Airborne cameras for natural grassland classification in the Pampa biome / Câmeras aerotransportadas para classificação da vegetação em pastagem natural no bioma Pampa

The extension of the area occupied by the inter tussock stratum and tussock stratum in natural pastures is essential for the productive performance of grazing animals. Images obtained from unmanned remote sensors can provide useful information, especially because they have a high spatial resolution. Thus, this study evaluated the performance of the supervised adaptive classification applied to aerial images obtained from an onboard drone camera to map the area covered by tussocks in a natural pasture of the Pampa biome. The study was carried out in a natural pasture area managed since 1986 under different forage allowances, considering treatments of 8, 12, and 16 kg of dry matter per 100 kg live weight (% LW). An aerial image from September 2017, obtained with a Canon S100 camera onboard a drone at an altitude of 120 m, with a spatial resolution of 5 cm, was used. The random forest and support vector machine classifiers were tested associated with specific classification rules. False-color images showed considerable visual similarity in the large patterns of the vegetation distribution and the validation performed with independent samples when compared to the classified images. The tested classifiers were able to measure the area covered by the tussock stratum, which could be an indicator of the quality vegetation in a natural grassland of the Pampa biome.

A quantidade de área ocupada por estrato inferior e superior em pastagens naturais tem grande importância sobre o desempenho produtivo dos animais em pastejo. Imagens obtidas de sensores remotos não tripulados podem fornecer informações úteis, especialmente por possuírem alta resolução espacial. O objetivo deste trabalho foi avaliar o desempenho de classificação supervisionada adaptativa aplicada a imagem aérea obtida por câmera a bordo de drone, no mapeamento da área coberta por touceiras em pastagem natural do bioma Pampa. O estudo foi realizado em área de pastagem natural, manejada desde 1986 sob diferentes ofertas de forragem, tendo sido considerados os tratamentos 8, 12 e 16 kg de matéria seca por 100 kg de peso vivo (% PV). Foi utilizada uma imagem aérea, de setembro de 2017, obtida com uma câmera Canon S100, a bordo de um drone a 120 m de altitude, correspondendo a resolução espacial de 5 cm. Foram testados dois classificadores, Random Forest e Support Vector Machine associados a regras específicas de classificação. As imagens de falsa cor, quando comparadas às imagens classificadas, apresentaram considerável semelhança visual nos grandes padrões de distribuição da vegetação, bem como na validação feita com amostras independentes. Os classificadores testados foram capazes de mensurar a área coberta por estrato superior, podendo ser um indicador da qualidade da vegetação, em pastagem natural do bioma Pampa.

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.(AU)

Airborne cameras for natural grassland classification in the Pampa biome / Câmeras aerotransportadas para classificação da vegetação em pastagem natural no bioma Pampa

ABSTRACT: The extension of the area occupied by the inter tussock stratum and tussock stratum in natural pastures is essential for the productive performance of grazing animals. Images obtained from unmanned remote sensors can provide useful information, especially because they have a high spatial resolution. Thus, this study evaluated the performance of the supervised adaptive classification applied to aerial images obtained from an onboard drone camera to map the area covered by tussocks in a natural pasture of the Pampa biome. The study was carried out in a natural pasture area managed since 1986 under different forage allowances, considering treatments of 8, 12, and 16 kg of dry matter per 100 kg live weight (% LW). An aerial image from September 2017, obtained with a Canon S100 camera onboard a drone at an altitude of 120 m, with a spatial resolution of 5 cm, was used. The random forest and support vector machine classifiers were tested associated with specific classification rules. False-color images showed considerable visual similarity in the large patterns of the vegetation distribution and the validation performed with independent samples when compared to the classified images. The tested classifiers were able to measure the area covered by the tussock stratum, which could be an indicator of the quality vegetation in a natural grassland of the Pampa biome.

