Scholarship & Research

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

Browse

Search Results

Now showing 1 - 10 of 23

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.
Increasing the scope and scale of agroecology in the Northern Great Plains
(Lyson Center for Civic Agriculture and Food Systems, 2024-04) Maxwell, Bruce D.; Duff, Hannah
Agroecology is a science, practice, and movement that is gaining momentum worldwide. It aims to provide local, stable, and diverse diets through diversified, resilient, and sustainable agricultural practices (Ewert et al. 2023). However, agroecology seeks to address food systems issues by replacing large-scale commodity-based agriculture with something very different. Agroecology is typically discussed within the scope and scale of smallholder farming while failing to address the issues embedded in large-scale commodity-based agriculture. While we do not take issue with an ideal system where food is produced on small farms, it does not need to exclude agroecology applied to current scales of agriculture in regions like the Northern Great Plains (NGP), where agriculture consists of spatially extensive crop and livestock farms. NGP farms have internal sustainability problems and harmful social, racial, and environmental externalities that can be addressed with agroecological principles. Despite the problems, the large scale of NGP agriculture is not likely to change much in coming decades, and so there is an imperative to apply agroecological principles at larger scales to address immediate issues. We emphasize that applying agroecological principles to large-scale farming could increase crop and forage diversity, conserve biodiversity, strengthen cross-boundary and multi-objective ecosystem management, address regional food security, and encourage co-innovation with crop and livestock producers in the NGP (Tittonell, 2020). If agroecologists don’t address the immediate issues of NGP such as climate change adaptation and mitigation, livestock-based protein production, unequal access to nutritious food, agriautomation, and pandemic food system disruption, then we may only expect industrialized agriculture to provide short-sited profit-motivated solutions repeating a pattern of the past.
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, Aníbal
Pinus 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).
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.
Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing
(MDPI AG, 2023-01) Morales, Giorgio; Sheppard, John W.; Hedgedus, Paul B.; Maxwell, Bruce D.
In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional Neural Networks (CNNs) given their unique ability to exploit the spatial information of small regions of the field. We present a novel CNN architecture called Hyper3DNetReg that takes in a multi-channel input raster and, unlike previous approaches, outputs a two-dimensional raster, where each output pixel represents the predicted yield value of the corresponding input pixel. Our proposed method then generates a yield prediction map by aggregating the overlapping yield prediction patches obtained throughout the field. Our data consist of a set of eight rasterized remotely-sensed features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), aspect, and two radar backscatter coefficients acquired from the Sentinel-1 satellites. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present leave-one-out cross-validation experiments for rain-fed winter wheat over four fields and show that our proposed methodology produces better predictions than five compared methods, including Bayesian multiple linear regression, standard multiple linear regression, random forest, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.
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.
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, Taro
Data-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.
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.
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.
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, Cathy
The 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.

Scholarship & Research

Browse

Filters

Settings

Sort By

Results per page

Search Results