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Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing T max , T min , RH mean , R s , and U 2 , with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R 2 ), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R 2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.

Our study from an ongoing research experiment initiated in Rabi 1967 at the Research Farm of CCS Haryana Agricultural University, Haryana, India, reports that during the 51st wheat cycle in pearl millet-wheat sequence, adding FYM in both seasons significantly impacted various soil parameters at different wheat growth stages compared to the rabi season. The application of 15 t of FYM ha −1 resulted in a considerable increase in dissolved organic carbon content (9.1–11.2%), available P (9.7–12.1%), and available S (12.6–17.1%), DHA levels by 7.3–22.0%, urease activity (10.1 and 17.0%), β-Glucosidase activity (6.2–8.4%), and APA activity (5.2–10.6%), compared to 10 t FYM ha −1 . Application of N 120 exhibited a considerable improvement in DHA (11.0–23.2%), β-Glucosidase (9.4–19.2%), urease (13.3–28.3%), and APA (3.3–6.2%) activity compared to control (N 0 ). At stage 3, the box plot revealed that 50% of the available N, P, and S values varied from 223.1 to 287.9 kg ha −1 , 53.0 to 98.2 kg ha −1 , and 50.0 to 97.6 kg ha −1 , respectively. Principal component analysis, with PC1 explaining 94.7% and PC2 explaining 3.15% of the overall variability, and SOC had a polynomial relationship with soil characteristics (R 2  = 0.89 to 0.99). Applying FYM 15  × N 120 treatment during both seasons proved beneficial in sustaining the health of sandy loam soil in North-West India.

Targeted profiling of polyphenols in trees may reveal valuable sources of natural compounds with major applications in pharmacology and disease control. The current study targeted the profiling of polyphenols using HPLC-DAD in Quercus robur, Q. macrocarpa and Q. acutissima bark extracts. Free radical scavenging of each extract was investigated using antioxidant assays. Antimicrobial activities against a wide spectrum of bacteria and fungi were explored, as well as anticancer activities against different cancer cell lines. The HPLC-DAD analyses revealed the availability of several polyphenols in high amounts, including ellagic acid (in Q. robur) and caffeic acid (in Q. macrocarpa) in all three species. The bioactivity assay revealed high antioxidant activity in Q. robur compared to that of the other species, as well as phenolic standards. The three oak bark extracts showed clear antibacterial activities against most bacteria tested, with the highest antibacterial activities in the extracts of Q. robur. In addition, the three extracts showed higher antibacterial activities against Pseudomonas aeruginosa, Micrococcus flavus, and Escherichia coli compared to that of other bacteria. There were strong antifungal activities against some fungi, such as Aspergillus flavus, Penicillium funiculosum, and Penicillium ochrochloron. There were also noticeable anticancer activities against MCF-7, HeLa, Jurkat, and HT-29 cell lines, with the highest anticancer activity in the extracts of Q. robur. This is the first study that reveals not only novel sources of important polyphenols (e.g., ellagic acid) in Q. robur, Q. macrocarpa and Q. acutissima bark but also their anticancer activities against diverse cancer cell lines.

In the context of degradation of soil health, environmental pollution, and yield stagnation in the rice–wheat system in the Indo-Gangetic Plains of South Asia, an experiment was established in split plot design to assess the long-term effect of crop residue management on productivity and phosphorus requirement of wheat in rice–wheat system. The experiment comprised of six crop residue management practices as the main treatment factor with three levels (0, 30 and 60 kg P 2 O 5 ha –1 ) of phosphorus fertilizer as sub-treatments. Significant improvement in soil aggregation, bulk density, and infiltration rate was observed under residue management (retention/incorporation) treatments compared to residue removal or residue burning. Soil organic carbon (SOC), available nutrient content (N, P, and K), microbial count, and enzyme activities were also significantly higher in conservation tillage and residue-treated plots than without residue/burning treatments. The residue derived from both crops when was either retained/incorporated improved the soil organic carbon (0.80%) and resulted in a significant increase in SOC (73.9%) in the topsoil layer as compared to the conventional practice. The mean effect studies revealed that crop residue management practices and phosphorus levels significantly influenced wheat yield attributes and productivity. The higher grain yield of wheat was recorded in two treatments, i.e. the basal application of 60 kg P 2 O 5 ha –1 without residue incorporation and the other with half the P-fertilizer (30 kg P 2 O 5 ha –1 ) with rice residue only. The grain yield of wheat where the rice and wheat residue were either retained/incorporated without phosphorus application was at par with 30 and 60 kg P 2 O 5 ha –1 . Phosphorus levels also significantly affected wheat productivity and available P content in the soil. Therefore, results suggested that crop residue retention following the conservation tillage approach improved the yield of wheat cultivated in the rice–wheat cropping system.

