pakistan: advanced training on monitoring of crops through

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Advanced Training on

Monitoring of Cropsthrough Satellite Technology

A joint FAO, UN & SUPARCO publication

P a k i s t a n S p a c e a n d U p p e r A t m o s p h e r e R e s e a r c h C o m m i s s i o n

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ACRONYMS

AF Area Frame

AFV Area Frame Villages

BPS Basic Pay Scale

CRS Crop Reporting Service

FAO Food & Agriculture Organization of UN

GIS Geographic Information System

GPS Global Positioning System

KP Khyber Pakhtunkhwa

LCCS Land Cover Classification System

NCRG National Center for Remote Sensing & Geo-Informatics

ICT Information and Communication Technology

MINFA Ministry of Food & Agriculture

PBS Pakistan Bureau of Statistics

PC1 Planning Commission 1

PSDP Public Sector Development Program

SRS Satellite Remote Sensing

SUPARCO Pakistan Space & Upper Atmosphere Research Commission

USDA United States Department of Agriculture

VMS Village Master Sample

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ISBN : 978-969-9102-07-3

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TABLE OF CONTENTS

CHAPTER 1

1.1 Principle of remote sensing 05

1.2 Remote sensing sensors, platforms and

resolution 06

1.2.1 Sensor 06

1.2.2 Platform 06

1.2.3 Resolution 06

CHAPTER 2

2.1 Image interpretation 07

2.2 The methodology 07

2.3 Elements of visual interpretation 07

2.4 Image enhancement 09

2.5 The Concept 09

2.6 The techniques 09

CHAPTER 3

3.1 Satellite based monitoring of crops 11

3.2 Crop calendar 11

3.3 Satellite data acquisition 11

3.4 Data requirements 13

3.5 Acquisition schedule 13

3.6 Satellite acquisition programming 14

3.7 Satellite data resolution 14

CHAPTER 4

4.1 Satellite based area frame 15

4.2 Satellitebasedimageclassification 16

4.3 Land use /land cover mapping 17

4.4 FAOlandcoverclassificationsystem(LCCS)

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4.5 UseofGPSforfielddatacollection 18

4.6 How GPS works? 19

4.6.1 GPS uses/applications 19

CHAPTER 5

5.1 Crop yield, production estimation and

forecasting technique 21

5.2 Crop yield modeling 21

5.2.1 Yield input parameter 21

5.3 Monitoring of natural hazards:

Impact of droughts on agriculture 23

CHAPTER 6

6.1 Increasing productivity of crops 25

6.1.1 Gaps in productivity 25

6.1.2 Important factors for increasing

productivity 25

CHAPTER 7

7.1 Report writing 27

7.2 Online crop situation alert system 27

7.3 Online crop alerts 27

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CHAPTER 1

investigation. Therefore, remote sensing offers an efficient and reliable means of collecting the information required for various purposes.

Due to its unique ability to furnish synoptic views of larger areas, satellite remote sensing is being effectively utilized in several areas for sustainable agricultural development and management. These areas include cropping system analysis; agro-ecological zonation; quantitative assessment of soil carbon dynamics and land productivity; soil erosion inventory; integrated agricultural drought assessment and management etc.

Information pertaining to status of crops is acquired through satellite remote sensing. This information and its calibration through ground truth surveys, provides timely and accurate data on crop acreage estimates, yield forecasts, early warning and crop stress. This helps in food security and better management of agriculture sector.

Energy Source, target, background, spectral, reflection,platform and sensor

Satellite Remote Sensing

A. Satellite remote sensing refers to the activities of recording and/or observing objects or events on earth through satellites in the space.The topics to be covered in this chapter are:

B. Principle of satellite remote sensing

C. Remote sensing tools:

› i. Sensor

› ii. Platform

› iii. Resolution

1.1 Principle of Remote Sensing

Remote Sensing is the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation. If the information is collected through satellites it is called Satellite Remote Sensing (SRS).

The basic principle of remote sensing is based on the interaction of electromagnetic radiation with atmosphere and the earth. Electromagnetic radiation reflected or emitted from an object is the usual source of remote sensing data. However any media such as gravity or magnetic fields can be utilized in remote sensing. The characteristics of objects can be determined, using a reflected or emitted electromagnetic radiation from the object. Each object has unique and different characteristics of reflection or emission under different environmental conditions. Remote sensing is the technology used to identify and understand the objects under different environmental conditions.

A sensor receives the electromagnetic radiation emitted and reflected by various earth surface features. These received radiations are analysed and converted into information about the object under

A.

B.

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1.2 Remote Sensing Sensors, Platforms and Resolution

1.2.1 Sensor

A device used to measure and record electromagnetic radiation reflected/emitted from the earth. Sensor can be mounted on aircrafts, balloons or satellites. There are three basic types of sensors as under:

a) Broad band devices receive radiation of many wavelengths and combined them into a single signal. Photographic film forms an image by compositing the range of wavelengths.

b) A narrow-band device detects limited range of wavelengths and does not separate them into individual wavelengths, or bands.

c) Multi-spectral devices simultaneously record radiation in two or more separate wavelength bands that can be later recombined.

1.2.2 Platform

The vehicle or carrier for remote sensing devices is called the platform. It may be situated on the

ground, on an aircraft or balloon or on a satellite outside of earth's atmosphere.

1.2.3 Resolution

Resolution is defined as the ability of the sensor to record smallest details on the ground, which is represented as pixel on the satellite images. Currently, commercially available satellites have the finest resolution of 0.5 meter.

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CHAPTER 2

The topics to be covered in this chapter are:

A. Image Interpretation

i. The Methodology

ii. Elements of Visual Interpretation

B. Image Enhancement

i. The Concept

ii. The Techniques

2.1 Image Interpretation

It is the extraction of qualitative and quantitative information, about the shape, location, structure, function, quality and condition of an object, by using human knowledge and experience.

2.2 The Methodology

Manual interpretation and analysis dates back to the beginnings of remote sensing from air photo interpretation. Digital processing and analysis is more recent with the advent of digital recording of remote sensing data as well as advents in computer technology. Both manual and digital techniques for interpretation of remote sensing data have their respective advantages and disadvantages. Generally, manual interpretation requires specific techniques and some equipment, while digital analysis requires specialized and often expensive equipment. Manual interpretation is a subjective process, meaning that the results will vary from one interpreter to another interpreter. Digital analysis is based on the manipulation of digital numbers in a computer and is more objective, generally resulting in more consistent results.

2.3 Elements of Visual Interpretation

Tone

Tone refers to the relative brightness or colour of objects in an image or the continuous gray scale varying from white to black. Generally, tone is the fundamental element for distinguishing between different targets or features. In panchromatic photographs, any object will reflect its unique tone according to the reflectance. For example dry sand reflects white, while wet sand reflects dark gray. In black and white near infrared photographs, water is black and healthy vegetation white to light gray.

Shape

Shape refers to the general form, structure, or outline of individual objects. Shape is a very distinctive clue for interpretation. For example, the crown of a conifer tree looks like a circle, while that of a deciduous tree has an irregular shape. Airports, harbours, factories etc., can also be identified by their shape.

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Size

Size of objects in an image is a function of scale. It is important to assess the size of a target relative to other objects in a scene, as well as the absolute size, to help in the interpretation of a target. For example, large buildings such as factories represent commercial property, whereas small buildings represent residential use.

Texture

Texture refers to the arrangement and frequency of tonal variation in particular areas of an image. Smooth textures are most often the result of uniform, even surfaces, such as fields, asphalt, or grasslands. Homogeneous grassland exhibits a smooth texture, coniferous forests usually show a coarse texture.

Shadow

Shadow is also helpful in interpretation as it may provide an idea of the profile and relative height of a target(s), which may make identification easier. Shadow is usually a visual obstacle for image interpretation. However, shadow may also give height information about towers, tall buildings etc., as well as shape information from the non-vertical perspective such as the shape of a bridge.

Pattern

Pattern refers to the spatial arrangement of visibly discernible objects. Pattern is regular usually repeated shape with respect to an object. For instance, rows of houses or apartments, regularly spaced rice fields, interchanges of highways, orchards are good examples of patterns.

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Association

Association is a specific combination of elements, geographic characteristics, configuration of the surroundings or the context of an object can provide the user with specific information for image interpretation.

The association of an object helps to distinguish between railway lines and roads, vegetation and canals.

› Roads have connections with human settlements

› Canals can be identified by natural vegetation around its sides and bridges on them

2.4 Image Enhancement

Image enhancement provides tool to support visual interpretation.

