Potato tuber sugars, starch and organic acids in relation to ozone exposure

Summary Data from two Swedish and one Finnish open-top chamber experiments were combined to investigate effects of ozone exposure on potato (Solanum tuberosum L.) tuber content of starch, sucrose, fructose, glucose, malic, citric and ascorbic acids. The glucose, fructose and malic acid concentrations showed strong negative correlations with ozone exposure, while citric acid, consistently increased with ozone exposure. No ozone effects could be demonstrated on starch, sucrose or ascorbic acid concentrations. It is discussed to what extent the changes found in potato tuber composition can be explained in terms of ozone effects on tuber maturity. Ozone exposure was expressed as the accumulated exposure over a cut-off concentration of 40 nmol mol⁻¹ (AOT40) and as the accumulated uptake of ozone over an ozone uptake rate threshold of 7 nmol m⁻² s⁻¹ (CUO 7). The difference in ability of the exposure indices to explain observed effects was small.     

Adaptation of regional digital soil mapping for precision agriculture

In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m² spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, ‘Farm Interactive’, in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with ‘ground truth’ maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.     

Producing nitrogen (N) uptake maps in winter wheat by combining proximal crop measurements with Sentinel-2 and DMC satellite images in a decision support system for farmers

Responsive fertilisation of winter wheat (Triticum aestivum L.) is often adopted, with N applied two or three times between the developmental stages of tillering and booting. Satellite-based decision support systems (DSS) providing vegetation index maps calculated from satellite data are available to aid farmers in adjusting the topdressing nitrogen (N) rate site-specifically to the current season and to variations in growth conditions within the field. One example is the freely available CropSAT DSS used in Scandinavia, which provides farmers with raster maps of the modified soil-adjusted vegetation index (MSAVI2) calculated mainly from data obtained from satellites Sentinel-2 (ESA, EU) and DMC (DMCii Ltd, Guildford, UK). This study investigated the possibility of calibrating MSAVI2 maps with data from handheld proximal sensor measurements of N uptake covering the main agricultural regions in Sweden during growth stages Z30-45 on the Zadok scale, in order to facilitate farmers’ decisions on N rate. More than 200 N-sensor measurements acquired during 2015 and 2016 in seven different winter wheat cultivars were combined with MSAVI2 values from CropSAT. It was found that N uptake could be predicted in a general, national model, i.e. for sites and dates other than those for which the calibration model was parameterised, with a mean absolute error of 11–15?kg?N?ha^?1. A cultivar-specific model performed better than this general model, but a regional model showed no improvement compared with the model parameterised with national data. Vegetation indices calculated from the two narrow bands of Sentinel-2 in the red edge-near infrared region of the crop canopy reflectance spectrum proved to be promising alternatives to the broadband index MSAVI2. Based on the results, we suggest that data from a monitoring programme involving handheld N sensor measurements can be integrated with a satellite-based DSS to upscale N uptake information.     

Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system

Precision Agriculture - Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full...     

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (EC_a), reflectance in the visible and near‐infrared (vis‐NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5‐cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm‐specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis‐NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross‐validation (RMSECV) of 0.3–0.5% for SOM, about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC_a reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis‐NIR sensor measurements with measurements of EC_a and or IF.