Incorporating Legacy Soil pH Databases into Digital Soil Maps

Soil data and reliable soil maps are imperative for environmental management. conservation and policy. Data from historical point surveys, e.g. experiment site data and farmers fields can serve this purpose. However, legacy soil information is not necessarily collected for spatial analysis and mapping such that the data may not have immediately useful geo-references. Methods are required to utilise these historical soil databases so that we can produce quantitative maps of soil propel-ties to assess spatial and temporal trends but also to assess where future sampling is required. This paper discusses two such databases: the Representative Soil Sampling Scheme which has monitored the agricultural soil in England and Wales from 1969 to 2003 (between 400 and 900 bulked soil samples were taken annually from different agricultural fields); and the former State Chemistry Laboratory, Victoria, Australia where between 1973 and 1994 approximately 80,000 soil samples were submitted for analysis by farmers. Previous statistical analyses have been performed using administrative regions (with sharp boundaries) for both databases, which are largely unrelated to natural features. For a more detailed spatial analysis that call be linked to climate and terrain attributes, gradual variation of these soil properties should be described. Geostatistical techniques such as ordinary kriging are suited to this. This paper describes the format of the databases and initial approaches as to how they can be used for digital soil mapping. For this paper we have selected soil pH to illustrate the analyses for both databases.

Modelled soil organic carbon stocks and changes in the Indo-Gangetic Plains, India from 1980 to 2030

The Global Environment Facility co-financed Soil Organic Carbon (GEFSOC) Project developed a comprehensive modelling system for predicting soil organic carbon (SOC) stocks and changes over time. This research is an effort to predict SOC stocks and changes for the Indian, Indo-Gangetic Plains (IGP), an area with a predominantly rice (Oryza sativa) - wheat (Triticum aestivum) cropping system, using the GEFSOC Modelling System and to compare output with stocks generated using mapping approaches based on soil survey data. The GEFSOC Modelling System predicts an estimated SOC stock for the IGP, India of 1.27, 1.32 and 1.27 Pg for 1990, 2000 and 2030, respectively, in the top 20 cm of soil. The SOC stock using a mapping approach based on soil survey data was 0.66 and 0.88 Pg for 1980 and 2000, respectively. The SOC stock estimated using the GEFSOC Modelling System is higher than the stock estimated using the mapping approach. This is due to the fact that while the GEFSOC System accounts for variation in crop input data (crop management), the soil mapping approach only considers regional variation in soil texture and wetness. The trend of overall change in the modelled SOC stock estimates shows that the IGP, India may have reached an equilibrium following 30-40 years of the Green Revolution. This can be seen in the SOC stock change rates. Various different estimation methods show SOC stocks of 0.57-1.44 Pg C for the study area. The trend of overall change in C stock assessed from the soil survey data indicates that the soils of the IGP, India may store a projected 1.1 Pg of C in 2030. (C) 2007 Elsevier B.V. All rights reserved.

Predicted soil organic carbon stocks and changes in Jordan between 2000 and 2030 made using the GEFSOC modelling system

Estimates of soil organic carbon (SOC) stocks and changes under different land use systems can help determine vulnerability to land degradation. Such information is important for countries in and areas with high susceptibility to desertification. SOC stocks, and predicted changes between 2000 and 2030, were determined at the national scale for Jordan using The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. For the purpose of this study, Jordan was divided into three natural regions (The Jordan Valley, the Uplands and the Badia) and three developmental regions (North, Middle and South). Based on this division, Jordan was divided into five zones (based on the dominant land use): the Jordan Valley, the North Uplands, the Middle Uplands, the South Uplands and the Badia. This information was merged using GIS, along with a map of rainfall isohyets, to produce a map with 498 polygons. Each of these was given a unique ID, a land management unit identifier and was characterized in terms of its dominant soil type. Historical land use data, current land use and future land use change scenarios were also assembled, forming major inputs of the modelling system. The GEFSOC Modelling System was then run to produce C stocks in Jordan for the years 1990, 2000 and 2030. The results were compared with conventional methods of estimating carbon stocks, such as the mapping based SOTER method. The results of these comparisons showed that the model runs are acceptable, taking into consideration the limited availability of long-term experimental soil data that can be used to validate them. The main findings of this research show that between 2000 and 2030, SOC may increase in heavily used areas under irrigation and will likely decrease in grazed rangelands that cover most of Jordan giving an overall decrease in total SOC over time if the land is indeed used under the estimated forms of land use. (C) 2007 Elsevier B.V. All rights reserved.

