Advanced machine learning-based kharif maize evapotranspiration estimation in semi-arid climate

Accurate Crop Evapotranspiration (ETc) estimation is crucial for understanding hydrological and agrometeorological processes, yet it's challenged by multiple parameters, data variations, and lack of continuity. These limitations restrict numerical methods application. To address this, the study aims to develop and assess ML models for daily maize ETc in semi-arid areas, utilizing varied weather inputs. Five ML models viz., Category Boosting (CB), Linear Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were developed and validated for the ICAR-IARI, New Delhi, Research Station. Penman-Monteith (PM) model estimated ETc values are used as the standard for comparing the performance of the ML model values. Results revealed that the SVM model achieved the highest coefficient of determination (R2) among all models, with a value of 0.987. Furthermore, the SVM model exhibited the lowest model errors (MAE = 0.121 mm day-1, RMSE = 0.172 mm day-1, and MAPE = 4.37%) compared to other models. The ANN model also demonstrated promising results, comparable to the SVM model. Notably, the wind speed parameter was found most influential input parameter. In conclusion, SVM or ANN could be considered reliable alternative methods for the accurate estimation of kharif maize crop ETc in the semi-arid climate.

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ET_o) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman-Monteith (PM56) model estimated daily ET_o. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ET_o modeling during the model training period, while in the testing period under scenarios S1(T_max) and S2 (T_max, and T_min), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ET_o in semi-arid climatic conditions.

Identification of suitable zones and sites for rainwater harvesting using GIS and multicriteria decision analysis

Identifying suitable zones for surface water storage and groundwater recharge is needed to enhance irrigation water availability. This study was undertaken to map rainwater harvesting (RWH) potential zones using geospatial tools and analytic hierarchy process (AHP). The suitable locations for RWH were also mapped using the Boolean logic concept. The surface runoff is a vital factor to demarcate the appropriate zones for RWH. The curve number approach was used to estimate the surface runoff potential. The runoff coefficient (RC) map was generated based on rainfall and surface runoff depth. Weights have been allocated to selected themes of RC, drainage density, and slope. The themes were integrated using geographic information system (GIS) and AHP to demarcate the suitable zones for RWH. The derived RWH potential map was categorized into zones like "very good," "good," "moderate," "poor," and "very poor" with an aerial extent of 14.3%, 24.7%, 28.3%, 20.2%, and 12.6%, respectively. The area suitable for farm ponds was found to be about 9% (408 km²), 13% (329 km²), and 4% (244 km²) in Mirzapur, Chandauli, and Sonbhadra districts, respectively. Furthermore, 22, 15, and 21 locations were found suitable for check dams in Mirzapur, Chandauli, and Sonbhadra districts, respectively. At a large scale, effective planning of water management strategies based on multicriteria decision analysis and GIS offers increased availability of surface and groundwater resources and may help for enhancing the agricultural land use options. The higher resolution maps may be further utilized to plan RWH strategies at village level.

Potent anti-mycobacterial and immunomodulatory activity of some bioactive molecules of Indian ethnomedicinal plants that have the potential to enter in TB management

Tuberculosis (TB) is one of the deadliest infectious diseases of human civilization. Approximately one-third of global population is latently infected with the TB pathogen Mycobacterium tuberculosis (M.tb). The discovery of anti-TB antibiotics leads to decline in death rate of TB. However, the evolution of antibiotic-resistant M.tb-strain and the resurgence of different immune-compromised diseases re-escalated the death rate of TB. WHO has already cautioned about the chances of pandemic situation in TB endemic countries until the discovery of new anti-tubercular drugs, that is, the need of the hour. Analysing the pathogenesis of TB, it was found that M.tb evades the host by altering the balance of immune response and affects either by killing the cells or by creating inflammation. In the pre-antibiotic era, traditional medicines were only therapeutic measures for different infectious diseases including tuberculosis. The ancient literatures of India or ample Indian traditional knowledge and ethnomedicinal practices are evidence for the treatment of TB using different indigenous plants. However, in the light of modern scientific approach, anti-TB effects of those plants and their bioactive molecules were not established thoroughly. In this review, focus has been given on five bioactive molecules of different traditionally used Indian ethnomedicinal plants for treatment of TB or TB-like symptom. These compounds are also validated with proper identification and their mode of action with modern scientific approaches. The effectiveness of these molecules for sensitive or drug-resistant TB pathogen in clinical or preclinical studies was also evaluated. Thus, our specific aim is to highlight such scientifically validated bioactive compounds having anti-mycobacterial and immunomodulatory activity for future use as medicine or adjunct-therapeutic molecule for TB management.

Development of a Composite Hydrologic Index for Semi-Arid Region of India

Rainfall is the major source for groundwater recharge in basins areas of central region of India. Now a day, the river basins are experiencing acute shortage of water which has resulted in lowering of groundwater level and drying up of water bodies. In order to maintain water sustainability; a composite hydrologic index was developed in the Betwa basin of Madhya Pradesh and Uttar Pradesh states, India. The index was developed using principal component analysis through hydrologic, topographic as well as geographic parameters derived from the Soil and Water Assessment Tool and MODFLOW model. The geomorphological parameters were categorized, on the basis of groundwater recharge potential and weight ranged from 1 to 4. The geomorphologic parameters, that is, soil type (T), slope (S), runoff ratio (R), and evapotranspiration (ET) were integrated into a single indicator of composite hydrologic index. Soil type and ET were the major factors that directly affected the groundwater recharge. These two parameters together explained 86% of total variability in the data. Based on the analysis of the four parameters that affected groundwater recharge, composite hydrologic index (CHI) was classified into very good, good, moderate, and low grade. The CHI was statistically validated using standardization methods. The index was developed as a water management tool to measure a sustainability state relative to a groundwater recharge potential, which allows for spatial and temporal comparison. This index will be helpful in natural resource management and will improve socioeconomic status of human population inhibiting in the semi-arid region.

