Nitrous oxide emissions from multiple combined applications of fertiliser and cattle slurry to grassland

Fertiliser and manure application are important sources of nitrous oxide (N2O) emissions from agricultural soils. The current default IPCC emission factor of 1.0% is independent of the type of fertiliser and manure, and application time, method and rate. However, in the IPCC Tiered system it is possible to use more specific emission factors that better reflect the actual fertiliser and manure management in a given country or region. The first and primary aim of this study was to determine whether the combination of cattle slurry injection with fertiliser application, which is common practice in intensively managed grasslands in the Netherlands and neighbouring countries, warrants an adjusted emission factor. A second aim was to evaluate whether alternative emission factors, based on N uptake and N surplus, respectively, give more insight in the N2O emission rates of various fertilisation strategies. In a 2-year field experiment on sandy soil in the Netherlands we measured the annual N2O emission from grasslands receiving repeated simultaneous applications of fertiliser and cattle slurry. The N2O fluxes and N uptake by grass were measured from plots receiving calcium ammonium nitrate (CAN) at four application rates, either with or without additional application of liquid cattle slurry, applied through shallow soil injection. The average emission factor for fertiliser-only treatments was 0.15%. The annual N2O emissions were similar for treatments receiving only fertiliser or only cattle slurry. In the first experimental year, application of cattle slurry increased the emission factor for fertiliser to 0.35%, but the second year showed no effect of cattle slurry on the emission from fertiliser. With regard to the first objective, we conclude that these results do not conclusively justify an adjusted emission factor for combined application of fertiliser and cattle slurry. To minimise risks however, it is sensible to avoid simultaneous application of fertiliser and cattle slurry. The N2O emission factor expressed as percentage of kg N uptake by grass was consistently higher after combined application of fertiliser and cattle slurry (0.29%), compared to fertiliser-only (0.17%). With regard to the second objective we conclude that an emission factor based on N uptake expresses the relatively inefficient N supply of cattle slurry to crop growth better than the traditional emission factor based on N application

A novel method to determine buffer strip effectiveness on deep soils

Unfertilized buffer strips (BS) generally improve surface water quality. High buffer strip effectiveness (BSE) has been reported for sloping shallow aquifers, but experimental data for plain landscapes with deeply permeable soils is lacking. We tested a novel method to determine BSE on a 20-m-deep, permeable sandy soil. Discharge from soil to ditch was temporarily collected in an in-stream reservoir to measure its quantity and quality, both for a BS and a reference (REF) treatment. Treatments were replicated once for the first, and three times for the next three leaching seasons. No significant BSE was obtained for nitrogen and phosphorus species in the reservoirs. Additionally, water samples were taken from the upper groundwater below the treatments. The effect of BS for nitrate was much bigger in upper groundwater than in the reservoirs that also collected groundwater from greater depths that were not influenced by the treatments. We conclude that measuring changes in upper groundwater to assess BSE is only valid under specific hydrogeological conditions. We propose an alternative experimental set-up for future research, including extra measurements before installing the BS and REF treatments to deal with spatial and temporal variability. The use of such data as covariates will increase the power of statistical tests by decreasing between-reservoir variability

Emissions of N2O from fertilized and grazed grassland on organic soil in relation to groundwater level

