Nitrogen is the main limiting nutrient after carbon, hydrogen and oxygen for photosynthetic process, phyto-hormonal, proteomic changes and growth-development of plants to complete its lifecycle. Excessive and inefficient use of N fertilizer results in enhanced crop production costs and atmospheric pollution. Atmospheric nitrogen (71%) in the molecular form is not available for the plants. For world’s sustainable food production and atmospheric benefits, there is an urgent need to up-grade nitrogen use efficiency in agricultural farming system. The nitrogen use efficiency is the product of nitrogen uptake efficiency and nitrogen utilization efficiency, it varies from 30.2 to 53.2%. Nitrogen losses are too high, due to excess amount, low plant population, poor application methods etc., which can go up to 70% of total available nitrogen. These losses can be minimized up to 15–30% by adopting improved agronomic approaches such as optimal dosage of nitrogen, application of N by using canopy sensors, maintaining plant population, drip fertigation and legume based intercropping. A few transgenic studies have shown improvement in nitrogen uptake and even increase in biomass. Nitrate reductase, nitrite reductase, glutamine synthetase, glutamine oxoglutarate aminotransferase and asparagine synthetase enzyme have a great role in nitrogen metabolism. However, further studies on carbon–nitrogen metabolism and molecular changes at omic levels are required by using “whole genome sequencing technology” to improve nitrogen use efficiency. This review focus on nitrogen use efficiency that is the major concern of modern days to save economic resources without sacrificing farm yield as well as safety of global environment, i.e. greenhouse gas emissions, ammonium volatilization and nitrate leaching.

/pdf/fate-of-nitrogen-in-agriculture-and-environment-agronomic-246qu8ghc0.pdf

Fate of nitrogen in agriculture and environment: agronomic, eco-physiological and molecular approaches to improve nitrogen use efficiency.

Optimal nitrogen (N) management is essential for profitable vegetable crop production and to minimize N losses to the environment that are a consequence of an excessive N supply. Proximal optical sensors placed in contact with or close to the crop can provide a rapid assessment of a crop N status. Three types of proximal optical sensors (chlorophyll meters, canopy reflectance sensors, and fluorescence-based flavonols meters) for monitoring the crop N status of vegetable crops are reviewed, addressing practical caveats and sampling considerations and evaluating the practical use of these sensors for crop N management. Research over recent decades has shown strong relationships between optical sensor measurements, and different measures of crop N status and of yield of vegetable species. However, the availability of both: (a) Sufficiency values to assess crop N status and (b) algorithms to translate sensor measurements into N fertilizer recommendations are limited for vegetable crops. Optical sensors have potential for N management of vegetable crops. However, research should go beyond merely diagnosing crop N status. Research should now focus on the determination of practical fertilization recommendations. It is envisaged that the increasing environmental and societal pressure on sustainable crop N management will stimulate progress in this area.

Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review

Do crop sensors promote improved nitrogen management in grain crops

Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon

The demand for improved decision-making products for cereal production systems has placed added emphasis on using plant sensors in-season, and that incorporate real-time, site specific, growing environments. The objectives of this work were to describe validated in-season sensor-based algorithms presently being used in cereal grain production systems for improving nitrogen use efficiency (NUE) and cereal grain yields. A review of research programs in the central Great Plains that have developed sensor-based N recommendations for cereal crops was performed. Algorithms included multiple land-grant university, government, and industry programs. A common thread in this review is the use of active sensors, particularly those using the normalized difference vegetation index (NDVI) for quantifying differences in fertilized and non-fertilized areas, within a specific cropping season. In-season prediction of yield potential over different sites and years is possible using NDVI, planting date, sensing date, cumulative growing degree days (GDD), and rainfall. Other in-season environment-specific inputs have also been used. Early passive sensors have advanced to by-plant N fertilization using active NDVI and by-plant statistical properties. Most recently, sensor-based algorithm research has focused on the development of generalized mathematical models for determining optimal crop N application. The development and promotion of fee-based modeling approaches for nutrient management continues. Nonetheless, several algorithms using active sensors for in-season N management are available from state and government sources at no cost and that have been extensively field tested and can be modified by producers.

Algorithms for In-Season Nutrient Management in Cereals

Published in Agron. J. 104:378–387 (2012) Posted online 12 Jan 2012 doi:10.2134/agronj2011.0249 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. T invention of analog-based, pulse-modulated, two-band, active lighting sensors (Beck and Vyse, 1994, 1995) and the equivalent digitally based sensor (Stone et al., 2003, 2005) have contributed to the potential use of these technologies for variable-rate application of N fertilizers. One of the more common reflectance indices used in agriculture is the normalized difference vegetation index (NDVI). The index is computed as (NIR – Red)/(NIR + Red), where NIR is the fraction of emitted near-infrared radiation returned from the sensed area (reflectance) and Red is the fraction of emitted red radiation returned from the sensed area (reflectance). Work by Filella and Penuelas (1994) and Liu et al. (2004a) noted that red edge reflectance can be indicative of plant chlorophyll content and biomass. Kanke et al. (2011) reported that NDVI better detected differences in plant growth, especially at early growth stages, than red edge reflectance. Spectral measurements of plants correlated with numerous physiological and morphological factors affecting growth and yield. Because of the difficulty in accounting for all confounding factors, models for computing N fertilizer rates are generally empirical and plant species specific and do not account for environmental factors, particularly rainfall, and their interactions with plant growth factors. Biggs et al. (2002) proposed a reference strip, where fertilizer is applied at a sufficient rate such that crop yield reaches a response plateau, that would subsequently be used to manage N fertilization. He patented a concept to measure reflectance with an optical sensor of the strip and the adjacent field rate and calculated the N application rate based on the ratio of the two readings (Biggs et al., 2002). The sensors were mounted on a center pivot irrigation system and paired measurements were made on-the-go. Researchers use linear or exponential models to describe the relationship between vegetative indices and plant yield. Linear relationships have been identified between yield and NDVI for corn (Diker et al., 2004), wheat (Nidumolu et al., 2008; Liu et al., 2004b), tomato (Solanum lycopersicum L.) (Bala et al., 2007), cotton lint (Gossypium hirsutum L.) (Plant et al., 2000), and barley (Hordeum vulgare L.) (Kancheva et al., 2007). Multiple linear regression was used for winter wheat (Salazar et al., 2006; Kumar et al., 1999). Exponential relationships were used for NDVI and yield in cotton lint (Plant et al., 2000), winter wheat (Enclona et al., 2004; Raun et al., 2005), spinach (Spinacia oleracea L.) (Jones et al., 2007), canola (Brassica napus L. var. napus) (Osborne, 2007), and corn (Raun et al., 2005). One model incorporated additional variables to account for other confounding factors such as the date of planting (Kumar et al., 1999). A comprehensive theory is needed to account for effects of the growth stage, rate of growth, date of sensing, and environment on crop growth and yield. Raun et al. (2005) recognized that N algorithms should account for the independence of the crop response to additional N and potential maximum yield. As such, they must be measured individually. Because N is highly mobile (Khosla and Alley, 1999), the maximum potential crop yield is temporally and spatially (Girma et al., 2007) variable, and the amount N ABSTRACT

/pdf/generalized-algorithm-for-variable-rate-nitrogen-application-3zurtw47iu.pdf

A. Dean Monroe

Papers

Generalized Algorithm for Variable-Rate Nitrogen Application in Cereal Grains