Soil structure is an important physical property of soil and has a great impact on the environment and agriculture. Dry aggregate size distribution and related soil structure indices are essential parameters in understanding the structural state of the soil. This study was conducted to determine the effects of different soil types and land uses on structure parameters and to relate them to selected soil properties. The investigation was performed on five soil types (Arenosols, Fluvisols, Chernozems, Gleysols and Solonetz), each from three different locations and under three different land uses (cropland, meadow and forest), so that a total of 135 undisturbed soil samples were collected. Dry sieving analysis was performed to obtain eight aggregate size classes (ASCs) (>10, 10-5, 5-3, 3-2, 2-1, 1-0.5, 0.5-0.25 and <0.25mm). The results suggest a highly significant impact of soil type on all ASCs and structure indices. Land use has a highly significant impact on the >10, 5-3 and 3-2 mm ASCs. Chernozems and Gleysols have more favorable structure than Arenosols, Fluvisols and Solonetz. Long term cultivation leads to the deterioration of soil structure and the formation of clods. Forest soils have a significantly better structure than soils under meadows and croplands. The application of principal component analysis and regression models identifies water retention at -33 kPa, bulk density and pH value as for the most important factors in predicting dMWD and dGMD.

The less productive soils present one of the major problems in wheat production. Because of unfavorable conditions, halomorphic soils could be intensively utilized using ameliorative measures and by selecting suitable stress tolerant wheat genotypes. This study examined the responses of ten winter wheat cultivars on stressful conditions of halomorphic soil, solonetz type in Banat, Serbia. The wheat genotypes were grown in field trails of control and treatments with two soil amelioration levels using phosphor gypsum, in amounts of 25 and 50 tha−1. Across two vegetation seasons, phenotypic variability and genotype by environment interaction (GEI) for yield traits of wheat were studied. The additive main effects and multiplicative interaction (AMMI) models were used to study the GEI. AMMI analyses revealed significant genotype and environmental effects, as well as GEI effect. Analysis of GEI using the IPCA (Interaction Principal Components) analysis showed a statistical significance of the first two main components, IPCA1 and IPCA2 for yield, which jointly explained 70% of GEI variation. First source of variation IPCA1 explained 41.15% of the GEI for the grain weight per plant and 78.54% for the harvest index. The results revealed that wheat genotypes responded differently to stressful conditions and ameliorative measures.

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