In-vitro method and model to estimate methane emissions from liquid manure management on pig and dairy farms in four countries.

Methane (CH4) emissions from manure management on livestock farms are a key source of greenhouse gas emissions in some regions and for some production systems, and the opportunities for mitigation may be significant if emissions can be adequately documented. We investigated a method for estimating CH4 emissions from liquid manure (slurry) that is based on anaerobic incubation of slurry collected from commercial farms. Methane production rates were used to derive a parameter of the Arrhenius temperature response function, lnA', representing the CH4 production potential of the slurry at the time of sampling. Results were used for parameterization of an empirical model to estimate annual emissions with daily time steps, where CH4 emissions from individual sources (barns, outside storage tanks) can be calculated separately. A monitoring program was conducted in four countries, i.e., Denmark, Sweden, Germany and the Netherlands, during a 12-month period where slurry was sampled to represent barn and outside storage on finishing pig and dairy farms. Across the four countries, lnA' was higher in pig slurry compared to cattle slurry (p < 0.01), and higher in slurry from barns compared to outside storage (p < 0.01). In a separate evaluation of the incubation method, in-vitro CH4 production rates were comparable with in-situ emissions. The results indicate that lnA' in barns increases with slurry age, probably due to growth or adaptation of the methanogenic microbial community. Using lnA' values determined experimentally, empirical models with daily time steps were constructed for finishing pig and dairy farms and used for scenario analyses. Annual emissions from pig slurry were predicted to be 2.5 times higher than those from cattle slurry. Changing the frequency of slurry export from the barn on the model pig farm from 40 to 7 d intervals reduced total annual CH4 emissions by 46 %; this effect would be much less on cattle farms with natural ventilation. In a scenario with cattle slurry, the empirical model was compared with the current IPCC methodology. The seasonal dynamics were less pronounced, and annual CH4 emissions were lower than with the current methodology, which calls for further investigations. Country-specific models for individual animal categories and point sources could be a tool for assessing CH4 emissions and mitigation potentials at farm level.

Challenges of accounting nitrous oxide emissions from agricultural crop residues.

Crop residues are important inputs of carbon (C) and nitrogen (N) to soils and thus directly and indirectly affect nitrous oxide (N2 O) emissions. As the current inventory methodology considers N inputs by crop residues as the sole determining factor for N2 O emissions, it fails to consider other underlying factors and processes. There is compelling evidence that emissions vary greatly between residues with different biochemical and physical characteristics, with the concentrations of mineralizable N and decomposable C in the residue biomass both enhancing the soil N2 O production potential. High concentrations of these components are associated with immature residues (e.g., cover crops, grass, legumes, and vegetables) as opposed to mature residues (e.g., straw). A more accurate estimation of the short-term (months) effects of the crop residues on N2 O could involve distinguishing mature and immature crop residues with distinctly different emission factors. The medium-term (years) and long-term (decades) effects relate to the effects of residue management on soil N fertility and soil physical and chemical properties, considering that these are affected by local climatic and soil conditions as well as land use and management. More targeted mitigation efforts for N2 O emissions, after addition of crop residues to the soil, are urgently needed and require an improved methodology for emission accounting. This work needs to be underpinned by research to (1) develop and validate N2 O emission factors for mature and immature crop residues, (2) assess emissions from belowground residues of terminated crops, (3) improve activity data on management of different residue types, in particular immature residues, and (4) evaluate long-term effects of residue addition on N2 O emissions.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Decline in climate resilience of European wheat.

Food security relies on the resilience of staple food crops to climatic variability and extremes, but the climate resilience of European wheat is unknown. A diversity of responses to disturbance is considered a key determinant of resilience. The capacity of a sole crop genotype to perform well under climatic variability is limited; therefore, a set of cultivars with diverse responses to weather conditions critical to crop yield is required. Here, we show a decline in the response diversity of wheat in farmers' fields in most European countries after 2002-2009 based on 101,000 cultivar yield observations. Similar responses to weather were identified in cultivar trials among central European countries and southern European countries. A response diversity hotspot appeared in the trials in Slovakia, while response diversity "deserts" were identified in Czechia and Germany and for durum wheat in southern Europe. Positive responses to abundant precipitation were lacking. This assessment suggests that current breeding programs and cultivar selection practices do not sufficiently prepare for climatic uncertainty and variability. Consequently, the demand for climate resilience of staple food crops such as wheat must be better articulated. Assessments and communication of response diversity enable collective learning across supply chains. Increased awareness could foster governance of resilience through research and breeding programs, incentives, and regulation.

