Abstract
Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.
Highlights
The amount of crop residue left in the field after harvest is of great importance for water storage [1], soil erosion control [2,3,4] and assessment and modeling of soil carbon sequestration [5]
This study has found that residue estimates from the FieldTRAKS app were in good agreement with those obtained with the photograph-grid method
This study found that residue cover estimated from the script method overestimates that obtained by the photograph-grid method they are strongly correlated (Bias= 4.2%; root mean squared error (RMSE) = 7.5; R2 = 0.96; regression results not shown)
Summary
The amount of crop residue left in the field after harvest is of great importance for water storage [1], soil erosion control [2,3,4] and assessment and modeling of soil carbon sequestration [5]. Sensors 2018, 18, 708 residue is of interest in agro-ecosystems in North America, such as the mid-West and Great Lakes states where agricultural practices, including tillage practices (preparing land for growing crops), can affect water quality of the Gulf of Mexico and Great Lakes [6]. Such practices are of greater importance in the Canadian agro-ecosystem of southwestern Ontario, an area where, retaining crop residue cover. Such quantitative information on the amount of crop residue cover by field, which can be extrapolated to regions, is essential to understand the state of soil management and the capacity for additional change in an area of interest
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