Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Assessment of soil total phosphorus storage in a complex topography along China's southeast coast based on multiple mapping scales

Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literature employ a single or narrow range of soil databases, which largely overlooks the impact of utilizing multiple mapping scales in estimating soil TP, especially in hilly topographies. In this study, Fujian Province, a subtropical hilly region along China’s southeast coast covered by a complex topographic environment, was taken as a case study. The influence of the mapping scale on soil TP storage (TPS)estimation was analyzed using six digital soil databases that were derived from 3 082 unique soil profiles at different mapping scales, i.e., 1:50 000 (S5),1:200 000 (S20), 1:500 000 (S50), 1:1 000 000 (S100), 1:4 000 000 (S400), and 1:10 000 000 (S1000). The regional TPS in the surface soil (0–20 cm) based on the S5, S20, S50, S100, S400, and S1000 soil maps was 20.72, 22.17, 23.06, 23.05, 22.04, and 23.48 Tg, respectively, and the corresponding TPS at0–100 cm soil depth was 80.98, 80.71, 85.00, 84.03, 82.96, and 86.72 Tg, respectively. By comparing soil TPS in the S20 to S1000 maps to that in the S5map, the relative deviations were 6.37%–13.32%for 0–20 cm and 0.33%–7.09%for 0–100 cm. Moreover, since the S20 map had the lowest relative deviation among different mapping scales as compared to S5, it could provide additional soil information and a richer soil environment than other smaller mapping scales. Our results also revealed that many uncertainties in soil TPS estimation originated from the lack of detailed soil information, i.e., representation and spatial variations among different soil types. From the time and labor perspectives, our work provides useful guidelines to identify the appropriate mapping scale for estimating regional soil TPS in areas like Fujian Province in subtropical China or other places with similar complex topographies. Moreover, it is of tremendous importance to accurately estimate soil TPS to ensure ecosystem stability and sustainable agricultural development, especially for regional decision-making and management of phosphate fertilizer application amounts.

Responses of greenhouse gas fluxes to experimental warming in wheat season under conventional tillage and no-tillage fields

Understanding the effects of warming on greenhouse gas（GHG, such as N2O, CH4 and CO2 ）feedbacks to climate change represents the major environmental issue. However, little information is available on how warming effects on GHG fluxes in farmland of North China Plain（NCP）. An infrared warming simulation experiment was used to assess the responses of N2O, CH4 and CO2 to warming in wheat season of 2012–2014 from conventional tillage（CT） and no-tillage（NT） systems. The results showed that warming increased cumulative N2O emission by 7.7% in CT but decreased it by 9.7% in NT fields（p 〈 0.05）. Cumulative CH4 uptake and CO2 emission were increased by 28.7%–51.7% and 6.3%–15.9% in both two tillage systems,respectively（p 〈 0.05）. The stepwise regressions relationship between GHG fluxes and soil temperature and soil moisture indicated that the supply soil moisture due to irrigation and precipitation would enhance the positive warming effects on GHG fluxes in two wheat seasons.However, in 2013, the long-term drought stress due to infrared warming and less precipitation decreased N2O and CO2 emission in warmed treatments. In contrast, warming during this time increased CH4 emission from deep soil depth. Across two years wheat seasons, warming significantly decreased by 30.3% and 63.9% sustained-flux global warming potential（SGWP） of N2O and CH4 expressed as CO2 equivalent in CT and NT fields, respectively. However, increase in soil CO2 emission indicated that future warming projection might provide positive feedback between soil C release and global warming in NCP.