Land Resource Areas and Spatial Analysis of Potential Location of Bioenergy Crops Production in Mississippi

Mississippi State is renowned for its land resource areas (LRA) and production of bioenergy crops which generate both agricultural and economic benefits. Agricultural commodities play a key role in economic growth, therefore the ability to produce more would enhance development. This paper offers an analysis of the production of bioenergy crops in Mississippi. Relative measures, time series graphs and descriptive statistics coupled with geographic information systems (GIS) mapping using ArcMap were employed to generate the outcome of this research. The outcome of the statistical analysis indicated that corn and soybeans were the most produced crops in Agricultural Districts 10 and 40. These districts produced more bioenergy crops than the other districts. GIS mapping results also showed that the potential area for bioenergy crops is in zone 131 of the Mississippi Land Resource Area (MLRA). This zone has an absolute advantage in the production of these crops which includes the diversity of biomass production such as corn, cotton, soybeans, wheat, rice, barley, grain sorghum, canola, camelina, algae, hardwoods, and softwood. The paper recommends a constant GIS mapping and land management systems for each agricultural district in Mississippi to enable researchers and farmers to determine the factors which contribute towards the increasing and decreasing trends in the production of the bioenergy crops.

Investigation on Sinipercine Fishes in the National Aquatic Germplasm Resources Protection Area in Jinjiang River of China

[Objective]The paper was to provide a scientific basis for protection and utilization of sinipercine fish resources in Jinjiang River,the national resources protection area. [Method]A total of 171 specimens of sinipercine were collected from Jinjiang River and its branches,identified and statistically counted.[Result]There were three species of wild sinipercine,including Siniperca scherzeri,Coreoperca whiteheadi and Siniperca kneri,with occurrence frequencies of100%,66. 7% and 50%,respectively. They were common species in the river,and S. scherzeri was dominant. The total allowable catch(TAC) of the three species was not big,and the individuals captured were generally small,and with rare mature individuals. [Conclusion]The resources of sinipercine had a preliminary recovery in the region after the foundation of the national resources protection area for aquatic resources in Jinjiang River in 2009.

Delineation of Potential Mineral Resources RegionBased on Geo-anomaly Unit

The geological anomaly unit method (GAUM) is a new way to delineate and evaluate ore finding targets in line with the “geological anomaly ore finding theory”. Comprehensive ore finding information from geological, geochemical and geophysical data is used for quantitative measurement of the “ore forming geological anomaly unit” in this paper. The main procedures are shown as follows: (1) The geo anomalous events associated with gold mineralization are analyzed in Tongshi gold field; (2) The zonation in the concentrated heavy minerals and the stream sediment elements of ore forming geo anomaly are studied in detail; (3) The deep geological structural framework is deduced by means of the synthetic geological interpretation of gravity and magnetic information; (4) The ore controlling geo anomalies and ore anomalies are chosen as the variables of the favorable ore forming indexes that can be used for the quantitative delineation and evaluation of the potential ore forming regions.

Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China

The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant,but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms,six impact factors,namely land surface temperature,fault density,Gutenberg–Liszt B value,formation combination entropy,distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF),weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF),the geothermal potential in the study area were predicted quantitatively. Based on the ECF results,the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%,area ratio is 0.867);(2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan,Panzhihua–Liangshan,Dali–Chuxiong,Nujiang–Baoshan,Diqing–Dali,and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt.

National Ecological Deficit Accounting of China with Detailed Ecological Footprint

Ecological footprint is a new method to assess sustainable development quantitatively. It translates production into biologically productive areas offering material flows to measure the utilized degree of nature by humankind. At present, China runs ecological deficits because footprints required for consumption are larger than available ones. In the paper, the ecological footprint model is applied to calculate the deficits by resource, environment and import-export accounting. It is clear that the deficits have caused enormous pressure to resource and environment, which would become bigger with more factors of resource and environment being accounted. In the primary production and energy trade, import footprint turned from deficit to surplus after 1996, which benefited the conservation of national resource. But compared with the huge depletion caused by resource and environment, they had small effects on deficits.

Estimating the spatial distribution of organic carbon density for the soils of Ohio, USA

Historical database of National Soil Survey Center containing 1424 geo-referenced soil profiles was used in this study for estimating the organic carbon （SOC） for the soils of Ohio, USA. Specific objective of the study was to estimate the spatial distribution of SOC density （C stock per unit area） to 1.0-m depth for soils of Ohio using geographically weighted regression （GWR）, and compare the results with that obtained from multiple linear regression （MLR）. About 80% of the analytical data were used for calibration and 20% for validation. A total of 20 variables including terrain attributes, climate data, bedrock geology, and land use data were used for mapping the SOC density. Results showed that the GWR provided better estimations with the lowest （3.81 kg m-2） root mean square error （RMSE） than MLR approach Total estimated SOC pool for soils in Ohio ranged from 727 to 742 Tg. This study demon strates that, the local spatial statistical technique, the GWR can perform better in capturing the spatial distribution of SOC across the study region as compared to other global spatial statistical techniques such as MLR. Thus, GWR enhances the accuracy for mapping SOC density.