A 104 295-308 5 Fig. 5 Tab. Hannover 1988 Geol. Jb. J. MARTIN RAGG, ROBERT J .A. JONES & MARY E. PROCTOR Soil Survey of England and Wales, project, database, land evaluation, agroclimatic data, mois- ture, field capacity, regression analysis, trend analysis United Kingdom Abstract: The creation of an information system for land evaluation (LandIS) by the Soil Sur- vey of England and Wales commenced in 1979. The philosophy behind LandIS was that it should be a 'small budget' system until its value had been proved, serve the soil scientist and those evaluat- ing land, have a user-friendly interface, include spatial datasets, interface with external software and extend our knowledge beyond that of just the soil data collected in the field. With the excep- tion of only limited progress with the user-friendly interface, all these aims have now been achieved and the system is operational. Individual datasets have been of great value but one of the most successful aspects of LandIS has been the incorporation of agroclimatic data for every 5 km x 5 km square in England and Wales. Moisture deficits (MD) for 970 meteorological stations and field capacity data (PC) for 10 km x 10 km pixels were expressed as linear functions of average annual rainfall (AAR) and altitude (AL T) by regression analysis. A trend-surface program was used to regularize x, y and z data. Adjustments to the processed data were made using the Information System and similar techniques were used to generate a regular field capacity (PC) dataset on a 5 km grid. The work demonstrated that well distributed soil data, together with appropriate environmental data and soundly based models, can transform a simple soil database into a land evaluation system. [Die AufbereituDg UDd DarstelluDg riumlicher DateD in eiDem IDformatioDS-System mit Hilfe statistischer UDd DBMS FuDktioDeD UDd TreDd-ADalyseD] Kurzfassung: 1979 begann der Soil Survey of England and Wales mit der Entwicklung eines Informations-Systems rur die Bodenschatzung (LandIS). Die Philosophie, die hinter LandIS steckte war, daJ3 es so lange ein System mit kleinem Budget sein sollte, bis bewiesen war, daJ3 es Bo- denkundler und Bodenschatzer untersttitzen kann, eine Benutzer-freundliche Bedienungsoberfla- che hat, raumliche Daten aufnehmen kann, Schnittstellen zu externer Software hat und unser Wis- sen Uber die im Gelande gesammelten Bodendaten hinaus erweitern kann. Bis auf begrenzte Fortschritte bei der Entwicklung der Benutzer-freundlichen Oberflache sind alle Ziele erreicht, und das System ist im Einsatz. Das Vorhalten der Daten in eigenstandigen Daten- sammlungen war von groJ3erBedeutung, aber eine der erfolgreichsten Einsatze von LandIS war die Vereinigung agroklimatischer Daten, die nun in einem 5 x 5 km2-Netz rur England und Wales vor- liegen. Bodenfeuchtigkeitsdefizite aus den Werten von 970 meteorologischen Stationen und Feld- kapazitats- W erte fur ein lOx 10 km2 Raster wurden als lineare Funktion des durchschnittlichen Authors' address: Dr. J .M. RAGG. Dr. R.J .A. JONES. M.E. PROCTOR. Soil Survey and Land Re- search Centre, Silsoe, Bedford MK45 4DT, U .K.
Transcript
A 104 295-308 5 Fig. 5 Tab. Hannover 1988Geol. Jb.
