Organic fertility inputs synergistically increase denitrification-derived nitrous oxide emissions in agroecosystems

Soil fertility in organic agriculture relies on microbial cycling of nutrient inputs from legume cover crops and animal manure. However, large quantities of labile carbon (C) and nitrogen (N) in these amendments may promote the production and emission of nitrous oxide (N₂ O) from soils. Better ecological understanding of the N₂ O emission controls may lead to new management strategies to reduce these emissions. We measured soil N₂ O emission for two growing seasons in four corn-soybean-winter grain rotations with tillage, cover crop, and manure management variations typical of organic agriculture in temperate and humid North America. To identify N₂ O production pathways and mitigation opportunities, we supplemented N₂ O flux measurements with determinations of N₂ O isotopomer composition and microbiological genomic DNA abundances in microplots where we manipulated cover crop and manure additions. The N input from legume-rich cover crops and manure prior to corn planting made the corn phase the main source of N₂ O emissions, averaging 9.8 kg/ha of N₂ O-N and representing 80% of the 3-yr rotations' total emissions. Nitrous oxide emissions increased sharply when legume cover crop and manure inputs exceeded 1.8 and 4 Mg/ha (dry matter), respectively. Removing the legume aboveground biomass before corn planting to prevent co-location of fresh biomass and manure decreased N₂ O emissions by 60% during the corn phase. The co-occurrence of peak N₂ O emission and high carbon dioxide emission suggests that oxygen (O₂ ) consumption likely caused hypoxia and bacterial denitrification. This interpretation is supported by the N₂ O site preference values trending towards denitrification during peak emissions with limited N₂ O reduction, as revealed by the N₂ O δ¹⁵ N and δ¹⁸ O and the decrease in clade I nosZ gene abundance following incorporation of cover crops and manure. Thus, accelerated microbial O₂ consumption seems to be a critical control of N₂ O emissions in systems with large additions of decomposable C and N substrates. Because many agricultural systems rely on combined fertility inputs from legumes and manures, our research suggests that controlling the rate and timing of organic input additions, as well as preventing the co-location of legume cover crops and manure, could mitigate N₂ O emissions.

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort.

We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Cover crops and drought: Maize ecophysiology and yield dataset

This dataset supports the research paper "Cover crop effects on maize drought stress and yield" by Hunter et al. [1]. Data is provided on ecophysiological and yield measurements of maize grown following five functionally diverse cover crop treatments. The experiment was conducted in Pennsylvania, USA from 2013-2015 with organic management. Cover crops were planted in August after winter wheat harvest. Cover crops were terminated in late May of the following year, manure was applied, and both were incorporated with full inversion tillage prior to planting maize. The five cover crop treatments included a tilled fallow control, medium red clover, cereal rye, forage radish, and a 3-species mixture of medium red clover, cereal rye, and Austrian winter pea. Drought was imposed with rain exclusion shelters starting in early July. Results are provided for two subplots per cover crop treatment representing ambient and drought conditions. The dataset includes: 1) soil moisture in spring and during the maize growing season; 2) maize height, leaf chlorophyll content, leaf area index, stomatal conductance, and pre-dawn leaf xylem water potential; 3) maize yield and yield components including kernel biomass, total biomass, harvest index, number of plants per subplot, ears per plant, kernel mass, and kernel number per ear, per plant, and per subplot; 4) modeled season-long radiation interception and radiation use efficiency of biomass production; and 5) maize rooting density by depth in one year only. Data was collected in the field and lab using ecophysiological instruments (e.g., SPAD meter, ceptometer, porometer, and pressure chamber). Biomass samples were taken to determine yield. Data presented have been averaged to the subplot level (ambient and drought). This dataset can inform future research focused on using cover crops and other cultural practices to improve climate adaptation in cropping systems and also may be useful for meta-analyses.

Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales

Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake's watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.

Can Crop Models Identify Critical Gaps in Genetics, Environment, and Management Interactions?

Increasing food demand under climate change constraints may challenge and strain agricultural systems. The use of crop models to assess genotypes performance across diverse target environments and management practices, i.e., the genetic × environment × management interaction (GEMI), can help understand suitability of genotype and agronomic practices, and possibly accelerate turnaround in plant breeding programs. However, the readiness of models to support these tasks can be debated. In this article, we point out modeling and data limitations and argue the need for evaluation and improvement of relevant process algorithms as well as model convergence. Under conditions suitable for plant growth, without meteorological extremes or soil limitation to root exploration, models can simulate resource capture, growth, and yield with relative ease. As stresses accumulate, the plant species- and genotype-specific attributes and their interactions with the soil and atmospheric environment generate a large range of responses, including conditions where resources become so limiting as to make yields very low. The space in between high and low yields is where most rainfed production occurs, and where the current model and user skill at representing GEMI varies. We also review studies comparing the performance of a large number of crop models and the lessons learned. The overall message is that improvement of models appears as a necessary condition for progress, and perhaps relevancy. Model ensembles help mitigate data input, model, and user-driven uncertainty for some but not all applications, sometimes at a very high cost. Successful model-based assessment of GEMI not only requires better crop models and knowledgeable users, but also a realistic representation of the environmental conditions of the landscape where crops are grown, which is not trivial given the 3D nature of water and nutrient transport. Models remain the best quantitative repository of our knowledge on crop functioning; they contain a narrative of plant, soil, and atmospheric functioning in computer language and train the mind to couple processes. But in our quest to tame GEMI, will they lead the way or just ride along history?

Integrating fast and slow processes is essential for simulating human-freshwater interactions

Integrated modeling is a critical tool to evaluate the behavior of coupled human-freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human-freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human-freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.

Analysis of climate signals in the crop yield record of sub-Saharan Africa

Food security and agriculture productivity assessments in sub-Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale-compatible climate data for the 1962-2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.