Managing landscapes to increase agricultural productivity and environmental stewardship can be informed by spatially‐distributed models that operate at spatial and temporal scales that are ...intervention‐relevant. This paper presents Cycles‐L, a landscape‐scale agroecosystem and hydrologic modeling system, using as a test case a watershed in Pennsylvania. Cycles‐L emerges from melding the landscape and hydrology structure of Flux‐PIHM, a 3‐D land surface hydrologic model, with the agroecosystem processes in the Cycles model. Consequently, Cycles‐L can simulate processes affected by topography, soil heterogeneity, and management practices, owing to its physically‐based hydrology that can simulate horizontal and vertical transport of solutes with water. The model was tested at a 730‐ha experimental watershed within the Mahantango Creek watershed. Cycles‐L simulated well stream water and mineral nitrogen discharge (Nash‐Sutcliffe coefficient 0.55 and 0.60, respectively) and grain yield (root mean square error 1.2 Mg ha−1). Cycles‐L outputs are as good or better than those obtained with the uncoupled Flux‐PIHM (water discharge) and Cycles (grain yield) models. Modeled spatial patterns of nitrogen fluxes like denitrification illustrate the combined control of crop management and topography. For example, denitrification is almost twice as high when simulated with Cycles‐L than when simulated with Cycles 1‐D. Due to its spatial and temporal resolution, Cycles‐L fills a gap in the availability of models that operate at a scale relevant to evaluate interventions in the landscape. Cycles‐L can become a central component in tools for climate change scenario analysis, precision agriculture, precision conservation, and artificial intelligence‐based decision support systems.
Key Points
Cycles‐L is a coupled agroecosystem hydrologic modeling system that couples an agroecosystem model with a 3‐D land surface hydrologic model
Cycles‐L simulated well stream discharge, grain crops yield, and nitrogen exports in the stream at a 730‐ha agricultural experimental watershed
Cycles‐L can simulate landscape level processes affected by climate, topography, soil heterogeneity, and management practices
Soil phosphorus (P) management remains a critical challenge for agriculture worldwide, and yet we are still unable to predict soil P dynamics as confidently as that of carbon (C) or nitrogen (N). ...This is due to both the complexity of inorganic P (Pi) and organic P (Po) cycling and the methodological constraints that have limited our ability to trace P dynamics in the soil–plant system. In this review, we describe the challenges for building parsimonious, accurate, and useful biogeochemical models that represent P dynamics and explore the potential of new techniques to usher P biogeochemistry research and modeling forward. We conclude that research efforts should focus on the following: (1) updating the McGill and Cole (1981) model of Po mineralization by clarifying the role and prevalence of biochemical and biological Po mineralization, which we suggest are not mutually exclusive and may co-occur along a continuum of Po substrate stoichiometry; (2) further understanding the dynamics of phytate, a six C compound that can regulate the poorly understood stoichiometry of soil P; (3) exploring the effects of C and Po saturation on P sorption and Po mineralization; and (4) resolving discrepancies between hypotheses about P cycling and the methods used to test these hypotheses.
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?
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.
We identify the climate signal in yields of maize, sorghum, and groundnut in sub‐Saharan Africa (SSA) during 1962–2014 using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology is three times more important than climate variables for explaining changes in crop yields and increasing temperatures reduced yields for all three crops, while precipitation increased yields. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively (see figure). We conclude that while we expect increases in future crop yields by technological improvements, the potential yields could be progressively reduced in warmer and drier climates.
Replacing row crops with perennial bioenergy crops may reduce nitrogen (N) loading to surface waters. We estimated the benefits, costs, and potential for replacing maize with switchgrass to meet ...required N loading reduction targets for the Chesapeake Bay (CB) of 26.9 Gg y−1. After subtracting the potential reduction in N loading due to improved N fertilizer practices for maize, a further 22.8 Gg y−1 reduction is required. Replacing maize with fertilized switchgrass could reduce N loading to the CB by 18 kg ha−1 y−1, meeting 31% of the N reduction target. The break-even price of fertilized switchgrass to provide the same profit as maize in the CB is 111 $ Mg−1 (oven-dry basis throughout). Growers replacing maize with switchgrass could receive an ecosystem service payment of 148 $ ha−1 based on the price paid in Maryland for planting a rye cover crop. For our estimated average switchgrass yield of 9.9 Mg ha−1, and the greater N loading reduction of switchgrass compared to a cover crop, this equates to 24 $ Mg−1. The annual cost of this ecosystem service payment to induce switchgrass planting is 13.29 $ kg−1 of N. Using the POLYSYS model to account for competition among food, feed, and biomass markets, we found that with the ecosystem service payment for switchgrass of 25 $ Mg−1 added to a farm-gate price of 111 $ Mg−1, 11% of the N loading reduction target could be met while also producing 1.3 Tg of switchgrass, potentially yielding 420 dam3 y−1 of ethanol.
