Incorporation of organic matter (OM) into soil can reduce its susceptibility to compaction. However, the significance of incorporated OM for different soil and soil water conditions is not well documented. We investigated the effectiveness of incorporated OM at different soil and soil water conditions and the OM effect on strength and structure recovery of compacted soils. A sandy soil (sandy Orthod), a silt loam (loamy Udalf), and a clay soil (clayey Umbrept) were amended with up to 80 g kg−1 of highly and slightly humified peat. The peat‐soil mixtures were compacted at different water contents using the standard Proctor procedure. Soil strength was determined with a penetrometer. Structure recovery was determined by bulk density changes and disintegration of clods through wetting‐drying cycles. For the cohesive silt loam and clay soils, OM was most effective at reducing compactibility at water contents lower than those for maximum Proctor compaction. For the sandy soil, OM was most effective at the Proctor‐optimum water content. The slightly humified peat had a greater effect than the highly humified. We found that OM is most effective for soils with high compactibility. The maximum penetration resistance, Pmax, of the clay soil was reduced from 0.49 to 0.30 MPa, and that of the sandy soil increased from 0.64 to 1.08 MPa. For the silt loam, 30 g kg−1 peat content had the highest Pmax. After five wetting‐drying cycles, bulk densities showed no significant differences among treatments. Clod disintegration was hindered by the OM incorporation. Although soil compactibility was reduced by OM incorporation, OM was more effective as soil compactibilty increased and at water contents lower than or close to the Proctor‐optimum water content. Structure recovery of compacted soils was not improved. The penetration resistance after compaction is not consistently related to the incorporated OM.
Including field-or even site-specific estimates of current net N mineralization into N fertilizer strategy is essential in order to further reduce N surpluses while maintaining crop yields, but adequate estimates are not available. Simulation models could account for many influencing factors, yet are not easily adjustable to different soil and site characteristics. Nowadays important input data for N mineralization models are digitally available. Thus, our objectives were (1) to experimentally determine specific temperature and soil water dependency functions for the rate coefficients of net N mineralization that could be allocated via digitally mapped data and (2) to find out the least necessary discrimination between soils. Specific and general functions for the rate coefficients of two organic N pools with first-order kinetics were derived using laboratory long-and short-term incubations from a broad variety of soils. Functions were evaluated using comparisons to field incubations of undisturbed soil columns from 27 sites. Interestingly, a differentiation between specific functions of not more than three soil groups was necessary for quite accurate simulations (r 2 = 0.87, P \ 0.001; RMSE = 23 kg N ha -1 , n-RMSE = 29%). The two criteria for grouping, soil texture (loess vs. sandy/loamy classes) and humus content class (applies only to temperature functions for sandy textures), can be taken from digital soil maps. Field studies, especially under suboptimal water contents, with plant cover and N-fertilization, will have to further prove the applicability of the derived functions. Pedotransfer functions for the pool sizes also based on digitally available data are needed for automatically calculating specific estimates of net N mineralization.
For temperate regions, such as Germany, a simple management model is developed, with which NO3 seepage losses during winter can be estimated, when the amount and distribution of soil NO3 in late fall are known. The semi‐analytical, one‐dimensional model is developed for homogeneous soils. The model is derived for steady‐state flow conditions at a constant soil water content (field capacity). Essential part of the model is a solute transport equation based on mixing‐cell theory. Sample calculations show that when the height Δz of the mixing‐cells is chosen such that Δz = 2D/v, where D is the soil dispersion coefficient and v is the pore water flow velocity, mixing‐cell model results compare well with results from convective‐dispersive models. The advantage of mixing‐cell models compared with convective‐dispersive models is that mixing‐cell model solutions frequently appear as simple mathematical expressions. With the model sample calculations concerning NO3 seepage losses during winter were carried out for a variety of site conditions. The calculations show that NO3 seepage losses vary considerably, even when the initial amount and the distribution of the soil NO3‐N are the same. The calculations also show that if a limit is set for the total amount of tolerable NO3 seepage losses during winter, site‐specific late fall upper limits for soil NO3‐N can be derived. A table of such values, for a wide range of site‐conditions, is presented.
Avoiding surplus N fertilization without reducing crop yields could be accomplished by accounting for current net N mineralization in N fertilizer recommendations. N simulation models would allow a quantitative consideration of important factors and could be based upon digitally mapped data. Soil-specific temperature and water functions that were derived in part I of the paper needed a differentiation between only three soil groups and the two allocating criteria were taken from digital soil maps. Here, the objectives were to experimentally determine pedotransfer functions (PTFs) for the pool sizes of two organic N pools (N fast , N slow ) that could be calculated via digitally available data and need a minimum set of easily accessible management data. Interestingly, most important input data for the PTFs of both pool sizes were mean clay contents of the texture class (German soil classification system). However, the underlying mechanisms might be different, as N slow could be positively influenced by clay-associated mineralizable SOM, whereas N fast could be positively related to clay content due to higher yield potential and thus more residues on finertextured soils. For N slow including the humus class improved the accuracy of the PTF (r 2 = 0.60; P \ 0.050). For N fast it was important to include a negative influence of the mean fall temperature of the preceding year (r 2 = 0.42; P \ 0.010), probably due to its influence on residue degradation before winter. Surprisingly, easily accessible management data, e.g. previous crop, did not improve the predictions in this study. Field studies with plant cover will have to further prove the applicability of the derived PTFs.
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