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Pakistan Agriculture Information System Role of University of Agriculture Faisalabad in Project Execution

Presenter: Umer Saeed

Focal Person: Prof. Dr. Ashfaq Ahmad Chattha

Agricultural Information System Centre

Department of Agronomy

University of Agriculture Faisalabad

Overview

� Team Members and Introduction

� University of Agriculture Faisalabad (UAF) Research and Projects

� Training of Borlaug Fellows in University of Maryland (UMD)

� Establishment of Agriculture Information System Centre at UAF

� UAF Expertise in Remote Sensing and Models

� Crop Reporting Service (CRS) Punjab Training

� Relationship of NDVI and Crop Yield

� Yield Forecasting and Hydrological Models

� Integration of Remote Sensing and Crop Models

� Conclusion

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Team Members

Name Position

Prof. Dr. Ashfaq Ahmad Focal Person Agronomist/Crop Modeler

Dr. Syed Aftab Wajid Agronomist/Crop Modeler

Dr. M. Jahanzeb Masud Cheema Hydrological Modeler/Remote Sensing

Dr. Ahsan Latif IT Expert

Dr. Hammad Ahmad Soil Scientist/GIS

Mr. Umer Saeed Agronomist/Remote Sensing

Mr. M. Habib Ur Rahman Agronomist/Crop Modeler

Introduction

� Crop monitoring

� Area estimation

� Yield forecasting

� Food security

� Conventional methods

� Remotely sensed data

� Crop reporting service (CRS)

� UAF was engaged in this project to train CRS to familiarize them with innovative techniques i.e. Remote Sensing and yield forecasting models

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UAF Projects and Research

1. Use of spectral reflectance to estimate growth, biomass and yield

of different wheat cultivars under moisture stress conditions

(ALP-2008-11, completed)

2. Agricultural models inter-comparison and improvement project

(AgMIP) to forecast yield of rice-wheat cropping system of Punjab

for 2040-2069 in the context of changing climate (DFID-2012-14, completed)

3. Global Earth Observation System of Systems/Asian Water Cycle

Initiative Indus River Basin Research Activities Under the

Framework of the GEOSS Asian Water Cycle Initiative (AWCI-Japan-Intializing)

4. Use of drone technology for crop monitoring and yield forecasting

(under process, Endowment Fund, UAF)

5. Yield forecasting of wheat for different irrigation and nitrogen levels using simulations and satellite imagery (PhD Project, UmerSaeed)

6. Use of optical remote sensing and DSSAT to assess the response of wheat yield for irrigation and nitrogen regimes (PhD Project)

7. Understanding water resources conditions in data scarce river basins using pixel information (Completed)

8. Performance assessment and evaluation of an irrigation system using RS and GIS techniques (Completed)

9. Using remote sensing and GIS to predict crop yields and crop water requirements (Completed)

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Achievements by UAF

Training of Borlaug Fellows in UMD

� Before training of CRS, it was necessary to train

faculty/staff members from UAF

� Two months training under the umbrella of Borlaug

Fellowship funded by USDA

� In UMD, yield of wheat and cotton for two districts of

Punjab was forecasted using LANDSAT data

� Faculty members were trained to use GLAM, MAGIS, QGIS,

PCI Geomatica

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Wheat

Results of work done at UMD

Classified Image Showing Wheat Mask

Yellow Colour is Showing the area under wheat crop

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Relationship between wheat yield and NDVI for District Khanewal (2005-2009)

Years NDVI Yield (tonnes ha-1)

2005 0.61 3.08

2006 0.56 2.83

2007 0.60 3.15

2008 0.52 2.68

2009 0.56 3.11 (2.87 Forecasted)

R² = 0.9331

2.6

2.7

2.8

2.9

3

3.1

3.2

0.5 0.55 0.6 0.65

Yie

ld (

tonnes

ha

-1)

NDVI

• NDVI obtained from satellite images

• Yield forecast one and half month before harvest of the crop

• Less gap between observed and predicted yield

Percent Difference Between Observed and Predicted Yield of wheat for Khanewal

YearsObserved Yield

(tonnes ha-1)

Predicted Yield

(tonnes ha-1)

Percentage

Difference (%)

