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The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables.

The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation.

Abstract Robotic milking systems are successful innovations in the development of dairy cattle. The objective of this study was to analyse the milking characteristics and behavior of dairy cows of different calving orders in “milk first” robotic milking systems. The data were collected from a commercial herd located in the Midwest region of Minas Gerais (Brazil), which uses an automatic milking system (AMS TM, DeLaval). Were analysed 26,574 observations of 235 Holstein cows were available. Data were evaluated by multivariate analysis of variance and the Tukey test. - Tthe characteristics milk flow and milking efficiency were more favourable for multiparous cows (p

A form of rural tourism in expansion is horse tourism. Equitourism is a form of hiking that allows for an intimate and natural use of landscapes and local territories. Equestrian tourism today no longer represents a small elite market niche, as it has become a true form of pastime for an increasing number of users. Trail planning and management need to consider the changes that are already occurring, as well as those that are predicted to lie ahead. The key trends that are expected to have an impact on recreational horse trail planning and management are highlighted in this research. Many of these trends have been emerging over decades and have rapidly increased due to the impacts of the COVID-19 pandemic. In recent years, in many Italian regions, especially in Tuscany, thanks to the enhancement of the Tuscan equestrian routes, projects for the promotion of equestrian tourism have been developed. In this work the methodology applied was based on the identification of an appropriate database created in the GIS context and specifically coded for equestrian tourism. The aim is to guarantee the creation of an innovative and functional product to meet the needs of this sustainable tourism sector, providing information capable of facilitating the end user, who, through their GPS, georeferenced images and maps, will have the possibility of planning and traveling the horse trail in complete safety. The final result of this research (the first case study in Italy) is the creation of a modern and complete information database of a horse trail that can be managed using GIS.

ABSTRACT: Allowing dairy cattle to access pasture or outdoor areas is known to be beneficial for cows' welfare and is considered important by the general public. However, in confinement-based operations with high-yielding cows, pasture access may be difficult to implement, especially for lactating animals. Providing pasture access to heifers and dry cows seems a more feasible option for most farms. The objective of this study was to investigate the effects of providing high-yielding dairy cows with free-choice pasture access during the dry period on their health, behavior, and milk production. Over the study period, a total of 78 Holstein cows were assigned to 1 of 2 treatments during the dry period (51 ± 8 d): housing with free-choice access to the pasture (PAST) or housing continuously without any access to the outdoors (CTRL). After calving, all cows from both treatments were mixed and housed continuously. To assess the effects of the treatment on cows' performance, all animals enrolled were monitored both before calving and during the first 100 d in milk of the following lactation. The behavior of all cows involved was monitored continuously during the whole observation period using collar-based sensors. All cows were inspected monthly to assess lameness, hock lesions, cleanliness, and body condition score. During the period after calving (0–100 d in milk), milk production and composition were also monitored. Results showed that free-choice pasture access affected cows' feeding behavior. Before calving, the animals in PAST spent more time feeding than in CTRL and, interestingly, this difference tended to persist for several weeks after calving. During the dry period, cows in PAST were cleaner than in CTRL but no differences in locomotion and body condition score were found between the 2 groups. Free-choice pasture access during the dry period also affected milk production during the following lactation. The cows that spent the dry period in PAST produced more milk than CTRL counterparts, particularly for the animals that calved during summer. In the current study we have found that providing free-choice pasture access during the dry period can positively affect the performance of dairy cattle and represents a desirable practice in confinement-based dairy production systems.

The main challenge in milk production has been to maintain a focus on efficient processes that enhance production outcomes while aligning with animal welfare and sustainability and being valued by society. As an alternative to improve cow welfare in production and provide better handling of the waste generated by the activity, the system called the compost-bedded pack barn (CBP) has been widely adopted in countries with temperate climates and higher milk production. This CBP has been attracting global interest, including from countries with tropical and subtropical climates, such as Brazil, where many producers have started to use it due to the response in terms of milk productivity. A CBP can be designed either in (a) an open facility with natural ventilation or a positive-pressure ventilation system or (b) totally closed facilities, equipped with negative-pressure ventilation systems and permanent thermal control. The latter system is being implemented in Brazil, despite insufficient knowledge about its efficiency. The objective of this study was, through a review, to gather and describe the most recent information on the use of open and closed CBP systems for dairy cattle housing, mainly covering how it is applied in tropical climate regions. To achieve the proposed objective, this review study included the following topics related to CBPs: (i) implementation, (ii) bedding, (iii) general construction and architectural characteristics, and (iv) ambient thermal conditioning. Knowledge gaps and directions for future research are also identified here.

