This study was conducted to explore whether hyperspectral data could be used to discriminate between the effects of different rates of nitrogen application to a potato crop. The field experiment was carried out in the Central Potato Research Station, Jalandhar, on seven plots with different nitrogen (N) treatments. Spectral reflectance was measured using a 512-channel spectroradiometer with a range of 395-1075 nm on two different dates during crop growth. An optimum number of bands were selected from this range based on band-band r 2 , principal component analysis and discriminant analysis. The four bands that could discriminate between the rates of N applied were 560, 650, 730, and 760 nm. An ANOVA analysis of several narrow-band indices calculated from the reflectance values showed the indices that were able to differentiate best between the different rates of N application. These were reflectance ratio at the red edge (R 740/720 ) and the structure insensitive pigment index (SIPI). To estimate leaf N, reflectance ratios were determined for each band combination and were evaluated for their correlation with the leaf N content. A regression model for N estimation was obtained using the reflectance ratio indices at 750 and 710 nm wavelengths (F-ratio = 32 and r 2 = 0.551, P \ 0.000).

PurposeThe manufacturing industry is presently experiencing technological disruption on a global scale. Consequently, to tackle such disruption, firms are identifying a volatile, uncertain, complex and ambiguous (VUCA) scenario and seeking ways to counter it. Accordingly, this paper aims to investigate the employee performance through assessing organizational citizenship behaviour (OCB) among the shop floor employees of the fast-moving consumer goods (FMCG) industry where a high-performance work system (HPWS) has been implemented.Design/methodology/approachA descriptive research design was used in the study, and 395 shop floor employees working in leading multinational firms, with a minimum global turnover of US$1bn, were interviewed. These manufacturing firms were located in three industrial clusters in the northern part of India.FindingsThe results indicate that HPWS influences OCB. Most of the dimensions of HPWS and OCB were found to be positively associated. The findings also disprove the labour process theory in the context of the study.Practical implicationsThe findings report a broad view of the relationship between HPWS and OCB in the Indian manufacturing context. The study offers the practical insights that HPWS is a universally accepted framework and that organizations should focus on the effective implementation of HPWS in a VUCA scenario, which is in line with past studies. The study also provides future directions for research.Originality/valueThis paper has established the relationship between HPWS and OCB in the manufacturing sector, especially for shop floor employees.

The effect of four nitrogen levels {0 kg N ha -1 (N 0 ), 50 kg N ha -1 (N 1 ), 100 kg N ha -1 (N 2 ) and 150 kg N ha -1 (N 3 )} two QPM hybrids {Shaktiman-2 (G 1 ) and Shaktiman-4 (G 2 )} and three levels of sulphur { 15 kg S ha -1 (S 1 ), 30 kg S ha -1 (S 2 ) and 45 kg S ha -1 (S 3 )} in quality protein maize (QPM) production for two years on an experimental field (Sandy clay loam) located at Agricultural Dryland Research Farm, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India. Each treatment was replicated three times in split plot design. The aim of research was to determine the influence of nitrogen and sulphur levels on yield, quality and economics of QPM hybrids under dryland condition.The significantly highest plant height, leaf area index (LAI), yield, net returns, benefit: cost ratio (B: C), lysine and tryptophan content were recorded with 150 kg N ha -1 (N 150 ) as compared to N 100 , N 50 and N 0 . On average QPM hybrids, Shaktiman-4 produced significantly taller plant (171.68cm), higher LAI (5.49), grain yield (60.54 q ha ), net return (Rs. 39825.09 ha -1 ), B: C ratio (2.26), tryptophan content (0.79%) and lysine content (3.91) than Shaktiman-2× S 15 .

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