In this study, diffuse reflectance spectroscopy (DRS) approach was examined for making input recommendations in the smallholder cocoa farms of Papua New Guinea (PNG). Soil samples were collected from four provinces of PNG. Soil samples from four different depths (0–10, 10–30, 30–60 and 60–90 cm) of 32 profiles in each of these site were used to create a database of soil chemical and physical properties. Spectral reflectance values at 1 nm interval covering visible to shortwave‐infrared (350–2,500 nm) were collected for each of these soil samples to develop partial least squares regression models. Soil textural fractions, soil organic carbon contents and available N were well predicted by the DRS approach with R2 values larger than 0.75. Moderate to poor estimation efficiencies were observed for remaining parameters. Nevertheless, the estimated soil attributes and their corresponding measured soil parameters were used as inputs to an input recommendation model of soil diagnosis to create input recommendation for a targeted cocoa yield of 1,000 kg dry cocoa beans ha‐1 Resulting input recommendations were similar for both of these input sources (measured and DRS‐estimated) suggesting that the DRS approach may provide an easy way to create input recommendations.
The concepts of soil security (especially relating to soil condition) provide a useful framework in building spectral libraries. Spectral libraries can be used with the purpose of assessing soil condition by measuring soil organic carbon (SOC) or increasing productivity through soil nutrient management. A spectral library was generated by measuring SOC and nutrients (nitrogen, phosphorous and potassium) and spectral reflectance data over the visible to near-infrared range (350-2,500 nm) in soil samples collected from four production systems in Papua New Guinea (PNG).The spectral library was analysed using SpecOptim, a software tool developed at the James Hutton Institute to explore spectral pre-processing and calibration options.From 192 model combinations of model, the best one was identified for each study area. Different combinations of data were also explored (e.g. by farm or all together).We believe that at the local-scale, soil carbon and nitrogen variability can be captured; however, the spectrally inactive properties such as phosphorous and potassium need to have a higher variability and therefore pooling is required in order to predict properties chemometrically. The SpecOptim software is a useful tool where analysis of spectral data can be difficult to determine. Specifically, it helped improve the accuracy of predictions by 2% for C and N (except for East New Britain site) compared with previously used pre-processing techniques and calibration models while automating identification of the optimal pre-processing approach. We believe that we have developed research-based evidence for using spectral libraries to fit with the soil priority areas of PNG.
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