The evaluation of the current and future impact of climate change on viticulture requires an integrated view on a complex interacting system within the soil-plant-atmospheric continuum under continuous change. Aside of the globally observed increase in temperature in almost all viticulture regions for at least four decades, we observe several clear trends at the regional level in the ratio of precipitation to potential evapotranspiration. Additionally the recently published 6^th assessment report of the IPCC (The physical science basis) shows case-dependent further expected shifts in climate patterns which will have substantial impacts on the way we will conduct viticulture in the decades to come.

Looking beyond climate developments, we observe rising temperatures in the upper soil layers which will have an impact on the distribution of microbial populations, the decay rate of organic matter or the storage capacity for soil organic carbon (SOC). All this influences the emission of greenhouse gases (GHGs) and the viscosity of water in the soil-plant pathway, altering the transport of water. Interactions between micro-organisms in the rhizosphere, the grapevine root system, degradation and fixation processes of SOC are complex and poorly understood but respond to environmental factors (such as increased soil temperatures), the plant material (rootstock for instance), and the cultivation system (for example bio-organic versus conventional, cover crop use versus open tillage). Increasing SOC stocks is discussed as a measure to reduce soil GHG emissions with the potential to improve the balance between GHG emissions and carbon removal from the atmosphere. Yet it is difficult to deduct the impact of climatic changes and cultivation practices on patterns of carbon storage or losses from soils. This paper presents a first attempt to quantify these potential impacts on SOC for a vineyard location using the RothC-model (Coleman and Jenkinson, 2005) in combination with the Geisenheim long-term (> 100-year) soil temperature record and climate predictions by the STAR II-model of the Potsdam Institute of Climate Impact using a medium realization run (Orlowsky et al., 2008).

It is shown that retaining pruning wood and using a full cover crop yielded a SOC increase of 16.2 t C ha^-1 over time. However, CO₂ emissions over the simulated time span were only slightly less than C-storage in the soil. It is concluded that cover crops in vineyards helps to achieve CO₂-neutrality but additional measures are required to make vineyards a significant C-sink.

Keywords: regional climate evolution, soil temperature, water balance, soil organic carbon, greenhouse gas emissions.

The Paris Agreement as a legally binding international treaty on climate change, which was adopted by 196 Parties on 12 December 2015, has at its core the aim to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 degrees Celsius (°C) above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 degrees °C. The European Union (EU) responded to the challenges implicit in such a goal by formulating the so-called “European Green Deal” (EGD) which was first published in December 2019. On 14 July 2021, the European Commission adopted a

series of legislative proposals

setting out how it intends to achieve

climate neutrality in the EU by 2050

. The package proposes to revise several pieces of EU climate legislation, including the EU Emissions Trading System, Effort Sharing Regulation, transport and land use legislation, setting out in real terms the ways in which the Commission intends to reach EU climate targets under the EGD. The main specifications for agriculture are: 1. reduce the use of pesticides by 50 % until 2030; 2. reduce fertilizer use by 20 %; 3. reduce nutrient losses by a minimum of 50 %; 4. increase the percentage of organic production to 25 % and at the same time implement the defined biodiversity strategy (European Commission, 2020).

Directly after the Paris agreement, the French Ministry of Agriculture launched the 4 per 1000 initiative to demonstrate that agriculture, and in particular soils, plays a crucial role where food security and climate change are concerned. Increasing the carbon content of soils by 0.4 % or 4 per 1000 per year, could compensate for the yearly anthropogenic release of CO₂ into the atmosphere (

https://www.4p1000.org

). The initiative was basically a result of preceding publications proposing the sequestration of carbon in soils as a win-win scenario to mitigate climate change (Lal, 2004; Lal, 2010a). The initiative also referred to the potential for GHG emission reductions through wise soil management that increases SOC, tightens the soil nitrogen (N) cycle which could enhance fertility and productivity, increase soil biodiversity, reduce erosion, runoff and water pollution and contribute to buffer crop and pasture systems against the impacts of climate change (Smith, 2012). General estimates assume that between 1,500 and 2,500 gigatons (1 gigaton = 1,000,000,000 tons) of carbon are stored in soils globally, more than in the atmosphere and vegetation on earth combined (Batjes, 1996; Oertel et al., 2016; Paustian et al., 2016). Thus, increasing net soil C storage by even a few percent could represent a substantial C sink potential.

Stabilizing global mean temperature at levels below 2 °C requires near-zero emissions of longer-lived GHGs such as CO₂ and N₂O by mid-century (Hong et al., 2021). Agricultural production and land-use changes are currently estimated to contribute about 14.6 gigatons of CO₂-equivalent (CO₂-eq.) (including GHG emissions such as methane and nitrous oxide recalculated into CO₂ equivalents) or 25 % of total anthropogenic GHG emissions. Global fruit production contributes about 1 % of total (Hong et al., 2021).