Predicted soil organic carbon stocks and changes in the Brazilian Amazon between 2000 and 2030

Currently we have little understanding of the impacts of land use change on soil C stocks in the Brazilian Amazon. Such information is needed to determine impacts'6n the global C cycle and the sustainability of agricultural systems that are replacing native forest. The aim of this study was to predict soil carbon stocks and changes in the Brazilian Amazon during the period between 2000 and 2030, using the GEFSOC soil carbon (C) modelling system. In order to do so, we devised current and future land use scenarios for the Brazilian Amazon, taking into account: (i) deforestation, rates from the past three decades, (ii) census data on land use from 1940 to 2000, including the expansion and intensification of agriculture in the region, (iii) available information on management practices, primarily related to well managed pasture versus degraded pasture and conventional systems versus no-tillage systems for soybean (Glycine max) and (iv) FAO predictions on agricultural land use and land use changes for the years 2015 and 2030. The land use scenarios were integrated with spatially explicit soils data (SOTER database), climate, potential natural vegetation and land management units using the recently developed GEFSOC soil C modelling system. Results are presented in map, table and graph form for the entire Brazilian Amazon for the current situation (1990 and 2000) and the future (2015 and 2030). Results include soil organic C (SOC) stocks and SOC stock change rates estimated by three methods: (i) the Century ecosystem model, (ii) the Rothamsted C model and (iii) the intergovernmental panel on climate change (IPCC) method for assessing soil C at regional scale. In addition, we show estimated values of above and belowground biomass for native vegetation, pasture and soybean. The results on regional SOC stocks compare reasonably well with those based on mapping approaches. The GEFSOC system provided a means of efficiently handling complex interactions among biotic-edapho-climatic conditions (> 363,000 combinations) in a very large area (similar to 500 Mha) such as the Brazilian Amazon. All of the methods used showed a decline in SOC stock for the period studied; Century and RothC simulated values for 2030 being about 7% lower than those in 1990. Values from Century and RothC (30,430 and 25,000 Tg for the 0-20 cm layer for the Brazilian Amazon region were higher than those obtained from the IPCC system (23,400 Tg in the 0-30 cm layer). Finally; our results can help understand the major biogeochemical cycles that influence soil fertility and help devise management strategies that enhance the sustainability of these areas and thus slow further deforestation. (C) 2007 Elsevier B.V. All rights reserved.

Estimating soil water fluxes from soil water records obtained using dielectric sensors

Accurate estimation of the soil water balance (SWB) is important for a number of applications (e.g. environmental, meteorological, agronomical and hydrological). The objective of this study was to develop and test techniques for the estimation of soil water fluxes and SWB components (particularly infiltration, evaporation and drainage below the root zone) from soil water records. The work presented here is based on profile soil moisture data measured using dielectric methods, at 30-min resolution, at an experimental site with different vegetation covers (barley, sunflower and bare soil). Estimates of infiltration were derived by assuming that observed gains in the soil profile water content during rainfall were due to infiltration. Inaccuracies related to diurnal fluctuations present in the dielectric-based soil water records are resolved by filtering the data with adequate threshold values. Inconsistencies caused by the redistribution of water after rain events were corrected by allowing for a redistribution period before computing water gains. Estimates of evaporation and drainage were derived from water losses above and below the deepest zero flux plane (ZFP), respectively. The evaporation estimates for the sunflower field were compared to evaporation data obtained with an eddy covariance (EC) system located elsewhere in the field. The EC estimate of total evaporation for the growing season was about 25% larger than that derived from the soil water records. This was consistent with differences in crop growth (based on direct measurements of biomass, and field mapping of vegetation using laser altimetry) between the EC footprint and the area of the field used for soil moisture monitoring. Copyright (c) 2007 John Wiley & Sons, Ltd.

Variograms of ancillary data to aid sampling for soil surveys

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograins of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site, Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograrns showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.

Determining nugget: sill ratios of standardized variograms from aerial photographs to krige sparse soil data

Maps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data.