2.5 The Concept

The Image enhancement includes both spatial and visual enhancements. Visual enhancement includes the use of suitable band combination, which discriminate the land features more easily and applying different data stretching and filtering techniques. Spatial enhancement is merging of the two different spatial and spectral datasets. In such a case low resolution multispectral data is compensated with high resolution panchromatic data to get high resolution multi spectral data set.

2. The Techniques

› Contrast stretching

› Colour Composites

› Filtering

› Principal component analysis

Contrast Stretching

The purpose of contrast stretching is to expand the narrow range of brightness values in an input image over a wide range of gray values. The result is an output image that is designed to emphasize the contrast between features of interest to the image analyst.

Colour Composites

Colour composites are generated to improve the radiometry of the images as human eyes are more capable to distinguish colour tones than the gray shades.

Different colour images may be obtained depending on the selection of three band images and the assignment of the three primary colours.

Enhanced ImageOriginal Image

Image Enhancement

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Types of Colour Composites

› False Colour Composite

› Natural Colour Composite

Filtering

Digital filter is a tool for changing the intensity of pixels within an image. The intensity changes depend upon the intensity of the target pixel and also upon the intensities of the pixel in vicinity.

False & Natural Colour Composite

Principal Component Analysis

Principal Component Analysis (PCA) is a technique used in image processing to reduce the correlation between bands of data. A characteristic of the principal component analysis is that, information common in all input bands is mapped to the first principal component analysis (PC-1) while the subsequent PCs account for progressively loss of information. The principal component analysis can be used for the following applications.

› Effective classification of land use with multi-band data

› Change detection with multi-temporal data

PC-1 shows area, which is similar to both images

PC-2 shows all the changed information

Raw & Filtered

PC-1

PC-2

PC-3

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CHAPTER 3

The topics to be covered are as under:

A. Satellite based Monitoring of Crops

B. Crop Calendar

C. Satellite Data Acquisition

D. Data Requirements

E. Acquisition Schedule

F. Satellite Acquisition Programming

G. Satellite Data Resolution

3.1 Satellite based Monitoring of Crops

SUPARCO being the National Space Agency has been utilizing satellite remote sensing technology for the monitoring and management of natural resources. In 2005, an initiative was taken for the "Monitoring of Crops through Satellite Technology". SUPARCO has a Satellite Ground Station (SGS) at Islamabad to acquire data from SPOT constellation of satellites, which ensure crops coverage during the cropping seasons. With foreign qualified manpower and international collaboration, SUPARCO was able to successfully monitor the crops.

3.2 Crop Calendar

There are two main crop growing seasons in Pakistan; the Kharif and the Rabi. By tradition, the crops are counted among their seasons of harvest. All crops harvested around spring or follow up are called Rabi as the word literally means spring. The crops harvested in autumn, on the same

analogy, are called Kharif crops. Similarly, the crops are counted in the financial year of harvest. A crop of sugarcane harvested in October 2011 onwards will be called sugarcane 2011-12, although it was sown in February 2011. Major Rabi crops include wheat, brassica, gram, fodders and others. The Kharif crops include sugarcane, cotton, rice, maize, fodders and legumes. Crop calendar plays a vital role to determine the satellite data acquisition schedule for crop area, yield and production estimation.

3.3 Satellite Data Acquisition

Remote sensing based crop area estimation is significantly dependent on the timely availability of quality satellite data. Satellite data acquisition is the first step towards crop area estimation through satellite acquisition. The main components are (a) the baseline standard of data to be used, (b) period of acquisition (c) and satellite programming for acquisition (d) acceptable quality level of processed data.

Acquisition Zone of Satellite Ground Station, Islamabad

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Crop Calendar

Cultivation Harvesting Cultivation & Harvesting

Crop Province Region Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Wheat Punjab Potohar

