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4 MATERIALS AND METHODS

MATERIALS AND METHODS

89

4.1 INTERPRETATIVE METHODOLOGY: THE MICROLEIS DECISION SUPPORT

SYSTEM A small number of systems such as MicroLEIS are widely used. One obvious reason

for MicroLEIS common use is the straightforward approach of the procedure,

which uses simple models. MicroLEIS predicts the optimum land use and the best

management practices, individualized for each soil type, to predict the optimum

biomass productivity, minimum environmental vulnerability and maximum CO2

sequestration. Climate change scenarios are considered together with other

important global changes, such as land use change, desertification, agricultural

extensification/intensification, etc. (Muñoz-Rojas, 2012).

MicroLEIS DSS is an agro-ecological decision support system (technology

developed by CSIC-IRNAS and transferred to Evenor-Tech, www.evenor-

tech.com). It is considered a very suitable tool to contain the soil and climatic

attributes for a better identification of vulnerable areas and formulation of action

programs (Anaya-Romero et al., 2010).

MicroLEIS DSS include three databases (SDBm, CDBm and MDBm) and thirteen

models. Seven models of the system can be applied in different hypothetical

scenarios of climate and agriculture management:

� Terraza, Cervatana and Sierra are models associated to evaluate soil

productivity as bioclimatic deficiency, general land capability and forestry

land suitability respectively.

� Raizal, ImpelERO and Pantanal are models related to land degradation

assessment as soil erosion risk, prediction of soil loss, specific soil

contamination risk, respectively; CarboSOIL is used to evaluate carbon

sequestration under different climate conditions.

� The other six models (Almagra, Albero, Marisma, Aljrafe, Alcor and

Arenal) are used to evaluate soil productivity and land degradation

depending on physical, chemical and pedological soil characteristics. All

the results of evaluation models can be spatialized by GIS integration

(Figure 4-1).

Applications of evaluation models are useful to assess climate change impacts,

planning of land use and intended for suitable soil management system.

CHAPTER 4

90

Figure 4-1. Conceptual design and components integration of the MicroLEIS DSS land evaluation decision support system. Environmental modeling: SS, simulation system; QA, qualitative approach; ES, expert system; ; NN, neural network; HM, hybrid model; SM, statistical model. Biophysical processes/outputs: BD, bioclimatic deficiency; LC, land capability; FS, forestry suitability (61 species); ER, erosion risk; IM, erosion impact/mitigation; SC, specific contamination; AS, agricultural suitability (12 crops); CP, crop productivity (3 crops); NF, natural fertility; PW, plasticity/workability; CT, compaction/trafficability; CR, contamination risk. CarboSOIL model is not included in this figure.

MicroLEISDecision Support System

Land evaluation modelbase

(12 models)

Climatic data included

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Soil data

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QA Cervatana LC

SS Terraza BD

NN Sierra FS

ES Raizal ER

HM ImpelERO IM

ES Pantanal SC

QA Almagra AS

SM Albero CP

QA Marisma NF

SM Aljarafe PW

SM Alcor CT

ES Arenal CR


91

4.1.1 DATA SOURCES AND HARMONIZATION

SOIL DATA BASE (SDBM)

GENERAL DESCRIPTION

The multilingual soil database SDBm plus (De la Rosa et al., 2002) is used to store

and manipulate the large amount of soil data extracted from soil profiles, These

soil attributes are input variables for applying the MicroLEIS models which have

been extracted by SDBm plus program, which is a geo-referenced soil attribute

database for storage of an exceptionally large number of morphological, chemical

and physical properties .

Figure 4-2. General scheme of the SDBm Plus database. Source: De la Rosa et al. (2002).

CHAPTER 4

92

Figure 4-3. Screen capture of SDBm data base.

SDBm Plus has been entirely re-designed and re-written as a WINDOWS

application (Figure 4-2). It is a user-friendly software designed to harmonize, store

and use large amounts of geo-referenced soil profile data in an efficient and

systematic way. Soil data sets can be stored in the SDBm Plus database, including

general information, horizon description, standard analyses, soluble salts/heavy

metals, physical analyses, water retention/hydraulic conductivity, photographs

and analytical methods.

Figure 4-3 represent a screen capture of the multilingual soil profile database

SDBm Plus with the stored soil profiles and how to add/modificate the soil

information contents for a specific soil profile to obtain the final soil profile

description. Figure 4-4 show an example of soil profiles description extracted from

the SDBm Plus database.


93

Figure 4-4. Example of soil profiles description extracted, from the SDBm Plus database.

CHAPTER 4

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Figure 4-4. (Cont.) Example of soil profiles description, extracted from the SDBm Plus database.


95

DATA COLLECTION

ANDALUSIAANDALUSIAANDALUSIAANDALUSIA

The multilingual soil database SDBm plus includes detailed information on soil

profiles such as site information, morphological descriptions and a detailed

physical and chemical analysis (1103 soil profiles of Andalusia) Figure 4-5-A. In this

study we have choosen the most representative profiles for Andalusia (62 profiles;

De la Rosa et al., 1984) (Table 4-1 and Figure 4-5-B).

Table 4-1. Soil taxonomy Classification (USDA, 2010) of 62 representative soil profile of Andalusia (87600 km

2). Codes are soil profile identification codes in the original soil data

base (SDBm). Source: adapted from De la Rosa et al. (1984). (*) Typical soil profiles of the Mediterranean region.

Order Sub-order Great group Sub-group Code Area, km2

Alfisols Xeralfs Haploxeralfs Aquic Haploxeralfs SE08 627 Calcic Haploxeralfs SE01 217 Typic Haploxeralfs CO06, CO05, JA07 5481

Palexeralfs Aquic Palexeralfs HU05 821 Typic Palexeralfs CA04 1645 Vertic Palexeralfs JA03 1491

Rhodoxeralfs Calcic Rhodoxeralfs CA03,GR10 1087 Lithic Rhodoxeralfs JA05 1102 Typic Rhodoxeralfs CA06,GR05, JA01, SE02 4835

Aridisols Argids Haplargids Vertic Haplargids AL05 1254 Cambids Haplocambids Xeric Haplocambids AL06 1196

Entisols Aquents Fluvaquents Typic Fluvaquents AL04, HU06, SE05 2202 Arents Udarents Haplic Udarents CA05 747 Fluvents Xerofluvents Aquic Xerofluvents GR11 1368

Typic Xerofluvents AL08, CO07, SE09 3719 Orthents Xerorthents Lithic Xerorthents HU02 1129

Typic Xerorthents CO01, GR08, MA03, AL01, GR01, GR03, GR06

9398

Inceptisols Xerepts Haploxerepts Typic Haploxerepts HU07 3013 Haploxerepts Calcic Haploxerepts AL02, GR07, MA01 4818

Lithic Haploxerepts CO03, HU01, GR04, MA02 7056 Typic Haploxerepts AL07, JA06, JA09, CO04 6075

Udepts Dystrudepts Typic Dystrudepts GR02 1139 Fluventic Dystrudepts HU04 1472

Molisols Rendolls Haprendolls Lithic Haprendolls JA08, AL03 2640 Ustolls Haplustolls Udic Haplustolls MA05 1374 Xerolls Haploxerolls Entic Haploxerolls SE04 589

Typic Haploxerolls MA04 1666 Ultisols Xerults Haploxerults Typic Haploxerults SE06 3748 Vertisols Xererts Haploxererts Entic Haploxererts GR09, HU03 1905

Typic Haploxererts CA02, CO02, JA04,SE03 JA02, SE07

11945

Chromic Haploxererts CA01 1841

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96

Figure 4-5. Spatial distribution of soil profiles in Andalusia. A: represents the spatial distribution all the stored soil profiles in SDBm; B: spatial distribution of the selected soil

profiles and the dominated land use in each soil profile.

