Prospects of satellite remote sensing in cereal disease

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: Webpage: jawadshah@hotmail. com, mails@nifa. org. com www. nifa. org. pk

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

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 (Fig 1) responsible for destabilizing production in Asia and other parts of the world. Fig 2: Countries with 2 third wheat acreage in asia Pakistan although with the 2 nd highest wheat acreage among the Southeast Asian countries has a national average yield at around 2. 5 t⁄ 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 3; Lead rust prone areas in Pakistan

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 1960 s. 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

Rust epidemic history and losses 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 $100 million to the Pakistan economy. Epidemic years in Pakistan Years Yellow Rust Leaf rust Stem Rust 1948 * * * Leaf rust: The sever epidemic of 1948 and 1954 reduced grain yield by 30 -50% while in 1978, it caused estimated national losses of 10% in yield with an economic value of 86 million US$. 1954 * * * 1959 * - - 1972 - * - 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. 1973 * * - 1976 * * - 1977 * - - 1978 * * - 1981 * * - 1993 * - - 1994 * - - 1995 * - - 2003 * - - 2005 * - -

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.

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

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 SPOT 5 image Zhang et al. , 2009 Wheat Yellow rust Landsat TM images Liu et al. , 2009 Wheat Powdery mildew -do- Liu et al. , 2009 Wheat Landsat-2 Nagarajan et al. , 2009 Wheat Yellow & leaf rust Leaf rust Earth Technology Satelite-1 Wheat Yellow rust Satellite images (HJ-CCD) Kanemasu et al. , 1974 Zhang et al. , 2011 Wheat Take-all Chen et al. , 2007 Wheat Leaf rust Powdery mildew Landsat Thematic Mapper imagery Airborne and space borne 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 1 C Ranganath et al. , 2004 Franke & Menz, 2007

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 Field. Spec 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 Field. Spec TM spectroradiometer and a multispectral CDD camera. Sens. & Instrumen. Food Qual. 2009 Sugarcane Orange rust EO-1 Hyperion imagery Apan et al. (2004 a), Australia Suger beet Powdery mildew Airborne hyperspectral imaging ROSIS, Hy. Map 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 Quick. Bird satellite image Geoinformatics 2004, Germany Tomato Early blight ASD Field. Spec 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 Sugarcane Stem rot Rust Space borne EO-1 Hyperion imagery Santoso et al. , 2011 Apan et al. , 2004 Oak Wilt Hyperspectral satellite imagery Blake et al. , 2005

Establishment of wheat rust monitoring and warning information system: A proposed collaborative program Secure funding Capacity building Information to farmers Precision Crop Protection Early detection & warning Infrastructure & facilities R & D work on integration of 3 S technologies

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 3 S 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

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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