Crops In India Research Articles

Microbial Interactions in the Management of Groundnut Stem Rot

Groundnut (Arachis hypogaea L.) is an important oil seed crop in the world belonging to the Leguminosae family. It is one of the essential food and cash crops of our country. In India, a large number of diseases attack groundnut [1]. Among soil borne diseases, stem rot or white mold caused by Sclerotium rolfsii Sacc. [2] is an important disease causing significant yield losses in several groundnut growing countries (Mehan et al., 1994). Sclerotium rolfsii Sacc. is a destructive soil borne fungal pathogen of oil seed crop in India. Different practices are recommended for management of groundnut stem rot such as deep summer ploughing, destruction of plant debris, crop rotation with jowar and bajra, seed treatment with carbendazim or captan or mancozeb or tebuconazole, soil drenching with hexaconazole, application of ammonium sulphate or calcium ammonium nitrate instead of urea and application of gypsum at flowering stage. Further, no single treatment is full proof and disease continues to cause losses in farmers’ fields. Biological control offers an interesting alternative to fungicides for sustainable management of soil borne diseases. Biocontrol is a non-chemical measure, could be effective as chemical control by various techniques. Among the techniques, the mixture of antagonists was studied to enchance the control efficacy. In most studies, the involvement of only mechanism of biological control is demonstrated. Involvement of more than one mechanism has been reported in only a few systems. Use of several biocontrol agents with several mechanisms of control fits in well with the concepts of integrated disease management, in which several means of disease suppression are applied concurrently. When single or more means of mechanisms are not effective, the others may compensate for the former absence. The present study, involved three major bacterial antagonists viz., Streptomyces violaceusniger, Streptomyces exfoliatus and Pseudomonas fluorescens to find out the effective dose of mixtures. The different doses from 10-1 to 10-5 were studied, the dose 10-1 of Streptomyces violaceusniger have more efficiency of 86.70, than S. exfoliatus. The efficacy of Streptomyces violaceusniger with combination of other antagonists were tested and found that, the mixture of Streptomyces violaceusniger and P. fluorescens have synergistic activity than any other combinations which have synergistic factor greater than one.

Germplasm conservation, evaluation, characterization and improvement of genetic resources of fruit crops in India: Preserving of future fruit crops for the future

Germplasm conservation, evaluation, characterization and improvement of genetic resources of fruit crops in India: Preserving of future fruit crops for the future

Brinjal Little Leaf Disease: An Emerging Threat to Brinjal Crop in India

Brinjal Little Leaf Disease: An Emerging Threat to Brinjal Crop in India

Emerging strategies in biological control of scale insects on fruit crops in India

Emerging strategies in biological control of scale insects on fruit crops in India

Study of resistance mechanism of Alternaria blight (Alternaria brassicicola) by biochemical markers in Indian Mustard (Brassica juncea L. Czern. &Coss.).

Indian mustard (Brassica juncea) is an important oilseed crop in India. Alternaria leaf spot (Alternaria blight) is incited by the fungus Alternaria brassicicola. It majorly affects crop production leading to a yield loss of up to 70%. To circumvent this problem, the study of the resistance mechanism and identification of biochemical markers is one of the important strategies for its management. In the present study, a total of 219 genotypes of Indian mustard with check were screened for Alternaria blight over two seasons. Based on the area under the disease progress curve (AUDPC) scores, ten consistently performing genotypes were selected for the screening of biochemical and yield attributes under artificial inoculated conditions of Alternaria brassicicola (Berk) Sacc. The result showed a negative correlation between disease and yield attributes. The catalase (CAT) activity was significantly increased in resistant genotypes compared to susceptible ones, indicating the crucial role of CAT in the resistance mechanism. Pathogen infection also increases the total protein content and the Alternaria-resistant genotype showed the highest total soluble protein while the susceptible genotype showed the lowest total soluble protein. The ten genotypes were categorized by SSI (stress susceptibility index) and Varuna was identified as a tolerant genotype and Giriraj as a susceptible genotype for Alternaria brassicicola (Berk) Sacc. Varuna and Giriraj were chosen for quantitative analysis of methionine and tryptophan amino acids from seeds using RP-HPLC (Reverse Phase-High Performance Liquid Chromatography) and there were significant differences in the levels of methionine and tryptophan between the Varuna and Giriraj genotypes. Varuna showed higher methionine and tryptophan content compared to the Giriraj genotype. Higher protein content demonstrated an increase in biotic stress-responsive amino acids, such as methionine and tryptophan, suggesting increased resistance to Alternaria diseases in these high-protein genotypes. These amino acids could be used as biochemical markers for Alternaria resistance of mustard.

