The changed C availability as influenced by tillage and residue management with fertilizer-N management, significantly impacts soil properties and eventually contributes towards crop productivity. We, therefore, investigated the impact of deep tillage DT (45 cm) vs. shallow tillage ST (15m) (in main plots), residue management (viz., Conventional tillage with residue (CT+R), Conventional tillage without residue (CT-R), Minimum tillage with residue (MT+R) and Minimum tillage without residue (MT-R) ) (in sub-plots) and fertilizer-N application (viz., 75%, 100% and 125% recommended fertilizer-N; N75- N125) (in sub-subplots) on soil physical, chemical and biological properties and crop yield of direct seeded rice and wheat. The results revealed that tillage depth had non-significant (p < 0.05) change in growth attributes and productivity of rice and wheat. Conventional tillage (CT) with or without residue retention (CT+R or CT-R) significantly (p < 0.05) increased the rice grain yield, compared with minimum tillage (MT). Unlike rice, wheat productivity was not significantly influenced by tillage - residue management treatments. Fertilizer-N application at N125 resulted in significantly higher crop (rice and wheat) yields, compared with N75, but was statistically at par with N100. DT significantly lowered the soil penetration resistance as compared to ST. The CT+R resulted in lowest penetration resistance among other tillage-residue management treatments; however, soil organic C (SOC), available-N, -P, -K, soil aggregation, and infiltration rate were not significantly influenced. Crop residue retention under MT and CT significantly increased the soil micro-organism count (by 5.5 to 8.4 %) and dehydrogenase enzyme activity (by 8.6 to 12.5%), compared with CT without residue. Therefore, it can be concluded that fertilizer-N management along with residue retention under variable tillage intensity has significant impact on crop productivity and physical and enzymatic properties of soils.. KEYWORDS :Tillage intensity, Crop residue management, Fertilizer-N application, Rice-wheat cropping system, Soil enzymatic properties.
In order to end hunger, achieve food security, improve nutrition and promote sustainable agriculture, forecasting of the production of rice plays an important role in state like Assam, as rice is the staple food and production of rice shares 96% of the total food grain production of the state. This study aims to forecast the total yearly production of rice in Assam. Autoregressive Integrated Moving Average Model (ARIMA) is applied for forecasting the rice production, by considering the time period 1951-2022. For construction of ARIMA model, at first stationarity of data series is tested with Augmented Dickey Fuller (ADF) test, which reveals that our considering series of yearly rice production in Assam becomes stationary at first order difference. ARIMA (4, 1, 3) model has been selected for forecasting by following the results of model diagnostic criterion-Akaike Information Criterion (AIC). Although, the production of rice in Assam shows an overall increasing trend but this could not keep pace with the population growth which results in an overall decline in per capita availability. Therefore, proper policies should be taken by both the central and state Governments along with various nodal agencies to increase the rice productivity in Assam to become a self-sufficient state.
This study contributes to agricultural forecasting by applying and comparing time-series models specific to paddy production in Tamil Nadu. This study's secondary data came from reliable government sources, including the Department of Economics and Statistics. ARIMA and Linear Holt's Exponential Smoothing are two widely used time-series forecasting models. Holt's approach uses weighted averages to identify trends, while ARIMA concentrates on autocorrelation structures and differencing. This study applies these methods to forecast the production of paddy in Tamil Nadu by using the timeseries data of 2000-2024. A comparative analysis of these two models was also conducted. Standard accuracy metrics, which include RMSE, MAE, MPE, MAPE and MASE, were used to evaluate the model's performance. The more appropriate model for predicting future paddy production was found with the aid of the comparative method. The results revealed that ARIMA (2,0,2) model consistently outperformed Holt's model across most evaluation metrices. The ARIMA demonstrated lower error rates, indicating greater forecasting accuracy, bringing out the significance of choosing appropriate models to ensure accurate predictions, thereby supporting effective agricultural decision making and resource allocation.
The changed C availability as influenced by tillage and residue management with fertilizer-N management, significantly impacts soil properties and eventually contributes towards crop productivity. We, therefore, investigated the impact of deep tillage DT (45 cm) vs. shallow tillage ST (15m) (in main plots), residue management (viz., Conventional tillage with residue (CT+R), Conventional tillage without residue (CT-R), Minimum tillage with residue (MT+R) and Minimum tillage without residue (MT-R) ) (in sub-plots) and fertilizer-N application (viz., 75%, 100% and 125% recommended fertilizer-N; N-75-N-125) (in sub-subplots) on soil physical, chemical and biological properties and crop yield of direct seeded rice and wheat. The results revealed that tillage depth had non-significant (p < 0.05) change in growth attributes and productivity of rice and wheat. Conventional tillage (CT) with or without residue retention (CT+R or CT-R) significantly (p < 0.05) increased the rice grain yield, compared with minimum tillage (MT). Unlike rice, wheat productivity was not significantly influenced by tillage & times; residue management treatments. Fertilizer-N application at N-125 resulted in significantly higher crop (rice and wheat) yields, compared with N-75, but was statistically at par with N-100. DT significantly lowered the soil penetration resistance as compared to ST. The CT+R resulted in lowest penetration resistance among other tillage & times;residue management treatments; however, soil organic C (SOC), available-N,-P,-K, soil aggregation, and infiltration rate were not significantly influenced. Crop residue retention under MT and CT significantly increased the soil micro-organism count (by 5.5 to 8.4 %) and dehydrogenase enzyme activity (by 8.6 to 12.5%), compared with CT without residue. Therefore, it can be concluded that fertilizer-N management along with residue retention under variable tillage intensity has significant impact on crop productivity and physical and enzymatic properties of soils.
This study conducts a fuzzy reliability analysis of a complex system called a non-series-parallel (NSP) system, accounting for time-varying failure rates. The system comprises seven distinct components organized into three subsystems. Two parallel subsystems: each consists of three components configured in series, while the third subsystem involves a single component linked with the extreme components of the parallel subsystems. To determine the system's reliability, the study employs the path tracing method, deriving an expression for the overall system reliability. It assumes the failure rates of components as time-varying triangular fuzzy numbers, employing the alpha-cut approach to de-fuzzify these numbers. The study computes intervals for fuzzy reliability and mean time to system failure (MTSF), while considering both identical and nonidentical components. To showcase the practical implications, the research applies these findings to an RLC system, shedding light on assessing complex systems' reliability despite uncertainties in component failure rates.
Rapid multiplication of elite sugarcane varieties is essential for meeting industrial demands. This study establishes a robust in vitro regeneration protocol for sugarcane var. CoLk 11206. Utilizing shoot tip explants, the research evaluated different concentrations of BAP, Kinetin, NAA, IBA and IAA. The highest shoot multiplication (6.60 +/- 0.80 shoots/explant) was achieved on MS medium supplemented with 0.5 mg/l BAP and 0.5 mg/l Kinetin. However, with BAP (0.5mg/l) and NAA (0.25mg/l), the number of shoots per culture (5.93) and shoot length (2.42 cm) was also found better. For rhizogenesis, a synergistic combination of 0.5 mg/l NAA and 2.5 mg/l IBA produced the highest root number (23.80 +/- 0.70), while 2.5 mg/l IAA + 0.5 mg/l IBA maximized root length (2.70 +/- 0.07 cm). Genetic stability of the micropropagated plantlets was rigorously validated using ISSR and SSR markers. All 209 amplified bands across 98 samples were monomorphic, confirming 100% genetic fidelity to the mother plant. This optimized micropropagation protocol ensures the rapid production of high quality, genetically uniform plantlets, facilitating the accelerated dissemination of elite sugarcane varieties.
The present investigation has been carried out for the yield forecasting of finger millet by using the Neural Network based Hybrid time series forecasting model. In this study, different statistical and machine learning models like Autoregressive Integrated Moving Average (ARIMA), Neural Network Autoregression (NNAR), Support Vector Machines (SVM) and Hybrid Models are used for crop yield predictions. Various models have been identified based on the different parameters such as the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study reveals that the Hybrid ARIMA (1,1,1) + NNAR (2,2,) is found to be the best fitted model due to the lowest value of RMSE and MAPE for the training data and also for the testing data, among all the comparing model. The best fitted Hybrid model is used to forecast the yield of millet for next 8 years in Odisha. From this study, it has been found that the yield of millet is expected to follow a cyclic trend in coming years, in few years it is expected to increase, and in few years, it is expected to decrease. This study will help the policy makers, government in decision making and also plays an important role in determining the yield gap, production and demand gap and differences for different millet in Odisha in the coming years.
