Integrating Machine Learning Model for Prediction of Total Factor Productivity of Agricultural Sector and Economic Growth in Pakistan
Date : 02-May-2024

Principal Investigator

Dr Sabahat Subhan

Assistant Professor, Department of Economics (FMS)

Agriculture sector gets key significance in the midst of the food crisis for Pakistan and the focus needs to be diverted towards the prediction of total factor productivity of the agricultural sector to meet the challenges of the agricultural productivity. The core objective of this study is to predict the TFP of the agricultural sector along with the population and economic growth rate in Pakistan for the period until 2073. To get the empirical evidence of the study, machine learning algorithms will be used for the prediction of TFP of agriculture sector along with population and economic growth rate of Pakistan. Data of Economic Growth Rate, Capital Stock, Labor Force, Arable Land, Agriculture Growth Rate, Population Growth Rate, and Total Factor Productivity of the agricultural sector from 1960 - 2023 will be used from national and international resources. The agricultural TFP of Pakistan is calculated by the Tornqvist-Theil (T-T) index method.

Completed Activites

1- Project Initiation:

     Description: The project initiation phase involved defining objectives, assembling the research team, and outlining the scope and


2- Data Collection and Analysis:

     Description: Successful collection and preprocessing of relevant data sets, ensuring quality and consistency for model training.

Ongoing Activites

3- AI Model Development:

     Description: Development of the AI-based framework, including algorithm design, training data preparation, and model testing.

Future Activites

4- Pilot Testing:

     Description: Conducting pilot tests to validate the effectiveness and accuracy of the developed AI framework.

5- Paper Writeup

     Description: Write up of research article highlighting major contribution of research work  that may be submitted to well reputed journal.

Completetion Status

   30% Completed