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“APEX-AGROTOOL” simulation environment and its use for long-term analysis of different crop rotation practices Alex Topaj, Sergey Medvedev1, Vladimir Badenko, Vitalij Terleev2 1Agrphysical Research Institute 2Saint-Petersburg Polytechnical University Workshop “Simulation in den Umwelt- und Geowissenschaften” Müncheberg, 25-27 March, 2015 Who are we… Agrophysical Research Institute Laboratory of Agroecosystem Simulation 1967-2012 Ratmir A. Poluektov 1930-2012 1973 –2014 Your Majesty Model… Model is a daily-step discrete recurrent operator describing agroecosystem dynamics from planting to harvesting. x1 x x 2 ... x i (k 1) f i ( x(k ), u (k ), w(k ), A) x(0) x0 k 1,2,...N xn x(k), x(k+1) –vector of state variables, u(k) – controlling management (agronomic treatments), w(k) – weather (daily data) A – model parameters. Agricultural crop as simulation object i 1,2,..., n Generic Crop Model AGROTOOL v 3.5. Modeling domain Limiting factors competition, pests, insects P, K, microelements N, C W, P Q, T Leaf Area Development & Light Interception Approach D LightVUtilization P-R (ecology) Yield Formation Y(PRT) IVPhenology Crop f(T,O) (mineral nutrition) Root Distribution over Depth EXP Stresses III Involved W, N (Nitrogen regime) Water Dynamics Evapo-transpiration II (Water regime) Soil CN-model I (Photosynthesis, phenological development) R PM CN, P(5) C:N Sub-model in AGROTOOL Model verification Leningrad region: Spring barley, summer wheat, winter rye, oat, potato, perennial grasses. Rotational and specialized field test. Saratov region (middle Volga): Summer wheat. Water stress field tests. Krasnodar region: Summer wheat, maize. Altai region (West Siberia): Alfalfa, summer wheat Muncheberg, Badlauchstadt, Germany: Summer wheat, spring barley, sugar beat. Kaliningrad region: Summer wheat, perennial grasses. Tver region: Summer wheat, spring barley, perennial grasses, rape, potato, oat. Landscape field test. Simulation software infrastructure & components Project AGROTOOL Stationary DataBase SIAM (MS Access, PostgreSQL…) Selector Adapter Agrotool Weather Generator Parameter Identification Desktop GUI ODB (MS Excel) System of Polyvariant Analysis APEX Weather Generator Model AGROTOOL Results Single Running Interface Input data Web-Interface Adapter Third-party model Project SIAM Results - data flows - control Project APEX Third-party model Frameworks of Crop Modeling Software Issues: Polyvariant analysis – multiple running of the model in package mode with various input datasets (multivariate computer experiment). Generic shell – versatile user interface for different thirdparty crop models. Structural identification (adaptation) – ability to design the dynamic algorithm of model proceeding from the set of alternative pre-developed modules Framework APEX APSIM / PMF Polyvariant analysis Generic shell DSSAT BioMA GUICS STICS / RECORD ROMUL / DLES Features Structural identification Challenges for polyvariant analysis Problem Source of multivariance Sensitivity analysis and parametric identification Parameter value variability Statistical analysis and productivity assessment Actual Weather Climate change influence on crop productivity Future Weather Scenarios Optimization of Agrotechnologiy Variants (dates and rates) of technological treatments Operative information support of field experiments Variants of technological treatments and future weather to the end of vegetation period Precision agriculture and GIS integration Spatial heterogeneity of agricultural field Long-term analysis of Crop Rotation Fields, Seasons and Cultures of Rotation under Investigation Factor 1 Multivariate running. Full factor experiment PROJECT Factor 2 RESULTS RUN Scenario Дата BLEAF WSOIL EPLANT 13/04/11 0.12 22.1 324 14/04/11 0.14 24.1 345 15/04/11 0.16 23.4 355 … Factors Gradations А – Project Creation Dialog; В – Scenario Table Factors in APEX Predefined qualitative factors: Soil Culture/Variety Weather Technology Location Initial State Disadvantages • No metrics/order relationships • Semantic constraints on the models being connected • Fixed set of factors for model analysis Advantages • Limited and manageable number of factors • «Semantically rich» data processing and result analysis Weather generator linking.Climate change investigation Parameters Actual Weather Data identification Location: 1. Leningrad region Cultures: 1. Spring barley 2. Potato 3. Winter rye variation Generated Weather Scenarios Parameters Weather generator generation Scenarios: 1. A2 2. B2 GCMs: 1. HadCM3 (GB) 2. ECHAM (Germany) Model Terms: 1. 2020 2. 2050 3. 2080 Decision making levels in crop management (yield programming) Level Decisions Planning horizon Simulation Tools Use Cases I Strategic (Project) Years, Decades Soil fertility simple regression models Land-use projects Crop rotation planning II Tactic (Planned) Vegetation Periods Crop production models Management and Optimization of Agrotechnologies Operative Days, Weeks, Months Crop production models On-line support and forecasting III Dynamic Crop Models for Crop Rotation Analysis: Challenges and Requirements Improving the accuracy and adequacy of simulation due to many factors taking into account Multi-variant computation caused by input data variability (e.g. Weather vs. Climate) Statistical interpretation of simulation results and risk analysis Big number of characteristics controlled and/or monitored by the model (productivity, physiology, ecology, fertility etc.) Advanced management of model uncertainties Simulation of several consequent vegetation periods according to chosen rotation scheme The model must simulate different cultures and take into consideration agroecosystem dynamics during non-growing season (wintering) The runtime framework must support the calculation of scenarios in a predetermined sequence and the transfer of data from the previous scenario to the next one «A-A» for Crop Rotation Analysis Requirement Crop model: Generic simulator Current state «Inherited» model variables: AGROTOOL: Versatile algorithm for all maintained cultures. Calibrated Shoot litter (aboveground biomass) models for cereals (summer and winter wheat, winter rye, Root litter (belowground biomass) barley, oats), maize, potato, root vegetables, annual and Humus content in 1m. Layer perennial forages, legumes. Total Mineral Nitrogen in 1m. Layer «Wintering» Predecessor influence Simulation infrastructure: Continuous calculation. Modified descriptions of snow Nitrogen melting and Nodule soil thermal regime. (for legumes) Separated calculation of litter and root residues in the module of carbon-nitrogen transfer and transformation in soil. Sub-model of symbiotic nitrogen fixation and nodule nitrogen dynamics. APEX: Multiple running Validated and implemented integrated environment for multivariate analysis and automation of computer experiments with crop models. Crop rotation support Special plug-in for planning not full factorial experiments and performing complex serial-parallel schemes of scenario computation. Forecasting Built-in stochastic generator of daily weather variables that takes into account possible climate changes. APEX functionality for continuous model computation «Factor joining» (not-full factor experiment). Clutch of gradations of several factors to the tuple Assigning of scenario running order Setting the transfer of data from the results of the previously performed scenario to the input data the following scenario APEX Crop Rotation Plugin selection tool dividing factor ordering tool Crop Continuity in AGROTOOL (“Groundhog Year” Test) SPIN-UP STEADY STATE http://agrotool.ru http://www.rpoluektov.ru