malaysian national paddy precision farming project

Malaysian National PaddyPrecision Farming Project

Outlines

INTRODUCTION

PRECISION FARMING

RESEARCH ON PF OF RICE

PADDY SOIL VARIABILITY

PADDY WATER MANAGEMENT

CONCLUSION

INTRODUCTION

• The precision farming project for rice was started and implemented in Block C, Sawah Sempadan, Tanjung Karang, Selangor Malaysia.

• This was one of the biggest pilot projects for Precision Farming Studies.

• This7-year (2001-2007) project was initiated by Malaysian remote sensing agency (MACRES) together with agriculture-related agencies and Universiti Putra malaysia (UPM)

1. Ensuring the ability of nations to meet their national food security needs with a declining natural resources especially, land and water

2. How to boost annual rice production from 545 m.t to 700 m.t to feed an additional 1.3 billion rice consumers by 2025.

3. How to do so using less water and land. Currently Water Productivity Index is between 0.2-0.3 kg/m3 or 1 kg rice for every 3000 to 5000 litres of fresh water.

Is it possible to go for WPI=1.0 or 1 kg/1000 litres?

CHALLENGES INVOLVING RICE CULTIVATION IN ASIAAsia is home for 56% of humanity and 70% of the world’s 1.3 billion poor.Annual increase in population is 51 millionAsia produces and consumes 92% of the world’s rice

MADA

Besut (KETARA)IADP-PP

Kerian-Sg. Manik

Northwest Selangor Project

8 MAJOR GRANARY AREAS IN MALAYSIA

Seberang Perak

(96,405 ha)

(9,941 ha)

(29,989 ha)

(8,701 ha)

(18,195 ha)

KADA (31,440 ha)Kemasin-Semerak

(10,629 ha)

(5,164 ha)

Total acreage: 210,464 ha

Cycle of Precision Farming for Paddy

PF

Soil sampling

Soil map

Fertilizer application map

Yield measurement Yield map

Harvesting

Soil chemical analysis

Basal dressing application

Plant growth analysis

Topdressing application

Fertilizerapplication map

2 2 2 2 2 2 2 22 2 2 2 2 2 2 22 2 2 4 4 2 2 22 2 2 4 4 2 2 22 2 4 4 4 4 2 22 2 4 4 4 4 2 22 2 2 2 2 2 2 22 2 2 2 2 2 2 2

Plant growth map

Variable rate application

NATIONAL PROGRAM PRECISION FARMING FOR RICE Components and Participants

(1) a) Soil mapping - DOA

b) Soil Fertility Mapping - ECa UPM

(2) Yield mapping(Yield Sensor, GPS)

UPM, DOA

(8)GIS Analysis

and DSSUPM, MACRES

(5)Water Management

DID, UPM

(4)Remote Sensing

(Satellite and Airborne)MACRES

(9)Variable Rate Treatment

Application UPM

(7)Nutrient

ManagementMARDI, MACRES

(6)Integrated Pest

Management(Early warning system)

DOA

PRECISION

FARMING

NATIONAL PROGRAM

(3)Advanced Rice

TechnologyMARDI

(10)Farm ManagersDOA, IADP NWS

Project Area

•The project was conducted at Sawah Sempadan underTanjung Karang Rice Irrigation Scheme in Malaysia.

•The area is managed by IADA Barat Laut Selangor,Malaysia.

• Sawah Sempadan compartment consists of 1,468 lots with the total area at about 2,300 hectares, divided into 24 blocks.

•Block C in Sawah Sempadan compartment had 86 individual farmers and was chosen as the main study area.

STUDY AREA

The plots involved in the study are plot number 3117, 3121, 3168, 3172, 3176, 3151, 3155 and 3221. The Bernam river is the only source for irrigation supply in the Sawah Sempadan.

