Tree contributions to climate change adaptation through reduced cattle heat stress and benefits to milk and beef production.

Cattle heat stress causes billions of dollars' worth of losses to meat and milk production globally, and is projected to become more severe in the future due to climate change. Tree establishment in pastoral livestock systems holds potential to reduce cattle heat stress and thus provide nature-based adaptation. We developed a general model for the impact of trees on cattle heat stress, which can project milk and meat production under future climate scenarios at varying spatial scales. The model incorporates the key microclimate mechanisms influenced by trees, including shade, air temperature, humidity, and wind speed. We conducted sensitivity analyses to demonstrate the relative influence of different mechanisms through which trees can impact cattle heat stress, and how tree impacts are influenced by climatic context globally. Trees hold the greatest potential to reduce cattle heat stress in higher latitudes and altitudes, with minor benefits in the lowland tropics. We projected the future contributions of current trees in mitigating climate change impacts on the dairy and beef herds of Aotearoa-New Zealand (A-NZ) in 2070-2080. Trees were simulated to contribute to A-NZ milk yields by over 491 million liters (lower CI = 112 million liters, upper CI = 850 million liters), and meat yields by over 8316 tonnes (lower CI = 2431 tonnes, upper CI = 13,668 tonnes) annually. The total economic contribution of existing trees in mitigating future cattle heat stress was valued at $US 244 million (lower CI = $US 58 million, upper CI = $US 419 million). Our findings demonstrate the importance of existing trees in pastoral landscapes and suggest that strategic tree establishment can be a valuable adaptation option for reducing cattle heat stress under climate change. Tree establishment in the next few years is critical to provide adaptation capacity and economic benefit in future decades.

Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting.

In New Zealand, nitrous oxide emissions from grazed hill pastures are estimated using different emission factors for urine and dung deposited on different slope classes. Allocation of urine and dung to each slope class needs to consider the distribution of slope classes within a landscape and animal behavior. The Nutrient Transfer (NT) model has recently been incorporated into the New Zealand Agricultural GHG Inventory Model to account for the allocation of excretal nitrogen (N) to each slope class. In this study, the predictive ability of the transfer function within the NT model was explored using urine deposition datasets collected with urine sensor and GPS tracker technology. Data were collected from three paddocks that had areas in low (<12°), medium (12-24°), and high slopes (>24°). The NT model showed a good overall predictive ability for two of the three datasets. However, if the urine emission factors (% of urine N emitted as N2 O-N) were to be further disaggregated to assess emissions from all three slope classes or slope gradients, more precise data would be required to accurately represent the range of landscapes found on farms. We have identified the need for more geospatial data on urine deposition and animal location for farms that are topographically out of the range used to develop the model. These new datasets would provide livestock urine deposition on a more continuous basis across slopes (as opposed to broad ranges), a unique opportunity to improve the performance of the NT model.

The Nutrient Transfer model allocates urine from grazing livestock to different slope classes. The predictive ability of the model was explored using urine sensor and tracker data of grazing livestock. The model showed a good overall predictive ability for two of the three datasets explored. There is a need for more geospatial urine deposition and animal location data on complex land.

The proportion of deposited urine patch intercepted by a delayed inhibitor application.

Nitrification inhibitors can reduce nitrous oxide (N2O) emissions and nitrate leaching losses from agricultural soils. Technologies have been developed to detect and target urine patches for inhibitor application, thereby reducing the total amount of inhibitor used. However, in practice there will be a time delay between the urine deposition and inhibitor application, potentially leading to physical separation of the inhibitor and urine that could reduce the effectiveness of the inhibitor compared to when the inhibitor and urine are well mixed. In this study, 2L of cattle urine was applied on two soil types in New Zealand. Twenty-four hours later the inhibitor dicyandiamide (DCD) was applied. The soil was sampled within 18 h and again after a rainfall event. DCD concentrations were measured in the 0-20 mm, 20-50 mm, and 50-100 mm depth ranges. The movement of the urine in the soil was simulated using the HYDRUS model. Before the rain most of the DCD was within the top 20 mm and intercepted 21-29% of the urine. After the rainfall event the DCD concentration decreased in the 0-20 mm layer and increased in the 20-50 mm layer. 18-55% or 63-79% of the urine was intercepted by DCD at a concentration of >4 ppm using the measured and modelled DCD concentrations, respectively. However, only 0-27% or 0-53% of the urine was intercepted at a DCD concentration >10 ppm.