RESUMO: A quantidade de área ocupada por estrato inferior e superior em pastagens naturais tem grande importância sobre o desempenho produtivo dos animais em pastejo. Imagens obtidas de sensores remotos não tripulados podem fornecer informações úteis, especialmente por possuírem alta resolução espacial. O objetivo deste trabalho foi avaliar o desempenho de classificação supervisionada adaptativa aplicada a imagem aérea obtida por câmera a bordo de drone, no mapeamento da área coberta por touceiras em pastagem natural do bioma Pampa. O estudo foi realizado em área de pastagem natural, manejada desde 1986 sob diferentes ofertas de forragem, tendo sido considerados os tratamentos 8, 12 e 16 kg de matéria seca por 100 kg de peso vivo (% PV). Foi utilizada uma imagem aérea, de setembro de 2017, obtida com uma câmera Canon S100, a bordo de um drone a 120 m de altitude, correspondendo a resolução espacial de 5 cm. Foram testados dois classificadores, Random Forest e Support Vector Machine associados a regras específicas de classificação. As imagens de falsa cor, quando comparadas às imagens classificadas, apresentaram considerável semelhança visual nos grandes padrões de distribuição da vegetação, bem como na validação feita com amostras independentes. Os classificadores testados foram capazes de mensurar a área coberta por estrato superior, podendo ser um indicador da qualidade da vegetação, em pastagem natural do bioma Pampa.

A comprehensive review on monitoring and purification of water through tunable 2D nanomaterials / Uma revisão abrangente sobre monitoramento e purificação de água através de nanomateriais 2D sintonizáveis

Instead of typical household trash, the heavy metal complexes, organic chemicals, and other poisons produced by huge enterprises threaten water systems across the world. In order to protect our drinking water from pollution, we must keep a close eye on the situation. Nanotechnology, specifically two-dimensional (2D) nanomaterials, is used in certain wastewater treatment systems. Graphene, g-C3N4, MoS2, and MXene are just a few examples of emerging 2D nanomaterials that exhibit an extraordinary ratio of surface (m3), providing material consumption, time consumption, and treatment technique for cleaning and observing water. In this post, we'll talk about the ways in which 2D nanomaterials may be tuned to perform certain functions, namely how they can be used for water management. The following is a quick overview of nanostructured materials and its possible use in water management: Also discussed in length are the applications of 2D nanomaterials in water purification, including pollutant adsorption, filtration, disinfection, and photocatalysis. Fluorescence sensors, colorimetric, electrochemical, and field-effect transistors are only some of the devices being studied for their potential use in monitoring water quality using 2D nanomaterials. Utilizing 2D content has its benefits and pitfalls when used to water management. New developments in this fast-expanding business will boost water treatment quality and accessibility in response to rising awareness of the need of clean, fresh water among future generations.

Em vez do lixo doméstico típico, os complexos de metais pesados, produtos químicos orgânicos e outros venenos produzidos por grandes empresas ameaçam os sistemas de água em todo o mundo. Para proteger nossa água potável da poluição, devemos ficar de olho na situação. A nanotecnologia, especificamente nanomateriais bidimensionais (2D), é usada em certos sistemas de tratamento de águas residuais. Grafeno, g-C3N4, MoS2 e MXene são apenas alguns exemplos de nanomateriais 2D emergentes que exibem uma extraordinária proporção de superfície (m3), proporcionando consumo de material, consumo de tempo e técnica de tratamento para limpeza e observação da água. Neste trabalho, trataremos das maneiras pelas quais os nanomateriais 2D podem ser ajustados para desempenhar determinadas funções, ou seja, como eles podem ser usados para o gerenciamento de água. A seguir, uma breve visão geral dos materiais nanoestruturados e seu possível uso no gerenciamento de água. Serão também discutidas detalhadamente as aplicações de nanomateriais 2D na purificação de água, incluindo adsorção de poluentes, filtração, desinfecção e fotocatálise. Sensores de fluorescência, colorimétricos, eletroquímicos e transistores de efeito de campo são apenas alguns dos dispositivos que estão sendo estudados para uso potencial no monitoramento da qualidade da água usando nanomateriais 2D. A utilização de conteúdo 2D tem seus benefícios e armadilhas quando utilizada para gerenciamento de água. Novos desenvolvimentos neste negócio em rápida expansão visam aumentar a qualidade e a acessibilidade do tratamento de água em resposta à crescente conscientização sobre a necessidade de água limpa e fresca entre as gerações futuras.

Nonlinear models for soil moisture sensor calibration in tropical mountainous soils

Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m³ m-³). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m³ m-³, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.