Brazil is the largest producer and exporter of coffee beans in the world. Given this relevance, it is important to monitor the crop to prevent attacks by pests. This study aimed to detect leaf miner (Leucoptera coffeella) infestation in a newly planted crop based on vegetation indices (VI) derived from aerial images obtained by a multispectral camera embedded in a remotely piloted aircraft (RPA) using random forest (RF). The study was conducted on the Cafua farm in the municipality of Lavras in southern Minas Gerais. The images were collected using a multispectral camera attached to a remotely piloted aircraft (RPA). Collections were carried out on 30 July 2019 (infested crop) and 16 December 2019 (post chemical control). The RF package in R software was used to classify the infested and healthy plants. The t test revealed significant differences in band means between healthy and infested plants, favouring higher means in healthy plants. VI also exhibited significant differences, with EXR being higher in infested plants and GNDVI, GOSAVI, GRRI, MPRI, NDI, NDRE, NDVI and SAVI showing higher averages in healthy plants, indicating distinct spectral responses and light absorption patterns between the two states of the plant. Due to the spectral differences between the classes, it was possible to classify the infested and healthy plants, and the RF algorithm performed very well.

Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach.

The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers.

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state.

In this study, a systematic review was performed to describe and discuss the main results available in the literature on physicochemical bedding quality in compost-bedded pack barn (CBP) systems for dairy cows. Experimental peer-reviewed articles in English were searched in the Scopus (ScP) and Web of Science (WoS) databases. The resulting articles (n = 162) underwent an evaluation process in four stages, following the PRISMA methodology and, based on a priori-defined eligibility criteria, 12 were selected. Several indicators of bedding quality were used, emphasizing bedding temperature in the aerobically active layer, evaluated in all studies. The decomposition activity was less intense in winter due to mild environmental conditions. During this period, appropriate management practices should be used (more frequent bedding replacement and turning, use of aeration systems under the bedding, lateral closures in the facilities, etc.) to maintain the fully active composting process. In conclusion, the physicochemical bedding quality in this system type is mainly affected by environmental conditions. However, some care is needed to extrapolate these results since this is a recent research area, which still requires further studies.

Viticulture began growing in Brazil in recent years, and this growth is due to the introduction of new techniques and improvements used in vine planting, especially the use of plastic covering in the vineyard. Thus, this study aimed to evaluate the economic viability of plastic covering installation, as well as the quality of grapes produced by two cultivars, in order to compare these results with those obtained by a conventional cultivation system. The experiment was performed at an experimental farm producing grapes in southern Brazil. The grape cultivars Niagara Branca and Niagara Rosada were studied and cultivated with and without plastic covering. Data related to the costs of vineyard planting, establishment, and seasonal management, in addition to the grape revenue, were assessed. Additionally, the grape yield and quality were evaluated. The analysis of return indicators showed that the net present value was higher for the plastic-covered system, which indicates greater profitability and consequent viability of the investment. Moreover, the grape quality varied according to the cultivar and production system. In this context, it is recommended to use the cultivar Niagara Rosada for production in a plastic covering system.

External egg quality is an essential parameter of egg production as it relates directly to economic losses. This study evaluated the spatial variability of external egg quality in five naturally ventilated caged vertical aviaries. Differences caused by bird age and thermal and luminous variability within aviaries during winter and summer were analyzed. Data on aviary air temperature, relative humidity, light intensity, and external egg quality were collected at evenly distributed points along the aviary length within three levels of cages. The experimental design was completely randomized in a factorial scheme. In the summer, the highest air temperature and lowest relative humidity were found in central cages, mainly in upper center cages; hens produced eggs with a lower weight and shape index in this area. Similar results were obtained in the winter. In the summer, eggs with lower shell weight and thickness were also produced by hens housed in the central cages, but in the winter, the opposite result was obtained. This study of the spatial variability of external egg quality proved efficient in detecting areas within an aviary with poor quality eggs; improvements to design and management in these areas could help management improve production efficiency and contribute to a sustainable egg supply.

The objective of this study was to characterize the dependence and spatial distribution of bedding attributes in an open compost-bedded pack barn (CBP) system with positive pressure ventilation during the winter period in Brazil. The study was conducted in July 2021, in the Zona da Mata region, Minas Gerais, Brazil. The bedding area (shavings and wood sawdust) was divided into a mesh with 44 equidistant points. At each point, the bedding temperature at the surface (tB-sur) and at a depth of 0.2 m (tB-20) and the air velocity at bedding level (vair,B) were measured, and bedding samples were collected. The bedding samples were used to determine the moisture level and pH at the surface (MB-sur e pHB-sur) and at a depth of 0.2 m (MB-20 and pHB-20). The spatial behavior of the variables was evaluated using geostatistics techniques. For all variables, the occurrence of strong spatial dependence was verified. Through the maps, it was observed that tB-sur, tB-20, MB-sur, MB-20, and vair,B showed high spatial variability, whereas pHB-sur and pHB-20 demonstrated low variation. On the surface, values of tB-sur < 20 °C and MB-sur > 60% were observed. At the subsurface, there was a predominance of tB-20 < 40 °C, MB-20 > 60%, and pH > 9, which are indications of low bedding composting activity.