Shifting Pest Paradigms in Indian Crops: A Comprehensive Review of Emerging Threats

Sustainable crop production directly relates to the insect repercussions in future climates. In recent times, it has been essential to look over the changes in the pest scenario as the categories of pests may shift. Such deviations in the pest status are due to multiple factors viz., increased temperature, rise in Co2 levels, etc. This is well-witnessed in a wide array of crops. Changes in these factors favour the pest species depending on their ecology. Monitoring the climate and pest population is vital for understanding the effect of climate change on insect pests. Therefore, the forecasting and prediction models must be adjusted considering the evolving circumstances. This review examines these changes in key crops, including pearl millet, maize, okra, cotton, tomato, chilli, cassava, mango and coconut. Significant pest shifts include the replacement of stem borer and shoot fly by fall armyworm in maize and the rise of sucking pests in Bt cotton. New invasive pests such as Tuta absoluta in tomato and Thrips parvispinus in chilly underscore the shifting pest landscape. The review highlights the importance of monitoring pest populations and advocates for integrated pest management (IPM) strategies, the development of resistant varieties and conservation of natural enemies to mitigate economic losses.

Effects of Herbicides and Nano Urea Mixture on Weeds, Yield Attributes and Yield of Wheat (Triticum aestivum)

Wheat (Triticum aestivum) is second most important staple crop in India after rice. Weeds have emerged as the major biotic stress in wheat. Post-emergence herbicidal combinations are used widely to control the complex weed flora in wheat crops. A field experiment was carried out to study the “Effects of herbicides and nano urea mixture on weeds, yield attributes and yield of wheat (Triticum aestivum)”. The experiment was performed in randomized blocked design with three replications and fourteen treatments in Rabi season 2022-23 at Agronomy Research Farm, CCS Haryana Agricultural University, Hisar. Significantly minimum weed count (1.52 m-2) and dry weight (1.49 g m-2) and maximum weed control efficiency (88.6%) of Phalaris minor and broadleaved weeds (87%) was recorded with application of clodinafop + metribuzin 60 + 210 g ha-1 with nano urea 3 ml L-1 followed by clodinafop + metribuzin 60 + 210 g ha-1 at 90 DAS. In case of yield attributes i.e. number of effective tillers per meter row length (92.5), spike length (11.4 cm), number of grains spike-1 (55.9) and 1000-grain weight (43.8) were higher with application of clodinafop + metribuzin 60 + 210 g ha-1 with nano urea 3 ml L-1 followed by clodinafop + metribuzin 60 + 210 g ha-1. Grain yield (5876 kg ha-1) and B:C (1.94) was recorded higher with application of clodinafop + metribuzin 60 + 210 g ha-1 with nano urea 3 ml L-1 followed by clodinafop + metribuzin 60 + 210 g ha-1 which was significantly higher than weedy check but at par with the weed free treatment.

In-vitro Evaluation of Fungicides and Botanicals against Uromyces ciceris arietini Inciting Chickpea Rust Disease

Chickpea, commonly known as gram is one of the important pulse crops in India. The crop faces numerous disease attacks, resulting in losses to both quality and quantity. Among the diseases, rust caused by Uromyces ciceris arietinum, is a sporadic disease of chickpea. Considering the notoriority of disease A study was conducted at department of plant Pathology and Agril Microbiology, PGI MPKV, Rahuri during 2021-22 which aimed to assess the efficacy of fungicides against the pathogen causing rust disease under in vitro conditions. Among five fungicides, Tebuconazole 50% + Trifloxystrobin 25% w/w WG and Azoxystrobin 18.2% + Difenoconazole 11.4% w/w SC were found cent per cent effective against U. ciceris arietini, which completely inhibited uredospore germination at all three concentrations, viz. 50 ppm, 100 ppm, 500 ppm. However, Propiconazole 25% EC, Tebuconazole 25% EC and Hexaconazole 5% SC were found cent per cent effective against test pathogen only at 500 ppm. Among botanicals, Azadirachtin and Pongamia oil showed effective treatments to some extent. Maximum uredospore germination (63%) was recorded in control (Tap water).

