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Development of a rice growth model for early warning and decision support systems

Development of a rice growth model for early warning and decision support systems. Agriculture and Food Research Organization (NARO) Japan National Agricultural Research Center (NARC) Agroinformatics Division Hiroe Yoshida, Kou Nakazono, Hiroyuki Ohno and Hiroshi Nakagawa. Background.

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Development of a rice growth model for early warning and decision support systems

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  1. Development of a rice growth model for early warning and decision support systems Agriculture and Food Research Organization (NARO) Japan National Agricultural Research Center (NARC) Agroinformatics Division Hiroe Yoshida, Kou Nakazono, Hiroyuki Ohno and Hiroshi Nakagawa

  2. Background Crop growth simulation models for rice have played important roles to help understand its yield responses to various environmental conditions. (Kropff et al., 1994; Horie et al., 1995; Bouman et al., 2001) 　　　　　・Evaluate plant ideotype ・Predict potential yield ・Asses the effect of climate change on crop performance ・Verify physiological hypotheses for further experimental research The crop growth model has also been utilized as a part of knowledge based decision support systems. (e.g. CERES series in DSSAT) Further development of early warning and decision support systems will be synchronized with that of crop growth simulation model.

  3. Early Warning and Decision Support System Sound early warning based on physiological knowledge and weather system Predict crop productivity based on crop model simulations for years(risk analysis) ・Transplanting date ・Cultivar ・Management plan Decision making! Propose management strategies in response to the early warning Decision making! New needs in crop modeling ・Water management ・Amount of top-dressingN fertilizer

  4. Contents （１）Rice Management in Japan -from statistical data （２）Development of a rice growth model for early warning and decision support systems Targets in process-based rice model

  5. （１）Rice Management in Japan

  6. （２）Development of a rice growth model for early warning and decision support systems

  7. Targets in process-based rice model Brown Rice Yield　 ←　Spikelet #, Grain filling ratio Appearance Quality　←　N and storage starch concentration Food Quality ←　Protein content in brown rice Crop response to N application Dynamics of storage starch accumulation N% in brown rice (protein concentration)

  8. Biomass growth Phenological development Photosynthesis Yield formation Development Spikelet sterility Sugar (Su) DVI Maintenance respiration Attainable Yield Root Root growth Spikelet number Grain growth Storage starch accumulation Vegetative tissue growth Differentiation Grain Yield (Y) Vegetative Tissues (V) Storage starch (ST) Translocation Spikelet # Degeneration Plant N dynamics Vegetative tissue N (NVT) (leaf N + stem N) Grain N (NY) Translocation Senescence LAI development Vegetative tissue N accumulation Recover ND Grain N accumulation Npool (leaf N + stem N) Expansion Npool accumulation LAI Plant N uptake Soil mineral N Senescence N uptake Root system Loss Root system development Indigenous supply fertilization Yoshida and Horie (2010) FCR 117, 122-130.

  9. Thank you! Fig．Decreased appearance quality of rice ‘Hatsuboshi’ grown under high air temperature condition

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