Abstract
A fully automatic methodology based on image processing is proposed to evaluate the quality of spray application sampled by water-sensitive papers (WSP). The methods proposed permit a computation of the fraction of spray coverage, an evaluation of the homogeneity of the spray spatial spread at various scales and directions, and extraction of stain and droplet size range and distribution. This allows the number of droplets per unit area and the standard droplet size spectra factors to be computed. The methods were tested with a number of test samples scanned at different resolutions, proving to be effective in situations where there is high spray coverage in the WSP, thus with considerable overlap between stains. The most suitable scanning resolution was found to be 600 dpi. The results obtained by the image processing methods were successfully compared with a manual (visual) counting of stains in a test sample.

Abstract
Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species' niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatiotemporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by Quinta da Cholda, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm - maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.

Abstract
Europe's 2020 strategy considers sustainable management as an increasingly important criterion for the several normative and incentives recently legislated. It is expectable that in short-term, political decision-makers and farmers will need to evaluate the type and degree of their activities' impact on the environment, economy and society. For this reason, tools are required to ease and translate these evaluations, capable of defining the main imbalance factors that need action. Despite a wide range of sustainability assessment tools available in the literature, the underlying methodologies are very similar and resort to indicators aggregated in hierarchical evaluation models that should be representative and adaptive for putative transferability. This paper provides an overview of the most relevant computational sustainability evaluation tools and their scope of application, in a benchmarking analysis. To ease the comprehension of these tools, a literature review was performed to analyse their structural elements and based on methodologies. Copyright © 2020 Inderscience Enterprises Ltd.

Abstract
This work aims to integrate Remote Sensing (RS) and cadastral data in QGIS software to perform the spatiotemporal mapping of Wine Yield (WY) cluster zones in the Douro region. Spatiotemporal modelling approach for prediction of wine yield was based on Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and topographic data. The results showed that 74% (R2 = 0.744, n=128, p<0.000) WY interannual variability at administrative division could be explained by the developed model. This information allows establishing wine production region pattern which can improve the agronomic and economic efficiency of vineyard and winery operations.

Abstract
Currently, copper is approved as an active substance among plant protection products and is considered effective against more than 50 different diseases in different crops, conventional and organic. Tomato has been cultivated for centuries, but many fungal diseases still affect it, making it necessary to control them through antifungal agents, such as copper, making it the primary form of fungal control in organic farming systems (OFS). The objective of this work was to determine whether exogenous copper applications can affect AOX mechanisms and nitrogen use efficiency in tomato plant grown in OFS. For this purpose, plants were sprayed with 'Bordeaux' mixture (SP). In addition, two sets of plants were each treated with 8 mg/L copper in the root substrate (S). Subsequently, one of these groups was also sprayed with a solution of 'Bordeaux' mixture (SSP). Leaves and roots were used to determine NR, GS and GDH activities, as well as proline, H2O2 and AsA levels. The data gathered show that even small amounts of copper in the rhizosphere and copper spraying can lead to stress responses in tomato, with increases in total ascorbate of up to 70% and a decrease in GS activity down to 49%, suggesting that excess copper application could be potentially harmful in horticultural production by OFS.

Abstract
Wildfire disturbances can profoundly impact many aspects of both ecosystem functioning and resilience. This study proposes a satellite-based approach to assess ecosystem resilience to wildfires based on post-fire trajec-tories of four key functional dimensions of ecosystems related to carbon, water, and energy exchanges: (i) vegetation primary production; (ii) vegetation and soil water content; (iii) land surface albedo; and (iv) land surface sensible heat. For each dimension, several metrics extracted from satellite image time-series, at the short, medium and long-term, describe both resistance (the ability to withstand environmental disturbances) and re-covery (the ability to pull back towards equilibrium). We used MODIS data for 2000-2018 to analyze trajectories after the 2005 wildfires in NW Iberian Peninsula. Primary production exhibited low resistance, with abrupt breaks immediately after the fire, but rapid recoveries, starting within six months after the fire and reaching stable pre-fire levels two years after. Loss of water content after the fire showed slightly higher resistance but slower and more gradual recoveries than primary production. On the other hand, albedo exhibited varying levels of resistance and recovery, with post-fire breaks often followed by increases to levels above pre-fire within the first two years, but sometimes with effects that persisted for many years. Finally, wildfire effects on sensible heat were generally more transient, with effects starting to dissipate after one year and overall rapid recoveries. Our approach was able to successfully depict key features of post-fire processes of ecosystem functioning at different timeframes. The added value of our multi-indicator approach for analyzing ecosystem resilience to wildfires was highlighted by the independence and complementarity among the proposed indicators targeting four dimensions of ecosystem functioning. We argue that such approaches can provide an enhanced characterization of ecosystem resilience to disturbances, ultimately upholding promising implications for post-fire ecosystem management and targeting different dimensions of ecosystem functioning.