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1. Does adopting a nitrogen best management practice reduce nitrogen fertilizer rates?

   https://doi.org/10.1007/s10460-021-10227-9  

   Houser, Matthew (June 2021, Agriculture and Human Values)

   null (Ed.)

   Technical best management practices are the dominant approach promoted to mitigate agriculture’s significant contributions to environmental degradation. Yet very few social science studies have examined how farmers actually use these practices. This study focuses on the outcomes of farmers’ technical best management practice adoption related to synthetic nitrogen fertilizer management in the context of Midwestern corn agriculture in the United States. Moving beyond predicting the adoption of nitrogen best management practices, I use structural equation modeling and data from a sample of over 2500 farmers to analyze how the number of growing season applications a farmer uses influences the rate at which synthetic nitrogen is applied at the field-level. I find that each additional application of N during the growing season is associated with an average increase of 2.4 kg/ha in farmers’ average N application rate. This result counters expectation for the outcome of this practice and may suggest that structural pressures are leading farmers to use additional growing season applications to ensure sufficiently high N rates, rather than allowing them to reduce rates. I conclude by discussing the implication of this study for future research and policy. 

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2. Soil health through farmers’ eyes: Toward a better understanding of how farmers view, value, and manage for healthier soils

   https://doi.org/10.2489/jswc.2023.00058  

   Irvine, R.; Houser, M.; Marquart-Pyatt, S.T.; Bogar, G.; Bolin, L.G.; Browning, E. Grennan; Evans, S.E.; Howard, M.M.; Lau, J.A.; Lennon, J.T. (January 2023, Journal of Soil and Water Conservation)

   Abstract: Improved soil health (SH) is critical in achieving agricultural resilience and miti- gating climate risks. Whether SH management practices are widely used depends greatly on US farmers’ voluntary decision-making. Toward understanding this point, much research has addressed factors that contribute to the adoption (or lack thereof) of SH-promoting practices, but less is known in terms of farmers’ perceptions of SH itself and the corresponding man- agement practices they see as related to achieving SH. To offer introductory insight on this knowledge gap and support better buy-in from farmers toward positive SH outcomes, our research draws upon qualitative interviews with 91 farmers across three key agricultural states in the Midwest (Illinois, Indiana, and Michigan). We develop a more detailed understanding of farmers’ views on SH, and why and how they manage for it. Nearly all interviewed farmers were familiar with the concept of SH and most viewed it favorably. A minority of farmers lacked familiarity with the term “SH” yet still managed for it. Skeptics of SH largely cited uncertainties related to over-zealous messaging by proponents of SH or lack of evidence for the return on investment of SH practices. Overall, farmers’ perceptions of SH largely aligned with the scientific community’s understanding of soils being a dynamic system, though farmers most dominantly defined SH by its biological component. Farmers perceived a host of benefits of SH, most often noting benefits to production, followed by improvements in physical aspects of the soil such as erosion control and increased organic matter. Notably, pro- duction and sustainability benefits were often cited together, suggesting that SH management is increasingly seen as a “win-win” by farmers. Additionally, we found that many farmers view themselves as active participants in SH outcomes and believe their management choices are indicators of positive SH outcomes, regardless of the practices they employ, including some strategies (such as tillage or tile drainage) that do not align with scientifically documented approaches to improving SH. Our findings show that farmers report engaging in an array of SH management practices that target both biotic and abiotic components of soils, and often use multiple practices in tandem to promote SH on their farms. Achieving better SH in agricultural production in the future will require engaging farmers in SH management by tailoring outreach and communication strategies to align with the perspectives and language farmers themselves use to conceptualize SH. 

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3. How farmers “repair” the industrial agricultural system

   https://doi.org/10.1007/s10460-020-10030-y  

   Houser, Matthew; Gunderson, Ryan; Stuart, Diana; Denny, Riva C. (March 2020, Agriculture and Human Values)

   Scholars are increasingly calling for the environmental issues of the industrial agricultural system to be addressed via eventual agroecological system-level transformation. It is critical to identify the barriers to this transition. Drawing from Henke’s (Cultivating science, harvesting power: science and industrial agriculture in California, MIT Press, Cambridge, MA, 2008) theory of “repair,” we explore how farmers participate in the reproduction of the industrial system through “discursive repair,” or arguing for the continuation of the industrial agriculture system. Our empirical case relates to water pollution from nitrogen fertilizer and draws data from a sample of over 150 interviews with row-crop farmers in the midwestern United States. We find that farmers defend this system by denying agriculture’s causal role and proposing the potential for within-system solutions. They perform these defenses by drawing on ideological positions (agrarianism, market-fundamentalism and techno-optimism) and may be ultimately led to seek system maintenance because they are unable to envision an alternative to the industrial agriculture system. 

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4. In defense of the original Type I functional response: The frequency and population-dynamic effects of feeding on multiple prey at a time

   https://doi.org/10.1101/2024.05.14.594210  

   Novak, Mark; Coblentz, Kyle E; DeLong, John P (May 2024, bioRxiv)

   Abstract Ecologists differ in the degree to which they consider the linear Type I functional response to be an unrealistic versus sufficient representation of predator feeding rates. Empiricists tend to consider it unsuitably non-mechanistic and theoreticians tend to consider it necessarily simple. Holling’s original rectilinear Type I response is dismissed by satisfying neither desire, with most compromising on the smoothly saturating Type II response for which searching and handling are assumed to be mutually exclusive activities. We derive a “multiple-prey-at-a-time” response and a generalization that includes the Type III to reflect predators that can continue to search when handling an arbitrary number of already-captured prey. The multi-prey model clarifies the empirical relevance of the linear and rectilinear models and the conditions under which linearity can be a mechanistically-reasoned description of predator feeding rates, even when handling times are long. We find support for linearity in 35% of 2,591 compiled empirical datasets and support for the hypothesis that larger predator-prey body-mass ratios permit predators to search while handling greater numbers of prey. Incorporating the multi-prey response into the Rosenzweig-MacArthur population-dynamics model reveals that a non-exclusivity of searching and handling can lead to coexistence states and dynamics that are not anticipated by theory built on the Type I, II, or III response models. In particular, it can lead to bistable fixed-point and limit-cycle dynamics with long-term crawl-by transients between them under conditions where abundance ratios reflect top-heavy food webs and the functional response is linear. We conclude that functional response linearity should not be considered empirically unrealistic but also that more cautious inferences should be drawn in theory presuming the linear Type I to be appropriate. 

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5. Denitrification and the challenge of scaling microsite knowledge to the globe

   https://doi.org/10.1002/mlf2.12080  

   Robertson, G. Philip (September 2023, mLife)

   Abstract Our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process‐level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well‐calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait‐based models into such efforts promises to improve predictions and understanding still further, but much more development is needed. 

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