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Agroforestry systems are alternative solutions for production and management of agricultural systems which may improve soil quality. In this study, we evaluated the potential of coconut Cocos nucifera-based Gliricidia (Gliricidia sepium) systems to improve soil quality of coconut lands in Sri Lanka in dry and wet regions. A three-year field experiment was conducted in a randomized complete block design with three treatments T0, T5 and T20, being respectively the control, five and twenty years aged Gliricidia intercropped coconut-based mixed systems. Three replicates of soil samples were taken at 0–15, 15–30 and 30–45 cm and differences in soil physical and chemical properties were evaluated among treatments and sites. We found significant effects of mixed system treatments on the soil chemical properties. In particular, organic matter, soil exchangeable potassium, total nitrogen and available phosphorus contents showed higher values in most coconut-gliricidia mixed systems’ soils, with highest values obtained for T20. Cumulatively for all soil depths, organic matter content (22%) and available phosphorus content (20%) were higher on the wet site, and total exchangeable potassium content (69%) higher on the dry site for T20. The pH, bulk density, microbial respiration and electric conductivity did not vary among treatments, but were influenced by the site characteristics, with the dry site showing higher values for pH and the wet site showing higher values for bulk density (5%), microbial respiration (33%) and electric conductivity (2%) in T20 treatment. The study demonstrates that the systems with Gliricidia differed in their soil chemical attributes and had higher levels of soil nutrients when compared to coconut monocrop even at early ages, underlying the potential of Gliricidia for the rehabilitation of coconut growing soils.

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

In many areas of South Asia and Southeast Asia, macaques inhabiting agricultural landscapes are considered serious crop pests by local farmers. In Nepal, for example, the expansion of monocultures, increased forest fragmentation, the degradation of natural habitats, and changing agricultural practices have led to a significant increase in the frequency of human-macaque conflict. In order to more fully understand the set of factors that contribute to macaque crop raiding, and the set of preventive measures that can be put in place to avoid human-macaque conflict, we examined patterns of crop raiding by a group of 52 rhesus macaques (Macaca mulatta) in the Kavrepalanchok district, Nepal. We present data on macaque inflicted crop damage in 172 agricultural plots (each plot measuring 380 m2) from August to October 2019. Our results indicate that farmland invasions by macaques were principally affected by crop type (maize was preferred over rice), nearness of farmland to both the forest edge and the major travel route used by the macaques, and the mitigation efforts applied by farmers to discourage crop raiding. We found that as the proportion of maize farmland in the most direct path from the macaque's main travel route to nearby crop raiding sites increased, the amount of maize damage decreased. This is likely explained by the fact that macaques traveling across several adjacent maize fields encounter multiple farmers protecting their crops. We estimated that the financial cost to individual farmer households of macaque maize and rice raiding was on average US$ 14.9 or 4.2% of their annual income from cultivating those two crops. As human-macaque conflict is one of the most critical challenges faced by wildlife managers in South Asia and Southeast Asia, studies of macaque crop raiding behavior provide an important starting point for developing effective strategies to manage human-macaque conflict while promoting both primate conservation and the economic well-being of the local community.

There is a growing concern over surface water dynamics due to an increased understanding of water availability and management with current climate trends. Remote sensing has now become an effective means of water extraction due to the availability of an enormous amount of data with diverse spatial, spectral, and temporal resolutions. However, water extraction from optical remote sensing data is associated with several major difficulties, such as the applicability of the extraction method over large areas and complex environments; shadow contamination from clouds, buildings, and mountains; and disclosure of shadowed water and exclusion of floating and submerged plants. To address these difficulties, a learning vector quantization (LVQ) neural network-based method was proposed and implemented to extract water using Landsat 8 imageries. This method is capable of separating water from clouds, build-up areas, shadows, and shadowed water by the ideal input of bands 1 to 7 and normalized difference vegetation index. This model learns water across Sri Lanka. Eight OLI scenes were tested, and the performance was compared with five widely used machine learning algorithms: support vector machine, K-nearest neighbor, discriminant analysis, combination of modified normalized difference water index and modified fuzzy clustering method, and K-means clustering methods. This method performed the best, achieving overall accuracies and the kappa coefficients between 97.8% and 99.7% and between 0.96 and 0.99, respectively. Results have demonstrated robustness, consistency, and preciseness in various dark surfaces, noisiest water environments, and highly water scarce scenes. LVQ revealed a good generalizing ability to detect all types of water with less amount of training samples. This method can be easily adaptable for other sensors and global water to support water resource studies.