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97

Soils were classified using the keys to soil taxonomy (USDA, 2010) at the sub-

group level of USDA Soil Taxonomy, resulting in 31 units. These included the

following soil orders within area km2 and the psentage of each of them from the

total area: Alfisols 17304.6 (19.8%), Aridisols 2450.0 (2.8%), Entisols 18563.2

(21.2%), Inceptisols 23573.2 (26,9%), Mollisols 6269.3 (7.2%), Ultisols 3748.1

(4.3%) and Vertisols 15691.8 (17.9%). The three major soil sub-groups are Typic

Xerorthents, Lithic Haploxerepts and Typic Haploxererts, representing 10.7 %,

8.1% and 13.6 respectively (Table 4-1).

Table 4-2 represents the morphologic proprieties of 24 profile selection that are

dominated in the Mediterranean areas

Table 4-2. Morphologic properties of the selected 24 soil profiles. Abbreviations: Texture class : c=clay; l=loam; si=silt; s=sand; cl=clay loam; scl=sandy clay loam; sl=sandy loam; ls=loamy sand; sic=silty clay; sicl=silty clay loam; sil=silty loam. Structure: Size: f=fine; m=medium; c=coarse; vc=very coarse. Grade: 0=structureless; 1=weak; 2=moderate; 3=strong. Type: m= massive; abk=angular blocky; sbk=subangular; pr=prismatic; cpr=columnar; gr=granular; sg=single grain. Soil consistence: when moist: lo=loose vfr=very friable fr=friable fi=firm sfir=slightly firm vfi=very firm. Boundary: c=clear; a=abrupt; g=gradual; d= diffuse; s= smooth; w=wavy; i=irregular; gs=gradual smooth; ds= diffuse smooth; cs=clear smooth; cw=clear wavy. Reaction: n=null; e=slight; es=strong; ev=violent.

Profile Horizon Depth Texture Structure Color Consistency Boundary Reaction

P-SE08 Ap 0-25 ls f1cr 10 YR 5/4 Mfi cs n

B1 25-40 sl c3sbk 10 YR 7/6 Mfr gw n B21t 40-70 sc c3pr 10 YR 5/6 Mfr d n B22g 70- sc c3pr 10 YR 5/6 Mfr gw n B3g >110 sc c2pr 10 YR 5/6 Mfr -- e P-SE01 Ap 0-20 scl f3cr 5 YR 4/8 Mfr gs e AB 20-45 sc c1cr 2.5 YR Mfr cs e B2t 45-60 sc c3abk 2.5 YR Mfi cs n B3ca 60-75 sl f3abk 5 YR 5/6 mfi gs es C1ca 75- l f2abk 5 YR 5/6 mfi gs ev IIC2ca >115 ls c3bk 10 YR 6/6 mfi -- ev P-CO06 A1 0-10 c c3gr 5 YR 4/4 mfi ci e B oct- c m2sbk 5 YR 5/6 mfi ai e R >50 P-CA03 Ap 0-15 scl m2cr 5 YR 4/4 mfr cs e B2 15-35 scl m2sbk 2.5 YR mfr ci n B3ca 35-60 c2cr 2.5 YR mfr g es Cca 60-80 f1sbk 2.5 YR ev C >80 2.5 YR ev

CHAPTER 4

98

Table 4-2. Continuation.


P-CA06 A1 0-10 sil m3cr 5 YR 4/2 mfr c n Bt oct- sic c2abk 2.5 YR mfi c n B+C 30-70 sil f2sbk 5 YR 5/4 mfr a ev R >70 P-AL05 A1 0-15 ls m2cr 10 YR 6/3 cs es B2 15- sl m2cpr 10 YR 5/6 es C >100 P- AL06 A 0-20 sic m2cr 2.5 Y 7/4 mfr ds ev AB 20-60 sic m2sbk 2.5 Y 6/6 mfr cs ev C >60 sic m2sbk 2.5 Y 7/ mfi -- ev P-SE05 A1sa 0-10 c m2sbk 10 YR 7/2 mfi cs es B11g oct- c c3m 10 YR 4/2 mvfi gs es B12g 37-65 c c2m mfi gs es IICg >56 c c3abk 2.5 Y 5/2 mfi es P-CA05 Ap1 0-10 sic m3sbk 2.5 Y 8/2 mfr cs es Ap2 oct- sic c2sbk 2.5 Y 8/2 mfr gs Ap3 20-60 sic m1sbk 2.5 Y 8/2 mfr d e AC 60-80 sic m3sbk 2.5 Y 8/2 mfr d e C >80 sicl c3abk 2.5 Y 7/2 e P-CO07 Ap 0-15 cl m1cr 10 YR 4/3 mvfr cs es AC 15-25 scl m2cr 10 YR 4/3 mfi as es C1 25-35 sl m2cr 10 YR 4/3 mfr gs es C2 35-65 scl m1sbk 10 YR 6/4 mfr gs es C3 >65 0 10 YR 6/3 es P-HU02 A1 0-9 l f1cr 10 YR 5/4 c n R >9 P-AL01 A1 0-25 sl m1gr 10 YR 5/2 cs e AC 25-80 cl m3sbk 5 YR 4/6 gs n C 80- n R >100 P-GR06 A 0-12 si m2gr 5 YR 7/1 es C >12 5 YR 7/1 es P-MA01 Ap 0-20 c m2sbk 5 YR 3/4 mfr d e AB 20-40 c c2sbk 5 YR 3/4 mfr c e B 40-60 c c2sbk 5 YR 4/4 mfi d es C >60 s c2sbk 5 YR 5/4 mfi ev P-HU01 A1 0-5 scl f3cr 7.5 YR mfr cs n B may- l c3sbk 10 YR 5/8 mfr cw C >25 P-CO04 A11 0-10 ls f1cr 10 YR 4/4 mvfr gs n A12 oct- sl f1cr 10 YR 4/4 mvfr cw n B 20-40 sl c2sbk 7.5 YR mfr ci n C >40 sl m2pr mfr n P-JA06 A1 0-35 c f2sbk 2.5 Y 8/ mfr cw es C >35 cl m 2.5 Y 8/3 mfi es P-GR02 A11 0-9 sl f3cr 7.5 YR mvfr cs n A12 sep- sl m2cr 7.5 YR mvfr cw n B2 20-33 sl m1gr 7.5 YR mfr gw n B3 33-65 sl f1sbk 2.5 Y 5/4 mfr cw n C1 65-95 sl m 2.5 Y 5/4 mfr d n C2 >95 sl m 2.5 Y 5/2 mfr d n P-HU04 A1 0-15 s f1cr 5 YR 2/1 mvfr gs AC 15-25 s 0 5 YR 3/1 mvfr g C1 25-50 s 0 10 YR 3/1 mvfr g C2 >50 s 0 10 YR 7/2 P-JA08 A11 0-15 sl f2cr 10 YR 3/2 mvfr d e A12 15-35 sl f2cr 10 YR 4/3 mvfr ci e R >35


99

Table 4-2. Continuation.


