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Sustainable agriculture plays a critical role in maintaining environmental health and also promotes long-term food security and the preservation of natural resources. We therefore, examined the response of intensified cropping systems over four years across five diverse cropland ecosystems viz. Basmati rice-Wheat-Cowpea, Basmati Rice-Potato-Wheat-Mixed Fodder (Maize + Cowpea + Charni), Basmati Rice-KnolKhol-Potato-Greengram, Basmati Rice-Radish-Green onion-French bean vegetable-Okra, and Rice-Fenugreek-KnolKhol-Green Onion-Dry Onion-Black gram to assess the changes in substrate availability and fertilizer application on nitrogen and phosphorus pools. Soil samples were collected from three different depths (0–5 cm, 5–15 cm, and 15–30 cm) during the kharif season. Significant results were observed in the mean values of mineralizable nitrogen, total nitrogen, ammonical nitrogen, nitrate nitrogen, and soil microbial biomass nitrogen at the 0–5 cm depth, with the highest values recorded under Rice-Fenugreek-KnolKhol-Green Onion-Dry Onion-Black gram. At the soil depth of 0–5 cm, available phosphorus and labile organic phosphorus exhibited significant differences, with the highest values observed in Basmati Rice-Potato-Wheat-Mixed Fodder. Moderately labile phosphorus reached its maximum values under T2 at both 0–5 cm, and at 5–15 cm depths. The peak values of non-labile organic phosphorus were found in T4 (Basmati Rice-Radish-Green onion-French bean vegetable-Okra) at the 0–5 cm soil depth. On a regional scale, the results suggest that more diversified cropping systems hold promise as sustainable agricultural practices that support nitrogen and phosphorus retention, contributing to overall soil sustainability.

In the current investigation, we have developed the efficient predictive estimator for finite population mean using auxiliary variable in case of post stratification. Up to the first order of approximation, the expressions for bias and mean square error (MSE) are derived for the proposed estimator. This also reveals the constant's ideal value, which reduces the MSE of the developed estimator. The developed estimator performs better than the existing estimators. Numerical study is also carried out by using the real data sets. [ABSTRACT FROM AUTHOR]

Ranked set sampling is an approach to data collection originally combines simple random sampling with the field investigator's professional knowledge and judgment to pick places to collect samples. Alternatively, field screening measurements can replace professional judgment when appropriate and analysis that continues to stimulate substantial methodological research. The use of ranked set sampling increases the chance that the collected samples will yield representative measurements. This results in better estimates of the mean as well as improved performance of many statistical procedures. Moreover, ranked set sampling can be more cost-efficient than simple random sampling because fewer samples need to be collected and measured. The use of professional judgment in the process of selecting sampling locations is a powerful incentive to use ranked set sampling. This paper is devoted to the study, we introduce an approach to the mean estimators in ranked set sampling. The amount of information carried by the auxiliary variable is measured with the on populations and samples and to use this information in the estimator, the basic ratio and the generalized exponential ratio estimators are as an improved form of a difference cum exponential ratio type estimator under the ranked set sampling in order to estimate the population mean ... of study variate Y using single auxiliary variable X. The expressions for the mean squared error of propose estimator under ranked set sampling is derived and theoretical comparisons are made with competing estimators. We show that the proposed estimator has a lower mean square error than the existing estimators. In addition, these theoretical results are supported with the aid of some real data sets using R studio. Therefore, Under RSS architecture, a better difference cum exponential ratio type estimator has been suggested. The estimator's mathematical form has been developed, and its efficiency requirements have been developed in relation to various already-existing estimators from the literature. By imputing various values for the constants used in the creation of our proposed estimator, we also provide several specific situations of our estimator. [ABSTRACT FROM AUTHOR]

In this article, a class of Poisson-regression based estimators has been proposed for estimating the finite population mean in simple random sampling without replacement (SRSWOR). The Poisson-regression model is the most common method used to model count responses in many studies. The expression for bias and mean square error (MSE) of proposed class of estimators are obtained up to first order of approximation. The proposed estimators have been compared theoretically with the existing estimators, and the condition under which the proposed class of estimators perform better than existing estimators have been obtained. Two real data sets are considered to assess the performance of the proposed estimators. Numerical findings confirms that the proposed estimators dominate over the existing estimators such as Koc (2021) and Usman et al. (2021) in terms of mean squared error. [ABSTRACT FROM AUTHOR]

In this study, we adapted the families of estimators from Ünal and Kadilar (2021) using the exponential function for the population mean in case of non-response for simple random sampling for the estimation of the mean of the population with the RSS (ranked set sampling) method. The equations for the MSE (mean square error) and the bias of the adapted estimators are obtained for RSS and it in theory shows that the proposed estimator is additional efficient than the present RSS mean estimators in the literature. In addition, we support these theoretical results with real COVID-19 real data and conjointly the simulation studies with different distributions and parameters. As a result of the study, it was observed that the efficiency of the proposed estimator was better than the other estimators. [ABSTRACT FROM AUTHOR]

To studied Bayesian aspect of small area estimation using Unit level model. In this paper we proposed and evaluated new prior distribution for the ratio σv²/σe²(=λ) of variance components in unit level model rather than uniform prior. To approximate the posterior moments of small area means, Laplace approximation method is applied. This choice of prior avoids the extreme skewness, usually present in the posterior distribution of variance components. This property leads to more accurate Laplace approximation. We apply the proposed model to the analysis of horticultural data and results from the model are compared with frequestist approach and with Bayesian model of uniform prior in terms of average relative bias, average squared relative bias and average absolute bias. The numerical results obtained highlighted the superiority of using the proposed prior over the uniform prior. Thus Bayes estimators (with new prior) of small area means have good frequentist properties such as MSE and ARB as compared to other traditional methods viz., Direct, Synthetic and Composite estimators. [ABSTRACT FROM AUTHOR]

In this paper, we suggest some new allocation schemes for non-response cases under Rank Set Stratified (RSS) sampling and show that new allocation schemes provide a gain in precision of the estimates when compared with stratified random sampling. A numerical example is given to illustrate the results. [ABSTRACT FROM AUTHOR]

In this paper, we have taken under considerations two auxiliary variables used as stratification variables and single study variable. For obtaining the optimum strata boundaries, we have solved the non linear equations using dynamic programming that resulted in more efficient results rather than obtained on using single study variable and single auxiliary variable. The proposed method is illustrated by using different distributions followed by stratification variables. [ABSTRACT FROM AUTHOR]