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Rainfall is a major component of the hydrological cycle. The lack of basic information on rainfall spatial variability is a major source of uncertainty in many fields of studies, e.g., meteorology, hydrology, and agriculture. Satellite rainfall measurements are becoming increasingly popular for their extensive database and reliable estimates. The objective of this study was to use the Tropical Rainfall Measuring Mission (TRMM) dataset along with rain gauges to characterize aspects of rainfall spatial variability and discuss the possible impacts from recent trends in the Brazilian savannah biome (Cerrado). Gauge stations were used to assess TRMM bias error and calibrate data for further analyses. Rainfall rates and their spatial variability showed a strong relationship to the transition zones between different biomes. Rainfall showed a decreasing trend for the eastern region of the Cerrado biome, a region characterized by a recent and significant expansion of crop areas. These trends agree with results from different studies which highlight the current drawdown of groundwater levels and reduced discharge, and possible lengthening of the dry season in the long run. As many conflicts have already been documented for this region, these decreasing trends are alarming for urgent and consistent hydroclimatic monitoring, and improved water resources planning and management. Positive trends for rainfall in the central Cerrado are likely a momentary recovery of recent-period drought.
Daniel Althoff; Helizani Couto Bazame; Roberto Filgueiras; Lineu Neiva Rodrigues. Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset. Journal of South American Earth Sciences 2021, 111, 103482 .
AMA StyleDaniel Althoff, Helizani Couto Bazame, Roberto Filgueiras, Lineu Neiva Rodrigues. Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset. Journal of South American Earth Sciences. 2021; 111 ():103482.
Chicago/Turabian StyleDaniel Althoff; Helizani Couto Bazame; Roberto Filgueiras; Lineu Neiva Rodrigues. 2021. "Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset." Journal of South American Earth Sciences 111, no. : 103482.
Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.
Lucas Corrêdo; Leonardo Maldaner; Helizani Bazame; José Molin. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors 2021, 21, 2195 .
AMA StyleLucas Corrêdo, Leonardo Maldaner, Helizani Bazame, José Molin. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors. 2021; 21 (6):2195.
Chicago/Turabian StyleLucas Corrêdo; Leonardo Maldaner; Helizani Bazame; José Molin. 2021. "Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy." Sensors 21, no. 6: 2195.
In this study, an algorithm is implemented with a computer vision model to detect and classify coffee fruits and map the fruits maturation stage during harvest. The main contribution of this study is with respect to the assignment of geographic coordinates to each frame, which enables the mapping of detection summaries across coffee rows. The model used to detect and classify coffee fruits was implemented using the Darknet, an open source framework for neural networks written in C. The coffee fruits detection and classification were performed using the object detection system named YOLOv3-tiny. For this study, 90 videos were recorded at the end of the discharge conveyor of a coffee harvester during the 2020 harvest of arabica coffee (Catuaí 144) at a commercial area in the region of Patos de Minas, in the state of Minas Gerais, Brazil. The model performance peaked around the ~3300th iteration when considering an image input resolution of 800 × 800 pixels. The model presented an mAP of 84%, F1-Score of 82%, precision of 83%, and recall of 82% for the validation set. The average precision for the classes of unripe, ripe, and overripe coffee fruits was 86%, 85%, and 80%, respectively. As the algorithm enabled the detection and classification in videos collected during the harvest, it was possible to map the qualitative attributes regarding the coffee maturation stage along the crop lines. These attribute maps provide managers important spatial information for the application of precision agriculture techniques in crop management. Additionally, this study should incentive future research to customize the deep learning model for certain tasks in agriculture and precision agriculture.
Helizani Couto Bazame; José Paulo Molin; Daniel Althoff; Maurício Martello. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture 2021, 183, 106066 .
AMA StyleHelizani Couto Bazame, José Paulo Molin, Daniel Althoff, Maurício Martello. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture. 2021; 183 ():106066.
Chicago/Turabian StyleHelizani Couto Bazame; José Paulo Molin; Daniel Althoff; Maurício Martello. 2021. "Detection, classification, and mapping of coffee fruits during harvest with computer vision." Computers and Electronics in Agriculture 183, no. : 106066.
The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis.
Jéssica G. Nascimento; Daniel Althoff; Helizani C. Bazame; Christopher M. U. Neale; Sergio N. Duarte; Anderson L. Ruhoff; Ivo Z. Gonçalves. Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil. Remote Sensing 2021, 13, 906 .
