Nitrification, a key process in the global nitrogen cycle that generates nitrate through microbial activity, may enhance losses of fertilizer nitrogen by leaching and denitrification. Certain plants can suppress soil-nitrification by releasing inhibitors from roots, a phenomenon termed biological nitrification inhibition (BNI). Here, we report the discovery of an effective nitrification inhibitor in the root-exudates of the tropical forage grass Brachiaria humidicola (Rendle) Schweick. Named ''brachialactone,'' this inhibitor is a recently discovered cyclic diterpene with a unique 5-8-5-membered ring system and a ␥-lactone ring. It contributed 60 -90% of the inhibitory activity released from the roots of this tropical grass. Unlike nitrapyrin (a synthetic nitrification inhibitor), which affects only the ammonia monooxygenase (AMO) pathway, brachialactone appears to block both AMO and hydroxylamine oxidoreductase enzymatic pathways in Nitrosomonas. global warming ͉ nitrogen pollution ͉ nitrous oxide emissions ͉ root exudation ͉ climate change M ost modern agricultural systems are based on large inputs of inorganic nitrogen (N), with ammonium (NH 4 ϩ ) being the primary N source (1, 2). Also, current crop management practices result in the development of highly nitrifying soil environments (3, 4). Nitrification results in the transformation of the relatively immobile NH 4 ϩ to highly mobile nitrate (NO 3 Ϫ ), making inorganic N susceptible to losses through leaching of NO 3 Ϫ and/or gaseous N emissions, potentially initiating a cascade of environmental and health problems (1, 2, 5, 6). Nitrous oxide (N 2 O) is one of the three major biogenic greenhouse gases contributing to global warming, produced primarily from denitrification processes in agricultural systems (5, 7). Also, assimilation of NO 3 Ϫ by plants can result in further N 2 O emissions directly from plant canopies (8). The low agronomic N-use efficiency (NUE) found in many agricultural systems is largely the result of N losses associated with nitrification (i.e., N losses from NO 3 Ϫ leaching and denitrification) (9-11). Most plants have the ability to assimilate both NH 4 ϩ and NO 3 Ϫ (12); therefore, nitrification does not need to be a dominant process in the N cycle for efficient N use.Nitrification is low in some forest and grassland soils (13-17). Since the early 1960s, some tropical grasses have been suspected of having the capacity to inhibit nitrification (18-21). However, this concept remained controversial due to the lack of direct evidence showing such inhibitory effects or the identification of specific inhibitors (22).We adopted a very sensitive bioassay using a recombinant luminescent Nitrosomonas europaea to detect biological nitrification inhibition (BNI) in plant-soil systems with the inhibitory activity of roots expressed in allylthiourea units (ATU) (23). Using this methodology, we were able to show that certain plants release nitrification inhibitors from their roots (23-26). Such BNI capacity appears to be relatively widespread among...

Background and Aims The forage grass Brachiaria humidicola (Bh) has been shown to reduce soil microbial nitrification. However, it is not known if biological nitrification inhibition (BNI) also has an effect on nitrogen (N) cycling during cultivation of subsequent crops. Therefore, the objective of this study was to investigate the residual BNI effect of a converted long-term Bh pasture on subsequent maize (Zea mays L.) cropping, where a long-term maize monocrop field (M) served as control. Methods Four levels of N fertilizer rates (0, 60, 120 and 240 kg N ha −1 ) and synthetic nitrification inhibitor (dicyandiamide) treatments allowed for comparison of BNI effects, while 15 N labelled micro-plots were used to trace the fate of applied fertilizer N. Soil was incubated to investigate N dynamics. Results A significant maize yield increase after Bh was evident in the first year compared to the M treatment. The second cropping season showed an eased residual effect of the Bh pasture. Soil incubation studies suggested that nitrification was significantly lower in Bh soil but this BNI declined one year after pasture conversion. Plant N uptake was markedly greater under previous Bh compared with M. The N balance of the 15 N micro-plots revealed that N was derived mainly (68-86%) from the mineralized soil organic N pool in Bh while plant fertilizer N recovery (18-24%) was not enhanced. Conclusions Applied N was strongly immobilized due to long-term root turnover effects, while a significant residual BNI effect from Bh prevented re-mineralized N from nitrification resulting in improved maize performance. However, a significant residual Bh BNI effect was evident for less than one year only.

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Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and BLUP models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield – those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.