Soil carbon insures arable crop production against increasing adverse weather due to climate change

Climate change is expected to bring more adverse weather conditions for agricultural production such as greater intra-annual climate variability and an increased likelihood of extreme weather events (Trnka et al 2014, Ray et al 2015, Moore and Lobell 2015). Adapting agriculture to climate change and improving farming's resilience is thus crucial for food security (Challinor et al 2014, Altieri and Nicholls 2017, IPCC 2019). The timing of adverse weather events can effect crop yields differently depending on when in the growing season they occur (Peltonen-Sainio et al 2011, Chenu et al 2013, Bourgault et al 2020). Thus, while there is a need to improve our understanding of the interplay between (intra-seasonal) weather variability and yields (Lobell and Burke 2008, Peltonen-Sainio et al 2011), we also need to include soil health in the analysis (Luo et al 2017).

Soil organic carbon (SOC) content is a fundamental indicator of soil health (Lal 2016). SOC correlates with different soil biodiversity dimensions such as microbial biomass, community structure, and its activities (Börjesson et al 2012, Mau et al 2015). Such soil-ecosystem structures are the base of soil-ecosystem functions and the production of a suite of ecosystem services that underpin yields (De Vries et al 2013, Bardgett and Van Der Putten 2014, Brady et al 2015, Oldfield et al 2019). Management practices that increase SOC tend to generate soils that hold more biodiversity, have better water holding capacity, provide more plant nutrients, and are less prone to erosion (Lal 2016, Minasny et al 2017, Manns and Martin 2018). However, common agricultural practices, such as intensive tillage can reduce SOC stocks and biodiversity (Tsiafouli et al 2015, Haddaway et al 2017). Intensification of arable crop production thereby degrades soil health and reduces production potential through loss of SOC (Lal 2016). Consequently, there is a trade-off between current intensification of arable crop production and soil health, and thus the resilience of future production. Soils can furthermore function as a carbon sink or contribute to emissions of greenhouse gases, depending on management systems (Griscom et al 2017, Schlesinger and Amundson 2019).

Implementing management practices that strengthen soil-ecosystem resilience could therefore be a prudent farming strategy for mitigating climate change, as well as simultaneously adapting to climate change (Locatelli et al 2015, Hamilton et al 2016, Paustian et al 2016). Soil health—unlike the weather—can be controlled by farmers; through appropriate management practices farmers can influence SOC content (Haddaway et al 2015, 2017, Sun et al 2020). How farmers manage their soils is not only instrumental for producing good yields under normal weather conditions, but also for producing stable yields despite the natural vagaries of the weather (Cong et al 2014, 2017, Manns and Martin 2018, Macholdt et al 2020). A resilience to adverse weather can generate what economists call an insurance value for arable farmers, if it reduces the uncertainty of future outcomes such as future yields for risk adverse beneficiaries (Baumgärtner and Strunz 2014, Bartkowski 2017, Quaas et al 2019).

Accordingly, climate change, crop growth, and soil management all matter simultaneously. Yet, the combined effects of temperature and precipitation variability during the different stages of crop growth remain underexplored. In order to identify climate change adaptation actions, we investigate the soil's ability to buffer the impacts of adverse weather events at different stages of the growing season on crop yields and the influence of crop management on SOC. To this end we pursue two research question: I) How does arable crop production respond to soil carbon levels under adverse weather across growing degree quartiles? II) Can crop rotations that promote soil carbon sequestration help farmers to adapt their management to an increasing likelihood of adverse weather events?

In this paper we combined historic weather data with yield data for two major cereal crops from long-term agricultural experiments from 12 sites and across 54 years in Sweden. The long-term experiments were originally set up to study soil fertility and provide fertilization recommendations to farmers (Carlgren and Mattsson 2001). They are based on a replicated set of fertilizer application rates for a set of crops and crop rotations with or without livestock manure application and grass leys. The crop rotations and the management have generated variations in SOC content within the sites. This enabled a production-function approach to estimate direct and interaction effects of soil carbon and weather variables on crop yields. To assess the effects of adverse weather at different stages of the growing period, we partitioned the growing season into four representative phases. Subsequently, we estimated the effects that management practices have on soil carbon by quantifying the effects of ley and manure application in crop rotations over time.