Irrigated Fallow

Irrigated after Kharif

Sindh Lower

Upper

KP Plains

Hilly Area

Balochistan Plains

Cotton Punjab Southern & Central

Sindh Mirpur Khas

Hyderabad, Badin

Upper

KP D I Khan

Balochistan Lasbela, Nasirabad

Sugarcane Punjab Spring

Sindh Autumn

Spring

KP Spring

Rice Punjab Basmati

Irri

Sindh Kotri

Sukkur

Guddu

KP Plain Areas

Hilly Areas

Balochistan

Potato Punjab Autumn

Spring

KP Autumn

Spring

Balochistan Summer

Onion Punjab All

Sindh Lower

Upper

KP Plains

Hilly Areas

Balochistan Uplands

Plains

Maize Punjab Autumn

Spring

KP Plain

Hilly

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3.4 Data Requirements

Satellite data vary in terms of their sensitivity to ground features with respect to sensors, its coverage in a single scene, spectral, spatial and temporal resolution and topographic effects. The SPOT satellite data cover an area of 3600 Km² to 4800 Km² in single observation with different acquisition angles. Spatial resolution is an important parameter of the satellite data for the estimation of crop area. Various land surface features such as field boundary, roads, canals etc. can be differentiated, once high resolution satellite data is available.SUPARCO uses SPOT 5 data for the crop area estimation. A maximum of 10 meter resolution data can be used to estimate crop area with more than 90% accuracy and the lowest sampling and non-sampling errors. SUPARCO generally uses 5 meter multispectral resolution data produced through spatial enhancement of 10 meter multispectral images from 5 meter panchromatic image. Data quality control through validation system makes the acquisition more reliable for area estimates. This helps to work out crop area estimates with better accuracy.

3.5 Acquisition Schedule

Acquisition dates or time of the satellite data is extremely crucial for the final crop area estimates by removing the impact of other crops. Acquisition dates correspond to the start and peak time of the photosynthetic activity and times of maturity.

In Pakistan, there is 6-8 weeks difference in cropping pattern from southern latitude of 24 degree to 34 degree in North. These two elements play important role in defining the time schedule for satellite data acquisitions. These dates change with zone to zone and cropping seasons. High resolution satellite data for crop area estimation are acquired twice during the growing season, once at sowing and secondly during peak growth season. Table shows generalized time period at national level. This period is adjustable for each cropping zone.

SPOT 5m Data of D. I. Khan



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3.6 Satellite Acquisition Programming

Accurate & timely programming for satellite data acquisition is a core element of remote sensing technology. Basic parameters of this aspect are the Area of Interest (AOI), programming type, programming period, acquisition data parameters (resolution) and acquired data quality control.

3.7 Satellite Data Resolution

The resolution of the acquired data is the core element of the programming request. SUPARCO generate the programming request with 10m multispectral and 5m panchromatic for crop area estimation. Data quality control through validation system makes the acquisition more reliable for area estimates.

Season Crops 1st Acquisition 2nd Acquisition

Rabi Wheat Dec - Jan Feb - Mar

Potato Oct -Nov

Maize Apr - May

Kharif Cotton Jun - Jul Aug - Sep

Rice Jun - Jul Aug - Sep

Sugarcane Jun - Jul Aug - Sep

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CHAPTER 4


A. Satellite Based Area Frame

B. Satellite Based Image Classification

C. Land Use /Land Cover Mapping

D. FAO Land Cover Classification System (LCCS)

E. Use of GPS for field Data Collection

F. How GPS Works?

G. GPS Uses/Applications

Different methodologies are used for crop area estimation. These methodologies are:

› Satellite Based Area Fame

› Satellite Based Image Classification

4.1 Satellite Based Area Frame

� An agricultural mask of the area of interests is prepared based on the satellite imagery of 5m resolution during the peak crop growth seasons i.e. February/March for Rabi crops and in September for Kharif crops.

� SUPARCO utilizes satellite-based area frame sampling developed in collaboration with Land & Water Geospatial Unit of FAO-UN, which is part of a fully operational system for the estimation of crop areas.

� The Satellite based area frame technique involves a three-stage stratification process to group districts and homogenous areas in order to use statistical inference to estimate crop areas.

� Stratification makes it possible to produce more accurate estimates by reducing variability between samples. Each stratum becomes a separate population.

� These statistical procedures are all part of a scientific probability survey system, implemented by SUPARCO. The following types of stratification are made on the satellite imagery with the help of GIS techniques:

a. Administrative stratification by grouping districts with similar cropping practices

b. Agriculture land and non-agriculture land

c. Agriculture land based on the cropping pattern, irrigation systems, and cropping intensity

Each agricultural zone is divided into Primary Sampling Units (PSU) of approximately 1000 Ha. These PSUs are then assigned strata IDs based on the cropping intensity as follows:

S# Strata ID Cropping Intensity

1 11 Greater than 75%

2 12 50 - 70%

3 21 25 to 50%

4 42 Less than 25%

PSU (Strata 11)

PSU (Strata 21)

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� A systematic random selection is made to select a sample of Primary Sampling Units (PSU).

