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Prospects of satellite remote sensing in cereal disease monitoring and precision crop protection for food security enhancement in Pakistan

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Title: Prospects of satellite remote sensing in cereal disease monitoring and precision crop protection for food security enhancement in Pakistan


1
Prospects of satellite remote sensing in cereal
disease monitoring and precision crop protection
for food security enhancement in Pakistan
  • Syed Jawad Ahmad Shah and Muhammad Ibrahim
  • Nuclear Institute for Food and Agricuture (NIFA),
    Tarnab, Peshawar, Pakistan

E-mail jawadshah_at_hotmail.com, mails_at_nifa.org.com
Webpage www.nifa.org.pk
2
  • Abstract
  • Among cereals, wheat is one of the most
    critically important staple foods in Pakistan
    where most of the population rely heavily on its
    production for their livelihoods. Population of
    Pakistan is expected to get doubled by 2050,
    making it 4th largest nation. To meet the needs
    of the growing population, wheat productivity on
    sustainable bases is of paramount importance for
    food security. Historically, a set of biotic
    stresses caused by airborne fungi can seriously
    affect wheat production in Pakistan which
    included yellow rust, leaf rust and stem rust.
    Historical presidents in our wheat dependent
    country indicate that a disease outbreak could
    cost millions of dollars in attempted control and
    lost agricultural output.. Such losses can be
    minimized by the application of modern outer
    space technologies such as satellite remote
    sensing which is not practiced in the ongoing
    phytopathological research in Pakistan. Prospects
    of satellite remote sensing in surveillance,
    monitoring and precision wheat crop protection of
    these rust diseases are presented for food
    security enhancement in Pakistan.

a
c
b
Figure 1 Three wheat rusts a yellow rust b
leaf rust and c stem rust
3
  • Introduction
  • Wheat acreage on global scale is around 215
    million hectares, 44(95 million hectares) is in
    Asia where it is grown on 62 million ha in China,
    India and Pakistan as shown in figure 2.
  • Increased wheat production for self sufficiency
    and food security is of strategic importance in
    most Asian countries where majority of the
    farmers are poor with small holdings and involved
    in subsistence farming.The three rust diseases of
    wheat have historically been the major biotic
    stress (Fig1) responsible for destabilizing
    production in Asia and other parts of the world.
  • Pakistan although with the 2nd highest wheat
    acreage among the Southeast Asian countries has a
    national average yield at around 2.5t/ ha..Wheat
    is cultivated on 22 million ha in Pakistan and it
    occupies 70 of the Rabi and 37 of the total
    cropped area. It is being consumed _at_ of 135
    kg/year with 72 total calories intake.
  • A worth of 6.4 billion US wheat produced in the
    country and diseases are one of the main causes
    for reducing its production in the country where
    one percent loss in production accounts for a
    loss of 61 milliion US. Leaf rust can attack
    80 of the wheat acreage in Pakistan (Fig 3).

