Sugarcane Water Productivity Assessments in the São Paulo state, Brazil.

São Paulo state, Brazil, has been highlighted by the sugarcane crop expansion. The actual scenario of climate and land use changes, bring attention for the large-scale water productivity (WP) analyses. MODIS images were used together with gridded weather data for these analyses. A generalized sugarcane growing cycle inside a crop land mask, from September 2011 to October 2012, was considered in the main growing regions of the state. Actual evapotranspiration (ET) is quantified by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm, the biomass production (BIO) by the RUE (Radiation Use Efficiency) Monteith?s model and WP is considered as the ratio of BIO to ET. During the four generalized sugarcane crop phases, the mean ET values ranged from 0.6 to 4.0 mm day-1; BIO rates were between 20 and 200 kg ha-1 day-1, resulting in WP ranging from 2.8 to 6.0 kg m-3. Soil moisture indicators are applied, indicating benefits from supplementary irrigation during the grand growth phase, wherever there is water availability for this practice. The quantification of the large-scale water variables may subsidize the rational water resources management under the sugarcane expansion and water scarcity scenarios.

MANEJO Y ORDENAMIENTO TERRITORIAL DE CUENCAS DE COSTA RICA. EL CASO DE LA MIÇROCUENCA DEL RÍO POÁS

La microcuenca del río Poás (ubicada entre el volcán Barva y el volcán Poás, hasta la confluencia con el río Grande cerca de la ciudad de Alajuela) posee un alto potencial para la formación de acuíferos de alta calidad. Por este motivo sus recursos naturales deben utilizarse adecuadamente. La mejor manera de lograr lo anterior es mediante la planificación del uso de la tierra. En esta investigación se plantea para ello el ordenamiento territorial y el manejo de cuencas. Para este propósito se realiza una zonificación mediante la cual se identifican las siguientes zonas: sin restricción de uso, uso restringido y uso muy restringido. La mayor parte de la microcuenca (64,6%) se encuentra en la categoríade “sin restricción de uso”. Sin embargo. se hace necesaria la intervención con rapidez en sectores ubicados en la parte alta de la microcuenca que se clasifican de “uso muy restringido”. En relación con el recurso hídrico, en la microcuenca en los últimos 14 años y de acuerdo con la metodología aplicada, se ha elevado la producción hídrica, específicamente en la escorrentía y la ganancia. En general aumentó en 1,6%.Abstract: The Poas river micro watershed (located between the Barva and Poas volcanoes reaching the confluence of the Grande river near the city of Alajuela) has high potential for developing high quality aquifers. thus, its natural resources should be utilized adequately. This is best done by proper land use planning. In this study guidelines are presented for land use planning and watershed management. Land use is zoned or classified for the following uses: unrestncted use, restricted use, and highly restricted use. Most of the micro watershed (64.6 percent) is classified or zoned as ‘unrestricted use.’ However, urgent intervention is needed in the upper areas of the micro watershed cla.ssified as ‘highly restricted use.’ In the Iast l4years. according Lo the methodology applied, hydrologic production has increased about 1.6 percent. specifically in runoff and soil moisture surpius.

Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin.

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman?Monteith and Shuttleworth?Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy?atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land?surface?atmosphere exchange parameterizations across a range of spatial scales.

Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin.

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (?ET) and evaporation (?EE) flux components of the terrestrial latent heat flux (?E), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman?Monteith and Shuttleworth?Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on ?ET and ?EE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, ?ET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on ?ET during the wet (rainy) seasons where ?ET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80?% of the variances of ?ET. However, biophysical control on ?ET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65?% of the variances of ?ET, and indicates ?ET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy?atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between ?ET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land?surface?atmosphere exchange parameterizations across a range of spatial scales.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

On the Democratization of AI: Bridging the Gap with AutoML

Riding the wave of recent groundbreaking achievements, artificial intelligence (AI) is currently the buzzword on everybody’s lips and, allowing algorithms to learn from historical data, Machine Learning (ML) emerged as its pinnacle. The multitude of algorithms, each with unique strengths and weaknesses, highlights the absence of a universal solution and poses a challenging optimization problem. In response, automated machine learning (AutoML) navigates vast search spaces within minimal time constraints. By lowering entry barriers, AutoML emerged as promising the democratization of AI, yet facing some challenges. In data-centric AI, the discipline of systematically engineering data used to build an AI system, the challenge of configuring data pipelines is rather simple. We devise a methodology for building effective data pre-processing pipelines in supervised learning as well as a data-centric AutoML solution for unsupervised learning. In human-centric AI, many current AutoML tools were not built around the user but rather around algorithmic ideas, raising ethical and social bias concerns. We contribute by deploying AutoML tools aiming at complementing, instead of replacing, human intelligence. In particular, we provide solutions for single-objective and multi-objective optimization and showcase the challenges and potential of novel interfaces featuring large language models. Finally, there are application areas that rely on numerical simulators, often related to earth observations, they tend to be particularly high-impact and address important challenges such as climate change and crop life cycles. We commit to coupling these physical simulators with (Auto)ML solutions towards a physics-aware AI. Specifically, in precision farming, we design a smart irrigation platform that: allows real-time monitoring of soil moisture, predicts future moisture values, and estimates water demand to schedule the irrigation.

