Reducing Nitrogen Export from the Corn Belt to the Gulf of Mexico: Agricultural Strategies for Remediating Hypoxia

McLellan, Eileen; Robertson, Dale M.; Schilling, Keith E.; Tomer, Mark D.; Kostel, Jill; Smith, Douglas G.; King, Kevin W.

doi:10.1111/jawr.12246

Cited by 80 publications

(80 citation statements)

References 71 publications

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“…With little documented progress in loads or hypoxic extent, clearly something more or something different is needed (57). Several analyses have demonstrated a range of approaches and potential pathways toward the desired load reduction, including altering fertilizer application rates (58), using cover crops (59), nutrient management (60, 61), alternatives to corn-based biofuels (62,63), and combinations of the above (12,34,(64)(65)(66)(67)(68). Most of these studies emphasize the value of targeting funding to locations and practices that make the most difference.…”

Section: Discussionmentioning

confidence: 99%

Ensemble modeling informs hypoxia management in the northern Gulf of Mexico

Scavia

Bertani

Obenour

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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A large region of low-dissolved-oxygen bottom waters (hypoxia) forms nearly every summer in the northern Gulf of Mexico because of nutrient inputs from the Mississippi River Basin and water column stratification. Policymakers developed goals to reduce the area of hypoxic extent because of its ecological, economic, and commercial fisheries impacts. However, the goals remain elusive after 30 y of research and monitoring and 15 y of goal-setting and assessment because there has been little change in river nitrogen concentrations. An intergovernmental Task Force recently extended to 2035 the deadline for achieving the goal of a 5,000-km 2 5-y average hypoxic zone and set an interim load target of a 20% reduction of the spring nitrogen loading from the Mississippi River by 2025 as part of their adaptive management process. The Task Force has asked modelers to reassess the loading reduction required to achieve the 2035 goal and to determine the effect of the 20% interim load reduction. Here, we address both questions using a probabilistic ensemble of four substantially different hypoxia models. Our results indicate that, under typical weather conditions, a 59% reduction in Mississippi River nitrogen load is required to reduce hypoxic area to 5,000 km 2 . The interim goal of a 20% load reduction is expected to produce an 18% reduction in hypoxic area over the long term. However, due to substantial interannual variability, a 25% load reduction is required before there is 95% certainty of observing any hypoxic area reduction between consecutive 5-y assessment periods.ensemble modeling | hypoxia | Gulf of Mexico | nitrogen-loading targets A large region of low-dissolved-oxygen (DO) bottom waters (hypoxia; DO < 2 mg·L −1 ) has formed nearly every summer in the Gulf of Mexico for at least the last three decades (1-3). Hypoxia forms because of respiration of organic matter in bottom waters and a vertically stratified water column restricting reaeration (4-6). Both factors are related to the outflow of freshwater and nutrients from the Mississippi River Basin, which typically peaks in March through May. Nutrients stimulate phytoplankton production, much of which settles in early summer, and bottom water DO (BWDO) is consumed during its decomposition. River outflows create a fresher, warmer surface layer above a colder, saltier bottom layer, limiting the vertical diffusion of DO (2).Policymakers developed hypoxia reduction goals because of ecological, economic, and commercial fisheries impacts (7-10). However, the goals remain elusive after >30 y of research and monitoring (3, 11) and >15 y of assessment and goal-setting (5, 12-16). A Gulf Task Force recently agreed to retain the 5-y moving average goal of a 5,000-km 2 hypoxic zone, but extended the deadline from 2015 to 2035 (17). The timeframe was reset because the 2015 hypoxic zone was 16,760 km 2 and the most recent 5-y average was 14,024 km 2 -greater than the long-term average of 13,751 km 2 (1). Missing the goal was not unexpected because the 5-y average load of late sp...

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Section: Discussionmentioning

confidence: 99%

Ensemble modeling informs hypoxia management in the northern Gulf of Mexico

Scavia

Bertani

Obenour

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…There is growing recognition that adaptation to increasingly variable weather will require implementation of diverse practices that address both on and off-farm impacts of weather extremes by reducing soil erosion, ameliorating nutrient loss, improving water infiltration and holding capacity, and so forth (Castellano and Helmers 2015;Drinkwater and Snapp 2007;ISU 2012;McLellan et al 2015;Walthall et al 2013). The survey provided a list of 18 BMPs that are recommended for adapting to the predicted impacts of climate change in the region (ISU 2012;Hatfield et al 2013;Janowiak et al 2016).…”

Section: Methodsmentioning

confidence: 99%

Climate change typologies and audience segmentation among Corn Belt farmers

Arbuckle¹,

Tyndall²,

Morton³

et al. 2017

Journal of Soil and Water Conservation

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Development of natural resource user typologies has been viewed as a potentially effective means of improving the effectiveness of natural resource management engagement strategies. Prior research on Corn Belt farmers' perspectives on climate change employed a latent class analysis (LCA) that created a six-class typology-the Concerned, Uneasy, Uncertain, Unconcerned, Confident, and Detached-to develop a better understanding of farmer perspectives on climate change and inform more effective climate adaptation and mitigation outreach strategies. The LCA employed 34 variables that are generally unobservable-beliefs about climate change, experience with extreme weather, perceived risks of climate change, and attitudes toward climate action-to identify types. The research reported in this paper builds on this typology of Corn Belt farmers by exploring 33 measures of observable farm enterprise characteristics, land management practices, and farmer demographics to assess whether variations in these observable characteristics between the six farmer classes display systematic patterns that might be sufficiently distinctive to guide audience segmentation strategies. While analyses detected some statistically significant differences, there were few systematic, meaningful observable patterns of difference between groups of farmers with differing perspectives on climate change. In other words, farmers who believe that anthropogenic climate change is occurring, that it poses risks to agriculture, and that adaptive action should be taken, may look very much like farmers who deny the existence of climate change and do not support action. The overall implication of this finding is that climate change engagement efforts by Extension and other agricultural advisors should use caution when looking to observable characteristics to facilitate audience segmentation. Additional analyses indicated that the farmer types that tended to be more concerned about climate change and supportive of adaptive action (e.g., Concerned and Uneasy) reported that they were more influenced by key private and public sector actors in agricultural social networks. On the other hand, farmers who were not concerned about climate change or supportive of adaptation (e.g., the Unconcerned, Confident, and Detached groups, comprising between one-third and one-half of respondents) were less integrated into agricultural networks. This suggests that Extension and other agricultural advisors should expand outreach efforts to farmers who are not already within their spheres of influence.

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“…As stakeholders identify and rank their interest in various practices and practice locations, it is possible to create a set of conservation scenarios that, if implemented, would achieve the target level of nutrient reduction at the watershed outlet. Tomer et al (2015) provide a spreadsheet-based approach for evaluating whether a proposed scenario will achieve the target reduction.…”

Section: Legendmentioning

confidence: 99%