KOREAN JOURNAL OF CROP SCIENCE (한국작물학회지)

The Korean Society of Crop Science (한국작물학회)
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Managing Within-Field Spatial Yield Variation of Rice by Site-Specific Prescription of Panicle Nitrogen Fertilizer

  • Published : 2005.09.01
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Abstract

Rice yield and protein content have been shown to be highly variable across paddy fields. In order to characterize this spatial variability of rice within a field, two-year experiments were conducted in 2002 and 2003 in a large-scale rice field of $6,600m^2$ In year 2004, an experiment was conducted to know if variable rate treatment (VRT) of N fertilizer, that was prescribed for site-specific management at panicle initiation stage, could reduce spatial variation in yield and protein content of rice while increasing yield compared to conventional uniform N topdressing (UN, 33kg N/ha at PIS) method. VRT nitrogen prescription for each grid was calculated based on the nitrogen (N) uptake (from panicle initiation to harvest) required for target rice protein content of $6.8\%$, natural soil N supply, and recovery of top-dressed N fertilizer. The required N uptake for target rice protein content was calculated from the equations to predict rice yield and protein content from plant growth parameters at panicle initiation stage (PIS) and N uptake from PIS to harvest. This model· equations were developed from the data obtained from the previous two-year experiments. The plant growth parameters for the calculation of the required N were predicted non-destructively by canopy reflectance measurement. Soil N supply for each grid was obtained from the experiment of year 2003, and N recovery was assumed to be $60\%$ according to the previous reports. The prescribed VRT N ranged from 0 to 110kg N/ha with an average of 57kg/ha that was higher than 33 kg/ha of UN. The results showed that VRT application successfully worked not only to reduce spatial variability of rice yield and protein content but also to increase rough rice yield by 960kg/ha. The coefficient of variation (CV) for rice yield and protein content was reduced significantly to $8.1\%$ and $7.1\%$ in VRT from $14.6\%$ and $13.0\%$ in UN, respectively. And also the average protein content of milled rice in VRT showed very similar value of target protein content of $6.8\%$. In conclusion the procedure used in this paper was believed to be reliable and promising method for reducing within-field spatial variability of rice yield and protein content. However, inexpensive, reliable, and fast estimation methods of natural N supply and plant growth and nutrition status should be prepared before this method could be practically used for site-specific crop management in large-scale rice field.

