Sclerotinia stem rot is one of the utmost important disease of mustard, causing considerable losses in seed yield and oil quality. The study of the genetic and proteomic basis of resistance to this disease is imperative for its effective utilization in developing resistant cultivars. Therefore, the genetic pattern of Sclerotinia stem rot resistance in Indian mustard was studied using six generations (P₁, P₂, F₁, F₂, BC₁P₁, and BC₁P₂) developed from the crossing of one resistant (RH 1222-28) and two susceptible (EC 766300 and EC 766123) genotypes. Genetic analysis revealed that resistance was governed by duplicate epistasis. Comparative proteome analysis of resistant and susceptible genotypes indicated that peptidyl-prolyl cis-trans isomerase (A0A078IDN6 PPIase) showed high expression in resistant genotype at the early infection stage while its expression was delayed in susceptible genotypes. This study provides important insight to mustard breeders for designing effective breeding programs to develop resistant cultivars against this devastating disease.

A GGE Biplot provides an efficient graphical procedure for identification of favourable genotypes and mega-environment analysis. It displays the Genotype main effect (G) and genotype-by-environment (GE) interaction in two dimensions. It also possesses an extra property in the evaluation of the test environment by discriminating power versus representativeness view. Wheat breeders aim to develop superior genotypes characterised by high grain yield and other desirable quality traits. Presence of G × E interaction makes it difficult to identify the high yielding and most stable genotypes. Mega-environment analysis and genotype evaluation were conducted for 23 genotypes of wheat in six environments/states of Northern India during 2013-14. Delhi environment was observed as the most discriminating while Uttar Pradesh was reported as the least discriminating. The genotype WH1105 was observed to be the most favourable followed by PBW698 for Northern Zone of India.

Advance estimates of significant cereal and commercial crops are given by the Directorate of Economics and Statistics and the central Ministry of Agriculture, Cooperation & Farmers’ Welfare. However, the final estimates are released a few months after the actual harvest of the crops. In this study, ARIMA and State-Space models have been developed for sugarcane yield forecasting in Ambala and Karnal districts of Haryana. The above-mentioned models have been developed using yield data of sugarcane crop for the time period 1966-67 to 2009-10 of Ambala and Karnal districts. The validity of fitted models has been tested over the years 2010-11 to 2016-17. The forecasting performance of the developed models has been studied using percent deviations of sugarcane yield forecasts in relation to the actual yield, and root means squared errors. It has been observed that state-space models outperform the popular ARIMA models for forecasting of sugarcane yield in Northern Agro-climatic Zone of Haryana.

The present study deals with the development disparities in districts of Haryana according to their level of development. The study utilized data over three points of time, viz. 1991-92, 2001-02, and 2011-12. Assessment of development in agricultural, industrial, infrastructural and socio-economic sectors has been studied using composite indices based on forty indicators. Out of the forty indicators, 19 were directly concerned with agricultural development, 4, 8 and 9 respectively reflected the progress of development in industrial, infrastructural, and socio-economic sectors. Sector-wise indices were combined to obtain weighted index for the overall development. The study indicated wide disparities in level of development among districts of Haryana in all the periods of study.The district of Mahendragarh lagged behind in almost all the sectors considered for this study. The districts of Faridabad and Gurgaon lagged behind in agriculture while the district of Karnal excelled in agriculture in all the three periods. The districts of Ambala, Faridabad and Gurgaon ranked first in overall development in 1991-92, 2001-02 and 2011-12, respectively, whereas Mahendragarh ranked last in 1991-92 and 2001-02 and the newly formed district Mewat in 2011-12. Spearman’s rank correlation was used to study relationships among sectoral developments. Kruskal Wallis test indicated significant changes in development level of industry and infrastructure sectors over the periods 1991-92, 2001-02 and 2011-12.

Unlike classical regression analysis, the state space models have time-dependent parameters and provide a flexible class of dynamic and structural time series models. The unobserved component model (UCM) is a special type of state space models widely used to analyze and forecast time series. The present investigation has been carried out to study the trend of sugarcane(gur) yield in five districts (Ambala, Karnal, Panipat, Yamunanagar and Kurukshetra) of Haryana state using the unobserved component models with level, trend and irregular components. For this purpose, the time series data on sugarcane yield from 1966-67 to 2016-17 of Ambala and Karnal, 1971-72 to 2016-17 of Kurukshetra and 1980-81 to 2016-17 of Panipat and Yamunanagar districts have been used. For all the districts, the irregular component was found to be highly significant (p=0.01) while both level and trend component variances were observed non-significant. Significance analysis of the individual component(s) has also been performed for possible dropping of the level and trend components by setting their variances equal to zero. The state space models may be effectively used pertaining to Indian agriculture data, as it takes into account the time dependency of the underlying parameters which may further enhance the predictive accuracy of the most popularly used ARIMA models with parameter constancy. Moreover, the unobserved component model is capable of handling both stationary as well as non-stationary time series and thus found more suitable for sugarcane yield modeling which is a trended yield (i.e. non-stationary in nature).