Improving the accuracy of nitrous oxide emission factors estimated for hotspots within dairy-grazed farms.

Nitrous oxide (N2O) emissions from dairy-grazing pastures can be dominated by large emissions from small areas ('hotspots') frequently used by grazing dairy cattle (i.e., water troughs and gateways). N2O emissions from these hotspots are quantified by investigating whether N2O emissions and emission factors (% of applied N emitted as N2O, EF3) from potential hotspots are different from non-hotspots. To better characterise N2O emissions from hotspots and non-hotspots of farms to understand their contributions to national agricultural greenhouse gas inventory calculations, a series of measurements were conducted during winter and spring on two NZ typical dairy farms with contrasting soil drainage (poorly versus well drained). Before measurements were taken, the soils either received a cow urine application or remained untreated. The results showed that changes in water-filled pore space (WFPS) and mineral N around water troughs and gateways, due to additional stock movements and disproportionate excreta-N deposition during previous grazing events, affected both background and total N2O emissions. But there was little impact on EF3 values (calculated using IPCC guidelines) from deposited urine between hotspot and pasture areas. These results suggest the same EF3 values can be used for both to calculate emissions from urine deposited on grazed pastures. However, these results raise concerns about higher background emission in hotspots subtracted from measured emissions from urine-N deposition in calculating EF3 values and discounting the effects of disproportionate N inputs in intensive agriculture on increased background emissions (legacy effect). This IPCC inventory method does not account for the legacy effect of N loading prior to the measurements which may underestimate the emissions. Thus, an allowance for higher hotspot background emissions could be included in the Inventory to accurately estimate total emissions from agriculture.

The role of soils in the regulation of air quality.

Soils play a key role in meeting the UN Sustainable Development Goals (SDGs). In this study, we review the contribution of soils to the regulation of air quality, which is one of 'Nature's Contributions to People' identified by the Intergovernmental-Policy Platform on Biodiversity and Ecosystem Services (IPBES). This is particularly relevant for SDG3 (health and well-being) and 11 (sustainable cities and well-being) but also impacts other SDGs. Soils can act as both a source and a sink of air pollutants (and their precursors). In addition, soils support plant growth which plays a major role in regulating air quality. The scale of the soil impacts on air quality range from global (e.g. greenhouse gas fluxes, stratospheric ozone depletion) to local (e.g. odours, particulates, pathogen transport). Harmful emissions from soil can be increased or decreased by anthropogenic activity, while climate change is likely to modify future emissions patterns, both directly and in response to human mitigation and adaption actions. Although soils are not the only source of these pollutants, it is worthwhile managing them to reduce erosion and nutrient losses to maintain soil health so we may continue to benefit from the contributions to good quality of life they provide. This article is part of the theme issue 'The role of soils in delivering Nature's Contributions to People'.

Soil-derived Nature's Contributions to People and their contribution to the UN Sustainable Development Goals.

This special issue provides an assessment of the contribution of soils to Nature's Contributions to People (NCP). Here, we combine this assessment and previously published relationships between NCP and delivery on the UN Sustainable Development Goals (SDGs) to infer contributions of soils to the SDGs. We show that in addition to contributing positively to the delivery of all NCP, soils also have a role in underpinning all SDGs. While highlighting the great potential of soils to contribute to sustainable development, it is recognized that poorly managed, degraded or polluted soils may contribute negatively to both NCP and SDGs. The positive contribution, however, cannot be taken for granted, and soils must be managed carefully to keep them healthy and capable of playing this vital role. A priority for soil management must include: (i) for healthy soils in natural ecosystems, protect them from conversion and degradation; (ii) for managed soils, manage in a way to protect and enhance soil biodiversity, health and sustainability and to prevent degradation; and (iii) for degraded soils, restore to full soil health. We have enough knowledge now to move forward with the implementation of best management practices to maintain and improve soil health. This analysis shows that this is not just desirable, it is essential if we are to meet the SDG targets by 2030 and achieve sustainable development more broadly in the decades to come. This article is part of the theme issue 'The role of soils in delivering Nature's Contributions to People'.

Could patterns of animal behaviour cause the observed differences in soil carbon between adjacent irrigated and unirrigated pastures?