This study was conducted to describe the building layout and dimensions, characterize the bedding material, and observe the management practices in 42 compost-bedded pack (CBP) barns in Kentucky (USA). The average herd size found in the study was 90 cows and the breeds consisted of Jersey (6.8%), Holstein (72.7%), and mixed (20.5%). The average CBP barn dimensions were 49.1 m (length) by 21.9 m (width). Many of these barns had feed alleys and driveways; overshot ridges with frequent orientation from NE to SW; and green sawdust, kiln-dried sawdust, or a mixture of both as the most common bedding materials. The bed-turning process was performed mechanically at depths of less than 0.25 m, and the loading of fresh material was performed every one to five weeks, varying by season, weather conditions, barn size, and cow density. The average bedding moisture content was found to be 59.0% (wet bulb—w.b.) and ranged from 36.2 to 71.8%. Coliforms were not present in barns that had a higher compost temperature, and the E. coli, Bacillus, and Streptococcus counts were higher in the barns that had a lower moisture content. In conclusion, it was observed that heterogeneous management was used among the barns and that the producers were satisfied with the compost barn system.

The thermal, chemical, and physical properties of compost bedding materials play an important role in every phase of compost production. Based on this, we aimed to assess the thermal, chemical and physical properties of bedding materials for compost-bedded pack (CBP) barns. The database for this study was registered from 42 CBP barns, distributed throughout the state of Kentucky (USA). The thermal conductivity showed a linear relationship with moisture content and bulk density, while thermal resistivity decreased with increasing particle size. The bedding moisture average was 46.8% (±11.5). The average finer index (p < 0.05) was the highest weight percentage (30.1%) in the samples studied. Water-holding capacity (WHC) increased with increasingly fine particle size. The higher bulk density value was 3.6 times that of the lowest bulk density value. The chemical characterization of the bedding material provided the following results: 42.7% (±3.8%) C, 1.6% (±0.4%) N, and 28.2 (±8.0) C:N ratio. However, thermal properties are strongly dependent on particle size. Producers can use the bedding material as fertilizer in their crops, due to the chemical characteristics of the materials. Beds with good physical and chemical properties improve their moisture content.

Tourism along the Via Francigena is a growing phenomenon. It is important to develop a direct survey of path’s users (pilgrims, tourists travel, day-trippers, etc.) able to define user’s profiles, phenomenon extent, and its evolution over time. This in order to develop possible actions to promote the socio-economic impact on rural areas concerned. With this research, we propose the creation of a monitoring network based on camera trapping system to estimate the number of tourists in a simple and expeditious way. Recently, the camera trapping, as well as the faunal field, is finding wide use even in population surveys. An innovative application field is the one in the tourist sector, becoming the basis of statistical and planning analysis. To carry out a survey of the pilgrims/tourists, we applied this type of sampling method. It is an interesting method since it allows to obtain data about type and number of users. The application of camera trapping along the Francigena allows to obtain several information about users profiles, such as sex, age, average lengths of pilgrimages, type of journey (by foot, by horseback or by bike), in a continuous time period distributed in the tourist months of the 2014.

A rational and modern network of riding trails involves the building of specific structures for the horses according to the new needs of equestrian tourists. These shelters require appropriate technical models that today cannot be found easily in the Italian or foreign literature. Over the years this gap has led to the development of the practice “do it yourself”, following old techniques of construction, not able to meet the new demands of the market of horse tourism. This research has highlighted two different solutions that can meet the needs of tourists riding today, in accordance with the laws about the construction and the health part. These structures have been designed observing the principles of low impact, low cost, easy installation, complete reuse. Two kinds of structures have been planned. Temporary stay horse shelter: building to put in resting places of interest along the path (historic villages, monasteries, etc.), where it is possible to arrange the horses for a few hours and allow to the riders to make a careful visit of places of interest. Overnight stay horse shelter: structure to put in particular points where horses and riders can spend the night. A new type of horse barn with annexed feed storage and saddle room. The structure is dynamic, due to the possibility to change quickly the position of the horses (max 8 places).