Forecast of agri-residues generation from rice, wheat and oilseed crops in India using machine learning techniques: Exploring strategies for sustainable smart management

Management of agri-residues generated in large quantities necessitates for its accurate estimation. Data analysis using machine learning methods can predict the agri-residues generation. The objective of the study was to forecast agri-residues generation from rice, wheat, and oilseed crops in India using ML methods and their sustainable uses. Prediction of agri-residues was done first by forecasting the crop production via the application of ML techniques for the period 2022 to 2030, and then the amount of crop residues generation calculated by multiplying the crop productions with the residues-to-product-ratio (RPR) values of the respective crops. RPR was estimated by using the gravimetric ratio of the residue to the actual crop production. The crop-specific RPR values were taken from various earlier studies in Indian context. The RPR values of 1.73 for the rice, 1.65 for wheat, and 2.6 for the oilseed crop were used as a conversion factor for residues calculation. Machine learning models linear regression, sequential minimal optimization regression (SMOreg), M5 Rule, and Gaussian process were used in the study. SMOreg performed better in models tested by coefficient of determination, root mean square error, and mean absolute error. The models predicted the generation of residues in 2030 as rice straw and husk 195.76 Mt to 277.68 Mt, wheat straw 188.62 Mt to 266.95 Mt, and oilseed stalk and oil cakes 55.61 Mt to 96.30 Mt in India. An overview of the management of agri-residues discussed. Estimation of agri-residues can provide an opportunity to utilize them with the best possible ways, lessen pollution and promote a zero-waste strategy.

Varietal Screening and Management of Basal Rot Disease of Onion

Onion (Allium cepa L.) belongs to the family Amaryllidaceae. It is known as common onion, bulb onion or biennial herb, and is one of the most important vegetable crops in India. However, the crop faces numerous diseases attack, resulting in losses to both quantity and quality. Among the diseases, the basal rot caused by Fusarium oxysporum f. sp. cepae, is one of the most significant soil-borne diseases. This work aimed to screen various onion varieties for resistance against basal rot in pots under glasshouse condition as well as to assess the efficacy of fungicides against the pathogen under both in vitro and in vivo conditions. It was observed that all onion varieties screened were highly susceptible while Bhima red was just susceptible; none of the varieties were resistant to basal rot. Under in vitro conditions the Fungicides Carbendazim and Tebuconazole, of the given concentration, were cent per cent effective in inhibiting mycelial growth of Fusarium oxysporum f. sp. cepae. For the field condition, the module of [carbendazim (2 g/kg) followed by tebuconazole (0.1 %) followed by azoxystrobin (0.1 %)] was the most effective.

Phenotypic characterization of Indian mustard using agronomic and quality traits under semi‐arid climate

AbstractIndian mustard is an important oilseed crop in India. The productivity of Indian mustard needs to be increased in marginal environments such as in arid and semi‐arid areas to boost production. For this, identification of promising genotypes with high yield and quality under arid and semi‐arid climate is required. In this study, 65 genotypes of Indian mustard were evaluated in Augmented Randomized Block Design in the Purulia district of West Bengal, a mustard growing zone characterized by red laterite soil and semi‐arid climate. Considerable variation was observed between the genotypes for most characters such as number of seeds per siliqua (coefficient of variation [CV] 7.39%), followed by days to first flowering (CV 6.9%) and total number of siliquae (CV 6.43%), respectively. However, variation was less for days to maturity and quality parameters. Strong positive correlations were observed between many traits such as number of branches and number of siliquae on branches (0.82), number of branches and siliquae per plant (0.73), number of branches and yield per plant (0.72), number of siliquae on branches and siliquae per plant (0.91), number of siliquae on branches and seed yield per plant (0.76). Relatively strong negative correlation was observed only between oil and glucosinolate content (−0.60). Most of the traits showed high heritability along with high genetic advance over mean. Principal component analysis and cluster analysis classified the genotypes into six clusters. Based on yield, its components, and other quality parameters, promising genotypes such as Pusa Mahak, Narendra Ageti Rai 4, and JM‐1 were identified. These genotypes may be targeted for further genetic improvement or be incorporated in breeding programmes for developing high‐yielding mustard varieties for arid and semi‐arid climate.