� Selected PSUs are sub-divided into Secondary Sampling Units (SSU) of nearly equal size (300-400 ha) keeping in view the sanctity of strata.

� The selected SSUs are further divided into Terminal Sampling Units (TSU) having a size of approximately 30 ha.

� Finally, a sample of TSU is selected and prepared for data collection in the field. The picture below shows PSU selected in Punjab province.

•

� Sindh province is divided into two ecological zones i.e. left bank of Indus and right bank of Indus.

� Professional enumerators travel to the selected segments and complete a total census on the

30-hectare sample areas.

� Every crop and land cover inside the segment is recorded.

� This total enumeration of a 30-hectare segment takes approximately 3 hours. Before the enumerator leaves the segment, it is ensured that the entire segment is covered and no field and area is left without survey (even areas of mosques and cemeteries etc are brought into account).

� In most cases, the enumerators do not measure any field because measurements have been accomplished on the satellite image.

� This ground information is fed into software and registered to identify the different crops on the satellite imagery during the digital image processing and classification processes.

4.2 Satellite based Image Classification

Satellite sensor records electromagnetic radiation in digital format, which are reflected from the surface of earth. The response of spectrum values of different features depends upon the internal characteristics of the object/features. Thematic layer is generated on the basis of spectral values called classification.

Ecological Zones in SIndh

Punjab South Zone broken down into PSUs (red are selected PSUs)

North East Punjab

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The overall objective of image classification techniques is to automatically categorize all pixels in a digital image into land cover classes or themes.

The Supervised classification is a standard image processing technique, which is based on clustering of image pixels into known classes called training data. These training data are collected during ground truth surveys.

There are many supervised classification algorithms which have been used for the remote sensing data, namely Maximum Likelihood (ML), Parallelepiped classifier (Box), Minimum-distance-to-means classifier (MDM) etc.

The supervised classification has been used on multispectral imagery to distinguish different crops. In supervised classification training areas/crop signatures were identified on the basis of ground observations. Sufficient and well distributed scattered training areas of each class were taken on each scene. Thirty-five training areas in each scene covering maximum diversity of spectral range were

Classified Imagery

selected for classification.

The Maximum Likelihood algorithm is based on grouping pixels in one class and assumes that the statistics for each class in each band is normally distributed and calculates the probability that a given pixel belongs to a specific class. Unless one selects a probability threshold, all pixels are classified. Each pixel is assigned to the class that has the highest probability (that is, the maximum likelihood). If the highest probability is smaller than the specified threshold, the pixel remains unclassified.

Guassian Maximum Likelihood Classification algorithm along with crops signatures were used to identify different crops. Once the crops have been distinguished the area can be easily classified on the basis of number of pixels in a crop multiplied by the area of a pixel.

4.3 Land Use /Land Cover Mapping

The agriculture and socio-economic development of Pakistan is based on land and water resource management and development. Due to increase in population, these resources are over stretched, often leading to resource depletion.

Land cover is the observed (bio) physical / cover on the earth's surface. When considering land cover in a very pure and strict sense, it should be confined to description of vegetation and man-made features.

Land use is characterized by the arrangements, activities and inputs people undertake in a certain land cover type to produce change or maintain it. Land use establishes a direct link between land cover and the actions of people in their environments.

Land cover maps have multiple uses. They are used for the management of natural resources, urban planning & management and flood risk modeling etc.

Satellite Imagery

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4.4 FAOLandCoverClassification System (LCCS)

The Land Cover Classification System (LCCS) is a comprehensive, standardized classification system, independent from sensors, scale and other constraints designed to meet specific user requirements, independent of the scale or means used to map. Any land cover identified anywhere in the world can be readily accommodated. The classification uses a set of independent diagnostic criteria that allow correlation with existing classifications and legends.

SUPARCO being a research organization has carried out National Landuse Plan project in the past. Its responsibility was to generate SRS data based thematic layers of the existing landuse in the country.

Presently, SUPARCO is carrying out SRS based land cover mapping using LCCS covering the entire country in coordination with FAO, UN. The land cover maps generated would act as a vital source of information which would easily be updated and sub-categorized according to the requirements of resource management and planning departments.