Fig 2 Countries with 2 third wheat acreage in
asia
Fig 3 Lead rust prone areas in Pakistan
4
Introduction
In Pakistan, wheat is cultivated on more than
eight million hectares and 70 of it is prone to
yellow rust. Infestation is severe in the
foothills in the north, but is also present in
the central region and western upland areas (Fig
4). Most of the Khyber Pakhtunkhwa is vulnerable
to the disease and the region is the gate way of
new races of the pathogen entering from
neighboring countries. Stem rust has been
under control since the semi dwarf spring wheats
of the green revolution, which were stem rust
resistant and occupied most of the area since
1960s. It reemerged in 2005 in Kaghan and is also
reported from Punjab and prevelent in most of
Sindh Provience (including Jhudo, Umarkot,
Khhipro, Tandojam, Kunri, Samaro, Tando Muhammad
Khan, Bulri Shah Karim, Khipro, NIA Tandojam.
Bulri Shah Karim, Rehmani Nagar, Jhudo, Shah
Bander, Thatta, Matli, Nasrpur, Kisana mori,
Sakrand, Gharo and Karach) of Pakistan (Fig 5).
Fig 4 Yellow rust prone areas in Pakistan
Fig 5 Stem rust prone areas in Pakistan
5
Rust epidemic history and losses
Epidemic years in Pakistan Epidemic years in Pakistan Epidemic years in Pakistan Epidemic years in Pakistan
Years Yellow Rust Leaf rust Stem Rust
1948
1954
1959 - -
1972 - -
1973 -
1976 -
1977 - -
1978 -
1981 -
1993 - -
1994 - -
1995 - -
2003 - -
2005 - -
Yellow rust Thirteen epidemics have been
recorded since 1948. Four major yellow rust
epidemics were recorded in 1978, 199798 and 2005
and caused respective losses of US244 million,
33 million and 100 million to the Pakistan
economy. Leaf rust The sever epidemic of 1948
and 1954 reduced grain yield by 30-50 while in
1978, it caused estimated national losses of 10
in yield with an economic value of 86 million
US. Stem rust Two epidemics were reported but
losses were not estimated. Most of the Pakistani
wheat genotypes were found susceptible to stem
rust during 2005-09.
6
Wheat rust surveillance monitoring methods
For effective control of wheat rusts, it is
essential to carry out disease surveillance and
monitoring to obtain the information on the
incidence of the disease timely and accurately.
Following three approaches are generally used and
being developed for wheat rust monitoring and
crop protection. ? Phenotypic rust assessments ?
Biochemical and molecular detection ? Remote
sensing technology Monitoring of rust diseases
in Pakistan is mainly done through field surveys
by human power, which is time-consuming, energy
consuming and error prone. The subjectivity of
the monitoring results seriously affect the
accuracy of disease forecast. Biochemical and
molecular detection is focusing on very early
stage of pathogen detection. Development and
implementation of remote sensing technologies
have facilitated the direct detection of foliar
diseases quickly, conveniently, economically and
accurately under field conditions.
7
Levels of wheat rust monitoring using remote
sensing technologies
In recent years, significant progress is made in
remote sensing technologies for monitoring wheat
rust at following four levels ? Single Leaf
scale (ground based) ? Canopy scale (ground
based) ? Field crop scale (aerial) ?
Countries/regional scale (satellite
based) Remote sensing data at single leaf,
canopy and field crop scale levels provide local
and limited experimental information. While
satellite based remote sensing can provide a
sufficient and inexpensive data base for rust
over large wheat regions or at spatial scale. It
also offers the advantage of continuously
collected data and availability of immediate or
archived data sets. Some examples of successful
satellite and other remote sensing techniques
used for detecting wheat rust and other crop
diseases are presented in Table 1.
Receiving station processing
Archiving
Distribution
8
Table 1 Satellite and other remote sensing techniques used for detecting wheat rust and other crop diseases Table 1 Satellite and other remote sensing techniques used for detecting wheat rust and other crop diseases Table 1 Satellite and other remote sensing techniques used for detecting wheat rust and other crop diseases Table 1 Satellite and other remote sensing techniques used for detecting wheat rust and other crop diseases
Hosts Diseases Approaches References
Wheat Yellow rust Landsat/TM Huang et al., 2004
Wheat Yellow rust SPOT5 image Zhang et al., 2009
Wheat Yellow rust Landsat TM images Liu et al., 2009
Wheat Powdery mildew -do- Liu et al., 2009
Wheat Yellow leaf rust Landsat-2 Nagarajan et al.,2009
Wheat Leaf rust Earth Technology Satelite-1 Kanemasu et al., 1974
Wheat Yellow rust Satellite images (HJ-CCD) Zhang et al., 2011
Wheat Take-all Landsat Thematic Mapper imagery Chen et al., 2007
Wheat Leaf rust Powdery mildew Airborne and space borne Franke Menz, 2007
Wheat Streak mosaic Landsat 5 Thematic Mapper Mirik et al., 2011
Rice Sheath blight ADAR system 5500 Zhihao et al., 2003
Soybean Cyst nematode Landsat 7 Nutter et al., 2002
Rubber leaf spot leaf fall Multi-date satellite data of IRS-1C Ranganath et al., 2004
9
Table 1 Continued Table 1 Continued Table 1 Continued Table 1 Continued
Hosts Diseases Approaches References
Wheat Yellow rust Pushbroom Hyperspectral Imager (PIH) sensor Int. J. Agric. Biol., 2012, China
Wheat Yellow rust ASD FieldSpec www.intechopen.com, 2002-03
Wheat yellow rust In-field spectral images Computers and Electronics in Agriculture,2004, UK
Wheat Powdery mildew Airborne hyperspectral imaging 2008 SPIE, Germany
Soybean Rust FieldSpec TM spectroradiometer and a multispectral CDD camera. Sens. Instrumen. Food Qual. 2009
