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Differences in barley grain yields as a result of soil variability

Published online by Cambridge University Press:  27 March 2009

P. A. Finke  and
D. Goense
P. A. Finke
Affiliation:
Department of Soil Science and Geology, Agricultural University, PO Box 37, 6700 A A Wageningen, The Netherlands
D. Goense
Affiliation:
Department of Agricultural Engineering and Physics, Agricultural University, Wageningen, The Netherlands
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Summary

Field scale variability in the grain yield of barley in 1989 was investigated in 62 field plots in a Dutch polder area, and compared to soil- and simulation-type characteristics. Total grain mass varied between 3409 and 6019 kg/ha, and grain moisture content between 131 and 14·7%. Soil profile descriptions and soil characteristics were used as basic input data for simulations. Soil water flow was simulated at 119 locations with the LEACHM model, for the purpose of quantifying spatial variability in transpiration deficits in the growing season. Both soil- and simulation-type characteristics were translated from point values to spatial averages for the harvested fields, using kriging. Kriged characteristics were correlated with yields, and used to construct transfer functions. Simulated transpiration deficits during sensitive crop development phases showed negative correlations with grain yield. Transfer functions explained at maximum 68·2% of the variance in the yields.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 120 , Issue 2 , April 1993 , pp. 171 - 180
Copyright
Copyright © Cambridge University Press 1993

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References

REFERENCES

Bouma, J. & Van Lanen, H.A.J. (1986). Extension functions and threshold values: from soil characteristicsm to land qualities. In Quantified Land Evaluation Procedures (Eds Beek, K. J., Burrough, P. A. & McCormack, D. E.), pp. 106–110. Enschede1: ITC.Google Scholar
Church, B. M. & Austin, R. B. (1983). Variability of wheat yields in England and Wales. Journal of Agricultural Science, Cambridge 100, 201–204.CrossRefGoogle Scholar
Corsten, L. C. A. (1989). Interpolation and optimal linear prediction. Statislica Neerlandica 43, 69–84.CrossRefGoogle Scholar
Feekes, W. (1941). De tarwe en haar milieu. Verslag van de technische Tarwe Commissie Groningen. Wheat and its environment. Report of the technical wheat committee Groningen (In Dutch.)Google Scholar
Finke, P. A. (1991). Soil survey to obtain basic simulation data for a heterogeneous field with stratified marine soils. In Eur 13501 – Soil and Groundwater Research Report II: Nitrate in soils, pp. 26–41. Luxemburg: Office for Official Publications of the European Communities.Google Scholar
Gales, K. (1983). Yield variation of wheat and barley in Britain in relation to crop growth and soil conditions – a review. Journal of the Science of Food and Agriculture 34, 1085–1104.CrossRefGoogle Scholar
Geisler, G. (1980). Pflanzenbau. Berlin: Verlag Paul Parey.Google Scholar
Goldberger, A. S. (1962). Best linear unbiased prediction in the generalized linear model. Journal of American Statistic Association 57, 369–375.CrossRefGoogle Scholar
Journel, A. G. & Huubregts, C. J. (1978). Mining Geo-statistics. New York: Academic Press.Google Scholar
Neeteson, J.J. (1989). Assessment of fertilizer nitrogen requirement of potatoes and sugar beet. PhD thesis, Agricultural University, Wageningen.Google Scholar
Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London Series A 193, 120–146.Google Scholar
Soil Survey Staff (1975). Soil taxonomy: a basic system of soil classification for making and interpretating soil surveys. USDA–SCS Agricultural Handbook 436. Washington DC: US Government Printing Office.Google Scholar
Stein, A. & Corsten, L. C. A. (1991). Universal kriging and cokriging as a regression procedure. Biometrics 47, 575–587.CrossRefGoogle Scholar
Stein, A., Van Dooremolen, W., Bouma, J. & Bregt, A. K. (1988). Cokriging point data on moisture deficit. Soil Science Society of America Journal 52, 1418–1423.CrossRefGoogle Scholar
Tits, M., Delcourt, H., Vervaeke, F., Vansichen, R. & De Baerdemaeker, J. (1989). Grain yield maps and related field characteristics. Proceedings of the 11th International Congress of Agricultural Engineers, Dublin, 4–8 September, 1989 1, 2791–2796.Google Scholar
Van Diepen, C. A., Rappoldt, C., Wolf, J. & Van Keulen, H. (1988). CWFS Crop Growth Simulation Model WOFOST documentation version 41. Staff working paper SOW-88-01. Centre for World Food Studies, Amsterdam/Wageningen.Google Scholar
Van Genuchten, M. Th. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44, 892–898.CrossRefGoogle Scholar
Van Keulen, H. & Wolf, J. (Eds) (1986). Modelling of Agricultural Production: Weather, Soils and Crops. Simu-lation monographs. Wageningen: Pudoc.Google Scholar
Wagenet, R. J. & Hutson, J. L. (1989). Leaching Estimation And Chemistry Model: A process based model of water and solute movement transformations, plant uptake and chemical reactions in the unsaturated zone. Continuum Vol. 2. Water Resources Institute. Ithaca, New York: Cornell University.Google Scholar
Wösten, J. H. M. (1987). Description of water retention and hydraulic conductivity characteristics from the Staring series with analytical functions. Internal Report no. 2019. Wageningen: Dutch Soil Survey Centre. (In Dutch.)Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415–421.CrossRefGoogle Scholar