Critical assessment of furrow openers and operational parameters for optimum performance under conservation tillage.

Conservation Agriculture (CA) is an innovative approach that promotes sustainable farming while enhancing soil health. However, residue management challenges often hinder its adoption, causing farmers to burn crop leftovers in fields. This study aimed to evaluate the effectiveness of various furrow openers under simulated soil bin conditions. Three types of furrow openers were examined: single disk (SD), Inverted T-type furrow opener with a plain rolling coulter (ITRC), and double disc (DD) furrow opener. Tests were conducted at different forward speeds (1.5, 2, and 2.5 km h-1) and with three straw densities (1, 2, and 3 t ha-1) at a consistent working depth of 5 cm. Draft measurements were obtained using load cells connected to an Arduino-based data-logging system. Results indicated that draft requirements increased with forward speed and straw density, while straw-cutting efficiency decreased with these factors. Average draft values for SD, ITRC, and DD were 290.3 N, 420 N, and 368.5 N, respectively, and straw-cutting efficiencies were 53.62%, 59.47%, and 74.89%, respectively. The DD furrow opener showed the highest straw-cutting efficiency (81.36%) at a working speed of 1.5 km h-1 and a straw density of 1 t ha-1, demonstrating optimal performance compared to other furrow openers.

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. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day-1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R2 = 0.71, mean biased error = 0.04 mm day-1, root mean squared error = 0.62 mm day-1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model's capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates.

Long-term application of FYM and fertilizer N improve soil fertility and enzyme activity in 51st wheat cycle under pearl millet-wheat.

Our study from an ongoing research experiment initiated in Rabi 1967 at the Research Farm of CCS Haryana Agricultural University, Haryana, India, reports that during the 51st wheat cycle in pearl millet-wheat sequence, adding FYM in both seasons significantly impacted various soil parameters at different wheat growth stages compared to the rabi season. The application of 15 t of FYM ha-1 resulted in a considerable increase in dissolved organic carbon content (9.1-11.2%), available P (9.7-12.1%), and available S (12.6-17.1%), DHA levels by 7.3-22.0%, urease activity (10.1 and 17.0%), ß-Glucosidase activity (6.2-8.4%), and APA activity (5.2-10.6%), compared to 10 t FYM ha-1. Application of N120 exhibited a considerable improvement in DHA (11.0-23.2%), ß-Glucosidase (9.4-19.2%), urease (13.3-28.3%), and APA (3.3-6.2%) activity compared to control (N0). At stage 3, the box plot revealed that 50% of the available N, P, and S values varied from 223.1 to 287.9 kg ha-1, 53.0 to 98.2 kg ha-1, and 50.0 to 97.6 kg ha-1, respectively. Principal component analysis, with PC1 explaining 94.7% and PC2 explaining 3.15% of the overall variability, and SOC had a polynomial relationship with soil characteristics (R2 = 0.89 to 0.99). Applying FYM15 × N120 treatment during both seasons proved beneficial in sustaining the health of sandy loam soil in North-West India.

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., cropping systems, fruit cropfodder crops and livestock components, in different proportions, and compared with rice-groundnut system which is a major farming approach in Telangana using randomized block design. The seven models were as follows: M1: Rice-Groundnut; M2: Rice-Groundnut, Pigeonpea + Sweetcorn (1:3)-Bajra, Bt cotton + Greengram (1:2)-Maize; M3: Rice-Groundnut, Pigeonpea + Sweetcorn (1:3)-Bajra, Pigeonpea + Maize (1:3)-Sunhemp; Napier grass, Sheep (5 + 1); M4: Rice-Groundnut, Pigeonpea + Sweetcorn (1:3)-Bajra, Bt cotton + Greengram (1:2)-Maize, Pigeonpea + Maize (1:3)-Sunhemp, Poultry unit; M5: Guava, Hedge Lucerne, Napier grass, Bt cotton + Greengram (1:2)-Maize, Sheep (5 + 1); M6: Guava, Bt cotton + Greengram (1:2)-Maize, Rice-Groundnut, Poultry; M7: Rice-Groundnut, Pigeonpea + Sweetcorn (1:3)-Bajra, Pigeonpea + Maize (1:3)-Sunhemp; Napier grass, Hedge lucerne, Poultry (100), Sheep (5 + 1). Based on a 2-year average, the Model M7 system produced 9980 Rice Grain Equivalent Yield(RGEY)kg of output per acre, with gross and net returns of ₹210,439 and ₹124,953 respectively, and recovered a B:C ratio of 2.46. It has recorded highest sustainable yield index (SYI) of 0.673 and value index of 0.772 with the lowest water footprint of 259.0 L/kg. This study reveals that adopting an integrated farming system is the optimal approach for effectively combining productive, financially rewarding, and diversified enterprises within a single acre of land.d. This system should be recommended for maximum benefits to smallto small and marginal farmers in Telangana's southern hills and plateau.

