The yield of the crop is a complex function of a number of dependent traits, which makes yield prediction a statistically difficult task. A number of work on yield prediction using morphological characters already exists in the literature. Most of the work used statistical techniques such as linear regression and crop yield models, which assume a linear relationship between yield and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With the advancement in the field of machine learning techniques, these methods can provide a viable alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using morphological traits. PCA was used for selection of the significant variables. These variables were later used as input variables in the ANN model with different hidden layers for predicting crop yield. The predictive performance of the model was evaluated using standard statistical tests. Sensitivity analysis was performed to find out the individual effects of each character on the apple yield. The study contributes to a better understanding of the complex relationships between crop yield and morphological traits.

Millets, recognized for their nutritional richness and adaptability to diverse environmental conditions, have regained global attention due to their sustainable agricultural practices and nutritional benefits. This paper explores the historical significance, global and Indian production trends, cost dynamics, export patterns, and governmental initiatives driving millet cultivation in India. By analyzing statistical data and research findings, the study underscores millets' potential to enhance food security, mitigate environmental impact, and foster socio-economic development.

India is a country with an agrarian economy in which majority of its population rely on agriculture directly as their source of livelihoof. Climate has a very significant role in agricultural production. It predominantly influences growth of the crop, development of the crop and eventually crop yield. Climate also significantly influences the outbreak of disease and pest; it affects the requirement of water by the crop. Possible changes in weather factors, like precipitation, temperature and CO2 concentration are expected to have a significant impact on crop growth. If farmers are able to predict the weather activities and are aware of the effect of these activities on crop production, then it will be beneficial to them as a feasible plan can be devised synchronizing the crop production activities as per changes in the climatic conditions. In view of tackling the aforementioned problem, this article describes various statistical techniques that can play a crucial role in forecasting production of agricultural commodities changing climatic conditions.

Millets, the earliest domesticated grains since the Indus Valley Civilization (3000 B.C.), are renowned as “Hardy Cereals” for their drought resilience and minimal irrigation requirements. Millets are nutritionally rich in protein, vitamins and minerals. As per the 2016-17 report of the Ministry of Agriculture and Farmers Welfare, India, the area under the cultivation of millets in India has reduced by 60% (14.72 million hectares). Despite the manifold benefits associated with millet cultivation, farmers face significant challenges, primarily stemming from the inadequate infrastructure required for processing, lack of extension services and marketing millets. The government is making efforts to increase millets production through various promotional campaigns such as the National Millets Year and International Millets Year. In this article we have attempted to illustrate the millet scenario in world and in India with the analysis of the trends in area, production and productivity of the major and the minor millets of the country.

A genome-wide association study (GWAS) rapidly scans DNA markers in many individuals to find genetic links to diseases. New findings aid in disease detection, treatment and prevention. Imputation predicts untyped genotypes in genetic studies when data is missing due to quality, cost, or design issues. It’s a proven statistical technique for estimating unobserved genotypes by borrowing haplotype segments from a densely genotyped reference panel. This allows estimation and testing of associations at unassayed variants.Genotype imputation is vital in analyzing genome-wide association scans, helping geneticists evaluate evidence for association at untyped genetic markers. This summary outlines missing data issues and various imputation methods.