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This work was funded by the European Research Council ERC Grant agreement 242658 (BIOCOM). CYTED funded networking activities (EPES, Acción 407AC0323). D.J.E. acknowledges support from the Australian Research Council (DP150104199) and F.T.M. support from the European Research Council (BIODESERT project, ERC Grant agreement no 647038), from the Spanish Ministerio de Economía y Competitividad (BIOMOD project, ref. CGL2013-44661-R) and from a Humboldt Research Award from the Alexander von Humboldt Foundation. M.D.-B. was supported by REA grant agreement no 702057 from the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Programme H2020-MSCA-IF-2016), J.R.G. acknowledges support from CONICYT/FONDECYT no 1160026.

A field experiment was carried out on the black cotton soil in the years 2018 and 2019 in the district of Sangli, Maharashtra, India to evaluate the sustainable agricultural practices for improving the growth and yield of the soybean (Glycine max L.) variety JS-335 along with the soil improvement. Twelve treatments were evaluated in a randomized block design with three replications. The results revealed that combined applications of chemical fertilizer (RDF-30:80:20:40) and organic manures (FYM and VCM) improved the growth attributes and seed yield as compared to control and other treatments along with a significant improvement in the soil health parameters. This agricultural practice emerged as a promising method for sustainable cultivation of soybean (C.V. JS-335) which improved the economic yield (kg.ha-1), agronomic efficiency (kg.ha-1), physiological efficiency (kg.ha-1), partial factor productivity (kg.ha-1), apparent recovery efficiency (kg.ha-1) and sustainable yield index (0.80) with maximum return having cost: benefit ratio (1:3.8). The results found statistically significant and correlated having a positive relationship between yield and sustainability parameters.

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

Soil is the most important natural resource for life on Earth after water. Given its fundamental role in sustaining the human population, both the availability and quality of soil must be managed sustainably and protected. To ensure sustainable management we need to understand the intrinsic functional capacity of different soils across Europe and how it changes over time. Soil monitoring is needed to support evidence-based policies to incentivise sustainable soil management. To this aim, we assessed which soil attributes can be used as potential indicators of five soil functions; (1) primary production, (2) water purification and regulation, (3) carbon sequestration and climate regulation, (4) soil biodiversity and habitat provisioning and (5) recycling of nutrients. We compared this list of attributes to existing national (regional) and EU-wide soil monitoring networks. The overall picture highlighted a clearly unbalanced dataset, in which predominantly chemical soil parameters were included, and soil biological and physical attributes were severely under represented. Methods applied across countries for indicators also varied. At a European scale, the LUCAS-soil survey was evaluated and again confirmed a lack of important soil biological parameters, such as C mineralisation rate, microbial biomass and earthworm community, and soil physical measures such as bulk density. In summary, no current national or European monitoring system exists which has the capacity to quantify the five soil functions and therefore evaluate multi-functional capacity of a soil and in many countries no data exists at all. This paper calls for the addition of soil biological and some physical parameters within the LUCAS-soil survey at European scale and for further development of national soil monitoring schemes.

A comprehensive 30×30 arc‐second resolution gridded soil characteristics data set of China has been developed for use in the land surface modeling. It includes physical and chemical attributes of soils derived from 8979 soil profiles and the Soil Map of China (1:1,000,000). We used the polygon linkage method to derive the spatial distribution of soil properties. The profile attribute database and soil map are linked under the framework of the Genetic Soil Classification of China which avoids uncertainty in taxon referencing. Quality control information (i.e., sample size, soil classification level, linkage level, search radius and texture) is included to provide “confidence” information for the derived soil parameters. The data set includes 28 attributes for 8 vertical layers at the spatial resolution of 30×30 arc‐seconds. Based on this data set, the estimated storage of soil organic carbon in the upper 1 m of soil is 72.5 Pg, total N is 6.6 Pg, total P is 4.5 Pg, total K is 169.9 Pg, alkali‐hydrolysable N is 0.55 Pg, available P is 0.03 Pg, and available K is 0.61 Pg. These estimates are reasonable compared with previous studies. The distributions of soil properties are consistent with common knowledge of Chinese soil scientists and the spatial variations over large areas are well represented. The data set can be incorporated into land models to better represent the role of soils in hydrological and biogeochemical cycles in China. Key Points A soil characteristics dataset of China was developed for land modeling A soil attribute database of China, comprising 8979 soil profiles, was developed The polygon linkage method was used to derive the spatial distribution of soils

We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.

