SUMMARY Intrinsic permeability to air of macropore space (ka) is related to macroporosity (ɛ) and organization of macropore space (O). Organization is defined as ka/ɛ. The use of ka for estimating saturated hydraulic conductivity (Ka) is also considered. The relationship between Log (O) and ɛ (Oɛ characteristic) can be used to describe changes to the macropore space of clay soils by amelioration and compaction. The effects of dominant macropore shape can also be identified and calculated as an empirical index of the efficiency of the pore organization E (E=log (O)/ɛ). Intrinsic permeability can then be related to E in a E:ka characteristic. Intrinsic permeability is the parameter most sensitive to structural change and E is mainly influenced by the dominant shapes of the macropores. Thus, the E:ka characteristic is suggested as a basis for studying differences in macropore space as may occur in response to external and internal stresses upon the soil and different systems of soil management, for example increases of packing pores by cultivation or of fissures by gypsum application and loss of packing pores by compaction. Empirical data indicate that Ks of the B horizons of Australian red‐brown earths can be estimated from ka of macropore space at a standard potential.

A system, ANOPOR, has been developed which uses the Quantimet 720 image analyser to recognize and measure the different types of pores in impregnated soil blocks. The system is concerned with three types of pores: channels, planar voids (cracks and fissures) and vughs but is not suitable for highly interconnected pore patterns. Each of the three types has a different origin and function and presents a different two-dimensional (2-D) shape when sectioned.A learning set consisting of these three pore classes was used to teach the system how to recognize statistically soil pores in images using measurements made by Quantimet. To describe the pore outlines in 2-D, shape factors giving the best class separation of the learning set in the pattern space were derived. Bayes equation was used to give the probability of a pore belonging to a particular class by comparing its position in the pattern space with the learning set. Class boundaries were determined which ANOPOR uses to allocate each pore in any image to the most likely class. The system measures the proportion of the pore space attributable to each class and the perimeter and intercept density for each. The system is used to measure the pore patterns in three soil horizons.

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