Selection of soil health indicators for modelling soil functions to promote smart urban planning.

The contribution of soil health to global health receives a growing interest, especially in urban environment. Therefore, there is a true need to develop methods to evaluate ecological functions provided by urban soils in order to promote smart urban planning. This work aims first at identifying relevant soil indicators based either on in situ description, in situ measurement or lab analysis. Then, 9 soil functions and sub-functions were selected to meet the main expectations regarding soil health in urban contexts. A crucial step of the present research was then to select adequate indicators for each soil function and then to create adapted reference frameworks; they were in the form of 4 classes with scores ranging from 0 to 3. All the reference frameworks were developed to evaluate soil indicators in order to score soil functions, either by using existing scientific or technical standards or references or based on the expertise of the co-authors. Our model was later tested on an original database of 109 different urban soils located in 7 cities of Western Europe and under various land uses. The scores calculated for 8 soil functions of 109 soils followed a Gaussian distribution. The scoring successfully expressed the strong contrasts between the various soils; the lowest scores were calculated for sealed soils and soils located in urban brownfields, whereas the highest were found for soils located in city parks or urban agriculture. Despite requiring a soil expertise, the proposed approach is easy to implement and could help reveal the true potential of urban soils in order to promote smart urban planning and enhance their contribution to global health.

Research on Tea Tree Growth Monitoring Model Using Soil Information.

Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.

Unearthing soil-plant-microbiota crosstalk: Looking back to move forward.

The soil is vital for life on Earth and its biodiversity. However, being a non-renewable and threatened resource, preserving soil quality is crucial to maintain a range of ecosystem services critical to ecological balances, food production and human health. In an agricultural context, soil quality is often perceived as the ability to support field production, and thus soil quality and fertility are strictly interconnected. The concept of, as well as the ways to assess, soil fertility has undergone big changes over the years. Crop performance has been historically used as an indicator for soil quality and fertility. Then, analysis of a range of physico-chemical parameters has been used to routinely assess soil quality. Today it is becoming evident that soil quality must be evaluated by combining parameters that refer both to the physico-chemical and the biological levels. However, it can be challenging to find adequate indexes for evaluating soil quality that are both predictive and easy to measure in situ. An ideal soil quality assessment method should be flexible, sensitive enough to detect changes in soil functions, management and climate, and should allow comparability among sites. In this review, we discuss the current status of soil quality indicators and existing databases of harmonized, open-access topsoil data. We also explore the connections between soil biotic and abiotic features and crop performance in an agricultural context. Finally, based on current knowledge and technical advancements, we argue that the use of plant health traits represents a powerful way to assess soil physico-chemical and biological properties. These plant health parameters can serve as proxies for different soil features that characterize soil quality both at the physico-chemical and at the microbiological level, including soil quality, fertility and composition of soil microbial communities.

Use of combined tools for effectiveness evaluation of tailings rehabilitated with designed Technosol.

Soil and water characteristics and biogeochemical processes can be improved by the application of an integrated technology based on circular economy: designed Technosol. The evaluation of the effectiveness of the superficial application of a designed Technosol, with andic and eutrophic properties, on the rehabilitation of sulfide tailings of a uranium mine (Fé mining area, Spain) was the aim of this study. After 20 months of the Technosol application, the tailing rehabilitation status (Rehabilitated tailing) was compared to a non-rehabilitated tailing (Tailing). To assess the rehabilitation of these systems, several properties were analyzed: chemical characteristics of the materials and their leachates, soil enzymatic activities (dehydrogenase, ß-glucosidase, acid phosphatase and urease), basal respiration and several plant endpoints from direct and indirect bioassays and pot experiment using Lolium perennse L. and Trifolium pratense L.. Potentially toxic concentrations of Co, Mn and Ni were identified in both available fraction and leachates, pointing out the serious environmental risk posed by the tailing. The improvement of overall physicochemical properties in the rehabilitated tailing materials (e.g., decrease of the hazardous element concentrations in leachates and available fraction, and improvement of the fertility and structure) allowed a quick plant cover with pasture species and provided a suitable habitat for active microbial community (evaluated by increasing dehydrogenase activity and basal respiration). This improvement in the rehabilitated tailing contributed to a significant decrease in the ecotoxicological risk and the spread of hazardous elements. The field application of this specific Technosol was a promising and lasting solution for rehabilitation of this type of tailings.

Fine-scale spatial homogenization of microbial habitats: a multivariate index of headwater wetland complex condition.

With growing public awareness that wetlands are important to society, there are intensifying efforts to understand the ecological condition of those wetlands that remain, and to develop indicators of wetland condition. Indicators based on soils are not well developed and are absent in some current assessment protocols; these could be advantageous, particularly for soils, which are complex habitats for plants, invertebrates, and microbial communities. In this study, we examine whether multivariate soil indicators, correlated with microbial biomass and community composition, can be used to distinguish reference standard (i.e., high condition) headwater wetland complexes from impacted headwater wetland complexes in central Pennsylvania, USA. Our reference standard sites existed in forested landscapes, while our impacted sites were situated in multi-use landscapes and were affected by a range of land-use legacies in the 1900s. We found that current assessment protocols are likely underrepresenting sampling needs to accurately represent site mean soil properties. On average, more samples were required to represent soil property means in reference standard sites compared to impacted sites. Reference standard and impacted sites also had noticeably different types of microbial habitats for the two multivariate soil indices assessed, and impacted sites were more homogenized in terms of the fine-scale (i.e., 1 and 5 m) spatial variability of these indices. Our study shows promise for the use of multivariate soil indices as indicators of wetland condition and provides insights into the sample sizes and scales at which soil sampling should occur during assessments. Future work is needed to test the generalizability of these findings across wetland types and ecoregions and establish definitive links between structural changes in microbial habitats and changes in wetland soil functioning.