Search

As organic waste is converted to usable amendments via composting, there are large CO2 emissions associated with the decomposition of organic matter via microorganisms. While the active composting phase produces the largest emissions over a short duration, compost can often be stored during and after the maturation phase for much longer periods of time, increasing cumulative emissions. As such, the objectives of this study were to examine the spatial and temporal variability associated with in situ emissions sampling while identifying the environmental and chemical controls on emissions in windrow composting facilities during and after the maturation phase. A total of 665 flux measurements were taken from four windrows representing different ages and compositions between June and November 2020. Factorial analysis of covariance (ANOVA) was used to determine the variability between sampling locations, while multiple linear regression was used to identify those parameters which had the most influence on CO2 flux. Emissions showed significant variability over time that were attributed to ambient temperatures. During the summer, each windrow reached peak emissions between 5.0 and 32.3 g CO2 m-2 hr-1. As temperatures cooled, the windrows saw a 62%-86% decline in emissions, generally falling below 2 g CO2 m-2 hr-1. Significant differences occurred between the top-most sampling location and all others on the windrow, emitting between 33%-100% more CO2. The environmental controls of surface temperature, moisture content, and internal temperature showed the highest influence on emissions (R2 = 0.62). Chemical properties including organic nitrogen, carbon, pH, magnesium, and nitrate also showed significant influence (R2 = 0.43). This research has shown that environmental factors including temperature and moisture show the strongest influence over emission rates in mature compost. A significant negative effect of organic nitrogen on CO2 flux was found, indicating that increased presence of organic nitrogen would aid in the retention of carbon after the maturation phase, acting to lower total emissions. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers' ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin's concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection. [ABSTRACT FROM AUTHOR]

Accurate soil information is critically important for forest management planning and operations but is challenging to map. Digital soil mapping (DSM) improves upon the limitations of conventional soil mapping by explicitly linking a variety of environmental data layers to spatial soil point data sets to continuously predict soil variability across a landscape. Thus far, much DSM research has focussed on the development of ultrafine-resolution soil maps within agricultural systems; however, increasing availability of light detection and ranging (LiDAR) data presents new opportunities to apply DSM to support forest resource applications at multiple scales. This project describes a DSM workflow using LiDAR-derived elevation data and machine learning models (MLMs) to predict key forest soil attributes. A case study in the Hearst Forest in northeastern Ontario, Canada, is used to illustrate the workflow. We applied multiple MLMs to the Hearst Forest to predict soil moisture regime and textural class. Both qualitative and quantitative assessment pointed to the random forest MLM producing the best maps (63% accuracy for moisture regime and 66% accuracy for textural class). Where error occurred, soils were typically misclassified to neighbouring classes. This standardized, flexible workflow is a valuable tool for practitioners that want to undertake DSM as part of forest resource management and planning. Key words: digital soil mapping, machine learning, forest management. Des informations precises sur les sols sont absolument essentielles pour la planification et les operations d'amenagement forestier mais elles sont difficiles a cartographies La cartographie numerique des sols (CNS) constitue un progres par rapport aux limites de la cartographie conventionnelle des sols en reliant une variete de couches de donnees environnementales a des ensembles de donnees spatiales ponctuelles des sols pour predire la variabilite a travers un paysage de facon continue. Jusqu'a maintenant, beaucoup de travaux de recherche sur la CNS ont mis l'accent sur le developpement de cartes des sols a tres haute resolution pour des systemes agricoles. Cependant, la disponibilite croissante de donnees lidar offre de nouvelles opportunites d'appliquer la CNS en support a des applications qui concernent les ressources forestieres a de multiples echelles. Ce projet decrit un flux de travail de CNS qui utilise des donnees altimetriques derivees du lidar et des modeles d'apprentissage automatique (MAA) pour predire des attributs importants des sols. Une etude de cas dans la foret de Hearst, dans le nord-est de l'Ontario, au Canada, est utilisee pour illustrer le flux de travail. Nous avons applique plusieurs MAA a la foret de Hearst pour predire le regime d'humidite et la classe de texture. Une evaluation tant qualitative que quantitative indiquait que le MAA de foret aleatoire produisait les meilleures cartes (precision de 63 % pour le regime d'humidite et de 66 % pour la classe de texture). Lorsqu'il y avait des erreurs, les sols mal classes etaient typiquement places dans les classes voisines. Ce flux de travail standardise et flexible est un outil precieux pour les praticiens qui veulent entreprendre la CNS en tant que composante de la planification et de la gestion des ressources forestieres. [Traduit par la Redaction] Mots-cles: cartographie numerique des sols, apprentissage automatique, amenagement forestier., 1. Introduction Soils are a critical element of forest ecosystems in that soil mineralogy, nutrient supply, moisture retention, texture, structure, and porosity collectively influence forest composition and productivity (Leniham 1993; [...]

Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial. Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability to obtain accurate information about soil. This research aims to apply machine learning algorithms using various environmental covariates to generate digital SOC maps for mineral topsoils in the Liberec and Domažlice districts, located in the Czech Republic. The soil class, land cover, and geology maps as well as terrain covariates extracted from the digital elevation model and remote sensing data were used as covariates in modelling. The spatial distribution of SOC was predicted based on its relationships with covariates using random forest (RF), cubist, and quantile random forest (QRF) models. Results of the RF model showed that land cover (vegetation) and elevation were the most important environmental variables in the SOC prediction in both districts. The RF had better efficiency and accuracy than the cubist and QRF to predict SOC in both districts. The greatest R² value (0.63) was observed in the Domažlice district using the RF model. However, cubist and QRF showed appropriate performance in both districts, too. [ABSTRACT FROM AUTHOR]

Introduction: The increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degraded soil properties when located near the surface. Methods: This study aimed to assess the potential of direct current resistivity measurements to predict the maximum peat thickness (MPT), defined as the nonlimnic peat thickness, to facilitate soil conservation andmanagement practices at the field-scale. The results were also compared to a regional map of the MPT from a previous study used and also tested as a covariate. This study was conducted in a shallow (MPT = 8-138 cm) cultivated organic soil from Québec, Canada. The MPT was mapped using the apparent electrical conductivity (ECa) from a Veris Q2800, and a digital elevation model, with and without a regional MPT map (RM) as a covariate to downscale it. Three machine-learning algorithms (Cubist, Random Forest, and Support Vector Regression) were compared to ordinary kriging (OK), multiple linear regression, and multiple linear regression kriging (MLRK) models. Results and discussion: The best predictive performance was achieved with OK (Lin's CCC = 0.89, RMSE = 13.75 cm), followed by MLRK-RM (CCC = 0.85, RMSE = 15.7 cm). All models were more accurate than the RM (CCC = 0.65, RMSE = 29.85 cm), although they underpredicted MPT > 100 cm. Moreover, the addition of the RM as a covariate led to a lower prediction error and higher accuracy for all models. Overall, a field-scale approach could better support precision soil conservation interventions by generatingmore accuratemanagement zones. Future studies should test multi-sensor fusion and other geophysical sensors to further improve the model performance and detect deeper boundaries. [ABSTRACT FROM AUTHOR]

This study explores the suitability of different biomass feedstocks for biochar production and their effects on soil health and crop yield. Two planting seasons were conducted, involving cucumber as the test crop and eleven treatments combining biochar and poultry manure. Soil analysis revealed initial soil conditions with high sand content and low pH. Poultry manure and biochar exhibited pH, organic carbon, and nutrient level variations. Significant differences in cucumber growth and yield were observed, with the longest vine length in plots treated with palm kernel husk biochar and poultry manure. Residual effects in the second planting season displayed similar trends. Soil pH, organic carbon, and total nitrogen remained consistent between seasons, while available phosphorus increased significantly in plots amended with goat manure biochar and poultry manure. Calcium, magnesium, potassium, and sodium contents also varied. Fruit length, weight, and yield were significantly improved by biochar treatments, with the combination of palm kernel husk biochar and poultry manure yielding the highest fruit weight. Correlation and structural equation analyses (p < 0.05) highlighted the relationships between plant characteristics, soil properties, and fruit indices, emphasizing the importance of nitrogen and phosphorus in supporting fruit development. The study suggests that biochar application enhances soil nutrients, crop growth, and fruit yields while reducing reliance on chemical fertilizers. It recommends considering biochar for land reclamation and as an alternative to traditional fertilizers, supported by appropriate regulations. [ABSTRACT FROM AUTHOR]

Soil properties can be influenced by slope location due to erosion, deposition, and mass movement. There are conflicting findings regarding the impact of the slope position on soil properties and elemental concentrations. This study aimed to examine the influence of slope position on the variability of soil properties and elemental concentrations along a hillslope landscape, using different multivariate approaches. This research was conducted in three distinct locations within the Cross River State, Nigeria: Ishibori in Ogoja, Agoi Ibami in Yakurr, and Mfamosing in Akamkpa. Three profile pits were dug on the crest, two on the middle slope, and three on the valley bottom, resulting in eight pits representing these locations. Fifty-three soil samples were collected from these pits and subjected to laboratory analyses. The lowest values were obtained for Bd, sand, and pH at all three slope positions. At the same time, the highest values were obtained for AP, SOC and Mn at the three different slope positions (crest, middle slope, and valley bottom). Soil properties, such as exchangeable acidity, bulk density, clay content, sand content, and soil organic carbon, were positively or negatively correlated (p < 0.05) with soil depth and elemental concentrations, depending on the location in the landscape. Exploring the interrelationship via a structural equation model (SEM), we observed that clay showed a strong positive correlation with sand and a moderate to weak negative correlation with sand and silt. The correlation coefficients between the studied slope positions were –0.88, –0.78, and –0.51, respectively. These results indicate that as the soil material at the crest decreased, there was a corresponding decrease in the materials at the middle slope and valley bottom. In the ANOVA, bulk density values in the crest and middle slope were comparable, but higher than those in the valley bottom soil. The potassium concentration was significantly different between the crest and middle slopes. Based on these findings, it can be concluded that topography significantly affects soil properties and that these differences should be considered when selecting and managing land for agricultural purposes. To improve soil fertility and productivity, it is recommended to implement proper soil management practices, such as nutrient management and erosion control, to reduce the impact of topographical differences on soil properties. [ABSTRACT FROM AUTHOR]

Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers. [ABSTRACT FROM AUTHOR]