Energy and greenhouse gas analysis of northeast U.S. dairy cropping systems

Climate‐smart agriculture emphasizes improving crop production while mitigating greenhouse gas (GHG) emissions. In recent years, machine learning (ML) models have been increasingly used to unlock complex problems in agriculture, such as an enhanced understanding of drivers regulating GHG emissions. However, these models have not been evaluated widely, specifically quantifying GHG emissions in arid and semi‐arid cropping systems. We evaluated soil GHG emissions in irrigated crop rotations with and without cover cropping using field‐based measurements and five ML models, that is, decision tree, random forest (RF), gradient boosting (GB), bagging regressor, and XGB booster. The ML model was trained and evaluated using >1600 data points of daily carbon dioxide (CO2), and nitrous oxide (N2O) measurements from four cover cropping practices (no‐cover crop and grass–brassica, grass–legume, and grass–brassica–legume cover crops) along with environmental (air temperature, precipitation), soil (moisture, temperature), and crop and management (cover crop carbon content, irrigation) variables and CO2 and N2O emissions was predicted. The RF model predicted CO2 emissions (R2 up to 0.68) more accurately, and N2O emissions were predicted better by the RF and GB models than other models with R2 up to 0.56 and 0.55, respectively. Irrigation was the most critical driver of CO2 emissions, and air temperature was the main driver of N2O emissions. ML models can effectively estimate GHG emissions using simple predictors, such as field management and environmental parameters, and help in designing climate‐smart management in arid and semi‐arid regions.

Mitigating greenhouse gas emissions and ammonia volatilization from cotton fields by integrating cover crops with reduced use of nitrogen fertilizer

Aims To use a commercially available, deterministic, whole-farm model to assess the impact on production (milk solids (MS)/ha), greenhouse gas (GHG) emissions (total and per kg MS), and gross margin per ha, from changes in the calving pattern alone or combined with changes in non-pregnancy and replacement rate, for a pasture-based dairy farming system in Waikato, New Zealand. Methods A baseline model of a dairy farm was developed. Reproductive data from actual Waikato dairy farms were used to assess the change and variability in GHG production (total and per kg MS), MS/ha and gross margin/ha of the baseline model farm. Two different scenarios were modelled using data reflecting a range in reproductive performance: firstly, calving pattern data from 82 farms were used to model, over the subsequent lactation, the range in outputs associated with these differences. Secondly, calving pattern and non-pregnancy rate data from 70 of these farms were used to model the range in outputs associated with differences in these combined metrics. Results Sequentially changing the calving pattern data to reflect the variation in the 82 farms demonstrated relatively small changes in the outputs: higher 6-week calving rates tended to produce more MS per ha and a higher gross margin per ha. These herds also had lower GHG emissions intensity but tended to produce more overall GHG. Including the variance in the calving and non-pregnancy rate also led to small changes in outputs. Herds with higher 6-week calving rates and lower non-pregnancy rates – necessitating the user to manually reduce the replacement rate – resulted in a decrease in emissions intensity and overall emissions. However, despite the large variation in both the non-pregnancy and 6-week calving rate in the actual farm data, there was much less variation in the model’s predicted production/ha, gross margin/ha and environmental emissions. Conclusions Although these herds demonstrated variation in reproductive performance, and a resultant variance in the replacement rate, the model predicted that the financial, production and environmental outputs were only slightly better for herds with the optimum reproductive performance. In particular, even for herds with the best reproductive performance, overall GHG emissions were only slightly reduced. Thus, our modelling suggests it is the opportunity to further manipulate the farming system – stemming from improvements in the reproductive performance – that is likely to create the greatest gains in the production, financial and environmental performance for a dairy farm.

Cropping system diversification can reduce the negative environmental impacts of agricultural production, including soil erosion and nutrient discharge. Less is known about how diversification affects energy use, climate change, and air quality, when considering farm operations and supply chain activities. We conducted a life cycle study using measurements from a nine-year Iowa field experiment to estimate fossil energy (FE) use, greenhouse gas (GHG) emissions, PM2.5-related emissions, human health impacts, and other agronomic and economic metrics of contrasting crop rotation systems and herbicide regimes. Rotation systems consisted of 2-year corn-soybean, 3-year corn-soybean-oat/clover, and 4-year corn-soybean-oat/alfalfa-alfalfa systems. Each was managed with conventional and low-herbicide treatments. FE consumption was 56% and 64% lower in the 3-year and 4-year rotations than in the 2-year rotation, and GHG emissions were 54% and 64% lower. Diversification reduced combined monetized damages from GHG and PM2.5-related emissions by 42% and 57%. Herbicide treatment had no significant impact on environmental outcomes, while corn and soybean yields and whole-rotation economic returns improved significantly under diversification. Results suggest that diversification via shifting from conventional corn-soybean rotations to longer rotations with small grain and forage crops substantially reduced FE use, GHG emissions, and air quality damages, without compromising economic or agronomic performance.

