Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

Jia, Renfu; Fang, Shibiao; Tu, Wenrong; Sun, Zhilin

doi:10.1155/2016/8957530

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…xlstat.com/en/solutions/ecology). Regression equations were derived as described elsewhere (Pal and Bhattacharya, 2013;Teng et al, 2015;French and Finch, 2016;Jia et al, 2016). Assuming the bacterial community is a dependent variable changing upon the independent environmental variables, the relative abundance of bacteria can be predicted by the environmental variables.…”

Section: Multiple Factor Regressionmentioning

confidence: 99%

“…Significant environmental factors were extracted from the correlation matrix and combined using a principal component analysis (PCA) and the Varimax Factor Rotation, which yielded the eigenvalues and scores. Relevant eigenvalues and scores were selected using default thresholds (Pal and Bhattacharya, 2013;French and Finch, 2016;Jia et al, 2016). The significant factors and their scores were combined in regression equation to estimate the dependent variable Y (represent the bacterial abundance) from the independent variables X 1 to X i (each of water physical variables) and ß 0 to ß i as coefficient constants, as follow.…”

Section: Multiple Factor Regressionmentioning

confidence: 99%

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Spatial and Seasonal Variability of Reef Bacterial Communities in the Upper Gulf of Thailand

et al. 2018

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Reefs at Ko Samae San (S), Khao Ma Cho (K), and Ko Tao Mo (T), in the Gulf of Thailand (GoT) represent a biodiversity hotspot, and bacteria play significant roles in maintaining the health of these coral reefs and their biogeochemical cycles. Therefore, this study analyzed bacterial communities (microbiota) from healthy corals and nearby seawater and sediment, using B-RISA and 16S rRNA gene sequencing. Sampling was done in one dry and one wet season to provide an initial assessment of variation with environmental conditions. The most prevalent coral species were Porites lutea, Platygyra sinensis, Acropora humilis, and Acropora millepora. The B-RISA and the sequencing results were correlated, which increased confidence the results. The microbiota varied among corals, seawater, and sediment and between the wet and dry seasons. Percentages of bacteria with known functions varied among sample types and seasons, and their relative abundances supported previously reported essential functions, such as prevention of disease (e.g., Pseudoalteromonas, Psychrobacter, and Cobetia were more abundant on corals in the dry season). Pearson's correlations and multiple factor regressions identified dissolved oxygen (DO), temperature, salinity, and density as significant influences on the microbiota. The equations estimated the relative abundance of a community comprising 147 bacterial genera, as well as the relative abundance of Pseudomonas, Clostridium, Verrucomicrobium, and Epulopiscium (R 2 ≥ 0.721). These results represent the first descriptions of microbiota from corals, and surrounding seawater and sediments in the upper GoT.

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Section: Multiple Factor Regressionmentioning

confidence: 99%

Section: Multiple Factor Regressionmentioning

confidence: 99%

Spatial and Seasonal Variability of Reef Bacterial Communities in the Upper Gulf of Thailand

et al. 2018

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“…This concept is similar to water delivery efficiency [3]. The factors affecting the efficiency of irrigation water use are complex and diverse, and research on various influencing factors has achieved many results [4][5][6][7]. A canal is the waterway that connect irrigation water sources and irrigated land.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Irrigation Canal System Characteristics in Heilongjiang Province and the Influence on Irrigation Water Use Efficiency

Sun

et al. 2018

Water

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Irrigation water use efficiency is a primary evaluation index that links economic production development with the efficient use of water resources. Canal water conveyance is an important part of irrigation, and the distribution characteristics of canal systems have an important influence on irrigation water use efficiency. In this paper, 75 irrigated districts in Heilongjiang Province in 2015 were selected as the study objects. The main, branch, lateral, and sublateral canals were graded into first-, second-, third-, and fourth-order classes, respectively. The irrigation districts were divided into three classes, that is, four-order, three-order, and two-order, according to the canal orders that the irrigation districts contained. The canal system framework was described by Horton's law. The fractal dimension of the canal system was calculated based on the bifurcation ratio and length ratio of the canals. The relationships between fractal dimensions and irrigation water use efficiency were evaluated. The results showed that the irrigation water use efficiency of the four-order and three-order irrigation districts initially increased and then decreased with increases in the fractal dimension (D). In the irrigation districts, an irrigation water use efficiency of more than 10 × 10 3 hm 2 and less than 0.67 × 10 3 hm 2 was proportional to the increase in the fractal dimension, whereas the opposite result was found for districts with (0.67-10) × 10 3 hm 2 . The irrigation water use efficiency of the four-order and two-order irrigation districts with less than 3.3 × 10 3 hm 2 had the greatest potential to increase the water use efficiency. Therefore, canal system reconstruction suggestions for different irrigation districts were provided. The results have important theoretical significance and practical value for the improvement of irrigation construction and the promotion of irrigation water efficiency planning.

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“…As a result, a concise and feasible index system of irrigation water-use efficiency was obtained. The effectiveness of PCA for simplifying the index system has been proved by Jia et al [30]. After PCA, all the data were transformed into "composite variables" in the cluster analysis in order to capture multiple questions in a ranked ordinal scale.…”

Section: Implications For Similar Researchmentioning

confidence: 99%

Assessing Factors Driving the Change of Irrigation Water-Use Efficiency in China Based on Geographical Features

Fang

Jia

et al. 2017

Water

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Abstract:Changes in irrigation water-use efficiency are related closely to agricultural development. Clarifying the driving factors of irrigation water-use efficiency change at different agricultural development stages is beneficial for buffering the contradiction between the protection of water resources and massive agricultural water consumption. It also has theoretical and application value when it comes to elucidating the driving characteristics of spatial changes in irrigation water-use efficiency observed among the different provinces of China. This paper analyzes driving factors of irrigation water-use change based on a study of literature and a field survey. It selects 21 indices from five aspects of climatic change, resource endowment, economic situation, technological level, and management mode as the system of driving factors for irrigation water-use change. This article then uses statistical data on economic and social development in the 31 provinces of China in 2009, and applies the principal component analysis (PCA) method to extract the main driving factors affecting irrigation water-use efficiency change. After calculation of factor scores, clustering analysis is conducted on the 31 provinces to explore regional differences among the driving factors of irrigation water-use efficiency change. The results show that these can be attributed to the factors of agricultural economic development, water-saving irrigation technology, water resource endowment, and dissipation. The 31 provinces can be divided into five types: agricultural economy strong driving type; agricultural economy dominant type; industrial economy dominant type; agriculture strong development type; and coordinated driving type. In highly agricultural provinces, mature irrigation district management and water-saving measures influence the efficiency of irrigation water-use, making these strong positive driving factors. In highly industrial provinces, changes in irrigation water-use efficiency are mainly driven by economic development and structural adjustment, making these weak driving factors.

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Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

Cited by 11 publications

References 36 publications

Spatial and Seasonal Variability of Reef Bacterial Communities in the Upper Gulf of Thailand

Spatial and Seasonal Variability of Reef Bacterial Communities in the Upper Gulf of Thailand

Analysis of Irrigation Canal System Characteristics in Heilongjiang Province and the Influence on Irrigation Water Use Efficiency

Assessing Factors Driving the Change of Irrigation Water-Use Efficiency in China Based on Geographical Features

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