Ero&Con User`s Manual & Technical Description
Contents
1. e e e VAW VAD VMW VMD e VCW VCD Figure 4 1 Index of the decision trees of the Raizal model 29 WWW MicroLEIS Ero amp Con module Table 4 3 Summary of environmental land management qualities and associated characteristics for each vulnerability type of Raizal evaluation model Land management characteristic Land management Vulnerability quality type Relief t W D Water soil erodibility k W Rainfall erosivity r W Wind soil erodibility e D Crop properties o Cultivation practices soil x Cultivation practices plant y Crop properties c Cultivation practices soil s Cultivation practices plant p Attainable erosion risks Landform Slope gradient Particle size distribution Superficial stoniness Organic matter Drainage Sodium saturation Mean monthly precipitation Max monthly precipitation Mean monthly temperature Latitude Groundwater table depth Particle size distribution Organic matter Mean monthly precipitation Mean monthly temperature Latitude Management erosion risks W W W D Landuse type Growing season length Leaf duration Specific leaf area Plant height Maximum rooting depth Sowing date Tillage practices Tillage depth Artificial drainage Soil conservation techniques Row spacing Residues treatment Crop rotation Landuse type Growing season length Leaf situation Plant height Structure of crop Tillage2. F AL06 Des rtico Campo Tabernas V3r V2c V3c V3g F AL07 Pardo calizo Los V lez V3c V4cr V3g F AL08 Aluvial Rio Nacimento V2 V3cd V3c V3g F CAO1 Tierra negra Campina V3r V4cdr V4cr V2r F CA02 Bujeo blanco Campo Gibraltar VI V3cd V3cr VI F CA03 Rojo Costa V2 V3cd V4cr V4g F CA04 Tierra parda Janda Aljibe VI V4cr V4cr V4r F CA05 Albariza Rincon Jerez V3 VA 4 2 F CA06 Terra rossa Sierra VI V4r V4r V3r F CO01 Albariza Campi a alta V4r V4lr V4cr V4r Example 6 2 Graphical presentation of the attainable contamination risks classes of the first 15 evaluating units of evaluating scenario Andalucia 41 WWW MicroLEIS Ero amp Con module MicroLEIS Ero amp Con PANTANAL model Scenario Andalucia Attainable risk 100 80 z 60 0 f a r 40 e a 20 0 None Lou Moderate High Inc Data Pesticides Contamination Example 6 3 CSV format of the first 15 evaluating units of evaluating scenario Andalucia MicroLEIS Ero amp Con PANTANAL model 42 WWW MicroLEIS Base Scenario Land Unit F ALO1 F AL02 F ALO03 F AL04 F ALOS F AL06 F AL07 F AL08 F CAO1 F CA02 F CA03 F CA04 F CA05 F CA06 F CO01 Andalucia Long Lat W00218 N3717 W00152 N3714 W00234 N3654 W00237 N3644 W00211 N3654 W00225 N3702 W00218 N3736 W00247 N3709 W00600 N3648 W00522 N3612 W00609 N3630 W00540 N3630 W00611 N3644 W00534 N3648 W00439 N3731 Alt 1260 21
3. Baca W00245 N3725 1200 CLIMATE RELATED CHARACTERISTICS JAN FEB MAR APR MAYJUN JUL AUG SEP OCT NOV DEC Pmen 25 0 23 0 40 0 480 42 0 28 0 7 0 6 0 15 0 42 0 46 0 56 0 Tmen 4 6 5 6 Jo MO ISO 18 0 99540 7 7 4 8 Pmen Mean Precipitation mm Tmen Mean Temperature Table 3 12 Data input screen for management related input variables MicroLEIS Ero amp Con PANTANAL model ENTER AND EDIT DATA 20 WWW MicroLEIS Ero amp Con module Evaluating unit F ALO1 Evaluating scenario AND Code Name Field Utilization Type Wheat Intensive MANAGEMENT RELATED CHARACTERISTICS Land Use Type 11 Use of Pesticides 1 Crop Rotation 4 Persistence of Pesticides 2 Land Use on Slopes 1 Toxity LD 50 of Pesticides 400 Application methods of Pesticides p P Fertilizer p Artificial Drainage p N Fertilizer 1 Artificial Groundwater Level 2 Animal Manure 2 Residues Treatment 3 Industrial Urban Waste 2 Soil Conservation Techniques 4 Time of Fertilization 1 Tillage Practices 4 The lt F1 gt key can be used for each qualitative variable It displays on screen all the possible valid codes and their meaning for this particular characteristic Table 3 13 Any code can be selected using the bar and that code is automatically introduced into the variable field Table 3 13 Pop up window corresponding to the qualitative land characteristic Drainage which opens when pressing the lt F1 gt key LC DRAINAGE Very poor
4. Method 2 Specific Leaf Area m2 kg Row spacing m 0 40 Plant Height m OSO Artificial Drainage 2 Maximum Rooting Depth m T00 Soil Conservation Techniq Water Sauce CIE Castore 2 Soil Conservation Techniq Wind Residues Treatment be gt E G S Crop Rotation The Fl key can be used for each qualitative variable It displays on screen all the possible valid codes and their meaning for this particular characteristic Table 3 6 Any code can be selected using the cursor and that code is automatically introduced into the variable field Table 3 6 Pop up window for the qualitative land characteristic Drainage which opens when pressing the lt F1 gt key LC Surface Drainage Very poor Poor Imperfect Moderately well Well Somewhat excessive Excessive Appendix A lists the names and classes of all variables Land Characteristics LCs and Management Characteristics MCs used within the Raizal model 15 WWW MicroLEIS Ero amp Con module Within the Raizal framework it is possible to delete an evaluating scenario or evaluating unit Table 3 7 shows the option to delete an evaluating scenario or unit Table 3 7 Menu to delete the evaluating scenario or unit DELETE INPUT DATA 1 Delete Evaluating Scenario 2 Delete Evaluating Unit H Explanation R Return to Input Menu 3 1 3 SDBm Interface The soil related data used as diagnostic criteria in Raizal can be automatical
5. Numerous shallow gullies will be present in the field The wind will remove from the soil a large amount of the A horizon that ordinary tillage brings up and will partly mix the B horizon or other underlying horizons with surface soil in the plow layer Rarely will this be condition uniform throughout a mappable land unit For these fields the effect of management change on the vulnerability classes could be considerable Class V7 Moderately High These field units present a moderately high vulnerability to water or wind erosion The soil will erode to the extent that practically all of the original surface soil or A horizon will be removed The plow layer will consist essentially of materials from the B or other underlying horizons Patches in which the plow layer is a mixture of the original A horizon and the B horizon or other underlying horizons will be included within the mapped field units Shallow gullies or a few deep ones will be common on some soil types 26 WWW MicroLEIS Ero amp Con module The wind will remove all of the A horizon and a small amount of the B or other underlying horizon The plow layer will consist of original horizons below the A horizon For these fields the more management erosive farming systems have adverse effects on the environment Class V8 High These field units present a high vulnerability to water or wind erosion The soil will erode to the extent that all of the original surface soil or A horizo
