Environmental evaluation with greenhouse gas emissions and absorption based on life cycle assessment for a Jatropha cultivation system in frost- and drought-prone regions of Botswana

Crop rotations are considered a promising strategy for mitigating greenhouse gas (GHG) emissions and enhancing soil organic matter in agricultural land. However, studies often focused solely on either GHG emissions or soil organic carbon (SOC) changes, rather than integrating both indicators. We conducted a 4‐year (2018–2021) crop rotation study to examine effects of six rotation systems in three ecoregions (sub‐humid, sub‐semiarid, and semiarid) on GHG emissions, SOC changes, and C footprints in Saskatchewan, Canada. The six rotation systems include (i) control, (ii) intensified, (iii) diversified, (iv) market‐driven, (v) high‐risk, and (vi) soil‐health cropping system. GHG emissions were estimated using the Holos model, and SOC changes were estimated using the Campbell model, and C footprints were calculated as the difference between GHG emissions and SOC changes. The 4‐year cumulative GHG emissions, expressed as CO2 equivalent (CO2e), were highest in the sub‐humid ecoregion due to higher background SOC levels, nitrogen (N) fertilizer inputs, and precipitation. The diversified and soil‐health systems reduced GHG emissions by reduced N fertilizer inputs. Carbon footprints revealed net CO2e emissions for the market‐driven system but net CO2e withdrawals for the soil‐health and diversified systems. The results indicated that the diversified systems significantly mitigated GHG emissions, increased soil C stocks, and withdrew CO2e.

The shift from straw incorporation to biofuel production entails emissions from production, changes in soil organic carbon (SOC) and through the provision of (co‐)products and entailed displacement effects. This paper analyses changes in greenhouse gas (GHG) emissions arising from the shift from straw incorporation to biomethane and bioethanol production. The biomethane concept comprises comminution, anaerobic digestion and amine washing. It additionally provides an organic fertilizer. Bioethanol production comprises energetic use of lignin, steam explosion, enzymatic hydrolysis and co‐fermentation. Additionally, feed is provided. A detailed consequential GHG balance with in‐depth focus on the time dependency of emissions is conducted: (a) the change in the atmospheric load of emissions arising from the change in the temporal occurrence of emissions comparing two steady states (before the shift and once a new steady state has established); and (b) the annual change in overall emissions over time starting from the shift are assessed. The shift from straw incorporation to biomethane production results in net changes in GHG emissions of (a) −979 (−436 to −1,654) and (b) −955 (−220 to −1,623) kg CO2‐eq. per tdry matter straw converted to biomethane (minimum and maximum). The shift to bioethanol production results in net changes of (a) −409 (−107 to −610) and (b) −361 (57 to −603) kg CO2‐eq. per tdry matter straw converted to bioethanol. If the atmospheric load of emissions arising from different timing of emissions is neglected in case (a), the change in GHG emissions differs by up to 54%. Case (b) reveals carbon payback times of 0 (0–49) and 19 (1–100) years in case of biomethane and bioethanol production, respectively. These results demonstrate that the detailed inclusion of temporal aspects into GHG balances is required to get a comprehensive understanding of changes in GHG emissions induced by the introduction of advanced biofuels from agricultural residues.

U.S. rice paddies, critical for food security, are increasingly contributing to non‐CO2 greenhouse gas (GHG) emissions like methane (CH4) and nitrous oxide (N2O). Yet, the full assessment of GHG balance, considering trade‐offs between soil organic carbon (SOC) change and non‐CO2 GHG emissions, is lacking. Integrating an improved agroecosystem model with a meta‐analysis of multiple field studies, we found that U.S. rice paddies were the rapidly growing net GHG emission sources, increased 138% from 3.7 ± 1.2 Tg CO2eq yr−1 in the 1960s to 8.9 ± 2.7 Tg CO2eq yr−1 in the 2010s. CH4, as the primary contributor, accounted for 10.1 ± 2.3 Tg CO2eq yr−1 in the 2010s, alongside a notable rise in N2O emissions by 0.21 ± 0.03 Tg CO2eq yr−1. SOC change could offset 14.0% (1.45 ± 0.46 Tg CO2eq yr−1) of the climate‐warming effects of soil non‐CO2 GHG emissions in the 2010s. This escalation in net GHG emissions is linked to intensified land use, increased atmospheric CO2, higher synthetic nitrogen fertilizer and manure application, and climate change. However, no/reduced tillage and non‐continuous irrigation could reduce net soil GHG emissions by approximately 10% and non‐CO2 GHG emissions by about 39%, respectively. Despite the rise in net GHG emissions, the cost of achieving higher rice yields has decreased over time, with an average of 0.84 ± 0.18 kg CO2eq ha−1 emitted per kilogram of rice produced in the 2010s. The study suggests the potential for significant GHG emission reductions to achieve climate‐friendly rice production in the U.S. through optimizing the ratio of synthetic N to manure fertilizer, reducing tillage, and implementing intermittent irrigation.