CHO

Eight selected plots of Block C in Sawah Sempadan,Tanjung Karang Selangor

Yield Mapping

Kriging or InverseSquared

Interpolation

Classification, contouring &

colouring

Calibration & Correction Representation and expert interpretation

Filtering and calibration

DGPS

Yield Sensor Latitude,

Longitude, Yield

Field data

Yield Map

Soil Variability Mapping

Soil Sampling

Electrical Conductivity

Lot X Y pH in H2O (uS/cm) % O.M % Carbon % Nitrogen (ppm)

Air Dry

3450 356426 382061 5.5 42 10.5 6.09 0.58 110.62576 359400 381712 5.2 49 7.11 4.12 0.43 46.42217 359906 380961 5.3 61 10.54 6.12 0.62 103.72133 360633 382066 5.2 54 6.85 3.97 0.38 23.82501 359500 383003 5.1 433 9.41 5.46 0.43 71.92114 360251 382597 5.1 54 10.48 6.08 0.51 502359 358806 380332 5.4 65 5.54 3.21 0.41 147.92879 357328 380417 5.6 63 5.54 3.21 0.41 413913 355850 382859 5.6 89 9.25 5.37 0.25 27.53403 356403 381295 5.4 47 5.04 2.92 0.3 13.63378 357138 381325 5.4 160 9.97 5.78 0.51 5.092955 358423 382128 5.2 276 7.54 4.38 0.33 65.42789 357709 381771 5.3 272 4.72 2.74 0.27 36.22284 359606 380024 5.4 69 5.76 3.34 0.32 34.13150 5 75 8.74 5.07 0.35 42.93943 5.1 335 6.13 3.56 0.34 632109 360829 382374 5.2 122 10.44 6.06 0.53 45.13736 5.2 66 3.41 1.98 0.21 15.6

INTERPOLATION

CALIBRATIONpHCECN,P,K, etc…

Water Management

SCADA SYSTEM

Variable Rate Treatment

Action MapHerbicides

Action MapFertilizer

Action MapInsecticides

Action MapWater Management

C1

C2

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

% sand

% c

lay

clay

siltyclay

siltyclay loam

silt loam

silt

loamsandy loam loamy

sand sand

sandy clay loam

sandy clay

clay loam

Textural Class of Paddy Soils at TAKRIS

Soil sampling locations

GIS MODELING

Statistical Analysis

Modeling

Action MapAction MapAction MapAction Map

Field Information

SCOPES•The research focused on the development of Spatial Decision Support System (SDSS) for efficient management of paddy farms.

•The targeted users of the system are the farmers representatives and the farm managers.

•The system was designed and developed only for paddy crop.

•Fertilizer recommendation maps were produced and derived from the soil data collected by the soil fertility mapping components and the nutrient management components

OUTCOMES

The outcomes of this research is the Paddy GIS program which contains the following modules such as: -

•Mapping modules•Water Management module•Pests and diseases module•Rice check module (farming activities)•Soil Plant Analysis Development (SPAD) meter module•Fertilizer management module•Record management module

OUTCOMES

The list of fertilizer recommendation maps produced by the program are:-

•Maps of Nitrogen (N), Phosphorus (P) and Potassium (K) From Soil Sample

•Maps of Nitrogen (N), Phosphorus (P) and Potassium (K) From EC Mapping

•Maps of Nitrogen from SPAD data

• Soil data collected from EC groups• Fertilizers algorithm Variable Rate

Technology Group• Yield data collected from harvester• Yield record collected from Integrated

Agriculture Development Area• Pest and disease records collected from crop

protection and Plant Quarantine Division, Department of Agriculture Malaysia

• Data from SPAD reading• Rice Irrigation Management Information

System (RIMIS) program

LIST OF INPUT DATA

DATA SOURCES

The sources of data used to access the nutrient contents

Data from various sources produces different maps. For example, data from soil sampling and soil EC are able to produce N, P and K fertilizer maps while data from SPAD only able to produce N fertilizer map.

Data sources and its data output

Variable Rate Technology Chlorophyll Meter

(SPAD) Reading

Rice Irrigation Management Information System (RIMIS) program

ADVANTAGES

♦ Paddy planting and related activities schedule♦ Fertilization schedule♦ Variable application map for fertilizer♦ Module for farmers♦ Module for managers♦ Special Bahasa Malaysia interface for farmers♦ Water management module♦ Pests management module♦ Diseases management module♦ Yield management module

COMPONENT OF PADDY GIS

There are three main components in the development of Paddy GIS program. The components of the Paddy GIS program are

• GIS

• Graphical User Interface (GUI)

• Database.

