Soil compaction is a critical issue in agriculture having a significant influence on crop growth. Sugar beet (Beta vulgaris L.) is accounted as a crop susceptible to compaction. Reduction of leaf area, final yield, and root quality parameters are reported in compacted soils. The most obvious visual indicator of topsoil compaction is root depth affected by agricultural tractor and machinery traffic up on the soil. Such indicators are mainly correlated to initial soil condition, tyre features, and number of passages. Monitoring and controlling frequency and position of machine traffic across the field, in such a way that passages are completed on specific, well-defined tracks, can assist with minimization of compaction effects on soil. The objective of the present work was to analyze the subsoil compaction during the growing period of sugar beet with different farming approaches including controlled traffic passages and random traffic. To this end, tests were carried out following each agro technical operation using penetrometer readings in order to monitor the state of cone-index after each step. In addition, at the harvesting time, root quality parameters were analyzed with particular attention to length and regularity of the taproot, total length, circumference, mass, and above-ground biomass. Such parameters were usefully implemented in order to evaluate the effects of controlled traffic passages compared to the random traffic in a cultivation of sugar beet. Results highlight how an increase in crop yield, derived from samples monitored, higher than 10% can be expected with implementation of a careful traffic management.Additional key words: soil; traffic management; compaction; crop parameters.Correspondence should be addressed to Andrea Pezzuolo: andrea.pezzuolo@unipd.itAbbreviations used: CI (cone index); CT (controlled traffic area); CTF (controlled traffic farming); CT0 (soil portion not affect by machines compaction); CT3 (lanes undergoing three machines passes); CT8 (lanes undergoing eight machines passes); RT (random traffic area); WW (work widths). Funding:The authors received no specific funding for this work.

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Abstract. Automatic Milking Systems (AMS), also known as robotic milking, are internationally accepted as a valid alternative to conventional milking parlour, and also as an advanced mean for dairy farm management. The continuous growth of labour and production costs are leading to the development of new improved AMS machines, especially for heaviest milking operations. Accordingly, AMS presence in European dairy farms is expected to continuously grow in the near future. AMS reduces heavy workload and allows milking frequency monitoring of each cow, based on its production level or lactation stage, without any additional labour cost. In this study, milking data of 15 dairy farms located in the Veneto region (North-Eastern Italy) were analyzed with the aim to estimate the Automatic Milking Systems performances, and eventually recognize operative limits and bottlenecks. Results are also of interest to allow definition of relations between the AMS capacity and milking time, which is useful to optimize operations and increase profitability. In particular, data relative to milk yield, daily milking sessions per cow, effective milking time, rejected milking time, cleaning time and machine downtime have been collected and used to evaluate the operative performance of each farm. Specifically, the analysis highlighted an average of 17 h·day -1 of milking activity, 5.6 h·day -1 of inactivity and 1.4 h·day -1 for cleaning and self-diagnosis. Additionally, 40 % of the AMS reported the use for milking activities lower than 16 h·day -1 with idle periods exceeding in some cases 7-8 h·day -1 .

Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.

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