Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, ...playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems.
Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production ...resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.
Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions ...offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.
Accurate mapping of crops with high spatiotemporal resolution plays a critical role in achieving the Sustainable Development Goals (SDGs), especially in the context of integrated crop-livestock ...systems (ICLS). Stakeholders can make informed decisions and implement targeted strategies to achieve multiple SDGs related to agriculture, rural development, and sustainable livelihoods by understanding the spatial dynamics of these systems. Accurate information on the extent of ICLS derived from multitemporal remote sensing and emerging map techniques such as deep learning can help in the implementation of sustainable agricultural practices. However, far too little attention has been paid to ICLS map accuracy because it may not be at the forefront of research agendas compared to those of other agricultural practices. This paper aims to map ICLS using high spatiotemporal resolution imagery and deep learning neural network classifiers at two different sites located in Brazil. The pipeline involves four interpretation approaches based on the ICLS class: evaluating deep neural network classifiers with different image composition intervals, explaining commission and omission errors, evaluating the temporal transferability of the method, and evaluating the influence of variables. The study area consists of two locations in São Paulo (study site 1, SS1) and Mato Grosso state (study site 2, SS2), Brazil. We derived nine spectral variables from PlanetScope (PS) images and four metrics through object-based image analysis (OBIA) using two time intervals, 10 and 15 days, to generate the image compositions. These input variables were used in three deep neural network classifiers: convolutional neural network in one dimension (Conv1D), long short-term memory (LSTM), and LSTM with a fully convolutional network (LSTM-FCN). Our results showed that mapping dynamic land use such as ICLS is possible by using high-spatiotemporal-resolution imagery and deep neural network classifiers. The 15-day LSTM-FCN classifier returned the highest map accuracies for both sites, with the following class-level accuracies: producer accuracy (PA) = 97.0% and user accuracy (UA) = 97.0% for SS1 and PA = 82.0% and UA = 96.5% for SS2. Meanwhile, we found map uncertainties arising from the diverse crop calendars and spectro-temporal similarities between ICLS and other land use. The best approaches revealed that temporal generalization was suitable for mapping ICLS, but some classifiers could not generalize due to the inherent characteristics of the class. Most variables were considered efficient for predicting ICLS, although spectral indices revealed better functional relationships, while the PS bands had a lower influence on the predictions. The accuracies achieved with the proposed method represent promising opportunities for the sufficiently accurate mapping of ICLS and other complex crop activities.
•A novel approach is proposed for integrated-crop livestock system (ICLS) mapping.•PlanetScope time series and deep learning effectively map ICLS.•Vegetation indices are powerful predictors for ICLS mapping.•ICLS mapping models can be transferred across years to avoid sample collection.
Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal ...resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area.
A minimal extension of the Standard Model (SM) featuring two scalar leptoquarks, an SU(2) doublet with hypercharge 1/6 and a singlet with hypercharge 1/3, is proposed as an economical benchmark model ...for studies of an interplay between flavor physics and properties of the neutrino sector. The presence of such type of leptoquarks radiatively generates neutrino masses and offers a simultaneous explanation for the current B-physics anomalies involving b→cℓνℓ decays. The model can also accommodate both the muon magnetic moment and the recently reported W-mass anomalies, while complying with the most stringent lepton flavor-violating observables.
•In animals the hypothalamic neuropeptide orexin A contributes to metabolic control.•The role of central nervous orexin A for metabolic function in humans is unclear.•We assessed the relationship ...between CSF orexin A and body composition in humans.•CSF orexin concentrations were inversely related to body weight and water content.•Findings suggest a link between orexin A and water homeostasis in humans.
The hypothalamic neuropeptide orexin A (hypocretin-1) is a key signal in sleep/wake regulation and promotes food intake. We investigated the relationship between cerebrospinal fluid orexin A concentrations and body composition in non-narcoleptic human subjects with a wide range of body weight to gain insight into the role of orexin A in human metabolism. We collected cerebrospinal fluid and blood samples and measured body composition by bioelectric impedance analysis in 36 subjects (16 women and 20 men) with body mass indices between 16.24 and 38.10 kg/m2 and an age range of 19–80 years. Bivariate Pearson correlations and stepwise multiple regressions were calculated to determine associations between orexin A and body composition as well as biometric variables. Concentrations of orexin A in cerebrospinal fluid averaged 315.6 ± 6.0 pg/ml, were comparable between sexes (p > 0.15) and unrelated to age (p > 0.66); they appeared slightly reduced in overweight/obese compared to normal-weight subjects (p = .07). Orexin A concentrations decreased with body weight (r = −0.38, p = .0229) and fat-free mass (r = −0.39, p = .0173) but were not linked to body fat mass (p > 0.24). They were inversely related to total body water (r = −0.39, p = .0174) as well as intracellular (r = −0.41, p = .0139) and extracellular water (r = −0.35, p = .0341). Intracellular water was the only factor independently associated with cerebrospinal fluid orexin A concentrations (p = .0139). We conclude that cerebrospinal fluid orexin A concentrations do not display associations with body adiposity, but are inversely related to intracellular water content. These cross-sectional findings suggest a link between orexin A signaling and the regulation of water homeostasis in humans.
