Estimating N status of winter wheat using a handheld spectrometer in the North China Plain

Excessive nitrogen (N) fertilizer application is very common in the North China Plain. Diagnosis of in-season N status in crops is critical for precision N management in this area. Remote sensing, as a timely and nondestructive tool, could be an alternative to traditional plant testing for diagnosing crop N status. The objectives of this study were to determine which vegetation indices could be used to estimate N status in winter wheat (Triticum aestivum L.) under high N input conditions, develop models to predict winter wheat N uptake using spectral vegetation indices and validate the models with data from farmers’ fields. An N rate experiment and a variety-N experiment were conducted in Huimin, Shandong Province from 2005/2006 to 2006/2007 to develop the models. Positive linear relationships between simple ratio vegetation indices (red vegetation index, RVI and green vegetation index, GVI) and N uptake were observed independent of growth stages and varieties (R², 0.48–0.74). In contrast, the relationships between normalized difference vegetation indices (NDVI and GNDVI), red and green normalized difference vegetation index (RGNDI), and red and green ratio vegetation index (RGVI) were exponentially related to N uptake (R², 0.43–0.79). Subsequently, 69 farmers’ fields in four different villages were selected as datasets to validate the developed models. The results indicated that the prediction using RVI had the highest coefficient of determination (R², 0.60), the lowest root mean square error (RMSE, 39.7 kg N ha⁻¹) and relative error (RE, 30.5%) across different years, varieties and growth stages. We conclude that RVI can be used to estimate nitrogen status for winter wheat in over-fertilized farmers’ fields before heading.     

Spatially resolved quantification of agrochemicals on plant surfaces using energy dispersive X‐ray microanalysis

BACKGROUND: In the present study the principle of energy dispersive X‐ray microanalysis (EDX), i.e. the detection of elements based on their characteristic X‐rays, was used to localise and quantify organic and inorganic pesticides on enzymatically isolated fruit cuticles. Pesticides could be discriminated from the plant surface because of their distinctive elemental composition. RESULTS: Findings confirm the close relation between net intensity (NI) and area covered by the active ingredient (AI area). Using wide and narrow concentration ranges of glyphosate and glufosinate, respectively, results showed that quantification of AI requires the selection of appropriate regression equations while considering NI, peak‐to‐background (P/B) ratio, and AI area. The use of selected internal standards (ISs) such as Ca(NO₃)₂ improved the accuracy of the quantification slightly but led to the formation of particular, non‐typical microstructured deposits. The suitability of SEM‐EDX as a general technique to quantify pesticides was evaluated additionally on 14 agrochemicals applied at diluted or regular concentration. CONCLUSION: Among the pesticides tested, spatial localisation and quantification of AI amount could be done for inorganic copper and sulfur as well for the organic agrochemicals glyphosate, glufosinate, bromoxynil and mancozeb. Copyright © 2009 Society of Chemical Industry