Methodology to filter out outliers in high spatial density data to improve maps reliability

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Objective lameness assessment in horses used for equine-assisted therapy in Rio Grande do Sul state, Brazil / Avaliação objetiva da claudicação em cavalos de equoterapia no Rio Grande do Sul, Brasil

Equine-assisted therapy is a method used since ancient times to rehabilitate individuals. The biomechanics provided by horses and the friction between their back and the riders' saddle generate impulses that are transmitted to riders' central nervous system; thus, these horses must be healthy enough to enable the desired therapeutic effect. The aim of the current study is to investigate lameness prevalence and intensity in equine-assisted therapy horses in Rio Grande do Sul State. The adopted methodology consisted of the objective evaluation of lameness based on Lameness Locator® wireless inertial sensors, which were placed in the 21 horses assessed in six equestrian centers in Rio Grande do Sul State. Results have shown that 90.1% of the assessed horses presented lameness in the hind (54.2%) and forelimbs (45.8%), as well as that 72% of them with presented mild lameness degree. This outcome has evidenced the need, and significance, of assessing these horses' locomotor system. To support effective therapy and protect equine welfare, it is essential that veterinarians should regularly monitor these animals in order to treat and prevent disease. Even subtle lameness can influence the generated stimuli; thus, it is an important factor to be taken into consideration when selecting equine-assisted therapy.

A equoterapia é um método de terapia, utilizada para reabilitação do praticante desde a antiguidade. A biomecânica proporcionada pelo cavalo e o atrito entre o dorso do cavalo e a sela do praticante geram impulsos que são transmitidos ao sistema nervoso central do mesmo, por isso a importância desse cavalo estar em condições aptas para promover o efeito terapêutico desejado. Nosso objetivo foi identificar a prevalência e intensidade da claudicação em equinos de equoterapia no estado do Rio Grande do Sul. A metodologia utilizada foi a avaliação objetiva da claudicação com base na utilização de sensores inerciais sem fio Lameness Locator® em seis centros equestres do Rio Grande do Sul, totalizando 21 equinos avaliados. Foi constatado que 90,1% dos equinos avaliados apresentavam claudicação, 54,2% do membro pélvico e 45,8% do membro torácico, quanto a intensidade 72% foram leves. A partir desse resultado, verifica-se a necessidade e a importância da avaliação no sistema locomotor destes animais. Para uma terapia eficaz e proteger o bem-estar dos equinos, é essencial o acompanhamento periódico por um médico veterinário para tratamento e prevenção de afecções. Claudicações, mesmo sutis, podem interferir nos estímulos gerados, sendo um importante fator de escolha do cavalo apto para a equoterapia.

Methodology to filter out outliers in high spatial density data to improve maps reliability

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Systolic blood pressure in anesthetized bitches measured with photoplethysmography

