Nitrogen use efficiency is an important item in organic farming. Modelling nitrogen dynamics can help to understand the impact of alternative agronomic practices and thus assist in decision making. In three examples in the Netherlands, the role of the NDICEA model is demonstrated. It is concluded that NDICEA is an easy to use and helpful tool for optimizing nitrogen efficiency and minimizing losses

Timmermans, Dr. B.G.H.

van der Burgt, Ir. G.J.H.M.

English

Organic Eprints

2nd Scientific Conference within the framework of the 9th European Summer Academy on Organic Farming, Lednice na Moravě, Czech Republic, June 24 - 26, 2009      The NDICEA model: a supporting tool for nitrogen management in arable farming Burgt, G.J.H.M. van der1 & Timmermans, B.G.H.1  Key words: nitrogen, model, mineralization, leaching Abstract Nitrogen  use  efficiency  is  an  important  item   in  organic  farm ing.  Modelling  nitrogen dynamics can  help to understand the impact of alternative agronom ic practices and thus  assist in decision  making.  In three examples in the Netherlands, the role  of the NDICEA  m odel is demonstrated. It is concluded that NDICEA is an  easy to use and helpful tool for optim izing nitrogen efficiency and m inim izing losses. Introduction Nutrient  management  is  a  key  factor  in  organic  agriculture.  For  some  nutrients  a balance  approach  will do, but  for  nitrogen  this is  not sufficient. Availability and crop demand  should  be  synchronized  as  close  as  possible  for  optimal  efficiency  and minimal  losses.  Because  of  the  number  and  complexity  of  processes  involved,  a model  approach  can  be  useful.  In  this  paper  we  present  aspects  of  the  use  of  the NDICEA model in optimizing nitrogen management by means of three examples.  Methods and results of the three examples For the  description  and interpretation of the nitrogen dynamics in  different situations we  used  the  NDICEA  model  (Nitrogen  Dynamics  In  Crop  rotations  in  Ecological Agriculture, Burgt et al.,  2006a). This  model describes soil water  dynamics,  nitrogen mineralization  and  inorganic  nitrogen  dynamics  in  relation  to  weather  and  crop demand.  Crop  yields  are  used  as  input,  making  the  model  target-oriented  which  is distinctive  from  most  other  nitrogen  models  (Kersebaum  et  al.,  2007).  The  model includes a two-layer soil model and calculations are based on time steps of one week. NDICEA has been  used in the Netherlands by farm  advisory services (Burgt et al., 2006b)  and  in  research  (Cuijpers  &  Hospers-Brands,  2009)  and  in  the  UK  for analysing nitrogen dynamics in organic crop rotations (Burgt et al., 2006c). In two of the following examples the model was used as decision support tool in arable farming,  one  on  tactical  and  one  on  strategic  level.  The  third  case  is  connected  to strategic questions in nitrogen use efficiency and is derived from a research project. For these  three  examples, all  agronomic  data relevant for NDICEA on soil  and soil water, fertilization, crop and green manures were gathered. This was done for at least three years: the year in which measurements took place, and the foregoing two years. The model performance was checked by soil inorganic nitrogen measurements, three or  more  per  season,  and  expressed  as  RMSE  (Wallach  &  Goffinet,  1989)  for measured and simulated soil mineral nitrogen. If the RMSE was beneath 20  kg N ha-1 model performance is considered to be acceptable (Burgt, 2006a)                                                  1 Louis Bolk Instituut,  Hoofdstraat 24, NL 3972 LA  The Netherlands, E-Mail g.vanderburgt@louisbolk.nl, Internet www.louisbolk.nl 2nd Scientific Conference within the framework of the 9th European Summer Academy on Organic Farming, Lednice na Moravě, Czech Republic, June 24 - 26, 2009      First example: tactical decision in practice of arable farm ing At the biodynamic farm ‘Loverendale’ (51o.34’34 N, 3o.34’ 55 E) the nitrogen dynamics of six fields are modelled. At the field ‘Iepenoord’ the crop sequence was 2007 barley (whole  plant  silage)  and  Italian  ryegrass  green  manure;  2008  potato  and  black radish/vetch  green  manure;  2009  beetroot.  