https://ccc.inaoep.mx/~mdprl/documentos/17062015.pdf

Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

Nitrogen (N) plays a key role in the plant life cycle. It is the main plant mineral nutrient needed for chlorophyll production and other plant cell components (proteins, nucleic acids, amino acids). Crop yield is affected by plant N status. Thus, the optimization of nitrogen fertilization has become the object of intense research due to its environmental and economic impact. This article focuses on reviewing current methods and techniques used to determine plant N status. Kjeldahl digestion and Dumas combustion have been used as reference methods for N determination in plants, but they are destructive and time consuming. By using spectroradiometers, reflectometers, imagery from satellite sensors and digital cameras, optical properties have been measured to estimate N in plants, such as crop canopy reflectance, leaf transmittance, chlorophyll and polyphenol fluorescence. High correlation has been found between optical parameters and plant N status, and those techniques are not destructive. However, some drawbacks include chlorophyll saturation, atmospheric and soil interference, and the high cost of instruments. Electrical properties of plant tissue have been used to estimate quality in fruits, and water content in plants, as well as nutrient deficiency, which suggests that they have potential for use in plant N determination.

https://www.mdpi.com/1424-8220/13/8/10823/pdf

A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances

1 The goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors. Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1537&context=agron_pubs

Strengths and limitations of Nitrogen rate recommendations for corn and opportunities for improvement

Improvements of nitrogen use efficiency (NUE) may be achieved through the use of sensing tools for N status determination Leaf and canopy chlorophyll, as well as leaf polyphenolics concentrations, are characteristics strongly affected by N availability that are often used as a surrogate to direct plant N status estimation Approaches with near-term operational sensors, handheld and tractor-mounted, for proximal remote measurements are considered in this review However, the information provided by these tools is unfortunately biased by factors other than N To overcome this obstacle, normalization procedures such as the well-fertilized reference plot, the no-N reference plot, and relative yield are often used Methods to establish useful relationships between sensor readings and optimal N rates, such as critical NSI (nitrogen sufficiency index), INSEY (in-season estimated yield), and the relationship between chlorophyll meter readings, grain yield, and sensor-determined CI (chlorophyll index) are also reviewed In a few cases, algorithms for translating readings into actual N fertilizer recommendation have been developed, but their value still seems limited to conditions similar to the ones where the research was conducted Near-term operational sensing can benefit from improvements in sensor operational characteristics (size and shape of footprint, positioning) or the choice of light wavebands more suitable for specific conditions (ie, genotype, growth stage, or crop density) However, one important limitation to their widespread use is the availability of algorithms that would be reliable in a variety of soil and weather conditions

Strategies to Make Use of Plant Sensors-Based Diagnostic Information for Nitrogen Recommendations

Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices

Active sensor refl ectance assessments of corn (Zea mays L.) canopy N status are advocated to direct variable N applications and improve N use effi ciency (NUE). Our goals were to determine: (i) growth stage and (ii) sensor vegetation index with greatest sensitivity in assessing N status and grain yield. Variable crop N was generated by supplying N at diff erent amounts and times in three fi eld studies. Chlorophyll meter (CM) and sensor data were gathered at two vegetative (V11 and V15) and two reproductive (R1 and R3) growth stages, using the Crop Circle sensor that measures refl ectance in visible (590 nm) and near infrared (NIR) (880 nm) bands. Sensor data were converted to the normalized diff erence vegetation index (NDVI 590 ) and chlorophyll index (CI 590 ) values. Grain yields were also determined. Sensor indices were more highly correlated with CM readings for vegetative vs. reproductive growth (r 2 of 0.85 vs. 0.55). Th e CM vs. CI 590 slope was over twice the NDVI 590 slope value, indicating CI 590 was more sensitive than NDVI 590 in assessing canopy greenness. Indices did not diff er in ability to distinguish yield variation. Results indicate sensor CI 590 values collected during vegetative growth are best suited to direct variable N applications.

/pdf/active-sensor-reflectance-measurements-of-corn-nitrogen-2x25jr7oa8.pdf

Active Sensor Reflectance Measurements of Corn Nitrogen Status and Yield Potential

It is commonly held that confined field trials (CFTs) used to evaluate the potential adverse environmental impacts of a genetically engineered (GE) plant should be conducted in each country where cultivation is intended, even when relevant and potentially sufficient data are already available from studies conducted elsewhere. The acceptance of data generated in CFTs “out of country” can only be realized in practice if the agro-climatic zone where a CFT is conducted is demonstrably representative of the agro-climatic zones in those geographies to which the data will be transported. In an attempt to elaborate this idea, a multi-disciplinary Working Group of scientists collaborated to develop a conceptual framework and associated process that can be used by the regulated and regulatory communities to support transportability of CFT data for environmental risk assessment (ERA). As proposed here, application of the conceptual framework provides a scientifically defensible process for evaluating if existing CFT data from remote sites are relevant and/or sufficient for local ERAs. Additionally, it promotes a strategic approach to identifying CFT site locations so that field data will be transportable from one regulatory jurisdiction to another. Application of the framework and process should be particularly beneficial to public sector product developers and small enterprises that develop innovative GE events but cannot afford to replicate redundant CFTs, and to regulatory authorities seeking to improve the deployment of limited institutional resources.

/pdf/transportability-of-confined-field-trial-data-for-3sbsom3vuv.pdf

Transportability of confined field trial data for environmental risk assessment of genetically engineered plants: a conceptual framework

Hybrid Maize Seed Production Yield Associations with Inbred Line Performance in Multienvironment Trials

In-season nitrogen management of irrigated maize using a crop canopy sensor

In previous research we found active canopy sensor reflectance assessments of corn (Zea mays L.) N status have potential for directing in-season N applications, but emphasized an algorithm was needed to translate sensor readings into appropriate N application (N app ) rates. The objectives of this work were to: (i) develop an active canopy sensor algorithm based on a SPAD chlorophyll meter algorithm and (ii) validate the active canopy sensor algorithm using data collected from a companion study. We derived the sensor algorithm using a linear relationship between sensor sufficiency index (SI sensor ) and SI SPAD values established in the previous research and a published SPAD algorithm employing a quadratic equation to calculate N app as a function of SI SPAD . The resulting equation: N app = 317√0.97-SI sensor represents the function for translating SI sensor to N app . To validate the algorithm, SI sensor values collected from small plots receiving varying N amounts were converted into N app using the algorithm. Then N app was converted into crop N balance (Nb balance estimates, where N balance = applied N-N app . Negative N balance values indicate N deficiency while positive values indicate excess N. The N balance values were compared with relative yields and a quadratic-plateau model fit to the data set for both growth stages (V11 and V15), producing an R 2 of 0.66. Relative yields plateaued at an N balance near zero (-11 kg N ha -1 ), indicating the algorithm provided reasonable estimates of N app for maximizing yields. The equation provides a basis for the use of active crop canopy sensors for in-season N management of irrigated corn.

An active sensor algorithm for corn nitrogen recommendations based on a chlorophyll meter algorithm.

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