COMPONENT OF PADDY GIS

Main components in Paddy GIS program

COMPONENTS OF PADDY GIS •The program used Microsoft Access database which is a Database Management System (DBMS) software to store yield, pest and diseases records in the database.

• GIS data are displayed in the computer based system. Users able to produces the digital and hardcopy maps of fertilizer recommendation input, printed maps and updates the pest,diseases, and yield records in the database

• GUI to enable the user to access the program in an easy manner.

STEPS ON DEVELOPMENT OF PADDY GIS

The development of Paddy GIS involved the following steps : -

•User Analysis study

•Data collection

•Database design and development

•Development of User Interface

•Development of fertilizer application maps

•Installation of Paddy GIS program

•Training

System architecture

• Program written in Visual Basic Development Environment

• Required the Spatial Analysis Extension

• Involved the programming in Visual Basic for Applications in ArcGIS development environment

• Used Microsoft Access as Database Management System (DBMS)

• Installed at the Integrated Agriculture Development Area (IADA) Barat Laut Selangor, Kuala SelangorSelangor

ABOUT THE PADDY GIS 1.5

PADDY GIS USERS

This program was designed to be used for many users including irrigation engineers of “BPSP”, manager (“Pengurus”) at Barat Laut Selangor and groups of farmers representatives.

IMPLEMENTATION

PADDY GIS MAIN MENU

Paddy GIS program is able to produce the fertilizer maps and provide decision tools for water management at the paddy field.

SAMPLE INTERFACE OF PRECISIONFARMER PROGRAM VERSION 1.5

SYSTEM USERS

MAIN MENU

WORK SCHEDULE

FERTERLIZER RECOMMENDATION INTERFACE

INTERPOLATION INTERFACE

MAP NAME :FERTILIZER RECOMMENDATION MAP OF NITROGEN (N) , PHOSPHORUS (P) POTASSIUM (K)

DATA SOURCE : FROM VERIES MACHINE, EC READING

SEASON : SEASON 1/2007.

PLOT NUMBER : 3221

MAP NAME :FERTILIZER RECOMMENDATION MAP OF NITROGEN (N)

DATA SOURCE : FROM CHLOROPHYLL METER

SEASON : 21-25 DAYS AFTER SEEDING SEASON 1/2007.

PLOT NUMBER : 3221




PLOT NUMBER : 3221

MAP NAME :FERTILIZER RECOMMENDATION MAP OF NITROGREN (N)

DATA SOURCE : FROM SOIL SAMPLING


PLOT NUMBER : 3221

MAP NAME :FERTILIZER RECOMMENDATION MAP OF PHOSPHORUS (P)

DATA SOURCE : FROM SOILSAMPLING


PLOT NUMBER : 3221

MAP NAME :FERTILIZER RECOMMENDATION MAP OF POTASSIUM (K)

DATA SOURCE : FROM SOILSAMPLING


PLOT NUMBER : 3221

DICUSSION

• Maps for N, P and K can be generated using three data sources which are data from SPAD meter, data from soil sampling and data recorded using Veris EC sensor.

• Nitrogen is supplied usually four times after the crop is planted in the paddy field. The reading to measure the Nitrogen contents in the leaf is taken using the SPAD meter. From the data taken by SPAD meter, maps for Nitrogen fertilizer application are generated by the Paddy GIS system

• Veris EC sensor also able to supply the data source for maps generation of N, P and K.

CONCLUSION

This research has shown how the principles of precision farming can be translated into practical use by the farmers. The ability to produce and use fertilizer recommendation maps will certainly optimize on the fertilizer inputs and maximize the yield of the precision farming.

Acknowledgements

Special thanks to Remote Sensing Malaysia, Department of Agriculture, IADA Barat Laut Selangor, BPSP, DID, MARDI, member of the Spatial Research group (SRG) and the members of the Precision Farming Engineering Research and Development (PREFERD) group at the Department of Biological and Agricultural Engineering and ITMA, UPM for their collaboration and cooperation.

malaysian national paddy precision farming project

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