<div>Background: E-learning is now being used in a variety of industries, including agriculture. The use of information and communication technologies (ICTs) is seen as essential for overcoming the barriers that a developing country faces in all areas and closing the digital divide. Farmers must be prepared with the most recent breakthroughs in ICTs in order to take advantage of new opportunities in the global market. 
 
Methods: By designing an e-learning module and providing farmers with a self-learning framework, the study focuses on the use of ICT instruments in climate-smart horticulture. The psychological factors in this study were investigated using the E-Learning Acceptance Model (ELAM), which was derived from the Technology Acceptance Model (TAM). A quantitative survey of 200 farmers were conducted. Structural equation modelling (SEM) was used to test the research hypotheses. 
 
Result: ELAM has enough predictive ability to explain farmers&rsquo; behavioural intention to utilise an e-learning module in a real world. In total, the result supports five ELAM hypotheses. The SEM findings indicate that ELAM is a valid model. In ELAM, partial mediation was also observed.</div>

Acceptance of an E-learning Module on Climate-smart Horticulture: A Structural Equation Model

<div style="text-align: justify;">The 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase is one of the essential enzymes of shikimate pathway and is verified as a specific target of broad-spectrum herbicide glyphosate. It is important to get insights into three-dimensional (3D) structure of this enzyme for engineering new herbicides as well as herbicide resistant crops. The information about 3D structure of EPSP synthase enzyme is not yet available in protein database for any of the plant species. Therefore, we revealed the homology model of Phaseolus vulgaris EPSP synthase protein using the structure of EPSP synthase from E. coli as template. The resulting model structure was refined by RAMPAGE server, ERRAT and Verify3D. Ramachandran plot analysis showed that conformations for 95.6% of amino acid residues are within the most favoured region. The phylogenetic tree (constructed using MAFFT) separated EPSP synthases of bacteria, monocot and dicot plants into distinct clusters. Our study generated reliable 3D model structure of EPSP synthase in P. vulgaris, which can be used for future studies.</div>

In silico analysis, structural modeling and phylogenetic analysis of EPSP synthase of Phaseolus vulgaris&nbsp; &nbsp;

<div>The objective of the present study was to study the effect of mastitis on milk yield in dairy cows by accounting the correlations among repeated records on monthly milk yield. The data were collected from monthly milk yield records of 482 dairy cows maintained at three Military Dairy Farms at Agra, Bareilly and Lucknow of Uttar Pradesh (India) for the period from 2011 to 2013. The overall prevalence of mastitis during study period was observed as 24.69%. Mixed modelling using four covariance structures (simple, compound symmetry, autoregressive and unstructured) was used to estimate effect of mastitis on milk yield by adjusting the effects of breed, mastitis, age, calving season, farm, month in lactation and random effects of each cow. On the basis of goodness fit criteria, unstructured covariance structure showed reliable results and estimated 36.76 kg losses due to mastitis in monthly milk yield in dairy cows.</div>

<div>Modelling the Effect of Mastitis on Milk Yield in Dairy Cows using Covariance Structures Fitted to Repeated Measures</div>

<div>Background: One of the major goals of plant breeding is the selection of high yielding superior cultivars having wide or specific adaptation. However, there is a fluctuation in the annual production due to the sensitive behaviour of the genotypes under different environmental conditions referred to as Genotype by Environment Interaction (GEI). The current study aimed to study the contribution of GEI for the adaptation of groundnut lines for spring and/or kharif season. 
 
Methods: To assess the contribution of GEI, Multi-Environment Trials (METs) were conducted for 40 confectionery purpose groundnut genotypes at F9 generation along with checks, across three locations for two seasons (spring and kharif). The contribution of environmental effects, genotypic values and genotype &times; environment interaction values were obtained from genotypic variance-covariance matrix Gi = &Sigma;g⨂A using mixed models (MM) in Best linear unbiased predictions (BLUPs).The pooled data was first partitioned into fixed effects of sites across the seasons and BLUP genotypic values (Ggge). The BLUP genotypic values are further partitioned into genetic value (Gg) and their interaction with the environment (Gge) for the adaptability of genotypes across seasons.&nbsp; 
 
Result: The results of MET revealed the presence of significant crossover interaction. The demarcation of advance breeding lines for adaptability across the environment as well as for season specific adaptation was done for variety testing. Genotypes having moderate to high Gge values along with high Gg values in spring than kharif, owing to their better performance during the spring season. CGL-11, CGL-23 and CGL-04 were the highest yielding genotypes, with quite high Gge values. This is due to the more favourable environmental conditions interacting positively with genotypes during the spring. Thus, the high Gg value(s) of genotype(s) alone is not a capable factor for commercialization as Gge value is the deciding factor for the adaptability for the targeted season.</div>

BLUP Estimation and Genotype Stability in Arachis hypogaea L. Variety Testing using Mixed Model Equations

Background: Wheat is an important crop of India That is grown in the winter season (October to April). A field study was conducted for nine plots having a dimension of 3 &times; 2 meter and different nutrient was applied to test the biomass yield. The objectives are to describe (a): To conduct field experiments under controlled conditions and vary the dosage of fertilizers for different treatments. (b): To observe the variation in crop yield of wheat with different dosages of fertilizers. (c): To model the crop yield of different treatments using InfoCrop and DSSAT crop models. 
 
