Emissions of N2O and NO from fertilized fields: summary of available measurement data

Information from 846 N2O emission measurements in agricultural fields and 99 measurements for NO emissions was summarized to assess the influence of various factors regulating emissions from mineral soils. The data indicate that there is a strong increase of both N2O and NO emissions accompanying N application rates, and soils with high organic-C content show higher emissions than less fertile soils. A fine soil texture, restricted drainage, and neutral to slightly acidic conditions favor N2O emission, while (though not significant) a good soil drainage, coarse texture, and neutral soil reaction favor NO emission. Fertilizer type and crop type are important factors for N2O but not for NO, while the fertilizer application mode has a significant influence on NO only. Regarding the measurements, longer measurement periods yield more of the fertilization effect on N2O and NO emissions, and intensive measurements (=1 per day) yield lower emissions than less intensive measurements (2–3 per week). The available data can be used to develop simple models based on the major regulating factors which describe the spatial variability of emissions of N2O and NO with less uncertainty than emission factor approaches based on country N inputs, as currently used in national emission inventories

Modelling global annual N2O and NO emissions from fertilized fields

Information from 846 N2O emission measurements in agricultural fields and 99 measurements for NO emissions was used to describe the influence of various factors regulating emissions from mineral soils in models for calculating global N2O and NO emissions. Only those factors having a significant influence on N2O and NO emissions were included in the models. For N2O these were (1) environmental factors (climate, soil organic C content, soil texture, drainage and soil pH); (2) management-related factors (N application rate per fertilizer type, type of crop, with major differences between grass, legumes and other annual crops); and (3) factors related to the measurements (length of measurement period and frequency of measurements). The most important controls on NO emission include the N application rate per fertilizer type, soil organic-C content and soil drainage. Calculated global annual N2O-N and NO-N emissions from fertilized agricultural fields amount to 2.8 and 1.6 Mtonne, respectively. The global mean fertilizer-induced emissions for N2O and NO amount to 0.9% and 0.7%, respectively, of the N applied. These overall results account for the spatial variability of the main N2O and NO emission controls on the landscape scal

Technologically achievable soil organic carbon sequestration in world croplands and grasslands

Reported potentials for sequestration of carbon in soils of agricultural lands are overly optimistic because they assume that all degraded cropland and grassland can be subjected to best management practices. Two approaches for estimating this potential are presented. Method 1 (M1) considers literature-derived best estimates for annual soil organic carbon (SOC) gains (Mg C ha−1) by bioclimatic zone; Method 2 (M2) assumes an annual C increase of 3 to 5 promille with respect to present SOC mass (similar to the French ‘4 pour mille’ initiative). Four management scenarios are considered, capturing the varying level of plausibility of meeting the full technological potential. According to M1, achievable gains range from 0.05–0.12 Pg C yr−1 to 0.14–0.37 Pg C yr−1, with a technological potential of 0.32–0.86 Pg C yr−1. For M2, these are 0.07–0.12 Pg C yr−1, 0.21–0.35 Pg C yr−1, and 0.60–1.01 Pg C yr−1. Consideration of the technological potential only and use of a proportional annual increase in SOC (M2), rather than using best estimates for soil carbon gains by bioclimatic zone (M1), will provide too ‘bright a picture’ in the context of rehabilitating degraded lands and mitigating/adapting to climate change. Further, M2 assumes that possible C gains will be greatest where present SOC stocks are highest, which is counter-intuitive. Although all measures aimed at increasing SOC content should be encouraged due to the creation of win-win situations, it is important to create a realistic picture of the amount of SOC gains that are feasible based on bioclimatic and management implementation constraints.</p

Better agronomic management increases climate resilience of maize to drought in Tanzania

Improved access to better seeds and other inputs, as well as to market and financing, provides greater harvest security for smallholder farmers in Africa, boosting their incomes and increasing food security. Since 2015, a variety of agronomic measures have been introduced and adopted by smallholder farmers under a program led by the United Nations’ World Food Program (WFP) called the Patient Procurement Platform (PPP). Here, we integrate a variety of agronomic measures proposed by the PPP to more than 20,000 smallholder farmers in Tanzania into 18 management strategies. We apply these across the country through grid-based crop model (DSSAT) simulations in order to quantify their benefits and risk to regional food security and smallholder farmers’ livelihoods. The simulation demonstrates current maize yields are far below potential yields in the country. Simulated yields across the nation were slightly higher than the mean of reported values from 1984 to 2014. Periodic droughts delayed farmers’ sowing and reduced maize yield, leading to high risk and low sustainability of maize production in most of the maize areas of the country. Better agronomic management strategies, particularly the combination of long-maturity, drought tolerance cultivars, with high fertilizer input, can potentially increase national maize production by up to five times, promoting Tanzania as a regional breadbasket. Our study provides detailed spatial and temporal information of the yield responses and their spatial variations, facilitating the adoption of various management options for stakeholders

