Energy budget, carbon and water footprint in perennial agro and natural ecosystems inside a Natura 2000 site as provisioning and regulating ecosystem services.

Energy flows in perennial agro and natural ecosystems are essential and could be related to greenhouse gas emissions and increasing water demands. Energy indices and carbon and water footprint are useful tools in assessing the interaction between production systems and environmental impact inside Natura 2000 sites and enhancing ecosystem services. The main aims of this study were to determine and compare energy indices, carbon and water footprint of olive groves, orange and kiwi orchards, and grasslands within and between 2 years, located in a Natura 2000 site, using an adjusted life cycle assessment (LCA) method; and to illustrate the importance of farming systems to ecosystem services. Proportional stratified sampling was used to select 36 farm types (12 olive groves, 12 oranges, and 12 kiwi orchards) and 12 grasslands from the municipality of Amfilochia, western Greece, during 2018 and 2019. Descriptive data analysis, correlational analysis, ANOVA, and hierarchical cluster analysis (HCA) were used to summarize and analyze the data. The main energy contributors for the three farm types were fertilizers and fuel consumption. The only input for the grasslands was animal manure. Energy efficiency showed the highest performance in olive groves followed in descending order by kiwi orchards, grasslands, and orange orchards. Concerning carbon-footprint, the values were the highest for orange orchards, followed in descending order by kiwi orchards, olive groves, and grasslands. Total water footprint values were the highest for grasslands and the lowest for olive groves. For both years, regarding HCA, each cluster consisted of the same ecosystems. According to the abovementioned indices, olive groves and grasslands are the most environmental friendly ecosystems. They could contribute in the environmental protection of the Amvrakikos Gulf, a Natura 2000 site. Kiwi orchards, although they are intensive along with the orange ones, seem to be more friendly to the environment than them and their cultivation in this Natura 2000 area is recommended. Energy indices and carbon and water footprint could be used, in order to highlight the related ecosystem (dis)services.

Assessment of Vegetation Indices Derived by UAV Imagery for Durum Wheat Phenotyping under a Water Limited and Heat Stressed Mediterranean Environment.

There is growing interest for using Spectral Vegetation Indices (SVI) derived by Unmanned Aerial Vehicle (UAV) imagery as a fast and cost-efficient tool for plant phenotyping. The development of such tools is of paramount importance to continue progress through plant breeding, especially in the Mediterranean basin, where climate change is expected to further increase yield uncertainty. In the present study, Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Green Normalized Difference Vegetation Index (GNDVI) derived from UAV imagery were calculated for two consecutive years in a set of twenty durum wheat varieties grown under a water limited and heat stressed environment. Statistically significant differences between genotypes were observed for SVIs. GNDVI explained more variability than NDVI and SR, when recorded at booting. GNDVI was significantly correlated with grain yield when recorded at booting and anthesis during the 1st and 2nd year, respectively, while NDVI was correlated to grain yield when recorded at booting, but only for the 1st year. These results suggest that GNDVI has a better discriminating efficiency and can be a better predictor of yield when recorded at early reproductive stages. The predictive ability of SVIs was affected by plant phenology. Correlations of grain yield with SVIs were stronger as the correlations of SVIs with heading were weaker or not significant. NDVIs recorded at the experimental site were significantly correlated with grain yield of the same set of genotypes grown in other environments. Both positive and negative correlations were observed indicating that the environmental conditions during grain filling can affect the sign of the correlations. These findings highlight the potential use of SVIs derived by UAV imagery for durum wheat phenotyping under low yielding Mediterranean conditions.