Effect of microcystins at different rice growth stages on its yield, quality, and safety

Author(s):  
Chanjuan Liang ◽  
Xudong Ma ◽  
Hongyue Liu

1997 ◽  
Vol 87 (12) ◽  
pp. 1226-1232 ◽  
Author(s):  
D. Shtienberg

The effects of Rhizopus head rot, caused by Rhizopus oryzae, on the yield of confectionery sunflower and its quality were studied in field experiments conducted from 1994 to 1996. The extent of yield loss was related to the crop growth stage at inoculation. When heads were inoculated at the budding stage, loss was not apparent, because inoculated heads were not infected. When inoculated at the anthesis stage, loss was relatively high (42.5 to 99.1%), and both the number of achenes per head and the individual achene weight were reduced. When heads were inoculated at the seed development stage, yield was not reduced significantly (although the entire receptacle was rotted). Effects of Rhizopus head rot on measures of yield quality were examined as well. Inoculation with R. oryzae did not affect the size of the achenes at any crop growth stage. In contrast, the incidence of discolored achenes (an external sign of nutmeats with a bitter off-flavor) was affected by the disease at all crop growth stages. A survey in eight commercial fields from 1992 to 1996 found that, by the end of the season, incidence of disease ranged from 2.3 to 17.4%. However, since disease intensified late, resultant yield losses were minor and did not exceed 3.1%. Loss figures were estimated by means of a model that was developed and validated in the field experiments. The disease did affect the incidence of discolored achenes. Thus, the conclusion drawn is that the effects of Rhizopus head rot in confectionery sunflower on crop yield is of minimal concern, at least when disease intensifies late, as was the case in the studied fields, but management of the disease should be considered in some situations. The objectives would be to prevent a reduction in yield quality, not yield quantity.



2019 ◽  
Vol 10 ◽  
Author(s):  
Wenhui Wang ◽  
Xue Luo ◽  
Yang Chen ◽  
Xianfeng Ye ◽  
Hui Wang ◽  
...  




2012 ◽  
Vol 32 (5) ◽  
pp. 1546-1552 ◽  
Author(s):  
陈宇 CHEN Yu ◽  
傅强 FU Qiang ◽  
赖凤香 LAI Fengxiang ◽  
罗举 LUO Ju ◽  
张志涛 ZHANG Zhitao ◽  
...  


Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5354
Author(s):  
Chin-Ying Yang ◽  
Ming-Der Yang ◽  
Wei-Cheng Tseng ◽  
Yu-Chun Hsu ◽  
Guan-Sin Li ◽  
...  

Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.



2020 ◽  
Vol 12 (21) ◽  
pp. 3613 ◽  
Author(s):  
Fadhlullah Ramadhani ◽  
Reddy Pullanagari ◽  
Gabor Kereszturi ◽  
Jonathan Procter

Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.



2017 ◽  
Vol 42 (2) ◽  
pp. 309-319
Author(s):  
P Mukherjee ◽  
MMH Khan

Studies were conducted to record the abundance of arthropod insect pests and natural enemies in rice fields as influenced by rice growth stages and neighboring crops at the experimental farm of Patuakhali Science and Technology University (PSTU), Dumki, Patuakhali during 2012 in Boro rice season following randomized complete block design. Results indicated that rice-tree habitat showed the highest abundance of leafhoppers (100.75) followed by cricket (16.50), grasshoppers (15.25) and stink bugs (15.25). The lowest abundance of all insect pests was in rice-sesame habitat. No significant differences were found on the abundance of rice bug, rice hispa and stem borer populations. At seedling stage, the highest abundance of leafhopper (94.25) was recorded followed by grasshopper (47.00) and stink bug (26.50) while the lowest was stem borer (0.57) and rice hispa (6.00). At early tillering stage, maximum number of grasshopper (17.25) was recorded followed by cricket (7.00). At maximum tillering stage, the highest abundance of leafhoppers (122.5) was obtained followed by rice bug (62.00) and the lowest was the stink bug (7.00). At panical initiation stage, the highest abundance of rice bug (334.00) was recorded which was followed by leafhoppers (65.25) and the lowest was the cricket (15.75). No population of rice hispa and stem borer was recorded at maximum tillering and panical initiation stages. In case of natural enemies, the highest abundance of lady bird beetle (45.27) and damselfly (16.73) was found in rice-rice habitat. The highest abundance of ichneumonid wasp (57.53) was in rice-tree habitat and ground beetle (28.80) was in rice-sesame habitat. No significant differences were observed on the abundance of dragonfly, spider and dipteran fly among different habitats. Among different growth stages of rice plant, significantly the highest abundance of lady bird beetle was recorded at maximum tillering stage. The highest abundance of ichneumonid wasp and ground beetle was recorded at seedling stage. The highest abundance of damselfly, spider and dipteran fly was at early tillering stage. No significant difference was observed on the abundance of dragonfly among different rice growth stages.Bangladesh J. Agril. Res. 42(2): 309-319, June 2017



2020 ◽  
pp. 185-203
Author(s):  
Dyah R. Panuju ◽  
David J. Paull ◽  
Amy L. Griffin ◽  
Bambang H. Trisasongko
Keyword(s):  


2020 ◽  
Vol 77 (2) ◽  
Author(s):  
Jintao Cui ◽  
Guangcheng Shao ◽  
Jia Lu ◽  
Larona Keabetswe ◽  
Gerrit Hoogenboom


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