AN INTERACTION MODEL BETWEEN ENVIRONMENTAL FACTORS AND BLACK RICE GROWTH IN IRRIGATED ORGANIC PADDY FIELD

Irrigation has been applied on a large scale for the improvement of grain yield per hectare and production stability. However, the dryland-to-paddy conversion affects the ecological environment of areas of long-term dry farming, especially soil microorganisms. Little attention has been paid to the changes in microbial communities and the interactions between their populations in this process. Therefore, in this paper, the compositions and diversity of soil bacterial and fungal communities were explored through a combination of high-throughput sequencing technology and molecular ecological network methods using bacterial 16S rRNA and fungal ITS. The results showed that: (1) both the abundance and diversity of soil bacteria and fungi decreased in a short time, and the abundance of Actinobacteria, Firmicutes and Olpidiomycota varied greatly. (2) Compared to dry land, the modular structure of interaction networks and interspecific relationships of bacterial and fungal communities in paddy soil were simpler, and the network became more unstable. A cooperative relationship dominated in the molecular ecological network of bacteria, while a competitive relationship was dominant in the network of fungi. Actinobacteria and Firmicutes were the dominant bacterial species in dry land and paddy field, respectively. Ascomycota was dominant in the fungal communities of both dry land and paddy field. (3) The change in soil environmental factors, such as pH, electrical conductivity (EC), organic matter (OM) and available potassium (AK), directly affected the soil microbial community structure, showing a significant correlation (p < 0.05). These environmental factors also influenced the dominant microbial species. Microorganisms are the most important link in the carbon and nitrogen cycles of soil, and a large-scale dryland-to-paddy conversion may reduce the ecological stability of regional soil.

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Short-term effects of wheat straw incorporation into paddy field as affected by rice transplanting time

Soil Research ◽

10.1071/sr07113 ◽

2008 ◽

Vol 46 (3) ◽

pp. 281 ◽

Cited By ~ 5

Author(s):

J. Ma ◽

H. Xu ◽

Y. Han ◽

Z. C. Cai ◽

K. Yagi

Keyword(s):

Wheat Straw ◽

Paddy Field ◽

Paddy Fields ◽

Short Term ◽

Mineral N ◽

Available N ◽

Rice Growth ◽

N Immobilisation ◽

Straw Incorporation ◽

Short Term Effects

Short-term effects of wheat straw incorporation into paddy field include stimulation of CH4 emissions, immobilisation of available N, suppression of rice growth, and accumulation of toxic materials. To study these short-term effects as affected by timing of rice transplantation, a field experiment was conducted at Dapu, China, in 2005. Two levels of wheat straw (0 and 3.75 t/ha) and 2 rice-transplanting times (normal and delayed) were adopted in this experiment. Methane emissions, concentrations of soil mineral N, dry matter accumulations, and grain yields were measured. Delayed rice transplantation had no effect on total CH4 emission from paddy fields incorporated with wheat straw (P > 0.05), but a significant effect on mineralisation of N (P < 0.05), in contrast to the net N immobilisation in the fields where seedlings were transplanted on the normal date. In paddy fields incorporated with wheat straw, delayed transplantation tended to promote rice growth and increase grain yield compared with transplantation on the normal date.

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Environmental factors influencing molinate biodegradation by a two-member mixed culture in rice paddy field floodwater

International Biodeterioration & Biodegradation ◽

10.1016/j.ibiod.2012.05.003 ◽

2012 ◽

Vol 72 ◽

pp. 52-58 ◽

Cited By ~ 8

Author(s):

Luisa Barreiros ◽

Joana Peres ◽

Nuno F. Azevedo ◽

Célia M. Manaia ◽

Olga C. Nunes

Keyword(s):

Environmental Factors ◽

Mixed Culture ◽

Paddy Field ◽

Rice Paddy ◽

Rice Paddy Field ◽

Factors Influencing

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Environmental Factors Affecting Fishes in a Paddy-field Area

Journal of Rainwater Catchment Systems ◽

10.7132/jrcsa.kj00004353082 ◽

2006 ◽

Vol 12 (1) ◽

pp. 11-16

Author(s):

Keiichi Nishimura ◽

Ikuo Takeda ◽

Akira Fukushima ◽

Hiroaki Somura

Keyword(s):

Environmental Factors ◽

Paddy Field ◽

Factors Affecting ◽

Field Area

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SNPxE: SNP-environment interaction pattern identifier

BMC Bioinformatics ◽

10.1186/s12859-021-04326-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hui-Yi Lin ◽

Po-Yu Huang ◽

Tung-Sung Tseng ◽

Jong Y. Park

Keyword(s):

Environmental Factors ◽

Specific Interaction ◽

False Negative ◽

Interaction Model ◽

Interaction Pattern ◽

Detection Accuracy ◽

Environment Interaction ◽

Interaction Patterns ◽

Full Interaction ◽

Environment Factor

Abstract Background Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. Results SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. Conclusions SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).

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Effects of the Autumn Incorporation of Rice Straw and Application of Lime Nitrogen on Methane and Nitrous Oxide Emissions and Rice Growth of a High-Yielding Paddy Field in a Cool-Temperate Region in Japan

Agriculture ◽

10.3390/agriculture11121298 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1298

Author(s):

Fumiaki Takakai ◽

Mimori Goto ◽

Haruki Watanabe ◽

Keiko Hatakeyama ◽

Kentaro Yasuda ◽

...

