Among the oilseed crops, Groundnut and Coconut prices are more unstable due to seasonality of production, perishable nature, production uncertainty etc. Lack of information on potential market as well as arrival and price behaviour of these oilseeds further worsen this situation of its growers. Since market integration helps in achieving price consistency and thus lead to marketing system efficiency, the present study examines the Market Integration of major oilseed crops?Coconut, and Groundnut?in India during the period of January 2013 to January 2024markets using Johansen?s cointegration test and Granger Causality test. In this study to test the stationarity of the price seriesAugmented Dickey Fuller test was used. The outcomes of the study strongly supported the presence of co-integration and interdependence of the selected markets from the result of Johansen cointegration test and Market integration analysis across intrastate and interstate regions identified causal relationships, such as unidirectional causality between certain regions for Coconut and bidirectional causality for Groundnut. The study stressed the need for systematic research to address production constraints, technical advancements, and policy influences, to manage emerging challenges effectively.

  This paper presents the estimation of missing responses in Randomized block Design (RBD), Latin Square Design (LSD) and Response Surface Design (RSD) using least squares method and expected maximization algorithm. The parameter relations also presented when initial values are taken as zero, mean of known responses and arbitrary. The methods are illustrated with suitable examples.

  Vegetables are rich in vitamins, minerals, and antioxidants that are essential for human body. However, increase in urbanization coupled with improved purchasing power of consumers and enormous wastage during post-harvest handling and marketing created a huge gap between per capita demand and supply. This loss can be minimized by promoting Farmers? Producer Organisation(FPO). In the present paper, status of FPOs engaged in perishable and important vegetable crops like tomato, potato and onion in different states of India have been studied. State-wise number of FPOs for tomato, potato and onion have been assessed and compared with production level of states using a composite index known as Vegetable Production Index. The Vegetable Production Index for each state has been constructed by combining production of potato, onion and tomato using principal component analysis and the states were categorized in high, medium and low categories using 75th percentile (0.40) and 25th percentile (0.01). The constructed index showed that Uttar Pradesh ranked first in production of these vegetables, followed by West Bengal and Maharashtra. It was investigated that except for few states, the states those were under high category, also had more number of FPOs (51%) engaged in potato, onion and tomato. The study showed that there is a need to increase number of FPOs for tomato, onion and potato in Chhattisgarh, Haryana and Odisha as they still have the opportunity to enhance marketing of vegetables through FPOs matching their production level.

  In experimental scenarios characterized by one source of heterogeneity within the experimental material, block designs offer significant value. Exploring the optimal replication(s) required for factorial experiments, conducted in blocks of size four has garnered significant attention among researchers. While experiments in blocks of size two have been extensively studied, there is growing recognition that experiments in blocks of size four might offer greater utility in practical applications. Particularly, when estimating main effects and specific two-factor interactions from two-level factorial experiments conducted within blocks, a considerable number of replicates may be necessary. This article delves into the exploration of designs that minimize the required number of replications for factorial experiments conducted in blocks of size four. The article presents methodologies aimed at obtaining such designs, which hold promise for enhancing the efficiency and effectiveness of experimental investigations.

  The study examines production of Nutri-cereals in India and the proportional contributions of area and productivity (yield) from 1950-51 to 2020- 21. Production instability raises production risks and discourages farmers from investing. The Cuddy-Della Valle Index (1978) was employed in the study to address the issue of instability in Nutri-cereal production. Furthermore, it employs decomposition analysis to disassemble area, yield, and interaction effects across time. To assess decadal change, the study divided the entire era into seven sub-periods. From 1950-51 to 2020-21, the area under cultivation for jowar, bajra, ragi, and Nutri-cereals showed a negative growth rate of (-) 1.87, (-) 0.62, (-) 1.26, and (-) 1.00 percent per year, respectively. Despite this, production of all crops except jowar increased at a positive CAGR during the time. From 1950-51 to 2020-21, productivity for bajra increased at the fastest rate (2.28 percent per year), followed by ragi at 1.41 percent per year. The goal of the decomposition method is to understand the driving forces that cause changes in the impact variable. For the first three decades of jowar production, the yield effect was particularly strong. However, data over the last four decades suggests that the area impact influences jowar output. It is evidenced that yield effect has always been the driving force for producing bajra and Nutri-cereals.