The inclusion of soil-centric mitigation projects within GHG offset markets (i.e. verified carbon standard (2022), American carbon registry (1996-2022), EU-COWI carbon farming initiative (2021)) and new initiatives to market “low-carbon products” indicate a growing role for GHG mitigation in agriculture (Kahiluoto et al., 2014). Therefore agriculture in general is putting more attention to soils and cultivation systems to reduce greenhouse gas (GHG) emissions and usage of soils as carbon storage component. Some publications have actually stated that under best practice management equal or even higher sequestration rates than those implicit in the 4 per 1000 initiative may be accomplished (i.e. Minasny et al., 2017; Chenu et al., 2019), whereas others have criticized the non-consideration of priming effects (addition of organic matter may at first increase decay-rates), climate induced changes in soil temperature and the equilibrium point of maximum C-storage in these studies and estimated that the C sequestration potential is much lower as strived for by the 4 per 1000 initiative, very much depends on soil status (degenerated soils have a higher potential, Flessa et al., 2019) and thus will not provide a major offset for greenhouse gas emissions (White, 2016; White et al., 2018; Baveye et al., 2018; Baveye et al., 2020).

In the recent past various funding programs on soils have been launched in different countries which can also be used to support Viticulture, such as the Emissions Reduction Fund in Australia or the Healthy Soils Program in California or the European COWI-EU farming initiative (2021), which has recently provided an outline about methods and sampling frequency for the determination of SOC. Pellerin et al. (2019) have calculated the costs and benefits additional carbon storage would have for different agricultural commodities to provide a baseline for financial carbon compensation. Nevertheless, these programs have also been criticized as being ineffective (White and Davidson, 2020).

Due to the uncertainty about the dimensions of the potential storage capacity in soils, several studies have attempted to quantify potential changes in C for vineyards. Depending on the environmental conditions and the cultivation practices, the estimates for C sequestration vary widely (see review by Longbottom and Petrie, 2015). In most cases C storage in above-ground biomass were found to be substantial (Williams and Smith, 1991; Brunori et al., 2016; Scandellari et al., 2016) with storage capacity below ground being highly variable depending on soil and root respiration rates (Franck et al., 2011; Scandellari et al., 2016) and induced by differences in cultivation practices and the absence or presence of soil amendments which consequently could result in a range from net GHG emissions or C-loss (Novara et al., 2020) to C-gain (Nistor et al., 2018; Novara et al., 2020; Marín-Matrínez et al., 2021). The Minasny et al. (2017) study on the 4 per 1000 initiative identified vineyards and orchards in France as areas with a high SOC sequestration potential. In agreement, Pellerin et al. (2019) in a study on all soils in France also estimated the potential of vineyards for a net CO₂ extraction from the atmosphere using models developed for corn, wheat, perennial plants and permanent pastures (STICS and PaSim) to be significant. In a recent meta-analysis of data on soil amendment practices to increase SOC, Payen et al. (2021) reported a positive effect irrespective of the amendment used but with large variations between amendments (for example prunings retained in the vineyard versus organic amendments such as manure, compost, sludge or biochar). The effects on SOC of the same amendment differed between climate zones and decreased with the duration of the experiment.

Based on the difficulties and uncertainties in estimating SOC and the potential effects of climate change and soil cultivation, the present study had three objectives. (1) Use an established soil carbon and CO₂-emission model and compare the results to measured values of SOC over time. (2) Estimate the effect of already observed long-term average changes in soil temperature on SOC with two different cultivation practices, one using a permanent cover crop and one with a six month autumn-winter cover crop. (3) Use output data from a climate simulation model to predict future changes in SOC if the two cultivation practices are permanently retained.

The vineyard chosen for SOC monitoring is located at Rüdesheim, Germany, 49.98 °North, and 7.91 °East and consists of an array of ancient terraces with soils ranging from loamy sand to loam to shallow stony patches primarily determined by quartzite (Löhnertz et al., 2004;

https://bodenviewer.hessen.de/mapapps/resources/apps/bodenviewer/index.html?lang=de

). The vineyard terraces were not cultivated for many years and re-established natural vegetation had to be removed. The terraces were planted starting in 2006 as part of a student project of Geisenheim University. In total 20 different Riesling clones were planted on 6 different rootstocks on a 1.35 m (between rows) by 1.0 m (between vines) spacing. Mechanisation of vineyard cultivation steps is not possible due to restricted access and the small structured terrace plots. The soil is covered with a permanent natural cover consisting mainly of different grass species with some legumes that slowly established. Depending on precipitation rates and thus cover crop growth, the floor is mowed between 1-3 times during the growing season. In infrequent intervals (not every year) and only when soil conditions permitted, a small inter row strip of about 20 cm was loosened with backhoes.