Last  manure  application  took  place  in  August 2007, 25 tons per hectare of cattle deep litter manure. The farmer feels unsure about the beetroot crop: does it need additional fertilizer to realize the expected  yield of 50 tons per hectare? The simulated and measured level of soil inorganic N is given in figure 1. In this figure, weather data up to  march 2009 are from a nearby meteo station; the rest of the 2009 weather data are average regional data. 01 02 03 04 05 00 5 0 1 0 0 1 5 0 2 0 0 2 5 0N   0 -2 5  c m   s im u la te d N  2 5 -7 0  c m   s im u la te dN   0 -2 5  c m   m e a s u re d N  2 5 -7 0  m e a s u re d Figure 1: Simulated (lines) and measured (labels) soil inorganic nitrogen. X-axis: week number, 0 = 1 January 2006.  Y-axis : kg ha-1 .   The simulation of the nitrogen dynamics of this field is adequate (RMSE = 12,6 ;  n = 4). The other five fields had comparable results (not shown, RMSE’s between 4,0 and 21,0).  The  simulation  of  the  2008  beetroot  crop,  yield  70  tons  ha-1  (not  shown), indicates a calculated shortage of 40 kg ha-1 nitrogen. The predicted available nitrogen in an ‘average’  year allows a reasonable  yield,  and this crop has shown in  2008 the potential  for  a  higher  yield,  even  when  the  NDICEA  simulation  showed  a  nitrogen shortage. The calculated shortage could be explained  by a  deeper root system than modelled  or  by  a  lower  nitrogen  content  of  the  crop  or  the  crop  residues.  Both possibilities could be checked in  future. This information made the farmer decide to reject an additional fertilizer application. Second example: strategic decision in practice of arable farm ing. The  organic  arable  farm  ‘Tongelaar’  (51o.42’.57  N  ,  5o.46’.37  E)  is  considering  the presence of  winter wheat in the rotation.  Weed control and  nitrogen  efficiency might be improved  by the replacement of winter  wheat by green  manure in autumn/winter followed  by  spring  wheat.  Nitrogen  measurements  and  NDICEA  simulations  were available from  four fields with  winter  wheat (P1, P2, P5 and P9). The simulation  was sufficiently accurate (RMSE of the four fields together was 12,5; n=12). 2nd Scientific Conference within the framework of the 9th European Summer Academy on Organic Farming, Lednice na Moravě, Czech Republic, June 24 - 26, 2009      Based on this information a simulation  was  done, replacing a winter  wheat crop by a green  manure  crop  followed  by  spring  wheat.  The  farmer  assumed  that  winter  and summer  wheat  yield  would  be  equal  and  that  the  black  radish/vetch  crop  would produce  2200  kg  dry  matter  ha-1.  N  content  of  the  cathcrop  was  82  kg  ha-1.  The simulations shows that leaching was reduced but the reduction in N losses was much lower than  82  kg ha-1 for several reasons. Part of the catch crop N-content (26  kg) is fixed  by  the  Vetch,  not  extracted  from  the  soil.  The  increased  N-release  after incorporation  of  the  catch  crop  increases  soil  inorganic  N  level  and  enhances  both denitrification and leaching. After the grain harvest, soil N level is higher in case of spring  wheat,  increasing  the  risk  of  leaching  after  the  main  crop.  Based  on  this information, the farmer has decided to change his rotation in favour of spring wheat: less weed problems, less leaching, higher N availability  with potentially higher yield or increased protein content, closed green cover of the field  during winter and additional organic matter application to the soil. Third example: strategic design of crop sequence; research project. To assess nitrogen use efficiency in courgette cultivation, a comparison study is done at  the  organic  farm  of  Rozendaal  in  Strijen  (51o,45’.24  N  ,  4o,29’.49  E).  Three precrop/fertilizer  treatments  were  studied.  1:  Precrop  cabbage,  autumn  ploughing, courgette  crop  with  120  kg  N-total  in  Vinasse  as  pre-planting  fertilizer.  2:  Precrop grassclover,  spring  ploughing,  no  fertilizer.  3:  Precrop  grassclover,  spring  rotary cultivation, no fertilizer. Soil mineral N was  monitored five  times, two of which  at two depths. The treatments  were  modelled, resulting in RMSE 8,8 , 15,5 and 10,6 for the treatments respectively (n=7 for each treatment). Results are shown in figure 2. 02 04 06 08 01 0 01 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 002 04 06 08 01 0 01 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 002 04 06 08 01 0 01 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 Figure 2: Simulated (lines) and measured (labels) soil inorganic nitrogen. X-axis: week number, 105 = 1 January 2008.  Y-axis : kg ha-1 .  