Lori Abendroth

What does it take to detect a change in soil carbon stock? A regional comparison of minimum detectable difference and experiment duration in the north central United States

Variability in soil organic carbon (SOC) results from natural and human processes interacting across time and space, and leads to large variation in the minimum difference in SOC that can be detected with a particular experimental design. Here we report a unique comparison of minimum detectable differences (MDDs) in SOC, and the estimated times required to observe those MDDs across the north central United States, calculated for the two most common SOC experiments: (1) a comparison between two treatments, e.g., moldboard plow (MP) and no-tillage (NT), using a randomized complete block design experiment; and (2) a comparison of changes in SOC over time for a particular treatment, e.g., NT, using a randomized complete block design experiment with time as an additional factor. We estimated the duration of the two experiment types required to achieve MDD through simulation of SOC dynamics. Data for the study came from 13 experimental sites located in Iowa, Illinois, Ohio, Michigan, Wisconsin, Missouri, and Minnesota. Soil organic carbon, bulk density, and texture were measured at four soil depths. Minimum detectable differences were calculated with probability of Type I error of 0.05 and probability of Type II error of 0.15. The MDDs in SOC were highly variable across the region and increased with soil depth. At 0 to 10 cm (0 to 3.9 in) soil depth, MDDs with five replications ranged from 1.04 g C kg−1 (0.017 oz C lb−1; 6%) to 7.15 g C kg−1 (0.114 oz C lb−1; 31%) for comparison of two treatments; and from 0.46 g C kg−1 (0.007 oz C lb−1; 3%) to 3.12 g C kg−1 (0.050 oz C lb−1; 13%) for SOC change over time. Large differences were also predicted in the experiment duration required to detect a difference in SOC between MP and NT (from 8 to >100 years with five replications), or a change in SOC over time under NT management (from 11 to 71 years with five replications). At most locations, the time required to detect a change in SOC under NT was shorter than the time required to detect a difference between MP and NT. Minimum detectable difference and experiment duration decreased with the number of replications and were correlated with SOC variability and soil texture of the experimental sites, i.e., they tended to be lower in fine textured soils. Experiment duration was also reduced by increased crop productivity and the amount of residue left on the soil. The relationships and methods described here enable the design of experiments with high power of detecting differences and changes in SOC and enhance our understanding of how management practices influence SOC storage.

Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems

The important questions about agriculture, climate, and sustainability have become increasingly complex and require a coordinated, multifaceted approach for developing new knowledge and understanding. A multistate, transdisciplinary project was begun in 2011 to study the potential for both mitigation and adaptation of corn-based cropping systems to climate variations. The team is measuring the baseline as well as change of the systems carbon (C), nitrogen (N), and water footprints, crop productivity, and pest pressure in response to existing and novel production practices. Nine states and 11 institutions are participating in the project, necessitating a well thought out approach to coordinating field data collection procedures at 35 research sites. In addition, the collected data must be brought together in a way that can be stored and used by persons not originally involved in the data collection, necessitating robust procedures for linking metadata with the data and clearly delineated rules for use and publication of data from the overall project. In order to improve the ability to compare data across sites and begin to make inferences about soil and cropping system responses to climate across the region, detailed research protocols were developed to standardize the types of measurements taken and the specific details such as depth, time, method, numbers of samples, and minimum data set required from each site. This process required significant time, debate, and commitment of all the investigators involved with field data collection and was also informed by the data needed to run the simulation models and life cycle analyses. Although individual research teams are collecting additional measurements beyond those stated in the standardized protocols, the written protocols are used by the team for the base measurements to be compared across the region. A centralized database was constructed to meet the needs of current researchers on this project as well as for future use for data synthesis and modeling for agricultural, ecosystem, and climate sciences.

Data management approach to multidisciplinary agricultural research and syntheses

Agricultural scientists are increasingly asked to address challenges related to natural resource stewardship, agricultural productivity, and environmental protection while simultaneously being mindful of the impact and risks associated with climate change (ASA CSSA SSSA 2011; Hatfield et al. 2011; OSTP CAST 2012). Measuring and predicting the effects of climate on agricultural systems adds a layer of complexity that is challenging using traditional data management methods; these methods also limit the potential for full data discovery and innovation (Overpeck et al. 2011; Wolkovich et al. 2012). To properly address challenges, access to multidisciplinary data spanning environments, timescales, treatments, and management is necessary (White and van Evert 2008; Reichman et al. 2011; Eigenbrode et al. 2014). Disciplinary scientists, data scientists, and data managers need to increasingly work in a collaborative manner in this data-rich era. While scientists generally desire to share data, time constraints, limited funding, a lackluster reward system, and reuse concerns are cited as barriers (Michener et al. 2011; Tenopir et al. 2011; Marx 2012; Wolkovich et al. 2012). A concerted and well-executed approach is necessary to overcome these barriers and move toward transformative science. The Climate and Corn-based Cropping Systems Coordinated Agricultural Project (CSCAP), referred to as “Sustainable Corn,”…

Crops, climate, culture, and change

The future of agriculture will depend on how well we negotiate change and adapt. A changing climate is one of many drivers of change. Increased variability in distribution and timing of precipitation, along with changing temperatures, bring about greater volatility in global agricultural production and markets. Growing competition for water resources and increased water pollutants are altering the hydrology, biology, and chemistry of streams, lakes, and rivers. Concurrently, long-term productivity of agricultural lands is reduced in many row crop fields due to soil erosion, compaction, and nutrient depletion. Each growing season, a series of invasive species, diseases, and pests challenge the practices put in place to control them, requiring ever more intensive management by farmers. End users and consumers will continue to demand efficient and economical production while desiring more varied and resource-intensive diets, biofuel feedstocks, and food security as populations grow and developing countries gain wealth (Garnett et al. 2013; Bennett et al. 2014). The Upper Midwest, with its fertile soils and abundant rainfall, reported an unprecedented 384 billion kg (15.1 billion bu) of corn (Zea mays L.) harvested in 2016, with many state averages reaching all-time highs: Indiana at 10,859 kg ha−1 (173 bu ac−1); Minnesota at 12,115…