Keywords

References

  1. Bahman Eghball and Gary E. Varvel. 1997. Fractal analysis of temporal yield variability of crop sequences implication for site-specific management. Agron. J. 89: 851-855 https://doi.org/10.2134/agronj1997.00021962008900060001x
  2. Beckett, P. H. T. and R. Webster. 1971. Soil variability: a review Soils Fert. 34, pp. 1-15
  3. Booltink, H. W. G, G. J. Alphen, W. D. Batchelor, J.O. Paz, J J. Stoorvogel, and R. Vargas. 2001. Tool for optimizing management of spatially variable fields. Agricultural System 70: 445-476 https://doi.org/10.1016/S0308-521X(01)00055-5
  4. Bouma, J. 1997. Precision agriculture. introduction to the spatial and temporal variability of environmental quality. In: Lake, J V, Bock, G.R., Goode, J A (Eds.), Precision Agriculture. Spatial and Temporal Variability of Environmental Quality. Ciba Foundation Symposium, 210. Wiley, Wageningen, The Netherlands, pp 5-17
  5. Cahn, M. D., J W. Humel, and B. H. Brouer. 1994. Spatial analysis of Soil fertility for site-specific crop management Soil Sci Soc. Am J 58.1240-1248 https://doi.org/10.2136/sssaj1994.03615995005800040035x
  6. Casanova, D , G. F. Epema, and J. Goudriaan. 1998. Monitoring rice reflectance at field level for estimating biomass and LAI Field Crops Res. 83-92
  7. Casanova, D., J. Goudriaan, J. Bouma, and G. F Epema. 1999. Yield gap analysis in relation to soil properties m direct-seeded flooded rice Geoderma 91(3-4): 191-216 https://doi.org/10.1016/S0016-7061(99)00005-1
  8. Cox, M. S., P. D. Gerard, M C. Wardlaw, and M. J. Abshire 2003. Variability of selected Soil properties and their relationships With soybean yield. Soil Sci. Soc. Am. J. 67. 1296-1302 https://doi.org/10.2136/sssaj2003.1296
  9. Cui, R. X. and B. W Lee. 2002 Spiklet number estimation model using nitrogen nutrition status and biomass at panicle intiaiton and headling stage of rice. Korean J. Crop Sci. 47 (5): 390-394
  10. Cui, R. X., M. H. Kim, J. H. Kim, H. S. Nam, and B. W. Lee. 2002. Determination of critical nitrogen dilution curves for nee growth. Korean J. Crop Sci. 47 (2). 127-131
  11. Delin, S. and B. Linden. 2002. Relations between net nitrogen mineralization and soil characteristics wthin an arable field. Acta Agr. Scand. B-S P 52, pp 78-85
  12. Dobermann, A, A. F Pampohno, and H. U. Neue. 1995. Spatial and temporal variability of transplanted rice at field scale. Agron. J. 87: 712-720 https://doi.org/10.2134/agronj1995.00021962008700040018x
  13. Dobermann, D. 1994 Factors causing field variation of direct-seeded flooded rice Geoderma 62(1-3).125-150 https://doi.org/10.1016/0016-7061(94)90032-9
  14. Environmental Systems Research Institute. Acview GIS (ESRI), 1996
  15. Hansen, P. M. and J. K. Schjoerring, 2003 Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression, Remote Sens. of Environ. 86: 542-553 https://doi.org/10.1016/S0034-4257(03)00131-7
  16. Jin J. and C. Jiang. 2002 Spatial variability of soil nutrient and site-specific management in the P.R. China Computer and Electronics in Agriculture 36 165-172 https://doi.org/10.1016/S0168-1699(02)00099-6
  17. Kahabka, J E, H. M. Van Es, E. J Mcclenahan, and W. J. Cox. 2004. Spatial analysis of maize response to nitrogen fertilizer in central New York. Precision Agriculture 5: 463-476 https://doi.org/10.1007/s11119-004-5320-2
  18. Machado, S., E. D. Bynum, T. L. Archer, R. J. Lascano, L T Wilson, J. Bordovsky, E. Segarra, K. Bronson, D. M Nesmith, and W Xu. 2000 Spatial and temporal variability of com gram yield: srtespecific relationships of biotic and abiotic factor. Precision Agriculture 2: 359-376 https://doi.org/10.1023/A:1012352032031
  19. McBratney, A. B. and M. J. Pnngle. 1997 Spatial variability in soil implications for precision agriculture. In: Stafford, J V. Ed. Spatial Variability in soil and Crop. Precision Agriculture '97 I BIOS, Oxford, pp 1-31
  20. Miller, P. M., M. J. Singer, and D R. Nielsen. 1995. Spatial variability of wheat yield and soil properties on complex hills. Soil Sci .Soc. Am J. 52: 1133-1141 https://doi.org/10.2136/sssaj1988.03615995005200040045x
  21. Naiqian, Z., M Wang, and N. Wang. 2002. Precision agriculture-a worldwide overview
  22. Nguyen T Hung and B. W. Lee. 2004 Selection of most sensitive waveband reflectance for normalized difference vegetation index calculation to predict nee crop growth and yield. Korean J. Crop. Sci. 49(5): 394-406
  23. Nguyen T. Hung, J. H. Kim, Nguyen Tuan Anh, and B.W. Lee. 2005 Application of hyperspectral canopy reflectance measurement and partial least square regression to prediction of crop growth and nitrogen status before heading stage of rice. Precision Agriculture 05 : 251-260
  24. Nguyen Tuan Anh, J. C. Shin, and B. W. Lee. 2004. Analysis of spatial variation of yields and soil chemical properties in a paddy field. Proc. Inter. Crop Sci Congress, pp. 425 Australia
  25. O'Neal, M. R., J. R. Frankenberger, and D. R. Ess. 2000 Spatial precipitation variability m the choice of nitrogen fertilization rates. Proceedings of fifth International Conference on Precision Agriculture (CD), July 16_/19, 2000. Bloomington, MN, USA
  26. Paz, J. O. 2000. Analysts of Spatial YIeld Variability and Economic Prescription for Precision Agriculture: a Modeling Approach. Ph D. dissertation, Iowa State University, Ames, Iowa
  27. Peng, S , F V. Garcia, R. C Laza, A. L. Samco, R. M. Visperas, and K. G Cassman. 1996. Increased N-use efficiency using a chlorophyll meter on high-yielding irrigated rice. Fields Crops Res. 47: 243-252 https://doi.org/10.1016/0378-4290(96)00018-4
  28. SAS Institute, 2001. SAS System for Windows Release 8.01. SAS Institute Inc. Cary, N.C
  29. Seney, G. B., A. O. Ward, J. G Lyon, N R Fausey, and S E. Nokes. 1998. Manipulation of high spatial resolution aircraft remote sensing data for use in site-specific farming. Trans. ASAE 41 (2) : 489-495 https://doi.org/10.13031/2013.17170
  30. Verhagen, J. 1997. Site specific fertiliser application for potato production and effects on N leachingusing dynamic simulation modeling Agri Ecos and Environ 66(2) . 165-175 https://doi.org/10.1016/S0167-8809(97)00086-8
  31. Verhagen, J., H. W. G. Booltmk, and J. Bouma 1995 Site specific management. balancing production and environmental requirements at farm level. Agric. Syst. 49: 369-384 https://doi.org/10.1016/0308-521X(95)00031-Y
  32. Yanai, J , C. K. Lee, M. Umeda, and T. Kosaki, 2000. Spatial of soil chemical properties in a paddy field Soil Sci. Plant Nutr. 4 : 473-482