Back to Search Start Over

An Alternative Statistical Model to Analysis Pearl Millet (Bajra) Yield in Province Punjab and Pakistan

Authors :
Muhammad Zeshan Arshad
Muhammad Zafar Iqbal
Festus Were
Ramy Aldallal
Fathy H. Riad
M. E. Bakr
Yusra A. Tashkandy
Eslam Hussam
Ahmed M. Gemeay
Source :
Complexity, Vol 2023 (2023)
Publication Year :
2023
Publisher :
Hindawi-Wiley, 2023.

Abstract

Background. A country’s agriculture reflects a backbone and performs a vital part in the betterment of the economy and individuals. Facts and figures of the agriculture sector offer a solid foundation and factual pathway intended for upcoming decisions in favor of a country. Accordingly, the probability models have a more significant influence not only in reliability engineering, hydrology, ecology, and medicine but also in agriculture sciences. Objective. The primary objective of this study is to propose a reliable and efficient model for pearl millet yield analysis, thereby empowering decision-makers to make informed decisions about their farming practices. With the successful implementation of this model, farmers can potentially increase their pearl millet yield, leading to higher incomes and improved livelihoods for the rural population of Pakistan. Model. This study proposes a novel probability model, namely, the alpha transformed odd exponential power function (ATOE-PF) distribution, for analyzing pearl millet yield in Punjab, Pakistan. Data. For data collection, two secondary data sets are explored that are electronically available on the site of the Directorate of Agriculture (Economics and Marketing) Punjab, Lahore, Pakistan. Results. The maximum likelihood estimation technique is used for estimating the model parameters. For the selection of a better fit model, we follow some accredited goodness of fit tests. The efficiency and applicability of the ATOE-PF distribution are discussed over the province of Punjab (with RMSE = 4.9176) and Pakistan (with RMSE = 4.5849). Better estimates and closest fit to data among the well-established neighboring models offer robust evidence in support of ATOE-PF distribution as well.

Details

Language :
English
ISSN :
10990526
Volume :
2023
Database :
Directory of Open Access Journals
Journal :
Complexity
Publication Type :
Academic Journal
Accession number :
edsdoj.2d0f9a422a4456a97582774c98f77f7
Document Type :
article
Full Text :
https://doi.org/10.1155/2023/8713812