10/13/2017. Rapid Nutrient Mapping System For Paddy Field in Malaysia. National Paddy Industry

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1 Business Driven Technology Oriented By Ezrin Mohd Husin Smart Farming Technology Center of Excellence Universiti Putra Malaysia Sustainable Development Rapid Nutrient Mapping System For Paddy Field in Malaysia As an essential industry to supply staple food to the nation. National Paddy Industry To the date, Malaysia only reach 72% self sufficient level in rice. The Government has targeted to reach self-sufficiency level in rice by The government s decision to achieve 100% self-sufficiency in rice is probably due to the height of the world food crisis in June 2008, where Malaysia suddenly found itself unable to guarantee sufficient rice for the nation in the following three months in mid Additional NPK Fertilizer, 12% Paddy Price Subsidy, 21% Certified Seed Incentive, 2% Ground Magnesium Limestone, 2% Sumber (FOA) Increasing Yield Incentive, 2% Pesticide, 8% Ploughing Subsidy, 3% Production Incentive, 16% Rice Miller Subsidy, 16% Federal Government Paddy Fertilizer Addition Subsidy al Scheme, 13% Fertilizer, 5% RM 1.78 billion was provided to paddy industry in insentive & subsidy scheme in year RM 250 juta was used for NPK fertilizer. In year 2011, RM3.8 billion was funded to increase agriculture production. Paddy industry in Malaysia is related to the poverty level. Farmers income for paddy farming in KADA & MADA (2010) = RM1,300/monthly WHY Subsidies??? In Malaysia, activities such paddy farming is relying on government subsidy to sustain the industry. But now its become a burden to the government. 1

2 Paddy Granary Area Area (ha) Ave. Potential Yield (m.t/ha) Current ave. yield (m.t/ha) Achievement Percentage (%) MADA 96, LOW PRODUCTIVITY??? Soil KADA 31, Kerian Sg. Manik 28, Barat Laut Selangor (IADA BLS) 19, Seberang Perak 8, Management Water KETARA 5, Kemasin Semerak 5, Seberang Perai 10, Weather Disease NPK Nutrients Class in the Soil Optimum nutrients requirement for paddy Nutrient Nitrogen (N) (g/kg) Phosphorus (P) (mg/kg) Very high >10 >45 >1.40 Potassium (K) (cmol/kg) high Moderate Low Very low <1 <3 <0.14 Nutrient Requirement Nitrogen (N) (%) Phosphorus (P) (mg/kg) >40 Potassium (K) (cmol/kg) > 0.1 ph CEC (mol/kg) > 16 2

3 Nutrients Level influenced by soil ph Nutrients Deficiency N deficiency P deficiency K deficiency Nutrients deficiency factors 1) Uniform fertilizer distribution 2) Standard formula of NPK fertilizer 3) Low CEC and soil ph An approach need to be taken 1) Increase rice yield 2) Increase farmers income 3) Assist the government to control the subsidy DEVELOPMENT OF AGRICULTURAL TECHNOLOGY 3

4 Objectives Soil Properties Mapping System SoilProp: Soil Properties Mapping System -A High Tech System to determine soil fertility level. - Appears in map form to ease farmer understand their farm conditions. SoilProp: Soil Properties Mapping System 1) To identify the soil fertility level at precise location. 2) To assist farmers to provide optimum input at the right amount, right place and at the right time. 3) To control fertilizer utilization 4) To reduce the input cost 5) To increse the yield DOUBLE PROFIT - SoilProp IS AN INTEGRATED SYSTEM OF: 1) Soil Sensor: Measure the electrical conductivity 2) GNSS : To locate precise position. 3) Robust computer: Processing the data 4) Software : To analyze and shows the result 4

5 SOFTWARE GNSS ROBUST COMPUTER POWER SOURCE DATA LOGGER SOIL SENSOR WHY ECa Sensor??? The ECa sensor is developed for on-the-go measurement of soil properties and is a very practical tool in mapping different soil properties as the soil ECa can be measured quickly with known locations. The apparent Electrical Conductivity (ECa), is affected by several soil properties such as soil water content, clay content, salinity, temperature, organic compounds and also metals. Although many soil factors affecting EC a are relatively fixed over time (e.g., clay content), others may exhibit strong seasonal dynamics [Brevik et al, 2002]. ECa measurements can also be a useful tool in soil fertility evaluation for site-specific agriculture because it has the potential to identify areas within fields where soil types, nutrient levels, and productivity differ. Sudduth et al. (1996) used ECa to map sitespecific N requirements because ECa was correlated with depth to clay, which was the major factor influencing yield levels in the locations studied. 5

6 N = 1.626ECAD P = e (-0.001ECAD) K = ( ECAS) Jenderata plantation, Teluk Intan: P = (ecas) (1) K = (ecas) (2) Sawah Sempadan, Tanjung Karang: The statistical analysis shows that soil ECa was significantly related to total N, available P and exchangeable K at 0.01 level. The model based on soil ECa can be described as follow: Total N = ( / ECad) Available P = e ( /ECad) Exchangeable K = e (32.065/ECad) Source : MACRES, 2008 Sungai Burong, Selangor D.E: N = 1.626ECad P = e (-0.001ECad) K = ( ECas) Descriptive Statistic Description Validation Process ECas (ms/m) ECad (ms/m) Number of Data Min Max Mean Range Std. Deviation Variance Coefficient of Variation (C.V.) Table 3: Descriptive statistics for actual NPK in the experimental area. Descriptive Statistic for Predicted NPK Predicted Exc. K Description Predicted (cmol/kg) Predicted Ava. P Total N (%) (mg/kg) Number of Data Min Max Mean Range Std. Deviation Variance Coefficient of Variation (C.V.) Validation Descriptive Statistic for Actual NPK Description Actual Total N (%) Actual Ava. P (mg/k g) Actual Exc. K (cmol/k g) Number of Data Min Max Mean Range Std. Deviation Variance Coefficient of Variation (C.V.) Actual Total Nitrogen Validation Predicted Total Nitrogen 6

