Role of Melatonin in Directing Plant Physiology

Melatonin (MT), a naturally occurring compound, is found in various species worldwide. In 1958, it was first identified in the pineal gland of dairy cows. MT is an "old friend" but a "new compound" for plant biology. It brings experts and research minds from the broad field of plant sciences due to its considerable influence on plant systems. The MT production process in plants and animals is distinct, where it has been expressed explicitly in chloroplasts and mitochondria in plants. Tryptophan acts as the precursor for the formation of phyto-melatonin, along with intermediates including tryptamine, serotonin, N-acetyl serotonin, and 5-methoxy tryptamine. It plays a vital role in growth phases such as the seed germination and seedling growth of crop plants. MT significantly impacts the gas exchange, thereby improving physio-chemical functions in plant systems. During stress, the excessive generation and accumulation of reactive oxygen species (ROS) causes protein oxidation, lipid peroxidation, nucleic acid damage, and enzyme inhibition. Because it directly acts as an antioxidant compound, it awakens the plant antioxidant defense system during stress and reduces the production of ROS, which results in decreasing cellular oxidative damage. MT can enhance plant growth and development in response to various abiotic stresses such as drought, salinity, high temperature, flooding, and heavy metals by regulating the antioxidant mechanism of plants. However, these reactions differ significantly from crop to crop and are based on the level and kind of stress. The role of MT in the physiological functions of plants towards plant growth and development, tolerance towards various abiotic stresses, and approaches for enhancing the endogenous MT in plant systems are broadly reviewed and it is suggested that MT is a steering compound in directing major physiological functions of plants under the changing climate in future

Integrated Assessment of Climate Change Impacts on Maize Farms and Farm Household Incomes in South India: A Case Study from Tamil Nadu

South India is characterized by a wide variety of landscapes, soils and climatic zones. It is comprised of tropical, semi-arid, humid-moist, and high-altitude environments, which support a diversity of agricultural systems. Our study focused on the state of Tamil Nadu, which is characterized by a generally tropical climate, and receive rainfall during both the southwest monsoon season (SWM, June to September) and the northeast monsoon (NEM, September to December). Agriculture continues to be an important sector in the state economy, as more than 56 of the people depend on agriculture and allied sectors for their livelihood. Analysis of land-use patterns in Tamil Nadu reveals that in the past decade there has been a reduction in net sown area and current fallow, while the share of cultivable wastelands has increased. The area under cereals, pulses, and oilseeds had marginally declined, although area under commercial crops like turmeric, sugar-cane, banana, fruits, and vegetables has shown an increasing trend. The production performance of major crops like cereals, pulses, and oilseeds has not shown any significant increase. Demand and supply gap of important crops in Tamil Nadu for the year 2010 indicates that the state is lagging far behind in the production of various crops

Speed Breeding: A Propitious Technique for Accelerated Crop Improvement

Development of climate-resilient genotypes with high agronomic value through conventional breeding consumes longer time duration. Speed breeding strategy involves rapid generation advancement that results in faster release of superior varieties. In this approach, the experimental crop is grown in a controlled environment (growth chambers) with manipulation provisions for temperature, photoperiod, light intensity, and moisture. The generation of the crop cycle can be hastened by inducing changes in the physiological process such as photosynthesis rate, flowering initiation, and duration. Speed breeding eases multiple trait improvement in a shorter span by integration of high-throughput phenotyping techniques with genotype platforms. The crop breeding cycle is also shortened by the implementation of selection methods such as single-seed descent, single plant selection, and marker-assisted selection

Carbon Accumulation, Soil Microbial and Enzyme Activities in Elephant Foot Yam-Based Intercropping System

Intercropping is a sustainable, eco-friendly, and economically beneficial cropping system. Elephant foot yam (EFY), a multifarious long-duration vegetable, takes 60 days or more to spread its canopy. Hence, this research assessed the impact of intercropping short duration vegetables, viz., cluster bean, radish, Amaranthus, and fenugreek, in elephant foot yam for two seasons (2021 and 2021/22). It included the analysis of parameters such as carbon accumulation, soil chemical properties, nutrient, enzyme, and microbial activities. The findings revealed that for both the seasons there was a significant (p < 0.01) rise in all the parameters examined in the intercropping patterns. Cluster bean (legume) outperformed the other intercrops utilised. Overall, carbon accumulation was improved by 54.40% when cluster beans were intercropped in EFY. Cluster bean intercropping increased the microbial and enzyme activities in the soil rhizosphere and improved soil organic carbon, microbial biomass carbon, nitrogen, phosphorus, and potassium by 31, 42, 28, 37, and 11%, respectively, compared to the sole crop. A positive correlation was observed between the soil microbes and enzyme activity with the soil chemical properties. As a result, the research concludes that intercropping cluster bean in EFY promotes carbon accumulation, soil nutrients, enzymes, and microbial community, which, in turn, favour the productivity of the elephant foot yam

Exploring DSSAT Model Genetic Coefficient Estimation Methodologies for Chickpea in Bundelkhand Region of Uttar Pradesh, India

In modern crop production, essential factors that contribute to narrowing yield gaps and minimizing production costs include making informed decisions about the selection of plant varieties, determining optimal sowing dates, determining appropriate plant populations, selecting suitable fertilizer rates, and implementing effective pest control methods. Two field experiments were conducted during the Rabi seasons of 2021 and 2022 at ICAR-Indian Institute of Pulses Research (IIPR), Kanpur using split-plot experimental design, where the main plots were three different sowing dates (20-25th October, November 10-15th, and 25th November-5th December), and the sub-plots were four chickpea cultivars (JG 16, RVG 202, IPC-07-66, and IPC-05-62), each with three replications. The genetic coefficients of the cultivars were estimated using both the iterative process (IP) and Generalized Likelihood Uncertainty Estimation (GLUE) methods in DSSAT v 4.7 to simulate the yields. Upon model validation, it was found that the average relative error (ARE) in predicting grain yield across the different sowing windows was between -25.7% to 29.1% when using the iterative process, while ARE was between -23.4% to 19% when using GLUE. The findings report more accurate simulations of chickpea growth and phenological development stages were recorded in normal sowings. And the model calibration suggest that GLUE provided superior estimates of genetic coefficients compared to the IP method. Therefore, it can be inferred that Glue is a more user-friendly and precise method

Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model

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

Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India

The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance of the different algorithms in predicting soil information for Indian landscapes. In this study, we compared a suite of six machine learning algorithms to predict quantitative (Cubist, decision tree, k-NN, multiple linear regression, random forest, support vector regression) and qualitative (C5.0, k-NN, multinomial logistic regression, naïve Bayes, random forest, support vector machine) soil information separately at a regional level. The soil information, including the quantitative (pH, OC, and CEC) and qualitative (order, suborder, and great group) attributes, were extracted from the legacy soil maps using stratified random sampling procedures. A total of 4479 soil observations sampled were non-spatially partitioned and intersected with 39 environmental covariate parameters. The predicted maps depicted the complex soil–environmental relationships for the study area at a 30 m spatial resolution. The comparison was facilitated based on the evaluation metrics derived from the test datasets and visual interpretations of the predicted maps. Permutation feature importance analysis was utilised as the model-agnostic interpretation tool to determine the contribution of the covariate parameters to the model’s calibration. The R2 values for the pH, OC, and CEC ranged from 0.19 to 0.38; 0.04 to 0.13; and 0.14 to 0.40, whereas the RMSE values ranged from 0.75 to 0.86; 0.25 to 0.26; and 8.84 to 10.49, respectively. Irrespective of the algorithms, the overall accuracy percentages for the soil order, suborder, and great group class ranged from 31 to 67; 26 to 65; and 27 to 65, respectively. The tree-based ensemble random forest and rule-based tree models’ (Cubist and C5.0) algorithms efficiently predicted the soil properties spatially. However, the efficiency of the other models can be substantially increased by advocating additional parameterisation measures. The range and scale of the quantitative soil attributes, in addition to the sampling frequency and design, greatly influenced the model’s output. The comprehensive comparison of the algorithms can be utilised to support model selection and mapping at a varied scale. The derived digital soil maps will help farmers and policy makers to adopt precision information for making decisions at the farm level leading to productivity enhancements through the optimal use of nutrients and the sustainability of the agricultural ecosystem, ensuring food security

Traditional Cultivars Influence on Physical and Engineering Properties of Rice from the Cauvery Deltaic Region of Tamil Nadu

Standard unit operations/equipment have not evolved for the traditional rice varieties of the Cauvery Deltaic region of Tamil Nadu. The fame of traditional rice is increasing nowadays owing to its health benefits. Non-standard unit operations may cause rice grains to crack during milling, accumulating more broken rice and yields in products of inferior quality. As a result, research into the physical properties of rice is crucial for the development of rice processing equipment that minimizes post-harvest losses during milling. Hence, an assessment was made to evaluate 30 traditional rice cultivars on their Physical (grain length, width, thickness, shape, and size), gravimetric (bulk, true, tapped density, porosity, Carr’s index, and Hausner ratio), and engineering characteristics (equivalent, arithmetic, square mean, and geometric mean diameter) using standard protocols, with the goal of reviving and preserving older varieties. The results from the analysis showed significant variations (p 2, respectively. Of the 30 varieties, 28 were under the high amylose category, and 2 belonged to the intermediate type. The Pearson correlation was established to study the interrelationships between the dimensions and engineering properties. Principal component analysis (PCA) reduced the dimensionality of 540 data into five principal components (PC), which explained 95.7% of the total variance. These findings suggest that it is possible to revive old landraces through careful selection and analysis of these properties. The superior characteristics of these traditional varieties can be further evaluated for breeding programs in order to improve the cultivation of these cherished rice landraces to enhance nutritional security

Role of Melatonin in Directing Plant Physiology

Melatonin (MT), a naturally occurring compound, is found in various species worldwide. In 1958, it was first identified in the pineal gland of dairy cows. MT is an “old friend” but a “new compound” for plant biology. It brings experts and research minds from the broad field of plant sciences due to its considerable influence on plant systems. The MT production process in plants and animals is distinct, where it has been expressed explicitly in chloroplasts and mitochondria in plants. Tryptophan acts as the precursor for the formation of phyto-melatonin, along with intermediates including tryptamine, serotonin, N-acetyl serotonin, and 5-methoxy tryptamine. It plays a vital role in growth phases such as the seed germination and seedling growth of crop plants. MT significantly impacts the gas exchange, thereby improving physio-chemical functions in plant systems. During stress, the excessive generation and accumulation of reactive oxygen species (ROS) causes protein oxidation, lipid peroxidation, nucleic acid damage, and enzyme inhibition. Because it directly acts as an antioxidant compound, it awakens the plant antioxidant defense system during stress and reduces the production of ROS, which results in decreasing cellular oxidative damage. MT can enhance plant growth and development in response to various abiotic stresses such as drought, salinity, high temperature, flooding, and heavy metals by regulating the antioxidant mechanism of plants. However, these reactions differ significantly from crop to crop and are based on the level and kind of stress. The role of MT in the physiological functions of plants towards plant growth and development, tolerance towards various abiotic stresses, and approaches for enhancing the endogenous MT in plant systems are broadly reviewed and it is suggested that MT is a steering compound in directing major physiological functions of plants under the changing climate in future