Pietro Marino Gallina

Near infrared monitoring of mineralisation of liquid dairy manure in agricultural soils

Liquid dairy manure is a major organic input to cultivated soils. Therefore, a method for monitoring the mineralisation of slurries should be a useful tool for managing soil fertilisation. In order to examine whether the biodegradation of cattle sludge can be monitored by near infrared (NIR) spectroscopy, soil samples from a laboratory incubation experiment were analysed using this rapid and inexpensive method. Five different cattle slurries were added to three soils with increasing clay content in such an amount as to give 130 ppm of total nitrogen. The resulting 18 experimental treatments (three control soils and 15 soil-slurry combinations) were incubated for 180 days under optimal temperature and soil water content. Each treatment was sampled at 0, 2, 8, 12, 16, 21, 29, 41, 72, 121 and 180 days: the respired CO2 was captured in alkali traps and mineral N was extracted using 1 M KCl. Three replicates of each sampling were analysed individually. The resulting 648 samples, air dried and ground at 0.5 mm, were analysed by NIR spectroscopy using an Antaris (Thermo Nicolet) Fourier transform-NIR spectrometer. Although the slurries and soil mineralised carbon represent only a very small part of the total soil organic carbon, the mineralisation of carbon can be clearly monitored by NIR spectroscopy in both amended and unamended soils. Whereas NO3–N evolution was difficult to predict using NIR data, the results for NH4–N were more encouraging. Using measurements of CO2–C respired, a two-pool mineralisation model was developed and the simulated concentration of carbon pools in the soils were used for the development of NIR equations. The results obtained in this work have demonstrated that NIR is a useful tool for monitoring the carbon mineralisation process when cattle sludge is incorporated into agricultural soils.

Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables

The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive measurements of above ground biomass, its N concentration and N uptake were taken at V6 and V9 development stages. Classical normalized difference vegetation indices (NDVI) and the indices adjusted by crop ground cover were calculated and regressed against the measured variables. Finally, image colorgrams were used to explore the potential of band-related information in variable estimation. A colorgram is a linear signal that summarizes the color content of each digital image. It is composed of a sequence of the frequency distribution curves of the camera bands, of their related parameters and of results of the principal components analysis applied to each image. The best predictors were found to be the ground cover and the adjusted green-based NDVI: regression equation at V9 resulted in R2 of 0.7 and RRMSE < 25% in external validation. Colorgrams did not improve prediction performance due to the spectral limitations of the camera. Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the modified camera was able to estimate maize biomass due to the very high spatial resolution. Since the above ground biomass is a robust proxy of N status, the modified camera could be a promising tool for a low-cost N fertilization support system.