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Whether green light promotes or represses plant growth is an unresolved but important question, warranting a global meta-analysis of published data. We collected 136 datasets from 48 publications on 17 crop species, and calculated the green light effect for a range of plant traits. For each trait the effect was calculated as the ratio between the trait value attained under a red/blue background light plus green, divided by the value attained under the background light only, both having the same light intensity. Generally, green light strongly increased intrinsic water use efficiency (15%), the shoot-to-root ratio (13%), and decreased stomatal conductance (−15%). Moreover, green light increased fresh weight to a small extent (4%), but not plant dry weight, resulting in a reduced dry matter content (−2%). Hence, green light is similarly effective at increasing biomass as red and blue light. Green light also showed to increase leaf area (7%) and specific leaf area (4%; i.e. thinner leaves). Furthermore, effects of green light were species-dependent, with positive effects on biomass for lettuce and microgreens, and negative effects in basil and tomato. Our data suggest that future research should focus on the role of green light in modulating water loss, its putative role as a shade signal, and the causes for its species-specific effects on crop biomass. [ABSTRACT FROM AUTHOR]

The rapid increases of the global population and climate change pose major challenges to a sustainable production of food to meet consumer demands. Process-based models (PBMs) have long been used in agricultural crop production for predicting yield and understanding the environmental regulation of plant physiological processes and its consequences for crop growth and development. In recent years, with the increasing use of sensor and communication technologies for data acquisition in agriculture, machine learning (ML) has become a popular tool in yield prediction (especially on a large scale) and phenotyping. Both PBMs and ML are frequently used in studies on major challenges in crop production and each has its own advantages and drawbacks. We propose to combine PBMs and ML given their intrinsic complementarity, to develop knowledge- and data-driven modelling (KDDM) with high prediction accuracy as well as good interpretability. Parallel, serial and modular structures are three main modes can be adopted to develop KDDM for agricultural applications. The KDDM approach helps to simplify model parameterization by making use of sensor data and improves the accuracy of yield prediction. Furthermore, the KDDM approach has great potential to expand the boundary of current crop models to allow upscaling towards a farm, regional or global level and downscaling to the gene-to-cell level. The KDDM approach is a promising way of combining simulation models in agriculture with the fast developments in data science while mechanisms of many genetic and physiological processes are still under investigation, especially at the nexus of increasing food production, mitigating climate change and achieving sustainability. [ABSTRACT FROM AUTHOR]

Potato (Solanum tuberosum L.) can reproduce sexually through flowering and asexually through tuberization. While tuberization has been thoroughly studied, little research has been done on potato flowering. Flower bud development in the strictly short-day tuberizing S. tuberosum group Andigena is impaired under short-day conditions. This impaired development may indicate that tuberization negatively influences flowering. Here, we determine how tuberization affects flower bud development. To find out whether the absence of tubers improves flowering, we prevented tuberization by: (i) grafting potato scions onto wild potato rootstocks, which were unable to form tubers; (ii) removing stolons, the underground structures on which tubers form; and (iii) using plants that were silenced in the tuberization signal StSP6A. Additionally, transgenic plants with increased StSP6A expression were used to determine if flower bud development was impaired. The absence of a tuber sink alone did not accelerate flower bud development, nor did it allow more plants to reach anthesis (open flowering stage) or have more open flowers. Interestingly, reducing StSP6A expression improved flower bud development, and increasing expression impaired it. Our results show that flower bud development in potato is repressed by the tuberization signal StSP6A, and not by competition with the underground tuber sink. [ABSTRACT FROM AUTHOR]

