Background: At the present time, various technologies have been used in the development of modern agriculture. The technology used to help in the development of agricultural farms is called a smart farm. Mashed Mushroom is a food due to their usefulness and high nutritional and good income, as this result, the researcher was interested in the use of technology and innovation of smart farms in the mashed mushroom cultivation house and find a mathematical model of the optimum temperature and humidity for growing mashed mushrooms. Furthermore, find a mathematical model of the yield amount of mashed mushrooms against temperature and humidity. 
 
Methods: There are 5 methods in this research, to be composed of studying the problem, planning the operation process, using technology and innovation in smart farms, collecting data and finally finding a mathematical model of the optimum temperature and humidity for growing mashed mushrooms. In addition, a mathematical model of the yield of mashed mushrooms was obtained.&nbsp; 
 
Result: The results showed that the mathematical model of the optimum temperature for mashed mushroom was T= 0.3267t3-2.83t2+7.1433t+20, the most appropriate temperature was 24.95&plusmn;5.557&deg;C at the average andthe mathematical model of the optimum humidity for mashed mushroom was H= -0.8417t3+4.82-0t2.1883t+59.65, the most appropriate humidity was 74.58&plusmn;15.451% at the average. In addition, the mathematical model of product yield of mashed mushrooms was&nbsp;<img alt="" src="data:image/png;base64,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" />= -1.479+0.055T+0.028H when temperature T and humidity H were determined.

Mathematical Model for Optimum Temperature and Humidity Determination of Mashed Mushroom in Greenhouses by using Smart Farm Technology and Innovation

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;">Homestead farming has been the backbone agricultural economy of Kerala but the &nbsp;productivity of the homesteads in Kerala has yet to reach an &nbsp;acceptable &nbsp;level. In this paper the possibility of increasing the contribution of this sector through proper crop planning is sought. The optimum model was developed by using linear programming (LP) technique. The constraints included in the analysis were total area, intercropped area, investment amount and population of each enterprise. The optimum model reveal the scope of 22.83 per cent enhancement in net return as compared to net return from the existing plan. Sensitivity analysis of the optimum model revealed that further enhancement of net return in The agro-ecological region could be achieved by increasing the cropping intensity in the underutilized intercropped area and changing the binding enterprises.</div>

Determination of optimum crop mix for crop cultivation in Kerala homesteads

<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;">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>Optimal allocation of surface water and groundwater is the key factor for conjunctive water management. Proper planning is necessary for optimal allocation of water for maximizing crop yield benefits and/or minimizing water quality problems. With the advent of computers, by the late 1960s, computer-aided mathematical models came into use to help decision-makers in planning conjunctive water management. Optimization models were popular in the early decades. Then researchers came to know the advantage of mimicking the situations and analyzing them before arriving at optimal solutions. Thus, simulation models and integrated simulation-optimization models came into existence. Multi-objective models were developed to solve more complicated physical situations. The computational burden of running thousands or lakhs of simulations before getting an optimum solution, generated evolution algorithms like ANN and GA to reduce the running time of models and obtain a more accurate solution. Even now we can&rsquo;t say the modelling approach is perfect to solve the real-world water-related issues that involve complicated physical phenomenon. A model which is more accurate, simpler and very near to real situations, may be developed in near future.</div>

Modelling Techniques for Conjunctive Water Management:&nbsp;A Review

<div style="text-align: justify;">Early selection of bulls having optimum age at first semen freezing play an important role in improving reproductive performance in a dairy herd. Twenty seven years data (1987-2013) &nbsp;on age at first semen freezing (AAFSF), conception rate based on first A.I. (CRFAI) , overall conception rate (OCR) and birth weight (B.WT) of 41 Sahiwal bulls belonging to 8 sets of Sahiwal improvement programme at ICAR-National Dairy Research Institute, Karnal, Haryana, India; were adjusted against environmental effects and subsequently analysed. Simple and multiple regression models were used for prediction of CRFAI and OCR of Sahiwal bulls. Among the three developed models (I to III), it was observed that Model III having age at first semen freezing and birth weight fulfil the accuracy of model i.e.; having high coefficient of determination (R2) value (CRFAI = 67% and OCR= 69%), low mean error sum of square (MSSe), low conceptual predictive value (CP value) and low Bayesian information criterion (BIC). The results revealed that optimum age at first semen freezing of Sahiwal bulls should be 2.5 - 3.0 years for 3.10% higher conception rate based on first A.I. (48.86%) and 4.39% higher overall conception rate (48.78%) in comparison to Sahiwal bulls with more than 3.5 years of age (CRFAI :- &nbsp;45.76% and OCR :- 44.39%).&nbsp;</div>

<div style="text-align: justify;">Optimum age at first semen freezing in relation to fertility of Sahiwal breeding bulls</div>

<div>Background: The determination of the soil moisture characteristic is a tedious, lengthy and costly laboratory methods. However, for the specific soil texture, once it is known, the soil moisture at various tension can be simulated through modelling approach which eliminates the requirements of performing the experiments every time. Therefore, the attempt was made for the determinations of the soil moisture characteristics during the year-2019 and validating the various models&rsquo; using these empirical data. 
&nbsp;&nbsp; 
Methods: The present research covers the experimentation during the year 2019 for determination of the soil moisture characteristics and fitting the obtained empirical data to mathematical frames of the various models. The moisture characteristics was obtained by measuring different moisture content values at different suction values through two experiments viz. wetting cycle (Absorption phase) and drying cycle (Desorption phase). The desorption curve was obtained by using the pressure plate apparatus and the wetting curve was measured through the capillary rise open tube method. 
&nbsp; 
Result: The saturation, field capacity, permanent wilting, residual moisture content, void ratio and saturated hydraulic conductivity were found as 43.5%v/v, 25.88%v/v, 12.05%v/v, 8.1%v/v. 0.77 and 0.2808 m/day respectively. Among the fitted models, the van Genuchten model was found best fit to observe data to simulate the desorption and sorption phase of the soil moisture retention characteristics. The soil moisture retention characteristics of the sorption and desorption phases indicated a hysteresis of approximately two log cycles.&nbsp;</div>

Mathematical Model for Optimum Temperature and Humidity Determination of Mashed Mushroom in Greenhouses by using Smart Farm Technology and Innovation

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

Determination of optimum crop mix for crop cultivation in Kerala homesteads

Use of WOFOST model in agriculture- A review

Modelling of vacuum drying of cherry pepper

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

Modelling Techniques for Conjunctive Water Management: A Review

Optimum age at first semen freezing in relation to fertility of Sahiwal breeding bulls

Validation of Models for Simulating the Soil Moisture Characteristics