Removing the inefficiencies of 3-dimensional canopy photosynthesis by the alteration of leaf light-response dynamics and plant architecture

植物的光合作用是叶片吸收二氧化碳并将其同化为特殊器官(叶绿体)中的碳水化合物的过程，这一过程需要叶绿素吸收光线。然而，在一段时间内，光合作用的速率会随着环境的变化而变化，如光照强度、叶龄、温度和其他因素。这对依赖高光合作用获得高产的作物的生产力产生了影响。生产力是冠层中大量树叶的总和，其中许多树叶遮荫(或部分相互遮蔽)，通常是不同年龄的树叶。我们可以根据叶片的光合作用属性和其他物理和生理因素来计算整个冠层的潜在生产力。当我们这样做时，理论生产率往往比测量的生产率高得多。原因尚不清楚，但很大一部分被认为是由于树叶重建成大型3D树冠时的反应方式。在这种状态下，植物作为一个群落存在，它具有我们不一定能从单独种植的植物中预测到的新特性。如果我们能够消除理论生产率和测量生产率之间的差距，我们就可以实现生产率的阶段性变化。光合作用速率对光强非常敏感。树冠内存在的光强度差异(从上到下呈指数递减)是显著的，并受到树冠的精确结构的影响，即单位地面面积的叶面积，叶的角度、形状和大小及其在3维空间中的位置。这意味着树冠内的光强在空间和时间上具有很大的变异性，例如频繁和瞬时地出现“光斑”。树冠的运动对产生快或慢的光斑以及它们在树冠中的位置起着重要的作用。光合作用应该根据这些快速变化的条件进行优化，但有迹象表明，情况并非如此。环境会导致田间光合作用的“下调”，这可以通过比较叶片的实际光合作用和最大光合作用来衡量。目前尚不清楚这种下调如何与树冠结构和树叶的反应相互作用。一个问题是，我们没有详细的3维作物树冠图像，也没有复杂的模型可以让我们将复杂的光强变化映射到光合作用。作物冠层发挥着许多重要的农学作用，有些具有光合作用，有些则不起作用。因此，我们需要从相反的角度来理解这个问题--也就是说，拍摄好静止和移动的作物冠层的3D图像，计算该冠层中发生的光强度的典型变化，然后改变光合作用动力学，使其与这些变化相匹配。我们将在一个特殊的温室里种植高产作物、水稻和小麦的树冠，这将使我们能够在静止时拍摄作物树冠，并使用激光扫描和相机技术生成3D高分辨率图像。我们将测试探测植物在风中移动的新技术，然后我们将对这些图像进行扭曲，以检查风引起的叶子‘颤动’和茎弯曲对光线分布的影响。我们将在数学光线跟踪程序中使用这些图像来精细地映射树冠内发生的灯光变化。这些变化将被用来预测哪些过程主导着冠层-生产力过程。这本质上是一个植物冠层的建模和成像项目：我们将开始下一个阶段，通过命令在这些模型确定的关键反应中改变水稻突变体，并种植它们来测试整个冠层的生产力。如果成功，我们可以通过消除冠层内发生的任何浪费过程来实现生产率的阶段性变化。

Photosynthesis in plants is the uptake of CO2 by leaves and its assimilation into carbohydrates within specialized organs (chloroplasts), a process that requires the absorption of light by chlorophyll. However the rate of photosynthesis over a given period of time varies according to environmental changes such as light intensity, leaf age, temperature and other factors.This has consequences for productivity of crops which depend on high rates of photosynthesis for high yields. Productivity is the sum total of a large number of leaves in a canopy, many of which shade (or partly shading each other) and are usually different ages. We can calculate the potential productivity of whole canopies based on leaf photosynthetic attributes and other physical and physiological factors. When we do this the theoretical productivity tends to be much higher than the measured productivity. The reasons are unclear but a large part is thought to be due to the way leaves respond when re-constructed into a large 3D canopy. In this state, plants exist as a community which has emergent properties that we cannot necessarily predict from plants grown individually. If we can eliminate the gap between the theoretical and measured productivity we can achieve a step change in productivity. Photosynthetic rate is very sensitive to light intensity. The difference in light intensities that exist within the canopy (an exponential decline from top to bottom) is significant and is affected by the precise architecture of the canopy i.e. the amount of leaf area per unit ground area, the angle, shape and size of leaves and their position within 3 dimensional space. This means that the light intensity has great variability in space and time within canopies e.g with frequent and transient appearance of 'light-flecks'. The movement of the canopy plays a major part in how fast or slow light flecks are generated, and where in the canopy they appear. Photosynthesis should be optimized to these rapidly changing conditions, but there are indications that it is not. The environment can cause a 'down-regulation' of photosynthesis in the field and this can be measured by comparing actual leaf photosynthesis against the maximum. It is not clear how this down-regulation interacts with canopy architecture and the responses of leaves.One problem is that we do not have detailed images of crop canopies in 3 dimensions and we do not have sophisticated models that allow us to map the complex changes in light intensity to photosynthesis. Crop canopies perform a number of important agronomic roles, some photosynthetic , others not. Therefore we need to understand the problem 'in reverse' - i.e. to take good 3D images of crop canopies, both still and moving, calculate the typical changes in light intensity that occur in that canopy and then change photosynthetic dynamics so that it matches those changes. We will grow canopies of productive crop plants, rice and wheat in a special glasshouse that will enable us to image crop canopies, when still, and produce 3D high resolution images using laser scanning and camera techniques. Novel techniques will be tested for detecting plant movement in wind and we will then distort these images to examine the effect of wind induced leaf 'flutter' and stem bending on light distribution. We will use these images in a mathematical ray-tracing program to finely map the changes in light that occur within the canopy. These changes will be used to predict which processes dominate the canopy-productivity process. This is essentially a modelling and imaging project of plant canopies: we will begin the following phase by ordering rice mutants altered in key reactions identified from these models and grow these to test whole canopy productivity. If successful we can achieve a step change in productivity by eliminating any wasteful processes that occur within the canopy.