ConstraintID: An online software tool to assist grain growers in Australia identify areas affected by soil constraints

Highlights

• Persistently poor-growing areas in fields can indicate a soil constraint.

• Remote-sensing data with a long history can reveal these areas within fields.

• ConstraintID is an online tool to help grains growers reveal persistent patterns.

• It can be used to guide soil sampling and help interpret the resulting soil data.

• It links soil data with critical values of soil constraints to different crops.

Abstract

Soil constraints, such as soil sodicity, salinity and acidity, are one cause of yield loss for grain growers globally. A first step towards reducing the associated yield gap is the identification of the area affected and the soil constraint/s responsible for the yield loss. Within a single field, persistent spatial variation of crop growth that repeats year after year is likely due to some form of soil constraint. Vegetation indices derived from satellite imagery can provide a valuable alternative to yield maps for assessing these persistent patterns. The variation identified from remote-sensing data can be compared with soil data from the field to help identify which soil constraints are potentially causing yield differences. This application note describes ConstraintID (www.constraintid.com.au), a web-based software tool that makes it easy for growers to use and interact with remote-sensing data to (1) reveal the persistent long-term variation of crop growth within their fields, (2) devise targeted soil sampling plans and (3) interpret the resulting soil data in view of critical values for certain soil constraints.

Introduction

Soil constraints, such as soil sodicity, salinity and acidity, are one cause of yield loss for grain growers globally (Bot et al., 2000). In some cases, there is potential for areas affected by persistent constraints to be managed differently—for instance, by growing varieties more tolerant to the identified constraint, or by ameliorating the soil to address the issue directly—and thereby reduce the associated yield gap (Hochman et al., 2016, Orton et al., 2018). A first step towards reducing this yield gap is the identification of the area affected and the particular soil constraint/s responsible for the yield loss. Although there is rarely a clear-cut boundary to the area affected, nor a single identifiable responsible constraint, the targeted acquisition of data with the greatest potential to help diagnose underlying issues driving yield variation can give growers important information to improve their management.

The within-field variation of crop growth in any single year might be related to various issues such as pests or diseases, or differential management within the field. However, patterns of within-field variation that repeat year after year are likely due to some form of soil constraint (Dang et al., 2011, Lobell et al., 2007). Long-term yield monitor data records, with the potential to identify persistent within-field variation of crop growth, are rarely kept by growers. However, vegetation indices derived from satellite imagery can give reasonable correlation with crop yields (Zhao et al., 2020, Ulfa et al., 2022) and provide a valuable alternative to yield maps for assessing persistent patterns of within-field variation of crop growth and defining management zones (Acevedo-Opazo et al., 2008). Soil measurements from areas of the field with higher or lower vegetation growth can be used to identify soil properties potentially causing yield differences (Dang et al., 2011, Lobell et al., 2007), and give growers useful information to improve their management. However, the processing, analysis and interpretation of data for such an investigation is a technical challenge and a barrier to its widespread use by growers and their advisers.

To overcome these barriers, we have developed ConstraintID (www.constraintid.com.au), a web-based software tool for growers in Australia’s northern grains growing region. The ConstraintID software tool makes it easy for growers to use and interact with remote-sensing data to (1) reveal the persistent long-term variation of crop growth within their fields, (2) devise targeted soil sampling plans and (3) interpret the resulting soil data in view of critical values for certain soil constraints (Page et al., 2021).