This study examined the pollution status of potential toxic elements (PTEs) in cultivated soils throughout the Poyang Lake region, Jiangxi Province, China. A total of 251 topsoil samples were collected from the 0–20 cm depth to determine the concentrations of seven PTEs (Hg, As, Pb, Cd, Cu, Cr, and Zn). Based on the concentrations of PTEs, we constructed an improved matter–element extension model to evaluate the level of soil pollution by PTEs. We also applied Hakanson’s toxic response factor to correct the weights of PTEs determined by a conventional weighting method. The mean concentrations of all seven PTEs in the soil samples exceeded their local background values in Jiangxi Province. The over-standard rates of different PTEs were ranked in the order of Cr > Cu > Cd > Hg > Zn > Pb > As (36.2–87.9%). These potentially harmful pollutants mainly came from the surrounding industrial and agricultural areas, and could enter cultivated soils through different pathways. Samples from Duchang County, Hukou County, and Gongqingcheng City were in a clean state, whereas samples from other areas was in a still clean state or at the warning limit. The evaluation results were consistent with those obtained using several conventional methods. The improved matter–element extension model can therefore be applied for the evaluation of soil pollution by PTEs and yield reliable results in cultivated land.

Rice panicle temperature (Tp) is a key factor for studying high temperature impacts on spikelet sterility. Comparing with measuring Tp by hand, a Tp simulation model could obtain Tp data readily. The two-layer energy budget model which divides the soil layer and canopy layer was widely used to predict rice canopy temperature (Tc), but panicle existed mostly in the upper layer canopy, and we have proved that Tc was different from the upper layer canopy temperature (Tc1), and the upper layer must be separated from the whole canopy for the purpose of estimating Tp. Thus, we developed the three-layer model, contained upper canopy layer with panicle (50–100 cm), lower rice canopy layer (10–40 cm), and water surface layer (≤10 cm) to estimate Tp with general meteorological and vegetation growth data. There were two steps to estimate Tp. The first step was calculating Tc1 and lower layer canopy temperature (Tc2) by solving heat balance equations with canopy resistances. And the second step was estimating Tp with following parameters: (a) the inclination factors of leaves and panicles (F1, F2, and Fp) which were decided by fitting the calculated transmissivity of downward solar radiation (TDSR) to the measured TDSR, (b) the aerodynamic resistance between the panicle and atmosphere (rap) denoted by wind speed, (c) the panicle resistance for transpiration (rp) denoted by days after heading, and (d) air temperature and humidity at the panicle’s height (Tac1 and eac1) calculated from the resistances of the pathways of sensible and latent heat fluxes in accordance with Ohm’s law. The model simulated fairly well the Tc1, Tc2, and Tp with root mean square errors (RMSEs) of 0.76°C, 0.75°C, and 0.81°C, respectively, where RMSE of measured Tp and predicted Tp by integrated micrometeorology model for panicle and canopy temperature (IM2PACT) including two-layer model was 1.27°C. This model was validated well by two other rice cultivars, and thus, it demonstrated the three-layer model was a new feasible way to estimate Tp.

Evaluation of the carrying capacity and spatial pattern matching of urban–rural construction land is critical for solving problems associated with irrational land use and the destruction of ecosystems. Here, we present a case study exploring the spatial matching relationship between the carrying capacity and current development status of urban–rural construction land in Nanchang, the capital city of Jiangxi Province, China. Land suitability evaluation for urban and rural construction was performed using the analytic hierarchical process and restrictive coefficient method. The spatial matching degree between current construction land and available construction land was obtained by a spatial overlay analysis. Results show that the area most suitable for construction land development (19.2% of the total) is mainly concentrated in the central urban districts, while the relatively suitable area (17.5% of the total) is present around the most suitable area. The ultimate development intensity (i.e., carrying capacity threshold) of construction land in the study region is 41.4%, and the residual development intensity (i.e., development potential) is 24.2%. The available construction land (including most suitable and relatively suitable areas) is generally abundant. The spatial matching degree of construction land ranges from 69.5% to 99.1% in different counties (districts). Pearson’s correlation analysis reveals that the spatial matching degree is positively correlated with the carrying capacity threshold of construction land (r = 0.926; p < 0.01) and the abundance of available construction land (r = 0.732; p < 0.05). The results could be useful for the rational development of urban–rural construction land and the optimization of land space at the city scale.

The trade-offs and synergies reveal the profit and loss relationship between ecosystem services, which is of great significance to the sustainable development of natural resources. The ecosystem services in Jiangxi Province, such as net primary productivity (NPP), soil conservation (SC) and water yield (WY) during 2000–2020, were estimated in this study. The correlation coefficient was adopted to analyze the trade-offs and synergies between the three ecosystem services by static space correlation and dynamic space correlation from such perspectives as Watershed, county and grid. Moreover, the influence of the three ecosystem services and the relations between them were explored from four aspects: landform, NDVI, accumulated temperature and precipitation. The results showed that the ecological environment quality in Jiangxi Province was improved and that the distribution of ecosystem services had significant regional characteristics. In the static analysis, ecosystem services at all scales were remarkably synergistic, and synergies weakened rapidly and even turned into trade-offs as the scale decreased. In the dynamic analysis, ecosystem services at all scales were mainly synergistic; the proportion of significant samples was much lower than that in the static analysis, the degree of trade-offs/synergies decreased with the decrease in scale, and the decrease was smaller than that in the static analysis. The major constraints for SC were landform and NDVI. The main constraint for WY was precipitation, and that for NPP was NDVI. Affected by various factors, NPP and SC were stably synergistic, NPP and WY were in a stable trade-off relationship, and the relationship between SC and WY was unstable. The trade-offs and synergies changed with factors and zoning.