J. MARTIN RAGG, ROBERT J .A. JONES & MARY E. PROCTOR
Soil Survey of England and Wales, project, database, land evaluation, agroclimatic data, mois-ture, field capacity, regression analysis, trend analysis United Kingdom
Abstract: The creation of an information system for land evaluation (LandIS) by the Soil Sur-vey of England and Wales commenced in 1979. The philosophy behind LandIS was that it shouldbe a 'small budget' system until its value had been proved, serve the soil scientist and those evaluat-ing land, have a user-friendly interface, include spatial datasets, interface with external softwareand extend our knowledge beyond that of just the soil data collected in the field. With the excep-tion of only limited progress with the user-friendly interface, all these aims have now been achievedand the system is operational.Individual datasets have been of great value but one of the most successful aspects of LandIS hasbeen the incorporation of agroclimatic data for every 5 km x 5 km square in England and Wales.Moisture deficits (MD) for 970 meteorological stations and field capacity data (PC) for 10 km x 10km pixels were expressed as linear functions of average annual rainfall (AAR) and altitude (AL T)by regression analysis. A trend-surface program was used to regularize x, y and z data.Adjustments to the processed data were made using the Information System and similar techniqueswere used to generate a regular field capacity (PC) dataset on a 5 km grid.The work demonstrated that well distributed soil data, together with appropriate environmentaldata and soundly based models, can transform a simple soil database into a land evaluation system.
Kurzfassung: 1979 begann der Soil Survey of England and Wales mit der Entwicklung einesInformations-Systems rur die Bodenschatzung (LandIS). Die Philosophie, die hinter LandISsteckte war, daJ3 es so lange ein System mit kleinem Budget sein sollte, bis bewiesen war, daJ3 es Bo-denkundler und Bodenschatzer untersttitzen kann, eine Benutzer-freundliche Bedienungsoberfla-che hat, raumliche Daten aufnehmen kann, Schnittstellen zu externer Software hat und unser Wis-sen Uber die im Gelande gesammelten Bodendaten hinaus erweitern kann.Bis auf begrenzte Fortschritte bei der Entwicklung der Benutzer-freundlichen Oberflache sind alleZiele erreicht, und das System ist im Einsatz. Das Vorhalten der Daten in eigenstandigen Daten-sammlungen war von groJ3er Bedeutung, aber eine der erfolgreichsten Einsatze von LandIS war dieVereinigung agroklimatischer Daten, die nun in einem 5 x 5 km2-Netz rur England und Wales vor-liegen. Bodenfeuchtigkeitsdefizite aus den Werten von 970 meteorologischen Stationen und Feld-kapazitats- W erte fur ein lOx 10 km2 Raster wurden als lineare Funktion des durchschnittlichen
Authors' address: Dr. J .M. RAGG. Dr. R.J .A. JONES. M.E. PROCTOR. Soil Survey and Land Re-
search Centre, Silsoe, Bedford MK45 4DT, U .K.
296
jahrlichen Niederschlags und der Hohe durch Regressions-Analyse bestim.mt. Ein Trendflachen-Programm wurde zur Ermittlung von x-, y- und z-Werten benutzt. Die Anpassung der unregelma-Big verteilt vorliegenden Werte der Bodenfeuchte an die interpolierten Hohendaten wurde mithilfedes Informations-Systems vorgenommen; ahnliche Techniken wurden dazu benutzt, ein gleichma-Biges 5 km-Raster ftir Feldkapazitats-Werte aufzubauen.Die vorliegende Arbeit zeigt, daB gut verteilte Bodendaten, zusammen mit geeigneten Umweltdatenund fundierten Modellen, in der Lage sind, eine einfache Bodendatenbank in ein System zurErmittlung von Bodenschatzungsdaten zu verwandeln.
Contents
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Introduction. Outline of System. Data Generation and Refinement. Data Sources. Moisture Deficit (MD) Field Capacity (FC) Altitude and Rainfall. Data Generation. Statistical Analysis. TrendSurfaceAnalysis Data Refinement using the Information System. Results from the Data Model. Conclusions. References.
