The project “FArming Tools for external nutrient Inputs and water Management” (FATIMA, H2020-SFS2) is developing satellite-based methodologies and information to support effective and efficient water and nitrogen input recommendations in agricultural production. This paper focuses on nitrogen recommendation for winter cereals in Austria and presents preliminary findings from the 2015/16 crop growing season. The Nitrogen Nutrition Index was applied using an empirical relationship to derive dry mass from Leaf Area Index (LAI) and %Na from a chlorophyll index. Results showed a very high correlation between LAI and above ground dry mass (R2=0.95) but a lower correlation between the chlorophyll index and %Na (R2=0.24). Despite various indices tested, the relationship to estimate %Na remains weak. Additional field data and research are needed to further study this aspect.

The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

Weeds cause crop loss indirectly by reducing the quantity of resources available for growth. Quantifying the effects of weed interference on nitrogen (N) supply, crop growth, and N nutrition may assist in making both N and weed management decisions. Experiments were conducted to quantify the effect of N addition and weed interference on soil nitrate-N (NO3-N) over time and the dependence of corn growth on NO3-N availability, determine the corn N nutrition index (NNI) at anthesis, and evaluate if relative chlorophyll content can be utilized as a reliable predictor of NNI. Urea was applied at 0, 60, and 120 kg N/ha to establish N treatments. Season-long weedy, weed-free, and five weed interference treatments were established by delaying weed control from time of crop planting to the V3, V6, V9, V15, or R1 stages of corn development. Soil NO3-N ranged from 20 kg N/ha without N addition to 98 kg N/ha with 120 kg N/ha added early in the season, but crop and weed growth reduced soil NO3-N to 10 kg N/ha by corn anthesis. Weed presence reduced soil NO3-N by up to 50%. Average available NO3-N explained 29 to 40% of the variation in corn shoot mass at maturity. Weed interference reduced corn biomass and NNI by 24 to 69%. Lack of N also reduced corn NNI by 13 to 46%, but reduced corn biomass by only 11 to 23%. Nondestructive measures of relative chlorophyll content predicted corn NNI with 65 to 85% accuracy. Although weed competition for factors other than N may be the major contributor to corn biomass reduction, the chlorophyll meter was a useful diagnostic tool for assessing the overall negative effects of weeds on corn productivity. Further research could develop management practices to guide supplemental N applications in response to weed competition.