Relationships between defoliation, leaf area index, canopy reflectance, and forage yield in the alfalfa-leaf spot pathosystem

Guan, Jie; Nutter, Forrest W.

doi:10.1016/s0168-1699(02)00113-8

Cited by 28 publications

(15 citation statements)

References 18 publications

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“…Most of the regression models developed for percentage reflectance and percentage defoliation in alfalfa caused by foliar pathogens were insignificant when disease levels were quite low two or more weeks prior to harvest, whereas nearly all models were significant on the day of harvest or one week earlier [79]. Similar regression models were developed that r 2 values between percentage reflectance and percentage defoliation caused by foliar pathogens in alfalfa increased as the measurements were made closer to harvest or on the day of harvest [77,78]. On the contrary, the discrimination of powdery mildew and leaf rust in winter wheat was achieved as early as one and two days post inoculation using blue-green and chlorophyll fluorescent [81].…”

Section: Discussionmentioning

confidence: 82%

“…Early stages of WSM symptom expression were <10% when only a few chlorotic streaks were present on leaves were not considered for identification of diseased-wheat in this study because earlier studies found that remote sensing images are not suitable for detecting initial symptoms of infection [52,[77][78][79][80]. Even though slightly symptomatic plants were not included in our study as diseased, this omission was not considered problematic because the goal was to quantify moderate to severe symptoms of WSM that ultimately would result in reduced forage or grain yields.…”

Section: Discussionmentioning

confidence: 99%

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Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Mırık¹,

Ansley²,

Price³

et al. 2013

ARS

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Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease monitoring and mapping over time and space during the course of a growing season.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Mırık¹,

Ansley²,

Price³

et al. 2013

ARS

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“…RS may provide a rapid alternative method to determine SCYLV impacted populations. Disease, as well as other stresses, affect the amount and quality of electromagnetic radiation reflected from leaves of plants, and difference in reflectance can, therefore, be used to detect pathogen infections (Guan and Nutter 2002;West et al 2003). Multispectral RS has been used effectively to monitor the incidence of a number of plant pathogens (Blazquez and Edwards 1986;Steddom et al 2003) however, a more recent trend has been the application of hyperspectral measurements for the detection of plant diseases (Apan et al 2003(Apan et al , 2004Bravo et al 2003;Mehl et al 2004;Vigier et al 2004).…”

Section: Mapping Of Sugarcane Yld Affected Areamentioning

confidence: 99%

Application of GPS and GIS in Sugarcane Agriculture

2011

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Technology certainly plays a significant part in the precision agriculture scheme, but understanding the appropriateness of the different forms of technology in response to a management problem is an essential element for successful precision agriculture implementation. Many of the enabling technologies discussed are at varying stages of development with many having restricted application and usefulness when applied at the within-paddock level. However, these technologies can still contribute to largerscale forms of precision agriculture at the region, mill, farm or paddock level. If the sugar industry decides that using global positioning system and geographic information system to chase the benefits of selective harvesting is potentially worthwhile, sorting out the spatial and temporal interactions between yield and CCS will be a critical research issue. Thus the adoption of theses technologies in a precision conservation philosophy could assist in managing the interactions between cane farming and environmental protection in the sensitive coastal floodplain ecosystems.

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“…Phoma medicaginis var. medicaginis and Stemphylium botryosum were reported as being responsible for leaf spotting and defoliation, which result in a significant reduction of forage yield and quality (Campbell and Madden 1990;Stuteville and Erwin 1990;Guan and Nutter 2002). In addition to quantitative yield losses, some foliar pathogens including Fusarium spp.…”

Section: Germination (%)mentioning

confidence: 99%

Seed-borne mycoflora of alfalfa (Medicago sativa L.) in the Riyadh Region of Saudi Arabia

2011

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Fifteen seed samples of local alfalfa (Medicago sativa L.) cultivar (Hegazy) collected from fields at different governorates of the Riyadh region in Saudi Arabia were screened for their seed-borne mycoflora. Standard moist blotter and deep-freezing blotter methods recommended by the International Seed Testing Association, in addition to an innovative alkaline seed-bed method were applied. A total of 24 genera and 35 species of fungi were isolated using the above-mentioned techniques. Alternaria alternata, Cladosporium sp., Aspergillus sp., Stemphylium sp. and Penicillium sp. were the genera most commonly isolated. Among the various techniques adopted for detection, the alkaline NaOH method was found to be effective and yielded the maximum number of pathogenic fungi. Syndromes of seed discoloration were also investigated. The alkaline seed-bed method using NaOH was used to detect saprophytic and pathogenic seed-borne fungi associated with discolored seeds. A total of 15 genera and 26 species of fungi were isolated. Cladosporium sp., Alternaria alternata and Aspergillus species were the saprophytic species detected most, while Stemphylium botryosum and Fusarium incarnatum were common pathogenic fungi found on discolored seeds. The average of incidence and occurrence percentages of most detected fungi were higher in discolored seeds than in normal seeds. Seed germination was also affected significantly by discoloration. The data also indicate that seed discoloration decreases seed germination significantly (by 26.3-60%).

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Relationships between defoliation, leaf area index, canopy reflectance, and forage yield in the alfalfa-leaf spot pathosystem

Cited by 28 publications

References 18 publications

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Application of GPS and GIS in Sugarcane Agriculture

Seed-borne mycoflora of alfalfa (Medicago sativa L.) in the Riyadh Region of Saudi Arabia

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