Treatments: 1 (left), 2 (middle), 3 (right). Solid line and squares: 0-30 cm, simulated and measured. Dotted line and triangles: 30-60 cm, simulated and measured. Courgette  yield  of  the  three  treatments  were  33107,  27907  and  32847  kg  ha-1 respectively  with an obviously lower yield for grassclover and ploughing (treatment 2). Precrop cabbage  and 120  kg N fertilizer (treatment 1) resulted in the same  yield as precrop  grassclover  without  fertilizer  and  with  rotary  cultivation  (treatment  3).  The difference between  ploughing and rotary cultivation is not accurately simulated in the modelled  nitrogen  mineralization, simply because soil cultivation is no  variable in the model.  The  measurements  in  mineral  N  tend  to  show  a  lower  level  in  case  of ploughing. This is reasonable: the decomposable material is brought deeper in the soil and is less mixed  with the soil compared to rotary cultivation, so  mineralization could have been be hampered. It was concluded that grassclover precrop can save fertilizer application, and that the type of soil cultivation is of mayor importance for the next crop, whether this  was due 2nd Scientific Conference within the framework of the 9th European Summer Academy on Organic Farming, Lednice na Moravě, Czech Republic, June 24 - 26, 2009      to the nitrogen dynamics or  to the soil structure. Based on these results the farmer decided to combine treatment 1 and 3 to optimize the cultivation of courgette following grassclover and rotary cultivation, using an limited fertilizer gift. Discussion The  use of NDICEA is relatively simple: it can be  downloaded from the internet for free, and  all information  needed to model a  field or rotation can  be acquired in a talk with the farmer or can be  estimated (Burgt, 2006a). The only exception are mineral N measurements needed for validation. In all three examples presented, the simulations of soil mineral nitrogen matched sufficiently with the measurements. No calibrations or changes  in  the  default  model  parameters  were  needed,  indicating  that  the  default model parameters are adequate  for these circumstances. Nevertheless a check  with at least four soil inorganic N measurements, spread over a growing season, should be part of the procedure before the model can be used as decision support tool.  The use of a dynamic model is interesting, not only because of the many processes it unites, but  also because it reveals effects both short-term  and long-term effects of an interference  (Christiansen  et  al.,  2006).  In  the  second  example  discussed  above  it became clear that less leaching in short terms might be followed by more leaching and other  losses  later.  This  also  plays  a  role  in  the  third  example:  with  grassclover  as precrop the production can do  without fertilizer, but  after harvest leaching of  nitrogen is much higher in case of the grassclover precrop. Conclusions In  the  three  cases  presented,  decision  making  is  supported  by  the  outcome  of  the NDICEA  model  calculations.  The  simulations  add  information  to  what  was  known already.  Except for additional soil  mineral N measurements, all data needed as input for the model can be deduced from the farmer in spoken form, making the use easy. References  Burgt G.J.H.M. van der, Oomen G.J.M., Habets A.S.J. & Rossing W.A.H. (2006) : The NDICEA model, a tool to improve nitrogen use efficiency in cropping systems. Nutrient Cycling in Agroecosystems 74: 275-294. Burgt G.J.H.M.  van der, Topp C.F.E., Watson C.A., Oomen G.J.M. & Rossing W.A.H. (2006): Predicting soil nitrogen dynamics for an organic rotation using NDICEA. Aspects of Applied Biology 80: 217-223. Burgt  G.J.H.M.  van  der,  Oomen  G.J.M.  &  Rossing W.A.H.  (2006): The  NDICEA  model  as  a learning tool: field experiences 2005. In Proceedings European Joint Organic Congress, 30-31 May 2006, Odense, Denmark, 236-237. Christiansen J.S., Thorup-Kristensen K. & Kristensen H.L. (2006) Root development of beetroot, sweet corn and celeriac, and soil N content after incorporation of green manure. Journal of Horticultural Science & Biotechnology 81(5): 831-838. Cuijpers W.J.M. and Hospers-Brands, M. (2009): Hulpmeststoffen – effect van gespreide mestgift op stikstofdynamiek in de bodem. Louis Bolk Instituut, Driebergen, 34p. Kersebaum K.Ch., Hecker J.-M., Mirschel W. & Wegehenkel M. (eds) (2007): Modelling water and nutrient dynamics in soil-crop systems. Springer, Dordrecht, 266 p. NDICEA software:  http://www.ndicea.nl Wallach D. & Goffinet B. (1989): Mean squared error of prediction as a criterion for evaluating and comaring system models. Ecol. Modell. 44: 209-306  

The NDICEA model: a supporting tool for nitrogen management in arable farming

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