Theses and Dissertations at Montana State University (MSU)

Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733

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Landscape effects on soil fertility across Belize
(Montana State University - Bozeman, College of Agriculture, 2022) Coe, Monica Rosemarie; Chairperson, Graduate Committee:
Globally there is an increasing rate of deforestation that leads to land deterioration, such as soil infertility. It is well established that the population is growing; therefore, there is a need to increase food security. To determine the causes of deforestation, I investigated the reasons underlying land conversion from forest to agricultural lands by answering the following questions: Was it because of accessible roads? If it is because of accessibility, how does it affect soil properties? To answer these questions, I focused on three agricultural communities: Shipyard (Conservative Mennonites), Spanish Lookout (Progressive Mennonites), and Maya Mountain North Forest Reserve (Mayas). These communities are to focus on the scale of agriculture related to each ethnicity and the rate of deforestation. I compared these parts of the country: Shipyard, Spanish Lookout, and Maya Mountain North Forest Reserve using the Geographic Information system (GIS) software, ArcPro version 2.9, to calculate the difference in deforestation by each Community using scanned maps dated 1958 by Charles Wright and datasets from secondary sources. Although the maps by Wright are outdated, the information is essential for the land use/land cover analysis. I also verified whether the forest lost was suitable (fertile) land according to Charles' Natural Vegetation and Provisional Soil Maps. The comparison helped me identify the trend in land cover change in small-scale farming, large-scale farming, and farming within a protected area in Belize. My results showed that the Shipyard and Spanish Lookout communities primarily contributed to forest loss and agriculture expansion on fertile lands. In contrast, the Mayan Community in the Maya Mountain North Forest Reserve showed much lower rates of forest less and these soils were relatively less fertile. These results also indicated that the Mennonites practiced large-scale agriculture compared to the Maya, who practiced small-scale agriculture, based on the quantity of clearing-cutting acres of forested lands. However, the Maya Mountain North Forest Reserve’s agricultural development appeared to be encroaching on non suitable (non-fertile) lands with soils of low pH and high rainfall, which could lead to higher rates of degradation. In addition, my analysis suggested road networks were not the primary reason for deforestation since decreasing road density trends are the converse of increasing deforestation trends. Together, my results predict that if deforestation within the Maya Mountain North Forest Reserve continues to increase, then it will eventually be higher than the deforestation rates of either Shipyard or Spanish Lookout.
Remote sensing to estimate above-ground biomass in Southeastern Montana grasslands
(Montana State University - Bozeman, College of Agriculture, 2022) Bauer, Shaelyn Joy; Chairperson, Graduate Committee: Scott Powell
Grasslands provide numerous ecological services, including, but not limited to, carbon capture and storage, and wildlife habitat for numerous species including pollinators, domestic ungulates, wild herbivores, and their predators. Grasslands account for more than 40% of Earth’s terrestrial surface and offer one of the most valuable land cover types, having the greatest diversity of grazing animals and predators on Earth. These grasslands serve as one of the largest reservoirs for terrestrial carbon, amounting to 10-30% of total world soil carbon (Hewins et al., 2018; Scurlock and Hall, 1998). Establishing carrying capacities of grasslands based on data, in both wildlife management and agricultural settings, is fundamental in grassland ecosystem management. However, ground observation of grassland biomass resources can be costly, time consuming, and is very limited in terms of scale, both spatially and temporally. Numerous remote-sensing applications have emerged to address the need for grassland biomass production data, with satellite-derived vegetation indices being the most widely implemented due to their wide variety of applications and the accessibility of free spectral imagery via federal agencies such as the U.S Geological Survey (USGS). Currently, there is no universal empirical model using statistical relationships between biomass and vegetation indices (VI), making custom-fitted models necessary for biomass estimation. Statistical analysis is limited by inadequate reference data and vulnerabilities to atmospheric factors. Results of multiple studies show strong correlations between field measured biomass and satellite-derived VI’s. However, correlation strength depends on matching the best VI, or multiple VI’s, to the land cover characteristics in question, coupled with validation tools and methodologies on the ground. Therefore, to explore the feasibility and accuracy of a site-specific biomass estimation model, I chose a privately owned ranch in eastern Montana for sampling and tested for combinations of indices that were best correlated with ground biomass measurements. I used several vegetation indices to compare the accuracy of univariate and multivariate modeling techniques for modeling above ground biomass. A multivariate model using the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI) and a native/introduced pasture classification variable arose as the model with the greatest predictive power for biomass estimation. This model accounted for 57% of the variation in response and was accurate to within 352.7 lbs/acre. My study lays out real-world applications for tailoring VIs to the application requirements of measuring biomass in an eastern Montana grassland coupled with ground validation. The methods in this study serve as a framework for fitting custom models to other grasslands under management.