Optimizing agriculture for sustainability and productivity by ICT - PowerPoint PPT Presentation

Optimizing agriculture for sustainability and productivity by ICT Seishi Ninomiya Institute for Sustainable Agro-ecosystem Services, The University of Tokyo 1

Agriculture and world population 10 10 6.5billion Agriculture Population 0.5billion Engineering 7 Tools Chemistry 10 (implements and fire) 5million 15000 4 10 6 5 4 3 2 1 10 10 10 10 10 10 Years ago Revised from Robert W.Kate(1994)

Grain productivity in last forty years 1961 2003 • Wheat 1.1 t/ha 2.9 t/ha (2.7 times) • Rice 1.9 t/ha 4.0 t/ha (2.1 times) • Corn 1.9 t/ha 4.7 t/ha (2.4 times) • Population 3 billion 6.3 billion (2.1 times) • Labor (hrs/ha)* 1,750 hrs 250hrs (1/7th) FAO statistics * Case of Japan 1 ha = 2.5 acre

Technologies to have increased crop productivity in 20th century • Chemical Fertilizers – Haber Process (1908) • Agro-chemicals – DDT (1938) Parathion (1944), Organic mercury, 2-4D (1944) • Machineries – Steam Locomotive Tractor (1902), Tractor with crawler • Irrigation – Pumping, dams, channels • Plant Breeding – Mendelian Low (1865) Agriculture based on chemistry and engineering along with high input = Maximization

Drawbacks of agriculture in 20th century • Serious impacts on environment – Agricultural chemicals – Water pollution, damage on ecosystem – Exhausted and unhealthy soil • Agriculture based on high energy consumption – Machinery, chemicals • Food safety and reliability Non-sustainable agriculture based on chemistry and engineering

Agriculture in 21st century need to fulfill • High productivity – To fulfill demand increase – Limited arable land, desertification, limit to deforestation • Stable production under unstable and varying climate – Global warming, floods, drought, unusual emergence of pests,.. • Sustainability – Lower impacts on environment, energy consumption, CO2 output • High quality and high functionality – High nutrition, good taste Safety and reliability • • Welfare of farmers Paradigm shift from maximization to optimization is needed

Optimization? e.g. Reduction of pesticide application • Results in – Cost reduction • Material cost, labor cost – Lower impact on environment – Lower CO2 output – Food safety and reliability • To reduce pesticide – Timely and pinpoint protection (application) • For timely and pinpoint protection – Prediction of pest occurrence – Optimal crop management ICT can help in many aspects

ICTs for reduction of pesticide application • Pesticide prediction model (early warning system) – Weather data (observed and forecasted) – To monitor field and crop condition (e.g. trap data to know trend) • Navigation to right use of pesticide – To follow complicated regulation in order not to violate it • Farm recording of pesticide application – To know cost (materials and labor) – To certify the correct use (GAP) and traceability information • Estimation of contribution for CO2 reduction – Data for farm level LCA

ICT helps optimization in many aspects • Cost reduction and competitive agriculture – Optimal farm planning, efficient management of large number of fields – Efficient distribution • Robust and stable farm production under extreme weather and global warming – Optimal crop / variety recommendation, optimal cropping timing – Early warning system of extreme weather • Sustainable agriculture – Optimal agro-chemical application • Food safety and reliability – Tractability, right use of pesticide – GAP risk management • High quality products – Visualization of quality 9

Approaches to reach the goal • Data collection – To know what is happening in each field quantitatively • Efficient Knowledge transfer – Quantify invisible empirical knowledge – To transfer Tacit Knowledge to Explicit Knowledge – Case base reasoning • Optimization and risk management – To support decision making based on acquired data and knowledge • Framework to support decision making

Data collection and recording To know present status of fields and crops • – Site-specific optimization is needed based on site-pacific data because of site-specificity of agriculture (no generalization) – Long term data collection is necessary • To know present status of farm management – Many farmers do not know income and expenditure balance of each parcel basis • Basis for risk management – GAP • Visualization of technology of each farmer – To show the level of skill a farmers has by quantitatively comparing the present level with a target level – e.g. nutrition content level, soil organic content, energy consumption Key points: • long term and continuous collection, low cost • minimization of manual handling, easy-to-use interface 11

