RESUMEN

Increasing soil organic carbon (SOC) confers benefits to soil health, biodiversity, underpins carbon sequestration and ameliorates land degradation. One recommendation is to increase SOC such that the SOC to clay ratio (SOC/clay) exceeds 1/13, yet normalising SOC levels based on clay alone gives misleading indications of soil structure and the potential to store additional carbon. Building on work by Poeplau & Don (2023) to benchmark observed against predicted SOC, we advance an alternative indicator: the ratio between observed and "typical" SOC (O/T SOC) for pan-European application. Here, "typical" SOC is the average concentration in different pedo-climate zones, PCZs (which, unlike existing SOC indicators, incorporate land cover and climate, alongside soil texture) across Europe, determined from mineral (<20 % organic matter) topsoils (0-20 cm) sampled during 2009-2018 in LUCAS, Europe's largest soil monitoring scheme (n = 19,855). Regression tree modelling derived 12 PCZs, with typical SOC values ranging 5.99-39.65 g kg-1. New index classes for comparison with SOC/clay grades were established from the quartiles of each PCZ's O/T SOC distribution; these were termed: "Low" (below the 25th percentile), "Intermediate" (between the 25th and 50th percentiles), "High" (between the 50th and 75th percentiles), and "Very high" (above the 75th percentile). Compared with SOC/clay, O/T SOC was less sensitive to clay content, land cover, and climate, less geographically skewed, and better reflected differences in soil porosity and SOC stock, supporting 2 EU Soil Health Mission objectives (consolidating SOC stocks; improving soil structure for crops and biota). These patterns held for 2 independent datasets, and O/T SOC grades were sensitive enough to reflect land management differences across several long-term field experiments. O/T SOC used in conjunction with several other physical, chemical and biological soil health indicators can help support the EU Soil Monitoring Law and achieve several United Nations Sustainable Development Goals.

RESUMEN

Efforts to improve soil health require that target values of key soil properties are established. No agreed targets exist but providing population data as benchmarks is a useful step to standardise soil health comparison between landscapes. We exploited nationally representative topsoil (0-15 cm) measurements to derive soil health benchmarks for managed and semi-natural environments across Great Britain. In total, 4587 soil organic matter (SOM), 3860 pH, 2908 bulk density (BD), and 465 earthworm abundance (EA) datapoints were used. As soil properties are sensitive to site-specific characteristics, data were stratified by habitat, soil type, and mean annual precipitation, with benchmarks defined as the middle 80 % of values in each distribution - yielding 135 benchmarks. BD and pH decreased with land management intensity (agriculture > semi-natural grasslands > woodlands > heathlands > wetlands), and vice versa for SOM and EA. Normalising benchmark ranges by medians revealed soil health indicator benchmark widths increased in the order: pH < BD < SOM < EA, while width increased with decreasing land management intensity. Arable and horticulture and improved grassland exhibited narrow benchmarks for SOM, pH and BD, yet the widest EA benchmark, suggesting additional drivers impact EA patterns. Upland wetlands had the widest BD benchmarks, important when determining carbon stocks. East Anglia currently possesses the largest proportions of atypical soils, including below typical SOM (19.2 %), above typical BD (17.4 %) and pH (39.1 %), and the smallest proportions of above typical SOM (2.4 %), and below typical BD (5.8 %) and pH (2.3 %). This is found even after land use, soil type and rainfall have been considered, underscoring how urgently soil health should be addressed here. Our benchmarking framework allows landowners to compare where their measured soil health indicators fall within expected ranges and is applicable to other biomes, national and multinational contexts.

Asunto(s)

Benchmarking , Suelo , Suelo/química , Ecosistema , Agricultura , Bosques , Carbono

RESUMEN

As agricultural land area increases to feed an expanding global population, soil erosion will likely accelerate, generating unsustainable losses of soil and nutrients. This is critical for Kenya where cropland expansion and nutrient loading from runoff and erosion is contributing to eutrophication of freshwater ecosystems and desertification. We used the Revised Universal Soil Loss Equation (RUSLE) to predict soil erosion rates under present land cover and potential natural vegetation nationally across Kenya. Simulating natural vegetation conditions allows the degree to which erosion rates are elevated under current land use practices to be determined. This methodology exploits new digital soil maps and two vegetation cover maps to model topsoil (top 20 cm) erosion rates, lifespans (the mass of topsoil divided by erosion rate), and lateral nutrient fluxes (nutrient concentration times erosion rate) under both scenarios. We estimated the mean soil erosion rate under current land cover at ~5.5 t ha-1 yr-1, ~3 times the rate estimated for natural vegetation cover (~1.8 t ha-1 yr-1), and equivalent to ~320 Mt yr-1 of topsoil lost nationwide. Under present erosion rates, ~8.8 Mt, ~315 Kt, and ~ 110 Kt of soil organic carbon, nitrogen and phosphorous are lost from soil every year, respectively. Further, 5.3 % of topsoils (~3.1 Mha), including at >25 % of croplands, have short lifespans (<100 years). Additional scenarios were tested that assume combinations of terracing and reduced tillage practices were adopted on croplands to mitigate erosion. Establishing bench terraces with zoned tillage could reduce soil losses by ≥75 %; up to 87.1 t ha-1 yr-1. These reductions are comparable to converting croplands to natural vegetation, demonstrating most agricultural soils can be conserved successfully. Extensive long-term monitoring of croplands with terraces and reduced tillage established is required to verify the efficacy of these agricultural support practices as indicated by our modelling.