Dry spectral diagnostic tools and methods for precise fertilizer application

This study explores the potential of soil spectroscopy to enhance fertilizer decision-making by providing cost-effective, portable instruments for farm-level soil property prediction. The study focused on assessing the performance of various spectrometers, including low-cost near-infrared (NIR) devices, compared to mid-infrared (MIR) bench-top instruments, using a case study on maize productivity in East Africa and soil data from Morocco’s semi-arid rainfed wheat- growing regions. The overall aim was to determine whether these spectroscopic methods could generate reliable predictions of key soil properties for nitrogen, phosphorus and potassium fertilizer recommendations. The results indicate that NIR spectroscopy, despite being the most affordable and portable option, demonstrated sufficient accuracy for predicting key soil properties such as soil pH, organic carbon, and exchangeable potassium, with concordance correlation coefficients (CCCs) ranging from 0.77 to 0.96. However, the prediction of phosphorus (Olsen P) showed considerable uncertainty, particularly for values above 15 mg P kg⁻¹, where deviations from measured values increased. Comparatively, the MIR spectrometer showed better prediction accuracy for phosphorus, though its higher cost and complexity limit its applicability in resource-limited settings. The NIR spectrometer, with a prediction accuracy suitable for nitrogen fertilization (deviation between -8 to 8 kg N ha⁻¹), emerged as a promising tool for cost-effective and rapid nutrient recommendations in developing countries. Furthermore, this research demonstrated that integrating spectroscopic data into crop models like QUEFTS for nutrient management enhances decision-making by considering both soil supply and crop response to nutrients. The findings underscore the necessity of developing region-specific calibration models to improve prediction reliability, with spatial autocorrelation analysis of soil spectra suggesting that proper calibration sample selection can improve prediction performance, especially for phosphorus and other key properties. Ultimately, this thesis contributes to the ongoing development of soil spectral libraries and highlights the potential of low-cost, field-friendly spectrometers to improve nutrient management and crop productivity in regions with limited soil data.