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The performance of the EU-Rotate_N model in predicting the growth and nitrogen uptake of rotations of field vegetable crops in a Mediterranean environment

Published online by Cambridge University Press:  23 August 2012

C. NENDEL ,
A. VENEZIA ,
F. PIRO ,
T. REN ,
R. D. LILLYWHITE  and
C. R. RAHN
C. NENDEL*
Affiliation:
Leibniz Centre for Agricultural Landscape Research, Institute of Landscape Systems Analysis, Eberswalder Straße 84, 15374 Müncheberg, Germany
A. VENEZIA
Affiliation:
Centro di Ricerca per l'Orticoltura, Via dei Cavalleggeri 25, Casella Postale 48, 84098 Pontecagnano, Italy
F. PIRO
Affiliation:
Centro di Ricerca per l'Orticoltura, Via dei Cavalleggeri 25, Casella Postale 48, 84098 Pontecagnano, Italy
T. REN
Affiliation:
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, People's Republic of China
R. D. LILLYWHITE
Affiliation:
School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK
C. R. RAHN
Affiliation:
School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK
*
*To whom all correspondence should be addressed. Email: [email protected]
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Summary

The EU-Rotate_N model was developed as a tool to estimate the growth and nitrogen (N) uptake of vegetable crop rotations across a wide range of European climatic conditions and to assess the economic and environmental consequences of alternative management strategies. The model has been evaluated under field conditions in Germany and Norway and under greenhouse conditions in China. The present work evaluated the model using Italian data to evaluate its performance in a warm and dry environment. Data were collected from four 2-year field rotations, which included lettuce (Lactuca sativa L.), fennel (Foeniculum vulgare Mill.), spinach (Spinacia oleracea L.), broccoli (Brassica oleracea L. var. italica Plenck) and white cabbage (B. oleracea convar. capitata var. alba L.); each rotation used three different rates of N fertilizer (average recommended N1, assumed farmer's practice N2=N1+0·3×N1 and a zero control N0). Although the model was not calibrated prior to running the simulations, results for above-ground dry matter biomass, crop residue biomass, crop N concentration and crop N uptake were promising. However, soil mineral N predictions to 0·6 m depth were poor. The main problem with the prediction of the test variables was the poor ability to capture N mineralization in some autumn periods and an inappropriate parameterization of fennel. In conclusion, the model performed well, giving results comparable with other bio-physical process simulation models, but for more complex crop rotations. The model has the potential for application in Mediterranean environments for field vegetable production.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 151 , Issue 4 , August 2013 , pp. 538 - 555
Copyright
Copyright © Cambridge University Press 2012 

