Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice

Abstract

Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system's capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties-needed to extend the system to new contexts-was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions.

Keywords: Critical nitrogen; NNI; PocketLAI; PocketN; sustainable N management.

Figure A1

Relationships between the PocketN index (–) and reference plant nitrogen content (PNC) (%, from elemental analyzer) for eighteen rice cultivars included in the study. See also Table 1. These relationships were used to derive cultivar-specific calibration curves to estimate PNC using PocketN.

Figure A2

Relationships between the PocketN index (–) and reference plant nitrogen content (PNC) (%, from elemental analyzer) for eighteen rice cultivars included in the study. See also Table 1. These relationships were used to derive cultivar-specific calibration curves to estimate PNC using PocketN.

Figure A3

Relationships between the PocketN index (–) and reference plant nitrogen content (PNC) (%, from elemental analyzer) for the cultivar Volano and for cultivar clusters not included in Figure 3. See also Table 1 and Table 2. These relationships were used to derive cultivar-specific and cluster-specific calibration curves to estimate PNC using PocketN.

Figure 1

Experimental design (left panel) and yield deviations compared to the field mean (right panel) for the experiments performed in 2014 in Rosasco and in 2015 in Gaggiano (experiment 1 and 2 in the text, respectively). In circles: nitrogen nutritional index (NNI) values derived using the PocketN and PocketLAI smart apps just before the topdressing fertilizations at tillering and at panicle initiation.

Figure 2

Response to different levels of topdressing fertilization at tillering in terms of aboveground biomass accumulation between tillering and panicle initiation.

Figure 3

Examples (charts for all other calibration curves are available in Appendix A) of the relationships between the PocketN index (–) and reference plant nitrogen content (PNC) (%, from elemental analyzer) for six widely grown cultivars and for two groups of cultivars after clustering the calibration curves. See also Table 1 and Table 2.

Figure 4

Example (for northern Italy) of the integration between satellite data and geo-located smart app-based N nutritional index (NNI) estimates. A cluster map from the 6 July 2018 NDRE (Normalized Difference Red-Edge) Sentinel 2 image was used to identify points where NNI was determined on 11 July (black boxes).