Every year (2008 to 2021) soil samples were taken with a Pürckhauer soil sampler in April/May randomly within and between rows and analysed for two depth layers (0-30 cm, 30-60 cm) for the main nutrient components and pH. The pH varied over time and depending on the terrace sampled between 6.7 and 7.6. Samples were collected on 5-10 different spots per terrace (depending on the size) and pooled into one sample for analyses. Between 4 and 5 terraces were sampled independently each year. Carbon and nitrogen were analysed in duplicate (300 mg) from the upper 30 cm soil samples according to the Dumas dry combustion method where the samples are burned at about 950 °C under oxygen dosage in an elementary analyser (Vario MAX CNS) being corrected for carbonate carbon following the FAO protocol on the standard operating procedure for soil total carbon (FAO, 2019). Soil bulk density was taken from Hendgen et al. (2020).

Soil temperature measurements started in Geisenheim on the University campus (49.9836 °North, 7.9602 °East) being serviced through the German Weather Service (Deutscher Wetterdienst, DWD) on April 1^st 1919 at four depths (10 cm, 20 cm, 50 cm, 100 cm), three times per day (7 am, 2 pm, 9 pm) with the exception for the 100 cm depth, which was only measured at 2 pm until January 1^st 1997. From then on, it was also measured three times per day. Starting July 1^st 1947, measurements at 5 cm depth were added three times per day. The measurement field was changed three times during history. On April 1^st 1936 it was moved to a vineyard location just outside the campus (49.9856 °North, 7.9563 °East), then again closer to the DWD station on August 1^st 1983 (49.9866 °North, 7.9548 °East) and finally to its current position with an automatized system on December 1^st 2006 (49.9859 °North, 7.9548 °East). Largest distance between sites is less than 300 m. Soil on all sites was described as deep, sandy loam to loamy with a very small stone fraction and a neutral pH (Löhnertz et al., 2004). Since no immediate differences in soil temperature data were noted each time the measurement location was changed, it was assumed that soil temperature values were unaffected. The observation that changes in the long-term soil temperature record occurred outside the near time-vicinity of changes in measurement sites (compare Figure 1) may serve as an additional indicator for the absence of a location effect.

The time series is not continuous. Missing values comprise the periods 21^st March 1945 – 1^st May 1945 for the 10 and 20 cm depths and from 1^st of May 1945 – 17^th January 1946 for all depths except at 50 cm. Periods with missing measurement values thereafter occurred for all depth during 10 days in February 1969, 7 days in February 1977 and on several individual days between 1^st January 1996 and 31^st December 2006 for the measurement depth at 5 cm, 10 cm, 20 cm, and 50 cm. To estimate missing values during this time period, linear extrapolation was used between the values of the neighbouring days.

Temperature measurements were conducted with standard mercury thermometers until December 1997 and with electronic resistance thermometers (Pt100) thereafter. Measurement plots were kept free of vegetation. Temperature data shown are those from the 2 pm measurements.

A weather data station is located on campus and in the past has been serviced by the DWD and the University. The climate in Geisenheim can be categorised as humid temperate. Annual precipitation is 544 mm (1981-2010) (DWD) and is approximately equally distributed throughout the year (maximum in July with 60 mm, minimum in April with 35 mm). Light precipitation events (< 10 mm/day) dominate and contribute 65 % of total precipitation, whereas daily precipitation events larger than 20 mm contribute only 9 %, respectively. Mean potential evapo-transpiration (ETp) between April 1 and September 30 is on average 605 mm but has been observed to increase over the last approximately 40 years (Schultz and Hofmann, 2016).

The model has been developed by Coleman and Jenkinson (2005) based on several earlier versions and original data from the Rothamsted classic experiment (Jenkinson and Rayner, 1977) and is freely available as a Windows version. The model has previously been used in studies on climate change effects on a large array of soils and climate conditions across German croplands (Riggers et al., 2019; Riggers et al., 2021) and also on conversion scenarios from cork oak forest to vineyards with different follow-up management systems (Francaviglia et al., 2012). It has also been included in global C cycling models (i.e. King et al., 1997). The model calculates the turnover of organic carbon and allows for the effects of soil type, temperature, moisture content and degree of plant cover on the turnover process. It consists of five different pools: decomposable and resistant plant material, microbial biomass, humified organic matter and inert carbon. It uses a monthly time step to calculate total organic carbon (t ha^-1) and CO₂-emissions (t ha^-1). The required climate data as input comprise monthly average air temperature (°C) with the argument that soil temperatures are not readily available and soil temperature values follow air temperature values. For the Geisenheim site this has been shown as a valid assumption (Schultz, 2019), although some deviations may occur (see Results and Discussion section). Due to the lack of sufficient data, likely differences in soil temperature for bare soil as compared to temperatures below cover crops (Yang et al., 2021) were ignored and model runs were performed for all scenarios with the same set of temperature data. For the prediction of GHG emissions, soil and air temperature data have been judged equally useful (i.e. Lopes de Gerenyu et al., 2005). Additionally monthly precipitation rates and ETp values are required where care needs to be taken to apply a correction factor for the conversion of ETp values into open-pan evaporation (Coleman and Jenkinson, 2005). The model allows two types of simulations: “direct” that uses the known input of organic carbon to the soil to calculate SOC, and “inverse” that evaluates the input of organic carbon required to maintain the stock of SOC.