We combined a data set from the long-term field experiments of the Swedish University of Agricultural Sciences providing data on yields, fertilizer rates and SOC levels (Carlgren and Mattsson 2001, SLU 2020, see section 2.1), with the European high‐resolution gridded dataset, E‐OBS version 17, providing weather data (ECAD 2018, Haylock et al 2008, see section 2.2). The final data set spans across the years 1962–2015 and includes yield, crop, crop rotation, crop variety, N- and PK fertilizer applications, topsoil carbon, topsoil pH, growing degree days (GDDs), and mean temperature and total precipitation data. The combined data set was analysed with a multilevel production-function approach (see section 2.3).

2.1. Long-term field experiment data

The data originates from six sites in southern Sweden (M-Series) and six sites in central Sweden (C, E, and R series, see figure 1(a)). The series differ in crops and rotations to represent a regionally typical type of crop rotation, but other than that follow the same experimental setup. At each site, two different types of crop rotations, four different rates of PK fertilizer, and four different rates of N fertilization were applied. All field experiments have two replicated blocks at each of the sites (figure A1) and there are 32 replicated plots for each of the sites per year (Carlgren and Mattsson 2001). The sites differ in soil types but have been selected so that for each of the agricultural regions in Sweden there is one productive and one less productive soil site (Kirchmann 1991, Kirchmann and Eriksson 1993, Kirchmann et al 1996, 1999, 2005). The crop rotations introduce ley and manure management. At the southern sites (M), one out of four crops in one crop rotation was substituted with a grass and clover ley, and manure applied every 4th year in winter to simulate livestock farming (Carlgren and Mattsson 2001). At the northern sites (C, E, R), two out of six crops were ley, and manure was applied every 6 years for one of the rotations (ibid.). The topsoil carbon was only measured every 4 years, we completed the data set by imputing the missing soil carbon observations through a non-parametric, random-forest method (Stekhoven and Bühlmann 2012), based on site, year, topsoil pH, and NPK fertilizer combinations (see tables A1 and A2).

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2.2. Historical weather data

Daily mean, minimum, and maximum temperature, and precipitation data was collected from the E-OBS observations (0.25 degree regular grid) for corresponding filed sites (ECAD 2018). There was a substantial warming effect in all seasons; mean annual temperature increased by ~2.6 °C from 1962 to 2015 across all 12 sites, while overall variation did not increase (figure 2 upper panels). The range between minimum and maximum daily temperatures was largest in summer with a mean range of 9.5 °C, compared to a mean winter range of ~5 °C. Regional temperature differentials between the southernmost M sites at around 56°N and the more northern C, E, and R sites (58–61°N) were most pronounced in autumn and winter. These and further temperature details such as site-specific patterns and the multi-annual variations in winter can be found in the supplementary information (figure s1 available online at https://stacks.iop.org/ERL/15/124034/mmedia). Precipitation patterns have remained relatively stable over spring and autumn but increase over summer and winter (figure 2 lower panels). Furthermore, the standard deviation of daily precipitation for each year increases, particularly in summer and to some extent in winter and spring (see also figure S2 in the supplementary information).

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We calculated GDDs for each cultivar with the following baseline temperatures (Miller et al 2001). Starting at the beginning of the year, we chose the following values: winter wheat 0 °C (Ruiz Castillo and Gaitán Ospina 2016), and spring barley 0 °C (Juskiw et al 2001). To analyse climate change effects along growth stages, we divided GDD into quartiles to approximate different growth stages of the plants (Peltonen-Sainio et al 2011). The GDD quartiles roughly correspond to the following plant development stages for cereals: i) tillering, ii) stem extension, iii) heading, and iv) ripening (Miller et al 2001). For each of the GDD quartiles, we computed an average of daily mean temperatures, and summed total daily precipitation. The data shows an overall warming, and corresponding increases in growing season length and GDD, indicating that the climatic conditions for agricultural production have become more favourable for cereal production.

