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Chandel, N. S.
- Digital Map-Based Site-Specific Granular Fertilizer Application System
Abstract Views :340 |
PDF Views:92
Authors
Affiliations
1 ICAR-Central Institute of Agricultural Engineering, Bhopal 462 038, IN
2 Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721 302, IN
3 ICAR-Central Potato Research Institute, Shimla 171 001, IN
1 ICAR-Central Institute of Agricultural Engineering, Bhopal 462 038, IN
2 Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721 302, IN
3 ICAR-Central Potato Research Institute, Shimla 171 001, IN
Source
Current Science, Vol 111, No 7 (2016), Pagination: 1208-1213Abstract
Variable rate application is the process of applying different rates of crop inputs according to the variability within an agricultural field. Variable rate fertilizer application is a technology that regulates the fertilizer application rate based on site-specific needs within a field. A GPS-based variable rate fertilizer application (VRFA) system was developed, which consisted of a differential global positioning system (DGPS), micro-processor, micro-controller, DC motor actuator, power supply, threaded screw arrangement and fluted roller metering mechanism. The digital soil nutrient availability and application maps for targeted yield were also developed. DGPS was used for real-time identification of grids. Based on the microcontroller algorithm, application rates were varied by changing the feed roller exposure length. The observed fertilizer application rate was 5 and 300 kg/ha for exposure length of 0 and 44 mm respectively. The results indicate that the fertilizer application rate changes according to the prescribed application rate at the identified grid with coefficient of variation (CV) of 11.7-15%. The values of ischolar_main mean square error and relative difference of the system for different levels of application rates were 2.62 and 3.71 respectively. It can be concluded that the developed VRFA system closely meets the target fertilizer application rate at the selected grid point.Keywords
Differential Global Positioning System, Fertilizer Applicator, Interpolation, Micro-Controller, Soil Nutrient Map.Full Text
References
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- Yield Prediction in Wheat (Triticum aestivum L.) using Spectral Reflectance Indices
Abstract Views :184 |
PDF Views:76
Authors
N. S. Chandel
1,
P. S. Tiwari
1,
K. P. Singh
1,
D. Jat
1,
B. B. Gaikwad
1,
H. Tripathi
1,
K. Golhani
1
Affiliations
1 ICAR-Central Institute of Agricultural Engineering, Bhopal - 462 038, IN
1 ICAR-Central Institute of Agricultural Engineering, Bhopal - 462 038, IN
Source
Current Science, Vol 116, No 2 (2019), Pagination: 272-278Abstract
Influence of nitrogen on vegetative growth of wheat is significant, and can be monitored and assessed using vegetation indices derived from canopy reflectance at different phenological growth stages. The aim of the present work was to establish a regression model for yield prediction of wheat using spectral reflectance indices (SRIs), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil adjusted vegetation index (SAVI) for selected phenological growth stages of wheat. The canopy spectral reflectance was recorded during three winter seasons (2014–2017) for irrigated wheat. A hyperspectral library of canopy reflectance was developed, which enables the study of spectra independent of different nitrogen management practices. It indicated that the precise level of nitrogen for irrigated wheat may be 90 kg ha-1 in vertisols under agro-climatic of central India. Coefficient of variation (CV) was determined based on significance test between eight levels of nitrogen and SRI values. On the basis of CV, NDVI and NDWI were selected among the four spectral indices for the study of correlation between grain and biomass yields and nitrogen levels for four growth stages, viz. tillering, booting, heading and milking. A regression model was developed to find the best representative stage for yield prediction among the four stages. The regression model indicated that the relations of NDVI with grain and biomass yields were stronger in the heading stage, and it resulted in 96% accurate estimation of grain and biomass yields in irrigated wheat.Keywords
Nitrogen Management, Spectral Reflectance, Vegetation Indices, Wheat, Yield Estimation.Full Text
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