Morphological, Molecular Identification and Pathogenicity of Rhizoctonia solani Kuhn: A Seed and Soil Borne Pathogen Infecting French Bean

French bean (Phaseolus vulgaris L.), holds significant importance as a both vegetable and pulse crop in India. Root rot disease has become threat for successful french bean cultivation. Fusarium spp., Sclerotium rolfsii, Rhizoctonia sp., Macrophomina, Pythium spp. and Thielaviopsis sp. may incite root rot disease in french bean. The root rot caused by Rhizoctonia soalni Kuhn is one among the root rot causing pathogens, is a major devasticating disease in this crop. The objective of this investigation was to perform the morphological, molecular identification and pathogenicity of R. solani Kuhn infecting french bean root rot. The pathogen was isolated from the naturally infected french bean field with characteristic symptoms like yellowing, drying at the soil level, stunting, poor seedling establishment, uneven growth, chlorosis, and premature defoliation in severely affected cases. It was isolated using standard tissue isolation technique and identified based on morphological traits. The fungus was molecular identified by sequencing ITS region of rDNA by using ITS1 and ITS4 primers. Further, pathogenicity of R. solani was proved according to Koch’s postulates.

Hybrid time series models with exogenous variable for improved yield forecasting of major Rabi crops in India

Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the agricultural sector. In this investigation, utilizing area under irrigation (%) as an exogenous variable, we have made an exertion to assess the suitability of different hybrid models such as ARIMAX (Autoregressive Integrated Moving Average with eXogenous Regressor)–TDNN (Time-Delay Neural Network), ARIMAX–NLSVR (Non-Linear Support Vector Regression), ARIMAX–WNN (Wavelet Neural Network), ARIMAX–CNN (Convolutional Neural Network), ARIMAX–RNN (Recurrent Neural Network) and ARIMAX–LSTM (Long Short Term Memory) as compared to their individual counterparts for yield forecasting of major Rabi crops in India. The accuracy of the ARIMA model has also been considered as a benchmark. Empirical outcomes reveal that the ARIMAX–LSTM hybrid modeling combination outperforms all other time series models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. For these models, an average improvement of RMSE and MAPE values has been observed to be 10.41% and 12.28%, respectively over all other competing models and 15.83% and 18.42%, respectively over the benchmark ARIMA model. The incorporation of the area under irrigation (%) as an exogenous variable in the ARIMAX framework and the inbuilt capability of the LSTM model to process complex non-linear patterns have been observed to significantly enhance the accuracy of forecasting. The performance supremacy of other hybrid models over their individual counterparts has also been evident. The results also suggest avoiding any performance generalization of individual models for their hybrid structures.

Wide hybridization in Rapeseed-Mustard

The Brassicas are an important group of crops in India yielding oils and many vegetables. For improving cultivated brassicas, the distant relatives are of considerable value. Wide hybridization is the way which could combine the valuable features of parental species into the hybrid. Crosses among Brassicaspecies viz., B. juncea (AABB, 2n = 36), B. Carinata (BBCC, 2n = 34), B. napus (AACC, 2n = 38), B. campestris (AA, 2n = 20), wild relative of cultivated species B. fruticulosa (2n = 4x=32, FFFF), and B. tournefortii (2n=20, TT) were attempted with the objective to find out crossability among them, analyse chromosome association in inter-specific hybrids, generate genetic variability and combine desirable characters in a targeted genotypes/species.

A New Report: Niger (Guizotia abyssinica), a new host of Phytoplasma in Madhya Pradesh India

Niger (Guizotia abyssinica) is one of the important minor oilseed crops in India. Phyllody disease on niger was noticed for the first time at Research Farm of Project Co-ordinating Unit, Sesame and Niger, JNKVV, Jabalpur (M.P.) during the year kharif 202021 and noticed again in 2021-22.

Identification of Weeds in Wheat Crop Using Artificial Intelligence Techniques

Wheat as an important cereal crop in India but presence of weeds results in significant damage in addition to insect pest and diseases. Weeds, which are unwanted plants that grow in agricultural crops, compete for essential elements like sunlight and water and are a major threat to food security. Conventional weed recognition approaches are very expensive, time consuming and require manual involvement by specialists. Researchers are actively investigating IT-based methods like computer vision and machine learning for weed identification. While models exist for identifying weeds in various crops, there is currently no specific model exists for weed identification in wheat crop. This paper proposed a mobile-based weed identification model using the ResNet50 deep learning architecture. The dataset used for training and testing the model consists of 1869 images of five prevalent weed species associated with wheat crop. After training, model demonstrated a notable accuracy of 93.25% on the validation dataset.

