Crop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase-wise crop advisory, and advisory for harvest were prepared using long-term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r 2 of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of India.
A field experiment was conducted during thekharif(June–September) andrabi(October–January) seasons of 2005–2006 to study the effect of a maize — sunflower cropping system on the weed flora shift. The results revealed a change in weed species, i.e. the appearance of new species and the elimination of certain weed species due to the cropping system. The density ofDinebra retroflexawas high during the 1styear maize cropping period, butPanicum repensbecame dominant when sunflower was grown after maize.Cyperus rotundus, originally the dominant sedge, was smothered byCynodon dactylondue to zero tillage.Dactyloctenium aegyptiumwas the dominant weed species in maize, whileParthenium hysterophoruswas the dominant weed species in sunflower. The proportions ofDatura fastuosa, Parthenium hysterophorus, Trianthema portulacastrum, Amaranthus viridis, Amaranthus polygamus, Flaveria austerlagica, Gynandropsis pentaphyllaandPortulaca quadrifidawere higher during the 1styear maize cropping season, while later their density was gradually reduced due to the inclusion of sunflower in the system.
In the field of medical diagnosis, identifying the illness in a right time and giving the best possible viable treatment avoids the critical situation of the patient. In this way, Heart disease is one of the most affecting illness faced by the populace everywhere throughout the world. Now-a-days, numerous soft computing based strategies include neural and fuzzy, have been created. In this work, Application of Fuzzy Inference System and Sugeno-Adaptive Neuro Fuzzy Inference System (S-ANFIS) to classify coronary illness in a person which analyses the various parameters of a person and give a criticism on the wellbeing component identified with Cardiovascular Diseases. Subsequent to preparing the system with adequate number of preparing pair got from standard informational index, testing is done on the different cases that demonstrate the viability of proposed approach.
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