<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 style="text-align: justify;">To full fill the demands of increasing population for agricultural products, increases pressure on land, water, and other natural resources, to fulfils this demands and to take decision about agricultural product requires information about production, productivity &nbsp;of crop also for proper management and carried out intercultural operations &nbsp;need advanced information about crop phenology, pest and diseases presently it is possible to generate such advanced information through well validated crop growth simulation models such as CERES, WOFOST, SUCROS, and APSIM. These models provided such advanced information based on integrate the effects of different factors viz., increasing temperature, soil productivity, also consider changes in climatic on productivity. The present paper describes the review of research work done on crop simulation model as well as covers detailed information about WOFOST model.&nbsp;</div>

Use of WOFOST model in agriculture- A review&nbsp;

<div style="text-align: justify;">The present investigation was carried out on 838 weekly test day milk yield records of first lactation pertaining to 411 HF x Deoni (Holdeo) crossbred cows spread over 38 years located at Cattle Cross Breeding Project, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani (Maharashtra). The comparison of four lactation curve models viz, Parabolic exponential function, Wood&rsquo;s gamma function, Quadratic model and Mixed log function was made in the present investigation. Mixed log function described as an excellent model for prediction of WTDY with the highest coefficient of determination (99.01 %) with comparatively lowest root-mean-square error (0.16 kg) value. The quadratic model was least fit model with the lowest coefficient of determination (90.76 %) and the highest root-mean-square error (0.51 kg) among the four models in Holdeo. The predicted error in WTDY in Parabolic exponential function, Wood&rsquo;s gamma function and mixed log function were the highest in WTDY5 and in Quadratic model it was highest in WTDY6. The errors between the observed values and predicted values were highest during the peak phase in all four models. The errors in estimated and predicted WTDMY values in initial milk yield of Holdeo cows were lower than the peak yield. The ascending phase of lactation curve was shorter than the descending phase. The gamma type function when compared to mixed log function, explained low peak yield and it is recommended that this function can give the best fit for the low yielding cows.</div>

<div style="text-align: justify;">Comparative study of fitting various lactation curve models in Holdeo crossbred cattle</div>

<div style="text-align: justify;">Drying is a complex phenomenon and study of drying kinetics and modelling is very important for describing the moisture movement with respect to time and predicting the dryer performance. Considering this fact, vacuum drying characteristics of cherry pepper was studied at different drying temperatures (50, 60 and 70oC). Cherry pepper were dried from initial moisture content of about 400% (dry basis) to 13 &ndash; 14.5% (dry basis) at different temperatures at 630 mm Hg vacuum. Time required to dry cherry peppers at 50, 60 and 70p C plate temperatures were 19 h, 9.75 h and 8 h respectively. Moisture reduction of cherry pepper at various temperatures was modelled using thin-layer models viz. Lewis, Page, Modified Page and Henderson &amp; Pabis model. Based on highest value of coefficient of determination, lowest values of reduced chi square and root mean square error, Modified Page model was found to be the best fit. Moisture diffusivity increased from 6.27 &times; 10-10 to 1.9&times; 10-9m2s-1 as plate temperature increased from 50 &ndash; 70oC. Activation energy was estimated to be 50.98 kJkgmol-1.</div>

Modelling of vacuum drying of cherry pepper

<div>Background: Lactation curve patterns are currently integrated in dairy cow&rsquo;s management software. Lactation curve modeling is useful for monitoring individual yields for diet planning, determining optimum strategies for insemination and genetic evaluation. It also helps for predicting expected missing values on field records and gives concise summary of biological efficiency and persistency of dairy cows. 
 
Methods: The study was aimed to characterize the lactation curve pattern for crossbred dairy cattle using different non-linear models. During the period 1991 to 2018, daily milk yield (DMY) consisted of 281698 records of 750 crossbred dairy cows maintained at Livestock Farms. GADVASU, Ludhiana, were collected for the study. Different non-linear models viz. exponential decline function (EDF), parabolic exponential model (PEM), inverse polynomial model (IPM), gamma-type function (GTF), mixed log function (MLF) and Ali and Schaeffer model (ASF) were used for the analysis. The model(s) that best fit and describe the curve characteristics was selected on the basis of coefficient of determination (R2), coefficient of variation (CV), Akaike information criterion (AIC)&nbsp;and mean square error (MSE). 
 
Result: The study clearly revealed that the PRM gave highest fit to DMY data with R2, MSE, AIC and CV values of 98.10%, 0.087, -743.31 and 2.37%, respectively. The IPM had also best fitted the observed DMY data with highest R2 (98.05%), lower MSE (0.089), low AIC (-735.8972) and lower CV (2.40%) values. The fitting of observed DMY data with predicted DMY were also found to be higher in the MLF (R2= 96.46%, MSE= 0.159, AIC= -558.16 and CV= 3.21%) and GTF (R2= 95.85%, MSE= 0.190, AIC= -505.24 and CV= 3.50%), whilst the EDF and PEM Models depicted relatively low fit to the DMY data when compared with the other non-linear models. However, IPM and GTF models can be used for accurate prediction of daily milk yield in the crossbred cattle population because they were typical standard lactation curves.</div>

<div>Characterization of Lactation Curve Patterns using Non-linear Models in Crossbred Dairy Cattle</div>