P-CO02 Ap 0-20 m3cr 2.5 Y 4/2 mfr cs es AC 20-90 m3sbk 2.5 Y 6/2 mfi d es C1 90- c3pr 2.5 Y 7/4 mvfi es C2 >120 m mvfi es P-SE03 Ap 0-25 c f2sbk 2.5 Y 4/2 gs es A1 25-35 c m3abk 2.5 Y 4/2 gs es C1ca 35-70 c m3pr 2.5 Y 4/2 gs es C2 70- c m3pr 2.5 Y 6/4 d es C3 120- c m 2.5 Y 6/6 ev P-CA01 Ap 0-10 c f3sbk 10 YR 5/1 mfr as e AB oct- c m3pr 10 YR 3/2 mvfi cs e B+C 45-80 c m 10 YR 5/2 mvfi gs es C >80 cl c3abk 2.5 Y 6/2 mfr es P-HU03 Ap1 0-20 c m3gr 10 YR 4/1 mfi gw e Ap2 20-60 c m3gr 10 YR 3/1 mvfi d es AC 60- c c3pr 10 YR 3/1 mvfi gw es C >140 c m 10 YR 8/4 mvfi ev

Table 4-3 represents the ranges and dominantvalues of site characteristics such as

landform, slope gradient, elevation and also exemplify the soil characteristics with

respect to useful depth, drainage, particle size distribution, superficial stoniness,

organic matter, pH, cation exchange capacity, sodium saturation.

Table 4-3. Ranges and dominant values of land characteristics of the 62 benchmark soils for Andalusia. (*) Soil parameters measured within the soil section 0 to 50 cm.

Land characteristics (Range) Dominant

Site – related characteristics Landform (plan - mountain) hill Slope gradient, % (0.7 - > 30) 2 Elevation, masl (1-2080) Soil- related characteristics Useful depth, cm (0-260) 150 Drainage (poor-excessive) well Particle size distribution* (sand-clay)clay Superficial stoniness (nill –abundant ) nill Organic matter,* % (0.14 – 4.32 ) 1.59 pH* (5.1 – 8.7) 7.4 Cation exchange capacity,* meq/100g (2.5- 50.4) 17.46 Sodium saturation,* % (0.2 – 11.9) 2.7

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100

ELELELEL----FAYOUMFAYOUMFAYOUMFAYOUM

The total soil profiles of 46 have been collected from previous research (Haroun,

2004; Ali, 2005); six representative soil profiles were selected to represent the soil

types of El-Fayoum area, (Figure 4-6): Vertic Torrifluvents, Typic Haplocalcids,

Typic Torrifluvents, Typic Haplogypsids, Typic Haplosalids, Typic Torripsamments.

Soil physical and chemical properties of each selected profile were included in the

SDBm database (Antoine et al., 1995; De la Rosa et al., 2002).

Figure 4-6. Spatial distributions of the collected soil profile in El- Fayoum area. Red points represent the representative soil profiles.

As illustrated inTable 4-4, the soil layers indicate that the sub-great group Vertic

Torrifluvents is the dominant soil sub-great group; it covers an area of 760 km2

representing 42.79% of the mapped soils. The sub-great group of Typic

Haplocalcids covers an area of 421 km2 representing 23.70% of soil units’ area. Its

geographic distribution is located on the edges of the depression exhibiting the

old river terraces. Typic Torrifluvents occurs within the depression, covering an

area of 141 km2, representing 7.94% of the mapped soils. These soils are

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FA-A04

FA-A03

FA-A02

FA-A01

Qarun Lake

0 10 205 km

Wad

i El r

ayan

lake

s

±El-Fayoum


101

associated with the recent terraces of the flood plain. The Gypsic soils, e.g. Typic

Haplogypsids, exist on the eastern borders of the Fayoum depression covering an

area of 87 km2 representing 4.90%. A very small patch of the sub-great group

Typic Haplosalids exists in the south east of the depression (58 km2; 3.27% of the

mapped soils) as shown in (Table 4-4) Typic Torripsamments cover small spots in

the south of El-Fayoum depression, covering an area of 26 km2 (1.36%). It should

be noticed that such variability of sub-great groups is unique for the Fayoum

Province due to its location, altitude, formation processes and patterns of

agricultural practices. The soil database SDBm-El-Fayoum physical, chemical and

morphological descriptions of 46 soil profiles of El-Fayoum depression.

Table 4-4.Classification of the soil taxonomic units (USDA, 2010) of six representative soil profile of El-Fayoum (1493 km

2) and corresponding area (km

2). (*) Source: H, Haroun

(2004); A: Ali (2005).

Order Sub-order Great group Sub-group Soil profile* Area Km

2

Aridisols Calcids Haplocalcids Typic Haplocalcids FA-H05 421 Gypsids Haplogypsids Typic Haplogypsids FA-A13 87 Salids Haplosalids Typic Haplosalids FA-H08 58

Entisols Fluvents Torrifluvents Vertic Torrifluvents FA-H19 760 Typic Torrifluvents FA-H07 141

Psamments Torripsamments Typic Torripsamments FA-H15 26

CLIMATE DATA BASE (CDBM)

GENERAL DESCRIPTION

The climate database CDBm developed for MicroLEIS DSS (Figure 4-7) is a

computer-based tool for the organization, storage, and manipulation of agro-

climatic data. These geo-referenced climate observations, at a particular

meteorological station, correspond to the mean values of such records for a

determinate period. It is precisely by this integration over a time period that

meteorology is distinguished from climate. The basic data of CDBm are the mean

values of the daily dataset for a particular month. The stored monthly mean

values correspond to a set of temperature and precipitation variables (maximum

CHAPTER 4

102

temperature, minimum temperature, accumulative precipitation, maximum

precipitation per day, and days of precipitation).

Figure 4-8 represents the screen capture of the main and calculations menus of

climate database also represents input/edit data form and summaries form.

Figure 4-7. General scheme of the CDBm database. De la Rosa et al. (2002).


103

Figure 4-8. Screen capture of CDBm data base.

ANNUAL CLIMATIC INDEX CALCULATIONS

Different climatic indices were included in this research in relation with crop

productivity (humidity, aridity and growing season) and land degradation

(precipitation concentration, Fournier and Arkley indices).

