Pan Evaporation Research Articles

Enhancing water security through advanced modeling: integrating deep learning and a novel metaheuristic optimization algorithm for accurate pan evaporation prediction

ABSTRACT This study evaluates and enhances machine learning models for predicting pan evaporation under diverse climatic conditions. Five fundamental machine learning models were employed and tested across four different stations. Subsequent comparisons were made with advanced techniques, including long short-term memory (LSTM) networks. An innovative approach was introduced, combining LSTM with Binary Al-Biruni Earth Radius (BER–LSTM). This hybrid method was benchmarked against other optimization techniques. The BER–LSTM model consistently outperformed other models across all stations and time scales, achieving up to a 97.54% improvement in root mean square error (RMSE) compared to standard LSTM on daily time scales. Compared to simpler models like Multilayer Perceptron and Support Vector Regressor, BER–LSTM showed even more substantial improvements, with up to a 99.03% reduction in RMSE. The BER–LSTM model demonstrates superior predictive capabilities for pan evaporation across varied climatic conditions, offering significant improvements over both traditional and advanced machine learning techniques. This approach shows promise for enhancing evaporation forecasting in diverse environmental contexts.

Influence of drip irrigation, crop geometry and mulching on morphological and nutritional traits of okra (Abelmoschus esculentus)

The experiment was conducted during rainy (kharif) seasons of 2019 and 2020 at College of Agriculture, Swami Keshwanand Rajasthan Agricultural University, Bikaner, Rajasthan to study the effect of crop geometry, mulching and different irrigation levels on the morphological and nutritional traits of okra [Abelmoschus esculentus (L.) Moench]. The experiment was conducted in a split plot design (SPD) comprised of 4 irrigation levels in the main plot (40%, 60%, 80% and 100% pan evaporation) and 2 different crop geometry (paired row sowing and normal sowing) and 3 types of mulches (no mulch, plastic mulch and straw mulch) were employed in the subplots and sub-subplots, respectively. Results indicated that 100% potential evapotranspiration (PE), drip irrigation (DI) and paired row sowing at 30 cm × 70 cm significantly increased the number of branches/plant, plant height (cm), length of fruit (cm), diameter of fruit (cm) and chlorophyll content of leaves compared to lower irrigation levels and normal sowing. Additionally, straw mulch positively influenced the above-mentioned parameters in the okra fruits as compared to without mulch. Drip irrigation, paired row sowing, and straw mulching also resulted in a significant increase in net returns per hectare. The study demonstrated that combining paired row sowing with straw mulch and irrigating at 100% potential evapotranspiration (PE) yielded the best growth attributes, highest fruit quality, and maximum net returns for okra.

Studying the Combined Impact of Salinity and Drought Stress-Simulated Conditions on Physio-Biochemical Characteristics of Lettuce Plant

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.

Climatic Parameters Use for Evaluating Water Budget and Drought Analysis in Erbil Sub-basin, Kurdistan Region, Northern-Iraq

Major theoretical and practical hydrological issues may be solved using methods to maintain water balance. Evaluating the climate characteristics and environmental circumstances to be employed in the methods of water managing approach used in the Erbil sub-basin, northern Iraq, is the primary goal of this study. The studied area is a part of the region influenced by the Mediterranean climatological system. The climatic parameters of the research region were evaluated using the climatic data collected in the Erbil meteorological station for the period 1980-2022. The research result shows that the mean annual rainfall is 411.26 mm/year and the average monthly relative humidity is 41.12 %, the mean temperature is 20.62 °C, mean sunshine duration 8.35 hr./day and annual pan evaporation is 1200.96 mm / year. Potential evapotranspiration considered by Thornthwaite method is 1588.94 mm. The water surplus and deficit are 216.14 mm and 1393.81 mm, respectively. The surface runoff calculating from soil conservation system (SCS) technique which is about 86 mm / year and groundwater recharge is about 130.14 mm / year. The groundwater Budgets is about 187661880 m3 / year. The climatic situation in the area is variety from winter its wet to the summer its dry climates. Also, it could be measured that the climate in the area is determined was (Humid to very humid). The draught index according to SPI analysis show that the drought was observed during Moderately drought in (1983, 1999, 2000, 2010, 2015, and 2021) to severely drought in (2022.