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. Modified Mann-Kendal, Sens Slope and Mann-Kendal tests were performed to determine the trend's variation and slope. In addition, the spatial-temporal changes in the quality of groundwater are studied through Geographic Information System (GIS) mapping and Piper diagram was applied to identify the unique hydrochemical properties. This is the first study conducted on this area using various trends analysis and no in-depth study is available highlighting the trends analysis of groundwater quality on a larger magnitude. In contrast, the correlation matrix reveals a high association between Mg2+ and SO42-, Na+ and Cl- that affects salinity and overall hardness at the majority of sites. The Piper diagram also demonstrates that the groundwater in Madaripur Sadar has major salinity issues. The analysis reveals a distinctive dominance of bicarbonate (HCO3-) ions across all sampling stations, with (HCO3-) equivalent fractions consistently ranging from 0.70 to 0.99 which can cause a significant impact on groundwater uses. This extensive analysis of long-term groundwater quality trends in the Dhaka Division enables researchers to comprehend the overall transition of groundwater quality for hardness related complications in future. Moreover, it can be a baseline study considering the valuable implications and future steps for sustainable water resource management in this region.

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 P2O5 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. Gene sequencing was done against various sources of phosphorus to analyze genes responsible for phosphatase activity. Alkaline phosphatase (ALP) gene amplicon of 380bp from all tested strains was showed in 1% w/v gel. Release pattern of P was also improved with coated fertilizer. The results showed that coated phosphatic fertilizer enhanced shoot dry weight by 43 and 46% under bacterial and metabolites coating respectively. Shoot and root length up to 44 and 42% with metabolites coated DAP and 41% with bacterial coated DAP. Physiological attributes also showed significant improvement with coated DAP over conventional. The results supported the application of coated DAP as a useful medium to raise crop yield even at lower application rates i.e., 50 and 75% DAP than non-coated 100% DAP application which advocated this coating technique a promising approach for advancing circular economy and sustainable development in modern agriculture.

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%). It is also found that peach activated carbon has a higher iodine number than walnut activated carbon, and this amount can be increased using chemical methods; Therefore, the highest amount of Iodine Number is related to Peach activated carbon that is made by chemical method (1230 mg/g), and the lowest amount of iodine number is seen in walnut activated carbon that is made by physical method (1020 mg/g). moreover, the pore diameter of physical activated carbon is lower than chemical activated carbon in all cases. So that the pore diameter of chemical activated peach carbon active is equal to 22.08 µm and the measured pore diameter of physical activated peach carbon active is equal to 20.42 µm. These values for walnut are obtained as 22.74 µm and 21.86 µm, respectively. Furthermore, the temperature and pH effects on the adsorption of different synthesized oily wastewater was studied and it was found that a decrease in adsorption can be seen with an increase in temperature or decreasing the pH value, which can be referred to this fact that the process of adsorption is an exothermic process. Finally, to analyze the compatibility of adsorption isotherms with experimental data and to predict the adsorption process, three different isotherms named Langmuir, Temkin, and Freundlich isotherms were applied and their parameters were correlated. The correlation results show that the Langmuir isotherm had the best correlation in all cases compared to the Freundlich and Temkin isotherms, based on the correlation coefficient, and the calculated R2 values which was greater than 0.99 in all the studied cases.

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. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

PAU Smart Seeder: a novel way forward for rice residue management in North-west India.

In winter, the paddy residues become wet during morning and late evening due to dew, which restricts the operation of sowing machines (Happy Seeder and Super Seeder) into paddy residues, as wet residues do not slide on furrow openers/tines. A PAU Smart Seeder (PSS) was developed and evaluated for a four-wheel tractor that can sow wheat with optimum crop establishment in combined harvested rice fields. The PSS were evaluated for its performance under varying straw load, forward speed, and rotor speed in terms of fuel consumption, field capacity, seed emergence, and grain yield. The crop establishment and wheat yield of PSS was also compared with the existing straw management machines Happy Seeder (HS) and Super Seeder (SS) under heavy paddy residue conditions. The effect of the straw load was more pronounced on dependent variables than the effect of the speed index. PSS performance was best at a forward speed of 2.6 km h-1, rotor speed of 127.5 rpm, and a straw load of 6 t ha-1. Average fuel consumption using PSS was lower than SS but higher than HS. Wheat emergence was higher by 15.6 and 25.7% on the PSS plots compared to HS and SS, respectively. Average wheat grain yield in PSS plots was significantly higher by 12.7 and 18.9% than SS and HS, respectively in one experiment, while the grain yield was similar for both PSS and HS in other experiments. PSS has a novel mechanism to manage paddy straw and simultaneously sow wheat into a heavy straw load (> 8 t ha-1) mixture of anchored and loose straw. In conclusion, PSS showed promise for in-situ management of rice straw as it eliminates most of the operational problems encountered by the existing seeders (HS and SS).