Background: Photoplethysmography is widely used in human medicine, with few studies on its use in veterinary medicine. Its sensor detects fluctuations in blood volume at the site, providing direct readings of cardiac pulse and peripheral oxygen saturation, as well as estimating cardiac output, respiratory rate and blood pressure. This study aimed to evaluate the use of photoplethysmography and compare it to vascular Doppler ultrasound as an indirect method of measuring systolic blood pressure in bitches undergoing elective ovariohysterectomy, using the invasive assessment of systolic blood pressure as a reference. Materials, Methods and Results: After clinical and laboratory evaluation, 34 healthy bitches were selected to undergo elective ovariohysterectomy. After food and water fasting, patients received pethidine hydrochloride intramuscularly as pre-anesthetic medication, followed by anesthetic induction with fentanyl citrate and propofol intravenously. General anesthesia was maintained by inhalation with isoflurane diluted in 100% oxygen. Intraoperative analgesia consisted of continuous infusion of fentanyl citrate intravenously. The animals were randomly divided into 2 groups, the thoracic limb group (TLG) and the pelvic limb group (PLG). In each patient, non-invasive blood pressure measurement was obtained simultaneously with Doppler (DOP) and photoplethysmography (PPG). The sensors of both devices were placed on the end of the same limb. The PPG sensor was positioned in the interdigital region. In patients belonging to the TLG, the Doppler sensor was placed in the ventral region of the thoracic limb, under the ulnar artery. In PLG patients, the Doppler sensor was placed in the dorsal region of the pelvic limb, over the dorsal artery of the foot. The sphygmomanometer was positioned close to the sensors. For systolic blood pressure (SBP) measurement, the cuff was inflated until the Doppler sound signal and the plethysmographic wave were lost. The cuff was then deflated until the Doppler pulse sound resumed and the photoplethysmography showed at least 2 continuous waves on a regular basis. The corresponding pressure value observed on the manometer consisted of the SBP. The same 2 evaluators performed all SBP measurements: 1 responsible for the DOP method and the other for the PPG method; both were blind to the other's findings, thus minimizing potential bias in the results. All animals underwent cannulation of the auricular artery for invasive measurement of systolic blood pressure, using a multiparameter monitor. All blood pressure measurements were performed at 5-min intervals, as well as obtaining additional parameters (heart and respiratory rate, esophageal temperature, partial tissue oxygen saturation, carbon dioxide concentration) and electrocardiographic monitoring. All parameters were documented for further statistical analysis. A strong correlation (r² = 0.95) was obtained between the DOP and PPG methods regardless of the limb on which the sensors were placed. There was a low correlation between the invasive method of measuring systolic blood pressure and the other methods. There was better agreement between the DOP and PPG methods (r2 = -0.0061; P = 0.85) when systolic blood pressure was measured in the TLG. Discussion: In the PLG, the values obtained with the DOP and PPG methods were significantly higher than those obtained with the invasive method, while the values obtained in the TLG differed slightly. It was found that the best measurement site by non-invasive methods was the thoracic limb. It was concluded that the non-invasive methods showed a low correlation with the invasive method; however, both methods had similar characteristics and photoplethysmography can be used to replace the vascular Doppler method.

Changes in soil temperature and moisture due to sugarcane straw removal in central-southern Brazil

Crop residues left in the field cover and protect the soil surface, and regulate key processes and functions, such as gas and water exchanges. However, the Brazilian sugarcane (Saccharum officinarum L.) sector has begun to use straw as feedstock to produce bioenergy. We conducted a field study to evaluate the effects of sugarcane straw removal in soil temperature and moisture changes at three sites (with different soil textures: Site 1 - clayey Oxisol, Site 2 - medium texture Oxisol, and Site 3 - sandy Ultisol) in the state of São Paulo, Brazil. The experimental design was randomized blocks with four rates of straw removal: i) no removal (NR); ii) moderate removal (MR); iii) substantial removal (SR), and iv) total removal (TR). Soil temperature was measured by sensors in the 0- to 5- and 5- to 10-cm soil layers. Undisturbed soil samples were collected from the 0- to 20- and 20- to 40-cm layers to determine soil moisture. Intensive straw removal (HR and TR) increased the soil temperature between 2 and 3 °C and the thermal amplitude between 5 and 9 °C in the 0- to 5-cm layer, compared to MR and NR. Soil moisture decreased between 0.03 and 0.07 g g-¹ in the 0- to 20-cm layer with intensive straw removal. The sandy soil was more susceptible to straw removal. Therefore, straw maintenance on the soil surface plays an essential role in temperature regulation and preservation of higher soil moisture, especially in regions with severe water deficits and long periods of water stress.

Methodology to filter out outliers in high spatial density data to improve maps reliability

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.(AU)

Citrus orchards under formation evaluated by UAV-Based RGB Imagery

Few studies have investigated the biometric attributes of citrus orchards under formation that use RGB sensors on board unmanned aerial vehicles (UAV) and the challenges are great. This study aimed to develop and validate a method of using aerial UAV images by automated routines to evaluate the biometric attributes of a crop of 'Tahiti' acid lime under formation. We used a multirotor UAV, programmed to capture images at three different map scales, with a frontal and side overlap of 80 %. Geoprocessing was carried out both with and without ground control points on each scale. An automated routine was developed in an open-source environment, consisting of three processing phases: i) Estimation of the plant biometric attributes, ii) Statistical analysis, and iii) Statistical Report Map (SRM). The use of the developed routine allowed to delimit and estimate the crown projection area with an accuracy of more than 95 % as well as identify and quantify the plants with an accuracy of over 97 %. The use of ground control points during the processing stage does not increase accuracy in estimating the biometric attributes under evaluation. On the other hand, map scale is strongly correlated with the quality of the estimates, especially plant height. The results allowed to define a method for the acquisition and analysis of aerophotogrammetric data using a UAV, which can be used to measure the plant biometric attributes under analysis and the method can be easily adapted to perennial crops.

