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.

The tropical forage grass Brachiaria humidicola (Bh) suppresses the activity of soil nitrifiers through biological nitrification inhibition (BNI). As a result, nitrate (NO3−) formation and leaching are reduced which is also expected to tighten the soil nitrogen (N) cycle. However, the beneficial relationship between reduced NO3− losses and enhanced N uptake due to BNI has not been experimentally demonstrated yet. Nitrification discriminates against the 15N isotope and leads to 15N depleted NO3−, but 15N enriched NH4+ in soils. Leaching of 15N depleted NO3− enriches the residual N pool in the soil with 15N. We hypothesized that altered nitrification and NO3− leaching due to diverging BNI magnitudes in contrasting Bh genotypes influence soil 15N natural abundance (δ15N), which in turn is reflected in distinct δ15N in Bh shoot biomass. Consequently, high BNI was expected to be reflected in low plant δ15N of Bh. It was our objective to investigate under controlled conditions the link between shoot value of δ15N in several Bh genotypes and leached NO3− amounts and shoot N uptake. Additionally, plant 15N and N% was monitored among a wide range of Bh genotypes with contrasting BNI potentials in field plots for 3 years. We measured leaf δ15N of young leaves (regrown after cutback) of Bh and combined it with nitrification rates (NRs) of incubated soil to test whether there is a direct relationship between plant δ15N and BNI. Increased leached NO3− was positively correlated with higher δ15N in Bh, whereas the correlation between shoot N uptake and shoot δ15N was inverse. Field cultivation of a wide range of Bh genotypes over 3 years decreased NRs in incubated soil, while shoot δ15N declined and shoot N% increased over time. Leaf δ15N of Bh genotypes correlated positively with NRs of incubated soil. It was concluded that decreasing plant δ15N of Bh genotypes over time reflects the long-term effect of BNI as linked to lower NO3− formation and reduced NO3− leaching. Accordingly, a low δ15N in Bh shoot tissue verified its potential as indicator of high BNI activity of Bh genotypes.

BackgroundCommon bean is an important legume crop with only a moderate number of short expressed sequence tags (ESTs) made with traditional methods. The goal of this research was to use full-length cDNA technology to develop ESTs that would overlap with the beginning of open reading frames and therefore be useful for gene annotation of genomic sequences. The library was also constructed to represent genes expressed under drought, low soil phosphorus and high soil aluminum toxicity. We also undertook comparisons of the full-length cDNA library to two previous non-full clone EST sets for common bean.ResultsTwo full-length cDNA libraries were constructed: one for the drought tolerant Mesoamerican genotype BAT477 and the other one for the acid-soil tolerant Andean genotype G19833 which has been selected for genome sequencing. Plants were grown in three soil types using deep rooting cylinders subjected to drought and non-drought stress and tissues were collected from both roots and above ground parts. A total of 20,000 clones were selected robotically, half from each library. Then, nearly 10,000 clones from the G19833 library were sequenced with an average read length of 850 nucleotides. A total of 4,219 unigenes were identified consisting of 2,981 contigs and 1,238 singletons. These were functionally annotated with gene ontology terms and placed into KEGG pathways. Compared to other EST sequencing efforts in common bean, about half of the sequences were novel or represented the 5' ends of known genes.ConclusionsThe present full-length cDNA libraries add to the technological toolbox available for common bean and our sequencing of these clones substantially increases the number of unique EST sequences available for the common bean genome. All of this should be useful for both functional gene annotation, analysis of splice site variants and intron/exon boundary determination by comparison to soybean genes or with common bean whole-genome sequences. In addition the library has a large number of transcription factors and will be interesting for discovery and validation of drought or abiotic stress related genes in common bean.

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