Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

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A correct soil moisture estimation is a fundamental prerequisite for many applications: agriculture, meteorological forecast, flood and drought prediction, and, in general, water accounting and management. Traditional methods typically provide point-like measurements, but suffer from soil heterogeneity, which can produce significant misinterpretation of the hydrological scenarios. In the last decade, cosmic-ray neutron sensing (CRNS) has emerged as a promising approach for the detection of soil moisture content. CRNS can average soil moisture over a large volume (up to tens of hectares) of terrain with only one probe, thus overcoming limitations arising from the heterogeneity of the soil. The present paper introduces the development of a new CRNS instrument designed for agricultural applications and based on an innovative neutron detector. The new instrument was applied and tested in two experimental fields located in Potsdam (DE, Germany) and Lagosanto (IT, Italy). The results highlight how the new detector could be a valid alternative and robust solution for the application of the CRNS technique for soil moisture measurements in agriculture.

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Abstract. Nitrogen management is an issue in agricultural sustainability. Compared to conventional tillage, precision agriculture practices can potentially improve nitrogen cycle efficiency, with positive benefits on crops, soils and environment. The present paper discusses the result of precision agriculture on nitrogen management, taking into account a 52 ha experimental site, in a private farm in a typical Po Valley field in north-eastern Italy, monitored from 2013 to 2015. The area is cultivated with corn (Zea mays) managed with variable rate application of fertilizer. Available data were combined in order to estimate with a 10×10 m resolution the actual efficiency in Nitrogen utilization and to quantify local losses, mainly due to denitrification, volatilization or leaching. Results show how an average loss of 76 kg·ha -1 per year was found in the experimental site, and only 11.5 % of the total area undergoing losses higher than 200 kg·ha -1 . Clearly the identification of critical areas can help definition of correction actions to be implemented to reduce and minimize the impact on agriculture on environment.Keywords: Nitrogen, precision agriculture, variable rate application, Automatic Resistivity Profiling. IntroductionIn the last years groundwater pollution has received an increasing amount of attention, especially in connection with intensive agriculture practices encompassing excessive application of nitrogen fertilizers [1; 2]. Indeed, if by one hand nitrogen is one of the most important plant nutrients, generally improving final crop yield and quality parameters, on the other hand it also plays an important role in agroecosystem pollution. The pressure of over-fertilization on the environment is getting more awareness as deterioration of fresh water and climate change are becoming more critical [2]. As a consequence, the European Union has encouraged several research actions and enacted a directive (the so called Nitrates Directive) aimed to promote good agricultural practices and to eventually reduce the risks of pollution of groundwater (91/676/EEC).Despite constant improvements in research and development of new techniques helping characterization of soil and plant status through implementation of proximal and remote sensors [3][4][5][6], determination of optimal nitrogen needs is still an issue, mainly due to the field temporal and spatial variability. Additionally, the response of the plants to N fertilization is very much influenced by soil as well as by weather conditions during the growing season.An interesting approach includes implementation of a simulation model which has the capability of integrating information on crop, soil, meteorological conditions and management practices, and allows quantification of nutrient needs and utilization of the same nutrients when fertilizations are carried out [7; 8]. However, such model is very much influenced by the specific microbiological activity of the soil, by the status of the vegetation and by its interaction with soil nutrients, therefore relia...