2.3. Response functions

We estimated yield response functions for winter wheat and spring barley through a multilevel model with site-specific random effects (intercepts) and site-specific means to account for all time-invariant, unobserved site-specific heterogeneity (Blanc and Schlenker 2017, Bell et al 2019). We controlled for standard quadratic fertilization yield functions, cultivation of new varieties, soil pH, and unobserved trends such as for example technological change and changing atmospheric carbon concentrations. We modelled the interaction between soil carbon and weather variables, to infer how the average effect of soil carbon plays out for various climatic conditions in terms of temperature and precipitation.

The general structure of the yield response functions is given by equation (1):

$$\begin{align} {Y_{it}} &= {\alpha _0} + {\beta _1}NFer{t_{it}} + {\beta _2}NFert_{it}^2 + {\beta _3}PKFer{t_{it}} \nonumber \\ & \quad + \gamma \log \left( {S{C_{it}}} \right) + {\delta _1}tem{p_{it}} + {\delta _2}pre{c_{it{\,}}} + {\delta _3}tem{p_{it}} \nonumber \\ & \quad \times pre{c_{it{\,}}} + {\mu _1}\log \left( {S{C_{it}}} \right) \times NFer{t_{it}} + {\mu _2}\log \left( {S{C_{it}}} \right) \nonumber\\ & \quad \times {\,}NFert_{it}^2 + {\sigma _1}\log \left( {S{C_{it}}} \right) \times {\,}tem{p_{it}} + {\sigma _2}\log \left( {S{C_{it}}} \right)\nonumber\\ & \quad \times {\,}pre{c_{it}} + \theta Control{s_{it}} + {\varepsilon _{it}}, \end{align} \tag{ 1 }$$

where ${\alpha _0}$ is the population level intercept, $NFer{t_{it}}$ is a vector of N fertilizer applications rates for site $i{\text{ }}$and year $t$, $PKFer{t_{it}}$ is a categorical PK fertilizer variable that allows to capture a non-linear relationship across four levels, $S{C_{it}}$ is soil carbon, $tem{p_{it}}$ is a vector of mean temperature, and $pre{c_{it}}$ represents total precipitation for each of the GDD quartiles. The temperature-precipitation interaction effect is only modelled within each GDD quartile. $Control{s_{it}}$ capture topsoil pH, crop rotation, crop variety and the site-specific soil carbon mean, and a linear time trend. The random effects, that is site-specific intercepts, are sampled from a normal distribution varying around the population level mean (Bates et al 2015, Bell et al 2019). For the numerical estimates see table A3 in the appendix.

The computations are conducted in the lme4 package (Bates et al 2015) in R (R Core Team 2019) through restricted maximum likelihood estimations. The analytical code and the data can be found in both the supplementary material and/or in a public repository [link to be inserted]. Based on the general estimates, we predicted the interaction effects for each GDD quartile for 5th, 50th, and 95th percentile values of both mean temperature total precipitation, using the ggeffects (Lüdecke 2018) package in R, and plotted results using ggplot2 (Wickham 2016) and sjPlot (Lüdecke 2019). All other variables were held constant at specific values for each of the interaction plots in figures 4 and 5.

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For the effect of ley (${\text{crop}}.{\text{rotation}} = {\text{II}}$) on soil carbon (${\text{SC}}$) we estimated a log-linear model to account for non-linear depletion rates over time, with the following specifications: a quadratic N-fertilization (${\text{NFert}}$) function, categorical PK fertilizer (${\text{PKFert}}$) application rates, topsoil pH ($topsoil\_pH$), mean temperature (${\text{temp}}$) and total precipitation (${\text{prec}}$) per GDD quartile, the interaction between precipitation and temperature per GDD quartile, a year trend (${\text{year}}$), and ley crop rotation dummy and the interaction between crop rotation, accounting for site-specific intercepts and year trends through a multilevel random effects estimation, see equation (2). For the numerical regression results for soil carbon see table A4.

