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APAN Geo-ICT and Sensor Network based Decision Support Geo ICT and Sensor Network based Decision Support Systems in Agriculture and Environment Assessmen 2011.8.24

Development of decision support system for optimal agricultural system for optimal agricultural production under global environment changes

M Mi hi d S Ni i

• M. Mizoguchi and S. Ninomiya

U i it f T k University of Tokyo

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BACKGROUND BACKGROUND

• Food shortage in the 21st century

– Population increase of 200,000 people daily – the transition to a carnivorous diet Limits of agric lt ral land e pansion and nstable ater – Limits of agricultural land expansion and unstable water supply – Stable agricultural production due to fears of global g p g warming and frequent extreme weather

The need to simultaneously achieve the following

• The need to simultaneously achieve the following

against the Global Environmental Change

– High productivity High productivity – High quality – Food safety – Low environmental impact, sustainability – Appropriate regional resource management

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Objectives j

• Among the long-term warming trend and the

freq enc of e treme eather in order to achie e a frequency of extreme weather, in order to achieve a robust and stable agricultural production We build a support system for optimizing agricultural

• We build a support system for optimizing agricultural

production

Optimal care management support profitable cultivation – Optimal care management support profitable cultivation (fertilizer and irrigation, crop rotation system) – Optimal water management in basin area Opt a ate a age e t bas a ea

• Expected ripple effect of the study

– Stable supply of food production Stable supply of food production – Stable management of the farmers base on food quality – Appropriate water resource management in watershed pp p g – Transition to sustainable agriculture in low-carbon and low environmental impacts

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Configuration and Overview of the study

Supply of down-scaling data

1 Development of high resolution weather

• 1. Development of high-resolution weather

model for local agricultural use

Generation of high-resolution data to in-situ agriculture model Prediction of Land and water resources Meteorological data Prediction of crop water demand

• 2. Development of crop quality model

Accurate predictions of crop quality and yield

• 3. Development of soil&water model

Prediction of water resources &soil moisture movement p Monitoring data for assimilation/tuning

• 4. Validation of support system by ground monitoring

Integration of multiple models User-friendly interface

• 5. Optimal Agricultural Production Support System
• 4. Validation of support system by ground monitoring

Construction of the ground monitoring system Optimal cultivation management considered the farmer's profitability (Fertilizers and irrigation, crop rotation system) Optimal water management in basin area On-site verification of the system

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Previous efforts toward achieving the goals

• 1. Development of high-resolution weather model for local agricultural use

– High-resolution mesh technology for meteorological data and climate model results (Univ. of Tokyo, NIAES)

• 2. Development of crop quality model

– Development of prediction models, such as rice growing in DIAS project (NARO)

• 3. Development of soil&water model

– Investigation of heat and soil moisture transfer model using meteorological model

• utput data (UT)

4 V lid ti f t t b d it i

• 4. Validation of support system by ground monitoring

– Development of Ground monitoring system using field server (NARO) – Database of ground monitoring data in DIAS project (UT)

• 5. Optimal Agricultural Production Support System

– Development of user interface for Growth model in DIAS project (UT) A i lt l t d i f t t f d t i t ti i DIAS j t (NARO) – Agriculture-related infrastructure for data integration in DIAS project (NARO)

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• 1. Development of high
• 1. Development of high-
• resolution weather model for local agricultural use

resolution weather model for local agricultural use (NIAES) (NIAES)

Re-analysis meteorological data (Uncertainty assessment using multiple climate scenarios)

Preparation of daily weather data in the

Near future climate prediction (CMIP 5)

target areas

Short&middle-term prediction (Such as seasonal Near future climate prediction by high-resolution atmosphere-

• cean model

(Innovative Program of Climate Statistical/dynamic downscaling of weather and climate data (Such as seasonal forecasting). Past to near future prediction · (1980 2030) Change the 21st Century)

(Collaboration with research

(1980 - 2030)

• 1. Spatial resolution

Each AMeDAS point (1 km mesh)

( groups downscaling)

• 2. Weather elements

Temperature, precipitation, Wind speed, humidity, Solar radiation, longwave radiation Snow radiation, Snow ...

