Constraints in Production and Marketing of Arecanut in Salem District of Tamil Nadu, India

Arecanut is an important cash crop in our country. The study was carried out to ascertain the constraints faced by arecanut farmers in Salem district of Tamil Nadu with a sample size of 120, by employing proportionate random sampling technique. Majority of the respondents expressed lack of specific grading of nuts in marketing as a constraint. More than three-fourths of the respondents suggested that there should be a mechanism to regulate import of nuts from other countries and to create market potential for nuts in the local markets

Association between somatic cell count early in the first lactation and the lifetime milk yield of cows in Irish dairy herds

Change in lifetime milk yield is an important component of the cost of diseases in dairy cows. Knowledge of the likelihood and scale of potential savings through disease prevention measures is important to evaluate how much expenditure on control measures is rational. The aim of this study was to assess the association between somatic cell count (SCC) at 5 to 30 d in milk during parity 1 (SCC1), and lifetime milk yield for cows in Irish dairy herds. The data set studied included records from 53,652 cows in 5,922 Irish herds. This was split into 2 samples of 2,500 and 3,422 herds at random. Linear models with lifetime milk yield and first-lactation milk yield as the outcomes and random effects to account for variation between herds were fitted to the data for the first sample of herds; data for the second sample were used for cross-validation. The models were developed in a Bayesian framework to include all uncertainty in posterior predictions and parameters were estimated from 10,000 Markov chain Monte Carlo simulations. The final model was a good fit to the data and appeared generalizable to other Irish herds. A unit increase in the natural logarithm of SCC1 was associated with a median decrease in lifetime milk yield of 864kg, and a median decrease in first-lactation milk yield of 105kg. To clarify the meaning of the results in context, microsimulation was used to model the trajectory of individual cows, and evaluate the expected outcomes for particular changes in the herd-level prevalence of cows with SCC1 ≥400,000cells/mL. Differences in mean lifetime milk yield associated with these changes were multiplied by an estimated gross margin for each cow to give the potential difference in milk revenue. Results were presented as probabilities of savings; for example, a 75% probability of savings of at least€97 or€115/heifer calved into the herd existed if the prevalence of cows with SCC1 ≥400,000cells/mL was reduced from ≥20 to <10 or <5%, respectively, and at least€71/heifer calved into the herd if the prevalence of cows with SCC1 ≥400,000cells/mL was reduced from ≥10 to <5%. The results indicate large differences in lifetime milk yield, depending on SCC early in the first lactation and the findings can be used to assess where specific interventions to control heifer mastitis prepartum are likely to be cost effective. Key words: dairy heifer, somatic cell count, lifetime milk yiel

Filtering Deterministic Layer Effects in Imaging

Sensor array imaging arises in applications such as nondestructive evaluation of materials with ultrasonic waves, seismic exploration, and radar. The sensors probe a medium with signals and record the resulting echoes, which are then processed to determine the location and reflectivity of remote reflectors. These could be defects in materials such as voids, fault lines or salt bodies in the earth, and cars, buildings, or aircraft in radar applications. Imaging is relatively well understood when the medium through which the signals propagate is smooth, and therefore nonscattering. But in many problems the medium is heterogeneous, with numerous small inhomogeneities that scatter the waves. We refer to the collection of inhomogeneities as clutter, which introduces an uncertainty in imaging because it is unknown and impossible to estimate in detail. We model the clutter as a random process. The array data is measured in one realization of the random medium, and the challenge is to mitigate cumulative clutter scattering so as to obtain robust images that are statistically stable with respect to different realizations of the inhomogeneities. Scatterers that are not buried too deep in clutter can be imaged reliably with the coherent interferometric (CINT) approach. But in heavy clutter the signal-to-noise ratio (SNR) is low and CINT alone does not work. The “signal,” the echoes from the scatterers to be imaged, is overwhelmed by the “noise,” the strong clutter reverberations. There are two existing approaches for imaging at low SNR: The first operates under the premise that data are incoherent so that only the intensity of the scattered field can be used. The unknown coherent scatterers that we want to image are modeled as changes in the coefficients of diffusion or radiative transport equations satisfied by the intensities, and the problem becomes one of parameter estimation. Because the estimation is severely ill-posed, the results have poor resolution, unless very good prior information is available and large arrays are used. The second approach recognizes that if there is some residual coherence in the data, that is, some reliable phase information is available, it is worth trying to extract it and use it with well-posed coherent imaging methods to obtain images with better resolution. This paper takes the latter approach and presents a first attempt at enhancing the SNR of the array data by suppressing medium reverberations. It introduces filters, or annihilators of layer backscatter, that are designed to remove primary echoes from strong, isolated layers in a medium with additional random layering at small, subwavelength scales. These strong layers are called deterministic because they can be imaged from the data. However, our goal is not to image the layers, but to suppress them and thus enhance the echoes from compact scatterers buried deep in the medium. Surprisingly, the layer annihilators work better than intended, in the sense that they suppress not only the echoes from the deterministic layers, but also multiply scattered ones in the randomly layered structure. Following the layer annihilators presented here, other filters of general, nonlayered heavy clutter have been developed. We review these more recent developments and the challenges of imaging in heavy clutter in the introduction in order to place the research presented here in context. We then present in detail the layer annihilators and show with analysis and numerical simulations how they work