Introduction
Although some computer methods were in use prior to 1979 by the Soil Survey of Eng-land and Wales, data manipulation by traditional methods had become so labour inten-sive and time consuming that the many analyses and comprehensive and widely distribut-ed field observations could only be related with difficulty and National or Regional ap-praisals were virtually impossible. In order to overcome these problems, the top level de-sign of Land Information System (LandIS) was made in 1979 with two dominant provi-sos. It was to serve the scientist and those evaluating land and, due to small resources(both of staff and hardware), it was to be a 'small bugdet' system until its value had beenproved. The main functions of the system were, and still are, to:
1. Accept all kinds of earth-science data -jncluding spatial databases.2. Validate and update data.3. Provide data safeguards.4. Relate data from many files.5. Interface with other systems.6. Provide a wide range of output in the form of reports, tabulations, statistical summa-
ries and graphics (including maps).7. Have a user-friendly image.8. Assemble data for publication.
Though, at present, the user-friendly interface is only partly complete, all these aimshave now been realized and the System is operational. Furthermore, the value of the sys-
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AUGER BORE.HOLE RECORDSBrief profile descriptions
STATISTICAL ANOGRAPHICS SOFTWARE
OTHER DATASYSTEMS
SOIL DESCRIPTIONS
Comprehensive soil profile
descriptions
ANALYTICAL DATA
Physical and chemical analyses
for several horizons at
points with soil profile descriptions/
ALPHA-NUMERIC OUTPUT
Tabular, formatted. with
text comments incorporated etc.
SOIL MAP UNITSRaster-digitised 1 ;250 000
map with 100 x 100m pixels
In-housesoftware
SOIL MAP LEGENDExtended legend for the
1 :250 000 mapGRAPHICAL OUTPUTBar-charts, graphs,
3D-plots. single andmulti factor maps etc
SOIL CLASSIFICATIONDefinitions of all soilclasses (Soil series)
TERMINAL
000000000
00000000
0000000
NATIONAL SOil INVENTORYDescriptive and analytical
data on a Skm grid
AGRO.CUMATIC DATASoil.climatic data for every Skm
square in England and Wales
USER
Fig. 1: An overview of LandIS.
tern to the Soil Survey and to the Ministry of Agriculture, Fisheries and Food has beenrecognized and further investment in hardware has b~en made this year. The system, re-presented diagrammatically in Figure 1 , is now functioning on a dedicated V AX 11/750with 670 Mbytes of mass storage together with a range of DEC and other software.
2. Outline of System
Following a thorough data systems analysis of our original requirements, softwarewas written in SPITBOL in order to validate, update, reformate and issue error/accep-tance reports for a wide range of earth-science data types. This general purpose softwarecan be used to serve any database management system (DBMS) and be adapted as ad-ditional data types are acquired. Although SPITBOL is an interpretive language, it waschosen because of string handling efficiency, flexibility and machine independence. TheInformation System has been built around relational file structured databases and DECsoftware comprising DATATRIEVE and a COMMON DATA DICTIONARY (DigitalEquipment Corporation 1982). DATATRIEVE is a fourth generation language for filedefinition, management, manipulation and retrieval with the relational 'join' and full
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Boolean logic. Around this kernel, a large number of DA T A TRIEVE procedures anddatabase views have been created which operate on the data and a number of soil-clima-te-crop models. These are linked by an hierarchy of menus and explanatory screens. It isthese which provide the user-friendly interface and transform a very large and complexset of related databases into an Information System, some facets of which can be re-garded as a 'knowledge-bases system'. DATATRIEVE can also be used as a commandlanguage for special retrievals, manipulations and data transformations. The agroclima-tic data generation and refinement described below is an example of this use of theDBMS.
As originally conceived, the main databases were to be soil oriented, supported by alimited amount of geological, topographic and environmental data. Since 1980, how-ever, soil-topography-climatic models have been used to extend what can now be called aknowledge-based system to help solve a wide range of agronomic and environmentalproblems. As far as the authors are aware, LandIS is unique in that it contains a largeamount of agroclimate data and the creation of this aspect of LandIS is described in thispaper. The main databases in the System are described by COMMISSION OF THE EuRO-PEAN COMMUNITIES (1986).
