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Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non-point source phosphorus (P) loss from farmland is one of the most serious causes of agricultural non-point source pollution. It is very necessary to identify the critical source areas and influence factors for the risk of P loss from farmland in a watershed, in order to prevent non-point source pollution. The objective of this study was to assess the risk of P loss from farmland in China from 2000 to 2020. P index model was also used. Among them, the soil available P content and fertilizer-P application rate were selected as the source factors. The soil-erosion modulus, annual runoff depth, and the normalized differential distance index between farmland and river network were used as the transport factors. Additionally, the GIS technology was then combined to identify the critical source areas of P loss from farmland. Random Forest (RF) was utilized to derive the critical influencing factors on the P loss from farmland in China. Structural Equation Modeling (SEM) was constructed to explore the relationship between the P index and influencing factors. The results show that: 1) The low, medium, high, and very high-risk areas of P loss from 2000 to 2020 accounted for 43.8%, 40.5%, 13.4%, and 2.4% of the total area of farmland, respectively. 2) The annual average percentage of the total area at high and very high risk of P loss from farmland in 2000, 2005, 2010, 2015, and 2020 was ranked in the descending order: the Huaihe River Basin, Yangtze River Basin, Pearl River Basin, Southeast River Basin, Songhua and Liaohe River Basin, Southwest River Basin, Yellow River Basin, Continental River Basin, and Haihe River Basin. 3) The RF results showed that the available P content and normalized differential distance index were the critical influencing factors of the P index, whose importance eigenvalues were 129.53 and 65.12, respectively. The available P content was the critical influencing factor of the P loss from the farmland. 4) SEM images showed that the P index was extremely significantly positively correlated with the source and transport factor indexes. The P index of the 14 selected index factors amounted to 0.62, in which the contribution rates of the source factor and the migration factor to the P index were 0.77 and 0.19, respectively (P<0.001). In conclusion, the findings can provide scientific references for the evaluation of non-point source pollution in farmland. It is of great significance for the decision-making on the prevention and control of agricultural surface pollution. [ABSTRACT FROM AUTHOR]

Agricultural carbon emission has been one of the major sources of carbon emission in the era of peak carbon and carbon neutrality. Therefore, it is of great significance to clarify the status quo of agricultural carbon emission in recent years, particularly for the spatial-temporal changes and influencing factors. Taking Jiangxi Province of China as the typical study area, the measurement system of agricultural carbon emission was established to calculate the agricultural carbon emission in 81 counties from 2000 to 2020. Four major carbon sources were selected as the paddy field planting, agricultural investment inputs, soil plowing, as well as livestock and poultry farming. The spatial pattern of agricultural carbon emission was analyzed by the spatial autocorrelation and center of gravity transfer at county level. The relevant influence factors were then determined by logarithmic mean Divisia index (LMDI). The results were summarized as follows: 1) The agricultural carbon emission in the study area was ranged from 10.98 to 14.72 million tons. An outstanding increasing trend was found in the agricultural carbon emission on the whole, whereas the carbon emission intensity was in an overall decreasing trend. The carbon emission intensity of planting industry showed a decreasing trend, ranging from 2.50 to 3.87 tons per ten thousand Yuan. Similarly, the carbon emission intensity of animal husbandry also showed a decreasing trend, ranging from 0.76 to 2.03 tons per ten thousand Yuan. Particularly, the total carbon emissions of each carbon source and the proportion in total agricultural carbon emissions were ranked in the descending order of: the paddy field planting (8.10 million tons, 61.15%) > livestock and poultry (2.43 million tons, 18.57%) > agricultural investment inputs (2.38 million tons, 18.02%) > soil plowing (0.27 million tons, 2.26%); 2) There was the apparent spatial characteristic of agricultural carbon emissions. For example, the high carbon emission areas were concentrated in the Poyang Plain and the Jitai Basin. Furthermore, the spatial distribution of agricultural carbon emission intensity was from the relative dispersion to concentration in the Northern, whereas, the carbon emission intensity in The Southern was relatively low. The center of gravity of total carbon emissions shifted northward, where the carbon emission in The Northern was higher than that in The Southern Jiangxi Province. 3) Agricultural production efficiency improvement was the most important factor to restrain the sustained growth of the agricultural carbon emissions. The factors were ranked on the agricultural carbon emission reduction and the proportion in the total agricultural carbon emission reduction: the agricultural production efficiency (18.28 million tons, 56.57%)> regional industrial structure (12.65 million tons, 39.15%) > rural population size (0.86 million tons, 2.66%) > agricultural industrial structure (0.52 million tons, 1.62%). Overall, the area order was given in the absolute value of agricultural carbon emission reduction by each factor: The North > The Middle >The South. The finding can provide the scientific strategy to estimate agricultural carbon emissions in Jiangxi Province, even the major grain producing areas [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We selected one, fourteen,and fifteen years old C.sinensis and neighboring broad leaf mixed forests as plots and operated experiments to measured soil physical properties and water-holding capacity of litter and soil.The principal component analysis was carried out to analyze study the effect of C.sinensis cultivation on water conservation capacity. The resuIts showed as fol lows. Firstly, the effective storage capacity of litter for broadleaf mixed forest was 166.87 times of 4a C.sinensis and and 61.50 times of 15a C.sinensis. Secondly, the variation of total porosity was ranged from 43. 92 % to 60. 13 %. Compared with broad leaf mixed forests, the average soil bulk densities of C.sinensis at three different planting years increased by 2. 17%,11.76%,and 20.74%,respectively.Thirdly,the Soil initial permeation rate and soil stable permeation rate for la C.sinensis were 20. 70% and 55.00% lower than the broad leaf mixed forests, respectively. The soil infiltration rate would be decrease with increased cultivation soil.The infiltration rate and soil bulk density were important factors that determinine the water conservation capacity. [ABSTRACT FROM AUTHOR]