A new digital technology to reduce fungicide use in vineyards.

There is a growing demand for technologies able to decrease the environmental impact of agricultural activities without penalizing quali-quantitative characteristics of productions. In the case of viticulture, one of the key problems is represented by the spray drift during fungicide treatments. The diffusion in operational farming contexts of technologies based on variable-rate and recycling tunnel sprayers is often limited by their cost and, for the latter, by their size and lower maneuverability, representing clear disadvantages especially in case of small farms or in hilly and mountain areas. We present a new digital technology implemented in a mobile app that supports the reduction of both the number of treatments and the amount of fungicide distributed per treatment. The technology is based (i) on an alert system that prevents unneeded treatments in case of no risk of infection and (ii) on the quantification of the optimal amounts of active ingredients and dilution water based on the sprayer type/settings and on leaf area index values estimated with a common smartphone. An internal database allows to adjust (in case of need) the active ingredient dose to assure full compliance with product's legal requirements. In case of heterogeneity in leaf area index values inside the vineyard, prescription maps are generated. Results from a 2-year case study in a vineyard in northern Italy are shown, where the system allowed to reduce by 26.4 % and 27.4 % (mean of two years), respectively, the seasonal amounts of fungicides and dilution water, and by 43.8 % the copper content in must. The high usability of the technology proposed (just a common smartphone is needed) and the fact that it does not require updating the farm machine park highlights the suitability of the proposed solution for operational farming conditions, including premium wine production districts often characterized by small farms in hilly areas.

May smart technologies reduce the environmental impact of nitrogen fertilization? A case study for paddy rice.

Precision agriculture is increasingly considered as a powerful solution to mitigate the environmental impact of farming systems. This is because of its ability to use multi-source information in decision support systems to increase the efficiency of farm management. Among the agronomic practices for which precision agriculture concepts were applied in research and operational contexts, variable rate (VR) nitrogen fertilization plays a key role. A promising approach to make quantitative, spatially distributed diagnoses to support VR N fertilization is based on the combined use of remote sensing information and few smart scouting-driven ground estimates to derive maps of nitrogen nutrition index (NNI). In this study, a new smart app for field NNI estimates (PocketNNI) was developed, which can be integrated with remote sensing data. The environmental impact of using PocketNNI and Sentinel 2 products to drive fertilization was evaluated using the Life Cycle Assessment approach and a case study on rice in northern Italy. In particular, the environmental performances of rice fertilized according to VR information derived from the integration of PocketNNI and satellite data was compared with a treatment based on uniform N application. Primary data regarding the cultivation practices and the achieved yields were collected during field tests. Results showed that VR fertilization allowed reducing the environmental impact by 11.0% to 13.6% as compared to uniform N application. For Climate Change, the impact is reduced from 937.3 to 832.7 kg CO2 eq/t of paddy rice. The highest environmental benefits - mainly due to an improved ratio between grain yield and N fertilizers - were achieved in terms of energy consumption for fertilizer production and of emission of N compounds. Although further validation is needed, these preliminary results are promising and provide a first quantitative indication of the environmental benefits that can be achieved when digital technologies are used to support N fertilization.

Quantifying Uncertainty Due to Stochastic Weather Generators in Climate Change Impact Studies.

Climate change studies involve complex processes translating coarse climate change projections in locally meaningful terms. We analysed the behaviour of weather generators while downscaling precipitation and air temperature data. With multiple climate indices and alternative weather generators, we directly quantified the uncertainty associated with using weather generators when site specific downscaling is performed. We extracted the influence of weather generators on climate variability at local scale and the uncertainty that could affect impact assessment. For that, we first designed the downscaling experiments with three weather generators (CLIMAK, LARS-WG, WeaGETS) to interpret future projections. Then we assessed the impacts of estimated changes of precipitation and air temperature for a sample of 15 sites worldwide using a rice yield model and an extended set of climate metrics. We demonstrated that the choice of a weather generator in the downscaling process may have a higher impact on crop yield estimates than the climate scenario adopted. Should they be confirmed, these results would indicate that widely accepted outcomes of climate change studies using this downscaling technique need reconsideration.

Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice.

Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system's capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties-needed to extend the system to new contexts-was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions.