How much biomass do we have? – Is UK supply from Miscanthus water-limited? tsec-biosys.ac.uk

1. How much biomass do we have? – Is UK supply from Miscanthus water-limited? www.tsec-biosys.ac.uk Dr. Goetz M Richter Rothamsted Research Biomass role in the UK energy futures The Royal Society, London: 28th & 29th July 2009

2. Contents • What were the hypotheses? • Objectives and Approaches • Regional estimates using a simple empirical model based on soil and climatic data • Uncertainties of estimates and optimising crop allocation • What can we learn from detailed process analysis? • How can we improve crop productivity? • What is the way forward?

3. What were the hypotheses? • Miscanthus has a higher productivity under lower water consumption than other local herbaceous crops due to its C4-photosynthetic pathway • Miscanthus is yielding robustly in areas with lower precipitation and particularly useful for eastern England • Miscanthus x Giganteus, is potentially a bioenergy crop ideally suited for marginal land, especially considering its low nutrient demand

4. Objectives and approaches Objective 1: Quantify yield effect of soil and agro-meteorological variables Approach • Evaluate harvestable Miscanthus yields (litter-free, 15 Feb; 3+ year) from local long-term experiment and a UK-wide series of experiments • Derive a universal empirical model for UK conditions • Up-scale empirical model to the agricultural landscape (yield maps) using spatially distributed input data (soil, weather)

5. Effect of soil water availability on yield • Available water capacity (AWC) in top 1.5 m from soil survey data base (NSRI) can be underestimated by up to 50% • Best estimate accounts for hydrological character of site (water from porous rock; depth to ground water; management) • AWC can be estimated using pedotransfer functions and applying first principles

6. Effect of potential soil moisture deficit • Potential soil moisture deficit (PMSD) is the cumulative difference between precipitation and potential ET • PSMD is averaged over the main growing season (April-Aug) and scaled in proportion to the AWC • For all 21 observations in 3 experiments at Rothamsted rPSMDexplained about 50% of the observed yield variability

7. Empirical grass yield model (EGM) TESTED INPUT DATA • Seasonal air temperature (Ta) • Global radiation (Rg) • Rainfall (P) • Average seasonal potential soil moisture deficit (PSMD) • available water capacity (AWC) • year planted (GY) for individual observations (year, a, location, l) FINAL MODEL • Y(local) = f(AWC, rPSMD); r2 ~ 0.7; RMSE 1.4 t ha-1 • Y(regional) = f (AWC, P, Ta); r2 ~ 0.5; RMSE 2.1 t ha-1

8. Spatial implementation of EGM • Transform soil map into database of input variables • Extract NATMAP variables: • Available Water Capacity (arable, grass) or primary soil variables for PTF • Make use of Hydrology of Soil Types (HOST classes) • Build database of weather • Inputs: precipitation and temperature • Local weather stations • Interpolated weather data (1 km2; Hijmans et al., 2005; http://www.worldclim.org

9. Revisiting the soil input data (AWC_PTF) • Expanded HYPRES pedotransfer function (Woesten et al., 1999) to E&W • Estimated AWC (PTF) from soil texture, bulk density and organic matter • Set of rules considered four different soil groups: • non-gley shallow soil overlying porous rock and other non-gleysol, and • deep gleysol and shallow gleysol above hard rock and sediments. • AWC is water retention between FC and WP (-1500 kPa), water at FC was estimated at -10kPa for gleysols, and -33 kPa for any other soil • For shallow soils over porous rock water was approximated for those soils classified as HOST classes 1 to 3 (Boorman et al., 1995) • AWC of porous rock was assumed to be between 10 vol% (chalk) and • 5 vol% (oolitic limestone, sandstone), estimated for the layers exceeding depth of rock to the maximum profile depth.

10. Estimating soil-series specific AWC • Only for the deep NonGleysoils both estimates of AWC were similar • Non-gley soils over porous rock (NG_PR) could provide on average an additional 17% of water • Gleyic soils (G, G_HR) can provide an additional 40 to 50% of water • Hydromorphic soils cover large areas of the UK

11. Impacts of BE expansion on land-use • Yield map for all soils except organic (~ 11 M ha) • Yield map for 9 (primary) constraints (<8 M ha) • Yield map 11 (secondary) constraints (<5 M ha) • Yield map for all constraints plus ALC 3 & 4 (~ 3 M ha) Lovett et al. 2009 Bioenergy Research 2, 17-28