Carbon footprints and social carbon cost assessments in a perennial energy crop system: A comparison of fertilizer management practices in a Mediterranean area

Dairy farms in the United States have changed in many ways over the past 50 yr. Milk production efficiency has increased greatly, with ∼30% fewer cows producing about twice the amount of milk today. Other improvements include increases in crop yields, fuel efficiency of farm equipment, and efficiency in producing most resources used on farms (e.g. electricity, fuel, fertilizer). These improvements have led to changes in the environmental impact of farms. Through simulation of representative dairy farms in 1971 and 2020, changes in nutrient losses and farmgate life cycle assessments of greenhouse gas (GHG) emissions, fossil energy use, and blue (ground and surface) water use were determined for 6 regions and the United States. For all environmental metrics studied, intensities expressed per unit of fat- and protein-corrected milk produced were reduced, but the total effects over all farms or milk produced increased for 5 of the 13 environmental metrics. Reductions in the impacts of dairy farms in the eastern United States were offset by large increases in western regions because of a major increase in cow numbers in the West. The national average intensity of GHG emissions decreased by 42%, which gave just a 14% increase in the total GHG emissions of all dairy farms over the 50-yr period. The intensity of fossil energy use decreased by 54%, with the total for all farms decreasing by 9%. Water use related to milk production decreased in intensity by 28%, but due to the large increase in dairy production in the dry western regions that have a greater dependence on irrigated feed crops, total blue water use increased by 42%. Major pathways of nitrogen loss included ammonia volatilization, leaching, and denitrification, where total ammonia emissions related to US dairy farms increased by 29%, while leaching losses decreased by 39%, with little change in nitrous oxide emissions. Simulated nitrogen and phosphorus runoff losses totaled for all dairy farms decreased by 27% to 51% through more efficient fertilizer use, reduced tillage, and greater use of cover crops. Emissions of methane and reactive non-methane volatile organic compounds increased by 32% and 53%, respectively, due to greater use of long-term manure storage and silage stored in bunkers and piles. Although much progress has been made in improving production efficiency, continued improvements with new strategies and technologies are needed to meet the demand for dairy products and mitigate total environmental impacts, particularly in view of projected climate variability.

Southland has witnessed a pronounced change in its agricultural landscape in recent years.Greater profitability of dairy relative to sheep farming has led to a large number of dairy conversions over the last 20 years, with the scope for further substantive conversions into the future.The economic and social benefits have been extensively reported, but less is understood about the environmental impacts associated with this land use change.To investigate the potential effect of land use change from sheep and beef to dairy on economic and environmental outcomes in the Southland region of New Zealand, farm-scale enterprise simulation models were linked with spatially explicit land resource information.By overlaying individual farm parcels with land resource information, land area and topography data for each farm were attained.Estimated pasture production (PP) for each land use capability (LUC) Class provided indicative data for the modelling exercise on the productive use of the land across the region.The approach provided a method for the expansion of farm scale modelling to a regional scale.A representative DairyNZ Production System 3 was used to investigate the influence of increasing dairy cow numbers and associated inputs at the farm level.A representative sheep and beef farm was also modelled.To account for a dairy support area, used to carry dry cows during the winter, a second step involved the modelling of a larger System 3 dairy farm that included a milking platform area and an adjacent support area.This farm system was considered for regional up-scaling to allow for a more comprehensive capture of nutrient losses and financial outcomes.Estimates of annual nitrogen (N) leaching values from dairy farms ranged from 21 to 44 kg N/ha, and were higher for farms with greater pasture production potential, due to the greater amount of N cycling and increased number of urine patches from the higher number of livestock numbers carried.Annual N leaching from the sheep and beef farms ranged from 8 to 17 kg N/ha.Annual greenhouse gas (GHG) emissions were also higher from farms with greater productive potential, ranging from 7.1 to 15.4 t CO 2 -e/ha for dairy and from 2.1 to 6.9 t CO 2 -e/ha for sheep and beef farms.In contrast to leaching, GHG emissions were higher from poorly-drained soils compared with well-drained soils; annual nitrous oxide (N 2 O) emissions accounted for 22% and 35% of total GHG emissions from dairy farms on well-and poorly-drained soils, respectively, and up to 40% from sheep and beef farms on poorly-drained soils.The new dairy farms resulting from conversion would largely fall in an N leaching range of 25 to 31 kg N/ha and have GHG emissions of 7.0 to 10.5 t CO 2 -e/ha.Depending on future regional regulations that may be implemented, a large number of potential dairy farms might leach more N than the allowable limit, and mitigation techniques will need to be implemented.A shift in land use from the current 15% of land area under dairying to a potential 46% led to a large increase in regional profit (76%).The environmental impact from this land use change, however, became substantial, with regional nitrate leaching increasing by 34% and GHG emissions by 24%.Conversion of more farms into dairying increased farm profit, N leaching and GHG emissions in the region compared with the current situation.It must be noted, however, that the up-scaling of potential dairy conversion was based on land resources defined by the productive potential of the landscapes found in Southland and that the actual level of conversion could differ substantially if additional or different farming scenarios were tested.

