Crop Rotations, Tillage and Cover Crops Influences on Soil Health, Greenhouse Gas Emissions and Farm Profitability

This study assessed the effects of three levels of crop rotation: [2-yr; corn (Zea mays L.)- soybean (Glycine max L.), 3-yr; corn-soybean-oat (Avena sativa L.) or 4-yr; cornsoybean- oat-winter wheat (Triticum aestivum L.)], two tillage [conventional-till (CT) and no-till (NT)], and two winter cover cropping systems [cover crop (CC) or fallow control (NC)] on soil biochemical and physical properties, greenhouse gas emissions (GHG), microbial community composition, crop yield and farm profitability under silty clay loam soil of south eastern South Dakota. Experimental design was a randomized complete block design in a split-split plot treatment arrangement with four replications. Rotations, tillage and cover cropping were, respectively, assigned as main-plot, sub-plot and subsub- plot factors. Results from soil samples collected for analyzing biochemical properties at surface 0-7.5 cm in 2017 indicate that these soil properties were enhanced by adopting CC and NT system. In general, the CC had 9, 17 and 19% higher geometric mean of enzyme activities (urease x β-D-glucosidase x phosphatase x arylsulfatase)1/4 than the NC at pre-planting, after planting and grain-filling stage of maize, respectively. Soil microbial biomass carbon (C) and β-glucosidase activity were 31 and 54%, respectively, higher with CC vs. NC under 4-yr rotation after planting of maize. At grain-filling stage, the hot water extractable C and nitrogen (N) contents were significantly greater under CC as compared to the NC plots. Results from soil physical measurements showed that CC reduced bulk density by 6% and increased saturated hydraulic conductivity and water infiltration rate by 1.5 times compared to the NC. Similarly, X-ray computed tomography (CT) measured total porosity, number of macropores and macroporosity were 43, 34, and 60%, respectively, higher with CC as compared to the fallow plots. Soils under 4-yr rotation had 16, 14, and 4% higher values of soil organic C, total N, and wet aggregate stability compared to those under 2-yr rotation, respectively. Also, 4-yr rotation significantly increased number of CT-measured pores, number of macropores, coarse mesopores, macroporosity, and mesoporosity than the 2-yr rotation. Soil surface GHG measurements were carried out during the growing seasons of maize and soybean phases under NT system in 2017 and 2018, respectively. Statistical differences in microbial community structure between treatments were few, however, in comparison to 2-yr and fallow management, the 4-yr rotation and CC had numerically greater specific biomarkers for bacterial or fungal populations. The 2-yr rotation had greater CO2 emissions than the 4-yr during growing season of 2017. However, 4-yr rotation increased the GHG fluxes during spring thaw of 2018. Cumulative CO2 emissions were greater under CC than the fallow when averaged over both the rotations during 2017, however, interaction effect during 2018 suggested that CC had lower CO2 emissions than the fallow only under 2-yr rotation. Measurements from 2017 and 2018 were further used to evaluate the ability of the DeNitrification-DeComposition (DNDC) model to predict field-measured soil surface CO2 and N2O emissions. Across all cropping treatments, model simulated soil (0–10 cm) temperature and moisture agreed well with the growing season field measurements. Predicted daily soil CO2 fluxes were accurate for corn phase in 2017, but model overestimated the simulated soil respiration compared to the measured data in 2018 for soybean. The statistics showed “poor” agreement between the simulated and measured N2O emissions because DNDC model underestimated the fluxes during both the crop phases. Nevertheless, these studies suggest that cropping system diversification achieved by extending length of rotations through small grains and by growing winter CC such as winter rye under NT system has the potential to enhance microbial community structure composition and mitigate GHG emissions. Yield and economic comparisons were conducted using the data collected from 2014 through 2018 years. Results suggest that NT system though reduced the corn yield but increased the soybean yield under 2-yr rotation as compared to the CT system. Therefore, both the tillage systems were economically equivalent, whereby NT improved benefit-cost ratio as compared to the CT system. In our study, while CC in its short-term did not contribute to economic benefit, our results indicated that incorporating CC in conventional rotation system under NT could provide an economically superior option to diversify the system. Increased length of crop rotations (3- and 4-yr) increased the corn and soybean yields as compared to the 2-yr rotation. In the context of overall profitability, however, the diversified cropping system in this study lagged the traditional corn-soybean system which could be attributed to the relatively lower profits of small grains. Therefore, it is important to identify other profitable crops to diversity the cornsoybean rotations those are beneficial for soils and the environment

