Arsenic Contamination of the Environment−Food Chain: A Survey on Wheat as a Test Plant To Investigate Phytoavailable Arsenic in Italian Agricultural Soils and as a Source of Inorganic Arsenic in the Diet

2010 ◽  
Vol 58 (18) ◽  
pp. 10176-10183 ◽  
Author(s):  
Francesco Cubadda ◽  
Silvia Ciardullo ◽  
Marilena D’Amato ◽  
Andrea Raggi ◽  
Federica Aureli ◽  
...  
Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 1036 ◽  
Author(s):  
Tiezhu Shi ◽  
Huizeng Liu ◽  
Yiyun Chen ◽  
Teng Fei ◽  
Junjie Wang ◽  
...  

Nematology ◽  
2013 ◽  
Vol 15 (4) ◽  
pp. 459-468 ◽  
Author(s):  
Yu Yu Min ◽  
Koki Toyota

A total of 12 soils collected from different agricultural fields, having different backgrounds of organic input, were evaluated for their suppressive potential against Meloidogyne incognita. Second-stage juveniles (J2) of M. incognita were inoculated into the soils and their survival was evaluated. The number of M. incognita J2 5 days after inoculation differed depending on soil and was significantly lower in two soils, suggesting higher suppressiveness against M. incognita in these soils. This was confirmed by an experiment using tomato as a test plant, in which the gall formation was significantly lower in the two soils than in other soils. To estimate the contribution of below-ground biota to the suppressiveness, numbers of nematodes (predator, omnivore, bacterivore and fungivore) and other soil fauna such as tardigrades and rotifers, were counted. Some soil chemical and biological properties were also measured. Results from multiple linear regression analysis suggested that the number of rotifers, microbial activity, soil pH and total C may be involved in the suppression. The relationship between the suppressiveness and soil chemical and biological parameters is discussed.


2010 ◽  
Vol 36 (6) ◽  
pp. 577-583 ◽  
Author(s):  
Ana Passuello ◽  
Montse Mari ◽  
Martí Nadal ◽  
Marta Schuhmacher ◽  
José L. Domingo

2017 ◽  
Vol 324 ◽  
pp. 526-534 ◽  
Author(s):  
Anamika Shrivastava ◽  
Anil Barla ◽  
Surjit Singh ◽  
Shivanand Mandraha ◽  
Sutapa Bose

2021 ◽  
Vol 54 (1) ◽  
pp. 89
Author(s):  
Jozef Kobza

<p>The article presents the current distribution of arsenic in agricultural soils of Slovakia. The current concentration of arsenic (extracted with <em>aqua regia</em>) was measured and evaluated based on 318 monitoring sites of national soil monitoring system in Slovakia. Based on the obtained results, one can state that the average content of arsenic is lower than the valid hygienic limit for arsenic (25 mg.kg-1) for predominated sandy-loamy and loamy soils in Slovakia. Increased values of arsenic were determined only for the Horná – Upper Nitra region (anthropogenic impact) – 24.5 mg.kg-1 and for the Stredný – Central Spiš region (mixed anthrophogenic and geogenic impact) – 129.5 mg.kg-1. These regions belong to the most arsenic-affected regions in Slovakia, where the content of bioavailable forms of arsenic is also increased in the range of 0.013–0.997 mg.kg-1. The hygienic limit for bioavailable arsenic in soils of Slovakia is 0.4 mg.kg-1. Finally, there is a serious risk of arsenic transport from soil into the plants and food chain especially in case of acid soils. A higher risk of As presence seems to be in anthropogenically affected soils.</p>


2011 ◽  
Vol 57 (No. 7) ◽  
pp. 307-314 ◽  
Author(s):  
J. Matula

Phosphorus concentration in the soil solution of agricultural soils should be a consensus of the agronomic and environmental aspect. Data from literary sources are inconsistent if the method of soil solution extraction from the soil and the method of phosphorus detection are not indicated. In the present paper a simplified procedure of soil solution extraction is used that is derived from the need of water to attain saturated soil paste. Based on barley cultivation in a plant growth chamber on 72 different soils the relationship between P concentration in simulated soil solution and the response of test plant (spring barley) was evaluated. Three approaches were used to derive an adequate P concentration in soil solution. Based on the diagnostics of P content in barley the following adequate P concentrations in soil solution were derived: 0.23&ndash;0.86 ppm P for colorimetry and 0.9&ndash;1.75 ppm P for ICP-AES. Using the concept of the boundary line of yield the critical P concentration in soil solutions was 0.8 ppm P for colorimetry and 1.3 ppm P for ICP-AES. The concept of the boundary line of P efficiency index enabled to define P concentrations in soil solution that can be considered as the lower limits of suitability from the agronomic aspect:<br />0.15 ppm P in simulated soil solution for colorimetry and 0.7 ppm P for ICP-AES.


2001 ◽  
Vol 136 (3) ◽  
pp. 331-344 ◽  
Author(s):  
N. A. BERESFORD ◽  
N. M. J. CROUT ◽  
R. W. MAYES

There is the potential for arsenic to enter the human food chain via ingestion by grazing animals. Data on the transfer of arsenic to ruminants have been too sparse to allow the development of dynamic models to predict changes in the arsenic contents of different tissues following ingestion. A study is described during which a group of 6-month-old lambs were given a single oral administration of 73AsCl3. Subsequently, concentrations of 73As in the tissues of groups of lambs slaughtered at intervals over a period of 181 days were determined. A true absorption coefficient of 0·46±0·055 (mean±S.E.) was determined which is considerably lower than expected from previous studies of non-ruminant animals which demonstrate complete absorption for inorganic arsenic. The resultant data were used to develop a compartment model to describe arsenic behaviour in sheep tissues. The derived model accounted for 80 % (n = 100) of the observed variation in the data. The model predicts that arsenic concentrations in tissues rapidly (< 40 days) reach equilibrium with the dietary intake level. Equilibrium transfer coefficient values (the ratio of the arsenic concentration in a tissue to the daily dietary intake of arsenic) for the important food-chain tissues were calculated as: (2·5±0·67)×10−3 days/kg for muscle, (9·1±1·96)×10−3 days/kg for liver and (1·1±0·14)×10−2 days/kg for kidney.


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