In addition, it would also provide a reliable base-line information for generation of multifaceted and versatile GIS.

4.5 UseofGPSReceiverforfield Data Collection

Extensive field survey campaign is carried out along with teams of the provincial Crop Reporting Services (CRS) using a mobile van, fully equipped with hi-tech equipment as high precision Garmin GPS, laptops, and latest satellite image maps to demarcate coordinates and locate position of different crops. Real time on-line GPS tracking is carried out for marking features in each segment.

A3 and A2 sized maps of SPOT-5 and Quick Bird Satellite Data (FCC and natural colour composite of band 4 2 1 and 3 2 1 (RGB) at 1: 10,000 scale were used during the survey).

Real-time GPS enabled Crops Spectral Signature Collection.

Crop related spectral signatures are the core element of the supervised image classification for the satellite based crop area estimation. Twice acquired satellite data during cropping season needs to be surveyed soon after its acquisition to reduce the element of biasness through change in vegetation surface. GPS receiver is the integral part of the ground survey campaign which enables the marking of different crops on the satellite images. The GPS receiver is the ground based device which connects to the laptop computer and associated software. The GPS receiver receives the location information from GPS satellites such as latitude, longitude and altitude. Mapping software has a tool which enables the real-time digitization of the crop fields based on GPS acquired positions. Vector files of GPS positions and digitized maps constitute the main spatial database for in situ development of spectral signatures for classification.

Real time Spectral Signature Collection

LCCS Product

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4.6 How GPS Works?

The measurement principle of GPS is based on 3D Trilateration (A mathematical method of determining the relative positions of objects using the geometry of triangles) from satellites.

A GPS receiver with line of site communication with a GPS satellite can determine how long the signal broadcast by the satellite has taken to reach its location, and therefore can determine the distance to the satellite. Thus, if the GPS receiver

GPS Receiver Location

can see three or more satellites and determine the distance to each, the GPS receiver can calculate its own position based on the known positions of the satellites. Basically, three satellites are required to find the 3D position of the receiver, but various inaccuracies mean that at least four satellites are generally required to determine a three-dimensional (3D) x, y, z position.

4.6.1 GPS Uses/Applications

› Search and rescue

› Disaster relief

› Surveying and Mapping

› Marine, aeronautical and terrestrial navigation

› Remote controlled vehicle and robot guidance

› Satellite positioning and tracking

› Shipping

› Recreation

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CHAPTER 5


A. Crop Yield, Production Estimation and Forecasting Technique

B. Crop Yield Modeling

C. Yield Input Parameter

D. Monitoring of Natural Hazards

E. Impact of Droughts on Agriculture

5.1 Crop Yield, Production Estimation and Forecasting Technique

The purpose of crop yield production modeling is to devise a system to predict/ estimate size and production of crops. Crop yield/production estimation models of wheat, sugarcane, rice and cotton are developed by SUPARCO in collaboration with FAO, UN.

Crop phenology refers to the stages of crop growth and it differs from area to area and variety to variety. This phenology is very important to define the cropping pattern and crop calendar of the area. The crop calendar helps to identify the seasonal performance of the crops in regard to their sowing times. Major component of crop yield/production models are development of calibration matrices. These cover vegetation (NDVI) image indices, meteorologically interpolated data and farm input statistics on temporal (decadal or monthly) basis for last ten years. Principal Component Analysis (PCA) is carried out to identify factors responsible for a significant change in crop yield for a particular year. After PCA, multi co-linear analysis is carried out to identify independent variables responsible

for a change. The multiple regression models with high coefficient of determination (R2) are used for yield/production forecasting/estimation.

5.2 Crop Yield Modeling

The Crop yield depends upon a number of variables including crop variety, farm management inputs and a wide range of management techniques. Weather is one of the most influential component affecting crop growth and yield.

Most common forms of crop yield models are statistical, semi-dynamic and mechanistic (simulation models). The yield and production depends upon weather, soil, water and other crop inputs.

5.2.1 Yield Input Parameter

The basis of all crop-yield forecasting methods is the long use in time-series of historical yield data. In our case, the official wheat statistical data at the country, province and district level were used.

