<div><strong>Background: </strong>The Malpura sheep is hardy breed of semi-arid region of Rajasthan. Farmers do not get the due share for their heavy weight lambs as animals are never sold on weight basis. Researchers have worked out the ways of predicting the live weight of sheep/goats using body measurements. However, very few studies have tried to address this issue, especially in India. Aim of this study was to construct a prediction scale for live weight of sheep using simple measure of heart girth and body length, such scale will further benefit the shepherds to guess the live weight of the lambs sold and expect the desired price from the customer.&nbsp;<br />
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<strong>Methods: </strong>The present study was conducted on 1164 records of 823 Malpura sheep at CSWRI, Avikanagar. The data on live weight (N=1164) and corresponding morphometric measures on heart girth and body length were digitalized and subjected to analysis. we used 14 linear predictors and regressed them on the live weight to obtain the estimates of regression, coefficient of determination and derived the prediction equations using the regression coefficients for prediction of live weights.&nbsp;<br />
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<strong>Result: </strong>Our study revealed that it is possible to create a measurement scale for prediction of live weight of the sheep using simple arithmetic. Several linear regression scales using body length and heart girth were developed. The best scale (L+G) had a prediction equation as: Y = -49.743+0.576L+0.562G, where Y is live weight. The R<sup>2</sup> was 0.883, which was not significantly more than linear regression scale (L+G). This will help the farmers to quite confidently score animals for their live weights across all the age groups. With the help of measurement tape, he can easily measure the heart girth and the body length of the sheep.&nbsp;</div>


<div>The Morphometric Scale to Predict the Live Weight of Malpura Sheep in Semi-arid Region of Rajasthan</div>


<div style="text-align:justify"><strong>Background:</strong> The soil nematodes can affect the crops in various ways. The plant-parasitic nematodes can lead to severe yield losses. The extent of crop yield loss depends on the susceptibility of the variety or host tolerance, population density of the nematode and various environmental variables. However, no tool is available for the prediction of nematode population buildup in soil therefore it has been difficult to issue advisories for timely management of these pathogens. Here we developed a method to accurately predict the nematode population buildup in soil for its timely management.&nbsp;<br />
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<strong>Methods:</strong> Nematode population index of a plant-parasitic nematode<em> Tylenchorynchus</em> was taken from two crops i.e. mung bean and crotalaria. The model was developed considering various weather variables to predict the population of the <em>Tylenchorynchus</em> in the fields of mung bean and crotalaria. Weather parameters such as maximum and minimum temperature, relative humidity, wind speed and sunshine hours were considered for developing the model for <em>Tylenchorynchus</em> population prediction. Stepwise regression method was applied to predict the nematode population.&nbsp;<br />
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<strong>Result: </strong>The regression analysis between estimated and observed values of <em>Tylenchorynchus</em> population gave the R<sup>2</sup> value as 0.98 for mung bean and 0.87 for crotalaria. Well timed prediction can help the growers to apply the required management practices to make it beneficial economically. This method can be extended to predict the population buildup of other serious nematode pests of crops.</div>


<div>Prediction of Nematode Population Dynamics using Weather Variables in Leguminous Crops</div>


<div><strong>Background:</strong> Deltamethrin, a type 2 pyrethroid, reported toxicity said to have atoxic effect upon chronic exposure on the immune system. Since bone marrow is the source of immune cells, it is appropriate to test impact of the widely used Deltamethrin on BMSCs (Bone marrow stem cells). It was found that long term exposure to these toxic pesticides used for domestic and agricultural purposes showed to cause severe hematotoxicity and degenerative marrow diseases, precisely hypoplastic bone marrow failure condition in mammals. The present study analysed the less explored predicted effects of Deltamethrin on blood stem cells through CD45 receptor and virtual effects on cultured cells.<br />
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<strong>Methods:</strong> The effect of deltamethrin on bone marrow-derived stem cells at different concentrations and time periods was analysed. To further understand the molecular basis of how these chemicals affect the protein-ligand binding, molecular docking studies were conducted.<br />
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<strong>Result:</strong> Our results on iGEMDOCK 2.1 software used to dock CD45 receptors with DLM and the binding mode indicated that the bound ligand and the active site of CD45 had a good binding score of -68.04 and formed the five H bonds with the amino acids residues of CD45. It is found from the current study that there is an increase in cytotoxicity with increased exposure to deltamethrin. Further studies can be done to note the precise target of such agents to quickly underst and and control the effect of such drugs on a safer lifestyle.</div>


<div>Predicted and <em>In vitro </em>Effect of Deltamethrin on Bone Marrow Progenitors</div>


<div><strong>Background:</strong> Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer&rsquo;s experience on a particular type of field and crop. Predicting the crop is done by the farmer&rsquo;s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities.&nbsp;<br />
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<strong>Methods: </strong>Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year&rsquo;s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield.&nbsp;<br />
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<strong>Result:</strong> This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool.</div>


<div>Soil Data Analysis and Crop Yield Prediction in Data Mining using R-Tool</div>