The Possibility of Consensus Regarding Climate Change Adaptation Policies in Agriculture and Forestry among Stakeholder Groups in the Czech Republic.

As ongoing research efforts contribute to elucidating the consequences of climate change as well as adaptation and mitigation options, aligning the current research knowledge with stakeholder opinions and perceptions remains critical for adopting effective climate change policies. This paper utilizes an interactive survey to (1) address the aforementioned gap in studies involving three groups of stakeholders and opinion makers and (2) perform a comparative primary study of the climate change assumptions, risk perceptions, policy preferences, observations, and knowledge of Czech farmers, governmental policy-makers and researchers. This study shows that the stakeholder groups agree that the climate is clearly changing, attribute this change mostly to man-made causes and expect the negative effects to either prevail or be unevenly geographically distributed. The large majority of all three groups consider unmitigated climate change a major threat even by 2050 and agree that preparing in advance is the best sectoral strategy. Importantly, while investment in adaptation measures is considered the most efficient tool for accelerating the implementation of adaptation measures, the CAP and EU rules (as valid in 2016) are believed to hinder such measures. The results of this study have ramifications for the wider region of Central Europe.

Soil carbon loss with warming: New evidence from carbon-degrading enzymes.

Climate warming affects soil carbon (C) dynamics, with possible serious consequences for soil C stocks and atmospheric CO2 concentrations. However, the mechanisms underlying changes in soil C storage are not well understood, hampering long-term predictions of climate C-feedbacks. The activity of the extracellular enzymes ligninase and cellulase can be used to track changes in the predominant C sources of soil microbes and can thus provide mechanistic insights into soil C loss pathways. Here we show, using meta-analysis, that reductions in soil C stocks with warming are associated with increased ratios of ligninase to cellulase activity. Furthermore, whereas long-term (≥5 years) warming reduced the soil recalcitrant C pool by 14%, short-term warming had no significant effect. Together, these results suggest that warming stimulates microbial utilization of recalcitrant C pools, possibly exacerbating long-term climate-C feedbacks.

Long-term nitrogen loading alleviates phosphorus limitation in terrestrial ecosystems.

Increased human-derived nitrogen (N) deposition to terrestrial ecosystems has resulted in widespread phosphorus (P) limitation of net primary productivity. However, it remains unclear if and how N-induced P limitation varies over time. Soil extracellular phosphatases catalyze the hydrolysis of P from soil organic matter, an important adaptive mechanism for ecosystems to cope with N-induced P limitation. Here we show, using a meta-analysis of 140 studies and 668 observations worldwide, that N stimulation of soil phosphatase activity diminishes over time. Whereas short-term N loading (≤5 years) significantly increased soil phosphatase activity by 28%, long-term N loading had no significant effect. Nitrogen loading did not affect soil available P and total P content in either short- or long-term studies. Together, these results suggest that N-induced P limitation in ecosystems is alleviated in the long-term through the initial stimulation of soil phosphatase activity, thereby securing P supply to support plant growth. Our results suggest that increases in terrestrial carbon uptake due to ongoing anthropogenic N loading may be greater than previously thought.

How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal.

There is growing international interest in better managing soils to increase soil organic carbon (SOC) content to contribute to climate change mitigation, to enhance resilience to climate change and to underpin food security, through initiatives such as international '4p1000' initiative and the FAO's Global assessment of SOC sequestration potential (GSOCseq) programme. Since SOC content of soils cannot be easily measured, a key barrier to implementing programmes to increase SOC at large scale, is the need for credible and reliable measurement/monitoring, reporting and verification (MRV) platforms, both for national reporting and for emissions trading. Without such platforms, investments could be considered risky. In this paper, we review methods and challenges of measuring SOC change directly in soils, before examining some recent novel developments that show promise for quantifying SOC. We describe how repeat soil surveys are used to estimate changes in SOC over time, and how long-term experiments and space-for-time substitution sites can serve as sources of knowledge and can be used to test models, and as potential benchmark sites in global frameworks to estimate SOC change. We briefly consider models that can be used to simulate and project change in SOC and examine the MRV platforms for SOC change already in use in various countries/regions. In the final section, we bring together the various components described in this review, to describe a new vision for a global framework for MRV of SOC change, to support national and international initiatives seeking to effect change in the way we manage our soils.

