Average grain weight (AGW) is a major component of wheat yield. When attempting to elucidate mechanisms behind treatments effects on AGW, the distribution of the weight of individual grains may be critical. Determining the individual weight of thousands of grains in each sample would be unmanageable. Then, when individual sizes must be considered, researchers either weigh individually a very minor proportion of the grains or determine for the complete sample individual linear dimensions (length, width, area) through an image processing equipment. We aimed to generate a single model equation to trustworthily convert grain linear dimensions to grain weights. Firstly, we used a set of data to build and calibrate a model for the relationship between weight and linear dimensions of individual grains. Then, we validated the model calibrated with independent data. Grain area was a better predictor of grain weight than length and width of grains. Initially, we generated a single linear model but (i) the intercept was incongruently negative and therefore (ii) we forced the linear regression through the origin, but that consistently overestimated the weight of small grains and underestimated large grains. Finally, we fitted the data again with a power curve model and forced the intercept to zero (with the log-transformed data) obtaining the model (ŷ = x^1.32) to estimate individual grain weight from grain area. The model was validated with (i) independent data from the same studies used to build the model, (ii) data from other completely independent experiments, and (iii) data from the literature. Considering the diversity of genotypes and environments in the model generation and validation, the proposed power curve model could be trustworthily used to estimate grain weights from measured areas.

平均谷物重量（AGW）是小麦产量的主要组成部分。当试图阐明对AGW的治疗作用背后的机制时，单个谷物重量的分布可能至关重要。确定每个样品中成千上万的谷物的单独重量将是难以管理的。然后，当必须考虑单个尺寸时，研究人员要么单独称重一小部分谷物，要么通过图像处理设备确定完整样品的单个线性尺寸（长度，宽度，面积）。我们旨在生成一个单一的模型方程，以可靠地将谷物的线性尺寸转换为谷物的重量。首先，我们使用一组数据来建立和校准单个谷物的重量和线性尺寸之间的关系模型。然后，我们验证了使用独立数据校准的模型。谷物面积比谷物的长度和宽度更好地预测了谷物的重量。最初，我们生成了一个线性模型，但（i）截距不一致，因此（ii）我们强制通过原点进行线性回归，但始终高估了小颗粒的重量，而低估了大颗粒的重量。最后，我们再次使用幂曲线模型对数据进行拟合，并将截距强制为零（对数转换后的数据），得到模型（但这始终高估了小谷物的重量，而低估了大谷物的重量。最后，我们再次使用幂曲线模型对数据进行拟合，并将截距强制为零（对数转换后的数据），得到模型（但这始终高估了小谷物的重量，而低估了大谷物的重量。最后，我们再次使用幂曲线模型对数据进行拟合，并将截距强制为零（对数转换后的数据），得到模型（ŷ= x ^1.32）从谷物面积估计单个谷物的重量。使用（i）来自用于构建模型的相同研究的独立数据，（ii）来自其他完全独立实验的数据以及（iii）来自文献的数据对模型进行了验证。考虑到模型生成和验证中基因型和环境的多样性，建议的功率曲线模型可以可靠地用于从测量区域估算谷物重量。