$$\begin{align}{\text{log}}(S{C_{it}}) & = {\alpha _0} + {\beta _1}crop.rotatio{n_{it}} + {\beta _1}NFert_{it}^2 \nonumber\\ & \quad + {\beta _2}PKFer{t_{it}} + \gamma {\text{ }}topsoi{l_{pH}} \nonumber\\ & \quad + {\delta _1}tem{p_{it}} + {\text{ }}{\delta _2}preci{p_{it{\text{ }}}} + {\text{ }}{\delta _3}tem{p_{it}} \nonumber\\ & \quad \times pre{c_{it{\text{ }}}} + {\text{ }}{\tau _1}yea{r_t} + {\tau _2}yea{r^2} \nonumber\\ & \quad + {{{\varphi }}_1}{\text{ }}crop.rotatio{n_{it}} \times yea{r_{\text{t}}} + {\varepsilon _{it}}.\end{align} \tag{ 2 }$$

3.1. Wheat yields

3.2. Spring barley yields

3.3. Soil carbon and management

We show that relatively higher soil carbon levels (all other things equal) are generally associated with higher yields for favourable climatic conditions. Furthermore, higher soil carbon reduces yield losses arising from adverse weather events at different stages of the growing season. These mechanisms can be explained by soil carbon increasing a set of ecosystem services that changes soil structure allowing infiltration and also water retention through larger soil aggregates integrating the soil organic matter with the mineral particles (Lal 2016). Soil carbon thereby provides farmers with insurance against adverse weather events. Soil carbon has differing effects on yield, depending on the growing degree quartile in which rainfall or temperature deviate from averages. Here we show the complex relations between soil carbon, and the timing of weather events over the growing season. We can confirm that yields are less variable and even increase with higher soil carbon content within sites (Manns and Martin 2018, Oldfield et al 2019). As our experiment was set up to derive fertilization recommendations, we can include N-fertilizer application rates (Macholdt et al 2020) and N–C interactions (Zhang et al 2018) such that we do not bias yield-effects estimates through omitted fertilization variables or their interaction see table A3. More importantly, however, our results contribute evidence that the interaction effects of soil carbon and weather variables on yield need to be differentiated by the timing of weather variations over the growing season. The results have clear implications for both science and society as they specify how to better estimate climate change effects on crop yields and suggest soil management practices for improving the potentials of soils to insure against climatic change.

Regarding our first research question: How does arable crop production respond to soil carbon levels under climatic variations across growing degree quartiles?, we need to interpret our results against the background of observable climate trends to understand the implications of our results for farmers and society. Climate change and production conditions vary regionally (Trnka et al 2011, Moore and Lobell 2015) and effects of soil management practices vary with climatic conditions (Sun et al 2020). For the Swedish long-term experiments, the precipitation primarily increases in winter and summer, and this is likely to continue (Rowell 2005, Trnka et al 2014). We can confirm that for northern latitudes, temperature increases can be beneficial for winter wheat but negative for barley (Peltonen-Sainio et al 2011). Yet, our predictions for values that accordingly resemble likely future conditions in Southern Sweden, namely high temperatures and high precipitation in winter and summer, soil carbon maximizes potential gains from favourable conditions for both crops (i.e. for first and fourth GDD quartiles). For wheat, soil carbon minimizes yield losses from unfavourable precipitation conditions, such as low temperature and precipitation in GDD quartile three. This exemplifies how soil carbon can reduce financial, down-side risks of yield losses for farming due to adverse weather (Cong et al 2017). For spring barley, however, we predict substantial yield losses at extremely high precipitation over second and third quartiles in the range of the 95th percentile and this effect is not outweighed by soil carbon (with considerable uncertainty). Potentially, this raises important implications for the adaptation potential of temperate climate barley production given predicted climatic change (see Dawson et al 2015). Yet, at less extreme temperature soil carbon enhances barley yield according to our results. Thus, we show the importance of accounting for the timing of weather conditions on yield in interaction with soil carbon. These results clearly indicate benefits from having higher soil carbon, for both barley and wheat. Soil carbon enhances yields in the event of favourable conditions while moderating potential yield losses due to adverse weather, and therefore insures agriculture against increasing temperature and precipitation variability.