12. Conclusions for Regional Scale Estimates • Improved our understanding of the control factors at the landscape scale • In spite of its high WUE yields of Miscanthus are clearly related to and limited by water supply • Estimates of the most limiting factor, soil AWC, are subjected to a rather large uncertainty • Mapped data need being replaced by more physically and hydrologically founded estimates (e.g groundwater depth) • There are no independent, regionally distributed yield data from on-farm trials or commercial fields to prove our estimates

13. Objectives and approaches Objective 2: Adapt a process-based crop growth model describing above / belowground carbon partitioning and yield Approach: • Parameterise model from literature and calibrate using initial growth curves from a local long-term experiment • Conduct a sensitivity analysis to identify most growth limiting parameters • Evaluate model using various indicators 14 years of the same experiment

14. Experimental basis for Process Model • Long-term, highly resolved data at Rothamsted • Light interception (LAI) • Dry matter • Leaf senescence, loss (litter) • Morphological data • Stem number, height & diameter • Leaf length, width • Growth dynamics of belowground biomass (rhizomes) Christian, D. G., Riche, A. B., Yates, N. E., Industrial Crops and Products28, 109 (2008)

15. Leaves LAI Stems Density (n), Ht, Wt Rhizomes RGR(T), SRWT, [RhDR(t)] Flowers A sink-source interaction model Physiology Asat, φ rs, ksen,,fW, fT rdr, halflife rad, P, T,.. Photo- synthesis Inter- ception ksen kfrost kext fT(A) Energy Balance Ta PER Phenology Phyllochron, nL Tb, TΣ(e, x, a), cv2g Carbohydrates fw cL/P fsht Tillering Morphology WD(L), SLA, nV, nG MaxHt, SSW(d) Water Balance crf Reserves 10-20% Roots θfc, θpw, depth, ... Source Formation Sink Formation

16. Sensitivity analysis (SA) for Miscanthus model • One-at-a-time SA (Morris, 1991) ranks parameters acc to the strength (μ) and variance (σ) of their yield effect (Δy/Δp) • Parameter contribution for different process traits • Phenology (e.g. transformation of vegetative to generative tillers, cv2g) • Morphology (e.g. partitioning to leaf, cL/P; shoot fsht; leaf width WDL etc.) • Physiology (photosynthesis at light saturation, Amax; quantum efficiency, φ; and their temperature dependence) • We explored the balance between parameters characterising the sink (morphological traits) and source size (physiological traits) • Model will be used to explore the traits of different species & varieties in aid of identifying optimal grass ideotypes

17. cL/P kext Asat φ fsht cSSW WDL SLAx Tb(A) Tn(A) Toptv2g Tb(sht) Tx(A) cv2g TΣ(x) Sensitivity analysis to rank parameters of Miscanthus yield model • Parameter effects on yield vary across and between process traits • Initial conditions (e.g. DMrhz) • Phenology (e.g. transformation of vegetative to generative tillers, cv2g) • Morphology (e.g. partitioning to leaf, cL/P; shoot fsht; leaf width WDL etc.) • Physiology (photosynthesis at light saturation, Amax; quantum efficiency, φ; and their temperature dependence) • Balance between size of sinks and sources (morphological and physiological traits) is dynamic DMrhz Preparing Submission for Global Change Biology- Bioenergy

18. Sink – Source Balance

19. What about water stress ? high stress tolerance low stress tolerance Sinclair, T. R., Field Crops Res.15, 125 (1986). Richter, G. M., Jaggard, K. W., Mitchell, R. A. C., Agric For Meteorol109, 13 (2001).

20. Leaf DM & GLAI dynamics

21. Leaf area dynamics and water stress

22. Yield prediction over 14 years

23. Conclusions for process-based model • A generic grass model was successfully adopted to simulate dry matter production of Miscanthus x giganteus • Identified important morphological traits • Calibrated & evaluated for one site, one variety • Ranked parameter using OAT sensitivity analysis • Explored sink-source balance, tillering dynamics • Future applications of this model are needed • For different species & varieties to identify optimal grass ideotypes • In different environments (G x E interaction)

24. Finally – where do we go from here? • We need feedback from the growers! • We need strengthening of the agronomy of these crops, SRC and Miscanthus • Regionally distributed on-farm trials and demonstrations on different soil types are needed • Research needs focus to improve our understanding (e.g. water use) and the varieties to be grown • Get on with the work!

25. Thank you for your attention! www.tsec-biosys.ac.uk