s from ZnO-NPs enhanced crop productivity, comparable to higher inputs from bulk-ZnO. This highlights a key benefit of nanofertilizers a reduction of nutrient inputs into agriculture without yield penalities. Copyright © 2020 Dimkpa, Andrews, Fugice, Singh, Bindraban, Elmer, Gardea-Torresdey and White.Evaluation of nitrogen (N) status by leaf color is a kind of classic nutritional diagnostic method. However, the color of leaves is influenced not only by N, but also by other nutrients such as potassium (K). Two-year field trials with a factorial combination of N and K were conducted to investigate the effects of different N and K rates on soil plant analysis development (SPAD) readings and leaf N, K, magnesium (Mg), and chlorophyll concentrations. Visual inspections in leaf greenness revealed darker green leaves with increasing N rates, while paler green leaves with increasing K rates. Data showed that SPAD readings, chlorophyll, N and Mg concentrations, and the chloroplast area increased significantly with raising N rates, while declined sharply with the increase in K rates due to the antagonistic relationships between K+ and NH4 + as well as Mg2+. It was also probable that the increase in K promoted the growth of leaves and diluted their N and Mg concentrations. The paler leaf appearance resulting from the application of K may overestimate the actual demand for N in the diagnosis of rice N status. The strong antagonistic relationships between K+, NH4 +, and Mg2+ should be considered in rice production and fertilization. Copyright © 2020 Hou, Tränkner, Lu, Yan, Huang, Ren, Cong and Li.Breeding higher yielding forage species is limited by current manual harvesting and visual scoring techniques used for measuring or estimation of biomass. Automation and remote sensing for high throughput phenotyping has been used in recent years as a viable solution to this bottleneck. Here, we focus on using RGB imaging and deep learning for white clover (Trifolium repens L.) and perennial ryegrass (Lolium perenne L.) yield estimation in a mixed sward. We present a new convolutional neural network (CNN) architecture designed for semantic segmentation of dense pasture and canopies with high occlusion to which we have named the local context network (LC-Net). On our testing data set we obtain a mean accuracy of 95.4% and a mean intersection over union of 81.3%, outperforming other methods we have found in the literature for segmenting clover from ryegrass. Comparing the clover/vegetation fraction for visual coverage and harvested dry-matter however showed little improvement from the segmentation accuracy gains. Further gains in biomass estimation accuracy may be achievable through combining RGB with complimentary information such as volumetric data from other sensors, which will form the basis of our future work. Copyright © 2020 Bateman, Fourie, Hsiao, Irie, Heslop, Hilditch, Hagedorn, Jessep, Gebbie and Ghamkhar.Chloroplastic glutamine phosphoribosylpyrophosphate amidotransferase (GPRATase) catalyzes the first committed step of de novo purine biosynthesis in Arabidopsis thaliana, and DAS734 is a direct and specific inhibitor of AtGPRAT, with phytotoxic effects similar to the leaf beaching phenotypes of known AtGPRAT genetic mutants, especially cia1 and atd2. However, the structure of AtGPRAT and the inhibition mode of DAS734 still remain poorly understood. In this study, we solved the structure of AtGPRAT2, which revealed structural differences between AtGPRAT2 and bacterial enzymes. Kinetics assay demonstrated that DAS734 behaves as a competitive inhibitor for the substrate phosphoribosyl pyrophosphate (PRPP) of AtGPRAT2. Docking studies showed that DAS734 forms electrostatic interactions with R264 and hydrophobic interactions with several residues, which was verified by binding assays. Collectively, our study provides important insights into the inhibition mechanism of DAS734 to AtGPRAT2 and sheds light on future studies into further development of more potent herbicides targeting Arabidopsis GPRATases. Copyright © 2020 Cao, Du, Han, Zhou, Ren, Wang, Chen and Zhang.Phosphorus (P) is the second most important nutrient after nitrogen (N) and can greatly diminish plant productivity if P supply is not adequate. Plants respond to soil P availability by adjusting root biomass to maintain uptake and productivity due to P use. In spite of our vast knowledge on P effects on plant growth, how to functionally model enhanced root biomass allocation in low P environments is not fully explored. We develop a dynamic plant model based on the principle of optimal carbon (C) and P allocation to investigate growth and functional response to contrasting levels of soil P availability. By describing plant growth as a balance of growth and respiration processes, we optimize C and P allocation in order to maximize leaf productivity and drive plant response. https://www.selleckchem.com/ We compare our model to a field trial and a set of hydroponic experiments which describe plant response at varying P availabilities. The model is able to reproduce long-term plant functional response to different P levels like change in root-shoot ratio (RSR), total biomass and organ P concentration. But it is not capable of fully describing the time evolution of organ P uptake and cycling within the plant. Most notable is the underestimation of organ P uptake during the vegetative growth stage which is due to the model's leaf productivity formalism. In spite of the model's parsimonious nature, which optimizes for and predicts whole plant response through leaf productivity alone, the optimal growth hypothesis can provide a reasonable framework for modelling plant response to environmental change that can be used in more physically driven vegetation models. Copyright © 2020 Kvakić, Tzagkarakis, Pellerin, Ciais, Goll, Mollier and Ringeval.Few proteins have been characterized as abscisic acid transporters. Several of them are NRT1/PRT Family (NPF) transporters which have been characterized in yeast using reporter systems. Because several members of the NPF4 subfamily members were identified in yeast as ABA transporters, here, we screened for ABA transport activity the seven members of the NPF4 subfamily in Xenopus oocytes using cRNA injection and 3H-ABA accumulation. The ABA transport capacities of NPF4.2, NPF4.5, NPF4.6, and NPF4.7 were confirmed. The transport properties of NPF4.5 and NPF4.6 were studied in more detail. Both ABA transporter activities are pH-dependent and slightly pH-dependent apparent Km around 500 μM. There is no competitive inhibition of the ABA-analogs pyrabactin and quinabactin on ABA accumulation demonstrating a different selectivity compared to the ABA receptors. Functional expression of these ABA transporters in Xenopus oocyte is an opportunity to start structure-function studies and also to identify partner proteins of these hormone transporters.