Wang J.-M.,University of Science and Technology of China |
Wang J.-M.,Key Laboratory of Land Regulation |
Yang P.-L.,China Agricultural University |
Bai Z.-K.,University of Science and Technology of China |
Bai Z.-K.,Key Laboratory of Land Regulation
Meitan Xuebao/Journal of the China Coal Society | Year: 2011
In order to choose the reasonable application rate and application mode of sodic soils reclaimed with desulphogypsum, control soil moisture, soil salinity and soil sodicity in the process of soil reclamation. A pot experiment was conducted to study union response of sunflower biological indicators (height, leaf area and dry mass), physiological indicators (photosynthesis rate, transpiration rate and stomata conductance) and yield to soil moisture, soil salinity and soil sodicity. Results indicate that crop growth is simultaneously affected by soil moisture, soil salinity and soil sodicity in the process of soil reclamation. Biological indicators and yield of sunflower decreases with the increase of soil moisture, soil salinity and soil sodicity. Sunflower photosynthesis rate, transpiration rate and stomata conductance increases with the increase of soil moisture, and decreases with the increase of soil salinity and soil sodicity. Soil salinity is the dominant factor of affecting sunflower yield and biological indicators, and effects of soil moisture on the sunflower physiological indicators is more obvious than the other two factors.
Gao X.,University of Science and Technology of China |
Gao X.,Key Laboratory of Land Regulation |
Wu K.,University of Science and Technology of China |
Wu K.,Key Laboratory of Land Regulation |
And 7 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2016
In order to scientifically calculate the feasible potential quality of cultivated land quality upgrade and improve the planning, design, and arrangement of land consolidation project, a new pre-evaluation method of promotion potential of arable land quality was proposed based on the natural grade and utilization grade index of agricultural land gradation. The existing evaluation methods of improving potential of arable land quality are generally based on the natural grade and utilization grade index, and to set the top grade of arable land in a specified region as the target value of promotion potential calculation. The principle of existing methods is to assume that the limiting factors of arable land quality could be reduced or improved by land consolidation project. However, this assumption is not verified. This might cause the overestimate or underestimate of the evaluation results. The method we proposed was based on improvable degree of sensitive factors of agricultural land gradation. By investigating and analyzing how well the relative techniques of land consolidation can improve the sensitive factors of agricultural land gradation, the difference value between realizable and actual natural grade can be accurately calculated. The utilization grade could be calculated based on the natural grade. The first step was to implement a targeted survey to determine the sensitive factors of agricultural land classification, such as the effective thickness of soil layer, degree of salinity, drainage condition, irrigation guarantee rate, irrigation water source, rock coverage of surface soil, and slope gradient. Afterwards, we analyzed the magnitude of the change of each sensitive factor improved by land consolidation project based on the gradation unit. The second step was to analyze the feasible natural quality grade promotion attributed to the most effective engineering measure for calculating the promotion situation of natural and utilized quality grades by upgrade potential models of natural and utilized grades. Finally, we converted results of the first two former steps into the feasible upgrade potentials of natural and utilized grades. Zhongjiang County of Sichuan Province was taken as a case study for our method. After analyzing the previous achievement of agricultural land gradation, the effective thickness of soil layer, slope gradient and rock coverage of surface soil, drainage condition and irrigation guarantee rate were determined as the sensitive factors of arable land quality. According to the adaptability of every sensitive factor, lifting scheme was enacted. Results showed that, both natural and utilized grade of 60 grading units in the studied area can be improved after the implementation of land consolidation project. The promotion ranges of provincial natural and utilized grades were 171.24 to 605.34 and 89.30 to 232.96, respectively. The provincial natural quality grade was promoted by an average of 0.66, and the provincial utilized grade was improved by an average of 0.63. After converting the provincial grade into national grade, the national natural quality grade was improved by an average of 0.84, and the national utilized grade was improved by an average of 0.62. The pre-evaluation method we proposed was adapted to the local situation of consolidation, with which pertinence focus on the cultivated land quality upgrade was obviously after the implementation of land consolidation projects, and was conformed to the practical situation of local land consolidation project. Research results could provide references for the future planning and design land consolidation project and the promotion of cultivated land quality. © 2016, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Yuan T.,University of Science and Technology of China |
Yuan T.,Key Laboratory of Land Regulation |
Zheng X.,University of Science and Technology of China |
Zheng X.,Key Laboratory of Land Regulation |
And 5 more authors.
PLoS ONE | Year: 2014
Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment. © 2014 Yuan et al.