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models

Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models

Identification and Screening of Leaf Rust Resistance Genes in Wheat Cultivars Through Microsatellites

Wheat is one of the important staple food being grown worldwide and in Pakistan. The wheat rust-causing fungi are very eco-adaptive and evolve rapidly to overcome genetic resistance of wheat varieties. Among all rust-causing fungi, Puccinia triticina causes leaf rust in wheat, and inflicts heavy yield losses. Deploying resistant varieties against leaf rust is the most effective, environmentally-friendly and economic way to control the disease. Therefore, new sources of genetic resistance are continually sought to develop rust resistance in wheat varieties, and nowadays rust resistant varieties have been developed through the accumulation of slow rusting or minor genes that would perform better in fields. Hence, the purpose of this study is to screen a few of the available leaf rust-resistant germplasm in field and evaluate their field response on genetic basis. For this purpose, minor genes were detected through the amplification of simple sequence repeat (SSR) in the selected wheat genotypes through screened primers. Field experiment was conducted. Molecular studies to identify the minor genes were performed. Meteorological data was recorded at the observatory laboratory of the same area. All the epidemiological factors (Temperature (maximum, minimum), Relative Humidity, and Pan Evaporation) showed significant correlation in tested 5 varieties ZARDANA89, ZARLASHTA99, RASKOH 05, BHAKKAR-2000, GA-2002 which were conducive for disease development except rainfall. PCR amplification of Xgwm118, and Xgwm165, showed the range of alleles in 14 genotypes. It was observed that 22 verities showed resistance, 3 were moderately resistant, 3 varieties were moderately susceptible, 8 varieties were moderately susceptible, and 5 varieties were moderately susceptible against leaf rust pathogen.

Self-attention transformer model for pan evaporation prediction: a case study in Australia

ABSTRACT In drought-prone regions like Australia, accurately assessing evaporation rates is essential for effectively managing and maximising the use of precious water resources and reservoirs. Current estimates show that evaporation reduces Australia's open water lake capacity by about 40% annually. With climate change, this water loss is expected to become an even greater concern. This study investigates a transformer-based neural network (TNN) to estimate monthly evaporation in three Australian locations. The models were trained and tested using monthly weather data spanning from 2009 to 2022. Input parameters were chosen based on Pearson's correlation coefficient values to identify the most impactful combinations. The developed TNN model was compared with two widely used empirical methods, namely Thornthwaite and Stephens and Stewart. The TNN model's impressive accuracy in evaporation prediction, attributed to its unique self-attention mechanism, suggests its promising potential for future use in evaporation forecasting. Additionally, the study revealed an intriguing result: Despite using the same input datasets, the TNN model surpassed traditional methods, achieving an average improvement of 18% in prediction accuracy. The TNN prediction model accurately predicts water loss (average R² = 0.970), supports irrigation management and agricultural planning and offers financial benefits to farming and related industries.

Using a random cross-validation technique to compare typical regression vs. Random Forests for modelling pan evaporation

