Application of microscopic traffic simulation beyond the corridor level analysis is not widely seen in literature. This is partly because of the fact that a simulation model cannot capture behavior responses such as peak spreading. This study develops a framework that integrates agent-based travel behavior models with large-scale traffic simulation to capture the regional impacts of new development. The proposed model is then applied to the I-270/I-495/I-95 corridor in the north Washington, DC metropolitan area in a case study. Findings from this study reveal the potential of the proposed model to capture network dynamics and behavioral reactions. This framework also provides a valuable tool for the evaluation of new transportation infrastructure, such as the intercounty connector (ICC) corridor currently under construction, and its operation strategies.

Rice-based cropping systems are the predominant sources of livelihood for farmers in South Asia, where soil quality decline is a major concern. An experiment was conducted at Bhubaneswar, Odisha, India, in 2018–2019 and 2019–2020. It comprised two rice cultivars, ‘Manaswini’ (M) and ‘Hasanta’ (H), and three nitrogen management practices—100% soil test-based nitrogen (STN) (N₁₀₀), 75% STN + in situ green manuring (Sesbania) (N₇₅+GM), and 50% STN + green manuring (N₅₀+GM)—in the main plot. Meanwhile, sub-plots consisted of three tillage methods for succeeding toria–sweet corn, namely zero tillage (ZT), conventional tillage (CT), and a furrow-irrigated raised bed (FIRB). The experiment was laid out in a split plot design with four replications to assess system nutrient uptake, productivity, and soil quality. Both rice cultivars were equal in terms of system nutrient uptake. The N₇₅+GM practice recorded the highest system NPK uptake (304.1, 70.34, and 343.5 kg ha⁻¹, respectively), enhancing the system N uptake by 10.7 and 7.4%, P uptake by 18.8 and 12.2%, and K uptake by 9.8 and 9.6% over N₁₀₀ and N₅₀+GM, respectively. ZT recorded the highest system NPK uptake (299.9, 70.6, and 339.7 kg ha⁻¹, respectively). The most promising treatments, M+(N₇₅+GM)-ZT and H+(N₇₅+GM)-ZT, recorded the maximum system economic yield (10.72 and 10.64 t ha⁻¹), residue yield (22.36 and 21.98 t ha⁻¹), biological yield (33.07 and 32.63 t ha⁻¹), and relative soil quality index (0.84 and 0.91). Correlation and principal component analyses exhibited positive and close relationships between all biological yield and soil quality parameters, except for micro water-stable aggregates, the fungal population, and the bulk density. The application of N₇₅+GM in M or H rice and ZT in succeeding toria–sweet corn can sustain productivity and soil quality in Eastern India.

An experiment was conducted to assess the existing variability in brinjal germplasm based on Mahalanobis’s D2 statistic based on nine quantitative traits. The germplasm accessions were classified into six clusters, in which the highest inter-cluster distance was recorded between clusters III and IV. Cluster I exhibited a low mean estimate for days to 50% flowering; cluster VI showed high estimates of the mean for plant height, the number of primary branches per plant, average fruit weight, and 1000 seed weight. Cluster V recorded the highest number of fruits and fruit yields per plant. Principal component analysis revealed total variability of 53.09 per cent, contributed by PC 1 (30.83 %) and PC 2 (23.07 %). Traits viz., days to 50% flowering, plant height, an average number of branches per plant, fruit length, and 1000 seed weight contributed positive significant component loadings for the first two PCs. High heritability and high GAM were also recorded for a number of fruits per plant, fruit yield per plant, fruit length, fruit girth, fruit weight, and 1000 seed weight. Fruit yield and the number of fruits per plant were positively and significantly associated with each other. Hence, simultaneous selection of these traits is advised for the genetic improvement in the brinjal breeding program.

Ragi is an important cereal crop in Koraput district of Odisha it is the richest source of Calcium, iron, and protein which makes it more important for health. This study aims the forecasting of Ragi production in Koraput district. Statistic data from 1985-86 to 2017-18 is used for forecasting purposes. For trend estimation, Auto-Regressive Integrated Moving Average (ARIMA) model was used. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were calculated for the knowledge. Appropriate Box-Jenkins ARIMA model was fitted. Validation of the model was tested by using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE). ARIMA (0,1,1) model was used for forecasting of the subsequent 5 years' production. The result shows 52.75 tonnes of Ragi production in 2022.

The environmental issue of biodiversity loss due to deforestation remains a significant concern, particularly in tropical and subtropical regions. This research employs the Species-Area Relationship (SAR) model to predict biodiversity loss in deforested areas by examining the relationship between habitat area and the number of species. To estimate the number of potential species extinctions from various degrees of forest loss, we utilize high-resolution land cover data and species inventories from selected priority biodiversity hotspots. The SAR model S = cA^z, where S is species richness, A is area, and c and z are constant values, helps predict the change in biodiversity loss as natural habitats are fragmented or lost. The results show similar patterns of species richness decline as the area of forest is reduced, with the impact being greater in areas of higher endemism. The predictions of the SAR model align with existing literature and patterns regarding species decline, suggesting that the SAR model is a valuable tool for conservation planning. This research paper highlights the importance of protecting remaining forests and informs policy discussions on biodiversity loss. Additionally, it highlights key takeaways regarding the use of spatial ecology models with land-use policy to achieve sustainable development and conserve ecosystems. Further research has been identified - we will revise model parameters using species-specific data to improve predictions.