Within field variations of plant available water capacity (PAWC) of soil is one of the major causes of spatial yield variability in dryland agriculture systems, as PAWC interacts with pre-season and in-season rainfall and other climatic variables to determine crop growth and final yield. Quantification of such variations helps to better understand the changes in soil texture and subsoil constraints to inform spatially explicit management practice. An inverse modelling approach to estimate PAWC from crop yields was developed as a more cost-effective alternative to traditional soil sampling methods. In this study, we further extend this approach to predict and map in-field variations of PAWC from yield maps of single and multiple crops. Soil PAWC maps were produced based on inversely predicted PAWC using crop yield maps together with in-field management information, and compared with: 1) available water capacity derived using laboratory-measured soil properties, and 2) soil types derived from proximally sensed soil spectra and ground geophysics for four representative farms in Australia. The results show that the predicted PAWC maps matched well with within-field spatial variation of soil types, and well reflected the impact of soil constraints (e.g. salinity), and soil classifications from soil survey and local experience. This demonstrates that the predicted PAWC from crop yield using inverse modelling can reflect the soil physicochemical variations within-field. The generated PAWC maps can be combined with process-based modelling to predict crop yield and yield zones and to inform spatial field management and soil sampling.

土壤植物有效水分容量 (PAWC) 的田间变化是旱地农业系统空间产量变异的主要原因之一，因为 PAWC 与季前和季节降雨量以及其他气候变量相互作用，以确定作物生长和最终结果。屈服。对这些变化进行量化有助于更好地了解土壤质地和底土限制的变化，从而为空间明确的管理实践提供信息。开发了一种从作物产量估计 PAWC 的逆向建模方法，作为传统土壤采样方法的更具成本效益的替代方法。在这项研究中，我们进一步扩展了这种方法，以从单一和多种作物的产量图预测和绘制 PAWC 的田间变化。土壤 PAWC 图是根据使用作物产量图和田间管理信息的反向预测 PAWC 生成的，并与以下内容进行比较：1) 使用实验室测量的土壤特性得出的可用水容量，以及 2) 从近端感应的土壤光谱得出的土壤类型澳大利亚四个代表性农场的地面地球物理学。结果表明，预测的PAWC图与土壤类型的田间空间变化匹配良好，并很好地反映了土壤约束（例如盐度）的影响，以及土壤调查和当地经验的土壤分类。这表明使用反演模型从作物产量预测的 PAWC 可以反映田间土壤的物理化学变化。