The use of computer simulation models in precision nutrient management

Transcription

1 14 July 2015 The use of computer simulation models in precision nutrient management 2015 ECPA, TELAVIV SESSION TITLE: MANAGEMENT, DATA ANALYSIS AND DSS 1 SESSION DATE/TIME: TUESDAY, JULY 14, 2015, 16:40-17:00 Finn Plauborg*, Kiril Manevski, Zhenjiang Zhou, Mathias N. Andersen *Dept. of Agroecology, Aarhus University, Foulum Blichers Allé 20, 8830 Tjele, Denmark præsen TATION

2 Location of Aarhus University main campus, its research centers and stations 1. Research Centre Foulum 2. Askov Experimental Station 3. Jyndevad Experimental Station 4. Research Centre Flakkebjerg 5. Aarhus University main campus

3 Department of Agroecology, Aarhus University, Denmark 3

4 Outline Background Precision Nutrient Management (PNM) Objectives To explore the possible use of soil mapping, SVAT and C/N turnover simulation models in PNM Results Model parameterization and effects Conclusions and perspectives

5 Precision Nutrient Management Within field management zones and application of different levels of nitrogen fertilizer, e.g. 140 kg ± 30 kg N ha -1 Potential for total higher crop DM and N yield at field level Potential for reducing nitrogen leaching and greenhouse gas emissions Potential for saving fertilizer BUT How to use soil mapping and simulations models

6 Soil mapping Knadel, pers com. Mobile NIR spectroscopy ( nm) in the plough layer plus EC-SH (0-30 cm) EC-DP (0-90 cm) 6

7 Soil mapping Mobile NIR spectroscopy ( nm) in the plough layer 7 Knadel, pers com.

8 Soil mapping Mobile TDR for determination of the water holding capacity in the plough layer 8 Thomsen, pers com.

9 Outline Background Precision Nutrient Management (PNM), sampling and GIS maps Objectives To explore the possible use of soil mapping, SVAT and C/N turnover models in PNM Results Model parameterization and effects Conclusions and perspectives

10 SVAT models and C/N turnover models Model candidates Review (Shaffer et al., 2001), among others, the models RZWQM and Daisy Daisy Carbon turnover 1. order kinetic governs changes in slow and fast pools of AOM, SMB, and SOM Water flow from Richards eq., nitrogen flow from convectiondispersion eq. Hydraulic functions, e.g. van Genuchten-Mualem with parameters obtained from measured retention and conductivity data and RETC optimization or assessed from pedo-transfer functions (e.g. HYPRES) with input of measured soil bulk density, texture and humus content 10 Shaffer, MJ, Ma, L., Hansen, S Modeling Carbon and Nitrogen Dynamics for Soil Management. Lewis Publisher, 651 pp.

11 Daisy carbon turnover governed by 1. order kinetics 11 Abrahamsen, pers com.

12 Daisy carbon turnover linked to N-MIT model by C/N ratios 12 Abrahamsen, pers com. (modified)

13 Initialization of Daisy SOM parameters Simulate ten year actual crop rotation to self-calibrate AOM and SMB fast pools Total SOM forms around 95% of total organic matter content and a semi automated calibration of the ratio SOM1/SOM2 and hence the ratio SOM1/(SOM1+SOM2) is needed At equilibrium we know dsom1 dt = f SOM1 k SOM2 SOM2 k SOM1 SOM1 = 0, then SOM1 (SOM1+SOM2) = 0.49 but how far are we from equilibrium? 13