Previous soil sampling from grazed pastures in New Zealand compared the changes of soil organic carbon (SOC) in adjacent irrigated and unirrigated portions of the same paddocks. It showed that irrigated portions had lower SOC stocks than unirrigated portions, with an average difference of 7.0 tC ha-1 or 0.6 tC ha-1 yr-1. These findings have formed the basis of an assessment for the net effect of conversion of New Zealand's grazed pastures to irrigation. However, since cattle could move freely between irrigated and unirrigated portions of the studied paddocks, there could have been different grazing intensities and/or excreta transfer between the irrigated and unirrigated portions of the same paddocks. Both these factors could have affected SOC stocks. In this study, we used the process-based model, CenW, to simulate the consequences of this possible carbon transfer via animal excreta and different grazing intensities. We found that the observed increase of 0.6 tC ha-1 yr-1 in SOC stock in the unirrigated portions could result from a transfer of 20% excreta from the irrigated to unirrigated portions (with an area ratio of 6:1) of a paddock and with the unirrigated portions being grazed only lightly with 2.0 tDM ha-1 in foliage biomass residuals remaining after grazing. That means that the observed higher SOC stocks in the unirrigated portions could potentially be attributable to the behaviour of grazing animals. We suggest that a realistic extent of carbon transfer and/or differences in grazing intensities could be sufficient to account for the observed differences in SOC stocks even if irrigation per se caused no differences in carbon stocks. It is therefore inappropriate to ascribe the change of SOC to irrigation effects based on experimental findings where SOC changes can be affected by the behaviour of grazing animals.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Global Research Alliance N2 O chamber methodology guidelines: Statistical considerations, emission factor calculation, and data reporting.

Static chambers are often used for measuring nitrous oxide (N2 O) fluxes from soils, but statistical analysis of chamber data is challenged by the inherently heterogeneous nature of N2 O fluxes. Because N2 O chamber measurements are commonly used to assess N2 O mitigation strategies or to determine country-specific emission factors (EFs) for calculating national greenhouse gas inventories, it is important that statistical analysis of the data is sound and that EFs are robustly estimated. This paper is one of a series of articles that provide guidance on different aspects of N2 O chamber methodologies. Here, we discuss the challenges associated with statistical analysis of heterogeneous data, by summarizing statistical approaches used in recent publications and providing guidance on assessing normality and options for transforming data that follow a non-normal distribution. We also recommend minimum requirements for reporting of experimental and metadata of N2 O studies to ensure that the robustness of the results can be reliably evaluated. This includes detailed information on the experimental site, methodology and measurement procedures, gas analysis, data and statistical analyses, and approaches to generate EFs, as well as results of ancillary measurements. The reliability, robustness, and comparability of soil N2 O emissions data will be improved through (a) application, and reporting, of more rigorous methodological standards by researchers and (b) greater vigilance by reviewers and scientific editors to ensure that all necessary information is reported in scientific publications.

Global Research Alliance N2 O chamber methodology guidelines: Summary of modeling approaches.

Measurements of nitrous oxide (N2 O) emissions from agriculture are essential for understanding the complex soil-crop-climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N2 O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process-based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high-level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N2 O modeling for different purposes and describe three commonly used process-based N2 O models (APSIM, DayCent, and DNDC).

Combining eddy covariance measurements with process-based modelling to enhance understanding of carbon exchange rates of dairy pastures.

Many temperate grasslands are used for dairying, and ongoing research aims to better understand these systems in order to increase animal production and soil organic carbon (SOC) stocks. However, it is difficult to fully understand management effects on SOC because most changes are slow and difficult to distinguish from natural variability, even if changes are important over years to decades. Eddy covariance (EC) measurements can overcome this problem by continuously measuring net carbon exchange from pastures, but net balances are very sensitive to even small systematic measurement errors. Combining EC measurements with detailed process-based modelling can reduce the risks inherent in total reliance on EC measurements. Modelling can also reveal information about the underlying processes that drive observed fluxes. Here, we describe carbon exchange patterns of five paddocks situated at four different locations in New Zealand and France where EC data and detailed physiological modelling were available. The work showed that respiration by grazing animals was often only incompletely captured in EC measurements. This was most problematic when fluxes were based on gap-filling, which could have estimated incorrect fluxes during grazing periods based on observations from periods without grazing. We then aimed to extract plant physiological insights from these studies. We found appreciable carbon uptake rates even at temperatures below 0 °C. After grazing, carbon uptake was reduced for up to 2 weeks. This reduction was larger than expected from reduced leaf area after grazing, but the factors contributing to that difference have not yet been identified. Detailed physiological models can also extrapolate findings to new management regimes, environmental conditions or plant attributes. This overcomes the limitation of experimental studies, which are necessarily restricted to actual site and weather conditions allowing models to make further progress on predicting management effects on SOC.