Temporal and spatial distribution of polycyclic aromatic hydrocarbons (PAHs) in the Danube River in Hungary.

The Danube is a significant transboundary river on a global scale, with several tributaries. The effluents from industrial operations and wastewater treatment plants have an impact on the river's aquatic ecosystem. These discharges provide a significant threat to aquatic life by deteriorating the quality of water and sediment. Hence, a total of 16 Polycyclic Aromatic Hydrocarbons (PAHs) compounds were analyzed at six locations along the river, covering a period of 12 months. The objective was to explore the temporal and spatial fluctuations of these chemicals in both water and sediment. The study revealed a significant fluctuation in the concentration of PAHs in water throughout the year, with levels ranging from 224.8 ng/L during the summer to 365.8 ng/L during the winter. Similarly, the concentration of PAHs in sediment samples varied from 316.7 ng/g in dry weight during the summer to 422.9 ng/g in dry weight during the winter. According to the Europe Drinking Water Directive, the levels of PAHs exceeded the permitted limit of 100 ng/L, resulting in a 124.8% rise in summer and a 265.8% increase in winter. The results suggest that the potential human-caused sources of PAHs were mostly derived from pyrolytic and pyrogenic processes, with pyrogenic sources being more dominant. Assessment of sediment quality standards (SQGs) showed that the levels of PAHs in sediments were below the Effect Range Low (ERL), except for acenaphthylene (Acy) and fluorene (Fl) concentrations. This suggests that there could be occasional biological consequences. The cumulative Individual Lifetime Cancer Risk (ILCR) exceeds 1/104 for both adults and children in all sites.

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). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

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. Herein, a soil-biochar incubation experiment was carried out to investigate the transformations of N and P in two different textured soils, namely clay loam and loamy sand, on mixing with rice straw biochar (RSB) and acacia wood biochar (ACB) at each level (0, 0.5, and 1.0% w/w). Ammonium N (NH4-N) decreased continuously with the increasing incubation period. The ammonium N content disappeared rapidly in both the soils incubated with biochars compared to the unamended soil. RSB increased the nitrate N (NO3-N) content significantly compared to ACB for the entire study period in both texturally divergent soils. The nitrate N content increased with the enhanced biochar addition rate in clay loam soil until 15 days after incubation; however, it was reduced for the biochar addition rate of 1% compared to 0.5% at 30 and 60 days after incubation in loamy sand soil. With ACB, the net increase in nitrate N content with the biochar addition rate of 1% remained higher than the 0.5% rate for 60 days in clay loam and 30 days in loamy sand soil. The phosphorus content remained consistently higher in both the soils amended with two types of biochars till the completion of the experiment.

Comparison activity of pure and chromium-doped nickel oxide nanoparticles for the selective removal of dyes from water.

The current study involves a synthesis of a composite of nickel oxide nanoparticles (NiONPs) with a chromium dopant to yield (Cr/NiONPs). Synthesis of nickel oxide was performed by the co-precipitation method. The synthesis of the composite was conducted by the impregnation method. FTIR, EDX, SEM, and XRD were used to characterize the synthesized materials. The synthesised materials' point zero charges (PZC) were performed using the potentiometric titration method. The obtained results show that the PZC for neat nickel oxide was around 5, and it was around 8 for Cr/NiONPs. The adsorption action of the prepared materials was examined by applying them to remove Reactive Red 2 (RR2) and Crystal Violate (CV) dyes from solutions. The outcomes demonstrated that Cr/NiONPs were stronger in the removal of dyes than NiONPs. Cr/NiONPs achieved 99.9% removal of dyes after 1 h. Adsorption isotherms involving Freundlich and Langmuir adsorption isotherms were also conducted, and the outcomes indicated that the most accurate representation of the adsorption data was offered by Langmuir adsorption isotherms. Additionally, it was discovered that the adsorption characteristics of the NiONPs and Cr/NiONPs correspond well with the pseudo-second-order kinetic model. Each of the NiONPs and Cr/NiONPs was reused five times, and the results display that the effectiveness of the removal of RR2 dye slightly declined with the increase in reuse cycles; it lost only 5% of its original efficiency after the 5 cycles. Generally, Cr/NiONPs showed better reusability than NiONPs under the same conditions.