•Producing switchgrass in the Chesapeake Bay basin could improve water quality.•A switchgrass price of 111 $ Mg−1 matches the average profitability of maize.•Our switchgrass production scenarios reduce nitrogen loading for 13–40 $ kg−1 of N.•Switchgrass production could meet 11% of the Bay nitrogen loading reduction target.•This switchgrass production could also supply 420 dam3 y−1 of ethanol.
Due to the nature of nitrogen cycling, policies designed to address water quality concerns have the potential to provide benefits beyond the targeted water quality improvements. For example, actions ...to protect water quality by reducing nitrate leaching from agriculture also reduce emissions of nitrous oxide, a potent greenhouse gas. These positive effects, which are incidental to the regulation's intended target, are termed “co‐benefits.” To quantify the co‐benefits associated with reduced nitrate leaching, we integrate an economic model of farmer decision making with a model of terrestrial nitrogen cycling for the watershed surrounding Lake Mendota, Wisconsin, USA. Our modeling approach provides a framework that links air and water pollutants in an agri‐environmental system and offers a direction for future studies. Our model results highlight the finding that the co‐benefits from nitrous oxide abatement are substantial, and their inclusion increases the benefit–cost ratio of water quality policies. Consideration of these co‐benefits has the potential to reverse the conclusions of benefit–cost analysis in the assessment of current water quality policies.
Abstract During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM ...threshold (θ crit ). Better quantification of θ crit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θ crit . We find an average global θ crit of 0.19 m 3 /m 3 , varying from 0.12 m 3 /m 3 in arid ecosystems to 0.26 m 3 /m 3 in humid ecosystems. θ crit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θ crit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θ crit , has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.
•We estimated the temporal dynamics of ecosystem services provided by cover crops.•Cover crops increased 8 of 11 ecosystem services in a 3-year grain rotation.•Estimates of cover crop benefits were ...sensitive to the point in time analyzed.•Trade-offs occurred between ecosystem benefits, profitability, and management risks.•This type of framework can improve agroecosystem management for multiple ecosystems services.
Cropping systems that provide ecosystem services beyond crop production are gaining interest from farmers, policy makers and society at large, yet we lack frameworks to evaluate and manage for multiple ecosystem services. Using the example of integrating cover crops into annual crop rotations, we present an assessment framework that: (1) estimates the temporal dynamics of a suite of ecosystem services; (2) illustrates ecosystem multifunctionality using spider plots; and (3) identifies key time points for optimizing ecosystem service benefits and minimizing trade-offs. Using quantitative models and semi-quantitative estimates, we applied the framework to analyze the temporal dynamics of 11 ecosystem services and two economic metrics when cover crops are introduced into a 3-year soybean (Glycine max)–wheat (Triticum aestivum)–corn (Zea mays) rotation in a typical Mid-Atlantic climate. We estimated that cover crops could increase 8 of 11 ecosystem services without negatively influencing crop yields. We demonstrate that when we measure ecosystem services matters and cumulative assessments can be misleading due to the episodic nature of some services and the time sensitivity of management windows. For example, nutrient retention benefits occurred primarily during cover crop growth, weed suppression benefits occurred during cash crop growth through a cover crop legacy effect, and soil carbon benefits accrued slowly over decades. Uncertainties exist in estimating cover crop effects on several services, such as pest dynamics. Trade-offs occurred between cover crop ecosystem benefits, production costs, and management risks. Differences in production costs with and without cover crops varied 3-fold over 10years, largely due to changes in fertilizer prices, and thus cover crop use will become more economical with increasing fertilizer prices or if modest cost-sharing programs are established. Frameworks such as that presented here provide the means to quantify ecosystem services and facilitate the transition to more multifunctional agricultural systems.
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 (N₂O) from soils. Better ecological understanding of the N₂O emission controls may lead to new management strategies to reduce these emissions. We measured soil N₂O 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 N₂O production pathways and mitigation opportunities, we supplemented N₂O flux measurements with determinations of N₂O 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 N₂O emissions, averaging 9.8 kg/ha of N₂O-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 N₂O emissions by 60% during the corn phase. The co-occurrence of peak N₂O emission and high carbon dioxide emission suggests that oxygen (O₂) consumption likely caused hypoxia and bacterial denitrification. This interpretation is supported by the N₂O site preference values trending towards denitrification during peak emissions with limited N₂O reduction, as revealed by the N₂O δ15N and δ18O 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 N₂O 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 N₂O emissions.
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.
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