Joint control of terrestrial gross primary productivity by plant phenology and physiology.

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r(2) = 0.90) and GPP recovery after a fire disturbance in South Dakota (r(2) = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

Reducing uncertainty of estimated nitrogen load reductions to aquatic systems through spatially targeting agricultural mitigation measures using groundwater nitrogen reduction.

The need to further abate agricultural nitrate (N)-loadings to coastal waters in Denmark represents the main driver for development of a new spatially targeted regulation that focus on locating N-mitigation measures in agricultural areas with high N-load. This targeting makes use of the spatial variation across the landscape in natural N-reduction (denitrification) of leached nitrate in the groundwater and surface water systems. A critical basis for including spatial targeting in regulation of N-load in Denmark is the uncertainty associated with the effect of spatially targeting measures, since the effect will be critically affected by uncertainty in the quantification of the spatial variation in N-reduction. In this study, we used 30 equally plausible N-reduction maps, at 100 m grid and sub-catchment resolutions, for the 85-km2 groundwater dominated Norsminde catchment in Denmark, applying set-aside as the measure on high N-load areas to reach a N-load reduction target of 20%. The uncertainty on these N-reduction maps resulted in uncertainty on the estimated N-load and on the required set-aside area. We tested several methods for spatially targeting set-aside that took into account the uncertainty on set-aside area and developed methods to reduce uncertainty on the estimated N-load reductions. These methods includes application of set-aside based on each individual N-reduction map compared to a mean N-reduction map, using spatial frequency of high N-load and using spatial frequency of low N-reduction. The results revealed that increasing the ensemble size for averaging the N-reduction maps would decrease the uncertainty on the estimated set-aside area with a stable effect when using an ensemble of 15 or more maps. The spatial resolution of the groundwater N-reduction map is essential for the effectiveness of set-aside, but uncertainty of the finer spatial resolution of N-reduction is greater compared to sub-catchment scale, and application of a spatially targeted strategy with uncertain N-reduction maps will result in incorrect set-aside area and uncertain estimations of N-load reductions. To reduce the uncertainty on estimated N-load reductions, this study finds the method of set-aside application based on spatial frequency of high N-load to be more effective than other methods tested.

Spatially differentiated strategies for reducing nitrate loads from agriculture in two Danish catchments.

Nutrient loss from agriculture is the largest source of diffuse water pollution in Denmark. To reduce nutrient loads a number of solutions have been implemented, but this has been insufficient to achieve the environmental objectives without unacceptable repercussions for agricultural production. This has substantiated the need to develop a new approach to achieve nitrogen (N) load reduction to the aquatic environments with lower costs to farmers. The new approach imply targeting N leaching mitigation to those parts of the landscape which contribute most to the N-loadings. This would involve either reducing the source loading or enhancing the natural reduction (denitrification) of N after it is leached from the root zone of agricultural crops. In this study, a new method of spatially differentiated analysis for two Danish catchments (Odense and Norsminde) was conducted that reach across the individual farms to achieve selected N-load reduction targets. It includes application of cover crops within current crop rotations, set-a-side application on high N-load areas, and changes in agricultural management based on maps of N-reduction available for two different spatial scales, considering soil type and farm boundaries as spatial constraints. In summary, the results revealed that considering spatial constraints for changes in agricultural management will affect the effectiveness of N-load reduction, and the highest N-load reduction was achieved where less constraints were considered. The results also showed that the range of variation in land use, soil types, and N-reduction potential influence the reduction of N-loadings that can originate from critical source areas. The greater the spatial variation the greater the potential for N load reduction through targeting of measures. Therefore, the effectiveness of spatially differentiated measures in term of set-a-side area in Odense catchment were relatively greater compared to Norsminde catchment. The results also showed that using a fine spatial N-reduction map provides greater potential for N load reductions compared to using sub-catchment scale N-reduction maps.