HUMIDITY INDEXHUMIDITY INDEXHUMIDITY INDEXHUMIDITY INDEX

Humidity index (HUi) is used to estimate the availability of water to the plants in a

general way. It is also often used to anticipate the needs of artificial drainage in an

area. To calculate the humidity index (HUI) the following formula is used:

��

Where: P is precipitation and ET0 is potential evapotranspiration (calculated

according to Thornthwaite’s or Hargreaves’ method).

ARIDITYARIDITYARIDITYARIDITY INDEXINDEXINDEXINDEX

Aridity index (ARi) is a simple procedure to estimate the general climate aridity.

The aridity index is calculated as the number of months of the year when

CHAPTER 4

104

potential evapotranspiration (calculated according to Thornthwaite’s or

Hargreaves’ method) exceeds precipitation.

GROWING SEASON INDEXGROWING SEASON INDEXGROWING SEASON INDEXGROWING SEASON INDEX

Growing season (GS) indicates the length of the vegetative period, calculated as

the number of months of the year when the temperature average exceeds 5 oC

(CEC, 1992).

PRECPRECPRECPRECIPITATION CONCENTRATIPITATION CONCENTRATIPITATION CONCENTRATIPITATION CONCENTRATION INDEXION INDEXION INDEXION INDEX

According to (Oliver, 1980) the precipitation concentration index (PCi) was

proposed to estimate the aggressiveness of rainfall from the temporal variability

of monthly rainfall. It is expressed as a percentage, according to the following

formula:

�� ∑ �� (∑ ��) ��

� ∙ 100

Where pi is the monthly precipitation in month i.

FOURNIER INDEXFOURNIER INDEXFOURNIER INDEXFOURNIER INDEX

The modified Fournier index (FRi) is frequently used to estimate the erosivity of

rainfall (factor R) during the soil erosion process , is defined by Arnoldus (1980)

using the following expression:

�� ∑ �� (∑ ��) ��

Where pi is the monthly precipitation in month i.

ARKLEY INDEXARKLEY INDEXARKLEY INDEXARKLEY INDEX

Arkley index (AKi) is used to estimate the effect of climate on the degree of soil

leaching. Arkley (1963) defined this index as the highest value either the sum of

the monthly precipitations minus potential evapotranspirations (calculated

according to Thornthwaite’s or Hargreaves’ method) of those months when the

precipitation is greater than evapotranspiration, or the total amount of

precipitation of wettest month

DATA COLLECTION

ANDALUSIAANDALUSIAANDALUSIAANDALUSIA

The climate database CDBm-Andalusia (mean monthly temperature, maximum

and minimum monthly rainfall and number of days of rainfall for each natural


105

region of Andalusia there is a representative meteorological station (62) that

includes geo-referenced climate observations, recorded during a period of 30

years) and CDBm-El-Fayoum (with data recorded during a period of 44 years).

ELELELEL----FAYOUMFAYOUMFAYOUMFAYOUM

Climatic data such as temperatures and precipitation for the last 44 consecutive

years (1962-2006) were collected from El- Fayoum meteorological station. These

data were integrated into the CDBm database (De la Rosa et al., 1986).

AGRICULTURE DATA BASE (MDBM)

GENERAL DESCRIPTION

The agriculture database MDBm is knowledge-based software to capture, store,

process, and transfer agricultural crop and management information obtained

through interviews with farmers (Figure 4-9). The MDBm dataset consists of geo-

referenced agricultural data on a particular land use system, structured and

stored as a file.

Figure 4-9. General structure of the MDBm database. De la Rosa et al. (2002).

CHAPTER 4

106

Figure 4-10. Screen capture of MDBm database. De la Rosa et al. (2002).

A menu system guides the user through a sequence of options to capture the

management practices followed on a site-specific farm. Input parameters include

farm and plot descriptions, crop characteristics, sequence of operations, and

behavioral observations. These parameters represent 59 management practices.

Variables can be modified or extended as appropriate.

Figure 4-10 shows a screen capture MDBm: agriculture management database

and a screen of coding, language change and data.

DATA COLLECTION

MDBm-Andalusia and MDBm-El-Fayoum contain information about agricultural

use and management of major crops. Original data were collected from the

SEIS.net database and published scientific literature, respectively.


107

4.2 SELECTED MODELS OF MICROLEIS DSS Three databases and five land evaluation models of MicroLEIS DSS have been used

for soil evaluation under climate or management change scenarios. Pantanal

(vulnerability of soil contamination by phosphorus, nitrogen, heavy metals and

pesticides), Raizal (water and wind soil erosion), Terraza (bioclimatic deficiency),

Cervatana (agricultural land use capability) and Almagra (soil suitability of for

annual, semi-annual and perennial Mediterranean crops). (Table 4-5 and

Figure 4-11)

Table 4-5. Applied MicroLEIS land evaluation models.

Purpose Model Land evaluation issue (Modelling approach)

Specific strategy supported

Agriculture productivity

Terraza Bioclimatic deficiency (parametric )

Crop water supply quantification and frost risk limitation

Cervatana General land capability (qualitative)

Segregation of best agricultural and marginal agricultural lands

Almagra Agricultural soil suitability (qualitative )

Diversification of crop rotation in best agricultural lands: for traditional crops (12)

Land degradation

Pantanal Specific soil contamination (expert system)

Rationalization of specific soil input application: N and P fertilizers, urban wastes,and pesticides

Raizal Soil erosion risk (expert system )

Identification of vulnerability areas with soil erosion problems

Figure 4-11. Selected components of MicroLEIS DSS and the integration between models applications result and GIS.

CHAPTER 4

108

Pantanal, Rhaizal and Terraza models require the following climate parameters to

run: monthly precipitation (mm), monthly mean and minimum temperature, oC.

Climate data for baseline and future climate change scenarios were obtained from

the time series of the CLIMA subsystem of the Environmental Information

Network of Andalusia (REDIAM5 ), which assimilates several databases from a set

of more than 2200 observatories since 1971. These data include climate spatial

datasets in raster format for different SRES emissions scenarios, obtained by

statistical downscaling of different GCMs. The downscaling techniques are based

on inverse distance interpolation and regression modelling of regional/local

physiographic features.

4.2.1 SOIL DEGRADATION MODELLING Land degradation models have been applied, emphasizing the study of soil

contamination and erosion to preserve this scarce resource Figure 4-12 shows an

overview with the general structure of the soil degradation models (Pantanal and

Raizal).

Figure 4-12. Overview with the general structure of the soil degradation models.

5 http://www.juntadeandalucia.es/medioambiente/site/rediam


109

Table 4-6. Input variable list (site, crop, cultivation and fertilizer characteristics) of the Raizal and Pantanal evaluation models.

