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 Momentum for agroecology in the USA(Springer Science and Business Media LLC, 2024-07) Ong, Theresa W.; Roman-Alcalá, Antonio; Jiménez-Soto, Estelí; Jackson, Erin; Perfecto, Ivette; Duff, HannahThe alarming convergence of ecological, health and societal crises underpins the urgent need to transform our agricultural and food systems. The global food system, with industrial agriculture at its core, poses a major threat to our planet’s health, contributing to climate change, biodiversity loss and food insecurity, which is known as the triple threat to humanity. The hidden costs of a global food system that relies on industrial agriculture are estimated to be US$12.7 trillion, with the vast majority driven by public-health crises due to unhealthy foods that disproportionately burden people on the lowest incomes.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 Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis(MDPI, 2023-02) Hegedus, Paul B.; Maxwell, Bruce; Sheppard, John; Loewen, Sasha; Duff, Hannah; Morales-Luna, Giorgio; Peerlinck, AmyFew mechanisms turn field-specific ecological data into management recommendations for crop production with appropriate uncertainty. Precision agriculture is mainly deployed for machine efficiencies and soil-based zonal management, and the traditional paradigm of small plot research fails to unite agronomic research and effective management under farmers’ unique field constraints. This work assesses the use of on-farm experiments applied with precision agriculture technologies and open-source data to gain local knowledge of the spatiotemporal variability in agroeconomic performance on the subfield scale to accelerate learning and overcome the bias inherent in traditional research approaches. The on-farm precision experimentation methodology is an approach to improve farmers’ abilities to make site-specific agronomic input decisions by simulating a distribution of economic outcomes for the producer using field-specific crop response models that account for spatiotemporal uncertainty in crop responses. The methodology is the basis of a decision support system that includes a six-step cyclical process that engages precision agriculture technology to apply experiments, gather field-specific data, incorporate modern data management and analytical approaches, and generate management recommendations as probabilities of outcomes. The quantification of variability in crop response to inputs and drawing on historic knowledge about the field and economic constraints up to the time a decision is required allows for probabilistic inference that a future management scenario will outcompete another in terms of production, economics, and sustainability. The proposed methodology represents advancement over other approaches by comparing management strategies and providing the probability that each will increase producer profits over their previous input management on the field scale.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.