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Abstract. Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south-north) × 6 km (east-west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha −1 to 8841.3 kg ha −1 , with an average of 1786.7 kg ha −1 . The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

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There is an increasing concern about heavy metal contamination in farmland in China and worldwide. In order to reveal the spatial features of heavy metals in the soil-rice system, soil and rice samples were collected from Nanxun, Southeastern China. Compared with the guideline values, elevated concentrations of heavy metals in soils were observed, while heavy metals in rice still remained at a safe level. Heavy metals in soils and rice had moderate to strong spatial dependence (nugget/sill ratios: 13.2% to 49.9%). The spatial distribution of copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) in soils illustrated that their high concentrations were located in the southeast part. The high concentrations of cadmium (Cd) in soils were observed in the northeast part. The accumulation of all the studied metals is related to the long-term application of agrochemicals and industrial activities. Heavy metals in rice showed different spatial distribution patterns. Cross-correlograms were produced to quantitatively determine the spatial correlation between soil properties and heavy metals composition in rice. The pH and soil organic matter had significant spatial correlations with the concentration of heavy metals in rice. Most of the selected variables had clear spatial correlation ranges for heavy metals in rice, which could be further applied to divide agricultural management zones.

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Abstract:The Agrobacterium-mediated transformation system is the most commonly used method in soybean transformation. Screening of soybean genotypes favorable for Agrobacterium-infection and tissue regeneration is the most important step to establish an efficient genetic transformation system. In this study, twenty soybean genotypes that originated from different soybean production regions in China were screened for transient infection, regeneration capacity, and stable transgenic efficiency. Three genotypes, Yuechun 04-5, Yuechun 03-3, and Tianlong 1, showed comparable stable transgenic efficiencies with that of the previously reported American genotypes Williams 82 and Jack in our experimental system. For the Tianlong 1, the average stable transformation efficiency is 4.59%, higher than that of control genotypes (Jack and Williams 82), which is enough for further genomic research and genetic engineering. While polymerase chain reaction (PCR), LibertyLink strips, and β-glucuronidase (GUS) staining assays were used to detect the insertion and expression of the transgene, leaves painted with 135 mg/L Basta could efficiently identify the transformants.

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The spatial variation of soil nutrients especially the soil test phosphorus (STP) in grassland soils is becoming important because of the use of soil‐nutrients information as a basis for policies such as the recently EU‐introduced Nitrates Directive. Up to now, the small‐scale spatial variation of soil nutrients in grassland has not been studied. The main aim of this study was to investigate the spatial patterns of soil nutrients in two grazed grassland plots with a long‐term (38 y) P‐application experiment, in order to better understand the spatial variation of soil nutrients and the correlation among soil nutrients in grasslands. Two small areas (one from a high‐P background and the other from a medium‐P background) were selected. Soil samples (304 per study area) were collected based on a 1 m × 1 m grid system. The samples were analyzed for STP, Mg, K, pH, and lime requirement (LR). The results were analyzed using conventional statistics, Moran's I, geostatistics, and a GIS.Based on the global Moran's I values, significant positive spatial autocorrelations were found for STP, Mg, pH, and LR in both study areas. Spatial clusters and spatial outliers were detected using the local Moran's I index. Clear linear‐shaped high‐high or low‐low value clusters of the studied variables except K were observed in the study areas due to long‐term usage of machine spreader or other agricultural‐management methods in the past. The corresponding linear patterns were further found in the spatial‐distribution maps. Small spatial patches were found for soil K revealing that it had a random spatial distribution related to the relatively uniform K fertilizer in the study areas. The spatial clusters revealed by local Moran's I were in line with the spatial patterns in the distribution maps.