Water erosion is one of the primary causes of agricultural soil degradation in Brazil, leading to diminished crop productivity and soil acidification, thereby impairing its ability to store carbon, nutrients, and water. Identifying the intensity of erosion can be achieved by utilizing indicator attributes that are highly sensitive to changes in the edaphic environment. The study analyzed the physical and chemical attributes of soil in areas with gullies exhibiting varying degrees of degradation/stabilization. The study was conducted across four areas with varying degrees of gully formation: a) initial, intermediate, mature, and senile. Samples were collected from both the external and internal sides of each gully at the end of the dry season. Among the physical attributes assessed, soil density and total porosity were found to be the most significantly altered. Evaluation of microaggregates provided insights into soil quality through fractions including water-dispersible clay, water-reflocculable clay, and non-water-dispersible clay. Total carbon, total nitrogen, and the C/N ratio elucidated the dynamics of soil and nutrient loss across different stages of erosion formation and stabilization processes, with lower values observed on the internal side of the gullies compared to the exterior side. Vegetation was observed to influence the results of the physical and chemical attributes. Overall, the values tended towards equilibrium between the faces at the senile stage, indicating greater stabilization. Keywords: agricultural sustainability, agroecosystem degradation, food security, soil attributes.

The increase in demand for more food production due to the population growth has caused land efficiency and sustainable soil management to be taken into consideration. Multi-criteria decision-making (MCDM) technique ranks the management alternatives via handling various data. The aim of this study was to determine efficient criteria and sub-criteria among the 16 soil properties in cropland, rangeland, and forestland based on the pair-wise comparison, weighting, and computing the influence percentage through fuzzy analytical hierarchy process (FAHP) and fuzzy TOPSIS and comparing their outcomes. Furthermore, the gap degree was calculated to understand which alternative and to what extent should be changed and improved to achieve the goal in the best way. The Best Non-fuzzy Performance Value (BNP) with the center of area (COA) method was applied to estimate the criterion weight. According to the final weights of criteria in cropland, it can be seen that the chemical attribute was preferred, since it had the highest weight (0.459), followed by the nutritional (0.332) and physical (0.209) properties; whereas, the most important criterion in rangeland and forestland was related to the physical property (weight = 0.76). The highest gap degree was obtained for bulk density (BD) (0.868), pH (0.567), and nitrogen (N) (0.845), respectively. The larger the gap degree, the more preferred the alternative. According to the computed relative closeness coefficient (RCCi), soil N and BD carried the highest priority. Application of FAHP to identify the most significant factor, which has critical effects on land sustainable management, also evaluation of environmental performance especially associated with soil properties in different land uses are very important due to guide decision-maker on picking FAHP model in considering and ranking decision criteria used in land use management.

Restoring forest ecosystems has become a global priority. Yet, soil dynamics are still poorly assessed among restoration studies and there is a lack of knowledge on how soil is affected by forest restoration process. Here, we compile information on soil dynamics in forest restoration based on soil physical, chemical, and biological attributes in temperate and tropical forest regions. It encompasses 50 scientific papers across 17 different countries and contains 1,469 points of quantitative information of soil attributes between reference (e.g., old-growth forest) and restored ecosystems (e.g., forests in their initial or secondary stage of succession) within the same study. To be selected, studies had to be conducted in forest ecosystems, to include multiple sampling sites (replicates) in both restored and reference ecosystems, and to encompass quantitative data of soil attributes for both reference and restored ecosystems. We recorded in each study the following information: (1) study year, (2) country, (3) forest region (tropical or temperate), (4) latitude, (5) longitude, (6) soil class, (7) past disturbance, (8) restoration strategy (active or passive), (9) restoration age, (10) soil attribute type (physical, chemical, or biological); (11) soil attribute, (12) soil attribute unit, (13) soil sampling (procedures), (14) date of sampling, (15) soil depth sampled, (16) soil analysis, (17) quantitative values of soil attributes for both restored and reference ecosystems, (18) type of variation (standard error of deviation) for both restored and reference ecosystems, and (19) quantitative values of the variation for both restored and reference ecosystems. These were the most common data available in the selected studies. This extensive database on the extent soil physical, chemical, and biological attributes differ between reference and restored ecosystems can fill part of the existing gap on both soil science and forest restoration in terms of (1) which are the critical soil attributes to be monitored during forest restoration? and (2) how do environmental factors affect soil attributes in forest restoration? The data will be made available to the scientific community for further analyses on both soil science and forest restoration. Soil information gaps during the forest restoration process and their general patterns can be addressed using this data set. There are no copyright or proprietary restrictions.

Sequestration of atmospheric carbon to the soil is a challenging task for the scientific community to mitigate the rising concentration of atmospheric carbon dioxide (CO2). Biochar, due to its aromatic structure and long mean residence time in the soil (more than 100 years) has the potential for long-term carbon sequestration in the soil. The trend obtained from the meagre published literature raised our hopes of achieving the goal of enhancing the productivity of different crops along with environmental sustainability. According to an estimate, global production of black carbon has been reported between 50 and 270 Tg yr−1, with as much as 80% of this remaining as residues in the soil. Biochar decomposition rate is slow in the soil, which indicates that it could be the possible answer to mitigation of elevated atmospheric CO2. It is reported that black carbon can produce significant benefits when applied to agricultural soils in combination with some fertilizers. Increase in crop yield to the tune of 45–250% has been reported by application of biochar along with chemical fertilizers. Soil water retention properties, saturated hydraulic conductivity and nutrients availability increased with the application of biochar. Biochar application reduced CO2 respiration, niturous oxide and methane production, and decreased dissipation rate of herbicide in the soil.