Screening cotton genotypes for their drought tolerance ability based on the expression level of dehydration-responsive element-binding protein and proline biosynthesis-related genes and morpho-physio-biochemical responses.

Drought stress adversely affects growth, development, productivity, and fiber quality of cotton (Gossypium hirsutum L). Breeding strategies to enhance drought tolerance require an improved knowledge of plant drought responses necessitating proper identification of drought-tolerant genotypes of crops, including cotton. The objective of this study was to classify the selected cotton genotypes for their drought tolerance ability based on morpho-physio-biochemical traits using Hierarchical Ward's cluster analysis. Five genotypes of cotton (Takfa 3, Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5) were selected as plant materials, and were grown under well-watered (WW; 98 ± 2% field capacity) and water-deficit (WD; 50 ± 2% field capacity) conditions for 16 days during the flower initiation stage. Data on morpho-physio-biochemical parameters and gene expression levels for these parameters were collected, and subsequently genotypes were classified either as a drought tolerant or drought susceptible one. Upregulation of GhPRP (proline-rich protein), GhP5CS (Δ1-pyrroline-5-carboxylate synthetase), and GhP5CR (Δ1-pyrroline-5-carboxylate reductase) in relation to free proline enrichment was observed in Takfa 3 genotype under WD condition. An accumulation of free proline, total soluble sugar, and potassium in plants under WD conditions was detected, which played a key role as major osmolytes controlling cellular osmotic potential. Magnesium and calcium concentrations were also enriched in leaves under WD conditions, functioning as essential elements and regulating photosynthetic abilities. Leaf greenness, net photosynthetic rate, stomatal conductance, and transpiration rate were also declined under WD conditions, leading to growth retardation, especially aboveground traits of Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5 genotypes. An increase in leaf temperature (1.1 - 4.0 °C) and crop water stress index (CWSI > 0.75) in relation to stomatal closure and reduced transpiration rate was recorded in cotton genotypes under WD conditions compared with WW conditions. Based on the increase of free proline, soluble sugar, leaf temperature, and CWSI, as well as the decrease of aboveground growth traits and physiological attributes, five genotypes were categorized into two cluster groups: drought tolerant (Takfa 3) and drought susceptible (Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5). The identified drought-tolerant cotton genotype, namely, Takfa 3, may be grown in areas experiencing drought conditions. It is recommended to further validate the yield traits of Takfa 3 under rainfed field conditions in drought-prone environments.

Alleviation of water-deficit stress in turmeric plant (Curcuma longa L.) using phosphate solubilizing rhizo-microbes inoculation.

The objective of this study was to assess the effects of phosphate solubilizing rhizo-microbes inoculants on nutrient balance, physiological adaptation, growth characteristics, and rhizome yield traits as well as curcuminoids yield at the secondary-rhizome initiation stage of turmeric plants, subsequently subjected to water-deficit (WD) stress. Phosphorus contents in the leaf tissues of Talaromyces aff. macrosporus and Burkholderia sp. (Bruk) inoculated plants peaked at 0.33 and 0.29 mg g-1 DW, respectively, under well-watered (WW) conditions; however, phosphorus contents declined when subjected to WD conditions (p ≤ 0.05). Similarly, potassium and calcium contents reached their maximum values at 5.33 and 3.47 mg g-1 DW, respectively, in Burk inoculated plants under WW conditions, which contributed to sustained rhizome fresh weight even when exposed to WD conditions (p ≤ 0.05). There was an increase in free proline content in T. aff. macrosporus and Burk inoculated plants under WD conditions, which played a crucial role in controlling leaf osmotic potential, thereby stabilizing leaf greenness and maximum quantum yield of PSII. As indicators of drought stress, there were noticeable restrictions in stomatal gas exchange parameters, including net photosynthetic rate, stomatal conductance, and transpiration rate, accompanied by an increase in leaf temperature. These changes resulted in reduced total soluble sugar levels. Interestingly, total curcuminoids and curcuminoids yield in Burk inoculated plants under WD conditions were retained, especially in relation to rhizome biomass. Burk inoculation in turmeric plants is recommended as a promising technique as it alleviates water-deficit stress, sustains rhizome biomass, and stabilizes curcuminoids yield. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-03922-x.

Ensemble modelling framework for groundwater level prediction in urban areas of India.

India is facing the worst water crisis in its history and major Indian cities which accommodate about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information theory (MI), genetic algorithm (GA), artificial neural network (ANN) and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data which consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate were pre-processed using mutual information theory, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre-processing predict groundwater levels accurately (R > 85%). It is also evident from the results that the pre-processing techniques enhance the prediction accuracy and results were improved for 66% of the monitored wells. Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels. The developed approach in this study for urban groundwater prediction can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling.