| dc.contributor.author | Navaneetha Srinivasagan, Subhashree | |
| dc.description.abstract | Forage availability was crucial for livestock production across the United States. Rangelands occupied vast areas 31 % of land and were the primary source of forage for livestock. However, extreme climatic conditions such as drought affect rangeland forage production and pose a serious threat to the rangeland enterprise. This increases the need to monitor forage in vast rangelands and adapt to other measures such as cultivating or buying forage to balance demand and supply. Despite this need, resources (studies and tools) on rangeland forage monitoring and existing forage production, handling, and economics were scattered and scarce. Therefore, a comprehensive systematic literature review was performed to gather the current understanding of the technology and resources used for monitoring and economics of forage production. Remote sensing technologies were widely used in recent research for their ability to scout vast areas frequently and machine learning (ML) in successfully comprehending complex relationships. Forage production economics was predominantly available for alfalfa forage crop, but other crops and bale collection logistics during production were ignored. Bale collection using conventional tractor carrying 1 and 2 bales/trip (BPT) and automatic bale picker (8-23 BPT) was simulated mathematically and analyzed with open-source R software using realistic equipment turning scenarios. Fuel consumption based on aggregation distance for ABP decreased on average by 72 % and 53 % compared to the tractor with 1 and 2 BPT. A web-based calculator tool was developed using open-source HTML, CSS, and JavaScript software for forage economic analysis including more than 10 varieties of forage crops involving the economics of bale collection (tractor and ABP). Pasture biomass yield prediction was performed with R software using vegetation index (VI) and climate data through ML approaches. Recursive feature selection (RFE) and random forest (RF) model for forage yield emerged as the best methodology based on accuracy. A web-based interactive tool was developed using the Shiny package in R to accommodate “field-specific,” pasture-scale inputs for predicting biomass yield. In conclusion, these successful results demonstrate the possibility of using open-source software for simulating logistics, developing models, and building tools for forage monitoring and analyzing the economics of forage production. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Rangeland Forage Growth Prediction, Logistics, Energy, and Economics Analysis and Tool Development Using Open-Source Software | en_US |
| dc.type | Dissertation | en_US |
| dc.type | Video | en_US |
| dc.date.accessioned | 2022-06-24T20:52:07Z | |
| dc.date.available | 2022-06-24T20:52:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/10365/32767 | |
| dc.subject | biomass | en_US |
| dc.subject | logistics | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | rangeland | en_US |
| dc.subject | remote sensing | en_US |
| dc.subject | web tool | en_US |
| dc.identifier.orcid | 0000-0002-5818-7715 | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Doctor of Philosophy (PhD) | en_US |
| ndsu.college | Agriculture, Food Systems and Natural Resources | en_US |
| ndsu.department | Agricultural and Biosystems Engineering | en_US |
| ndsu.program | Agricultural and Biosystems Engineering | en_US |
| ndsu.advisor | Cannayen, Igathinathane | |
| dc.identifier.doi | 10.48655/10365/32767 | |