The synthesis and characterization of one oxidoethoxidovanadium(V) VVO(L1)(OEt) (1) and two nonoxidovanadium(IV) complexes, VIV(L2–3)2 (2 and 3), with aroylhydrazone ligands incorporating ...naphthalene moieties, are reported. The synthesized oxido and nonoxido vanadium complexes are characterized by various physicochemical techniques, and their molecular structures are solved by single crystal X-ray diffraction (SC-XRD). This revealed that in 1 the geometry around the vanadium atom corresponds to a distorted square pyramid, with a O4N coordination sphere, whereas that of the two nonoxido VIV complexes 2 and 3 corresponds to a distorted trigonal prismatic arrangement with a O4N2 coordination sphere around each “bare” vanadium center. In aqueous solution, the VVO moiety of 1 undergoes a change to VVO2 species, yielding VVO2(L1)− (1′), while the nonoxido VIV-compounds 2 and 3 are partly converted into their corresponding VIVO complexes, VIVO(L2–3)(H2O) (2′ and 3′). Interaction of these VVO2, VIVO, and VIV systems with two model proteins, ubiquitin (Ub) and lysozyme (Lyz), is investigated through docking approaches, which suggest the potential binding sites: the interaction is covalent for species 2′ and 3′, with the binding to Glu16, Glu18, and Asp21 for Ub, and His15 for Lyz, and it is noncovalent for species 1′, 2, and 3, with the surface residues of the proteins. The ligand precursors and complexes are also evaluated for their in vitro antiproliferative activity against ovarian (A2780) and prostate (PC3) human cancer cells and in normal fibroblasts (V79) to check the selectivity of the compounds for cancer cells.
The heat waves of 2003 in Western Europe and 2010 in Russia, commonly labelled as rare climatic anomalies outside of previous experience, are often taken as harbingers of more frequent extremes in ...the global warming-influenced future. However, a recent reconstruction of spring–summer temperatures for WE resulted in the likelihood of significantly higher temperatures in 1540. In order to check the plausibility of this result we investigated the severity of the 1540 drought by putting forward the argument of the known soil desiccation-temperature feedback. Based on more than 300 first-hand documentary weather report sources originating from an area of 2 to 3 million km², we show that Europe was affected by an unprecedented 11-month-long Megadrought. The estimated number of precipitation days and precipitation amount for Central and Western Europe in 1540 is significantly lower than the 100-year minima of the instrumental measurement period for spring, summer and autumn. This result is supported by independent documentary evidence about extremely low river flows and Europe-wide wild-, forest- and settlement fires. We found that an event of this severity cannot be simulated by state-of-the-art climate models.
Regardless the materials properties, the vast majority of ceramic restorations could require an individualization through the extrinsic staining to improve aesthetics. This study aimed to compare the ...staining wear durability of different monolithic ceramics. Specimens of high translucent zirconia (YZHT), zirconia reinforced lithium silicate (ZLS), hybrid ceramic (HC) and feldspathic ceramic (FLD) were divided in five groups according to each material staining technique. The ZLS ceramic was tested with stained prior (ZLS1) and after crystallization (ZLS2). All specimens were extrinsically characterized, i.e. stained, and crystallized or sintered in specific ovens, according to the manufacturer's recommendation. The specimens were submitted to three-body wear tests in ACTA wear machine, simulating the presence of food bolus and antagonist (pH 7, 15 N, 1 Hz). The wear rate of the stain surface was determined after 5 intervals of 200,000 cycles, using a profilometer. The ceramic surface before and after staining, and after wear were inspected by Scanning Electron Microscopy (SEM). The wear rates were analyzed using two-way ANOVA and post-hoc Tukey test. The wear rates of the staining were affected by ceramic and the number of cycles (P < 0.001). 100% of staining was removed after 200,000 cycles for HC, and after 600,000 cycles for YZHT and ZLS1. Staining of ZLS2 and FLD remained on ceramic surface even after 1,000,000 cycles. Furthermore, FLD showed a significant higher staining durability than ZLS2. SEM revealed different surface morphologies for each group with and without staining and after the wear test. Ceramics with fired staining showed higher durability compared to the polymerized one. The feldspar ceramic presented superior staining durability, followed by zirconia reinforced lithium silicate and high translucent zirconia. The conventional two steps staining technique showed improved durability for zirconia reinforced lithium silicate.
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