Land characteristic, class or unit Raizal Pantanal

Site-related characteristics LC Landforms, 21 classes � �

LC Slope gradient, % � �

LC Groundwater table depth, m � �

Soil-related characteristics LC Drainage, 7 classes � �

LC Particle size distribution, 23 classes � �

LC Superficial stoniness, % � LC Organic matter, % � �

LC pH �

LC Cation exchange capacity, meq/100g �

LC Sodium saturation, % � Climate-related characteristics LC Mean monthly precipitation, mm � �

LC Max monthly precipitation, mm � �

LC Mean monthly temperature, °C � �

LC Latitude, ° � �

Crop-related characteristics MC Land use type, 11 classes � �

MC Crop rotation, 4 classes � �

MC Land use on slopes, 2 classes �

MC Growing season length, days � MC Leaf duration, 2 classes � MC Leaf situation, 2 classes � MC Specific leaf area, m2/kg � MC Plant height, m � MC Maximum rooting depth, m � MC Structure of crop, 2 classes � Cultivation-related characteristics MC Sowing date, 2 classes � MC Tillage practices, 5 classes � �

MC Tillage depth, 2 classes � MC Row spacing, m � MC Artificial drainage, 2 classes � �

MC Artificial groundwater level, 2 classes �

MC Soil conservation techniques (water), 4 classes � �

MC Soil conservation techniques (wind), 5 classes � MC Residues treatment, 3 classes � �

Fertilizer-related characteristics MC Use of P-fertilizer, 3 classes �

MC Use of N-fertilizer, 3 classes �

MC Use of animal manure, 2 classes MC Use of industrial /urban waste, 2 classes �

MC Time of fertilization, 2 classes �

Pesticides-related characteristics MC Use of pesticides, 2 classes �

MC Persistence of pesticides, 3 classes �

MC Toxicity (LD-50) of pesticides, 3 classes �

MC Application methods, 2 classes �

CHAPTER 4

110

SOIL CONTAMINATION RISK: PANTANAL MODEL

Within the MicroLEIS framework, the Pantanal model was developed as a

qualitative evaluative approach for assessing limitations to the use of land, or the

vulnerability of the land, in respect to specified agricultural degradation risks. The

Pantanal model focuses on diffuse ‘soil agro-contamination’ from agricultural

substances, i.e. phosphorus (P), nitrogen (N), heavy metals (H), and pesticides (X),

which predict soil vulnerability or risk classes. It is a very complex expert model

for spatially distributed systems, using easily available parameters and being

applicable to large geographic regions (De la Rosa and Crompvoets, 1998).

Table 4-7. Summary of environmental Land/management Qualities (11) and associated Land Characteristics (27), for each vulnerability type, considered in Pantanal model. Source: From De la Rosa and Crompvoets. (1998). Vulnerability type: P = phosphorus, N = nitrogen, H = heavy metals, and X = pesticides.

Land/management quality

Vulnerability type

Land/management characteristic (input variables)

Attainable contamination risks Surface run-off, r P, N, H, X Relief; soil erodibility; rainfall erosivity. Leaching degree, l P, N, H, X Monthly precipitation; monthly temperature;

groundwater table depth; drainage; particle size distribution.

Phosphate fixation, f P pH; particle size distribution; organic matter. Cation retention, c N, H pH; particle size distribution; CEC; organic matter. Denitrification, d N Monthly temperature; groundwater table depth;

organic matter; pH. Pesticide sorption, o X Organic matter; pH; particle size distribution; CEC. Pesticide degradation, g X Monthly temperature; monthly precipitation; pH;

organic matter. Management contamination risks Phosphate incidence, i P Landuse type; use of P-fertilizer; artificial drainage. Nitrogen incidence, j N Landuse type; use of N-fertilizer; crop rotation; soil

ploughing; time of fertilization; straw incorporation. Heavy metals incidence, q H Landuse type; crop rotation; use of pesticides; use of

fertilizers; use of waste. Pesticides incidence,t X Landuse type; persistence in soil; toxicity of pesticides;

application methods; artificial groundwater level.


111

The model has been developed for spatially distributed systems and uses easily

available parameters, being applicable to large geographic regions, also the model

can be use at different scales. The biophysical variables or land-related

characteristics were used to calculate the attainable or potential contamination

risk, and the agricultural practices or management-related characteristics were

used to calculate the management contamination risk. The characteristic values,

classes for the qualitative variables and ranges for the quantitative variables, were

grouped into generalization levels. For each vulnerability type, the land evaluation

procedure that follows is based on decision trees rather than on matching tables.

Through a total of 29 decision trees the qualities (severity levels) are related to

the characteristics (generalization levels), and the final decision or vulnerability

classes are derived from the qualities.

Table 4-8. Pathway of the decision tree branch constructed to relate the Land Quality “Leaching degree” with the associated Land Characteristics in Pantanal model. Source:

From De la Rosa and Crompvoets. (1998). Under each class the symbol >>>> followed by a letter (B to R) is used to direct to the next step of the decision tree. The path is followed until a severity level (Low, Moderate, High or Extreme) of the Land Quality is encountered.

Evaluation step Land characteristics Severity level

1 2 3 4

A Humidity index > B > C > D > E B Groundwater table depth Low > F > G

C Groundwater table depth Low > H > I

D Groundwater table depth > J > K > L

E Groundwater table depth > M Extreme Extreme

F Drainage Low Low > N

G Drainage Moderate Moderate High

H Drainage Low > N Moderate

I Drainage High High > O

J Drainage > N Moderate > P

K Drainage > Q High > R

L Drainage > O Extreme Extreme

M Drainage High High Extreme

N Particle size distribution Low Moderate Moderate

O Particle size distribution High Extreme Extreme

P Particle size distribution Moderate Moderate High

Q Particle size distribution Moderate High High

R Particle size distribution High High Extreme

CHAPTER 4

112

This empirically based model also includes a simple precipitation partitioning sub-

model to calculate surface runoff and leaching degree, by using the humidity

index as the relation between yearly amounts of precipitation and potential

evapotranspiration. Information about the soil and water contamination

processes was also obtained from questionnaires, interviews and discussions with

a range of specialists, experts and land users

Following this expert system or decision trees approach, the agrochemical

vulnerability classes established by Pantanal for each type of contamination for

the Land, Management, are defined as Class V1 (None), V2 (Low), V3 (Moderate)

and V4 (High).

The physically-related contamination risk (land vulnerability classes, are calculated

separately from the management-related contamination risk (management

vulnerability classes), and then both are combined to produce the actual

contamination risk (field vulnerability classes). So, the actual vulnerability is

grouped in five classes as follows:

Class V1 (None) field units of this class are almost invulnerable to agrochemical

contamination because of their biophysical condition and management system.

The actual vulnerability to soil, surface and groundwater diffuse pollution are very

low. This management system is not considered to be a controlling factor and

almost any other farming system could be implemented.

Class V2 (Low) field units are slightly vulnerable to agrochemical contamination

because the combination of the management system with the biophysical

conditions of the classified field unit does almost no harm to the soil, surface and

groundwater quality.

Class V3 (Moderate) field units are moderately vulnerable to agrochemical

contamination; the combination of the management system and biophysical

characteristics of the field unit harms the quality of soil, surface and groundwater.

