<div style="text-align: justify;">Anthracnose of green gram caused by Colletotrichum truncatum (Schwein.) Andrus &amp; W.D. Moore occurs severely in Indo- gangetic plains every year. Experiment was conducted for two consecutive years (2013 and 2014) to assess yield losses to develop yield loss models. The linear regression models were developed by two parameters i. e. percent yield loss and avoidable yield loss as dependant variable and area under disease progress curve (AUDPC) as independent variable. The two years pooled mean revealed that a drop of 0.3 to 0.33 qha-1 in pod yield and a drop of 0.16 to 0.20% in avoidable yield loss of pod yield due to every 1% increase in AUDPC. Three sprays of Propiconazole 25 EC (0.1%) at 10 days interval beginning from 20 days after sowing resulted in lowest anthracnose under moderate disease pressure and four sprays for high disease pressure resulted in highest pod yield of 5.17q/ha and 5.08q/ha respectively. Highest avoidable losses of pod yield and percent yield loss over potential yield due to anthracnose were 48.82% and 3.55% for three sprays and 49.74% and 1.77% for four sprays respectively. The result also showed that three sprays of fungicide (Propiconazole 25 EC @ 0.1%) resulted in minimizing the disease and maximizing the profit by high benefit cost ratio (B:C) ratio.</div>

Measuring and assessing the yield loss and yield loss model of green gram due to Anthracnose of green gram (Vigna radiata)

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

Background: Finger millet cultivation in Alfisols (Typic rhodustalf, Palaviduthi soil series) demands an optimal integrated plant nutrient supply to achieve desired yields. In order to develop the soil test crop response to fertilizer and farm yard manure (FYM) through integrated plant nutrition system (STCR-IPNS) in terms of nutrient uptake, yield and pre-sowing STVs, a field investigation on Finger millet [Eleusine coracana (L.) Gaertn.] was conducted at the farmer&rsquo;s field of Thoppupatti village, Dindigul district, Southern Agro-climatic zone of Tamil Nadu, Southern India during December 2021-August 2022.&nbsp; 
 
Methods: By adopting the Inductive cum Targeted Yield Model before the test crop of Finger millet, a artificial soil fertility gradient was created by the application of graded levels of NPK fertilizers with fodder sorghum as a gradient crop. 
 
Result: The basic data viz., N, P and K nutrients for producing 1 tonne of finger millet grain were 48 kg, 23 kg and 44 kg; the soil contributed 14.86%, 19.18% and 18.95%; fertilizer contributed 44.16%, 32.79% and 59.75% for N, P and K and farmyard manure (FYM) contributed 28.47%, 11.77% and 33.78%, respectively. Fertilizer prescription equations for finger millet and ready reckoners for the operational range of soil test results for the intended yield target under NPK alone and IPNS (NPK + FYM) were produced using the basic data.

Sustaining Soil Fertility and Yield by Inductive cum Targeted Yield Model-based Fertilizer Prescriptions for Finger millet (Eleusine coracana L.) in Alfisols of Tamil Nadu, Southern India

<div>Background: Weed populations reduce crop yields by influencing the pattern of crop growth and development throughout the season and by directly competing with the crop for limiting precious resources, like light, water or nutrients. A number of the factors that influence magnitude of crop yield losses from weed interference include the timing of weed emergence relative to the crop, weed density, pattern of weed growth and development. Weed management is one among the foremost critical factors influencing crop yield. By providing a window of weed-free growth early within the season, the size advantage that crop seedlings have over weeds can be utilized to reduce the intensity of direct competition for resources at the stages of crop development when yield is being determined. Weeds are identified as a significance drawback since they create biotic stress in realizing the genetic yield potential of this valuable crop. 
 
Methods: A field experiment was carried out during the rabi season of 2010-11 to 2014-15 at ICAR- National Research Centre on Seed Spices, Ajmer, Rajasthan to estimate the yield losses because of weed infestation and to work out the critical period for weed control (CPWC) in fenugreek (Trigonella foenum-graecum L.). There were 14 treatments comprising of initial weedy periods of 15, 30, 45, 60, 75, 90 days after sowing (DAS) and weed free period upto 15, 30, 45, 60, 75, 90 DAS along with weedy till harvest (un-weeded check) and weed free till harvest (weed free check). The treatments were replicated three times in a randomized block design. 
 
