Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-25T18:01:41.989Z Has data issue: false hasContentIssue false

Modelling of yields and soil nitrogen dynamics for crop rotations by HERMES under different climate and soil conditions in the Czech Republic

Published online by Cambridge University Press:  08 January 2013

P. HLAVINKA*
Affiliation:
Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic Global Change Research Centre AS CR, v.v.i., Bělidla 986/4a, 603 00 Brno, Czech Republic
M. TRNKA
Affiliation:
Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic Global Change Research Centre AS CR, v.v.i., Bělidla 986/4a, 603 00 Brno, Czech Republic
K. C. KERSEBAUM
Affiliation:
Leibniz-Centre for Agricultural Landscape Research (ZALF), Institute of Landscape Systems Analysis, 14 Eberswalder Str. 84, 15374 Müncheberg, Germany
P. ČERMÁK
Affiliation:
Crop Research Institute, Drnovská 507/73, 161 06 Prague 6 - Ruzyně, Czech Republic
E. POHANKOVÁ
Affiliation:
Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
M. ORSÁG
Affiliation:
Global Change Research Centre AS CR, v.v.i., Bělidla 986/4a, 603 00 Brno, Czech Republic
E. POKORNÝ
Affiliation:
Department of Agrochemistry, Soil Science, Microbiology and Plant Nutrition, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
M. FISCHER
Affiliation:
Global Change Research Centre AS CR, v.v.i., Bělidla 986/4a, 603 00 Brno, Czech Republic
M. BRTNICKÝ
Affiliation:
Department of Agrochemistry, Soil Science, Microbiology and Plant Nutrition, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
Z. ŽALUD
Affiliation:
Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
*
*To whom all correspondence should be addressed. Email: [email protected]

Summary

The crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0·82 to 0·96 for the calibration database (the median coefficient of determination (R2) was 0·71), while IA for verification varied from 0·62 to 0·93 (median R2 was 0·78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0·76 and 0·72 for calibration and verification, respectively, and analogous medians of R2 were 0·50 and 0·49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0·49 to 0·74 for calibration and from 0·43 to 0·68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Němčice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0–0·3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.