Abstract. Sustainable intensification schemes such as integrated soil fertility management (ISFM) are a proposed strategy to close yield gaps, increase soil fertility, and achieve food security in sub-Saharan Africa. Biogeochemical models such as DayCent can assess their potential at larger scales, but these models need to be calibrated to new environments and rigorously tested for accuracy. Here, we present a Bayesian calibration of DayCent, using data from four long-term field experiments in Kenya in a leave-one-site-out cross-validation approach. The experimental treatments consisted of the addition of low- to high-quality organic resources, with and without mineral nitrogen fertilizer. We assessed the potential of DayCent to accurately simulate the key elements of sustainable intensification, including (1) yield, (2) the changes in soil organic carbon (SOC), and (3) the greenhouse gas (GHG) balance of CO2 and N2O combined. Compared to the initial parameters, the cross-validation showed improved DayCent simulations of maize grain yield (with the Nash–Sutcliffe model efficiency (EF) increasing from 0.36 to 0.50) and of SOC stock changes (with EF increasing from 0.36 to 0.55). The simulations of maize yield and those of SOC stock changes also improved by site (with site-specific EF ranging between 0.15 and 0.38 for maize yield and between −0.9 and 0.58 for SOC stock changes). The four cross-validation-derived posterior parameter distributions (leaving out one site each) were similar in all but one parameter. Together with the model performance for the different sites in cross-validation, this indicated the robustness of the DayCent model parameterization and its reliability for the conditions in Kenya. While DayCent poorly reproduced daily N2O emissions (with EF ranging between −0.44 and −0.03 by site), cumulative seasonal N2O emissions were simulated more accurately (EF ranging between 0.06 and 0.69 by site). The simulated yield-scaled GHG balance was highest in control treatments without N addition (between 0.8 and 1.8 kg CO2 equivalent per kg grain yield across sites) and was about 30 % to 40 % lower in the treatment that combined the application of mineral N and of manure at a rate of 1.2 t C ha−1 yr−1. In conclusion, our results indicate that DayCent is well suited for estimating the impact of ISFM on maize yield and SOC changes. They also indicate that the trade-off between maize yield and GHG balance is stronger in low-fertility sites and that preventing SOC losses, while difficult to achieve through the addition of external organic resources, is a priority for the sustainable intensification of maize production in Kenya.

Including a one-year grass ley increases soil organic carbon and decreases greenhouse gas emissions from cereal-dominated rotations – A Swedish farm case study

Agroecosystems provide a range of benefits that are strongly influenced by cropping practice. Crop productivity and C, N, and greenhouse gas (GHG) balances were evaluated in an 18‐yr cropping system study on an Aridic Haplustoll in the northern Great Plains. Application of synthetic fertilizers consistently increased crop yield and soil organic carbon (SOC), with greatest impact in perennial grass and continuous wheat ( Triticum aestivum L.) rotations and least impact in rotations with fallow or annual legumes. Based on N balance, N inputs other than fertilizer were 16 to 30 kg N ha −1 yr −1 in rotations without legumes and 62 kg N ha −1 yr −1 in a legume‐wheat (LW) rotation, while losses of synthetic fertilizer N were 32% in annual crop rotations and 3% in perennial grass. Due to large gains in SOC, perennial grass reduced atmospheric GHG by 20 to 29 Mg CO 2 equivalent (eq.) ha −1 during the 18 yr of this study. For annual crop rotations, seed yield ranged from 1.2 to 2.5 Mg ha −1 yr −1 , protein yield from 0.20 to 0.41 Mg ha −1 yr −1 , and GHG intensity from 0 to 0.5 Mg CO 2 eq. Mg −1 seed. Fertilized continuous wheat had the highest crop productivity and lowest net GHG intensity, while an annual LW rotation had the highest protein productivity and among the lowest GHG intensities (0.2 Mg CO 2 eq. Mg −1 seed). Further evaluation at broader temporal and spatial scales is necessary to account for future changes in SOC and differences in use of crop products.