Crop yield and N2O emission affected by long-term organic manure substitution fertilizer under winter wheat-summer maize cropping system.

Application of organic manure combined with synthetic fertilizer can maintain crop yield and improve soil fertility, but the long-term effects of substituting different proportions of synthetic fertilizers with organic manure on N2O emission remain unclear. In this study, field experiments and DNDC model simulations were used to study the long-term effects of substituting synthetic fertilizers with organic manure on crop yield and N2O emission. The field experiment was conducted at Guanzhong Plain, northern China, under a wheat-maize cropping system. Six treatments were included: no fertilization (CK); synthetic nitrogen (N), phosphorus (P) and potassium (K) fertilizers (NPK); and 25%, 50%, 75% and 100% of the synthetic N substituted by dairy manure (25%M, 50%M, 75%M, and 100%M), respectively. The DNDC model was calibrated using the field data from the NPK treatment from 2014 to 2017 and was validated for the other treatments. The results showed that the DNDC model can successfully simulate the crop yield (e.g. nRMSE < 5%) and annual N2O emission (nRMSE < 20%). In addition, a 30-year simulation found that organic manure substitution treatments could maintain wheat yield well, and the yield variation between different years was small. However, relative to the NPK treatment, the maize yields for the first 6 and 7 years were lower under 50%M and 75%M, and under 100%M maize yields were reduced for the first 15 years. The long-term simulation showed that N2O emission of fertilized treatment had an increasing trend over time, especially the 75%M treatment where the N2O emission was higher than that of NPK treatment after 25 years of fertilization. The annual mean N2O emission under different treatments was, in decreasing order, NPK > 25%M > 50%M > 75%M > 100%M > CK. The yield-scale N2O emission and emission factor were highest for the NPK treatment. Considering crop yield, yield stability and N2O emission, substitution of 25% synthetic fertilizer by organic manure can simultaneously ensure crop productivity and environmental protection under the tested environment.

Modelling the effects of pasture renewal on the carbon balance of grazed pastures.

In New Zealand, pasture renewal is a routine management method for maintaining pasture productivity. However, knowledge of the renewal effects on soil organic carbon (SOC) stocks is still limited. Here we use a process-based model, CenW, to comprehensively assess the effects of pasture renewal on the carbon balance of a temperate pasture in the Waikato region of New Zealand. We investigated the effects of renewal frequency, length of fallow period, renewal timing, and the importance and quantification of age-related reductions in productivity. Our results suggest that SOC change depends on the combined effects of renewal on gross primary productivity (GPP), autotrophic and heterotrophic respiration, carbon removal by grazing and carbon allocation to roots. Pasture renewal reduces grazing removal proportionately more than GPP because newly established plants need to allocate more carbon to re-build their root system following renewal which limits foliage production. That lengthens the time before above-ground biomass has grown sufficiently to be grazed again. New plants have a lower ratio of autotrophic respiration to GPP, however, which partly compensates for the GPP loss during renewal. Our simulations suggested an average SOC loss of 0.16 tC ha-1 yr-1 if pastures were renewed every 25 years, but could gain an average of 0.3 tC ha-1 yr-1 if pastures were renewed every year. For maximizing pasture production, the optimal renewal frequency depends on the rate of pasture deterioration with more rapid deterioration rates favouring more frequent renewal. Additionally, the length of the fallow period, renewal timing, and associated environmental conditions are important factors that can affect SOC temporally, but the importance of those effects diminishes at the annual or longer time scales. A major uncertainty for a full understanding of the renewal effect on SOC lies in the rate of pasture deterioration with time since previous renewal.

Upscaling NZ-DNDC using a regression based meta-model to estimate direct N2O emissions from New Zealand grazed pastures.