Large-scale socio-economic surveys include independent central and state samples with identical designs and sizes. However, only the central sample is used for official estimates, leaving the state sample underutilized despite its value.This study examines methods to jointly use both samples for more efficient and reliable inference. National surveys aimed at state and national statistics often struggle to provide precise district-level estimates due to small sample sizes. To address this, small area estimation techniques are applied to combine information from both samples. These methods improve the precision of district-level estimates by reducing sampling variability. Their performance is evaluated through simulation experiments. Results show substantial gains in efficiency, especially in relative bias and relative RMSE.
In a consecutive cropping sequence experiment, multiple treatments have been administered over two or more successive seasons. In these instances, it is essential to evaluate the main effects for each season, along with the residual effects and their interaction with the treatment of the subsequent season. Experimenters do not always choose to estimate all two-factor interactions; they may instead focus on estimating all main effects and subsequent two-factor interactions, as is often necessary in cropping sequence experiments. In factorial experiments, it is often preferable to estimate only the twofactor interaction between successive factors while accounting for all main effects, particularly in cropping sequence experiments. The current study established general construction methods for two specific scenarios: (i) consecutive two-factor interactions assessed with full efficiency, and (ii) all main effects and two-factor interactions exhibiting equal efficiency.
This study introduces a random walk based greedy algorithm with stochastic updates for interpreting black-box machine learning model. A Bayesian hierarchical probabilistic surrogate model was formulated to quantify the uncertainty of the latent parameters of the machine learning model using a hyper-prior. Bayesian credible intervals were obtained to determine the significance of each parameter. Furthermore, the proposed algorithm is compared with the standard logistic regression surrogate model. The algorithm simulation performance was tested using Crop Recommendation based on Soil Properties and Weather Prediction Dataset.
The importance of calibration approach has been increasing prominently in recent years. Calibration estimation plays an important role in enhancing the accuracy of estimators of population parameters by incorporating auxiliary information. In this study, we aim to propose a calibration estimator specifically for population mean estimation under stratified random sampling. In our approach, we introduced calibration constraints based on the known coefficient of variation (CV) of the multi-auxiliary variable. Through a simulation study using real dataset, the performance of the recommended estimator has been compared with estimators by Tracy et al. (2003), Rao et al. (2012), and Garg and Pachori (2020). The results established that the developed calibration estimator performed more effectively compared to the existing ones.
The goal of this paper is to develop some new set of estimators for estimating population coefficient of variation (CV) Cy of the study variable y using information on known population mean X and population variance Sx2 of the auxiliary variable x. We have obtained the expressions of bias and mean squared error (MSE) of the proposed classes of estimators up to first order of approximation under simple random sampling without replacement (SRSWOR) scheme. Optimum conditions are obtained at which the suggested classes of estimators attained the minimum mean squared error. Conditions are obtained under which proposed classes of estimators are more efficient than some existing estimators. An empirical study is carried out to show the performance of the suggested classes of estimators over some existing estimators.
A two-year field experiment was conducted during the Kharif seasons of 2023 and 2024 at the Crop Research Centre, Sardar Vallabhbhai Patel University of Agriculture & Technology, Meerut, Uttar Pradesh, to evaluate the interactive effects of tillage-cum-crop establishment methods and nutrient management strategies on the productivity and growth performance of basmati rice (Oryza sativa L.) grown on Typic Ustochrept soils. The experiment employed a split-plot design with the aromatic rice cultivar Pusa Basmati-1692, comparing three crop establishment methods: Conventional puddle transplanted rice (CT-TPR), Wide raised beds transplanted rice (WB-TPR) and Unpuddled transplanted rice (UN-TPR). Subplot treatments consisted of four nutrient management regimes: control (N1), recommended dose of fertilizers (150:60:40 kg NPK ha1) + Zn (N2), 75% RDF + FYM 5 t ha1 (N3) and 75% RDF + FYM 5 t ha1 + bio-fertilizer (N4). The results demonstrated that CT-TPR significantly increased key yield attributes effective tillers, panicle length, number of panicles per unit area, grains per panicle, and 1000-grain weight resulting in superior grain and straw yields compared to alternative establishment methods. Among nutrient management strategies, application of the recommended dose of NPK plus zinc (N2) consistently produced the highest yields across both seasons. In contrast, control plots exhibited the lowest growth and yield performance. The findings underscore the critical role of conventional puddle transplanting in combination with scientifically balanced nutrient regimes for optimizing basmati rice productivity in Indo-Gangetic plains, with direct implications for resourceefficient and sustainable rice cultivation.
