Source: TEXAS A&M UNIVERSITY submitted to
THE IMPACT OF CLIMATE CHANGE, CARBON MARKETS AND CLIMATE SMART AGRICULTURE AND FORESTRY PRACTICES ON U.S. AGRICULTURAL SECTOR AND MARKET
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1030585
Grant No.
2023-67023-39814
Project No.
TEX06932
Proposal No.
2022-10003
Multistate No.
(N/A)
Program Code
A1651
Project Start Date
Jun 1, 2023
Project End Date
May 31, 2026
Grant Year
2023
Project Director
McCarl, B.
Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
(N/A)
Non Technical Summary
Climate change has been found to have a negative impact on society. In the face of such impacts there are numerous efforts to reduce GHG net emissions to protect the future economy and environment. Adaptation is also essential as it will take time for mitigation to have an effect and agriculture is vulnerable. Climate smart agriculture and forestry (CSAF) practices address both adaptation and mitigation sometimes individually and sometimes jointly. Those practices also influence carbon and commodity markets along with effects of conservation programs. To the best of our knowledge, there is not a comprehensive integrated model that portrays climate change impacts covering crop and livestock impacts along with the implications of the wide array of potential CSAF practices plus associated policies as they influence the economic, environmental, geographic and temporal dimensions of agriculture. In this study, we will develop and use a modeling approach to evaluate carbon markets and CSAF practices oriented incentives and other policies. We will develop and use the model to do an integrated analysis with the consideration of interactions among climate effects, adopted CSAF practices, adaptation, regional, national, and international commodity markets, mitigation extent, and implications for regional incomes which has never been done in the literature. The characteristics of CSAF practices, such as permanence, leakage, uncertainty, additionality, and transactions cost will also be considered. We will also evaluate carbon market, carbon policy and conservation programs design and provide information for policy makers and farmer. Climate change has been found to have a negative impact on society. In the face of such impacts there are numerous efforts to reduce GHG net emissions to protect the future economy and environment. Adaptation is also essential as it will take time for mitigation to have an effect and agriculture is vulnerable. Climate smart agriculture and forestry (CSAF) practices address both adaptation and mitigation sometimes individually and sometimes jointly. Those practices also influence carbon and commodity markets along with effects of conservation programs. To the best of our knowledge, there is not a comprehensive integrated model that portrays climate change impacts covering crop and livestock impacts along with the implications of the wide array of potential CSAF practices plus associated policies as they influence the economic, environmental, geographic and temporal dimensions of agriculture. In this study, we will develop and use a modeling approach to evaluate carbon markets and CSAF practices oriented incentives and other policies. We will develop and use the model to do an integrated analysis with the consideration of interactions among climate effects, adopted CSAF practices, adaptation, regional, national, and international commodity markets, mitigation extent, and implications for regional incomes which has never been done in the literature. The characteristics of CSAF practices, such as permanence, leakage, uncertainty, additionality, and transactions cost will also be considered. We will also evaluate carbon market, carbon policy and conservation programs design and provide information for policymakers and farmer.
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
6056110301070%
1020199107030%
Goals / Objectives
Climate change has been found to have a negative impact on society. In the face of such impacts there are numerous efforts to reduce GHG net emissions to protect the future economy and environment. Adaptation is also essential as it will take time for mitigation to have an effect and agriculture is vulnerable. Climate smart agriculture and forestry (CSAF) practices address both adaptation and mitigation sometimes individually and sometimes jointly. Those practices also influence carbon and commodity markets along with effects of conservation programs. To the best of our knowledge, there is not a comprehensive integrated model that portrays climate change impacts covering crop and livestock impacts along with the implications of the wide array of potential CSAF practices plus associated policies as they influence the economic, environmental, geographic and temporal dimensions of agriculture. In this study, we will develop and use a modeling approach to evaluate carbon markets and CSAF practices oriented incentives and other policies. We will develop and use the model to do an integrated analysis with the consideration of interactions among climate effects, adopted CSAF practices, adaptation, regional, national, and international commodity markets, mitigation extent, and implications for regional incomes which has never been done in the literature. The characteristics of CSAF practices, such as permanence, leakage, uncertainty, additionality, and transactions cost will also be considered. We will also evaluate carbon market, carbon policy and conservation programs design and provide information for policy makers and farmer. Climate change has been found to have a negative impact on society. In the face of such impacts there are numerous efforts to reduce GHG net emissions to protect the future economy and environment. Adaptation is also essential as it will take time for mitigation to have an effect and agriculture is vulnerable. Climate smart agriculture and forestry (CSAF) practices address both adaptation and mitigation sometimes individually and sometimes jointly. Those practices also influence carbon and commodity markets along with effects of conservation programs. To the best of our knowledge, there is not a comprehensive integrated model that portrays climate change impacts covering crop and livestock impacts along with the implications of the wide array of potential CSAF practices plus associated policies as they influence the economic, environmental, geographic and temporal dimensions of agriculture. In this study, we will develop and use a modeling approach to evaluate carbon markets and CSAF practices oriented incentives and other policies. We will develop and use the model to do an integrated analysis with the consideration of interactions among climate effects, adopted CSAF practices, adaptation, regional, national, and international commodity markets, mitigation extent, and implications for regional incomes which has never been done in the literature. The characteristics of CSAF practices, such as permanence, leakage, uncertainty, additionality, and transactions cost will also be considered. We will also evaluate carbon market, carbon policy and conservation programs design and provide information for policy makers and farmer.