Multi-sensor data collection Cell phone with GPS and camera Fieldserver • Air temp., humidity, solar radiation, • soil moisture, CO2, etc. • Camera (0.3 to 10 M pixels) • WIFI hot spots

Automatic detection of farm action by image analysis and IC tags Subject material IC Tag Automatic record of farm action

Field Doctor: Integrated field monitoring and diagnosis service Fixed point field monitoring Air temp., soil temp., solar radiation.,. soil moisture, humidity, Fieldserver image etc. Collected data On-site evaluation and analysis � Evaluation � Comparison � Technical support Patrol wagon Collected data Data analysis and archive Simplified elementary analysis Heavy metal analysis � Periodical screening and diagnosis of field and crops Analysis results � Quantification of farmer’s skill by achievement level to target goal � Farmers can know the gap between their level and ideal level Infra red sensor Thermograph Laser induced florescent Florescent X ray Leaf color � Guidance for improvement analysis Analysis results In-laboratory analysis Residual pesticide test Color distribution Micro array micro- Digital pen record Spectrum analysis organism analysis

Efficient knowledge transfer • Knowledge of skillful farmers is disappearing along with aging of them • Empirical knowledge takes an important role in agriculture – Quantify invisible empirical knowledge – To convert Tacit Knowledge to Explicit Knowledge • Technologies – Case base reasoning (CBR) to utilize cases – Text-mining to extract knowledge from text – Automatic detection of farmers’ actions

Cyfar’s (Cyber farmer) diary • Mobile phone based blog system with photos To share farm information among neighboring farmers • 10 years of data collection is now working as a • valuable case database to make decisions cyfars@yahoo. co.jp 馬鈴薯 42 09120001.jpg 南大成 選別 収穫 終了 トヨシロ

Optimization and risk management • Risk management and optimization by maximally utilizing collected data, knowledge and models • Simple data mining is the first step • Risk management for human mistakes and farming optimization – GAP – Farm management system • Optimal management against environmental risks – Extreme weather – Pesticide • Fundamental databases are extremely important – Weather DB, soil DB, farming system DB, market price DB, map DB, etc. 17

Simple data mining to find out rules Images Yield Temperature Farm work records Humidity etc… Growth rate etc… Fieldserver Fieldserver Farmer Farmer Heuristic findings by comparison using data viewer e.g. High relationship between yield and air temperatures of 4 to 7 days before harvest

Identification of best timing of harvest 19

Pesticide navigation system: To support proper use of pesticide • Adjudication of proper use of pesticide by mobile phone. • Result of adjudication is automatically recorded as farm record ログイン メニュー 事前判定 予定を入力 判定結果 履歴登録へ 計画を参照 計画からの入力 予定を入力 事前判定 GPS ２９人の農家の方で、５０歳未満の方は 携帯電話による事前判定と履歴記帳 全員今後も携帯を利用したいという回答

Farm management system for GAP • To navigate farmers to most optimal farming based on GAP standard linking several databases Farming record Pesticide navigation Field data collection Fertilizer DB Pesticide DB Farming system database GAP Rule DB Market Price DB

Airborne pest immigration prediction • Weather forecast + diffusion model + insect behavior model + crop growth model + satellite image analysis • Optimization of pesticide application 4 mm 3 mg Rice Hopper Immigration Route

Utilization of satellite images / remote sensing • To identify the best timing of wheat harvest – Water content estimation of wheat grain to keep the grain quality best • Rice grain quality estimation – Estimation of nitrogen contents per field – For quality classification and guidance for next cropping • Rice paddy damage estimation for agricultural insurance – Substitution of complete enumeration sampling by humans Examples practically used in Japan

Framework to support decision making • Data integration is necessary in many of agricultural decision making • To provide efficient data and program usage, a framework to seamlessly integrate and exchange data is necessary 24