Simulating greenhouse gas budgets of four California cropping systems under conventional and alternative management

A framework for cost-effectiveness analysis of greenhouse gas mitigation measures in dairy industry with an application to dairy farms in China

The dairy industry is a significant contributor to global greenhouse gas emissions (GHG). Although much effort has been directed to explore the cost-effective measures for many sectors such as electricity, building infrastructure, transportation, research on mitigation measures within dairy industry remains limited. A notable obstacle is the absence of a cost-effectiveness analysis (CEA) framework to guide decision-makers and practitioners in this sector. In response, we propose a comprehensive CEA framework tailored to mitigate GHG emissions in the dairy industry. Our conceptual framework consists of six steps: defining the system boundary to determine the activities generating GHG emissions; identifying GHG emission sources within the system boundary; identifying potential mitigation measures; determining methods to quantify GHG emissions; collecting data to estimate both GHG emissions and mitigation costs; and applying general econometric methodologies to analyze the cost-effectiveness of mitigation measures. We further conducted a case study focusing on dairy farms in China, analyzing three categories of mitigation measures: feed, energy, and manure management. The results indicate that implementing effective feed and energy measures is a cost-saving strategy, reducing the cost per unit of milk production. Conversely, adopting effective manure management measures may lead to increased costs for dairy farms. The findings offer strategic recommendations for reducing GHG emissions from dairy production in China and provide analytical insights and strategic references applicable to other developing countries.

Cover crop performance under a changing climate in continuous corn system over Nebraska

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.) on no-till continuous corn yield and soil properties under historical (1991-2020) and projected climate (2041-2070) in eastern Nebraska. Local weather data during the historical period were used, while climate change projections were based on the Canadian Earth System Model 2 dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 under two representative concentration pathways (RCP), namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were nonsignificant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. CC reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha- 1 ) during the historical period. CC resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) and 22% (27 mm) reduction in plant available water compared to no cover crop during historical period. CC reduced cumulative seasonal surface runoff/soil evaporation and increased the rate of soil organic carbon buildup. This research provides valuable information on how changes in climate can impact the performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.

Soil Management Practices to Mitigate Nitrous Oxide Emissions and Inform Emission Factors in Arid Irrigated Specialty Crop Systems

Greenhouse gas (GHG) emissions from arid irrigated agricultural soil in California have been predicted to represent 8% of the state’s total GHG emissions. Although specialty crops compose the majority of the state’s crops in both economic value and land area, the portion of GHG emissions contributed by them is still highly uncertain. Current and emerging soil management practices affect the mitigation of those emissions. Herein, we review the scientific literature on the impact of soil management practices in California specialty crop systems on GHG nitrous oxide emissions. As such studies from most major specialty crop systems in California are limited, we focus on two annual and two perennial crops with the most data from the state: tomato, lettuce, wine grapes and almond. Nitrous oxide emission factors were developed and compared to Intergovernmental Panel on Climate Change (IPCC) emission factors, and state-wide emissions for these four crops were calculated for specific soil management practices. Dependent on crop systems and specific management practices, the emission factors developed in this study were either higher, lower or comparable to IPCC emission factors. Uncertainties caused by low gas sampling frequency in these studies were identified and discussed. These uncertainties can be remediated by robust and standardized estimates of nitrous oxide emissions from changes in soil management practices in California specialty crop systems. Promising practices to reduce nitrous oxide emissions and meet crop production goals, pertinent gaps in knowledge on this topic and limitations of this approach are discussed.

Human-induced and natural carbon storage in floodplains of the Central Valley of California

Active floodplains can putatively store large amounts of organic carbon (SOC) in subsoils originating from catchment erosion processes with subsequent floodplain deposition. Our study focussed on the assessment of SOC pools associated with alluvial floodplain soils that are affected by human-induced changes in floodplain deposition and in situ SOC mineralisation due to land use change and drainage. We evaluated depth-dependent SOC contents based on 23 soil cores down to 3 m and 10 drillings down to 7 m in a floodplain area of the lower Cosumnes River. An estimate of 266 Mg C ha^-1 or about 59% of the entire SOC stored within the 7 m profiles was found in the upper 2 m. Most profiles (n = 25) contained discrete buried A horizons at depths of approximately 0.8 m. These profiles had up to 130% higher SOC stocks. The mean δ¹³C of all deep soil profiles clearly indicated that arable land use has already altered the stable isotopic signature in the first meter of the profile. Radiocarbon dating showed that the ¹⁴C age in the buried horizon was younger than in overlaying soils indicating a substantial sedimentation phase for the overlaying soils. An additional analysis of total mercury contents in the soil profiles indicated that this sedimentation was associated with upstream hydraulic gold mining after the 1850s. In summary, deep alluvial soils in floodplains store large amounts of SOC not yet accounted for in global carbon models. Historic data give evidence that large amounts of sediment were transported into the floodplains of most rivers of the Central Valley and deposited over organically rich topsoil, which promoted the stabilization of SOC, and needs to be considered to improve our understanding of the human-induced interference with C cycling.

