Novel moisture retaining dustable powder containing Steinernema abbasi effectively controls damage of subterranean termite in wheat and chickpea.

The application of biocontrol agents in farm operations for pest control programs is gaining priority and preference globally. Effective delivery, infectivity of the biocontrol agents, and quality shelf-life products containing these bioagents are vital parameters responsible for the success of biopesticides under field conditions. In the present study, moisture-retaining bio-insecticidal dustable powder formulation (SaP) of Steinernema abbasi (Sa) infective juveniles (IJs) was developed and assessed for its shelf life, physicochemical profile, and bio-efficacy against subterranean termite under field conditions. Formulation exhibited free-flowing character, with pH of 6.50-7.50, and apparent density in the range 0.50-0.70 g cm-3. The bioefficacy study for two rabi seasons (2020-2021, and 2021-2022) in wheat and chickpea grown in an experimental farm heavily infested with subterranean termites (Odontotermes obesus) revealed a significant reduction in plant damage due to pest attack in formulation-treated plots, monitored in terms of relative number of infested tillers in wheat and infested plants in chickpea fields. The reduced damage to the crop caused by termite was reflected in the relative differences in the growth and yield attributes as well. The study establishes the potential of the developed product as a biopesticide suitable for organic farming and integrated pest management operations.

Development of a QuEChERS-LCMS/MS method for simultaneous estimation of tebuconazole and chlormequat chloride in wheat crop.

Tebuconazole (TBZ) and Chlormequat chloride (CCC) combination has been established as highly effective in reducing plant height of lodging prone wheat varieties. In this work, a novel analytical method employing the quick, easy, cheap, effective, rugged and safe (QuEChERS) cleanup technique and LC-MS/MS (liquid chromatography-tandem mass spectroscopy) was developed for simultaneous estimation of TBZ and CCC in wheat grains and harvest stage plant leaves. A total of 10 mL of acetonitrile and 50 mg of primary secondary amine (PSA) sorbent was consumed in the optimized QuEChERS process for leaves and grain samples. The LC-MS/MS analysis was performed using a C-18 column operating under electrospray ionization in positive mode. The QuEChERS approach achieved extraction recoveries in the acceptable range of 70%-120%, for both the compounds and was validated in terms of accuracy, precision, sensitivity and linearity. Persistence study was conducted using Lihocin (CCC 50% SL), Folicur (TBZ 25.9% EC) and their combination tank mix (Lihocin + Folicur-50% SL + 25.9% EC) applied as foliar spray twice in wheat crop (tester tall variety C-306). The results demonstrated that the developed QuEChERS-LCMS/MS is rapid and confirmatory for simultaneous quantification of both the test analytes in wheat crop.

Nanocomposite-based smart fertilizers: A boon to agricultural and environmental sustainability.

Fertilizers are indispensable agri-inputs to accomplish the growing food demand. The injudicious use of conventional fertilizer products has resulted in several environmental and human health complications. To mitigate these problems, nanocomposite-based fertilizers are viable alternative options. Nanocomposites, a novel class of materials having improved mechanical strength, barrier properties, and mechanical and thermal stability, are suitable candidates to develop eco-friendly slow/controlled release fertilizer formulations. In this review, the use of different nanocomposite materials developed for nutrient management in agriculture has been summarized with a major focus on their synthesis and characterization techniques, and application aspects in plant nutrition, along with addressing constraints and future opportunities of this domain. Further detailed studies on nanocomposite-based fertilizers are required to evaluate the cost-effective synthesis methods, in-depth field efficacy, environmental fate, stability, etc. before commercialization in the field of agriculture. The present review is expected to help the policy makers and all the stakeholders in the large-scale commercialization and application of nanocomposite-based smart fertilizer products with greater societal acceptance and environmental sustainability in near future.

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models.

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires-Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires-Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers' fields for developing better disease management options.

Performance appraisal of Trichoderma viride based novel tablet and powder formulations for management of Fusarium wilt disease in chickpea.

In developing a Trichoderma viride-based biocontrol program for Fusarium wilt disease in chickpea, the choice of the quality formulation is imperative. In the present study, two types of formulations i.e. powder for seed treatment (TvP) and tablet for direct application (TvT), employing T. viride as the biocontrol agent, were evaluated for their ability to control chickpea wilt under field conditions at three dosages i.e. recommended (RD), double of recommended (DD) and half of recommended (1/2 RD). A screening study for the antagonistic fungi strains based on volatile and non-volatile bioassays revealed that T. viride ITCC 7764 has the most potential among the five strains tested (ITCC 6889, ITCC 7204, ITCC 7764, ITCC 7847, ITCC 8276), which was then used to develop the TvP and TvT formulations. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of volatile organic compounds (VOCs) of T. viride strain confirmed the highest abundance of compositions comprising octan-3-one (13.92%), 3-octanol (10.57%), and 1-octen-3-ol (9.40%) in the most potential T. viride 7764. Further Physico-chemical characterization by standard Collaborative International Pesticides Analytical Council (CIPAC) methods revealed the optimized TvP formulation to be free flowing at pH 6.50, with a density of 0.732 g cm-3. The TvT formulation showed a pH value of 7.16 and density of 0.0017 g cm-3 for a complete disintegration time of 22.5 min. The biocontrol potential of TvP formulation was found to be superior to that of TvT formulation in terms of both seed germination and wilt incidence in chickpea under field conditions. However, both the developed formulations (TvP and TvT) expressed greater bioefficacy compared to the synthetic fungicide (Carbendazim 50% WP) and the conventional talc-based formulation. Further research should be carried out on the compatibility of the developed products with other agrochemicals of synthetic or natural origin to develop an integrated disease management (IDM) schedule in chickpea.

Modeling and Optimization of Fractal Dimension in Wire Electrical Discharge Machining of EN 31 Steel Using the ANN-GA Approach.

To achieve enhanced surface characteristics in wire electrical discharge machining (WEDM), the present work reports the use of an artificial neural network (ANN) combined with a genetic algorithm (GA) for the correlation and optimization of WEDM process parameters. The parameters considered are the discharge current, voltage, pulse-on time, and pulse-off time, while the response is fractal dimension. The usefulness of fractal dimension to characterize a machined surface lies in the fact that it is independent of the resolution of the instrument or length scales. Experiments were carried out based on a rotatable central composite design. A feed-forward ANN architecture trained using the Levenberg-Marquardt (L-M) back-propagation algorithm has been used to model the complex relationship between WEDM process parameters and fractal dimension. After several trials, 4-3-3-1 neural network architecture has been found to predict the fractal dimension with reasonable accuracy, having an overall R-value of 0.97. Furthermore, the genetic algorithm (GA) has been used to predict the optimal combination of machining parameters to achieve a higher fractal dimension. The predicted optimal condition is seen to be in close agreement with experimental results. Scanning electron micrography of the machined surface reveals that the combined ANN-GA method can significantly improve the surface texture produced from WEDM by reducing the formation of re-solidified globules.