<div style="text-align:justify"><strong>Background:</strong> In this study 11 height and diameter prediction models were fitted and evaluated for Himalayan Chir pine (<em>Pinus roxburghii</em>) in Jammu region of UT J and K (India). The data were collected from 50 permanent sample plots in uneven aged stands of <em>Pinus roxburghii</em> and total of 500 individual tree height and diameter measurement were used for this study. At initial stage all the models fitted resulted in significant coefficients, besides various selection criteria&rsquo;s were also used to test the predictive performance of fitted models. The results of these criteria were generated from various libraries of R studio (version 3.5.1, 2018). The models were further cross validated and results revealed Manfred (MG) and Michaelis-Menten2 (MJ) models described the highest amount of height variation in terms of fit statistics and more crucially with lowest prediction error rate as compared to other models.<br />
&nbsp;</div>

<div style="text-align:justify"><strong>Methods: </strong>The study was carried out in Jammu region of UT J and K (India). Data used in this study were collected on 50 permanent sample plots of 0.25 ha in size.In order to achieve stipulated objectives, Height diameter data on 500 trees from Jammu forest division was utilized in this study.<br />
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<div style="text-align:justify"><strong>Result:</strong> The summary statistics of height and diameter variable and the overall summary of the coefficients of various height and diameter models in Jammu forest division are presented. Almost all the coefficients of the statistical models were statistically significant which is an indication that fitted models are capturing the height diameter relationship an important aspect in context to biological realism.</div>


<p>Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine</p>


<div style="text-align: justify;">Total 40 pregnant bitches with known history of breeding; irrespective of age, parity belonging to different breeds were classified into normal gestation (NG) and threatened abortion (TA) and further divided into four groups (n=10) according to dam&rsquo;s body weight (Group I-NG, &gt;25kg BW; Group II- NG, &lt;25kg BW; Group III- TA, &gt;25kg BW and Group IV- TA&lt;25 kg BW). In impending abortion conventional treatment of antibiotics and progesterone was given for 7 days and 15/20 bitches parturited. The puppies were evaluated at birth, at 30 minutes and at 60 minutes after birth by using Apgar scoring System. The mean Apgar scores at birth, 30, 60 minutes were 7.33 &plusmn; 0.22, 9.19 &plusmn; 0.23, 10.55 &plusmn; 0.12; 6.53 &plusmn; 0.16, 9.26 &plusmn; 0.20, 10.88 &plusmn; 0.12; 6.28 &plusmn; 0.20, 7.88 &plusmn; 0.21, 9.35 &plusmn; 0.18 and 5.39 &plusmn; 0.15, 7.36 &plusmn; 0.21, 9.81 &plusmn; 0.19, respectively in group I, II, III and IV. The Apgar scores of the puppies at birth, 30 and 60 minutes were significantly higher in normal gestation than in threatened abortion irrespective of body weights. At birth 6.54, 86.27, 7.19 and 17.17, 82.83,0 per cent puppies from normal gestation and threatened abortion had low, medium and high Apgar scores, respectively. At 60 minutes, 0, 10.79, 89.21 and 0, 41.46, 58.54 per cent puppies had low, medium, high scores in normal gestation and threatened abortion, respectively.</div>

<div style="text-align: justify;">Evaluation of Puppies by Apgar Scoring System in Normal Gestation and Threatened Abortion in Canines</div>

<p><strong>Background: </strong>Timely feeding of adequate colostrum to new born calf is important to ensure transfer of passive immunity to prevent several infections. Present study has evaluated effect of time and amount of colostrum feeding on physiological parameters and health scores in Surti buffalo calves.&nbsp;<br />
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<strong>Methods:</strong> Based on amount and time of feeding colostrum after birth, 24 Surti buffalo calves were divided into four groups as T<sub>1</sub> (7%; 0-4), T<sub>2</sub> (7%; 4-8), T<sub>3</sub> (10%; 0-4) and T<sub>4</sub> (10%; 4-8). Physiological parameters (twice daily upto day 5) and naval, nasal, eye and faecal scores on day 1, 7, 14, 21, 28, 35 and 42 were recorded. Lying behavior was recorded on day 2.<br />
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<strong>Result: </strong>Significant (p&lt;0.05) interaction between treatment and weeks and nonsignificant effect of treatment was observed. Calves that received colostrum late had significantly (p&lt;0.05) higher faecal score as well as evening heart and respiration rate. Calves that received colostrum early had significant (p&lt;0.05) higher lying time. Normal scores of naval, nasal, eye and faecal indicated good health of calves. It was concluded that early feeding of colostrum @ 7 or 10% body weight within 4 hours after birth is beneficial for optimum health in buffalo calves.</p>


<p>Effect of Time and Amount of Colostrum Feeding on Physiological Parameters and Health Scores in Surti Buffalo Calves</p>