Intensively managed grasslands on organic soils are a major source of nitrous oxide (N2O) emissions. The Intergovernmental Panel on Climate Change (IPCC) therefore has set the default emission factor at 8 kg N–N2O ha-1 year-1 for cultivation and management of organic soils. Also, the Dutch national reporting methodology for greenhouse gases uses a relatively high calculated emission factor of 4.7 kg N–N2O ha-1 year-1. In addition to cultivation, the IPCC methodology and the Dutch national methodology account for N2O emissions from N inputs through fertilizer applications and animal urine and faeces deposition to estimate annual N2O emissions from cultivated and managed organic soils. However, neither approach accounts for other soil parameters that might control N2O emissions such as groundwater level. In this paper we report on the relations between N2O emissions, N inputs and groundwater level dynamics for a fertilized and grazed grassland on drained peat soil. We measured N2O emissions from fields with different target groundwater levels of 40 cm (‘wet’) and 55 cm (‘dry’) below soil surface in the years 1992, 1993, 2002, 2006 and 2007. Average emissions equalled 29.5 kg N2O–N ha-1 year-1 and 11.6 kg N–N2O ha-1 year-1 for the dry and wet conditions, respectively. Especially under dry conditions, measured N2O emissions exceeded current official estimates using the IPCC methodology and the Dutch national reporting methodology. The N2O–N emissions equalled 8.2 and 3.2% of the total N inputs through fertilizers, manure and cattle droppings for the dry and wet field, respectively and were strongly related to average groundwater level (R 2 = 0.74). We argue that this relation should be explored for other sites and could be used to derive accurate emission data for fertilized and grazed grasslands on organic soil

Earthworms increase crop yield. But how? A meta-analysis.

ISEE 10

Synthesizing the evidence of nitrous oxide mitigation practices in agroecosystems

Nitrous oxide (N$_2$O) emissions from agricultural soils are the main source of atmospheric N$_2$O, a potent greenhouse gas and key ozone-depleting substance. Several agricultural practices with potential to mitigate N$_2$O emissions have been tested worldwide. However, to guide policymaking for reducing N$_2$O emissions from agricultural soils, it is necessary to better understand the overall performance and variability of mitigation practices and identify those requiring further investigation. We performed a systematic review and a second-order meta-analysis to assess the abatement efficiency of N$_2$O mitigation practices from agricultural soils. We used 27 meta-analyses including 41 effect sizes based on 1119 primary studies. Technology-driven solutions (e.g. enhanced-efficiency fertilizers, drip irrigation, and biochar) and optimization of fertilizer rate have considerable mitigation potential. Agroecological mitigation practices (e.g. organic fertilizer and reduced tillage), while potentially contributing to soil quality and carbon storage, may enhance N$_2$O emissions and only lead to reductions under certain pedoclimatic and farming conditions. Other mitigation practices (e.g. lime amendment or crop residue removal) led to marginal N$_2$O decreases. Despite the variable mitigation potential, evidencing the context-dependency of N$_2$O reductions and tradeoffs, several mitigation practices may maintain or increase crop production, representing relevant alternatives for policymaking to reduce greenhouse gas emissions and safeguard food security