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References

Agostini, F., Tei, F., Silgram, M., Farneselli, M., Benincasa, P. & Aller, M. F. (2010). Decreasing nitrate leaching in vegetable crops with better N management. In Genetic Engineering, Biofertilisation, Soil Quality and Organic Farming (Ed. Lichtfouse, E.), pp. 147–200. Sustainable Agriculture Reviews 4. Dordrecht, NL: Springer.CrossRefGoogle Scholar
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56. Rome: FAO.Google Scholar
Cao, L. K., Chen, G. J. & Lu, Y. T. (2005). Nitrogen leaching in vegetable fields in the suburbs of Shanghai. Pedosphere 15, 641–645.Google Scholar
Cavero, J., Plant, R. E., Shennan, C., Friedman, D. B., Williams, J. R., Kiniry, J. R. & Benson, V. W. (1999). Modeling nitrogen cycling in tomato-safflower and tomato-wheat rotations. Agricultural Systems 60, 123–135.CrossRefGoogle Scholar
Crews, T. E. & Peoples, M. B. (2005). Can the synchrony of nitrogen supply and crop demand be improved in legume and fertilizer-based agroecosystems? A review. Nutrient Cycling in Agroecosystems 72, 101–120.CrossRefGoogle Scholar
Crohn, D. M. & Valenzuela-Solano, C. (2003). Modeling temperature effects on decomposition. Journal of Environmental Engineering 129, 1149–1156.CrossRefGoogle Scholar
de Paz, J. M. & Ramos, C. (2004). Simulation of nitrate leaching for different nitrogen fertilization rates in a region of Valencia (Spain) using a GIS-GLEAMS system. Agriculture Ecosystems and Environment 103, 59–73.CrossRefGoogle Scholar
Dusenbury, M. P., Engel, R. E., Miller, P. R., Lemke, R. L. & Wallander, R. (2008). Nitrous oxide emissions from a northern great plains soil as influenced by nitrogen management and cropping systems. Journal of Environmental Quality 37, 542–550.CrossRefGoogle ScholarPubMed
Gallardo, M., Thompson, R. B., Lopez-Toral, J. R., Fernandez, M. D. & Granados, R. (2006). Effect of applied N concentration in a fertigated vegetable crop on soil solution nitrate and nitrate leaching loss. In Acta Horticulturae (ISHS): Proceedings of the International Symposium towards Ecologically Sound Fertilisation Strategies for Field Vegetable Production 700, 221–224.CrossRefGoogle Scholar
Greenwood, D. J. (2001). Modeling N-response of field vegetable crops grown under diverse conditions with N_ABLE: a review. Journal of Plant Nutrition 24, 1799–1815.CrossRefGoogle Scholar
Greenwood, D. J., Rahn, C. R., Draycott, A., Vaidyanathan, L. V. & Paterson, C. (1996). Modelling and measurement of the effects of fertilizer-N and crop residue incorporation on N-dynamics in vegetable cropping. Soil Use and Management 12, 13–24.CrossRefGoogle Scholar
Guertal, E. A. (2009). Slow-release nitrogen fertilizers in vegetable production: a review. Horttechnology 19, 16–19.CrossRefGoogle Scholar
Guo, R. Y., Nendel, C., Rahn, C. R., Jiang, C. G. & Chen, Q. (2010). Tracking nitrogen losses in a greenhouse crop rotation experiment in North China using the EU-Rotate_N simulation model. Environmental Pollution 158, 2218–2229.CrossRefGoogle Scholar
Halvorson, A. D., Bartolo, M. E., Reule, C. A. & Berrada, A. (2008). Nitrogen effects on onion yield under drip and furrow irrigation. Agronomy Journal 100, 1062–1069.CrossRefGoogle Scholar
Hansen, S., Jensen, H. E., Nielsen, N. E. & Svendsen, H. (1991). Simulation of nitrogen dynamics and biomass production in winter-wheat using the Danish simulation-model DAISY. Fertilizer Research 27, 245–259.CrossRefGoogle Scholar
Jackson, L. E. (2000). Fates and losses of nitrogen from a nitrogen-15-labeled cover crop in an intensively managed vegetable system. Soil Science Society of America Journal 64, 1404–1412.CrossRefGoogle Scholar
Jensen, L. S., Salo, T., Palmason, F., Breland, T. A., Henriksen, T. M., Stenberg, B., Pedersen, A., Lundström, C. & Esala, M. (2005). Influence of biochemical quality on C and N mineralisation from a broad variety of plant materials in soil. Plant and Soil 273, 307–326.CrossRefGoogle Scholar
Kersebaum, K. C., Hecker, J.-M., Mirschel, W. & Wegehenkel, M. (2007). Modelling water and nutrient dynamics in soil-crop systems: a comparison of simulation models applied on common data sets. In Modelling Water and Nutrient Dynamics in Soil-Crop Systems (Eds Kersebaum, K. C., Hecker, J.-M., Mirschel, W. & Wegehenkel, M.), pp. 1–17. Dordrecht, NL: Springer.CrossRefGoogle Scholar
Lafolie, F., Bruckler, L., de Cockborne, A. M. & Laboucarie, C. (1997). Modeling the water transport and nitrogen dynamics in irrigated salad crops. Irrigation Science 17, 95–104.CrossRefGoogle Scholar
Leenhardt, D., Lafolie, F., Bruckler, L. & de Cockborne, A. M. (1998). Evaluating irrigation strategies for lettuce by simulation: 2. Nitrogen budget. European Journal of Agronomy 8, 267–282.CrossRefGoogle Scholar
