Medidas de comparação de mapas gerados por métodos geoestatísticos

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps

COMPARISON MEASURES OF MAPS GENERATED BY GEOSTATISTICAL METHODS

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.CNPqCNPqCAPESCAPESFundacao AraucariaFundacao Araucari

Regional hydrogeology and groundwater budget modeling in the arid Middle Drâa Catchment (South-Morocco)

This study deals with the regional hydrogeology in Middle Drâa Catchment (MDC) in South-Morocco that features 15,000 km2 of semi-arid to hyper-arid conditions and a heterogeneous geological setting. The MDC covers the southern flank of the central Anti-Atlas Mountains reaching the southerly adjacent Saharan Foreland. Pasture and migration are of significant meaning for existence. Important agriculture and settlement concentrate mainly in six date palm oases along the Wadi Drâa relying on both stream flow and uncontrolled groundwater pumping for irrigation. The dominant groundwater exploitation taps the most important alluvial Drâa aquifers that relate to the six Drâa oases. The Drâa aquifers form an interrupted chain of shallow groundwater reservoirs embedded in a hard rock aquitard system veined by a tributary wadi network. The pressure of climate and global change particularly demands the analysis of the groundwater system, the quantification of water availability and scenario projections to derive options of adaptation and mitigation. Accordingly, results of the analysis of groundwater level data, hydrogeochemical and hydrogeological information lead to the characterization of the current state of the groundwater system and the development of the groundwater budget model BIL. The BIL model simulates the lumped annual groundwater availability and response at the Drâa aquifers. Lithological information from mapping surveys and bore log descriptions form the basis of a hydrofacies framework refining the existing concept of the aquifer system in the MDC. The groundwater level response of the Drâa aquifers relates to the re-interpreted distribution of specific yield values. The recent recharge of the Drâa aquifers depends mostly on transmission losses from the regulated inflow from the Upper Drâa Catchment to the Wadi Drâa. Indirect re-charge from floods generating after intense rainfall within the MDC is another source of aquifer replenishment. The analysis of inorganic groundwater composition and stable isotope signature verifies the interpretation of the aquifer system and the main groundwater flow paths. The distribution of the hydrochemical facies and the state of hydrogeochemical evolution hint on significant influence of groundwater pumping for irrigation. Based on the preceding analysis, items of the groundwater balance are pre-processed for each Drâa aquifer individually considering groundwater discharge from one aquifer to another. Ac-cordingly, the BIL model assesses the annual groundwater budget of the Drâa aquifers for the 33 year period 1974-2006. The model results are highly sensitive to changes in indirect recharge from stream flow infiltration, aquifer properties and irrigation-related parameters. The plausibility tests of the model results reveal satisfying accordance with observed piezometric data as available. So, scenarios of climate and global change are analyzed using the BIL model. As climate change has a significant impact on the groundwater availability of the Drâa aquifers and global change even worsens the situation, options of groundwater management are derived from the hydro-geological analysis and the groundwater budget modeling

Plant Adaptation to Global Climate Change

Plant Adaptation to Global Climate Change discusses the issues of the impact of climate change factors (abiotic and biotic) on vegetation. This book also deals with simulation modeling approaches to understanding the long-term effects of different environmental factors on vegetation. This book is a valuable resource for the environmental science research community, including those interested in assessing climate change impacts on vegetation and researchers working on simulation modeling

Feasibility of Remote Sensing Based Deep Learning in Crop Yield Prediction

In this dissertation the applicability of novel machine learning methods with remote sensing data was studied in the context of agricultural decision support systems in smart farming. The main focus was the utilization of high-resolution unmanned aerial vehicle (UAV) data to perform in-season crop yield estimation with spatial and spatio-temporal deep learning model architectures in a Finnish coastal habitat. While open-access satellite data has already been utilized in crop-related modelling, such as crop type classification and yield prediction, intra-field scale prediction for the smaller fields common in the Nordic countries requires images with higher resolution than currently available from open-access satellite systems. In addition to using UAV remote sensing data, various combinations of crop field related sensor data, data from open-access sources and satellite data were evaluated. Data quality is also an important aspect with remote sensing data, with high altitude satellite-based earth observation suffering from occasional obstructions by the cloud canopy. A decision tree model was employed to estimate cloud coverage by using UAV data as cloudless ground truth. In this dissertation it is shown that crop yield prediction with convolutional neural networks (CNNs) is feasible with high-resolution UAV data and produces results accurate enough for performing corrective farming actions in-season. Using UAV data time series not only improves the modelling performance (post-season prediction) with high-resolution UAV RGB data but also improves the predictive capabilities (in-season prediction). Furthermore, the use of various data sources for crop yield prediction in addition to UAV RGB data is shown to improve the predictive capabilities of the model. In summary, the use of deep learning techniques can be seen to improve the smart farming decision support pipeline by providing performant and reliable decision engines