Eco-friendly substitutes for inorganic priming materials are necessary for the sustainable production of quality cut flowers. This study examined the effect of organic bulb priming materials; moringa leaf extract (MLE), coconut water (CW), and Beejamrutha on the flowering traits of Asiatic lily cv. Indian Summerset in the two growing seasons. Bulbs primed with 5% MLE produced the maximum number of floral buds (3.53 +/- 0.06 and 3.56 +/- 0.00), longest floral bud length (11.39 +/- 0.34 cm and 11.31 +/- 0.01 cm), and largest flower diameter (20.15 +/- 0.23 cm and 20.54 +/- 0.14 cm), while 75% CW accelerated floral initiation, recorded the shortest days to first flower opening (97.33 +/- 0.57 and 97.06 +/- 0.13 days). Moreover, in control conditions, there is a decrease number of buds per plant, smaller flower size and delayed flowering. By using of these organic formulations has the potential to improve productivity, quality, and market value of lily cut flowers with maintaining environmental balance. Principal component analysis (PCA) confirmed strong positive correlations between floral traits with PC1 explaining over 97% of the total variance. The findings demonstrate that natural bio-stimulants such as MLE and CW can effectively enhance floral growth, improve bud initiation, and accelerate flowering, offering sustainable and ecologically safe substitutes to chemical priming agents. PCA also helped in visualizing trait divergence and identifying ideotypes for future hybridization.
Accurate prediction of rice yield is crucial for strengthening food security and improving agricultural decisionmaking in North-East Nigeria, where production systems are constrained by fluctuating input use and environmental variability. Regression (SVR), and K-Nearest Neighbours (KNN)to model rice yield using five primary farm inputs: Labour (B), Fertilizer (F), Herbicides (H), Seeds (S), and land (L)area. All models were optimized through hyper-parameter tuning to ensure reliable performance. The results show that SVM and XGB produced the strongest predictive accuracy, with RF achieving an RMSE of 14.6451 and MAD of 12.5955, while XGB achieved RMSE of 14.7739 and MAE of 12.5341. In contrast, RF and KNN recorded higher error values, indicating weaker predictive capability. To determine whether SVM and XGB differ statistically, a Wilcoxon signed-rank test was performed, yielding a non-significant p-value of 0.6756. This confirms that both ensemble model and SVM perform equivalently despite slight numerical differences. Overall, the findings demonstrate the robustness of ensemble learning techniques for rice yield prediction and provide a methodological foundation for developing datadriven agricultural decision support tools in resource-constrained environments..
Women entrepreneurship has emerged as a significant driver of socio-economic development in India, contributing to employment generation, innovation, and inclusive growth. In the agricultural sector, women play a vital role in farming, livestock management, food processing, and allied enterprises, yet their recognition and participation as entrepreneurs remain comparatively low. Despite their substantial contribution to agriculture, many women continue to face structural, financial, technological and social barriers that limit their entrepreneurial potential. Therefore, understanding the challenges and opportunities associated with women-led agribusinesses is essential for sustainable rural development. Women entrepreneurs in India's agriculture sector encounter multiple constraints, including limited access to land ownership, credit facilities, modern technologies, market linkages, training programs, and decision-making platforms. Social norms, gender discrimination, inadequate mobility, low digital literacy, and limited institutional support further restrict business growth. However, increasing policy support, self-help groups, cooperatives, digital agriculture platforms, e-commerce, microfinance institutions, startup ecosystems, and skill development initiatives have created new avenues for women to establish and expand agricultural enterprises. Opportunities are particularly strong in organic farming, value-added food products, dairy, poultry, floriculture, beekeeping, agri-tourism, and sustainable agribusiness models. In conclusion, women entrepreneurs possess immense potential to transform India's agricultural economy by enhancing productivity, rural livelihoods, and food security. Addressing institutional and socio-economic barriers through targeted policies, financial inclusion, capacity building, technological access, and market integration is crucial for empowering women in agriculture. Strengthening women-led entrepreneurship can significantly contribute to gender equality, rural prosperity, and sustainable agricultural development in India.