<p>In the agriculture field farmers are dependent on crops and once it is caused by disease, they can lose the proper yield. Plant illnesses are the common cause of low yields and reduced income for farmers. So, to overcome this, machine learning approach in the agriculture field is showing exponential improvement for interdisciplinary research, but in modern disease prediction systems, it is a different process that can takes more time to identify and understand the type of disease. Disease prediction is related to computer vision and machine learning to detect types of leaf disease on different plants. It&rsquo;s a long-term system that includes a variety of real-world scenarios such as, Prediction system for vine leaf disease, pomegranate leaf disease, corn leaf disease, etc. Disease prediction is the accurate determination of the disease state of plant leaves. Accurate disease prediction is one of the key requirements in data science. Presently, machine learning based methods have improved prediction accuracy for plant leafs like grapes, pomegranates, maize. However, the disease prediction performance is still required to be improved in this challenging environment. Existing disease prediction models require high computational time and storage facilities in the agriculture field. To overcome this, we have proposed a comparative study of ML for prediction and classification of crop diseases to improve the efficiency of early prediction of crop diseases.</p>


<p>Machine Learning Models for Plant Disease Prediction and Detection: A Review</p>


<div><strong>Background:</strong> Additive main and multiplicative interaction (AMMI) analysis had been exploited for multi environment trials for most of the crops. Usage of the best linear unbiased prediction (BLUP), along with AMMI tools, of the genotypes would improve the estimation of interaction effects.&nbsp;<br />
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<strong>Methods:</strong> AMMI based measures of adaptability have been enriched with the incorporation of BLUP of genotypes by new Superiority index that allowed variable weights for stability and yield of genotypes.<br />
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<strong>Result: </strong>Stability measure weighted average of absolute scores (WAASB) based on all significant interaction principal components ranked suitability of KB1754, RD3000, NDB1445 genotypes. Superiority index while weighting 0.65 and 0.35 for mean yield and stability arranged DWRB201, NDB1445, RD2552 as of stable high yield performance of barley genotypes. Corrected measure Modified AMMI Stability Value (MASV1) found RD2552, DWRB201, KB1762 and Modified AMMI Stability Value (MASV) ranked DWRB201, RD2552, KB1762. ASTAB measure achieved the desirable lower values for DWRB201 DWRB207, HUB268 genotypes. Biplot graphical analysis based on 60.7% of variation of the stability measures observed MASV1, ASTAB (AMMI based stability parameter), EV(Averages of the squared eigenvector values), SIPC (Sums of the absolute value of the IPC scores), Za (Absolute value of the relative contribution of IPCs to the interaction), W3, WAASB and MASV had been clubbed together. For the second year lower value of WAASB measure had observed for RD3016, KB1815 HUB273. Ranking of genotypes as per Superiority index found RD3017, RD2907, HUB274 as of stable high yield performance. Genotypes RD3017, RD2907 and NDB1173 pointed out by MASV1 while RD3017, RD2907, NDB1173 identified by MASV as the genotypes of choice. RD3017 NDB1173, RD2907 genotypes were selected as per values of ASTAB measure. Total of 71.8% of variation of the considered measures in biplot analysis expressed larger cluster comprised of AMMI based measures and a separate cluster of Superiority indexes as per mean, Geometric Adaptability Index (GAI) and HMGV also observed.</div>


<p>​Best Linear Unbiased Prediction with Additive Main and Multiplicative Interaction for Stability Analysis of Barley Genotypes Evaluated in Coordinated Program</p>


<div>The present study was conducted on 2100 first lactation bimonthly test day milk yield (BTDY) records of 350 Murrah buffaloes calved in between 1993 and 2017 at ICAR-NDRI, Karnal. A total of 6 BTDY records were taken from each animal at an interval of 60 days, from 6th day to 305th day of lactation. The prediction of First Lactation 305-Day Milk Yield (FL305DMY) was done by utilizing five conventional and machine learning methods viz., Centering Date Method (CDM), Test Interval Method (TIM), Ratio Method (RM), Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). Error in prediction was estimated by absolute error, percentage absolute error, average error, percentage average error, Root Mean Square Error (RMSE) and percentage RMSE. MLR was found to be the best method with the least error in prediction (5.71% RMSE), followed by ANN (5.77% RMSE). The accuracy (R2) of MLR equation including all 6 BTDY records was 91.86%. The best MLR equation for an early prediction of FL305DMY included 3 BTDY records viz., BTDY-2 (65th day), BTDY-3 (125th day) and BTDY-4 (185th day) with 85.29% R2. The study compared the conventional and computational methods for prediction of first lactation milk yield which could be used for early selection of the animals.</div>


The Morphometric Scale to Predict the Live Weight of Malpura Sheep in Semi-arid Region of Rajasthan

Prediction of Nematode Population Dynamics using Weather Variables in Leguminous Crops

Predicted and In vitro Effect of Deltamethrin on Bone Marrow Progenitors

Soil Data Analysis and Crop Yield Prediction in Data Mining using R-Tool

Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine

Evaluation of Puppies by Apgar Scoring System in Normal Gestation and Threatened Abortion in Canines

Effect of Time and Amount of Colostrum Feeding on Physiological Parameters and Health Scores in Surti Buffalo Calves

Machine Learning Models for Plant Disease Prediction and Detection: A Review

Best Linear Unbiased Prediction with Additive Main and Multiplicative Interaction for Stability Analysis of Barley Genotypes Evaluated in Coordinated Program

Prediction of First Lactation 305-day Milk Yield Based on Bimonthly Test Day Milk Yield Records in Murrah Buffaloes