RMSD = 1.6 mm d⁻¹, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, P_Bias = −6.44%, MAE = 3.58 mm d⁻¹, RMSD = 4.2 mm d⁻¹, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques. Full article