Rubber tree (Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.) is a key industrial crop for expanding Ethiopia’s latex production. Early growth performance and structural stability of eight introduced clones (IRCA230, PB217, RRIC100, RRIC121, IRCA41, PB86, RRIC101, and RRIM623) were evaluated at the Tepi Agricultural Research Station. A randomized complete block design with three replicates was employed. The survival (
Salt-affected soils of semi-arid northern India severely limit ecosystem functioning and agroforestry productivity through elevated salinity, low organic carbon, restricted phosphorus availability, and alkaline pH. Eleven amendment treatments comprising enriched charcoal (Ench) and enriched soil (EnS) substrates co-inoculated with four indigenously characterised plant growth-promoting rhizobacteria (PGPR: Klebsiella aerogenes, Lysinibacillus acetophenoni, Pseudomonas stutzeri, and Pseudomonas mosselii) were evaluated across three soil depth intervals (0–30, 30–60, and 60–90 cm) in a randomised split-plot design at three sites (Hisar, Karnal, Bhiwani) in Haryana. Soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P), and potassium (K) were analysed using split-plot linear mixed-effects models (LMMs). Ten amendments significantly reduced EC, with nine achieving significant reductions across all three depth intervals and the largest reductions reaching 0.31 dS m⁻1 (67
Habitat conversion threatens the Greater Kafue Ecosystem (GKE), Zambia, one of southern Africa’s most important protected-area landscapes, yet its spatial predictors remain poorly quantified. We used a Random Forest classifier to map susceptibility to natural-habitat conversion across Kafue National Park (KNP) and its surrounding Game Management Areas (GMAs). The binary response was derived from Landsat classifications for 1984 and 2024, treating natural-to-anthropogenic cover transitions as conversion and fourteen socio-economic, climatic, topographic, proximity, and conservation predictors were evaluated. Because most predictors are contemporary, the model characterises spatial correlates of cumulative conversion rather than temporal drivers. On an independent, spatially blocked test set (n = 1,751), it achieved an accuracy of 0.77, AUC-ROC of 0.83, F1-score of 0.81, and Cohen’s Kappa of 0.53, remaining stable under ten-fold spatial cross-validation (accuracy = 0.84 ± 0.07). A higher false-positive rate (0.34) than false-negative rate (0.14) indicated a precautionary bias suited to screening. Percentage cultivated land was the strongest predictor (mean decrease in impurity = 33.6
Agroforestry is considered a sustainable land-use practice that contributes to soil improvement, climate resilience, and livelihood security; however, its effects on soil properties in Eastern Plains agro-climatic zone of Uttar Pradesh is not well quantified. This study was conducted during 2024–2026 to evaluate the influence of different tree-based agroforestry systems on soil physico-chemical properties across 11 blocks of Ayodhya district in Eastern Plains of Uttar Pradesh, India. Soil samples were collected from various agroforestry systems at three depths (0–20, 20–40, and 40–60 cm), and tree density was assessed usingquadrant method. The results revealed that soil organic carbon (SOC) and total nitrogen (TN) differed significantly among agroforestry practices and were higher under tree-based systems. SOC was highest in woodlots established in 2019 (1.37
The complex terrain and increasing forest fire incidents in recent years make high-mountain environments vulnerable to forest fires, posing a threat to ecological and economic well-being. However, research related to forest fires remains limited in the mountain region. The study aimed to assess the forest fire risk by integrating eleven predictive layers (biophysical, topographical, climatic, and anthropogenic factors) and a Random Forest (RF) model in Rasuwa, a mountainous district of Nepal. However, the precipitation factor was excluded due to multicollinearity concerns. The model was validated using Area Under the Curve (AUC) and True Skill Statistics (TSS). With an AUC of 0.9 and TSS of 0.67, the model shows strong predictive performance and high reliability of the generated fire risk map. The most significant factors in the model’s ability to predict the forest fire risk were elevation, temperature, Normalized Difference Vegetation Index (NDVI), and proximity to settlements. The study shows that 29.66