Project Methods
U.S. Forest and Agricultural Sector Optimization Model with Greenhouse Gas (FASOMGHG) and Soil and Water Assessment Tool (SWAT) are the two major models will be used in the project.SWAT modelhas a nutrient and carbon cycling algorithm that can be used to analyze GHG implications of CSAF practices. Previously, SWAT has shown success in simulating crop growth (Haney et al., 2018), soil carbon (Haney et al., 2018; Zhang, 2018; Zhang et al., 2013), nutrient cycling (Haney et al., 2018), and GHGs ( Gao et al., 2020; Huang et al., 2022) along with its ecohydrological modeling strengths. Considering SWAT's capabilities and wide success, the USDA is considering it as a core watershed model for applications in the Conservation Effects Assessment Project (CEAP) (Richardson et al., 2008) across watersheds in the United States.The HAWQS, a web-based interface for SWAT, will be implemented to develop SWAT projections under this objective. HAWQS integrates high-quality datasets such as multi-year crop incidence data to account for crop rotations, a modified National Hydrography Dataset Plus (NHDPlus) medium-resolution surface water network, hydrological calibration parameters, a soil database that combines the Soil Survey Geographic Database (SSURGO) and Digital General Soil Map of the United States (STATSGO2) datasets, and climate datasets giving historical (NCDC-NOAA/PRISM/NEXRAD) and future (CMIP5/CMIP6) scenarios.The hydrological calibrated SWAT models will be further calibrated for crop growth simulations. The multi-parameter calibrated SWAT projects will be further utilized to simulate land-based CSAF scenarios to assess the impacts of various CSAF practices on the soil hydrological properties, crop biomass and yields, soil organic matter, soil carbon and nutrient cycling, and GHG emissions.Figure 1: FASOMGHG model structure (Adams et al., 2005)The Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG) will be employed to investigate the integrated effects of CSAF practices combined with climate change effects and adaptation effects FASOMGHG is a price endogenous, dynamic mathematical program that simulates US agricultural and forest production, input use, markets and GHG emission changes from agricultural and forest sector for every five years with the maximum time span from 2020 to 2100 (Adams et al., 2005). It models price endogenous markets for over 70 primary crops and 20 livestock commodities along with over 100 secondary food, feed and fuel products and byproducts based on the theoretical framework described in McCarl and Spreen (1980). The agricultural part of FASOMGHG simulates irrigated/dryland crop mix, livestock mix, livestock feeding, land use, water use, basic processing and management in a fashion that models some degree of farmer, processor, and consumer adaptation. The GHG emissions from agricultural management, production and processing are also counted in the model. The model structure summary is shown in Figure 1.FASOMGHG has been used to investigate the impact of climate change effects, adaptation and mitigation incentives on the U.S. agricultural and forestry sectors (Beach et al., 2008, 2015; Beach & McCarl, 2012; Fei et al., 2017, 2021). The current version of FASOMGHG already has the tillage management, and fertilizer management strategies embedded and has the capability to simulate CSAF adoption among tillage and fertilizer application options. The characteristics of FASOMGHG provide a framework to comprehensively evaluate the effects of more CSAF practices, and the impact of carbon markets and policies. In particular, FASOMGHG has: 1) the flexibility to add mitigation and adaptation strategies, and carbon market design rules into mitigation and adaptation evaluation. 2) the dynamic model feature can allow us to embed dynamic differences in carbon sequestration amounts and data on approach to equilibrium along with payment by practices to better evaluate the short- and longer-term implications of actions. 3) the model covers carbon dioxide, nitrous oxide and methane allowing consideration of the total GHG implications of CSAF actions; 4) the model has been adapted to simulate both a capped sector and a sector which can voluntarily opt in to provide offsets (Wang et al., 2021), which will be used to evaluate the carbon market and policy, 5) transactions costs are modeled causing gaps between farm and seller prices (Kim, 2011), 6) the price endogenous market procedure provides the opportunity to count the impact of mitigation and adaption on the market equilibrium and the producer's reaction to altered prices and 7) regional income commodity production, cost and GHG payments are also accounted by region allowing identification of how regions and commodity producer groups are differentially effected.