Non Parametric Trend Analysis of Tea Cultivation in India

With the second-largest producer and the highest consumer in the world, tea is one of the most important beverage crops in India. Considering the importance of tea industry to India’s national economy, a trend analysis of tea cultivation has been done here. Using data on the acreage, production, and productivity of tea in India from 1960 to 2021, and following a non-parametric approach Modified Mann-Kendall test has been used to detect the trend in the data. Addition to this, Sen’s slope estimator and innovative trend analysis have been performed to analyze the magnitude of trend whereas the Pettitt Mann-Whitney test has been used to detect the single change point. The findings of tests revealed that the area, production and productivity of tea in India have significantly increasing trend over the years.

Deep Learning Approaches for Disease Detection in Groundnut Crops using CNN Models

A major oilseed crop grown in tropical and subtropical parts of the world, groundnuts are a major crop in India. In the sixteenth century, groundnuts were likely transported from Brazil to West Africa, later making their way to India and the African east coast. According to earlier research, various strategies are employed to prevent diseases of groundnut leaves. The main methods include artificial intelligence (AI), machine learning (ML), convolutional neural networks (CNN), and more. Several CNN techniques for leaf disease identification and methodology will be employed in this study. Different CNN models, such as MobileNet, VGG-16, and EfficientNet, are compared to determine which model is most frequently used to identify leaf disease. The accuracy and precision will be computed and presented as a result of utilizing the dataset.

Biodiesel production: An updated review of evidence

This review paper highlights the production of biodiesel from different plant based feedstocks via the transesterification process. Biodiesel is a renewable, non-toxic, environment-friendly and an economically feasible option to tackle the depleting fossil fuels and its negative environmental impact. Biodiesel in general possess higher kinematic viscosity and density than conventional diesel. However, because of food security concerns, the use of edible oil in biodiesel production is criticized globally. Non-edible plant oils, waste cooking oils, and edible oil industry byproducts are suggested as effective biodiesel feedstocks because nonedible feedstock does not compete with food from human consumption. High-potential second-generation feedstock for biodiesel production uses waist cooking oil, acid oil, and animal tallow. Non edible crops in India as a feedstock for biodiesel production are yellow oleander oil (Cascabela thevetia), Pongamia (Pongamia pinnata), Jatropha curcas, Mahua (Madhuca longifolia), Candlenut (Aleurites moluccanus), Rubber (Hevea brasiliensis), Soapnut (Sapindus mukorossi), Jojoba (Simmondsia chinensis), Tobacco (Nicotiana tabacum), Neem (Azadirachta indica), Karanja (Millettia pinnata), Castor (Ricinus communis), Polanga (Calophyllum inophyllum L), Cotton (Gossypium), Kusum (Carthamus tinctorius), Yellow oleander (Cascabela thevetia), Sea mango (Cerbera odollam), Tung (Vernicia fordii), and Bottle tree (Brachychiton rupestris). Biodiesel is a sustainable liquid bio-energy resource that might be used to replace diesel fuel. Despite having numerous advantages over conventional diesel, the biodiesel industry is still struggling in India because of various reasons and challenges like availability, high feedstock pricing, operational hurdles, and supply-chain management challenges.

Examining the Impact of Integrated Nutrient Management and Vermicompost on Mustard Growth and Nutrient Composition

This study examines the impact of Integrated Nutrient Management (INM) on the growth attributes and nutrient composition of Indian mustard (Brassica juncea L.), a vital rabi season oilseed crop in India. Nine different treatment combinations were evaluated in a Randomized Block Design at Agronomy Research Farm. The treatments included various combinations of chemical fertilizers, vermicompost, sulfur, and zinc. The results indicate that the application of INM significantly influenced various growth parameters, including plant height and dry matter accumulation. Treatment T4 (75% RDF +Vermicompost@ 4t ha-1 + S @ 20kg ha-1) demonstrated the highest plant height and dry matter accumulation at different stages of growth. Additionally, seed yield, stover yield, and biological yield were positively impacted by INM, with T4 exhibiting the highest seed yield. Furthermore, the study highlights the importance of INM in enhancing the overall quality of Indian mustard. The application of these nutrient management practices not only led to increased seed yield but also improved the nutrient composition of the crop. The findings underscore the potential of INM as a sustainable approach to optimize crop productivity and improve food security in India. Overall, this research provides valuable insights for farmers, agricultural practitioners, and policymakers, offering a promising strategy for enhancing oilseed production and contributing to the agricultural economy of India. The positive outcomes of this study support the adoption of INM practices to ensure sustainable and productive mustard cultivation in the region.