Underestimation of N2 O emissions in a comparison of the DayCent, DNDC, and EPIC models

Process-based models are increasingly used to study agroecosystem interactions and N₂ O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N₂ O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N₂ O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH₄⁺ , and soil NO₃^- , which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N₂ O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Simulated Effects of Soil Texture on Nitrous Oxide Emission Factors from Corn and Soybean Agroecosystems in Wisconsin

Soil texture is known to have an influence on the physical and biological processes that produce NO emissions in agricultural fields, yet comparisons across soil textural types are limited by considerations of time and practicality. We used the DayCent biogeochemical model to assess the effects of soil texture on NO emissions from agriculturally productive soils from four counties in Wisconsin. We validated the DayCent model using field data from 2 yr of a long-term (approximately 20-yr) cropping systems trial and then simulated yield and NO emissions from continuous corn ( L.) and corn-soybean ( L.) cropping systems across 35 Wisconsin soil series classified as either silt loam, sandy loam, or loamy sand. Silt loam soils had the highest NO emissions of all soil types, exhibiting 80 to 158% greater mean emissions and 100 to 282% greater emission factors compared with loamy sand and sandy loam soils, respectively. The model predicts that for these soils under these cropping systems, denitrification constituted the majority of the NO flux only in the silt loam soils. However, across all soil textures, locations, and years, denitrification explained the most variation (74-98%) in total NO emissions. Our results suggest that soil texture is an important factor in determining a range of NO emission characteristics and is critical for estimating future NO emissions from agricultural fields.

Modeling the impacts of temperature and precipitation changes on soil CO2 fluxes from a Switchgrass stand recently converted from cropland

Switchgrass (Panicum virgatum L.) is a perennial C4 grass native to North America and successfully adapted to diverse environmental conditions. It offers the potential to reduce soil surface carbon dioxide (CO2) fluxes and mitigate climate change. However, information on how these CO2 fluxes respond to changing climate is still lacking. In this study, CO2 fluxes were monitored continuously from 2011 through 2014 using high frequency measurements from Switchgrass land seeded in 2008 on an experimental site that has been previously used for soybean (Glycine max L.) in South Dakota, USA. DAYCENT, a process-based model, was used to simulate CO2 fluxes. An improved methodology CPTE [Combining Parameter estimation (PEST) with "Trial and Error" method] was used to calibrate DAYCENT. The calibrated DAYCENT model was used for simulating future CO2 emissions based on different climate change scenarios. This study showed that: (i) the measured soil CO2 fluxes from Switchgrass land were higher for 2012 which was a drought year, and these fluxes when simulated using DAYCENT for long-term (2015-2070) provided a pattern of polynomial curve; (ii) the simulated CO2 fluxes provided different patterns with temperature and precipitation changes in a long-term, (iii) the future CO2 fluxes from Switchgrass land under different changing climate scenarios were not significantly different, therefore, it can be concluded that Switchgrass grown for longer durations could reduce changes in CO2 fluxes from soil as a result of temperature and precipitation changes to some extent.

Simulating greenhouse gas mitigation potentials for Chinese Croplands using the DAYCENT ecosystem model

Understanding the potential for greenhouse gas (GHG) mitigation in agricultural lands is a critical challenge for climate change policy. This study uses the DAYCENT ecosystem model to predict GHG mitigation potentials associated with soil management in Chinese cropland systems. Application of ecosystem models, such as DAYCENT, requires the evaluation of model performance with data sets from experiments relevant to the climate and management of the study region. DAYCENT was evaluated with data from 350 cropland experiments in China, including measurements of nitrous oxide emissions (N2 O), methane emissions (CH4 ), and soil organic carbon (SOC) stock changes. In general, the model was reasonably accurate with R(2) values for model predictions vs. measurements ranging from 0.71 to 0.85. Modeling efficiency varied from 0.65 for SOC stock changes to 0.83 for crop yields. Mitigation potentials were estimated on a yield basis (Mg CO2 -equivalent Mg(-1) Yield). The results demonstrate that the largest decrease in GHG emissions in rainfed systems are associated with combined effect of reducing mineral N fertilization, organic matter amendments and reduced-till coupled with straw return, estimated at 0.31 to 0.83 Mg CO2 -equivalent Mg(-1) Yield. A mitigation potential of 0.08 to 0.36 Mg CO2 -equivalent Mg(-1) Yield is possible by reducing N chemical fertilizer rates, along with intermittent flooding in paddy rice cropping systems.

Greenhouse gas fluxes from agricultural soils under organic and non-organic management--a global meta-analysis

It is anticipated that organic farming systems provide benefits concerning soil conservation and climate protection. A literature search on measured soil-derived greenhouse gas (GHG) (nitrous oxide and methane) fluxes under organic and non-organic management from farming system comparisons was conducted and followed by a meta-analysis. Up to date only 19 studies based on field measurements could be retrieved. Based on 12 studies that cover annual measurements, it appeared with a high significance that area-scaled nitrous oxide emissions from organically managed soils are 492 ± 160 kg CO2 eq. ha(-1) a(-1) lower than from non-organically managed soils. For arable soils the difference amounts to 497 ± 162 kg CO2 eq. ha(-1) a(-1). However, yield-scaled nitrous oxide emissions are higher by 41 ± 34 kg CO2 eq. t(-1) DM under organic management (arable and use). To equalize this mean difference in yield-scaled nitrous oxide emissions between both farming systems, the yield gap has to be less than 17%. Emissions from conventionally managed soils seemed to be influenced mainly by total N inputs, whereas for organically managed soils other variables such as soil characteristics seemed to be more important. This can be explained by the higher bioavailability of the synthetic N fertilisers in non-organic farming systems while the necessary mineralisation of the N sources under organic management leads to lower and retarded availability. Furthermore, a higher methane uptake of 3.2 ± 2.5 kg CO2 eq. ha(-1) a(-1) for arable soils under organic management can be observed. Only one comparative study on rice paddies has been published up to date. All 19 retrieved studies were conducted in the Northern hemisphere under temperate climate. Further GHG flux measurements in farming system comparisons are required to confirm the results and close the existing knowledge gaps.