Carbon and Water Budgets in Multiple Wheat-Based Cropping Systems in the Inland Pacific Northwest US: Comparison of CropSyst Simulations with Eddy Covariance Measurements

Abstract

Accurate carbon and water flux simulations for croplands are greatly dependent on high quality representation of management practices and meteorological conditions, which are key drivers of the surface-atmosphere exchange processes. Fourteen site-years of carbon and water fluxes were simulated using the CropSyst model over four agricultural sites in the inland Pacific Northwest (iPNW) US from October 1, 2011 to September 30, 2015. Model performance for field-scale net ecosystem exchange of CO 2 (NEE) and evapotranspiration (ET) was evaluated by comparing simulations with long-term eddy covariance measurements. The model captured the temporal variations of NEE and ET reasonably well with an overall r of 0.78 and 0.80, and a low RMSE of 1.82 g C m −2 d −1 and 0.84 mm d −1 for NEE and ET, respectively. The model slightly underestimated NEE and ET by 0.51 g C m −2 d −1 and 0.09 mm d −1 , respectively. ET simulations showed better agreement with eddy covariance measurements than NEE. The model performed much better for the sites with detailed initial conditions (e.g., SOC content) and management practice information (e.g., tillage type). The CropSyst results showed that the winter wheat fields could be annual net carbon sinks or close to neutral with the net ecosystem carbon balance (NECB) ranging from 92 to −17 g C m −2 , while the spring crop fields were net carbon sources or neutral with an annual NECB of −327 to −3 g C m −2 . Simulations for the paired tillage sites showed that the no-till site resulted in lower CO 2 emissions for the crop rotations of winter wheat-spring garbanzo, but had higher carbon loss into the atmosphere for spring canola compared to the conventional tillage site. Water budgets did not differ significantly between the two tillage systems. Winter wheat in the high-rainfall area had higher crop yields and water use efficiency but emitted larger amounts of CO 2 into the atmosphere than in the low-rainfall area. Based Chi et al.Agricultural Carbon and Water Budgets on model evaluations in this study, CropSyst appears promising as a tool to simulate field-scale carbon and water budgets and assess the effects of different management practices and local meteorological conditions for the wheat-based cropping systems in this region.

Performance Assessment of MOD16 in Evapotranspiration Evaluation in Northwestern Mexico

“…A relationship between the vapor pressure deficit (VPD) and the relative humidity (RH) is assumed [66], which is highly questionable [3] and could lead to the underestimation identified in this work. Previous model validations of MOD16 showed a similar underestimation of ET in sparse natural vegetation, such as crops [67], savanna [40], and grassland [68]. As pointed out by these authors, the differences may be due to the uncertainty of the input variables (Leaf area index (LAI) or meteorological data).…”

“…A fundamental requirement is to maintain a homogeneous surface without disturbance (Fetch), which is proportional to the instrument height [18]; a Fetch 100:1 is recommended and should be higher for smoother surfaces and smaller for rough surfaces. This height is unknown for sites located in areas with wheat crops; however, it has been demonstrated that with an instrument that is 2 m high, a homogeneous area of 1.5-2.5 ha can be observed depending on the direction and speed of the wind and on the atmospheric stability [68]; in the case of Rayón, which has a rough surface, with an instrument height of 9 m, a Fetch of at least 900 m is required, a requirement that is easily fulfilled because of the type of coverage; in the case of El Mogor, the height at which the measuring instrument was placed, i.e., 3.5 m, requires a Fetch of at least 350 m, and, in La Paz, the height of 13 m can easily represent a Fetch of 1300 m. The uncertainties associated with the measurements of the flux towers can influence the indicators bias, error, and coefficient of determination [43]. The advantage of having complete measurements at intervals of 8 days was evident in the improvement of the relationship between the flux towers and MOD16 in the sites with more data [7], as was observed in EC1 and EC2.…”

Confronting Climate Change Challenges to Dryland Cereal Production: A Call for Collaborative, Transdisciplinary Research, and Producer Engagement

“…Although the mitigation of agricultural GHG emissions generally does not provide a monetized return to farmers and is not currently encouraged by direct public policy incentives or regulations (Brown et al, 2017); effective adaptation practices could achieve "win-win" benefits in which cropping system profitability is increased through more efficient use of applied nitrogen, resulting in both improved farm productivity and reduced N 2 O emissions (Millar et al, 2010). Roughly 1% of the nitrogen applied results in N 2 O production, but emissions are variable, influenced by climate, soil organic carbon (SOC), soil texture, soil drainage, soil pH, crop management practices, soil nutrient conditions, and soil O 2 status (IFA/FAO, 2001;McSwiney and Robertson, 2005;Del Grosso et al, 2010;Lehuger et al, 2011;Chi et al, 2016Chi et al, , 2017Waldo et al, 2016).…”

“…It aimed to improve knowledge of the production systems, identify opportunities to improve their efficiency and sustainability, promote farmer participation, provide decision support tools, educate producers and citizens at all levels. The conceptual framework, outputs and outcomes of the REACCH project can be accessed through its web site: https://www.reacchpna.org, and in publications, including some appearing in this special issue of Frontiers in Ecology and Evolution: (1) Develop a theoretical framework integrating cropping system, economic and climate modeling (Abatzoglou et al, 2014;Antle et al, 2017;Stöckle et al, 2017), (2) Monitor greenhouse gas (GHG) emissions and nitrogen and carbon dynamics in the production systems (Chi et al, , 2017Waldo et al, 2016;Kostyanovsky et al, 2017), (3) Compare current and aspirational production systems for productivity and GHG emission potential under current and projected climate Brown et al, 2017;Maaz T. M. et al, 2017;Stöckle et al, 2017), (4) Address the environmental, social, and economic factors influencing agriculture and technology adoption (Antle et al, 2017;Karimi et al, 2017;Kaur et al, 2017), (5) Anticipate climate change related changes in crop protection requirements (Davis et al, 2015a(Davis et al, ,b, 2017Eigenbrode et al, 2015;Foote et al, 2017), (6) Work closely with producers to develop and guide project activities (Kruger and Yorgey, 2017;Yorgey et al, 2017), (7) Educate students from elementary through graduate levels to prepare coming generations for challenges related to climate change in agriculture (White et al, 2014), (8) Ensure data from the project and related projects are managed to facilitate detecting trends and interdisciplinary collaboration (Flathers et al, 2017), and (9) Coordinate all these activities under an integrated, transdisciplinary framework Morton et al, 2015).…”

Integrating Historic Agronomic and Policy Lessons with New Technologies to Drive Farmer Decisions for Farm and Climate: The Case of Inland Pacific Northwestern U.S.

“…Further research is needed to determine what extent direct seeding in combination with other cfBMPs such as diversified crop rotations with more prolific roots and/or increased stable organic C inputs can more fully replenish and build SOC within the limitations imposed by climatic conditions. Recent developments in monitoring technologies have enabled the ability to assess system C balances between crops and soils while tracking CO 2 fluxes of managed fields to assess the effects of diversified crops, soils and fluctuating weather (Waldo et al, 2016;Chi et al, 2017).…”