Influence of climatic conditions on tympanic temperature and milk production in grazing cows

The present study aimed to analyze large volumes of tympanic temperature (TT) data to identify its use as a physiological indicator of climatic conditions and its relationship with milk production in grazing cows under tropical lowland conditions. Three dairy farms and 21 multiparous early lactation cows were included in the study. Seven animals were equipped with tympanic temperature wireless sensors within each farm, and permanent information was collected hourly for 22 days on average. Ambient temperature (AT), relative humidity (RH), wind speed (WS), precipitation (PP), and THI information were obtained from meteorological stations located close to each farm. Statistical analyses included Spearman correlations and random coefficient regression models (P < 0.05). TT presented moderate and significant correlations with AT (0.35 to 0.49), SR (0.25 to 0.32), THI (0.35 to 0.49), and RH (-0.35 to -0.49). Climatic variables like AT, PP, SR, and WS were the most contributing factors to TT prediction (R2 =0.42 to 0.86). Grazing dairy cows in tropical scenarios accumulate heat during the day and dissipate it at nighttime, although higher producing animals deal with more problems to reach thermal homeostasis. Correlations between TT and daily milk production varied according to animal yield; however, higher TT values were related to the most productive cows. The effect of TT on milk production prediction was not conclusive among farms, possibly by animal management or others characteristics of the systems. TT determination through remote sensors allows a reliable diagnosis of the physiological temperature response to climatic conditions.(AU)

Influence of climatic conditions on tympanic temperature and milk production in grazing cows

The present study aimed to analyze large volumes of tympanic temperature (TT) data to identify its use as a physiological indicator of climatic conditions and its relationship with milk production in grazing cows under tropical lowland conditions. Three dairy farms and 21 multiparous early lactation cows were included in the study. Seven animals were equipped with tympanic temperature wireless sensors within each farm, and permanent information was collected hourly for 22 days on average. Ambient temperature (AT), relative humidity (RH), wind speed (WS), precipitation (PP), and THI information were obtained from meteorological stations located close to each farm. Statistical analyses included Spearman correlations and random coefficient regression models (P < 0.05). TT presented moderate and significant correlations with AT (0.35 to 0.49), SR (0.25 to 0.32), THI (0.35 to 0.49), and RH (-0.35 to -0.49). Climatic variables like AT, PP, SR, and WS were the most contributing factors to TT prediction (R2 =0.42 to 0.86). Grazing dairy cows in tropical scenarios accumulate heat during the day and dissipate it at nighttime, although higher producing animals deal with more problems to reach thermal homeostasis. Correlations between TT and daily milk production varied according to animal yield; however, higher TT values were related to the most productive cows. The effect of TT on milk production prediction was not conclusive among farms, possibly by animal management or others characteristics of the systems. TT determination through remote sensors allows a reliable diagnosis of the physiological temperature response to climatic conditions.

A short review: Comparisons of high-throughput phenotyping methods for detecting drought tolerance

Drought is a major threat worldwide for crop production, especially due to the rapid climate changes. Current drought solutions involve improving irrigation system, rainwater harvesting, damming, cloud seeding, and changes of cultivation methods. Despite effective, each solution has economic, environmental, and temporal drawbacks. Among all solutions, the most effective, inexpensive and manageable method is the use of drought-tolerant cultivars via plant breeding. However, conventional plant breeding is a time-consuming and laborious task, especially for phenotypic data acquisition of target traits of numerous progenies. High-throughput phenotyping (HTP) is a recently developed method and has potential to overcome the mentioned issues. HTP offers massive, accurate, rapid, and automatic data acquisition in the breeding procedure and can be a breakthrough for developing drought resistant/tolerant cultivars. This study introduces various methods of HTP to detect drought stress, which can accelerate the breeding processes of drought-tolerant cultivars to provide helpful guidelines for breeders and researchers to choose appropriate methods.(AU)