Result: Maximum pod length (cm), number of pods/plant, number of seed/pod, test weight (g), seed yield (kg/ha), straw yield (kg/ha) and harvesting index (%) to the tune of 9.47, 41.2, 16.67, 12.60, 2297, 4954 and 31.73 were recorded at weed free throughout growth period. Weed infestation up to 30, 45, 60, 75, 90 DAS and weedy throughout growth period reduced the seed yield significantly to the magnitude of 567.3, 801.7, 913.3, 1253.3, 1393.7 and 1736.3 kg/ha. Yield losses in fenugreek increase from 2.95 kg/ha/day at 15 days weed infestation to 12.31 kg/ha/day at 135 days weed infestation. Maximum gross return (`104248/ha), net return (`67018/ha) were obtained at weed free throughout growth period with the B:C ratio 2.80 among the weed free treatments. The employment of response curves with weed free or weed competition period showed that seed yield of fenugreek&nbsp;were&nbsp;the highest with the field&nbsp;free&nbsp;from weeds throughout the growth period and the critical period of weed/fenugreek competition was 32 days after sowing and lies between 30-45 days after sowing. In regression approach for fenugreek seed yield and weed free or weed competition period the quadratic function was fitted to estimate the expected yields which had the high significant with the data recorded and have the highest values of R2. If we predict the seed yield losses based on the quadratic model for weed free period model, it declined from 42.04 at 0 DAS to 0.0% at 135 days. Similarly, weed infestation period model predicted the seed yield losses increased from 0.00 at 0 days weed infestation to 73.42% at 135 days. Critical period for weed competition in fenugreek was found to be 32 days after sowing. The CPWC is helpful for making decisions on the necessity for and timing of weed control.</div>

<div>Yield Losses Due to Weeds, Critical Period for Weed-crop Competition in Fenugreek (Trigonella foenum-graecum L.)&nbsp;</div>

<div>Background: The present study is region specific carried out to see the relation of bean anthracnose with weather in Kashmir and also to assess the yield loss in bean vis-&agrave;-vis anthracnose. Such studies form basis for deciding disease suppressive crop geometry and also sowing date alterations besides helping in disease predictions. The yield loss study in particular reveals the importance of a disease before any management strategies are worked out. 
 
Methods: Epidemiology was conducted under natural epiphytotic conditions using a susceptible bean cv. Shalimar Rajmash-1 for recording disease development at weekly intervals. Yield loss assessment was also recorded on Shalimar Rajmash-1 where different disease levels were created by inoculating at different phonological stages after maturity the crop stand disease free. 
 
Conclusion: During cropping season, the incidence and intensity of bean anthracnose developed to the extent of 77.06 and 54.51 per cent, when average temperature, average RH and weekly rain fall ranged from 16.6-23.8oC, 62.2-76.5 per cent and 1.7-48.2 mm, respectively. However, the highest periodic increase in disease incidence (12.62%) was recorded during 24th standard metrological week which coincided with highest rain fall and the increase in periodic disease intensity was higher (&gt; 10%) when RH was above 70 per cent. The yield loss in bean vis-a-vis variable levels of disease as created by inoculating different unit population of bean plants at different phenological stages revealed significant and positive correlation of bean anthracnose and yield loss. Highest yield loss (68.42%) was recorded in populations where the disease started at 1st trifoliate stage which then reached maximum intensity of 81.09 per cent at physiological maturity. The terminal disease intensity in population where disease started at pod filling was significantly low (18.59%) and caused least yield loss of 10.95 per cent. The study reveals that bean anthracnose caused significant yield loss when it appeared at any stage upto flowering.</div>

Bean Anthracnose (Colletotrichum lindemuthianum) in Kashmir: Epidemiology and Yield Loss Assessment

Six genotypes viz., Palam Basmati-1, Palam Red Rice, HPR 1068, Arize 6129, HPR 2143 and Kasturi Basmati were evaluated to assess the crop/ yield losses by paddy leaf folder, Cnaphalocrocis medinalis at CSK Himachal Pradesh Krishi Vishvidyalaya, Rice and Wheat Research Centre, Malan, during Kharif 2012. The leaf infestation (47.1%) at maturity stage was significantly lower in HPR 2143 as compared to other varieties. However, Kasturi Basmati was having significantly higher leaf infestation (77.2%) as compared to Arize 6129 (59.2%), HPR 1068 (57.7%), Palam Red Rice (57.0%) and HPR 2143 (47.1%) and was found at par with Palam Basmati-1 (73.3%).Yield data revealed that the grain yield was higher in protected plots as compared to unprotected plots irrespective of the varieties. The highest grain yield was recorded in protected plots of Arize 6129 (76.45 q/ha) followed by Kasturi Basmati (59.07 q/ha), HPR 1068 (57.15 q/ha), Palam Basmati-1 (56.52 q/ha), HPR 2143 (52.84 q/ha) and Palam Red Rice (46.22 q/ha). The grain yield varied from 35.26 to 53.40 q/ha in the unprotected plots. Assessment of yield losses revealed that the maximum per cent avoidable losses was found in Kasturi Basmati (37.9%) and least in HPR 2143 (11.9%).&nbsp;

Assessment of yield losses of rice caused by paddy&nbsp;leaf folder, Cnaphalocrocis medinalis Guenee

<div>Background: Soybean anthracnose is currently a severe threat to India&rsquo;s soybean cultivation. There is limited information about estimated yield loss due to anthracnose and foliar fungicide application for its management. 
 