A potato model intercomparison across varying climates and productivity levels.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Effects of climate and nutrient load on the water quality of shallow lakes assessed through ensemble runs by PCLake.

Complex ecological models are used to predict the consequences of anticipated future changes in climate and nutrient loading for lake water quality. These models may, however, suffer from nonuniqueness in that various sets of model parameter values may yield equally satisfactory representations of the system being modeled, but when applied in future scenarios these sets of values may divert considerably in their simulated outcomes. Compilation of an ensemble of model runs allows us to account for simulation variability arising from model parameter estimates. Thus, we propose a new approach for aquatic ecological models creating a more robust prediction of future water quality. We used our ensemble approach in an application of the widely used PCLake model for Danish shallow Lake Arreskov, which during the past two decades has demonstrated frequent shifts between turbid and clear water states. Despite marked variability, the span of our ensemble runs encapsulated 70­90% of the observed variation in lake water quality. The model exercise demonstrates that future warming and increased nutrient loading lead to lower probability of a clear water, vegetation-rich state and greater likelihood of cyanobacteria dominance. In a 6.0°C warming scenario, for instance, the current nutrient loading of nitrogen and phosphorus must be reduced by about 75% to maintain the present ecological state of Lake Arreskov, but even in a near-future 2.0°C warming scenario, a higher probability of a turbid, cyanobacteria-dominated state is predicted. As managers may wish to determine the probability of achieving a certain ecological state, our proposed ensemble approach facilitates new ways of communicating future stressor impacts.

Effects of exogenous organic/inorganic nitrogen addition on carbon pool distribution and transformation in grassland soil.

AIMS: Increases in nitrogen (N) deposition may significantly affect the organic carbon (OC) cycle in soil. The inconsistent findings of the influence of added N on soil OC pools highlight the need of quantifying responses of the OC pool distribution to N addition. Moreover, the influence of N addition with a mixture of organic and inorganic N on OC pool distribution and stabilization in grassland soil remains unclear. METHODS: We carried out a five-year field experiment with adding N to examine the effects of different types of N addition on soil OC pool distribution and transformation in a meadow steppe in Inner Mongolia. We applied N in the ratios of inorganic N (IN) and organic N (ON) at 10:0 (N1), 7:3 (N2), 5:5 (N3), 3:7 (N4), 0:10 (N5), and 0:0 (CK), respectively. We measured OC content in bulk soil, particulate organic matter (POM), and mineral-associated organic matter (MAOM) fractions. Additionally, a short-term soil incubation was conducted to assess potential OC mineralization. RESULTS: Our study showed no significant effect on soil organic carbon content of different ratios of IN/ON addition. N addition reduced microbial biomass C/N ratio, the fraction of mineral-associated organic matter, cumulative CO2 emission, and microbial metabolic quotient. Compared with ON addition alone, IN addition alone showed a stronger effect on the C in different soil fractions and soil OC mineralization. The particulate organic matter (POM) fraction was more sensitive to N addition than the mineral-associated organic matter (MAOM) fraction. CONCLUSIONS: Our results suggest that the contribution of N in organic and inorganic forms affecting OC pool distribution with different turnover rates should be considered when assessing the effects of N addition types on soil OC processes in grassland.

Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Watershed land use effects on lake water quality in Denmark.

Mitigating nutrient losses from anthropogenic nonpoint sources is today of particular importance for improving the water quality of numerous freshwater lakes worldwide. Several empirical relationships between land use and in-lake water quality variables have been developed, but they are often weak, which can in part be attributed to lack of detailed information about land use activities or point sources. We examined a comprehensive data set comprising land use data, point-source information, and in-lake water quality for 414 Danish lakes. By excluding point-source-influenced lakes (n = 210), the strength in relationship (R2) between in-lake total nitrogen (TN) and total phosphorus (TP) concentrations and the proportion of agricultural land use in the watershed increased markedly, from 10-12% to 39-42% for deep lakes and from 10-12% to 21-23% for shallow lakes, with the highest increase for TN. Relationships between TP and agricultural land use were even stronger for lakes with rivers in their watershed (55%) compared to lakes without (28%), indicating that rivers mediate a stronger linkage between landscape activity and lake water quality by providing a "delivery" mechanism for excess nutrients in the watershed. When examining the effect of different near-freshwater land zones in contrast to the entire watershed, relationships generally improved with size of zone (25, 50, 100, 200, and 400 m from the edge of lake and streams) but were by far strongest using the entire watershed. The proportion of agricultural land use in the entire watershed was best in explaining lake water quality, both relative to estimated nutrient surplus at agricultural field level and near-lake land use, which somewhat contrasts typical strategies of management policies that mainly target agricultural nutrient applications and implementation of near-water buffer zones. This study suggests that transport mechanisms within the whole catchment are important for the nutrient export to lakes. Hence, the whole watershed should be considered when managing nutrient loadings to lakes, and future policies should ideally target measures that reduce the proportion of cultivated land in the watershed to successfully improve lake water quality.