Keyword(s):

Rice Straw ◽

Paddy Field ◽

N2o Emission ◽

Nitrous Oxide Emissions ◽

Nitrogen Application ◽

Straw Decomposition ◽

Ch4 Emissions ◽

Rice Growth ◽

Ch4 And N2o ◽

Set Up

The effects of autumn plowing and lime nitrogen application on rice straw decomposition, CH4 and N2O emission and rice growth in the following year in a high-yielding rice cultivated paddy field were evaluated for two years. The experimental plots were set up, combining different times of rice straw (750 g m−2) incorporation into the soil by plowing (autumn or the following spring), with and without lime nitrogen application in autumn (5 g-N m−2). Autumn plowing promoted the decomposition of rice straw, but the application of lime nitrogen did not show a consistent trend. The soil pH was high (7.3) at the studied site, and the alkaline effect of lime nitrogen may not have been significant. As with straw decomposition, CH4 emissions were suppressed by autumn plowing, and no effect from the lime nitrogen application was observed. It was also suggested that the straw decomposition period may be shorter and the CH4 emissions may be higher in high-yielding cultivars that require a longer ripening period than in normal cultivars. The effect of both treatments on N2O emission was not clear. Both the autumn plowing of rice straw and lime nitrogen application were effective in promoting rice growth and increasing rice yield.

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Physiological Response of Operators during Power Tiller Use for Paddy Field Cultivation

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120004402241 ◽

2000 ◽

Vol 44 (22) ◽

pp. 651-654

Author(s):

V. M. Salokhe ◽

D. U. Mamansari ◽

Jianxia Zhang

Keyword(s):

Environmental Factors ◽

Paddy Field ◽

Physiological Response ◽

Field Tests ◽

Work Capacity ◽

Physical Work ◽

Physical Work Capacity ◽

Humid Climate ◽

Physical Response ◽

Power Tiller

Power tillers (single axle, two-wheel hand tractors) are widely used for paddy field cultivation in Thailand. Currently, over one million power tillers are being used as they suit both the economical and physical conditions of paddy farming in developing countries. Controlling these hand tractors while walking behind is often hard work. Safety and health concerns associated with the use of these machines have not been well documented. This study was aimed at evaluating the physical response of operators during various operations by power tillers in paddy field. The most popular power tiller model in Thailand was used for this investigation. Field tests were conducted to determine the physical response of operators while using this machine. The response was assessed through measurement of operator heart rates during different operations. The comfort, working posture and the task were also evaluated. The physical work capacity was established with the help of a bicycle ergometer. The field operations carried out were first-plowing, second-plowing and harrowing. The tillage equipment used were moldboard plow, disc plow and peg tooth harrow. A statistical analysis was conducted to compare the effects of environmental factors and working intensity in each activity and results were compared with the standards. It was observed that the physical response during some of the operations exceeded the physical work capacity of the operators. It was noticed that the excessive physiological response was contributed by power tiller use as well as environmental factors. Working with power tiller in muddy paddy fields in a hot and humid climate also contributed to excessive workload. Surface conditions in the field also had a direct effect on physiological response of the operator. Energy cost during wetland operation was also due to unfavorable working conditions. Some ways to improve the design of the power tiller to make it more comfortable for the operator were suggested.

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Estimation of rice vegetation coverage from DVI of Landsat 7 and 8 data

Science and Technology Development Journal ◽

10.32508/stdj.v19i2.678 ◽

2016 ◽

Vol 19 (2) ◽

pp. 138-145

Author(s):

Thu Thi Thanh Phan ◽

Rikimaru Atsushi ◽

Kenta Sakata ◽

Kazuyoshi Takahashi ◽

Junki Abe

Keyword(s):

Paddy Field ◽

Vegetation Index ◽

Near Infrared ◽

Digital Camera ◽

Vegetation Coverage ◽

Observation Data ◽

Landsat Images ◽

Rice Varieties ◽

Rice Growth ◽

Landsat 7

Monitoring of rice growth is a requirement for high quality rice production. In addtion to plant height, number stem and rice leaf color, vegetation coverage (VC) which represents for percentage of ground covered by rice plant is also considered as an important index to validate rice growth. Thus, the study is to estimate rice vegetation coverage from difference vegetation index (DVI) calculated from reflectance of near-infrared and red band of Landsat 7 and 8 images. The field observations of the reflectance and the VC were carried out in two paddy rice varieties in 2013. Paddy field reflectance was observed by spectrometer Ocean Optics SD2000. The photos of paddies were taken from the height of 1 m by a digital camera in order to calculate the VC. The reflectances of paddy field corresponding to red and near-infrared bands of Landsat 7 and 8 were calculated from the field observation data. Satellite reflectance was also converted from pixel value of Landsat images. According to the data analysis, VC rapidly increased in two fields and got saturation status (VC>90%) at 65 days after transplanting (DAT) in the early July. DVI was approximately 25% when VC saturated. Additionally, DVI had strong correlation with VC with high determination coefficient (r2 =0.9) when VC was less than 90%. Thus, VC were computed from DVI, calculated from reflectances of Landsat images, using a regression model of VC and DVI. From the result of comparison between the estimated and computed VC, the possibility of estimating VC from DVI calculated from Landsat reflectance is confirmed.

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