Mark Lieffering

Changes in source-sink relations during development influence photosynthetic acclimation of rice to free air CO2 enrichment (FACE)

Relationships between photosynthetic acclimation and changes in the balance between source-sink supply and demand of carbon (C) and nitrogen (N) were tested using rice (Oryza sativa L. cv. Akitakomachi). Plants were field-grown in northern Japan at ambient CO2 partial pressure [p(CO2)] or free air CO2 enrichment (FACE; p(CO2) ~ 26-32 Pa above ambient) with low, medium or high N supplies. Leaf CO2 assimilation rates (A) and biochemical parameters were measured at 32-36 (eighth leaf) and 76-80 (flag leaf) d after transplanting, representing stages with a contrasting balance between C and N supply and demand in sources and sinks. Acclimation due to FACE was pronounced in flag leaves at each N supply. This was not fully accounted for by reductions in leaf N concentrations, because A/N and Vcmax/N were lower in FACE-grown flag leaves. Acclimation did not occur in the eighth leaf, and A/N and Vcmax/N was not significantly increased in FACE-grown leaves. Soluble protein / sucrose and amino acid / sucrose concentrations decreased under FACE, whereas sucrose phosphate synthase protein levels increased. At flag leaf stage, there was a discrepancy between the demand and supply of N, which was resolved by enhanced leaf N remobilization, associated with the lower Rubisco concentrations under FACE. In contrast to the early growth stage, enhanced growth of rice plants was accompanied by increased plant N uptake in FACE. We conclude that photosynthetic acclimation in flag leaves occurs under FACE because there is a large demand for N for reproductive development, relative to supply of N from root uptake and remobilization from leaves.

Paddy Rice Responses to Free-Air CO2 Enrichment

Rice (Oryza sativa L.) is one of the world’s three major crops. It differs from wheat and maize, the world’s top two crops, in the distribution of its production areas: a predominant proportion of the global rice harvest comes from regions at latitudes between 30° N and 30° S, mostly in Asia (FAOSTAT 2004). In contrast, the majority of the wheat and maize crops are produced at higher latitudes. Rice is also quite unique in that the majority (ca. 90 %) of the world’s harvest comes from flooded fields. As such, rice is grown under natural and socioeconomic environments that are different from those for the other major crops. This would further imply that the impacts of global change on rice may differ from those on other crops due to these differences in the growing environment. The importance of rice as the most important food crop in Asia justified the commencement of the Rice FACE project in Japan in 1996. The primary objective of the project was to improve our capability to predict responses of rice plants and paddy ecosystems to increasing atmospheric CO2 concentration ([CO2]). The FACE experiment was conducted for 3 years (1998–2000, Phase 1), and after a 2-year hiatus, for an additional 2 years (2003–2004, Phase 2). This chapter summarizes rice plant responses to elevated [CO2] in the Rice FACE experiment during Phase 1.

Greenhouse gas and energy balance of dairy farms using unutilised pasture co-digested with effluent for biogas production

Greenhouse gas (GHG) emissions from New Zealand dairy farms are significant, representing nearly 35% of New Zealand’s total agricultural emissions. Although there is an urgent need for New Zealand to reduce agricultural GHG emissions in order to meet its Kyoto Protocol obligations, there are, as yet, few viable options for reducing farming related emissions while maintaining productivity. In addition to GHG emissions, dairy farms are also the source of other emissions, most importantly effluent from milking sheds and feed pads. It has been suggested that anaerobic digestion for biogas and energy production could be used to deal more effectively with dairy effluent while at the same time addressing concerns about farm energy supply. Dairy farms have a high demand for electricity, with a 300-cow farm consuming nearly 40 000 kWh per year. However, because only ~10% of the manure produced by the cows can be collected (e.g. primarily at milking times), a maximum of only ~16 000 kWh of electricity per year can be produced from the effluent alone. This means that anaerobic digestion/electricity generation schemes are currently economic only for farms with more than 1000 cows. A solution for smaller farms is to co-digest the effluent with unutilised pasture sourced on the farm, thereby increasing biogas production and making the system economically viable. A possible source of unutilised grass is the residual pasture left by the cows immediately after grazing. This residual can be substantial in the spring–early summer, when cow numbers (demand) can be less than the pasture growth rates (supply). The cutting of ungrazed grass (topping) is also a useful management tool that has been shown to increase pasture quality and milk production, especially over the late spring–summer. In this paper, we compare the energy and GHG balances of a conventional farm using a lagoon effluent system to one using anaerobic digestion supplemented by unutilised pasture collected by topping to treat effluent and generate electricity. For a hypothetical 300-cow, 100-ha farm, topping all paddocks from 1800 to 1600 kg DM/ha four times per year over the spring–summer would result in 80 tonnes of DM being collected, which when digested to biogas would yield 50 000 kWh (180 GJ) of electricity. This is in addition to the 16 000 kWh from the effluent digestion. About 90 GJ of diesel would be used to carry out the topping, emitting ~0.06 t CO2e/ha. In contrast, the anaerobic/topping system would offset/avoid 0.74 t CO2e/ha of GHG emissions: 0.6 t CO2e/ha of avoided CH4 emissions from the lagoon and 0.14 t CO2e/ha from biogas electricity offsetting grid electricity GHGs. For the average dairy farm, the net reduction in emissions of 0.68 CO2e/ha would equate to nearly 14% of the direct and indirect emissions from farming activities and if implemented on a national scale, could decrease GHG emissions nearly 1.4 million t CO2e or ~10% of New Zealand’s Kyoto Protocol obligations while at the same time better manage dairy farm effluent, enhance on-farm and national energy security and increase milk production through better quality pastures.

C and N models Intercomparison – benchmark and ensemble model estimates for grassland production

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.