In epidemiology, we study the association of disease with exposure and various risk factors that cause the occurrence of disease. Hypertension is one of the most common disorders in India and globally. The present study is conducted to estimate the risk related to hypertension among women under the reproductive age group in the Bayesian and classical approaches, and a comparison is made. The comparative study is done to evaluate the performance of risk for hypertension using various risk factors like smoking, alcohol consumption, diabetes, different categories of wealth index, different categories of BMI, and other categories of age of women under the reproductive age group. The Bayesian estimation utilizes the Monte Carlo Markov Chain (MCMC) technique in WinBUGS-14 software to obtain the results. The present study focuses on the use of a Bayesian approach with a non-informative prior on different risk factors to evaluate the odds ratio for hypertension. The results of the Bayesian approach were found to be more optimal than the results of the classical approach in estimating risk for hypertension. Also, the trace and density of each risk factor provided a better understanding of the MCMC simulations of the different odds ratios. The results of the Bayesian approach were compared to the classical approach, and it is observed that in estimating risk related to hypertension, the Bayesian approach provides a better estimate and has a lower standard error than the classical approach among women under the reproductive age group. The Bayesian approach used for different risk factors in estimating odds ratios with credible intervals provides a narrower interval width than the width of the confidence interval of the classical approach. So, from our results, we can conclude that the Bayesian approach provides a better estimate than the classical approach, and it can be used as an alternative to the classical approach for estimating risk analysis in epidemiological investigations.
In classical inventory models, demand rates are supposed to be constant; however, in real-world scenarios, they may vary and be dependent on several factors. Additionally, demand rates can vary according to seasonality and other factors. Nowadays, advertising plays a very important part in any business plan as it helps businesses to reach their target audience and expand. Moreover, it is very important to maintain an adequate stock so as to attract more and more customers in very competitive business situations. Price is one of the primary determinants of a product's demand. Generally, when quantities demanded tend to fall, prices tend to rise, and when prices tend to fall, numbers demanded generally tend to grow. Three of them price, stock, and advertising can make a fundamental difference to demand. As we can see from the literature survey many researchers work on the effect price and stock on demand but here we will work on advertisement effect on demand. In this work, these elements are integrated and described quantitatively by a fuzzy inventory model. This study aims to find out a suitable replenishment policy that will minimize the total inventory cost. A significant factor in inventory policy is deterioration. Here, the suggested model inventory policy for degrading items is established. The inventory model is evaluated using a numerical example. Additionally, sensitivity analysis is done.
Random censoring finds its applications in various domains such as reliability engineering (while life testing of items) and survival analysis (while conducting clinical trials on patients). In this paper, we discuss the estimation of reliability characteristics of inverse Pareto distribution when the failure time data are observed from multiple testing facilities under the presence of test facility specific variations i.e., the block randomly censored setup. The method of maximum likelihood estimation has been used for computing the point estimates and asymptotic confidence intervals of the parameters and reliability characteristics under classical estimation. Under the Bayesian setup, gamma distribution has been used as the prior distribution for the parameters and Markov chain Monte Carlo techniques have been used for obtaining the samples from posterior distribution. These techniques are based on the method of Metropolis Hastings algorithm and Gibbs sampling and an algorithm is proposed for computing the Bayes estimates and highest posterior density credible intervals of the parameters and reliability characteristics. A simulation study is conducted to assess and compare the performance of the estimation methods followed by a real data illustration pertaining to medical study.
This paper presents a new Searls type family of ratio estimators that use various auxiliary measures to estimate the population mean of a study variable under a simple random sampling without replacement framework. The study examines specific cases that include auxiliary data such as the median, quartile deviation, and coefficient of variation, maintains a first-order approximation for the introduced estimators' bias and Mean Square Error (MSE), and provides theoretical conditions for comparing their efficiency with existing estimators. Numerical analysis demonstrates that the proposed family of estimators are more effective than other ratio-type estimators, making them appropriate for use in a range of real-world scenarios including Agriculture, Biological Science, Commerce, Defence, Economics and Engineering, Forestry, Mathematical Sciences etc.