The impact of uncertainties on predicted greenhouse gas emissions of dairy cow production systems

This review paper synthesizes the current understanding of greenhouse gas (GHG) emissions from field cropping systems. It examines the key factors influencing GHG emissions, including crop type, management practices, and soil conditions. The review highlights the variability in GHG emissions across different cropping systems. Conventional tillage systems generally emit higher levels of carbon dioxide (CO2) and nitrous oxide (N2O) than no-till or reduced tillage systems. Crop rotation, cover cropping, and residue management can significantly reduce GHG emissions by improving soil carbon sequestration and reducing nitrogen fertilizer requirements. The paper also discusses the challenges and opportunities for mitigating GHG emissions in field cropping systems. Precision agriculture techniques, such as variable rate application of fertilizers and water, can optimize crop production while minimizing environmental impacts. Agroforestry systems, which integrate trees and crops, offer the potential for carbon sequestration and reducing N2O emissions. This review provides insights into the latest research on GHG emissions from field cropping systems and identifies areas for further study. It emphasizes the importance of adopting sustainable management practices to reduce GHG emissions and enhance the environmental sustainability of agricultural systems.

Climate change is one of the main global issues of modern time. Ever increasing demand for food/feed and the need for higher environmental standards require shaping of the agricultural activities toward ecological and more sustainable efficient systems. One of the principal ways of attaining higher productivity and environmental standards is identification and adoption of beneficial management practices (BMP) by reviewing the conventional agricultural activities. The BMP are agricultural practices that promote sustainable land stewardship and maintain/increase profitability of farms. The BMP are from both crop and animal production systems and tradeoffs between the two systems could provide several opportunities in reducing, removing and/or avoiding of greenhouse gases (GHG) emissions. Despite that, few reviews have presented them together. This review covers GHG emissions related to the BMP in the crop and animal production systems of farms relevant to Canadian Prairie. These BMP include: (1) use of inorganic N fertilizers, (2) livestock and feed management, (3) manure management, (4) cropping systems, (5) tillage practices and (6) improved pasture and grazing management. In addition, sources of variations, quantification methods and adoptability are discussed. Quantified GHG emissions from direct and indirect measurements of researches from Canada and other part of the world are included. Since most experiments are conducted under multiple biophysical scenarios while adopting various methodologies, summarizing the findings was difficult. The effect of BMP on GHG is determined by ecological processes. Such determinants are discussed and knowledge gaps are identified. Integration of crop and livestock production systems could further lead toward higher energy and resource use efficiency; hence less GHG emissions.

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.

Rivers play an important role in greenhouse gas emissions. Over the past decade, because of global urbanization trends, rapid land use changes have led to changes in river ecosystems that have had a stimulating effect on the greenhouse gas production and emissions. Presently, there is an urgent need for assessments of the greenhouse gas concentrations and emissions in watersheds. Therefore, this study was designed to evaluate river-based greenhouse gas emissions and their spatial-temporal features as well as possible impact factors in a rapidly urbanizing area. The specific objectives were to investigate how river greenhouse gas concentrations and emission fluxes are responding to urbanization in the Liangtan River, which is not only the largest sub-basin but also the most polluted one in Chongqing City. The thin layer diffusion model method was used to monitor year-round concentrations of pCO2, CH4, and N2O in September and December 2014, and March and June 2015. The pCO2 range was (23.38±34.89)-(1395.33±55.45) Pa, and the concentration ranges of CH4 and N2O were (65.09±28.09)-(6021.36±94.36) nmol·L-1 and (29.47±5.16)-(510.28±18.34) nmol·L-1, respectively. The emission fluxes of CO2, CH4, and N2O, which were calculated based on the method of wind speed model estimations, were -6.1-786.9, 0.31-27.62, and 0.06-1.08 mmol·(m2·d)-1, respectively. Moreover, the CO2 and CH4 emissions displayed significant spatial differences, and these were roughly consistent with the pollution load gradient. The greenhouse gas concentrations and fluxes of trunk streams increased and then decreased from upstream to downstream, and the highest value was detected at the middle reaches where the urbanization rate is higher than in other areas and the river is seriously polluted. As for branches, the greenhouse gas concentrations and fluxes increased significantly from the upstream agricultural areas to the downstream urban areas. The CO2 fluxes followed a seasonal pattern, with the highest CO2 emission values observed in autumn, then successively winter, summer, and spring. The CH4 fluxes were the highest in spring and the lowest in summer, while N2O flux seasonal patterns were not significant. Because of the high carbon and nitrogen loads in the basin, the CO2 products and emissions were not restricted by biogenic elements, but levels were found to be related to important biological metabolic factors such as the water temperature, pH, DO, and chlorophyll a. The carbon, nitrogen, and phosphorus content of the water combined with sewage input influenced the CH4 products and emissions. Meanwhile, N2O production and emissions were mainly found to be driven by urban sewage discharge with high N2O concentrations. Rapid urbanization accelerated greenhouse gas emissions from the urban rivers, so that in the urban reaches, CO2/CH4 fluxes were twice those of the non-urban reaches, and all over the basin N2O fluxes were at a high level. These findings illustrate how river basin urbanization can change aquatic environments and aggravate allochthonous pollution inputs such as carbon, nitrogen, and phosphorus, which in turn can dramatically stimulate river-based greenhouse gas production and emissions; meanwhile, spatial and temporal differences in greenhouse gas emissions in rivers can lead to the formation of emission hotspots.

