Search Results (9)

Early sowing to avoid stress later in the season is limited by low early spring temperatures and unpredictable cold spells within recommended sowing dates. To achieve successful crop production, it is essential to understand plant stress responses, enabling breeders and producers to better address climate change challenges. Researching genetic variability for cold stress is key to developing cold-tolerant crops. In response, a study investigating the effects of low-temperature treatment and cold priming in the early vegetative development on soybean biomass, chlorophyll a fluorescence (ChlF) and pigment-related spectral reflectance indices (PR_SRIs) was conducted in a controlled environment with 12 soybean genotypes. Priming began 16 days after sowing (DAS), followed by a 48-h recovery and a subsequent 48-h low-temperature treatment. During priming and stress treatments, temperatures and relative air humidity were set to 10/5 °C and 70/90% (day/night), with a light intensity of 300 μmol/m²/s. The results showed that low temperatures negatively impacted biomass and physiological parameters, with priming having neutral or negative effects. The parameters ET₀/TR₀, RE₀/RC, TR₀/DI₀, F_m, F_v, ARI1, and ARI2 were identified as relatively appropriate non-destructive alternatives for biomass analysis, aiding in genotype screening and stress detection. Genotypic variation in response to cold stress suggests potential for selecting cold-tolerant varieties. Full article

Healthy eating behaviour of women is critical not only for their health but also for their children’s health and well-being. The present study examined the dietary pattern, nutrition-related knowledge, and attitudes of working women in Western Province, Sri Lanka. In addition, this study identified the factors associated with dietary diversity score (DDS). A cross-sectional study was conducted among 300 working women, aged 20–60 years, in Western Province, Sri Lanka. The data on socio-demography, dietary patterns, and nutrition-related knowledge and attitudes were collected. Overall, 38% of the women were overweight and 13% were obese. The median frequency of intake of chicken, fish, eggs, milk and milk products, green leafy vegetables, and fruits were 2, 5, 2, 9, 5, and 10 respectively, per week. A large majority of the women (70%) had tea/coffee with sugar and snacks (60%) at least four times a week. Only a third of the women met the minimum DDS, while more than half of the women had good nutrition-related knowledge and attitudes. Women with good nutrition-related knowledge were more frequent consumers of roots/tubers, shellfish, vegetables, fruit, fruit juice, nuts and oils, and fast food. Women with good nutrition-related attitudes had a significantly lower frequency of consumption of soya meat, while having a higher frequency of consumption of fast food. Multiple regression analysis showed that age and household income were significantly independently related to DDS, while attitudes were negatively associated. While there was a trend, the association of nutrition-related knowledge with DDS was not statistically significant (p = 0.057). The overall F ratio (8.46) was highly significant (p = 0.001) and the adjusted R² was 0.093. The results demonstrate that a significant proportion of working women have good basic nutrition-related knowledge and attitudes, while two-thirds of them do not meet the minimum DDS. Furthermore, age, family income, and knowledge were positively associated with DDS, while attitudes were negatively associated. Before designing any intervention, further research is needed using a qualitative approach to understand how nutrition knowledge and eating behaviour are related in this population group. Full article

This research aimed to apply the geospatial techniques and Analytical Hierarchy Process (AHP) approach to find vulnerable areas in terms of flooding in the Neluwa area, Sri Lanka. The study incorporated nine relevant criteria for the vulnerability classification under three sub-criteria; the built environment, physical environment, and socio-economic environment. Under the built environment, road networks and buildings were chosen as sub-criteria. The Normalized Difference Vegetation Index (NDVI), slope, elevation, water bodies, and stream density were taken as physical criteria. Land use and population density were considered as socio-economic criteria. All the criteria are set correctly in raster data, and their contents were well adduced. The study consisted of the use of different levels of criteria and combinations of different processes. The analytical results reveal that 14.24% and 30.24% of the total area are at a very-high risk and high risk for flooding, respectively. Only 5.17% of the land was classified as a risk-free area. Eastern, central, and western divisions of the study area are highly vulnerable to floods due to their low slopes. Based on the produced maps, the spatial extents and levels of risk were systematically identified. Data obtained through qualitative judgments related to the field were validated based on the approach used. The potential of this approach is effective in assessing the spatial vulnerability of these flood-affected areas. Using such criteria and a model-based approach will be constructive in identifying different flood scenarios and in providing a remunerative guideline for potential anticipatory measures and better land-based planning in the area. Full article