Pan evaporation (Epan) of class A pan evaporimeter under local semi-arid conditions was modelled in this study based on meteorological observations as input data using an integrated regression approach that includes three steps: a) first step: appropriate selection of transformations for reducing normality departures of independent variables and ridge regression for selecting variables with low collinearity based on variance inflation factors, b) second step (RCV-REG): regression (REG) of the final model with selected transformed variables of low collinearity implemented using an iterative procedure called “Random Cross-Validation” (RCV) that splits multiple times the data in calibration and validation subsets considering a random selection procedure, c) robustness control of the estimated regression coefficients from RCV-REG by analyzing the sign (+ or -) variation of their iterative solutions using the 95% interval of their Highest Posterior Density Distribution (HPD). The iterative procedure of RCV can also be implemented on machine learning methods (MLs) and for this reason, the ML method of Random Forests (RF) was also applied with RCV (RCV-RF) as an additional case in order to be compared with RCV-REG. Random splitting of data into calibration and validation set (70% and 30%, respectively) was performed 1,000 times in RCV-REG and led to a respective number of solutions of the regression coefficients. The same number of iterations and random splitting for validation was also used in the RCV-RF. The results showed that RCV-REG outperformed RCV-RF at all model performance criteria providing robust regression coefficients associated to independent variables (constant signs of their 95% HPD interval) and better distribution of validation solutions in the iterative 1:1 plots from RCV-RF (RCV-RG: R2=0.843, RMSE=0.853, MAE=0.642, MAPE=0.081, NSE=0.836, Slope(1:1 plot)=0.998, Intercept(1:1 plot)=0.011, and RCV-RF: R2=0.835, RMSE=0.904, MAE=0.689, MAPE= 0.088, NSE=0.818, Slope(1:1 plot)=1.120, Intercept(1:1 plot)=-1.011, based on the mean values of 1,000 iterations). The use of RCV approach in various modelling approaches solves the problem of subjective splitting of data into calibration and validation sets, provides a better evaluation of the final modelling approaches and enhances the competitiveness of typical regression models against machine learning models.

Study on the optimal water−biochar management mode based on pan evaporation for solar greenhouse tomato (Solanum lycopersicum L.) in Northeast China

Biochar is commonly employed to enhance soil structure. However, the soil structure degradation due to multi-year continuous cropping poses a significant obstacle to the advancement of solar greenhouse agriculture. This study aimed to optimize the water−biochar management mode for seasonal solar greenhouse tomatoes (Solanum lycopersicum L.), which have been continuously cultivated over three years, with irrigation guided by the evaporation of a 20 cm evaporation pan (Ep-20). The experimental design employed a split–plot arrangement with two factors. The primary factor, irrigation amount, was tested at three levels: 0.6 Ep-20 (W1), 0.8 Ep-20 (W2), and 1.0 Ep-20 (W3). The secondary factor, biochar amount, was tested at three levels: 0 (B0), 20 t ha−1 (B1), and 40 t ha−1 (B2). The results revealed that biochar reduced soil bulk density and enhanced field capacity, tomato plant height, and photosynthesis. Increased irrigation improved tomato appearance quality but reduced nutritional quality, whereas higher biochar application improved both aspects. Principal component analysis (PCA) and grey relational analysis (GRA) results identified W2B2 as the optimal mode for the quality of spring tomato and autumn tomato. Compared to the control (W3B0), W2B2 exhibited an average increase of 18.0 % in yield and 47.5 % in irrigation water productivity (WPI) with a 20 % reduction in irrigation amount across two seasons. The technique for order preference by similarity to ideal solution (TOPSIS) comprehensive evaluation determined that the W2B2 mode maximized comprehensive benefits, enhancing both the overall quality of tomato and achieving higher yield and WPI. This study utilized the Ep-20 to characterize the reference crop evapotranspiration (ETo) and the crop evapotranspiration (ETc). The investigation demonstrated the feasibility of utilizing Ep-20 to guide irrigation under biochar application conditions and develop ETc estimation models for water−biochar management modes to optimize solar greenhouse tomato production.