Sugarcane Orange rust EO-1 Hyperion imagery Apan et al. (2004a), Australia
Suger beet Powdery mildew Airborne hyperspectral imaging ROSIS, HyMap sensor systems Proc. of SPIE Vol. 7472, 747228 2009 SPIE
Pinus radiata Needle blight Hyperspectral imagery (CASI-2) Phytopathology, 2003, Australia
Sugar beet Root rot QuickBird satellite image Geoinformatics 2004, Germany
Tomato Early blight ASD FieldSpec Proceedings of SSC 2005, Austr.
Rice Sheath blight ADAR System 5500 Int J of Applied Earth Observation Geoinformation, 2005, USA
Mustard Alternaria Space borne Datta et al., 2006
Oil Palm Stem rot Space borne Santoso et al., 2011
Sugarcane Rust EO-1 Hyperion imagery Apan et al., 2004
Oak Wilt Hyperspectral satellite imagery Blake et al., 2005
10
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11
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12
Establishment of wheat rust monitoring and
warning information system A proposed
collaborative program
Precision Crop Protection
13
Food for thought
Remote sensing, GIS, and GPS technologies have
the potential to revolutionize the way in which
farmers manage diseases in their crops, however,
key conceptual and quantitative links concerning
the relationships between remote sensing data and
biological systems are often lacking. Where we
stand in Pakistan?
Our ultimate goal is to develop methodologies to
provide timely assessments concerning the wheat
and other crops health for regional as well as
for individual fields to facilitate the
generation and delivery of timely, reliable, and
cost effective disease/crop management
information. Needs research and development
efforts in Pakistan?
To fully maximize the yield benefits that might
be achieved by integrating 3S technologies into
disease management programs, it is first
necessary to ? Accurately differentiate
vegetation types ? Recognize when wheat plants
in fields are stressed ? Accurately determine
what is causing plant stress ?Quantify the
degree of plant stress within fields ? Quantify
and relate remote sensing assessments for plant
stress with ground assessments for plant
health. ? Develop crop stress thresholds for
plant populations that alert the farmer when
available and mitigation measures should be
developed to optimize the farmers net
return on investment
14
Wheat Rust Collaborating Institutions
? Cereal Disease Laboratory, St. Paul, Minnesota, USA
? Washington State University, USA
? International Maize and Wheat Improvement Center, Mexico.
? Institut National de la Recherche Agronomique (INRA), France.
? Plant Breeding Institute, University of Sydeny, Australia.
? National Crop Disease Research Program, NARC, Islamabad.
? National Wheat Improvement Program, NARC, Islamabad.
? Agricutural University, Peshawar.
? International Islamic University, Islamabad.
? Khyber Pakhtunkhwa Agricuture Research and Extension System.
15
References
Huang, M.Y., Huang, Y.D., Huang, W.J., Liu, L.Y., Wang, J.H., Wan, A.M. The Physiological Changes of Winter Wheat Infected with Stripe Rust and the Remote Sensing Mechanism of Disease Incidence (in Chinese). Journal of Anhui Agricultural Sciences 32, 132134 (2004)
Zhang, Y.P., Guo, J.B., Wang, S., Wang, H.G., Ma, Z.H. Relativity Research on Near ground and Satellite Remote Sensing Reflectance of Wheat Stripe Rust (in Chinese). ActaPhytophylacica Sinica 36, 119122 (2009)
Liu, L.Y., Song, X.Y., Li, C.J., Qi, L., Huang, W.J., Wang, J.H. Monitoring and Evaluation of the Diseases and Yield Winter Wheat from Multi-temporal Remotely-sensed Data (in Chinese). Transactions of the CSAE 25, 137143 (2009)
Nagarajan, S., Seibold, G., Kranz, J., Saari, E. E., and Joshi, L. M. 1984. Monitoring wheat rust epidemics with the Landsat-2 satellite. Phytopathology 74585-587.
Kanemasu ET, Niblett CL, Manges H, Lenhert D, Newman MA (1974) Wheat its growth and disease severity as deduced from ERTS-1. Remote Sens Environ 3255260
Zhang, J.C., W.J. Huang, J.Y. Li, G.J. Yang, J.H. Luo, X.H. Gu and J.H.Wang, 2011. Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat. Precis. Agric., 12 716731
Chen X., J. Ma, H. Qiao, D. Cheng, Y. Xu and Y. Zhao (2007) Detecting infestation of take-all disease in wheat using Landsat Thematic Mapper imagery. International Journal of Remote Sensing. 28 5183-5189
Franke, J. and G. Menz, 2007. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis. Agric., 8161172
Mirik, M., Jones, D. C., Price, J. A., Workneh, F., Ansley, R. J., and Rush, C. M. 2011. Satellite remote sensing of wheat infected by Wheat streak mosaic virus. Plant Dis. 954-12.
Zhihao Q, Minghua Z, Thomas C, Wenjuan L and Huajun T (2003) Remote Sensing Analysis of Rice Disease Stresses for Farm Pest Management Using Wide-band Airborne Data. International Geosciences and Remote Sensing Symposium, IV 2215 - 2217, July 21-25, 2003, Toulouse, France
Nutter F. W., Jr. G. L. Tylka, J. Guan, A. J. D. Moreira, C. C. Marett, T. R. Rosburg, J. P. Basart and C. S. Chong (2002) Use of Remote Sensing to Detect Soybean Cyst Nematode-Induced Plant Stress. Journal of Nematology 34(3)222231.
Ranganath BK, Pradeep N, Manjula VB, Gowda B, Rajanna MD, Shettigar D, Nageswar Rao PP (2004) Detection of diseased rubber plantations using satellite remote sensing. J Remote Sens 32(1)4957
Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane orange rust disease using EO-1 Hyperion hyperspectral imagery. Int J Remote Sens 25489498
Blake W, Hongjie X and Paul J (2005) Early detection of oak wilt disease in quercus ssp. A hyperspectral approach. Pecora 16 Global Priorities in Land Remote Sensing October 23 27, 2005 Sioux Falls, South Dakota, USA.
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