No abstract

Recognising the various sources of nitrate pollution and understanding system dynamics are fundamental to tackle groundwater quality problems. A comprehensive GIS database of twenty parameters regarding hydrogeological and hydrological features and driving forces were used as inputs for predictive models of nitrate pollution. Additionally, key variables extracted from remotely sensed Normalised Difference Vegetation Index time-series (NDVI) were included in database to provide indications of agroecosystem dynamics. Many approaches can be used to evaluate feature importance related to groundwater pollution caused by nitrates. Filters, wrappers and embedded methods are used to rank feature importance according to the probability of occurrence of nitrates above a threshold value in groundwater. Machine learning algorithms (MLA) such as Classification and Regression Trees (CART), Random Forest (RF) and Support Vector Machines (SVM) are used as wrappers considering four different sequential search approaches: the sequential backward selection (SBS), the sequential forward selection (SFS), the sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). Feature importance obtained from RF and CART was used as an embedded approach. RF with SFFS had the best performance (mmce=0.12 and AUC=0.92) and good interpretability, where three features related to groundwater polluted areas were selected: i) industries and facilities rating according to their production capacity and total nitrogen emissions to water within a 3km buffer, ii) livestock farms rating by manure production within a 5km buffer and, iii) cumulated NDVI for the post-maximum month, being used as a proxy of vegetation productivity and crop yield.

No abstract

No abstract

Purpose Current organizations face a complex competitive landscape driven by globalization and technology that puts them in the course of a new economic age. This complexity stresses learning and innovation as fundamental mechanisms for organizational survival. This paper aims to propose that how learning and innovation emerge and affect organizational performance can be better understood through the complexity leadership theory. Design/methodology/approach The authors review literature on complexity leadership theory, learning and innovation in complex bureaucratic environments and then present reflections regarding how learning and innovation can be achieved through the interaction of three complexity leadership functions: adaptive, administrative and enabling. This conceptual framework suggests that individuals are in constant interaction, exchange information, influence each other and collectively produce emergent properties that promote effective learning and innovation. Findings We propose that learning and innovation can be better achieved in organizations if the complexity leadership theory is applied as an alternative to centralized forms of influence and control. Originality/value This paper presents a reflection on the benefits of the complexity leadership theory as an alternative framework to understand organizational leadership. Furthermore, this paper proposes that the complexity leadership theory is more adequate to generate learning and innovation in complex, fast-changing work environments.

This work evidences the importance of models, which evaluate pesticides environmental behaviour, namely their water contamination potential (as Mackay multicompartimental fugacity model) and, specially, groundwater contamination potential (as GUS and Bacci and Gaggi leaching indices), in pesticide selection. Moreover, it reveals the importance to adapt proper statistical methods according to level of left-censored data. Using JCA was still possible to establish relations between pesticides and their temporal trend in a case study where there were more than 80% of data censored. This study will contribute to the Tagus river basin management plan with information on the patterns of pesticide occurrence in the alluvial aquifer system.

No abstract

The cork oak (Quercus suber L.) is periodically harvested for bark (cork) throughout its lifetime. Trees undergo physiological changes as they age which affect stem diameter growth and their sensitivity to climate. However, little is known about trees age-or size-related growth changes and it remains unclear if trees of different ages (sizes) have similar climate-growth relationships. In this study, we examined the increment in stem basal area of 47 randomly selected (large and small) cork oaks over a 12-year period to assess divergent climate-growth relationships. Our approach, using a machine learning algorithm on unlabelled data sets of basal area increments, successfully filtered out tree-clusters that suggested a size (age)-dependent growth response to climate. On average, the basal area increment was more than three times larger in larger-trees clusters compared with smaller-trees clusters. A large tree (diameter >75 cm) on average added 105 cm 2 y −1 to its basal area against 25 cm 2 y −1 in a small tree (diameter <35 cm). Additionally, in smaller-trees, cork harvesting intensified the negative impact of drought on tree growth, and worsened post-drought recovery. These findings highlight the need to consider biological growth trends for accurate predictions of trees responses to drought.