Statistical investigation to explore the impact of soil and other characteristics on cotton yield

Hayat, Azhar; Amin, Muhammad; Afzal, Saima

doi:10.1080/00103624.2020.1781152

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Nevertheless, farm mechanization and the application of new technologies lead to the transformation of crop management from a rather qualitative science, which was mainly based on observations of a more quantitative science, which is now based on measurements. In this new data-driven area, a variety of different production features including soil and climate conditions, irrigation management, and nutrient availability significantly influence the potential cotton yield and growth [1][2][3][4][5]. Therefore, the prediction of cotton's yield, as well as the factors which mostly affect it, could lead to production optimization through the early modification of harvest settings and adjustments.…”

Section: Introductionmentioning

confidence: 99%

A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

et al. 2020

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Cotton constitutes a significant commercial crop and a widely traded commodity around the world. The accurate prediction of its yield quantity could lead to high economic benefits for farmers as well as for the rural national economy. In this research, we propose a multiple-input neural network model for the prediction of cotton’s production. The proposed model utilizes as inputs three different kinds of data (soil data, cultivation management data, and yield management data) which are treated and handled independently. The significant advantages of the selected architecture are that it is able to efficiently exploit mixed data, which usually requires being processed separately, reduces overfitting, and provides more flexibility and adaptivity for low computational cost compared to a classical fully-connected neural network. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece (Thessaly) in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and state-of-the-art models. The numerical experiments revealed the superiority of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

et al. 2020

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Exploring Spatial Variability and Yield-Determining Factors in Cotton Production: A Multivariate Analysis

Afzal,

Hayat,

Amin

et al. 2024

Communications in Soil Science and Plant Analysis

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

Hayat

Amin

Afzal

et al. 2022

Advances in Materials Science and Engineering

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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.

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Statistical investigation to explore the impact of soil and other characteristics on cotton yield

Cited by 3 publications

References 14 publications

A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

Exploring Spatial Variability and Yield-Determining Factors in Cotton Production: A Multivariate Analysis

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

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