Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing

Virtually everyone in Minnesota and bordering states whose profession has a direct stake in Water Conservation assembled in Downtown St. Paul’s RiverCentre October 17-18 for the annual Water Resources Conference. I had the honor of chairing a Special Session at the conference titled “Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing.” The session featured two very foundational efforts that we are attempting to bridge together in partnered research at GEMS: (1) The Minnesota Department of Agriculture’s (MDA) Minnesota Agricultural Water Quality Certification Program (MAWQCP), directed by Brad Jordahl Redlin, and (2) the Department of Forest Resources’ remote-sensed Water Quality assessment for our 10,000+ lakes, led by Leif Olmanson.

The 90-minute session was organized so that each of the three speakers (one had dropped out last minute for logistical reasons) had 20 minutes to speak plus two minutes for burning questions. At the end the audience had 15 minutes for questions with the entire panel of speakers. There were about 30 very inquisitive people in the audience, who peppered the speakers with questions throughout.

Updates on the MDA’s certification program

Brad Jordahl Redlin kicked off the session with an outstanding talk that outlined the principles on which the certification program he founded and directs was created, and highlighted the progress that has been made. Literally over a million acres have now been certified, spread across over 1400 producers. In exchange for regulatory certainty for a period of time, these producers allow his government agency to (1) assess their farm practices, (2) suggest new management practices (sometimes with additional incentives such as subsidized equipment from a grant), and (3) carry out audits of their new practices. The program, which has been operational for nearly a decade, is well beyond the stage of corralling early-adopters. New participants span the spectrum of producer demographics in age, farm size, and more.
 

Scaling lake quality measurement

Leif Olmanson stepped up to review an activity that he first spearheaded twenty years ago – using remote sensing to measure the water quality of Minnesota lakes. In the interim, he has overseen steady progress using new generations of satellites (from Landsat to Sentinel), multiple metrics (clarity, chlorophyll, organic matter), and the frequency of data reporting (from once every 5 years to ~weekly lake snapshot and monthly pixel-level composites of all 10,000+ lakes). Data is made available to the casual user via a friendly Lake Browser interface, and in numeric form pixel-by-pixel for data wizards on GEMS Exchange.
 

GEMS bridging the two programs

David Porter rounded out the session discussing the improvements he has made in creating algorithms that properly remove clouds and aerosols from the satellite images. This is a prerequisite for feeding all of the regularly spaced (~5 day) images of a field through a growing season into a sophisticated Recurrent Neural Network (RNN, a type of Machine Learning algorithm) that his colleague Anubha Agrawal has been preparing. As described in this case study, David, Anubha, and I intend to use the RNN trained on known, certified management practices (e.g., strip till / no till, cover crops) from the MAWQCP to make a prediction of what practices growers are making on uncertified farmland. We then will trace these producer fields to the downstream lakes in each watershed, and see what covariates (e.g., slope, soil type, distance to waterway) affect the correlation of % farm practice adoption vs. downstream lake quality in a watershed.

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

Behavioral economists are studying “nudges” to prompt behavior change in agriculture. Accounting for production economics can help make nudges more effective for reducing agricultural run-off.

Water pollution caused by agricultural nitrogen runoff is a pervasive problem worldwide. In a recently published paper (Food Policy), GEMS researchers (Yuan Chai and Philip Pardey) teamed up with University of Western Australia collaborator (David Pannell) to provide a new slant on this age-old problem. Combining insights from both production economics and behavioral science opens up new and practical avenues for nudging farmers towards eco-friendly practices that can mitigate water pollution. 

Production Economics Insights

The authors highlight three key empirical findings that offer opportunities to reshape farming practices and reduce nitrogen pollution. First, many farmers tend to apply more nitrogen than necessary, driven by yield maximizing or other motives. This can lead farmers to use more fertilizer than is economically optimal, which not only incurs avoidable costs but also exacerbates the water pollution problem. Second, farmers may imagine that, contrary to the evidence, using more fertilizer is a risk-reducing strategy. Thirdly, the profit-to-fertilizer-rate relationship is flat near the optimum, suggesting that farmers can curtail their fertilizer usage without incurring significant, if any, private costs (through foregone income).

Nudges

Leveraging these three insights, the authors draw on emerging findings from behavioral economics to identify a number of potentially transformative approaches to curbing agricultural pollution. The authors make specific suggestions for ways in which behavioral science could contribute in these areas, including understanding farmers’ perceptions about nitrogen, yield and profit (detecting biases), understanding the influences of these biases on behavior, understanding farmers’ perceptions of and preferences for different policy responses, and designing behavioral nudges that capitalize on the specific insights we have highlighted regarding the production economics of nitrogen fertilizer. Nudges could include strategies that aim to correct biases in perceptions (e.g., about yield increases or risk reductions from applying N),or tap into social and psychological factors that influence farmers’ decision making.

Behavioral changes that are deemed nudgeable (i.e., where farmers bear little or no cost) have a lot of merit. The nudges revealed through this research could go a long way to cleaning up water bodies in ways that do not undermine food security or farmer livelihood concerns: a rare win-win result.

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota