<div>Background: The dairy industry is one of the largest industries in India. The exports were made to 105 countries in the world. Dairy production is one of the major sustenance factors for the rural economy of India. Notably, India ranks first in Isabgol production (98%) and is the sole supplier of seeds and husk in the international market. Among medicinal plants, Isabgol is the first ranked foreign exchange earner for the country. India is the largest producer and the main supplier of seed and husk to the world market. It contains a significant number of proteins and husk yields colloidal mucilage which are valued for medicinal application and is used in Ayurveda, unani and allopathic systems of medicines. It is the main constituent of a number of laxative preparations. The psyllium is high in soluble fibre content with detoxing effect over digestive system makes it a very apt nutraceutical.&nbsp;Basundi is one of the heats desiccated indigenous products popular in Western part of India, mostly Maharashtra and Gujarat. It can be classified in the condensed milk group along with rabri, khoa, mithai and kheer and can be considered similar to sweetened condensed whole milk. 
 
Methods: The present investigation indicates utilization of encapsulated lactic acid bacteria culture (Lactobacillus acidophilus, Lactobacillus bulgaricus) and modified psyllium husk for the preparation of probiotic basundi. The prepared probiotic basundi was analyzed for sensorial, physicochemical and microbial quality parameters. Probiotic basundi was prepared from 1000 ml of milk, 90 gm sugar, 02-03 pieces of cardamom, saffron and encapsulated LAB culture having (107, 108 and 109 cfu/gm containing equal proportions of Lactobacillus acidophilus and Lactobacillus bulgaricus) with 0.65 per cent hydrochloric acid modified psyllium husk. The probiotic basundi was then stored at refrigerated conditions at 4&deg;C for 08 hrs. 
 
Result: The organoleptic evaluation of probiotic basundi was carried out. As per the score of hedonic scale, basundi with encapsulated 10 per cent probiotic culture (109 cfu/gm) with 0.65 per cent hydrochloric acid modified psyllium husk had shown maximum consumer acceptability (8.3) among all samples.</div>

Exploration of Encapsulated Modified Psyllium Husk for Preparation of Probiotic Basundi

Nowadays, Ischemia Modified Albumin (IMA) has been regarded as an indicator for various maladies and complicated pregnancy in humans. Therefore, it is hypothesized that evaluation of the maternal serum IMA degrees can serve as an early detection of abortion in dairy cows. 252 Holstein cows&rsquo; serum samples were taken both in the 2nd and 6th months of their pregnancy. Twenty-two cattle were selected as a positive control group among the non aborted ones. Furthermore, blood samples from 20 non-pregnant cows were taken to identify the effects of physiological changes of pregnancy. IMA and Ischemia Modified Albumin Rate (IMAR) were markedly higher (p&lt;0.01); and albumin was markedly lower (p&lt;0.01) in aborted cows and healthy-pregnant cows compared with non-pregnant cows in both months. In aborted cows, IMA, IMAR and Oxidative stress index (OSI) degrees were found to be more increased than healthy pregnancies both in 2nd and 6th months of pregnancy; however, there were statistically significant only at 6th month (p&lt;0.01). Total Oxidative Stress (TOS) levels were higher than healthy pregnancies. So, as a result of IMA which was caused by oxidative stress rising from hypoxia/ischemia could serve as an early bioindicator for miscarriage in cattle.&nbsp;

Ischemia modified albumin as an early biomarker in diagnosing the late stages abortus in cows

<div>The aim of this retrospective study was to evaluate the clinical outcome, complications, recurrence rate, and results of the pocket technique in the treatment of prolapse of the third eyelid gland, cherry eye namely. Thirty eyes of 26 dogs diagnosed with prolapse of the third eyelid gland were included the study. Conjunctivitis and ocular discharges were noticed in the affected eyes. The four cases were (15%) bilateral and twenty-two (85%) were unilateral. Of the dogs with unilateral disease, the affected eye was on the left side in 10 (45%) dogs and the right side in 12 (55%). Eighteen dogs were male and eight were female. Twenty-six dogs, 30 eyes with protrusion of the third eyelid gland were treated using Modified Morgan&rsquo;s pocket technique. In the postoperative period, the dogs were controlled 3 times with 7 day intervals and no clinical problem was detected. Modified Morgan pocket technique was used with 96.1 percent success rate for the re-positioning of prolapsed gland of the third eyelid in dogs.&nbsp;</div>

<div>Surgical Treatment of Prolapse of the Third Eyelid Gland in Dogs using Modified Morgan Pocket Technique</div>

<div>Background: Chickpea is an important pulse crop. The area, production and productivity of Kabuli chickpea are very low as compared to Desi type chickpea. While, worldwide chickpea production is predominated by Desi type (80%) compared to Kabuli type (20%). One of constraints limiting the Kabuli chickpea seed production is the fast seed deterioration process as, they possesses thin and fragile seed coat which makes it more susceptible to infestation of pulse beetle and storage fungi. In addition, the day by day increase in usage of chemical pesticides and increase in health awareness among the people and demand towards organic based products necessitate a new alternative method of seed storage than the routinely used storage methods. Modified atmosphere storage is an alternative method to avoid chemical fumigants, which are said to cause residual effect on seed material and development of resistance by storage pest against insecticides. 
 
Methods: The present investigation on modified atmosphere packaging (MAP) was carried out at the Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur during March 2017. Further, its storage and seed quality assessment were conducted at Seed Quality and Research Laboratory of National Seed Project (Crops), Seed Unit, University of Agricultural Sciences, Dharwad during March 2017 to May 2018. 
 
Result: The results revealed that the Kabuli chickpea seeds stored in the gaseous atmosphere with the combination of CO2 (80%) + N2&nbsp;(20%) + O2 (0%) [C2] have maintained the maximum germination (92%) followed by Vaccum Packaging [C5] by using 700 gauge polyethylene bag for 14 months under ambient conditions above the Indian Minimum Seed Certification Standards.</div>

​Effect of Modified Atmosphere Storage Conditions on Seed Quality and Longevity of Kabuli Chickpea Varieties

<div>Agriculture in Ethiopia is reliant on the poor farmer&rsquo;s income. Crop production in the country is hampered by a variety of factors, among them subsistence farming, inefficient use of farm inputs, climate change, poor export performance, policy uncertainty and low interest in agricultural credit are the major ones. Plant pests also pose a threat to food crops, resulting in significant losses for smallholder farmers and jeopardizing food security. Thus, Ethiopian policymakers recently recognized genetically engineered crops as a viable method of increasing food productivity and quality. A thorough empirical review had conducted, focusing on the status, potential benefits and drawbacks of genetically modified crop production in Ethiopia. Descriptive statistics had used, followed by raw data interpretation. Genetically modified crop production is an option for addressing food insecurity, malnutrition and the production of low-cost feedstock. However, as genetically modified crops become more prevalent in Ethiopian agriculture, humans, wildlife and the environment must all be considered. As a result, GM crops can help poor farmers improve their agricultural livelihoods if better national biosafety regulations had implemented.</div>

Potential Impact of using Genetically Modified Crops in Ethiopia: A Review

Background: Legume crops are an essential component of global agriculture and are widely supplied for human consumption, livestock feed and soil improvement due to their vital nutritional nature. The economic and nutritional significance of legumes is threatened by a multitude of diseases that can cause substantial yield losses. Traditional methods for disease detection, relying on visual inspection, are often subjective and inefficient, leading to delayed intervention. 
 
Methods: This study investigates the utilization of machine learning algorithms for the early identification of diseases affecting legume crops. A comprehensive evaluation is conducted on machine learning algorithms, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) with respect to the domain of disease detection. Through a comparative analysis of their performance across different environmental conditions and phases of crop development, this study also explores their strengths and weaknesses. 
 
Result: The findings and comparative examination offered significant perspectives on the potential of machine learning algorithms in the realm of early legume crop disease detection. In addition to enhancing crop health and disease management, the research provides support for sustainable agricultural practices and possesses the capacity to augment environmental sustainability and food security through the application of machine learning techniques.&nbsp;

Machine Learning Algorithms for Early Detection of Legume Crop Disease

<div>Background: Chickpea is the third major pulse produced globally, with 11.6 million tonnes produced per annum (Merga and Haji, 2019).&nbsp;Sugar alcohols, inulin, starch are all prebiotic carbohydrates found in chickpeas (Johnson et al., 2020).&nbsp;Near-Infrared (NIR) spectroscopy is a non-destructive, versatile and powerful analytical technique. 
&nbsp;&nbsp; 
Methods: Spectral data obtained from NIR spectroscopy requires application of various techniques to extract useful information from spectral data which is further used for building various models for prediction of physical or chemical components presents in agricultural crops. The main aim of this study is to apply various machine learning algorithms especially effective in predicting sugar concentration in chickpea. Sugar prediction models are developed using Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR) algorithms. Performance of the models is evaluated using measures namely, Root Mean Square Error (RMSE), Residual Standard Error (RSE), Coefficient of Determination (R2) and Adjusted Coefficient of Determination (adjusted R2). 
 
