Long-term conservation agriculture to enhance soil properties and quality in rice-wheat cropping system.

Conservation agriculture (CA) presents a promising substitute to the tillage-intensive rice-wheat cropping system (RWS) prevalent in the Indo-Gangetic plains (IGPs). In the northwestern IGPs, on-farm studies examining the impact of CA durations on soil properties and quality are limited. This study assessed the effects of CA practised for 2 (CA2), 4 (CA4), 8 (CA8), and 12 (CA12) years and conventional tillage (CT) on soil quality in the Nilokheri block of Haryana, India.

SPAD dynamics in maize crop with precision nitrogen management under rain-fed and irrigated conditions.

Real-time monitoring of canopy chlorophyll content is crucial for understanding crop growth and guiding precision agricultural management. The SPAD chlorophyll meter is a valuable tool for assessing nitrogen status in maize (Zea mays L.), a key cereal crop used for food, feed, and biofuels.

Optimizing cultivation practices to enhance growth and yield of Indian mustard.

Since mustard is a significant oilseed crop in India, improving cultivation practises is essential for enhancing the productivity. The experiment was laid out in randomized block design with three replications at the farm of Department of Agronomy, Lovely Professional University, Phagwara during the rabi season. The work was carried out for two years to analyse the pooled data (2022-2023; 2023-2024) of Indian mustard.

Optimizing irrigation and nitrogen levels to achieve sustainable rice productivity and profitability.

The global scarcity of irrigation water poses a significant challenge to the sustainable production of rice and its availability worldwide. With a growing population driving increased demand for rice, it is crucial to enhance rice production while minimizing water usage. Achieving this requires a comprehensive understanding of the complex interactions between water and nitrogen dynamics and the formulation of strategies to optimize the application of irrigation water and nitrogen fertilizers.

Development of a sustainable portable Archimedes screw turbine for hydropower generation.

Portable hydropower turbines are turbines with a scale below 5 kW and which can be carried from one place to another easily by hand due to their light weight. This study was carried out to evaluate the potential of Archimedes Screw Turbine (AST) as an improved portable hydro-power turbine (PHPT) to address shortcomings in available portable turbines. The design of Archimedes screw hydro-power turbine is mainly concerned with screw geometry, which is determined by a variety of internal and external characteristics, including its length, external and internal diameter, Pitch of blades, and Number of the blades, which were 80 cm, 18 cm, 9.

Estimation of yield and nitrogen use efficiencies in hybrid maize varieties through site specific nitrogen management based on leaf color chart (LCC).

Exorbitant praxis of nitrogen pioneered and opened up the usage of time-specific and need-based nitrogen management. The leaf color chart (LCC), being one of the handy tool, is put to use for the estimation of the indirect leaf N, and also heightens the competence of crop N administration. Integrating leaf color chart assessments into maize cultivation practices can provide a practical and cost-effective approach for tailoring nitrogen applications, leading to improved resource-use efficiency and sustainable maize production.

Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates.

Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco.

Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas.

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction.

Forecasting urban water demand using different hybrid-based metaheuristic algorithms' inspire for extracting artificial neural network hyperparameters.

This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid-based metaheuristic algorithms' behaviour, modified PSO, and PSO as benchmarking techniques. Based on the findings, it is possible to enhance the standard of initial data and select optimal predictions that drive urban water demand through effective data processing.

Water volumes and mulches affect plant growth, leaf nutrient status and orchard soil mineral content of sweet orange cv. Mosambi.

Day-by-day increasing irrigation water scarcity requires the application of water-saving irrigation techniques to sustain agriculture production. A two-year field investigation was conducted during 2018 to 2020 to determine the effects of various mulches and irrigation volumes on the growth, leaf chemicals and soil properties of one-year-old sweet oranges (Citrus sinensis) cv. Mosambi.

Estimation of the crop evapotranspiration for Udham Singh Nagar district using modified Priestley-Taylor model and Landsat imagery.

The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022-23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET.

GIS based geostatistical modelling and trends analysis of groundwater quality for suitable uses in Dhaka division.

Preserving the quality of groundwater has become Bangladesh's primary challenge in recent years. This study explores temporal trend variations in groundwater quality on a broader scale across 18 stations within the Dhaka division over 35 years. The data set encompasses an analysis of 15 distinct water quality parameters.

Designing a productive, profitable integrated farming system model with low water footprints for small and marginal farmers of Telangana.

In the years 2021-2022 and 2022-2023, an experiment was carried out at the IFS Unit, College of Agriculture, PJTSAU, Rajendranagar in order to determine the best one-acre integrated farming system model for Telangana's small and marginal farmers. Seven farm models among which six models were developed by combining the various components i.e.

Efficacy of DAP coated with bacterial strains and their metabolites for soil phosphorus availability and maize growth.

Phosphorus (P) use efficiency in alkaline/calcareous soils is only 20% due to precipitation of PO with calcium and magnesium. However, coating Diammonium Phosphate (DAP) with phosphorus solubilizing bacteria (PSB) is more appropriate to increase fertilizer use efficiency. Therefore, with the aim to use inorganic fertilizers more effectively present study was conducted to investigate comparative effect of coated DAP with PSB strains Bacillus subtilis ZE15 (MN003400), Bacillus subtilis ZR3 (MN007185), Bacillus megaterium ZE32 (MN003401) and Bacillus megaterium ZR19 (MN007186) and their extracted metabolites with uncoated DAP under axenic conditions.

Thermodynamic and structural investigation of oily wastewater treatment using peach kernel and walnut shell based activated carbon.

Despite the many articles about activated carbon with different precursors in adsorption process, no in-depth research has been carried out to understand the causes of the difference in surface adsorption characteristics of activated carbon with different precursors and different activation processes. In this work, the ability of two active carbon adsorbents made of walnut shell and peach kernel by two chemical and physical methods (totally 4 different types of activated carbon) in treatment of oily wastewater including diesel, gasoline, used oil or engine lubricant has been compared. The results show that the chemical activated peach carbon active with 97% hardness has provided the highest hardness and physical activated walnut carbon active has obtained the lowest hardness value (87%).

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs).

Biochar influences nitrogen and phosphorus dynamics in two texturally different soils.

Nitrogen (N) and phosphorus (P) are vital for crop growth. However, most agricultural systems have limited inherent ability to supply N and P to crops. Biochars (BCs) are strongly advocated in agrosystems and are known to improve the availability of N and P in crops through different chemical transformations.

Heatwaves in Peninsular Malaysia: a spatiotemporal analysis.

One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future.