Nitrogen accumulation in grains, remobilization and post-anthesis uptake under tan spot and leaf rust infections on wheat

Nitrogen (N) fluxes involve important processes determining grain yield and quality in wheat (Triticurn aestivum L.) and could be affected by major fungal diseases like leaf rust (Puccinia triticina Eriks) and tan spot [Pyrenophora tritici-repentis (Died.) Drechs., anamorph Drechslera tritici-repentis) (Died.) Shoem.]. Although effects of foliar diseases on N accumulation and translocation have already been studied, none of these studies performed inoculations in field conditions. This study aimed to determine the effects of independent inoculations to ensure the predominance of each pathogen of 1) Pyrenophora tritici-repentis (necrotroph) and 2) Puccinia triticina (biotroph) on N accumulation and allocation in wheat crops. Experiments were carried out using a split-split plot design where the main plots were both diseases, subplots were three inoculation treatments and ten cultivars were the sub-subplots. Healthy area duration (HAD), crop growth rate (CGR), N remobilization (NREM), N postanthesis uptake (NPU), %N in grains and N stored in grains were calculated. The results indicate that the nutrition habit of the pathogens would cause a differential effect on crop N fluxes with more noticeable effects for P.triticina, explained by the retention of N in green tissues, which prevents the normal translocation of N to the grains. Higher rates of N stored in grains, NREM and a negative rate for % N in grains were manifested due to longer extensions in the HAD for leaf rust compared to tan spot. Inoculations with Py.tritici-repentis caused increases in the %N in grains up to 14.9%, while P.triticina reduced it by 9.89%. Both NREM (-30.9%) and N stored in grains (-24.4%) showed significant reductions under increases in the inoculum dose of P.triticina, but not under Py.tritici-repentis infections. Relating HAD and CGR to N accumulation under foliar diseases infections could be an effective tool to improve the modelling of yield and quality losses more accurately.