AMA StyleJéssica G. Nascimento, Daniel Althoff, Helizani C. Bazame, Christopher M. U. Neale, Sergio N. Duarte, Anderson L. Ruhoff, Ivo Z. Gonçalves. Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil. Remote Sensing. 2021; 13 (5):906.
Chicago/Turabian StyleJéssica G. Nascimento; Daniel Althoff; Helizani C. Bazame; Christopher M. U. Neale; Sergio N. Duarte; Anderson L. Ruhoff; Ivo Z. Gonçalves. 2021. "Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil." Remote Sensing 13, no. 5: 906.
The objective of the present work was to evaluate the use of spectral sensors to determine nitrogen fertilizer requirements for pastures of Urochloa brizantha cv. Xaraés in Brazil. The experimental design was a randomized block design with 4 replications of 4 treatments: a control treatment (TT) without application of N; a reference treatment (TR) with N applied at a standard predetermined fixed rate (150 kg urea/ha/cycle); a treatment using GreenSeekerTM (TG) to determine N requirement by the canopy normalized difference vegetation index (NDVI); and a treatment using SPAD 502 (TS) to determine N requirement by foliar chlorophyll assessment. For treatments involving spectral sensors, N fertilizer was applied at half the rate of that in the reference treatment at the beginning of each cycle and further N was applied only when the nitrogen sufficiency index dropped below 0.85. The sensors used in the work indicated that no additional N fertilizer was required by these pastures above the half rates applied. Applying N at the reduced rates to the pastures was more efficient than the pre-determined fixed rate, as both sensor treatments and the fixed rate treatment produced similar total forage yields, with similar crude protein concentrations. All fertilized pastures supported similar stocking rates, while the sensor treatments used less N fertilizer, i.e. 75 kg urea/ha/cycle less than the reference plot. Longer-term studies to verify these findings are warranted followed by promotion of the technology to farmers to possibly reduce fertilizer application rates, improve profitability and provide environmental benefits.
Helizani C. Bazame; Francisco A.C. Pinto; Domingos S. Queiroz; Daniel M. De Queiroz; Daniel Althoff. Spectral sensors prove beneficial in determining nitrogen fertilizer needs of Urochloa brizantha cv. Xaraés grass in Brazil. Tropical Grasslands-Forrajes Tropicales 2020, 8, 60 -71.
AMA StyleHelizani C. Bazame, Francisco A.C. Pinto, Domingos S. Queiroz, Daniel M. De Queiroz, Daniel Althoff. Spectral sensors prove beneficial in determining nitrogen fertilizer needs of Urochloa brizantha cv. Xaraés grass in Brazil. Tropical Grasslands-Forrajes Tropicales. 2020; 8 (2):60-71.
Chicago/Turabian StyleHelizani C. Bazame; Francisco A.C. Pinto; Domingos S. Queiroz; Daniel M. De Queiroz; Daniel Althoff. 2020. "Spectral sensors prove beneficial in determining nitrogen fertilizer needs of Urochloa brizantha cv. Xaraés grass in Brazil." Tropical Grasslands-Forrajes Tropicales 8, no. 2: 60-71.
Improving irrigation water management is an important asset when facing increased water shortages. The Hargreaves–Samani (HS) method is a simple method that can be used as an alternative to the Penman–Monteith (PM) method, which requires only temperature measurements for estimating reference evapotranspiration (ETo). However, the applicability of this method relies on its calibration to local meteorological specificities. The objective of this study was to investigate the effects of local calibration on the performance of the HS equation. The study was carried out for the middle portion of the São Francisco River Basin (MSFB), Brazil, and considered four calibration approaches: A1—single calibration for the entire MSFB; A2—separate calibration by clusters of months; A3—by clusters of stations; and A4—for all contexts resulting by combining A2 and A3. Months from the wet season showed larger improvements by the calibration of the HS model, since mean air temperature and its daily range showed stronger correlations to ETo. On the other hand, the months from the dry season and stations from the eastern region of MSFB performed poorly regardless of the calibration approach adopted. This occurred because, in those cases, ETo presented larger correlation to variables that are missing in the HS equation, and the use of the full PM equation seems unavoidable.
Daniel Althoff; Robson Argolo Dos Santos; Helizani Couto Bazame; Fernando França Da Cunha; Roberto Filgueiras. Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration. Water 2019, 11, 2272 .