Crop cuttings data

For yield estimation, crop cuts are used as a standard sampling technique in Pakistan. It is based on a well-defined procedure, using random tables to determine the location of samples. For each crop three samples of 4.57m x 8.09 m (15 feet x 20 feet) plot in duplicate are selected in each area frame village. These small plots have harvesting experiments, which can be considered as determination of the biological yield. After taking into consideration the significant losses, which are determining the economic yield (a measure of grain that is available to the market place). These economic yield values are used to extrapolate the yield at larger spatial-scale by provincial Crop Reporting Services (CRSs).

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zone (Nasirabad and Jafarabad districts mainly).

Canal Water Withdrawal data

Water is one of the main limiting factors for agricultural production. The Provincial Irrigation Departments collect accurate and up-to-date information about the water withdrawal from main canals on daily basis. Time-series of decadal (10 days) sums of canal water withdrawal values are provided by these departments.

Tube-well Irrigation Data

The tube well irrigation plays a role in agriculture and is important component in crop yield model. Nowadays, the tube-well irrigation provides half of total irrigation water supply to the wheat during Rabi season.

Meteorological Data

The source of meteorological data is the Pakistan Meteorological Department (PMD). The data from 42 different meteorological stations, covering the territory of Punjab, Sindh, Khyber Pakhtunkhwa and part of Balochistan were gathered. The spatial distribution of the stations is important.

Fertilizer Data

The crop fertilization plays a key-role for increasing yields in Pakistan. In well irrigated areas the nitrogen fertilizers supply could be the limiting factor for agricultural production. The yearly district-wise nitrogen, phosphorous, potash and total nutrient off-take data from 1994-2012 are used in crop yield model.

Irrigation Data

The irrigation data also play a key role in Pakistan's agriculture. In Punjab along with upper & lower Sindh regions, 90% of the wheat is sown in irrigated areas. The two provinces produce more than 90 percent of wheat in the country. In Khyber Pakhtunkhwa (KP) 40 percent wheat comes from irrigated areas and 60 percent from rain-fed areas. In Balochistan, major wheat growing areas are rain-fed, and irrigated wheat is confined to Pat Feeder

Patfeeder Canal

Tube well at work

Fertilizer application to chilies & tomato crop

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5.3 Monitoring of Natural Hazards: Impact of Droughts on Agriculture

Agriculture is a vital economic sector at global level with influential impact on rural communities across the world. Extreme events such as droughts have a devastating impact on rural communities. Drought is mainly due to rain deficit during the southwest monsoon in Pakistan with some association to El Nino and La Nina events. Increasing frequency of drought in Pakistan is affecting all provinces.

NDVI March 2010 Drought in Potohar region 2009

Cotton Picking

Manual cutting of Wheat in Punjab

In the country, annual water availability per capita has decreased from 5600 cubic meter during 1947 to 1200 cubic meter during 2001.

Since late 1980's satellite data have been used for drought monitoring and agricultural crop damage assessment.

PMD stations

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CHAPTER 6


A. Increased Productivity of Crops

B. Gaps in Productivity

C. Important Factors for Increasing Productivity

6.1 Increasing Productivity of Crops

Commissioning of Mangla & Tarbella Dams in 1970's and later on a quantum jump in water supplies has caused saturation in the scope of horizontal expansion of the crop area. The major scope is now in vertical expansion through improving farm productivity levels. This can be accomplished through raising productivity of subsistent farming community to bridge the gap between the national yields and yields of the progressive growers. The gaps in productivity and the factors for improvement are as follows:

6.1.1 Gaps in Productivity

The productivity levels of crops in Pakistan need to be increased. Even within Pakistan there are wide variations in productivity of crops at the farm of progressive growers and the subsistent growers. In wheat the progressive growers are harvesting yield of 5.5 tons/ha and the national level yield is 2.3 tons per ha. In cotton, the yield of progressive growers for phutti is 3.5 tons per ha and the national average yield is almost half of that. In sugarcane the yield of progressive growers is 110 tons per ha and national average yield is 48 tons per ha. Similarly, in basmati rice the progressive growers

are harvesting 3.5 tons per ha while national average yield is 1.5 tons per ha. In IRRI rice, the yield of progressive growers is 8 tons per ha and the national yield is only 2.3 tons per ha. As the scope for horizontal expansion is limited, the agricultural policy is designed for vertical expansion through increase in the productivity of the important crops viz. wheat, cotton, sugarcane, rice and oilseeds. The focus is the subsistent farmers who lack behind in harvesting good yields. To achieve this objective, the productivity will be increased through improvement in agronomic practices.