(Data input) Data Integration Analysis System Data Integration Analysis System （DIAS） S&W model Crop model

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• 2. Development of crop quality model （NARO)

Biomass growth and yield formation Phenological development

Summer crop: rice

Agro-meteorological model (high-resolution weather data) Ground monitoring (assimilation/tuning data)

Crop growth model

Storage starch (ST) Storage starch Accumulation Vegetative tissue growth Root Photosynthesis Sugar (Su) Maintenance respiration Grain growth Root growth Development DVI Spikelet # Differentiation Spikelet number

Summer crop: rice Winter crop: wheat, barley /Crop growth dynamics /Crop quality as well as yield! / f l l

Vegetative Tissues (V) Grain Yield (Y) Translocation T l i Attainable Yield Spikelet sterility Degeneration Plant N dynamics

/Forecast of agricultural meteorological disasters

S&W model

leaf N leaf N accumulation Grain N soil N uptake Grain N accumulation Senescence Translocation dead N Expansion LAI Senescence LAI development stem N accumulation stem N Senescence Translocation

(water resources, soil moisture) （crop water demand）

Decision Support Systems rice-wheat rotation system Decision Support Systems rice wheat rotation system

/Optimal configuration of cropping season /Selection of varieties /Risk reduction in agriculture meteorological disaster /Land productivity optimization /Optimal resource utilization

Advanced use of rice paddies to improve food self-sufficiency

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• 3. Development of soil&water model in rural area （UT)

Development of material circulation modelunder climate change

Changes in the quality and quantity

• f local water resources

Ch i

climate change (Changes in rainfall- temperature characteristics)

Changes in soil environment F d P d i Available quantity Water use plan Changes in demand for water use patterns /Local water cycle /Nutrient cycling /Chemical load Changes in soil environment Nutrient water temperature Food Production Changes in Crop species/Cultivation system Ecosystem/Land use

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)

Crop model

• 10

10 20 30 50 100 150 200 250 300 350 Temperature(℃ )

10cm

Temperature(℃)

Soil&water model under climate change

Prediction of quality and quantity of water resources/Rational water planning Prediction of soil hydrothermal environment/Linkages to crop model y g p Evaluation of soil&water environmental changes Development of reducing the environmental impact based on prediction

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4.Validation of support system by ground monitoring （UT&NARO) （UT&NARO)

Ground monitoring data

Data assimilation/tuning

Field-level validation of models (Agricultural Experiment Station) Development of the systems (UT)

Accuracy verification

Improvement of Field Server p Compact monitoring system

Can be installed in poor infrastructure regions with lectrical power and telecommunications

Basin-level support System Validation (Tedori-gawa Land

Verification support system

Improvement District)

Verification support system Productivity and profitability

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• 5. Optimal Agricultural Production Support System （UT)

Hi h l ti d li li t d l

Optimal Agricultural Production Support System

Crop quality model High-resolution downscaling climate model

Data

DIAS database syetem

Soil&water model in rural area

exchang

Downscaling data Ground monitoring data Ground monitoring system

ge platfor

High-resolution weather data for agriculture

rm

Optimal cultivation management support tool Regional Water Management Tools

User interface

The best guidance on how to manage Conscious cultivation profitability

Input of fertilizer, irrigation timing, timing of planting, variety selection and crop rotation system

Regional water management guidelines

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Hokuriku prefectures selected as target area

• Agricultural Experiment Stations

– Toyama Prefecture I hik P f t – Ishikawa Prefecture – Fukui Prefecture – Ishikawa Prefecture University – Tedori-gawa Land Improvement District

• Reasons for selecting

Reasons for selecting

– Urgency

• Quality deterioration has already occurred in recent years by a global warming
• Significant change snowfall due to warming can reduce the amount of water

g g g

• The rising temperatures and changes in snowfall could change from barley to

wheat crops in winter season best

– Advantageous to build and validate models and systems

Has a variety of meteorological and environmental conditions and terrain

• Has a variety of meteorological and environmental conditions and terrain
• The main barley growing regions
• suitable as a venue to verify the productivity by the alternation of land usage

system and the rice-wheat double cropping system

• Groundwater, river water quality and soil data have been prepared prepared in

the previous project

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Expected research results

• Model developed in this study group does not depend on

the region and contribute to world agricultural production the region and contribute to world agricultural production

– Meteorological data will be generated with the resolution and accuracy for agricultural use accuracy for agricultural use – The crop quality model will be developed in a real agricultural field – Mutual coordination of crop and soil water model groups will be made, and will

• contribute to improving quality yield prediction
• optimize regional water resource management

O ti l i t ill b d

• Optimal cropping system will be proposed

– to produce a stable yield and quality to be under the Global Change t th t bl l f i lt l d t – to the stable supply of agricultural products

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Thank you very much Thank you very much

This presentation was made based on the proposal to MEXT submitted on 2010.7.16

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