Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors   总被引：1，自引：0，他引：1  

Georges F. Backoulou  Norman C. ElliottKristopher Giles  Mpho PhoofoloVasile Catana  Mustafa MirikJerry Michels 《Computers and Electronics in Agriculture》2011,78(2):123-129

The Russian wheat aphid (RWA) Diuraphis noxia (Mordvilko) is a major pest of winter wheat and barley in the United States. RWA induces stress to the wheat crop by damaging plant foliage, lowering the greenness of plants, and affecting productivity. The utilization of multispectral remote sensing is effective at detecting plant stress in agricultural crops. Stress to wheat plants detected in fields can be caused by several factors that can vary spatially in their presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can occur individually or collectively. The present study investigated the potential of using spatial pattern metrics derived from multispectral images in combination with topographic and edaphic variables to identify a set of variables to differentiate the stress induced by RWA from other stress causing factors. A discriminant function analysis was applied to 15 discriminating variables. A set of 13 variables were retained to develop a model to differentiate the three types of stress. Overall, 97 percent of patches of stress used to validate the model were correctly categorized. Stressed patches caused by RWA were 98 percent correctly classified, patches caused by drought were 94 percent correctly classified, and patches caused by agronomic conditions were 99 correctly classified. It is possible to discriminate stress induced by RWA from other stress causing factors in multispectral data when spatial attributes of the stress causing factors are incorporated in the analysis.  相似文献   

An LC-MS method for analyzing total resveratrol in grape juice, cranberry juice, and in wine.   总被引：6，自引：0，他引：6  

Yan Wang  Florentina Catana  Yanan Yang  Robin Roderick  Richard B van Breemen 《Journal of agricultural and food chemistry》2002,50(3):431-435

Resveratrol is an antioxidant found in grapes, grape products, and some other botanical sources with antiinflammatory and anticancer properties. In grapes and wine, it occurs both as free resveratrol and piceid, the 3beta-glucoside of resveratrol. Here we report a liquid chromatography-mass spectrometry method to analyze total resveratrol (including free resveratrol and resveratrol from piceid) in fruit products and wine. Samples were extracted using methanol, enzymatically hydrolyzed, and analyzed using reversed phase HPLC with positive ion atmospheric pressure chemical ionization (APCI) mass spectrometric detection. Following APCI, the abundance of protonated molecules was recorded using selected ion monitoring (SIM) of m/z 229. An external standard curve was used for quantitation, which showed a linear range of 0.52-2260 pmol of trans-resveratrol injected on-column with a correlation coefficient 0.9999. The coefficient of variance of the response factor over the same concentration range was determined to be 5.8%, and the intra-assay coefficient of variance was determined to be 4.2% (n = 7). The limit of quantitation, defined as signal-to-noise 10:1, was determined to be 0.31 pmol injected on-column. The extraction efficiency of the method was determined to be 92%. The stability of resveratrol under different conditions was also examined. For example, resveratrol was stable for up to 5 days at 4 degrees C in the dark but was not stable at room temperature without protection from light. Resveratrol was detected in grape, cranberry, and wine samples. Concentrations ranged from 1.56 to 1042 nmol/g in Concord grape products, and from 8.63 to 24.84 micromol/L in Italian red wine. The concentrations of resveratrol were silmilar in cranberry and grape juice at 1.07 and 1.56 nmol/g, respectively.  相似文献   

Angora goat gastrointestinal parasite knowledge and control practices among Lesotho farming communities     

Moiloa  Morai Johannes  Phoofolo  Mpho  Matebesi-Ranthimo  Puleng  Molapo  Setsomi  Phalatsi  Moeketsi  Mahlehla  Motšelisi 《Tropical animal health and production》2020,52(6):3077-3083

Tropical Animal Health and Production - Smallholder Angora goat farming is widespread throughout Lesotho, resulting in the country being ranked second in global mohair production. The Lesotho...  相似文献   

No evidence of Trichinella spp. in domestic pig carcasses at a selected abattoir in southern Botswana     

Segwagwe  Basiamisi Ernest  Machete  James  Ntwaetsile  Mpho  Mushonga  Borden  Kandiwa  Erick 《Tropical animal health and production》2019,51(5):1273-1275

Tropical Animal Health and Production - Trichinellosis is a worldwide zoonosis with genotypes affecting different domestic and wild animals and is widely distributed throughout the world. Species...  相似文献   

Correction to: No evidence of Trichinella spp. in domestic pig carcasses at a selected abattoir in southern Botswana     

Segwagwe  Basiamisi Ernest  Machete  James  Ntwaetsile  Mpho  Mushonga  Borden  Kandiwa  Erick 《Tropical animal health and production》2019,51(5):1277-1277

Tropical Animal Health and Production - The article “No evidence of Trichinella spp. in domestic pig carcasses at a selected abattoir in southern Botswana”, written by Basiamisi Ernest...  相似文献   

Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia     

Georges F. Backoulou  Norman C. ElliottKristopher Giles  Mpho PhoofoloVasile Catana 《Computers and Electronics in Agriculture》2011,75(1):64-70

The Russian wheat aphid, Diuraphis noxia, is an important pest of winter wheat, Triticum aestivum, and barley, Hordeum vulgare that has caused an annual economic loss estimated at over 1 billion dollars since it first appeared in the United States. The objective of this study was to determine the potential of combining multispectral imagery with spatial pattern recognition to identify and spatially differentiate D. noxia infestations in wheat fields. Multispectral images were acquired using an MS3100-CIR multispectral camera. D. noxia, drought, and agronomic conditions were identified as major causes for stresses found in wheat fields. Seven spatial metrics were computed for each stress factor. The analysis of spatial metrics quantitatively differentiated the three types of stress found within wheat fields. Detection and differentiation of wheat field stress may help in mapping stress and may have implications for site-specific monitoring systems to identify D. noxia infestations and help to target pesticide applications.  相似文献