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2018 - 20192
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Author: Aditi × Start date: 1982 × Type: Thesis ×
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    ThesisItemOpen Access
    Role of crop condition based dummy regressor alongwith weather parameters for pre-harvest yield prediction of cotton crop in Western Agro-climatic zone of Haryana
    (CCSHAU, 2019) Aditi; Verma, Urmil
    Crop yield models are abstract presentation of interaction of crop with its environment and can range from simple correlation of yield with a finite number of variables to the complex statistical models with predictive end. The pre-harvest forecasts are useful to farmers to decide in advance their future prospects and course of action. An efficient crop predicting infrastructure is pre-requisite for information system about food supply, especially export–import policies, procurement and price-fixation. Multiple Linear regression was used to develop zonal yield models for obtaining cotton yield prediction in Hisar, Bhiwani, Sirsa and Fatehabad districts of Haryana. Linear time-trend has been obtained using cotton yield data of the period 1980-81 to 2011-12. The fortnightly weather data along with trend yield have been utilized for the same period for building the zonal weather-yield models. Models have been validated for subsequent years i.e. 2012-13 to 2016-17, not included in the development of the models. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/CCT/dummy variables as regressors. The predictive performance(s) of the contending models were observed in terms of average absolute percent deviations of cotton yield forecasts in relation to the observed yield(s) and root mean square error(s). The adequacy of the fitted models was examined through histogram, normal-probability plot for the residuals and residual plot against fitted values for the selected models. The yield(s) estimated by zonal weather-yield models had sometimes higher percent deviations from the real-time yield(s) i.e. too high than considered to be tolerable for reliable yield prediction in the districts under consideration. Consequent upon, an attempt was made to improve the predictive accuracy of the developed models by adding trend yield based crop condition term to the zonal weather-yield model and that significantly improved the predictive accuracy of forecast models. The CCT is an indicator variable generated by splitting the DOA crop yield series into different non-overlapping classes. The level of accuracy achieved by zonal yield model(s) using CCT as categorical covariate along with weather variables was considered adequate for estimating the district-level cotton yield(s) at least 4-5 weeks in advance of the crop harvest. The average absolute percent deviations of postsample period forecasts falling between 4-9 percent favour the use of developed models for cotton yield prediction in western zone of Haryana. Zonal yield models incorporating CCT and weather variables consistently showed the satisfactory results pertaining to cotton yield prediction and performed well with lower error metrics as compared to the remaining models in all time regimes for the district under consideration.
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    ThesisItemOpen Access
    EST-SSR and RGA polymorphism for diversity analysis and bacterial leaf blight resistance in clusterbean [Cyamopsis tetragonoloba (L.)Taub.
    (CCSHAU, 2018) Aditi; Yadav, Neelam R.
    Cyamopsis tetragonoloba (L.) Taub., commonly known as “guar”, traditionally used as forage crop, has gained the status of an industrial crop, due to the presence of galactomannan (guar gum) in its endosperm. Bacterial Leaf Blight is one of the most devastating disease in cluster bean and the crop has undergone severe yield loses due to disease incidence for last few years. This investigation was carried out to study EST-SSRs which are the most preferred molecular markers and RGAs polymorphism for genetic diversity analysis. In the study 158 cluster bean germplasm lines were grown in field at two different locations. Morphological evaluation was carried out for various agronomical and yield attributes. Galactomannan content was estimated and was found in the range of 14.25 to 42.23 per cent. The molecular analysis was carried out using 218 molecular markers out of which 85 were found to be polymorphic and used for diversity analysis. The dendrogram obtained on the basis of molecular marker analysis divided the genotypes into two major clusters at the similarity coefficient of 0.63. The cross transferability EST-SSRs and SSRs from other legume species was studied and found to be 18.75 per cent. The markers used in the study were observed to have PIC value in the range of 0.322-0.791. Multiple sequence alignment of RGA sequences of HG75 and C. serrata including HES1401 resulted into the presence of SNPs and In/Dels. In silico analysis of sequences of PCR amplified product of RGA (RGK03HD07) and EST-SSR (GDR 10) resulted into similarity of wild species with NBS-LRR of Medicago trunculata which further provide opportunity for identification of resistance gene in cluster bean. Further, the morphological and molecular data was used for association mapping. Association analysis resulted into population structure with K=2 (number of cluster) with eight possible sub groups in STRUCTURE (2.3.4). A total of 22 different marker trait associations were observed using TASSEL (2.1) for P<0.05. Three markers (CBN22, CTF229 and GDR8) were found to be associated with seed yield.