The availability of detailed input data frequently limits the application of process-based models at large scale. In this study, we produced simplified meta-models of the simulated nitrous oxide (N2O) emission factors (EF) using NZ-DNDC. Monte Carlo simulations were performed and the results investigated using multiple regression analysis to produce simplified meta-models of EF. These meta-models were then used to estimate direct N2O emissions from grazed pastures in New Zealand. New Zealand EF maps were generated using the meta-models with data from national scale soil maps. Direct emissions of N2O from grazed pasture were calculated by multiplying the EF map with a nitrogen (N) input map. Three meta-models were considered. Model 1 included only the soil organic carbon in the top 30cm (SOC30), Model 2 also included a clay content factor, and Model 3 added the interaction between SOC30 and clay. The median annual national direct N2O emissions from grazed pastures estimated using each model (assuming model errors were purely random) were: 9.6GgN (Model 1), 13.6GgN (Model 2), and 11.9GgN (Model 3). These values corresponded to an average EF of 0.53%, 0.75% and 0.63% respectively, while the corresponding average EF using New Zealand national inventory values was 0.67%. If the model error can be assumed to be independent for each pixel then the 95% confidence interval for the N2O emissions was of the order of ±0.4-0.7%, which is much lower than existing methods. However, spatial correlations in the model errors could invalidate this assumption. Under the extreme assumption that the model error for each pixel was identical the 95% confidence interval was approximately ±100-200%. Therefore further work is needed to assess the degree of spatial correlation in the model errors.

Investigating a method for estimating direct nitrous oxide emissions from grazed pasture soils in New Zealand using NZ-DNDC.

In this study, we developed emission factor (EF) look-up tables for calculating the direct nitrous oxide (N2O) emissions from grazed pasture soils in New Zealand. Look-up tables of long-term average direct emission factors (and their associated uncertainties) were generated using multiple simulations of the NZ-DNDC model over a representative range of major soil, climate and management conditions occurring in New Zealand using 20 years of climate data. These EFs were then combined with national activity data maps to estimate direct N2O emissions from grazed pasture in New Zealand using 2010 activity data. The total direct N2O emissions using look-up tables were 12.7±12.1 Gg N2O-N (equivalent to using a national average EF of 0.70±0.67%). This agreed with the amount calculated using the New Zealand specific EFs (95% confidence interval 7.7-23.1 Gg N2O-N), although the relative uncertainty increased. The high uncertainties in the look-up table EFs were primarily due to the high uncertainty of the soil parameters within the selected soil categories. Uncertainty analyses revealed that the uncertainty in soil parameters contributed much more to the uncertainty in N2O emissions than the inter-annual weather variability. The effect of changes to fertiliser applications was also examined and it was found that for fertiliser application rates of 0-50 kg N/ha for sheep and beef and 60-240 kg N/ha for dairy the modelled EF was within ±10% of the value simulated using annual fertiliser application rates of 15 kg N/ha and 140 kg N/ha respectively.

Quantifying the climate-change consequences of shifting land use between forest and agriculture.

Land-use change between forestry and agriculture can cause large net emissions of carbon dioxide (CO2), and the respective land uses associated with forest and pasture lead to different on-going emission rates of methane (CH4) and nitrous oxide (N2O) and different surface albedo. Here, we quantify the overall net radiative forcing and consequent temperature change from specified land-use changes. These different radiative agents cause radiative forcing of different magnitudes and with different time profiles. Carbon emission can be very high when forests are cleared. Upon reforestation, the former carbon stocks can be regained, but the rate of carbon sequestration is much slower than the rate of carbon loss from deforestation. A production forest may undergo repeated harvest and regrowth cycles, each involving periods of C emission and release. Agricultural land, especially grazed pastures, have much higher N2O emissions than forests because of their generally higher nitrogen status that can be further enhanced through intensification of the nitrogen cycle by animal excreta. Because of its longevity in the atmosphere, N2O concentrations build up nearly linearly over many decades. CH4 emissions can be very high from ruminant animals grazing on pastures. Because of its short atmospheric longevity, the CH4 concentration from a converted pasture accumulates for only a few decades before reaching a new equilibrium when emission of newly produced CH4 is balanced by the oxidation of previously emitted CH4. Albedo changes generally have the opposite radiative forcing from those of the GHGs and partly negate their radiative forcing. Overall and averaged over 100 years, CO2 is typically responsible for 50% of radiative forcing and CH4 and N2O for 25% each. Albedo changes can negate the radiative forcing by the three greenhouse gases by 20-25%.