Methods: An experiment was conducted for Wheat crop having a dimension of 3 &times; 2 meter at the Indian Institute of Technology, UK-India. Three plots (C1, C2, C3) was given control fertilizer supply while plot (F1, F2, F3) given -25%, +25%, +50% and plot (N1, N2, N3) given -25%, +25%, +50% w.r.t to control. The simulation model Infocrop and DSSAT were simulated to check test the model. 
 
Result: In this study, two crop simulation models namely Infocrop and DSSAT were used to predict the yield of wheat crops. Field experiments were carried out at a farm site at Roorkee, India and different treatments of fertilizers are applied to the field plots. In total, seven fertilizer treatments are used: C (controlled with a normal dosage of fertilizers), F1, F2, F3 (varying amount of applied farmyard manure), N1, N2 and N3 (varying amount of applied urea). The objective of the study was to evaluate yields of the seven treatments using Infocrop and DSSAT crop models and compare them to the observed values of yields. The grain yield of the controlled treatment (C1, C2, C3) was predicted accurately by both models. There was a significant variation in the grain yields of the F1, F2, F3 treatments with the application of farm yard manure (FYM). DSSAT under-predicted the grain yields for the plot F1, F2, F3 treatments and over-predicted these for N1, N2, N3 treatments. Overall, Infocrop, which was developed and validated for Indian conditions, simulated the grain yield more accurately as compared to the DSSAT model under varying applications of FYM and urea dosages.

Wheat Yield Modelling using Infocrop and DSSAT Crop Simulation Models

<div>Background: A reliable method for controlling the sex of farm animals has the potential to revolutionize dairy farming. This can only be achieved by use of a method capable of separating X and Y chromosome bearing spermatozoa at lower cost and causing no significant damages to sperm viability. Realizing significance of buffaloes in Indian dairy farming, present study was aimed to develop appropriate density gradient model for enrichment of X chromosome bearing spermatozoa population in semen of Murrah buffalo bull.&nbsp; 
 
Methods: Density gradient centrifugation (DGC) technique was employed for enriching X chromosome spermatozoa in buffalo bull semen using four gradient media viz., Percoll, Optiprep, Ficoll and Sucrose. Percentage enrichment of X chromosome bearing spermatozoa in all the DGC models was determined by SYBR green based Real Time PCR. 
 
Result: Our investigations revealed that number of layers and centrifugation speed (g) factors in a density gradient centrifugation model, have significant effect on the percentage enrichment of X chromosome content in semen samples.Three layers, four layers and five layers density gradient centrifugation models showed significant differences (P&lt;0.05) in the sex ratio towards X spermatozoa population in semen (61.72%&plusmn;0.81, 64.55%&plusmn;0.26 and 67.31&plusmn;0.33%) respectively. Density gradient centrifugation models with centrifugation speed (g) 200x g and 300x g also showed significant differences (P&lt;0.05) in the sex ratio towards X spermatozoa population in semen (66.36%&plusmn;0.25 and 62.69&plusmn;0.22%) respectively. Out of all DGC models, Optiprep density gradient model with 5 layers and centrifugation at 200xg (O3) yielded maximum enrichment (72.4%&plusmn;1.38). In conclusion, O3 poses to be a promising model for enrichment of X spermatozoa in buffalo bull semen.</div>

<div>Five Layered Optiprep based Density Gradient Model is a Promising Model for Enrichment of Viable X Chromosome Bearing Spermatozoa in Bubalus bubalis</div>

<div>Background: Expression of the heat shock proteins (Hsp) is responsible for the protection from adverse climatic changes particularly heat stress in Common carp&nbsp;(Cyprinus carpio). Although, with advancement of molecular techniques, Hsp70 protein has been isolated but this protein needs to be characterized by both physicochemically and structurally for the functional annotation of fish genome. So this current study was undertaken with aim of generation of various protein models and also for thorough physiochemical characterization of this protein. 
 
Methods: In this study, Hsp70 protein of common carp&nbsp;was characterized by both physiochemical and structurally through insilco platform and as the crystal structure of this protein is not available, protein models were created though homology modelling upon Modeller version 9.21, Phyre2 and Swiss-model and then the generated predicted models were evaluated through Rampage, Errat, Verify 3D, ProQ and ProSA analysis. 
 