Type
Climate Change and Agriculture Research Papers
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56. Rome: FAO.Google Scholar
Askegaard, M. & Eriksen, J. (2007). Growth of legume and nonlegume catch crops and residual-N effects in spring barley on coarse sand. Journal of Plant Nutrition and Soil Science 170, 773780.CrossRefGoogle Scholar
Barraclough, P. B. (1986). The growth and activity of winter wheat roots in the field: nutrient inflows of high yielding crops. Journal of Agricultural Science, Cambridge 106, 5359.CrossRefGoogle Scholar
Beven, K. (1989). Changing ideas in hydrology – the case of physically-based models. Journal of Hydrology 105, 157172.CrossRefGoogle Scholar
Boons-Prins, E. R., de Koning, G. H. J., van Diepen, C. A. & Penning de Vries, F. W. T. (1993). Crop Specific Simulation Parameters for Yield Forecasting across the European Community. Simulation reports No. 32 CABO-TT. Wageningen, The Netherlands: Wageningen URL.Google Scholar
Colnenne, C., Meynard, J. M., Reau, R., Justes, E. & Merrien, A. (1998). Determination of critical nitrogen dilution curve for winter oilseed rape. Annals of Botany 81, 311317.CrossRefGoogle Scholar
Davies, J. A. & McKay, D. C. (1989). Evaluation of selected models for estimating solar radiation on horizontal surfaces. Solar Energy 43, 153168.Google Scholar
De Willigen, P. (1991). Nitrogen turnover in the soil–crop system; comparison of fourteen simulation models. Fertilizer Research 27, 141149.Google Scholar
Eckersten, H., Blombäck, K., Kätterer, T. & Nyman, P. (2001). Modelling C, N, water and heat dynamics in winter wheat under climate change in southern Sweden. Agriculture, Ecosystems and Environment 86, 221235.Google Scholar
Giebel, A., Wendroth, O., Reuter, H. I., Kersebaum, K. C. & Schwarz, J. (2006). How representatively can we sample soil mineral nitrogen? Journal of Plant Nutrition and Soil Science 169, 5259.Google Scholar
Greenwood, D. J., Lemaire, G., Gosse, G., Cruz, P., Draycott, A. & Neeteson, J. J. (1990). Decline of percentage N of C3 and C4 crops with increasing plant mass. Annals of Botany 66, 425436.CrossRefGoogle Scholar
Grunwald, S. (1997). GIS gestürtzte Modellierung des Landschaftswasser und Stoffhaushalts mit dem Modifizerten Modell AGNPSm. Giessen, Germany: Justus Liebig University Giessen.Google Scholar
Habekotté, B. (1997). A model of the phonological development of winter oilseed rape (Grassica napus L.). Field Crops Research 54, 127136.CrossRefGoogle Scholar
Hoffmann, F. (1995). Fagus, a model for growth and development of beech. Ecological Modelling 83, 327348.Google Scholar
Hoogenboom, G. (2000). Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology 103, 137157.Google Scholar
Jego, G., Martinez, M., Antiguedad, I., Launay, M., Sanchez-Perez, J. M. & Justes, E. (2008). Evaluation of the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar beet using the STICS soil-crop model. Science of the Total Environment 394, 207221.Google Scholar
Kersebaum, K. C. (1995). Application of a simple management model to simulate water and nitrogen dynamics. Ecological Modelling 81, 145156.Google Scholar
Kersebaum, K. C. (2007). Modelling nitrogen dynamics in soil–crop systems with HERMES. Nutrient Cycling in Agroecosystems 77, 3952.Google Scholar
Kersebaum, K. C. (2011). Special features of the HERMES model and additional procedures for parameterization, calibration, validation, and applications. In Methods of Introducing System Models into Agricultural Research (Eds Ahuja, L. R. & Ma, L.), pp. 6594. Advances in Agricultural Systems Modeling Series 2. Madison, WI: ASA, CSSA and SSSA.Google Scholar
Kersebaum, K. C. & Beblik, A. J. (2001). Performance of a nitrogen dynamics model applied to evaluate agricultural management practices. In Modeling Carbon and Nitrogen Dynamics for Soil Management (Eds Ma, M.. Shaffer, L. & Hansen, S.), pp. 549569. Boca Raton, FL: Lewis Publishers.Google Scholar
Kersebaum, K. C., Lorenz, K., Reuter, H. I., Schwarz, J., Wegehenkel, M. & Wendroth, O. (2005). Operational use of agro-meteorological data and GIS to derive site specific nitrogen fertilizer recommendations based on the simulation of soil and crop growth processes. Physics and Chemistry of the Earth 30, 5967.Google Scholar
Kersebaum, K. C., Nendel, C., Mirschel, W., Manderscheid, R., Weigel, H. J. & Wenkel, K.-O. (2009). Testing different CO2 response algorithms against a face crop rotation experiment and application for climate change impact assessment at different sites in Germany. Idöjárás 113, 7988.Google Scholar
Klik, A. & Eitzinger, J. (2010). Impact of climate change on soil erosion and the efficiency of soil conservation practice in Austria. Journal of Agricultural Science, Cambridge 148, 529–451.Google Scholar
Mirschel, W. & Wenkel, K. O. (2007). Modelling soil–crop interactions with AGROSIM model family. In Modelling Water and Nutrient Dynamics in Soil–Crop Systems (Eds Kersebaum, K. C., Hecker, J. M., Mirschel, W. & Wegehenkel, M.), pp. 5973. Dordrecht, The Netherlands: Springer.Google Scholar
Monteith, J. L. (1965). Evaporation and environment. Symposium of the Society for Experimental Biology 19, 205234.Google Scholar