Result: It was observed that, RF outperformed all other models in terms of accuracy for predicting sugar component from preprocessed spectra, with RMSE, RSE, R2 and adjusted R2 values of 0.054, 0.062, 0.954 and 0.937, respectively. The accuracy of the ANN model is similar to that of the RF, with minor differences in RMSE, RSE, R2 and adjusted R2 , values of 0.057, 0.067, 0.952 and 0.935.</div>

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy

<div>Background:&nbsp;Dairying in India has witnessed a radical transformation from a largely unorganised activity into a thriving organised industry. However, there are only a limited number of earlier attempts that specifically evaluated the milk production pattern of India. Moreover, most of these earlier studies are quite dated and have employed autoregressive integrated moving average (ARIMA) models, which suffer from the problem of local optima. To overcome this lacuna, we have utilised a genetic algorithm (GA) in the ARIMA framework. Its suitability to forecast the annual milk production in India has also been assessed comparatively with respect to the traditional ARIMA methodology. 
 
Methods: For the current study, data on annual milk production (in million tonnes) in India for the period from 1980 to 2019 have been utilised. For both the approaches under investigation, the whole data series is first divided into two sets, namely the training set and the testing set. The production data of 1980-2016 have been utilised for the model building purpose while retaining the last 3 years&rsquo; data for the post-sample evaluation. 
 
Result:&nbsp;Outcomes emanated from the post-sample assessment clearly suggest that the ARIMA-GA approach has outperformed the traditional ARIMA methodology in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. It is also evident that GA has substantially minimised the error related to the parameter estimation.</div>

​​ARIMA-Genetic Algorithm Approach for Forecasting Milk Production in India

Background: Further research is needed to estimate the marketable live weight of lambs with high accuracy and reliability while minimizing contact and measurement. This study aimed to estimate the 120th-day marketing weight of Morkaraman lambs by different machine learning algorithms, considering the variables of dam age, sex, birth type, birth weight, as well as 30th day, 60th day and 90th day live weights.&nbsp; 
 
Methods: Artificial neural networks (ANN), classification and regression trees (CART), support vector machines with radial basis function kernel (SVMR) and Random Forest (RF) algorithms for estimation of the marketing weight were performed for training (75%) and testing (25%) datasets. Models used in this study were compared based on mean absolute error (MAE), root mean squared error (RMSE) and mean absolute per cent error (MAPE) performance metrics. The most significant predictor of the marketing live weight in all models was the 90th day live weight, whereas the birth weight, birth type and dam age were the least important predictors. The correlation coefficients between live weight values estimated by the SVMR, CART, RF and ANN models and the actual marketing live weight were determined as 0.82, 0.82, 0.82 and 0.84, respectively. 
 
Result: The best prediction for the marketing live weight of Morkaraman lambs in the 4th month was obtained from the ANN model. Using artificial neural networks to determine the marketing weight of lambs can save time and labor because of the reduced number of weighings. It may improve decisions made in flock management.

Estimation of Marketing Live Weight of Lambs by Different Machine Learning Algorithms

Background: BPT-5204 is one of the popular high yielding rice variety which is highly susceptible to biotic and abiotic stresses. Embyogenic callus based transformation turned out as an efficient method for development of climate resilient varieties through genetic transformation studies. Keeping in the view, the present study was undertaken to investigate the role of modified MS media compounds on somatic embryogenesis in rice. 
 
Methods: The effect of modified MS media components such as plant growth regulators, total inorganic nitrogen concentration and carbohydrate sources on somatic embryogenesis of BPT-5204 was investigated in three in vitro experiments. The experiments were conducted at Department of Crop Physiology, Acharya N.G Ranga Agricultural University, Agricultural College, Bapatla. The data pertaining to biochemical parameters was analyzed statistically. 
 
Result: The role of various modified MS media components in inducing somatic embryogenesis was observed in the present study. Modified MS media containing both NH4+ and NO3- in the ratio of 2:1 (40 mM NH4+: 20 mM NO3-) along with 10 &micro;M 2,4-D + 2.5 &micro;M kinetin has produced creamish to light yellow and compact calli which developed into compact embryogenic calli with globular embryos on surface after subculturing. Irrespective of total nitrogen concentration, the ratio of NH4+ and NO3- played a significant role in somatic embryogenesis. Higher ammonium concentration of 40mM and nitrate concentration of 20 mM in the culture media resulted in the induction of somatic embryos than the ammonia or nitrate alone. Besides this, culture media with disaccharide, 3% maltose as a carbohydrate source gave consistently higher number of somatic embryos/explant when compared with sucrose and other monosaccharides. All the nitrogenous compounds like ammonical nitrogen, nitrate nitrogen, nitrite nitrogen, total nitrogen and protein content of the calli showed the declining trend at 21 days after inoculation in all the three invitro experiments due rapid metabolism of nitrogen during embryogenesis.

Effect of Plant Growth Regulators, Modified Nitrogen and Carbohydrate Sources on Somatic Embryogenesis of Rice (Oryza sativa L.) Cultivar BPT-5204