Innovative Spent Coffee Ground-Based Biofertilizer: Effects on Soil Microbiome and Crop Health

Aim: Crop health is strictly related to soil biodiversity. The transition from chemical fertilizers to biofertilizers may represent a sustainable way to restore soil biodiversity, improve crop health and productivity. Spent coffee ground (SCG) is a waste material with a high potential for the development of innovative biofertilizers. However, previous research showed that the use of SCG as fertilizer inhibits plants growth, mainly due to the presence of phytotoxic compounds. On the contrary, SCG-treated plants showed high nutritional values with an increased content in antioxidants and mineral elements. The aim of this research was to test the effects of an innovative SCG-based biofertilizer (BF) on salad crops (Lactuca sativa), and on their rhizosphere microbial communities. And thus, to evaluate its potential in mitigating SCG’s negative effects. Method: A randomized block design experiment was carried to compare the effects of BF and SCG at 3 different concentrations, each with 5 replicates. Plants functional traits and nutritional values were evaluated. 16S rRNA gene metabarcoding was used to assess the effects on rhizosphere communities. Results: BF-treated plants showed a higher polyphenols level and less stress signs compared to the SCG-treated and control ones. At the highest concentrations, BF modify bacterial communities with an increase of plant growth-promoting rhizobacteria (e.g., Azospirillium, Arachidicoccus) and other beneficial bacterial taxa, such as those involved in disease suppression, aromatic compounds degradation, nutrients cycling. Conclusions: BF resulted capable of reducing SCG negative effects on plant health, promoting the growth of plants beneficial microbial taxa and, therefore, favouring a healthier growth of crops

The Protists of Tree Canopies

Eukaryotic microorganisms, i.e. protists, are the base of all multicellular life on Earth, which independently evolved in three of the 26 major eukaryotic lineages. Apart from dominating eukaryotic diversity, protists play fundamental roles in many Earth system processes – representing all trophic levels, including autotrophs, mixotrophs, saprotrophs, eukaryvores, omnivores as well as parasites and their hyperparasites. Recent progress in molecular methods for the first time opens a window into the black box of protistan diversity in terrestrial ecosystems. Among terrestrial habitats, the highly heterogeneous habitat represented by tree canopies is presumed to harbour diverse but, until now, poorly characterized communities of these microbial eukaryotes. A majority of protists that have been identified to strongly interact with plant surfaces can be assigned to the Stramenopiles-Alveolata-Rhizaria (SAR) supergroup, in particular representatives belonging to the monophyletic group of Cercozoa (Rhizaria). Their ability to rapidly excyst, feed and multiply within short generation times, represents an advantageous evolutionary adaptation to the highly variable microclimatic conditions prevailing up in the tree crowns. For our investigation we perfomed a series of metabarcoding analyses with highly group-specific primers to comprehensively analyse communities of Cercozoa in various ecological compartments (microhabitats), from the forest floor to the canopy region, across two biomes (temperate and tropical forest). With the findings described herein, we were the first to assess quantitative and qualitative informations on canopy associated cercozoan communities, which, in turn, contribute to the understanding of the processes governing distribution and selection of microbial eukaryotes in terrestrial ecosystems. Finally, our investigations provide new insights into the ecosystem functioning as well as ecosystem services tree canopies provide

Influence of Organic and Conventional Management Systems on Soil Microarthropods in Protected and Non-Protected Areas

Aim: The EU Biodiversity Strategy 2030 aims to increase land-protected areas at 30% and organic farming at 25% of agricultural lands. But which measure could be more effective in preserving soil biodiversity? The aim of the study is, therefore, to assess soil health of arable lands under organic and conventional managements in Non-protected (NPAorg) and Protected (PAcon) areas of Marche region (Italy) and compare the influence of the applied farming practices on soil microarthropods in two seasons, characterized by different intensities of soil management practices: spring (lower) and autumn (higher). Method: Soil health has been assessed through the Biological Quality of Soil index based on arthropods (QBS-ar). Novel approaches (QBS-ab and FAI indices) which consider microarthropods’ abundance in the index calculation, have been also applied. Density (ind/m2), Acari/Collembola ratio, % of Oribatid mites on total mites, biodiversity indices, correlations with chemical-physical parameters, and ordination analysis (nMDS) have been evaluated. Results: In both seasons, different communities have been found according to management and, particularly, PAcon sites showed significantly higher levels of biodiversity compared to NPAorg. However, in autumn, microarthropod communities present higher stability in NPAorg sites, showing an opposite trend and fewer fluctuations of the indices compared to PAcon. Conclusions: PA, even in conventional managed soils, seem to enhance soil biodiversity, while organic farming in NPA, confers a higher resilience to soil, making microarthropod communities more stable. Results showed that agricultural intensity reduction combined with the increased integration of agroecosystems in protected areas may represent an effective, and sustainable measure to preserve soil biodiversity and its ecological services