Circular economy strategies in sedimentary phosphate mine reclamation: Development of technosol from phosphate waste rock.

Proper management of mine waste plays a crucial role in minimizing environmental impacts. One potential solution to tackle this problem involves transforming mine waste rock into soil to facilitate the process of mine restoration. The aim of this study was to assess the mineralogical, chemical, and physical characteristics of technosol derived from phosphate mine waste dumps. Following this evaluation, a novel rehabilitation strategy was proposed. For this purpose, a total of 32 samples were systematically collected across a 4 ha area of technosols, which had been established in accordance with the waste rock soil rehabilitation strategy involving geomorphic reshaping. According to the findings, phosphate mining left the soil with a sandy texture, resulting in a degraded soil structure with severely unfavorable crop growth conditions, notably poor stability, and low water retention. The chemistry of the studied soils was characterized by the dominance of CaO (29.02 wt%± 1.01) > SiO2 (27.61 wt% ± 0.61) > P2O5 (11.34 wt% ± 0.23) > MgO (5.97 wt%±0.16). Mineralogically, the samples were mainly formed by quartz, dolomite, calcite, apatite, and clay minerals. The prevalence of dolomite played a significant role in enhancing the accessibility of Mg as an essential nutrient and the occurrence of apatite in the soil resulted in the presence of P2O5. However, the abundance of Ca was linked to three major minerals: calcite, apatite, and dolomite. X-ray fluorescence analyses demonstrated that the concentrations of Fe2O3, K2O, and SO3 did not exceed 2 wt%.Organic matter, represented by SOC <0.2% and N < 0.02%, demonstrated an extraordinary deficiency in the study area. The analysis of element bioavailability confirmed that the soil was rich in Ca (10383,26 mg/kg), Mg (278,47 mg/kg), Zn (12,82 mg/kg), and Cu (3,7 mg/kg) but deficient in other essential nutrients such as P, K, S, Mn, and Fe. Our research results provide a set of recommendations aimed at enhancing existing mine rehabilitation practices applicable to both pre- and post-rehabilitation phases, leveraging automated mineralogy and circular economy principles. Notably, we propose a rehabilitation strategy to be implemented prior to the geomorphic reshaping phase, which is intended to reduce costs and efforts associated with soil reconstitution.

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review.

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.

A review of organic and inorganic amendments to treat saline-sodic soils: Emphasis on waste valorization for a circular economy approach.

Soil salinization poses a significant challenge to the sustainable advancement of agriculture on a global scale. This environmental issue not only hampers plant growth and soil fertility but also hinders the advancement of the national economy due to restrictions on plant development. The utilization of organic and/or inorganic amendments has demonstrated the ability to mitigate the detrimental impacts of salt stress on plant life. At the outset, this review, in addition to summarizing current knowledge about soil amendments for saline-sodic soils, also aims to identify knowledge gaps requiring further research. The organic or inorganic amendments modify soil conditions and impact plant development. For instance, organic amendments have the potential to improve the structure of the soil, augment its capacity to retain water, and stimulate microbial activity. As this occurs, salts gradually leach through the porous structure of the soil. Conversely, inorganic amendments, such as gypsum and phosphogypsum, displace sodium from soil-negative sorption sites reducing the salinity, they also increase base saturation, altogether positively impacting plant growth conditions. This review emphasizes that, under adequate rates, the combination of organic and inorganic amendment has a high potential to enhance the poor physicochemical properties of saline-sodic soils, thereby reducing their salinity. Consequently, an in-depth examination of the mineral composition, texture, and chemical composition of the soil is required to choose the most effective amendment to implement. Future research necessitates a thorough investigation of techno-economic and life cycle assessment, with active involvement from stakeholders, to enhance the decision-making process of the amendments in specific localities.

Soil data for mapping paludification in black spruce forests of eastern Canada.

Soil data and soil mapping are indispensable tools in sustainable forest management. In northern boreal ecosystems, paludification is defined as the accumulation of partially decomposed organic matter over saturated mineral soils, a process that reduces tree regeneration and forest growth. Given this negative effect on forest productivity, spatial prediction of paludification in black spruce stands is important in forest management. This paper provides a description of the soil database to predict organic layer thickness (OLT) as a proxy of paludification in northeastern Canada. The database contains 13,944 OLT measurements (in cm) and their respective GPS coordinates. We collected OLT measurements from georeferenced ground plots and transects from several previous projects. Despite the variety of sources, the sampling design for each dataset was similar, consisting of manual measurements of OLT with a hand probe. OLT measurements were variable across the study area, with a mean ± standard deviation of 21 ± 24 cm (ranging from a minimum of 0 cm to a maximum of 150 cm), and the distribution tended toward positive skewing, with a large number of low OLT values and fewer high OLT values. The dataset has been used to perform OLT mapping at 30-m resolution and predict the risk of paludification in northeastern Canada (Mansuy et al., 2018) [1]. The spatially explicit and continuous database is also available to support national and international efforts in digital soil mapping.

Using a Mobile Device "App" and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields.

Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as "app" method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph-grid and script methods (R² = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by -6.3% and -10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., -5.3% vs. -7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.