Nitrous oxide production from soybean and maize under the influence of weedicides and zero tillage conservation agriculture.

Current experiment envisages evaluating N2O production from nitrification and denitrification under the influence of weedicides, cropping systems and conservation agriculture (CA). The weed control treatments were conventional hand weeding (no weedicide), pre emergence weedicide pendimethalin and post emergence weedicide imazethapyr for soybean, atrazine for maize. Experiment was laid out in randomized block design with three replicates. Soils were collected from different depths and incubated at different moisture holding capacity (MHC). N2O production from nitrification varied from 2.77 to 6.04 ng N2O g-1 soil d-1 and from denitrification varied from 0.05 to 1.34 ng N2O g-1 soil d-1. Potential nitrification rate (0.16-0.39 mM NO3 produced g-1 soil d-1) was higher than potential denitrification rate (0.45-0.93 mM NO3 reduced g-1 soil d-1). N2O production, nitrification, denitrification, and microbial gene abundance were higher in maize than soybean. Both N2O production and nitrification decreased (p < 0.05) with soil depth, while denitrification increased (p < 0.05) with soil depth. Abundance of eubacteria and ammonia oxidizing bacteria (AOB) were high (p < 0.01) at upper soil layer and declined with depth. Abundance of ammonia oxidizing archaea (AOA) increased (p < 0.05) with soil depth. Study concludes that intensive use of weedicides in CA may stimulate N2O production.

Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy.

The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications.