After field checking [MARSMAN and DE GRUIJTER, 
1986] this model was found to provide correct 
grazing suitability predictions in 95% of cases. 
3.2 Soil Drainage Status 
  
Drainage status is linked to the height of the 
water table, and more particularly its Mean 
Highest Water Level (or GHG value), as follows: 
  
Drainage Status Level|GHG cm below the surface 
  
1 »80 
2 40-80 
3 25-40 
4 15-25 
5 «15 
  
  
  
  
Following field testing [MARSMAN and DE GRUIJTER, 
1986] it was found that the standard deviation of 
the GHG is 14cm. Using estimation by confidence 
intervals the probability of land parcel vith a 
certain measured GHG value being in a specified 
Drainage Status Level can be calculated. For 
example with a GHG value of 60cm, the probability 
of the parcel being in Drainage Status Level 2 is 
85%. 
3.3 Soil Bearing Capacity 
  
Bearing capacity (3 classes) is related to 
Soiltype (5 classes) and GHG, as follows: 
  
  
Soiltype ] 2 3 4 5 
GHG(cm) 
0-12 3- 3-3 3-3 
13-24 3:99: 9:72 
235-33 3 22 3 2 
34-40 2. à 3 2 | 
41-60 2 2.2.2 1 
61-80 1 12 22 
80-140 1:1 T2 1 
  
  
  
Thus, e.g., Soiltype 3 with a water table 41-60 cm 
below the surface has a Bearing Capacity Class of 
2, 
In its turn Soiltype is related to Soiltexture as 
follows: 
  
  
Soiltype Organic | Clay 
content | content 
1. Peat 15-100% 0-8% 
2. Clay with peat underlay | 22-70% 8-100% 
3. Clay 0-15% 25-100% 
4. Clayey sand 0-2.5% 8-25% 
5. Sand 0-2.5% 0-8% 
  
  
  
  
  
As bearing capacity is determined from GHG, 
organic content, and clay content, the qualities 
of all three need to be known. Tests have shown 
that the probability of these particular organic 
content and clay content classes being correct is 
98% [MARSMAN and DE GRUIJTER, 1986]. The quality of 
GHG data was discussed in the previous section, 
and an example landparcel was shown to have a 
probability of 85% that it was in its stated 
Drainage Status Level (or GHG level). Thus in the 
same example landparcel, the probability of its 
Bearing Capacity Class being correct (Pbc) is: 
360 
Pbc = 0.85 x 0.98 x 0.98 = 0.82 = 82% 
3.4 Soil Moisture Supply Capacity 
Moisture supply capacity is recorded in 
millimeters and is calculated using a polynomial 
of twenty coefficients and three variables 
(rooting depth, mean lowest water-table depth, and 
mean spring water-table depth) [RAMLAL, 1991]. In 
this application it is reclassified into 5 
discrete classes: 
  
Moisture Supply 
Capacity Class 
Moisture Supply 
Capacity (mm) 
  
>200 
150-200 
100-150 
50-100 
<50 
LW = 
  
  
  
  
Following error propagations carried out by the 
Dutch Soil Research Institute [MARSMAN and DE 
GRUIJTER, 1986] it was found that the standard 
deviation of Moisture Supply determinations is 
17mm. With this information, and using estimation 
by confidence intervals the pro- bability (e.g.) 
of a landparcel having Moisture Supply Capacity 
Class 2, when its Moisture Supply Capacity has 
been measured to be 166mm, is 81%. 
3.5 Quality of the 
Classification 
Grazing Suitability 
Taking into account the quality of the model (see 
section 3.1), the quality of the Soil Drainage 
Status Level (section 3.2), the Soil Bearing 
Capacity Class (section 3.3), the Moisture Supply 
Capacity Class (section 3.4), and using Crisp Set 
Theory it is possible to estimate the probability 
(P) of the given landparcel (referred to in 
sections 3.2, 3.3, 3.4) having the predicted 
Grazing Suitability to be: 
P =:0.98(0-85 x 0.82 » 0.81) = .55 = 55% 
Applying Fuzzy Sub-Set Theory and using these 
probabilities as Certainty Factors, the overall 
Certainty Factor associated with the predicted 
Grazing Suitability is 0.81, i.e. MIN(0.98, 
0.85,0.82,0.81). 
4. RESULTS OF EXPLORATION OF PROPOSAL 
In this study a database was built which held Soil 
Polygons supplied by the Dutch Soil Research 
Institute, the land parcel boundaries supplied by 
the Dutch Topographic Service, and database tables 
holding the soil characteristics and the relevant 
soil characteristics quality parameters of the 
those soil polygons. 
Using the existing facilities of ILWIS the Grazing 
Suitability Model was inserted and a multicoloured 
5-class grazing suitability map produced. Then 
using the procedures outlined in Section 3 and 
implemented in ILVIS the quality parameters were 
processed to give i) a 2-class probability map 
(«502 probability, >50% probability); ii) a 
3-class probability map (low, average, and good 
probability); and iii) a 5-class probability map 
(<10%, 10-30%, 30-40%, 40-50%, and 50-60%). The 
5-class map is shown below, in FIGURE 2. 
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