Constrained optimisation of spatial sampling : a geostatistical approach

AimsThis thesis aims at the development of optimal sampling strategies for geostatistical studies. Special emphasis is on the optimal use of ancillary data, such as co-related imagery, preliminary observations and historic knowledge. Although the object of all studies is the soil, the developed methodology can be used in any scientific field dealing with geostatistics.In summary, the objectives of this study were:Formulation of a range of optimisation criteria that honour a wide variety of aims in soil-related surveys.Development of an optimisation algorithm for spatial sampling that is able to handle these different optimisation criteria.Incorporation of ancillary data such as co-related imagery, historic knowledge and expert knowledge in the sampling strategy.Comparison of the performances of the developed optimisation algorithms with established sampling strategies.Application of developed optimisation techniques in practical soil sampling studies.Outline of major toolsChapter 2 shows how a phased sampling procedure can optimise environmental risk assessment. Using indicator kriging, probability maps of exceeding environmental threshold levels are used to direct subsequent sampling. The method is applied in a lead-pollution study in the city of Schoonhoven, The Netherlands. It is tested using stochastic simulations, and results are compared to conventional sampling schemes in terms of type-I and type-II errors. The phased sampling schemes have much lower type-I errors than the conventional sampling schemes with comparable type-II errors. They predict almost 70% of the area correctly (polluted or not-polluted), as compared to 55% by conventional schemes.Chapter 3 introduces the spatial simulated annealing (SSA) algorithm as a general, flexible optimisation method for spatial sampling. Sampling schemes are optimised at the point level, taking into account sampling constraints and preliminary observations. Different optimisation criteria can be handled. SSA is demonstrated using two optimisation criteria from the literature. The first (the MMSD criterion) aims at even spreading of points over the area. The second (WM criterion) optimises the realised point pair distribution for variogram estimation. For several examples it is shown that SSA is superior to conventional sampling strategies. Improvements up to 30% occur for the first criterion, while an almost complete solution is found for the second criterion. SSA is flexible in adding extra criteria.Optimising sampling for spatial interpolationChapter 4 introduces the MEAN_OK algorithm in SSA, which aims at minimisation of the mean ordinary kriging variance over the research area. It is applied on texture and phosphate content on a river terrace in Thailand. First, sampling is conducted for estimation of the variogram. The variograms thus obtained are used to optimise additional sampling for minimal kriging variance using SSA. This reduces kriging variance of sand percentage from 28.2 to 23.7 (%) 2. The variograms are used subsequently in a geomorphologically similar area. Optimised sampling schemes for anisotropic variables differ considerably from isotropic ones. Size of kriging neighbourhood has a small but distinct effect on the schemes. The schemes are especially efficient in reducing high kriging variances near boundaries of the area.Chapter 5 further explores the possibilities of minimising kriging variance using SSA. Next to the MEAN_OK criterion, the MAX_OK criterion is introduced, which minimises maximum kriging variance. Both criteria are compared to a regular grid. Using SSA, the mean kriging variance reduces from 40.64 [unit] 2to 39.99 [unit] 2. The maximum kriging variance reduces from 68.83 [unit] 2to 53.36 [unit] 2. An additional sampling scheme of 10 observations is optimised for an irregularly scattered data set of 100 observations. This reduces the mean kriging variance from 21.62 [unit] 2to 15.83 [unit] 2, and maximum kriging variance from 70.22 [unit] 2to 34.60 [unit] 2. The influence of variogram parameters on the optimised sampling schemes is investigated. A Gaussian variogram produces a very different sampling scheme than an exponential variogram with similar nugget, sill and (effective) range. A very short range results in random sampling schemes, with observations separated by distances larger than twice the range. For a spherical variogram, magnitude of the relative nugget effect does not effect the sampling schemes, except for high values.Chapter 6 introduces the WMSD criterion into SSA, which optimises sampling using a spatial weight function. This allows distinguishing between different areas of priority. A multivariate contamination study in the Rotterdam harbour with five contaminants at two depths shows two subsequent sampling stages with two spatial weight functions. The first stage combines earlier observations and historic knowledge, with emphasis on areas with high priority. The resulting scheme shows a contamination at 17.4% of the samples, with 1.5% heavily contaminated. The second stage uses probability maps of exceeding intermediate threshold values to guide additional sampling to possible hot spots. This yields 26.7% contaminated samples, with 16.7% heavily contaminated. These include new locations that were not detected during the first stage. The WMSD criterion can be used as a valuable tool in decision making processes.Optimising sampling for model estimationChapter 7 focuses on the use of ancillary data to optimise sampling for precision farming research. Using a cheap, low-tech scoring technique yield maps were predicted for millet in an on-farm study in Niger. Yield varied from 0 to 2500 kg ha -1. Subsequently, SSA was used to optimise three different sampling schemes. Scheme 1 optimised coverage of the whole area. Scheme 2 covered the whole yield range, and scheme 3 covered the low producing areas. Using correlation coefficients, scheme 2 found significant correlations between 5 variables and yield. Scheme 1 found only one significant correlation. Using multivariate regression of yield on soil variables, scheme 2 explained 70% of the yield variation. For scheme 1 this was only 37%. Differences between scheme 3 and scheme 1 proved to be significant for distance to shrubs, micro-relief, pH-H2O and CEC. From this study we concluded that shrubs are the main factor influencing yield by catching eroded particles and improving soil fertility. In general, we concluded that the sampling strategy of scheme 2 should be recommended for establishing yield/soil relations. Variograms of micro-relief and yield suggested that spatial correlation is largely confined to distances of 3 to 5 m.Chapter 8 evaluates a number of sampling strategies for variogram estimation. In the first part, a regular grid is compared to a sampling scheme that optimises the point pair distribution for variogram estimation. This yields unbiased experimental variograms. However, the fluctuation of the experimental variograms is much lower with a regular grid. We concluded from this that the point pair distribution alone is not a useful optimisation criterion for variogram estimation. In the second part, additional observations selected for optimal point pair distribution are compared with randomly drawn additional observations. The random observations result in much higher standard deviations at shorter distances. We concluded from this that for additional short distance observations the point pair distribution is a very useful optimisation criterion. The third part focusses on optimal variogram use. A sampling grid of 81observations is completed, after preliminary estimation of the variogram, with 19 additional observations for minimal kriging variance.The scheme is compared to a regular grid of 100 observations. For an exponential field without nugget effect, the use of the phased sampling scheme reduces the mean squared kriging error from 0.39 [unit] 2to 0.31 [unit] 2, and the maximum squared kriging error from 6.05 [unit] 2to 4.24 [unit] 2. For a spherical field with a nugget effect of 33%, mean squared kriging error does not change and maximum squared kriging error decreases from 15.98 [unit] 2to 11.52 [unit] 2. We concluded that minimisation of the squared kriging error is often more relevant than accurate estimation of the variogram. Taking samples just outside the area improved the quality of the prediction in terms of both kriging variance and squared kriging error.</p

