Mathematical Modeling In Agriculture
Download Mathematical Modeling In Agriculture PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mathematical Modeling In Agriculture book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Mathematical Modeling in Agriculture
Author: Sabyasachi Pramanik
language: en
Publisher: John Wiley & Sons
Release Date: 2024-10-16
The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable. The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers’ choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is the internet. However, few FMIS have fully tapped into the internet’s possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems’ deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.
Mathematical Models in Agriculture
Role of mathematical models; Dynamic deterministic models; Mathematical programming; Basic biological processes; Growth functions; Simple dynamic growth models; Simple ecological models; Envinment and weather; Plant and crop processes; Crop models; Crop husbandry; Plant diseases and pests; Animal processes; Animal organs; Whole-animal models; Animal products; Animal husbandry; Animal diseases; Solutions exercises; Mathematical glossary.
Data Driven Mathematical Modeling in Agriculture
The research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Technical topics discussed in the book include: Precision agriculture Machine learning Wireless sensor networks IoT Deep learning