7 Validation Validation Actual available phosphorus Predicted available phosphorus Actual Exc. Potassium Predicted Exc. Potassium Validation Description Actual Ava. P (mg/kg) Actual Exc. K (cmol/kg) Number of Data Min Max Mean Range Std. Deviation Variance Coefficient of Variation (C.V.) Validation 7

8 kg/ha Validation Verification Test Verification Test ,630 5,981 5,722 5, ,194 4,778 T1 T (a) Comparison of yield map before (a) and after (b) implementing precision farming (b) 0 Season 1/2009 Season 2/2009 Season 1/2010 8

9 Verification Test Economics Comparison for T1 and T0 Conducted for 2 Seasons Comparison of rice yield before (1/2010) and after (1/2011) implementing Precision Farming concept for 40 participating lots Average yield 1/2010 (before) = 3.93 t/ ha Average yield 1/2011 (after ) = 5.3 t/ ha 26% increase yield 1/2010 yield 1/ ROI VALUE Verification Test The comparison of Return on Investment (ROI) for 40 farmers in Sawah Sempadan, Tanjung Karang, Selangor D.E. COMPARISON BETWEEN 1/2011 AND 2/2011 (ROI) ROI 1/2011 ROI 2/2011 LOT NO Average ROI 1/2011 = 1.76 Average yield 2/2011 = 2.25 NO ADVANTAGE SOIL PROP CONVENTIONAL 1 TIME CONSUMPTION 3-4 days / 100 hectares 3-4 weeks / 100 samples 2 ENERGY CONSUMPTION Tractors driver -Labourfor sampling -Science Officer -Chemical used 3 OUTPUT - User friendly Map - Scientific and complicated table form 4 COST - RM500/ 300 data/ ha - RM100/ 1 data/ ha 5 GUARANTEED Up to 50% (depending on cultivation sesson) No Information 6 YIELD PERFORMANCE 9-26 % increment No Information 7 Return on Investment (ROI)

10 MODERN FARMING PACKAGE SMART FARMING COMMUNITY CENTER HAPPY FARMERS SMART FARMING COMMUNITY CENTER INTRODUCTION -Smart Farming Community Center or PKPP is a knowledge transfer center for the farming community enabled by ICT. - PKPP is fully equipped with ICT facilities to educate farmers on precision farming and ICT knowledge. - Farmers are exposed to the technology used in paddy farming and trained on paddy management through the concept of precision farming. -PKPP also provides e-services such as e-banking, e-learning, and so on. SMART Farming Community Center SMART Farming Community Center The SFCC is equipped with ICT tools such as high end computers, internet service and related software. Soil & Yield Data 10

11 Web Smart Farmer (WSF) A web-based Precision Farming information centre. A source of information for the farmers, to prompt them to undertake the required activities at the right time, place and amount of inputs. Web Smart Farmer 11

12 Features 1. Prompt farmers to undertake the required activities at the right time, place and amount of inputs. The system allows farmers to keep records of their farming activities such as the amount and cost of fertilizer and chemical use. The system determine the profit of their farming activities based on Return on Investment (ROI). This will enhance the farmers understanding of their cost of rice productions and real profit being gained by them for each particular season. 2. Farmers view their field soil variability maps through a web portal and take remedial actions using variable rate treatment of their management zones with the help of rice check available on-line. 3. Facilitate precision farming practices based on ECa variable rate remedial actions map. 12

13 4. As a bookkeeping of the activities carried out by the farmers including costs involved. 5. Generate report on an individual farmer s lot to show costs of inputs, labor, income and profit. CAPABILITIES OF WEB SMART FARMER I. Farmers can keep track of the production costs for each lot. II. III. IV. Increase profitability from optimizing the inputs such as fertilizers and chemical use. Remind farmers of their next activities for the specific lot. Precision farming can easily be practiced with this guide. V. User friendly and easy-to-use application. VI. Accessible anytime and anywhere because of web based application. VII. Government agencies are able to monitor their support services to the farmers. VIII. Bridging the digital divide between urban and rural communities. Awards and Recognition GOLD MEDAL GOLD MEDAL MALAYSIA TECHNOLOGY EXPO PAMERAN REKACIPTA, 2012 PENYELIDIKAN DAN INOVASI

14 Awards and Recognition News Straits Times, 24 th June Awards and Recognition GOLD MEDAL MALAYSIA TECHNOLOGY EXPO 2012 Awards and Recognition Awards and Recognition 14

15 Awards and Recognition DRONES TECHNOLOGY 15

16 Through experiments at rice paddy fields which is situated at Saga Prefectural Research Institute of Agriculture: SPRIA in Saga city, Japan, it is found that protein content in rice crops is highly correlated with NDVI which is acquired with visible and Near Infrared: NIR camera mounted on radiocontrol helicopter. It also is found that nitrogen content in rice leaves is correlated to NDVI as well. Protein content in rice crop is negatively proportional to rice taste. Therefore rice crop quality can be evaluated through NDVI observation of rice paddy field. (Kohei Arai et al, 2014) Thank you EZRIN MOHD HUSIN Smart Farming Technology Research Center Faculty of Engineering Universiti Putra Malaysia 16