Pupal Development of Ceratitis capitata (Diptera: Tephritidae) and Diachasmimorpha longicaudata (Hymenoptera: Braconidae) at Different Moisture Values in Four Soil Types

This study aimed to evaluate adult emergence and duration of the pupal stage of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), and emergence of the fruit fly parasitoid, Diachasmimorpha longicaudata (Ashmead), under different moisture conditions in four soil types, using soil water matric potential Pupal stage duration in C capitata was influenced differently for males and females In females, only soil type affected pupal stage duration, which was longer in a clay soil In males, pupal stage duration was individually influenced by moisture and soil type, with a reduction in pupal stage duration in a heavy clay soil and in a sandy clay, with longer duration in the clay soil As allude potential decreased, duration of the pupal stage of C capitata males increased, regardless of soil type C capitata emergence was affected by moisture, regardless of soil type, and was higher in drier soils The emergence of D longicaudata adults was individually influenced by soil type and moisture factors, and the number of emerged D longicaudata adults was three times higher in sandy loam and lower in a heavy clay soil Always, the number of emerged adults was higher at higher moisture conditions C capitata and D longicaudata pupal development was affected by moisture and soil type, which may facilitate pest sampling and allow release areas for the parasitoid to be defined under field conditions.

A piezoelectric based energy harvester interface for a CMOS wireless sensor IC

In this thesis a piezoelectric energy harvesting system, responsible for regulating the power output of a piezoelectric transducer subjected to ambient vibration, is designed to power an RF receiver with a 6 mW power consump-tion. The electrical characterisation of the chosen piezoelectric transducer is the starting point of the design, which subsequently presents a full-bridge cross-coupled rectifier that rectifies the AC output of the transducer and a low-dropout regulator responsible for delivering a constant voltage system output of 0.6 V, with low voltage ripple, which represents the receiver’s required sup-ply voltage. The circuit is designed using CMOS 130 nm UMC technology, and the system presents an inductorless architecture, with reduced area and cost. The electrical simulations run for the complete circuit lead to the conclusion that the proposed piezoelectric energy harvesting system is a plausible solution to power the RF receiver, provided that the chosen transducer is subjected to moderate levels of vibration.

A reusable smart interface for gas sensor resistance measurement

The advances of the semiconductor industry enable microelectromechanical systems sensors, signal conditioning logic and network access to be integrated into a smart sensor node. In this framework, a mixed-mode interface circuit for monolithically integrated gas sensor arrays was developed with high-level design techniques. This interface system includes analog electronics for inspection of up to four sensor arrays and digital logic for smart control and data communication. Although different design methodologies were used in the conception of the complete circuit, high-level synthesis tools and methodologies were crucial in speeding up the whole design cycle, enhancing reusability for future applications and producing a flexible and robust component.

A current-mode interface circuit for a piezoresistive pressure sensor

An interfacing circuit for piezoresistive pressure sensors based on CMOS current conveyors is presented. The main advantages of the proposed interfacing circuit include the use of a single piezoresistor, the capability of offset compensation, and a versatile current-mode configuration, with current output and current or voltage input. Experimental tests confirm linear relation of output voltage versus piezoresistance variation.

Tillage systems and nutrient sources affecting soil cover, temperature and moisture in a clayey oxisol under corn

Tillage affects soil physical properties, e.g., porosity, and leads to different amounts of mulch on the soil surface. Consequently, tillage is related to the soil temperature and moisture regime. Soil cover, temperature and moisture were measured under corn (Zea mays) in the tenth year of five tillage systems (NT = no-tillage; CP = chisel plow and single secondary disking; CT = primary and double secondary disking; CTb = CT with crop residues burned; and CTr = CT with crop residues removed). The tillage systems were combined with five nutrient sources (C = control; MF = mineral fertilizer; PL = poultry litter; CS = cattle slurry; and SS = swine slurry). Soil cover after sowing was greatest in NT (88 %), medium in CP (38 %) and lowest in CT treatments (< 10 %), but differences decreased after corn emergence. Soil temperature was related with soil cover, and significant differences among tillage were observed at the beginning of the growing season and at corn maturity. Differences in soil temperature and moisture in the surface layer of the tilled treatments were greater during the corn cycle than in untilled treatments, due to differences in intensity of soil mobilization and mulch remaining after soil management. Nutrient sources affected soil temperature and moisture in the most intense part of the corn growth period, and were related to the variation of the corn leaf area index among treatments

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Monitoring of praseodymium(III) ions in aqueous solutions, soil and sediment samples by a PVC membrane sensor based on a furan-triazole derivative

A furan-triazole derivative has been explored as an ionophore for preparation of a highly selective Pr(III) membrane sensor. The proposed sensor exhibits a Nernstian response for Pr(III) activity over a wide concentration range with a detection limit of 5.2×10-8 M. Its response is independent of pH of the solution in the range 3.0-8.8 and offers the advantages of fast response time. To investigate the analytical applicability of the sensor, it was applied successfully as an indicator electrode in potentiometric titration of Pr(III) solution and also in the direct and indirect determination of trace Pr(III) ions in some samples.