Result: Our investigation showed that this protein is very stable, hydrophilic with no disulphide bonds and the protein models which were generated from this study, are of good quality with z value of - 9.58, -9.48 and -10.93 and quality factor of 82.56, 59.43 and 95.27 respectively. So this study was concluded that the generated Hsp70 protein models would provide an avenue for the other researchers for development of high-throughput gene function assignment in fish.</div>

Comparative Homology Modeling and Physicochemical Characterization of Cyprinus carpio Hsp70 Protein

<div>The present study was conducted to estimate and compare the three types of growth models in Hanwoo steer (Bos aurus coreanae). The Gompertz, Von Bertalanffy, and Logistic nonlinear models were used. A total of 2,239 Hanwoo steers (Bos taurus coreanae) from 6 months to 24 months old (2003 to 2014) and 8,916 growth data from the Hanwoo improvement Center were used to estimate the growth model which included three parameters. These parameters were A, mature body weight; b, growth ratio; and k, intrinsic growth rate. Regression equations using the Gompertz, Von Bertalanffy, and Logistic models were calculated as respectively. The mean square errors (MSEs) for each model were 1945.9, 1958.7, and 1935.0, respectively. The equation using the Logistic model showed the lowest value among three models. The estimated birth weights from the Gompertz, Von Bertalanffy, and Logistic models were 50.35 kg, 36.94 kg, and 74.13 kg, respectively. Furthermore, the estimated mature weights from the Gompertz, Von Bertalanffy, and Logistic models were 919.0 kg, 1043.3 kg, and 770.0 kg, respectively. In addition, the estimated age and body weight at inflection from the Gompertz, Von Bertalanffy, and Logistic models were 349.0 days and 338.1 kg, 317.9 days and 308.2 kg, and 397.8 days and 385.0 kg, respectively. Based on the results, we concluded that the regression equation using the Logistic model was the most appropriate among the growth models for measuring data. However, further studies would be needed in order to obtain more accurate parameters using a much wider period of data from birth to shipping age.&nbsp;</div>

<div>Comparison and estimation of the growth curve models of Hanwoo steer (Bos taurus coreanae)</div>

Background: Breeding programme is mainly structured and evaluated on the basis of first lactation 305-day milk yield (FL305DMY) of dairy animals. Early prediction of 305-day milk yield using test-day records is crucial for early evaluation of elite animals and to reduce the cost of data recording and animal rearing.&nbsp; 
 
Methods: In the study, the prediction efficiency of conventional models viz. centering date method (CDM), test interval method (TIM), ratio method (RM) and multiple linear regression (MLR) was compared with the newly evolved machine learning connectionist model named Artificial Neural Network (ANN). Data on 3,850 monthly test-day milk yield (MTDY) records of 809 Murrah buffaloes were utilized for the prediction of FL305DMY. The prediction efficiency of the models was compared based on absolute error, average error, root mean square error (RMSE) and their respective percentages. An attempt was thereafter made for the early-stage prediction of FL305DMY.&nbsp; 
 
Result: MLR was identified as the most accurate model with least error in prediction (4.19% RMSE), followed by ANN model (4.28% RMSE). The prediction accuracy for the regression equation incorporating all the 11 MTDY records was found to be 95.68 per cent. The optimal regression equation for early-stage prediction of FL305DMY consisted of four variables viz. MTDY-3 (65th day), MTDY-4 (95th day), MTDY-5 (125th day) and MTDY-7 (185th day) showed a R2 value of 87.02 per cent. It was inferred from the study that the most effective early-stage prediction of FL305DMY could be achieved by 185th day of lactation, offering a valuable tool for early and efficient selection of elite animals using monthly test-day milk yield records.

Comparative Efficiency of Conventional and Connectionist Models in Prediction of First Lactation 305-day Milk Yield in Murrah Buffaloes

<div>Background: A properly designed drip irrigation system increases the efficiency of irrigation water and its design is based on the soil wetting pattern. 
&nbsp; 
Methods: In this study, a numerical model has been developed using one-dimensional Richard&rsquo;s Equation to simulate soil wetting pattern under drip irrigation. A fully implicit finite-difference scheme has been used to solve the governing equations with suitable initial and boundary conditions. An experiment was also conducted to analyse the wetting pattern in sandy loam soil. Wetting front computed by numerical model has been compared with the experimental wetting front at different time interval of drip irrigation system. The present model is also validated with the results available in the literature. 
 
Result:&nbsp;Statistical indices - root mean square error, percentage bias and coefficient of determination have been used to evaluate the model&rsquo;s performance. The computed results are closely matched with the and experimental results with RMSE is less than 3, PBIAS less than 6% and R2 greater than 0.96 and also with the earlier models with RMSE is less than 0.01, PBIAS less than 4% and R2 greater than 0.98, It shows that the present model can simulate soil wetting pattern in the drip irrigation system.</div>