6. The effect of climate change on agricultural and horticultural potential in Europe Research Report No 2 Environmental Change Unit Oxford Loveland P ed 1995 ACCESS Agroclimatic change and European Soil Suitability Volume I Technical Report In press Thornthwaite C W 1948 An approach toward a rational classification of climate The Geogr Rev 38 55 94 44
7. data 3 2 2 Enter Edit and Delete Data From the keyboard it is possible to enter edit and delete input data Table 3 10 shows an example of Enter and Edit Data screen for soil related data The same structure is used in climate and managment related variables screens Table 3 11 and Table 3 12 Only those variables which are necessary to evaluate the chosen specific compound vulnerability are highlighted on the screens From all climate data input the model calculates automatically the Humidity Index Annual Temperature and Annual Rainfall These calculated climate variables represent the climate influence on the contamination vulnerability of a land unit Table 3 10 Data input screen for soil related input variables MicroLEIS Ero amp Con PANTANAL Model ENTER AND EDIT DATA 19 WWW MicroLEIS Ero amp Con module Evaluating scenario AND Evaluating unit F ALO1 Representative area km2 2000 00 Code Name Long Latit Altit Benchmark Soil ALO1 Greda roja W 00218 N3717 1260 SOIL RELATED CHARACTERISTICS Landform MO Drainage W Slope Gradient 30 Particle Size Distribution SCL Groundwater Table Depth m W Organic Matter 1 7 pH 7 1 C E C meq 100 gr 18 1 Table 3 11 Data input screen for climate related input variables MicroLEIS Ero amp Con PATANAL Model ENTER AND EDIT DATA Evaluating unit F ALO1 Evaluating Scenario AND Code Long Latit Altit Climate Station 1 Almanzora Alto
8. practices Tillage depth Tillage method Soil conservation techniques Residues treatment Crop rotation Vulnerability types Water erosion W Wind erosion D 30 WWW MicroLEIS Ero amp Con module 4 2 Pantanal Model 4 2 1 Vulnerability Classes The agrochemical contamination vulnerability classes 4 established by Pantanal for the attainable and management vulnerability are defined as follows Class VI None Almost invulnerable to agrochemical contamination by agricultural activities and the biophysical risks to soil surface and groundwater diffuse pollution are very low The corresponding fields have a very big storage capacity for agrochemical compounds and or the amount of leaching and run off of the contaminants is very low The management system of this class does not lower the quality of the soil surface and groundwater of the field unit Class 2 Low A reasonably low vulnerability to agrochemical compounds which diffusely contaminate the soil surface and groundwater The agropollutant storage capacity of the corresponding fields is high and or the amount of leaching and run off are low The management system of this class could harm the quality of the soil surface and groundwater of the field unit on a small scale Class V3 Moderate A reasonably high vulnerability to agrochemical compounds which diffusely contaminate the soil surface and groundwater The agropollutant storage capacity of the corresponding fi
9. two previous single hypothetical evaluations the changes of input variables are not saved in the climate and management input database of Ero amp Con models 6 Output Evaluation Results 38 WWW MicroLEIS Ero amp Con module Three types of presentation tabular graphical and CSV format are generated by Ero amp Con models These outputs are stored in files whose names were asked for by the program and placed in the directory C MLERO OUTPUT The Evaluation Results menu Table 6 1 gives access to all these output files of the models Table 6 1 Evaluation results menu of the RAIZAL model EVALUATION RESULTS 1 Display Results 2 Delete Results H Explanation R Return to Main Erosion Menu 6 1 Display Results The Display Results option is used to show the files of evaluation results both original and hypothetical referring to all the evaluating units of a particular evaluating scenario For the three types of vulnerability class Attainable Management and Actual the evaluation results are shown in the following formats 1 The tabular presentation of the evaluating scenario with the evaluating unit location water and wind vulnerability classes subclasses and total index For the actual vulnerability the following construction is used Class attainable subclass management subclass e g V9 kr oz A summary of the vulnerability classes and their representative area in km2 for this evaluated scenario i
10. 0 900 90 300 1080 700 53 70 20 450 60 200 240 d BRR WR NF WN RF WN FR Re 2 SES Sm 50 Sous os oy am LLY YY gt lt BWNDP HE C0 YU YU a Ero amp Con module 7 Bibliography CEC 1992 CORINE soil erosion risks and important land resources Commission of 43 WWW MicroLEIS Ero amp Con module the European Communities DGXII EUR 13233 EN Brussels CSIC 1995 CDB Climate database Monthly data module IRNAS Pub Sevilla CSIC 1995 MDBm Multilingual management database First approximation IRNAS Pub Sevilla Crompvoets J W Mayol F and de la Rosa D 1994 An expert evaluation system for assessing agricultural soil erosion vulnerability In Soil responses to climate change M D Ronsevell and P J Loveland eds NATO ASI Series Springer Verlag Heidelberg De la Rosa D Crompvoets J and Mayol F 1995 Risk modelling land vulnerability expert system In ACCESS Agroclimatic Change and European Soil Suitability Volume I Technical Report P Loveland ed In press FAO 1976 A framework for land evaluation Soils Bulletin No 26 FAO Pub Rome FAO 1984 Provisional methodology for assessment and mapping of desertification FAO UNEP Pub Rome FAO ISRIC CSIC 1995 SDBm Multilingual soil database World Soil Resources Report No 81 FAO Pub Rome Kenny G J Harrison and Parry M L eds 1993
11. 2 310 55 011 5 SE0061 N374044 0060218 200 MO Castilblanco SL 2592 0 5 2 0 6 5 0064 N375204 W060524 490 VA Almaden SL 1 69 12 6 0 3 3 2 Pantanal Model On the main menu of the Pantanal model Table 3 8 the first option is Input Data Generation 17 WWW MicroLEIS Ero amp Con module Table 3 8 Main menu of the Pantanal model AGROCHEMICAL CONTAMINATION RISKS 1 Input Data Generation 2 Base Evaluations 3 Hypothetical Evaluations 4 Output Evaluation Results H Explanation R Return to Ero amp Con Menu 3 2 1 New Evaluating Scenarios As presented in Table 3 9 the first option of input data is the creation of new evaluating scenarios To develop a Pantanal application it is necessary to create an evaluating scenario with its code within located all the evaluating units to be evaluated The number of evaluating units within an evaluating scenario is almost unlimited It is also possible to select a previously defined evaluating scenario by using the lt F1 gt option Table 3 9 Input data generation menu of Pantanal model INPUT DATA GENERATION 18 WWW MicroLEIS Ero amp Con module New Evaluating Scenarios 2 Enter amp Edit Data 3 Delete Data 4 SDBm Interface H Explanation R Return to main menu For each evaluating unit the input data can be generated 1 from the keyboard for soil climate and management related data and 11 through the SDBm interface only for the soil related
12. 50 I 0 3 0 l2 20 5 2 Management Change A Management Change evaluation can be made by changing All the Management Characteristics FUT Selecting a particular Field Utilization Type FUT by using the lt F1 gt key imposes it on all the evaluation units of an evaluating scenario One or more Management Characteristics MC Selecting the classes of the MCs imposes them on all the evaluating units of an evaluating scenario In both cases the option Compute Evaluation selects the evaluating scenario When selected the model automatically evaluates the hypothetical management change scenario Finally the model asks for names of files of tabular graphical and CSV format presentation to display the output evaluation results for this hypothetical scenario 37 WWW MicroLEIS Ero amp Con module 5 3 Climate and Management Change Ero amp Con models can also make hypothetical evaluations considering climate and management changes simultaneously This option combines two of the previous changes climate factors and management characteristics The option Compute Evaluation selects an evaluating scenario When selected the model automatically evaluates the hypothetical climate management change scenario Finally the model asks for names of files of tabular graphical and CSV format presentation to display the output evaluation results for this hypothetical scenario For this double hypothetical evaluation and for the
13. ND Evaluating unit F ALO1 Representative area km2 2000 00 Code Name Longitude Latitude Altitude m Benchmark Soil 101 Greda roja W00218 N3717 1260 SOIL RELATED CHARACTERISTICS Landforms MO Particle Size Distribution SL Slope Gradient 30 Organic Matter 1L Groundwater Table Depth m 20 0 Sodium Saturation 360 Superficial Stoniness 25 Drainage W Table 3 4 Data input screen for climate related input variables MicroLEIS Ero amp Con RAIZAL Model Evaluating Unit F ALO1 Evaluating Scenario AND Code Name Longitude Latitude Altitude Climate Station AALO1 Almanzora Alto W00245 N3725 1200 CLIMATE RELATED CHARACTERISTICS JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Pmax 58 0 62 0 170 0 180 0 122 0 85 0 5940 2 0 72 0 131 0 158 0 191 0 Pmean P25 510 23 51 40 0 48 0 42 2350 ESO Dun US 42 0 46 0 56 0 Tmean 4 6 5 6 Tell 10 2 ISO ASO 29 9 2350 Pmax Maximum Precipitation mm Pmean Mean Precipitation mm Tmean Mean Temperature C Table 3 5 Data input screen for management related input variables MicroLEIS Ero amp Con RAIZAL Model 14 WWW MicroLEIS Ero amp Con module Evaluating unit F ALO1 Evaluating Scenario AND Code Name Field Utilization Type FUTO1 Wheat Intensive MANAGEMENT RELATED CHARACTERISTICS Crop Properties Cultivation Practices Land Use Type Sowing Date 1 Growing Season Length days 80 Tillage Practices Leaf Duration 2 Tillage Depth 1 Leaf Situation 2 Tillage
14. Poor Imperfect Moderately well Well Somewhat excessive Excessive Appendix A lists the names and classes of all variables Land Characteristics LCs and Management Characteristics MCs used within the Pantanal model Within the Pantanal framework it is possible to delete an evaluating scenario or evaluating unit Table 3 14 shows the option to delete an evaluating scenario or unit 21 WWW MicroLEIS Ero amp Con module Table 3 14 Menu to delete the evaluating scenario or unit INPUT DATA DELETE 1 Evaluating Scenario 2 Evaluating Unit 3 Benchmark Soil 4 Climate Station 5 Field Utilization R Return to Input Menu 3 2 3 SDBm Interface The soil related data used as diagnostic criteria in Pantanal can be automatically captured from the SDBm database FAO CSIC 1994 by using the Soil Layer Generator option of SDBm When using this option the input files generated by SDBm must be placed in directory CAMLEROUNPUT to be imported The soil profile variables stored in SDBm which are used in Pantanal are the following 1 Site Characteristics Latitude LAT Longitude LON Altitude ELEV Landform LAFO Slope Gradient SLGR Stoniness STON Drainage Depth of Groundwater Table GWAT Location LOC 2 Horizon layer and Analytical characteristics Texture TEX1 pH water PHW Organic carbon OC Cation Exchange Capacity CECS Sodium
