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Field-specific simulations of net N mineralization based on digitally available soil and weather data: II. Pedotransfer functions for the pool sizes

机译:基于可数字获取的土壤和天气数据的净氮矿化的特定领域模拟:II。 Pedotransfer函数适用于池大小

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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 (Nfast, Nslow) 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 Nslow could be positively influenced by clay-associated mineralizable SOM, whereas Nfast could be positively related to clay content due to higher yield potential and thus more residues on finer-textured soils. For Nslow including the humus class improved the accuracy of the PTF (r² = 0.60; P < 0.050). For Nfast it was important to include a negative influence of the mean fall temperature of the preceding year (r² = 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.
机译:通过考虑氮肥推荐中当前的净氮矿化量,可以避免氮肥过剩而不降低作物产量。 N个仿真模型将允许对重要因素进行定量考虑,并且可以基于数字地图数据。本文第一部分中得出的土壤特定温度和水功能仅需区分三个土壤组,而两个分配标准均来自数字土壤图。在这里,目的是通过实验确定两个有机N池(N fast ,N slow )的池大小的pedotransfer函数(PTF),可以通过数字方式计算得出数据,并且需要最少的一组易于访问的管理数据。有趣的是,两个池大小的PTF的最重要的输入数据是质地类别的平均粘土含量(德国土壤分类系统)。然而,其潜在机理可能有所不同,因为N slow 可能受到与粘土相关的可矿化SOM的正影响,而N fast 则可能由于粘土含量高而正相关。产生潜力,因此在质地较细的土壤上残留更多。对于N slow ,包括腐殖质类,提高了PTF的准确性(r²= 0.60; P <0.050)。对于N fast ,重要的是要包括对前一年平均下降温度的负面影响(r²= 0.42; P <0.010),这可能是由于其对冬季前残留物降解的影响。令人惊讶的是,易于访问的管理数据,例如以前的作物,并未改善本研究的预测。具有植物覆盖率的田间研究将不得不进一步证明衍生的PTF的适用性。

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