Boron (B) is an essential micronutrient, crucial for the growth and development of crop plants. However, the essential to a toxic range of B in the plant is exceptionally narrow, and symptoms develop with a slight change in its concentration in soil. The morphological and anatomical response, such as leaf chlorosis, stunted growth, and impairment in the xylem and phloem development occurs under B-toxicity. The transport of B in the plant occurs via transpiration stream with the involvement of B-channels and transporter in the roots. The higher accumulation of B in source and sink tissue tends to have lower photosynthetic, chlorophyll content, infertility, failure of pollen tube formation and germination, impairment of cell wall formation, and disruption of membrane systems. Excess B in the plant hinders the uptake of other micronutrients, hormone transport, and metabolite partitioning. B-mediated reactive oxygen species production leads to the synthesis of antioxidant enzymes which help to scavenge these molecules and prevent the plant from further oxidative damage. This review highlights morpho-anatomical, physiological, biochemical, and molecular responses of the plant under B toxicity and thereby might help the researchers to understand the related mechanism and design strategies to develop B tolerant cultivars. Graphical Abstract The physio-biochemical and molecular responses and mechanism of B uptake under its toxic condition have been illustrated. The spatial distribution of boron under its toxic condition and its accumulation in the plant might be regulated with sugar alcohols (polyols). This review throws light on the elevated level of B in the soil-plant system and provides management strategies for alleviating B toxicity in the plant.

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha⁻¹ with a mean of 3654 kg ha⁻¹. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha⁻¹ with the mean of 3076 kg ha⁻¹. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha⁻¹ with the mean of 3855 kg ha⁻¹. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R² greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Rice (Oryza sativa L.) grain yield is reduced by drought stress during reproductive stages. In this work, the effects of foliar selenium (Se) spray on the reduction of drought stress are measured and the most critical stage of rice is identified. Three field experiments were carried out. In experiments I and II, the sensitive stage was determined by imposing drought stress at the panicle initiation and flowering stages by withholding irrigation for 10, 15, 20, and 25 days. In experiment III, drought stress was induced by depriving plants of moisture for 20 or 25 days. Se spray was then applied at rates of 0, 10, and 20 mg L⁻¹ to alleviate the drought, and its effects were evaluated. Overall, it was reported that the panicle initiation stage was more vulnerable to drought stress than the flowering stage, as evidenced by a larger reduction in grain yield. Employing Se as a foliar spray at a rate of 20 mg L⁻¹ under drought stress enhanced the number of filled grains m⁻², the total number of grains m⁻², and grain yield ha⁻¹ by 22.0, 4.3, and 11.0%, respectively, over water spray. This shows a positive interaction or synergistic interaction between drought tolerance and selenium application. These results demonstrate that foliage supplementation of Se at a dosage of 20 mg L⁻¹ alleviate the adverse effects of drought stress at the panicle initiation stage in rice cultivation. Se mitigated the negative effects of drought by improving the membrane stability index, relative water content, and proline content.

The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.