AMA StyleDaniel Althoff, Robson Argolo Dos Santos, Helizani Couto Bazame, Fernando França Da Cunha, Roberto Filgueiras. Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration. Water. 2019; 11 (11):2272.
Chicago/Turabian StyleDaniel Althoff; Robson Argolo Dos Santos; Helizani Couto Bazame; Fernando França Da Cunha; Roberto Filgueiras. 2019. "Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration." Water 11, no. 11: 2272.
The net primary productivity is one of the main indicators of an ecosystem’s health. The objectives of the present study were to assess the performance of machine learning techniques in net primary productivity modeling and to assess regional trends for the Brazilian territory. Net primary production was modeled using evapotranspiration estimates, the normalized difference vegetation index, hypsometry and meteorological data. The models adopted for estimating net primary productivity were stepwise regression, Bayesian regularized neural network and Cubist regression. A linear trend model was applied pixel by pixel in order to verify a significant change in net primary productivity across the Brazilian territory. The Cubist model performed best among the evaluated models, with root-mean-squared error of 135.6 g C m−2 year−1 and R2 equal to 0.78. While assessing the net primary productivity time series, an increased trend was observed for the Brazilian Savannah biome, which may be attributed to the replacement of some Savannah formations and degraded grasslands to agriculture. The developed model has shown a great potential for filling the gap of spatial net primary productivity data in large scales.
Helizani Couto Bazame; Daniel Althoff; Roberto Filgueiras; Maria Lúcia Calijuri; Julio Cesar de Oliveira. Modeling the Net Primary Productivity: A Study Case in the Brazilian Territory. Journal of the Indian Society of Remote Sensing 2019, 47, 1727 -1735.
AMA StyleHelizani Couto Bazame, Daniel Althoff, Roberto Filgueiras, Maria Lúcia Calijuri, Julio Cesar de Oliveira. Modeling the Net Primary Productivity: A Study Case in the Brazilian Territory. Journal of the Indian Society of Remote Sensing. 2019; 47 (10):1727-1735.
Chicago/Turabian StyleHelizani Couto Bazame; Daniel Althoff; Roberto Filgueiras; Maria Lúcia Calijuri; Julio Cesar de Oliveira. 2019. "Modeling the Net Primary Productivity: A Study Case in the Brazilian Territory." Journal of the Indian Society of Remote Sensing 47, no. 10: 1727-1735.
The Brazilian Savanna biome is the main agricultural region of Brazil and is facing a growing water shortage and conflicts that tend to expand to new areas and intensify in those already stressed, which is hindering the economic development of the region. Small reservoirs play an important role in supporting the local economy in the savanna areas of Brazil. Hundreds of small reservoirs have been built in the last twenty years in the region. They were constructed by individual farmers or projects funded by different agencies without any coordination and knowledge that the reservoirs are part of a complex hydrologic system. The lack of reservoir basic information and appropriate methods to quantify water supply, demand and losses are hindering efficient water management. In this context, evaporation is a non beneficial loss to the water system and should be better quantified. The present study aimed to improve methods for estimating small reservoir evaporation in the Brazilian savanna region. The study area was the Buriti Vermelho watershed, where the evaporation were measured in Class A pans installed inside and outside of a small reservoir. Pan coefficients were calculated on a monthly, seasonal and annual basis. In addition, equations for estimating evaporation based on climatic variables were developed. The pan coefficients values varied throughout the year from 0.72 to 0.92, with mean absolute error (MAE) ranging from 0.32 to 0.52 mm d−1. The two best climatic equations presented MAE of 0.44 mm d−1, while equations that used solar radiation and relative humidity as input also presented good performance.
Daniel Althoff; Lineu Neiva Rodrigues; Demetrius David da Silva; Helizani Couto Bazame. Improving methods for estimating small reservoir evaporation in the Brazilian Savanna. Agricultural Water Management 2019, 216, 105 -112.
AMA StyleDaniel Althoff, Lineu Neiva Rodrigues, Demetrius David da Silva, Helizani Couto Bazame. Improving methods for estimating small reservoir evaporation in the Brazilian Savanna. Agricultural Water Management. 2019; 216 ():105-112.
Chicago/Turabian StyleDaniel Althoff; Lineu Neiva Rodrigues; Demetrius David da Silva; Helizani Couto Bazame. 2019. "Improving methods for estimating small reservoir evaporation in the Brazilian Savanna." Agricultural Water Management 216, no. : 105-112.