  The unexpected fluctuation of prices of agricultural commodities may have impactful repercussions on the producers. The price volatility can be modeled by applying time series analysis. A time series consists of linear and non-linear components. The linear component can be modeled by the autoregressive integrated moving average (ARIMA) methodology. Again, long-term dependencies amongst the realizations of any time series can also be observed. This long-term dependency can be addressed by incorporating fractional differencing in the ARIMA model which is known as the autoregressive fractionally integrated moving average (ARFIMA) model. To address both the linear and non-linear components effectively, hybrid time series models can be used. In a hybrid model, more than one model is clubbed together such that one is used for capturing the linear component and another captures the nonlinear counterpart. In this article, a hybrid ARFIMA-LRNN (Layer recurrent neural network) model is employed for modeling the price series of arhar for the Mumbai market. The forecasting accuracy of the hybrid model outperformed the standalone models.

  Unlike ordinary least square model, the geographically weighted regression model takes into account spatial non-stationarity and can capture the spatially varying relationship between several variables. Although, a particular model should contain all pertinent covariates but too many insignificant covariates make the model unnecessarily complex. Therefore, it is important to choose important covariates having significantly high correlation with the study variable. Here, a forward stepwise variable selection procedure under the geographically weighted regression model framework has been proposed for choosing significant covariates and compared with the existing forward stepwise ordinary least square method. Further, an estimator of finite population total incorporating spatial information has been developed. The performance of the proposed spatial estimator was compared empirically under both forward stepwise geographically weighted regression and forward stepwise ordinary least square method through a spatial simulation study. It was found that the performance of the spatial estimator using forward stepwise geographically weighted regression method is better than the forward stepwise ordinary least square method.

  Agroforestry systems involving both tree and crop components usually produce multiple outputs which should all be considered in evaluating the productivity of a system. The problem of multiple outputs arising from tree and crop components can be tackled by developing an index that synthesizes these components into a single value. Therefore, this study aimed to develop a new selection index called Agroforestry System Productivity Index (ASPI) that can be used for easy assessment and comparison of agroforestry systems. The ASPI may be defined as a sum of the relative proportions of the equivalently scaled yields or products of tree and crop components of an agroforestry system. ASPI scores are calculated by converting the outputs of an agroforestry system to a common scale and then ranking the proportions of the converted values for each year of production. The index is shown to be reliable in ranking agroforestry systems and therefore recommended for use in comparing different agroforestry systems involving tree-crop components.

  Small area estimation (SAE) is a critical statistical approach used to generate credible estimates for subpopulations or regions with small sample numbers. In agricultural research, reliable crop production estimation at small geographical scales is critical for policy development, resource allocation, and decision-making. Ranked set sampling (RSS), which is recognised for being a cost-effective and accurate data gathering method, is combined with auxiliary data to increase accuracy of the estimates for small regions. This study proposes synthetic ratio type estimators by combining RSS with auxiliary information to improve SAE efficiency and precision, notably in agricultural production estimation. The mean square error (MSE) of the proposed synthetic estimator is obtained to the first order approximation. The approach uses auxiliary information to lower the MSE of the estimators. Comparative investigations with certain well-known adapted SAE estimators reveal that using RSS and auxiliary data considerably improves estimation accuracy for small regions. The theoretical results are supported with a simulation study carried out over an artificially rendered population. The practical benefits of the suggested estimator are demonstrated by an application to crop production data. The findings indicate that this technique is not only more efficient, but also highly applicable in real-world agricultural surveys, making it a useful tool for improving small area estimates in resource-constrained contexts.

  Government and non-government organizations regularly initiate developmental programs in successive phases. Each phase plays a significant role in the development process. These programs aim to enhance the socio-economic conditions of communities and individuals by addressing issues such as poverty, health, education, women empowerment, and infrastructure. Every year, many such programs are implemented in several areas for various purposes, including health awareness programs, women empowerment programs, cleanliness programs, vaccination campaigns, and educational programs to improve health and public participation. Chandra et al. (2018a) considered the linear impact of programs across successive phases and used a multiplicative model that linked with predefined survey and response variables. They employed the model to estimate the population mean of the response variable using the Ranked Set Sampling (RSS) on the survey variable, which led to the linear impact valuation of developmental programs. In this paper, we have suggested an exponential impact to reflect more realistic growth patterns in numerous development processes. We have proposed an estimator of the response variable under RSS based on the survey variable with exponential impact and compared to an estimator of RSS with Linear impact in terms of relative precision (RP). The pattern of various RPs is explored using the real-life example of Education for all towards quality with equity.

  Hindi Summaries of research articles