Nash, J. E. & Sutcliffe, I. V. (1970). Riverflow forecasting through conceptual model. Journal of Hydrology 273, 282290.CrossRefGoogle Scholar
Nendel, C., Berg, M., Kersebaum, K. C., Mirschel, W., Specka, X., Wegehenkel, M., Wenkel, K. O. & Wieland, R. (2011). The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecological Modelling 222, 16141625.Google Scholar
Nuske, A. (1983). Ein Modell für die Stickstoff-Dynamik von Acker-Lößböden im Winterhalbjahr – Messungen und Simulationen. Ph.D. Thesis, University of Hannover, Germany.Google Scholar
Olesen, J. E., Trnka, M., Kersebaum, K. C., Skjelvag, A. O., Seguin, B., Peltonen-Sainio, P., Rossi, F., Kozyra, J. & Micale, F. (2011). Impacts and adaptation of European crop production systems to climate change. European Journal of Agronomy 34, 96112.Google Scholar
Palosuo, T., Kersebaum, K. C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J. E., Patil, R. H., Ruget, F., Rumbaur, C., Takac, J., Trnka, M., Bindi, M., Caldag, B., Ewert, F., Ferrise, R., Mirschel, W., Saylan, L., Siska, B. & Rotter, R. (2011). Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. European Journal of Agronomy 35, 103114.Google Scholar
Pedersen, A., Petersen, B. M., Eriksen, J., Hansen, S. & Jensen, L. S. (2007). A model simulation analysis of soil nitrate concentrations – does soil organic matter pool structure or catch crop growth parameters matter most? Ecological modelling 205, 209220.Google Scholar
Petr, J. (1988). Rukovět’ Agronoma. Prague: SZN.Google Scholar
Plénet, D. & Lemaire, G. (1999). Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops. Plant and Soil 216, 6582.Google Scholar
Reidsma, P. & Ewert, F. (2008). Regional farm diversity can reduce vulnerability of food production to climate change. Ecology and Society 13, 38. URL: http://www.ecologyandsociety.org/vol13/iss1/art38/CrossRefGoogle Scholar
Rötter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., Ferrise, R., Hlavinka, P., Moriondo, M., Nendel, C., Olesen, J. E., Patil, R. H., Ruget, F., Takac, J. & Trnka, M. (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crops Research 133, 2326.CrossRefGoogle Scholar
Sapkota, T. B., Askegaard, M., Laegdsmand, M. & Olesen, J. E. (2012). Effects of catch crop type and root depth on nitrogen leaching and yield of spring barley. Field Crops Research 125, 129138.Google Scholar
Selles, F., Campbell, C. A., McConkey, B. G., Brandt, S. A. & Messer, D. (1999). Relationships between biological and chemical measures of N supplying power and total soil N at field scale. Canadian Journal of Soil Science 79, 353366.Google Scholar
Schoeneberger, P. J., Wysocki, D. A., Benham, E. C. & Broderson, W. D. (1998). Field Book for Describing and Sampling Soils. Lincoln, NE: Natural Resources Conservation Service, USDA, National Soil Survey Center.Google Scholar
Smith, P. & Olesen, J. E. (2010). Synergies between the mitigation of, and adaptation to, climate change in agriculture. Journal of Agricultural Science, Cambridge 148, 543552.Google Scholar
Thaler, S., Eitzinger, J., Trnka, M. & Dubrovsky, M. (2012). Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. Journal of Agricultural Science, Cambridge 150, 537555.Google Scholar
Trnka, M., Dubrovsky, M. & Zalud, Z. (2004 a). Climate change impacts and adaptation strategies in spring barley production in the Czech Republic. Climatic Change 64, 227255.Google Scholar
Trnka, M., Dubrovsky, M., Semeradova, D. & Žalud, Z. (2004 b). Projections of uncertainties in climate change scenarios into expected winter wheat yields. Theoretical and Applied Climatology 77, 229249.Google Scholar
Trnka, M., Kocmánková, E., Balek, J., Eitzinger, J., Ruget, F., Formayer, H., Hlavinka, P., Schaumberger, A., Horáková, V., Možný, M. & Žalud, Z. (2010). Simple snow cover model for agrometeorological applications. Agricultural and Forest Meteorology 150, 11151127.Google Scholar
USDA-SCS (1972). National Engineering Handbook, Section 4: Hydrology. Washington, D.C.: United States Department of Agriculture – Soil Conservation Service.Google Scholar
van Heemst, H. D. J. (1988). Plant Data Values Required for Simple Crop Growth Simulation Models: Review and Bibliography. Simulation reports CABO-TT 17. Wageningen, The Netherlands: Centre for Agrobiological Research and Agricultural University.Google Scholar
Van Keulen, H., Penning de Vries, F. W. T. & Drees, E. M. (1982). A summary model for crop growth. In Simulation of Plant Growth and Crop Production (Eds Penning de Vries, F. W. T. & van Laar, H. H.), pp. 8797. Wageningen, The Netherlands: Pudoc.Google Scholar
Willmott, J. C. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63, 13091313.Google Scholar
Yu, Q., Goudriaan, J. & Wang, T. D. (2001). Modelling diurnal courses of photosynthesis and transpiration of leaves on the basis of stomatal and non-stomatal responses, including photoinhibition. Photosynthetica 39, 4351.Google Scholar
Zhang, Y., Li, C. S., Zhou, X. J. & Moore, B. (2002). A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture. Ecological Modelling 151, 75108.Google Scholar