2005 3.08 - -

2006 2.83 - -

2007 3.15 - -

2008 2.68 - -

2009 3.11 2.87 7.80

RMSE 0.114

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Wheat Yield Forecasting of Punjab For 2014

UAF-UMD Collaboration

Landsat February, 2014

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Winterwheat, 2014

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Cotton

Relationship between NDVI and cotton

yield for Khanewal (2005-2009)

Years NDVI Yield (tonnes ha-1)

2005 0.55 2.35

2006 0.49 2.27

2007 0.45 2.07

2008 0.38 1.85

2009 0.55 2.18 (2.38 Forecasted)

R² = 0.961

1.7

1.9

2.1

2.3

2.5

0.35 0.45 0.55 0.65

NDVI

Yie

ldto

nnes

ha

-1

• NDVI obtained from satellite images

• Yield forecast one and half month before harvest of the crop

• Less gap between observed and predicted yield

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Percent Difference Between Observed and Predicted Cotton yield for Khanewal

YearsObserved Yield

(tonnes ha-1)

Predicted Yield

(tonnes ha-1)

Percentage

Difference (%)

2005 2.348 - -

2006 2.266 - -

2007 2.075 - -

2008 1.851 - -

2009 2.187 2.38 9.08

RMSE 0.095

Establishment of Agriculture Information System Centre at UAF

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Experts from UMD working with UAF for Capacity Building of

UAF staff

Conti…

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Conti…

Conti…

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Conti…

Facilities Provided at Centre

S.No. Description of Items Quantity

1a) Dell PowerEdge T620 Server

b) 19 inch Monitors

01

02

2

a) Dell precision T3500 Workstations (Intel Xeon

Processor)

b) 24” monitors

03

06

3 APC Symmetra UPS 01

4 HP A3 Color laser Printer- CP552dn 01

5 Networking Equipment (Switch 28 ports) 01

6 Samsung Glaxy Note 2 04

7 PTCL Evo Nitro (9.3 Mb) 04

8 PCI Geomatica License 01 server (4 clients)

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What can be Done in Next Phase?

UAF Expertise in Remote Sensing and Modeling

� Highly trained faculty (RS/GIS)

� Pivot between governmental agencies, researchers and

farmers

� Departmental labs on RS and GIS

• Remote sensing and hydrological modeling lab

• Remote sensing and GIS lab

• Crop modeling lab

• Advanced computer and GIS lab

• Agriculture Information System Lab

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NDVI and Wheat Crop Phenology for Different Irrigation Levels

NDVI changes with Crop Development

NDVI and Wheat Crop Phenology for Different Nitrogen Levels

NDVI changes with Crop Development

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Yield Forecasting and Hydrological Models

� UAF has been working on yield forecasting models since 2002

� Crop growth model describe the growth mechanism and dynamics

on daily basis from sowing to maturity

� Calibrated models can also be integrated with remotely sensed data i.e.

LAI from satellite data

• DSSAT (Decision Support System for Agro-technology Transfer)

• APSIM (Agricultural Production Systems Simulator)

• CropWat (Crop water requirement)

• WEB-DHM (Water and Energy Budget based Distributed Hydrological

Model)

CRS Punjab Training

� In near future, UAF is in a position now to train personnel of CRS Punjab

� We will enhance their capacity in the use of RS for crop monitoring, area estimation and yield forecasting

� UAF will also help in NDVI interpretations of different crops in terms of crop phenology

� Use of Mobile Agriculture Geo-tagging information system (MAGIS)

� Crop models integration with RS and transfer of these expertise to CRS Punjab will also be done

� CRS has field data and UAF will collaborate with them in developing yield forecasting models

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Conclusion

� Real time crop monitoring, area estimation and yield

forecasting is need of the hour

� UAF has expertise in RS and yield forecasting strengthened by

UMD, FAO, USDA and SUPARCO

� We will also help understand CRS regarding NDVI and yield

relationship interpretations

� Crop models integrated with RS data would be very helpful to

forecast yield as models covers all aspects of crop on daily

basis

� UAF will help achieve all these goal by training CRS Punjab in

collaboration with UMD, FAO, USDA and SUPARCO

Thanksdel@uaf.edu.pk