Methods: A novel fungicide, Picoxystrobin 7.05 per cent + Propiconazole 11.71 per cent (w/w) was evaluated for two consecutive years (2018 and 2019) in three different concentrations as a foliar application in combination with commercially available fungicides as standard checks to identify an alternative fungicide for management of soybean anthracnose disease. The yield loss was estimated from the data observed for two years in a row. 
 
Result: After three foliar applications of the novel fungicide, Picoxystrobin 7.05 per cent + Propiconazole 11.71 per cent w/w @ 0.20 per cent, the maximum disease control (PDC), the lowest disease index (PDI) and the area under the disease progress curve (AUDPC) were obtained. The maximum gross return was obtained following three foliar applications of Picoxystrobin 22.52 per cent (w/w) SC @ 0.08 per cent, but the maximum B:C ratio was estimated with foliar application of Hexaconazole 5.00 per cent EC @ 0.100 per cent. Anthracnose severity was found to be negatively correlated with soybean grain yield (r = -0.91). In every 1% increase in anthracnose disease severity reduced soybean yield by 115 kg per hectare.</div>

Evaluation of Novel Fungicide for the Management of Soybean Anthracnose Disease and Yield Loss Estimation

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

<div>First lactation records of 351 Karan-Fries cows born to 27 bulls, having 3 or more progeny were used to evaluate sires by animal model (AM). Heritabilities, genetic and phenotypic correlations between the traits were estimated by univariate and bivariate linear animal models. The heritability estimates for 305 days or less milk yield (305MY), fat based energy corrected milk yield (FECMY), fat protein based energy corrected milk yield (FPECMY) and fat protein lactose based energy corrected milk yield (FPLECMY) were 0.23, 0.65, 0.11 and 0.39, respectively. There was a high (0.99) genetic and phenotypic correlation between 305MY, FECMY and FPLECMY. The high and positive genetic correlations between energy traits are indication of common genetic and physiological mechanism governing these traits. Comparison of ranks of the sires based on 305MY, FECMY and FPLECMY by animal model depicted a notable100 per cent level of similarity except for FPECMY in which the ranks were entirely different. There was a high heritability and genetic correlation between FECM and FPLECM. Genetic evaluation of Karan-Fries bulls based on energy corrected milk yield will increase the accuracy of selection than the conventional mil yield evaluation. It can help in mitigating the nutritional insecurity of the country in the long run.</div>

<div>Genetic Parameters and Animal Model Evaluation of First Lactation 305-d Milk Yield and Energy Traits in Karan Fries Cattle</div>

<div>Background: Aerial blight (Rhizoctonia solani Kuhn) and Anthracnose/pod blight (Colletotrichum truncatum) are important soybean diseases, affect most of the present varieties with varying intensity, in India. There are also few reports of resistance against both the diseases.&nbsp; 
 
Methods: To identify resistance, a set of 121 diverse soybean genotypes including six susceptible checks i.e. JS 93-05, JS 335, JS 72-280, Punjab 1, RKS 18 and VLS 58 were evaluated under high disease pressure field conditions during 2017, 2018 and 2019. Moreover, assessment of yield losses due to these diseases were also worked out in highly infected plants of susceptible checks. 
 
Result: It was observed that aerial blight (0.0-46.8 per cent) and anthracnose/pod blight (0.0-56.2 per cent) were affected soybean genotypes from R1 to R7 and V3 to R7 stages, respectively. The genotypes responded differently and showed absolute resistance to susceptible reaction. Out of 121 genotypes, only five genotypes i.e. JS 20-57, JSM 222, MACS 1407, PS 1611 and Cat 2126 B were found to be highly resistant against both the diseases. Per cent pod and yield losses were significantly correlated with varying severity of aerial blight (0.966** and 0.995**) and anthracnose/pod blight (0.957** and 0.995**), respectively. However, the highest yield loss of 41.0 and 64.8 per cent were recorded on 55.6 and 75.2 per cent disease index (at 90 days) of aerial blight and anthracnose/pod blight, respectively.</div>

Measuring and assessing the yield loss and yield loss model of green gram due to Anthracnose of green gram (Vigna radiata)

Wheat Yield Modelling using Infocrop and DSSAT Crop Simulation Models

Sustaining Soil Fertility and Yield by Inductive cum Targeted Yield Model-based Fertilizer Prescriptions for Finger millet (Eleusine coracana L.) in Alfisols of Tamil Nadu, Southern India

Yield Losses Due to Weeds, Critical Period for Weed-crop Competition in Fenugreek (Trigonella foenum-graecum L.)

Bean Anthracnose (Colletotrichum lindemuthianum) in Kashmir: Epidemiology and Yield Loss Assessment

Assessment of yield losses of rice caused by paddy leaf folder, Cnaphalocrocis medinalis Guenee

Evaluation of Novel Fungicide for the Management of Soybean Anthracnose Disease and Yield Loss Estimation

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

Genetic Parameters and Animal Model Evaluation of First Lactation 305-d Milk Yield and Energy Traits in Karan Fries Cattle

Identification of Sources of Resistance and Yield Loss Assessment for Aerial Blight and Anthracnose/Pod Blight Diseases in Soybean