Use of LTE data for cross-scale cropping system modelling and assessment

Transcription

1 Use of LTE data for cross-scale cropping system modelling and assessment Frank A Ewert Leibniz Center for Agricultural Landscape Research (ZALF) e.v., Germany INRES-Crops Science, University of Bonn, Germany Rothamsted, 21 st 23 rd May 2018 Background Use of LTEs for modelling Selected results Challenges and opportunities Date: 29 th May 2018

2 Breeding Management Crop and cropping system models Crop models Physiological processes Weather and CO 2 Atmosphere Radiation interception Crop Soil Phenology Assimilation Respiration Allocation Plant N ET Soil H 2 O, N and C Organ growth Leaves, stems,... Roots 2

3 Crop and cropping system models g m Variables Stems Roots Grains Total Biomass LAI Dynamic Deterministic Model type Process-based Numerical simulation Day of the year LAI Climate Temperature Precipitation Radiation CO 2 Soil H 2 0 C, N, P, K Factors Management Irrigation Fertilization Varieties Sowing date Pests, diseases Rotation Tillage 3

4 Breeding Management Crop and cropping system models Crop model Cropping system model Atmosphere Crop Crop 1 Crop 2 Crop n Soil time 4

5 Main areas of model application Research Teaching Decision support Communication Impact assessment Markets Land use Crop Integrated modelling Observational data analysis Farming systems Yield gap ananlysis Organism Organ Soil Model comparison and improvement Functional Structural Plant Modelling Genetic modelling Virtual phenotyping 5

6 Aspect of scale Scaling crop system models Example, wheat yield Global Asseng et al, 2015 Germany NRW Bonn Sources of uncertainty Ewert et al, 2013 Input data Climate, soil, management Genetic characteristics Model Structure Parameters Output data Post processing Observations 6

7 Crop modelling and LTE Model inputs (potentially) available from LTE Climate, CO 2 Soil characteristis Crop characteristics Management (NPK, manure, ) Data Broadbalk Winter Wheat experiment, Rothamsted Goudriaan and Zadoks (1995) in Fuhrer (2003) Time series (but changes in M, G) (Spacial variability multiple sites) Rotations E (C+S) x M G x E G x M G x E x M E Environment M Management C Climate S Soil (NPK, C, H2O, ) G Genotype Johnston and Poulton (2018) 7

8 Examples of using LTE data for modelling Modelling soil properties Example: soil C Multi-site experiments Example: soil C Example: RothC with data from LTE Rothamsted, spring barley Sim und Obs organic carbon Johnston et al., 2009 in Johnston and Poulton (2018) 8

9 Examples of using LTE data for modelling Few studies to model soil and crop processes Müncheberg, D Bad Lauchstädt, D Kersebaum, (2007) 9

10 Examples of using LTE data for modelling Few studies to model soil and crop processes multi-model simulations 5 Locations Treatment effects reproduced by all models and observation Crop-specific (normalised mean absolute) errors of yield sims across all models, sites and treatments. Kollas et al., (2015) 10

11 Examples of using LTE data for modelling? Modelling long time series of crop and soil variables?? Parameterisation for changing varieties? Yield change and variability of winter wheat in the long term fertilization experiment, Dikopshof, Bonn, Rueda-Ayala, et al., (2018) 11

12 31 May 2016 Examples of using LTE data for modelling? Modelling long time series of crop (and soil) variables; Example phenology Dikopshof, Bonn (D), Direct comparison of winter wheat varieties grown at Dikopshof, Bonn, New cultivar (Tommi) Old cultivar (Heines VII) Rezaei, et al., (2018) 12

13 Challenges and opportunities (scaling) Up-scaling crop/soil responses Sampling Aggregation Soil Climate Management Crop rotations Phenology observation stations (>6000), Ewert et al,

14 Challenges and opportunities (scaling) Up-scaling Sampling Zhao et al,

15 Challenges and opportunities (scaling) Aggregation of rotation Space a) b) c) a) M i Monocrop, initialised per a a) M c Monocrop, continouos b) R s Crop rotation, instance c) R s Crop rotation, aggregate Ewert, 2004 Cohorts Time Teixeira et al., 2015

16 Challenges and opportunities (scaling) Aggregation of rotation Example: New Zealand Autumn wheat Silage maize Forage kale Green-feed wheat Effect of aggregation methods depends on: Crop Output variablw M i Monocrop, initialised M c Monocrop, continouos R s Crop rotation, instance R s Crop rotation, aggregate Teixeira et al., 2015

17 Challenges and opportunities Aggregation / Sampling of rotation Soil Climate Management Crop rotations The Use of Long Term Field Experiments in Soil Research The example of Bonares, M Grosse Tuesday,

18 Fair principles, data management and sharing, repositories and LTE data Tuesday (C Hoffmann, The Bonares Repository) Often analogue Metadata sporadic Low accessibility Difficult to cite & reuse Archiving unsafe Recent data situation BonaRe Data Repository is FAIR plus follows the FAIR principles AND data are freely available Standards 203 LTFEs Running times >20 a 18

19 Challenges and opportunities (outlook) Sensing (FLEX/Sentinel 3), ZALF-Drone) Adjacent experiments Farm and landscape Monitoring 19

20 Use of LTE data for cross-scale cropping system modelling and assessment Frank A Ewert Leibniz Center for Agricultural Landscape Research (ZALF) e.v., Germany INRES-Crops Science, University of Bonn, Germany Thank you Rothamsted, 21 st 23 rd May 2018 Date: 29 th May 2018