Land Resources & Environmental Sciences
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/11
The Department of Land Resources and Environmental Sciences at Montana State Universityoffers integrative, multi-disciplinary, science-based degree programs at the B.S., M.S., and Ph.D. levels.
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Item Landscape context affects patch habitat contributions to biodiversity in agroecosystems(Wiley, 2024-06) Duff, Hannah; Debinski, Diane; Maxwell, Bruce D.Effective conservation schemes are needed to advance the dual objectives of biodiversity conservation and agronomic production in agricultural landscapes. Understanding how plant and arthropod taxa respond to both local habitat patch characteristics and landscape complexity is crucial for planning effective agri-environment schemes. This study investigated the relative effects of local variables (plant and insect diversity ≤100 m from patch habitat center) and landscape variables (landscape composition and configuration metrics ≤5 km from patch habitat center) on the diversity of plants and arthropods within noncrop habitat patches (1) at different spatial extents ranging from 0.1 to 5 km, while (2) quantifying differential effects of local and landscape variables on particular components of diversity (i.e., species richness and abundance), and accounting for (3) particular components of landscape extent (0.1-, 0.5-, 1-, 2-, and 5-km radii) and complexity (i.e., landscape composition and configuration). Landscape variables were significantly correlated with local plant and arthropod species richness and abundance at all spatial extents. Biodiversity responses to landscape variables were largely scale-dependent, as pairwise comparisons were significantly different between all spatial extents except between 1- and 2-km extents, and correlations were lowest at the 5-km extent. Partial R2 values for predicting local biodiversity were highest when both local and landscape variables were included as predictors of species richness and abundance, increasing from 0.163 to 0.469 when landscape variables were included, underscoring the importance of considering both local and landscape effects on local diversity. Landscape configuration variables accounted for more variation in plant and arthropod species richness than composition variables. However, models performed best when composition and configuration were considered together rather than alone, suggesting that both components of landscape complexity should be considered for identifying and managing conservation areas in crop fields. Existing conservation schemes that incentivize farmers to create or conserve seminatural patch habitat within crop fields may be more effective when combined with landscape-scale designs that enhance landscape complexity across the Northern Great Plains. Local conservation efforts should be coordinated with landscape-level efforts to ultimately enhance biodiversity and desired ecosystem service outcomes across agricultural landscapes.Item Pinus contorta Alters Microenvironmental Conditions and Reduces Plant Diversity in Patagonian Ecosystems(MDPI AG, 2023-02) García, Rafael A.; Fuentes-Lillo, Eduardo; Cavieres, Lohengrin; Cóbar-Carranza, Ana J.; Davis, Kimberley T.; Naour, Matías; Núñez, Martín A.; Maxwell, Bruce D.; Lembrechts, Jonas J.; Pauchard, AníbalPinus contorta is considered one of the most invasive tree species worldwide, generating significant impacts on biodiversity and ecosystems. In several Patagonian ecosystems in southern Chile, it has escaped from plantations established mainly in the 1970s, and is now invading both forests and treeless environments. In this study, we evaluated the impact of the invasion of P. contorta on microenvironmental conditions in Araucaria araucana forest and Patagonian steppe ecosystems, and assessed how these changes related to the richness and abundance of native and non-native plant species. In each ecosystem, 24 plots of 100 m2 were established along a gradient of P. contorta biomass, where 18 environmental variables and the composition of native and non-native vegetation were measured at a local scale. Our results indicated that increased pine biomass was associated with differences in microclimatic conditions (soil and air temperature, photosynthetically active radiation (PAR), and soil moisture) and soil properties (potassium, nitrate, pH, and litter accumulation). These changes were ecosystem dependent, however, as well as associated with the level of invasion. Finally, the reduction in the richness and abundance of native plants was associated with the changes in soil properties (accumulation of leaf litter, pH, and organic matter) as well as in the microclimate (minimum air temperature, PAR) generated by the invasion of P. contorta. Overall, our results confirm that the invasion of P. contorta impacts microenvironmental conditions (i.e., canopy cover, litter accumulation, minimum air temperature, and maximum soil temperature) and reduces native plant diversity. For future restoration plans, more emphasis should be given to how environmental changes can influence the recovery of invaded ecosystems even after the removal of the living pine biomass (i.e., legacy of the invasion).Item Using spatially variable nitrogen application and crop responses to evaluate crop nitrogen use efficiency(Springer Science and Business Media LLC, 2023-05) Hegedus, Paul B.; Ewing, Stephanie A.; Jones, Claim; Maxwell, Bruce D.Low nitrogen use efficiency (NUE) is ubiquitous in agricultural systems, with mounting global scale consequences for both atmospheric aspects of climate and downstream ecosystems. Since NUE-related soil characteristics such as water holding capacity and organic matter are likely to vary at small scales (< 1 ha), understanding the influence of soil characteristics on NUE at the subfield scale (< 32 ha) could increase fertilizer NUE. Here, we quantify NUE in four conventionally managed dryland winter-wheat fields in