Complexity of hydrometeorological variables: false nearest neighbour algorithm

ABSTRACT This study examines the complexity of hydrometeorological variables in the Savitri River basin in India. Specifically, the study estimates the dimensionality of daily rainfall, runoff, maximum temperature, minimum temperature, pan evaporation, relative humidity, sunshine duration, and wind speed observed during 2000–2010 at two stations (Kangule and Birwadi). The false nearest neighbour (FNN) algorithm is employed to estimate the dimensionality of each variable. The dimensionality represents the number of variables dominantly governing the system. The FNN dimension values of the eight daily hydrometeorological time series from each station range between 4 and 7, which may be considered as exhibiting a medium level of complexity. Among the eight series, wind speed is found to be the least complex, whereas minimum temperature and sunshine duration are the most complex. An attempt is also made to examine the effect of temporal scale on the complexity of the hydrometeorological variables, by analysing the hourly rainfall and runoff series from the two stations. The results indicate that, for both rainfall and runoff, hourly data (finer scale) exhibits greater complexity, with two to three additional influential variables. The present results have important implications for hydrometeorological modelling, prediction, and disaster management in the small and flood-prone Savitri River basin.

Evaluation of the impacts of turbidity on Gilgel-Gibe I reservoir storage, Omo-Gibe River Basin, Ethiopia

Quantity and quality of the water held in the reservoir fluctuates due to turbidity alterations. The influence of turbidity on the amount of the water held in a reservoir was described explicitly in this research. This study aimed to evaluate turbidity's impact on the Gilgel-Gibe I reservoir water. The samples were obtained by longitudinally stratifying the reservoir water throughout its course. Ten burrowed pools wrapped in transparent white plastic were used to retain water, for detection of the association between turbidity and surface water temperature, and to demonstrate the vertical variation in water temperature. The pan evaporation rate was measured using two Class A pans placed in the field to indicate the disparity in the amount of water evaporated from reservoir owing to reservoir turbidity variation. SPSS and MS Excel spreadsheet softwares were used to analyze the data. According to the results of this study, turbidity and water temperature have a significant direct relationship that is positive at 9:00 and 13:00 and negative at 17:00 observation hours. From the top layer of pool water to the bottom layer, the water temperature decreased vertically. Intensity of the light rays absorbed and scattered alters with turbidity variation and significant amounts of light rays was absorbed and scattered in the most turbid water. The reported water temperature differences between the top and bottom layers at 13:00 observation hour were 9.78 °C and 1.53 °C, for the most and least turbid pool water, respectively. Turbidity directly affects reservoir water by increasing both the water temperature and evaporation rates. Among all turbid-water samples, substantial quantity of water evaporated from the most turbid-water. For the most and least turbid water samples, the volume difference of the evaporated water from the reservoir was approximately 65.812 m3. According to these findings, if the reservoir water turbidity increases, the amount of water held in the reservoir significantly reduced due to substantial water loss.

Field Comparison Studies of the Rate of Evaporation Between Colorado Sunken Evaporation Pan and Class A Evaporation Pans in the Arid Areas (Case Study: Semnan City)

Field Comparison Studies of the Rate of Evaporation Between Colorado Sunken Evaporation Pan and Class A Evaporation Pans in the Arid Areas (Case Study: Semnan City)

Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

A reliable pan evaporation (Epan) estimation over a daily scale is vital for sustainable water and agriculture management, especially for designing water use allocations, irrigation system management, and demand and utilization assessments. However, designing a reliable computational methodology is a challenging task for Epan estimation due to its stochastic and random characteristics. In this study, the potential of five machine learning (ML) models namely Hammerstein-Weiner (HW), Random Forest (RF), Boosted Regression Tree (BRT), Long Short-Term Memory (LSTM), and Stepwise Linear Regression (SWLR) models were evaluated for Epan modeling in Lake Hawassa catchment, Ethiopia. Afterward, hybrid SWLR-LSTM, SWLR-RF, SWLR-BRT, and SWLR-HW models were developed to benefit from the strength of the non-linear and linear characteristics of the models. The estimation results were evaluated using determination coefficient (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and Root Mean Square Error (RMSE), Index of Agreement (IA) and Kling-Gupta efficiency (KGE). The performance analysis result of individual models showed that the RF model led to more accurate estimation with NSE = 0.938, IA = 0.956, R2 = 0.94, KGE = 0.852, RMSE = 0.483 mm/day and Pbias = -1.193 mm/day during the validation phase. Among the hybrid models, SWLR-RF provides the best predictive performance improving SWLR, HW, LSTM, BRT and RF by 26.563 %, 21.348 %, 15.577 %, 9.091 % and 3.514 %, respectively based on the validation phase NSE value. Generally, the results of the current study demonstrated the capability of deep learning, ensemble tree and hybrid linear-nonlinear models for Epan estimation in the data-scarce catchment.

