National Greenhouse Gas Emission Reduction Potential from Adopting Anaerobic Digestion on Large-Scale Dairy Farms in the United States.

Waste-to-energy systems can provide a functional demonstration of the economic and environmental benefits of circularity, innovation, and reimagining existing systems. This study offers a robust quantification of the greenhouse gas (GHG) emission reduction potential of the adoption of anaerobic digestion (AD) technology on applicable large-scale dairy farms in the contiguous United States. GHG reduction estimates were developed through a robust life cycle modeling framework paired with sensitivity and uncertainty analyses. Twenty dairy configurations were modeled to capture important differences in housing and manure management practices, applicable AD technologies, regional climates, storage cleanout schedules, and methods of land application. Monte Carlo results for the 90% confidence interval illustrate the potential for AD adoption to reduce GHG emissions from the large-scale dairy industry by 2.45-3.52 MMT of CO2-eq per year considering biogas use only in renewable natural gas programs and as much as 4.53-6.46 MMT of CO2-eq per year with combined heat and power as an additional biogas use case. At the farm level, AD technology may reduce GHG emissions from manure management systems by 58.1-79.8% depending on the region. Discussion focuses on regional differences in GHG emissions from manure management strategies and the challenges and opportunities surrounding AD adoption.

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

Despite the importance of agriculture in California's Central Valley, the potential of alternative management practices to reduce soil greenhouse gas (GHG) emissions has been poorly studied in California. This study aims at (1) calibrating and validating DAYCENT, an ecosystem model, for conventional and alternative cropping systems in California's Central Valley, (2) estimating CO2, N2O, and CH4 soil fluxes from these systems, and (3) quantifying the uncertainty around model predictions induced by variability in the input data. The alternative practices considered were cover cropping, organic practices, and conservation tillage. These practices were compared with conventional agricultural management. The crops considered were beans, corn, cotton, safflower, sunflower, tomato, and wheat. Four field sites, for which at least five years of measured data were available, were used to calibrate and validate the DAYCENT model. The model was able to predict 86-94% of the measured variation in crop yields and 69-87% of the measured variation in soil organic carbon (SOC) contents. A Monte Carlo analysis showed that the predicted variability of SOC contents, crop yields, and N2O fluxes was generally smaller than the measured variability of these parameters, in particular for N2O fluxes. Conservation tillage had the smallest potential to reduce GHG emissions among the alternative practices evaluated, with a significant reduction of the net soil GHG fluxes in two of the three sites of 336 +/- 47 and 550 +/- 123 kg CO2-eq x ha(-1) x yr(-1) (mean +/- SE). Cover cropping had a larger potential, with net soil GHG flux reductions of 752 +/- 10, 1072 +/- 272, and 2201 +/- 82 kg CO2-eq x ha(-1) x yr(-1). Organic practices had the greatest potential for soil GHG flux reduction, with 4577 +/- 272 kg CO2-eq x ha(-1) x yr(-1). Annual differences in weather or management conditions contributed more to the variance in annual GHG emissions than soil variability did. We concluded that the DAYCENT model was successful at predicting GHG emissions of different alternative management systems in California, but that a sound error analysis must accompany the predictions to understand the risks and potentials of GHG mitigation through adoption of alternative practices.

Short-term sustainability of drainage water reuse: spatio-temporal impacts on soil chemical properties.

Greater urban demand for finite water resources, increased frequency of drought resulting from erratic weather, and increased pressure to reduce drainage water volumes have intensified the need to reuse drainage water. A study was initiated in 1999 on a 32.4-ha saline-sodic field (Lethent clay loam series; fine, montmorillonitic, thermic, Typic Natrargid) located on the west side of California's San Joaquin Valley (WSJV) with the objective of evaluating the sustainability of drainage water reuse with respect to impact on soil quality. An evaluation after 5 yr of irrigation with drainage water is presented. Geo-referenced measurements of apparent soil electrical conductivity (EC(a)) were used to direct soil sampling at 40 sites to characterize the spatial variability of soil properties (i.e., salinity, Se, Na, B, and Mo) crucial to the soil's intended use of growing Bermuda grass (Cynodon dactylon (l.) Pers.) for livestock consumption. Soil samples were taken at 0.3-m increments to a depth of 1.2 m at each site in August 1999, April 2002, and November 2004. Drainage water varying in salinity (0.8-16.2 dS m(-1)), SAR (5.4-52.4), Mo (80-400 microg L(-1)), and Se (<1-700 microg L(-1)) was applied to the field since July 2000. An analysis of the general temporal trend shows that overall soil quality has improved due to leaching of B from the top 0.6 m of soil; salinity and Na from the top 1.2 m, but primarily from 0 to 0.6 m; and Mo from the top 1.2 m. Short-term sustainability of drainage water reuse is supported by the results.

Sugar Beet Performance with Curly Top Is Related to Virus Accumulation and Age at Infection.

Resistance to curly top disease caused by Beet curly top virus (BCTV) and related curtoviruses has been important to sustainable sugar beet (Beta vulgaris) production in the western United States for most of the last century. Recent advances in sugar beet genetics have led to the development of high-yielding cultivars, but these cultivars have little resistance to curly top disease. These cultivars are highly effective when disease management practices or environmental factors minimize curly top incidence, but can result in significant losses in years with early infection or abundant curly top. A greenhouse assay has been developed to rapidly test cultivars for a broad array of factors affecting performance in the presence of curly top. Previous studies have shown that sugar beet plants were more susceptible and losses more severe when seedlings were infected by BCTV, but less severe when plants were larger at the time of infection. To evaluate more precisely the relationship between age at infection, disease severity, virus accumulation, and yield loss in modern cultivars that were not bred for curly top resistance, individual sugar beet plants varying in degree of resistance and susceptibility to curly top were inoculated by viruliferous beet leafhoppers (Circulifer tenellus) when plants had two, four, or six true leaves, and maintained in a greenhouse for 6 weeks. When plants were inoculated at the two-leaf stage, all cultivars became severely stunted, with high disease ratings and similar rates of symptom development, regardless of resistance or susceptibility of the cultivar. Plants inoculated at four-and six-leaf stages exhibited increasing separation between resistant and susceptible phenotypes, with highly resistant cultivars performing well with low disease ratings and increased plant weights relative to susceptible cultivars. High-yielding cultivars performed only slightly better than the susceptible control cultivar. Results from greenhouse trials matched those from field trials conducted under heavy curly top pressure. Importantly, low virus concentration was directly correlated with lower disease ratings and higher plant weight, while elevated virus concentrations corresponded to higher disease ratings and lower weights. This demonstrates that a rapid greenhouse assay involving multiple traits can provide a rapid and effective means of selecting cultivars with improved curly top control, and could lead to more rapid incorporation of resistance into high-yielding sugar beet.