Usefulness of techniques to measure and model crop growth and yield at different spatial scales

Abstract

ContextWithin-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. ObjectiveThis study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. MethodsWe collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. ResultsWheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial variation in yield due largely to uncertainties in input data. However, APSIM simulations captured the average crop growth dynamics and yield well at 1 ha strip- and 10 ha whole field scales. ConclusionsThe results highlight the need for caution when using yield maps and remote sensing data to quantify spatial variability and inform spatially explicit management decisions at a fine resolution (e.g., 1 m2). In our case, remote sensing data and yield maps only became consistent and process-based modelling became skilful at scales larger than a 1 ha strip. ImplicationsDespite an increasing amount of high-resolution spatial data, the usefulness at fine resolution needs further investigation, particularly under heterogeneous field conditions. Such data need to be analysed in conjunction with the landscape, soil and climate data to understand the drivers of spatial variability and inform process understanding and modelling. This further implies potential problems in developing spatial management practices at finer scales using such data.