A Closed Loop Automated Drip Irrigation System Based on Arduino Uno

In recent years, the limited availability of irrigation water has made the optimal use of water a necessity. Successful irrigation practices in agriculture reduce excessive water usage and increase water use efficiency. Economical use of water can be made possible through modern irrigation systems. Nowadays, automatic control systems in agriculture have made significant progress and offer important advantages. In this study, a mini evaporation pan consisting of nested two cylinders was used (outer cylinder diameter 32 cm, inner cylinder diameter 27 cm, and both with a height of 20 cm) and the water remained at a constant level in both cylinders due to the notch beneath the inner cylinder. A water level sensor was placed inside the inner cylinder, and irrigation was activated based on the signal received from the sensor. The data from the sensor was processed by the microcontroller on the Arduino Uno, and three different irrigation applications were implemented by activating pumps connected to relays at 07:00, 14:00, and 21:00 o’clock, and then the mini-evaporation pan was refilled, and the system waited for the next irrigation period. The automation drip irrigation system maintained irrigation as a closed loop throughout the entire growth period of the lettuce plants using this approach. The addition of a fertilization unit to this system could make it more effective in plant production. The performance of the system will be significantly enhanced by enabling hardware that provides irrigation and fertilization to the plant according to its specific needs in terms of timing and quantity, along with appropriate software integration.

Improvement of Yield and Quality of Peanut (Arachis hypogaea L.), Some Enzymatic and Non-Enzymatic Antioxidants Activity in Response to Zn-Nano Fertilizer in Different Irrigation Regimes

ABSTRACT Peanut, as an oilseed, have high nutritional value for humans and livestock and poultry. The seed yield (SY) of peanut decreases under drought stress conditions, and the zinc (Zn) nano-fertilizer can reduce the negative impacts of water deficiency on peanut plants and improves its SY. This experiment carried out as split plot based on randomized complete block design with three replications in Astara, Iran, during the 2018 and 2019 cropping seasons. Three irrigation regimes at 50 mm (IR50), 75 mm (IR75) and 100 mm (IR100) evaporation from class A evaporation pan in main plot and three levels of Zn nano-chelate, including 0.5 g. l−1; (Zn0.5), 1 g. l−1; (Zn1) and 1.5 g. l−1; (Zn1.5) in sub plot, comprised experimental treatments. The results indicated that the highest pod yield (PY), SY, oil yield (OY) and protein yield (PRY) of peanut (3633, 2900, 1582 and 826 kg. ha−1, respectively) were obtained in response to Zn nano-chelate with a dose of 1.0 g. l−1 in IR50 treatment. Nonetheless, under foliar spraying of Zn1 and severe drought stress conditions (IR100) were observed the highest proline (14.84 µmol g−1 FW), superoxide dismutase (53.42 Unit mg−1 proteinmin−1), catalase (72.87 nmol mg−1 proteinmin−1), peroxidase (64.17 nmol mg−1 protein min−1), soluble sugar content (172.04 mg g−1 FW), soluble protein content (114.16 mg g−1 DW) and anthocyanin pigments (11.20 µmol g−1 FW). Generally, application of Zn1 treatment, through increasing antioxidant enzymes activity and non-enzymatic antioxidants, improved peanut SY. Hence, foliar application of Zn nano-chelate could be recommended to enhance peanut yield.

Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India

ABSTRACT Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi's semi-arid climate, data spanning 31 years (1990–2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash–Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model's robust performance underscores its potential application in water resource management.

