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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: [email protected] , [email protected] Webpage: www.nifa.org.pk

Syed Jawad Ahmad Shah and Muhammad Ibrahim

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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. Abstract. - PowerPoint PPT Presentation

Text of Syed Jawad Ahmad Shah and Muhammad Ibrahim

Page 1: Syed Jawad Ahmad Shah and Muhammad Ibrahim

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 IbrahimNuclear Institute for Food and Agricuture (NIFA),

Tarnab, Peshawar, Pakistan

E-mail: j[email protected], [email protected]: www.nifa.org.pk

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AbstractAmong 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.

Figure 1: Three wheat rusts a: yellow rust; b: leaf rust and c: stem rust

a cb

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IntroductionWheat 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 @ 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

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IntroductionIn 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

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Epidemic years in PakistanYears

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, 1997–98 and 2005 and caused respective losses of US$244 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.

Rust epidemic history and losses

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Wheat rust surveillance & monitoring methodsFor 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 technologyMonitoring 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.

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Receiving stationprocessing



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.

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Table 1: Satellite and other remote sensing techniques used for detecting wheat rust and other crop diseasesHosts Diseases Approaches ReferencesWheat Yellow rust Landsat/TM Huang et al., 2004Wheat Yellow rust SPOT5 image Zhang et al., 2009

Wheat Yellow rust Landsat TM images Liu et al., 2009Wheat 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 rustPowdery 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

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Table 1: ContinuedHosts Diseases Approaches ReferencesWheat Yellow rust Pushbroom Hyperspectral

Imager (PIH) sensorInt. J. Agric. Biol., 2012, China

Wheat Yellow rust ASD FieldSpec www.intechopen.com, 2002-03Wheat yellow rust In-field spectral images Computers and Electronics in

Agriculture,2004, UKWheat 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), AustraliaSuger beet Powdery mildew Airborne hyperspectral imaging

ROSIS, HyMap sensor systemsProc. of SPIE Vol. 7472, 747228· © 2009 SPIE

Pinus radiata Needle blight Hyperspectral imagery (CASI-2) Phytopathology, 2003, AustraliaSugar beet Root rot QuickBird satellite image Geoinformatics 2004, GermanyTomato 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., 2006Oil Palm Stem rot Space borne Santoso et al., 2011Sugarcane Rust EO-1 Hyperion imagery Apan et al., 2004

Oak Wilt Hyperspectral satellite imagery Blake et al., 2005

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Establishment of wheat rust monitoring and warning information system: A proposed collaborative program

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Food for thoughtRemote 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 farmer’s net return on investment

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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.

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ReferencesHuang, 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, 132–134 (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, 119–122 (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, 137–143 (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 74:585-587.

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 3:255–260

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: 716–731

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., 8:161–172

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. 95:4-12.

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):222–231.

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):49–57

Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane orange rust disease using EO-1 Hyperion hyperspectral imagery. Int J Remote Sens 25:489–498

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.