The decreasing mineral concentrations in the grains of cereals have recently stimulated research to better understand the cropping determinants of grain mineral composition. This study aimed to analyze the effects of liming on the mineral concentrations in the grains of three cereal crops: barley, oat, wheat. The hypothesis tested was that soil pH is the main driver of the grain nutrient concentrations in crops, through its influence on the soil extractable minerals. Macro nutrients (Ca, K, Mg, P, S), micro-nutrients (Cu, Fe, Mn, Se, Zn) and some trace elements (As, Cd, Pb) were analyzed. Two long term liming trials in SE England (1962 -) were studied, with the same crops sown in the same years. On each site, four liming rates were applied to 32 plots to create a pH range from approximately 4.5 to 7.5. The trials were subdivided into two P fertiliser treatments, consisting of a nil and regular P inputs. For a given crop, the effects of pH, soil type, concentrations of nutrients in soil extracts and of P treatment on the grain mineral concentrations were tested. This pairwise analysis was followed by a multiple linear regression analysis in order to determine the main explanatory variable for crop mineral concentration. Liming had a significant impact on most of the soil extractable mineral concentrations, except extractable K and Mg. The grain mineral concentrations exhibited significant differences between crops, the concentrations in wheat being the smallest. pH proved to have a larger direct effect on mineral concentrations in grain (e.g. Ca, Mg, P, Mn) than through its influence on extractable nutrients (e.g. Cd). Grain nutrients responses to pH were, however, not the same in the three crops. Differences in Cu and Zn were mostly accounted for by the effect of soil type, the soil with the higher CEC leading to the higher grain concentrations. For Fe, Pb and K, no correlation could be found between the grain mineral concentrations and the explanatory variables. Difficulties in explaining the grain mineral concentrations are due to specific crop responses to nutrients, usefulness of soil extractions, and complex physiological processes in mineral translocation from roots to grains. The results underline the difficulty of using ordinary soil analysis for predicting the quality of cereal grains for nutrition, and caution in the use of grain testing to recommend soil fertility enhancing practices.

谷物中矿物质含量的降低最近刺激了研究，以更好地了解谷物矿物质组成的决定因素。这项研究旨在分析石灰对三种谷类作物（大麦，燕麦，小麦）谷物中矿物质含量的影响。检验的假设是，土壤pH值通过影响土壤可提取矿物质而成为作物中谷物营养素浓度的主要驱动因素。分析了常量养分（Ca，K，Mg，P，S），微量养分（Cu，Fe，Mn，Se，Zn）和一些微量元素（As，Cd，Pb）。在英格兰东南部（1962-）进行了两次长期浸石灰试验，研究了相同年份播种的相同农作物。在每个站点上，对32个样地施加四个限制速率，以创建约4.5至7.5的pH范围。试验被分为两种磷肥处理，包括零磷和常规磷输入。对于给定的农作物，测试了pH，土壤类型，土壤提取物中养分浓度和磷处理对谷物矿物质浓度的影响。在此成对分析之后进行多元线性回归分析，以确定作物矿物质浓度的主要解释变量。除可提取的钾和镁外，石灰对大多数土壤可提取的矿物质浓度都有显着影响。作物之间的谷物矿物质浓度差异显着，小麦中的矿物质浓度最小。与对可提取养分（例如Cd）的影响相比，pH值对谷物中的矿物质浓度（例如Ca，Mg，P，Mn）具有更大的直接影响。然而，三种作物的谷物营养素对pH的反应不同。铜和锌的差异主要由土壤类型的影响引起，CEC较高的土壤导致较高的谷物浓度。对于Fe，Pb和K，谷物矿物质浓度与解释变量之间没有相关性。难以解释谷物矿物质浓度的原因是农作物对养分的特定反应，土壤提取物的有用性以及矿物质从根向谷物的转运过程中复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。铜和锌的差异主要是由土壤类型的影响造成的，CEC较高的土壤导致较高的谷物浓度。对于Fe，Pb和K，谷物矿物质浓度与解释变量之间没有相关性。难以解释谷物矿物质浓度的原因是农作物对养分的特定反应，土壤提取物的有用性以及矿物质从根向谷物的转运过程中复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。铜和锌的差异主要是由土壤类型的影响造成的，CEC较高的土壤导致较高的谷物浓度。对于Fe，Pb和K，谷物矿物质浓度与解释变量之间没有相关性。难以解释谷物矿物质浓度的原因是农作物对养分的特定反应，土壤提取物的有用性以及矿物质从根向谷物的转运过程中复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。铅和钾，在谷物矿物质浓度和解释变量之间没有发现相关性。难以解释谷物矿物质浓度的原因是农作物对养分的特定反应，土壤提取物的有用性以及矿物质从根向谷物的转运过程中复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。铅和钾，在谷物矿物质浓度和解释变量之间没有发现相关性。难以解释谷物矿物质浓度的原因是农作物对养分的特定反应，土壤提取物的有用性以及矿物质从根向谷物的转运过程中复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。矿物质从根到谷转运的复杂而复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。矿物质从根到谷转运的复杂而复杂的生理过程。结果强调了使用普通土壤分析法预测谷物谷物营养品质的难度，并谨慎使用谷物测试法来建议提高土壤肥力的做法。