Project Abstract

Abstract
This research project aimed to investigate the factors that affect millet yield using multiple regression analysis with the SPSS statistical package. Millet is an important cereal crop in many regions of the world, providing food security and income for millions of people. Understanding the factors that influence millet yield is crucial for improving agricultural practices and increasing production. The study collected data on various variables that could potentially affect millet yield, including rainfall, temperature, soil nutrients, pest infestation, and farming techniques. A sample of millet farms was selected, and data was collected over a period of one year to capture the seasonal variations in the factors under study. The data was analyzed using multiple regression analysis in SPSS to identify the significant factors that affect millet yield. The results indicated that rainfall, soil nutrients, and pest infestation were the most significant predictors of millet yield. Farms with higher levels of rainfall and soil nutrients generally had higher yields, while farms with high levels of pest infestation experienced lower yields. Furthermore, the study found that farming techniques, such as irrigation and fertilizer application, also had a significant impact on millet yield. Farms that employed modern farming techniques tended to have higher yields compared to those using traditional methods. Overall, the findings of this study provide valuable insights into the factors that influence millet yield and highlight the importance of sustainable agricultural practices for improving crop production. By understanding the factors that affect millet yield, farmers and policymakers can make informed decisions to enhance agricultural productivity and food security. The results of this research have practical implications for millet farmers and agricultural extension services, as they provide guidance on the factors that should be prioritized to increase millet yield. Additionally, the study contributes to the existing literature on millet production and serves as a foundation for future research on improving crop yields through targeted interventions and sustainable farming practices. In conclusion, this research project demonstrates the utility of multiple regression analysis with the SPSS statistical package in understanding the complex relationships between various factors and millet yield. By identifying the key factors that influence millet production, this study contributes to efforts aimed at enhancing food security and livelihoods in regions where millet is a staple crop.

Project Overview

INTRODUCTION

1.0   BACKGROUND OF STUDY

In Africa, Millet is primarily grown for human consumption serving as the staple food in some of the poorest countries and regions of the continent the grain is mainly used in three different ways as a grain-like flour (couscous), as a dough and as a gruel (Brunken et al.,1977; counting and Harris,1968).

Despite the enormous use to which millet can be put, there are some constraints which limit the production of the crop in savannah environment of Northern Nigeria. Among these are nature of soils, climate of the region and cultural techniques and management practise. Other is disease pests, weed and other parasites whose effect seriously affect the yield of the millet.

Millet is believed to have descended from the west African wild grass which was domesticated more than 40,000 years ago (National Research Council, 1996) it spread from west African to East Africa and then to India.

Areas planted with millet are estimated at 15 million hectares annually in Africa and 14 million hectares in Asia. Global production exceeds 10million tone a year (national research council, 1966). The food value of millet is high. Trial in India has shown that millet is nutritionally more superior to maize and rice for human growth. The protein content of millet is higher than maize and has a relatively high vitamin A content (Gallagher 1984).

1.1   AIMS AND OBJECTIVE OF THE STUDY

This study aims at relating the effectiveness components of millet to millet yield. The parameter include, Establishment score count at two weeks (ESTAB), days of 50% flowering after sowing/germination (DHF), Plant height (PLHT),Pinnacle length (PNCL), 100 seed weigh(HSN) .

Using multiple regression analysis in a bid to determine:

1. Which factors have a statistically significant effect on the yield of millet.
2. To build a regression model of yield on the identified factors in the experiment.
3. To possibly recommend for a further experiment on other factor that influence the yield of millet that are not included in these experiment.

 

1.2    SIGNIFICANCE OF STUDY

After the factors have been identified the researcher will improve to the improvement of millet yield when the factors that influence it are identified.

 

1.3   LIMITATION AND SCOPE OF STUDY

The result and recommendation from this project apply to the existing commercially available hybrid verity of millet.

And all the data used and all the observation made are limited to Lake Chad Research Institute., Federal Ministry of Agriculture and Natural Resources.

 

1.4   SOURCE OF DATA COLLECTION

Data were collected on a number of parameters, these include seedling Establishment, days of 50% flowering, Plant height, Pinnacle length, 100 seed weight, Biomass, Grain yield. All these were obtained from the Lake Chad Research Institute, federal Ministry of Agriculture and Natural Resources.

1.5   DEFINTION OF TERMS

1. ESTAB: Establishment score count at two weeks
2. DHF: Day of 50% flowering after sowing / germination
3. PLHT: Plant height  
4. PNCL: Pinnacle Length
5. HSN: 100 seed weight
6. YIELD: Yield component of millet

 

1.6   METHODOLOGY

The analytical part of work will be done using multiple regression analysis, correlation analysis, regression coefficient and coefficient of determinant through the aid of SPSS Statistical package