Regarding our second question: Can crop rotations that promote soil carbon help farmers to adapt to an increasing likelihood of adverse weather events?, there exists a multitude of possible measures to increase soil carbon content in arable production systems (West et al 2004, Haddaway et al 2015, Keel et al 2019), such as no-tillage (Haddaway et al 2017, Ogle et al 2019), cover crops (Poeplau and Don 2015) and ley years (Prade et al 2017, Zhou et al 2019). The more and longer soils are covered, and root biomass is accumulated, e.g. through fertilization, the more organic carbon will be stored below ground (for a recent overview see Sykes et al 2020). We observe both a generally positive trend for crop yields at the experimental sites over time, but also increasing yield variation. Moreover, current agricultural practices, such as those used in the Swedish long-term experiments, on average loose soil carbon. We show that declining soil carbon levels are associated with lower yields. Yet, effects in terms of declining yields are not directly observable by farmers as yields still increase over time. This may be explained because losses in soil carbon have so far been offset by technological development such as improved varieties (Fischer and Edmeades 2010). Such a hidden deterioration is problematic given the broader potential societal benefits from maintaining healthy soils (Lal 2016). In particular arable soils could even function as a carbon sink; and the 1.5 °C climate goals can only be achieved by including agricultural land use into emissions reductions (IPCC 2019). Given the long-time scales needed for SOC to recover, there is a mismatch between short- and long-term benefits (Brady et al 2015). As has been shown for the same experiments, ley-manure management can provide carbon storage (Carlgren and Mattsson 2001, Albizua et al 2015). Yet, as we show, this is not enough to stop the depletion of soil carbon stocks (for soil carbon balance calculations in the Swedish long-term experiments, see Kätterer et al 2014, Börjesson et al 2018, Keel et al 2019).

Overall, enhancing soil carbon can contribute to making agriculture more resilient to climate change by reducing the production risks that come with increasing frequency of adverse and extreme weather over the growing season, while enhancing the effects of favourable climatic conditions in higher latitudes. Our results furthermore imply that predictions of the impacts of climate change based on scenarios of average annual temperature and precipitation changes, general variability increases, or general GDD increase, are omitting the effects from weather variations over the growing season. Yet, it is important to note that, the societal benefits such as food security and carbon storage (Vermeulen et al 2019, Bossio et al 2020) provided by management options that promote soil carbon such as inclusion of ley or other measures (Albizua et al 2015, Poeplau and Don 2015, Bradford et al 2019) come at a cost for the farmer in terms of foregone yield (Bartkowski et al 2018). Our results indicate that a substantial increase from 0.5%–2.5% soil carbon comes with a maximum increased yield of 2.5 tonnes for both wheat (in a warm year with high precipitation) and barley (in a median year with low precipitation). In our case, to restore such a carbon stock would require foregoing yield every 6 years to grow ley for more than 200 years. As this is hardly a reasonable scenario, but there are public benefits of soil restoration in terms of food security, land degradation neutrality, and climate change mitigation, measures enhancing soil carbon could be supported by corresponding reforms of land-use and agricultural policies to compensate farmers for the provision of such public goods that comes at their expense in terms of current yield (Paustian et al 2016, Pe'er et al 2017, 2020, Cowie et al 2018, Hristov et al 2020). To halt soil degradation, improved soil management may thereby require agronomic and policy solutions to optimize SOC storage while minimizing short-term trade-offs with farmer incomes.

The authors gratefully acknowledge funding by the strategic research area Biodiversity and Ecosystem services in a Changing Climate (BECC, www.becc.lu.se/). Special thanks go to the Swedish University of Agricultural Science (SLU) for the maintenance of the Long-Term Experiments and the provision of data (www.slu.se/en/faculties/nj/this-is-the-nj-faculty/collaborative-centres-and-major-research-platforms/long-term-field-experiments-/). The authors also acknowledge the E-OBS dataset and the data providers in the ECA&D project (www.ecad.eu).

The authors declare no competing interests.

ND conducted the data preparation and analysis and wrote the first draft of the document. WM conceptualized the project and gathered the climate data. YC participated in the conceptualization of project and analysis. GB provided the agricultural experiment data and helped organizing it. MB led the project and conceptualized the analysis. KH co-led the project and conceptualized the analysis. All participated in interpretation of results and revision of the initial draft.

The analytical code is available open access at: https://github.com/NilsDroste/SoilCarbonInsurance. Climate and yield data are available from the authors upon reasonable request.

Appendix. Experimental design

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Appendix. Descriptive statistics

Appendix. Regression analysis