Jeffrey S. Amthor

Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO₂ assimilation to variable intercellular CO₂ concentrations (A/C_i response curves) are used to model the maximum (potential) rate of carboxylation by ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco, V_cmax) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; J_PAR). The standard Farquhar‐von Caemmerer‐Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO₂ (g_m) derived from tobacco that may be inapplicable across species. To study the significance of using such parameters for other species, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g_m in cotton (Gossypium hirsutum cv. Sicot 71) and derived V_cmax and J₂₀₀₀ (J_PAR at 2000 µmol m⁻² s⁻¹ PAR) from cotton A/C_i curves incrementally measured at 15°C–40°C using cotton and other species‐specific sets of input parameters with our new automated fitting R package ‘OptiFitACi’. Notably, parameterisation by a set of tobacco parameters produced unrealistic J₂₀₀₀:V_cmax ratio of V_cmax above 15°C, up to 2.3‐fold higher estimates of J₂₀₀₀ and more variable estimates of V_cmax and J₂₀₀₀, for our cotton data compared to model parameterisation with cotton‐derived values. We determined that errors arise when using a g_m,25 of 2.3 mol m⁻² s⁻¹ MPa⁻¹ or less and Rubisco CO₂‐affinities in 21% O₂ (K_C^21%O2) at 25°C outside the range of 46–63 Pa to model A/C_i responses in cotton. We show how the A/C_i modelling capabilities of ‘OptiFitACi’ serves as a robust, user‐friendly, and flexible extension of ‘plantecophys’ by providing simplified temperature‐sensitivity and species‐specificity parameterisation capabilities to reduce variability when modelling V_cmax and J₂₀₀₀.

Summary: Leaf day respiration ( R _d ) strongly influences carbon‐use efficiencies of whole plants and the global terrestrial biosphere. It has long been thought that R _d is slower than respiration in the dark at a given temperature, but measuring R _d by gas exchange remains a challenge because leaves in the light are also photosynthesizing. The Kok method and the Laisk method are widely used to estimate R _d . We highlight theoretical limitations of these popular methods, and recent progress toward their improvement by using additional information from chlorophyll fluorescence and by accounting for the photosynthetic reassimilation of respired CO ₂ . The latest evidence for daytime CO ₂ and energy release from the oxidative pentose phosphate pathway in chloroplasts appears to be important to understanding R _d .

Leaf gas exchange measurements provide an important tool for inferring a plant’s photosynthetic biochemistry. In most cases, the responses of photosynthetic CO ₂ assimilation to variable intercellular CO ₂ concentrations ( A / C _i response curves) are used to model the maximum rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V _cmax ) and the rate of electron transport at a given photosynthetically active radiation (PAR; J _PAR ). The standard Farquhar-Von Caemmerer-Berry model is typically used with default parameters of Rubisco kinetic values and mesophyll conductance to CO ₂ ( g _m ) derived from tobacco that impairs analytical reliability across species. To study this, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g _m in cotton ( Gossypium hirsutum cv. Sicot 71) and derived V _cmax and J ₂₀₀₀ ( J at 2000 µmol m ^-2 s ^-1 PAR) from cotton A / C _i curves incrementally measured at 15°C to 40°C using cotton and tobacco parameters with our new automated fitting R package ‘OptiFitACi’. When applied to cotton, the tobacco parameters produced unrealistic J ₂₀₀₀ : V _cmax ratio of V _cmax , approximately 50% higher estimates of J ₂₀₀₀ and more variable estimates of V _cmax and J ₂₀₀₀ , compared to model parameterisation with cotton-derived values. We determined that errors arise when using a g _m of 0.23 mol m ^-2 s ^-1 bar ^-1 or below and Rubisco CO ₂ -affinities under ambient O ₂ ( K _C ^21%O2 ) outside 461 µbar to 627 µbar to model A / C _i responses in cotton. We show how the multi- A / C _i modelling capabilities of ‘OptiFitACi’ serves as a robust, user-friendly extension of ‘plantecophys’ by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to enable higher accuracy estimates of V _cmax and J ₂₀₀₀ .

Nonphotosynthetic plant metabolic processes are powered by respiratory energy, a limited resource that metabolic engineers—like plants themselves—must manage prudently.

Forest management practices might act as nature-based methods to remove CO ₂ from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO ₂ in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO ₂ concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

Quantitative models are crucial to almost every area ofecosystem science. They provide a logical structure that guides andinforms empirical observations of ecos...

Roughly half the carbon that crop plants fix by photosynthesis is subsequently lost by respiration. Nonessential respiratory activity leading to unnecessary CO₂ release is unlikely to have been minimized by natural selection or crop breeding, and cutting this large loss could complement and reinforce the currently dominant yield-enhancement strategy of increasing carbon fixation. Until now, however, respiratory carbon losses have generally been overlooked by metabolic engineers and synthetic biologists because specific target genes have been elusive. We argue that recent advances are at last pinpointing individual enzyme and transporter genes that can be engineered to (1) slow unnecessary protein turnover, (2) replace, relocate, or reschedule metabolic activities, (3) suppress futile cycles, and (4) make ion transport more efficient, all of which can reduce respiratory costs. We identify a set of engineering strategies to reduce respiratory carbon loss that are now feasible and model how implementing these strategies singly or in tandem could lead to substantial gains in crop productivity.