The Role of Coffee in the Development of Southwest Ethiopia's Forests: Farmers' Strategies,Investor Speculation,and Certification Projects

This article examines the role of coffee in the agriculture of southwest Ethiopian highlands, a context characterized by recent huge private coffee estate developments, coffee certification projects and smallholdings. The analysis shows that smallholder farmers prioritize staple crops, and allocate land and labor to coffee only once food production objectives have been reached. It also underscores the diversity of the local peasantry, often treated as a homogeneous group in development projects designed to boost farmers' income through coffee production. Based on this analysis, the article evaluates the effects of recent coffee-oriented development policies: state forest concessions for coffee investment, and two coffee certification projects (UTZ-Certified and Fair-Trade/Organic). Because they omit the differentiated farmers' strategies, these certification initiatives fail to improve local populations' livelihoods and accelerate incipient socioeconomic inequalities. Moreover, when applied to big private plantations, they participate in farmers' dispossession from critical forest resources essential to households' food self-sufficiency.     

Geochemical and isotopic investigation of the aquifer system in the Djerid-Nefzaoua basin, southern Tunisia

In the Djerid-Nefzaoua region, southern Tunisia, about 80% of agricultural and domestic water supply is provided by the complex terminal (CT) aquifer. However, 20% of this demand is provided by other hydraulically connected aquifers, namely the continental intercalaire (CI) and the Plio-Quaternary (PQ). Overexploitation of the CT aquifer for agricultural practices has contributed to the loss of the artesian condition and the decline of groundwater level which largely increased the downward leakage from the shallow PQ aquifer. Excess irrigation water concentrates at different rates in the irrigation channels and in the PQ aquifer itself. Then, it returns to the CT aquifer and mixes with water from the regional flow system, which contributes to the salinization of the CT groundwater. A geochemical and isotopic study had been undertaken over a 2-years period in order to investigate the origin of waters pumped from the CT aquifer with an emphasis on its hydraulic relationships with the underlying and the overlying CI and PQ aquifers. Geochemistry indicates that groundwater samples collected from different wells show an evolution of the water types from Na-Cl to Ca-SO₄-Cl. Dissolution of halite, gypsum and anhydrite-bearing rocks is the main mechanism that leads to the salinization of the groundwater. Isotopic data indicate the old origin of all groundwater in the aquifer system. Mixing and evaporation effects characterizing the CT and the PQ aquifers were identified using δ²H and δ¹⁸O relationship and confirmed by the conjunction of δ²H with chloride concentration.     

Petrography and geochemistry of the Upper Cretaceous Nubia sandstones,Eastern Desert,Egypt: implications for provenance,paleoweathering, and tectonic setting

The provenance of the Upper Cretaceous Nubia sandstones from four vertical sections along Qena-Safaga and Qena-Quseir roads in central Eastern Desert of Egypt was investigated based on their modal composition and geochemical data. The Nubia sandstone samples are abundant in quartz content with low feldspar and lithic fragments. Their average modal composition (Q_94.2F_1.3R_4.5) classifies them as quartz arenites with subordinate quartz wackes which is consistent with geochemistry data. The average CIA, CIW, PIA, and Th/U ratio values revealed that the intensity of weathering in the studied areas was similar, varying from moderate to intensive weathering, and may reflect low-relief and warm humid climatic conditions in the source area. The ICV (<?1) and SiO₂/Al₂O₃ ratio (>?5) indicate that the Nubia sandstones are texturally and mineralogically mature. The petrographical and geochemical analyses suggest that the Nubia sandstones were mainly derived from felsic (granitic) and/or recycled sand sources. The major element-based multidimensional tectonic discrimination diagrams suggested the Nubia sandstones were deposited in a passive continental margin of a syn-rift basin. This result agreed with the general geology of central Eastern Desert of Egypt during the Upper Cretaceous.     

Exploring the diverse potentials of <Emphasis Type="Italic">Planococcus</Emphasis> sp. TRC1 for the deconstruction of recalcitrant kraft lignin

Kraft lignin (KL) is the chief contaminant which is responsible for dark coloration, toxicity and high chemical oxygen demand (COD) of paper pulp mill effluent. The present study investigated the diverse potentials of Planococcus sp. TRC1 in the biodegradation of KL. Preliminary evaluation indicated that the strain was able to grow on broad spectrum of lignin-derived compounds, decolorize lignin-mimicking dyes and catabolize substrates of ligninolytic enzymes. Response surface methodology (RSM) was executed to perform the optimization of different process parameters. The results displayed that Planococcus sp. TRC1 could completely utilize 100 mg L^?1 of KL and 78% of 200 mg L^?1 of KL as sole source of carbon with concurrent reduction in COD and color. The biokinetic details of KL biodegradation showed that the values of \(\mu^{*}\), µ _max, \(q^{*}\) and q _max were 0.018 h^?1, 0.01 h^?1, 0.023 g g^?1 h^?1 and 0.05 g g^?1 h^?1, respectively. UV–visible spectrophotometry, SEM and FTIR indicated the significant alterations in the surface morphology, functional groups and chromophores during the course of biodegradation. XRD revealed the emergence of peak signifying the formation of low molecular weight intermediates after bacterial treatment. Considering the environmental impact, bacterial-treated KL illustrated less phytotoxicity using Vigna radiata seed bioassay. These results suggested that Planococcus sp. TRC1 could be a promising strain for the degradation of KL in an ecofriendly way.     

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya,India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps(LSMs) using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model(CNN) and three popular machine learning techniques, i.e., random forest model(RF), artificial neural network model(ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points. A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve(AUC) of the receiver operating characteristics(ROC) curve and statistical methods like root mean square error(RMSE) and mean absolute error(MAE). In both datasets(training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.