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A simple evaluation of soil quality of waterlogged purple paddy soils with different productivities.

Liu Z, Zhou W, Lv J, He P, Liang G, Jin H - PLoS ONE (2015)

Bottom Line: Most measured soil properties showed significant differences (P ≤ 0.05) among HPPS, MPPS and LPPS.Low levels of TN, AP and soil microbial activities were considered to be the major constraints limiting the productivity in LPPS.Managers in our study area should employ more appropriate management in the LPPS to improve its rice productivity, and particularly to any potential limiting factor.

View Article: PubMed Central - PubMed

Affiliation: Ministry of Agriculture Key Laboratory of Crop Nutrition and Fertilization, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Resource and Environmental Science, Northwestern University of A & F, Yangling 712100, China.

ABSTRACT
Evaluation of soil quality can be crucial for designing efficient farming systems and ensuring sustainable agriculture. The present study aimed at evaluating the quality of waterlogged purple paddy soils with different productivities in Sichuan Basin. The approach involved comprehensive analyses of soil physical and chemical properties, as well as enzyme activities and microbial community structure measured by phospholipid fatty acid analysis (PLFA). A total of 36 soil samples were collected from four typical locations, with 12 samples representing high productivity purple paddy soil (HPPS), medium productivity purple paddy soil (MPPS) and low productivity purple paddy soil (LPPS), respectively. Most measured soil properties showed significant differences (P ≤ 0.05) among HPPS, MPPS and LPPS. Pearson correlation analysis and principal component analysis were used to identify appropriate soil quality indicators. A minimum data set (MDS) including total nitrogen (TN), available phosphorus (AP), acid phosphatase (ACP), total bacteria (TB) and arbuscular mycorrhizal fungi was established and accounted for 82.1% of the quality variation among soils. A soil quality index (SQI) was developed based on the MDS method, whilst HPPS, MPPS and LPPS received mean SQI scores of 0.725, 0.536 and 0.425, respectively, with a ranking of HPPS > MPPS > LPPS. HPPS showed relatively good soil quality characterized by optimal nutrient availability, enzymatic and microbial activities, but the opposite was true of LPPS. Low levels of TN, AP and soil microbial activities were considered to be the major constraints limiting the productivity in LPPS. All soil samples collected were rich in available N, K, Si and Zn, but deficient in available P, which may be the major constraint for the studied regions. Managers in our study area should employ more appropriate management in the LPPS to improve its rice productivity, and particularly to any potential limiting factor.

No MeSH data available.


Correlation between soil quality index and rice yield of 36 paddy fields.** P < 0.01.
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pone.0127690.g004: Correlation between soil quality index and rice yield of 36 paddy fields.** P < 0.01.

Mentions: Correlation analysis indicated that SQI and rice yield were linearly related (Fig 4). The results followed the observations of Li et al. (2013) [17], who also reported significant positive correlation between SQI and ric yield. Our results also support previous conclusions that the selected MDS indicators could effectively evaluate soil quality status as a rice-production medium and the SQI approach is an appropriate way to develop a quantitative procedure to evaluate different land management practices [11,12].


A simple evaluation of soil quality of waterlogged purple paddy soils with different productivities.

Liu Z, Zhou W, Lv J, He P, Liang G, Jin H - PLoS ONE (2015)

Correlation between soil quality index and rice yield of 36 paddy fields.** P < 0.01.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440743&req=5

pone.0127690.g004: Correlation between soil quality index and rice yield of 36 paddy fields.** P < 0.01.
Mentions: Correlation analysis indicated that SQI and rice yield were linearly related (Fig 4). The results followed the observations of Li et al. (2013) [17], who also reported significant positive correlation between SQI and ric yield. Our results also support previous conclusions that the selected MDS indicators could effectively evaluate soil quality status as a rice-production medium and the SQI approach is an appropriate way to develop a quantitative procedure to evaluate different land management practices [11,12].

Bottom Line: Most measured soil properties showed significant differences (P ≤ 0.05) among HPPS, MPPS and LPPS.Low levels of TN, AP and soil microbial activities were considered to be the major constraints limiting the productivity in LPPS.Managers in our study area should employ more appropriate management in the LPPS to improve its rice productivity, and particularly to any potential limiting factor.

View Article: PubMed Central - PubMed

Affiliation: Ministry of Agriculture Key Laboratory of Crop Nutrition and Fertilization, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Resource and Environmental Science, Northwestern University of A & F, Yangling 712100, China.

ABSTRACT
Evaluation of soil quality can be crucial for designing efficient farming systems and ensuring sustainable agriculture. The present study aimed at evaluating the quality of waterlogged purple paddy soils with different productivities in Sichuan Basin. The approach involved comprehensive analyses of soil physical and chemical properties, as well as enzyme activities and microbial community structure measured by phospholipid fatty acid analysis (PLFA). A total of 36 soil samples were collected from four typical locations, with 12 samples representing high productivity purple paddy soil (HPPS), medium productivity purple paddy soil (MPPS) and low productivity purple paddy soil (LPPS), respectively. Most measured soil properties showed significant differences (P ≤ 0.05) among HPPS, MPPS and LPPS. Pearson correlation analysis and principal component analysis were used to identify appropriate soil quality indicators. A minimum data set (MDS) including total nitrogen (TN), available phosphorus (AP), acid phosphatase (ACP), total bacteria (TB) and arbuscular mycorrhizal fungi was established and accounted for 82.1% of the quality variation among soils. A soil quality index (SQI) was developed based on the MDS method, whilst HPPS, MPPS and LPPS received mean SQI scores of 0.725, 0.536 and 0.425, respectively, with a ranking of HPPS > MPPS > LPPS. HPPS showed relatively good soil quality characterized by optimal nutrient availability, enzymatic and microbial activities, but the opposite was true of LPPS. Low levels of TN, AP and soil microbial activities were considered to be the major constraints limiting the productivity in LPPS. All soil samples collected were rich in available N, K, Si and Zn, but deficient in available P, which may be the major constraint for the studied regions. Managers in our study area should employ more appropriate management in the LPPS to improve its rice productivity, and particularly to any potential limiting factor.

No MeSH data available.