<div style="text-align: justify;">In this study, four lactation curve models viz. Gamma-type function (GF), Mixed log function (MLF), Exponential function (EF) and Polynomial regression function (PRF) were fitted to predict the weekly test-day milk yields pertaining to 961 Murrah buffaloes progeny of 101 sires spread over a period of 36 years (1977-2012) maintained at ICAR-NDRI, Karnal. The PRF was the best fit with highest R2 (99.3%) and lowest RMSE (0.3%) which was used subsequently to estimate the first lactation 305-day or less milk yield (FL305DMY) from weekly test-day milk yields. The sires were evaluated using four linear models viz. Least Squares(LSQ), Simple Regressed Least Squares (SRLS), Sire (BLUP-SM) and animal (BLUP-AM) models from actual as well as PRF predicted FL305DMYs. BLUP-AM was the most efficient, among the sire evaluation models, in predicting the breeding values. Sire evaluation on the basis of predicted data (using PRF) was similar to that on the basis of actual data indicating that the best fitted lactation curve function can be used as an alternative for early animal evaluation and reduction of generation interval.</div>

Modeling lactation curve for genetic evaluation of Murrah buffaloes

The purpose of this study was to classify Anatolian buffalo using Gaussian mixture regression model according to discrete and continuous environmental effects. Gaussian mixture model performs separately regression analysis both within and between groups. This is an important property of Gaussian mixture models which makes it different from other multivariate statistical methods. The data were obtained from 1455 Anatolian buffalo lactation milk yield records reared in seven different locations in Bitlis province, Turkey. Age of dam, lactation duration and locations were considered as environmental effects on lactation milk yield. Data set was divided into three homogenous subgroups with respect to AIC and BIC in the Gaussian mixture regression, based on environmental effects on lactation milk yield. Estimated mean for lactation milk yields and mixing probabilities for the first, second and third subgroups were determined as 1494.33 kg (16.9%), 540.33 kg (45.2%) and 847.61 (37.9%), respectively. The numbers of buffalo in each subgroup according to mixing probability were obtained as 159, 756, and 540 for the first, second, and third groups, respectively. The effects of lactation period, age of dam and villages were found statistically significant on lactation milk yield in subgroup 1 that was highest mean for lactation milk yield (p&lt;0.01). &nbsp; In conclusion, results showed that Gaussian mixture regression was an important tool for classifying quantitative traits considering environmental effects in animal breeding.

Modeling with Gaussian mixture regression for lactation&nbsp;milk yield in Anatolian buffaloes

Background: The profitability of ruminant production hinges on the reliable availability of top-quality fodder. Nevertheless, a concerning issue is emerging: a substantial gap between the supply and demand of fodder. This imbalance predominantly affects small and marginal dairy farmers, who possess limited land. These farmers prioritize cash crop cultivation on their available land while dedicating only minimal space to fodder production. The rising cost of external feed supplements further intensifies the burden, affecting both milk yield and animal health. Inadequate fodder leads to nutritional deficiencies, reduced reproductive efficiency and increased susceptibility to diseases. Ensuring year-round access to diverse, nutrient-rich green fodder is, therefore, essential for improving livestock productivity and farmer income. Integrated fodder production models that maximize yield even in limited spaces can serve as a sustainable solution. Empowering farmers with low-cost, space-efficient techniques for cultivating multiple fodder types can significantly bridge the gap and support resilient dairy farming systems. 
 
Methods: In this regard, to address the prevailing fodder scarcity challenge, Bhagavatula Charitable Trust&ndash;Krishi Vigyan Kendra (BCT-KVK), Anakapalli, has been actively demonstrating a multi-cut fodder production model in a compact area of 10 cents. This model is specifically designed to enable small to medium-scale dairy farmers to access a consistent and diverse supply of green fodder throughout the year in its operational villages across the Anakapalli district. The demonstration plot incorporates a strategic mix of high-biomass-yielding grasses like Super Napier, cereal fodders (such as fodder maize and sorghum) and leguminous fodders (like cowpea and hedge lucerne) thereby ensuring a balanced nutritional profile for ruminants. In addition, tree fodders such as Subabul, Sesbania, Moringa and Glyricidia are planted around the boundaries to serve as a perennial source of green fodder and to provide essential vitamins and minerals. This integrated approach contrasts with the conventional practice of feeding carbohydrate-dominant grasses alone, which often leads to poor nutrient balance, reduced milk productivity and subclinical deficiencies. The model is designed for continuous harvesting and re-growth, ensuring sustainable fodder availability and minimizing the dependency on purchased feed resources. 
 
Result: With the multi-cut fodder production model, farmers obtained average milk yields of 2,500-3,000 litres per lactation in crossbred HF cows. The average milk fat and SNF content were consistently above 4% and 8%, respectively. This improvement is attributed to the year-round availability of nutritious green fodder, which enhanced rumen function, digestion and overall animal health. The inclusion of leguminous and tree fodders helped bridge nutrient gaps, leading to better metabolic efficiency and sustained lactation performance. Farmers also reported reduced dependency on commercial feed and improved profitability per animal due to better feed conversion efficiency and reduced health-related issues. 
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BLUP Estimation and Genotype Stability in Arachis hypogaea L. Variety Testing using Mixed Model Equations

Wheat Yield Modelling using Infocrop and DSSAT Crop Simulation Models

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

Use of WOFOST model in agriculture- A review

Comparative study of fitting various lactation curve models in Holdeo crossbred cattle

Modelling of vacuum drying of cherry pepper

Characterization of Lactation Curve Patterns using Non-linear Models in Crossbred Dairy Cattle

Modeling lactation curve for genetic evaluation of Murrah buffaloes

Modeling with Gaussian mixture regression for lactation milk yield in Anatolian buffaloes

Empowering Small Farmers with the 10- Cent Multi-cut Fodder Model