6.1.2 Important Factors for Increasing Productivity

The important factors for increasing productivity are identified as follows:

Quality Seeds

The quality seed has a major role to play in bridging the gaps of productivity in crops. The contribution of quality seeds has been estimated at bringing additionality in productivity of crops by 25 to 30%. This area will be focused as an important element of agricultural policy in improving Pakistan's productivity of crops.

Fertilizers

Fertilizers are known to be the biggest factor in increasing productivity of crops. It is generally believed that grain nutrient ratio is 8:1. The application of fertilizers at appropriate time through placement and in proper balance is known to increase productivity of crops by 40-50%. Extensive efforts are being made through education and demonstration to increase fertilizer use efficiency and improve yield turnover per unit of fertilizer nutrients applied.

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Pest / Weed Management

Pests and weeds take a heavy toll on productivity of crops. The crops with the largest pressure of insects and pests are cotton, fruits, vegetables, rice, sugarcane and other crops. Excessive and indiscriminate uses of chemicals have not only burdened farm economy eroding already fragile profit margins but have also disturbed the biological equilibrium through eliminating the farmer friendly predators. The pest-scouting program has been launched to educate the farmers on identification of insects and their control.

Programs on Integrated Pest Management have been launched for an effective control of insects and for assuring the survival of predators. The Cotton Leaf Curl Virus (CLCV) has done colossal damage to cotton crop, which has been controlled through breeding of resistant varieties. The Burewala Strain of cotton virus is mutant of the Multan Virus and breeding/agronomic programs have been started to contain the disease. Similarly, weeds are detrimental to increased crop productivity. These compete for nutrition, soil moisture and solar radiation. The Punjab province has focused on this issue and through proper weed management and increased herbicides application has achieved higher per acre yield in case of wheat. It is desired that the other provinces should focus on weed management and popularize use of herbicides.

Transfer of Technology

There have been heavy investments in agriculture sector in the areas of research, extension, development, water management and promotion of the use of quality seeds.

The synergy from all these production factors bears fruit when these technologies are transferred to the farming community at the grass root levels. It is planned to place major emphasis on transmission of technologies through electronic media.

GPS based Field Training

Training of CRS Officials at Lahore

Field Survey

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CHAPTER 7


A. Report Writing

B. Online Crop situation alert System

C. Online Crop Alerts

7.1 Report Writing

Report writing is based on one's professional vision and experience. However a good report is expected to communicate the message in very clear and concise manner. It should also conform to professional norms. The reports generally should have four components as follows:

i. Introduction describing the problems and issues

ii. Analysis of the issues

iii. Solutions to the problems

iv. Suggestions on implementation strategy

7.2 Online Crop situation alert System

Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) embarked upon the challenging task of creating an online crop situation alert system. A modest beginning was made a few years back in this regard. The satellite based agriculture masks were prepared using 5 meter satellite imagery during peak growing seasons

in February for winter crops and September for summer crops. The satellite acquisitions are made at 4 and 8 weeks interval after crop emergence.

The crop yield and production estimates are based on the data across the growing season of the crops. These include 10 daily satellite Vegetation (VGT) indices of 1Km resolution along with ground information on daily agro-meteorological parameters, 10 daily irrigation water deliveries in canals and monthly fertilizer off take. An attempt was made to integrate and predict crop response at various stages of crop growth. Some of these include (a) time of emergence (b) time of peak growth (c) time of ripening /senescence.

7.3 Online Crop Alerts

After this system was established, an attempt was made to ensure that this information is available online and is updated frequently, at least on decadal basis. The crop forecasts and estimations involving area, production and yield of crops are issued on monthly basis. To cite an example of timelines, the first forecast for wheat crop during the year 2012 was made on 31st January followed by update on 28th February. The final estimate of wheat was issued on 31st March. The main wheat harvesting periods are March in southern areas of Pakistan, April in central and May in northern regions of the country. Likewise the temporal crop forecasts and estimations for cotton, sugarcane, rice, maize, and potato are issued frequently on monthly basis. The common pattern of release of crop statistics by conventional system involves a delay due to various reasons. The satellite system has overcome this deficiency and has helped to devise food security system to address inadequacies in the ground based system.

pakistan: advanced training on monitoring of crops through

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