White mold of pea caused by Sclerotinia sclerotiorum is a common disease in China.However,we discovered that the diverse Sclerotinia species could cause white mold on pea plants in Chongqing and Sichuan of China durin...White mold of pea caused by Sclerotinia sclerotiorum is a common disease in China.However,we discovered that the diverse Sclerotinia species could cause white mold on pea plants in Chongqing and Sichuan of China during recent disease surveys.Thus,the objective of this study was to confirm the causal agents from diseased pea plants.The obtained isolates of white mold from Chongqing and Sichuan were identified by morphological characters and molecular characterization to determine the pathogen species,and their pathogenicity was confirmed on pea through completing Koch’s postulates.Fungal isolates of Sclerotinia-like were obtained from diseased plants or sclerotia.Based on morphological characteristics and molecular characterization,30 isolates were identified to three species,six isolates as S.minor,seven as S.sclerotiorum,and 17 as S.trifoliorum.In pathogenicity tests on pea cultivars Zhongwan 4 and Longwan 1,all 30 isolates caused typical symptoms of white mold on the inoculated plants,and the inoculated pathogens were re-isolated from the diseased plants.This study confirmed that white mold of pea was caused by three Sclerotinia species,S.sclerotiorum,S.minor and S.trifoliorum in Chongqing and Sichuan.It is the first report that S.minor and S.trifoliorum cause white mold of pea in Southwest China.展开更多
White Mold of soybeans (Glycine Max), also known as Sclerotinia stem rot (Sclerotinia sclerotiorum), is among the most important fungal diseases that affect soybean yield and represents a recurring annual threat to so...White Mold of soybeans (Glycine Max), also known as Sclerotinia stem rot (Sclerotinia sclerotiorum), is among the most important fungal diseases that affect soybean yield and represents a recurring annual threat to soybean production in South Dakota. Accurate quantification of white mold in soybean would help understand white mold impact on production;however, this remains a challenge due to a lack of appropriate data at a county and state scales. This study used Landsat images in combination with field-based observations to detect and quantify white mold in the northeastern part of South Dakota. The Random Forest (RF) algorithm was used to classify the soybean and the occurrence of white mold from Landsat images. Results show an estimate of 132 km2, 88 km2, and 190 km2 of white mold extent, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties, respectively, in 2017. Compared with ground observations, it was found that soybean and white mold in soybean fields were respectively classified with an overall accuracy of 95% and 99%. These results highlight the utility of freely available remotely sensed satellite images such as Landsat 8 images in estimating diseased crop extents, and suggest that further exploration of consistent high spatial resolution images such as Sentinel, and Rapid-Eye during the growing season will provide more details in the quantification of the diseased soybean.展开更多
基金supported by the China Agriculture Research System of MOF and MARA(CARS-08)the National Crop Germplasm Resources Center of China(NCGRC-2020-09)the Scientific Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘White mold of pea caused by Sclerotinia sclerotiorum is a common disease in China.However,we discovered that the diverse Sclerotinia species could cause white mold on pea plants in Chongqing and Sichuan of China during recent disease surveys.Thus,the objective of this study was to confirm the causal agents from diseased pea plants.The obtained isolates of white mold from Chongqing and Sichuan were identified by morphological characters and molecular characterization to determine the pathogen species,and their pathogenicity was confirmed on pea through completing Koch’s postulates.Fungal isolates of Sclerotinia-like were obtained from diseased plants or sclerotia.Based on morphological characteristics and molecular characterization,30 isolates were identified to three species,six isolates as S.minor,seven as S.sclerotiorum,and 17 as S.trifoliorum.In pathogenicity tests on pea cultivars Zhongwan 4 and Longwan 1,all 30 isolates caused typical symptoms of white mold on the inoculated plants,and the inoculated pathogens were re-isolated from the diseased plants.This study confirmed that white mold of pea was caused by three Sclerotinia species,S.sclerotiorum,S.minor and S.trifoliorum in Chongqing and Sichuan.It is the first report that S.minor and S.trifoliorum cause white mold of pea in Southwest China.
文摘White Mold of soybeans (Glycine Max), also known as Sclerotinia stem rot (Sclerotinia sclerotiorum), is among the most important fungal diseases that affect soybean yield and represents a recurring annual threat to soybean production in South Dakota. Accurate quantification of white mold in soybean would help understand white mold impact on production;however, this remains a challenge due to a lack of appropriate data at a county and state scales. This study used Landsat images in combination with field-based observations to detect and quantify white mold in the northeastern part of South Dakota. The Random Forest (RF) algorithm was used to classify the soybean and the occurrence of white mold from Landsat images. Results show an estimate of 132 km2, 88 km2, and 190 km2 of white mold extent, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties, respectively, in 2017. Compared with ground observations, it was found that soybean and white mold in soybean fields were respectively classified with an overall accuracy of 95% and 99%. These results highlight the utility of freely available remotely sensed satellite images such as Landsat 8 images in estimating diseased crop extents, and suggest that further exploration of consistent high spatial resolution images such as Sentinel, and Rapid-Eye during the growing season will provide more details in the quantification of the diseased soybean.
