Assessing long-term impacts of cover crops on soil organic carbon in the central US Midwestern agroecosystems.

Cover crops have been reported as one of the most effective practices to increase soil organic carbon (SOC) for agroecosystems. Impacts of cover crops on SOC change vary depending on soil properties, climate, and management practices, but it remains unclear how these control factors affect SOC benefits from cover crops, as well as which management practices can maximize SOC benefits. To address these questions, we used an advanced process-based agroecosystem model, ecosys, to assess the impacts of winter cover cropping on SOC accumulation under different environmental and management conditions. We aimed to answer the following questions: (1) To what extent do cover crops benefit SOC accumulation, and how do SOC benefits from cover crops vary with different factors (i.e., initial soil properties, cover crop types, climate during the cover crop growth period, and cover crop planting and terminating time)? (2) How can we enhance SOC benefits from cover crops under different cover crop management options? Specifically, we first calibrated and validated the ecosys model at two long-term field experiment sites with SOC measurements in Illinois. We then applied the ecosys model to six cover crop field experiment sites spanning across Illinois to assess the impacts of different factors on SOC accumulation. Our modeling results revealed the following findings: (1) Growing cover crops can bring SOC benefits by 0.33 ± 0.06 MgC ha-1  year-1 in six cover crop field experiment sites across Illinois, and the SOC benefits are species specific to legume and non-legume cover crops. (2) Initial SOC stocks and clay contents had overall small influences on SOC benefits from cover crops. During the cover crop growth period (i.e., winter and spring in the US Midwest), high temperature increased SOC benefits from cover crops, while the impacts from larger precipitation on SOC benefits varied field by field. (3) The SOC benefits from cover crops can be maximized by optimizing cover crop management practices (e.g., selecting cover crop types and controlling cover crop growth period) for the US Midwestern maize-soybean rotation system. Finally, we discussed the economic and policy implications of adopting cover crops in the US Midwest, including that current economic incentives to grow cover crops may not be sufficient to cover costs. This study systematically assessed cover crop impacts for SOC change in the US Midwest context, while also demonstrating that the ecosys model, with rigorous validation using field experiment data, can be an effective tool to guide the adaptive management of cover crops and quantify SOC benefits from cover crops. The study thus provides practical tools and insights for practitioners and policy-makers to design cover crop related government agricultural policies and incentive programs for farmers and agri-food related industries.

The important but weakening maize yield benefit of grain filling prolongation in the US Midwest.

A better understanding of recent crop yield trends is necessary for improving the yield and maintaining food security. Several possible mechanisms have been investigated recently in order to explain the steady growth in maize yield over the US Corn-Belt, but a substantial fraction of the increasing trend remains elusive. In this study, trends in grain filling period (GFP) were identified and their relations with maize yield increase were further analyzed. Using satellite data from 2000 to 2015, an average lengthening of GFP of 0.37 days per year was found over the region, which probably results from variety renewal. Statistical analysis suggests that longer GFP accounted for roughly one-quarter (23%) of the yield increase trend by promoting kernel dry matter accumulation, yet had less yield benefit in hotter counties. Both official survey data and crop model simulations estimated a similar contribution of GFP trend to yield. If growing degree days that determines the GFP continues to prolong at the current rate for the next 50 years, yield reduction will be lessened with 25% and 18% longer GFP under Representative Concentration Pathway 2.6 (RCP 2.6) and RCP 6.0, respectively. However, this level of progress is insufficient to offset yield losses in future climates, because drought and heat stress during the GFP will become more prevalent and severe. This study highlights the need to devise multiple effective adaptation strategies to withstand the upcoming challenges in food security.

Ancient DNA from the Schild site in Illinois: Implications for the Mississippian transition in the Lower Illinois River Valley.

Archaeologists have long debated whether rapid cultural change in the archaeological record is due to in situ developments, migration of a new group into the region, or the spread of new cultural practices into an area through existing social networks, with the local peoples adopting and adapting practices from elsewhere as they see fit (acculturation). Researchers have suggested each of these explanations for the major cultural transition that occurred at the beginning of the Mississippian period (AD 1050) across eastern North America. In this study, we used ancient DNA to test competing hypotheses of migration and acculturation for the culture change that occurred between the Late Woodland (AD 400-1050) and Mississippian (AD 1050-1500) periods in the Lower Illinois River Valley. We obtained sequences of the first hypervariable segment of the mitochondrial genome (mtDNA) from 39 individuals (17 Late Woodland, 22 Mississippian) interred in the Schild cemetery in western Illinois, and compared these lineages to ancient mtDNA lineages present at other sites in the region. Computer simulations were used to test a null hypothesis of population continuity from Late Woodland to Mississippian times at the Schild site and to investigate the possibility of gene flow from elsewhere in the region. Our results suggest that the Late Woodland to Mississippian cultural transition at Schild was not due to an influx of people from elsewhere. Instead, it is more likely that the transition to Mississippian cultural practices at this site was due to a process of acculturation.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery.

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.

Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning approach.

Climate change is exacerbating environmental pollution from crop production. Spatially and temporally explicit estimates of life-cycle environmental impacts are therefore needed for suggesting location and time relevant environmental mitigations strategies. Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanistic models, capable of capturing the heterogeneity, tend to be very complicated and time-consuming. Efficient prediction of life-cycle environmental impacts from agricultural production is lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW) and eutrophication (EU) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate future life-cycle environmental impacts of corn production in U.S. Midwest counties under four emissions scenarios for years 2022-2100. Results from BRT models indicate that the cross-validation (R2) for predicting life cycle GW and EU impacts ranged from 0.78 to 0.82, respectively. Furthermore, results show that future life-cycle GW and EU impacts of corn production will increase in magnitude under all four emissions scenarios, with the highest environmental impacts shown under the high-emissions scenario. Moreover, this study found that changes in precipitation and temperature played a significant role in influencing the spatial heterogeneity in all life-cycle impacts across Midwest counties. The BRT model results indicate that machine learning can be a useful tool for predicting spatially and temporally explicit future life-cycle environmental impacts associated with corn production under different climate scenarios.

Assessing the Prevalence and Association of Acculturation and Health Literacy Levels in Spanish-Speaking Hispanic Patients at Midwestern Community Health Centers in the USA.

BACKGROUND: Predictions from the US census indicate that by 2060 one of every three individuals living in the nation will be of Hispanic origin. Hispanics experience health disparities manifested with higher rates of chronic conditions such as hypertension and diabetes in contrast with other US population groups. Health literacy and acculturation have been well identified as key contributors for this issue. However, very few studies have examined the association between health literacy and levels of acculturation in Spanish-speaking Hispanics living in rural and urban areas of the USA. OBJECTIVE: To assess the prevalence and association of acculturation with low levels of health literacy and demographic variables in Spanish-speaking Hispanics in the US Midwest. METHODS: We measured levels of acculturation in Spanish-speaking adult Hispanics (N = 401) accessing community health centers in rural and urban Nebraska. We used a survey that included the Bidimensional Acculturation Scale for Hispanics/Linguistic Proficiency Subscale (BAS/LP) and the Short Assessment of Health Literacy in Spanish (SAHL-S). RESULTS: We observed similar acculturation levels with rural and urban status participants. A total of 85% of the total participants had low levels of acculturation to US culture. Of the 15% of the participants who showed themselves to be bicultural, 81% reported adequate levels of health literacy in Spanish. CONCLUSIONS: Our findings are consistent with previous studies that report the associations between acculturation and health literacy levels are significant. This study underscores the importance of considering the cultural aspects of the Hispanic community for the development of health literacy interventions and education programs.