Research on the mechanism of how climate change affects cultivated soil organic carbon is the basis for the management of cultivated land quality in the context of climate change. Crop phenological responses to climate change have an important effect on cultivated soil organic carbon as well. However, previous research primarily focused on the independent effects of climate change or crop phenological responses on the changes in soil organic carbon, and few studies have analyzed the changes in cultivated soil organic carbon under the combined influence of both factors or quantified their contribution rates to the changes in cultivated soil organic carbon. Based on topsoil samples in 2008 and 2021, annual pre-season and mid-season climate data from 2008 to 2021, and the phenological parameters extracted from the enhanced vegetation index （EVI） time series from 2007 to 2022, a soil organic carbon predictive model was constructed using the random forest algorithm. The total change in soil organic carbon from 2008 to 2021, the change in soil organic carbon under climate change alone, and the change in soil organic carbon under the synergistic influence of climate change and crop phenological responses were simulated. Furthermore, the contributions of climate change and crop phenological responses to the changes in cultivated soil organic carbon were distinguished and quantified. Moreover, the dominant influencing factors of soil organic carbon changes and their spatial distributions were identified and analyzed. The results were as follows： ① Under the synergistic influence of climate change and crop phenological responses, a decrease was observed in soil organic carbon in 74.15% of the cultivated land area in Fujian Province during the years 2008-2021, with an average decrease of 2.20 g·kg-1. Additionally, there was an increase in soil organic carbon in 25.85% of the cultivated area, with an average increase of 1.48 g·kg-1. ②The average contribution rates of pre-season climate, crop phenological responses to climate change, mid-season climate, and phenological changes resulting from cultivars shifts or other adjustments of agricultural measures to soil organic carbon changes were 34.08%, 28.56%, 22.75%, and 14.61%, respectively. Overall, climate change had a greater impact on the changes in cultivated soil organic carbon in Fujian Province than the crop phenological response to climate change. ③ The regions where climate change and phenological response jointly acted as dominant influencing factors held the largest area, accounting for 47.06% of the total cultivated land area in Fujian Province, and the regions where climate change was the dominant influencing factor alone held the second-largest area, accounting for 28.64% of the total cultivated land area. ④ Higher contribution rates of pre-season climate factors and phenological changes resulting from cultivar shifts or other adjustments of agricultural measures tended to be distributed in higher-altitude areas, whereas higher contribution rates of mid-season climate factors and phenological responses to climate change tended to be distributed in lower-altitude areas. These research findings can provide a theoretical basis for decision making regarding the management of cultivated land quality and the safeguarding of food security in the context of climate change.

It is difficult to identify farm management practices that consistently provide greenhouse gas (GHG) abatement at different locations because effectiveness of practices is greatly influenced by climates and soils. We address this knowledge gap by identifying practices that provide abatement in eight case studies located across diverse conditions in Australian’s grain-producing areas. The case studies focus on soil-based emissions of nitrous oxide (N2O) and changes in soil organic carbon (SOC), simulated over 100 years for 15 cropping management scenarios. Average changes in the balance of GHG from both N2O emissions and SOC sequestration (∆GHG balance) and gross margins compared to a high emissions baseline were determined over 25 and 100 simulated years. Because scenarios providing the greatest abatement varied across individual case studies, we aggregated the data over all case studies and analysed them with a random forest data mining approach to build models for predicting ∆GHG balance. Increased cropping intensity, achieved by including cover crops, additional grains crops, or crops with larger biomass in the rotation, was the leading predictor of ∆GHG balance across the scenarios and sites. Abatement from increased cropping intensity averaged 774 CO2-e ha−1 year−1 (25 years) and 444 kg CO2-e ha−1 year−1 (100 years) compared to the baseline, with reduced emissions from SOC sequestration offsetting increased N2O emissions for both time frames. Increased cropping intensity decreased average gross margins, indicating that a carbon price would likely be needed to maximise GHG abatement from this management. To our knowledge, this is the first time that the random forest approach has been applied to assess management practice effectiveness for achieving GHG abatement over diverse environments. Doing so provided us with more general information about practices that provide GHG abatement than would have come from qualitative comparison of the variable results from the case studies.

