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Liming with 'structure lime', comprising approximately 80–85% ground limestone and 15–20% slaked lime, has been promoted in subsidised environmental schemes in Sweden since 2010 to increase clay aggregate stability and mitigate particulate phosphorus losses to surface waters. To date, approximately 65,000 ha have been structure-limed. Apart from stabilising aggregates, liming may also improve other physical properties, such as soil strength. This study examined the effect of increasing application rate (0–16 t ha-1) of structure lime on soil strength, approximated by horizontal (draught requirement) and vertical (penetrometer resistance) measurements, in eight field soils (clay content 26–38%) to which structure lime had been applied two, three, four or six years previously. Draught requirement when cultivating with a multipurpose cultivator significantly decreased (by 11%) with the highest application rate of structure lime (16 t ha-1) compared with an unlimed control. This reduced the wheel power requirement by 7.1 kW and diesel consumption by 1.2–1.4 L ha-1, and lowered CO 2 emissions by 3–4 kg ha-1. To clarify the general effect of structure liming, the mean value of all limed treatments was compared with that of the unlimed control. This showed that structure liming in general significantly reduced the draught requirement (by 7%). However, penetrometer resistance measurements revealed no significant effects of structure liming and no relation between draught requirement and penetrometer resistance measurements. Overall, the results indicate that structure liming can reduce fuel consumption, due to easier soil tillage, and thus lower CO 2 emissions. • Cultivator draught requirement was measured in structure limed field trials. • A towing eye with sensors that can measure forces in three directions was used. • Structure liming reduced horizontal draught requirement by 7%. • Reductions corresponded to decreases of 1.2–1.4 L diesel ha-1 and 3–4 kg CO 2 ha-1. • Structure liming did not affect penetrometer resistance in this study. [ABSTRACT FROM AUTHOR]

Abstract: The study investigated proximal sensor data fusion for topsoil clay mapping on a 22hectare agricultural field in southwest Sweden. Eight different predictor sets and two different prediction methods were tested in an orthogonal design. The predictor sets were different combinations of proximally measured gamma (γ) ray spectrometry and apparent electrical conductivity (ECa), four terrain attributes (elevation, slope and the cosine and the sine of the aspect) and the digital numbers (DNs) of an aerial photo. The two prediction methods were partial least squares regression (PLS-R) and k nearest neighbor prediction (kNN). It was found that the γ ray spectrometry variables (232Th, 40K and total count of decays) were good predictors of topsoil clay content (mean absolute error of about 1.5% clay) and predictions were neither much improved nor deteriorated by addition of any of the other predictors. The ECa measurements, which are affected also by the subsoil, did not perform as well. Predictions were improved when the ECa data were integrated with the aerial photo DN but were deteriorated by addition of elevation data. The kNN method yielded slightly better predictions than the PLS-R method but overall it was more important which input data were used than how the predictions were made. It was observed that even though dense soil sampling was used for calibration (three samples per hectare), use of proximal soil sensor data was almost always better than mere interpolation of the calibration samples. [Copyright &y& Elsevier]