3. Data Generation and Refinement
JONES & THOMASSON (1985) compiled moisture deficits for 970 irregularly distributedrainfall stations, and field capacity data for 10 km squares delineated by the main 10 kmlines of the National Grid, an orthogonal metric grid with its origin to the south west ofEngland. However, climatic variability in England and Wales is such that all data are re-quired for 5km x 5km grid intersections. Generation of moisture deficit and field capac-ity data at these intervals with the aid of statistical and trend surface analyses and an In-formation System has now been completed. The techniques used, which are equally ap-plicable to many irregularly spaced earth-science data, are described in detail in the sec-tions below.
3.1 Data Sources
Before considering the procedures used for data refinement, it is appropriate to ex-amine the data sources in detail. .
3.1.1 Moisture Deficit (MD)
Potential month-end, seasonal maximum and crop-adjusted soil moisture deficits(1961-75), MD data, were calculated for 970 stations in England and Wales, mostly re-cording daily rainfall totals (Fig. 2). The rainfall totals R (mm), for these stations, andestimates of potential evapotranspiration PT (mm), were supplied by the British Meteo-rological Office. The MD dataset is based on over 400,000 monthly moisture balances of(R- PT) for the years 1961-75 (JONES & THOMASSON 1985).
, These balances give potential soil moisture deficits (PSMD) for a crop, such as grass,which completely covers the ground all year. Adjustments are needed in calculatingmoisture deficits for cereal and root crops, to allow for limited ground cover early in theseason and consequently reduced PT (THOMASSON 1979, JONES & THOMASSON 1985).The adjusted deficits, termed MD (crop adjusted), are calculated for winter wheat,
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$00 600200 300 400
50 100 1500
kilometresFig. 2: Rainfall stations used for calculating moisture deficits (isopleths show mean max
PSMD (mm».spring barley, sugar beet and potatoes. The MD grass is equivalent to the maximumPSMD. The MD data are used, together with profile available water contents, for assess-ing average soil droughtiness.
0 ~
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3.1.2 Field Capacity {FC)
Median and quartile dates for return and end of field capacity {condition of zero soilmoisture deficit) and its duration in days, have been calculated for each of 1737 10km x10km National Grid squares in England and Wales. The dataset was prepared by regres-sion of return and end dates for FC, given by SMITH & TRAFFORD {1976), on annual ave-rage rainfall {AAR) for 1941-70. It provides the only nationally consistent assessment ofmeteorological field capacity {JONES & THOMASSON 1985) and the data are useful for pre-dicting soil wetness {JONES 1985) and the periods suitable for landwork {THOMASSON
1982).
3.1.3 Altitude and Rainfall
Altitudes (AL T5km), in metres above sea level, were recorded at 5 km grid intersec-tions during survey for the National Soil Map (1979-83). Average annual rainfall(AAR5km) at the same grid intersections were supplied by the British Meteorological Of-fice.
3.2 Data Generation
The datasets described above are held as ASCII files in the Information System. Thekey field for these files is the National Grid Reference which is related to the longitudeand latitude of a site.
3.2.1 Statistical Analysis
The GENSTAT statistical program (ALVEY 1977) was used to examine the relationshipof the MD parameters (Month-end PSMD, max PSMD and crop adjusted MD) to alti-tude (AL T), average annual rainfall (AAR), latitude (LA T) and longitude (LON) for the970 moisture deficit stations, the relevant station data first being extracted from the In-formation System. The model fitted to moisture deficit data (MD) is shown below:
MD = Bo + Bl .ALT + B2 .AAR + BJ .LAT + B4. LON
For field capacity data {FC), a simpler model was fitted, again using GENST A T:
FC = Bo + B2 .AAR + BJ .LAT + B4 .LON
The AL T and AAR data for all the 970 moisture deficit stations -termed AL Tstn,AARstn -are stored in the database; LA T and LON are the eastings and northings res-pectively of the National Grid Reference. Separate AARstn data also exist for 10km gridsquares for which the FCstn data were originally calculated. The regression coefficientsand variation in the MD parameters accounted for by the GENST AT analysis are shownin Table 1. Similar data have been calculated for FC.
Table I. Regression Coefficients from GENSTAT analysisAgroclimatic coefficients Variance
Bl B2 OJoVariable
-0.19569
-0.14036
-0.07182-0.06196-0.090450.05530
90899292
MDgrassMDpotatoesReturn to Field CapacityEnd of Field Capacity
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3.2.2 Trend Surface Analysis
The SURF ACE II graphics system (DA VIS 1978) is a computer program that accepts ir-regularly distributed x,y ,z data and forms a matrix which is then interpolated to give zvalues at regular x,y intervals. It was used to generate MD data on a 5km x 5km grid(MDgrid). The interpolation procedure is based on a nearest neighbour analysis andhence regularizing datasets such as MD in this way prevents other data such as altitude(AL T) and average annual rainfall (AAR) (which can influence the refinement process)being taken into account. The AL Tstn and AARstn data (for the moisture deficit sta-tions) were also processing using SURF ACE II to give values on a 5km grid (AL Tgrid,AARgrid). In addition, the FCstn data, originally on a 10km x 10km grid, were resolvedto 5km intervals (FCgrid) using SURFACE II.
3.2.3 Data Refinement using the Information System
Direct comparison of AL T5km and AAR5km, already stored in LandlS, with the ma-thematically interpolated data (AL Tgrid, AARgrid) was possible once the gridded data-sets were stored in LandlS. A short DATATRIEVE procedure was written to make ad-justments to the data at each 5km grid intersect based on the altitude and rainfall diffen-
A simpler procedure was used to adjust the field capacity (FC) data:
FC5km = FCgrid + (AAR5km -AARgrid) .B2 .
3.2.4 Results from the Data Model
A list of terms used in the text and taples is shown in Table 2.The first two characters of the National Grid References in Tables 3 to 5 identify the
100km x 100km square -NY for the square in north-west England bordering Scotland,SH for the high mountain area of North Wales, SK for the East Midlands, SX for south-west England and TF, TL for East Anglia. The remaining digits, 4 eastings and 4 nor-things, locate the station within the 100km x 100km square. The spatial resolution ofAL Tgrid and AARgrid compared with AL T5km and AAR5km is shown in Figures 3 and4. The improvement in the spatial resolution of the MD grass data by adjusting for thesedifferences is shown in Figure 5. As would be expected from using a trend surface pa-ckage like SURF ACE II, the statistically interpolated 5km values (AL Tgrid, AARgrid)are very close to the nearest station data (AL Tstn, AARstn) -see Table 3. There are,however, significant differences between the interpolated and the true grid intersect data(AL T5km, AAR5km) already available in the database but derived from other sources -
field survey in the case of AL T5km and the British Meteorological Office in the case ofAAR5km. Station No.5 in nothern England has an AL Tgrid of 365m but an AL T5kmof only 282m (Table 3). There is virtually no difference in the corresponding AAR dataand hence it is the altitude difference which accounts for MD5km (grass) being 15mm
larger than MDgrid (Table 4).For Station 14 in south-west England, AARgrid (1488mm) is significantly smaller than
AAR5km (1610mm) resulting in MD5km (grass) being 15mm smaller than MDgrid(grass). Similarly, there is a reduction in MD5km (potatoes) compared with MDgrid (po-
tatoes).
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Only the AAR difference (AAR5km -AARgrid) is used to adjust the field capacitydata and the effect of this is seen in Table 5. For station No.3 with AAR5km 49mm lar-ger than AARgrid, FC5km (for return day code -RFC) is smaller, and hence earlier ,than the corresponding FCgrid.
Month-end PSMD and other crop-adjusted MD data have been similarly treated andimproved as have the FC data (median and quartile return (RFC) and end (EFC) of fieldcapacity and duration in days).