Leaves are often exposed to fluctuating irradiance, which limits assimilation. Elevated CO2 enhances dynamic photosynthesis (i.e. photosynthesis in fluctuating irradiance) beyond its effects on steady-state photosynthesis rates. Studying the role of CO2 in dynamic photosynthesis is important for understanding plant responses to changing atmospheric CO2 partial pressures. The rise of photosynthesis after a step-wise increase to 1000 µmol m-2 s-1, the loss of photosynthetic induction after irradiance decreases, and rates of photosynthesis during sinusoidal changes in irradiance were studied in tomato (Solanum lycopersicum L.) leaves, using three CO2 partial pressures (200, 400, and 800 µbar). Initial irradiance was set to 0, 50, 100, and 200 µmol m-2 s-1 to vary the initial induction state. Most responses at 200 µbar were not different from those at 400 µbar. In contrast, CO2 at 800 µbar increased the relative carbon gain by 12% after an increase in irradiance, decreased the loss of photosynthetic induction by 14%, and increased dynamic photosynthesis during sine waves by 17%, compared with 400 µbar. These effects were additional to steady-state effects of elevated CO2 on photosynthesis. The enhancement of dynamic photosynthesis rates by elevated CO2 may therefore additionally increase photosynthesis in a future, CO2-enriched climate. [ABSTRACT FROM AUTHOR]

Background and Aims Plants depend on photosynthesis for growth. In nature, factors such as temperature, humidity, CO2 partial pressure, and spectrum and intensity of irradiance often fluctuate. Whereas irradiance intensity is most influential and has been studied in detail, understanding of interactions with other factors is lacking. Methods We tested how photosynthetic induction after dark-light transitions was affected by CO2 partial pressure (20, 40, 80 Pa), leaf temperatures (15·5, 22·8, 30·5 °C), leaf-to-air vapour pressure deficits (VPDleaf-air; 0·5, 0·8, 1·6, 2·3 kPa) and blue irradiance (0-20 %) in tomato leaves (Solanum lycopersicum). Key Results Rates of photosynthetic induction strongly increased with CO2partial pressure, due to increased apparent Rubisco activation rates and reduced diffusional limitations. High leaf temperature produced slightly higher induction rates, and increased intrinsic water use efficiency and diffusional limitation. High VPDleaf-air slowed down induction rates and apparent Rubisco activation and (at 2·3 kPa) induced damped stomatal oscillations. Blue irradiance had no effect. Slower apparent Rubisco activation in elevated VPDleaf-air may be explained by low leaf internal CO2partial pressure at the beginning of induction. Conclusions The environmental factors CO2 partial pressure, temperature and VPDleaf-air had significant impacts on rates of photosynthetic induction, as well as on underlying diffusional, carboxylation and electron transport processes. Furthermore, maximizing Rubisco activation rates would increase photosynthesis by at most 6-8 % in ambient CO2 partial pressure (across temperatures and humidities), while maximizing rates of stomatal opening would increase photosynthesis by at most 1-3 %. [ABSTRACT FROM AUTHOR]

Incident irradiance on plant leaves often fluctuates, causing dynamic photosynthesis. Whereas steady-state photosynthetic responses to environmental factors have been extensively studied, knowledge of dynamic modulation of photosynthesis remains scarce and scattered. This review addresses this discrepancy by summarizing available data and identifying the research questions necessary to advance our understanding of interactions between environmental factors and dynamic behaviour of photosynthesis using a mechanistic framework. Firstly, dynamic photosynthesis is separated into sub-processes related to proton and electron transport, non-photochemical quenching, control of metabolite flux through the Calvin cycle (activation states of Rubisco and RuBP regeneration, and post-illumination metabolite turnover), and control of CO2 supply to Rubisco (stomatal and mesophyll conductance changes). Secondly, the modulation of dynamic photosynthesis and its sub-processes by environmental factors is described. Increases in ambient CO2 concentration and temperature (up to ~35°C) enhance rates of photosynthetic induction and decrease its loss, facilitating more efficient dynamic photosynthesis. Depending on the sensitivity of stomatal conductance, dynamic photosynthesis may additionally be modulated by air humidity. Major knowledge gaps exist regarding environmental modulation of loss of photosynthetic induction, dynamic changes in mesophyll conductance, and the extent of limitations imposed by stomatal conductance for different species and environmental conditions. The study of mutants or genetic transformants for specific processes under various environmental conditions could provide significant progress in understanding the control of dynamic photosynthesis. [ABSTRACT FROM AUTHOR]