Lugato, E., Paustian, K. & Giardini, L. (2007). Modelling soil organic carbon dynamics in two long-term experiments of north-eastern Italy. Agriculture Ecosystems and Environment 120, 423–432.CrossRefGoogle Scholar
Mei, B. L., Zheng, X. H., Xie, B. H., Dong, H., Zhou, Z. X., Wang, R., Deng, J., Cui, F., Tong, H. & Zhu, J. G. (2009). Nitric oxide emissions from conventional vegetable fields in southeastern China. Atmospheric Environment 43, 2762–2769.CrossRefGoogle Scholar
Muhammetoglu, A. & Yardimci, A. (2006). A fuzzy logic approach to assess groundwater pollution levels below agricultural fields. Environmental Monitoring and Assessment 118, 337–354.CrossRefGoogle ScholarPubMed
Nash, J. E. & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models, Part I – A discussion of principles. Journal of Hydrology 10, 282–290.CrossRefGoogle Scholar
Nendel, C. (2009). Evaluation of best management practices for N fertilisation in regional field vegetable production with a small-scale simulation model. European Journal of Agronomy 30, 110–118.CrossRefGoogle Scholar
Nendel, C., Schmutz, U., Venezia, A., Piro, F. & Rahn, C. R. (2009). Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels. Plant and Soil 325, 319–334.CrossRefGoogle Scholar
Palosuo, T., Kersebaum, K. C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J. E., Patil, R. H., Ruget, F., Rumbaur, C., Takáč, J., Trnka, M., Bindi, M., Caldağ, B., Ewert, F., Ferrise, R., Mirschel, W., Saylan, L., Šiška, B. & Rötter, 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, 103–114.CrossRefGoogle Scholar
Pedersen, A., Zhang, K. F., Thorup-Kristensen, K. & Jensen, L. S. (2010). Modelling diverse root density dynamics and deep nitrogen uptake - A simple approach. Plant and Soil 326, 493–510.CrossRefGoogle Scholar
R Development Core Team (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rahn, C. R., Zang, K. F., Lillywhite, R. D., Ramos, C., Doltra, J., de Paz, J. M., Riley, H., Fink, M., Nendel, C., Thorup-Kristensen, K., Pedersen, A., Piro, F., Venezia, A., Firth, C., Schmutz, U., Rayns, F. & Strohmeyer, K. (2010). EU-Rotate_N – a European decision support system – to predict environmental and economic consequences of the management of nitrogen fertiliser in crop rotations. European Journal of Horticultural Science 75, 20–32.Google Scholar
Ramos, C., Agut, A. & Lidon, A. L. (2002). Nitrate leaching in important crops of the Valencian Community region (Spain). Environmental Pollution 118, 215–223.CrossRefGoogle ScholarPubMed
Rinaldi, M., Ventrella, D. & Gagliano, C. (2007). Comparison of nitrogen and irrigation strategies in tomato using CROPGRO model. A case study from Southern Italy. Agricultural Water Management 87, 91–105.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., Ruget, F., Takáč, 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, 23–36.CrossRefGoogle Scholar
Saxton, K. E., Rawls, W. J., Romberger, J. S. & Papendick, R. I. (1986). Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal 50, 1031–1036.CrossRefGoogle Scholar
Shaeffer, D. L. (1980). Model evaluation methodology applicable to environmental assessment models. Ecological Modelling 8, 275–295.CrossRefGoogle Scholar
Sierra, J. (1997). Temperature and soil moisture dependence of N mineralization in intact soil cores. Soil Biology and Biochemistry 29, 1557–1563.CrossRefGoogle Scholar
Stanski, H. R., Wilson, L. J. & Burrows, W. R. (1989). Survey of Common Verification Measures in Meteorology. WMO World Weather Watch Technical Report 8, WMO/TD No. 358. Geneva, Switzerland: WMO.Google Scholar
Sutton, M. A., Oenema, O., Erisman, J. W., Leip, A., van Grinsven, H. & Winiwarter, W. (2011). Too much of a good thing. Nature 472, 159–161.CrossRefGoogle ScholarPubMed
Wang, Q. R., Li, Y. C. & Klassen, W. (2007). Changes of soil microbial biomass carbon and nitrogen with cover crops and irrigation in a tomato field. Journal of Plant Nutrition 30, 623–639.CrossRefGoogle Scholar
Westerveld, S. M., McKeown, A. W. & McDonald, M. R. (2006). Seasonal nitrogen partitioning and nitrogen uptake of carrots as affected by nitrogen application in a mineral and an organic soil. Hortscience 41, 1332–1338.CrossRefGoogle Scholar
Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York, USA: Springer.CrossRefGoogle Scholar
Willmott, C. J. & Wicks, D. E. (1980). An empirical method for the spatial interpolation of monthly precipitation within California. Physical Geography 1, 59–73.CrossRefGoogle Scholar
Zambrano-Bigiarini, M. (2010). hydroGOF: Goodness-of-fit Functions for Comparison of Simulated and Observed Hydrological Time Series. R package, Version 0.2-1. Available from: http://www.rforge.net/hydroGOF/ (verified 23 July 2012).Google Scholar
Zotarelli, L., Dukes, M. D., Scholberg, J. M. S., Munoz-Carpena, R. & Icerman, J. (2009). Tomato nitrogen accumulation and fertilizer use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling. Agricultural Water Management 96, 1247–1258.CrossRefGoogle Scholar