A Photographer’s World: The Art of Randeep Maddoke

An introductory text to photo artist Randeep Maddoke's wor

The Politics of Caste and Textual Cleansing: A Case of Babu Rajab Ali’s Poetry

On September 15, 2012, some Punjabi book publishers and editors1 were arrested under the Scheduled Castes and Scheduled Tribes (Prevention of Atrocities) Act for reprinting some works of the 20th century Punjabi poet and kavishar Babu Rajab Ali that allegedly contain derogatory caste names for the Dalits. The police had arrested these publishers suspecting the books could cause unrest in the state and could lead to rioting or division among communities. While one of the publishers chose not to circulate the books in the market after the controversy, in the subsequent editions of two other publications, the texts of Rajab Ali were changed, sanitized of the alleged caste names included in them, with terms more acceptable in contemporary times. We examine the deeper reasons behind this zealous and reactionary response to reprinting texts written more than seventy years ago. It becomes imperative to analyse if it is correct to reproduce old poetical texts through the process of “cleansing” and if the textual cleansing subverts the original meaning the poet wanted to convey, in his particular spatial-temporal context. The political, social and cultural dynamics behind the caste-based and censor-filtered purging of text reproduction need to be probed into, along with the role of state agencies and institutional structures that allow it to exist. Should the publishers be held responsible for the content they publish? Should the debate of cleansing/sanitizing, when understood in the context of the prevalent caste relations in Punjab, be reduced only to the notions of ‘freedom of expression’ and ‘freedom of individual’? Further, we analyse the significance of Rajab Ali’s writing on its own merit, while also demonstrating and critiquing his proclivity to perpetuate existing caste norms. These are the questions this paper seeks to address, while also conveying how poetry can be used as an alternative historical source for early-twentieth century Malwa

A Content Based Region Separation and Analysis Approach for SAR Image Classification

SAR images are the images captured through satellite or radar to monitor the specific geographical area or to extract any information regarding the geographical structure. This information can be used to recognize the land areas or regions with specific features such as identification of water area or flood area etc. But the images captured from satellite covers larger land regions with multiple scene pictures. To recognize the specific land area, it is required to process all the images with defined constraints to identify the particular region. The images or the image features can be trained under some classification method to categorize the land regions. There are various supervised and unsupervised classification methods to classify the SAR images. But the SAR images are high resolution images with multiple region types in same images. Because of this, the existing methods are not fully capable to classify the regions accurately. There is the requirement of more effective classification that can identify the land regions more adaptively

A Novel Approach in Image Encryption Using AES Algorithm

As the information trade in electronic way is quickly expanding, it is likewise similarly vital to shield the classification of information from unapproved get to. The breaks in security influence client's protection and notoriety. The information traded can be content, picture, sound, video and so on. Each sort of information has its own particular highlights and diverse methods are utilized to shield classified picture information from unapproved get to. Thus encryption of information is done to affirm security in open systems. Cryptography is the investigation of procedures for secure correspondence within the sight of a foe. It manages issues like encryption, validation, and key appropriation to give some examples. Image encryption is a system that gives security to pictures by changing over the first picture into a picture which is hard to get it. In the base paper, main approach was that they have added a key stream generator (A5/1W7) to AES to ensure improving the encryption performance; mainly for images characterized by reduced entropy. The implementation of both techniques has been realized for experimental purposes. Detailed results in terms of security analysis and implementation are given. Comparative study with traditional encryption algorithms is shown the superiority of the modified algorithm. But the comparative study showed that encryption time as well as decryption time of this algorithm is quite high. The parameters used in their research such as entropy, co-relation and PSNR are also needed to be analyzed. To improvise their algorithm, we have proposed an algorithm which deals with XOR operation of the sub keys. The methodology is described below

A Clustering and Associativity Analysis Based Probabilistic Method for Web Page Prediction

Today all the information, resources are available online through websites and web page. To access any instant information about any product, institution or organization, users can access the online available web pages. In this work, a three stage model is provided for more intelligent web page prediction. The method used the clustering and associativity analysis with rule formulation to improve the prediction results. The CMeans clustering is applied in this prior stage to identify the sessions with high and low usage of web pages. Once the clustering is done, the rule is defined to identify the sessions with page occurrence more than average. In the final stage, the neuro-fuzzy is applied to perform the web page prediction. The result shows that the model has provided the effective derivation on web page visits

Machine Learning: A Potential Forecasting Toll

Technical analysis involves predicting asset price movements from analysis of historical prices. Many studies have been conducted to determine the profitability of technical analysis. A composite prediction is considered here by using the buy and sell signals from technical indicators as inputs. Both machine learning methods like neural networks and statistical methods like logistic regression are used to get composite forecasts. Signals from trend-following and mean-reversal technical indicators are used in addition to variance of prices as inputs. Variance is added to help technical indicators switch between trend-following and mean-reversal systems. Five commodities from agricultural, livestock and foreign exchange futures markets are selected to test the hypothesis of profitability of technical indicators. Special care is taken to avoid data snooping error. None of the individual indicators or machine learning models generate significant profit in single day forecasts. In twenty-day forecasts, only random forest and pipeline models are profitable. Neural networks and statistical models both failed to deliver here. The out of sample failure of the neural networks is partly due to the relatively large number of parameters. Managed futures, however also did poorly in the out of sample period so the results could also be due to picking a time period where technical analysis did poorly. Individual indicators did occasionally show significant profits. Random forests and decision tree find variance as the most important input. Future research should consider alternative time periods, commodities, systems, and machine learning algorithms. If a scale neutral variable for variance could be developed, it should be used so that the models could be trained on data from multiple commodities to provide more training data.Agricultural Economic