The life cycle based greenhouse gas (GHG) balances of Fatty Acid Methyl Esters (FAME also called "Biodiesel") from various resources have been set in the Renewable Energy Directive (RED). Due to technology and scientific progress there are various options to improve the GHG balances of FAME. In this Supporting Action 10 most interesting options were assessed: 1) "Biomethanol": Substitution of fossil methanol with biomethanol; 2) "Bioethanol": Substitution of fossil methanol with bioethanol; 3) "CHP residues": Use of residues and co-products in an CHP plant; 4) "New plant species": Examination of new plants for vegetable oils, that could increase the biomass weight without any detrimental effect on the oil seed; 5) "Bioplastics and biochemicals": Production of bioplastics and biochemicals from process residues; 6) "Advanced agriculture": Advanced agricultural practices in terms of N2O emissions and soil carbon accumulation; 7) "Organic residues": Use of organic versus mineral fertilizer for feedstock cultivation; 8) "FAME as fuel": Use of FAME in machinery for cultivation, transportation and distribution; 9) "Retrofitting multi feedstock": Retrofitting of single feedstock plants for blending fatty residues; and 10) "Green electricity": Use of renewable electricity produced in a PV plant on site. The assessment approach started with the GHG standard values of the RED and the corresponding background data documented in BioGrace. For the most relevant FAME production possibilities in Europe, characterized by the feedstock (rapeseed, sunflower, palm oil, soybean, used cooking oil, animal fat) and FAME production capacity (50 - 200 kt/a), the technical and economic data of "Best Available Technology in 2015" (BAT) were used as starting point to assess the improvement options. Based on the calculation of GHG emissions (g CO2-eq/MJ) and production cost (€/tFAME) an overall assessment (incl SWOT-Analyses and Stakeholder involvement) of the options was made and summarized in "Fact Sheets". A significant GHG reduction compared to the RED values in processing is possible, if best available technology (BAT) is applied. The GHG emissions of cultivation compared to RED are higher due to improved data on the correlation between fertilizer input and yields. The assessed GHG improvements options show that the potential to reduce emissions is relatively large in agriculture cultivation, but a relatively low in processing. The production cost analysis shows that revenues from co-produced animal feed and oil yield per hectare have a strong influence on total production costs, e.g. mainly animal feed from soybeans. The total FAME production cost of BAT are 280 – 1,000 €/tFAME, including revenues from co-products. Cost ranges arise due to different feedstock and capacities. The greenhouse gas analysis of the improvement options results in a GHG reduction potential of 0 - 37 g CO2-eq/MJ compared to BAT. The greenhouse gas mitigation costs of improvement options range between -260 and +1,000 €/t CO2-eq. Options with negative greenhouse gas mitigation costs generate economic benefits compared to the base case. Summing up the assessment one can conclude that the future FAME production has several options to further improve its GHG balance thus contributing substantially to a more sustainable transportation sector.

The influence of the long-term combination of rice straw removal and rice straw compost application on methane (CH4) and nitrous oxide (N2O) emissions and soil carbon accumulation in rice paddy fields was clarified. In each of the initial and continuous application fields (3 and 39−51 years, respectively), three plots with different applications of organic matter were established, namely, rice straw application (RS), rice straw compost application (SC) and no application (NA) plots, and soil carbon storage (0−15 cm), rice grain yield and CH4 and N2O fluxes were measured for three years. The soil carbon sequestration rate by the organic matter application was higher in the SC plot than in the RS plot for both the initial and continuous application fields, and it was lower in the continuous application field than in the initial application field. The rice grain yield in the SC plot was significantly higher than those in the other plots in both the initial and continuous application fields. Cumulative CH4 emissions followed the order of the NA plot < the SC plot < the RS plot for both the initial and continuous application fields. The effect of the organic matter application on the N2O emissions was not clear. In both the initial and continuous application fields, the increase in CH4 emission by the rice straw application exceeded the soil carbon sequestration rate, and the change in the net greenhouse gas (GHG) balance calculated by the difference between them was a positive, indicating a net increase in the GHG emissions. However, the change in the GHG balance by the rice straw compost application showed negative (mitigating GHG emissions) for the initial application field, whereas it showed positive for the continuous application field. Although the mitigation effect on the GHG emissions by the combination of the rice straw removal and rice straw compost application was reduced by 21% after 39 years long-term application, it is suggested that the combination treatment is a sustainable management that can mitigate GHG emissions and improve crop productivity.