A short review: Comparisons of high-throughput phenotyping methods for detecting drought tolerance

Drought is a major threat worldwide for crop production, especially due to the rapid climate changes. Current drought solutions involve improving irrigation system, rainwater harvesting, damming, cloud seeding, and changes of cultivation methods. Despite effective, each solution has economic, environmental, and temporal drawbacks. Among all solutions, the most effective, inexpensive and manageable method is the use of drought-tolerant cultivars via plant breeding. However, conventional plant breeding is a time-consuming and laborious task, especially for phenotypic data acquisition of target traits of numerous progenies. High-throughput phenotyping (HTP) is a recently developed method and has potential to overcome the mentioned issues. HTP offers massive, accurate, rapid, and automatic data acquisition in the breeding procedure and can be a breakthrough for developing drought resistant/tolerant cultivars. This study introduces various methods of HTP to detect drought stress, which can accelerate the breeding processes of drought-tolerant cultivars to provide helpful guidelines for breeders and researchers to choose appropriate methods.

Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing / Variabilidade espaço-temporal de parâmetros biofisícos do milho irrigado utilizando sensoriamento remoto orbital

This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc ) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.(AU)

Objetivou-se com o presente estudo estimar a evapotranspiração real da cultura por meio do modelo SAFER, calcular o Kc em função do NDVI e a biomassa da cultura do milho irrigado, utilizando para isso imagens dos sensores Operacional Land Imager (OLI) e Thermal Infrared Sensor (TIRS) do satélite Landsat-8. Foram selecionados os pivôs 21 ao 26 de uma fazenda comercial localizada nos municípios de Bom Jesus da Lapa e Serra do Ramalho, situadas no oeste do estado da Bahia, Brasil. As épocas de semeadura dentro dos pivôs são ordenadas em Norte e Sul ou Leste e Oeste, iniciando o cultivo primeiro em uma das orientações e posteriormente na outra. Verifica-se com base na relação entre NDVI e KcFAO, um alto valor do coeficiente de determinação (R2=0,7921), evidenciando que a variância do KcFAO pode ser explicada pelo NDVI na cultura do milho. Considerando-se os pivôs centrais com diferentes datas de plantio, os valores dos pixels da ETc variaram de 0,0 a 6,0 mm d-1 durante o ciclo fenológico. Os maiores valores foram encontrados para o DOY 199, correspondendo ao DAS em torno de 100 dias. Os valores mais baixos da BIO ocorrem aos 135 DOY em torno de 20 DAS. É observado que existe uma relação entre a ETc e BIO, os DOY mais elevados da BIO são equivalentes com os maiores valores de ETc . Além desta relação, a BIO é fortemente influenciada pela disponibilidade hídrica no solo.(AU)

Spectral detection of nematodes in soybean at flowering growth stage using unmanned aerial vehicles / Detecção espectral de nematóides na soja em estádio de florescimento por meio de veículos aéreos não-tripulados

Soybean is one of the main crop species grown in the world. However, there is a decline in productivity due to the various types of stress, including the nematodes Heterodera glycines and Pratylenchus brachyurus. The objectives were to determine the best spectral band for detecting H. glycines and P. brachyurus at the beginning of flowering (R1). Soil and root sampling was conducted at nine sampling sites in each of the five nematode-infested regions, totaling 45 sampling points. Flights were made at all regions using Phantom 4 Advanced, Sequoia and 14-band customized Sentera. For H. glycines, the red spectral band best explained the variability on soil and root nematode counts as well as the second stage of juveniles in soil. For P. brachyurus, Sentera RedEdge best explained the variability in root nematode counts and Sequoia NIR best explained soil juveniles. A multiple linear regression model using spectral data for detecting P. brachyurus and H. glycines improved R² compared to simple linear regressions. At flowering growth stage (R1), soybean spectral reflectance was associated with the number of H. glycines and P. brachyurus on soil and roots using low-cost and multispectral sensors.(AU)

A soja é uma das principais espécies de planta cultivadas no mundo. Todavia, perdas de produtividade são ocasionadas por vários tipos de estresses, incluindo os nematoides H. glycines e P. brachyurus. Como objetivo, buscou-se determinar a melhor banda espectral para a detecção do H. glycines e P. brachyurus com o uso de modelos de regressões lineares simples e definir um modelo matemático de regressão linear múltiplo para sua detecção, no início do florescimento (R1). Para isto, foram definidos nove pontos de coleta em cinco reboleiras, totalizando 45 pontos. As coletas foram feitas em um padrão específico de distâncias, de forma a ter amostras com tipos variados de populações de nematoides. Foram realizados voos com o Phantom 4 Advanced, Sequoia e Sentera sobre cada uma das reboleiras. O comprimento de onda do vermelho melhor explicou a variabilidade dos dados para H. glycines no solo e na raiz, bem como dos juvenis de segundo estádio no solo. Para P. brachyurus, a RedEdge da Sentera foi a que explicou melhor a variabilidade dos dados para nematoide na raiz e a NIR da Sequoia a que melhor explicou para juvenis no solo. Quando se utilizou um modelo matemático para a detecção do P. brachyurus e H. glycines, percebe-se uma grande melhora no R² e p-valor com relação às regressões lineares simples. No início da floração (R1), a refletância espectral da soja foi associada ao número de H. glycines e P. brachyurus no solo e nas raízes, usando sensores de baixo custo e multiespectrais.(AU)

Use of the rumination profile through collar sensors for mastitis diagnosis in dairy cows

Background: Mastitis is an inflammatory disease of the mammary gland, mostly associated with bacterial infections. Itis responsible for great economic losses due to decreased milk yield, discarded milk, milk composition alterations andtreatment costs, besides it impairs the animal health and welfare. The rumination time is an important behavioral markerand its assessment can be used as an early diagnosis tool, which can improve cure rate. Therefore, the aim of the presentstudy was to evaluate the sensitivity of behavior monitoring system collars in the diagnosis of mastitis and the averagerumination time (RT) of Holstein cows during the healthy period and affected by the disease.Materials, Methods & Results: The study was conducted on a commercial property located in the municipality of RioGrande, Rio Grande do Sul, Brazil. The RT data from 39 multiparous Holstein cows with an average milk yield of 38.4 L/day was collected. RT monitoring was performed using C-Tech1 collars combined with CowMed® software, which assessbehavior data from the animals and emits warning signals when it finds abnormalities in any parameter. In order to verifywhether the animals were determined correlated with diseases, the sensitivity of the data was evaluated, when the systemhad given the alert to animals considered ill, they underwent to a further clinical evaluation performed by a veterinarian toconfirm the diagnosis. From the diagnosis, the cows were divided into subclinical mastitis (SM) and clinical mastitis (CM)groups. SM was detected by the California Mastitis Test (CMT) and cows that were graded 1 (++), 2 (++) or 3 (+++) withoutthe presence of any other clinical sign were assigned to the SM group. CM was assessed by observation of abnormalitiesin milk such as changes in color and consistency, as well as the presence of lumps, clots or blood; and clinical examinationof the udder was performed for detection of hot, hard, swollen or painful quarters...

Temporal variations in the air, soil, and fiddler crab (Austruca perplexa) burrow temperatures in southern Thailand

Fiddler crabs live in an intertidal habitat and face several environmental constraints. Extreme environmental conditions, especially temperature affects their growth and reproduction. They use several strategies to deal with extreme temperatures. Among these, constructing burrows is important. Burrows act as a refuge during very high or low temperatures. This study investigates the temporal variations in air temperature, burrow temperature of large-sized male and female Austruca perplexa crabs, and the soil temperature near their burrows in Nakhon Si Thammarat province, southern Thailand (tropical climate). Air, burrow and soil temperatures were measured every 30 min in a day using temperature sensors. We observed that from 8:00 up to and including 17:30, burrow temperature was lower than soil temperature, but other times, burrow temperature was higher than soil temperature. In the case of air temperature, it was lower than soil or burrow temperatures most of the time in a day. When we compared temperatures among air, soil, and burrows at day (6:00 up to 17:30) and night (18:00 up to 5:30), burrow temperature was lower than soil temperature during the day but was higher at night. The air temperature was lower than soil or burrow temperatures on both day and night. This study shows that A. perplexa crab burrows can modulate the inside temperature and maintain a suitable temperature for the crabs.(AU)