Numerical simulation to assess potential groundwater recharge and net groundwater use in a semi-arid region

Accurate assessment of deep percolation (potential groundwater recharge) under different field crops is essential for sustainable management of scarce water resources and proper planning of crop rotation in irrigated, semi-arid regions of the world. The potential recharge from commonly grown field crops in semi-arid Indo-Gangetic Plain (IGP) of India was estimated using HYDRUS-1D model, where, irrigation, evapotranspiration, and soil moisture dynamics were simulated. Simultaneously, net groundwater use by different cropping patterns was also calculated. Among the hydraulic parameters, n was found most sensitive for water percolation. During rainy season, 293.8 and 159.1 mm water was percolated below the root zone of cotton and soybean, respectively, which accounted for 39.4 and 32.9% of the water input. During winter season, 66.8 and 30.3 mm water was percolated below the root zone of winter maize and mustard, respectively, accounting for 20.5 and 10.6% of added water. It was observed that net groundwater use was positive for cotton, soybean, and summer maize with the values of 168.8, 159.1, and 18.0 mm year^-1, respectively, and negative for rice, wheat, winter maize, and mustard. For the eight most important cropping patterns of semi-arid IGP, the net groundwater use was negative and varied between - 4.4 mm year^-1 for cotton-maize and -423 mm year^-1 for rice-wheat. With these cropping patterns, the overall rate of decline of groundwater was 231 mm year^-1. It was found that maize-wheat and soybean-wheat cropping patterns consume much less water than rice-wheat cropping pattern and therefore are suitable to arrest the declining trend of groundwater in semi-arid IGP of India.

Spatial variability of groundwater depth and quality parameters in the National Capital Territory of Delhi

The groundwater quantity and quality scenario is of much concern in the National Capital Territory of Delhi, India, which necessitates an investigation to envisage the extent of spatial variability of groundwater depth and pollutant concentration levels in this region. Therefore, in this study, an effort was made to generate the spatial variability map of groundwater depth and quality parameters (viz. chloride, electrical conductivity, fluoride, magnesium, and nitrate). Ordinary kriging was used to analyze the spatial variability of groundwater depth and quality parameters, whereas indicator kriging was used to analyze groundwater quality parameters equal to or greater than the pollution threshold values. It was observed that the semivariogram parameters fitted well in the exponential model for water depth and in the spherical model for water quality parameters. The generated spatial variability maps indicated that in 43% of the study area, groundwater depth was within 20 m. The salinity level was higher than 2.5 dS m(-1) in 69% of the study area and the nitrate concentration exceeded 45 mg l(-1) in 36% of the area. The probability maps showed that about 24% of the area had the highest probability (0.8-1.0) of exceedence of the threshold electrical conductivity value and an area of 2% exhibited the highest probability of exceedence of the threshold value of nitrate concentration in the groundwater. The generated spatial variability and probability maps will assist water resource managers and policymakers in development of guidelines in judicious management of groundwater resources for agricultural and drinking purposes in the study area.

Variability of soil-water quality due to Tsunami-2004 in the coastal belt of Nagapattinam district, Tamilnadu

In this study, the Tsunami-caused deterioration of soil and groundwater quality in the agricultural fields of coastal Nagapattinam district of Tamilnadu state in India is presented by analyzing their salinity and sodicity parameters. To accomplish this, three sets of soil samples up to a depth of 30 cm from the land surface were collected for the first six months of the year 2005 from 28 locations and the ground water samples were monitored from seven existing dug wells and hand pumps covering the study region at intervals of 3 months. The EC and pH values of both the soil and ground water samples were estimated and the spatial and temporal variability mappings of these parameters were performed using the geostatistical analysis module of ArcGIS((R)). It was observed that the spherical semivariogram fitted well with the data set of both EC and pH and the generated kriged maps explained the spatial and temporal variability under different ranges of EC and pH values. Further, the recorded EC and pH data of soil and ground water during pre-Tsunami periods were compared with the collected data and generated variability soil maps of EC and pH of the post-Tsunami period. It was revealed from this analysis that the soil quality six months after the Tsunami was nearing the pre-Tsunami scenario (EC< 1.5 dS m(-1); pH<8), whereas the quality of ground water remained highly saline and unfit for irrigation and drinking. These observations were compared with the ground scenarios of the study region and possible causes for such changes and the remedial measures for taking up regular agricultural practices are also discussed.

Ligand-dependent activation of the hedgehog pathway in glioma progenitor cells

The hedgehog (Hh) signaling pathway regulates progenitor cells during embryogenesis and tumorigenesis in multiple organ systems. We have investigated the activity of this pathway in adult gliomas, and demonstrate that the Hh pathway is operational and activated within grade II and III gliomas, but not grade IV de novo glioblastoma multiforme. Furthermore, our studies reveal that pathway activity and responsiveness is confined to progenitor cells within these tumors. Additionally, we demonstrate that Hh signaling in glioma progenitor cells is ligand-dependent and provide evidence documenting the in vivo source of Sonic hedgehog protein. These findings suggest a regulatory role for the Hh pathway in progenitor cells within grade II and III gliomas, and the potential clinical utility of monitoring and targeting this pathway in these primary brain tumors.

Iron in sickle cell disease

Iron deficiency anaemia was detected in 23% of cases with homozygous sickle cell disease. The aetiology of iron deficiency was similar to the other population in the community. High serum ferritin level was detected in 15.4% of the cases and was well correlated to the number of transfusions. Tissue haemosiderosis was not detected in any case. Patients with heterozygous sickle cell had either normal or low serum ferritin levels.