基于生命周期评价的上海市水稻生产碳足迹研究

A LCA Based Biofuel Supply Chain Analysis Framework

This three-year study was conducted on a commerce silt loam soil at the Northeast Research and Experiment Station near St. Joseph, Louisiana to evaluate the following objectives: 1) evaluate crop yield response to tillage, winter, and summer cover crops in wheat double-cropping systems. 2) evaluate the effects of tillage, winter and summer cover crops on soil properties under different cropping systems. 3) quantify the economic benefits of double-cropping, cover cropping and monoculture systems under conventional and no tillage practices. A winter cover crop (WCC) mix (winter wheat (Triticum aestivum and Austrian winter pea (Pisum sativum) and summer cover crop (SCC) mix sunn hemp (Crotalaria juncea) and sorghum sudangrass (Sorghum bicolor) were incorporated in the treatments (cropping systems). Treatments included conventional tillage (CY)and no-tillage (NT) while the cropping systems are wheat-fallow (W-F), wheat-cotton (W-C), wheat-soybean (W-S), wheat-summer cover crops (W-SCC), WCC-S, F-C,WCC-S and W-S. Tillage influence on crop yield were not consistent and varied across years. In year one wheat yield in the NT (2091 kg ha-1) was 8.5% greater than the wheat yield in the CT (1913 kg ha-1). In year two and three, wheat yield in the CT was higher than the NT and was significantly than the NT in year three with the CT (2621 kg ha-1) outyielding the NT (2140 kg ha-1) with 18.4%. On a three-year average, wheat yield in the CT (2122 kg ha-1) were relatively higher than the NT (1961 kg ha-1) with 7.6%. Like the wheat, tillage influence on cotton and soybean yield followed the same trend with the CT having higher yield than the NT. In general, the fallow system for each cropping system treatment had the lowest yield compared to double-cropping and cover cropping treatments. Soybean yield in the W-S (4107 kg ha-1) and WCC-S (3780 kg ha-1) treatments were 18.6 and 11.5% respectively higher than the F-S (3345 kg ha-1). On a three-year average this yield advantage of the double-cropped and cover cropping systems was also observed in wheat and cotton. Tillage and cropping system influence on soil properties followed same pattern with the fallow systems have the least influence on soil properties. The CT tend to improve soil properties more at the 0 -15 cm soil depth, a layer within the depth of our CT where most of the residues were incorporated. This is one reason why our soil organic matter in the CT at the 0 -15 cm soil depth were more than that of the NT and it was significant in the cotton cropping system in one year with the CT having 1.65% while the NT was 1.45%. Beyond the top 0 -15 cm soil depth, the NT seems to accumulate more soil organic matter. Cropping systems with cover crops and double-cropped systems had the highest soil organic matter in all cropping systems. The highest soil organic matter of 1.81% was observed in both W-SCC and WCC-S at the 0 -15cm soil depth. Evidently, we observed lower bulk densities in the NT but no significant difference between tillage practice or cropping systems. The CT had the highest bulk density of 1.35 g cm-3compared to the NT with 1.27 g cm-3 across all cropping systems. Our results also demonstrated that cropping system treatments with cover crops incorporated (W-SCC) and double-cropped systems (WCC-C and WCC-S) had higher net returns compared to the fallow systems (W-F, F-C and F-S).