Photosynthetic limits on carbon sequestration in croplands

This is the final version. Available on open access from Elsevier via the DOI in this recordHow much C can be stored in agricultural soils worldwide to mitigate rising carbon dioxide (CO2) concentrations, and at what cost? This question, because of its critical relevance to climate policy, has been a focus of soil science for decades. The amount of additional soil organic C (SOC) that could be stored has been estimated in various ways, most of which have taken the soil as the starting point: projecting how much of the SOC previously lost can be restored, for example, or calculating the cumulative effect of multiple soil management strategies. Here, we take a different approach, recognizing that photosynthesis, the source of C input to soil, represents the most fundamental constraint to C sequestration. We follow a simple “Fermi approach” to derive a rough but robust estimate by reducing our problem to a series of approximate relations that can be parameterized using data from the literature. We distinguish two forms of soil C: ‘ephemeral C’, denoting recently-applied plant-derived C that is quickly decayed to CO2, and ‘lingering C,’ which remains in the soil long enough to serve as a lasting repository for C derived from atmospheric CO2. First, we estimate global net C inputs into lingering SOC in croplands from net primary production, biomass removal by humans and short-term decomposition. Next, we estimate net additional C storage in cropland soils globally from the estimated C inputs, accounting also for decomposition of lingering SOC already present. Our results suggest a maximum C input rate into the lingering SOC pool of 0.44 Pg C yr−1, and a maximum net sequestration rate of 0.14 Pg C yr−1 – significantly less than most previous estimates, even allowing for acknowledged uncertainties. More importantly, we argue for a re-orientation in emphasis from soil processes towards a wider ecosystem perspective, starting with photosynthesis.Biotechnology and Biological Sciences Research Council (BBSRC

Earthworms increase plant production: a meta- analysis

To meet the challenge of feeding a growing world population with minimal environmental impact, we need comprehensive and quantitative knowledge of ecological factors affecting crop production. Earthworms are among the most important soil dwelling invertebrates. Their activity affects both biotic and abiotic soil properties, in turn affecting plant growth. Yet, studies on the effect of earthworm presence on crop yields have not been quantitatively synthesized. Here we show, using meta-analysis, that on average earthworm presence in agroecosystems leads to a 25% increase in crop yield and a 23% increase in aboveground biomass. The magnitude of these effects depends on presence of crop residue, earthworm density and type and rate of fertilization. The positive effects of earthworms become larger when more residue is returned to the soil, but disappear when soil nitrogen availability is high. This suggests that earthworms stimulate plant growth predominantly through releasing nitrogen locked away in residue and soil organic matter. Our results therefore imply that earthworms are of crucial importance to decrease the yield gap of farmers who can't -or won't- use nitrogen fertilizer