Montana following multiple years of sub-field scale variation in experimental N fertilizer applications. To inform farmer decisions that incorporates NUE, we developed a generalizable model to predict subfield scale NUE by comparing six candidate models, using ecological and biogeochemical data gathered from open-source data repositories and from normal farm operations, including yield and protein monitoring data. While NUE varied across fields and years, efficiency was highest in areas of fields with low N availability from both fertilizer and estimated mineralization of soil organic N (SON). At low levels of applied N, distinct responses among fields suggest distinct capacities to supply non-fertilizer plant-available N, suggesting that mineralization supplies more available N in locations with higher total N, reducing efficiency for any applied rate. Comparing modelling approaches, a random forest regression model of NUE provided predictions with the least error relative to observed NUE. Subfield scale predictive models of NUE can help to optimize efficiency in agronomic systems, maximizing both economic net return and NUE, which provides a valuable approach for optimization of nitrogen fertilizer use.Item Rationale for field-specific on-farm precision experimentation(Elsevier BV, 2022-10) Hegedus, Paul B.; Maxwell, Bruce D.Uncertainties in farming necessitate detailed knowledge of the production efficiencies to maintain sustainability. To accomplish ecologically based agriculture, with the goal of intensification by maximizing production and profit as well as minimizing environmental impact, we hypothesized that a site-specific knowledge base can be efficiently achieved through modern precision agriculture (PA) technologies at the field scale. The two goals of this study were to quantify the spatiotemporal variation of crop responses and the variables driving crop production, crop quality, and field-scale farmer net-return. We conducted on-farm experimentation (OFE) on several fields for three years where we varied nitrogen fertilizer rate as a management input, to induce changes in crop response. Using a Monte Carlo approach, we assessed the probability that crop responses varied across fields and between years. To determine the drivers of crop production, quality, and net-return, we performed sensitivity analyses to assess the impact of variation in the environment with the most influence on crop responses and farmer profits. Our analysis provided evidence that the degree of the response of winter wheat yield and protein content to variable nitrogen fertilizer rates are not homogenous across time and space. Elevation as a covariate to nitrogen fertilizer rate was the primary influence on predicted yields and protein across most fields, yet not among all fields and across years in fields. The drivers of net-return varied among fields and across years primarily between yield and protein. However, in some cases the most influential factor was the base price received, controlled by the grain elevators that growers sell to, indicating that in some fields and years, farmer’s net-returns are dictated by variables outside of a farmer’s control or ability to manage. These results provide basic evidence justifying the use of OFE for farm management and suggest that management needs to be specific to each field and point in time, with recommendations being made specifically for a field based on information gathered from that field. On-farm experimentation will enable farmers to identify these drivers and understand how their inputs influence yield and protein within fields. Using information provided by OFE with decision support systems can enable farmers to make informed management decisions that maximize their profits and increase the efficiency of chemical inputs, such as nitrogen fertilizer.Item Assessing performance of empirical models for forecasting crop responses to variable fertilizer rates using on-farm precision experimentation(Springer Nature, 2022-10) Hegedus, Paul B.; Maxwell, Bruce D.; Mieno, TaroData-driven decision making in agriculture can be augmented by utilizing the data gathered from precision agriculture technologies to make the most informed decisions that consider spatiotemporal specificity. Decision support systems utilize underlying models of crop responses to generate management recommendations, yet there is uncertainty in the literature on the best model forms to characterize crop responses to agricultural inputs likely due for the most part to the variability in crop responses to input rates between fields and across years. Seven fields with at least three years of on-farm experimentation, in which nitrogen fertilizer rates were varied across the fields, were used to compare the ability of five different model types to forecast crop responses and net-returns in a year unseen by the model. All five model types were fit for each field using all permutations of the three years of data where two years were used for training and a third was held out to represent a “future” year. The five models tested were a frequentist based non-linear sigmoid function, a generalized additive model, a non-linear Bayesian regression model, a Bayesian multiple linear regression model and a random forest regression model. The random forest regression typically resulted in the most accurate forecasts of crop responses and net-returns across most fields. However, in some cases the model type that produced the most accurate forecast of grain yield was not the same as the model producing the most accurate forecast of grain protein concentration. Models performed best when the data used for training models was collected from years with similar weather conditions to the forecasted year. The results are important to developers of decision support tools because the underlying models used to simulate management outcomes and calculate net-returns need to be selected with consideration for the spatiotemporal specificity of the data available.