Understanding the changes in climate and land use/land cover (LULC) over time is important for developing policies for minimizing the socio-economic impacts of riverine floods. The present study evaluates the influence of hydro-climatic factors and anthropogenic practices related to LULC on floods in the Kelani River Basin (KRB) in Sri Lanka. The gauge-based daily precipitation, monthly mean temperature, daily discharges, and water levels at sub-basin/basin outlets, and both surveyed and remotely sensed inundation areas were used for this analysis. Flood characteristics in terms of mean, maximum, and number of peaks were estimated by applying the peak over threshold (POT) method. Nonparametric tests were also used to identify the climatic trends. In addition, LULC maps were generated over the years 1988–2017 using Landsat images. It is observed that the flood intensities and frequencies in the KRB have increased over the years. However, Deraniyagala and Norwood sub-basins have converted to dry due to the decrease in precipitation, whereas Kithulgala, Holombuwa, Glencourse, and Hanwella showed an increase in precipitation. A significant variation in atmospheric temperature was not observed. Furthermore, the LULC has mostly changed from vegetation/barren land to built-up in many parts of the basin. Simple correlation and partial correlation analysis showed that flood frequency and inundation areas have a significant correlation with LULC and hydro-climatic factors, especially precipitation over time. The results of this research will therefore be useful for policy makers and environmental specialists to understand the relationship of flood frequencies with the anthropogenic influences on LULC and climatic factors. Full article

An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs. Full article

Fragmented and degraded ecosystems should be restored to ensure the biological connectivity among fragmented forest landscapes. The tropical rainforests of Southwestern Sri Lanka are highly degraded and fragmented due to plantation agriculture and human settlements. However, the high spatial variation in environmental factors and ecosystem functions challenge the success rate of restoration interventions. The aim of this study was to assess the vegetation composition and stand structure in relation to the spatial variation in key soil physicochemical parameters in the Endane Biodiversity Corridor that links peripheral forest reserve to the Sinharaja Rainforest Complex (SRC). The site that extends over 24 ha was classified into five land-use categories (productive tea lands, marginal tea lands, scrub—abandoned three years ago, and two woodlands—abandoned 15 years ago) in which the vegetation composition, stand structure, and physicochemical parameters of soil were assessed and mapped. Results revealed that the Shannon diversity index in the scrub and the woodlands were higher than in the tea lands. The diversity among the secondary forest patches was similar. However, with a mean record of 14 species, the species richness was high in sites close to the SRC. In comparison to the SRC (358 Mg ha⁻¹), there was a substantial potential to sequester more carbon in the restoration sites (12–108 Mg ha⁻¹). While explaining 31% of abundance and species distribution, the ordination results revealed a close relationship of the soil parameters to vegetation composition and species abundance. The calculated coefficient variation values for soil parameters (TN, EC, Av.P, Ex.K, OC, and BD) were beyond 12%, indicating high or moderate soil spatial variability among the land use categories. Coefficient of variation for soil pH was estimated to be 9%, revealing low soil spatial variability among the land use categories. The maps of these soil parameters corresponded with the type of land use and fertilizer application to tea fields. The highest and the lowest total N contents were observed in the scrub and woodlands, respectively, which appears to be mediated by the relative composition of N-fixing trees between the two groups. Our results facilitate effective matching of sites to species for restoration of the Endane Biodiversity Corridor that may be replicated in similar restoration contexts in tropical Asia. Full article

Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R² = 1.0) and testing (R² = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions. Full article

The application of proximal hyperspectral sensing, using simple vegetation indices, offers an easy, fast, and non-destructive approach for assessing various plant variables related to salinity tolerance. Because most existing indices are site- and species-specific, published indices must be further validated when they are applied to other conditions and abiotic stress. This study compared the performance of various published and newly constructed indices, which differ in algorithm forms and wavelength combinations, for remotely assessing the shoot dry weight (SDW) as well as chlorophyll a (Chla), chlorophyll b (Chlb), and chlorophyll a+b (Chlt) content of two wheat genotypes exposed to three salinity levels. Stepwise multiple linear regression (SMLR) was used to extract the most influential indices within each spectral reflectance index (SRI) type. Linear regression based on influential indices was applied to predict plant variables in distinct conditions (genotypes, salinity levels, and seasons). The results show that salinity levels, genotypes, and their interaction had significant effects (p ≤ 0.05 and 0.01) on all plant variables and nearly all indices. Almost all indices within each SRI type performed favorably in estimating the plant variables under both salinity levels (6.0 and 12.0 dS m⁻¹) and for the salt-sensitive genotype Sakha 61. The most effective indices extracted from each SRI type by SMLR explained 60%–81% of the total variability in four plant variables. The various predictive models provided a more accurate estimation of Chla and Chlt content than of SDW and Chlb under both salinity levels. They also provided a more accurate estimation of SDW than of Chl content for salt-tolerant genotype Sakha 93, exhibited strong performance for predicting the four variables for Sakha 61, and failed to predict any variables under control and Chlb for Sakha 93. The overall results indicate that the simple form of indices can be used in practice to remotely assess the growth and chlorophyll content of distinct wheat genotypes under saline field conditions. Full article

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them. Full article