Mathematical modeling for improved greenhouse gas balances, agro‐ecosystems, and policy development: lessons from the Australian experience

Moore, Andrew D.; Eckard, Richard; Thorburn, Peter J.; Grace, Peter R.; Wang, Enli; Chen, Deli

doi:10.1002/wcc.304

Cited by 17 publications

(11 citation statements)

References 78 publications

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“…The utility of data from long-term field experiments to help formulate, parameterize and validate predictive models of soil carbon stock change has long been acknowledged [e.g. 20,97]; expanding the compilation of data from high-quality experiments across the globe, and making the data easily available for modelers and analysts, can accelerate the development and improvement of models [65]. Soil monitoring networks, in which periodic soil measurements are made on actual working lands, have been established in several countries [84] and can play a vital role in reducing model uncertainty [47].…”

Section: Toward a New Global Soil Information Systemmentioning

confidence: 99%

Quantifying carbon for agricultural soil management: from the current status toward a global soil information system

et al. 2019

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The importance of building/maintaining soil carbon, for soil health and CO 2 mitigation, is of increasing interest to a wide audience, including policymakers, NGOs and land managers. Integral to any approaches to promote carbon sequestering practices in managed soils are reliable, accurate and cost-effective means to quantify soil C stock changes and forecast soil C responses to different management, climate and edaphic conditions. While technology to accurately measure soil C concentrations and stocks has been in use for decades, many challenges to routine, cost-effective soil C quantification remain, including large spatial variability, low signal-to-noise and often high cost and standardization issues for direct measurement with destructive sampling. Models, empirical and process-based, may provide a cost-effective and practical means for soil C quantification to support C sequestration policies. Examples are described of how soil science and soil C quantification methods are being used to support domestic climate change policies to promote soil C sequestration on agricultural lands (cropland and grazing land) at national and provincial levels in Australia and Canada. Finally, a quantification system is outlinedconsisting of well-integrated datamodel frameworks, supported by expanded measurement and monitoring networks, remote sensing and crowd-sourcing of management activity datathat could comprise the core of a new global soil information system. Take Home messages:Increasing soil organic carbon (SOC) stocks would improve the performance of working (managed) soils especially under drought or other stressors, increase agricultural resilience and fertility, and reduce net GHG emissions from soils.There are many improved management practices that can be and are currently being applied to cropland and grazing lands to increase SOC.Land managers are decision makers who operate in larger contexts that bound their agricultural and financial decisions (e.g. market forces, crop insurance, input subsidies, conservation mandates, etc.).Any effort to value improvements in the performance of agricultural soils through enhanced levels of SOC will require feasible, credible and creditable assessment of SOC stocks, which are governed by dynamic and complex soil processes and properties. This paper evaluates currently accepted methods of quantifying and forecasting SOC that, when augmented and pulled together, could provide the basis for a new global soil information system.

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Section: Toward a New Global Soil Information Systemmentioning

confidence: 99%

Quantifying carbon for agricultural soil management: from the current status toward a global soil information system

et al. 2019

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“…Cropping Systems GHG emissions from SOC, N 2 O, and CH 4 are determined by the interaction of many factors, so a simulation-based approach was used in this study because it allowed the trade-offs between GHGs to be examined as a system with common conditions and over the long-term (Moore, 2014;Moore et al, 2015). The approach adopted to simulate GHGs from the cropping systems defined for the case study farms is summarized in Figure 2.…”

Section: Overview Of Approach For Simulating Crop and Livestock Scenamentioning

confidence: 99%

Greenhouse Gas Emissions From Cropping and Grazed Pastures Are Similar: A Simulation Analysis in Australia

Meier

Thorburn

Bell

et al. 2020

Front. Sustain. Food Syst.

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The agricultural sector has potential to provide greenhouse gas (GHG) mitigation by sequestering soil organic carbon (SOC). Replacing cropland with permanent pasture is one practice promoted for its potential to sequester soil carbon. However, pastures frequently support livestock, which produce other GHG emissions that could negate the abatement from increased SOC, especially given the declining rate of SOC sequestration through time. Our purpose was to determine whether the abatement provided by SOC storage in permanent pastures was offset by livestock emissions, and to thus compare emissions from grazed pasture systems with those from cropping systems. We investigated this question for three case study farms in locations with contrasting climate, soils and management representative of Australian cropping and livestock systems. Three cropping scenarios were defined that had increasing amounts of SOC inputs: Crop burn , crop residues burned before sowing (lowest SOC input); Crop stubble , crop residues retained; and Crop intensity , uncropped fallow phases replaced with short-term green manure legume crops. The on-farm GHG emissions profiles of these cropping scenarios were compared with those from two livestock scenarios utilizing continuous stocking: Livestock grass , stocked permanent grass pasture; and Livestock legume , stocked permanent legume pasture; the latter having higher SOC input than the former. Crop yields, pasture growth rates and emissions of carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) from the soil were simulated with the APSIM farming systems model. Livestock emissions were predicted using Australian GHG accounting emission factors. For the farms in this study, the SOC sequestered in the stocked permanent pastures was offset by emissions from livestock, and emissions from cropping scenarios were similar to or significantly less than those from the livestock scenarios. These findings: (1) demonstrate the importance of using net GHG abatement potentials from combined emissions rather than a single GHG abatement process when evaluating potential abatement practices, and (2) demonstrate that characteristics of different locations can alter the abatement potential of management practices.

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“…With a long-term vision of creating in-season N management decision support tools, we now discuss several important attributes to consider for geospatial framework development for corn production systems of the United States ( Figure 1 ). The aim of our proposed tool would be to estimate crop yield and environmental loss in the growing season and identify site-specific actions for farmers in the regional sustainability context ( Moore et al, 2014 ). The first step would be designing a user interface by integrating the selected crop model with daily weather, site-specific soils, information and hydrological data from United States Geological Survey (USGS).…”

Section: Turning Available Crop Models Into Real-time N Management Tomentioning

confidence: 99%

“…First, the underlying source codes for both Adapt-N and FieldView are not available in the public domain, which hinders other researchers from improving and integrating new mechanisms associated with soil N supply, crop growth, and environmental loss. For instance, Moore et al (2014) suggested that quality assurance processes, documentation procedures, and access to model source codes are important aspects while selecting crops models for assessing proposed greenhouse gas abatement methodologies in Australian agriculture. Moreover, previous research has suggested that there are number of non-technological factors such as broad social learning for participatory development of extension specialists, farmers, and scientists to ensure effective adoption of such decision support tools by farmers ( Jakku and Thorburn, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production

et al. 2017

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Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

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Mathematical modeling for improved greenhouse gas balances, agro‐ecosystems, and policy development: lessons from the Australian experience

Cited by 17 publications

References 78 publications

Quantifying carbon for agricultural soil management: from the current status toward a global soil information system

Quantifying carbon for agricultural soil management: from the current status toward a global soil information system

Greenhouse Gas Emissions From Cropping and Grazed Pastures Are Similar: A Simulation Analysis in Australia

A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production

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