The effect on the intensity of the management system to actual vulnerability class

can change considerably.

Class V4 (High) field units of this class are highly vulnerable to agrochemical

contamination, because the simultaneous impact of the management system and

the biophysical characteristics damages the quality of the soil, surface and

groundwater of the field unit on a high scale. More intensive farming systems

have negative effects on the environment.


113

Figure 4-13. Screen captures of Pantanal model. Further information about Pantantal model can be found in De la Rosa and Crompvoets (1998).

Class V5 (Extreme) field units are extremely vulnerable to agrochemical

contamination, because the intensity of the agricultural activities on the field unit

and the high biophysical vulnerability of the field unit itself harm the soil, surface

and groundwater quality on an extremely high scale. The water management and

the quantity and toxicity of the pollutants have to be carefully applied to the field

unit. Figure 4-13 shows a screen capture of input data processes of Pantanal

model.

SOIL EROSION VULNERABILITY: RAIZAL MODEL

In the decision trees of Rizal model different classes of each Land Characteristic

(LC) or Management Characteristic (MC) are connected with the severity levels of

the corresponding Land Quality (LQ) or Management Quality (MQ) by complex

decision trees, based on the approach of expert systems. The connections

between the severity levels of the Land and Management Qualities and the

vulnerability classes of the Attainable, Management and Actual types are through

decision trees (Table 4-9).

CHAPTER 4

114

Table 4-9. Summary of environmental land/management qualities and associated characteristics, for each vulnerability type of Raizal evaluation model. Vulnerability types: Water erosion (W), Wind erosion (D). Source: From Crompvoets et al. (1994).

Land/management quality Type Land/management characteristic

Attainable erosion risks Relief, t

W, D Landform; Slope gradient.

Water soil erodibility, k

W

Particle size distribution; Superficial

stoniness; Organic matter; Drainage; Sodium

saturation.

Rainfall erosivity, r

Mean monthly precipitation; Max monthly

precipitation; Mean monthly temperature;

Latitude.

Wind soil erodibility, e D Groundwater table depth; Particle

size distribution; Organic matter; Mean

monthly precipitation; Mean monthly

temperature; Latitude.

Management erosion risks Crop properties, o W Landuse type; Growing season length; Leaf

duration; Specific leaf area; Plant height;

Maximum rooting depth; Sowing date. Cultivation practices (soil), x W Tillage practices; Tillage depth;

Artificial drainage; Soil conservation

techniques.

Cultivation practices

(plant), y

W Row spacing; Residues treatment. Crop

rotation.

Crop properties, c D Landuse type; Growing season length; Leaf

situation; Plant height; Structure of crop.

Cultivation practices (soil), s D Tillage practices; Tillage depth; Tillage

method; Soil conservation techniques.

Cultivation practices

(plant), p

D Residues treatment; Crop rotation.


115

Table 4-10. The involved Land and Management Qualities in Raizal model. Source: From Crompvoets et al. (1994).

Land Qualities Management Qualities LQ1 Relief, t MQ1 Crop properties to water erosion, o

LQ2 Soil erodibility to water erosion, k MQ2 Cultivation practices to water erosion, z LQ3 Rainfall erosivity, r MQ2.1 Cultivation practices to water erosion:

soil, x LQ4 Soil erodibility to wind erosion, e MQ2.2 Cultivation practices to water erosion:

plant, y MQ3 Crop properties to wind erosion, c MQ4 Cultivation practices to wind erosion, u MQ4.1 Cultivation practices to wind erosion:

soil, s MQ4.2 Cultivation practices to wind erosion:

plant, p

The following (Table 4-10) Land and Management Qualities are involved in Raizal

model to make a decision about erosion risk.

Almost all the Land and Management Qualities separate four severity levels, as

follows: Very low, Moderately low, Moderately high and Very high. The whole

Raizal model is based on 19 decision trees (Figure 4-14). All the decision trees can

be observed by selecting the option "Decision Trees Observation" from the

Original Evaluation Menu.

Figure 4-15 shows the main input menus of Raizal model, the first option of input

data is the formation of evaluating-scenarios. To progress a Raizal application it is

needed to define the evaluating-scenario with its internal code locating all the

evaluating-units to be evaluated. The number of evaluating-units within an

evaluating-scenario is almost unlimited.

The assessment of soil erosion management vulnerability is classified into four

classes into four classes: Class V1 Very Low; Class V2 Moderately Low; Class V3

Moderately High and Class V4 Very High. On the other hand, the soil erosion

vulnerability classes (10) established by Raizal for the Attainable and Actual

Vulnerability risks (VAW, VAD and VCW, VCD) are defined as follows: Class V1

None; Class V2 Very Low; Class V3 Low; Class V4 Moderately Low; Class V5

Slightly Low; Class V6 Slightly High; Class V7 Moderately High; Class V8 High; Class

V9 Very High and Class V10 Extreme.

CHAPTER 4

116

Figure 4-14. Index of the decision trees of the Raizal model. Source: From Crompvoets et al. (1994).

Figure 4-15. Screen capture of Raizal model. Further information about Raizal model can be found in Farroni et al. (2002) and Crompvoets et al. (1994).

LQ1 LQ2 LQ3 LQ4 MQ2.1 MQ2.2 MQ4.1 MQ4.2

MQ1 MQ2 MQ3 MQ4

VAW VAD VMW VMD

VCW VCD


117

4.2.2 PRODUCTION AND ECOSYSTEM MODELLING

In the initial development of MicroLEIS DSS, qualitative methods were used to

predict the general land capability of the most important crops and the specific

suitability for individual crops or for a selection of forest species (i.e. Cervatana,

Almagra, and Sierra models, respectively; De la Rosa et al., 1992). The bioclimatic

classes are established by combining the classes of water deficiency and frost risk,

following the criterion of maximum limitation. Frost risk was estimated according

to the criteria of Verheye (1986) and adapted for the Mediterranean regions. The

frost risk was defined as the number of months with minimum average

temperature below 6 oC (as the complement of the frost-free period). According

to De la Rosa et al. (1992), the calculations of Terraza and Cervatana models are

empirical, formulated and calibrated using expert knowledge and scientific

literature. The models have been recalibrated and validated using benchmark

data from various regions of Andalucia (Spain) and other sites. Figure 4-16

represent an integration scheme of soil factors, site factors, climate factors and

soil qualities to evaluate bioclimatic deficiency and agriculture land capability.

Figure 4-16. Scheme of agriculture suitability models (Terraza and Cervatana models).

Land capability(CERVATANA model)

Bioclimatic deficiency(TERRAZA model)

UsefulDepth, l

Texture, l Carbonatecontent, l

Latitude

Maximumtemperature

Stoniness, l Drainage, l Salinity, l

Slope t

Minimumtemperature

Density of vegetation

Monthlyprecipitation

ET0

Plantcharacteristics

WaterAvailability, b

OxygenAvailability

Nutrientavailability

Rainfall erosivity,r

Soil Erodiblity,r

Frost Risk, b

Humidityindex

So

ilfa

cto

rsS

oil

qu

alit

ies

Sit

efa

cto

rsC

limat

efa

cto

rsC

rop

fact

or

Ag

ricu

ltu

resu

itab

iliti

es

CHAPTER 4

118

BIOCLIMATIC DEFICIENCY: TERRAZA MODEL

Bioclimatic calculation begins by determining the monthly potential

evapotranspiration (ET0), using the method of Thornthwaite (1948), as follows:

�� 16 ∙ �� ∙ 10 ∙ �� ∙ �

Where: Tm is monthly mean temperature (oC); Nm ismonthly coefficient of light correction, depending on the site latitude; I and a, constants for each site, which are calculated as:

� � � ��5 �

.! " �

� � 0.000000675 ∙ I% − 0.0000771 ∙ I� + 0.01792 ∙ I + 0.49239

Crop monthly evapotranspiration (ETc) is calculated as:

��, � �� ∙ -,

The monthly real evapotranspiration (ETa) is given by:

��. � ��, − /

Where D is the monthly water deficit. The difference between monthly

evapotranspiration and precipitation at a site can be positive or negative. If

positive, there is a surplus or excess (S) of water; if negative, there is a deficit or

lack (D). During the seasonal period of a crop, this difference is calculated

between the precipitation and evapotranspiration of the crop (ETc).

The monthly reduction of yield (Ry) is calculated using the following formula:

1 − 0�0� � -1 �1 − ��.

��,�

Substituting:

1 − 0�0� � �1


119

Where: Ya is real crop production and Ym is potential crop production. So, we

have

�1 � -1 ∙ 21 − 345346

7 ∙ 100(expressed as %).

The annual reduction in crop production (Rys) is calculated as follows:

�18 � -18 �1 − 9��.�9�,

� ∙ 100

Where SETa, is the sum of the monthly real evapotranspiration during the

phenological period of the crop and SETc, is the sum of the monthly

evapotranspiration of the crop during its phenological period.

Figure 4-16 shows a conceptual scheme considered in Terraza and Cervatana

models to relate soil, site, climate, soil qualities and agriculture suitability model.

Figure 4-17 shows screen captures of the Terraza model (Pro& Eco package), at

the stages of entering soil information and obtaining final results.

Figure 4-17. Screen capture of Terraza model.

CHAPTER 4

120

GENERAL LAND CAPABILITY: CERVATANA MODEL

The Cervatana model predicts the general land use capability or suitability for a

specific agricultural uses, depending on information about topography (t), soil

factors (l), erosion risk (r) and bioclimatic deficiency (b) Figure 4-16, the model

application results are grouped in four classes: S1-optimum, S2-good, S3-moderat

and N-marginal for each specific soils and crops. Figure 4-18 shows screen

captures of the Cervatana model (Pro& Eco package), at the stages of entering soil

information and obtaining final results.

CROP SUITABILITY: ALMAGRA MODEL

Almagra model represents a biophysical evaluation that uses as diagnostic criteria

those soil factors or conditions favorable for crop development in function of

suitability (De la Rosa et al., 1977). The reference zone chosen for this work is on

the left bank of the lower Guadalquivir valley, northwest of the city of Seville. The

area is 690 km2, and its characteristics are typical of a Mediterranean region (De la

Rosa, 1974). Figure 4-19 shows the level of generalization of Almagra model

considering the soil factors according to Antoine et al., 1995 (profile depth (p),

texture (t), drainage (d), carbonate(c),

Figure 4-18. Screen capture of Cervatana model.


121

Figure 4-19. Levels of generalization for each soil input factors and evaluated crop.

Level of generalization

Texture, %(t)

Profile depth, cm (p)

Profile development (g)

Drainage(d)

Carbonate,%(c)

Salinity, ds/m (s)

Sodium saturation, % (a)

Very poor (d3,d4,d5)

Poor(d2,d3, d4)

Moderate (d1,d2)

Good (d1)

Excessive (d2)

V. Exces.(d4,d3)

>40 (c3,c4)

20 – 40 (c2,c3)

10-20(c2,c1)

0.5- 10(c1,c2)

< 0.5(c2,c3)

> 16 (s5)

10 – 16(s5, s4)

8 – 10 (s4,s3,s5)

6 – 8(s3,s2,s4)

4 – 6(s2,s3,s1)

2 – 4(s2,s1)

< 2 (s1)

> 25 (a5)

20 – 25(a4,a5)

15 – 20(a3,a4)

10 – 15 (a2,a3,a1)

5 – 10(a2,a1)

< 5(a1)

Grade 1 (g1)

Grade 2(g1,g2)

Grade 3(g2)

Grade 4(g2,g3)

To imperm. material

To sand or gravel

To perm. limestone

0-15 % gravels

15-25 % gravels

> 25 % gravels

0-15 %gravels

15-25 %gravels

> 25 %gravels

Coarse (t4,t3,t2)

M. coarse (t3,t2)

Medium (t2,t1)

M. fine(t1,t2)

Fine(t2,t4)

Coarse (t5,t4)

M. coarse (t4,t3)

Medium (t3,t2)

M. heavy (t2,t3,t4)

Fine(t3,t5)

Coarse (t5,t4)

M. coarse (t5,t4)

Medium (t4,t3)

M. fine (t3,t4,t5)

Fine(t4,t5)

>120 (p1)

90-120 (p1, p2)

60-90 (p2,p3,p1)

6 – 8(s3,s2,s4)

45-60 (p3,p4,p2)

35-45 (p4,p5,p3

25-35 (p5,p4)

To impermeable material

To sand or gravel

To permeable limestone

<25 (p5)

>90 (p1)

60-90 (p1,p2)

45-60 (p2,p3,p1)

45-60 (p3,p4,p2)

35-45 (p3,p4,p2)

25-35 (p4,p5,p3)

10-25(p5,p4)

<10(p5)

>60(p1)

45-60 (p1,p2)

35-45(p2,p3)

25-35(p3,p4)

10-25(p4,p5)

<10 (p5,p4)

Depth sublevel

Texture sublevel

CHAPTER 4

122

salinity(s), sodium saturation (a) and profile development (g)) and also the

evaluation of these factors depending on the level of generalization and the

different crops. The agricultural land suitability evaluation considered the

following traditional crops: wheat (T), maize (M), melon (Me), potato (P), soybean

(S), cotton (A), sunflower (G) and sugar beet (R) as annuals; alfalfa (Af) as

semiannual; and peach (Me), citrus fruits (C) and olive (O) as perennials. The

control section of soil factors measured as texture, carbonates, salinity and

sodium character were established by adapting the criteria developed for the

differentiation of Families and Series in the Soil Taxonomy (Soil Survey Staff,

1975). Development, inputs and validity of this model described in De la Rosa et

al. (1992).

Figure 4-20 shows screen captures of the Almagra model (Pro& Eco package), at

the stage of entering soil parameter information and final results.