Charge transfer complex-based spectrophotometric analysis of famotidine in pure and pharmaceutical dosage forms.

A straightforward and efficient spectrum technique was created using Ortho-chloranil as the electron acceptor (-acceptor) in a charge transfer (CT) complex formation reaction to determine the concentration of famotidine (FMD) in solutions. Compared to the double-distilled blank solution, the reaction result detected a definite violet colour at a maximum absorption wavelength of 546 nm, For concentrations range 2-28 µg/ml, the technique demonstrated excellent compliance with Beer-Law and Lambert's, as evidenced by its molar absorptivity of 2159.648 L mol-1 cm-1. Lower detection limits of 0.3024 µg/ml and 1.471 µg/ml, respectively, were discovered. The complexes of famotidine and Ortho-chloranil were found to have a 2:1 stoichiometry. Additionally, the suggested approach effectively estimated famotidine concentrations in pharmaceutical formulations, particularly in tablet form.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash-Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit-cost ratio.

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. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950-2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.

Interactive effects of long-term management of crop residue and phosphorus fertilization on wheat productivity and soil health in the rice-wheat.

In the context of degradation of soil health, environmental pollution, and yield stagnation in the rice-wheat system in the Indo-Gangetic Plains of South Asia, an experiment was established in split plot design to assess the long-term effect of crop residue management on productivity and phosphorus requirement of wheat in rice-wheat system. The experiment comprised of six crop residue management practices as the main treatment factor with three levels (0, 30 and 60 kg P2O5 ha-1) of phosphorus fertilizer as sub-treatments. Significant improvement in soil aggregation, bulk density, and infiltration rate was observed under residue management (retention/incorporation) treatments compared to residue removal or residue burning. Soil organic carbon (SOC), available nutrient content (N, P, and K), microbial count, and enzyme activities were also significantly higher in conservation tillage and residue-treated plots than without residue/burning treatments. The residue derived from both crops when was either retained/incorporated improved the soil organic carbon (0.80%) and resulted in a significant increase in SOC (73.9%) in the topsoil layer as compared to the conventional practice. The mean effect studies revealed that crop residue management practices and phosphorus levels significantly influenced wheat yield attributes and productivity. The higher grain yield of wheat was recorded in two treatments, i.e. the basal application of 60 kg P2O5 ha-1 without residue incorporation and the other with half the P-fertilizer (30 kg P2O5 ha-1) with rice residue only. The grain yield of wheat where the rice and wheat residue were either retained/incorporated without phosphorus application was at par with 30 and 60 kg P2O5ha-1. Phosphorus levels also significantly affected wheat productivity and available P content in the soil. Therefore, results suggested that crop residue retention following the conservation tillage approach improved the yield of wheat cultivated in the rice-wheat cropping system.

Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice-wheat cropping system.

In the plains of western North India, traditional rice and wheat cropping systems (RWCS) consume a significant amount of energy and carbon. In order to assess the long-term energy budgets, ecological footprint, and greenhouse gas (GHG) pollutants from RWCS with residual management techniques, field research was conducted which consisted of fourteen treatments that combined various tillage techniques, fertilization methods, and whether or not straw return was present in randomized block design. By altering the formation of aggregates and the distribution of carbon within them, tillage techniques can affect the dynamics of organic carbon in soil and soil microbial activity. The stability of large macro-aggregates (> 2 mm), small macro-aggregates (2.0-2.25 mm), and micro-aggregates in the topsoil were improved by 35.18%, 33.52%, and 25.10%, respectively, over conventional tillage (0-20 cm) using tillage strategies for conservation methods (no-till in conjunction with straw return and organic fertilizers). The subsoil (20-40 cm) displayed the same pattern. In contrast to conventional tilling with no straw returns, macro-aggregates of all sizes and micro-aggregates increased by 24.52%, 28.48%, and 18.12%, respectively, when conservation tillage with organic and chemical fertilizers was used. The straw return (aggregate-associated C) also resulted in a significant increase in aggregate-associated carbon. When zero tillage was paired with straw return, chemical, and organic fertilizers, the topsoil's overall aggregate-associated C across all aggregate proportions increased. Conversely, conventional tillage, in contrast to conservation tillage, included straw return as well as chemical and organic fertilizers and had high aggregate-associated C in the subsurface. This study finds that tillage techniques could change the dynamics of microbial biomass in soils and organic soil carbon by altering the aggregate and distribution of C therein.