A new series of thiazolyl-pyrazoline derivatives (4a–d, 5a–d 6a, b, 7a–d, 8a, b, and 10a, b) have been designed and synthesized through the combination of thiazole and pyrazoline moieties, starting from the key building blocks pyrazoline carbothioamides (1a–b). These eighteen derivatives have been designed as anticipated EGFR/HER2 dual inhibitors. The efficacy of the developed compounds in inhibiting cell proliferation was assessed using the breast cancer MCF-7 cell line. Among the new synthesized thiazolyl-pyrazolines, compounds 6a, 6b, 10a, and 10b displayed potent anticancer activity toward MCF-7 with IC50 = 4.08, 5.64, 3.37, and 3.54 µM, respectively, when compared with lapatinib (IC50 = 5.88 µM). In addition, enzymatic assays were also run for the most cytotoxic compounds (6a and 6b) toward EGFR and HER2 to demonstrate their dual inhibitory activity. They revealed promising inhibition potency against EGFR with IC50 = 0.024, and 0.005 µM, respectively, whereas their IC50 = 0.047 and 0.022 µM toward HER2, respectively, compared with lapatinib (IC50 = 0.007 and 0.018 µM). Both compounds 6a and 10a induced apoptosis by arresting the cell cycle of the MCF-7 cell line at the G1 and G1/S phases, respectively. Molecular modeling studies for the promising candidates 6a and 10a showed that they formed the essential binding with the crucial amino acids for EGFR and HER2 inhibition, supporting the in vitro assay results. Furthermore, ADMET study predictions were carried out for the compounds in the study.

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017–2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h −1 . At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash–Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h −1 , at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm , respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm 3  cm −3 just after irrigation in 2 L h −1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm 3  cm −3 were in 4 L h −1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm 3  cm −3 were in 8 L h −1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (N ES ), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Q t-n ) and suspended sediment concentration (S t-n ) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (S t-1 , S t-2 , S t-3 , S t-4 ) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest N ES  = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest N ES  = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

The lack of knowledge about genetic variation in cassava is a problem for Fiji’s efforts to improve its genetics. Using agro-morphological features, this study aimed to assess the genetic diversity and interrelationships among 33 cassava cultivars. A field investigation was conducted at the Dobuilevu Research Station using a randomized complete block design. Morphological analysis, based on qualitative and quantitative characteristics, divided the germplasm into three groups. In both the qualitative and quantitative trait datasets, two principal components were found to account for 36.31% and 43.45% of the total genetic variance, respectively. Qualitative features, such as branching habit and stem cortex color (r = 0.19) , petiole color and root cortex color (r = 0.32) , and leaf color and root shape (r = 0.40) were shown to have significant positive correlations. Similarly, quantitative parameters like starch content (r = 0.25) and the number of leaf lobes with yield (r = 0.17) showed significant associations. Based on morphological and genetic similarities, hierarchical clustering grouped the cultivars into three qualitative and five quantitative clusters. While the quantitative traits emphasized variability in yield, starch content, and iron content. The qualitative traits’ descriptive statistics revealed diverse phenotypic expressions, with dark green leaf color and cylindrical root form being the most common. These results demonstrate significant genetic variation across cassava cultivars, which can be used for genetic improvement initiatives, germplasm conservation, and short-term varietal release programs. To facilitate the development of more resilient and productive cassava cultivars, targeted breeding efforts aimed at improving yield, quality, and stress tolerance are recommended based on the significant phenotypic and genetic variation observed.

The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R 2 , PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.