The Almagra model has been applied on the studied soil transects in Seville and

El-Fayoum.

Figure 4-20. Screen capture of Almagra model.


123

4.3 CLIMATE AND MANAGEMENT CHANGE SCENARIOS Climate database CDBm-Andalusia, includes 62 climatic stations data that

contains monthly average values of climate variables: mean temperature,

maximum and minimum rainfall, number of days of rain. Climate variables have

been collected during a consecutive period of 40 years (1960-2000) to represent

current climate conditions, and the hypothetical future climate change scenarios

for three periods (2040- 2070-2100) have been calculated according to the

average of 18 global climate models of A1B scenario (Table 4-11).

The present scenario of climatic condition of the study area was derived from the

climatic data during the latest 20 (1989–2009). In the light of the results of

IPCC,2007a the study area will be affected by increase in atmospheric

temperature and decreasing precipitation in a systematic manner. The predicted

scenario of climatic conditions of the study area was worked out using results of

the conference; according the achieved data the annual precipitation will be

decreased to 7.6 mm/year in 2080 instead of 11.3 mm/year. On the other hand, it

was found that climatic changes have a significant impact on the amount of water

entering El-Fayoum Province, in this turn has a close relationship with soil

degradation and lack of productivity.

In El-Fayoum area the soil contamination risk have been evaluated under different

management of major crops in the studied area. Table 4-12 shows the different of

input management parameters between the studied crops using Pantanal model.

Table 4-11. Climate change scenarios for different climate parameter in Spring, Summer, Autumn and Winter seasons; ΔT, change in temperature (d C); ΔP, change in precipitation (%) Source: Adapted from State Meteorological Agency (AEMET, 2011).

Period

Current climate Future scenarios of climate change

1961-2000 2040 2070 2100

T

max,

T min,

°C

P,

mm

∆T

max

∆T

min

∆P ∆T

max

∆T

min

∆P

∆T

max

∆T

min

∆P

Spring 20.5 8.2 160.5 2.0 1.7 -17.0 4.0 3.0 -38.0 5.6 4.0 -42.0 Summer 31.6 16.3 31.6 2.5 2.5 -25.0 4.5 4.0 -13.0 7.2 6.1 -32.0 Autumn 23.2 11.0 170.9 1.9 1.7 -22.0 3.6 3.4 -17.0 5.8 5.0 -35.0 Winter 14.4 4.4 253.3 2.1 2.1 10.0 2.5 2.5 -10.0 3.3 2.8 -12.0 Annual 22.4 10.0 616.2 2.1 2.0 -13.5 3.7 3.2 -19.5 5.5 4.5 -30.3

CHAPTER 4

124

Table 4-12. Evaluation model application: Input crop management parameters of Pantanal model.

Crops Maize Beans Wheat Sorghum

Land use type Arable. irrigated Arable. irrigated Arable. irrigated Arable. irrigated Crop rotation Winter-summer

crop combination Winter-summer crop combination

Winter-summer crop combination

Winter-summer crop combination

P-fertilizer Excessive Excessive Controlled Controlled N-fertilizer Excessive Controlled Excessive Excessive Animal manure Yes Yes Yes Yes Industrial / urban waste

Yes No No Yes

Time of fertilization

Spring/summer Autum /winter Autum /winter Spring/summer

Use of Pesticides Yes Yes Yes Yes Persistence of pesticides (months)

Low (< 6) Low (< 6) Low (< 6) Low (< 6)

Toxicity (LD-50, ppm)

Low (>1000) Low (>1000) Low (>1000) Low (>1000)

Application methods of pesticides

Foliage Foliage Foliage Foliage

Artificial drainage

Yes Yes Yes Yes

Artificial ground water

Yes yes Yes Yes

Residues Treatment

Burning Mulching. ploughed-in

Mechanical Burning

Soil conservation techniques

Nil Nil Nil Nil

Tillage practices type

Conventional tillage

Minimum tillage Minimum tillage Conventional tillage

Also hypothetical recommended management scenarios ( under maize crop) were

established for each soil type to formulate sustainable agriculture practices by

reducing inputs use like water, fertilizers and pesticides, and so minimizing the

risk of soil degradation due to contamination. This in in agreement with Carsan et

al. (2014), who suggested that low-input practices (including reducing tillage,

using legumes in rotations or intercrops) can contribute to restoration of soil

nutrients, improve soil carbon contents and reduce dependence on fertilizer use

by 50% (Carsan et al., 2014).


125

Figure 4-21. Steps of extracting the studied soil profile information using Arc.GIS 10.2.

4.4 ANALYSIS OF TOPOGRAPHIC TRANSECTS

4.4.1 PROVINCE OF SEVILLE Two transects (S-N and W-E) were considered in the province of (Figure 4-21).

These points were subsequently represented by 41 soil profiles from the soil

database SDBm-Seville. In the first stage, the exact location of 576 soil profiles

from the database was represented (Figure 4-21-1). Then, representative selected

topographic transects were selected (Figure 4-21-2) and representative points at

regular 4 km intervals were stablished (Figure 4-21-3). Data from the nearest soil

profile were considered as representative of each point.

Climate change scenarios have been calculated according to the global climate

model (CNRMCM3) (Andalusian Environmental Information Network, REDIAM,

2011) by extracting spatial climate data under IPCC scenario A1B for the current

period (average data from 1960-2000), 2040, 2070 and 2100.

4.4.2 EL FAYOUM

One topographic transect (SE-NW) was considered in El-Fayoum, including 10

representative soil profiles at regular 3.3 km intervals. Climate data from El-

Fayoum weather station were collected and soil degradation risks under wheat,

sunflower and olive crops under different management scenarios were studied.

CHAPTER 4

126

4.5 SPATIAL DATA COLLECTION AND PROCESSING

All results have been integrated in a GIS to perform spatial analysis of degradation

(contamination + erosion), land capability and crop suitability and produce

graphical outputs. Spatial data processing was carried out using ArcGIS 10.2.

Soil spatial information was obtained by digitalization of the Andalusia (De la Rosa

et al., 1984), Seville (De la Rosa et al., 2009) and El-Fayoum preexisting soil maps

(Ali, 2005; ASRT, 2009; Haroun, 2004). Data from selected soil profiles of

Andalusia, Sevilla province and El-Fayoum, including morphological, chemical and

physical properties, were attached to the attribute table of the digital soil map.

Digital Elevation Models (DEM) of the study areas have been obtained from the

Shuttle Radar Topographic Mission (SRTM). The SRTM uses precisely positioned

radar to map the Earth surace at intervals of 1-arc second (~30 meters). The DEM

extracted from SRTM data can be used in conjunction with controlled imagery

sources to provide better visualization of the terrain. DEMs have a key role in

improving accuracy in the field of soil and agricultural characterizing (Matinfar et

al., 2011). The SRTM image of El-Fayoum and Seville Provinces were processed in

ENVI 4.7 software to extract the surface elevation, slope gradient and slope

direction.

Pattern of land uses in Andalusia.

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