Sunflower grain yield and oil content affected by zinc fertilization and genotype in drought stress conditions

ContextDrought stress is a major environmental factor limiting sunflower (Helianthus annus L.) growth and productivity, worldwide. Although there has been research on the use of zinc (Zn) to alleviate drought stress in sunflower, more has yet to be indicated on how Zn may affect sunflower yield and biochemical properties in drought stress conditions as the objective of the present research. MethodsThe field experiment was a split plot on the basis of a complete randomized block design with 18 treatments and 3 replicates conducted in the province of Fars, Iran, in 2018. The experimental treatments of drought (main plots) including irrigating at 70 (control, S1), 105 (mild, S2) and 140 mm (severe, S3) evaporation from the evaporation pan and the sub plots (factorial arrangement) of sunflower genotypes (Shams and Barzegar) and Zn fertilizer (ZnSO4) at 0, 40 and 70 kgha−1 were tested. Different sunflower, growth, yield and biochemical parameters were determined. ResultsStress significantly affected leaf area index (LAI), tray diameter (TD), grain weight (GW), infertile seeds (I), water use efficiency (WUE) and proline (Pr), and genotype was significant on LAI, TD, number of grains per tray (NGT), GW, WUE, and grain protein (PP). However, Zn significantly increased LAI, PP, GW and WUE by 50, 5.9, 35 and 32 %, respectively. ConclusionZn fertilization can alleviate the unfavourable effects of drought stress on sunflower yield and biochemical properties by improving plant growth (LAI), yield (grain weight), and physiology (water use efficiency and protein percentage), and genotype is a determining factor.

The effect of foliar application of plant growth regulators on functional and qualitative characteristics of wheat (Triticum aestivum L.) under salinity and drought stress conditions

This study aimed to investigate the effect of salinity and water stress on the physiological and functional characteristics of winter wheat (Triticum aestivum L.) under the foliar application of plant growth regulators (PGRs). The experiment was carried out as a split plot based on a randomized complete block design with three replications in two environments. In each environment, water stress at two irrigation levels (after 90 and 120 mm of pan evaporation) and with two EC of 1.5 and 10 dS/m in the main plots and spraying of PGRs including salicylic acid (SA), gibberellic acid (GA3), and cytokinins (CK) (purine) content with a concentration of 100 ppm and the control treatment (spraying solution with normal water) were placed in subplots. Results indicated that all treatments caused significant increases in functional and qualitative characteristics and yield of Triticum aestivum L. The saline environment and irrigation level after 120 mm of pan evaporation caused a reduction in grain yield in all traits except for seed proline, seed nitrogen content, and seed protein content. Also, the combined foliar application of GA3 + CK + SA increased yield in most traits. The highest RWC of flag leaves was observed in the foliar application of GA3 + SA (3.36 kg/ha) and then in the foliar application of GA3 + SA + CK (57.87 kg/ha). GA3 interacts with PGR spraying to balance another development under saline and non-saline conditions.

Effects of different irrigation methods and mulching on yield, growth and water use efficiency of strawberry

ABSTRACT A greenhouse experiment was conducted from 2015–2016 to 2017–2018 on an Aeric Haplaquept to evaluate the effect of irrigation (surface irrigation at furrows at irrigation water: cumulative pan evaporation = 1, drip irrigation to meet 100%, 80% and 60% of actual crop evapotranspiration demand), and mulch (nonwoven jute agro textile, biodegradable plastic film, rice straw and no mulch) regimes on strawberry. Average soil water storage in root zone (86 mm) and fraction of potential plant available water (61%) was highest in jute agrotextile mulched and drip irrigated at 80% actual evapotranspiration. This combination of mulch and irrigation regime increased belowground (root length density, 7.15 × 103 mm−3) and above ground (leaf area index, 9.4) crop growth and emerged as the best option with three- and four-times higher yield (31.1 Mg ha −1) and water use efficiency (23.1 kg m−3), respectively than surface irrigated unmulched strawberry. Results suggest that these findings be incorporated into packages and practices of strawberry cultivation in the moist tropical sub-humid region of Bengal basin and other similar agroecological regions of South Asia.