Scholarship & Research
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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.Item Precision Agroecology(MDPI AG, 2021-12) Duff, Hannah; Hegedus, Paul B.; Loewen, Sasha; Bass, Thomas; Maxwell, Bruce D.In response to global calls for sustainable food production, we identify two diverging paradigms to address the future of agriculture. We explore the possibility of uniting these two seemingly diverging paradigms of production-oriented and ecologically oriented agriculture in the form of precision agroecology. Merging precision agriculture technology and agroecological principles offers a unique array of solutions driven by data collection, experimentation, and decision support tools. We show how the synthesis of precision technology and agroecological principles results in a new agriculture that can be transformative by (1) reducing inputs with optimized prescriptions, (2) substituting sustainable inputs by using site-specific variable rate technology, (3) incorporating beneficial biodiversity into agroecosystems with precision conservation technology, (4) reconnecting producers and consumers through value-based food chains, and (5) building a just and equitable global food system informed by data-driven food policy. As a result, precision agroecology provides a unique opportunity to synthesize traditional knowledge and novel technology to transform food systems. In doing so, precision agroecology can offer solutions to agriculture’s biggest challenges in achieving sustainability in a major state of global change.Item A satellite-driven hydro-economic model to support agricultural water resources management(2020-12) Maneta, Marco P.; Coburn, K.; Kimball, John S.; He, Mingzhu; Silverman, N. L.; Chaffin, Brian C.; Ewing, Stephanie A.; Ji, X.; Maxwell, Bruce D.The management of water resources among competing uses presents a complex technical and policy challenge. Integrated hydro-economic models capable of simulating the hydrologic system in irrigated and non-irrigated regions including the response of farmers to hydrologic constraints and economic and policy incentives, provide a framework to understand biophysical and socioeconomic implications of changing water availability. We present a transformative hydro-economic model of agricultural production driven by multi-sensor satellite observations, outputs from regional climate models, and socioeconomic data. Our approach overcomes the limitations of current decision support systems for agricultural water management and provides policymakers and natural resource managers with satellite data-driven, state-wide, operational models capable of anticipating how farmers allocate water, land, and other resources when confronted with new climate patterns, policy rules, or market signals. The model can also quantify how farming decisions affect agricultural water supplies. We demonstrate the model through an application in the state of Montana.Item CLIMATE CHANGE AND HUMAN HEALTH IN MONTANA: A Special Report of the Montana Climate Assessment(Montana Institute on Ecosystems, 2020-12) Adams, Alexandra K.; Byron, Robert; Maxwell, Bruce D.; Higgins, Susan; Eggers, Margaret; Byron, Lori; Whitlock, CathyThe purpose of this assessment is to a) present understandable, science-based, Montana-specific information about the impacts of climate change on the health of Montanans; and b) describe how our healthcare providers, state leaders, communities, and individuals can best prepare for and reduce those impacts in the coming decades. This assessment draws from, and is an extension to, the 2017 Montana Climate Assessment (MCA1) (Whitlock et al. 2017), which provides the first detailed analysis of expected impacts to Montana’s water, forests, and agriculture from climate change. MCA explains historical, current, and prospective climate trends for the state based on the best-available science. The 2017 Montana Climate Assessment did not address the impact of climate change on the health of Montanans. This special report of the MCA fills that important knowledge gap; it represents a collaboration between climate scientists and Montana’s healthcare community and is intended to help Montanans minimize the impacts of climate on their health.Item Simulation model suggests that fire promotes lodgepole pine (Pinus contorta) invasion in Patagonia(2019-07) Davis, Kimberley T.; Maxwell, Bruce D.; Caplat, Paul; Pauchard, Anibal; Nunez, Martin A.To best understand plant invasions and predict unexpected outcomes it is necessary to integrate information on disturbance, the local environment, and demography. Disturbance by fire has been shown to promote invasions worldwide, but precise interactions between fire, native and invading species remain unclear. Indeed, trade-offs exist between fire-induced mortality of seed sources and increased establishment, driving invasion outcomes. A positive feedback between lodgepole pine (Pinus contorta) invasions and fire has been identified but only above a certain pine density. Above this threshold, fire resulted in increased pine dominance at the plot level, however below this threshold establishment rates did not change. We used a spatially explicit invasion simulation model modified to include fire to explore the implications of these complex interactions between pine invasions and fire. We asked if fire promoted P. contorta invasion across a Patagonian steppe site and if this depended on the age of the invasion when it burned. Our simulations indicated that, although fire was not necessary to initiate invasion, fire in communities where pine invasions were at least 10 years old resulted in increased spatial extent and maximum invasion density compared to unburned simulations. Fire through younger invasions did not alter the progression of the invasion compared to unburned simulations. Pine invasions should be managed before they reach an advanced stage where positive feedbacks between fire and pine invasion could lead to dramatic increases in invasion rate.Item Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data(2018-03) He, Mingzhu; Kimball, John S.; Maneta, Marco P.; Maxwell, Bruce D.; Moreno, Alvaro; Begueria, Santiago; Wu, XiaocuiAccurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008-2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.Item Vulnerability of dryland agricultural regimes to economic and climatic change(2018) Lawrence, Patrick G.; Maxwell, Bruce D.; Rew, Lisa J.; Ellis, Colter; Bekkerman, AntonLarge-scale agricultural systems are central to food production in North America, but their ubiquity could be threatened by vulnerability to economic and climatic stressors during the 21st century. Prior research has focused on understanding the influence of climatic changes on physiological processes in these systems and has increasingly recognized that other factors such as social, economic, and ecological variation and the interaction among these factors may cause unexpected outcomes. We assess the vulnerability of large-scale agricultural systems to variation in multiple stressors and investigate alternative adaptation strategies under novel conditions. We examine dryland farms in Montana’s northern Great Plains (NGP), which represent large-scale semiarid agricultural systems that are likely to be affected by climate change. Farmers in the NGP have experienced three distinct periods of economic- and drought-related stressors since the 1970s, primarily driven by uncertainty in soil moisture, but at times amplified by uncertainty in nitrogen fertilizer and wheat prices. We seek to better understand how farmers evaluate and respond to these conditions. The results indicate that although farmers perceived few alternative agronomic options for adapting to drought, strategies for adapting to high input prices were more plentiful. Furthermore, we find that increasing the overall resilience of dryland agricultural systems to economic and climatic uncertainty requires intrinsic valuation of crop rotations and their field-specific response to inputs.