15. Tillage practices Time of fertilization Residues treatment Heavy metals incidence q Landuse type Crop rotation Use Pesticides incidence t of pesticides Use of fertilizers Use of waste X Landuse type Persistence in soil Toxicity of pesticides Application methods Artificial groundwater level Vulnerability types Phosphorus P Nitrogen N Heavy metals H and Pesticides X 34 WWW MicroLEIS Ero amp Con module 5 Hypothetical Evaluations The user has the option to generate hypothetical predictions by changing the climate or and management related variables So it will be possible to predict for example the impact of climate changes on the field vulnerability to water erosion or to predict the impact of the residues treatment of a field unit to pesticides contamination vulnerability Table 5 1 shows the different options to run the hypothetical prediction Table 5 1 Menu to select type of hypothetical evaluation HYPOTHETICAL EVALUATIONS 1 Climatic Change 2 Management Change 3 Climate and Management Change 4 Compute Evaluation H Explanation R Return to Main Erosion Menu 5 1 Climate Change Selection of the Hypothetical Climate Change option displays the screen shown in Table 5 2 At first it is necessary to define the climate perturbation s 35 WWW MicroLEIS Ero amp Con module Table 5 2 Data input screen to generate a hypothetical prediction by changing the climate rel
16. WWW MicroLEIS Exploring the Agro ecological Limits of Sustainabilit Ero amp Con Agro ecological Field Vulnerability Evaluation System User s Manual amp Technical Description developed by D de la Rosa J Crompvoets F Mayol and J A Moreno Consejo Superior de Investigaciones Cientificas Instituto de Recursos Naturales y Agrobiologia Sevilla WWW MicroLEIS Ero amp Con module Contents 1 The Approach Overview 2 Ero amp Con Front end 2 1 General Structure 2 2 Installation 2 3 System Requirements 3 Input Data Generation 3 1 Raizal Model 3 1 1 New Evaluating Scenarios 3 1 2 Enter Edit and Delete Data 3 1 3 SDBm Interface 3 2 Pantanal Model 3 2 1 New Evaluating Scenarios 3 2 2 Enter Edit and Delete Data 3 2 3 SDBm Interface 4 Base Evaluations 4 1 Raizal Model 4 1 1 Vulnerability Classes 4 1 2 Decision Trees 4 2 Pantanal Model 4 2 1 Vulnerability Classes 4 2 2 Decision Trees 5 Hypothetical Evaluations 5 1 Climate Change 5 2 Management Change 5 3 Climate Management Change 6 Output Evaluation Results 6 1 Display Results 6 2 Delete Results 7 Bibliography WWW MicroLEIS Ero amp Con module 1 The Approach Overview Within the new MicroLEIS framework the Ero amp Con package is a database expert system evaluation approach for assessing limitations to the use of the land or vulnerability of the land to specified agricultural degradation risks Soil erosion salinization and di
17. ated factors AP and AT MicroLEIS Ero amp Con RAIZAL Model CLIMATIC CHANGE A Pmax 0 0 0 0 ORG 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pmean 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 A Tmean 0 0 020 0 0 0 0 0 0 020 0 0 0 0 0 0 0 0 0 0 0 0 gt Pmax Increment or of Maximum Precipitation A Pmean Increment or of Mean Precipitation A Tmean Increment or of Mean Temperature C Three climate perturbation results from GCMs assumptions for the Mediterranean South can be selected using the lt F1 gt key Table 5 3 To apply these increments AP and AT the months December January and February are included in the winter and July August and September in the summer No changes are considered in the other months of the year However any arbitrary set of climate perturbations can be used The option Compute Evaluation selects the evaluating scenario When selected the model automatically evaluates the hypothetical climate change scenario Finally the model asks for names of files of tabular graphical and CSV format presentation to display the output evaluation results 36 WWW MicroLEIS Ero amp Con module Table 5 3 Popup screen showing three climate perturbations MicroLEIS Ero amp Con RAIZAL Model Predicted climate perturbations after Kenny et al 1993 iz OC AP Year Winter Summer Winter Summer Mediterranean South 2010 0 5 0 25 6 0 2030 df Jf e 0 30 20
18. ation using data information of 62 representative sites of the Andalucia region Spain and of 42 sites of the European Union A spatial application of the models was made for the Province of Sevilla 1 400 000 ha Spain The hypothetical predictions considering climate and management changes can be useful as tools in designing adaptation strategies to climate changes and in formulating sustainable landuse scenarios by changing agricultural management practices These what if scenarios are critical pieces of the puzzle in the understanding of global change Figure 1 3 shows an overview with the general structure of the Ero amp Con system WWW MicroLEIS Potentialities climate site soil qualities Biophysical factors data base Limitations erosion contamination salinization Attainable land productivity strategic crops Attainable land degradation risks Application scales E European scenarios R Regional scenarios Management productivity indices Management factors knowledge base Management degradation indices Ero amp Con module Phase 2 Actual field suitability classes classes Major procedures used a Qualitative classification b Simulation modeling c Expert systems Figure 1 1 General scheme of the new MicroLEIS framework incorporating sustainability in land evaluation WWW MicroLEIS Ero amp Con module Profile Standard Soluble Salts Soil Physical Description Anal
19. elds is low and or the amount of leaching and run off ranges from moderate to high The management system of this damages the quality of the soil surface and groundwater of the field unit on a high scale Class V4 High Very vulnerable to agrochemical contamination by agricultural activities and the risks to soil surface and groundwater diffuse pollution are very severe The fields have a very small storage for agrochemical compounds and therefore leaching could be very high Also fields which are strongly vulnerable to run off could damage the quality of surface water The field management system of this class harms the soil surface and groundwater quality of the field unit on an extremely high scale The assessment of the actual vulnerability is classified into five actual vulnerability classes Class VI None Field units of this actual 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 of this actual class are slightly vulnerable to agrochemical contamination because the combination of the management system with 31 WWW MicroLEIS Ero amp Con module the biophysical conditions of the classified field unit does almost no harm to t
20. ffuse agrochemical contamination are separately considered by several program models These empirical knowledge based models also combine simple process sub models such as to predict rainfall erosivity by the Thornthwaite Fournier formula In this sense Ero amp Con can be considered as a set of hybrid methods Special attention is given to the management technological aspects at farm level Therefore agricultural management factors are detailed and combined with land characteristics in order to define the field units to be evaluated within each model Physical attainable risks are calculated separately from management related risks and the two are combined to produce the actual vulnerability classes Figure 1 1 Ero amp Con models are automated applications of the developed rural resources databases SDBm Soil CDB Climate and MDB Management Figure 1 2 The created expert models are based on decision trees as hierachical multiway keys in which the leaves are results such as land quality LQ ratings and the interior nodes of the tree are decision criteria such as land characteristic LC values The models were partly constructed in accordance with the criteria of the FAO framework for Land Evaluation The created models were initially formulated and calibrated by using expert knowledge experience of specialists and land users and collected literature knowledge Then the models were recalibrated and validated by point to point applic
21. haracterisics of the soil are favorable and erosion is controlled The wind will remove a big part of the soil profile The plow layer consists of the original horizons below the A horizon An occasional blow out part of the field unit will be included Class VIO Extreme These field units present an extremely high vulnerability to water or wind erosion The field will erode until it has an intricate pattern of moderately deep or deep gullies Soil profiles will be destroyed except in small areas between gullies Such fields will not be useful for crops in this condition Reclamation for crop production or for improved pasture is very difficult but will be practical if the other characteristics of the soil are favorable and erosion is controlled by soil conservation techniques for example by construction of terraces The assessment of the soil erosion management vulnerability VMW and VMD is classified into four classes Class VI Very Low The human influence on these field units is very small regarding the vulnerability to water or wind erosion In general this class represents good soil conservation methods and the actual vulnerability of a field unit will be dependent only on the attainable risk This class represents the best field management 27 WWW MicroLEIS Ero amp Con module methods for a particular field unit to become a possible sustainable land use system economical issues are not involved Class V2 Moderately Low The influe
22. he soil surface and groundwater quality Class V3 Moderate Fields units of this actual class 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 actual 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 adverse effects on the environment Class V5 Extreme Field units of this actual class 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 Besides classes subclasses are also presented as evaluation outputs The subclasses show the user the vulnerability limitations of the evaluated field unit and help in understanding the evaluated classification 4 2 2 Decision Trees The classes of each Land Characteris
23. ion trees Table 4 3 Appendix A shows the different classes of the Land and Management Characteristics The following Land and Management Qualities are involved Land Qualities with their subclass code LQ1 Relief t LQ2 Soil erodibility to water erosion LQ3 Rainfall erosivity r LQ4 Soil erodibility to wind erosion e Management Qualities with their subclass code MQ1 Crop properties to water erosion o MQ2 Cultivation practices to water erosion z MQ2 1 Cultivation practices to water erosion soil x MQ2 2 Cultivation practices to water erosion plant y MQ3 Crop properties to wind erosion c MQA Cultivation practices to wind erosion u MQA 1 Cultivation practices to wind erosion soil s 28 WWW MicroLEIS MQ4 2 Cultivation practices to wind erosion plant p Ero amp Con module Almost all the Land and Management Qualities separate four severity levels as follows Very low Moderately low Moderately high Very high The whole Raizal model is based on 19 decision trees Figure 4 1 All the decision trees can be observed by selecting the option Decision Trees Observation from the Original Evaluation Menu and are presented in Appendix B MicroLEIS Ero amp Con RAIZAL Model LQ1 LQ2 LQ3 LQ4 MQ2 1 MQ22 MQ41 MQ42 MQI MQ3
24. ld The soil of the areas in this class can be eroded to the extent that ordinary implements reach through the remaining A horizon The wind will remove a small amount of the soil For these fields the effect of management change on the vulnerability classes could be important There are small differences in use capabilities and management requirements from the uneroded soils Class V5 Slightly Low These field units present a slightly low vulnerability to water or wind erosion The soil will erode to the extent that ordinary tillage implements reach through the remaining A horizon or well below the depth of the original plowed layer in soils with thin A horizons Generally the plow layer will consist of a mixture of the original A horizons and underlying horizons Mapped land units will have patches in which the plow layer consists wholly of the original A horizon and others in which it consists wholly of underlying horizons A few shallow gullies will be present in the field The wind will remove from the soil a sufficient amount of the A horizon that ordinary tillage brings up For these fields the effect of management change on the vulnerability classes could be considerable Class V6 Slightly High The field units present a slightly high vulnerability to water or wind erosion The soil will erode to the extent that a big part of the original surface soil or A horizon will be removed Water erosion processes will be active during each year
25. ly captured from the SDBm database FAO CSIC 1994 by using the Soil Layer Generator option of SDBm When useing this option the input files generated by SDBm must be placed in directory C MLERO INPUT to be imported The soil profile variables stored in SDBm which are used in Raizal are the following 1 Site characteristics Latitude LAT Longitude LON Altitude ELEV Land form LAFO Slope gradient SLGR Stoniness STON Drainage DRAT Ground water table depth GWAT Location LOC 2 Horizon layer characteristics Texture TEX1 Organic carbon OC Cation exchange capacity CECS Sodium saturation NA 16 WWW MicroLEIS Ero amp Con module The structure of SDBm files as created by the Soil Layer Generator is shown For this example a layer or control section of 0 to 50 cm from the surface was considered for the soil horizon and analytical characteristics in Example 3 1 Example 3 1 An SDBm input file for six soil profiles and one control section including the SDBm soil related characteristics which are diagnostic criteria for the Raizal module Reg PRNO LAT LON ELEV LAFO SLGR STON DRAI GWAT LOC TEXT OC CECS NA Ju SE0052 N374715 w054015 320 HI Coustanting ILIO 5 2 5 0058 N374300 0060600 490 VA Almaden sue 15215 825 0 0 3 5 0059 N374520 0060020 400 VA Casiciloleameas SCL 0 8 21 1 0 0 4 5 0060 N374341 0055629 350 VA Castilblanco LS 05
26. ly precipitation mm XXX LC Mean monthly temperature C LC Latitude XXX XXX WWW MicroLEIS Ero amp Con module Table 1 1 Cont Input variable list of the Raizal and Pantanal evaluation models Management characteristic class or unit Raizal Pantanal Crop related characteristics MC Land use type 11 classes XXX XXX MC Crop rotation 4 classes XXX XXX MC Land use on slopes 2 classes XXX MC Growing season length days XXX MC Leaf duration 2 classes XXX MC Leaf situation 2 classes XXX MC Specific leaf area m2 kg XXX MC Plant height m XXX MC Maximum rooting depth m XXX MC Structure of crop 2 classes XXX Cultivation related characteristics MC Sowing date 2 classes XXX MC Tillage practices 5 classes XXX XXX MC Tillage depth 2 classes XXX MC Tillage method 2 classes XXX XXX MC Row spacing m XXX MC Artificial drainage 2 classes XXX XXX MC Artificial groundwater level 2 classes XXX MC Soil conservation techniques water 4 classes xxx XXX MC Soil conservation techniques wind 5 classes xxx MC Residues treatment 3 classes XXX XXX Fertilizer related characteristics MC Use of P fertilizer 3 classes XXX MC Use of N fertilizer 3 classes XXX MC Use of animal manure 2 classes XXX MC Use of industrial urban waste 2 classes XXX MC Time of fertilization 2 classes XXX Pesticides related characteristics MC Use of pesticides 2 classes XXX MC Persistence of pesticides 3 cla
27. n will be removed The plow layer will consist essentially of materials from the B or other underlying horizons Patches in which the plow layer is a mixture of the original A horizon and the B horizon or other underlying horizons will be included within the mapped field units Shallow and moderately deep gullies will be present in the field unit Where land is afforested or drained there will be often a slug of erosion before channels are stabilized by vegetation but the rates of erosion will continue to be greater than those prevailing before The wind will remove all of the A horizon and a part of the B or other underlying horizon The plow layer will consist mainly of the original horizons below the A or below the original plowed layer in the soils with thin A horizons although some patches will have much of the original A horizon remains in the field unit Sometimes an occasional blow out area of the field unit will be included For these fields the more management erosive farming systems have adverse effects on the environment Class V9 Very High These field units present a very high vulnerability to water or wind erosion The field will erode until it has partly an intricate pattern of moderately deep gullies Soil profiles will be destroyed except in small areas between gullies Such fields will not be useful for crops in this condition Reclamation for crop production or for improved pasture is difficult but will be practical if other c
28. nable and Actual Vulnerability risks VAW VAD and VCW VCD are defined as follows Class VI None These field units are not vulnerable to water or wind erosion the risks to these processes could be considered as nil and the land unit will be uneroded For these fields management erosivity is not considered to be a controlling factor and almost any farming system can be implemented Class V2 Very Low These field units present a very low vulnerability to wind or soil erosion Soil erosion will occur rarely and only during extreme climate conditions For these fields the influence of the management systems on the soil degradation is low 25 WWW MicroLEIS Ero amp Con module Class V3 Low These field units present a low vulnerability to water or wind erosion Soil erosion will occur rarely The soil could have a few rills or places with thin A horizons that give evidence of accelarated erosion but not to such an extent as to alter greatly the thickness and character of the A horizon The wind will remove only a very small amount of the soil For these fields the effect of management change on the vulnerability classes could be important There are no differences in use capabilities and management requirements from the uneroded soils Class V4 Moderately Low These field units present a moderately low vulnerability to wind or wind erosion Only a few properties of water erosion especially inter rill erosion will be visible in the fie
29. nce of field management is small regarding water or wind erosion vulnerability In the sense of sustainable land use when the attainable risk is high then the risk of field management has to be Class 1 or Class 2 land use with higher vulnerability is excluded Class V3 Moderately High Field units with this risk class are in danger of becoming less suitable in the agricultural sense and their field methods are vulnerable to water or wind erosion The field use with this management risk is considered not to be sustainable Class V4 Very High Field units with this management class are in real danger of becoming unsuitable because their management methods greatly accelerate the processes of water or wind erosion To sustain the field it is necessary to avoid this management class Besides vulnerability classes subclasses are also presented as evaluation outputs The subclasses show the user the vulnerability limitations of the evaluated field unit and help in understanding the evaluated classification 4 1 2 Decision Trees The 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 decis
30. ntamination H Explanation Q Quit 10 WWW MicroLEIS Ero amp Con module 2 2 Installation Ero amp Con is simple to install and to run Installation is by the following steps during which a few questions are asked insert the diskette 1 in drive A or B or change to drive A or B or type MLEROINS to install the software on a hard disk follow the instructions on the screen Ero amp Con will be installed in directory MLERO Finally to run Ero amp Con from the directory MLERO type MLERO and press lt Enter gt A few examples of evaluating scenarios are included 2 3 System Requirements To use Ero amp Con you will require a personal computer PC MS DOS version 3 0 or higher with about 1 Mbytes of free space It will work with 1 Mbytes of RAM but is likely to be slow if handling large quantities of data 11 WWW MicroLEIS Ero amp Con module 3 Input Data Generation 3 1 Raizal Model On the main menu of the Raizal model Table 3 1 the first option is Input Data Generation Table 3 1 Main menu of the soil erosion risks Raizal model SOIL EROSION RISKS 1 Input Data Generation 2 Base Evaluations 3 Hypothetical Evaluations 4 Output Evaluation Results H Explanation R Return to Ero amp Con Menu 3 1 1 New Evaluating Scenarios As presented in Table 3 2 the first option of input data is the creation of evaluating scenarios To develop a Raizal application it is neces
31. p Con PANTANAL Model VAX VAX2 VAP VAN VAH VAX Figure 4 2 Index of the decision trees of the Pantanal model 33 WWW MicroLEIS Ero amp Con module Table 4 4 Summary of environmental land management qualities and associated characteristics for each vulnerability type of Pantanal evaluation model Land management quality Vulnerability Land management type characteristic Surface run off r Leaching degree Phosphate fixation f Cation retention c Denitrification d Pesticide sorption Pesticide degradation g Phosphate incidence 1 Nitrogen incidence j Attainable contamination risks P N H X Landform Soil erodibility Rainfall erosivity P N H X Monthly precipitation Monthly temperature Groundwater table depth Drainage Particle size distribution P pH Particle size distribution Organic matter N H pH Particle size distribution CEC Organic matter N Monthly temperature Groundwater table depth Organic matter pH X Organic matter pH Particle size distribution CEC X Monthly temperature Monthly precipitation pH Organic matter Management contamination risks P Landuse type Use of P fertilizer Artificial drainage N Landuse type Use of N fertilizer Crop rotation
32. s also included 2 The graphical presentation shows the water and wind vulnerability class distribution in percentage of the area for the evaluated scenario 3 The CSV format presentation as the best manner to link the Ero amp Con models with a GIS is used on a similar way to the tabular presentation including longitude latitude and altitude of each evaluating unit 39 WWW MicroLEIS Ero amp Con module The results of hypothetical evaluations are presented in the same formats although a reference to the climate or and management change is also included The following pages show these output evaluation results for a scenario formed by a set of benchmark fields of Sevilla province The original evaluation of results is followed by the corresponding results for a hypothetical evaluation of Sevilla province Example 6 1 Tabular presentation of the attainable contamination risks classes of the first 15 evaluating units of evaluating scenario Andalucia MicroLEIS PANTANAL model 40 WWW MicroLEIS Ero amp Con module Base Scenario Andalucia Attainable contamination vulnerability classes Evaluating Location Land Vulnerability types unit Phosphorus Nitrogen Heavy Pesticides metals F ALO1 Greda Roja Almanzaro alto V2r V3r V4or F AL02 Pardo calizo Almanzaro bajo VI V2c V3c V3g F AL03 Rendsina Andarax Gabor VI V2c V3c V3g F AL04 Salino Campo Dalias VI V3cd V3cr V3g F ALOS Volc nico Campo N jar V2 V2c V3c V3g
33. sary 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 It is also possible to select a previously defined evaluating scenario by using the lt F1 gt option 12 WWW MicroLEIS Ero amp Con module Table 3 2 Input data generation menu of Raizal model INPUT DATA GENERATION 1 New Evaluating scenarios 2 Enter amp Edit Data 3 Delete Data 4 SDBm Interface H Explanation R Return to Main Menu RAIZAL For each evaluating scenario the input data can be generated i from the keyboard for soil climate and management related data and 11 through the SDBm interface only for the soil related data 3 1 2 Enter Edit and Delete Data From the keyboard it is possible to enter edit and delete input data Table 3 3 shows an example of Enter and Edit Data screen for soil related data The same structure is uded in climate and management related variables screens Table 3 4 and Table 3 5 From all climate data input the model calculates automatically the Humidity Index and the Derived Fournier Humidity Index These calculated climate variables represent the climate erosivity of a land unit Table 3 3 Data input screen for soil related input variables MicroLEIS Ero amp Con RAIZAL Model 13 WWW MicroLEIS Ero amp Con module ENTER AND EDIT DATA Evaluating scenario A
34. saturation NA 22 WWW MicroLEIS Ero amp Con module The structure of SDBm files as created by the Soil Layer Generator is shown in Example 3 2 For this example a layer or control section from 0 to 50 cm from the surface was considered for the soil horizon and analytical characteristics Example 3 2 An SDBm input file for six soil profiles and one control section including the SDBm soil related characteristics which are diagnostic criteria for the Pantanal model PRNO LAT LON ELEV LAFOSLGR STON DRAI GWAT LOC TEXIPHW OC CECS SE052 N374715 W054015 320 HI SE058 N374300 W060600 490 VA SE059 N374520 W060020 400 VA SE060 N374341 W055629 350 VA SE061 N374044 W060218 200 MO SE064 N375204 W060524 490 VA SIC 5 8 13 23 6 5 5 325 SCL 5 9 0 8 21 1 LS 6 0 0 8 0 6 SL 6 1 2 5 20 0 SL 6 0 IET 2 Ao RU 0 On Key Wi KS 23 WWW MicroLEIS Ero amp Con module 4 Base Evaluations Ero amp Con models evaluate the vulnerability risks of an agricultural field to land degradation considering separately three types of vulnerability attainable management and actual and for each degradation factor water and wind erosion and nitrogen phosphorus heavy metals Cu Zn Cd Hg Pb and pesticides general hydrophile and hydryphobe contamination Table 4 1 The attainable vulnerability considers the biophysical risk of the capability of the soil being harmed in one or more of its ecological func
35. sses XXX MC Toxicity LD 50 of pesticides 3 classes XXX MC Application methods 2 classes XXX WWW MicroLEIS Ero amp Con module 2 Ero amp Con Front end 2 1 General Structure Following the scheme of the MicroLEIS system a set of computer programmes was developed to automate the application of Ero amp Con models However new facilities are included in this package The major characteristics of the Ero amp Con front end are Interface with SDBm database FAO CSIC 1994 multilingual Soil Database Pop up screens showing codes types and classes Batch processing modes Hypothetical predictions option Link with GIS These programmes are largely self explanatory They use menus as shown in Table 2 1 to inform about alternatives and prompt you to respond whenever needed From each menu the Explanation option gives detailed information on the corresponding step Many fields in Ero amp Con input screens use codes These codes are included in the Ero amp Con software in the form of indices and can be accessed from the F1 key while entering editing data Output results for an evaluating scenario in tabular graphic or CSV format presentation can be displayed or deleted by selecting the corresponding file Table 2 1 Main menu of the Ero amp Con Agro vulnerability Field Evaluation system MAIN MENU 1 RAIZAL model Soil Erosion 2 ARENAL model General Soil Contamination 3 PANTANAL model Specific Soil Co
36. tic LC and Management Characterististics MC are connected with the severity levels of the corresponding Land Quality LQ and Management Quality MQ by complex decision trees The connections between the severity levels of the Land and Management Qualities and the vulnerability classes of the Attainable and Management vulnerability types are through decision trees Table 4 4 Appendix A shows the division of the Land and Management Characteristics The following Land and Management Qualities are involved Land Qualities with their subclass LQ1 Surface Run off LQ1 1 Surface Run off Relief LQ1 2 Surface Run off Soil erodibility LQ1 3 Surface Run off Rainfall erosivity LQ2 Leaching Degree LQ3 Pesticides Sorption o LQ4 Biodegradation g LQ5 Denitrification d 32 WWW MicroLEIS Ero amp Con module LQ6 Cation Adsorption Capacity c LQ7 Phosphate Fixation f Management Qualities with their subclass MQI Phosphorus Arsenic Management i MQ2 Nitrogen Management j MQ3 Heavy Metals Management q MQ4 Pesticides Management t 5 Soil Erosion Management Almost all the Land and Management Qualities separate four severity levels as follows None Low Moderate High The Pantanal model is based on decision trees Figure 4 2 All the decision trees can be observed by selecting the option Decision Trees Observation from the Base Evaluation Menu and are presented in Appendix B MicroLEIS Ero am
37. tions The management vulnerability considers the risk of a particular Field Utilization Type to land degradation The actual vulnerability considers simultaneously the biophysical and management risk factors of a particular field unit Table 4 1 Combination of vulnerability classes according to attainable management and actual degradation risks Vulnerability type Land degradation factor Attainable risk Management risk Actual risk Raizal model Water erosion W VAW VMW VCW Wind erosion D VAD VMD VCD Pantanal model Phosphorus contamination P VAP VMP VCP Nitrogen contamination N VAN VMN VCN Heavy metals contamination H VAH VMH VCH Pesticides contamination X VAX VMX VCX Any type of the evaluation is made in batch running mode for all the evaluating units included within the selected evaluating scenario So the option Scenario Selection of Table 4 2 is the first step in making an evaluation Having selected the scenario the type of vulnerability which you want to evaluate is chosen 24 WWW MicroLEIS Ero amp Con module Table 4 2 Menu to compute original evaluations of RAIZALmodel ORIGINAL EVALUATIONS 1 Scenario Selection 2 Attainable Vulnerability 3 Management Vulnerability 4 Actual Vulnerability T Decision Trees Observation R Return to Main Erosion Menu 4 1 Raizal Model 4 1 1 Vulnerability Classes The soil erosion vulnerability classes 10 established by Raizal for the Attai
38. ytical Data Data Data 2 s Data NY gt 4 A Language w Dictionary r ES Input Data sDBm Coding System d gt Soil Layer 4 Generator z Y 4 Graphics Printed Data Display Data Representation Figure 1 2 General scheme of the SDBm soil database WWW MicroLEIS Ero amp Con module Presentation Main Menu B ARENAL PANTANAL e Original Hypothetical i Output Evaluations Evaluations Evaluations Input w Input T let EN SDBm Scenario Climatic Management Clim amp Manag Scenarios Field Units EE Interface Selection Change Change Change Attainable Management Actual E hit Display CN Delete Risks Risks Risks NONE 2g Results Results Figure 1 3 The basic structure of Ero amp Con front end WWW MicroLEIS Ero amp Con module Table 1 1 Input variable list of the Raizal and Pantanal evaluation models Land characteristic class or unit Raizal Pantanal Site related characteristics LC Landforms 21 classes XXX XXX LC Slope gradient XXX XXX LC Groundwater table depth m XXX XXX Soil related characteristics LC Drainage 7 classes XXX XXX LC Particle size distribution 23 classes XXX XXX LC Superficial stoniness XXX LC Organic matter XXX XXX LC pH XXX LC Cation exchange capacity meq 100g XXX LC Sodium saturation XXX Climate related characteristics LC Mean monthly precipitation mm XXX XXX LC Max month
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