14 SOM1/(SOM1+SOM2) SOM1/(SOM1+SOM2) Styczen, M., S. Hansen, L. S. Jensen, H. Svendsen, P. Abrahamsen, C. D. Borgesen, C. Thirup, and H. S. Ostergaard Standardopstillinger til Daisy-modellen: Vejledning og baggrund. Technical report (in Danish). Copenhagen, Denmark: Royal Veterinary and Agricultural University and Danish Institute of Agricultural Sciences. Available at: Accessed 1 September AARHUS Initialization of Daisy SOM parameters and long term effects If miss-match exists between estimated SOM ratio, yearly C input and humus % then a new equilibrium will slowly appear Input 2000 kg C/ha, 2.0% humus (1.16% C ) and C/N 11 (long term 0.65% C) kg C ha -1 depth -1 Input 4300 kg C/ha, 2.0% humus (1.16% C ) and C/N 11 (long term 1.3 %C) kg C ha -1 depth -1 Year Year 14

15 Outline Background Precision Nutrient Management (PNM), sampling and GIS maps Objectives To explore the possible use of SVAT and C/N turnover models in PNM Results Model parameterizations and effects Conclusions and perspectives

16 Effect of humus% on the Daisy setting of SOM parameters and hence the obtained background C and N mineralisation Yearly C input (kg/ha) Humus% Daisy self setting SOM1/ (SOM1+SOM2) Change in C content (kg C ha -1 year -1 ) Background N mineralization (kg N ha -1 year -1 ) The Daisy self calibration procedure makes a balanced parameter setting of the SOM ratio, which creates and important trend in the C and N background mineralisation

17 Effect of top soil depth on N uptake in potatoes 17

18 Daisy output affected by within field variations in hydraulic properties Simulated and measured concentrations of NO 3 -N (mg/l) (y-axis) at 57 points within an area of 0.25 ha on a Jyndevad coarse sand. Djurhuus et al., Modelling mean nitrate leaching from spatially variable fields using effective hydraulic parameters. Geoderma 87 (1999):

19 Daisy output affected by within field variations in hydraulic properties Modified from Djurhuus et al..,

20 Potato data compared to simulations with Daisy Experiment with drip irrigated and N fertigated potato in Fully randomized experiment (four replicates) in a coarse sand at Jyndevad Simulated with Daisy based on one set of C/N and hydraulic parameters Treatments: I0N0 : No irrigation No nitrogen I0N3 : No irrigation, 140 kg N/ha I1N0 : Full irrigation, No nitrogen I1N3 : Full irrigation, 140 kg N/ha IdaisyNdaisy : Full irrigation, 100 kg N/ha Irrigation and N fertigation guided by Daisy If spatial variation in hydraulic and C/N parameters are introduced in the simulations, will that then explain the variations found within treatments?? 20

21 Outline Background Precision Nutrient Management (PNM), sampling and GIS maps Objectives To explore the possible use of SVAT and C/N turnover models in PNM Results Model parameterization and effects Conclusions and perspectives 21

22 Conclusions and perspectives Daisy is quite sensitive to parameterisation of hydraulic properties Daisy is quite sensitive to parameterisation of C/N parameters, especially the humus % and depth of the top soil Daisy simulated quite well DM yield and N uptake in potatoes based on one set of hydraulic and C/N parameters Soil mapping combined with the use of the Daisy model (or similar) show great potential for PNM, especially if spatial variations in top soil depth and soil carbon content can be assessed and introduced into the model. 22

23 Conclusions and perspectives Simulation models needs continuously to be verified/validated If simulation models are a bit off How to correct? Is data assimilation and auto-recalibration the way forward How can crop maps from e.g. tractor-mounted or UAV be helpful? 23

24 Conclusions and perspectives Crop mapping MobilLas canopy sensor 24 Thomsen, pers com.

25 Conclusions and perspectives Crop mapping MobilLas canopy sensor 25 Thomsen, pers com.

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27 Acknowledgement Figaro project, EU FP7 Grant agreement no: Prof. Merete Styczen, Prof. Søren Hansen, and Computer Scientist Per Abrahamsen, University of Copenhagen Postdoc Maria Knadel, Senior scientist Anton Thomsen, Surveyor Henrik Nørgaard, Academic employee Mette Balslev Greve, Dept. of Agroecology, Aarhus University 27