Rice is generally grown under completely flooded condition and providing food for more than half of the world's population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R and root mean square error (RMSE) of the models varied between 0.22-0.98 and 24.02-607.29 kg ha, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35-981.89 kg ha and 0.98-36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.

A liquid chromatography/tandem mass spectrometric method for the quantification of 6-(3-benzoyl-ureido)-hexanoic acid hydroxyamide (EX-2), a novel histone deacetylase (HDAC) inhibitor, in mouse plasma was developed to support in-house pharmacokinetic (PK) studies in the lead optimization stage. In order to determine the PK parameters for EX-2 in comparison to other HDAC inhibitors such as suberoylanilide hydroxamic acid (SAHA), PXD-101 and LBH-589, which are currently in different stages of clinical trials, research-grade bio-analytical method validations were carried out for EX-2 and these reference HDAC inhibitors, which were synthesized by in-house medicinal chemists. The components of validation consisted of specificity, extraction efficiency, signal-response of calibration standards, lower limit of quantification, autosampler stability and accuracy and precision of quality control samples. The validated LC/MS/MS methods were accurate and precise. The calibration curve ranged from 1 to 1600 ng/mL for all the analytes. The methods developed were used to quantify EX-2 and other HDAC inhibitors in mouse plasma obtained from pharmacokinetic studies. The results suggest that EX-2 has better PK parameters compared with the reference drugs and is a promising drug development candidate.

This paper describes a convenient method for the separation and simultaneous determination of six anti-diabetic drugs viz., glibenclamide (GLB), gliclazide (GLC), glipizide (GLZ), pioglitazone (PGL), repaglinide (RPG) and rosiglitazone (RGL) in pharmaceutical formulations. Also, the assay has been shown applied to support quantification of the six anti-diabetic drugs in human plasma. The analytes were either injected directly onto the column after suitable dilution (pharmaceutical formulation analysis) or a simple extraction procedure, using acetonitrile, from human plasma spiked with anti-diabetic drugs and internal standard (IS). Ternary gradient elution at a flow rate of 1 mL/min was employed on an Intertisl ODS 3V column (4.6 x 250 mm, 5 microm) at ambient temperature. The mobile phase consisted of 0.01 m formic acid (pH 3.0), acetonitrile, Milli Q water and methanol. Celecoxib was used as an IS. The six anti-diabetic drugs were monitored at a wavelength of 260 nm. The nominal retention times of RGL, PGL, GLZ, GLC, GLB, IS and RGL were 11.4, 13.3, 14.8, 17.6, 20.78, 22.1 and 25.4 min, respectively. The assay developed for formulation analysis was found to be accurate and precise. The calibration curves ranged from 0.1 to 100 microg/mL for all analytes with the exception of GLB, where the range was 0.3-100 microg/mL. The plasma assay was validated for parameters such as specificity, accuracy and extraction recovery. The proposed method is simple, selective and can be extended for routine analysis of anti-diabetics in pharmaceutical preparations and in biological matrices.

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Integrated farming systems (IFS) entail a holistic approach to farming aimed at meeting the multiple demands (impart farm resilience, farmer livelihoods, food security, ecosystem services, and making farms adaptive and resilient, etc.). IFS are characterized by temporal and spatial mixing of crops, livestock, fishery, and allied activities in a single farm. It is hypothesized that these complex farms are more productive at a system level, are less vulnerable to volatility, and produce less negative externalities than simplified farms. Thereby, they cater the needs of small and marginal farmers, who are the backbone of agriculture in India. Our review of literature shows that IFS have the potential to improve farm profitability (265%) and employment (143%) compared to single enterprise farms. The literature showed that IFS enhance nutrient recycling through composting, mulching, and residue incorporation and, as a consequence, have the capacity to reduce the external purchase of inputs. The nutrient recycling in turn helps to increase the soil quality indicators such as soil nutrient availability and also improves soil microbial activity. The IFS play a major role in biodiversity conservation through adoption of diversified cropping system and through integration of indigenous livestock breeds. IFS also played important role in improving soil organic carbon from 0.75 to 0.82%. Due to increased carbon sequestration, biomass production by trees, reduced consumption of fertilizers, and pesticides the greenhouse gas emission could be reduced significantly. This results in a linked system making it sustainable and climate‐resilient. The main challenge associated with adoption of IFS is it requires skill, knowledge, resources, labor, and capital which are not always available with small and marginal farmers. There is a need for integrating productivity, profitability, and environmental sustainability variables in a single evaluation framework to effectively generate information toward enhancing adaptability of IFS.

The fall armyworm of maize, Spodoptera frugiperda (J. E. Smith) (Lepidoptera; Noctuidae) is capable of causing a 100% yield loss due to its unforeseen occurrence from the seedling to the cob formation stage. To manage this serious pest, maize growers are tending to apply a high dosage of pesticides. This indiscriminate usage of pesticides has resulted in an unacceptable amount of insect resurgence in maize, harming maize production and consumption. In this review, we prepared a list of practical pest management options, including host plant resistance, agronomical, cultural, biological, botanical, chemical, and biotechnology approaches. It was found that cultivation of tolerant genotypes, adjusting sowing windows, and practicing specific intercultural and cropping systems measures in addition to chemical and non-chemical pest management strategies showed encouraging results for sustainable management of fall armyworm, which could protect the crop. This review highlights novel and successful management options advocated in various parts of the world. Recommendations documented in this paper would certainly pave the way for successful management of fall armyworm in maize and other concerned crops.