The prediction of crop biomass, grain yield and grain quality using fluorescence sensing in cereals

Potassium (K) is a macronutrient which plays a vital role on crop growth and metabolism. After N the requirements for K are greatest for most arable crops and so the availability of K is of critical importance to optimise production. The precision nutrient management of arable crops requires accurate and timely assessment of crop nutrient status. Much research and practice has focused on crop N status, while there has been a lack of focus on other important nutrients such as K. Therefore, in this study we assess the robustness of 12 fluorescence channels and several indices to predict nutrient status (K, Mg and Ca) across two cereal crops with different row management and lime status on an acidic K deficient soil. A multi-factorial experiment was used with the following treatment factors: crop (barley, wheat), K fertilizer rates (0, 25, 50, 100 kg K/ ha), lime (nil, 1 t/ ha) and two management factors (inter-row, windrow). At flowering the crop was sampled for biomass and nutrient content and proximal sensing (using a Multiplex fluorometer) undertaken of the crop canopy. Crop variables showed significant treatment effects. For instance, all crop variables were greater under the windrow treatment than the inter-row, K rate significantly increased grain yield and TGW, but K rate decreased protein and grain Ca and Mg content, also the grain yield was significantly greater under lime compared with the nil treatment. These crop effects enabled the identification of significant crop-fluorescence relationships. For instance, SFR_R (a chlorophyll index) predicted crop biomass (regardless of crop species) and FLAV predicted with the grain protein of windrow-grown barley. These results are promising and suggest crop-fluorescence relationships can be used to inform crop nutrient status which could be used to aid management decisions. Thus, there is good potential for fluorescence sensing to quantify crop K status and the opportunity to improve the timing and precision of K management for application within a precision agriculture system.