Terrestrial organic carbon storage in a British moorland

Accurate estimates for the size of terrestrial organic carbon (C) stores are needed to determine their importance in regulating atmospheric CO2 concentrations. The C stored in vegetation and soil components of a British moorland was evaluated in order to: (i) investigate the importance of these ecosystems for C storage and (ii) test the accuracy of the United Kingdom's terrestrial C inventory. The area of vegetation and soil types was determined using existing digitized maps and a Geographical Information System (GIS). The importance of evaluating C storage using 2D area projections, as opposed to true surface areas, was investigated and found to be largely insignificant. Vegetation C storage was estimated from published results of productivity studies at the site supplemented by field sampling to evaluate soil C storage. Vegetation was found to be much less important for C storage than soil, with peat soils, particularly Blanket bog, containing the greatest amounts of C. Whilst the total amount of C in vegetation was similar to the UK national C inventory's estimate for the same area, the national inventory estimate for soil C was over three times higher than the value derived in the current study. Because the UK's C inventory can be considered relatively accurate compared to many others, the results imply that current estimates for soil C storage, at national and global scales, should be treated with caution

Global data set of derived soil properties, 0.5-degree grid (ISRIC-WISE)

The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree ... longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables: Total available water capacity (mm water per 1 m soil depth) soil organic carbon density (kg C/m**2 for 0-30cm depth range) soil organic carbon density (kg C/m**2 for 0-100cm depth range) soil carbonate carbon density (kg C/m**2 for 0-100cm depth range) soil pH (0-30 cm depth range) soil pH (30-100 cm depth range) Data Citation: The data set should be cited as follows: Batjes, N. H. (ed). 2000. Global Data Set of Derived Soil Properties, 0.5-Degree Grid (ISRIC-WISE). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A

SoilGrids: using big data solutions and machine learning algorithms for global soil mapping

The SoilGrids system (www.soilgrids.org) uses machine learning algorithms to predict soil type and basic soil properties at seven depths on global extent. These algorithms (i.e., random forests, gradient boosting) are trained with soil observations assembled from 150 000 locations across the globe as stored in WoSIS ..

Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019)

The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broadscale levels. Since the release of the first "WoSIS snapshot", in July 2016, many new soil data were shared with us, registered in the ISRIC data repository and subsequently standardised in accordance with the licences specified by the data providers. Soil profile data managed inWoSIS were contributed by a wide range of data providers; therefore, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values (and units of measurement) and soil analytical method descriptions. We presently consider the following soil chemical properties: organic carbon, total carbon, total carbonate equivalent, total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity. We also consider the following physical properties: soil texture (sand, silt, and clay), bulk density, coarse fragments and water retention. Both of these sets of properties are grouped according to analytical procedures that are operationally comparable. Further, for each profile we provide the original soil classification (FAO, WRB, USDA), version and horizon designations, insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e. location) of the point data, as well as a first approximation for the uncertainty associated with the operationally defined analytical methods, are presented for possible consideration in digital soil mapping and subsequent earth system modelling. The latest (dynamic) set of quality-assessed and standardised data, called "wosis-latest", is freely accessible via an OGC-compliant WFS (web feature service). For consistent referencing, we also provide time-specific static "snapshots". The present snapshot (September 2019) is comprised of 196 498 geo-referenced profiles originating from 173 countries. They represent over 832 000 soil layers (or horizons) and over 5.8 million records. The actual number of observations for each property varies (greatly) between profiles and with depth, generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to fill gradually gaps in the geographic distribution and soil property data themselves, this subject to the sharing of a wider selection of soil profile data for so far under-represented areas and properties by our existing and prospective partners. Part of this work is foreseen in conjunction within the Global Soil Information System (GloSIS) being developed by the Global Soil Partnership (GSP). The "WoSIS snapshot-September 2019" is archived and freely accessible at https://doi.org/10.17027/isric-wdcsoils.20190901 (Batjes et al., 2019).</p