Regional values for prospective soil organic carbon (SOC) change in Australian cropland were derived via state-of-the-art modelling. This paper evaluates the applicability of the results in the context of life cycle assessment (LCA). Results of soil carbon modelling need to align with LCA requirements in order to be applicable. The following aspects were investigated in more detail: effect of SOC and variability on product carbon footprint results, data symmetry and consistency, attribution of the SOC change to activity and allocation of the SOC change to crops in rotations. Results show that greenhouse gas (GHG) emissions or removals associated with SOC change even in the absence of recent land use change or land management change can potentially change Australian crop carbon footprints considerably. Over a modelling period of 62 years, the SOC continues to change. In attributional LCA, issues with attribution, allocation and data symmetry of the SOC change values are complex. Without a comprehensive understanding of the causal link between individual crops and pasture in a rotation and the change in SOC, and without a SOC change figure for an appropriate reference baseline, application in attributional LCA is limited to certain types of studies only. In consequential LCA, the data symmetry issues as well as the need for allocation and attribution can be avoided. The SOC change results cannot be allocated to individual crops and are therefore valid only for a full rotation cycle. This means results may be applied in attributional LCA but only in certain contexts. The main applicability is foreseen as a business-as-usual baseline for consequential LCA. A set of SOC change values will be derived for this purpose and made available with accompanying guidance for use and interpretation.

Sub-surface soil carbon changes affects biofuel greenhouse gas emissions

Carbon savings from sugarcane straw-derived bioenergy: Insights from a life cycle perspective including soil carbon changes

Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life‐cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life‐cycle GHG emissions affect biofuels' attractiveness and eligibility under a number of renewable fuel policies in the USA and abroad. Modeling was used to refine the spatial resolution and depth extent of domestic estimates of SOC change for land (cropland, cropland pasture, grassland, and forest) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow) at the county level in the USA. Results show that in most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. SOC change results were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life‐cycle GHG emissions of corn and cellulosic ethanol. Total LUC GHG emissions (g CO2eq MJ−1) were 2.1–9.3 for corn‐, −0.7 for corn stover‐, −3.4 to 12.9 for switchgrass‐, and −20.1 to −6.2 for Miscanthus ethanol; these varied with SOC modeling assumptions applied. Extending the soil depth from 30 to 100 cm affected spatially explicit SOC change and overall LUC GHG emissions; however, the influence on LUC GHG emission estimates was less significant in corn and corn stover than cellulosic feedstocks. Total life‐cycle GHG emissions (g CO2eq MJ−1, 100 cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18–26 for switchgrass ethanol, and −7 to −0.6 for Miscanthus ethanol. The LUC GHG emissions associated with poplar‐ and willow‐derived ethanol may be higher than that for switchgrass ethanol due to lower biomass yield.

Oil palm (OP) plantations account for 1.7 % of global CO2 emissions. Numerous studies have focused primarily on greenhouse gas (GHG) emissions from peatlands, constituting 20% of total OP area in the two largest OP producing countries, Indonesia and Malaysia. Few studies have investigated the potential for reducing GHG emissions in OP plantations. Strategies to reduce emissions and sequester carbon must consider how different practices affect production and the environment. Understanding the spatial distribution of GHG intensity and how the environment affects GHG intensity is therefore key to sustainable oil palm production.GHG intensity was used as a metric to map the potential for sustainable OP plantations. GHG intensity represents the GHG emissions / removals (ton C ha-1) per unit of oil palm yields (ton ha-1). The approach for analysing the change in GHG emissions/ removals, referred to as the IPCC tier 1 method, is based on changes in soil organic carbon due to C and N emissions in drained peatlands and the associated change in aboveground biomass due to land use change. Changes in GHG intensity were investigated spatially for a case study in an industrial OP plantation located in Riau Province, Indonesia, from 2015 to 2019. Linear regression was used to analyse the relationships between GHG intensity and agri-environmental variables including NDVI, NPP, GPP, evapotranspiration, soil moisture in the root zone, soil moisture in deeper layer, C and N emissions from organic soils, and soil organic carbon (SOC).The results show that around 90% of the new oil palm plantations in 2019 were converted from timber plantation, swamp scrubland, and bare land in 2015. Consequently, biomass growth from land use change acted as a carbon sink in this period. However, drained organic soils contributed significantly to GHG emissions. The change in GHG intensity in OP plantation in this study varied spatially from emitting (0.19 to 4.10 Ton C eq Ton-1 yields) to removing the GHG (0.23 to 2.40 Ton C eq Ton-1 yields). Among the environmental variables, NDVI and soil moisture showed the strongest relationship with GHG emissions/ removals (R2 = 0.23,&amp;#160;&amp;#160; p value = &lt; 2.2e-16) and yields (R2 = 0.2&amp;#160;&amp;#160; p value = &lt; 2.2e-16) in OP plantations.These initial findings are advantageous for spatially identifying potential OP plantations that remove or emit GHG. Understanding the relationship between GHG emissions/removals and yields to environment variables provides insight into monitoring and enhancing OP sustainability, both from production and environmental perspectives. Future work will examine non-linear approaches to better model this relationship.&amp;#160;&amp;#160;&amp;#160;