Mapping the Sinorhizobium meliloti 1021 solute-binding protein-dependent transportome

The number of solute-binding protein-dependent transporters in rhizobia is dramatically increased compared with the majority of other bacteria so far sequenced. This increase may be due to the high affinity of solute-binding proteins for solutes, permitting the acquisition of a broad range of growth-limiting nutrients from soil and the rhizosphere. The transcriptional induction of these transporters was studied by creating a suite of plasmid and integrated fusions to nearly all ATP-binding cassette (ABC) and tripartite ATP-independent periplasmic (TRAP) transporters of Sinorhizobium meliloti. In total, specific inducers were identified for 76 transport systems, amounting to approximate to 47% of the ABC uptake systems and 53% of the TRAP transporters in S. meliloti. Of these transport systems, 64 are previously uncharacterized in Rhizobia and 24 were induced by solutes not known to be transported by ABC- or TRAP-uptake systems in any organism. This study provides a global expression map of one of the largest transporter families (transportome) and an invaluable tool to both understand their solute specificity and the relationships between members of large paralogous families.

Shoot zinc (Zn) concentration varies widely within Brassica oleracea L. and is affected by soil Zn and phosphorus (P) levels

The low availability of zinc (Zn) in soils and crops affects dietary Zn intake worldwide. This study sought to determine if the natural genetic variation in shoot Zn concentrations (Zn(shoot)) is sufficient to pursue a crop improvement breeding strategy in a leafy vegetable crop. The gene-pool of Brassica oleracea L. was sampled using a large (n = 376) diversity foundation set (DFS), representing almost all species-wide common allelic variation, and 74 commercial varieties (mostly F(1)). The DFS genotypes were grown at low and high soil phosphorus (P) levels under glasshouse and field conditions, and also in a Zn-deficient soil, with or without Zn-fertilisation, in a glasshouse. Despite the large variation in Zn(shoot) among genotypes, environment had a profound effect on Zn(shoot) The heritability of Zn(shoot) was significant, but relatively low, among 90 doubled-haploid (DH) lines from a mapping population. While several quantitative trait loci (QTL) associated with Zn(shoot) occurred on chromosomes C2, C3, C5, C7, and C9, these were generally weak and conditional upon growth conditions. Breeding for Zn(shoot) in B. oleracea is therefore likely to be challenging. Shoot P concentrations increased substantially in all genotypes under low soil Zn conditions. Conversely, only some genotypes had increased Zn(shoot) at low soil P levels. Sufficient natural genetic variation may therefore exist to study some of the interactions between Zn and P nutrition.

Regulatory hotspots are associated with plant gene expression under varying soil phosphorus supply in Brassica rapa

Gene expression is a quantitative trait that can be mapped genetically in structured populations to identify expression quantitative trait loci (eQTL). Genes and regulatory networks underlying complex traits can subsequently be inferred. Using a recently released genome sequence, we have defined cis- and trans-eQTL and their environmental response to low phosphorus (P) availability within a complex plant genome and found hotspots of trans-eQTL within the genome. Interval mapping, using P supply as a covariate, revealed 18,876 eQTL. trans-eQTL hotspots occurred on chromosomes A06 and A01 within Brassica rapa; these were enriched with P metabolism-related Gene Ontology terms (A06) as well as chloroplast-and photosynthesis-related terms (A01). We have also attributed heritability components to measures of gene expression across environments, allowing the identification of novel gene expression markers and gene expression changes associated with low P availability. Informative gene expression markers were used to map eQTL and P use efficiency-related QTL. Genes responsive to P supply had large environmental and heritable variance components. Regulatory loci and genes associated with P use efficiency identified through eQTL analysis are potential targets for further characterization and may have potential for crop improvement.

Simple measures of climate, soil properties and plant traits predict national scale grassland soil carbon stocks

1. Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. 2. Using data from an extensive national survey of English grasslands we show that surface soil (0-7cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. 3. Soil C stocks in the largest pool, of intermediate particle size (50-250 µm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0.45-50 µm), was explained by soil pH and the community abundance weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N rich vegetation. The C stock in the small active fraction (250-4000 µm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. 4. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. 5. Synthesis and Applications: Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1-100,000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.