Application of Regression Analysis to Identify the Soil and Other Factors Affecting the Wheat Yield

Abstract

In farming and related fields, numerous connections exist that should be distinguished quantitatively. Several factors affect the various crop yields in different dimensions. These factors may have relation with farmer’s practices or with quality of soil. In this study, our main focus is to explore the effect of soil and other factors on the wheat yield. Regression modeling plays an important role in the identification of such factors that greatly affect the crops yield. For reliable and valid results, one has to check the data for outliers and other critical results. In this study, we have fitted the regression models with and without satisfying some regression assumptions to determine the factors affecting yield of wheat. For analysis purposes, the required data were collected from the district Multan. It was observed that when the regression assumptions were satisfied, then coefficient of determination (R2) was improved from 45% to 48%, R2 (adjusted) was improved from 40% to 46%, and the standard error of the estimates was reduced from 2.772 to 2.649. These results indicate that the soil characteristics, such as saturation, electrical conductivity, organic matter, phosphorus, potassium, calcium carbonate, and micronutrients (zinc, copper, iron, manganese, and boron), are the significant factors for wheat yield. While among all other factors, urea, chemical coating of seed, use of compost, and previously sown crops are the significant factors for wheat yield.

A Concentration Prediction-Based Crop Digital Twin Using Nutrient Co-Existence and Composition in Regression Algorithms

“…One of the promises of a digital twin in crop management is for the automatic prediction system to support in deciding the appropriate fertilization period [22][23][24]. Deploying the sensors which monitor the concentration of nutrients present in soil, humidity, and temperature in the real fields to make consistent quality checks.…”

“…One study employed an artificial neural network-based prediction algorithm to assess the influence of individual nutrients (N, P, K, Zn, and S) on various rice plant parameters. The algorithm indicated that optimal growth often occurs with nutrient doses below the maximum applied levels, while maximum yield is achieved at a 100% nutrient dose [22].…”

Unraveling New Insights: Exploring Risk Factors of Under-Five Mortality in Somaliland using the First-Ever SLDHS 2020 Data and Count Data Regression Models

“…The ZIP regression model combines two components: a zero component and a non-zero component [18,23]. The zero component captures the excess zeros in the data, while the non-zero component models the counts for non-zero outcomes.…”

Concentration Prediction‐Based Crop Digital Twin Using Nutrient Co‐Existence and Composition in Regression Algorithms

“…One study employed an artificial neural network-based prediction algorithm to assess the influence of individual nutrients (N, P, K, Zn, and S) on various rice plant parameters. The algorithm indicated that optimal growth often occurs with nutrient doses below the maximum applied levels, while maximum yield is achieved at 100% nutrient dose [10].…”