The raster maps displayed in Figures 3, 4 and 5 were plotted using a Sigmex colourgraphics workstation operating on the V AX. Each of the grid matrices saved (in binaryform) from SURFACE II was used as input to a FORTRAN program (K. BICKNELL,pers.comm.) which creates a file of colour codes suitable for plotting on an ink-jet prin-ter (Tektronix type).
Table 2. Abbreviations used in text and Tables 3 to 5
AARAAR5km
AARgrid
AARstnALTALT5kmAL Tgrid
AL TstnEFCFCFC5kmFCgrid
FCstnMDMDSkm
MDgrid
MDstn
PT
R
RFC
Average annual rainfall, mmAverage annual rainfall at a Skm grid intersect (from Meteorological
Office)Average annual rainfall for meteorological stations interpolated ata Skm grid intersect by SURF ACE IIAverage annual rainfall for meteorological stationsAltitude, m above sea levelAltitude from soil survey at a Skm grid intersectAltitude for meteorological stations interpolated at a Skm gridintersect by SURF ACE IIAltitude for meteorological stationsEnd of field capacity (day code)Field capacity, return or end dates or durationField capacity at a Skm grid intersectField capacity for a lOkm grid intersect interpolated at Skm bySURF ACE IIField capacity for a lOkm grid intersectMoisture deficit (Potential) -negative sum of (R -PT)Moisture deficit at a Skm grid intersect (MDgrid adjusted for AL Tand AAR) -end product of refinement using DBMSMoisture deficit for meteorological stations interpolated at aSkm grid intersect by SURF ACE IIMoisture deficit for meteorological stationsPotential transpiration mm -(monthly totals)Rainfall mm (monthly totals)Return to field capacity (day code)
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Table 3. Comparison of altitudcs (m) abovc sca Icvcl and avcragc annual rainfall (mm)
Most of the data processing described above could have been accomplished usingFORTRAN or some other high level language but an early appraisal indicated that, al-though much machine-time might be required, programming would be greatly simplifiedusing the DBMS query language. Events proved this to be true and the machine time wasfound to be acceptable. The processing of raw meteorological data using FORTRAN(not referred to in this paper) was onerous in comparison to the work described. Further-more, the combination of well distributed soil and regularized environmental data, suchas the agroclimatic datasets described in this paper, with soundly based models have pro-vided an opportunity to transform simple soil databases into a comprehensive land eva-luation system.
5. References
ALVEY, N. (1977): GENSTAT: a general statistical program. -Numerical Algorithm Group; Ox-
ford.
COMMISSION OF THE EUROPEAN COMMUNITIES (1986): Agriculture. Computerized land evaluationdatabases in the European Communities. -In: NORR, A.H. (Ed.): Catalogue of a Ques-
DAVIS, J.C. (Ed.) (1978): SURFACE II graphics system. -Manchester (Univ. Manchester Regional
Computing Centre).
308
DIGITAL EQUIPMENT CORPORATION (1982): VAX-11 DATATRIEVE. -Reference Manual, Version
1.2, AA-K-O79A- TE. DEC; Maynard, Massachusetts.
JONES. R.J .A. (1985): The field capacity dataset of the Soil Survey of England and Wales: an aid toassessing soil wetness. -Soil Survey and Land Evaluation, s: 1-12; Norwich.
-&THOMASSON, A.J. (1985): An agroclimatic databank for England and Wales. -Soil Surv.tech. Monogr. , 16; Harpenden.
RAGG. J.M. & PROCTOR. M. (1983): The Soil Survey Information System. -Rep. Rothamsted exp.Stn for 1982: 232-236; Harpenden.
SMITH, L.P. & TRAFFORD, B.D. (1976): Climate and drainage. -Techn. Bull. Minist. Agric. Fish
-(1982): Soil and climatic aspects of workability and trafficability. -Proc. 9th Conf. int.Soil Tillage Research Organization: 551-557; Osijek, Yugosl.
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