Inputs are also required on the clay content of the soil (in our case 24 % was used) since this adjusts the partitioning between CO₂ evolved and the microbial biomass and humified organic matter during decomposition and the depth of the soil layer in question (25 cm). For the type of soil cultivation, it is only possible to distinguish between 100 % cover crop and bare soil. The addition of plant residues per month (t C ha^-1) is also required. In this study, rates of 0.3 t C ha^-1 for pruning wood in January and February and 0.15 t C ha^-1 for March were used based on estimates from the vineyard site and data from a literature review of Carlisle et al. (2010). The only other additional input was on C from leaf drop in autumn, with 0.36 t C ha^-1 for October and November estimated from spacing and canopy height and based on cited values of Carlisle et al. (2010). An estimate of the decomposability of the incoming plant material is also required. This needs to be estimated by the ratio of Decomposable Plant Material (DPM) to Resistant Plant Material (RPM). The model provides four choices since in most cases these data are not known, agricultural crops and improved grassland (DPM/RPM 1.44), unimproved grassland and scrub (DPM/RPM 0.67), and deciduous and tropical woodland (DPM/RPM 0.25). For the initial model runs in this study a ratio of 0.25 was used assuming pruning wood consists of a large amount of relatively resistant plant material.

In order to estimate future changes in SOC and CO₂ emissions from the study vineyard, a data file on the required inputs previously constructed for the estimation of future changes in ETp (Schultz and Hofmann, 2016; Schultz, 2017) was used that also contained temperature and precipitation data. This file is based on model-outputs of a regionalized version of the STARII model of the Potsdam Institute of Climate Impact (Orlowsky et al., 2008). The STARII model constructed time series from 2007-2060 by resampling of observed weather data according to trend information of the Global climate model ECHAM5/OM with the A1B scenario (SRES, Special Report on Emission Scenario) (Jacob, 2005). This scenario is roughly equivalent to the RCP 6.0 (Representative Concentration Pathways) currently used to simulate different scenarios (see

https://www.globalchange.gov/browse/multimedia/emissions-concentrations-and-temperature-projections

) with an estimated CO₂-concentration of 650 ppm by 2100 (IPCC, 2021). This approach provides physical consistency of the combination of the weather variables and is in close agreement compared to the statistics of observed climatology (Orlowsky et al., 2008).

Table

Data are seasonal means for summer (June, July, August; JJA) and autumn (September, October, November; SON) from daily measurements at 2 pm. Lines show the 10-year running-mean.

Table

Data are mean values ± SD from 4-5 terraces. The year 2008 was the post-planting year when soil was manually kept mostly free of vegetation. Since 2009 a natural cover crop was left to develop achieving 100 % cover by 2010. Dashed lines represent runs with the RothC-model (soil organic carbon (t ha^-1)). The green line shows a simulation with open soil in 2008-2009 and 100 % cover thereafter. The blue line shows a simulation with open soil in 2008-2009, a cover crop from October to March and open soil for the remaining year. Yearly input of carbon through pruning wood and leaves were equal for both treatments. Continuous lines show soil temperatures (bare soil) from the Geisenheim site for summer (June, July, August; JJA) and autumn (September, October, November; SON).

Table

Simulations were based on real climate data for that particular period (continuous lines) and a second run adding the observed temperature changes for the four seasons 2000-2021 as compared to 1961-1990 (see Figure 2) at a depth of 20 cm.

Without many “generations” of students working that particular experimental vineyard described and taking the soil samples, this study would not have happened. Thanks is due to the laboratory crew of the Department of Soil Science and Plant Nutrition for analysing soil C. I greatly appreciate the many colleagues of the German Weather Service, DWD, who over more than a century have measured soil temperature, in particular Andreas Ehlig, the “gatekeeper” of the data and currently observing the measurements at the University. Comments on the manuscript by my soil science and plant nutrition colleagues Otmar Löhnertz and Christoph-Martin Geilfus are also very much appreciated.

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