Potential for the adoption of measures to reduce N2O emissions from crop residues in Denmark.

Crop residues represent a climate change dilemma: they can promote carbon (C) sequestration, but they may also stimulate emissions of the powerful greenhouse gas nitrous oxide (N2O). Although there are crop residue management measures to reduce N2O emissions, N2O reductions achieved at national scale with these measures have been seldom studied, and how farmers' willingness to accept the measures constrains their potential remains largely unknown. Using Denmark as a case study, we combined a survey (completed by 592 farmers) and national data to assess the practical potential and obstacles for the successful implementation of management strategies to reduce N2O emissions from crop residues. Crop residue removal (particularly from vegetables and cover crops) and nitrification inhibitors were identified as effective in reducing N2O emissions from a biophysical perspective. If all aboveground crop residues from vegetables and cover crops were removed, N2O emissions could be reduced by 0.181 Gg N2ON, corresponding to 11% of the total N2O emissions from crop residues nationally. However, a low percentage of farmers would be willing to remove crop residues from the field, especially for vegetables and cover crops (25%), in connection to the possible short- to medium-term reduction in C sequestration. Similarly, use of nitrification inhibitors would reduce emissions by 0.247 Gg N2ON, corresponding to 15% of the total residue N2O emissions, and only 37% of all farmers would accept their use. Our results highlight that farmer' preferences for the adoption of measures can constrain the use of the few available effective mitigation options. Better knowledge dissemination and advisory services are crucial to address this challenge; farmers may be motivated to remove aboveground crop residues by highlighting the proportionally more important contribution of belowground residues to C sequestration, and that aboveground residues may have commercial value (biorefining, biogas, biofuel), although these options need further development.

Interactive effects of straw management, tillage, and a cover crop on nitrous oxide emissions and nitrate leaching from a sandy loam soil.

Minimum tillage, residue recycling and the use of cover crops are key elements of conservation agriculture that play important roles in soil carbon (C) and nitrogen (N) dynamics. This study determined the long-term effects of tillage practice (conventional ploughing vs. direct seeding), straw management (retained vs. removed), and the presence of a cover crop (CC; fodder radish in this study) on nitrous oxide (N2O) emissions, nitrate (NO3-) leaching, and soil mineral N dynamics between October 2019 and June 2020. In the factorial experiment with eight treatment combinations, cumulative N2O emissions ranged from 0.04 to 0.8 kg N ha-1, whereas NO3- leaching varied between 4 and 28 kg N ha-1. The study did not find effects of straw retention on NO3- leaching or N2O emissions. No-till reduced N2O emissions by on average 46% compared to ploughing. Fodder radish reduced NO3- leaching by 80-84%, and there was little N2O emission in the presence of the cover crop; however, after termination in spring there was a flush of N2O, cumulative N2O-N averaged 0.1 and 0.5 kg N ha-1 without and with a cover crop. With information about long-term soil C retention from straw and fodder radish, an overall greenhouse (GHG) balance was calculated for each system. Without straw retention after harvest, there was always a positive net GHG emission, and the indirect N2O emission from NO3- leaching was similar to, or greater than direct N2O emissions. However, in the presence of fodder radish, the direct N2O emissions after termination were much more important than indirect emissions, and negated the C input from fodder radish. Direct seeding, straw retention and the use of a cover crop showed positive effects on N retention and/or GHG balance and could substantially improve the carbon footprint of agroecosystems on sandy soil in a wet temperate climate.